self-driving car

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pages: 472 words: 80,835

Life as a Passenger: How Driverless Cars Will Change the World by David Kerrigan

3D printing, Airbnb, airport security, Albert Einstein, autonomous vehicles, big-box store, Boeing 747, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, data science, deep learning, DeepMind, deskilling, disruptive innovation, Donald Shoup, driverless car, edge city, Elon Musk, en.wikipedia.org, fake news, Ford Model T, future of work, General Motors Futurama, hype cycle, invention of the wheel, Just-in-time delivery, Lewis Mumford, loss aversion, Lyft, Marchetti’s constant, Mars Rover, megacity, Menlo Park, Metcalfe’s law, Minecraft, Nash equilibrium, New Urbanism, QWERTY keyboard, Ralph Nader, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Sam Peltzman, self-driving car, sensor fusion, Silicon Valley, Simon Kuznets, smart cities, Snapchat, Stanford marshmallow experiment, Steve Jobs, technological determinism, technoutopianism, TED Talk, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, trolley problem, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, warehouse robotics, Yogi Berra, young professional, zero-sum game, Zipcar

g=43194ac7-9d44-46f5-9e4c-9c759f8e3641 https://www.bloomberg.com/amp/news/articles/2017-05-16/waymo-s-next-challenge-making-driverless-passengers-feels-safe http://newsroom.aaa.com/2017/03/americans-feel-unsafe-sharing-road-fully-self-driving-cars/ https://www.fastcompany.com/40419374/the-future-of-autonomous-vehicles-relies-on-middle-america Negative articles include: http://www.computerworld.com/article/2599426/emerging-technology/did-you-know-googles-self-driving-cars-cant-handle-99-of-roads-in-the-us.html https://www.technologyreview.com/s/530276/hidden-obstacles-for-googles-self-driving-cars/ https://www.theguardian.com/commentisfree/2016/dec/15/the-guardian-view-on-self-driving-cars-apply-the-brakes https://www.nytimes.com/2016/12/19/opinion/google-wants-driverless-cars-but-do-we.html?_r=0 Blogs: A selection of blogs on the topic of Driverless cars: http://penguindreams.org/blog/self-driving-cars-will-not-solve-the-transportation-problem/# http://utilware.com/autonomous.html http://ideas.4brad.com/rodney-brooks-pedestrian-interaction-andrew-ng-infrastructure-and-both-human-attitudes https://medium.com/@alexrubalcava/a-roadmap-for-a-world-without-drivers-573aede0c968 http://www.newgeography.com/content/005024-preparing-impact-driverless-cars http://blog.piekniewski.info/2017/05/11/a-car-safety-myths-and-facts/ https://medium.com/@christianhern/self-driving-cars-as-the-new-toolbar-8c8a47a3c598 https://backchannel.com/self-driving-cars-will-improve-our-cities-if-they-dont-ruin-them-2dc920345618#.4va0brsyg Videos: A selection of Videos on the topic of Driverless cars: Video of Tesla Auto pilot - https://thescene.com/watch/arstechnica/cars-technica-hands-on-with-tesla-s-autopilot https://youtu.be/tiwVMrTLUWg (15 Minute TED Talk by Chris Urmson of Google, 2015) * * * [1] http://www.mckinsey.com/~/media/McKinsey/Business%20Functions/McKinsey%20Digital/Our%20Insights/Disruptive%20technologies/MGI_Disruptive_technologies_Full_report_May2013.ashx [2] http://www.morganstanley.com/articles/autonomous-cars-the-future-is-now [3] http://www3.weforum.org/docs/Media/WEF_FutureofJobs.pdf [4] https://en.wikipedia.org/wiki/Roy_Amara [5] https://twitter.com/BenedictEvans/status/763209924302090240 [6] https://en.wikipedia.org/wiki/Zeno%27s_paradoxes#Dichotomy_paradox [7] https://twitter.com/BenedictEvans/status/771115479393906688 [8] https://lilium.com/ [9] https://www.uber.com/info/elevate/ [10] The Salmon of Doubt, Douglas Adams, 2002 [11] http://farmerandfarmer.org/mastery/builder.html [12] https://global.oup.com/academic/product/innovation-and-its-enemies-9780190467036?

John Jordan, a Professor at Penn State[318] sees parallels in the past: “About 125 years ago, when the internal combustion engine supplanted equine power for personal mobility, there was much talk regarding ‘horseless carriages’, defining the future in terms of the past. We are at much the same juncture today. Much of the conversation starts with what we know human drivers do: ‘How will self-driving cars avoid bicyclists? How will self-driving cars merge in construction zones? How will self-driving cars make left turns across oncoming traffic with solar glare?’ All of these questions must be answered, of course, but I believe it’s not too early to ask what we want of the next car.” Sociologist and economist Thorstein Veblen introduced the concept of Technological Determinism[319] in the 1920s, which proposed that a society's technology determines the development of its social structure and cultural values.

mbid=nl_31516 http://www.nlc.org/article/new-autonomous-vehicle-guide-helps-cities-prepare-for-a-driverless-future http://www.nctr.usf.edu/wp-content/uploads/2016/11/Implications-for-Public-Transit-of-Emerging-Technologies-11-1-16.pdf http://globalpolicysolutions.org/wp-content/uploads/2017/03/Stick-Shift-Autonomous-Vehicles.pdf https://www.technologyreview.com/s/607841/a-single-autonomous-car-has-a-huge-impact-on-alleviating-traffic/ Chapter 7 - Regulation & Acceptance https://www.transportation.gov/AV/federal-automated-vehicles-policy-september-2016 https://www.scientificamerican.com/article/when-it-comes-to-safety-autonomous-cars-are-still-teen-drivers1/# http://www.newsweek.com/when-will-we-know-self-driving-cars-are-safe-501270 http://www.huffingtonpost.com/entry/how-safe-are-self-driving-cars_us_5908ba48e4b03b105b44bc6b?ncid=engmodushpmg00000004 http://www.reuters.com/article/us-germany-autos-self-driving-idUSKBN1881HY http://techcrunch.com/2016/01/28/security-and-privacy-standards-are-critical-to-the-success-of-connected-cars/ https://techcrunch.com/2016/11/06/why-the-department-of-transportations-self-driving-car-guidelines-arent-enough/ https://electrek.co/2016/10/19/elon-musk-says-the-media-is-killing-people-when-writing-negative-articles-about-self-driving-cars/ http://readwrite.com/2017/05/07/responsible-autonomous-car-regulations-tl1/ https://www.enotrans.org/wp-content/uploads/2015/09/AV-paper.pdf http://www.lexology.com/library/detail.aspx?


Autonomous Driving: How the Driverless Revolution Will Change the World by Andreas Herrmann, Walter Brenner, Rupert Stadler

Airbnb, Airbus A320, algorithmic bias, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, clean tech, computer vision, conceptual framework, congestion pricing, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, deep learning, demand response, digital map, disruptive innovation, driverless car, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, John Zimmer (Lyft cofounder), Lyft, manufacturing employment, market fundamentalism, Mars Rover, Masdar, megacity, Pearl River Delta, peer-to-peer rental, precision agriculture, QWERTY keyboard, RAND corporation, ride hailing / ride sharing, self-driving car, sensor fusion, sharing economy, Silicon Valley, smart cities, smart grid, smart meter, Steve Jobs, Tesla Model S, Tim Cook: Apple, trolley problem, uber lyft, upwardly mobile, urban planning, Zipcar

Microsoft is also developing connectivity and telematics services and is supporting Toyota’s research into artificial intelligence and self-driving cars. In 2017, Korean tech giant Samsung received permission from the Korean government to test self-driving cars on public roads, using Hyundai vehicles equipped with cameras and sensors. NEW PLAYERS Shanghai has recently joined Beijing and Shenzhen as one of the centres of the Chinese automobile industry. Numerous startups have been established in and around this megacity that are developing autonomous electric vehicles. There are about 20 companies working on self-driving cars, one of which is especially prominent. Jia Yueting, billionaire and CEO of LeEco, a company that has grown enormously with smartphones, TV transmitters and streaming services, has now entered the race.

It is clear that these calculations can, at best, indicate the rough magnitudes involved in autonomous driving. One must also be aware that all analyses refer to a traffic situation in which there are only self-driving cars on the roads. However, accidents might still occur with autonomous vehicles, but V-to-X communication based on the new long-term evolution (LTE) vehicular 65 Autonomous Driving 66 Table 8.1. Potential Savings from Self-Driving Cars and Trucks. Expected Savings from the Use of Autonomous Cars and Trucks in the United States Savings Cars Trucks Less fuel $158 billion $35 billion Less labour $70 billion Less injuries and fatalities $542 billion $36 billion Productivity gains $507 billion Less congestion $149 billion $27 billion Total $1.3 trillion $168 billion Source: Morgan Stanley Research [94].

Another example is the US National Highway Traffic 146 Autonomous Driving Safety Administration’s (NHTSA) Federal Automated Vehicles Policy and Cybersecurity Best Practices for Modern Vehicles, both of which provide cyber security guidelines for the automotive industry. More recently, in June 2017, a bipartisan group of legislators including Senators John Thune, Bill Nelson and Gary Peters introduced a set of principles to guide legislation on self-driving cars that address many cyber security concerns. Also in June, House Representatives started circulating the drafts of a 14-bill package in an effort to pass federal laws regarding self-driving cars. Finally, on 28 June 2017, the Federal Trade Commission (FTC) and the NHTSA held a workshop to examine consumer privacy and cyber security issues in autonomous and connected vehicles. Despite these guidelines and any federal laws that may be passed, and given the potential damage of an attack, the automotive industry must proactively procure and establish solutions and standards themselves.


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apollo 11, Apollo Guidance Computer, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, Bletchley Park, blockchain, Boston Dynamics, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, Computing Machinery and Intelligence, congestion charging, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, electricity market, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, Ford Model T, future of work, gamification, Geoffrey Hinton, gig economy, gigafactory, Google Glasses, Google X / Alphabet X, Hans Lippershey, high-speed rail, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kim Stanley Robinson, Kiva Systems, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Neal Stephenson, Neil Armstrong, Network effects, new economy, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, retail therapy, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, Snow Crash, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, synthetic biology, systems thinking, TaskRabbit, technological singularity, TED Talk, telemarketer, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, TSMC, Turing complete, Turing test, Twitter Arab Spring, uber lyft, undersea cable, urban sprawl, V2 rocket, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks, yottabyte

In fact, based on Google’s beta testing of its self-driving cars, the existing units are about ten times safer than human drivers. For every 1 million miles that the existing fleet of self-driving cars undertakes, without causing an accident, we will see that number effectively double. Probability means that at some point a self-driving car will cause an accident, and that at some point it will probably be involved in a fatality, but these self-driving cars will still be demonstrably safer than human drivers. As Brad Templeton, a Singularity University professor who worked with Google on the self-driving car, articulated to me during a recent interview: “Self-driving cars don’t get tired, don’t get drunk, don’t get distracted, don’t get road rage and don’t need a rest, unless it might be to charge.”

Let me give you a simple example of why the banking system that today requires a person’s identity to be tied to a bank account cannot survive this shift. When Your Self-driving Car Has a Bank Account While owning a car will definitely be an option in the future, many Millennials and their descendants will opt to participate in a sharing economy where ownership is distributed, or where self-driving car time is rented. So let’s take a scenario in 2025 to 2030 when a Millennial subscribes to a personalised car service guaranteeing access to an autonomous, self-driving car for a certain number of hours each day, or where they buy a “share” in a self-driving car. The car picks up the Millennial and takes them to work.

Any cars that are being made that don’t have full autonomy will have negative value. It will be like owning a horse. You will only be owning it for sentimental reasons.” Elon Musk, CEO of Tesla, Tesla Earnings call, November 2015 Living with Self-driving Cars At the CES in Las Vegas in 2015, Mercedes launched a self-driving car called the Mercedes F015. In fact, it reportedly drove itself to the venue. Where it differed from the likes of Google’s self-driving car, or those from Volvo, Audi or Tesla, is that it was specifically designed as a “third place” for consumers, as Dieter Zetsch, head of the German company, put it at the time. If our home is our first place and our office or school our second place, then where is the next place we spend a large portion of our time?


pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

"Susan Fowler" uber, 1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, Big Tech, bitcoin, Buckminster Fuller, Charles Babbage, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data science, deep learning, Dennis Ritchie, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, fake news, Firefox, gamification, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Greyball, Hacker Ethic, independent contractor, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, John Perry Barlow, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, machine translation, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, Nate Silver, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, One Laptop per Child (OLPC), opioid epidemic / opioid crisis, PageRank, Paradox of Choice, payday loans, paypal mafia, performance metric, Peter Thiel, price discrimination, Ray Kurzweil, ride hailing / ride sharing, Ross Ulbricht, Saturday Night Live, school choice, self-driving car, Silicon Valley, Silicon Valley billionaire, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, TechCrunch disrupt, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, traumatic brain injury, Travis Kalanick, trolley problem, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, women in the workforce, work culture , yottabyte

The best case for considering how artificial intelligence works both really well and not at all is the case of the self-driving car. The first time I rode in a self-driving car, in 2007, I thought I was going to die. Or vomit. Or both. So, when I heard in 2016 that self-driving cars were coming to market, that Tesla had created software called Autopilot and that Uber was testing self-driving cars in Pittsburgh, I wondered: What had changed? Did the reckless engineers I met in 2007 actually manage to embed an ethical decision-making entity inside a two-ton killing machine? It turned out that perhaps not as much has changed as I might have thought. The story of the race to build a self-driving car is a story about the fundamental limits of computing.

He’s warning of a likely future path for self-driving cars that is neither safe nor ethical nor toward the greater good. The problem seems to be that few people are listening. “Self-driving cars are nifty and coming soon” seems to be the accepted wisdom, and nobody seems to care that the technologists have been saying “coming soon” for decades now. To date, all self-driving car “experiments” have required a driver and an engineer to be onboard at all times. Only a technochauvinist would call this success and not failure. A few useful consumer advances have come out of self-driving car projects. My car has cameras embedded in all four sides; the live video from these cameras makes it easier to park.

When people don’t have a framework or a sense of commitment to others, however, they tend to make decisions that seem aberrant. In the case of self-driving cars, there’s no way to make sure that the decisions made by individual technologists in corporate office buildings will match with actual collective good. This leads us to ask, again: Who does this technology serve? How does it serve us to use it? If self-driving cars are programmed to save the driver over a group of kindergarteners, why? What does it mean to accept that programming default and get behind the wheel? Plenty of people, including technologists, are sounding warnings about self-driving cars and how they attempt to tackle very hard problems that haven’t yet been solved.


pages: 477 words: 75,408

The Economic Singularity: Artificial Intelligence and the Death of Capitalism by Calum Chace

"World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, AlphaGo, Alvin Toffler, Amazon Robotics, Andy Rubin, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Boston Dynamics, bread and circuses, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, digital divide, Douglas Engelbart, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Flynn Effect, full employment, future of work, Future Shock, gender pay gap, Geoffrey Hinton, gig economy, Google Glasses, Google X / Alphabet X, Hans Moravec, Herman Kahn, hype cycle, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kiva Systems, knowledge worker, lifelogging, lump of labour, Lyft, machine translation, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, Neil Armstrong, new economy, Nick Bostrom, Occupy movement, Oculus Rift, OpenAI, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, SoftBank, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, TED Talk, The future is already here, The Future of Employment, Thomas Malthus, transaction costs, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, warehouse automation, warehouse robotics, working-age population, Y Combinator, young professional

[clxxix] We drive rather than use public transport because there is no appropriate public transport available, or sometimes because we prefer travelling in our own space. Self-driving cars could give us the best of both worlds, allowing us to read, sleep, watch video or chat as we travel. Finally, self-driving cars will enable us to use our environments more sensibly, especially our cities. Most cars spend 95% of their time parked.[clxxx] This is a waste of an expensive asset, and a waste of the land they occupy while sitting idle. We will consider later how far self-driving cars could alleviate this problem. To autonomy and beyond Self-driving cars, like our artificially intelligent digital assistants, are still waiting to receive their generic name.

[clxxxii] The US Department of Transport draws a distinction between (partly) autonomous cars and (fully) self-driving cars.[clxxxiii] The former still have steering wheels, and require a human driver to take over when they encounter a tricky situation. Self-driving cars, by contrast, are fully independent, and the steering wheel has been removed to save space. Autonomous cars will probably be merely a staging post en route to the completely self-driving variety. In fact the US DoT grades cars on a scale from L0, where the driver does everything, to L4, where the car does everything. Google’s initial idea was that the first self-driving cars in general use would be L3, meaning that the human driver should be ready to take over at a moment’s notice if anything went wrong, just as airplane pilots are.

[clxxxv] With many technology projects, resolving the last few issues is more difficult than the bulk of the project: edge cases are the acid test. Nevertheless, those edge cases are being tackled, and will be resolved. It is well-known that Google's self-driving cars have travelled well over a million miles in California without causing a significant accident, but what is less well-known is that the cars also drive millions of miles every day in simulators. Chris Urmson, head of the Google project, expects self-driving cars to be in general use by 2020.[clxxxvi] Sceptics point out that Google's self-driving cars depend on detailed maps. But producing maps for the roads outside California doesn't sound like an insurmountable obstacle, and in any case, systems like SegNet from Cambridge University enable cars to produce maps on the fly.


pages: 362 words: 97,288

Ghost Road: Beyond the Driverless Car by Anthony M. Townsend

A Pattern Language, active measures, AI winter, algorithmic trading, Alvin Toffler, Amazon Robotics, asset-backed security, augmented reality, autonomous vehicles, backpropagation, big-box store, bike sharing, Blitzscaling, Boston Dynamics, business process, Captain Sullenberger Hudson, car-free, carbon footprint, carbon tax, circular economy, company town, computer vision, conceptual framework, congestion charging, congestion pricing, connected car, creative destruction, crew resource management, crowdsourcing, DARPA: Urban Challenge, data is the new oil, Dean Kamen, deep learning, deepfake, deindustrialization, delayed gratification, deliberate practice, dematerialisation, deskilling, Didi Chuxing, drive until you qualify, driverless car, drop ship, Edward Glaeser, Elaine Herzberg, Elon Musk, en.wikipedia.org, extreme commuting, financial engineering, financial innovation, Flash crash, food desert, Ford Model T, fulfillment center, Future Shock, General Motors Futurama, gig economy, Google bus, Greyball, haute couture, helicopter parent, independent contractor, inventory management, invisible hand, Jane Jacobs, Jeff Bezos, Jevons paradox, jitney, job automation, John Markoff, John von Neumann, Joseph Schumpeter, Kickstarter, Kiva Systems, Lewis Mumford, loss aversion, Lyft, Masayoshi Son, megacity, microapartment, minimum viable product, mortgage debt, New Urbanism, Nick Bostrom, North Sea oil, Ocado, openstreetmap, pattern recognition, Peter Calthorpe, random walk, Ray Kurzweil, Ray Oldenburg, rent-seeking, ride hailing / ride sharing, Rodney Brooks, self-driving car, sharing economy, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, SoftBank, software as a service, sovereign wealth fund, Stephen Hawking, Steve Jobs, surveillance capitalism, technological singularity, TED Talk, Tesla Model S, The Coming Technological Singularity, The Death and Life of Great American Cities, The future is already here, The Future of Employment, The Great Good Place, too big to fail, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, uber lyft, urban planning, urban sprawl, US Airways Flight 1549, Vernor Vinge, vertical integration, Vision Fund, warehouse automation, warehouse robotics

Notes Preface xivprivate-sector funding . . . surged tenfold: Aparna Narayanan, “Self-Driving Cars Run into Reality—and Are Further Away Than You Think,” Investors Business Daily, May 24, 2019, https://www.investors.com/news/self-driving-cars-hit-delays-driverless-cars-timeline/. xvperfecting the technology will be trickier: Jeffrey Rothfeder, “For Years, Automakers Wildly Overpromised on Self-Driving Cars and Electric Vehicles—What Now?” Fast Company, July 10, 2019, https://www.fastcompany.com/90374083/for-years-automakers-wildly-overpromised-on-self-driving-cars-and-electric-vehicles-what-now. xvGM’s cruise . . . software sometimes failed: Cohen Coberly, “GM’s Self-Driving Car Fails to See Pedestrians and Detect ‘Phantom’ Bicycles, Report Claims,” Techspot, October 24, 2018, https://www.techspot.com/news/77083-gm-self-driving-car-division-facing-technical-challenges.html. 1.

Hawkins, “Waymo and GM Still Lead the Pack in California’s New Self-Driving Report Cards,” The Verge, January 31, 2018, https://www.theverge.com/2018/1/31/16956902/california-dmv-self-driving-car-disengagement-2017. 42more than doubling the average distance between disengagements: Alan Ohnsman, “Waymo Tops Self-Driving Car ‘Disengagement’ Stats as GM Cruise Gains and Tesla Is AWOL,” Forbes, February 13, 2019, https://www.forbes.com/sites/alanohnsman/2019/02/13/waymo-tops-self-driving-car-disengagement-stats-as-gm-cruise-gains-and-tesla-is-awol/#7b83615131ec. 42“freedom from external control or influence”: English Oxford Living Dictionaries, s.v.

Cheape, Moving the Masses: Urban Public Transit in New York, Boston, and Philadelphia, 1880–1912 (Cambridge, MA: Harvard University Press, 1980), 174. 89as many as three sets of tracks and overhead power lines ran: Cheape, Moving the Masses, 159. 89commuters paid at least two fares to get to work: Cheape, Moving the Masses, 159. 89PTC’s ridership had tripled: Cheape, Moving the Masses, 174. 89the company’s monopoly was secure: Cheape, Moving the Masses, 162–67. 94“As a driverless taxi was finally introduced”: Keller, “The Living Machine,” 1470. 94drivers take home about 80 percent of ride-for-hire fees: Alex Rosenblat, Uberland: How Algorithms Are Rewriting the Rules of Work (Oakland: University of California Press, 2018). 94all but eliminate the share that goes to labor: International Transport Board of the OECD, Urban Mobility System Upgrade: How Shared Self-Driving Cars Could Change City Traffic, ITF Corporate Partnership Board Report, 2015. 94Today’s cabs spend half of their working hours empty: Judd Cramer and Alan B. Krueger, “Disruptive Change in Taxi Business: The Case of Uber,” American Economic Review 106, no. 5 (2016): 177–82. 94could grow to $285 billion annually by 2030: David Welch and Elisabeth Behrmann, “Who’s Winning the Self-Driving Car Race?” Bloomberg, May 7, 2018, https://www.bloomberg.com/news/features/2018-05-07/who-s-winning-the-self-driving-car-race. 95more than one billion taxibots: Author’s calculation using estimates from UBS, Longer Term Investments: Smart Mobility (Chief Investment Office Americas, October 19, 2017). 95SilverRide targets senior citizens: Mitchell Hartman, “Wanted: Elder Transportation Solutions,” Marketplace, January 30, 2019, https://www.marketplace.org/2019/01/30/business/wanted-elder-transportation-solutions. 95“transported everything from leopards”: Ted Trauter, “Pet Chauffeur Tried to Adapt to Tough Economy,” You’re the Boss (blog), New York Times, August 26, 2011, https://boss.blogs.nytimes.com/2011/08/26/pet-chauffeur-tries-to-adapt-to-tough-economy. 96no-cost rides to prenatal-care appointments and grocery stores: Laura Bliss, “In Columbus, Expectant Moms Will Get On-Demand Rides to the Doctor,” CityLab, December 27, 2018, https://www.citylab.com/transportation/2018/12/smart-city-columbus-prenatal-ride-hailing/579082/. 97the company could soon be serving up to a million passengers: Alexis Madrigal, “Finally, the Self-Driving Car,” The Atlantic, December 5, 2018, https://www.theatlantic.com/technology/archive/2018/12/test-ride-waymos-self-driving-car/577378/. 97Singapore could make do with half: MIT Senseable City Lab, “Unparking,” Massachusetts Institute of Technology, accessed 20 February 2019, http://senseable.mit.edu/unparking/. 97could swap one private self-driving cab for every six: International Transport Board of the OECD, Urban Mobility System Upgrade. 98eliminate upwards of 75 percent of its yellow cabs: Javier Alonso-Mora et al., “On-Demand High-Capacity Ride-Sharing via Dynamic Trip-Vehicle Assignment,” Proceedings of the National Academy of Sciences of the United States of America 114, no. 3 (2017): 462–67. 98high cost of remote human safety monitors: Ashley Nunes and Kristen Hernandez, “The Cost of Self-Driving Cars Will Be the Biggest Barrier to Their Adoption,” Harvard Business Review, January 31, 2019, https://hbr.org/2019/01/the-cost-of-self-driving-cars-will-be-the-biggest-barrier-to-their-adoption. 98“We are going to also offer third-party”: “Full Video and Transcript: Uber CEO Dara Khosrowshahi at Code 2018,” Recode, June 4, 2018, https://www.recode.net/2018/5/31/17397186/full-transcript-uber-dara-khosrowshahi-code-2018. 99flubbed their geometry too: For more, see Jarett Walker, “Does Elon Musk Understand Urban Geometry?”


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The Driver in the Driverless Car: How Our Technology Choices Will Create the Future by Vivek Wadhwa, Alex Salkever

23andMe, 3D printing, Airbnb, AlphaGo, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, benefit corporation, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, CRISPR, deep learning, DeepMind, distributed ledger, Donald Trump, double helix, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, gigafactory, Google bus, Hyperloop, income inequality, information security, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Mary Meeker, Menlo Park, microbiome, military-industrial complex, mobile money, new economy, off-the-grid, One Laptop per Child (OLPC), personalized medicine, phenotype, precision agriculture, radical life extension, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, seminal paper, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, synthetic biology, Tesla Model S, The future is already here, The Future of Employment, Thomas Davenport, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, uranium enrichment, Watson beat the top human players on Jeopardy!, zero day

Most important, car sharing will cost a fraction of what car ownership today costs. Owning a car for daily, personal transportation will seem impractical. Self-driving cars will also deliver incontrovertible social benefits. With self-driving cars, the disabled will no longer struggle to find transportation; they will have an on-demand personal driver. Several years ago, as the New York Times in November 2014 relates it, Google’s self-driving car team contacted Steve Mahan, Executive Director of the Santa Clara Valley Blind Center.5 The team wanted feedback and let Mahan come along for test drives in earlier self-driving Prius models as well as in the latest Google car.

For me, it’s already a toss-up between driving and flying when I want to travel from San Francisco to Santa Barbara, which is four and a half hours away by car and takes four hours by plane and taxis (provided there are no flight delays). The self-driving cars will easily tip the balance; for any trips on the West coast, I’ll forgo the flights. Imagine the disruptions to the railroad and airline industries when we all start making this choice. And all of this begins to happen by the early 2020s. If I can rely on Elon Musk, my Tesla will become fully autonomous as early as 2018; 14 and Uber’s CEO, Travis Kalanick, has signed a pact with Volvo to have self-driving cars on the roads by 2021.15 Does the Technology Foster Autonomy Rather Than Dependence? I simply can’t wait for self-driving cars to take over our roads; I see them as increasing our personal autonomy as much as, if not more than, anything else discussed in this book.

.), 28 June 2014, http://www.telegraph.co.uk/finance/newsbysector/banksandfinance/10933273/Addison-Lee-owner-flags-sale.html (accessed 21 October 2016). 3. Johana Bhuiyan, “Why Uber has to be first to market with self-driving cars,” Recode 29 September 2016, http://www.recode.net/2016/9/29/12946994/why-uber-has-to-be-first-to-market-with-self-driving-cars (accessed 21 October 2016). 4. Alison Griswold, “Uber wants to replace its drivers with robots. So much for that ‘new economy’ it was building,” Slate 2 February 2015, http://www.slate.com/blogs/moneybox/2015/02/02/uber_self_driving_cars_autonomous_taxis_aren_t_so_good_for_contractors_in.html (accessed 21 October 2016). 5. Ray Kurzweil, How to Create a Mind: The Secret of Human Thought Revealed, New York: Viking, 2012. 6.


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Right of Way: Race, Class, and the Silent Epidemic of Pedestrian Deaths in America by Angie Schmitt

active transport: walking or cycling, autonomous vehicles, car-free, congestion pricing, COVID-19, crossover SUV, desegregation, Donald Trump, Elaine Herzberg, gentrification, global pandemic, high-speed rail, invention of air conditioning, Lyft, megacity, move fast and break things, off-the-grid, Ralph Nader, Richard Florida, Ronald Reagan, self-driving car, Silicon Valley, Skype, subprime mortgage crisis, super pumped, Uber and Lyft, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, white flight, wikimedia commons

Then her face is obscured, at close distance, by a motion blur, and the video is clipped, right before impact, for her sake and viewers’. Will Self-Driving Cars Save Us? Some people believe that self-driving cars will someday eliminate pedestrian crashes entirely—or nearly so. A system of perfectly calibrated self-driving cars could reduce traffic fatalities 90 percent or more, the Atlantic’s Adrienne LaFrance, among others, has said,1 but that assertion relies on many flawed assumptions—including that 94 percent of crashes can be attributed to so-called human error. Nevertheless, the companies pursuing the self-driving cars tout safety as a foundational moral imperative for the technology, and on some level, it is a compelling vision.

Pedestrian Safety on the Technological Frontier 1. Adrienne LaFrance, “Self-Driving Cars Could Save 300,000 Lives Per Decade in America,” Atlantic, September 9, 2015, https://www.theatlantic.com/technology/archive/2015/09/self-driving-cars-could-save-300000-lives-per-decade-in-america/407956/?utm_source=SFTwitter. 2. Hannah Knowles, “Uber’s Self-Driving Cars Had a Major Flaw: They Weren’t Programmed to Stop for Jaywalkers,” Washington Post, November 6, 2019, https://www.boston.com/cars/car-news/2019/11/06/ubers-self-driving-cars-had-a-major-flaw-they-werent-programmed-to-stop-for-jaywalkers. 3.

Doug Ducey’s Face,” Arizona Republic, May 23, 2018, https://www.azcentral.com/story/opinion/op-ed/laurieroberts/2018/05/23/uber-arizona-gov-doug-ducey-experiment-blows-up-his-face/639118002/. 6. Julia Carrie Wong, “California Threatens Legal Action against Uber Unless It Halts Self-Driving Cars,” Guardian, December 16, 2016, https://www.theguardian.com/technology/2016/dec/16/uber-defies-california-self-driving-cars-san-francisco. 7. Simon Romero, “Wielding Rocks and Knives, Arizonans Attack Self-Driving Cars,” New York Times, December 31, 2018, https://www.nytimes.com/2018/12/31/us/waymo-self-driving-cars-arizona-attacks.html. 8. Governors Highway Safety Association, “Autonomous Vehicles,” accessed February 29, 2020, https://www.ghsa.org/state-laws/issues/autonomous%20vehicles. 9.


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Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, backpropagation, Bernie Sanders, Big Tech, Boston Dynamics, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, dark matter, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, driverless car, Elon Musk, en.wikipedia.org, folksonomy, Geoffrey Hinton, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, machine translation, Mark Zuckerberg, natural language processing, Nick Bostrom, Norbert Wiener, ought to be enough for anybody, paperclip maximiser, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tacit knowledge, tail risk, TED Talk, the long tail, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, trolley problem, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, world market for maybe five computers

The success of self-driving cars is crucially dependent on machine learning (especially deep learning), particularly for the cars’ computer-vision and decision-making components. How can we determine if these cars have successfully learned all that they need to know? This is the billion-dollar question for the self-driving car industry. I’ve encountered conflicting opinions from experts on how soon we can expect self-driving cars to play a significant role in daily life, with predictions ranging (at the time of this writing) from a few years to many decades. Self-driving cars have the potential to vastly improve our lives. Automated vehicles could substantially reduce the millions of annual deaths and injuries due to auto accidents, many of them caused by intoxicated or distracted drivers.

In 2016, three researchers published results from surveys of several hundred people who were given trolley-problem-like scenarios that involved self-driving cars, and were asked for their views of the morality of different actions. In one survey, 76 percent of participants answered that it would be morally preferable for a self-driving car to sacrifice one passenger rather than killing ten pedestrians. But when asked if they would buy a self-driving car programmed to sacrifice its passengers in order to save a much larger number of pedestrians, the overwhelming majority of survey takers responded that they themselves would not buy such a car.21 According to the authors, “We found that participants in six Amazon Mechanical Turk studies approved of utilitarian AVs [autonomous vehicles] (that is, AVs that sacrifice their passengers for the greater good) and would like others to buy them, but they would themselves prefer to ride in AVs that protect their passengers at all costs.”

There are also experimental vehicles that can operate fully autonomously in fairly wide circumstances, but these vehicles still need human “safety drivers” who remain ready to take over at a moment’s notice. Several fatal accidents caused by self-driving cars, including the experimental ones, have occurred when a human was supposed to have been ready to take over but was not paying attention. The self-driving car industry desperately wants to produce and sell fully autonomous vehicles (that is, level 5); indeed, full autonomy is what we, the consumers, have long been promised in all the buzz around self-driving cars. What are the obstacles to getting to true autonomy in our cars? The primary obstacles are the kinds of long-tail situations (“edge cases”) that I described in chapter 6: situations that the vehicle was not trained on, and that might individually occur rarely, but that, taken together, will occur frequently when autonomous vehicles are widespread.


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Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, algorithmic bias, Alignment Problem, AlphaGo, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, Big Tech, bitcoin, Boeing 747, Boston Dynamics, business intelligence, business process, call centre, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, CRISPR, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, fake news, Fellow of the Royal Society, Flash crash, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, hype cycle, ImageNet competition, income inequality, industrial research laboratory, industrial robot, information retrieval, job automation, John von Neumann, Large Hadron Collider, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, Mustafa Suleyman, natural language processing, new economy, Nick Bostrom, OpenAI, opioid epidemic / opioid crisis, optical character recognition, paperclip maximiser, pattern recognition, phenotype, Productivity paradox, radical life extension, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, seminal paper, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, sparse data, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, synthetic biology, systems thinking, Ted Kaczynski, TED Talk, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, workplace surveillance , zero-sum game, Zipcar

We flew down to Victorville, and it was the first time I saw so many self-driving cars in the same place. The whole Stanford team were all fascinated for the first five minutes, watching all these cars zip around without drivers, and the surprising thing was that after five minutes, we acclimatized to it, and we turned our backs to it. We just chatted with each other while self-driving cars zipped passed us 10 meters away, and we weren’t paying attention. One thing that’s remarkable about humanity is how quickly we acclimatize to new technologies, and I feel that it’s not going to be too long before self-driving cars are no longer called self-driving cars, they’re just called cars.

ANDREW NG: I don’t like hype, and I feel like a few companies have spoken publicly and described what I think of as unrealistic timelines about the adoption of self-driving cars. I think that self-driving cars will change transportation, and will make human life much better. However, I think that everyone having a realistic roadmap to self-driving cars is much better than having CEOs stand on stage and proclaim unrealistic timelines. I think the self-driving world is working toward more realistic programs for bringing the tech to market, and I think that’s a very good thing. MARTIN FORD: How do you feel about the role of government regulation, both for self-driving cars and AI more generally? ANDREW NG: The automotive industry has always been heavily regulated because of safety, and I think that the regulation of transportation needs to be rethought in light of AI and self-driving cars.

For the ecosystem as well, I think public-private partnerships will accelerate the growth of domestic industry, and governments that make thoughtful regulation about self-driving cars will see self-driving accelerate in their communities. I’m very committed to my home state of California, but California regulations do not allow self-driving car companies to do certain things, which is why many self-driving car companies can’t have their home bases in California and are now almost forced to operate outside of California. I think that both at the state level as well as at the nation level, countries that have thoughtful policies about self-driving cars, about drones, and about the adoption of AI in payment systems and in healthcare systems, for example—those countries with thoughtful policies in all of these verticals will see much faster progress in how these amazing new tools can be brought to bear on some of the most important problems for their citizens.


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Rule of the Robots: How Artificial Intelligence Will Transform Everything by Martin Ford

AI winter, Airbnb, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, basic income, Big Tech, big-box store, call centre, carbon footprint, Chris Urmson, Claude Shannon: information theory, clean water, cloud computing, commoditize, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, deskilling, disruptive innovation, Donald Trump, Elon Musk, factory automation, fake news, fulfillment center, full employment, future of work, general purpose technology, Geoffrey Hinton, George Floyd, gig economy, Gini coefficient, global pandemic, Googley, GPT-3, high-speed rail, hype cycle, ImageNet competition, income inequality, independent contractor, industrial robot, informal economy, information retrieval, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, John Markoff, Kiva Systems, knowledge worker, labor-force participation, Law of Accelerating Returns, license plate recognition, low interest rates, low-wage service sector, Lyft, machine readable, machine translation, Mark Zuckerberg, Mitch Kapor, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Ocado, OpenAI, opioid epidemic / opioid crisis, passive income, pattern recognition, Peter Thiel, Phillips curve, post scarcity, public intellectual, Ray Kurzweil, recommendation engine, remote working, RFID, ride hailing / ride sharing, Robert Gordon, Rodney Brooks, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, social distancing, SoftBank, South of Market, San Francisco, special economic zone, speech recognition, stealth mode startup, Stephen Hawking, superintelligent machines, TED Talk, The Future of Employment, The Rise and Fall of American Growth, the scientific method, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, universal basic income, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator

Mary Chris Jaklevic, “MD Anderson Cancer Center’s IBM Watson project fails, and so did the journalism related to it,” Health News Review, February 23, 2017, www.healthnewsreview.org/2017/02/md-anderson-cancer-centers-ibm-watson-project-fails-journalism-related/. 56. Mark Anderson, “Surprise! 2020 is not the year for self-driving cars,” IEEE Spectrum, April 22, 2020, spectrum.ieee.org/transportation/self-driving/surprise-2020-is-not-the-year-for-selfdriving-cars. 57. Alex Knapp, “Aurora CEO Chris Urmson says there’ll be hundreds of self-driving cars on the road in five years,” Forbes, October 29, 2019, www.forbes.com/sites/alexknapp/2019/10/29/aurora-ceo-chris-urmson-says-therell-be-hundreds-of-self-driving-cars-on-the-road-in-five-years/. 58. Lex Fridman, “Chris Urmson: Self-driving cars at Aurora, Google, CMU, and DARPA,” Artificial Intelligence Podcast, episode 28, July 22, 2019, lexfridman.com/chris-urmson/.

The reality is that the routine operation of autonomous cars on both highways and in more urban environments—in other words, situations where things work more or less as expected—has largely been solved. If public roads were anything like the inside of an Amazon warehouse in terms of the overall level of predictability, self-driving cars might already be widely deployed. The problem, of course, is in the so-called edge cases, the virtually infinite number of unusual interactions and situations that are difficult or impossible for a self-driving car to accurately predict or, in many cases, to correctly interpret. Most self-driving car initiatives depend on highly precise advanced mapping of the streets being traveled. Therefore, unexpected road closings, construction or traffic accidents can create problems.

This ability to reliably predict the outcome of a robotic operation and work around failure is really the bright line between a controlled warehouse-type environment, where robots are likely to thrive in the relatively near future, and the far more chaotic outside world, where the challenges for technologies like self-driving cars are likely to be far more daunting. A warehouse robot that can predictably handle half the items it might encounter can be highly useful. A self-driving car operating on a public road that can reliably navigate ninety-nine percent of the situations it encounters may be worse than useless because that outlying one percent virtually guarantees disaster. A partially capable fulfillment robot is likely made even more valuable by the fact that Amazon’s sales are governed by a long-tail distribution in which a relatively small fraction of the products stocked in a warehouse constitute the lion’s share of the items that customers tend to order.


Driverless: Intelligent Cars and the Road Ahead by Hod Lipson, Melba Kurman

AI winter, Air France Flight 447, AlphaGo, Amazon Mechanical Turk, autonomous vehicles, backpropagation, barriers to entry, butterfly effect, carbon footprint, Chris Urmson, cloud computing, computer vision, connected car, creative destruction, crowdsourcing, DARPA: Urban Challenge, deep learning, digital map, Donald Shoup, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, General Motors Futurama, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Hans Moravec, high net worth, hive mind, ImageNet competition, income inequality, industrial robot, intermodal, Internet of things, Jeff Hawkins, job automation, Joseph Schumpeter, lone genius, Lyft, megacity, Network effects, New Urbanism, Oculus Rift, pattern recognition, performance metric, Philippa Foot, precision agriculture, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, Silicon Valley, smart cities, speech recognition, statistical model, Steve Jobs, technoutopianism, TED Talk, Tesla Model S, Travis Kalanick, trolley problem, Uber and Lyft, uber lyft, Unsafe at Any Speed, warehouse robotics

Daimler Press Release, “AUDI AG, BMW Group and Daimler AG agree with Nokia Corporation on Joint Acquisition of HERE Digital Mapping Business,” August 3, 2015, http://media.daimler.com/dcmedia/0-921-656186-1-1836824-1-0-0-0-0-0-0-0-0-0-0-0-0-0-0.html 15. “Volvo Car Group tests road magnets for accurate positioning of self-driving cars,” Volvo Car Group Press Release, March 11, 2014, https://www.media.volvocars.com/global/en-gb/media/pressreleases/140760/volvo-car-group-tests-road-magnets-for-accurate-positioning-of-self-driving-cars 16. Bill Vlasic, “U.S. Proposes Spending $4 Billion on Self-Driving Cars,” New York Times, January 14, 2016, http://www.nytimes.com/2016/01/15/business/us-proposes-spending-4-billion-on-self-driving-cars.html?_r=0 17. Brad Templeton “California DMV Regulations May Kill the State’s Robocar Lead,” 4brad.com, December 17, 2015, http://ideas.4brad.com/california-dmv-regulations-may-kill-states-robocar-lead 18.

“Autonomous Cars: Self-Driving the New Auto Industry Paradigm,” Morgan Stanley Blue Paper, November 6, 2013. 13. Google Official Blog, “The Latest Chapter for the Self-Driving Car: Mastering City Street Driving,” April 28, 2014, https://googleblog.blogspot.nl/2014/04/the-latest-chapter-for-self-driving-car.html 14. Google Annual Report, 2007. 15. Burkhard Bilger, “Has the Self-Driving Car at Last Arrived?” New Yorker, November 25, 2013, http://www.newyorker.com/reporting/2013/11/25/131125fa_fact_bilger?currentPage=all 16. Mark Harris “The Unknown Start-up That Built Google’s First Self-Driving Car,” IEEE Spectrum Online, November 19, 2014, http://spectrum.ieee.org/robotics/artificial-intelligence/the-unknown-startup-that-built-googles-first-selfdriving-car 9 Anatomy of a Driverless Car Driverless cars “see” and “hear” by taking in real-time data that flows in from several different types of on-board sensors.

Sommers, “Teen Drivers’ Perceptions of Inattention and Cell Phone Use While Driving,” Traffic Injury Prevention 16 (supp. 2) (2015): S52. DOI: 10.1080/15389588.2015.1062886 19. Quote comes from Google Self-Driving Car Project Monthly Report, October 2015, https://static.googleusercontent.com/media/www.google.com/en//selfdrivingcar/files/reports/report-1015.pdf 20. Description of the bus incident here, https://static.googleusercontent.com/media/www.google.com/en//selfdrivingcar/files/reports/report-0216.pdf 21. Kevin Root, “Self Driving Cars, Autonomous Vehicles, and Shared Mobility,” Slideshare, http://www.slideshare.net/traveler138/self-driving-cars-v11 4 A Mind of Its Own There’s an old joke that made the rounds on the internet back in the 1990s.


Driverless Cars: On a Road to Nowhere by Christian Wolmar

Airbnb, autonomous vehicles, Beeching cuts, bitcoin, Boris Johnson, BRICs, carbon footprint, Chris Urmson, cognitive dissonance, congestion charging, connected car, deskilling, Diane Coyle, don't be evil, driverless car, Elon Musk, gigafactory, high net worth, independent contractor, RAND corporation, ride hailing / ride sharing, self-driving car, Silicon Valley, smart cities, technological determinism, Tesla Model S, Travis Kalanick, wikimedia commons, Zipcar

Financial Times, 23 May (http://on.ft.com/2qiE2cB). 57. G. Paton. 2017. Self-driving cars could run on unlit roads to con- serve energy. The Times, 9 October (http://bit.ly/2zMycZo). 58. International Transport Forum. 2017. Shared Mobility: Simula- tions for Helsinki. OECD (http://bit.ly/2zjWxTa). 118 Photo credits ‘A Google self-driving car at the intersection of Junction Ave and North Rengstorff Ave in Mountain View’ (page 17). By Grendelkhan (own work) [CC BY-SA 4.0 (http://creativecommons.org/licenses/​ by‑sa/​4.0)], via Wikimedia Commons. ‘Uber’s self-driving car test driving in downtown San Francisco’ (page 21).

The article continued: ‘The pizza delivery company is testing out a novel way of carrying out deliveries.’ 14 This, again, had become a global story, and a website called TechCrunch was typical, illustrating it with the picture of a young woman picking up a pizza from a ‘self-driving’ car. The truth, however, was again far more banal. The ‘self-driving’ cars will have drivers but they will stay in the car ‘behind darkened windows’ while delivering the pizzas. TechCrunch revealed that the point of the 23 Driverless Cars: On a Road to Nowhere experiment, run jointly by Domino’s and Ford, was not to test the technology but ‘to see how people react to receiving pizzas via self-driving vehicles’.

While it is not possible to disentangle precisely the proportion of that huge sum being spent on electric and autonomous car technology, according to the PriceWaterhouseCoopers report on connectivity cited in the last chapter, ‘the self-driving car will be the most valuable contribution to automakers’ top and bottom lines in a generation’.32 Therefore, it is highly likely that much of this money is being spent on the search for the Holy Grail of the self-driving car, and the actual sums mentioned by various companies back this up. Predictions are constantly being made and updated – invariably pushed further into the future – and some of them will therefore be out of date or abandoned even before this book is published.


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The Second Intelligent Species: How Humans Will Become as Irrelevant as Cockroaches by Marshall Brain

Amazon Web Services, basic income, clean water, cloud computing, computer vision, digital map, driverless car, en.wikipedia.org, full employment, Garrett Hardin, income inequality, job automation, knowledge worker, low earth orbit, mutually assured destruction, Neil Armstrong, Occupy movement, ocean acidification, Search for Extraterrestrial Intelligence, self-driving car, Stephen Hawking, Tragedy of the Commons, working poor

The system also includes front- and rear-facing radar with a longer range, as well as an optical camera that is helpful in determining, among other things, whether a traffic light is red or green. In addition, self-driving cars drive on roads that have been pre-scanned. How can a self-driving car tell where the lanes are if it is night time, raining and the lane marking lines have faded? The car knows where the lanes are because of this pre-scanning. By putting all of this together, a self-driving car can do a great job on public roads. In fact, self-driving cars are far better at driving than human beings are. Self-driving cars never get distracted, never blink, never doze off, never talk on cell phones, never get drunk, etc. In addition, the self-driving car has a 360-degree view and multiple sensors that humans will never have.

Chapter 5 - How Computer Vision Systems will Destroy Jobs If you look back at the description of self-driving cars in the previous chapter, notice that computer vision does not really play a role. Current self-driving cars do not have two eyes on the roof or the hood looking out at the road and deciding what to do based on visual input. Self-driving cars do have an optical camera, but it plays a small role. For example, it helps the car decide if a traffic light at an intersection is red or green. This might seem odd to many people. When humans drive a car, visual input through our eyes is essential. Why don't self-driving cars do it the same way? Why doesn't a self-driving car use optical cameras and binocular vision in the same way that human beings use their eyes to sense the world?

To get an idea of how this will unfold, let's focus on one specific group of people who will be affected early and completely by the process of automation: truck drivers. As mentioned in Chapter 2, self-driving cars have existed firmly in the public consciousness since 2012. In August, 2012, Google announced that it had developed self-driving car technology that had logged over 300,000 miles on public roadways, co-mingling with normal and oblivious human traffic. Since then, many more self-driving cars have entered the space. The sensor technology and computer technology that makes self-driving cars possible is impressive, but nothing exotic. A LIDAR unit sends out infrared laser pulses all around the vehicle to form a 360 degree 3D image of everything around the car.


pages: 175 words: 54,755

Robot, Take the Wheel: The Road to Autonomous Cars and the Lost Art of Driving by Jason Torchinsky

autonomous vehicles, barriers to entry, call centre, commoditize, computer vision, connected car, DARPA: Urban Challenge, data science, driverless car, Elon Musk, en.wikipedia.org, interchangeable parts, job automation, Philippa Foot, ransomware, self-driving car, sensor fusion, side project, Tesla Model S, trolley problem, urban sprawl

v=PgnsapPGaaw. 27 Wikipedia, “Edge Detection,” https://en.wikipedia.org/wiki/Edge_detection. 28 Torchinsky, Jason, “Why Nissan Built Realistic Inflatable Versions of Its Most Popular Cars,” Jalopnik, October 18, 2012, https://jalopnik.com/why-nissan-built-realistic-inflatable-versions-of-its-m-5952415. 29 Condliffe, Jamie, “This Image Is Why Self-Driving Cars Come Loaded with Many Types of Sensors,” MIT Technology Review, July 21, 2017, https://www. technologyreview.com/s/608321/this-image-is-why -self-driving-­cars-come-­loaded-­with-many-types-of-sensors/. 30 Antunes, João, “Performance over Price: Lumina’s Novel Lidar Tech for Autonomous Vehicles,” SPAR 3D, May 5, 2017, https://www.spar3d.com/news/lidar/performance-price-luminars -novel-lidar-tech-autonomous-vehicles/. 31 Dwivedi, Priya, “Tracking a self-driving car with high precision,” Towards Data Science, April 30, 2017, https://towardsdatascience.com/helping-a-self-driving-car-localize-itself-88705f419e4a. 32 Kichun Jo; Yongwoo Jo; Jae Kyu Suhr; Ho Gi Jung; Myoungho Sunwoo, “Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras,” IEEE Transactions on Intelligent Transportaion Systems, vol, 16, 6, December 2015, https://ieeexplore.ieee.org/document/7160754/. 33 Silver, David, “How Self-Driving Cars Work,” Medium, December 14, 2017, https://medium.com/udacity/how-self-driving-cars-work -f77c49dca47e. 34 Website of the Australian Government Department of Infrastructure, Regional Development and Cities, https://infrastructure.gov.au/vehicles/mv_standards_act/files/Sub136_Austroads.pdf.

v=PgnsapPGaaw. 27 Wikipedia, “Edge Detection,” https://en.wikipedia.org/wiki/Edge_detection. 28 Torchinsky, Jason, “Why Nissan Built Realistic Inflatable Versions of Its Most Popular Cars,” Jalopnik, October 18, 2012, https://jalopnik.com/why-nissan-built-realistic-inflatable-versions-of-its-m-5952415. 29 Condliffe, Jamie, “This Image Is Why Self-Driving Cars Come Loaded with Many Types of Sensors,” MIT Technology Review, July 21, 2017, https://www. technologyreview.com/s/608321/this-image-is-why -self-driving-­cars-come-­loaded-­with-many-types-of-sensors/. 30 Antunes, João, “Performance over Price: Lumina’s Novel Lidar Tech for Autonomous Vehicles,” SPAR 3D, May 5, 2017, https://www.spar3d.com/news/lidar/performance-price-luminars -novel-lidar-tech-autonomous-vehicles/. 31 Dwivedi, Priya, “Tracking a self-driving car with high precision,” Towards Data Science, April 30, 2017, https://towardsdatascience.com/helping-a-self-driving-car-localize-itself-88705f419e4a. 32 Kichun Jo; Yongwoo Jo; Jae Kyu Suhr; Ho Gi Jung; Myoungho Sunwoo, “Precise Localization of an Autonomous Car Based on Probabilistic Noise Models of Road Surface Marker Features Using Multiple Cameras,” IEEE Transactions on Intelligent Transportaion Systems, vol, 16, 6, December 2015, https://ieeexplore.ieee.org/document/7160754/. 33 Silver, David, “How Self-Driving Cars Work,” Medium, December 14, 2017, https://medium.com/udacity/how-self-driving-cars-work -f77c49dca47e. 34 Website of the Australian Government Department of Infrastructure, Regional Development and Cities, https://infrastructure.gov.au/vehicles/mv_standards_act/files/Sub136_Austroads.pdf.

* * * 60 Siler, Wes, “Science Shows People Prefer Angry, Aggressive Cars,” Jalopnik, October 7, 2008, https://jalopnik.com/5060127/science-shows-people-prefer-angry-aggressive-cars. 61 Warren, Tamara, “Google’s Self-Driving Car Design Boss Speaks on Her Strategy,” The Verge, October 25, 2016, https://www.theverge.com/2016/10/25/13307364/google-self-driving-car-design-yoojung -ahn-interview. 62 Torchinsky, Jason, “Honda Once Made a Car Specifically For People to Bone In,” Jalopnik, November 2, 2015, https://jalopnik.com/honda-once-made-a-car-specifically-for-people-to-bone-i-1740050375. 63 Hanlon, Mike, “Honda’s Fuya-jo Party on Wheels Concept,” New Atlas, April 16, 2005, https://newatlas.com/go/3950/. 64 “Branding Nano as the Cheapest Car Was a Big Mistake, Says Ratan Tata,” India Today Online, November 30, 2013, https://www.indiatoday.in/business/story/nano-branding-big-mistake -ratan-tata-219211-2013-11-30.


pages: 444 words: 127,259

Super Pumped: The Battle for Uber by Mike Isaac

"Susan Fowler" uber, "World Economic Forum" Davos, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, always be closing, Amazon Web Services, Andy Kessler, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Bay Area Rapid Transit, Benchmark Capital, Big Tech, Burning Man, call centre, Cambridge Analytica, Chris Urmson, Chuck Templeton: OpenTable:, citizen journalism, Clayton Christensen, cloud computing, corporate governance, creative destruction, data science, Didi Chuxing, don't be evil, Donald Trump, driverless car, Elon Musk, end-to-end encryption, fake news, family office, gig economy, Google Glasses, Google X / Alphabet X, Greyball, Hacker News, high net worth, hockey-stick growth, hustle culture, impact investing, information security, Jeff Bezos, John Markoff, John Zimmer (Lyft cofounder), Kevin Roose, Kickstarter, Larry Ellison, lolcat, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Masayoshi Son, mass immigration, Menlo Park, Mitch Kapor, money market fund, moral hazard, move fast and break things, Network effects, new economy, off grid, peer-to-peer, pets.com, Richard Florida, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, selling pickaxes during a gold rush, shareholder value, Shenzhen special economic zone , Sheryl Sandberg, side hustle, side project, Silicon Valley, Silicon Valley startup, skunkworks, Snapchat, SoftBank, software as a service, software is eating the world, South China Sea, South of Market, San Francisco, sovereign wealth fund, special economic zone, Steve Bannon, Steve Jobs, stock buybacks, super pumped, TaskRabbit, tech bro, tech worker, the payments system, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, union organizing, upwardly mobile, Vision Fund, WeWork, Y Combinator

v=TS0NuV-zLZE. 244 “We will impound the vehicle”: Victor Fiorillo, “Uber Launches UberX In Philadelphia, but PPA Says ‘Not So Fast,’ ” Philadelphia, October 25, 2014, https://www.phillymag.com/news/2014/10/25/uber-launches-uberx-philadelphia/. 244 “UBERX: REMINDER”: Documents held by author. 245 a behavior engineers called “eyeballing”: Mike Isaac, “How Uber Deceives the Authorities Worldwide,” New York Times, March 3, 2017, https://www.nytimes.com/2017/03/03/technology/uber-greyball-program-evade-authorities.html. 247 “Uber has for years used”: Isaac, “How Uber Deceives the Authorities Worldwide.” 247 Uber’s security chief, prohibited employees: Daisuke Wakabayashi, “Uber Seeks to Prevent Use of Greyball to Thwart Regulators,” New York Times, March 8, 2017, https://www.nytimes.com/2017/03/08/business/uber-regulators-police-greyball.html. 247 Department of Justice opened a probe: Mike Isaac, “Uber Faces Federal Inquiry Over Use of Greyball Tool to Evade Authorities,” New York Times, May 4, 2017, https://www.nytimes.com/2017/05/04/technology/uber-federal-inquiry-software-greyball.html. 247 the inquiry widened to Philadelphia: Mike Isaac, “Justice Department Expands Its Inquiry into Uber’s Greyball Tool,” New York Times, May 5, 2017, https://www.nytimes.com/2017/05/05/technology/uber-greyball-investigation-expands.html. 248 He called it The Rideshare Guy: Harry Campbell, “About the Rideshare Guy: Harry Campbell,” The Rideshare Guy (blog), https://therideshareguy.com/about-the-rideshare-guy/. 248 directly due to the string of controversies: Kara Swisher and Johana Bhuiyan, “Uber President Jeff Jones Is Quitting, Citing Differences Over ‘Beliefs and Approach to Leadership,’ ” Recode, March 19, 2017, https://www.recode.net/2017/3/19/14976110/uber-president-jeff-jones-quits. 250 “there’s just a bunch of models”: Emily Peck, “Travis Kalanick’s Ex Reveals New Details About Uber’s Sexist Culture,” Huffington Post, March 29, 2017, https://www.huffingtonpost.com/entry/travis-kalanick-gabi-holzwarth-uber_us_58da7341e4b018c4606b8ec9. 253 “I am so sorry for being cold”: Amir Efrati, “Uber Group’s Visit to Seoul Escort Bar Sparked HR Complaint,” The Information, March 24, 2017, https://www.theinformation.com/articles/uber-groups-visit-to-seoul-escort-bar-sparked-hr-complaint. 253 reporter’s cell phone number: Efrati, “Uber Group’s Visit to Seoul Escort Bar.” Chapter 26: FATAL ERRORS 254 who called the maneuver illegal: Mike Isaac, “Uber Expands Self-Driving Car Service to San Francisco. D.M.V. Says It’s Illegal.,” New York Times, December 14, 2016, https://www.nytimes.com/2016/12/14/technology/uber-self-driving-car-san-francisco.html. 254 Uber issued a statement: Isaac, “Uber Expands Self-Driving Car Service to San Francisco.” 255 Uber’s narrative was false: Mike Isaac and Daisuke Wakabayashi, “A Lawsuit Against Uber Highlights the Rush to Conquer Driverless Cars,” New York Times, February 24, 2017, https://www.nytimes.com/2017/02/24/technology/anthony-levandowski-waymo-uber-google-lawsuit.html. 255 Levandowski was unceremoniously terminated: Mike Isaac and Daisuke Wakabayashi, “Uber Fires Former Google Engineer at Heart of Self-Driving Dispute,” New York Times, May 30, 2017, https://www.nytimes.com/2017/05/30/technology/uber-anthony-levandowski.html. 256 “possible theft of trade secrets”: Aarian Marshall, “Google’s Fight Against Uber Takes a Turn for the Criminal,” Wired, May 12, 2017, https://www.wired.com/2017/05/googles-fight-uber-takes-turn-criminal/. 256 expressed contrition in a press interview: Mike Isaac, “Uber Releases Diversity Report and Repudiates Its ‘Hard-Charging Attitude,’ ” New York Times, March 28, 2017, https://www.nytimes.com/2017/03/28/technology/uber-scandal-diversity-report.html. 257 existence of Uber’s program “Hell”: Efrati, “Uber’s Top Secret ‘Hell’ Program.” 257 The team kept tabs: Kate Conger, “Uber’s Massive Scraping Program Collected Data About Competitors Around the World,” Gizmodo, December 11, 2017, https://gizmodo.com/ubers-massive-scraping-program-collected-data-about-com-1820887947. 257 recorded private conversations: Paayal Zaveri, “Unsealed Letter in Uber-Waymo Case Details How Uber Employees Allegedly Stole Trade Secrets,” CNBC, December 15, 2017, https://www.cnbc.com/2017/12/15/jacobs-letter-in-uber-waymo-case-says-uber-staff-stole-trade-secrets.html. 261 personal, private medical files: Kara Swisher and Johana Bhuiyan, “A Top Uber Executive, Who Obtained the Medical Records of a Customer Who Was a Rape Victim, Has Been Fired,” Recode, June 7, 2017, https://www.recode.net/2017/6/7/15754316/uber-executive-india-assault-rape-medical-records. 262 it was over for Eric Alexander: Mike Isaac, “Uber Fires Executive Over Handling of Rape Investigation in India,” New York Times, June 7, 2017, https://www.nytimes.com/2017/06/07/technology/uber-fires-executive.html. 262 Kalanick accepted her resignation: Mike Isaac, “Executive Who Steered Uber Through Scandals Joins Exodus,” New York Times, April 11, 2017, https://www.nytimes.com/2017/04/11/technology/ubers-head-of-policy-and-communications-joins-executive-exodus.html. 263 “The last note I got from her”: Kalanick, “Dad is getting much better in last 48 hours.” 265 “Over the last seven years”: Unpublished letter, obtained by author.

Chapter 18 CLASH OF THE SELF-DRIVING CARS Travis Kalanick was fuming in the grand ballroom of the Terranea Resort—a seaside haven for the rich off the coast of Rancho Palos Verdes, California. It was the opening night of the 2014 annual Code Conference, a confab for the tech elite. On stage, Sergey Brin was in the middle of a historic speech, but Kalanick was on his iPhone firing off messages to David Drummond. Brin—who was ostensibly Kalanick’s partner and investor—had just unveiled something that could threaten Uber’s existence: a fully autonomous self-driving car. “The reason I’m excited for this self-driving car project is the ability for it to change the world around you,” Brin told the audience.

Uber’s “competitive intelligence” operation—that is, the sprawling, systematic COIN program led by Joe Sullivan and his lieutenant, Mat Henley—grew larger by the day. Kalanick often heard whispers of Google’s self-driving car project, or occasionally, an errant rumor that Google was starting a self-driving taxi service. Every time Kalanick would hear a rumor like this, he’d fire off an email to Drummond. “We get stuff like this more than I would like,” Kalanick once wrote to Drummond, forwarding intel about a Google self-driving car service. “A meeting with Larry [Page] could calm this down if it’s not true but he has been avoiding any meeting with me since last fall.


pages: 190 words: 62,941

Wild Ride: Inside Uber's Quest for World Domination by Adam Lashinsky

"Susan Fowler" uber, "World Economic Forum" Davos, Airbnb, always be closing, Amazon Web Services, asset light, autonomous vehicles, Ayatollah Khomeini, Benchmark Capital, business process, Chuck Templeton: OpenTable:, cognitive dissonance, corporate governance, DARPA: Urban Challenge, Didi Chuxing, Donald Trump, driverless car, Elon Musk, Erlich Bachman, gig economy, Golden Gate Park, Google X / Alphabet X, hustle culture, independent contractor, information retrieval, Jeff Bezos, John Zimmer (Lyft cofounder), Lyft, Marc Andreessen, Mark Zuckerberg, megacity, Menlo Park, multilevel marketing, new economy, pattern recognition, price mechanism, public intellectual, reality distortion field, ride hailing / ride sharing, Salesforce, San Francisco homelessness, Sand Hill Road, self-driving car, side hustle, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, Snapchat, South of Market, San Francisco, sovereign wealth fund, statistical model, Steve Jobs, super pumped, TaskRabbit, tech worker, Tony Hsieh, transportation-network company, Travis Kalanick, turn-by-turn navigation, Uber and Lyft, Uber for X, uber lyft, ubercab, young professional

This all felt quaintly futuristic in 2013. Still, the one company that could trounce Uber by creating a taxi-like service was already developing a self-driving car, seemingly on a whim. It was a tech powerhouse with vast resources that had already created one of the best digital street-mapping systems in the world. And that company, Google, was on the verge of making a major investment in Uber. And yet, these were still early days for self-driving cars. The concept largely resided in the labs of robotics departments of engineering schools. When Kalanick was first able to check under the hood of Google’s autonomous program, he wasn’t the least bit impressed.

When Kalanick was first able to check under the hood of Google’s autonomous program, he wasn’t the least bit impressed. In preparation for a meeting with Google CEO Larry Page, Kalanick was invited to take a ride in one of Google’s self-driving cars, which were tooling around the company’s Mountain View, California, campus. Such rides had become a staple for visiting dignitaries, kind of like a trip to the Great Wall of China or a stroll down the empty street of a movie set. If you were important enough, you were offered a ride in a Google self-driving car. It took more than razzle-dazzle to impress Kalanick, however, and for all the gee-whiz promise of Google’s bold technological experiment, he was underwhelmed.

Several years after the DARPA Grand Challenge, Larry Page and Sergey Brin decided they wanted to build a self-driving car. Never mind that it had little to do with Google’s information-quest mission. It was “moonshot” technology they wanted to advance. They persuaded Thrun to leave Stanford in 2010 to help start an in-house research arm called Google X. The group would go on to develop diverse technology such as antiaging drugs and computers that could be printed on eyeglasses and contact lenses. Its first project would be a self-driving car. Thrun helped develop software called Street View and sent cars driven by Google engineers onto city streets to map everything from street signs to the placement of barriers.


pages: 307 words: 88,180

AI Superpowers: China, Silicon Valley, and the New World Order by Kai-Fu Lee

"World Economic Forum" Davos, AI winter, Airbnb, Albert Einstein, algorithmic bias, algorithmic trading, Alignment Problem, AlphaGo, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, bike sharing, business cycle, Cambridge Analytica, cloud computing, commoditize, computer vision, corporate social responsibility, cotton gin, creative destruction, crony capitalism, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, deskilling, Didi Chuxing, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, full employment, future of work, general purpose technology, Geoffrey Hinton, gig economy, Google Chrome, Hans Moravec, happiness index / gross national happiness, high-speed rail, if you build it, they will come, ImageNet competition, impact investing, income inequality, informal economy, Internet of things, invention of the telegraph, Jeff Bezos, job automation, John Markoff, Kickstarter, knowledge worker, Lean Startup, low skilled workers, Lyft, machine translation, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, Neil Armstrong, new economy, Nick Bostrom, OpenAI, pattern recognition, pirate software, profit maximization, QR code, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, risk tolerance, Robert Mercer, Rodney Brooks, Rubik’s Cube, Sam Altman, Second Machine Age, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, SoftBank, Solyndra, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, TED Talk, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, vertical integration, Vision Fund, warehouse robotics, Y Combinator

Every one of these downside risks presents thorny ethical questions. How should we balance the livelihoods of millions of truck drivers against the billions of dollars and millions of hours saved by autonomous vehicles? What should a self-driving car “optimize for” in situations where it is forced to choose which car to crash into? How should an autonomous vehicle’s algorithm weigh the life of its owner? Should your self-driving car sacrifice your own life to save the lives of three other people? These are the questions that keep ethicists up at night. They’re also questions that could hold up the legislation needed for autonomous-vehicle deployment and tie up AI companies in years of lawsuits.

CHINA’S “TESLA” APPROACH When managing a country of 1.39 billion people—one in which 260,000 people die in car accidents each year—the Chinese mentality is that you can’t let the perfect be the enemy of the good. That is, rather than wait for flawless self-driving cars to arrive, Chinese leaders will likely look for ways to deploy more limited autonomous vehicles in controlled settings. That deployment will have the side effect of leading to more exponential growth in the accumulation of data and a corresponding advance in the power of the AI behind it. Key to that incremental deployment will be the construction of new infrastructure specifically made to accommodate autonomous vehicles. In the United States, in contrast, we build self-driving cars to adapt to our existing roads because we assume the roads can’t change.

“the best company”: April Glaser, “DJI Is Running away with the Drone Market,” Recode, April 14, 2017, https://www.recode.net/2017/4/14/14690576/drone-market-share-growth-charts-dji-forecast. 1.5 million miles: Fred Lambert, “Google’s Self-Driving Car vs Tesla Autopilot: 1.5M Miles in 6 Years vs 47M Miles in 6 Months,” Electrek, April 11, 2016, https://electrek.co/2016/04/11/google-self-driving-car-tesla-autopilot/. $583 billion: “Xiong’an New Area: China’s Latest Special Economic Zone?” CKGSB Knowledge, November 8, 2017, http://knowledge.ckgsb.edu.cn/2017/11/08/all-articles/xiongan-china-special-economic-zone/. 6.


pages: 414 words: 109,622

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World by Cade Metz

AI winter, air gap, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, Amazon Robotics, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Big Tech, British Empire, Cambridge Analytica, carbon-based life, cloud computing, company town, computer age, computer vision, deep learning, deepfake, DeepMind, Demis Hassabis, digital map, Donald Trump, driverless car, drone strike, Elon Musk, fake news, Fellow of the Royal Society, Frank Gehry, game design, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Googley, Internet Archive, Isaac Newton, Jeff Hawkins, Jeffrey Epstein, job automation, John Markoff, life extension, machine translation, Mark Zuckerberg, means of production, Menlo Park, move 37, move fast and break things, Mustafa Suleyman, new economy, Nick Bostrom, nuclear winter, OpenAI, PageRank, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, profit motive, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Sam Altman, Sand Hill Road, self-driving car, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Ballmer, Steven Levy, Steven Pinker, tech worker, telemarketer, The Future of Employment, Turing test, warehouse automation, warehouse robotics, Y Combinator

That December, while visiting his parents back in Toronto during the holiday break, he’d received an email from a woman named Anelia Angelova, who wanted help with the Google self-driving car. She didn’t actually work on the self-driving car. She worked with Krizhevsky at Google Brain. But she knew the lab’s ongoing research in computer vision—an extension of Krizhevsky’s efforts at the University of Toronto—would remake the way the company built its autonomous vehicles. The Google self-driving car project, known inside the company as Chauffeur, was nearly five years old. That meant Google had spent nearly five years building autonomous vehicles without help from deep learning.

There, he did what he’d wanted to do at Microsoft: build a self-driving car. The company launched its project years after Google, but Lu was sure it would put cars on the road far faster than its American rival. This wasn’t because Baidu had better engineers or better technology. It was because Baidu was building its car in China. In China, government was closer to industry. As Baidu’s chief operating officer, he was working with five Chinese municipalities to remake their cities so that they could accommodate the company’s self-driving cars. “There is no question in my mind this will get commercialized way sooner in China than in the United States.

It was increasingly obvious, however, that this was, at best, a partial solution. For years, companies like Google and Uber had promised that self-driving cars would soon be on the roads, shuttling everyday people across cities in the United States and abroad. But even the popular press began to realize these claims were grossly exaggerated. Although deep learning had significantly improved their ability to recognize people, objects, and signs on the road—and accelerated their ability to predict events and plan routes forward—self-driving cars were still a long way from dealing with the chaos of the daily commute with the same agility as people.


pages: 343 words: 91,080

Uberland: How Algorithms Are Rewriting the Rules of Work by Alex Rosenblat

"Susan Fowler" uber, Affordable Care Act / Obamacare, Airbnb, algorithmic management, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, basic income, big-box store, bike sharing, Black Lives Matter, business logic, call centre, cashless society, Cass Sunstein, choice architecture, cognitive load, collaborative economy, collective bargaining, creative destruction, crowdsourcing, data science, death from overwork, digital divide, disinformation, disruptive innovation, don't be evil, Donald Trump, driverless car, emotional labour, en.wikipedia.org, fake news, future of work, gender pay gap, gig economy, Google Chrome, Greyball, income inequality, independent contractor, information asymmetry, information security, Jaron Lanier, Jessica Bruder, job automation, job satisfaction, Lyft, marginal employment, Mark Zuckerberg, move fast and break things, Network effects, new economy, obamacare, performance metric, Peter Thiel, price discrimination, proprietary trading, Ralph Waldo Emerson, regulatory arbitrage, ride hailing / ride sharing, Salesforce, self-driving car, sharing economy, side hustle, Silicon Valley, Silicon Valley ideology, Skype, social software, SoftBank, stealth mode startup, Steve Jobs, strikebreaker, TaskRabbit, technological determinism, Tim Cook: Apple, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, urban planning, Wolfgang Streeck, work culture , workplace surveillance , Yochai Benkler, Zipcar

It offered a flimsy premise, that its particular technology was simply not subject to the rules the DMV had developed.14 The most important part of Uber’s encounter with the California DMV was the company’s rejection of the government’s authority on principle. Citing Tesla’s autopilot technology as an example of self-driving-car technology that doesn’t require a permit, Uber argued that the testing permits the DMV devised for self-driving cars didn’t apply to Uber’s self-driving cars because they were, in fact, not yet capable of autonomous driving without a human overseer. Anthony Lewandowski, Uber’s lead engineer on self-driving cars (who was accused of stealing Google’s self-driving Internet protocol when he left and came to Uber), announced, “We cannot in good conscience sign up to regulation for something we’re not doing.”15 Uber directly contravened the law with no remorse, and with no real impact.

For context: Uber provided these comments to me and my coauthor Ryan Calo as part of our fact-checking effort in preparation for our law review article, “The Taking Economy: Uber, Information and Power.” 14. Sam Levin, “Uber Admits to Self-Driving Car ‘Problem’ in Bike Lanes as Safety Concerns Mount,” The Guardian, December 19, 2016, www.theguardian.com/technology/2016/dec/19/uber-self-driving-cars-bike-lanes-safety-san-francisco. 15. Julia Carrie Wong, “California Threatens Legal Action against Uber Unless It Halts Self-Driving Cars,” The Guardian, December 16, 2016, www.theguardian.com/technology/2016/dec/16/uber-defies-california-self-driving-cars-san-francisco. 16. Mike Isaac (@Mikeisaac) wrote, “The state attorney generals office is not happy with Uber.”

Twitter, December 16, 2016, https://twitter.com/MikeIsaac/status/809936567078965248. 17. Marisa Kendall, “Uber Sends Self-Driving Cars to Arizona after Failed San Francisco Pilot,” Mercury News, December 23, 2016, www.mercurynews.com/2016/12/22/uber-ships-self-driving-cars-to-arizona-after-failed-san-francisco-pilot/. 18. Selena Larson, “Arizona Suspends Uber’s Self-Driving Car Tests after Fatal Crash,” CNN Tech, March 27, 2018, http://money.cnn.com/2018/03/26/technology/arizona-suspends-uber-self-driving-cars/index.html. 19. Oliver Laughland, Jessica Glenza, Steven Thrasher, and Paul Lewis, “‘We Can’t Breathe’: Eric Garner’s Last Words Become Protestors’ Rallying Cry,” The Guardian, December 4, 2014, www.theguardian.com/us-news/2014/dec/04/we-cant-breathe-eric-garner-protesters-chant-last-words. 20.


pages: 295 words: 81,861

Road to Nowhere: What Silicon Valley Gets Wrong About the Future of Transportation by Paris Marx

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, A Declaration of the Independence of Cyberspace, Airbnb, An Inconvenient Truth, autonomous vehicles, back-to-the-land, Berlin Wall, Bernie Sanders, bike sharing, Californian Ideology, car-free, carbon credits, carbon footprint, cashless society, clean tech, cloud computing, colonial exploitation, computer vision, congestion pricing, corporate governance, correlation does not imply causation, COVID-19, DARPA: Urban Challenge, David Graeber, deep learning, degrowth, deindustrialization, deskilling, Didi Chuxing, digital map, digital rights, Donald Shoup, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Elaine Herzberg, Elon Musk, energy transition, Evgeny Morozov, Extinction Rebellion, extractivism, Fairchild Semiconductor, Ford Model T, frictionless, future of work, General Motors Futurama, gentrification, George Gilder, gig economy, gigafactory, global pandemic, global supply chain, Google Glasses, Google X / Alphabet X, green new deal, Greyball, high-speed rail, Hyperloop, independent contractor, Induced demand, intermodal, Jane Jacobs, Jeff Bezos, jitney, John Perry Barlow, Kevin Kelly, knowledge worker, late capitalism, Leo Hollis, lockdown, low interest rates, Lyft, Marc Benioff, market fundamentalism, minimum viable product, Mother of all demos, move fast and break things, Murray Bookchin, new economy, oil shock, packet switching, Pacto Ecosocial del Sur, Peter Thiel, pre–internet, price mechanism, private spaceflight, quantitative easing, QWERTY keyboard, Ralph Nader, Richard Florida, ride hailing / ride sharing, Ronald Reagan, safety bicycle, Salesforce, School Strike for Climate, self-driving car, Sidewalk Labs, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, smart cities, social distancing, Southern State Parkway, Steve Jobs, Stewart Brand, Stop de Kindermoord, streetcar suburb, tech billionaire, tech worker, techlash, technological determinism, technological solutionism, technoutopianism, the built environment, The Death and Life of Great American Cities, TikTok, transit-oriented development, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban renewal, VTOL, walkable city, We are as Gods, We wanted flying cars, instead we got 140 characters, WeWork, Whole Earth Catalog, Whole Earth Review, work culture , Yom Kippur War, young professional

Part One—Understanding Uber’s Bleak Operating Economics,” Naked Capitalism (blog), November 30, 2016, Nakedcapitalism.com. 25 Ibid. 26 Anthony Ha, “California Regulator Passes First Ridesharing Rules, a Big Win for Lyft, Sidecar, and Uber,” TechCrunch, September 19, 2013, Techcrunch.com. 27 Ken Jacobs and Michael Reich, “The Uber/Lyft Ballot Initiative Guarantees Only $5.64 an Hour,” UC Berkeley Labor Center, October 21, 2019, Laborcenter.berkeley.edu. 28 Wilfred Chan, “Can American Labor Survive Prop 22?,” Nation, November 10, 2020, Thenation.com. 5. Self-Driving Cars Did Not Deliver 1 James Niccolai, “Self-driving Cars a Reality for ‘Ordinary People’ within 5 Years, Says Google’s Sergey Brin,” Computer World, September 25, 2012, Computerworld.com. 2 John Paczkowski, “Google’s Self-Driving Cars Now Legal in California,” All Things D, September 25, 2012, Allthingsd.com. 3 Nicholas Carlson, “Uber Is Planning for a World Without Drivers—Just a Self-Driving Fleet,” Business Insider, May 28, 2014, Businessinsider.com. 4 Kara Swisher, “Self-Driving into the Future: Full Code Conference Video of Google’s Sergey Brin,” Recode, June 11, 2014, Vox.com. 5 Ibid. 6 Adrienne LaFrance, “Your Grandmother’s Driverless Car,” Atlantic, June 29, 2016, Theatlantic.com. 7 Doc Quigg, “Reporter Rides Driverless Car,” Press-Courier, June 7, 1960, News.google.com. 8 “A Brief History of Autonomous Vehicle Technology,” Wired, n.d., Wired.com. 9 Robert D.

., “Editorial Patterns in Bicyclist and Pedestrian Crash Reporting,” Transportation Research Record: Journal of the Transportation Research Board 2673:2, 2019. 15 Russell Brandom, “Self-driving Cars Are Headed toward an AI Roadblock,” Verge, July 3, 2018, Theverge.com. 16 Eric A. Taub, “How Jaywalking Could Jam Up the Era of Self-Driving Cars,” New York Times, August 1, 2019, Nytimes.com. 17 Ibid. 18 Matthew Sparkes, “Should We All Wear Sensors to Avoid Being Run Over by Driverless Cars?,” New Scientist, March 5, 2021, Newscientist.com. 19 Edward Taylor, “Volkswagen Says Driverless Vehicles Have Limited Appeal and High Cost,” Reuters, March 5, 2019, Reuters.com. 20 Daisuke Wakabayashi, “Uber’s Self-Driving Cars Were Struggling Before Arizona Crash,” New York Times, March 23, 2018, Nytimes.com. 21 “Collision between Vehicle Controlled by Developmental Automated Driving System and Pedestrian, Tempe, Arizona, March 18, 2018,” Highway Accident Report NTSB/HAR-19/03 prepared by the National Transportation Safety Board, 2019, pp. 43–5, Ntsb.gov. 22 Ibid. 23 Ibid. 24 Lora Kolodny, “Tesla Faces Another NHTSA Investigation after Fatal Driverless Crash in Spring, Texas,” CNBC, April 19, 2021, Cnbc.com. 25 Shara Tibken, “Waymo CEO: Autonomous Cars Won’t Ever Be Able to Drive in All Conditions,” CNET, November 13, 2018, Cnet.com. 26 A.

Brin pitched a set of ugly smart glasses called Google Glass as the next big consumer tech product in 2012, but they were a flop. However, his vision for an urban future where self-driving cars would take over streets around the world initially seemed like it might avoid the same fate. In September 2012, then California governor Jerry Brown appeared alongside Brin at Google’s headquarters in Silicon Valley to sign a law aimed at accelerating the testing of autonomous vehicles. At the event, Brin told reporters that he believed “self-driving cars are going to be far safer than human-driven cars.”1 On top of that, the vehicles would reduce traffic congestion, improve fuel efficiency, be prepared for any eventuality that could arise, and better serve those “underserved by the current transportation system.”


pages: 307 words: 90,634

Insane Mode: How Elon Musk's Tesla Sparked an Electric Revolution to End the Age of Oil by Hamish McKenzie

Airbnb, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, basic income, Bay Area Rapid Transit, Ben Horowitz, business climate, car-free, carbon footprint, carbon tax, Chris Urmson, Clayton Christensen, clean tech, Colonization of Mars, connected car, crony capitalism, Deng Xiaoping, Didi Chuxing, disinformation, disruptive innovation, Donald Trump, driverless car, Elon Musk, Fairchild Semiconductor, Ford Model T, gigafactory, Google Glasses, Hyperloop, information security, Internet of things, Jeff Bezos, John Markoff, low earth orbit, Lyft, Marc Andreessen, margin call, Mark Zuckerberg, Max Levchin, megacity, Menlo Park, Nikolai Kondratiev, oil shale / tar sands, paypal mafia, Peter Thiel, ride hailing / ride sharing, Ronald Reagan, self-driving car, Shenzhen was a fishing village, short selling, side project, Silicon Valley, Silicon Valley startup, Snapchat, Solyndra, South China Sea, special economic zone, stealth mode startup, Steve Jobs, tech worker, TechCrunch disrupt, TED Talk, Tesla Model S, Tim Cook: Apple, Tony Fadell, Uber and Lyft, uber lyft, universal basic income, urban planning, urban sprawl, Zenefits, Zipcar

For the first applications of its technology, Uisee trialed self-driving vehicles for cargo and passengers at the international airports in Singapore and Guangzhou, in southern China. China could certainly use self-driving cars. More than seven hundred people are killed on its roads a day, according to the World Health Organization. In a phone interview two days before I met Wu, Wang Jing, the head of Baidu’s autonomous driving unit, said Baidu believed self-driving cars could reduce the death toll on China’s roads by 90 percent—since that’s the proportion of accidents caused by human error. Self-driving cars would also save time, Wang said. A commute in a megacity like Beijing or Shanghai commonly takes an hour or two, but most cars on the road have only one occupant.

He also argued that China provides the ideal testing conditions for autonomous vehicles, because the roads are so crowded and driving behaviors are so erratic, presenting a panoply of challenges for the cars’ supercomputers to contend with—and ultimately overcome. “Technically, if you can make it work in China, you can make it work anywhere.” A day before my call with Wang, Baidu had announced a partnership with Wuhu, a city of nearly four million people in Anhui province, to start a self-driving-car pilot program in the central city. For the first three years, self-driving cars, vans, and buses would be introduced to downtown areas purely for testing. Beyond three years, the plan is to start commercializing the program and allowing passengers in the vehicles. Ultimately, the program would spread across the whole city. Wang cited Wuhu as an example of how regulation in China might actually work in favor of autonomous vehicles.

There’s Tesla, of course, with its autonomous electric semitruck, and other electric truck start-ups Nikola, Thor, and Starsky Robotics. A crack team of engineers from Google’s self-driving car team left the company to establish the San Francisco–based Otto, which said in August 2016 that it was moving “with urgency” to get commercially ready autonomous trucks on the road within two years. Two days later, Uber announced that it had acquired Otto. The company’s cofounder Anthony Levandowski—a pioneering engineer on Google’s self-driving car team—would head up the ride-sharing company’s autonomous vehicle efforts, and Otto would also lead Uber’s efforts in trucking.


The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski

AI winter, Albert Einstein, algorithmic bias, algorithmic trading, AlphaGo, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, backpropagation, Baxter: Rethink Robotics, behavioural economics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, data science, deep learning, DeepMind, delayed gratification, Demis Hassabis, Dennis Ritchie, discovery of DNA, Donald Trump, Douglas Engelbart, driverless car, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, Jim Simons, John Conway, John Markoff, John von Neumann, language acquisition, Large Hadron Collider, machine readable, Mark Zuckerberg, Minecraft, natural language processing, Neil Armstrong, Netflix Prize, Norbert Wiener, OpenAI, orbital mechanics / astrodynamics, PageRank, pattern recognition, pneumatic tube, prediction markets, randomized controlled trial, Recombinant DNA, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, world market for maybe five computers, X Prize, Yogi Berra

Rather than follow the traditional AI approach by writing a computer program to anticipate every contingency, Thrun drove Stanley around the desert (figure 1.2), and it learned for itself to predict how to steer based on sensory inputs from its vision and distance sensors. Thrun later founded Google X, a skunk works for high-tech projects, where the technology for self-driving cars was developed further. Google’s self-driving cars have since logged 3.5 million miles driving around the San Francisco Bay Area. Uber has deployed a fleet of self-driving cars in Pittsburgh. Apple is moving into self-driving cars to extend the range of Figure 1.1 Sebastian Thrun with Stanley, the self-driving automobile that won the 2005 DARPA Grand Challenge. This breakthrough jump-started a technological revolution in transportation.

General Motors paid $1 billion for Cruise Automation, a Silicon Valley start-up that is developing driverless technology, and invested an additional $600 million in 2017 in research and development.2 In 2017, Intel purchased Mobileye, a company that specializes in sensors and computer vision for self-driving cars, for $15.3 billion dollars. The stakes are high in the multitrillion-dollar transportation sector of the economy. Self-driving cars will soon disrupt the livelihoods of millions of truck and taxi drivers. Eventually, there will be no need to own a car in a city when a self-driving car can show up in a minute and take you safely to your destination, without your having to park it. The average car today is only used 4 percent of the time, which means it needs to be parked somewhere 96 percent of the time. But because self-driving cars can be serviced and parked outside cities, vast stretches of city land now covered with parking lots can be repurposed for more productive uses.

Waymo, the self-driving spin-off from Google, has invested $1 billion over 8 years and has constructed a secretive testing facility in California’s central valley with a 91-acre fake town, including fake bicycle riders and fake auto breakdowns.6 The goal is to broaden the training data to include special and unusual circumstances, called edge cases. Rare driving events that occur on highways often lead to accidents. The difference with self-driving cars is that when one car experiences a rare event, the learning experience will propagate to all other self-driving cars, a form of collective intelligence. Many similar test facilities are being constructed by other self-driving car companies. These create new jobs that did not exist before, and new supply chains for the sensors and lasers that are needed to guide the cars.7 Self-driving cars are just the most visible manifestation of a major shift in an economy being driven by information technology (IT).


pages: 290 words: 85,847

A Brief History of Motion: From the Wheel, to the Car, to What Comes Next by Tom Standage

accelerated depreciation, active transport: walking or cycling, autonomous vehicles, back-to-the-city movement, bike sharing, car-free, carbon footprint, Cesare Marchetti: Marchetti’s constant, Chris Urmson, City Beautiful movement, Clapham omnibus, congestion charging, coronavirus, COVID-19, deep learning, Didi Chuxing, Donald Shoup, driverless car, Elaine Herzberg, Elon Musk, flex fuel, Ford Model T, Ford paid five dollars a day, garden city movement, General Motors Futurama, Ida Tarbell, Induced demand, interchangeable parts, invention of the wheel, James Watt: steam engine, Jane Jacobs, jitney, Joan Didion, John Zimmer (Lyft cofounder), Lewis Mumford, lockdown, Lyft, Marshall McLuhan, minimum wage unemployment, oil shock, Own Your Own Home, peak oil, prompt engineering, Ralph Nader, Richard Florida, ride hailing / ride sharing, Rosa Parks, safety bicycle, self-driving car, social distancing, Steve Jobs, streetcar suburb, tech bro, The Death and Life of Great American Cities, trade route, Travis Kalanick, Uber and Lyft, uber lyft, unbiased observer, Unsafe at Any Speed, Upton Sinclair, urban planning, urban sprawl, Victor Gruen, W. E. B. Du Bois, walkable city, white flight, wikimedia commons, Yom Kippur War, Zipcar

.), here General Motors, here car loans, introduction of, here, here carmakers acquired by, here closed body designs, adoption of, here and color choice as selling point, here, here electric cars of 1990s, here ethanol fuels and, here founding of, here growth into world’s largest company, here influence on business practices, here, here introduction of style changes to create “dynamic obsolescence,” here, here and lead as gas additive, here multiple brand (marque) strategy of, here and self-driving cars, here, here, here strategy vs. Ford, here, here, here, here and streetcar lines, purchase of, here surpassing of Ford, here and turn to sale of mobility services, here and used car trades, introduction of, here and World’s Fair Futurama exhibit, here, here General Motors Acceptance Corporation, here, here Good Road Show (Chicago, 1922), here Good Roads movement (U.S.), here Google, self-driving car program (Waymo), here, here, here, here, here Grand Challenge races for self-driving cars, here Gruen, Victor, here Highway Act of 1956, here highways car companies’ support for, here in cities, opposition to, here first parkways in New York City, here, here history of concept, here postwar construction boom, here as supposed remedy for traffic congestion, here, here, here urban, and poor neighborhoods, here and urban planning, here, here, here, here, here Hollingshead, Richard, here horsecars (trams), here, here horse-drawn vehicles in ancient Rome, here, here and coach fad, here in Europe, as status symbols, here, here limited use in medieval world, here use by kings and gods, here, here, here, here use in warfare, here, here, here, here See also chariots; coaches; wheeled vehicles, early horse-drawn vehicles in cities popularity of coaches and, here problems caused by, here, here, here, here railroads’ increase of demand for, here replacement by cars, here, here See also horsecars (trams); omnibus service horseless carriages (runabouts), here horses domestication of, here as transport for high-status men, here use in warfare, here, here, here Howard, Ebenezer, here, here internal combustion engine cars and engine knocking, here first models of, here, here in Paris–Bordeaux race (1895), here, here in Paris–Rouen race (1894), here, here, here, here superiority to other early designs, here, here, here internal combustion engines bicycle as first vehicle powered by, here Daimler-Maybach engines, here, here, here, here, here, here invention and early uses, here internet and new transportation options, here replacement of car for many uses, here, here internet of motion, here see also mobility as a service (MaaS) Iranian Revolution, and oil shock of 1979, here jaywalking, campaign against, here jitneys, here, here Kadesh, Battle of (1274 B.C.E.), here, here King, Martin Luther, Jr., here League of Nations, and traffic standards, here Le Corbusier, here, here Levassor, Émile, here, here, here, here Levitt, William, here, here lidar, in self-driving cars, here Ligroin, as early fuel, here, here Lime, here, here lithium-ion batteries, here, here, here, here Ljubljana Marshes Wheel, here, here, here London carriageway in, here, here and coach traffic, here early omnibus service in, here London Steam Carriage, here, here Lyft, here, here, here, here MaaS.

Since then they and other participants in the various DARPA contests have gone on to work on autonomous-vehicle technology at Google, Uber, Tesla, and a host of start-ups, from Aurora to Zoox. Prototype self-driving cars first took to America’s public roads in 2012; they have since traveled millions of miles. Just as the Paris–Rouen race provided a glimpse of the future of horseless carriages, the DARPA challenges did the same for driverless cars. But predictions that self-driving “robotaxis” would be ubiquitous by 2020 proved overly optimistic. Autonomous vehicles (AVs), as self-driving cars are known in the industry, can do extraordinary things, such as navigating busy downtown streets and handling complex junctions with multiple traffic lights.

Ride-hailing apps can summon a taxi with a few taps. App-based car-rental and car-sharing services provide access to a vehicle for a few hours or days. Bikes and scooters can be found on street corners in many cities for rental by the minute. And even more radical approaches are coming over the horizon. Proponents of autonomous or self-driving cars predict that summoning a robotaxi when needed will eventually be cheaper than car ownership, and that such vehicles could reduce traffic congestion and road deaths. More ambitious still are the start-ups working on flying cars—giant aerial drones that are large enough to carry people. Revisiting the history of the car, and how it changed the world, can provide a roadmap to help make sense of these new transport options, by showing how social, political, and technological forces interact to produce both expected and unexpected outcomes.


pages: 416 words: 112,268

Human Compatible: Artificial Intelligence and the Problem of Control by Stuart Russell

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, algorithmic bias, AlphaGo, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, behavioural economics, Bletchley Park, blockchain, Boston Dynamics, brain emulation, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, complexity theory, computer vision, Computing Machinery and Intelligence, connected car, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, fake news, Flash crash, full employment, future of work, Garrett Hardin, Geoffrey Hinton, Gerolamo Cardano, Goodhart's law, Hans Moravec, ImageNet competition, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the wheel, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Nash: game theory, John von Neumann, Kenneth Arrow, Kevin Kelly, Law of Accelerating Returns, luminiferous ether, machine readable, machine translation, Mark Zuckerberg, multi-armed bandit, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, OpenAI, openstreetmap, P = NP, paperclip maximiser, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, smart cities, smart contracts, social intelligence, speech recognition, Stephen Hawking, Steven Pinker, superintelligent machines, surveillance capitalism, Thales of Miletus, The Future of Employment, The Theory of the Leisure Class by Thorstein Veblen, Thomas Bayes, Thorstein Veblen, Tragedy of the Commons, transport as a service, trolley problem, Turing machine, Turing test, universal basic income, uranium enrichment, vertical integration, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, web application, zero-sum game

In such cases—as in the examples given above—the AI system will often find an optimal solution that sets the thing you do care about, but forgot to mention, to an extreme value. So, if you say to your self-driving car, “Take me to the airport as fast as possible!” and it interprets this literally, it will reach speeds of 180 miles per hour and you’ll go to prison. (Fortunately, the self-driving cars currently contemplated won’t accept such a request.) If you say, “Take me to the airport as fast as possible while not exceeding the speed limit,” it will accelerate and brake as hard as possible, swerving in and out of traffic to maintain the maximum speed in between.

Machines designed in this way will defer to humans: they will ask permission; they will act cautiously when guidance is unclear; and they will allow themselves to be switched off. While these initial results are for a simplified and idealized setting, I believe they will survive the transition to more realistic settings. Already, my colleagues have successfully applied the same approach to practical problems such as self-driving cars interacting with human drivers.1 For example, self-driving cars are notoriously bad at handling four-way stop signs when it’s not clear who has the right of way. By formulating this as an assistance game, however, the car comes up with a novel solution: it actually backs up a little bit to show that it’s definitely not planning to go first.

Millions of students have taken online AI and machine learning courses, and experts in the area command salaries in the millions of dollars. Investments flowing from venture funds, national governments, and major corporations are in the tens of billions of dollars annually—more money in the last five years than in the entire previous history of the field. Advances that are already in the pipeline, such as self-driving cars and intelligent personal assistants, are likely to have a substantial impact on the world over the next decade or so. The potential economic and social benefits of AI are vast, creating enormous momentum in the AI research enterprise. What Happens Next? Does this rapid rate of progress mean that we are about to be overtaken by machines?


pages: 328 words: 90,677

Ludicrous: The Unvarnished Story of Tesla Motors by Edward Niedermeyer

autonomous vehicles, barriers to entry, Bear Stearns, bitcoin, business climate, call centre, carbon footprint, Clayton Christensen, clean tech, Colonization of Mars, computer vision, crowdsourcing, disruptive innovation, Donald Trump, driverless car, Elon Musk, en.wikipedia.org, facts on the ground, fake it until you make it, family office, financial engineering, Ford Model T, gigafactory, global supply chain, Google Earth, housing crisis, hype cycle, Hyperloop, junk bonds, Kaizen: continuous improvement, Kanban, Kickstarter, Lyft, Marc Andreessen, Menlo Park, minimum viable product, new economy, off grid, off-the-grid, OpenAI, Paul Graham, peak oil, performance metric, Ponzi scheme, ride hailing / ride sharing, risk tolerance, Sand Hill Road, self-driving car, short selling, short squeeze, side project, Silicon Valley, Silicon Valley startup, Skype, smart cities, Solyndra, stealth mode startup, Steve Jobs, Steve Jurvetson, tail risk, technoutopianism, Tesla Model S, too big to fail, Toyota Production System, Uber and Lyft, uber lyft, union organizing, vertical integration, WeWork, work culture , Zipcar

There was more than friendship behind Google’s interest in Tesla. Since 2009, Google had been developing a technological play that held even more transformative potential for the auto industry than Tesla’s batteries and electric motors: self-driving car technology. Headed up by Sebastian Thrun, the German innovator and computer scientist who had led a Stanford University team to victory in the 2005 Defense Advanced Research Projects Agency Grand Challenge, Google’s self-driving car program (which would be spun off into a company called Waymo in December 2016) was the undisputed leader in a technology that threatened to make EVs look like old news. Autonomous-drive technology is actually a collection of technologies, each serving different aspects of the driving task.

Some of the core challenges of autonomous drive are related to the work Google had done for its map products, which involved multisensor scanning to create realistic models of roadways and machine learning to derive turn-by-turn directions from them. But the near-infinite complexity of traffic made it an order of magnitude more challenging, and until 2013, Google’s self-driving car program was widely seen as a speculative moon shot funded by its massive ad revenues. The first public clue that Google’s self-driving cars were moving beyond mere experimentation came in early May 2013, when Musk revealed that Tesla was discussing possible collaboration on autonomous-drive technology with the search giant. But, in typical Musk fashion, collaboration with Google was only one option, and Tesla would go its own way if necessary.

Silicon Valley’s “move fast and break stuff” ethos, which fostered innovation in non-safety-critical software, was beginning to look like a poor approach to developing self-driving car systems, which hold the power of life and death. Using a sensor suite designed for a driver-assist system came across as the ultimate example of Silicon Valley’s “minimum viable product” paradigm, applied not to a smartphone app but to an activity that is a leading cause of death. Any plan to simply update software whenever an underbaked autonomous-drive system runs into a situation it can’t handle has to assume that a number of those situations will involve a customer dying. The challenge of self-driving car development was not simply convincing the public that a car could “drive itself,” which could theoretically be achieved with as little hardware as a brick placed on the accelerator, but by making a car drive itself safely.


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WTF?: What's the Future and Why It's Up to Us by Tim O'Reilly

"Friedman doctrine" OR "shareholder theory", 4chan, Affordable Care Act / Obamacare, Airbnb, AlphaGo, Alvin Roth, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, behavioural economics, benefit corporation, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, Blitzscaling, blockchain, book value, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Carl Icahn, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, congestion pricing, corporate governance, corporate raider, creative destruction, CRISPR, crowdsourcing, Danny Hillis, data acquisition, data science, deep learning, DeepMind, Demis Hassabis, Dennis Ritchie, deskilling, DevOps, Didi Chuxing, digital capitalism, disinformation, do well by doing good, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Firefox, Flash crash, Free Software Foundation, fulfillment center, full employment, future of work, George Akerlof, gig economy, glass ceiling, Glass-Steagall Act, Goodhart's law, Google Glasses, Gordon Gekko, gravity well, greed is good, Greyball, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, independent contractor, index fund, informal economy, information asymmetry, Internet Archive, Internet of things, invention of movable type, invisible hand, iterative process, Jaron Lanier, Jeff Bezos, jitney, job automation, job satisfaction, John Bogle, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Zimmer (Lyft cofounder), Kaizen: continuous improvement, Ken Thompson, Kevin Kelly, Khan Academy, Kickstarter, Kim Stanley Robinson, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Ellison, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, machine readable, machine translation, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, Network effects, new economy, Nicholas Carr, Nick Bostrom, obamacare, Oculus Rift, OpenAI, OSI model, Overton Window, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, post-truth, race to the bottom, Ralph Nader, randomized controlled trial, RFC: Request For Comment, Richard Feynman, Richard Stallman, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, Ronald Coase, Rutger Bregman, Salesforce, Sam Altman, school choice, Second Machine Age, secular stagnation, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart contracts, Snapchat, Social Responsibility of Business Is to Increase Its Profits, social web, software as a service, software patent, spectrum auction, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, stock buybacks, strong AI, synthetic biology, TaskRabbit, telepresence, the built environment, the Cathedral and the Bazaar, The future is already here, The Future of Employment, the map is not the territory, The Nature of the Firm, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Davenport, Tony Fadell, Tragedy of the Commons, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, two-pizza team, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, yellow journalism, zero-sum game, Zipcar

There will be significant costs to achieve the kind of availability for passengers that Uber or Lyft currently have using centrally owned self-driving cars. Remember that the total number of cars in the system must be sufficient to satisfy peak demand. If the company itself owns the self-driving cars, and uses them to compete with its human drivers for the busiest, most lucrative times, it risks making them less willing to participate. If the goal is truly to make transportation as reliable as running water or electricity, rather than simply to maximize company profit, these companies should deploy self-driving cars not to compete with their drivers but to supplement them, providing services in areas that are currently not well served, even though those cars might be utilized less often.

Even when Siri’s attempts to understand human speech failed, it was remarkable that we were now talking to our devices and expecting them to respond. Siri even became the best friend of one autistic boy. The year 2011 was also the year that Google announced that its self-driving car prototype had driven more than 100,000 miles in ordinary traffic, a mere six years after the winner of the DARPA Grand Challenge for self-driving cars had managed to go only seven miles in seven hours. Self-driving cars and trucks have now taken center stage, as the media wrestles with the possibility that they will eliminate millions of human jobs. This fear, that this next wave of automation will go much further than the first industrial revolution in making human labor superfluous, is what makes many say “this time is different” when contemplating technology and the future of the economy.

For example, Uber and Lyft have made much of their plans to incorporate self-driving cars into their future. With a shallow understanding of their business, you might quickly conclude that the reason to do this is that eliminating the 70–80% of the fare that is paid out to drivers will make these businesses more profitable. With the aid of the business model map I outlined above, you’d ask different questions. If the company currently depends on a liquid marketplace of drivers who bring their own cars and work only when they believe they can make a decent wage, what happens when the platforms introduce self-driving cars into the mix? They potentially destabilize their own marketplace.


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Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, algorithmic bias, algorithmic management, augmented reality, autonomous vehicles, backpropagation, Brixton riot, Cambridge Analytica, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, data science, deep learning, DeepMind, Douglas Hofstadter, driverless car, Elon Musk, fake news, Firefox, Geoffrey Hinton, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta-analysis, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, sparse data, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, systematic bias, TED Talk, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, trolley problem, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche, you are the product

Alex Davies, ‘An oral history of the DARPA Grand Challenge, the grueling robot race that launched the self-driving car’, Wired, 8 March 2017, https://www.wired.com/story/darpa-grand-challenge-2004-oral-history/. 9. ‘Desert race too tough for robots’, BBC News, 15 March, 2004, http://news.bbc.co.uk/1/hi/technology/3512270.stm. 10. Davies, ‘An oral history of the DARPA Grand Challenge’. 11. Denise Chow, ‘DARPA and drone cars: how the US military spawned self-driving car revolution’, LiveScience, 21 March 2014, https://www.livescience.com/44272-darpa-self-driving-car-revolution.html. 12. Joseph Hooper, ‘From Darpa Grand Challenge 2004 DARPA’s debacle in the desert’, Popular Science, 4 June 2004, https://www.popsci.com/scitech/article/2004-06/darpa-grand-challenge-2004darpas-debacle-desert. 13.

Project Report (Bristol: University of the West of England, June 2016), http://eprints.uwe.ac.uk/29167/1/Venturer_WP5.2Lit%20ReviewHandover.pdf. 60. Langewiesche, ‘The human factor’. 61. Evan Ackerman, ‘Toyota’s Gill Pratt on self-driving cars and the reality of full autonomy’, IEEE Spectrum, 23 Jan. 2017, https://spectrum.ieee.org/cars-that-think/transportation/self-driving/toyota-gill-pratt-on-the-reality-of-full-autonomy. 62. Julia Pyper, ‘Self-driving cars could cut greenhouse gas pollution’, Scientific American, 15 Sept. 2014, https://www.scientificamerican.com/article/self-driving-cars-could-cut-greenhouse-gas-pollution/. 63. Raphael E. Stern et al., ‘Dissipation of stop-and-go waves via control of autonomous vehicles: field experiments’, arXiv: 1705.01693v1, 4 May 2017, https://arxiv.org/abs/1705.01693. 64.

Jean-François Bonnefon, Azim Shariff and Iyad Rahwan (2016), ‘The social dilemma of autonomous vehicles’, Science, vol. 35, 24 June 2016, DOI 10.1126/science.aaf2654; https://arxiv.org/pdf/1510.03346.pdf. 36. All quotes from Paul Newman are from private conversation. 37. Naaman Zhou, ‘Volvo admits its self-driving cars are confused by kangaroos’, Guardian, 1 July 2017, https://www.theguardian.com/technology/2017/jul/01/volvo-admits-its-self-driving-cars-are-confused-by-kangaroos. 38. All quotes from Jack Stilgoe are from private conversation. 39. Jeff Sabatini, ‘The one simple reason nobody is talking realistically about driverless cars’, Car and Driver, Oct. 2017, https://www.caranddriver.com/features/the-one-reason-nobody-is-talking-realistically-about-driverless-cars-feature. 40.


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Ways of Being: Beyond Human Intelligence by James Bridle

Ada Lovelace, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Anthropocene, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big Tech, Black Lives Matter, blockchain, Californian Ideology, Cambridge Analytica, carbon tax, Charles Babbage, cloud computing, coastline paradox / Richardson effect, Computing Machinery and Intelligence, corporate personhood, COVID-19, cryptocurrency, DeepMind, Donald Trump, Douglas Hofstadter, Elon Musk, experimental subject, factory automation, fake news, friendly AI, gig economy, global pandemic, Gödel, Escher, Bach, impulse control, James Bridle, James Webb Space Telescope, John von Neumann, Kickstarter, Kim Stanley Robinson, language acquisition, life extension, mandelbrot fractal, Marshall McLuhan, microbiome, music of the spheres, negative emissions, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, peer-to-peer, planetary scale, RAND corporation, random walk, recommendation engine, self-driving car, SETI@home, shareholder value, Silicon Valley, Silicon Valley ideology, speech recognition, statistical model, surveillance capitalism, techno-determinism, technological determinism, technoutopianism, the long tail, the scientific method, The Soul of a New Machine, theory of mind, traveling salesman, trolley problem, Turing complete, Turing machine, Turing test, UNCLOS, undersea cable, urban planning, Von Neumann architecture, wikimedia commons, zero-sum game

That mind was perched precariously on the passenger seat; I sat at the wheel, still in control, for now. All this took place a few years ago, in the winter of 2017, when I decided to try and build myself a self-driving car. And although it never – quite – drove itself, it did take me to some pretty interesting places. The idea of a self-driving car is fascinating to me. Not really for its capabilities, but for its place in our imagination. The self-driving car is one of those technologies which in the space of just a few years has gone from space-age, ‘Life in the Twenty-First Century’ fantasy to humdrum reality, without ever passing through a period of critical reflection or assimilation.

In transferring this analogy to the world of AI, it seems evident that thus far we have mostly created domesticated machines of the first kind, we have begun to corral a feedlot of the second, and we live in fear of unleashing the third. Where does my self-driving car sit in this taxonomy? It’s mostly ‘pet’ – a domesticated machine under my control – but it’s also productive, in harness, a working animal; and, because of my insistence that it goes where it pleases, it’s a little wild, a little unpredictable. With careless handling, the self-driving car might be considered among the most damaging applications of AI. Not only does it contribute directly to the destruction of the planet through material extraction and carbon emissions – at least, until we get fully solar, sustainable versions – but it also steals from us the very real, if guilty, pleasure of driving.

But even this intentionally limited proposal was met with opposition, in the form of an open letter, signed by 150 experts in medicine, robotics, AI and ethics, calling the plans ‘inappropriate’ and ‘ideological, nonsensical and non-pragmatic’.18 The European Parliament resolution, however, was a response to a very real problem: that of a legal lack of clarity concerning autonomous systems which have real-world impacts on human lives. Self-driving cars are one such example; autonomous weapons platforms like military drones and robotic sentries are others. If a self-driving car runs someone over – as has already happened – the law remains uncertain as to where the blame should lie, and legal frameworks are urgently required to handle this situation. Likewise, while military drones, missiles and machine-gun posts remain for now under the control of human operators, they will soon operate fully autonomously, with consequences which are both predictable and unpredictable – but almost certainly horrifying.


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Rise of the Robots: Technology and the Threat of a Jobless Future by Martin Ford

3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic management, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, bond market vigilante , business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, Charles Babbage, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, data science, debt deflation, deep learning, deskilling, digital divide, disruptive innovation, diversified portfolio, driverless car, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Ford Model T, Fractional reserve banking, Freestyle chess, full employment, general purpose technology, Geoffrey Hinton, Goldman Sachs: Vampire Squid, Gunnar Myrdal, High speed trading, income inequality, indoor plumbing, industrial robot, informal economy, iterative process, Jaron Lanier, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kenneth Arrow, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, large language model, liquidity trap, low interest rates, low skilled workers, low-wage service sector, Lyft, machine readable, machine translation, manufacturing employment, Marc Andreessen, McJob, moral hazard, Narrative Science, Network effects, new economy, Nicholas Carr, Norbert Wiener, obamacare, optical character recognition, passive income, Paul Samuelson, performance metric, Peter Thiel, plutocrats, post scarcity, precision agriculture, price mechanism, public intellectual, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Robert Solow, Rodney Brooks, Salesforce, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, single-payer health, software is eating the world, sovereign wealth fund, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Steven Pinker, strong AI, Stuxnet, technological singularity, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Coming Technological Singularity, The Future of Employment, the long tail, Thomas L Friedman, too big to fail, Tragedy of the Commons, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce

Tom Simonite, “Data Shows Google’s Robot Cars Are Smoother, Safer Drivers Than You or I,” Technology Review, October 25, 2013, http://www.technologyreview.com/news/520746/data-shows-googles-robot-cars-are-smoother-safer-drivers-than-you-or-i/. 10. See ibid. for Chris Urmson’s comments. 11. “The Self-Driving Car Logs More Miles on New Wheels” (Google corporate blog), August 7, 2012, http://googleblog.blogspot.co.uk/2012/08/the-self-driving-car-logs-more-miles-on.html. 12. As quoted in Heather Kelly, “Driverless Car Tech Gets Serious at CES,” CNN, January 9, 2014, http://www.cnn.com/2014/01/09/tech/innovation/self-driving-cars-ces/. 13. For US accident statistics, see http://www.census.gov/compendia/statab/2012/tables/12s1103.pdf; for global accident statistics, see http://www.who.int/gho/road_safety/mortality/en/. 14.

Most people who have given serious thought to the optimal role of self-driving cars seem to agree that, at least in densely populated areas, they are likely to be a shared resource. This has been Google’s intent from the start. As Google co-founder Sergey Brin explained to the New Yorker’s Burkhard Bilger, “[L]ook outside, and walk through parking lots and past multilane roads: the transportation infrastructure dominates. It’s a huge tax on the land.”15 Google hopes to smash the prevailing owner-operator model for the automobile. In the future, you’ll simply reach for your smart phone or other connected device and call for a self-driving car whenever you need it.

The Rise—and Stumble—of the MOOC Free Internet-based courses like those offered by edX are part of the trend toward massive open online courses—or MOOCs—that exploded into the public consciousness in the late summer of 2011, when two computer scientists at Stanford University, Sebastian Thrun and Peter Norvig, announced that their introductory artificial intelligence class would be available to anyone at no cost over the Internet. Both of the course’s instructors were celebrities in their field with strong ties to Google; Thrun had led the effort to develop the company’s self-driving cars, while Norvig was the director of research and co-author of the leading AI textbook. Within days of the announcement, more than 10,000 people had signed up. When John Markoff of the New York Times wrote a front-page article5 about the course that August, enrollment rocketed to more than 160,000 people from over 190 countries.


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Surviving AI: The Promise and Peril of Artificial Intelligence by Calum Chace

3D printing, Ada Lovelace, AI winter, Airbnb, Alvin Toffler, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, Bletchley Park, blockchain, brain emulation, Buckminster Fuller, Charles Babbage, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, deep learning, DeepMind, dematerialisation, Demis Hassabis, discovery of the americas, disintermediation, don't be evil, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Geoffrey Hinton, Google Glasses, hedonic treadmill, hype cycle, industrial robot, Internet of things, invention of agriculture, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, life extension, low skilled workers, machine translation, Mahatma Gandhi, means of production, mutually assured destruction, Neil Armstrong, Nicholas Carr, Nick Bostrom, paperclip maximiser, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, radical life extension, Ray Kurzweil, Robert Solow, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, South Sea Bubble, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Jobs, strong AI, technological singularity, TED Talk, The future is already here, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

Politicians worldwide have understood that they need to agree and implement policies and procedures to cope with the arrival of this technology. (The impetus to introduce self-driving cars is enormous. Around 1.2m lives are lost on the world’s roads each year and most of these deaths are due to driver error. Self-driving cars don’t get tired, distracted or drunk. Accidents are also a major cause of traffic congestion, so average journey times would be significantly reduced if most cars were self-driving. Car-sharing is expected to become more common, and parking should become much easier. There are always unforeseen consequences, of course.

Some of this would come from self-driving vehicles, which are likely to appear on our roads in significant numbers from 2017. Some 30 US cities will be experimenting with self-driving cars by the end of 2016, for instance. (22) There are 3.5 million truck drivers in the US alone, (23) 650,000 bus drivers (24) and 230,000 taxi drivers. (25) There are numerous hurdles to be overcome before all these jobs become vulnerable. At the time of writing, Google’s self-driving cars have travelled a million miles without causing an accident. As we saw in chapter 1 they are not perfect, but none of the challenges facing them look insurmountable: Google was recently awarded a patent for a system which can tell whether a cyclist is signalling a turn.

from intelligent algorithms which match adverts with readers and viewers, and it is busily looking for more and more new ways to exploit its world-leading expertise in AI in as many industries as it can manage. The huge collection of servers which comprise the distributed computing platform for the AI which drives the company’s numerous services is often called the Google Brain. Sometimes Google enters a new industry using home-grown talent, as with its famous self-driving cars, and with Calico, which is looking to apply Big Data to healthcare. Other times it acquires companies with the expertise not already found inside Google, or “acqui-hires” their key talent. Its rate of acquisition reached one company a week in 2010, and by the end of 2014 it had acquired 170 of them.


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The Business of Platforms: Strategy in the Age of Digital Competition, Innovation, and Power by Michael A. Cusumano, Annabelle Gawer, David B. Yoffie

activist fund / activist shareholder / activist investor, Airbnb, AltaVista, Amazon Web Services, AOL-Time Warner, asset light, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business logic, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, commoditize, CRISPR, crowdsourcing, cryptocurrency, deep learning, Didi Chuxing, distributed ledger, Donald Trump, driverless car, en.wikipedia.org, fake news, Firefox, general purpose technology, gig economy, Google Chrome, GPS: selective availability, Greyball, independent contractor, Internet of things, Jeff Bezos, Jeff Hawkins, John Zimmer (Lyft cofounder), Kevin Roose, Lean Startup, Lyft, machine translation, Mark Zuckerberg, market fundamentalism, Metcalfe’s law, move fast and break things, multi-sided market, Network effects, pattern recognition, platform as a service, Ponzi scheme, recommendation engine, Richard Feynman, ride hailing / ride sharing, Robert Metcalfe, Salesforce, self-driving car, sharing economy, Silicon Valley, Skype, Snapchat, SoftBank, software as a service, sovereign wealth fund, speech recognition, stealth mode startup, Steve Ballmer, Steve Jobs, Steven Levy, subscription business, Susan Wojcicki, TaskRabbit, too big to fail, transaction costs, transport as a service, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, Vision Fund, web application, zero-sum game

CHAPTER 7: LOOKING FORWARD: PLATFORMS AND THE FUTURE 1.Georgia Wells, “Snapchat Zigs Where Facebook Zags,” Wall Street Journal, June 14, 2018. 2.Khari Johnson, “Everything Amazon’s Alexa Learned to Do in 2017,” Venture Beat, December 29, 2017; Paul Cutsinger, “2017 Alexa Skills Kit Year in Review: More Than 100 New Products, Programs, Features, and Tools” January 5, 2018, https://developer.amazon.com/blogs/alexa/post/829a615b-301f-407c-96e7-6956fb988570/2017-alexa-skills-kit-year-in-review-more-than-100-new-products-programs-features-and-tools (accessed May 2, 2018); and Monica Chin, “Amazon Is Killing the Skill (as We Know It),” Tom’s Guide, September 13, 2018. 3.Jake Swearingen, “Amazon Could Give the Echo Dot Away and Still Make Money,” New York Magazine, January 3, 2018. 4.James Stables, “Google Assistant Aces Accuracy Study—but Alexa Is Catching Up Fast,” Ambient, April 30, 2018. 5.Rob Verger, “Someday, You Might Subscribe to a Self-Driving Taxi Service, Netflix-Style,” Popular Science, March 15, 2018. 6.Ibid. 7.Phil LeBeau, “General Motors Plans to Take On Ride-Sharing Services with Self-Driving Cars by 2019,” CNBC, November 30, 2018, https://www.cnbc.com/2017/11/30/gm-to-take-on-ride-sharing-services-with-self-driving-cars-by-2019.html (accessed June 2018). 8.Caitlin Huston, “Driverless Cars Could Cost 35 Cents per Mile for the Uber Consumer,” Marketwatch, September 19, 2016, https://www.marketwatch.com/story/demand-for-driverless-cars-could-boost-uber-to-2016-09-19 (accessed June 2018). 9.Christopher Mims, “How Self-Driving Cars Could End Uber,” Wall Street Journal, May 7, 2017. 10.Max Chafkin, “Uber’s First Self-Driving Fleet Arrives in Pittsburgh This Month,” Bloomberg, August 18, 2016. 11.See Lyft, “The Open Autonomous Era,” https://take.lyft.com/open-platform/ (accessed June 2018). 12.Mike Isaac, “Lyft Adds Ford to Its List of Self-Driving Car Partners,” New York Times, September 27, 2018, https://www.nytimes.com/2017/09/27/technology/lyft-ford-self-driving-cars.html?

Ultimately, we expect voice to be a classic platform battle, where the winner(s) will depend on who can build up the largest installed base of users, who can create the best ecosystem for producing innovative applications, and who (if anyone) can lock in their customer base, limit multi-homing in the future, and create a sufficiently compelling solution to reduce competition from niche players and differentiation in the market. RIDE SHARING AND SELF-DRIVING CARS: FROM PLATFORM TO SERVICE While AI will spawn a range of new platforms, it will also enable new capabilities that may destroy existing platforms. One of the most exciting AI applications has been the emergence of self-driving cars. Ironically, this new technology may replace some of the most widely used platforms in the world: Uber, Lyft, Didi Chuxing, and other ride-sharing businesses. Despite the strong cross-side network effects, the ride-sharing platform revolution could actually disappear.

Uber’s cofounder and then CEO Travis Kalanick stressed the importance of winning the race: “The minute it was clear to us that our friends in Mountain View [i.e., Google] were going to be getting in the ride-sharing space, we needed to make sure there is an alternative [self-driving car]. Because if there is not, we’re not going to have any business.” Kalanick added that developing a self-driving car “is basically existential for us.”10 Uber announced in November 2017 that it would buy 24,000 self-driving vehicles from Volvo, giving it a fleet to test and later deploy in an autonomous ride-hailing service. Lyft has taken a different approach.


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New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale

affirmative action, Affordable Care Act / Obamacare, Airbnb, algorithmic bias, Amazon Mechanical Turk, Anthropocene, augmented reality, Automated Insights, autonomous vehicles, basic income, battle of ideas, Bernie Sanders, Big Tech, Bill Joy: nanobots, bitcoin, blockchain, Brexit referendum, call centre, Cambridge Analytica, carbon tax, citizen journalism, Clayton Christensen, collective bargaining, commoditize, computer vision, conceptual framework, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, critical race theory, cryptocurrency, data is the new oil, data science, decarbonisation, deep learning, deepfake, deskilling, digital divide, digital twin, disinformation, disruptive innovation, don't be evil, Donald Trump, Douglas Engelbart, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fake news, Filter Bubble, finite state, Flash crash, future of work, gamification, general purpose technology, Google Chrome, Google Glasses, Great Leap Forward, green new deal, guns versus butter model, Hans Moravec, high net worth, hiring and firing, holacracy, Ian Bogost, independent contractor, informal economy, information asymmetry, information retrieval, interchangeable parts, invisible hand, James Bridle, Jaron Lanier, job automation, John Markoff, Joi Ito, Khan Academy, knowledge economy, late capitalism, lockdown, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, medical malpractice, megaproject, meta-analysis, military-industrial complex, Modern Monetary Theory, Money creation, move fast and break things, mutually assured destruction, natural language processing, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, nuclear winter, obamacare, One Laptop per Child (OLPC), open immigration, OpenAI, opioid epidemic / opioid crisis, paperclip maximiser, paradox of thrift, pattern recognition, payday loans, personalized medicine, Peter Singer: altruism, Philip Mirowski, pink-collar, plutocrats, post-truth, pre–internet, profit motive, public intellectual, QR code, quantitative easing, race to the bottom, RAND corporation, Ray Kurzweil, recommendation engine, regulatory arbitrage, Robert Shiller, Rodney Brooks, Ronald Reagan, self-driving car, sentiment analysis, Shoshana Zuboff, Silicon Valley, Singularitarianism, smart cities, smart contracts, software is eating the world, South China Sea, Steve Bannon, Strategic Defense Initiative, surveillance capitalism, Susan Wojcicki, tacit knowledge, TaskRabbit, technological solutionism, technoutopianism, TED Talk, telepresence, telerobotics, The Future of Employment, The Turner Diaries, Therac-25, Thorstein Veblen, too big to fail, Turing test, universal basic income, unorthodox policies, wage slave, Watson beat the top human players on Jeopardy!, working poor, workplace surveillance , Works Progress Administration, zero day

Self-driving vehicles promise to eliminate many thousands of traffic fatalities each year. So the problem may seem easy at first glance. On the other hand, they would also put hundreds of thousands of paid drivers out of work. Does that harm entitle governments to ban or slow down the adoption of self-driving cars? Asimov’s three laws are not clear on such matters. Nor do they have much to say about a recent demand of self-driving car evangelists: that pedestrians be trained to act in ways that make it easier for self-driving vehicles to operate, and penalized if they fail to do so. These ambiguities and many more are why the statutes, regulations, and court cases affecting robotics and AI in our world are finer grained than Asimov’s laws.

Both law and norms will shape that new identity over time.64 None of these decisions should be made solely—or even predominantly—by the programmers and corporations developing algorithms for self-driving cars. They involve governance and participation by a much wider range of experts, ranging from urban-studies scholars to regulators to police and attorneys. Negotiations among affected parties are likely to be protracted—but that is the price of a democratic and inclusive transition toward new and better technology. And these are only a few of the ethical, legal, and social implications of a widespread transition to self-driving cars.65 Nevertheless, some futurists argue that AI obviates the need for professions.

Marc Canellas and Rachel Haga, “Unsafe at Any Level: The U.S. NHTSA’s Levels of Automation Are a Liability for Automated Vehicles,” Communications of the ACM 63, no. 3 (2020): 31–34. 65. Nor are the strictly technological challenges easy to address. Roberto Baldwin, “Self-Driving Cars are Taking Longer to Build Than Everyone Thought,” Car and Driver, May 10, 2020, https://www.caranddriver.com/features/a32266303/self-driving-cars-are-taking-longer-to-build-than-everyone-thought/. 66. AI Now Institute, AI Now 2019 Report, December 2019, 8, 45–47, https://ainowinstitute.org/AI_Now_2019_Report.pdf. 67. Henry Mance, “Britain Has Had Enough of Experts, says Gove,” Financial Times, June 3, 2016, https://www.ft.com/content/3be49734-29cb-11e6-83e4-abc22d5d108c. 68.


pages: 278 words: 91,332

Carmageddon: How Cars Make Life Worse and What to Do About It by Daniel Knowles

active transport: walking or cycling, autonomous vehicles, Bandra-Worli Sea Link, bank run, big-box store, bike sharing, Boeing 747, Boris Johnson, business cycle, car-free, carbon footprint, congestion charging, congestion pricing, coronavirus, COVID-19, Crossrail, decarbonisation, deindustrialization, Detroit bankruptcy, Donald Shoup, Donald Trump, driverless car, Elaine Herzberg, Elon Musk, first-past-the-post, Ford Model T, Frank Gehry, garden city movement, General Motors Futurama, gentrification, ghettoisation, high-speed rail, housing crisis, Hyperloop, Induced demand, James Watt: steam engine, Jane Jacobs, Jeremy Corbyn, Jevons paradox, Lewis Mumford, lockdown, Lyft, megacity, megastructure, New Urbanism, Northern Rock, parking minimums, Piers Corbyn, Richard Florida, ride hailing / ride sharing, safety bicycle, self-driving car, Silicon Valley, Southern State Parkway, Steve Jobs, TED Talk, Tesla Model S, The Death and Life of Great American Cities, the High Line, Traffic in Towns by Colin Buchanan, Uber and Lyft, uber lyft, upwardly mobile, urban planning, urban renewal, walkable city, white flight, white picket fence, Yom Kippur War, young professional

And that is kind of the problem with self-driving cars. They are perhaps not pure bionic duckweed. Eventually, Google or Tesla or some other competitor may be able to map enough of the world, and improve the technology enough that you really will be able to hop into a car with nobody at the wheel in quite a few cities. That, however, is many years away. And in any case, when self-driving car advocates argue that their rise will mean that technologies such as trains will be made redundant, or that traffic will be solved, they are almost certainly wrong. Self-driving cars still take up space. And however brilliant the engineers designing them are, they will simply never work well in the congested, complicated big cities of the world.

Electric cars, for all their faults, are not bionic duckweed. They are very real bits of technology that do, on the important measure of carbon emissions at least, starkly improve the damage done by cars to the environment. But much else is. Chief among them is the idea of autonomous, “self-driving” cars. According to their boosters, self-driving cars are about to change the planet. In December 2021, Elon Musk told a conference hosted by the Wall Street Journal that they are “absolutely coming,” and “will be one of the biggest transformations ever in human civilization.” The idea is that when all cars are self-driving, they will be able to far more efficiently use the road space available, hugging each other like train cars.

Uber settled a lawsuit; Vasquez has been charged with negligent homicide, though she pleaded not guilty, and the trial is still pending. Some projects are still ongoing. You can take a Waymo taxi around a relatively smaller suburban part of Phoenix, Arizona, and unlike self-driving cars elsewhere, it does not have to have a driver. Phoenix is probably as perfect a city as you can develop for self-driving cars. It has 291 sunny days per year, which are less confusing to cameras than rainy or overcast days. It is also sprawling, with wide, fairly easy to navigate roads, with not many pedestrians or cyclists to have to navigate around. But the cars still struggle occasionally, getting confused by things like traffic cones.


pages: 301 words: 85,126

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Abraham Wald, Air France Flight 447, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, Big Tech, Black Lives Matter, Bletchley Park, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, fake news, Flash crash, Grace Hopper, Gödel, Escher, Bach, Hans Moravec, Harvard Computers: women astronomers, Higgs boson, index fund, information security, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, machine translation, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, Salesforce, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, systems thinking, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

It’s to tell the algorithm how to train itself what to do, using data and the rules of probability. How Did We Get Here? Modern AI systems, like a self-driving car or a home digital assistant, are pretty new on the scene. But you might be surprised to learn that most of the big ideas in AI are actually old—in many cases, centuries old—and that our ancestors have been using them to solve problems for generations. For example, take self-driving cars. Google debuted its first such car in 2009. But you’ll learn in chapter 3 that one of the main ideas behind how these cars work was discovered by a Presbyterian minister in the 1750s—and that this idea was used by a team of mathematicians over 50 years ago to solve one of the Cold War’s biggest blockbuster mysteries.

Another algorithm formats the response into a coherent English sentence. A final algorithm verbalizes that sentence in a non-robotic-sounding way: “The best breakfast tacos in Austin are at Julio’s on Duval Street. Would you like directions?” And that’s AI. Pretty much every AI system—whether it’s a self-driving car, an automatic cucumber sorter, or a piece of software that monitors your credit card account for fraud—follows this same “pipeline-of-algorithms” template. The pipeline takes in data from some specific domain, performs a chain of calculations, and outputs a prediction or a decision. There are two distinguishing features of the algorithms used in AI.

For if pulsating stars are the candles of the universe, then Henrietta Leavitt was the one who fashioned their candlestick, giving us an equation that we could hold up to the heavens to shine light into the darkness. 3 THE REVEREND AND THE SUBMARINE Q: What do a bicycle, snow, a kangaroo, and a submarine have in common? A: They’re all important for building a car that can drive itself. TAKE BICYCLES. BICYCLES are trouble. The sensors on a self-driving car are really good at identifying things like pedestrians and squirrels, which move a lot slower than cars and which look basically the same from every angle. Other cars are a piece of cake; they’re giant reflective blobs of metal that light up like a Christmas tree on radar. But bicycles? Bicycles can be fast or slow, big or small, metal or carbon fiber—and depending on your viewing angle, they can be as wide as a car or as narrow as a book.


pages: 308 words: 84,713

The Glass Cage: Automation and Us by Nicholas Carr

Airbnb, Airbus A320, Andy Kessler, Atul Gawande, autonomous vehicles, Bernard Ziegler, business process, call centre, Captain Sullenberger Hudson, Charles Lindbergh, Checklist Manifesto, cloud computing, cognitive load, computerized trading, David Brooks, deep learning, deliberate practice, deskilling, digital map, Douglas Engelbart, driverless car, drone strike, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, gamification, global supply chain, Google Glasses, Google Hangouts, High speed trading, human-factors engineering, indoor plumbing, industrial robot, Internet of things, Ivan Sutherland, Jacquard loom, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, knowledge worker, low interest rates, Lyft, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, natural language processing, new economy, Nicholas Carr, Norbert Wiener, Oculus Rift, pattern recognition, Peter Thiel, place-making, plutocrats, profit motive, Ralph Waldo Emerson, RAND corporation, randomized controlled trial, Ray Kurzweil, recommendation engine, robot derives from the Czech word robota Czech, meaning slave, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, systems thinking, tacit knowledge, TaskRabbit, technological determinism, technological solutionism, technoutopianism, TED Talk, The Wealth of Nations by Adam Smith, turn-by-turn navigation, Tyler Cowen, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche

Chapter One: PASSENGERS 1.Sebastian Thrun, “What We’re Driving At,” Google Official Blog, October 9, 2010, googleblog.blogspot.com/2010/10/what-were-driving-at.html. See also Tom Vanderbilt, “Let the Robot Drive: The Autonomous Car of the Future Is Here,” Wired, February 2012. 2.Daniel DeBolt, “Google’s Self-Driving Car in Five-Car Crash,” Mountain View Voice, August 8, 2011. 3.Richard Waters and Henry Foy, “Tesla Moves Ahead of Google in Race to Build Self-Driving Cars,” Financial Times, September 17, 2013, ft.com/intl/cms/s/0/70d26288-1faf-11e3-8861-00144feab7de.html. 4.Frank Levy and Richard J. Murnane, The New Division of Labor: How Computers Are Creating the Next Job Market (Princeton: Princeton University Press, 2004), 20. 5.Tom A.

.: Oxford University Press, 2005), 247. 58.Andy Clark, Natural-Born Cyborgs: Minds, Technologies, and the Future of Human Intelligence (New York: Oxford University Press, 2003), 4. 59.Quoted in Fallows, “Places You’ll Go.” Chapter Seven: AUTOMATION FOR THE PEOPLE 1.Kevin Kelly, “Better than Human: Why Robots Will—and Must—Take Our Jobs,” Wired, January 2013. 2.Jay Yarow, “Human Driver Crashes Google’s Self Driving Car,” Business Insider, August 5, 2011, businessinsider.com/googles-self-driving-cars-get-in-their-first-accident-2011-8. 3.Andy Kessler, “Professors Are About to Get an Online Education,” Wall Street Journal, June 3, 2013. 4.Vinod Khosla, “Do We Need Doctors or Algorithms?,” TechCrunch, January 10, 2012, techcrunch.com/2012/01/10/doctors-or-algorithms. 5.Gerald Traufetter, “The Computer vs. the Captain: Will Increasing Automation Make Jets Less Safe?

And by processing all the streams of incoming information instantaneously—in “real time”—its onboard computers are able to work the accelerator, the steering wheel, and the brakes with the speed and sensitivity required to drive on actual roads and respond fluidly to the unexpected events that drivers always encounter. Google’s fleet of self-driving cars has now racked up close to a million miles, and the vehicles have caused just one serious accident. That was a five-car pileup near the company’s Silicon Valley headquarters in 2011, and it doesn’t really count. It happened, as Google was quick to announce, “while a person was manually driving the car.”2 Autonomous automobiles have a ways to go before they start chauffeuring us to work or ferrying our kids to soccer games.


pages: 265 words: 74,807

Our Robots, Ourselves: Robotics and the Myths of Autonomy by David A. Mindell

Air France Flight 447, air gap, Apollo 11, Apollo 13, Apollo Guidance Computer, autonomous vehicles, Beryl Markham, Boeing 747, Captain Sullenberger Hudson, Charles Lindbergh, Chris Urmson, digital map, disruptive innovation, driverless car, drone strike, Easter island, en.wikipedia.org, Erik Brynjolfsson, fudge factor, Gene Kranz, human-factors engineering, index card, John Markoff, low earth orbit, Mars Rover, Neil Armstrong, ride hailing / ride sharing, Ronald Reagan, self-driving car, Silicon Valley, telepresence, telerobotics, trade route, US Airways Flight 1549, William Langewiesche, zero-sum game

“without traffic accidents or congestion”: Sebastian Thrun, “Self-Driving Cars Can Save Lives, and Parking Spaces,” New York Times, December 5, 2011, http://www.nytimes.com/2011/12/06/science/sebastian-thrun-self-driving-cars-can-save-lives-and-parking-spaces.html. Sebastian Thrun, “What We’re Driving At,” Google official blog, http://googleblog.blogspot.com/2010/10/what-were-driving-at.html, accessed July 10, 2014. John Markoff, “A Trip in a Self-Driving Car Now Seems Routine,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/13/a-trip-in-a-self-driving-car-now-seems-routine, accessed July 10, 2014. John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html.

John Markoff, “Police, Pedestrians and the Social Ballet of Merging: The Real Challenges for Self-Driving Cars,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/29/police-bicyclists-and-pedestrians-the-real-challenges-for-self-driving-cars/, accessed July 10, 2014. We know that driverless cars will be susceptible: John Markoff, “Collision in the Making Between Self-Driving Cars and How the World Works,” New York Times, January 23, 2012, http://www.nytimes.com/2012/01/24/technology/googles-autonomous-vehicles-draw-skepticism-at-legal-symposium.html. Will Knight, “Proceed with Caution toward the Self-Driving Car,” MIT Technology Review, April 16, 2013, http://www.technologyreview.com/review/513531/proceed-with-caution-toward-the-self-driving-car/.

The Google car’s successful driving tests: Mark Harris, “How Google’s Autonomous Car Passed the First U.S. State Self-Driving Test,” IEEE Spectrum Online, September 10, 2014, http://spectrum.iee.org. Idem., “These Are the Secrets Google Wanted to Keep about Its Self-Driving Cars,” Quartz, http://qz.com/252817/these-are-the-secrets-google-wanted-to-keep-about-its-self-driving-cars/, accessed November 18, 2014. Mark Harris, “How Much Training Do You Need to Be a Robocar Test Driver? It Depends On Whom You Work For,” IEEE Spectrum Cars That Think, February 24, 2015, http://spectrum.ieee.org/cars-that-think/transportation/human-factors/how-much-training-do-you-need-to-be-a-robocar-test-driver-it-depends-on-whom-you-work-for.


pages: 491 words: 77,650

Humans as a Service: The Promise and Perils of Work in the Gig Economy by Jeremias Prassl

3D printing, Affordable Care Act / Obamacare, Airbnb, algorithmic management, Amazon Mechanical Turk, Andrei Shleifer, asset light, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, death from overwork, Didi Chuxing, disruptive innovation, Donald Trump, driverless car, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, Greyball, hiring and firing, income inequality, independent contractor, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, low skilled workers, Lyft, machine readable, Mahatma Gandhi, Mark Zuckerberg, market friction, means of production, moral hazard, Network effects, new economy, obamacare, pattern recognition, platform as a service, Productivity paradox, race to the bottom, regulatory arbitrage, remote working, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Rosa Parks, scientific management, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, TechCrunch disrupt, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, transaction costs, transportation-network company, Travis Kalanick, two tier labour market, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, warehouse automation, work culture , working-age population

Justin McCurry, ‘South Korean woman’s hair eaten by robot vacuum cleaner as she slept’, The Guardian (9 February 2015), https://www.theguardian.com/ world/2015/feb/09/south-korean-womans-hair-eaten-by-robot-vacuum- cleaner-as-she-slept, archived at https://perma.cc/86YB-RF49; Aarian Marshall, ‘Puny humans still see the world better than self-driving cars, Wired (5 August 2017), https://www.wired.com/story/self-driving-cars-perception- humans/, archived at https://perma.cc/B8L9-7K32; Marty Padget, ‘Ready to pay billions for self-driving car roads?’, Venture Beat (17 May 2017), https:// venturebeat.com/2017/05/17/ready-to-pay-trillions-for-self-driving-car-roads/, archived at https://perma.cc/ZJ9K-LSXF. There is, furthermore, an important distinction between jobs that could be automated and those that actually are: see David Kucera, New Automation Technologies and Job Creation and Destruction Dynamics (International Labour Organization 2016). 14.

McAfee and Brynjofsson disagree: because ‘humanity has recently become much better at building machines that can figure things out on their own,’ they suggest, ‘ “Polanyi’s paradox” is not the barrier it once was; machines can learn even when humans can’t teach them.’12 It is true that * * * Rethinking Employment Law for the Future of Work 139 engineers have been working hard to develop cleaning robots, self-driving cars, and image-recognition software. Even after years of work and billions of investments, however, the algorithms continue to struggle—from a robotic cleaner getting tangled in its owner’s hair until she could be freed by paramedics, to self-driving cars confused by ice, snow, faded road mark- ings, and stray plastic bags.13 Artificial Artificial Intelligence In the long run, the gig economy will not remain beyond the reach of algo- rithms.

Entrepreneurs’ reliance on cheap out- workers was said to ‘perpetuate the use of imperfect and inferior machin- ery . . . and thus prevent the adoption of improved and more economical models of production’.74 Two immediate counter-arguments come to mind. First, what about Uber’s well-publicized efforts to develop self-driving cars? And, in any event, might a delay in innovative automation not be to the benefit of workers whose jobs would otherwise be threatened by a rise of the robots? As regards the first of these arguments, Uber’s emphasis on autonomous vehicles has increasingly been questioned by experts from both technological and eco- nomic perspectives: the company’s efforts seem to lag significantly behind competitors’ technological advances.75 In any event, why would Uber replace its current asset-light model, under which drivers bear the full cost of pro- viding cars, petrol, and their time, with a massive investment in an expensive fleet of self-driving cars?


pages: 386 words: 113,709

Why We Drive: Toward a Philosophy of the Open Road by Matthew B. Crawford

1960s counterculture, Airbus A320, airport security, augmented reality, autonomous vehicles, behavioural economics, Bernie Sanders, Big Tech, Boeing 737 MAX, British Empire, Burning Man, business logic, call centre, classic study, collective bargaining, confounding variable, congestion pricing, crony capitalism, data science, David Sedaris, deskilling, digital map, don't be evil, Donald Trump, driverless car, Elon Musk, emotional labour, en.wikipedia.org, Fellow of the Royal Society, Ford Model T, gamification, gentrification, gig economy, Google Earth, Great Leap Forward, Herbert Marcuse, hive mind, Ian Bogost, income inequality, informal economy, Internet of things, Jane Jacobs, labour mobility, Lyft, mirror neurons, Network effects, New Journalism, New Urbanism, Nicholas Carr, planned obsolescence, Ponzi scheme, precautionary principle, Ralph Nader, ride hailing / ride sharing, Ronald Reagan, Sam Peltzman, security theater, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, smart cities, social graph, social intelligence, Stephen Hawking, surveillance capitalism, tacit knowledge, tech worker, technoutopianism, the built environment, The Death and Life of Great American Cities, the High Line, time dilation, too big to fail, traffic fines, Travis Kalanick, trolley problem, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, Wall-E, Works Progress Administration

They are consistent with results in surveys by Pew Research Center, the American Automobile Association and others.” Paul Lienert and Maria Caspani, “Americans Still Don’t Trust Self-Driving Cars, Reuters/Ipsos Poll Finds,” Reuters, April 1, 2019, https://www.reuters.com/article/us-autos-selfdriving-poll/americans-still-dont-trust-self-driving-cars-reuters-ipsos-poll-finds-idUSKCN1RD2QS. Other findings consistent with these are collected from opinion polls conducted by various industry groups, insurance institutes, and consumer advocacy groups and available at Saferoads.org. 8.Christopher Mele, “In a Retreat, Uber Ends Its Self-Driving Car Experiment in San Francisco,” New York Times, December 22, 2016, http://www.nytimes.com/2016/12/21/technology/san-francisco-california-uber-driverless-car-.html?

Other findings consistent with these are collected from opinion polls conducted by various industry groups, insurance institutes, and consumer advocacy groups and available at Saferoads.org. 8.Christopher Mele, “In a Retreat, Uber Ends Its Self-Driving Car Experiment in San Francisco,” New York Times, December 22, 2016, http://www.nytimes.com/2016/12/21/technology/san-francisco-california-uber-driverless-car-.html?hp&action=click&pgtype=Homepage&clickSource=story-heading&module=first-column-region&region=top-news &WT.nav=top-news&_r=0. Mike Isaac, “Uber Defies California Regulators with Self-Driving Car Service,” New York Times, December 16, 2016, https://www.nytimes.com/2016/12/16/technology/uber-defies-california-regulators-with-self-driving-car-service.html. 9.John Harris, “With Trump and Uber, the Driverless Future Could Turn into a Nightmare,” Guardian, December 16, 2016, https://www.theguardian.com/commentisfree/2016/dec/16/trump-uber-driverless-future-jobs-go. 10.These are the findings of the city’s transport department as characterized by Nicole Gelinas in “Why Uber’s Investors May Lose Their Lunch,” New York Post, December 26, 2017, available at https://www.manhattan-institute.org/html/why-ubers-investors-may-lose-their-lunch-10847.html. 11.

The proposed book is political in spirit, if we may take that term in its broadest sense. As we become ever more administered and pacified in so many domains of life, I want to explore this one domain of skill, freedom, and individual responsibility—driving—before it is too late, and make a case for defending it. For self-driving cars to realize their full potential to reduce traffic and accidents, we can’t have rogue dissidents bypassing the system of coordination that they make possible.6 Their inherent logic presses toward their becoming mandatory—if not by fiat of the state, then by the prohibitive calculations of insurance companies, who will have to distribute risk among fewer human drivers.


pages: 688 words: 147,571

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

"World Economic Forum" Davos, Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic bias, algorithmic trading, AlphaGo, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, backpropagation, Basel III, bitcoin, Black Monday: stock market crash in 1987, blockchain, brain emulation, Brexit referendum, Cambridge Analytica, Charles Babbage, Clapham omnibus, cognitive dissonance, Computing Machinery and Intelligence, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, Demis Hassabis, distributed ledger, don't be evil, Donald Trump, driverless car, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hallucination problem, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, machine readable, machine translation, medical malpractice, Nate Silver, natural language processing, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, nudge unit, obamacare, off grid, OpenAI, paperclip maximiser, pattern recognition, Peace of Westphalia, Philippa Foot, race to the bottom, Ray Kurzweil, Recombinant DNA, Rodney Brooks, self-driving car, Silicon Valley, Stanislav Petrov, Stephen Hawking, Steve Wozniak, strong AI, technological singularity, Tesla Model S, The Coming Technological Singularity, The Future of Employment, The Signal and the Noise by Nate Silver, trolley problem, Turing test, Vernor Vinge

Ngo, “Redesign of the Vehicle Bonnet Structure for Pedestrian Safety”, Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering, Vol. 226, No. 1 (2012), 70–84. 109Many commentators have pointed out the applicability of the Trolley Problem to self-driving cars, but beyond articulating the issue, few have actually suggested a legal or moral answer. See, for example, Matt Simon, “To Make Us All Safer, Robocars Will Sometimes Have to Kill”, Wired, 17 March 2017, https://​www.​wired.​com/​2017/​03/​make-us-safer-robocars-will-sometimes-kill/​, accessed 1 June 2018; Alex Hern, “Self-Driving Cars Don’t Care About Your Moral Dilemmas”, The Guardian, 22 August 2016, https://​www.​theguardian.​com/​technology/​2016/​aug/​22/​self-driving-cars-moral-dilemmas, accessed 1 June 2018; Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan, “The Social Dilemma of Autonomous Vehicles”, Science, Vol. 352, No. 6293 (2016), 1573–1576; Noah J.

Ministry of Justice [2014] UKSC 38, where the Supreme Court declined to find that a terminally ill person had a right to be administered euthanasia, in the absence of any imprimatur from Parliament to that effect. 56Jack Stilgoe and Alan Winfield, “Self-Driving Car Companies Should Not Be Allowed to Investigate Their Own Crashes”, The Guardian, 13 April 2018, https://​www.​theguardian.​com/​science/​political-science/​2018/​apr/​13/​self-driving-car-companies-should-not-be-allowed-to-investigate-their-own-crashes, accessed 1 June 2018. 57“Homepage”, Website of the House of Lords Select Committee on A.I., http://​www.​parliament.​uk/​ai-committee, accessed 1 June 2018. 58“Homepage”, Website of the All-Party Parliamentary Group on A.I., http://​www.​appg-ai.​org/​, accessed 1 June 2018. 59Another area of focus for discussions on AI and law which is outside of the problems addressed by this book is the impact of AI on the legal industry itself, for example as a replacement for lawyers and judges.

See further below at s. 4.1. 6Ibid. 7“Homepage”, Website of AI2, http://​allenai.​org/​, accessed 1 June 2018. 8Oren Etzioni, “How to Regulate Artificial Intelligence”, 1 September 2017, New York Times, https://​www.​nytimes.​com/​2017/​09/​01/​opinion/​artificial-intelligence-regulations-rules.​html, accessed 1 June 2018. 9For a similar formulation to Walsh see Tim Wu, “Please Prove You’re Not a Robot”, New York Times, 15 July 2017, https://​www.​nytimes.​com/​2017/​07/​15/​opinion/​sunday/​please-prove-youre-not-a-robot.​html, accessed 1 June 2018. 10Toby Walsh, Android Dreams (London: Hurst & Company, 2017), 113–114. 11Though a 2018 accident in Arizona, where a woman was killed after walking in front of a self-driving vehicle travelling at 40 miles per hour, suggests that—at least at the time of writing—autonomous vehicles remain imperfect in this regard. See, for the issue and a potential solution: Dave Gershgorn, “An AI-Powered Design Trick Could Help Prevent Accidents like Uber’s Self-Driving Car Crash”, Quartz, 30 March 2018, https://​qz.​com/​1241119/​accidents-like-ubers-self-driving-car-crash-could-be-prevented-with-this-ai-powered-design-trick/​, accessed 1 June 2018. 12For an example of a system which is designed to test whether AI has “common sense”, see the discussion of the AI2 Reasoning Challenge in Will Knight, “AI Assistants Say Dumb Things, and We’re About to Find Out Why”, MIT Technology Review, 14 March 2018, https://​www.​technologyreview​.​com/​s/​610521/​ai-assistants-dont-have-the-common-sense-to-avoid-talking-gibberish/​, accessed 1 June 2018.


User Friendly by Cliff Kuang, Robert Fabricant

A Pattern Language, Abraham Maslow, Airbnb, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, Apple II, augmented reality, autonomous vehicles, behavioural economics, Bill Atkinson, Brexit referendum, Buckminster Fuller, Burning Man, business logic, call centre, Cambridge Analytica, Chuck Templeton: OpenTable:, cognitive load, computer age, Daniel Kahneman / Amos Tversky, dark pattern, data science, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Elaine Herzberg, en.wikipedia.org, fake it until you make it, fake news, Ford Model T, Frederick Winslow Taylor, frictionless, Google Glasses, Internet of things, invisible hand, James Dyson, John Markoff, Jony Ive, knowledge economy, Kodak vs Instagram, Lyft, M-Pesa, Mark Zuckerberg, mobile money, Mother of all demos, move fast and break things, Norbert Wiener, Paradox of Choice, planned obsolescence, QWERTY keyboard, randomized controlled trial, replication crisis, RFID, scientific management, self-driving car, seminal paper, Silicon Valley, skeuomorphism, Skinner box, Skype, smart cities, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, tacit knowledge, Tesla Model S, three-martini lunch, Tony Fadell, Uber and Lyft, Uber for X, uber lyft, Vannevar Bush, women in the workforce

And he started to see that a great many of those buttons were dedicated to little tidbits of assisted driving. He thought, Why don’t I put those all together on a touchscreen? This was around 2010, when self-driving cars were just beginning to be real. A team at Stanford had figured out how to rig up an Audi to drive itself in a race up the fabled Pikes Peak. Anyone could see that the promise of self-driving cars was too tantalizing for them to remain in the lab for long. It happened that Lathrop was particularly well positioned for the problem. He’d cut his teeth at NASA, trying to create helmet displays for pilots.

Victor Cruz Cid, “Volvo Auto Brake System Fail,” YouTube, May 19, 2015, www.youtube.com/watch?v=_47utWAoupo. 3. RockTreeStar, “Tesla Autopilot Tried to Kill Me!” YouTube, October 15, 2015, www.youtube.com/watch?v=MrwxEX8qOxA. 4. Andrew J. Hawkins, “This Map Shows How Few Self-Driving Cars Are Ac-tually on the Road Today,” The Verge, October 23, 2017, www.theverge.com/2017/10/23/16510696/self-driving-cars-map-testing-bloomberg-aspen. 5. There is an irony in this: Audi is owned by Volkswagen, which at the same time was embroiled in a scandal over untrustworthy emissions performance. The engineers and designers in this story had no involvement in that. 6.

Presented at TED2017, April 2017. www.ted.com/talks/tristan_harris_the_manipulative_tricks_tech_companies_use_to_capture_your_attention. ———. “How Technology Is Hijacking Your Mind—from a Magician and Google Design Ethicist.” Medium, May 18, 2016. Hawkins, Andrew J. “This Map Shows How Few Self-Driving Cars Are Actually on the Road Today.” The Verge, October 23, 2017. www.theverge.com/2017/10/23/16510696/self-driving-cars-map-testing-bloomberg-aspen. Helft, Miguel. “The Class That Built Apps, and Fortunes.” New York Times, May 7, 2011. www.nytimes.com/2011/05/08/technology/08class.html. Hempel, Jessi. “What Happened to Facebook’s Grand Plan to Wire the World?”


pages: 533

Future Politics: Living Together in a World Transformed by Tech by Jamie Susskind

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, algorithmic bias, AlphaGo, Amazon Robotics, Andrew Keen, Apollo Guidance Computer, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, bitcoin, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, British Empire, business process, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, Citizen Lab, cloud computing, commons-based peer production, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, digital divide, digital map, disinformation, distributed ledger, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Erik Brynjolfsson, Ethereum, ethereum blockchain, Evgeny Morozov, fake news, Filter Bubble, future of work, Future Shock, Gabriella Coleman, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, Large Hadron Collider, Lewis Mumford, lifelogging, machine translation, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, natural language processing, Neil Armstrong, Network effects, new economy, Nick Bostrom, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, Philippa Foot, post-truth, power law, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Bannon, Steve Jobs, Steve Wozniak, Steven Levy, tech bro, technological determinism, technological singularity, technological solutionism, the built environment, the Cathedral and the Bazaar, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, Tragedy of the Commons, trolley problem, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, work culture , working-age population, Yochai Benkler

Indeed, machine learning algorithms are all around us:32 Amazon’s algorithm, more than any one person, determines what books are read in the world today. The NSA’s algorithms decide whether you’re a potential terrorist. Climate models decide what’s a safe level of carbon dioxide in the atmosphere. Stock-picking models drive the economy more than most of us do. When the time comes for you to take your first ride in a self-driving car, remember that:33 no engineer wrote an algorithm instructing it, step-by-step, how to get from A to B. No one knows how to program a car to drive, and no one needs to, because a car equipped with a learning algorithm picks it up by observing what the driver does. Machine learning, to borrow from Domingos, is the automation of automation itself.34 It’s a profound development because it liberates AI systems from the limitations of their human creators.

It is also about increasing connectivity between people and machines—through Siri-like ‘oracles’ which answer your questions and ‘genies’ that execute commands.35 In the future, when you leave your house, ‘the same conversation you OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS 48 FUTURE POLITICS were having with your vacuum cleaner or robot pet will be carried on seamlessly with your driverless car, as if one “person” inhabited all these devices’.36 Samsung is looking to put its AI voice assistance Bixby into household appliances, like TVs and refrigerators, to make them responsive to human voice command.37 Self-driving cars will communicate with one another to minimize traffic and avoid collisions. Within the home, Bluetooth Mesh technology could increasingly be used to connect ‘smart’ devices with one another, using every nearby device as a range booster to create a secure network connection between devices that would previously have been out of range.38 (It’s important to note, however, that one of the challenges for the ‘internet of things’ will be developing a unified protocol that enables devices to communicate seamlessly with one another.)39 Looking further ahead, developments in hardware could yield new and astonishing ways of communicating.

This was the first scientific instance of ‘mind-to-mind’ communication, also known as telepathy.40 You can already buy basic brainwave-reading devices, such as the Muse headband, which aims to aid meditation by providing real-time feedback on brain activity.41 Companies such as NeuroSky sell headsets that allow you to operate apps and play games on your smartphone using only thoughts.The US army has (apparently not very well) flown a helicopter using this kind of technology.42 Brain–computer interfaces have been the subject of a good deal of attention in Silicon Valley.43 Overall, increasingly connective technology appears set to deliver the vision of Tim Berners-Lee, inventor of the world wide web, of ‘anything being potentially connected with anything’.44 OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Increasingly Integrated Technology 49 Sensitive In the future, we can expect a dramatic rise in the number of ­sensors in the world around us, together with a vast improvement in what they are able to detect.This is increasingly sensitive technology. Our handheld devices already contain microphones to measure sound, GPS chips to determine location, cameras to capture images, and several other sensors. Increasingly, the devices around us will use radar, sonar, lidar (the system used in self-driving cars to measure the distance to an object by firing a laser at it), motion sensors, bar code scanners, humidity gauges, pressure sensors, magnetometers, barometers, accelerometers, and other means of sensing, and hence interacting with, the physical world. There are many reasons why we might want more sensors in our own homes and devices—for recovering lost or stolen items using GPS, for instance, or monitoring the security or temperature of our homes from afar.45 Industrial entities, too, benefit from real-time feedback on their machinery, whether in relation to humidity, air pressure, electrical resistivity, or chemical presence.


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Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future by Luke Dormehl

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, Bletchley Park, book scanning, borderless world, call centre, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, deep learning, DeepMind, driverless car, drone strike, Elon Musk, Flash crash, Ford Model T, friendly AI, game design, Geoffrey Hinton, global village, Google X / Alphabet X, Hans Moravec, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, machine translation, Marc Andreessen, Mark Zuckerberg, Menlo Park, Mustafa Suleyman, natural language processing, Nick Bostrom, Norbert Wiener, out of africa, PageRank, paperclip maximiser, pattern recognition, radical life extension, Ray Kurzweil, recommendation engine, remote working, RFID, scientific management, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, tech billionaire, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, Tony Fadell, too big to fail, traumatic brain injury, Turing machine, Turing test, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!

In the end, the car drove 2,797 miles coast-to-coast from Pittsburgh, Pennsylvania to San Diego, California – including a crossing of the Hoover Dam carried out autonomously. In one memorable highlight, a Businessweek reporter who was covering the event was pulled over by a Kansas State Trooper. Pomerleau and Jochem sailed by in their self-driving car, hands exaggeratedly off the steering wheel. It would be another fifteen years, until October 2010, before Google announced its own self-driving car initiative. However, thanks to his groundbreaking work in neural nets, Dean Pomerleau had proved his point. Welcome to Deep Learning The next significant advance for neural networks took place in the mid-2000s.

In shops, bars, theme parks and museums, bluetooth beacons will transmit user-relevant information to your smartphone or wearable device depending on your location and personal preferences. On the street, by far the biggest visible change likely to happen in the next several decades will be the mass arrival of self-driving cars. Following on from the work of Dean Pomerleau, as described in the last chapter, both Google and Apple have invested in this field and look set to play a key role in bringing autonomous vehicles to the mainstream. Self-driving cars won’t only affect us on an individual level, but also collectively by helping to reduce traffic congestion in cities. The data that they gather will be vital to town planners as cities continue to expand.

However, some went further and gave the networks themselves the autonomous power to buy and sell. Not coincidentally, the finance sector quickly joined the video game business as an industry ready to throw money at AI researchers. The age of algorithmic trading had begun. Another eye-catching application of neural nets during this time was the invention of the self-driving car. Autonomous vehicles had been a long-time dream of technologists. In 1925, the inventor Francis Houdina demonstrated a radio-controlled car, which he drove through the streets of Manhattan without anyone at the steering wheel. Later, autonomous vehicle tests used guidewires and on-board sensors to follow painted white lines on the road or seek out the alternating current of buried cables.


pages: 345 words: 75,660

Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

Abraham Wald, Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, algorithmic bias, AlphaGo, Amazon Picking Challenge, artificial general intelligence, autonomous vehicles, backpropagation, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, carbon tax, Charles Babbage, classic study, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, data science, deep learning, DeepMind, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, financial engineering, fulfillment center, general purpose technology, Geoffrey Hinton, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, Jeff Hawkins, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, Nick Bostrom, On the Economy of Machinery and Manufactures, OpenAI, paperclip maximiser, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Robert Solow, Salesforce, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Levy, strong AI, The Future of Employment, the long tail, The Signal and the Noise by Nate Silver, Tim Cook: Apple, trolley problem, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, vertical integration, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

However, it does this precisely when those changes mean that “old data” is less useful for prediction. 7. Daniel Ren, “Tencent Joins the Fray with Baidu in Providing Artificial Intelligence Applications for Self-Driving Cars,” South China Morning Post, August 27, 2017, http://www.scmp.com/business/companies/article/2108489/tencent-forms-alliance-push-ai-applications-self-driving. 8. Ren, “Tencent Joins the Fray with Baidu in Providing Artificial Intelligence Applications for Self-Driving Cars.” Chapter 16 1. The theory of adaptation and incentives outlined here comes from Steven Tadelis, “Complexity, Flexibility, and the Make-or-Buy Decision,” American Economic Review 92, no. 2 (May 2002): 433–437. 2.

When high-quality machine prediction became cheap, human prediction declined in value, so the cabbies were worse off. The number of rides in London’s black cabs fell. Instead, other people provided the same service. These others also had driving skills and human sensors, complementary assets that went up in value as prediction became cheap. Of course, self-driving cars might themselves end up substituting for those skills and senses, but we will return to that story later. Our point here is that understanding the impact of machine prediction requires an understanding of the various aspects of decisions, as described by the anatomy of a decision. Should You Take an Umbrella?

You will try foods you don’t like. If you keep trying new foods in the hope of finding some ideal, you are missing out on a lot of good meals. Judgment, whether by deliberation or experimentation, is costly. Knowing Why You Are Doing Something Prediction is at the heart of a move toward self-driving cars and the rise of platforms such as Uber and Lyft: choosing a route between origin and destination. Car navigation devices have been around for a few decades, built into cars themselves or as stand-alone devices. But the proliferation of internet-connected mobile devices has changed the data that providers of navigation software receive.


pages: 257 words: 64,285

The End of Traffic and the Future of Transport: Second Edition by David Levinson, Kevin Krizek

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, American Society of Civil Engineers: Report Card, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, big-box store, bike sharing, carbon tax, Chris Urmson, collaborative consumption, commoditize, congestion pricing, crowdsourcing, DARPA: Urban Challenge, dematerialisation, driverless car, Dutch auction, Elon Musk, en.wikipedia.org, Ford Model T, Google Hangouts, high-speed rail, Induced demand, intermodal, invention of the printing press, jitney, John Markoff, labor-force participation, Lewis Mumford, lifelogging, Lyft, means of production, megacity, Menlo Park, Network effects, Occam's razor, oil shock, place-making, pneumatic tube, post-work, printed gun, Ray Kurzweil, rent-seeking, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, tacit knowledge, techno-determinism, technological singularity, Tesla Model S, the built environment, The future is already here, Thomas Kuhn: the structure of scientific revolutions, transaction costs, transportation-network company, Uber and Lyft, Uber for X, uber lyft, urban renewal, women in the workforce, working-age population, Yom Kippur War, zero-sum game, Zipcar

_r=2&src=sch&pagewanted=all Erico Guizzo (2011-10-18) How Google's Self-Driving Car Works IEEE Spectrum http://spectrum.ieee.org/automaton/robotics/artificial-intelligence/how-google-self-driving-car-works 156 These hires included Sebastian Thrun of Stanford and Chris Urmson of CMU, for their own internal secret project, which they announced in 2010. 157 Figure 7.1 Source: Data on Google Cars from 140,000 - http://googleblog.blogspot.com/2010/10/what-were-driving-at.html 300,000 http://googleblog.blogspot.com/2012/08/the-self-driving-car-logs-more-miles-on.html 500,000 http://www.businessinsider.com/google-self-driving-car-problems-2013-3?

In this event, unlike the previous Grand Challenges, cars had to have more sophisticated and intelligent sensors. Though road quality was better (paved rather than off-road), the challenge was far more challenging. Fast forward just a few years and we see that Google hired many of the leaders of the Stanford and Carnegie Mellon teams.155 156 Google Self-Driving Cars have since traveled 1.5 million miles (2.4 million km) autonomously, mostly around the San Francisco Bay Area, but also more recently in Austin, Texas and Kirkland, Washington (Figure 7.1).157 Google's cars are map-dependent, operating where the roads have been mapped out in detail, so that they can compare what they see with what they expect to see158—a strategy with obvious strengths and weaknesses.159 In Fall of 2015, the electric vehicle automaker Tesla remotely upgraded its most recent model year cars (about 50,000 vehicles) with “auto-pilot”, making them semi-autonomous (late Level 2, early Level 3).160 Elon Musk, the CEO of Tesla, says he expects fully autonomous vehicles within 3 years (i.e. by 2018).

Thus we anticipate that autonomous vehicles will go from their current status of essentially 0% market share to an end state of 100% of all new car sales (i.e. autonomous capability will be a requirement of new car purchases) by 2030. Furthermore, older human-driven vehicles will be phased out except for special purposes (car shows, races, parades) during the 2030s. Self-driving cars in specific contexts (e.g. freeways or isolated campuses) are expected enter the market before 2020. In other words, human drivers will eventually (around 2040) be prohibited on public roads most of the time, just as horses no longer gallop down our streets. Consumer acceptance remains an unknown, and depends on the quality of the product being offered.


Big Data at Work: Dispelling the Myths, Uncovering the Opportunities by Thomas H. Davenport

Automated Insights, autonomous vehicles, bioinformatics, business intelligence, business process, call centre, chief data officer, cloud computing, commoditize, data acquisition, data science, disruptive innovation, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, lifelogging, Mark Zuckerberg, move fast and break things, Narrative Science, natural language processing, Netflix Prize, New Journalism, recommendation engine, RFID, self-driving car, sentiment analysis, Silicon Valley, smart grid, smart meter, social graph, sorting algorithm, statistical model, Tesla Model S, text mining, Thomas Davenport, three-martini lunch

., to allow self-driving cars on the nation’s highways). It’s not entirely clear what the viable business model for each of these innovations is—how companies can make money with them. It’s also not clear that customers will want these innovations—­particularly Chapter_02.indd 41 03/12/13 11:42 AM 42 big data @ work those like the pet store video cameras that pose a risk to human and pet ­privacy. However, it seems likely that some organizations will pull them off, and that they will make those organizations very successful. Just as Google, for example, decided to make the self-driving car a reality, there are other organizations that will succeed with integrating it into a comprehensive travel management capability.

Without any actions on Lynda’s part, she receives a proposed itinerary with the f­ ollowing components: • A flight on her preferred airline, with a frequent flyer upgrade already arranged and her preferred aisle seat reserved • A hotel reservation for all the nights of the conference • A self-driving rental car reservation at the airport (because the conference hotel is forty miles away, and the travel management application has compared the cost at prevailing rates of taxi, limo, and rental car for that distance) • A reservation at the best Italian restaurant in the conference city—Lynda’s favorite dining option—for the “on your own” night of the conference, with three suggestions (and three alternate suggestions) for dining companions who are valued members of her social network and who will also be attending the conference; Lynda needs only to touch her tablet screen once to invite them Chapter_02.indd 33 03/12/13 11:42 AM 34 big data @ work Lynda’s self-driving car delivers her to the conference hotel with no problems; the travel management system had downloaded her destination address, preferred air-conditioning temperature, and favorite satellite music station to the car. Lynda’s only complaint about selfdriving rental cars is that antiquated regulations force her to sit in the driver’s seat, which limits her tablet access.

(Note to skeptics: Although many of these automated travel features are not yet available, travel management experts I interviewed suggested that they would be plausible in the fairly near future. And we Chapter_02.indd 34 03/12/13 11:42 AM How Big Data Will Change Your Job, Company, and Industry   35 know that the self-driving car already exists—described by Google as a big data project—and will probably be incorporated into the transportation system in some fashion.) A Big Data Scenario for Energy Management David Byron is a corporate facilities and energy manager for ­Bathworks, a large US plumbing fixtures manufacturing company.


pages: 562 words: 201,502

Elon Musk by Walter Isaacson

4chan, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, AltaVista, Apollo 11, Apple II, Apple's 1984 Super Bowl advert, artificial general intelligence, autism spectrum disorder, autonomous vehicles, basic income, Big Tech, blockchain, Boston Dynamics, Burning Man, carbon footprint, ChatGPT, Chuck Templeton: OpenTable:, Clayton Christensen, clean tech, Colonization of Mars, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, crowdsourcing, cryptocurrency, deep learning, DeepMind, Demis Hassabis, disinformation, Dogecoin, Donald Trump, Douglas Engelbart, drone strike, effective altruism, Elon Musk, estate planning, fail fast, fake news, game design, gigafactory, GPT-4, high-speed rail, hiring and firing, hive mind, Hyperloop, impulse control, industrial robot, information security, Jeff Bezos, Jeffrey Epstein, John Markoff, John von Neumann, Jony Ive, Kwajalein Atoll, lab leak, large language model, Larry Ellison, lockdown, low earth orbit, Marc Andreessen, Marc Benioff, Mars Society, Max Levchin, Michael Shellenberger, multiplanetary species, Neil Armstrong, Network effects, OpenAI, packet switching, Parler "social media", paypal mafia, peer-to-peer, Peter Thiel, QAnon, Ray Kurzweil, reality distortion field, remote working, rent control, risk tolerance, Rubik’s Cube, Salesforce, Sam Altman, Sam Bankman-Fried, San Francisco homelessness, Sand Hill Road, Saturday Night Live, self-driving car, seminal paper, short selling, Silicon Valley, Skype, SpaceX Starlink, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steve Wozniak, Steven Levy, Streisand effect, supply-chain management, tech bro, TED Talk, Tesla Model S, the payments system, Tim Cook: Apple, universal basic income, Vernor Vinge, vertical integration, Virgin Galactic, wikimedia commons, William MacAskill, work culture , Y Combinator

“This has the potential to be the far biggest thing we ever do, even bigger than a self-driving car,” he told his chief designer, Franz von Holzhausen. “Once we hear a recurring theme from Elon, we start working on it,” von Holzhausen says. They began meeting in the Tesla design studio in Los Angeles, where the Cybertruck and Robotaxi models were on display. Musk gave the specs: the robot should be about five-foot-eight, with an elfish and androgenous look so it “doesn’t feel like it could or would want to hurt you.” Thus was born Optimus, a humanoid robot to be made by the Tesla teams working on self-driving cars. Musk decided that it should be announced at an event called “AI Day,” which he scheduled for Tesla’s Palo Alto headquarters on August 19, 2021.

He was comparing his project at Tesla to the artificial intelligence chatbot that had just been released by OpenAI, the lab that Musk had cofounded with Sam Altman in 2015. For almost a decade, Musk had been working on various forms of artificial intelligence, including self-driving cars, Optimus the robot, and the Neuralink brain-machine interface. Shroff’s project involved the latest machine-learning frontier: devising a self-driving car system that would learn from human behavior. “We process an enormous amount of data on how real humans acted in a complex driving situation, and then we train a computer’s neural network to mimic that.” Musk had asked to meet with Shroff—who had occasionally served as a fourth musketeer with James, Andrew, and Ross—because he was thinking about persuading him to leave Tesla’s Autopilot team and come work at Twitter.

It had a fleet of almost two million Teslas around the world collecting billions of video frames per day. “We are uniquely positioned to do this,” Elluswamy said at the meeting. The ability to collect and analyze vast flows of real-time data would be crucial to all forms of AI, from self-driving cars to Optimus robots to ChatGPT–like bots. And Musk now had two powerful gushers of real-time data, the video from self-driving cars and the billions of postings each week on Twitter. He told the Autopilot meeting that he had just made a major purchase of 10,000 more GPU data-processing chips for use at Twitter, and he announced that he would hold more frequent meetings on the potentially more powerful Dojo chips being designed at Tesla.


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Robots Will Steal Your Job, But That's OK: How to Survive the Economic Collapse and Be Happy by Pistono, Federico

3D printing, Albert Einstein, autonomous vehicles, bioinformatics, Buckminster Fuller, cloud computing, computer vision, correlation does not imply causation, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Firefox, future of work, gamification, George Santayana, global village, Google Chrome, happiness index / gross national happiness, hedonic treadmill, illegal immigration, income inequality, information retrieval, Internet of things, invention of the printing press, Jeff Hawkins, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, labor-force participation, Lao Tzu, Law of Accelerating Returns, life extension, Loebner Prize, longitudinal study, means of production, Narrative Science, natural language processing, new economy, Occupy movement, patent troll, pattern recognition, peak oil, post scarcity, QR code, quantum entanglement, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, seminal paper, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, synthetic biology, technological singularity, TED Talk, Turing test, Vernor Vinge, warehouse automation, warehouse robotics, women in the workforce

As a result, just because a technology exists and it helps us live better, it will not necessarily be adopted right away, because of many social factors. To explain how this process unfolds, I will try to predict what I think is a possible future scenario for the case of self-driving cars. Needless to say, I do not possess the power of precognition, but I will try to make an educated guess. Some of these events, at the time of writing, have already happened. Many have not. Time will tell how wrong I was. 7.8 A (possible) History of Self-Driving Cars Google announced that they have invented self-driving cars. After a few years of research, with very little money and a small team, they were able to harness the power of machines to solve a very challenging problem of our times.

The situation seemed to be changing, and public opinion is now mostly favourable. Then, the first major accident happened. A self-driving car was roaming around as usual, when another car, driven by a human, crashed into it. The person driving the old-fashioned vehicle was exceeding the speed limit and did not care to follow the street signs either. In short, it was his fault. The cybernetic car tried to avoid the collision, but the other car was simply too fast and it all happened to quickly. The result: the driver of the old car, and his friend next to him, died. News stories went nuts. Headlines like “First self-driving car kills 2 people”, “The killer-machine”, and “Who ’s going to pay for this?”

But I also received very different answers: “I don’t trust machines, they’ll never be like us. I will never get into a car like that, I want to have control. People won’t accept that, they’ll never have automated cars running on the streets.” This vision is shared by many others I interviewed, some of whom were particularly disturbed by the idea of self-driving cars (surprisingly enough even young people). There are many factors to consider, and the evolution of progress goes through various steps. First, there is the development of a new technology. Computer scientists, mathematicians, physicists, and engineers form a small research team somewhere, and decide they want to tackle a specific problem.


The Ethical Algorithm: The Science of Socially Aware Algorithm Design by Michael Kearns, Aaron Roth

23andMe, affirmative action, algorithmic bias, algorithmic trading, Alignment Problem, Alvin Roth, backpropagation, Bayesian statistics, bitcoin, cloud computing, computer vision, crowdsourcing, data science, deep learning, DeepMind, Dr. Strangelove, Edward Snowden, Elon Musk, fake news, Filter Bubble, general-purpose programming language, Geoffrey Hinton, Google Chrome, ImageNet competition, Lyft, medical residency, Nash equilibrium, Netflix Prize, p-value, Pareto efficiency, performance metric, personalized medicine, pre–internet, profit motive, quantitative trading / quantitative finance, RAND corporation, recommendation engine, replication crisis, ride hailing / ride sharing, Robert Bork, Ronald Coase, self-driving car, short selling, sorting algorithm, sparse data, speech recognition, statistical model, Stephen Hawking, superintelligent machines, TED Talk, telemarketer, Turing machine, two-sided market, Vilfredo Pareto

If you want to fly commercially from Ithaca, New York, to the island town of Lipari in Italy, you can’t simply direct American Airlines to take a nonstop route along the great circle between the two locations—instead you’ll have multiple flight legs and layovers, all for the sake of macroscopic efficiency at the expense of your own time and convenience. In a similar vein, it would be natural for a massive network of self-driving cars to be coordinated so as to implement navigation schemes that optimize for collective average driving time (and perhaps other considerations, such as fuel efficiency) rather than individual self-interest. But even before the self-driving cars arrive en masse, we can imagine other ways Maxwell might be effectively deployed. One is that if, as in our two-route example above, Maxwell randomly chooses the drivers who are given nonselfish routes, users might have a stronger incentives to use the app, since over time the assignment of nonselfish routes will balance out across users, and then each individual user would enjoy lower average driving time.

Some of the popular and scientific discussion of algorithmic morality has focused on thought experiments highlighting the difficult ethical decisions that self-driving cars and other systems might soon confront on a regular basis. The Moral Machine project at MIT presents users with a series of such dilemmas in an effort to poll human perspectives on AI and machine learning morality. While it might seem like an extended parlor game, perhaps projects such as this will eventually gather valuable subjective data on moral perception, somewhat akin to the suggestion of surveying user groups to advance algorithmic transparency. Fig. 29. Illustration of standard hypothetical moral dilemmas faced by self-driving cars, in which the controlling algorithm must decide whether to sacrifice its passengers or the pedestrians.

But the incentive problems that Maxwell faces are arguably even worse, because they are not simply about drivers lying to the app; rather, the problem is drivers disregarding its recommendations entirely when they are not best responses. There are a couple of reasonable replies to this concern. The first is that we may eventually (perhaps even soon) arrive at an era of mostly or even entirely self-driving cars, in which case the Maxwell solution could simply be implemented by centralized fiat. Public transportation systems are generally already designed and coordinated for collective, not individual, optimality. If you want to fly commercially from Ithaca, New York, to the island town of Lipari in Italy, you can’t simply direct American Airlines to take a nonstop route along the great circle between the two locations—instead you’ll have multiple flight legs and layovers, all for the sake of macroscopic efficiency at the expense of your own time and convenience.


pages: 389 words: 119,487

21 Lessons for the 21st Century by Yuval Noah Harari

"World Economic Forum" Davos, 1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, behavioural economics, Bernie Sanders, bitcoin, blockchain, Boris Johnson, Brexit referendum, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, carbon-based life, Charlie Hebdo massacre, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, DeepMind, deglobalization, disinformation, Donald Trump, Dr. Strangelove, failed state, fake news, Filter Bubble, Francis Fukuyama: the end of history, Freestyle chess, gig economy, glass ceiling, Google Glasses, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, job automation, knowledge economy, liberation theology, Louis Pasteur, low skilled workers, Mahatma Gandhi, Mark Zuckerberg, mass immigration, means of production, Menlo Park, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-truth, post-work, purchasing power parity, race to the bottom, RAND corporation, restrictive zoning, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, TED Talk, transatlantic slave trade, trolley problem, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, Watson beat the top human players on Jeopardy!, zero-sum game

If we teach Kant, Mill and Rawls to write code, they can carefully program the self-driving car in their cosy laboratory, and be certain that the car will follow their commandments on the highway. In effect, every car will be driven by Michael Schumacher and Immanuel Kant rolled into one. Thus if you program a self-driving car to stop and help strangers in distress, it will do so come hell or high water (unless, of course, you insert an exception clause for infernal or high-water scenarios). Similarly, if your self-driving car is programmed to swerve to the opposite lane in order to save the two kids in its path, you can bet your life this is exactly what it will do.

When considering automation it is therefore wrong to compare the abilities of a single human driver to that of a single self-driving car, or of a single human doctor to that of a single AI doctor. Rather, we should compare the abilities of a collection of human individuals to the abilities of an integrated network. For example, many drivers are unfamiliar with all the changing traffic regulations, and they often violate them. In addition, since every vehicle is an autonomous entity, when two vehicles approach the same junction at the same time, the drivers might miscommunicate their intentions and collide. Self-driving cars, in contrast, can all be connected to one another.

Tesla will produce two models of the self-driving car: the Tesla Altruist and the Tesla Egoist. In an emergency, the Altruist sacrifices its owner to the greater good, whereas the Egoist does everything in its power to save its owner, even if it means killing the two kids. Customers will then be able to buy the car that best fits their favourite philosophical view. If more people buy the Tesla Egoist, you won’t be able to blame Tesla for that. After all, the customer is always right. This is not a joke. In a pioneering 2015 study people were presented with a hypothetical scenario of a self-driving car about to run over several pedestrians.


System Error by Rob Reich

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, AlphaGo, AltaVista, artificial general intelligence, Automated Insights, autonomous vehicles, basic income, Ben Horowitz, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, Blitzscaling, Cambridge Analytica, Cass Sunstein, clean water, cloud computing, computer vision, contact tracing, contact tracing app, coronavirus, corporate governance, COVID-19, creative destruction, CRISPR, crowdsourcing, data is the new oil, data science, decentralized internet, deep learning, deepfake, DeepMind, deplatforming, digital rights, disinformation, disruptive innovation, Donald Knuth, Donald Trump, driverless car, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, fulfillment center, future of work, gentrification, Geoffrey Hinton, George Floyd, gig economy, Goodhart's law, GPT-3, Hacker News, hockey-stick growth, income inequality, independent contractor, informal economy, information security, Jaron Lanier, Jeff Bezos, Jim Simons, jimmy wales, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Lean Startup, linear programming, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, minimum wage unemployment, Monkeys Reject Unequal Pay, move fast and break things, Myron Scholes, Network effects, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, NP-complete, Oculus Rift, OpenAI, Panopticon Jeremy Bentham, Parler "social media", pattern recognition, personalized medicine, Peter Thiel, Philippa Foot, premature optimization, profit motive, quantitative hedge fund, race to the bottom, randomized controlled trial, recommendation engine, Renaissance Technologies, Richard Thaler, ride hailing / ride sharing, Ronald Reagan, Sam Altman, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, spectrum auction, speech recognition, stem cell, Steve Jobs, Steven Levy, strong AI, superintelligent machines, surveillance capitalism, Susan Wojcicki, tech billionaire, tech worker, techlash, technoutopianism, Telecommunications Act of 1996, telemarketer, The Future of Employment, TikTok, Tim Cook: Apple, traveling salesman, Triangle Shirtwaist Factory, trolley problem, Turing test, two-sided market, Uber and Lyft, uber lyft, ultimatum game, union organizing, universal basic income, washing machines reduced drudgery, Watson beat the top human players on Jeopardy!, When a measure becomes a target, winner-take-all economy, Y Combinator, you are the product

The CEO and shareholders of a ride-hailing service that deploys millions of self-driving cars stand to gain untold wealth, while millions of unemployed taxi, bus, and truck drivers will have to grapple with the consequences of technology over which they have no power. When it comes to material well-being, the age of smart machines may be wonderful for some but livelihood destroying for others. What Is Freedom Worth to You? One hidden yet important cost of automation is that it may undermine our freedom to live our lives as we want. Let’s return to the example of self-driving cars, which could save up to 300,000 lives per decade in the United States alone—something one reporter called potentially the “greatest public-health achievement of the 21st century.”

And although this may cost more than what is currently spent in the United States, as the Europeans have shown, it’s not only how much you spend but how you spend it that makes all the difference. * * * Because Silicon Valley is an epicenter of the design and testing of self-driving cars, it is not uncommon to pull up to a stoplight and see an autonomous vehicle operating right beside you, its sensors visibly sitting atop the roof of the car. For most people, the sight of a self-driving car causes excitement and perhaps spurs the imagination. But when we stop to consider the vast amount of automation that awaits us, it is difficult to look away from the important decisions we will be confronting.

more than 90 percent: National Highway Traffic Safety Administration, “Traffic Safety Facts: 2017 Data,” US Department of Transportation, May 2019, https://crashstats.nhtsa.dot.gov/Api/Public/ViewPublication/812687. making commuting time more productive: Peter Diamandis, “Self-Driving Cars Are Coming,” Forbes, August 13, 2014, https://www.forbes.com/sites/peterdiamandis/2014/10/13/self-driving-cars-are-coming/. Accordingly, most participants: Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan, “The Social Dilemma of Autonomous Vehicles,” Science 352, no. 6293 (June 24, 2016): 1573–76, https://doi.org/10.1126/science.aaf2654. IBM’s Deep Blue computer had “unseated humanity”: Bruce Weber, “Swift and Slashing, Computer Topples Kasparov,” New York Times, May 12, 1997, https://www.nytimes.com/1997/05/12/nyregion/swift-and-slashing-computer-topples-kasparov.html.


pages: 625 words: 167,349

The Alignment Problem: Machine Learning and Human Values by Brian Christian

Albert Einstein, algorithmic bias, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, butterfly effect, Cambridge Analytica, Cass Sunstein, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, data science, deep learning, DeepMind, Donald Knuth, Douglas Hofstadter, effective altruism, Elaine Herzberg, Elon Musk, Frances Oldham Kelsey, game design, gamification, Geoffrey Hinton, Goodhart's law, Google Chrome, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, hedonic treadmill, ImageNet competition, industrial robot, Internet Archive, John von Neumann, Joi Ito, Kenneth Arrow, language acquisition, longitudinal study, machine translation, mandatory minimum, mass incarceration, multi-armed bandit, natural language processing, Nick Bostrom, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, OpenAI, Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, precautionary principle, premature optimization, RAND corporation, recommendation engine, Richard Feynman, Rodney Brooks, Saturday Night Live, selection bias, self-driving car, seminal paper, side project, Silicon Valley, Skinner box, sparse data, speech recognition, Stanislav Petrov, statistical model, Steve Jobs, strong AI, the map is not the territory, theory of mind, Tim Cook: Apple, W. E. B. Du Bois, Wayback Machine, zero-sum game

For a video explanation of the research, see “Quadcopter Navigation in the Forest Using Deep Neural Networks,” https://www.youtube.com/watch?v=umRdt3zGgpU. 54. Bojarski et al., “End to End Learning for Self-Driving Cars.” The Nvidia team further augmented its side-pointing camera images with “Photoshop” manipulations for a greater diversity of angles. These suffered from similar constraints as the ALVINN images, but they were good enough in practice. For a more informal discussion, see Bojarski et al., “End-to- End Deep Learning for Self-Driving Cars,” https://devblogs.nvidia.com/deep-learning-self-driving-cars/. For the video of the car in action on the roads of Monmouth County, see “Dave-2: A Neural Network Drives a Car,” https://www.youtube.com/watch?

Some models must, for better or worse, deal not with human abstractions like “GRE score” and “number of prior offenses” but with raw linguistic, audio, or visual data. Some medical diagnostic tools can be fed human inputs, like “mild fever” and “asthmatic,” while others might be shown an X-ray or CAT scan directly and must make some sense of it. A self-driving car, of course, must deal with a stream of radar, lidar, and visual data directly. In such cases we have little choice but the kinds of large, multimillion-parameter “black box” neural networks that have such a reputation for inscrutability. But we are not without resources here as well, on the science of transparency’s other, wilder frontier.

“So that makes sense,” she says, “if you’re from an area where fire engines are red.”82 It’s almost always true in the United States, for instance, but not in Australia, where, depending on the district, fire trucks can sometimes be white—or, in Canberra, neon yellow. This would suggest, say, that a self-driving-car model developed on a US-centric dataset might need modification before it was safe to deploy down under. Kim also found that the concept of “arms” was important to identifying “dumbbells,” corroborating the earlier visual findings of the DeepDream group from Google, and hinting that the network might struggle to identify a dumbbell on a rack or on the ground.83 The concept “East Asian” was important to “ping-pong ball,” and the concept “Caucasian” was important to “rugby.”


pages: 308 words: 85,880

How to Fix the Future: Staying Human in the Digital Age by Andrew Keen

"World Economic Forum" Davos, 23andMe, Ada Lovelace, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, AlphaGo, Andrew Keen, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, basic income, Bernie Sanders, Big Tech, bitcoin, Black Swan, blockchain, Brewster Kahle, British Empire, carbon tax, Charles Babbage, computer age, Cornelius Vanderbilt, creative destruction, crowdsourcing, data is the new oil, death from overwork, DeepMind, Demis Hassabis, Didi Chuxing, digital capitalism, digital map, digital rights, disinformation, don't be evil, Donald Trump, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, European colonialism, fake news, Filter Bubble, Firefox, fulfillment center, full employment, future of work, gig economy, global village, income inequality, independent contractor, informal economy, Internet Archive, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Jeff Bezos, jimmy wales, job automation, Joi Ito, Kevin Kelly, knowledge economy, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, move fast and break things, Network effects, new economy, Nicholas Carr, Norbert Wiener, OpenAI, Parag Khanna, peer-to-peer, Peter Thiel, plutocrats, post-truth, postindustrial economy, precariat, Ralph Nader, Ray Kurzweil, Recombinant DNA, rent-seeking, ride hailing / ride sharing, Rutger Bregman, Salesforce, Sam Altman, Sand Hill Road, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, Skype, smart cities, Snapchat, social graph, software is eating the world, Stephen Hawking, Steve Jobs, Steve Wozniak, subscription business, surveillance capitalism, Susan Wojcicki, tech baron, tech billionaire, tech worker, technological determinism, technoutopianism, The Future of Employment, the High Line, the new new thing, Thomas L Friedman, Tim Cook: Apple, Travis Kalanick, Triangle Shirtwaist Factory, Uber and Lyft, Uber for X, uber lyft, universal basic income, Unsafe at Any Speed, Upton Sinclair, urban planning, WikiLeaks, winner-take-all economy, Y Combinator, Yogi Berra, Zipcar

In January 2015 Uber “gutted” the robotics lab of Carnegie Mellon University to “poach” the fifty people who were working on self-driving cars.57 As I write this, a little over two years later, Uber is already conducting trials of self-driving vehicles in both Pennsylvania and Arizona. Google, Apple, and many other conventional car companies are conducting similar trials. By the time you read this, we will be ever closer to the reality of self-driving cars on our roads. The logic for this enormous investment in the technology of self-driving cars—particularly from Uber’s point of view—is chillingly obvious. “Uber’s future depends greatly on solving self-driving,” the technology website Recode explains.

Farhad Manjoo, “One Way to Fix Uber: Think Twice Before Using It,” New York Times, June 14, 2017. 56. Mike Isaac, “Uber CEO to Leave Trump Advisory Council After Criticism,” New York Times, February 2, 2017. 57. Josh Lowensohn, “Uber Gutted Carnegie Mellon’s Top Robotics Lab to Build Self-Driving Cars,” Verge, May 19, 2015. 58. Johana Bhuiyan, “Inside Uber’s Self-Driving Car Mess,” Recode, March 24, 2017. 59. More, Utopia, 18. Chapter Ten 1. More, Utopia, 51. 2. Ibid., 50. 3. Ibid., 53. 4. John Thornhill and Ralph Atkins, “Money for Nothing,” Financial Times, May 27, 2016. 5. “Sighing for Paradise to Come,” Economist, June 4, 2016. 6.

See regulation Apple centralized power of, 60–70 Macintosh, 9, 10 regulation issues, 128–129, 142–144, 146, 151, 160 Siri, 25 social responsibility and, 205 apps, by Singapore, 113, 114 APT 28 (Russia), 96 Archimedes point, 195–196 artificial intelligence (AI) cobots, 277 humanity and, 23–28, 268–272 open technology platforms for, 31–33 robots replaced by craftsmen, by Toyota, 271 self-driving cars, 256–257 social responsibility for, 197–200 as “treachery,” 52–56 artists on copyright issues, 237–245 on streaming services, 226–233 ArtScience Museum (Singapore), 112–115, 124 Austin (Texas), on worker and consumer choice issues, 240, 254 Autodesk, 118 automobile industry competitive innovation by, 182–188, 191 self-driving cars, 256–257 Babbage, Charles, 26 Balakrishnan, Vivian, 119–120 Bannon, Stephen, 94 Basu, Kaushik, 72 Battery (San Francisco club), 217 Bell, Emily, 64 BeneStream, 219 Benioff, Marc, 205, 280–281 Berkeley Food Movement, 224 Berners-Lee, Tim, 32, 58–63, 65, 150–151 Betaworks, 29–31 Bezos, Jeff, 205, 211–213, 223.


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Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World by Mo Gawdat

3D printing, accounting loophole / creative accounting, AI winter, AlphaGo, anthropic principle, artificial general intelligence, autonomous vehicles, basic income, Big Tech, Black Lives Matter, Black Monday: stock market crash in 1987, butterfly effect, call centre, carbon footprint, cloud computing, computer vision, coronavirus, COVID-19, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Demis Hassabis, digital divide, digital map, Donald Trump, Elon Musk, fake news, fulfillment center, game design, George Floyd, global pandemic, Google Glasses, Google X / Alphabet X, Law of Accelerating Returns, lockdown, microplastics / micro fibres, Nick Bostrom, off-the-grid, OpenAI, optical character recognition, out of africa, pattern recognition, Ponzi scheme, Ray Kurzweil, recommendation engine, self-driving car, Silicon Valley, smart contracts, Stanislav Petrov, Stephen Hawking, subprime mortgage crisis, superintelligent machines, TED Talk, TikTok, Turing machine, Turing test, universal basic income, Watson beat the top human players on Jeopardy!, Y2K

Those machines will not only become smarter, they will know more (as they have access to the entire internet as their memory pool) and they will communicate between each other better, thus enhancing their knowledge. Think about it: when you or I have an accident driving a car, you or I learn, but when a self-driving car makes a mistake, all self-driving cars learn. Every single one of them, including the ones that have not yet been ‘born’. By 2049, probably in our lifetimes and surely in those of the next generation, AI is predicted to be a billion times smarter (in everything) than the smartest human. To put this into perspective, your intelligence, in comparison to that machine, will be comparable to the intelligence of a fly in comparison to Einstein.

There, I was privileged to lead the launch of Google’s operations and technologies in close to half of Google’s offices worldwide, encompassing more than a hundred languages. My time there concluded when I assumed the role of Chief Business Officer of Google [X], the infamous innovation arm of Google that incubated some of the artificial intelligence development projects such as Google’s self-driving cars, Google Brain and most of Google’s robotics innovation. My insights into the very core of the artificial intelligence developments that have led us to where we are today, derived in part from my time at Google [X], are unique. I am combining my direct experience with AI development with my work in the field of happiness research (documented in my internationally bestselling book Solve for Happy, a very successful podcast, Slo Mo, and the non-profit organization I founded, OneBillionHappy.org) to bring you a unique perspective on the challenges we face in the age of the rise of superintelligence.

The world champion of Go is Google’s AlphaGo (Go is an abstract strategy board game invented in China more than 2,500 years ago and is known to be one of the most complex strategy games because of its infinite number of possible board configurations). Machines with incredible image recognition systems power our security systems simply because they see better than us, and the world’s safest driver by far is a self-driving car that not only sees further but pays undivided attention to the road. Using multiple sensor technologies for communication with other cars around it, it can even ‘see’ round corners. With enough ‘training’, no matter what the task, machines have been learning to do it better. Into the Unknown It is predicted that by the year 2029, which is relatively just around the corner, machine intelligence will break out of specific tasks and into general intelligence.


pages: 590 words: 152,595

Army of None: Autonomous Weapons and the Future of War by Paul Scharre

"World Economic Forum" Davos, active measures, Air France Flight 447, air gap, algorithmic trading, AlphaGo, Apollo 13, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Black Monday: stock market crash in 1987, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, data science, deep learning, DeepMind, DevOps, Dr. Strangelove, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fail fast, fault tolerance, Flash crash, Freestyle chess, friendly fire, Herman Kahn, IFF: identification friend or foe, ImageNet competition, information security, Internet of things, Jeff Hawkins, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Korean Air Lines Flight 007, Loebner Prize, loose coupling, Mark Zuckerberg, military-industrial complex, moral hazard, move 37, mutually assured destruction, Nate Silver, Nick Bostrom, PalmPilot, paperclip maximiser, pattern recognition, Rodney Brooks, Rubik’s Cube, self-driving car, sensor fusion, South China Sea, speech recognition, Stanislav Petrov, Stephen Hawking, Steve Ballmer, Steve Wozniak, Strategic Defense Initiative, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, Tyler Cowen, universal basic income, Valery Gerasimov, Wall-E, warehouse robotics, William Langewiesche, Y2K, zero day

The organization kept at it, sponsoring a follow-up Grand Challenge the next year. This time, it was a resounding success. Twenty-two vehicles beat the previous year’s distance record and five cars finished the entire course. In 2007, DARPA hosted an Urban Challenge for self-driving cars on a closed, urban course complete with traffic and stop signs. These Grand Challenges matured autonomous vehicle technology in leaps and bounds, laying the seeds for the self-driving cars now in development at companies like Google and Tesla. DARPA has since used the Grand Challenge approach as a way to tackle other truly daunting problems, harnessing the power of competition to generate the best ideas and launch a technology forward.

Department of Defense officials state that the option of deploying fully autonomous weapons should be “on the table.” BETTER THAN HUMAN? Armed robots deciding who to kill might sound like a dystopian nightmare, but some argue autonomous weapons could make war more humane. The same kind of automation that allows self-driving cars to avoid pedestrians could also be used to avoid civilian casualties in war, and unlike human soldiers, machines never get angry or seek revenge. They never fatigue or tire. Airplane autopilots have dramatically improved safety for commercial airliners, saving countless lives. Could autonomy do the same for war?

When the swarm commanders click go, the swarms close on each other once again. This time the battle comes out dead even, 3–3. In Round 3, Red pulls out a decisive win, 7–4. Red Swarm commander is happy to take credit for the win. “I pushed the button,” he says with a chuckle. Just as robots are transforming industries—from self-driving cars to robot vacuum cleaners and caretakers for the elderly—they are also transforming war. Global spending on military robotics is estimated to reach $7.5 billion per year in 2018, with scores of countries expanding their arsenals of air, ground, and maritime robots. Robots have many battlefield advantages over traditional human-inhabited vehicles.


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Lab Rats: How Silicon Valley Made Work Miserable for the Rest of Us by Dan Lyons

"Friedman doctrine" OR "shareholder theory", "Susan Fowler" uber, "World Economic Forum" Davos, Airbnb, Amazon Robotics, Amazon Web Services, antiwork, Apple II, augmented reality, autonomous vehicles, basic income, Big Tech, bitcoin, blockchain, Blue Ocean Strategy, business process, call centre, Cambridge Analytica, Clayton Christensen, clean water, collective bargaining, corporate governance, corporate social responsibility, creative destruction, cryptocurrency, data science, David Heinemeier Hansson, digital rights, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, fake news, full employment, future of work, gig economy, Gordon Gekko, greed is good, Hacker News, hiring and firing, holacracy, housing crisis, impact investing, income inequality, informal economy, initial coin offering, Jeff Bezos, job automation, job satisfaction, job-hopping, John Gruber, John Perry Barlow, Joseph Schumpeter, junk bonds, Kanban, Kevin Kelly, knowledge worker, Larry Ellison, Lean Startup, loose coupling, Lyft, Marc Andreessen, Mark Zuckerberg, McMansion, Menlo Park, Milgram experiment, minimum viable product, Mitch Kapor, move fast and break things, new economy, Panopticon Jeremy Bentham, Parker Conrad, Paul Graham, paypal mafia, Peter Thiel, plutocrats, precariat, prosperity theology / prosperity gospel / gospel of success, public intellectual, RAND corporation, remote working, RFID, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Ruby on Rails, Sam Altman, San Francisco homelessness, Sand Hill Road, scientific management, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, six sigma, Skinner box, Skype, Social Responsibility of Business Is to Increase Its Profits, SoftBank, software is eating the world, Stanford prison experiment, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, stock buybacks, super pumped, TaskRabbit, tech bro, tech worker, TechCrunch disrupt, TED Talk, telemarketer, Tesla Model S, Thomas Davenport, Tony Hsieh, Toyota Production System, traveling salesman, Travis Kalanick, tulip mania, Uber and Lyft, Uber for X, uber lyft, universal basic income, web application, WeWork, Whole Earth Catalog, work culture , workplace surveillance , Y Combinator, young professional, Zenefits

But Tesla also has raced ahead of Detroit in developing the two biggest new car technologies: electric motors and autonomous vehicles. Tesla is not the only Silicon Valley company that threatens Ford. Google and Uber are working on self-driving cars. Apple is rumored to be operating a secret automotive laboratory. The Silicon Valley guys realize that transportation is becoming a technology business. Self-driving cars depend on artificial intelligence, which means sensors and lots of software, stuff they know how to do. To them, a car is just a container for an AI computer, a bunch of software that happens to have wheels attached.

“‘Tech Bro’ Calls San Francisco ‘Shanty Town,’ Decries Homeless ‘Riffraff’ in Open Letter.” Chicago Tribune, February 18, 2016. http://www.chicagotribune.com/bluesky/technology/ct-tech-bro-letter-san-francisco-homeless-20160218-story.html. Mims, Christopher. “In Self-Driving-Car Road Test, We Are the Guinea Pigs.” Wall Street Journal, May 13, 2018. https://www.wsj.com/articles/in-self-driving-car-road-test-we-are-the-guinea-pigs-1526212802. Mishel, Lawrence, and Jessica Schieder. “CEO Pay Remains High Relative to the Pay of Typical Workers and High-Wage Earners.” Economic Policy Institute, July 20, 2017. https://www.epi.org/publication/ceo-pay-remains-high-relative-to-the-pay-of-typical-workers-and-high-wage-earners.

Ford has been hiring artificial intelligence engineers, built a tech lab in Silicon Valley, and struck a deal with a San Francisco software company whose engineers will teach Ford’s coders about Agile development. Ford wants us to know that it’s in the midst of a huge transformation, and that it’s not falling behind. Earlier, we all took turns going for rides in Ford’s prototype self-driving car, which Ford vows to have in production by 2021. Now we’ve come indoors for an event that is meant to evoke the atmosphere of a big Silicon Valley conference, or an Apple product announcement. Tim Brown, the head of IDEO, a cooler-than-thou Silicon Valley design shop, hangs out in the hallway.


pages: 301 words: 89,076

The Globotics Upheaval: Globalisation, Robotics and the Future of Work by Richard Baldwin

agricultural Revolution, Airbnb, AlphaGo, AltaVista, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, basic income, Big Tech, bread and circuses, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, commoditize, computer vision, Corn Laws, correlation does not imply causation, Credit Default Swap, data science, David Ricardo: comparative advantage, declining real wages, deep learning, DeepMind, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, Fairchild Semiconductor, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, Hans Moravec, hiring and firing, hype cycle, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, Kevin Roose, knowledge worker, laissez-faire capitalism, Les Trente Glorieuses, low skilled workers, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, manufacturing employment, Mark Zuckerberg, mass immigration, mass incarceration, Metcalfe’s law, mirror neurons, new economy, optical character recognition, pattern recognition, Ponzi scheme, post-industrial society, post-work, profit motive, remote working, reshoring, ride hailing / ride sharing, Robert Gordon, Robert Metcalfe, robotic process automation, Ronald Reagan, Salesforce, San Francisco homelessness, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, social intelligence, sovereign wealth fund, standardized shipping container, statistical model, Stephen Hawking, Steve Jobs, supply-chain management, systems thinking, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income, warehouse automation

Imagine how much faster the Industrial Revolution would have spread if Newcomen’s steam engine could have been reproduced costlessly, instantly, and perfectly. Self-driving cars are an example of Varian’s law. They are one of the sure-fire, high-tech wonders of the future. Yet they use no breakthrough technology. They are a recombination of existing technologies like GPS, Wi-Fi, advanced sensors, anti-lock brakes, automatic transmission, traction and stability control, adaptive cruise control, lane control, and mapping software—all integrated by tons of processing power, and an AI-powered white-collared robot. Yet, despite being a mash-up of off-the-shelf tech, self-driving cars will create a $7 trillion market. This is not an isolated example.

All that is needed is a populist politician to capture their imagination. In fact, there already is a populist trying to unite blue-collar and white-collar anger: Andrew Yang. Yang, who already entered the 2020 presidential race, argues that the US needs radically new policies to prevent mass unemployment and a violent backlash. “All you need is self-driving cars to destabilize society . . . That one innovation will be enough to create riots in the street. And we’re about to do the same thing to retail workers, call center workers, fast-food workers, insurance companies, accounting firms.”3 Yang is—as New York Times writer Kevin Roose puts it—“a longer-than-long shot” presidential candidate, but his themes are likely to be taken up by more electable candidates.

Psychologists define intelligence as: “A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.”13 Today’s AI is not intelligent in this sense. Machine learning does only the last two items in the psychologists’ list: learn quickly and learn from experience. Even the revolutionary machine learning applications we see today—like Siri and self-driving cars—are just computer programs that recognize patterns in data and then act, or make suggestions based on the patterns they find. The pattern recognition is astonishing, often superhuman in specific areas. But pattern recognition is not “intelligence” as the word is generally used when speaking about intelligent animals like humans, chimpanzees, or dolphins.


pages: 413 words: 119,587

Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots by John Markoff

A Declaration of the Independence of Cyberspace, AI winter, airport security, Andy Rubin, Apollo 11, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Baxter: Rethink Robotics, Bill Atkinson, Bill Duvall, bioinformatics, Boston Dynamics, Brewster Kahle, Burning Man, call centre, cellular automata, Charles Babbage, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, cognitive load, collective bargaining, computer age, Computer Lib, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deep learning, DeepMind, deskilling, Do you want to sell sugared water for the rest of your life?, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, driverless car, dual-use technology, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, factory automation, Fairchild Semiconductor, Fillmore Auditorium, San Francisco, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, General Magic , Geoffrey Hinton, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, haute couture, Herbert Marcuse, hive mind, hype cycle, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Ivan Sutherland, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, Jeff Hawkins, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, Kaizen: continuous improvement, Kevin Kelly, Kiva Systems, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, military-industrial complex, Mitch Kapor, Mother of all demos, natural language processing, Neil Armstrong, new economy, Norbert Wiener, PageRank, PalmPilot, pattern recognition, Philippa Foot, pre–internet, RAND corporation, Ray Kurzweil, reality distortion field, Recombinant DNA, Richard Stallman, Robert Gordon, Robert Solow, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, Seymour Hersh, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Strategic Defense Initiative, strong AI, superintelligent machines, tech worker, technological singularity, Ted Nelson, TED Talk, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Tony Fadell, trolley problem, Turing test, Vannevar Bush, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

“Electronic Stability Control Systems for Heavy Vehicles,” National Highway Traffic Safety Administration, 2012, http://www.nhtsa.gov/Laws+&+Regulations/Electronic+Stability+Control+(ESC). 7.John Markoff, “Police, Pedestrians and the Social Ballet of Merging: The Real Challenges for Self-Driving Cars,” New York Times, May 29, 2014, http://bits.blogs.nytimes.com/2014/05/29/police-bicyclists-and-pedestrians-the-real-challenges-for-self-driving-cars/?_php=true&_type=blogs&_r=0. 8.Lawrence D. Burns, William C. Jordan, and Bonnie A. Scarborough, “Transforming Personal Mobility,” The Earth Institute, Columbia University, January 27, 2013, http://sustainablemo bility.ei.columbia.edu/files/2012/12/Transforming-Personal-Mobility-Jan-27-20132.pdf. 9.William Grimes, “Philippa Foot, Renowned Philosopher, Dies at 90,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/us/10foot.html. 10.

Winograd’s conversion coincided with the collapse of a nascent artificial intelligence industry known as the “AI Winter.” Several decades later, Winograd, who was faculty advisor for Google cofounder Larry Page at Stanford, famously counseled the young graduate student to focus on the problem of Web search rather than self-driving cars. In the intervening decades Winograd had become acutely aware of the importance of the designer’s point of view. The separation of the fields of AI and human-computer interaction, or HCI, is partly a question of approach, but it’s also an ethical stance about designing humans either into or out of the systems we create.

Even the most successful entrant had gotten stuck in the dust just seven miles from the starting line in a 120-mile race, with one wheel spinning helplessly as it teetered off the edge of the road. When the dust settled, a reporter flying overhead in a light plane saw brightly colored vehicles scattered motionless over the desert floor. At the time it seemed obvious that self-driving cars were still years away, and Tether was criticized for organizing a publicity stunt. Now, just a little more than a year later, Thrun was behind the wheel in a second-generation robot contestant. It felt like the future had arrived sooner than expected. It took only a dozen miles, however, to realize that techno-enthusiasm is frequently premature.


pages: 424 words: 114,905

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

"World Economic Forum" Davos, 23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Apollo 11, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, Big Tech, bioinformatics, blockchain, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, Computing Machinery and Intelligence, conceptual framework, creative destruction, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, data science, David Brooks, deep learning, DeepMind, Demis Hassabis, digital twin, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fake news, fault tolerance, gamification, general purpose technology, Geoffrey Hinton, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, machine translation, Mark Zuckerberg, medical residency, meta-analysis, microbiome, move 37, natural language processing, new economy, Nicholas Carr, Nick Bostrom, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, post-truth, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Skinner box, speech recognition, Stephen Hawking, techlash, TED Talk, text mining, the scientific method, Tim Cook: Apple, traumatic brain injury, trolley problem, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

As you might anticipate, companies are not enthusiastic about government regulation; many firms, including Microsoft and Google, have set up their own internal ethics boards, arguing that regulatory involvement might be counterproductive, delaying the adoption of self-driving cars over fringe issues when it already seems clear that autonomous vehicles will reduce traffic fatalities overall. But we don’t think of it in the big picture way. More than 1.25 million people are killed by human drivers each year, most by human error, but we as a society don’t bat an eye at the situation.62 The introduction of computers into the mix sets up a cognitive bias, not acknowledging the net benefit. When a self-driving car kills a person, there’s an outcry over the dangers of self-driving cars. The first fatality of a pedestrian hit by a driverless car occurred in an Uber program in Arizona in 2018.

The potential for human takeover of the car—conditional automation—is Level 3. Most people are familiar with Level 2, which is like cruise control or lane keeping, representing very limited automation. FIGURE 4.8: Self-driving cars and medicine. The Society of Automotive Engineers’ five levels of self-driving. Source: Adapted from S. Shladover, “The Truth About ‘Self-Driving” Cars,’ Scientific American (2016): www.scientificamerican.com/article/the-truth-about-ldquo-self-driving-rdquo-cars/. The whole auto industry clearly has its sights on Level 4—with limited need for human backup—which relies on multiple, coordinated technologies.

TO DEVELOP THE conceptual framework of deep medicine, I’ll start with how medicine is practiced now and why we desperately need new solutions to such problems as misdiagnosis, errors, poor outcomes, and runaway costs. That, in part, hinges on the basics of how a medical diagnosis is made today. To understand the reward and risk potential of AI, we will explore the AI precedents, the accomplishments ranging from games to self-driving cars. Of equal, and perhaps even greater, importance will be an exploration of AI’s liabilities, such as human bias, the potential for worsening inequities, its black-box nature, and concerns for breaches of privacy and security. The transfer of tens of millions of people’s personal data from Facebook to Cambridge Analytica, who then used AI to target individuals, illustrates one critical aspect of what could go wrong in the healthcare context.


pages: 116 words: 31,356

Platform Capitalism by Nick Srnicek

"World Economic Forum" Davos, 3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Big Tech, Californian Ideology, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, collaborative economy, collective bargaining, data science, deindustrialization, deskilling, Didi Chuxing, digital capitalism, digital divide, disintermediation, driverless car, Ford Model T, future of work, gig economy, independent contractor, Infrastructure as a Service, Internet of things, Jean Tirole, Jeff Bezos, knowledge economy, knowledge worker, liquidity trap, low interest rates, low skilled workers, Lyft, Mark Zuckerberg, means of production, mittelstand, multi-sided market, natural language processing, Network effects, new economy, Oculus Rift, offshore financial centre, pattern recognition, platform as a service, quantitative easing, RFID, ride hailing / ride sharing, Robert Gordon, Salesforce, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, software as a service, surveillance capitalism, TaskRabbit, the built environment, total factor productivity, two-sided market, Uber and Lyft, Uber for X, uber lyft, unconventional monetary instruments, unorthodox policies, vertical integration, warehouse robotics, Zipcar

This means that, despite their differences, companies like Facebook, Google, Microsoft, Amazon, Alibaba, Uber, and General Electric (GE) are also direct competitors. IBM, for instance, has moved into the platform business, purchasing Softlayer for cloud computing, and BlueMix for software development. The convergence thesis helps explain why Google is lobbying with Uber on self-driving cars and why Amazon and Microsoft have been discussing partnerships with German automakers on the cloud platform required by self-driving cars.28 Alibaba and Apple have made major investments in Didi, Apple’s partnership being particularly strategic, given that iPhones are the major interface to taxi services. And nearly all of the major platforms are working to develop medical data platforms.

In the twenty-first century, however, the technology needed for turning simple activities into recorded data became increasingly cheap; and the move to digital-based communications made recording exceedingly simple. Massive new expanses of potential data were opened up, and new industries arose to extract these data and to use them so as to optimise production processes, give insight into consumer preferences, control workers, provide the foundation for new products and services (e.g. Google Maps, self-driving cars, Siri), and sell to advertisers. All of this had historical precedents in earlier periods of capitalism, but what was novel with the shift in technology was the sheer amount of data that could now be used. From representing a peripheral aspect of businesses, data increasingly became a central resource.

While they are similar in this respect, their business models are significantly different. Zipcar owns the assets it rents out – the vehicles; Uber does not. The former is a product platform, while the latter is a lean platform that attempts to outsource nearly every possible cost. (Uber aims, however, eventually to command a fleet of self-driving cars, which would transform it into a product platform.) Zipcar, by contrast, might be considered a ‘goods as a service’ type of platform. Product platforms are perhaps one of the biggest means by which companies attempt to recuperate the tendency to zero marginal costs in some goods. Music is the best example, as in the late 1990s downloading music for free became as simple as installing a small program.


pages: 242 words: 73,728

Give People Money by Annie Lowrey

Abraham Maslow, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, airport security, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, Black Lives Matter, carbon tax, clean water, collective bargaining, computer age, crowdsourcing, cryptocurrency, deindustrialization, desegregation, Donald Trump, driverless car, Edward Glaeser, Elon Musk, ending welfare as we know it, everywhere but in the productivity statistics, full employment, gender pay gap, gentrification, gig economy, Google Earth, Home mortgage interest deduction, income inequality, indoor plumbing, information asymmetry, Jaron Lanier, jitney, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, Kodak vs Instagram, labor-force participation, late capitalism, Lyft, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, mass incarceration, McMansion, Menlo Park, mobile money, Modern Monetary Theory, mortgage tax deduction, multilevel marketing, new economy, obamacare, opioid epidemic / opioid crisis, Overton Window, Peter Thiel, post scarcity, post-work, Potemkin village, precariat, public intellectual, randomized controlled trial, ride hailing / ride sharing, Robert Bork, Robert Solow, Ronald Reagan, Rutger Bregman, Sam Altman, self-driving car, Silicon Valley, single-payer health, Steve Jobs, TaskRabbit, tech billionaire, The future is already here, The Future of Employment, theory of mind, total factor productivity, Turing test, two tier labour market, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, War on Poverty, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y Combinator

* * * It goes as far back as the spear, the net, the plow. Man invents machine to make life easier; machine reduces the need for man’s toil. Man invents car; car puts buggy driver and farrier out of work. Man invents robot to help make car; robot puts man out of work. Man invents self-driving car; self-driving car puts truck driver out of work. The fancy economic term for this is “technological unemployment,” and it is a constant and a given. You did not need to go far from the auto show to see how the miracle of invention goes hand in hand with the tragedy of job destruction. You just need to take a look at its host city.

Heck, even throw in any number of construction and retail workers who move goods around, as well as the kid who delivers your pizza and the part-timer who schleps your groceries to your doorstep. President Barack Obama’s White House estimated that self-driving vehicles could wipe out between 2.2 and 3.1 million jobs. And self-driving cars are not the only technology on the horizon with the potential to dramatically reduce the need for human work. Today’s Cassandras are warning that there is scarcely a job out there that is not at risk. If you have recently heard of UBI, there is a good chance that it is because of these driverless cars and the intensifying concern about technological unemployment writ large.

“I don’t think we’re going to have a choice,” he said of a UBI. “I think it’s going to be necessary.” In Detroit, that risk felt ominously real. The question I wondered about as I wandered the halls of the Cobo Center and spoke with technology investors in Silicon Valley was not whether self-driving cars and other advanced technologies would start putting people out of work. It was when—and what would come next. The United States seems totally unprepared for a job-loss Armageddon. A UBI seemed to offer a way to ensure livelihoods, sustain the middle class, and guard against deprivation as extraordinary technological marvels transform our lives and change our world


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The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl

3D printing, algorithmic bias, algorithmic trading, Alvin Toffler, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, classic study, Clayton Christensen, commoditize, computer age, death of newspapers, deferred acceptance, disruptive innovation, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Ford Model T, Frank Levy and Richard Murnane: The New Division of Labor, fulfillment center, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kodak vs Instagram, Lewis Mumford, lifelogging, machine readable, machine translation, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, Panopticon Jeremy Bentham, Paradox of Choice, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, scientific management, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, stable marriage problem, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, technological determinism, technological solutionism, TED Talk, the long tail, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator

As a straightforward example, what would happen if the passenger in the car needed to reach a hospital as a matter of urgency—and that this meant breaking the speed limit on a largely empty stretch of road? It is one thing if the driver/passenger was ticketed at a later date thanks to the car’s built-in speed tracker. But what if the self-driving car, bound by fixed Ambient Laws, refused to break the regulated speed limit under any conditions? You might not even have to wait for the arrival of self-driving cars for such a scenario to become reality. In 2013, British newspapers reported on road-safety measures being drawn up by EC officials in Brussels that would see all new cars fitted with “Intelligent Speed Adaptation” measures similar to those already installed in many heavy-goods vehicles and buses.

Medicine and the Reign of Technology (Cambridge, UK; New York: Cambridge University Press, 1978). 44 Gusfield, Joseph. The Culture of Public Problems: Drinking-Driving and the Symbolic Order (Chicago: University of Chicago Press, 1981). 45 “Google’s Self-Driving Cars Are Safer Than Human Drivers.” Macworld, August 8, 2012. macworld.com.au/news/googles-self-driving-cars-are-safer-than-human-drivers-67261/#.Uh2-DLyE5eo. 46 Owen, Glen. “Britain Fights EU’s ‘Big Brother’ Bid to Fit Every Car with Speed Limiter.” Daily Mail, August 31, 2013. dailymail.co.uk/news/article-2408012/Britain-fights-EUs-Big-Brother-bid-fit-car-speed-limiter.html. 47 Moskvitch, Katia, and Richard Fisher.

This issue will become even more pressing as the rise of Ambient Law continues—with technologies not only having the power to regulate behavior but to dictate it as well, sometimes by barring particular courses of action from being taken. Several years ago, Google announced that it was working on a fleet of self-driving cars, in which algorithms would be used for everything from planning the most efficient journey routes, to changing lanes on the motorway by determining the smoothest path combining trajectory, speed and safe distance from nearby obstacles. At the time of writing, these cars have completed upward of 300,000 miles of test drives in a wide range of conditions, without any reported accidents—leading to the suggestion that a person is safer in a car driven by an algorithm than they are in one driven by a human.45 Since cars driven by an algorithm already conform to a series of preprogrammed rules, it is understandable why specific laws would become just more to add to the collection.


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Designing Great Data Products by Jeremy Howard, Mike Loukides, Margit Zwemer

AltaVista, data science, Filter Bubble, PageRank, pattern recognition, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, text mining

We need to define the models we will need, such as physics models to predict the effects of steering, braking and acceleration, and pattern recognition algorithms to interpret data from the road signs. As one engineer on the Google self-driving car project put it in a recent Wired article, “We’re analyzing and predicting the world 20 times a second.” What gets lost in the quote is what happens as a result of that prediction. The vehicle needs to use a simulator to examine the results of the possible actions it could take. If it turns left now, will it hit that pedestrian? If it makes a right turn at 55 mph in these weather conditions, will it skid off the road? Merely predicting what will happen isn’t good enough. The self-driving car needs to take the next step: after simulating all the possibilities, it must optimize the results of the simulation to pick the best combination of acceleration and braking, steering and signaling, to get us safely to Santa Clara.

Engineers start by defining a clear objective: They want a car to drive safely from point A to point B without human intervention. Great predictive modeling is an important part of the solution, but it no longer stands on its own; as products become more sophisticated, it disappears into the plumbing. Someone using Google’s self-driving car is completely unaware of the hundreds (if not thousands) of models and the petabytes of data that make it work. But as data scientists build increasingly sophisticated products, they need a systematic design approach. We don’t claim that the Drivetrain Approach is the best or only method; our goal is to start a dialog within the data science and business communities to advance our collective vision.


pages: 225 words: 70,241

Silicon City: San Francisco in the Long Shadow of the Valley by Cary McClelland

affirmative action, Airbnb, algorithmic bias, Apple II, autonomous vehicles, barriers to entry, Black Lives Matter, Burning Man, clean water, cloud computing, cognitive dissonance, Columbine, computer vision, creative destruction, driverless car, El Camino Real, Elon Musk, Fairchild Semiconductor, full employment, gamification, gentrification, gig economy, Golden Gate Park, Google bus, Google Glasses, high net worth, housing crisis, housing justice, income inequality, John Gilmore, John Perry Barlow, Joseph Schumpeter, Loma Prieta earthquake, Lyft, mass immigration, means of production, Menlo Park, Mitch Kapor, open immigration, PalmPilot, rent control, Salesforce, San Francisco homelessness, self-driving car, sharing economy, Silicon Valley, Skype, Social Justice Warrior, Steve Jobs, Steve Wozniak, TaskRabbit, tech bro, tech worker, transcontinental railway, Travis Kalanick, Uber and Lyft, uber lyft, urban planning, vertical integration, William Shockley: the traitorous eight, young professional

You look at the highway, if you take a picture, I think 92 percent of it is empty and only 8 percent is filled by cars, because humans are bad drivers and need all this safety distance between them. With a self-driving car, you could drive much more efficiently, faster and closer together with a lower accident rate. You don’t need parking. Look out of the window, a big part of the city is parking lots. You waste all this real estate in the best locations storing stuff that rusts. You can change urban architecture. It allows elderly people to participate in life because, when they can’t drive anymore, they get cut off from their friends. So, that vision for self-driving cars is great, but the part that’s really hard and, honestly, isn’t solved yet by any of the players, is to make it really robust and reliable.

They call it “the tipping point.” The only problem with the tipping point is you can only see it in the rearview mirror. HENDRIK DAHLKAMP He specializes in teaching machines how to see. Born and raised in Germany, he came to California to study computer science and landed a job at Google working on the self-driving car. He left to launch his own start-up out of his apartment in SoMa. The living room is flanked on two sides with floor-to-ceiling windows. You can look out in 180 degrees and see nothing but sky and city, like you are floating above San Francisco. You can see all the way to the highways—which are jammed solid with cars inching their way east to the Bay Bridge and south toward Google, Facebook, and the peninsula.

We put all of that data on a map: you could just click on any point and you saw the panorama of the street. The tech wasn’t all that complicated, but it made for a pretty cool demo because you click on any address and get immersed. You could hit a button and travel along the road. Larry Page, the CEO of Google, he loves self-driving cars. He’s a big alpha geek in technology. He actually came to the DARPA Grand Challenge. Half my team joined Google and quickly built up a fleet of these cars, not just one. We had a hundred cars in the country and four hundred worldwide. We turned the mapping function into a product that became Google Street View.


pages: 332 words: 100,601

Rebooting India: Realizing a Billion Aspirations by Nandan Nilekani

Airbnb, Atul Gawande, autonomous vehicles, barriers to entry, bitcoin, call centre, carbon credits, cashless society, clean water, cloud computing, collaborative consumption, congestion charging, DARPA: Urban Challenge, data science, dematerialisation, demographic dividend, digital rights, driverless car, Edward Snowden, en.wikipedia.org, energy security, fail fast, financial exclusion, gamification, Google Hangouts, illegal immigration, informal economy, information security, Khan Academy, Kickstarter, knowledge economy, land reform, law of one price, M-Pesa, machine readable, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, mobile money, Mohammed Bouazizi, more computing power than Apollo, Negawatt, Network effects, new economy, off-the-grid, offshore financial centre, price mechanism, price stability, rent-seeking, RFID, Ronald Coase, school choice, school vouchers, self-driving car, sharing economy, Silicon Valley, single source of truth, Skype, smart grid, smart meter, software is eating the world, source of truth, Steve Jobs, systems thinking, The future is already here, The Nature of the Firm, transaction costs, vertical integration, WikiLeaks, work culture

http://archive.darpa.mil/grandchallenge05/ 16. Fisher, Adam. 18 September 2013. ‘Inside Google’s Quest To Popularize Self-Driving Cars’. Popular Science. http://www.popsci.com/cars/article/2013-09/google-self-driving-car Winkler, Rolfe, and Macmillan, Douglas. 2 February, 2015. ‘Uber Chases Google in Self-Driving Cars With Carnegie Mellon Deal’. Wall Street Journal. http://blogs.wsj.com/digits/2015/02/02/uber-chases-google-in-self-driving-cars/ Taylor, Edward, and Oreskovic, Alexei. 14 February 2015. ‘Apple studies self-driving car, auto industry source says’. Reuters. http://www.reuters.com/article/2015/02/14/us-apple-autos-idUSKBN0LI0IJ20150214. 17. 18 September 2014.

Through DARPA—coincidentally the agency that also birthed the internet—the US government has been funding such endeavours for over a decade.15 The underlying technology has now entered the commercial space; Google is testing self-driving cars using its Google Chauffeur platform, Uber has just announced an academic collaboration with Carnegie Mellon University to ‘develop driverless car and mapping technology’, and Apple is reportedly investigating technologies for building electric and self-driving cars.16 While we may not see a fleet of self-driving cars taking over our streets in the near future, it’s worthwhile to consider that various US state governments are already starting to pass laws that permit driverless cars to operate on state roads.17 Once again, government regulations need to anticipate innovation by keeping a close eye on emerging trends and assessing their potential impact and chances of widespread adoption.

In fact, the data generated by GSTN can be combined with data from various other e-commerce platforms, payment systems, and MCA21—a platform launched by the ministry of corporate affairs—to make a data-based credit assessment of a business. Data can also become the basis by which individuals are granted loans, thanks to new credit-scoring models that combine information from payment transactions, tax filings, social media activity, and so on. In the same way that a computer algorithm can end up giving us a self-driving car, so also algorithms can unlock credit for millions of people and small businesses. Finally, in the last six grand challenges, we extrapolate our learnings and experiences from the previous six. The use of technology already ensures free and fair elections in India today. As the Election Commission undertakes its stated goal of linking voter IDs to Aadhaar, the errors of inclusion and exclusion that plague our voter rolls and weaken our democracy can be fixed.


pages: 285 words: 58,517

The Network Imperative: How to Survive and Grow in the Age of Digital Business Models by Barry Libert, Megan Beck

active measures, Airbnb, Amazon Web Services, asset allocation, asset light, autonomous vehicles, big data - Walmart - Pop Tarts, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, crowdsourcing, data science, disintermediation, diversification, Douglas Engelbart, Douglas Engelbart, future of work, Google Glasses, Google X / Alphabet X, independent contractor, Infrastructure as a Service, intangible asset, Internet of things, invention of writing, inventory management, iterative process, Jeff Bezos, job satisfaction, John Zimmer (Lyft cofounder), Kevin Kelly, Kickstarter, Larry Ellison, late fees, Lyft, Mark Zuckerberg, Mary Meeker, Oculus Rift, pirate software, ride hailing / ride sharing, Salesforce, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, software as a service, software patent, Steve Jobs, subscription business, systems thinking, TaskRabbit, Travis Kalanick, uber lyft, Wall-E, women in the workforce, Zipcar

Those assets sitting on a balance sheet seem costly to maintain compared with intangibles. The major auto companies, for example, had enormous real estate holdings, many of them factories. Managing and maintaining these holdings drained cash, diluted focus, made the automakers sclerotic, and became a hindrance to innovation. So who then is creating self-driving cars? Why, Apple and Google, of course. These great innovators have few tangible assets relative to their size, and yet they enjoy some of the highest equity values in the world. Starwood Hotels is another great example. With more than twelve hundred properties under management, Starwood is currently pursuing an asset-light strategy, selling about $1.5 billion in property from 2013 to 2015.

Lyft’s president John Zimmer stated, “We strongly believe that autonomous vehicle go-to-market strategy is through a network, not through individual car ownership.” According to executives at both GM and Lyft, they will start work on developing a network of self-driving vehicles—a challenge to Google, Tesla, and Uber, which are also devoting resources to this innovation.2 Openness Makes Space for Ongoing Change Will GM’s self-driving-car aspiration create value for the firm? Will its investment in Lyft lead to automotive leadership in ten years? We couldn’t say. But so far its openness to adaptation and new ideas shows potential for future growth and transformation. We’ve now reached the last of the principles to be considered for a network orchestrator business model, and it points us to the mental model.

We have talked about Facebook, but Google is another classic open organization. From its policy of encouraging employees to work 20 percent of their time on their own projects—whatever they think will benefit the company—to its mission to “organize the world’s information and make it universally accessible and useful,” to its eagerness to take on projects (such as self-driving cars and glucose-checking contact lenses) far outside its core competencies, it could be said that Google has openness in its DNA. In fact, Google has become so open that founders Larry Page and Sergey Brin have had to create a newer, bigger company—Alphabet—as a part of continuous business model innovation.


pages: 208 words: 57,602

Futureproof: 9 Rules for Humans in the Age of Automation by Kevin Roose

"World Economic Forum" Davos, adjacent possible, Airbnb, Albert Einstein, algorithmic bias, algorithmic management, Alvin Toffler, Amazon Web Services, Atul Gawande, augmented reality, automated trading system, basic income, Bayesian statistics, Big Tech, big-box store, Black Lives Matter, business process, call centre, choice architecture, coronavirus, COVID-19, data science, deep learning, deepfake, DeepMind, disinformation, Elon Musk, Erik Brynjolfsson, factory automation, fake news, fault tolerance, Frederick Winslow Taylor, Freestyle chess, future of work, Future Shock, Geoffrey Hinton, George Floyd, gig economy, Google Hangouts, GPT-3, hiring and firing, hustle culture, hype cycle, income inequality, industrial robot, Jeff Bezos, job automation, John Markoff, Kevin Roose, knowledge worker, Kodak vs Instagram, labor-force participation, lockdown, Lyft, mandatory minimum, Marc Andreessen, Mark Zuckerberg, meta-analysis, Narrative Science, new economy, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, OpenAI, pattern recognition, planetary scale, plutocrats, Productivity paradox, QAnon, recommendation engine, remote working, risk tolerance, robotic process automation, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, social distancing, Steve Jobs, Stuart Kauffman, surveillance capitalism, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, TikTok, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, warehouse robotics, Watson beat the top human players on Jeopardy!, work culture

Several years ago, when I started as a tech columnist for the Times, most of what I heard about AI mirrored my own optimistic views. I met with start-up founders and engineers in Silicon Valley who showed me how new advances in fields like deep learning were helping them build all kinds of world-improving tools: algorithms that could increase farmers’ crop yields, software that would help hospitals run more efficiently, self-driving cars that could shuttle us around while we took naps and watched Netflix. This was the euphoric peak of the AI hype cycle, a time when all of the American tech giants—Google, Facebook, Apple, Amazon, Microsoft—were pouring billions of dollars into developing new AI products and shoving machine learning algorithms into as many of their apps as possible.

My more tech-skeptical friends and colleagues generally approved. They’d been hearing the gloomy predictions about job-killing robots, and it worried them. They wanted me to confirm their fears of a looming automation crisis, and affirm their suspicions that even if AI didn’t cause mass unemployment, it would bring new harms—creepy surveillance, runaway self-driving cars, brain-melting social media apps—that would outweigh its benefits. Among people in Silicon Valley, though, more typical was the reaction I got from Aaron Levie, the CEO of the enterprise software company Box. “Oh God,” he said. “Please tell me you’re not writing one of those ‘robots are taking all the jobs’ books that makes everyone terrified and depressed.”

Maybe we’ll all want personal trainer robots to follow us around, reminding us to eat healthier and exercise more. Maybe we’ll have cities filled with connected sensors that can dynamically adjust traffic patterns to avoid congestion, or spot disease outbreaks by analyzing our wastewater. Maybe in addition to self-driving cars, we’ll build self-driving restaurants, which will shuttle us from place to place as we dine. All those new projects will require humans—not just to write the code, but to give the advice, install the sensors, and provide the hospitality. We’ve always been good at coming up with new, interesting work for ourselves as technology opens new doors, the optimists say, and our bottomless desires will keep us from running out of things to do


pages: 332 words: 93,672

Life After Google: The Fall of Big Data and the Rise of the Blockchain Economy by George Gilder

23andMe, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AlphaGo, AltaVista, Amazon Web Services, AOL-Time Warner, Asilomar, augmented reality, Ben Horowitz, bitcoin, Bitcoin Ponzi scheme, Bletchley Park, blockchain, Bob Noyce, British Empire, Brownian motion, Burning Man, business process, butterfly effect, carbon footprint, cellular automata, Claude Shannon: information theory, Clayton Christensen, cloud computing, computer age, computer vision, crony capitalism, cross-subsidies, cryptocurrency, Danny Hillis, decentralized internet, deep learning, DeepMind, Demis Hassabis, disintermediation, distributed ledger, don't be evil, Donald Knuth, Donald Trump, double entry bookkeeping, driverless car, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, fake news, fault tolerance, fiat currency, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, floating exchange rates, Fractional reserve banking, game design, Geoffrey Hinton, George Gilder, Google Earth, Google Glasses, Google Hangouts, index fund, inflation targeting, informal economy, initial coin offering, Internet of things, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, Jim Simons, Joan Didion, John Markoff, John von Neumann, Julian Assange, Kevin Kelly, Law of Accelerating Returns, machine translation, Marc Andreessen, Mark Zuckerberg, Mary Meeker, means of production, Menlo Park, Metcalfe’s law, Money creation, money: store of value / unit of account / medium of exchange, move fast and break things, Neal Stephenson, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, OSI model, PageRank, pattern recognition, Paul Graham, peer-to-peer, Peter Thiel, Ponzi scheme, prediction markets, quantitative easing, random walk, ransomware, Ray Kurzweil, reality distortion field, Recombinant DNA, Renaissance Technologies, Robert Mercer, Robert Metcalfe, Ronald Coase, Ross Ulbricht, Ruby on Rails, Sand Hill Road, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Singularitarianism, Skype, smart contracts, Snapchat, Snow Crash, software is eating the world, sorting algorithm, South Sea Bubble, speech recognition, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, stochastic process, Susan Wojcicki, TED Talk, telepresence, Tesla Model S, The Soul of a New Machine, theory of mind, Tim Cook: Apple, transaction costs, tulip mania, Turing complete, Turing machine, Vernor Vinge, Vitalik Buterin, Von Neumann architecture, Watson beat the top human players on Jeopardy!, WikiLeaks, Y Combinator, zero-sum game

As Claude Shannon showed, these success rates of 95 percent, or even 99.999 percent, are deceptive, because you have no way of telling which instances are the errors.6 The vast majority of the home loans in the mortgage crisis were sound, but because no one knew which ones were not, all the securities crashed. You don’t want that problem with self-driving cars. In a joint appearance in 2012 in Aspen, Peter Thiel chided Eric Schmidt: “You don’t have the slightest idea of what you are doing.” He pointed out that the company had amassed some $50 billion in cash at the time and was allowing it to sit in the bank at near-zero interest rates while its vast data centers still could not identify cats as well as a three-year-old could.7 Thiel is the leading critic of Silicon Valley’s prevailing philosophy of “inevitable” innovation.

That span, traveled in every step in the computation, is governed by the speed of light, which on a chip is around nine inches a nanosecond—a significant delay on chips that now bear as much as sixty miles of tiny wires. What Dally saw is that the serial computer has reached the end of the line. Most computers (smartphones and tablets and laptops and even self-driving cars) are not plugged into the wall any more. Even supercomputers and data centers suffer from power constraints, manifested in the problems of cooling the machines, whether by giant fans and air conditioners or by sites near rivers or glaciers. As Hölzle comments, “By classic definitions, there is little ‘work’ produced by a datacenter since most of the energy is converted to heat.”

Tegmark makes the case as well as it can be made that the attainments of AI programs—“Watson” the quiz-show winner and occasionally superior medical diagnostician; Big Blue the chess champion; Google’s DeepMind game players, which learned to outperform human players from scratch in dozens of electronic games; the face-recognizers; the natural language translators; the self-driving car programs—portend a super-intelligence that will someday be so superior to the human mind that we will no more comprehend its depths than a dog grasps the meaning of our own cerebrations. It is just a matter of time. Although shunning the dystopian interpretation, Kurzweil boldly offers a date: 2049.


pages: 237 words: 64,411

Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan

Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Bob Noyce, Brian Krebs, business cycle, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, Computing Machinery and Intelligence, corporate governance, crowdsourcing, driverless car, drop ship, Easter island, en.wikipedia.org, Erik Brynjolfsson, estate planning, Fairchild Semiconductor, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, information asymmetry, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kiva Systems, Larry Ellison, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Nick Bostrom, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, short squeeze, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Vitalik Buterin, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

Imagine that my car is crossing a narrow bridge and a school bus full of children suddenly enters from the other side. The bridge can’t accommodate both vehicles, so to avoid destroying both it’s clear that one of them will have to go over the edge. Would I buy a car that is willing to sacrifice my life to save the children? Will the aggressiveness of a self-driving car become a selling point like gas mileage? Moral quandaries like this, no longer confined to the musings of philosophers, will urgently arrive on our courthouse steps. The emergence of synthetic intellects and forged laborers that act as our individual agents will raise a raft of practical conundrums.

Looking further to the future while staying rooted in today’s technologies, imagine the fire extinguishers, shrunk to the size of insects, digging themselves into miniature foxholes awaiting a command to spring into action. When summoned, they might self-assemble to form a protective dome or blanket around homes, infrastructure, even individual people. Research on concepts like this is active enough to have earned the name “swarm robotics.” Even self-driving cars aren’t going to be nearly as self-contained or autonomous as they appear. Standards for vehicles and roadside sensors to share information wirelessly, essentially becoming one interconnected system of eyes and ears, are close to completion. The U.S. Department of Transportation, among other institutions, is developing so-called V2V (vehicle to vehicle) communications protocols by piggybacking on the Federal Communications Commission’s allocation of radio spectrum for dedicated short-range communications (DSRC) specifically intended for automotive applications.

This same principle, appropriately generalized, can apply to just about any circumstance where electronic agents compete with humans—not just to lines. Do the participants differ in their ability, or the cost they pay, to access the resource? This question needs to be answered on a case-by-case basis, but the concept is clear. For instance, suppose I send my robot to move my car every two hours to avoid a parking ticket, or instruct my self-driving car to repark itself. Will we judge that cost sufficiently equivalent to doing it myself to consider it fair to those without a robotic driver or car to spare? What if it costs me as much to send the robot as it would for you to send your human administrative assistant? I contend that the brawl for the right to display an ad to you seems a lot fairer than having HFT programs participate in the securities markets.


pages: 339 words: 88,732

The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies by Erik Brynjolfsson, Andrew McAfee

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, American Society of Civil Engineers: Report Card, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, falling living standards, Filter Bubble, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, full employment, G4S, game design, general purpose technology, global village, GPS: selective availability, Hans Moravec, happiness index / gross national happiness, illegal immigration, immigration reform, income inequality, income per capita, indoor plumbing, industrial robot, informal economy, intangible asset, inventory management, James Watt: steam engine, Jeff Bezos, Jevons paradox, jimmy wales, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kiva Systems, knowledge worker, Kodak vs Instagram, law of one price, low skilled workers, Lyft, Mahatma Gandhi, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mars Rover, mass immigration, means of production, Narrative Science, Nate Silver, natural language processing, Network effects, new economy, New Urbanism, Nicholas Carr, Occupy movement, oil shale / tar sands, oil shock, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Simon Kuznets, six sigma, Skype, software patent, sovereign wealth fund, speech recognition, statistical model, Steve Jobs, Steven Pinker, Stuxnet, supply-chain management, TaskRabbit, technological singularity, telepresence, The Bell Curve by Richard Herrnstein and Charles Murray, the Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

But our experience on the highway convinced us that it’s a viable approach for the large and growing set of everyday driving situations. Self-driving cars went from being the stuff of science fiction to on-the-road reality in a few short years. Cutting-edge research explaining why they were not coming anytime soon was outpaced by cutting-edge science and engineering that brought them into existence, again in the space of a few short years. This science and engineering accelerated rapidly, going from a debacle to a triumph in a little more than half a decade. Improvement in autonomous vehicles reminds us of Hemingway’s quote about how a man goes broke: “Gradually and then suddenly.”5 And self-driving cars are not an anomaly; they’re part of a broad, fascinating pattern.

Joseph Hooper, “DARPA’s Debacle in the Desert,” Popular Science, June 4, 2004, http://www.popsci.com/scitech/article/2004-06/darpa-grand-challenge-2004darpas-debacle-desert. 4. Mary Beth Griggs, “4 Questions About Google’s Self-Driving Car Crash,” Popular Mechanics, August 11, 2011, http://www.popularmechanics.com/cars/news/indus try/4-questions-about-googles-self-driving-car-crash; John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html. 5. Ernest Hemingway, The Sun Also Rises (New York: HarperCollins, 2012), p. 72. 6.

According to an initial specification supplied by the agency, they will have to be able to drive a utility vehicle, remove debris blocking an entryway, climb a ladder, close a valve, and replace a pump.34 These seem like impossible requirements, but we’ve been assured by highly knowledgeable colleagues—ones competing in the DRC, in fact—that they’ll be met. Many saw the 2004 Grand Challenge as instrumental in accelerating progress with autonomous vehicles. There’s an excellent chance that the DRC will be similarly important at getting us past Moravec’s paradox. More Evidence That We’re at an Inflection Point Self-driving cars, Jeopardy! champion supercomputers, and a variety of useful robots have all appeared just in the past few years. And these innovations are not just lab demos; they’re showing off their skills and abilities in the messy real world. They contribute to the impression that we’re at an inflection point—a bend in the curve where many technologies that used to be found only in science fiction are becoming everyday reality.


pages: 339 words: 94,769

Possible Minds: Twenty-Five Ways of Looking at AI by John Brockman

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alignment Problem, AlphaGo, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Bletchley Park, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Computing Machinery and Intelligence, CRISPR, Daniel Kahneman / Amos Tversky, Danny Hillis, data science, David Graeber, deep learning, DeepMind, Demis Hassabis, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, fake news, finite state, friendly AI, future of work, Geoffrey Hinton, Geoffrey West, Santa Fe Institute, gig economy, Hans Moravec, heat death of the universe, hype cycle, income inequality, industrial robot, information retrieval, invention of writing, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Watt: steam engine, Jeff Hawkins, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Large Hadron Collider, Loebner Prize, machine translation, market fundamentalism, Marshall McLuhan, Menlo Park, military-industrial complex, mirror neurons, Nick Bostrom, Norbert Wiener, OpenAI, optical character recognition, paperclip maximiser, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, public intellectual, quantum cryptography, RAND corporation, random walk, Ray Kurzweil, Recombinant DNA, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, synthetic biology, systems thinking, technological determinism, technological singularity, technoutopianism, TED Talk, telemarketer, telerobotics, The future is already here, the long tail, the scientific method, theory of mind, trolley problem, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, you are the product, zero-sum game

., capable of accepting a purpose as input and then achieving it—rather than special purpose, with their goal implicit in their design. For example, a self-driving car should accept a destination as input instead of having one fixed destination. However, some aspects of the car’s “driving purpose” are fixed, such as that it shouldn’t hit pedestrians. This is built directly into the car’s steering algorithms rather than being explicit: No self-driving car in existence today “knows” that pedestrians prefer not to be run over. Putting a purpose into a machine that optimizes its behavior according to clearly defined algorithms seems an admirable approach to ensuring that the machine’s “conduct will be carried out on principles acceptable to us!”

Each of these stages was heralded as a revolutionary advance over the limitations of its predecessors, yet all effectively do the same thing: They make inferences from observations. How these approaches relate can be understood by how they scale—that is, how their performance depends on the difficulty of the problem they’re addressing. Both a light switch and a self-driving car must determine their operators’ intentions, but the former has just two options to choose from, whereas the latter has many more. The AI-boom phases have started with promising examples in limited domains; the bust phases came with the failure of those demonstrations to handle the complexity of less-structured, practical problems.

The third problem that scaling solved for AI was coming up with the rules for reasoning without having to hire a programmer for each problem. Wiener recognized the role of feedback in machine learning, but he missed the key role of representation. It’s not possible to store all possible images in a self-driving car, or all possible sounds in a conversational computer; they have to be able to generalize from experience. The “deep” part of deep learning refers not to the (hoped-for) depth of insight but to the depth of the mathematical network layers used to make predictions. It turned out that a linear increase in network complexity led to an exponential increase in the expressive power of the network.


pages: 180 words: 55,805

The Price of Tomorrow: Why Deflation Is the Key to an Abundant Future by Jeff Booth

3D printing, Abraham Maslow, activist fund / activist shareholder / activist investor, additive manufacturing, AI winter, Airbnb, Albert Einstein, AlphaGo, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, Bretton Woods, business intelligence, butterfly effect, Charles Babbage, Claude Shannon: information theory, clean water, cloud computing, cognitive bias, collapse of Lehman Brothers, Computing Machinery and Intelligence, corporate raider, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, dark matter, deep learning, DeepMind, deliberate practice, digital twin, distributed ledger, Donald Trump, Elon Musk, fiat currency, Filter Bubble, financial engineering, full employment, future of work, game design, gamification, general purpose technology, Geoffrey Hinton, Gordon Gekko, Great Leap Forward, Hyman Minsky, hype cycle, income inequality, inflation targeting, information asymmetry, invention of movable type, Isaac Newton, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, John von Neumann, Joseph Schumpeter, late fees, low interest rates, Lyft, Maslow's hierarchy, Milgram experiment, Minsky moment, Modern Monetary Theory, moral hazard, Nelson Mandela, Network effects, Nick Bostrom, oil shock, OpenAI, pattern recognition, Ponzi scheme, quantitative easing, race to the bottom, ride hailing / ride sharing, self-driving car, software as a service, technoutopianism, TED Talk, the long tail, the scientific method, Thomas Bayes, Turing test, Uber and Lyft, uber lyft, universal basic income, winner-take-all economy, X Prize, zero-sum game

How the Economy Works, Part II: Creative Destruction Out with the old, in with the new The BuildDirect journey The windows of opportunity The rise of the platforms On the eve of destruction 3. It Is Hard to Think Differently Building on weak foundations Two-speed thinking Myths we live by How do we overcome our errors? 4. The Technology Boom Doubling up Self-driving cars Virtual and augmented reality Additive manufacturing and 3D printing The coming sonic boom 5. The Future of Energy The laws of energy Let the sun shine in Changing the price of tomorrow 6. The Future of Intelligence The impact of artificial intelligence A brief history of intelligence The beginning of AI 7.

Remember that the underpinnings of the technology revolution are continuing to double. There are developments on the horizon that will make what we have now look primitive. And many of these technologies are not independent. They feed back to each other, which in turn drives more acceleration. For example, the same data captured through visualization in self-driving cars, drones, or robots provides more data to the network to learn faster. If it feels like it’s hard keeping up with the rate of progress today, just wait for what’s to come. Technological advances have been hugely beneficial, enhancing our ability to live our lives better. As we are seeing, though, most of our jobs today come from the same inefficiencies and waste that technology replaces over the longer term.

And all of it is undermining the very basis of our economies: growth and inflation. Let’s take a deeper look at three technologies that should be entering the mainstream in the not-too-distant future. You are likely very aware of these technologies, but because adoption is still quite early, beyond the hype, their impact on society so far has been limited. Self-driving cars We have come a long way since the first DARPA Grand Challenge launched in 2004 to spur development of the first fully autonomous ground vehicles. None of the entrants in that first contest even finished the race. Fifteen years later, the time is nearing when truly autonomous self-driving automobiles will start their march across industries.


pages: 300 words: 76,638

The War on Normal People: The Truth About America's Disappearing Jobs and Why Universal Basic Income Is Our Future by Andrew Yang

3D printing, Airbnb, assortative mating, augmented reality, autonomous vehicles, basic income, Bear Stearns, behavioural economics, Ben Horowitz, Bernie Sanders, call centre, corporate governance, cryptocurrency, data science, David Brooks, DeepMind, Donald Trump, Elon Musk, falling living standards, financial deregulation, financial engineering, full employment, future of work, global reserve currency, income inequality, Internet of things, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: technological unemployment, Khan Academy, labor-force participation, longitudinal study, low skilled workers, Lyft, manufacturing employment, Mark Zuckerberg, megacity, meritocracy, Narrative Science, new economy, passive income, performance metric, post-work, quantitative easing, reserve currency, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Ronald Reagan, Rutger Bregman, Sam Altman, San Francisco homelessness, self-driving car, shareholder value, Silicon Valley, Simon Kuznets, single-payer health, Stephen Hawking, Steve Ballmer, supercomputer in your pocket, tech worker, technoutopianism, telemarketer, The future is already here, The Wealth of Nations by Adam Smith, traumatic brain injury, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, unemployed young men, universal basic income, urban renewal, warehouse robotics, white flight, winner-take-all economy, Y Combinator

Rio Tinto has 73 autonomous mining trucks hauling iron ore 24 hours a day in Australia. Europe saw its first convoys of self-driving trucks cross the continent in 2016. In 2016 Uber bought the self-driving truck company Otto for $680 million and now employs 500 engineers to perfect the technology. Google spun off its self-driving car company Waymo, which is working on self-driving trucks with the big truck manufacturers Daimler and Volvo. Jim Scheinman, a venture capitalist at Maven Ventures who has backed startups in both autonomous trucks and cars, says that self-driving trucks will arrive significantly before cars because highway driving is so much easier.

Department of Transportation is throwing its full support behind development of autonomous vehicles as a way to improve safety on our roadways.” In 2016 the trucking industry spent $9.1 million on lobbying, and the Ohio government has already committed $15 million to set up a 35-mile stretch of highway outside Columbus for testing self-driving trucks. Arizona, California, and Nevada have begun allowing self-driving car trials in their states, and others will follow. Will truckers and the industry fight back? Back in the 1950s, truckers were highly unionized, with the Teamsters being legendary in their aggressiveness. Today, only about 13 percent of U.S. truckers are unionized, and 90 percent of the trucking industry is made up of small businesses with 10 or fewer trucks.

History repeats itself until it doesn’t. No one has an incentive to sound the alarm. To do so could make you seem uneducated and ignorant of history, and perhaps even negative and shrill. It also would make you right in this case. There has never been a computer smarter than humans until now. Self-driving cars are a different type of leap forward than the invention of cars themselves. Data is about to supplant human judgment. And on and on. It’s like the warning you get when investing—sometimes the past is not the best indicator of the present or future. It’s important also to remember that things got quite rough during the Industrial Revolution; in America this is the period between 1870 and 1914 when factories and assembly lines absorbed millions of workers before World War I.


pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Peter Beck" AND "Rocket Lab", Alan Greenspan, Anthropocene, autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Boston Dynamics, Bretton Woods, Brexit referendum, capital controls, capitalist realism, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, CRISPR, David Ricardo: comparative advantage, decarbonisation, deep learning, dematerialisation, DIY culture, Donald Trump, double helix, driverless car, electricity market, Elon Musk, energy transition, Erik Brynjolfsson, fake news, financial independence, Francis Fukuyama: the end of history, future of work, Future Shock, G4S, general purpose technology, Geoffrey Hinton, Gregor Mendel, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, Jevons paradox, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, Leo Hollis, liberal capitalism, low earth orbit, low interest rates, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private spaceflight, Productivity paradox, profit motive, race to the bottom, rewilding, RFID, rising living standards, Robert Solow, scientific management, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, SoftBank, stem cell, Stewart Brand, synthetic biology, technological determinism, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, We are as Gods, Whole Earth Catalog, working-age population

‘Drivers Could Lose up to 25,000 Jobs per Month when Self-Driving Cars Hit, Goldman Sachs Says’. CNBC, 22 May 2017. Bomey, Nathan. ‘US Vehicle Deaths Topped 40,000 in 2017, National Safety Council Estimates’. USA Today, 15 February 2018. Darter, Michael. ‘DARPA’s Debacle in the Desert’. Popular Science, 4 June 2004. Dillow, Clay. ‘Revealed: Google’s Car Fleet Has Been Driving around Unmanned for 140,000 Miles Already’. Popular Science, 11 October 2010. Ford, Martin. The Rise of the Robots: Technology and the Threat of Mass Unemployment. Oneworld, 2017. Marshall, Aarian. ‘As Uber Flails, Its Self-driving Car Research Rolls On’.

While the challenge had been ambitious – after all, the point was to stretch the entrants’ abilities – few thought it would descend into such farce. One observer even labelled the episode ‘the debacle in the desert’. To any reasonable person the possibility of autonomous vehicles seemed decades away. And yet, just six years later in 2010, Google announced their self-driving cars had ‘logged in over 140,000 miles’ with seven test vehicles completing over 1,000 miles each without any human intervention – including difficult terrain like San Francisco’s notoriously steep Lombard Street. Since then the likes of Apple, Tesla and Uber have entered the game, not to mention the older incumbents of the automobile industry.

In the span of just eleven years the technology underpinning autonomous vehicles had improved so dramatically that they went from a totem of public ridicule to influencing the business models of some of the world’s most valuable companies. That is how exponential technologies work: ponderously at first, and then a sudden transformation – a tendency historically visible with personal computing, smartphones, the internet and soon the descendants of Atlas. For now, however, the technology that will turn self-driving cars from engineering possibility to background feature in our everyday lives remains to be perfected. Importantly, the way this challenge is being approached by the likes of Google and Uber offers an insight into how automation may diffuse across other parts of the economy and eliminate jobs. The strategy runs something like this: begin by acquiring massive amounts of data to allow algorithms to model and reproduce outcomes and work their way through highly repetitive tasks.


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The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World by Pedro Domingos

Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, backpropagation, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is not the new oil, data is the new oil, data science, deep learning, DeepMind, double helix, Douglas Hofstadter, driverless car, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, Geoffrey Hinton, global village, Google Glasses, Gödel, Escher, Bach, Hans Moravec, incognito mode, information retrieval, Jeff Hawkins, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, large language model, lone genius, machine translation, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, Nick Bostrom, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, power law, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, scientific worldview, Second Machine Age, self-driving car, Silicon Valley, social intelligence, speech recognition, Stanford marshmallow experiment, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the long tail, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, Thomas Bayes, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight, yottabyte, zero-sum game

The candidate with the best voter models wins, like Obama against Romney. Unmanned vehicles pilot themselves across land, sea, and air. No one programmed your tastes into the Amazon recommendation system; a learning algorithm figured them out on its own, by generalizing from your past purchases. Google’s self-driving car taught itself how to stay on the road; no engineer wrote an algorithm instructing it, step-by-step, how to get from A to B. No one knows how to program a car to drive, and no one needs to, because a car equipped with a learning algorithm picks it up by observing what the driver does. Machine learning is something new under the sun: a technology that builds itself.

We know how to drive cars and decipher handwriting, but these skills are subconscious; we’re not able to explain to a computer how to do these things. If we give a learner a sufficient number of examples of each, however, it will happily figure out how to do them on its own, at which point we can turn it loose. That’s how the post office reads zip codes, and that’s why self-driving cars are on the way. The power of machine learning is perhaps best explained by a low-tech analogy: farming. In an industrial society, goods are made in factories, which means that engineers have to figure out exactly how to assemble them from their parts, how to make those parts, and so on—all the way to raw materials.

In machine learning, knowledge is often in the form of statistical models, because most knowledge is statistical: all humans are mortal, but only 4 percent are Americans. Skills are often in the form of procedures: if the road curves left, turn the wheel left; if a deer jumps in front of you, slam on the brakes. (Unfortunately, as of this writing Google’s self-driving cars still confuse windblown plastic bags with deer.) Often, the procedures are quite simple, and it’s the knowledge at their core that’s complex. If you can tell which e-mails are spam, you know which ones to delete. If you can tell how good a board position in chess is, you know which move to make (the one that leads to the best position).


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Exponential: How Accelerating Technology Is Leaving Us Behind and What to Do About It by Azeem Azhar

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 23andMe, 3D printing, A Declaration of the Independence of Cyberspace, Ada Lovelace, additive manufacturing, air traffic controllers' union, Airbnb, algorithmic management, algorithmic trading, Amazon Mechanical Turk, autonomous vehicles, basic income, Berlin Wall, Bernie Sanders, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, book value, Boris Johnson, Bretton Woods, carbon footprint, Chris Urmson, Citizen Lab, Clayton Christensen, cloud computing, collective bargaining, computer age, computer vision, contact tracing, contact tracing app, coronavirus, COVID-19, creative destruction, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, data science, David Graeber, David Ricardo: comparative advantage, decarbonisation, deep learning, deglobalization, deindustrialization, dematerialisation, Demis Hassabis, Diane Coyle, digital map, digital rights, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, Elon Musk, emotional labour, energy security, Fairchild Semiconductor, fake news, Fall of the Berlin Wall, Firefox, Frederick Winslow Taylor, fulfillment center, future of work, Garrett Hardin, gender pay gap, general purpose technology, Geoffrey Hinton, gig economy, global macro, global pandemic, global supply chain, global value chain, global village, GPT-3, Hans Moravec, happiness index / gross national happiness, hiring and firing, hockey-stick growth, ImageNet competition, income inequality, independent contractor, industrial robot, intangible asset, Jane Jacobs, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, Just-in-time delivery, Kickstarter, Kiva Systems, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, lockdown, low skilled workers, lump of labour, Lyft, manufacturing employment, Marc Benioff, Mark Zuckerberg, megacity, Mitch Kapor, Mustafa Suleyman, Network effects, new economy, NSO Group, Ocado, offshore financial centre, OpenAI, PalmPilot, Panopticon Jeremy Bentham, Peter Thiel, Planet Labs, price anchoring, RAND corporation, ransomware, Ray Kurzweil, remote working, RFC: Request For Comment, Richard Florida, ride hailing / ride sharing, Robert Bork, Ronald Coase, Ronald Reagan, Salesforce, Sam Altman, scientific management, Second Machine Age, self-driving car, Shoshana Zuboff, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, software as a service, Steve Ballmer, Steve Jobs, Stuxnet, subscription business, synthetic biology, tacit knowledge, TaskRabbit, tech worker, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, Thomas Malthus, TikTok, Tragedy of the Commons, Turing machine, Uber and Lyft, Uber for X, uber lyft, universal basic income, uranium enrichment, vertical integration, warehouse automation, winner-take-all economy, workplace surveillance , Yom Kippur War

But those are big ifs. When our scientific understanding of a subject is still developing, predictions are sometimes little better than guesswork.22 Self-driving cars create similar, if rather smaller, headaches. In 2019, Elon Musk envisioned that Tesla, the car firm, would have a fleet of 1 million self-driving taxis, what he called ‘robo-taxis’, on the roads by the end of 2020.23 The actual number was zero. And Tesla is not alone. Every self-driving car company has missed its targets. It turns out that the problem is much harder, from a purely technical perspective, than the teams building the technologies were willing to acknowledge.

Sagaria, ‘Misperception of Exponential Growth’, Perception & Psychophysics, 18(6), November 1975, pp. 416–422 <https://doi.org/10.3758/BF03204114>. 14 Fernand Braudel, The Mediterranean and the Mediterranean World in the Age of Philip II (New York: Harper & Row, 1972), p. 20. 15 Pascal Boyer and Michael Bang Petersen, ‘Folk-Economic Beliefs: An Evolutionary Cognitive Model’, Behavioral and Brain Sciences, 41, 2018, E158 <https://doi.org/10.1017/S0140525X17001960>. 16 Duff McDonald, The Firm: The Story of McKinsey and Its Secret Influence on American Business (New York: Simon & Schuster, 2014), pp. 178–179. 17 ‘Planet of the Phones’, The Economist, 26 February 2015 <https://www.economist.com/leaders/2015/02/26/planet-of-the-phones> [accessed 15 March 2021]. 18 Simon Evans, ‘Solar Is Now “Cheapest Electricity in History”, Confirms IEA’, Carbon Brief, 13 October 2020 <https://www.carbonbrief.org/solar-is-now-cheapest-electricity-in-history-confirms-iea> [accessed 18 December 2020]. 19 Ray Kurzweil, The Age of Spiritual Machines: When Computers Exceed Human Intelligence (New York, NY: Penguin, 2000). 20 Suzana Herculano-Houzel, ‘The Human Brain in Numbers: A Linearly Scaled-up Primate Brain’, Frontiers in Human Neuroscience, 3 November 2009 <https://doi.org/10.3389/neuro.09.031.2009>. 21 Carl Zimmer, ‘100 Trillion Connections: New Efforts Probe and Map the Brain’s Detailed Architecture’, Scientific American, January 2011 <https://doi.org/10.1038/scientificamerican0111-58>. 22 Even if we could build a machine with the complexity of the human brain – comprising artificial rather than real neurons, and connections between them – it isn’t clear this would give rise to anything that can do what the human brain does. 23 Graham Rapier, ‘Elon Musk Says Tesla Will Have 1 Million Robo-Taxis on the Road Next Year, and Some People Think the Claim Is So Unrealistic That He’s Being Compared to PT Barnum’, Business Insider, 23 April 2019 <https://www.businessinsider.com/tesla-robo-taxis-elon-musk-pt-barnum-circus-2019-4> [accessed 11 January 2021]. 24 Andrew Barclay, ‘Why Is It So Hard to Make a Truly Self-Driving Car?’, South China Morning Post, 5 July 2018 <https://www.scmp.com/abacus/tech/article/3028605/why-it-so-hard-make-truly-self-driving-car> [accessed 11 January 2021]. 25 Rani Molla, ‘How Apple’s iPhone Changed the World: 10 Years in 10 Charts’, Vox, 26 June 2017 <https://www.vox.com/2017/6/26/15821652/iphone-apple-10-year-anniversary-launch-mobile-stats-smart-phone-steve-jobs> [accessed 22 July 2020]. 26 Ritwik Banerjee, Joydeep Bhattacharya and Priyama Majumdar, ‘Exponential-Growth Prediction Bias and Compliance with Safety Measures Related to COVID-19’, Social Science & Medicine, 268, January 2021, 113473 <https://doi.org/10.1016/j.socscimed.2020.113473>. 27 Robert C.

Katz, Christina Patterson, Chicago Booth and John Van Reenen, ‘The Fall of the Labor Share and the Rise of Superstar Firms’, The Quarterly Journal of Economics, 135(2), May 2020, pp. 645–709 <https://doi.org/10.1093/qje/qjaa004> Banerjee, Ritwik, Joydeep Bhattacharya and Priyama Majumdar, ‘Exponential-Growth Prediction Bias and Compliance with Safety Measures Related to COVID-19’, Social Science & Medicine, 268, January 2021, 113473 <https://doi.org/10.1016/j.socscimed.2020.113473> Barclay, Andrew, ‘Why Is It So Hard to Make a Truly Self-Driving Car?’, South China Morning Post, 5 July 2018 <https://www.scmp.com/abacus/tech/article/3028605/why-it-so-hard-make-truly-self-driving-car> [accessed 11 January 2021] Benson, Christopher L., Giorgio Triulzi, and Christopher L. Magee, ‘Is There a Moore’s Law for 3D Printing?’, 3D Printing and Additive Manufacturing, 5(1), March 2018, pp. 53–62 <https://doi.org/10.1089/3dp.2017.0041> Bessen, James, Learning by Doing: The Real Connection between Innovation, Wages, and Wealth (New Haven: Yale University Press, 2015) Biancotti, Claudia, ‘India’s Ill-Advised Pursuit of Data Localization’, Peterson Institute for International Economics, 9 December 2019 <https://www.piie.com/blogs/realtime-economic-issues-watch/indias-ill-advised-pursuit-data-localization> [accessed 20 March 2021] Bo, Marta, ‘Autonomous Weapons and the Responsibility Gap in Light of the Mens Rea of the War Crime of Attacking Civilians in the ICC Statute in Weapons and Targeting’, Journal of International Criminal Justice, 2021, mqab005 <https://doi.org/10.1093/jicj/mqab005> Bobier, Jean-François, Philipp Gerbert, Jens Burchardt, and Antoine Gourévich, ‘A Quantum Advantage in Fighting Climate Change’, BCG Global, 22 January 2020 <https://www.bcg.com/publications/2020/quantum-advantage-fighting-climate-change> [accessed 23 March 2021] Boyer, Pascal, and Michael Bang Petersen, ‘Folk-Economic Beliefs: An Evolutionary Cognitive Model’, Behavioral and Brain Sciences, 41, 2018, E158 <https://doi.org/10.1017/S0140525X17001960> Bradshaw, Samantha, and Philip N.


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Seriously Curious: The Facts and Figures That Turn Our World Upside Down by Tom Standage

"World Economic Forum" Davos, agricultural Revolution, augmented reality, autonomous vehicles, Big Tech, blood diamond, business logic, corporate governance, CRISPR, deep learning, Deng Xiaoping, Donald Trump, Dr. Strangelove, driverless car, Elon Musk, failed state, financial independence, gender pay gap, gig economy, Gini coefficient, high net worth, high-speed rail, income inequality, index fund, industrial robot, Internet of things, invisible hand, it's over 9,000, job-hopping, Julian Assange, life extension, Lyft, M-Pesa, Mahatma Gandhi, manufacturing employment, mega-rich, megacity, Minecraft, mobile money, natural language processing, Nelson Mandela, plutocrats, post-truth, price mechanism, private spaceflight, prosperity theology / prosperity gospel / gospel of success, purchasing power parity, ransomware, reshoring, ride hailing / ride sharing, Ronald Coase, self-driving car, Silicon Valley, Snapchat, South China Sea, speech recognition, stem cell, supply-chain management, transaction costs, Uber and Lyft, uber lyft, undersea cable, US Airways Flight 1549, WikiLeaks, zoonotic diseases

Why biggest isn’t fastest in the animal kingdom Geek speak: getting technical What is a brain-computer interface? The link between video games and unemployment What do robots do all day? Why 5G might be both faster and slower than previous wireless technologies Mobile phones are more common than electricity in much of sub-Saharan Africa Why self-driving cars will mostly be shared, not owned How ride-hailing apps reduce drink-driving What is augmented reality? Why we’re still waiting for the space elevator How astronomers spotted the first interstellar asteroid Why drones could pose a greater risk to aircraft than birds What is the point of spam e-mail?

At the Olympics, for example, many contestants were followed by 360-degree video cameras. At special venues sports fans could don virtual-reality goggles to put themselves right into the action. 5G is also supposed to become the connective tissue for the internet of things, interconnecting everything from smartphones and wireless sensors to industrial robots and self-driving cars. This will be made possible by a technique called “network slicing”, which allows operators to create bespoke networks that give each set of devices exactly the kind of connectivity they need to job a particular job. Despite its versatility, it is not clear how quickly 5G will take off. The biggest brake will be economic.

Mobile-money services, which enable people to send cash straight from their phones, have in effect created personal bank accounts that people can carry in their pockets. By one estimate, the M-Pesa mobile-money system alone lifted about 2% of Kenyan households out of poverty between 2008 and 2014. Technology cannot solve all of Africa’s problems, but it can help with some of them. Why self-driving cars will mostly be shared, not owned When will you be able to buy a driverless car that will work anywhere? This commonly asked question contains three assumptions: that autonomous vehicles (AVs) will resemble cars; that people will buy them; and that they will be capable of working on all roads in all conditions.


pages: 349 words: 95,972

Messy: The Power of Disorder to Transform Our Lives by Tim Harford

affirmative action, Air France Flight 447, Airbnb, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, assortative mating, Atul Gawande, autonomous vehicles, banking crisis, Barry Marshall: ulcers, Basel III, Berlin Wall, Bletchley Park, British Empire, Broken windows theory, call centre, Cass Sunstein, Chris Urmson, cloud computing, collateralized debt obligation, Computing Machinery and Intelligence, crowdsourcing, deindustrialization, Donald Trump, Erdős number, experimental subject, Ferguson, Missouri, Filter Bubble, financial engineering, Frank Gehry, game design, global supply chain, Googley, Guggenheim Bilbao, Helicobacter pylori, high net worth, Inbox Zero, income inequality, industrial cluster, Internet of things, Jane Jacobs, Jeff Bezos, Loebner Prize, Louis Pasteur, machine readable, Marc Andreessen, Mark Zuckerberg, Menlo Park, Merlin Mann, microbiome, out of africa, Paul Erdős, Richard Thaler, Rosa Parks, self-driving car, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, Stewart Brand, Susan Wojcicki, tacit knowledge, TED Talk, telemarketer, the built environment, The Death and Life of Great American Cities, the strength of weak ties, Turing test, Tyler Cowen, urban decay, warehouse robotics, William Langewiesche

Klein, Streetlights and Shadows, pp. 123–124. 19. “Will Self-Driving Cars Spell the End of the American Road Trip?” 99% Invisible (podcast), available on The Eye: Slate’s Design Blog, July 3, 2015, http://www.slate.com/blogs/the_eye/2015/07/03/self_driving_cars_and_the_paradox_of_automation_from_99_invisible.html. Raj Rajkumar’s comments below are from the same podcast. 20. Jack Stewart, “What May Be Self-Driving Cars’ Biggest Problem,” BBC Future, August 25, 2015, http://www.bbc.com/future/story/20150824-what-may-be-self-driving-cars-biggest-problem. 21. Cited in Langewiesche, “The Human Factor.” 22.

Once the veterans retire, the human expertise to intuit when the computer has screwed up will be lost forever.18 • • • We’ve seen the problems with GPS systems and with autopilot. Put the two ideas together, and you get the self-driving car. Chris Urmson, who runs Google’s self-driving car program, hopes that the cars will soon be so widely available that his sons will never need to have a driving license. (His oldest son will be sixteen in 2020—Urmson is in a hurry.) There’s a revealing implication in that target: that unlike a plane’s autopilot, a self-driving car will never need to cede control to a human being. True to form, Google’s autonomous vehicles have no steering wheel, though one hopes there will be some way to jump out if they start heading for the ocean.19 Not everyone thinks it is plausible for cars to be completely autonomous—or, at least, not soon enough for Urmson junior.


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Zero to One: Notes on Startups, or How to Build the Future by Peter Thiel, Blake Masters

Airbnb, Alan Greenspan, Albert Einstein, Andrew Wiles, Andy Kessler, Berlin Wall, clean tech, cloud computing, crony capitalism, discounted cash flows, diversified portfolio, do well by doing good, don't be evil, Elon Musk, eurozone crisis, Fairchild Semiconductor, heat death of the universe, income inequality, Jeff Bezos, Larry Ellison, Lean Startup, life extension, lone genius, Long Term Capital Management, Lyft, Marc Andreessen, Mark Zuckerberg, Max Levchin, minimum viable product, Nate Silver, Network effects, new economy, Nick Bostrom, PalmPilot, paypal mafia, Peter Thiel, pets.com, power law, profit motive, Ralph Waldo Emerson, Ray Kurzweil, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Singularitarianism, software is eating the world, Solyndra, Steve Jobs, strong AI, Suez canal 1869, tech worker, Ted Kaczynski, Tesla Model S, uber lyft, Vilfredo Pareto, working poor

Computers already have enough power to outperform people in activities we used to think of as distinctively human. In 1997, IBM’s Deep Blue defeated world chess champion Garry Kasparov. Jeopardy!’s best-ever contestant, Ken Jennings, succumbed to IBM’s Watson in 2011. And Google’s self-driving cars are already on California roads today. Dale Earnhardt Jr. needn’t feel threatened by them, but the Guardian worries (on behalf of the millions of chauffeurs and cabbies in the world) that self-driving cars “could drive the next wave of unemployment.” Everyone expects computers to do more in the future—so much more that some wonder: 30 years from now, will there be anything left for people to do?

Non-monopolists exaggerate their distinction by defining their market as the intersection of various smaller markets: British food ∩ restaurant ∩ Palo Alto Rap star ∩ hackers ∩ sharks Monopolists, by contrast, disguise their monopoly by framing their market as the union of several large markets: search engine ∪ mobile phones ∪ wearable computers ∪ self-driving cars What does a monopolist’s union story look like in practice? Consider a statement from Google chairman Eric Schmidt’s testimony at a 2011 congressional hearing: We face an extremely competitive landscape in which consumers have a multitude of options to access information. Or, translated from PR-speak to plain English: Google is a small fish in a big pond.

Kaczynski, Ted Karim, Jawed Karp, Alex, 11.1, 12.1 Kasparov, Garry Katrina, Hurricane Kennedy, Anthony Kesey, Ken Kessler, Andy Kurzweil, Ray last mover, 11.1, 13.1 last mover advantage lean startup, 2.1, 6.1, 6.2 Levchin, Max, 4.1, 10.1, 12.1, 14.1 Levie, Aaron lifespan life tables LinkedIn, 5.1, 10.1, 12.1 Loiseau, Bernard Long-Term Capital Management (LTCM) Lord of the Rings (Tolkien) luck, 6.1, 6.2, 6.3, 6.4 Lucretius Lyft MacBook machine learning Madison, James Madrigal, Alexis Manhattan Project Manson, Charles manufacturing marginal cost marketing Marx, Karl, 4.1, 6.1, 6.2, 6.3 Masters, Blake, prf.1, 11.1 Mayer, Marissa Medicare Mercedes-Benz MiaSolé, 13.1, 13.2 Michelin Microsoft, 3.1, 3.2, 3.3, 4.1, 5.1, 14.1 mobile computing mobile credit card readers Mogadishu monopoly, monopolies, 3.1, 3.2, 3.3, 5.1, 7.1, 8.1 building of characteristics of in cleantech creative dynamism of new lies of profits of progress and sales and of Tesla Morrison, Jim Mosaic browser music recording industry Musk, Elon, 4.1, 6.1, 11.1, 13.1, 13.2, 13.3 Napster, 5.1, 14.1 NASA, 6.1, 11.1 NASDAQ, 2.1, 13.1 National Security Agency (NSA) natural gas natural secrets Navigator browser Netflix Netscape NetSecure network effects, 5.1, 5.2 New Economy, 2.1, 2.2 New York Times, 13.1, 14.1 New York Times Nietzsche, Friedrich Nokia nonprofits, 13.1, 13.2 Nosek, Luke, 9.1, 14.1 Nozick, Robert nutrition Oedipus, 14.1, 14.2 OfficeJet OmniBook online pet store market Oracle Outliers (Gladwell) ownership Packard, Dave Page, Larry Palantir, prf.1, 7.1, 10.1, 11.1, 12.1 PalmPilots, 2.1, 5.1, 11.1 Pan, Yu Panama Canal Pareto, Vilfredo Pareto principle Parker, Sean, 5.1, 14.1 Part-time employees patents path dependence PayPal, prf.1, 2.1, 3.1, 4.1, 4.2, 4.3, 5.1, 5.2, 5.3, 8.1, 9.1, 9.2, 10.1, 10.2, 10.3, 10.4, 11.1, 11.2, 12.1, 12.2, 14.1 founders of, 14.1 future cash flows of investors in “PayPal Mafia” PCs Pearce, Dave penicillin perfect competition, 3.1, 3.2 equilibrium of Perkins, Tom perk war Perot, Ross, 2.1, 12.1, 12.2 pessimism Petopia.com Pets.com, 4.1, 4.2 PetStore.com pharmaceutical companies philanthropy philosophy, indefinite physics planning, 2.1, 6.1, 6.2 progress without Plato politics, 6.1, 11.1 indefinite polling pollsters pollution portfolio, diversified possession power law, 7.1, 7.2, 7.3 of distribution of venture capital Power Sellers (eBay) Presley, Elvis Priceline.com Prince Procter & Gamble profits, 2.1, 3.1, 3.2, 3.3 progress, 6.1, 6.2 future of without planning proprietary technology, 5.1, 5.2, 13.1 public opinion public relations Pythagoras Q-Cells Rand, Ayn Rawls, John, 6.1, 6.2 Reber, John recession, of mid-1990 recruiting, 10.1, 12.1 recurrent collapse, bm1.1, bm1.2 renewable energy industrial index research and development resources, 12.1, bm1.1 restaurants, 3.1, 3.2, 5.1 risk risk aversion Romeo and Juliet (Shakespeare) Romulus and Remus Roosevelt, Theodore Royal Society Russia Sacks, David sales, 2.1, 11.1, 13.1 complex as hidden to non-customers personal Sandberg, Sheryl San Francisco Bay Area savings scale, economies of Scalia, Antonin scaling up scapegoats Schmidt, Eric search engines, prf.1, 3.1, 5.1 secrets, 8.1, 13.1 about people case for finding of looking for using self-driving cars service businesses service economy Shakespeare, William, 4.1, 7.1 Shark Tank Sharma, Suvi Shatner, William Siebel, Tom Siebel Systems Silicon Valley, 1.1, 2.1, 2.2, 2.3, 5.1, 5.2, 6.1, 7.1, 10.1, 11.1 Silver, Nate Simmons, Russel, 10.1, 14.1 singularity smartphones, 1.1, 12.1 social entrepreneurship Social Network, The social networks, prf.1, 5.1 Social Security software engineers software startups, 5.1, 6.1 solar energy, 13.1, 13.2, 13.3, 13.4 Solaria Solyndra, 13.1, 13.2, 13.3, 13.4, 13.5 South Korea space shuttle SpaceX, prf.1, 10.1, 11.1 Spears, Britney SpectraWatt, 13.1, 13.2 Spencer, Herbert, 6.1, 6.2 Square, 4.1, 6.1 Stanford Sleep Clinic startups, prf.1, 1.1, 5.1, 6.1, 6.2, 7.1 assigning responsibilities in cash flow at as cults disruption by during dot-com mania economies of scale and foundations of founder’s paradox in lessons of dot-com mania for power law in public relations in sales and staff of target market for uniform of venture capital and steam engine Stoppelman, Jeremy string theory strong AI substitution, complementarity vs.


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The Runaway Species: How Human Creativity Remakes the World by David Eagleman, Anthony Brandt

active measures, Ada Lovelace, agricultural Revolution, Albert Einstein, Andrew Wiles, Apollo 13, Burning Man, cloud computing, computer age, creative destruction, crowdsourcing, Dava Sobel, deep learning, delayed gratification, Donald Trump, Douglas Hofstadter, en.wikipedia.org, Frank Gehry, Gene Kranz, Google Glasses, Great Leap Forward, haute couture, informal economy, interchangeable parts, Isaac Newton, James Dyson, John Harrison: Longitude, John Markoff, Large Hadron Collider, lone genius, longitudinal study, Menlo Park, microbiome, Netflix Prize, new economy, New Journalism, pets.com, pneumatic tube, QWERTY keyboard, Ray Kurzweil, reversible computing, Richard Feynman, risk tolerance, Scaled Composites, self-driving car, Simon Singh, skeuomorphism, Solyndra, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, synthetic biology, TED Talk, the scientific method, Watson beat the top human players on Jeopardy!, wikimedia commons, X Prize

Likewise, it’s hard for us to imagine that some decades from now, our children may have their own self-driving cars. Your six-year-old child will be able to commute to school on her own: just strap her in and wave goodbye. Meanwhile, in case of an emergency, your own self-driving car could be turned into an ambulance: if your heart starts beating irregularly, the car’s built-in biological monitoring can detect it and reroute to the nearest hospital. And there’s no reason why you have to be the only one in the car. You could be picked up in a self-driving car and get a manipedi or a dental appointment while moving to your next destination: offices can be entirely mobile.

In recent decades, the world has found itself transitioning from a manufacturing economy to an information economy. But that is not where this road ends. As computers become better at digesting mountains of data, people are being freed up to work on other tasks. We’re already seeing the first glimpses of this new model: the creativity economy. Synthetic biologist, app developer, self-driving car designer, quantum computer designer, multimedia engineer – these are positions that didn’t exist when most of us were in school, and they represent the vanguard of what’s coming. When you grab your morning coffee ten years from now, you may be walking into a job that looks very different from the one you’re working now.

It’s not always about the particular solution, but instead about the variation. Why do humans adapt to everything around us so quickly? It’s because of a phenomenon known as repetition suppression. When your brain gets used to something, it displays less and less of a response each time it sees it. Imagine, for example, that you come across a new object – say, a self-driving car. The first time you see it, your brain shows a large response. It’s absorbing something new and registering it. The second time you see it, your brain shows slightly less response. It doesn’t care quite as much about it, because it’s not quite as novel. The third time: less response again. The fourth time: even less.


pages: 361 words: 81,068

The Internet Is Not the Answer by Andrew Keen

"World Economic Forum" Davos, 3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, AOL-Time Warner, augmented reality, Bay Area Rapid Transit, Berlin Wall, Big Tech, bitcoin, Black Swan, Bob Geldof, Boston Dynamics, Burning Man, Cass Sunstein, Charles Babbage, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, creative destruction, cuban missile crisis, data science, David Brooks, decentralized internet, DeepMind, digital capitalism, disintermediation, disruptive innovation, Donald Davies, Downton Abbey, Dr. Strangelove, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, fail fast, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, fulfillment center, full employment, future of work, gentrification, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, holacracy, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Perry Barlow, Joi Ito, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kevin Roose, Kickstarter, Kiva Systems, Kodak vs Instagram, Lean Startup, libertarian paternalism, lifelogging, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Mary Meeker, Metcalfe’s law, military-industrial complex, move fast and break things, Nate Silver, Neil Armstrong, Nelson Mandela, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Norman Mailer, Occupy movement, packet switching, PageRank, Panopticon Jeremy Bentham, Patri Friedman, Paul Graham, peer-to-peer, peer-to-peer rental, Peter Thiel, plutocrats, Potemkin village, power law, precariat, pre–internet, printed gun, Project Xanadu, RAND corporation, Ray Kurzweil, reality distortion field, ride hailing / ride sharing, Robert Metcalfe, Robert Solow, San Francisco homelessness, scientific management, Second Machine Age, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, subscription business, TaskRabbit, tech bro, tech worker, TechCrunch disrupt, Ted Nelson, telemarketer, The future is already here, The Future of Employment, the long tail, the medium is the message, the new new thing, Thomas L Friedman, Travis Kalanick, Twitter Arab Spring, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, urban planning, Vannevar Bush, warehouse robotics, Whole Earth Catalog, WikiLeaks, winner-take-all economy, work culture , working poor, Y Combinator

“Society is reshaping itself to fit the contours of the new computing infrastructure. The infrastructure orchestrates the instantaneous data exchanges that make fleets of self-driving cars and armies of killer robots possible. It provides the raw materials for the predictive algorithms that inform the decisions of individuals and groups. It underpins the automation of classrooms, libraries, hospitals, shops, churches, and homes.”24 With its massive investment in the development of intelligent labor-saving technologies like self-driving cars and killer robots, Google—which has imported Ray Kurzweil, the controversial evangelist of “singularity,” to direct its artificial intelligence engineering strategy25—is already invested in the building and management of the glass cage.

“The Internet,” Joi Ito, the director of the MIT Media Lab, notes, “is not a technology; it’s a belief system.”14 Everything and everyone are being connected in a network revolution that is radically disrupting every aspect of today’s world. Education, transportation, health care, finance, retail, and manufacturing are now being reinvented by Internet-based products such as self-driving cars, wearable computing devices, 3-D printers, personal health monitors, massive open online courses (MOOCs), peer-to-peer services like Airbnb and Uber, and currencies like Bitcoin. Revolutionary entrepreneurs like Sean Parker and Kevin Systrom are building this networked society on our behalf.

Google, for example, still prides itself as being an “uncompany,” a corporation without the traditional structures of power—even though the $400 billion leviathan is, as of June 2014, the world’s second most valuable corporation. It’s active and in some cases brutally powerful in industries as varied as online search, advertising, publishing, artificial intelligence, news, mobile operating systems, wearable computing, Internet browsers, video, and even—with its fledgling self-driving cars—the automobile industry. In the digital world, everyone wants to be an unbusiness. Amazon, the largest online store in the world and a notorious bully of small publishing companies, still thinks of itself as the scrappy “unstore.” Internet companies like the Amazon-owned shoe store Zappos, and Medium, an online magazine founded by billionaire Twitter founder Ev Williams, are run on so-called holacratic principles—a Silicon Valley version of communism where there are no hierarchies, except, of course, when it comes to wages and stock ownership.


pages: 481 words: 125,946

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence by John Brockman

Adam Curtis, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Anthropocene, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, basic income, behavioural economics, bitcoin, blockchain, bread and circuses, Charles Babbage, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, data science, deep learning, DeepMind, Demis Hassabis, digital capitalism, digital divide, digital rights, discrete time, Douglas Engelbart, driverless car, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, financial engineering, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, Great Leap Forward, Hans Moravec, hive mind, Ian Bogost, income inequality, information trail, Internet of things, invention of writing, iterative process, James Webb Space Telescope, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, Large Hadron Collider, lolcat, loose coupling, machine translation, microbiome, mirror neurons, Moneyball by Michael Lewis explains big data, Mustafa Suleyman, natural language processing, Network effects, Nick Bostrom, Norbert Wiener, paperclip maximiser, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, Recombinant DNA, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, synthetic biology, systems thinking, tacit knowledge, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, We are as Gods, Y2K

In theory it could happen, but we have more pressing things to worry about. Once we put aside the sci-fi disaster plots, the possibility of advanced artificial intelligence is exhilarating—not just for the practical benefits, like the fantastic gains in safety, leisure, and environment-friendliness of self-driving cars but also for the philosophical possibilities. The computational Theory of Mind has never explained the existence of consciousness in the sense of first-person subjectivity (though it’s perfectly capable of explaining the existence of consciousness in the sense of accessible and reportable information).

First, and most simply, it matters because we regularly find ourselves in everyday situations where we need to know why. Why was I denied a loan? Why was my account blocked? Why did my condition suddenly get classified as “severe”? And sometimes we need to know why in cases where the machine made a mistake. Why did the self-driving car abruptly go off the road? It’s hard to troubleshoot problems when you don’t understand why they’re happening. There are deeper troubles, too; to talk about them, we need to understand more about how these algorithms work. They’re trained on massive quantities of data and they’re remarkably good at picking up on the subtle patterns these data contain.

Call the first “Humanoid Thinking,” or Humanoid AI, and the second “Alien Thinking,” or Alien AI. Almost all AI today is Humanoid Thinking. We use AI to solve problems too difficult, time-consuming, or boring for our limited brains to process: electrical-grid balancing, recommendation engines, self-driving cars, face recognition, trading algorithms, and the like. These artificial agents work in narrow domains with clear goals their human creators specify. Such AI aims to accomplish human objectives—often better, with fewer cognitive errors, distractions, outbursts of bad temper, or processing limitations.


The New Map: Energy, Climate, and the Clash of Nations by Daniel Yergin

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", 3D printing, 9 dash line, activist fund / activist shareholder / activist investor, addicted to oil, Admiral Zheng, Albert Einstein, American energy revolution, Asian financial crisis, autonomous vehicles, Ayatollah Khomeini, Bakken shale, Bernie Sanders, BRICs, British Empire, carbon tax, circular economy, clean tech, commodity super cycle, company town, coronavirus, COVID-19, decarbonisation, deep learning, Deng Xiaoping, Didi Chuxing, disruptive innovation, distributed generation, Donald Trump, driverless car, Edward Snowden, Elon Musk, energy security, energy transition, failed state, Ford Model T, geopolitical risk, gig economy, global pandemic, global supply chain, green new deal, Greta Thunberg, hydraulic fracturing, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), inventory management, James Watt: steam engine, John Zimmer (Lyft cofounder), Kickstarter, LNG terminal, Lyft, Malacca Straits, Malcom McLean invented shipping containers, Masayoshi Son, Masdar, mass incarceration, megacity, megaproject, middle-income trap, Mikhail Gorbachev, mutually assured destruction, new economy, off grid, oil rush, oil shale / tar sands, oil shock, open economy, paypal mafia, peak oil, pension reform, power law, price mechanism, purchasing power parity, RAND corporation, rent-seeking, ride hailing / ride sharing, rolling blackouts, Ronald Reagan, Russian election interference, self-driving car, Silicon Valley, smart cities, social distancing, South China Sea, sovereign wealth fund, Suez crisis 1956, super pumped, supply-chain management, TED Talk, trade route, Travis Kalanick, Twitter Arab Spring, Uber and Lyft, uber lyft, ubercab, UNCLOS, UNCLOS, uranium enrichment, vertical integration, women in the workforce

Page opened up the trunk of his car and pulled out the robot for Thrun to examine. Thrun quickly pulled together a team, and the robot was fixed.5 The decisive race was two years later—that 2007 Grand Challenge on that deserted Air Force base in Victorville, California. An empty desert was one thing. But could a self-driving car navigate the streets of an American city, even if it was a ghost city? Eleven teams made it into the 2007 competition, but once again it was Carnegie Mellon versus Stanford. Stanford’s team was back with a Volkswagen named “Junior,” after Leland Stanford Jr., for whom the university was named.

“There was no way, before 2000, to make something interesting,” Thrun said. “The sensors weren’t there, the computers weren’t there, and the mapping wasn’t there. Radar was a device on a hilltop that cost two hundred million dollars.”9 Even with all the competitors and competing visions, there is at least a consensus on the benchmarks for defining a self-driving car. The Society of Automotive Engineers has classified cars by level of automation. The first three levels go from “no automation” at Level 0 up to Level 3, which is cruise control and autopilot that controls acceleration under the supervision of the driver. Level 4 is “high automation”—capable of driving and monitoring the environment without human supervision but only in what is called a “geofenced area,” which might be a college campus, a central business district, or using the “pods” to go from Terminal 5 at Heathrow Airport to the business car park.

What happens if a vehicle goes to the wrong destination? Or if there is construction or an accident en route? Or weather causes a malfunction? Or the car has to make a decision whether to hit a person or crash on the side of the road? Since people are handing over control to a machine, in order to instill confidence these self-driving cars will have to function at much higher levels of performance than cars driven by humans. While millions of miles have now been driven by test driverless cars, humans drive more than eight billion miles every day in the United States. Will some groups create havoc by hacking the software in tens of thousands of cars?


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

"World Economic Forum" Davos, 23andMe, 3D printing, Airbnb, Alan Greenspan, algorithmic bias, algorithmic trading, AltaVista, Anne Wojcicki, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, bioinformatics, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, Brian Krebs, British Empire, business intelligence, call centre, carbon footprint, clean tech, cloud computing, collaborative consumption, connected car, corporate governance, Credit Default Swap, cryptocurrency, data science, David Brooks, DeepMind, Demis Hassabis, disintermediation, Dissolution of the Soviet Union, distributed ledger, driverless car, Edward Glaeser, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fiat currency, future of work, General Motors Futurama, global supply chain, Google X / Alphabet X, Gregor Mendel, industrial robot, information security, Internet of things, invention of the printing press, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Joi Ito, Kevin Roose, Kickstarter, knowledge economy, knowledge worker, lifelogging, litecoin, low interest rates, M-Pesa, machine translation, Marc Andreessen, Mark Zuckerberg, Max Levchin, Mikhail Gorbachev, military-industrial complex, mobile money, money: store of value / unit of account / medium of exchange, Nelson Mandela, new economy, off-the-grid, offshore financial centre, open economy, Parag Khanna, paypal mafia, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Thiel, precision agriculture, pre–internet, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rubik’s Cube, Satoshi Nakamoto, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, social graph, software as a service, special economic zone, supply-chain management, supply-chain management software, technoutopianism, TED Talk, The Future of Employment, Travis Kalanick, underbanked, unit 8200, Vernor Vinge, Watson beat the top human players on Jeopardy!, women in the workforce, work culture , Y Combinator, young professional

Accidents are caused by the four Ds: Bilger, “Auto-Correct.” There remain many gaps: Lee Gomes, “Hidden Obstacles for Google’s Self-Driving Cars,” MIT Technology Review, August 28, 2014, http://www.technologyreview.com/news/530276/hidden-obstacles-for-googles-self-driving-cars/. Uber has already built: John Biggs, “Uber Opening Robotics Research Facility in Pittsburgh to Build Self-Driving Cars,” TechCrunch, February 2, 2015, http://techcrunch.com/2015/02/02/uber-opening-robotics-research-facility-in-pittsburgh-to-build-self-driving-cars/. At last count there were 162,037 active drivers: Emily Badger, “Now We Know How Many Drivers Uber Has—and Have a Better Idea of What They’re Making,” Washington Post, January 22, 2015, http://www.washingtonpost.com/blogs/wonkblog/wp/2015/01/22/now-we-know-many-drivers-uber-has-and-how-much-money-theyre-making%E2%80%8B/.

As Sebastian Thrun explained in a TED talk, his best friend was killed in a car accident, spurring his personal crusade to innovate the car accident out of existence: “I decided I’d dedicate my life to saving 1 million people every year.” Google has hired the former deputy director of the National Highway Traffic Safety Administration, Ron Medford, to be its director of safety for self-driving cars. Medford explained that Americans collectively drive approximately 3 trillion miles per year, and more than 30,000 people die in the process. Worldwide, those statistics are enormous; approximately 1.3 million people die every year in car crashes. Google, of course, also has an interest in allowing consumers to have more time on their hands—quite literally, to have their hands free.

This isn’t just about taxi drivers; the delivery driver may be replaced by Amazon’s airborne delivery drones or automated delivery trucks. UPS and Google are also testing their own versions of the delivery drone. Two and a half million people in the United States make their living from driving trucks, taxis, or buses, and all of them are vulnerable to displacement by self-driving cars. It’s hard to wrap your head around all the changes this might mean. I met the CEO of a company that develops high-tech access control systems (like the new parking garage system at the airport that tells you how many open spaces are available on each floor) and asked him what worries him about the future.


pages: 380 words: 109,724

Don't Be Evil: How Big Tech Betrayed Its Founding Principles--And All of US by Rana Foroohar

"Susan Fowler" uber, "World Economic Forum" Davos, accounting loophole / creative accounting, Airbnb, Alan Greenspan, algorithmic bias, algorithmic management, AltaVista, Andy Rubin, autonomous vehicles, banking crisis, barriers to entry, behavioural economics, Bernie Madoff, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, book scanning, Brewster Kahle, Burning Man, call centre, Cambridge Analytica, cashless society, clean tech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, data science, deal flow, death of newspapers, decentralized internet, Deng Xiaoping, digital divide, digital rights, disinformation, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Etonian, Evgeny Morozov, fake news, Filter Bubble, financial engineering, future of work, Future Shock, game design, gig economy, global supply chain, Gordon Gekko, Great Leap Forward, greed is good, income inequality, independent contractor, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, junk bonds, Kenneth Rogoff, life extension, light touch regulation, low interest rates, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, military-industrial complex, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, Paul Volcker talking about ATMs, paypal mafia, Peter Thiel, pets.com, price discrimination, profit maximization, race to the bottom, recommendation engine, ride hailing / ride sharing, Robert Bork, Sand Hill Road, search engine result page, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Shoshana Zuboff, side hustle, Sidewalk Labs, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, SoftBank, South China Sea, sovereign wealth fund, Steve Bannon, Steve Jobs, Steven Levy, stock buybacks, subscription business, supply-chain management, surveillance capitalism, TaskRabbit, tech billionaire, tech worker, TED Talk, Telecommunications Act of 1996, The Chicago School, the long tail, the new new thing, Tim Cook: Apple, too big to fail, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, warehouse robotics, WeWork, WikiLeaks, zero-sum game

The energy in the room, the excitement to simply be in the same space with the uber-Bro, was visceral. We all enjoyed the high-end snacks in the company canteen while Kalanick was peppered with questions about his career history, Uber’s new ventures into self-driving cars, and whether the company would ever consider augmenting its already hefty pay and benefits (self-driving-car engineers in the Valley can make around $2 million) by handing out perks like subsidized MBAs, as other larger tech firms do. “Oooh, it’s getting hot in here,” he quipped, to laughs and earnest nods of agreement around the room. But in another session, a somewhat different view of the company emerged.

With each buzz and beep of our phones, each automatically downloaded video, each new contact popping up in our digital networks, we get just a glimmer of a vast new world that is, frankly, beyond most people’s understanding, a bizarre land of information and misinformation, of trends and tweets, and of high-speed surveillance technology that has become the new normal. Just think: Russian election-hacking; hate-mongering Twitter feeds; identity theft; big data; fake news; online scams; digital addiction; self-driving car crashes; the rise of the robots; creepy facial recognition technology; Alexa eavesdropping on our every conversation; algorithms that watch us work, play, and sleep; and companies and governments that control them. The list of technology-driven social disruption is endless—and all of it has appeared in just the past few years.

Netflix, Amazon, and, even to a certain extent, Apple, who are relative newcomers to the entertainment business, are no longer content being the uncontested leaders in the video streaming market; now they are also dominant content producers, becoming in effect TV and movie studios, spending billions of dollars (in the case of Netflix and Amazon) on original television programming,42 a move that has left the previous titans of the entertainment business scrambling to match them (hence the recent massive industry mergers of AT&T and Time Warner). Google has lurched into the transportation business with its bid to create a self-driving car, and Facebook is trying to launch its own finance system with the creation of a bespoke cryptocurrency, Libra (Apple has already teamed up with Goldman Sachs on a credit card). Big Tech, in other words, doesn’t just want to become a leader in one sector. It wants to become the platform for everything, the operating system for your life.


pages: 240 words: 65,363

Think Like a Freak by Steven D. Levitt, Stephen J. Dubner

Albert Einstein, Anton Chekhov, autonomous vehicles, Barry Marshall: ulcers, behavioural economics, call centre, carbon credits, Cass Sunstein, colonial rule, Donald Shoup, driverless car, Edward Glaeser, Everything should be made as simple as possible, fail fast, food miles, gamification, Gary Taubes, Helicobacter pylori, income inequality, information security, Internet Archive, Isaac Newton, medical residency, Metcalfe’s law, microbiome, prediction markets, randomized controlled trial, Richard Thaler, Scramble for Africa, self-driving car, Silicon Valley, sunk-cost fallacy, Tony Hsieh, transatlantic slave trade, Wayback Machine, éminence grise

: See Robert Hornik, Lela Jacobsohn, Robert Orwin, Andrea Piesse, Graham Kalton, “Effects of the National Youth Anti-Drug Media Campaign on Youths,” American Journal of Public Health 98, no. 12 (December 2008). 174 SELF-DRIVING CARS: Among the many people who informed our thinking on the driverless-car future, we are especially indebted to Raj Rajkumar and his colleagues at Carnegie Mellon, who let us ride in their driverless vehicle and answered every question. / 175 Google has already driven its fleet of autonomous cars: See Angela Greiling Keane, “Google’s Self-Driving Cars Get Boost from U.S. Agency,” Bloomberg.com, May 30, 2013; “The Self-Driving Car Logs More Miles on New Wheels,” Google official blog, August 7, 2012. (Our text contains updated mile figures from a Google spokesperson as of October 2013.) / 174 Ninety percent of traffic deaths due to driver error: Per Bob Joop Goos, chairman of the International Organization for Road Accident Prevention; also per National Highway Traffic Safety Administration (NHTSA) statistics. / 174 Worldwide traffic deaths: Most of the statistics in this section are drawn from World Health Organization and NHTSA reports. / 175 In many U.S. cities, 30 to 40 percent of the downtown surface area is devoted to parking: See Stephen J.

Panaceas are almost nonexistent. If you paper over the shortcomings of your plan, that only gives your opponent reason to doubt the rest of it. Let’s say you’ve become a head-over-heels advocate for a new technology you think will change the world. Your argument goes like this: The era of the self-driving car—a.k.a. the driverless car, or autonomous vehicle—is just around the corner, and we should embrace it as vigorously as possible. It will save millions of lives and improve just about every facet of our society and economy. You could go on and on. You could talk about how the toughest challenge—the technology itself—has largely been conquered.

Wade, 93 Rolling Stone, 140 Romania, witches in, 30–31 Roth, David Lee, 152, 154 and game theory, 142–43 and King Solomon, 137–38, 142–43 and M&M clause, 141–42 and Van Halen, 137, 138, 140–42 running with the herd, 10, 112–15, 172 salt sensitivity, 76–77 Sargent, Thomas, 26–27 “Save to Win,” 99 savings: prize-linked (PLS) account, 98–99 rate of, 97–99 scams, 154–61 schoolteachers, early retirement of, 180–81 “Scramble for Africa,” 74 Seeger, Pete, 138 self-assessment, 27 self-driving car, 174–77 self-interest, 7 self-sterilizing surface, invention of, 194–95 Sen, Amartya, 66 separating equilibrium, 143, 154 September 11 attacks, 22, 161–62 seriousness, 96 shame, fear of, 6 Shaw, George Bernard, 10–11 shoes, selling, 128–30 Silva, Rohan, 12 simplicity, 94 Singer, Isaac Bashevis, “Why I Write for Children,” 104 slavery: and Caribbean blacks, 77 and salt sensitivity, 76–77 in South America, 74–77 Smile Pinki, 120 Smile Train, 119–24, 130 Smith, Adam, 58 Smith, Billie June, 99 soccer, penalty kick in, 3–7, 29 Soccer Boy, 119 social-gaming site, 100 social issues: and corruption, 66–67 experiments in, 39–40 incentives in, 112, 113 problem solving, 66–67 Society of Fellows, Harvard, 42 Solomon, King, 152, 165 and David Lee Roth, 137–38, 142–43 First Temple built by, 137 and game theory, 142–43 maternity dispute settled by, 58, 139–40, 154, 187 Solomon method, 58, 140n solution, “perfect,” 173–74 sophistication, 88n South America: colonialism in, 74 slavery in, 74–77 Spanish Prisoner, 156 speculation, 90 Spenkuch, Jörg, 71–72 SpinForGood.com, 100 sports: brain as critical organ in, 63 competitive eating, 62–64 expectations in, 64 training for, 62 tricking athletes into improvement, 63 Springsteen, Bruce, 208 Standards of Conduct Office, 184 starvation, causes of, 66–67 status quo, 10 status-quo bias, 206 stock markets, predictions of, 24–25, 29–30 stomach acid, 78, 79–80, 95 Stone, Alex, 101–3 storytelling, 181–88 anecdotes vs., 181–82 in the Bible, 185–88 data in, 182 and narcissism, 183 teaching via, 183 time frame in, 182 truth vs. falsity of, 182–83 suicide, 32–34 getting help, 34 impulse toward, 34 “no one left to blame” theory of, 33–34 sunk-cost fallacy, 191, 192, 199 Sunstein, Cass, 172 SuperFreakonomics, 11–12, 161, 164 swimming accidents, 91 table manners, Japanese, 57 talent: as overrated, 96 self-assessment of, 27 teacher quality, 50 Teach Your Garden to Weed Itself, 143, 145, 149, 154 Ten Commandments, 185–86 terrorists: and banks, 161–65 and education, 171 and life insurance, 163–65 Tetlock, Philip, 23–25, 171 Thaler, Richard, 172 thinking: big, 89 with different muscles, 8 like a child, 87, 92, 95, 100 like a Freak, 8, 10–11, 87 small, 88–92 time spent in, 10–11 Thomas, Sonya, 61 time frame, 182 total internal reflection, 195 tradition, 39, 78, 82 traffic accidents, 178–79 “transpoosion,” 87 trial by ordeal, 144–49, 154 tricks: fun in, 152 improving athletes via, 63 “Turn!


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Gigged: The End of the Job and the Future of Work by Sarah Kessler

"Susan Fowler" uber, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, basic income, bitcoin, blockchain, business cycle, call centre, cognitive dissonance, collective bargaining, crowdsourcing, data science, David Attenborough, do what you love, Donald Trump, East Village, Elon Musk, financial independence, future of work, game design, gig economy, Hacker News, income inequality, independent contractor, information asymmetry, Jeff Bezos, job automation, law of one price, Lyft, Mark Zuckerberg, market clearing, minimum wage unemployment, new economy, opioid epidemic / opioid crisis, payday loans, post-work, profit maximization, QR code, race to the bottom, ride hailing / ride sharing, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, TaskRabbit, TechCrunch disrupt, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, union organizing, universal basic income, working-age population, Works Progress Administration, Y Combinator

Workers and US Government Cheated Out of Billions in Stolen Wages and Lost Tax Revenue. National Employment Law Project. February 19, 2014. 2   Newton, Casey. Uber Will Eventually Replace All of Its Drivers with Self-Driving Cars. The Verge. May 28, 2014. https://hbr.org/2017/07/lots-of-employees-get-misclassified-as-contractors-heres-why-it-matters. 3   Kessler, Sarah. A Timeline of When Self-Driving Vehicles Will Be on the Road, According to the People Making Them. Quartz. March 29, 2017. https://qz.com/943899/a-timeline-of-when-self-driving-cars-will-be-on-the-road-according-to-the-people-making-them/. 4   McKinsey Global Institute. What the Future of Work Will Mean for Jobs, Skills, and Wages.

“The reason Uber could be expensive is because you’re not just paying for the car—you’re paying for the other dude in the car,” Travis Kalanick said on stage at a conference in 2014. “When there’s no other dude in the car, the cost of taking an Uber anywhere becomes cheaper than owning a vehicle.”2 Uber started picking up passengers in its first tests of self-driving cars in Pittsburgh in 2016. Toyota, Nissan, General Motors, and Google have all estimated that automated cars will be on the road by 2020.3 In the United States, 1.8 million people make a living driving trucks; another 687,000 drive buses; another 1.4 million deliver packages; and another 305,000 work as taxi drivers and chauffeurs.

See also retirement security Perez, Thomas Pollack, Ethan Postmates (courier delivery service) poverty Prehype (startup accelerator) Quartz (business news website) QuickTrip racism Reich, Robert remote talent workers retirement security 401(k) contingent workers and decline in economics and Honest Dollar (independent worker retirement savings) Managed by Q and Peers.org (sharing-economy support) pensions portable benefit programs Social Security traditional employees and ride-hailing services A-Ryde income of drivers Juno platform cooperativism and tips and See also Lyft; Uber Rolf, David Salehi, Niloufar Samaschool. See also Davenport, Terrence; Foster, Gary; Green, Shakira; Logan, Kristen Samasource Schneider, Nathan Scholz, Trebor Schwartz, Emma (Managed by Q employee) Schwarzenegger, Arnold Screen Actors Guild self-driving cars Shea, Katie Shieber, Jon Shyp (shipping service) sick days Silberman, Six Snapchat So Lo Mo (social, local, mobile) Social Security SpaceX Sprig (restaurant delivery service) Starbucks Stern, Andy Stocksy (stock photo cooperative) subcontractors Arise and earnings Managed by Q and Silicon Valley and Sundararajan, Arun Sweet, Julie SXSW (South by Southwest) Taft-Hartley Act Take Wonolo (staffing agency) Target TaskRabbit (odd job marketplace) taxi industry EU regulation and New York Taxi Workers Alliance tips and Uber and US statistics See also Lyft; ride-hailing services; Uber TechCrunch (blog) TechCrunch Disrupt temp workers and agencies early history of earnings freelancers versus injury rate Kelly Services (“Kelly Girls”) Manpower permanent employees versus Silicon Valley and “temp worker” as a category US statistics work satisfaction Teran, Dan Tischen (labor marketplace) Ton, Zeynep Trader Joe’s trucking industry Trudeau, Kevin Trump, Donald Try Caviar (food delivery service) Turker Nation (online forum) Turkopticon Twitch (live streaming video platform) Twitter Uber (ride-hailing service) 180 days of change affiliate marketing program driver-led activism and protests Drivers’ Guild and FTC charges of exaggerated earnings funding growth of guaranteed fares history of independent contractor model lawsuits and legal issues “No shifts.


pages: 340 words: 92,904

Street Smart: The Rise of Cities and the Fall of Cars by Samuel I. Schwartz

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, active transport: walking or cycling, Affordable Care Act / Obamacare, American Society of Civil Engineers: Report Card, autonomous vehicles, bike sharing, car-free, City Beautiful movement, collaborative consumption, congestion charging, congestion pricing, crowdsourcing, desegregation, Donald Shoup, driverless car, Enrique Peñalosa, Ford Model T, Ford paid five dollars a day, Frederick Winslow Taylor, high-speed rail, if you build it, they will come, Induced demand, intermodal, invention of the wheel, lake wobegon effect, Lewis Mumford, Loma Prieta earthquake, longitudinal study, Lyft, Masdar, megacity, meta-analysis, moral hazard, Nate Silver, oil shock, parking minimums, Productivity paradox, Ralph Nader, rent control, ride hailing / ride sharing, Rosa Parks, scientific management, self-driving car, skinny streets, smart cities, smart grid, smart transportation, TED Talk, the built environment, the map is not the territory, transportation-network company, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban decay, urban planning, urban renewal, walkable city, Wall-E, white flight, white picket fence, Works Progress Administration, Yogi Berra, Zipcar

Even better: so long as more autonomy equals more safety, there is no point where the cost of the technology exceeds its added value. The most prominent player in the world of autonomous driving, though, isn’t Allstate or Geico. It isn’t Mercedes-Benz or Ford, or even Tesla. It’s Google. The Google Self-Driving Car is a project that the Internet giant saw as a natural outgrowth of its existing mapping software, particularly the technology from Google Street View, which stitches together panoramic photos of more than five million miles of roads in more than forty countries. Google’s versions of the driverless car—refitted Toyotas, Audis, and Lexuses—combine real-time access to all that data with a laser rangefinder that creates and refreshes three-dimensional maps of the area immediately around the car.

Transit riders use more than 20 percent more calories than drivers on a per-trip basis, which gives buses, subways, and streetcars a giant health advantage over cars. In fact, after five years of taking transit, the obese percentage of a given population—those with a Body Mass Index greater than 30—drops by more than half. And, as long as cities create plazas and piazzas where cars are banned but not people, self-driving cars offer no advantage, even without recognizing the mathematical impossibility of moving thousands of people through a city center in single-occupant vehicles. This doesn’t mean there isn’t a place for cars, with or without laser-rangefinders and GPS mapping. In less dense parts of cities, suburbs, and rural areas, all the safety aspects developed by automated cars make sense.

A group of simulated driverless cars negotiating a typical urban intersection at the same (slow) acceleration of a commuter train increases the time needed to cross the intersection by anywhere from 36 percent to more than 2,000 percent. If you want to browse the Internet while commuting, and still want to get to work on time, trains look like a much better option. There are other reasons to be suspicious of the brave new world represented by Google’s self-driving cars and others of similar ambition. On a purely personal level, I’m a little taken aback by the promise that autonomous vehicles will be able to collect you at your front door and deposit you at the front door of a supermarket or shopping mall—or even at your desk or workstation—without your feet ever touching the ground.


pages: 501 words: 114,888

The Future Is Faster Than You Think: How Converging Technologies Are Transforming Business, Industries, and Our Lives by Peter H. Diamandis, Steven Kotler

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, barriers to entry, Big Tech, biodiversity loss, bitcoin, blockchain, blood diamond, Boston Dynamics, Burning Man, call centre, cashless society, Charles Babbage, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, CRISPR, crowdsourcing, cryptocurrency, data science, Dean Kamen, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital twin, disruptive innovation, Donald Shoup, driverless car, Easter island, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, fake news, food miles, Ford Model T, fulfillment center, game design, Geoffrey West, Santa Fe Institute, gig economy, gigafactory, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, impact investing, indoor plumbing, industrial robot, informal economy, initial coin offering, intentional community, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, Kiva Systems, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, Masayoshi Son, mass immigration, megacity, meta-analysis, microbiome, microdosing, mobile money, multiplanetary species, Narrative Science, natural language processing, Neal Stephenson, Neil Armstrong, Network effects, new economy, New Urbanism, Nick Bostrom, Oculus Rift, One Laptop per Child (OLPC), out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, planned obsolescence, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, robo advisor, Satoshi Nakamoto, Second Machine Age, self-driving car, Sidewalk Labs, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, SoftBank, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, tech billionaire, technoutopianism, TED Talk, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Vision Fund, VTOL, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

Right now, the system is an array of cameras and GPS sensors, but soon models will include microphones, speakers, and the ability—via AI-driven natural language processing—to communicate with customers. Since 2016, Starship has carried out fifty thousand deliveries in over one hundred cities in twenty countries. Along similar lines, Nuro, the company cofounded by Jiajun Zhu, one of the engineers who helped Google develop their self-driving car, has a miniature self-driving car of their own. Half the size of a sedan, the Nuro looks like a toaster on wheels, except with a mission. This toaster has been designed to carry cargo—about twelve bags of groceries (version 2.0 will carry twenty)—which it’s been doing for select Kroger stores since 2018 (in 2019, Domino’s also partnered with Nuro).

Next, Holden went to another startup, Groupon—which is hard to remember as a disruptive enterprise today, but was then part of the first wave of “power to the people” internet companies. From there, he went to Uber, where, despite the turmoil the company experienced, Holden strung together a series of unlikely wins: UberPool, Uber Eats, and, most recently, Uber’s self-driving car program. So when he proposed an even zanier product line—that Uber take to the skies—it wasn’t all that surprising that the company’s leadership took him seriously. And for good reason. The theme of the second annual Uber Elevate wasn’t actually flying cars. The cars have already arrived. Instead, the theme of the second Uber Elevate was the path to scale.

Traditional automotive players like BMW, Mercedes and Toyota were competing for this emerging market with tech giants like Apple, Google (via Waymo), Uber, and Tesla, trying out different designs, gathering data, and honing neural networks. Out of these, Waymo seems well positioned for early market dominance. Formerly Google’s self-driving car project, Waymo began its work in 2009 by hiring Sebastian Thrun, the Stanford professor who won the DARPA Grand Challenge. Thrun helped develop the AI system that would become the brains behind Waymo’s self-driving fleet. About ten years later, in March 2018, Waymo purchased that fleet, buying twenty thousand sporty, self-driving Jaguars for its forthcoming ride-hailing service.


pages: 237 words: 74,109

Uncanny Valley: A Memoir by Anna Wiener

autonomous vehicles, back-to-the-land, basic income, behavioural economics, Blitzscaling, blockchain, blood diamond, Burning Man, call centre, charter city, cloud computing, cognitive bias, cognitive dissonance, commoditize, crowdsourcing, cryptocurrency, dark triade / dark tetrad, data science, digital divide, digital nomad, digital rights, end-to-end encryption, Extropian, functional programming, future of work, gentrification, Golden Gate Park, growth hacking, guns versus butter model, housing crisis, Jane Jacobs, job automation, knowledge worker, Lean Startup, means of production, medical residency, microaggression, microapartment, microdosing, new economy, New Urbanism, Overton Window, passive income, Plato's cave, pull request, rent control, ride hailing / ride sharing, San Francisco homelessness, Sand Hill Road, self-driving car, sharing economy, Shenzhen special economic zone , side project, Silicon Valley, Silicon Valley startup, Social Justice Warrior, social web, South of Market, San Francisco, special economic zone, subprime mortgage crisis, systems thinking, tech bro, tech worker, technoutopianism, telepresence, telepresence robot, union organizing, universal basic income, unpaid internship, urban planning, urban renewal, warehouse robotics, women in the workforce, work culture , Y2K, young professional

A social network everyone said they hated but no one could stop logging in to went public at a valuation of one-hundred-odd billion dollars, its grinning founder ringing the opening bell over video chat, a death knell for affordable rent in San Francisco. Two hundred million people signed on to a microblogging platform that helped them feel close to celebrities and other strangers they’d loathe in real life. Artificial intelligence and virtual reality were coming into vogue, again. Self-driving cars were considered inevitable. Everything was moving to mobile. Everything was up in the cloud. The cloud was an unmarked data center in the middle of Texas or Cork or Bavaria, but nobody cared. Everyone trusted it anyway. It was a year of new optimism: the optimism of no hurdles, no limits, no bad ideas.

Instead, the men discussed work projects using secret code names. They discussed their graduate research. One had spent seven years trying to teach robots to tie different kinds of knots, like Boy Scouts. I asked if he was studying robotics at one of the universities in the Bay Area. No, he said, looking me up and down—he was a professor. Talk turned to self-driving cars. One of the engineers mentioned a recent Take Your Child to Work Day, where the autonomous-car unit had asked visiting children to jump and dance and roll around in front of the sensors. The technology was world-class, but it still needed to train on nonadults. It was an incredibly exciting moment for transportation, he said: the hurdles they faced weren’t technical, but cultural.

I wanted attention, some acknowledgment. I wanted to make sure everyone knew I wasn’t just some engineer’s girlfriend who stood around at parties waiting for him to finish geeking out—though of course that’s exactly what I was doing. I was skeptical, I told the men. The media hype seemed more than overblown: self-driving cars were part of a future vision that seemed not just unlikely, but beyond fantasy. Hadn’t we just established that the cars didn’t even know how to identify children? The group turned toward me. The scout-leader professor looked amused. “What did you say you do?” one of the men asked. I explained that I worked at a mobile analytics company, hoping they would assume I was an engineer.


pages: 269 words: 70,543

Tech Titans of China: How China's Tech Sector Is Challenging the World by Innovating Faster, Working Harder, and Going Global by Rebecca Fannin

"World Economic Forum" Davos, Adam Neumann (WeWork), Airbnb, augmented reality, autonomous vehicles, Benchmark Capital, Big Tech, bike sharing, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, clean tech, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, data science, deep learning, Deng Xiaoping, Didi Chuxing, digital map, disruptive innovation, Donald Trump, El Camino Real, electricity market, Elon Musk, fake news, family office, fear of failure, fulfillment center, glass ceiling, global supply chain, Great Leap Forward, income inequality, industrial robot, information security, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, Mary Meeker, megacity, Menlo Park, money market fund, Network effects, new economy, peer-to-peer lending, personalized medicine, Peter Thiel, QR code, RFID, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart transportation, Snapchat, social graph, SoftBank, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, stock buybacks, supply-chain management, tech billionaire, TechCrunch disrupt, TikTok, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, Vision Fund, warehouse automation, WeWork, winner-take-all economy, Y Combinator, young professional

—Gary Rieschel Founding managing partner, Qiming Venture Partners Rieschel knows full well the key factors driving China’s push to win the global tech race, from working and living in Shanghai for eight years on the front lines of China’s techno-charged environment. China’s workaholic, determined entrepreneurs are quick to get new technologies commercialized. Chinese people voraciously embrace the latest apps, games, payment services, social media, and online shopping. Venture capitalists fund cutting-edge startups in artificial intelligence, self-driving cars, electric vehicle batteries, biotech, robotics, drones, and augmented and virtual reality. China’s huge digital markets—the world’s largest for the internet, smartphones, e-commerce, and mobile payments—are spurring advancements that go mainstream quickly. Not least of all, the Chinese government’s protectionism and concerted nationalistic policies propel China to become a world-leading innovative country.

Like Mark Zuckerberg, Jeff Bezos, and Larry Page, who confront a tech backlash and constant challenges to their clout, China’s leaders face daunting issues that could weaken them: privacy concerns, counterfeit charges, restrictions on their most addictive products, and competitive threats. Baidu faces a possible reentry of Google to the Middle Kingdom some 10 years after googling didn’t knock out China’s search leader. Baidu’s bid to own the future for AI with self-driving cars and facial recognition for payments is uncertain after Li lost two experts in a row who were leading Baidu’s AI charge while rivals chip into the sector: Alibaba in smart-city traffic management, Tencent in medical imaging and diagnostic tools, and startups SenseTime and Face++ with AI-enhanced face-matching technologies for IDs and public security.

The New York City Police Department is reportedly monitoring citizens using cameras and facial recognition software developed in China, from SenseTime partner Hikvision.1 In the United States, tech giants Google, Microsoft, Amazon, Facebook, and IBM dominate AI for many futuristic and practical uses. Google self-driving cars are being tested on California’s Highway 101; Facebook spins out posts based on deep learning of content preferences; Amazon’s Alexa powers lights, TVs, and speakers by voice activation; and Microsoft’s Azure relies on cognitive computing for speech and language applications, while IBM Watson’s AI-based computer system increases productivity and improves customer service for call centers, production lines, and warehouses.


pages: 372 words: 101,174

How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, anesthesia awareness, anthropic principle, brain emulation, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer age, Computing Machinery and Intelligence, Dean Kamen, discovery of DNA, double helix, driverless car, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Hans Moravec, Isaac Newton, iterative process, Jacquard loom, Jeff Hawkins, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Nick Bostrom, Norbert Wiener, optical character recognition, PalmPilot, pattern recognition, Peter Thiel, Ralph Waldo Emerson, random walk, Ray Kurzweil, reversible computing, selective serotonin reuptake inhibitor (SSRI), self-driving car, speech recognition, Steven Pinker, strong AI, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Wall-E, Watson beat the top human players on Jeopardy!, X Prize

Calculations per second per (constant) thousand dollars of different computing devices.10 Floating-point operations per second of different supercomputers.11 Transistors per chip for different Intel processors.12 Bits per dollar for dynamic random access memory chips.13 Bits per dollar for random access memory chips.14 The average price per transistor in dollars.15 The total number of bits of random access memory shipped each year.16 Bits per dollar (in constant 2000 dollars) for magnetic data storage.17 Even the predictions that were “wrong” were not all wrong. For example, I judged my prediction that we would have self-driving cars to be wrong, even though Google has demonstrated self-driving cars, and even though in October 2010 four driverless electric vans successfully concluded a 13,000-kilometer test drive from Italy to China.18 Experts in the field currently predict that these technologies will be routinely available to consumers by the end of this decade.

People are already talking to their phones in natural language (via Siri, for example, which was also contributed to by Nuance). These natural-language assistants will rapidly become more intelligent as they utilize more of the Watson-like methods and as Watson itself continues to improve. The Google self-driving cars have logged 200,000 miles in the busy cities and towns of California (a figure that will undoubtedly be much higher by the time this book hits the real and virtual shelves). There are many other examples of artificial intelligence in today’s world, and a great deal more on the horizon. As further examples of the LOAR, the spatial resolution of brain scanning and the amount of data we are gathering on the brain are doubling every year.

If all the AI systems decided to go on strike tomorrow, our civilization would be crippled: We couldn’t get money from our bank, and indeed, our money would disappear; communication, transportation, and manufacturing would all grind to a halt. Fortunately, our intelligent machines are not yet intelligent enough to organize such a conspiracy. What is new in AI today is the viscerally impressive nature of publicly available examples. For example, consider Google’s self-driving cars (which as of this writing have gone over 200,000 miles in cities and towns), a technology that will lead to significantly fewer crashes, increased capacity of roads, alleviating the requirement of humans to perform the chore of driving, and many other benefits. Driverless cars are actually already legal to operate on public roads in Nevada with some restrictions, although widespread usage by the public throughout the world is not expected until late in this decade.


pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

"World Economic Forum" Davos, algorithmic bias, Amazon Web Services, Andrew Keen, augmented reality, autonomous vehicles, barriers to entry, Bartolomé de las Casas, behavioural economics, Berlin Wall, Big Tech, bitcoin, blockchain, blue-collar work, book scanning, Broken windows theory, California gold rush, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, choice architecture, citizen journalism, Citizen Lab, classic study, cloud computing, collective bargaining, Computer Numeric Control, computer vision, connected car, context collapse, corporate governance, corporate personhood, creative destruction, cryptocurrency, data science, deep learning, digital capitalism, disinformation, dogs of the Dow, don't be evil, Donald Trump, Dr. Strangelove, driverless car, Easter island, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, facts on the ground, fake news, Ford Model T, Ford paid five dollars a day, future of work, game design, gamification, Google Earth, Google Glasses, Google X / Alphabet X, Herman Kahn, hive mind, Ian Bogost, impulse control, income inequality, information security, Internet of things, invention of the printing press, invisible hand, Jean Tirole, job automation, Johann Wolfgang von Goethe, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kevin Roose, knowledge economy, Lewis Mumford, linked data, longitudinal study, low skilled workers, Mark Zuckerberg, market bubble, means of production, multi-sided market, Naomi Klein, natural language processing, Network effects, new economy, Occupy movement, off grid, off-the-grid, PageRank, Panopticon Jeremy Bentham, pattern recognition, Paul Buchheit, performance metric, Philip Mirowski, precision agriculture, price mechanism, profit maximization, profit motive, public intellectual, recommendation engine, refrigerator car, RFID, Richard Thaler, ride hailing / ride sharing, Robert Bork, Robert Mercer, Salesforce, Second Machine Age, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, slashdot, smart cities, Snapchat, social contagion, social distancing, social graph, social web, software as a service, speech recognition, statistical model, Steve Bannon, Steve Jobs, Steven Levy, structural adjustment programs, surveillance capitalism, technological determinism, TED Talk, The Future of Employment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, two-sided market, union organizing, vertical integration, Watson beat the top human players on Jeopardy!, winner-take-all economy, Wolfgang Streeck, work culture , Yochai Benkler, you are the product

AI evolves differently. When one of the self-driving cars makes an error, all of the self-driving cars learn from it. In fact, new self-driving cars are “born” with the complete skill set of their ancestors and peers. So collectively, these cars can learn faster than people. With this insight, in a short time self-driving cars safely blended onto our roads alongside human drivers, as they kept learning from each other’s mistakes.… Sophisticated AI-powered tools will empower us to better learn from the experiences of others.… The lesson with self-driving cars is that we can learn and do more collectively.33 This is a succinct but extraordinary statement of the machine template for the social relations of an instrumentarian society.

Google was also among the wealthiest of all registered lobbyists in the EU, second only to a lobbying group that represents a confederation of European corporations.108 The firm also learned to engineer sophisticated lobbying efforts at the state level, primarily geared to fight back any proposed legislation that would augment privacy and curtail behavioral surplus operations. For example, Google won the right to put its self-driving cars on the road—anticipated as important supply chains—after enlisting Obama officials to lobby state regulators for key legislation.109 Both Google and Facebook currently lead aggressive state-level lobbying campaigns aimed at repelling or weakening statutes to regulate biometric data and protect privacy.

Indeed, under the direction of surveillance capitalism the global reach of computer mediation is repurposed as an extraction architecture. This process originated online but has spread to the real world as well, a fact that we will examine more closely in Part II. If Google is a search company, why is it investing in smart-home devices, wearables, and self-driving cars? If Facebook is a social network, why is it developing drones and augmented reality? This diversity sometimes confounds observers but is generally applauded as visionary investment: far-out bets on the future. In fact, activities that appear to be varied and even scattershot across a random selection of industries and projects are actually all the same activity guided by the same aim: behavioral surplus capture.


When Computers Can Think: The Artificial Intelligence Singularity by Anthony Berglas, William Black, Samantha Thalind, Max Scratchmann, Michelle Estes

3D printing, Abraham Maslow, AI winter, air gap, anthropic principle, artificial general intelligence, Asilomar, augmented reality, Automated Insights, autonomous vehicles, availability heuristic, backpropagation, blue-collar work, Boston Dynamics, brain emulation, call centre, cognitive bias, combinatorial explosion, computer vision, Computing Machinery and Intelligence, create, read, update, delete, cuban missile crisis, David Attenborough, DeepMind, disinformation, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Ernest Rutherford, factory automation, feminist movement, finite state, Flynn Effect, friendly AI, general-purpose programming language, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, industrial robot, Isaac Newton, job automation, John von Neumann, Law of Accelerating Returns, license plate recognition, Mahatma Gandhi, mandelbrot fractal, natural language processing, Nick Bostrom, Parkinson's law, patent troll, patient HM, pattern recognition, phenotype, ransomware, Ray Kurzweil, Recombinant DNA, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, sorting algorithm, speech recognition, statistical model, stem cell, Stephen Hawking, Stuxnet, superintelligent machines, technological singularity, Thomas Malthus, Turing machine, Turing test, uranium enrichment, Von Neumann architecture, Watson beat the top human players on Jeopardy!, wikimedia commons, zero day

There is no need nor reason to give most of them a humanoid shape, although humanoid robots also exist.) Mine sites can be controlled fairly tightly, but robots are now working in much more natural and unstructured environments. The first of these technologies that is likely to have widespread impact is self driving cars and trucks. Google self driving car. News www.mirror.co.uk The famous Google driverless car can negotiate urban traffic autonomously, and is purported to have covered 500,000 kilometres with only one accident caused by another car running into it from behind. What that really means is unclear because the cars also have drivers that could take over if the computer was about to cause an accident.

Computers note your licence plate when you drive down the road, and much of your day to day communication is via computer networks that are carefully monitored. The computers that do this are locked away in secure data centres so you personally cannot turn them any of them off. More directly, robots in many shapes and sizes will soon be leaving the factory. Initially, there will be self driving cars and automated cleaners, fruit pickers, and systems for maintaining racks of computers in data centres. Computers already fly military drones and the military is investing heavily in semi-autonomous robot soldiers. By the time computers become truly intelligent they will be in a good position to directly control the physical world.

Robots leaving the factory The most significant change that is likely to be seen over the next ten years is the practical application of robots that are working outside of carefully structured factory environments. The earliest have been the automated vacuum cleaners, the better of which actively map out the rooms that are cleaning. Probably the most significant in the short term will be autonomous, self-driving cars. Huge trucks have been autonomously driving around mine sites for several years. Mercedes already ships driver assist technology that senses other cars, while BMW expects to move their completely automatic freeway driving system into production by 2020. The Google driverless car has received considerable attention, but all vehicle manufactures have invested in the technology.


pages: 97 words: 31,550

Money: Vintage Minis by Yuval Noah Harari

23andMe, agricultural Revolution, algorithmic trading, AlphaGo, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, DeepMind, European colonialism, Flash crash, Ford Model T, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, low interest rates, Nick Bostrom, pattern recognition, peak-end rule, Ponzi scheme, self-driving car, Suez canal 1869, telemarketer, The future is already here, The Future of Employment, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Watson beat the top human players on Jeopardy!, zero-sum game

And it is sobering to realise that, at least for armies and corporations, the answer is straightforward: intelligence is mandatory but consciousness is optional. Armies and corporations cannot function without intelligent agents, but they don’t need consciousness and subjective experiences. The conscious experiences of a flesh-and-blood taxi driver are infinitely richer than those of a self-driving car, which feels absolutely nothing. The taxi driver can enjoy music while navigating the busy streets of Seoul. His mind may expand in awe as he looks up at the stars and contemplates the mysteries of the universe. His eyes may fill with tears of joy when he sees his baby girl taking her very first step.

Taxi drivers are highly likely to go the way of horses. Indeed, if we forbid humans to drive not only taxis but vehicles altogether, and give computer algorithms a monopoly over traffic, we can then connect all vehicles to a single network, thereby rendering car accidents far less likely. In August 2015 one of Google’s experimental self-driving cars had an accident. As it approached a crossing and detected pedestrians wishing to cross, it applied its brakes. A moment later it was hit from behind by a sedan whose careless human driver was perhaps contemplating the mysteries of the universe instead of watching the road. This could not have happened if both vehicles had been guided by interlinked computers.

You ask a question, the oracle replies, but it is up to you to make a decision. If the oracle wins your trust, however, the next logical step is to turn it into an agent. You give the algorithm only a final aim, and it acts to realise that aim without your supervision. In the case of Waze, this may happen when you connect Waze to a self-driving car, and tell Waze ‘take the fastest route home’ or ‘take the most scenic route’ or ‘take the route which will result in the minimum amount of pollution’. You call the shots, but leave it to Waze to execute your commands. Finally, Waze might become sovereign. Having so much power in its hands, and knowing far more than you, it may start manipulating you and the other drivers, shaping your desires and making your decisions for you.


pages: 286 words: 87,401

Blitzscaling: The Lightning-Fast Path to Building Massively Valuable Companies by Reid Hoffman, Chris Yeh

"Susan Fowler" uber, activist fund / activist shareholder / activist investor, adjacent possible, Airbnb, Amazon Web Services, Andy Rubin, autonomous vehicles, Benchmark Capital, bitcoin, Blitzscaling, blockchain, Bob Noyce, business intelligence, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, CRISPR, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, database schema, DeepMind, Didi Chuxing, discounted cash flows, Elon Musk, fake news, Firefox, Ford Model T, forensic accounting, fulfillment center, Future Shock, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, Greyball, growth hacking, high-speed rail, hockey-stick growth, hydraulic fracturing, Hyperloop, initial coin offering, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, margin call, Mark Zuckerberg, Max Levchin, minimum viable product, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, PalmPilot, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, Quicken Loans, recommendation engine, ride hailing / ride sharing, Salesforce, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, SoftBank, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, synthetic biology, Tesla Model S, thinkpad, three-martini lunch, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, work culture , Y Combinator, yellow journalism

Google has since used the financial power of its gross margins to place big bets that other companies might shy away from, such as investing in Android and Chrome, two products that were going up against dominant competitors (Apple’s iOS in mobile phone software and Microsoft and Firefox in Web browsers). Google has also used its margins to fund radical experiments like X (formerly Google X) and Waymo (self-driving cars). These bets may or may not pay off, but even if they fail, Google’s margins give it the ability to recover quickly and keep going. Network Effects Google has leveraged network effects quite a bit in its major business lines, though not, ironically enough, in its core search product!

For example, we suspect that the market for food delivery from existing restaurants—a pure commodity business—is unlikely to offer any lasting competitive advantages that would justify an expensive blitzscaling campaign. LEARNING CURVE Another way to use blitzscaling to create a lasting competitive advantage is to be the first to climb a steep learning curve. Some opportunities, such as self-driving cars, require you to solve hard, complex problems. The more rapidly you scale, the more data you have to drive learning (or train machine learning), which improves your product, making it easier to scale further in the market while your competitors who have just begun to learn lag far behind. Netflix is the leader in streaming video entertainment, but it only achieved this status by being willing to climb a series of steep learning curves.

ADVANTAGE #3: LONGEVITY While the ability to undertake multiple attempts at blitzscaling is an advantage, so is the ability to be patient with a single attempt. Large companies can (if they have patient shareholders) have longer time horizons than start-ups, which need to show immediate results to continue raising money. Google often plays this long game with technologies ranging from self-driving cars to a cure for aging. Facebook is also playing the long game with Oculus Rift and VR. The key is knowing when to scale up. Microsoft tried to scale smartphones too early with Windows CE; as it turns out, the modern smartphone only became practical once Moore’s Law made mobile CPUs powerful enough, and Apple combined software with capacitive touch screens, Corning’s damage-resistant Gorilla Glass, and high-volume Chinese manufacturing.


pages: 247 words: 81,135

The Great Fragmentation: And Why the Future of All Business Is Small by Steve Sammartino

3D printing, additive manufacturing, Airbnb, augmented reality, barriers to entry, behavioural economics, Bill Gates: Altair 8800, bitcoin, BRICs, Buckminster Fuller, citizen journalism, collaborative consumption, cryptocurrency, data science, David Heinemeier Hansson, deep learning, disruptive innovation, driverless car, Dunbar number, Elon Musk, fiat currency, Frederick Winslow Taylor, game design, gamification, Google X / Alphabet X, haute couture, helicopter parent, hype cycle, illegal immigration, index fund, Jeff Bezos, jimmy wales, Kickstarter, knowledge economy, Law of Accelerating Returns, lifelogging, market design, Mary Meeker, Metcalfe's law, Minecraft, minimum viable product, Network effects, new economy, peer-to-peer, planned obsolescence, post scarcity, prediction markets, pre–internet, profit motive, race to the bottom, random walk, Ray Kurzweil, recommendation engine, remote working, RFID, Rubik’s Cube, scientific management, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, software is eating the world, Steve Jobs, subscription business, survivorship bias, The Home Computer Revolution, the long tail, too big to fail, US Airways Flight 1549, vertical integration, web application, zero-sum game

Who knows, the crowd may come up with significant safety enhancements that use sensors to wake sleepy drivers. glass cockpit: an aircraft cockpit that features electronic (digital) instrument displays, typically large LCD screens (Wikipedia) Moreover, cars are very soon going to evolve into lounge rooms once self-driving cars become the norm. The technology for safe self-driving cars already exists. Millions of kilometres have been driven without incident. The cost of the technology that makes it possible is in rapid freefall. It’s hard to predict when autonomous driving cars will be available to the public, and estimates range from a few years to up to 20 years.1 Google, a leading developer of the technology, claims its technology will be ready to commercialise with major auto manufacturers by the year 2018.

Given we’re talking about years, rather than decades, car companies should probably prepare for the inevitable now. A world of entirely new revenue streams awaits the auto industry if they follow the playbook already evidenced in both media evolution and personal computing technology. All they need is to have the courage to let other people get involved. The technology for safe self-driving cars already exists. From products to platforms Being able to thrive going forward is about removing the finality that comes with the launch mentality: not assuming that a product is finished when we deliver it to the market. Brands that survive the current reconfiguration of economics will understand that a product or service is a continuum of development, a continuum that people take from the company and invent the next stages of.

As car ownership widens and we start to seal the roads, we make highways, roundabouts and traffic lights. We invent maps for directions as the number and complexity of roads increases. We use GPS devices and live traffic reports (via the web) for more efficient movement on the roads. And our next stack will be the self-driving car, which will do it all for us. Each layer is needed before the subsequent layer can make any sense or be needed. While the industrial revolution created a machine-based layer of technology in business and lifestyle, we’re now entering a stage where a digital layer is being added. Cheap, disposable technology will give us a new layer that augments both how we live and how we do business.


pages: 291 words: 80,068

Framers: Human Advantage in an Age of Technology and Turmoil by Kenneth Cukier, Viktor Mayer-Schönberger, Francis de Véricourt

Albert Einstein, Andrew Wiles, Apollo 11, autonomous vehicles, Ben Bernanke: helicopter money, Berlin Wall, bitcoin, Black Lives Matter, blockchain, Blue Ocean Strategy, circular economy, Claude Shannon: information theory, cognitive dissonance, cognitive load, contact tracing, coronavirus, correlation does not imply causation, COVID-19, credit crunch, CRISPR, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, deep learning, DeepMind, defund the police, Demis Hassabis, discovery of DNA, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, en.wikipedia.org, fake news, fiat currency, framing effect, Francis Fukuyama: the end of history, Frank Gehry, game design, George Floyd, George Gilder, global pandemic, global village, Gödel, Escher, Bach, Higgs boson, Ignaz Semmelweis: hand washing, informal economy, Isaac Newton, Jaron Lanier, Jeff Bezos, job-hopping, knowledge economy, Large Hadron Collider, lockdown, Louis Pasteur, Mark Zuckerberg, Mercator projection, meta-analysis, microaggression, Mustafa Suleyman, Neil Armstrong, nudge unit, OpenAI, packet switching, pattern recognition, Peter Thiel, public intellectual, quantitative easing, Ray Kurzweil, Richard Florida, Schrödinger's Cat, scientific management, self-driving car, Silicon Valley, Steve Jobs, Steven Pinker, TED Talk, The Structural Transformation of the Public Sphere, Thomas Kuhn: the structure of scientific revolutions, TikTok, Tim Cook: Apple, too big to fail, transaction costs, Tyler Cowen

The car braked hard and decelerated quickly—but not before it slammed into the boundary and was poised to go off the road. In the end, it came to a rest . . . on a thin, purple line of pixels on the screen. The minor accident was a digital simulation. It happened only in the computer servers of Waymo, Google’s self-driving-car company. The simulation is designed to overcome a serious shortcoming of all autonomous vehicles: a lack of data on rare events because they are, well, rare. For well over a decade, the industry has been collecting real-world road data to train the AI models that power the self-driving systems.

See also: Sam Greenspan, “11 Most Absurd Inventions Created by MacGyver,” 11 Points, March 18, 2018, https://11points.com/11-absurd-inventions-created-macgyver/. Our sense of predictability and control: Keith D. Markman et al., “The Impact of Perceived Control on the Imagination of Better and Worse Possible Worlds,” Personality and Social Psychology Bulletin 21, no. 6 (June 1995): 588–95. Introductory example of self-driving cars: Videos from research in an Arxiv paper at: Mayank Bansal, Alex Krizhevsky, and Abhijit Ogale, “ChauffeurNet: Learning to Drive by Imitating the Best and Synthesizing the Worst,” Waymo, accessed November 2, 2020, https://sites.google.com/view/waymo-learn-to-drive. The scenario was “recovering from a trajectory perturbation (M2 = M1 + environment losses).”

The scenario was “recovering from a trajectory perturbation (M2 = M1 + environment losses).” The paper itself is at “ChauffeurNet,” Waymo, Arxiv, December 7, 2018, https://arxiv.org/abs/1812.03079. On Carcraft: An article with superb reporting is: Alexis C. Madrigal, “Inside Waymo’s Secret World for Training Self-Driving Cars,” Atlantic, August 23, 2017, https://www.theatlantic.com/technology/archive/2017/08/inside-waymos-secret-testing-and-simulation-facilities/537648. Data on the amount of simulation driving: “The Virtual World Helps Waymo Learn Advanced Real-World Driving Skills,” Let’s Talk Self-Driving, accessed November 2, 2020, https://letstalkselfdriving.com/safety/simulation.html.


pages: 253 words: 84,238

A Thousand Brains: A New Theory of Intelligence by Jeff Hawkins

AI winter, Albert Einstein, artificial general intelligence, carbon-based life, clean water, cloud computing, deep learning, different worldview, discovery of DNA, Doomsday Clock, double helix, en.wikipedia.org, estate planning, Geoffrey Hinton, Jeff Hawkins, PalmPilot, Search for Extraterrestrial Intelligence, self-driving car, sensor fusion, Silicon Valley, superintelligent machines, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Turing machine, Turing test

We, the designers of intelligent machines, have to go out of our way to design in motivations. Why would we design a machine that accepts our first request but ignores all others after that? This is as likely as designing a self-driving car that, once you tell it where you want to go, ignores any further requests to stop or go someplace else. Further, it assumes that we designed the car so that it locks all the doors and disconnects the steering wheel, brake pedal, power button, etc. Note that a self-driving car isn’t going to develop goals on its own. Of course, someone could design a car that pursues its own goals and ignores the requests of humans. Such a car might cause harm.

We learn thousands of skills in our lifetime, and although we may not be the best at any one of these skills, we are flexible in what we can learn. Deep learning AI systems exhibit almost no flexibility. A Go-playing computer may play the game better than any human, but it can’t do anything else. A self-driving car may be a safer driver than any human, but it can’t play Go or fix a flat tire. The long-term goal of AI research is to create machines that exhibit human-like intelligence—machines that can rapidly learn new tasks, see analogies between different tasks, and flexibly solve new problems. This goal is called “artificial general intelligence,” or AGI, to distinguish it from today’s limited AI.

The chess-playing computer uses this reference frame to represent the location of each piece, to represent legal chess moves, and to plan movements. A chessboard reference frame is inherently two-dimensional and has only sixty-four locations. This is fine for chess, but it is useless for learning the structure of staplers or the behaviors of cats. Self-driving cars typically have multiple reference frames. One is GPS, the satellite-based system that can locate the car anywhere on Earth. Using a GPS reference frame, a car can learn where roads, intersections, and buildings are. GPS is a more general-purpose reference frame than a chess board, but it is anchored to Earth, and therefore can’t represent the structure or shape of things that move relative to Earth, such as a kite or a bicycle.


pages: 344 words: 104,077

Superminds: The Surprising Power of People and Computers Thinking Together by Thomas W. Malone

Abraham Maslow, agricultural Revolution, Airbnb, Albert Einstein, Alvin Toffler, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, Asperger Syndrome, Baxter: Rethink Robotics, bitcoin, blockchain, Boeing 747, business process, call centre, carbon tax, clean water, Computing Machinery and Intelligence, creative destruction, crowdsourcing, data science, deep learning, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental economics, Exxon Valdez, Ford Model T, future of work, Future Shock, Galaxy Zoo, Garrett Hardin, gig economy, happiness index / gross national happiness, independent contractor, industrial robot, Internet of things, invention of the telegraph, inventory management, invisible hand, Jeff Rulifson, jimmy wales, job automation, John Markoff, Joi Ito, Joseph Schumpeter, Kenneth Arrow, knowledge worker, longitudinal study, Lyft, machine translation, Marshall McLuhan, Nick Bostrom, Occupy movement, Pareto efficiency, pattern recognition, prediction markets, price mechanism, radical decentralization, Ray Kurzweil, Rodney Brooks, Ronald Coase, search costs, Second Machine Age, self-driving car, Silicon Valley, slashdot, social intelligence, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, technological singularity, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, Tragedy of the Commons, transaction costs, Travis Kalanick, Uber for X, uber lyft, Vernor Vinge, Vilfredo Pareto, Watson beat the top human players on Jeopardy!

But we also need to understand how new electronic information technologies will profoundly transform these superminds. Many people today believe that the most important new kind of information technology will be artificial intelligence (AI), embodied in robots and other software programs that do smart things only humans could do before. It’s certainly true that machines like Amazon’s Alexa and Google’s self-driving cars are getting smarter, and it’s possible that someday, in the future, we will have artificially intelligent machines that are as smart and broadly adaptable as humans. But most experts estimate that, if this happens, it probably won’t be for at least several decades and quite possibly much longer.

As we move further along the continuum toward greater machine control, Google Assistant and Amazon’s Alexa are examples of automated systems that strive to be assistants rather than just tools, especially when they do things like volunteering information you never asked for—such as reminding you that you need to leave for the airport now to make your flight. Similarly, a fully self-driving car would be a clear example of an assistant. Like a human taxi (or Uber) driver, this automated assistant will take a great deal of initiative to navigate through traffic to the destination you specify. Another example of an automated assistant is the software used by the online clothing retailer Stitch Fix to help its human stylists recommend items to customers.3 Stitch Fix customers first fill out detailed questionnaires about their style, size, and price preferences.

Peers Some of the most intriguing uses of computers will involve roles where the machines operate as peers of the humans more than as assistants or tools, even in cases where there isn’t much actual artificial intelligence being used. In many cases, this will happen because a program that acts as an assistant for one human acts as a peer of another. For example, if you are riding in a self-driving car, and I am driving myself on the same road, then your driving assistant is my driving peer. If you are a stock trader, you may already be transacting with someone else’s automated program trading system without even knowing it. And if you are bidding in an eBay auction, you may be competing with someone else who uses an automated “sniping” assistant that is programmed to outbid you in the last few seconds of an auction.


pages: 588 words: 131,025

The Patient Will See You Now: The Future of Medicine Is in Your Hands by Eric Topol

23andMe, 3D printing, Affordable Care Act / Obamacare, Anne Wojcicki, Atul Gawande, augmented reality, Big Tech, bioinformatics, call centre, Clayton Christensen, clean water, cloud computing, commoditize, computer vision, conceptual framework, connected car, correlation does not imply causation, creative destruction, crowdsourcing, dark matter, data acquisition, data science, deep learning, digital divide, disintermediation, disruptive innovation, don't be evil, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Firefox, gamification, global village, Google Glasses, Google X / Alphabet X, Ignaz Semmelweis: hand washing, information asymmetry, interchangeable parts, Internet of things, Isaac Newton, it's over 9,000, job automation, Julian Assange, Kevin Kelly, license plate recognition, lifelogging, Lyft, Mark Zuckerberg, Marshall McLuhan, meta-analysis, microbiome, Nate Silver, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, personalized medicine, phenotype, placebo effect, quantum cryptography, RAND corporation, randomized controlled trial, Salesforce, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Snapchat, social graph, speech recognition, stealth mode startup, Steve Jobs, synthetic biology, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, traumatic brain injury, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, WikiLeaks, X Prize

Sullivan, “Google’s New Driverless Car Has No Brakes or Steering Wheel,” Washington Post, May 28, 2014, http://www.washingtonpost.com/news/morning-mix/wp/2014/05/28/googles-new-driverless-car-has-no-brakes-or-steering-wheel//?print=1. 29. R. Lawler, “Google X Built a Fully Self-Driving Car from Scratch, Sans Steering Wheel and Pedals,” TechCrunch, May 27, 2014, http://techcrunch.com/2014/05/27/google-x-introduces-a-fully-self-driving-car-sans-steering-wheel-and-pedals/. 30. L. Gannes, “Google’s New Self-Driving Car Ditches the Steering Wheel,” Recode, May 27, 2014, http://recode.net/2014/05/27/googles-new-self-driving-car-ditches-the-steering-wheel/. 31. R. W. Lucky, “The Drive for Driverless Cars,” IEEE Spectrum, June 26, 2014, http://spectrum.ieee.org/computing/embedded-systems/the-drive-for-driverless-cars. 32.

Pai, “In-Depth: Providers’ Inevitable Acceptance of Patient Generated Health Data,” MobiHealthNews, March 21, 2014, http://mobihealthnews.com/31268/in-depth-providers-inevitable-acceptance-of-patient-generated-health-data/. 25. J. Markoff, “A Trip in a Self-Driving Car Now Seems Routine,” New York Times, May 13, 2014, http://bits.blogs.nytimes.com/2014/05/13/a-trip-in-a-self-driving-car-now-seems-routine/?smid=tw-nytimesbits. 26. A. Salkever, “What Google’s Driverless Car Future Might Really Look Like,” Read Write, May 28, 2014, http://readwrite.com/2014/05/28/googles-driverless-car-future?awesm=readwr.it_p20r-awesm=~oFWmYrlzbbCpi0. 27.

The Google driverless car is now electric without brakes, an accelerator, or a steering wheel.25–31 It has a 360-degree field of view—eliminating any blind spots—with hundreds of laser and radar sensors. It can now recognize pedestrians and bicyclists, along with their hand gestures, better than human beings can, and has a sterling safety record that surpasses driving by humans. And it can be summoned by a smartphone. If we can build self-driving cars with this sensor and computing technology, are we ready to develop doctorless patients? I think the answer is much more autonomous patients, yes, without question, but truly doctorless, no. Much of the practice of medicine will reboot and bypass the current deeply engrained, sacrosanct doctor-dependent operations.32–34 Just as you can do your electrocardiogram by your smartphone today and get an immediate computer algorithm interpretation, so it will be the case for many diagnostics in the future, such as whether you have sleep apnea or hypertension—anything with simple quantitative data to record, process, and quickly return to you.


Virtual Competition by Ariel Ezrachi, Maurice E. Stucke

"World Economic Forum" Davos, Airbnb, Alan Greenspan, Albert Einstein, algorithmic management, algorithmic trading, Arthur D. Levinson, barriers to entry, behavioural economics, cloud computing, collaborative economy, commoditize, confounding variable, corporate governance, crony capitalism, crowdsourcing, Daniel Kahneman / Amos Tversky, David Graeber, deep learning, demand response, Didi Chuxing, digital capitalism, disintermediation, disruptive innovation, double helix, Downton Abbey, driverless car, electricity market, Erik Brynjolfsson, Evgeny Morozov, experimental economics, Firefox, framing effect, Google Chrome, independent contractor, index arbitrage, information asymmetry, interest rate derivative, Internet of things, invisible hand, Jean Tirole, John Markoff, Joseph Schumpeter, Kenneth Arrow, light touch regulation, linked data, loss aversion, Lyft, Mark Zuckerberg, market clearing, market friction, Milgram experiment, multi-sided market, natural language processing, Network effects, new economy, nowcasting, offshore financial centre, pattern recognition, power law, prediction markets, price discrimination, price elasticity of demand, price stability, profit maximization, profit motive, race to the bottom, rent-seeking, Richard Thaler, ride hailing / ride sharing, road to serfdom, Robert Bork, Ronald Reagan, search costs, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, smart meter, Snapchat, social graph, Steve Jobs, sunk-cost fallacy, supply-chain management, telemarketer, The Chicago School, The Myth of the Rational Market, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, Travis Kalanick, turn-by-turn navigation, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, Watson beat the top human players on Jeopardy!, women in the workforce, yield management

For a discussion of the “nowcasting radar,” see Stucke and Grunes, Big Data and Competition Policy. Yoko Kubota, “Toyota Aims to Make Self-Driving Cars by 2020,” Wall Street Journal, October 6, 2015, http://www.wsj.com/articles/toyota-aims-to-make -self-driving-cars-by-2020 -1444136396; Yoko Kubota, “Behind Toyota’s Late Shift into Self-Driving Cars,” Wall Street Journal, January 12, 2016, 338 25. 26. 27. 28. 29. 30. 31. 32. Notes to Pages 239–240 http://www.wsj.com/articles/behind-toyotas-late-shift-into-self-driving-cars -1452649436 (“In the battle for global pre-eminence, traditional car makers fear soft ware makers will steal the auto’s soul and profitability, putting incumbents in a similar position to Chinese factories making smartphones for global brands.”).

Nathaniel Mott, “Uber Should Fear the Company Formerly Known as Google,” Gigaom (August 11, 2015), https://gigaom.com/2015/08/11/uber-vs -alphabet-google/. Weinberger, “Microsoft Could See an Opportunity to Poke Google in the Eye with Uber Investment.” Douglas MacMillan, “GM Invests $500 Million in Lyft, Plans System for Self-Driving Cars,” Wall Street Journal, January 4, 2016, http://www.wsj.com /article _email/gm-invests-500-million-in-lyft-plans-system-for-self-driving -cars-1451914204-lMyQjAxMTI2NTA2NDEwODQyWj. Coupons.com, Form 10-K for 2014 (2014), 17; Yelp Inc., Form 10-Q for the Quarterly Period Ended June 30, 2015 (2015), 33, http://www.sec.gov /Archives/edgar/data/1345016/000120677415002479/yelp_10q.htm.

In quickly accessing and analyzing our personal data, the super-platforms have powerful tools that the monopolies of yesteryear lacked, such as the ability to discern trends and threats well before others, including the government.23 Their superior market position enables them to dictate the interaction not only with us—the customers—but also with small and medium size companies. The latter, just like us, may lack the resources, data, and algorithms to effectively curb the power of the super-platforms when they ex- Final Reflections 239 pand into their markets. Even Goliaths like General Motors are wary that with self-driving cars, the lion’s share of profits will go to those tech firms that develop the algorithms and collect the data.24 The auto manufacturers’ profits (and their unionized workers’ salaries) are squeezed. The winners in the data-collection arms race benefit in several ways: first, in improving their self-learning algorithms; second, in capturing greater value from the data (either directly or indirectly through advertisingrelated ser vices or behavioral discrimination); third, in using the profits to expand their platform, thereby attracting more users, advertisers, and personal data; and finally, as their platforms evolve into super-platforms, in becoming the lords of the new market order—keepers of the data—who can promote or disrupt competition at their will.


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Inventing the Future: Postcapitalism and a World Without Work by Nick Srnicek, Alex Williams

3D printing, additive manufacturing, air freight, algorithmic trading, anti-work, antiwork, back-to-the-land, banking crisis, basic income, battle of ideas, blockchain, Boris Johnson, Bretton Woods, business cycle, call centre, capital controls, capitalist realism, carbon footprint, carbon tax, Cass Sunstein, centre right, collective bargaining, crowdsourcing, cryptocurrency, David Graeber, decarbonisation, deep learning, deindustrialization, deskilling, Doha Development Round, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Ferguson, Missouri, financial independence, food miles, Francis Fukuyama: the end of history, full employment, future of work, gender pay gap, general purpose technology, housing crisis, housing justice, income inequality, industrial robot, informal economy, intermodal, Internet Archive, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, Kiva Systems, late capitalism, liberation theology, Live Aid, low skilled workers, manufacturing employment, market design, Martin Wolf, mass immigration, mass incarceration, means of production, megaproject, minimum wage unemployment, Modern Monetary Theory, Mont Pelerin Society, Murray Bookchin, neoliberal agenda, New Urbanism, Occupy movement, oil shale / tar sands, oil shock, Overton Window, patent troll, pattern recognition, Paul Samuelson, Philip Mirowski, post scarcity, post-Fordism, post-work, postnationalism / post nation state, precariat, precautionary principle, price stability, profit motive, public intellectual, quantitative easing, reshoring, Richard Florida, rising living standards, road to serfdom, Robert Gordon, Ronald Reagan, Second Machine Age, secular stagnation, self-driving car, Slavoj Žižek, social web, stakhanovite, Steve Jobs, surplus humans, synthetic biology, tacit knowledge, technological determinism, the built environment, The Chicago School, The Future of Employment, the long tail, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, wages for housework, warehouse automation, We are all Keynesians now, We are the 99%, women in the workforce, working poor, working-age population

These are tasks that computers are perfectly suited to accomplish once a programmer has created the appropriate software, leading to a drastic reduction in the numbers of routine manual and cognitive jobs over the past four decades.22 The result has been a polarisation of the labour market, since many middle-wage, mid-skilled jobs are routine, and therefore subject to automation.23 Across both North America and Western Europe, the labour market is now characterised by a predominance of workers in low-skilled, low-wage manual and service jobs (for example, fast-food, retail, transport, hospitality and warehouse workers), along with a smaller number of workers in high-skilled, high-wage, non-routine cognitive jobs.24 The most recent wave of automation is poised to change this distribution of the labour market drastically, as it comes to encompass every aspect of the economy: data collection (radio-frequency identification, big data); new kinds of production (the flexible production of robots,25 additive manufacturing,26 automated fast food); services (AI customer assistance, care for the elderly); decision-making (computational models, software agents); financial allocation (algorithmic trading); and especially distribution (the logistics revolution, self-driving cars,27 drone container ships and automated warehouses).28 In every single function of the economy – from production to distribution to management to retail – we see large-scale tendencies towards automation.29 This latest wave of automation is predicated upon algorithmic enhancements (particularly in machine learning and deep learning), rapid developments in robotics and exponential growth in computing power (the source of big data) that are coalescing into a ‘second machine age’ that is transforming the range of tasks that machines can fulfil.30 It is creating an era that is historically unique in a number of ways.

New pattern-recognition technologies are rendering both routine and non-routine tasks subject to automation: complex communication technologies are making computers better than humans at certain skilled-knowledge tasks, and advances in robotics are rapidly making technology better at a wide variety of manual-labour tasks.31 For instance, self-driving cars involve the automation of non-routine manual tasks, and non-routine cognitive tasks such as writing news stories or researching legal precedents are now being accomplished by robots.32 The scope of these developments means that everyone from stock analysts to construction workers to chefs to journalists is vulnerable to being replaced by machines.33 Workers who move symbols on a screen are as at risk as those moving goods around a warehouse.

On a technical level, machines today remain worse than humans at jobs involving creative work, highly flexible work, affective work and most tasks relying on tacit rather than explicit knowledge.46 The engineering problems involved in automating these tasks appear insurmountable for the next two decades (though similar claims were made about self-driving cars ten years ago), and a programme of full automation would aim to invest research money into overcoming these limits. A second barrier to full automation occurs for economic reasons: certain tasks can already be completed by machines, but the cost of the machines exceeds the cost of the equivalent labour.47 Despite the efficiency, accuracy and productivity of machine labour, capitalism prefers to make profits, and therefore uses human labour whenever it is cheaper than capital investment.


pages: 294 words: 96,661

The Fourth Age: Smart Robots, Conscious Computers, and the Future of Humanity by Byron Reese

"World Economic Forum" Davos, agricultural Revolution, AI winter, Apollo 11, artificial general intelligence, basic income, bread and circuses, Buckminster Fuller, business cycle, business process, Charles Babbage, Claude Shannon: information theory, clean water, cognitive bias, computer age, CRISPR, crowdsourcing, dark matter, DeepMind, Edward Jenner, Elon Musk, Eratosthenes, estate planning, financial independence, first square of the chessboard, first square of the chessboard / second half of the chessboard, flying shuttle, full employment, Hans Moravec, Hans Rosling, income inequality, invention of agriculture, invention of movable type, invention of the printing press, invention of writing, Isaac Newton, Islamic Golden Age, James Hargreaves, job automation, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, lateral thinking, life extension, Louis Pasteur, low interest rates, low skilled workers, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Lou Jepsen, Moravec's paradox, Nick Bostrom, On the Revolutions of the Heavenly Spheres, OpenAI, pattern recognition, profit motive, quantum entanglement, radical life extension, Ray Kurzweil, recommendation engine, Rodney Brooks, Sam Altman, self-driving car, seminal paper, Silicon Valley, Skype, spinning jenny, Stephen Hawking, Steve Wozniak, Steven Pinker, strong AI, technological singularity, TED Talk, telepresence, telepresence robot, The Future of Employment, the scientific method, Timothy McVeigh, Turing machine, Turing test, universal basic income, Von Neumann architecture, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, women in the workforce, working poor, Works Progress Administration, Y Combinator

The other kind of AI is referred to by three different names: general AI, strong AI, or artificial general intelligence (AGI). Although the terms are interchangeable, I will use AGI from this point forward to refer to an artificial intelligence as smart and versatile as you or me. A Roomba vacuum cleaner, Siri, and a self-driving car are powered by narrow AI. A hypothetical robot that can unload the dishwasher would be powered by narrow AI. But if you wanted a robot MacGyver, that would require AGI, because MacGyver has to respond to situations that he has not previously considered. AGI does not currently exist, nor is there agreement on how to build it—or even if it is possible.

In another example, a European rocket exploded in flight with $7 billion worth of loss because a 64-bit number was too large to convert to a 16-bit number, causing both a metaphoric and a literal crash. While expensive, these disasters were at least well contained. Imagine a similar problem affecting the self-driving car network, the power grid, or—gasp—your company’s payroll system. I point these issues out not to suggest that we should rethink our march toward a more mechanical future. Machines are, on the whole, more reliable than people in what they do. However, generally speaking, machine failures have a larger potential to cascade.

Lucky for him, a deus ex machina in the form of the sorcerer, awakened by the commotion, comes down and puts a stop to the whole affair, retrieving his hat from a contrite apprentice. 13 * * * The Human Brain We’ve thoroughly explored the world of narrow AI. Narrow AI powers the self-driving car, the thermostat that learns the temperatures you prefer, and the spam filter of your email folder. Yes, these are technical marvels, but don’t ask any of them what you should get your spouse for Christmas. Artificial general intelligence (AGI), on the other hand, is an intelligence that is at least as smart as you and me.


pages: 305 words: 93,091

The Art of Invisibility: The World's Most Famous Hacker Teaches You How to Be Safe in the Age of Big Brother and Big Data by Kevin Mitnick, Mikko Hypponen, Robert Vamosi

4chan, big-box store, bitcoin, Bletchley Park, blockchain, connected car, crowdsourcing, data science, Edward Snowden, en.wikipedia.org, end-to-end encryption, evil maid attack, Firefox, Google Chrome, Google Earth, incognito mode, information security, Internet of things, Kickstarter, Laura Poitras, license plate recognition, Mark Zuckerberg, MITM: man-in-the-middle, off-the-grid, operational security, pattern recognition, ransomware, Ross Ulbricht, Salesforce, self-driving car, Silicon Valley, Skype, Snapchat, speech recognition, Tesla Model S, web application, WikiLeaks, zero day, Zimmermann PGP

However, only a few automakers, such as Mercedes, Tesla, and Ford, send over-the-air updates to all their cars. The rest of us still have to go into the shop to get our automobile software updated. If you think the way Tesla and Uber are tracking every ride you take is scary, then self-driving cars will be even scarier. Like the personal surveillance devices we keep in our pockets—our cell phones—self-driving cars will need to keep track of where we want to go and perhaps even know where we are at a given moment in order to be always at the ready. The scenario proposed by Google and others is that cities will no longer need parking lots or garages—your car will drive around until it is needed.

But it’s predicted that by 2025 a majority of the cars on the road will be connected—to other cars, to roadside assistance services—and it’s likely that a sizable percentage of these will be self-driving.28 Imagine what a software bug in a self-driving car would look like. Meanwhile, every trip you take will be recorded somewhere. You will need an app, much like the Uber app, that will be registered to you and to your mobile device. That app will record your travels and, presumably, the expenses associated with your trip if they would be charged to the credit card on file, which could be subpoenaed, if not from Uber then from your credit card company. And given that a private company will most likely have a hand in designing the software that runs these self-driving cars, you would be at the mercy of those companies and their decisions about whether to share any or all of your personal information with law enforcement agencies.

The scenario proposed by Google and others is that cities will no longer need parking lots or garages—your car will drive around until it is needed. Or perhaps cities will follow the on-demand model, in which private ownership is a thing of the past and everyone shares whatever car is nearby. Just as our cell phones are less like copper-wire phones than they are like traditional PCs, self-driving cars will also be a new form of computer. They’ll be self-contained computing devices, able to make split-second autonomous decisions while driving in case they are cut off from their network communications. Using cellular connections, they will be able to access a variety of cloud services, allowing them to receive real-time traffic information, road construction updates, and weather reports from the National Weather Service.


pages: 239 words: 70,206

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else by Steve Lohr

"World Economic Forum" Davos, 23andMe, Abraham Maslow, Affordable Care Act / Obamacare, Albert Einstein, Alvin Toffler, Bear Stearns, behavioural economics, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, Carl Icahn, classic study, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, data science, David Brooks, driverless car, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, financial engineering, Frederick Winslow Taylor, Future Shock, Google Glasses, Ida Tarbell, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, machine translation, Mark Zuckerberg, market bubble, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, planned obsolescence, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Robert Solow, Salesforce, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, SimCity, six sigma, skunkworks, speech recognition, statistical model, Steve Jobs, Steven Levy, The Design of Experiments, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Tony Fadell, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!, yottabyte

But Brynjolfsson sees a pattern playing out with big data that is comparable to past technologies. Innovations that have been percolating for years in research labs are making their way into products. An industry or two leads the way, like online advertising, and showcase projects point toward the future, like IBM’s Watson or Google’s self-driving cars (robotic incarnations of big data). Enthusiasm fans investment by companies and start-ups. But a broad-based payoff has not yet emerged. Debate rages between the techno-optimists and the pessimists. In his office, I ask Brynjolfsson to describe the steps that led him to become a big-data believer.

By December of 2013, however, Krugman had become more impressed by advances in computing and he wrote an article, published on the Times’s Web site, explaining why he thinks Gordon is “probably wrong.” A decade ago, Krugman writes, “the field of artificial intelligence had marched from failure to failure. But something has happened—things that were widely regarded as jokes not long ago, like speech recognition, machine translation, self-driving cars, and so on, have suddenly become more or less working reality.” Data and software, Krugman observes, have forged the path to working artificial intelligence. “They’re using big data and correlations and so on,” he writes, “to implement algorithms—mindless algorithms, you might say. But if they can take people’s place, does it matter?”

The human users are interested partners and can override the machine, but most of the time they let the Nest algorithms take over. The issue of when to trust the machine—a mechanical one or a virtual one, a software algorithm—is going to play out repeatedly in the future. Appeals to efficiency alone will not carry the day. Advocates for self-driving cars marshal safety statistics and logical-sounding arguments to push their case—about accident rates and the human foibles of drowsiness, distractedness, and drunkenness. Those arguments help, but they do not speak to the issues of trust and comfort with the machines. People are not aggregates; we all experience the world as individuals.


pages: 535 words: 149,752

After Steve: How Apple Became a Trillion-Dollar Company and Lost Its Soul by Tripp Mickle

"World Economic Forum" Davos, Airbnb, airport security, Apple II, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, banking crisis, Boeing 747, British Empire, business intelligence, Carl Icahn, Clayton Christensen, commoditize, coronavirus, corporate raider, COVID-19, desegregation, digital map, disruptive innovation, Donald Trump, Downton Abbey, driverless car, Edward Snowden, Elon Musk, Frank Gehry, General Magic , global pandemic, global supply chain, haute couture, imposter syndrome, index fund, Internet Archive, inventory management, invisible hand, John Markoff, Jony Ive, Kickstarter, Larry Ellison, lateral thinking, Mark Zuckerberg, market design, megacity, Murano, Venice glass, Ralph Waldo Emerson, self-driving car, Sheryl Sandberg, Silicon Valley, skeuomorphism, Stephen Fry, Steve Jobs, Steve Wozniak, Steven Levy, stock buybacks, Superbowl ad, supply-chain management, thinkpad, Tim Cook: Apple, Tony Fadell, Travis Kalanick, turn-by-turn navigation, Wayback Machine, WikiLeaks, Y2K

In his youth, he had worked: Buster Hein, “These Are the Fabulous Rides of Sir Jony Ive,” Cult of Mac, February 27, 2014, https://www.cultofmac.com/254380/jony-ives-cars/. His vision differed from: Daisuke Wakabayashi, “Apple Scales Back Its Ambitions for a Self-Driving Car,” New York Times, August 22, 2017, https://www.nytimes.com/2017/08/22/technology/apple-self-driving-car.html. As the debate simmered: Jack Nicas, “Apple, Spurned by Others, Signs Deal with Volkswagen for Driverless Car,” New York Times, May 23, 2018, https://www.nytimes.com/2018/05/23/technology/apple-bmw-mercedes-volkswagen-driverless-cars.html.

In a bid to restart: Daisuke Wakabayashi, “Apple Taps Bob Mansfield to Oversee Car Project,” Wall Street Journal, July 25, 2016, https://www.wsj.com/articles/apple-taps-bob-mansfield-to-oversee-car-project-1469458580; Daisuke Wakabayashi and Brian X. Chen, “Apple Is Said to Be Rethinking Strategy on Self-Driving Cars,” New York Times, September 9, 2016, https://www.nytimes.com/2016/09/10/technology/apple-is-said-to-be-rethinking-strategy-on-self-driving-cars.html. Shortly after the iTunes shutdown: Paul Mozur and Jane Perlez, “Apple Services Shut Down in China in Startling About-Face,” New York Times, April 21, 2016, https://www.nytimes.com/2016/04/22/technology/apple-no-longer-immune-to-chinas-scrutiny-of-us-tech-firms.html.

At the time, Tesla was in the process of doubling its staff and plowing money into the development of more sophisticated batteries for its electric vehicles. The electric vehicle company was recruiting dozens of Apple engineers, who told former colleagues that the company’s founder, Elon Musk, was going to be the next Jobs. In nearby Mountain View, Google had been working on its own self-driving car and trying to partner with established automakers to bring it to roads nationwide in a few years. The entire peninsula buzzed with the possibility of a transportation revolution. A group of engineers gathered in a conference room to discuss how to proceed. They reviewed marketing analysis drawn up by the consulting firm McKinsey & Company.


pages: 179 words: 43,441

The Fourth Industrial Revolution by Klaus Schwab

"World Economic Forum" Davos, 3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Anthropocene, augmented reality, autonomous vehicles, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, Buckminster Fuller, call centre, circular economy, clean water, collaborative consumption, commoditize, conceptual framework, continuous integration, CRISPR, cross-border payments, crowdsourcing, digital divide, digital twin, disintermediation, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, epigenetics, Erik Brynjolfsson, future of work, global value chain, Google Glasses, hype cycle, income inequality, Internet Archive, Internet of things, invention of the steam engine, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, life extension, Lyft, Marc Benioff, mass immigration, megacity, meta-analysis, more computing power than Apollo, mutually assured destruction, Narrative Science, Network effects, Nicholas Carr, nuclear taboo, OpenAI, personalized medicine, precariat, precision agriculture, Productivity paradox, race to the bottom, randomized controlled trial, reshoring, RFID, rising living standards, Sam Altman, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, smart cities, smart contracts, social contagion, software as a service, Stephen Hawking, Steve Jobs, Steven Levy, Stuxnet, supercomputer in your pocket, synthetic biology, TaskRabbit, The Future of Employment, The Spirit Level, total factor productivity, transaction costs, Uber and Lyft, uber lyft, Watson beat the top human players on Jeopardy!, Wayback Machine, WikiLeaks, winner-take-all economy, women in the workforce, working-age population, Y Combinator, Zipcar

Positive impacts – Improved safety – More time for focusing on work and/or consuming media content – Effect on the environment – Less stress and road rage – Improved mobility for those older and disabled, among others – Adoption of electric vehicles Negative impacts – Job losses (taxi and truck drivers, car industry) – Upending of insurance and roadside assistance (“pay more to drive yourself”) – Decreased revenue from traffic infringements – Less car ownership – Legal structures for driving – Lobbying against automation (people not allowed to drive on freeways) – Hacking/cyber attacks The shift in action In October 2015, Tesla made its cars that were sold over the last year in the US semi-autonomous via a software update. Source: http://www.wired.com/2015/10/tesla-self-driving-over-air-update-live Google plans to make autonomous cars available to the public in 2020. Source: Thomas Halleck, 14 January 2015, “Google Inc. Says Self-Driving Car Will Be Ready By 2020”, International Business Times: http://www.ibtimes.com/google-inc-says-self-driving-car-will-be-ready-2020-1784150 In the summer of 2015, two hackers demonstrated their ability to hack into a moving car, controlling its dashboard functions, steering, brakes etc., all through the vehicle’s entertainment system. Source: http://www.wired.com/2015/07/hackers-remotely-kill-jeep-highway/ The first state in the United States (Nevada) to pass a law allowing driverless (autonomous) cares did so in 2012.

In doing so, they are making (and even “growing”) objects that are continuously mutable and adaptable (hallmarks of the plant and animal kingdoms).4 In The Second Machine Age, Brynjolfsson and McAfee argue that computers are so dexterous that it is virtually impossible to predict what applications they may be used for in just a few years. Artificial intelligence (AI) is all around us, from self-driving cars and drones to virtual assistants and translation software. This is transforming our lives. AI has made impressive progress, driven by exponential increases in computing power and by the availability of vast amounts of data, from software used to discover new drugs to algorithms that predict our cultural interests.


pages: 138 words: 40,787

The Silent Intelligence: The Internet of Things by Daniel Kellmereit, Daniel Obodovski

Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, barriers to entry, business intelligence, call centre, Clayton Christensen, cloud computing, commoditize, connected car, crowdsourcing, data acquisition, driverless car, en.wikipedia.org, Erik Brynjolfsson, first square of the chessboard, first square of the chessboard / second half of the chessboard, Freestyle chess, Google X / Alphabet X, Internet of things, lifelogging, Metcalfe’s law, Network effects, Paul Graham, Ray Kurzweil, RFID, Robert Metcalfe, Salesforce, self-driving car, Silicon Valley, smart cities, smart grid, software as a service, Steve Jobs, The future is already here, the long tail, Tony Fadell, vertical integration, web application, Y Combinator, yield management

Machines are often not only faster than humans, but also more accurate, and they dramatically minimize the chance of human error caused by fatigue. As things around us become smarter due to remotely controlled sensors, machines will take over more and more tasks. One of the amazing things coming down the pipeline is the self-driving car. Says Astro Teller: Self-driving cars in the not-too-distant future are just going to be meaningfully better than people. It will become irresponsible and antiquated for people to drive cars. That is absolutely going to happen in the next decade. I believe that very strongly. Whether Google does it or not, reasonable people could disagree, but whether that generally is going to happen, that I feel very strongly about.

In addition, modern cars are filled with sensors: detecting light for the mirrors and headlights and rain for the windshield wipers, tire pressure monitors, accelerometers, gyroscopes, and compasses. Going further down this path, the digitalization of mechanics in cars allows for the arrival of driverless cars or self-driving cars, which Google has successfully tested over the past few years. But connectivity offers even more: communication between cars to optimize traffic flow and make better decisions on behalf of the driver. Volvo, for example, has successfully demonstrated road trains as part of the EU’s SARTRE (Safe Road Trains for the Environment) Project, which has several cars following one another in a platoon formation; the lead car has a professional driver taking responsibility for the platoon, while following vehicles operate in a semi-autonomous mode, reducing the distance between the vehicles, and reducing drag and fuel consumption, while getting to their destination faster.23 You may be familiar with the crowd-sourced navigation application Waze, which is one of the most accurate personal navigation applications today because it uses real-time traffic and construction information provided by users.


pages: 387 words: 119,409

Work Rules!: Insights From Inside Google That Will Transform How You Live and Lead by Laszlo Bock

Abraham Maslow, Abraham Wald, Airbnb, Albert Einstein, AltaVista, Atul Gawande, behavioural economics, Black Swan, book scanning, Burning Man, call centre, Cass Sunstein, Checklist Manifesto, choice architecture, citizen journalism, clean water, cognitive load, company town, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, deliberate practice, en.wikipedia.org, experimental subject, Fairchild Semiconductor, Frederick Winslow Taylor, future of work, Google Earth, Google Glasses, Google Hangouts, Google X / Alphabet X, Googley, helicopter parent, immigration reform, Internet Archive, Kevin Roose, longitudinal study, Menlo Park, mental accounting, meta-analysis, Moneyball by Michael Lewis explains big data, nudge unit, PageRank, Paul Buchheit, power law, Ralph Waldo Emerson, Rana Plaza, random walk, Richard Thaler, Rubik’s Cube, self-driving car, shareholder value, Sheryl Sandberg, side project, Silicon Valley, six sigma, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, survivorship bias, Susan Wojcicki, TaskRabbit, The Wisdom of Crowds, Tony Hsieh, Turing machine, Wayback Machine, winner-take-all economy, Y2K

More than fifty billion apps have been downloaded from the Google Play store. Chrome, launched as a safer, faster, and open-source Web browser in 2008, has over 750 million active users and has grown into an operating system powering “Chromebook” laptops.10 And Google is just beginning to explore what is possible, from self-driving cars to Project Loon, which aims to provide Internet access by balloon to the hardest-to-reach parts of the globe. From wearable computing products like Google Glass, which blends the Web and the world in a tiny lens that sits above your right eye (we’re working on a version for lefties), to the Google Smart Contact Lens, a contact lens that doubles as a blood glucose monitor for people with diabetes.

Thus far, the innovation and learning from having both systems outweigh the costs of deciding on one or the other. We also use an unfortunately named technique common in technology firms called “dogfooding,” where Googlers are the first to try new products and provide feedback.ix Dogfooders were the first to test-ride in our self-driving cars, supplying valuable feedback on how they work in daily use. This way, Googlers learn what’s going on, and teams get valuable, early feedback from real users. One of the serendipitous benefits of transparency is that simply by sharing data, performance improves. Dr. Marty Makary, a surgeon at the Johns Hopkins Hospital in Baltimore, Maryland, points to when New York State started requiring hospitals to post death rates from coronary artery bypass surgeries.

Japanese college grades are virtually useless as a hiring signal, but knowing which college someone attended is helpful, at least for hiring new graduates. Our professional recruiters are also familiar with many jobs across Google, no small feat considering our business currently includes search, self-driving cars, futuristic glasses, fiber-based Internet services, manufacturing, video studios, and venture capital! This is important, because when someone applies to a job at your company, they don’t know everything your company does. In fact, most large companies have distinct recruiting teams for different divisions.


pages: 521 words: 118,183

The Wires of War: Technology and the Global Struggle for Power by Jacob Helberg

"World Economic Forum" Davos, 2021 United States Capitol attack, A Declaration of the Independence of Cyberspace, active measures, Affordable Care Act / Obamacare, air gap, Airbnb, algorithmic management, augmented reality, autonomous vehicles, Berlin Wall, Bernie Sanders, Big Tech, bike sharing, Black Lives Matter, blockchain, Boris Johnson, Brexit referendum, cable laying ship, call centre, Cambridge Analytica, Cass Sunstein, cloud computing, coronavirus, COVID-19, creative destruction, crisis actor, data is the new oil, data science, decentralized internet, deep learning, deepfake, deglobalization, deindustrialization, Deng Xiaoping, deplatforming, digital nomad, disinformation, don't be evil, Donald Trump, dual-use technology, Edward Snowden, Elon Musk, en.wikipedia.org, end-to-end encryption, fail fast, fake news, Filter Bubble, Francis Fukuyama: the end of history, geopolitical risk, glass ceiling, global pandemic, global supply chain, Google bus, Google Chrome, GPT-3, green new deal, information security, Internet of things, Jeff Bezos, Jeffrey Epstein, John Markoff, John Perry Barlow, knowledge economy, Larry Ellison, lockdown, Loma Prieta earthquake, low earth orbit, low skilled workers, Lyft, manufacturing employment, Marc Andreessen, Mark Zuckerberg, Mary Meeker, Mikhail Gorbachev, military-industrial complex, Mohammed Bouazizi, move fast and break things, Nate Silver, natural language processing, Network effects, new economy, one-China policy, open economy, OpenAI, Parler "social media", Peter Thiel, QAnon, QR code, race to the bottom, Ralph Nader, RAND corporation, reshoring, ride hailing / ride sharing, Ronald Reagan, Russian election interference, Salesforce, Sam Altman, satellite internet, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart grid, SoftBank, Solyndra, South China Sea, SpaceX Starlink, Steve Jobs, Steven Levy, Stuxnet, supply-chain attack, Susan Wojcicki, tech worker, techlash, technoutopianism, TikTok, Tim Cook: Apple, trade route, TSMC, Twitter Arab Spring, uber lyft, undersea cable, Unsafe at Any Speed, Valery Gerasimov, vertical integration, Wargames Reagan, Westphalian system, white picket fence, WikiLeaks, Y Combinator, zero-sum game

Stanford PhD students Larry Page and Sergey Brin had incorporated the company in 1998, after working out of the garage of Susan Wojcicki (now YouTube’s CEO). By the time I joined, nearly two decades later, Google had grown into one of the most iconic companies on the planet. There were more than 60,000 Googlers working around the world, on everything from perfecting search engines to testing self-driving cars.1 Those products and services brought in an astonishing $90 billion in annual revenue.2 Within a year Google would briefly dethrone Apple as the most valuable brand in the world.3 Google had become the kind of company every scrappy start-up sought to unseat. Yet as I began my new job, nothing about Google seemed boring or bureaucratic.

As ridiculous as these articles might seem, the Russians’ firehosing might have managed to trick Google’s algorithms. For those outside the tech world, an algorithm can best be understood as a set of rules in the form of math or some other problem-solving operation. It’s the process that allows computers to produce an output (like a search result) or make a decision about something (like whether a self-driving car should hit the brakes when it comes to a Stop sign). As the British futurist Jamie Susskind writes, algorithms are all around us—even offline. “A set of driving directions is one form of algorithm, specifying what to do under various conditions,” he notes. “ ‘Go down the street, turn right at the post office and then left at the lights.’ ” Susskind points out that we even use algorithmic decision-making in our romantic relationships.

It poses a direct threat to American—and global—security. And the problem isn’t simply the tremendous data collection capabilities 5G networks would offer foreign intelligence services. The more items are connected to the Internet of Things via 5G, the more those items could potentially be weaponized against us. What if China directed a fleet of self-driving cars to mow down pedestrians? What if your Internet-enabled pacemaker stopped working? What if your thermostat was cranked up to 120 degrees in the heat of summer? What if China were able to pinpoint the exact geographic cellular location of Indian soldiers along its disputed border? Testifying in favor of restricting cities from buying buses or trains from Chinese manufacturers—infrastructure that will no doubt soon rely on 5G networks—Scott Paul, the president of the Alliance for American Manufacturing, said, “Putting railcars manufactured by a Chinese state-owned firm underneath the Pentagon in Washington, DC, or near sensitive locations in New York City or anywhere else in America is a horrible idea.”


pages: 602 words: 177,874

Thank You for Being Late: An Optimist's Guide to Thriving in the Age of Accelerations by Thomas L. Friedman

3D printing, additive manufacturing, affirmative action, Airbnb, AltaVista, Amazon Web Services, Anthropocene, Apple Newton, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Berlin Wall, Bernie Sanders, Big Tech, biodiversity loss, bitcoin, blockchain, Bob Noyce, business cycle, business process, call centre, carbon tax, centre right, Chris Wanstrath, Clayton Christensen, clean tech, clean water, cloud computing, cognitive load, corporate social responsibility, creative destruction, CRISPR, crowdsourcing, data science, David Brooks, deep learning, demand response, demographic dividend, demographic transition, Deng Xiaoping, digital divide, disinformation, Donald Trump, dual-use technology, end-to-end encryption, Erik Brynjolfsson, fail fast, failed state, Fairchild Semiconductor, Fall of the Berlin Wall, Ferguson, Missouri, first square of the chessboard / second half of the chessboard, Flash crash, fulfillment center, game design, gig economy, global pandemic, global supply chain, Great Leap Forward, illegal immigration, immigration reform, income inequality, indoor plumbing, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the steam engine, inventory management, Irwin Jacobs: Qualcomm, Jeff Bezos, job automation, John Markoff, John von Neumann, Khan Academy, Kickstarter, knowledge economy, knowledge worker, land tenure, linear programming, Live Aid, low interest rates, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Maui Hawaii, Menlo Park, Mikhail Gorbachev, mutually assured destruction, Neil Armstrong, Nelson Mandela, ocean acidification, PalmPilot, pattern recognition, planetary scale, power law, pull request, Ralph Waldo Emerson, ransomware, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, Solyndra, South China Sea, Steve Jobs, subscription business, supercomputer in your pocket, synthetic biology, systems thinking, TaskRabbit, tech worker, TED Talk, The Rise and Fall of American Growth, Thomas L Friedman, Tony Fadell, transaction costs, Transnistria, uber lyft, undersea cable, urban decay, urban planning, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y2K, Yogi Berra, zero-sum game

When you keep doubling something for fifty years you start to get to some very big numbers, and eventually you start to see some very funky things that you have never seen before. The authors argued that Moore’s law just entered the “second half of the chessboard,” where the doubling has gotten so big and fast we’re starting to see stuff that is fundamentally different in power and capability from anything we have seen before—self-driving cars, computers that can think on their own and beat any human in chess or Jeopardy! or even Go, a 2,500-year-old board game considered vastly more complicated than chess. That is what happens “when the rate of change and the acceleration of the rate of change both increase at the same time,” said McAfee, and “we haven’t seen anything yet!”

This mismatch, as we will see, is at the center of much of the turmoil roiling politics and society in both developed and developing countries today. It now constitutes probably the most important governance challenge across the globe. Astro Teller’s Graph The most illuminating illustration of this phenomenon was sketched out for me by Eric “Astro” Teller, the CEO of Google’s X research and development lab, which produced Google’s self-driving car, among other innovations. Appropriately enough, Teller’s formal title at X is “Captain of Moonshots.” Imagine someone whose whole mandate is to come to the office every day and, with his colleagues, produce moonshots—turning what others would consider science fiction into products and services that could transform how we live and work.

Think of the introduction of the printing press, the telegraph, the manual typewriter, the Telex, the mainframe computer, the first word processors, the PC, the Internet, the laptop, the mobile phone, search, mobile apps, big data, virtual reality, human-genome sequencing, artificial intelligence, and the self-driving car. A thousand years ago, Teller explained, that curve representing scientific and technological progress rose so gradually that it could take one hundred years for the world to look and feel dramatically different. For instance, it took centuries for the longbow to go from development into military use in Europe in the late thirteenth century.


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Radical Markets: Uprooting Capitalism and Democracy for a Just Society by Eric Posner, E. Weyl

3D printing, activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, anti-communist, augmented reality, basic income, Berlin Wall, Bernie Sanders, Big Tech, Branko Milanovic, business process, buy and hold, carbon footprint, Cass Sunstein, Clayton Christensen, cloud computing, collective bargaining, commoditize, congestion pricing, Corn Laws, corporate governance, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, Donald Trump, Elon Musk, endowment effect, Erik Brynjolfsson, Ethereum, feminist movement, financial deregulation, Francis Fukuyama: the end of history, full employment, gamification, Garrett Hardin, George Akerlof, global macro, global supply chain, guest worker program, hydraulic fracturing, Hyperloop, illegal immigration, immigration reform, income inequality, income per capita, index fund, informal economy, information asymmetry, invisible hand, Jane Jacobs, Jaron Lanier, Jean Tirole, Jeremy Corbyn, Joseph Schumpeter, Kenneth Arrow, labor-force participation, laissez-faire capitalism, Landlord’s Game, liberal capitalism, low skilled workers, Lyft, market bubble, market design, market friction, market fundamentalism, mass immigration, negative equity, Network effects, obamacare, offshore financial centre, open borders, Pareto efficiency, passive investing, patent troll, Paul Samuelson, performance metric, plutocrats, pre–internet, radical decentralization, random walk, randomized controlled trial, Ray Kurzweil, recommendation engine, rent-seeking, Richard Thaler, ride hailing / ride sharing, risk tolerance, road to serfdom, Robert Shiller, Ronald Coase, Rory Sutherland, search costs, Second Machine Age, second-price auction, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, special economic zone, spectrum auction, speech recognition, statistical model, stem cell, telepresence, Thales and the olive presses, Thales of Miletus, The Death and Life of Great American Cities, The Future of Employment, The Market for Lemons, The Nature of the Firm, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, trade route, Tragedy of the Commons, transaction costs, trickle-down economics, Tyler Cowen, Uber and Lyft, uber lyft, universal basic income, urban planning, Vanguard fund, vertical integration, women in the workforce, Zipcar

It was around that time that both the volume of data collected and the speed and depth of computation became sufficient to allow applications that made a difference in users’ lives. Around that time the first ML-powered personal digital assistants and dictation services emerged; Siri, Google Assistant, and Cortana became familiar features of everyday life. Even more ambitious applications are being developed, including virtual and augmented reality, self-driving cars, and drones that deliver goods to consumers at the click of a button. Because these services have high “sample complexity,” they require vast stores of data on which to train the ML systems. Thus, the vast data sets collected by Google, Facebook, and others as a by-product of their core business functions became a crucial source of revenue and competitive advantage.

Instead, they are smaller companies, academic researchers, and financial firms with no direct access to data. Many of these businesses have exciting prospects. Work Fusion, for example, offers a sophisticated incentive scheme to workers to help train AIs to automate business processes. Might AI firms hire workers to label maps and road images and sell the labeled data to companies producing self-driving cars? However, the total size of these markets is tiny compared to the number of users who produce data used by the siren servers. The number of workers on mTurk is in the tens of thousands, compared to billions of users of services offered by Google and Facebook.25 The data titans (Google, Facebook, Microsoft, etc.) do not pay for most of their data.

A lonely middle-aged spinster from a corner of Asia was soon the voice of those who spurned the robots and apps, the visa papers and the voice credits. She was the sound of a homeland, a life lost. Yet for all the attention she received, Tuyên was disappointed by the narrowness of the response and the jeering her movement received. Why couldn’t the others see that teaching a computer to cook phở or keeping tabs on robots at some Korean self-driving car factory were not the jobs they had grown up dreaming of, grown up with a right to deserve? Some had initially responded, but the moment that American markets began to sneeze, even Tuyên’s neighbors became jittery about her protests. Why did she have to risk all of their dividends? So Tuyên began to travel around the country and world looking for pockets of fellow travelers unwilling to sell their soul to the demons of data and commonly owned capital.


The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do by Erik J. Larson

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Bletchley Park, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, deep learning, DeepMind, driverless car, Elon Musk, Ernest Rutherford, Filter Bubble, Geoffrey Hinton, Georg Cantor, Higgs boson, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Large Hadron Collider, Law of Accelerating Returns, Lewis Mumford, Loebner Prize, machine readable, machine translation, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, public intellectual, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, TED Talk, The Coming Technological Singularity, the long tail, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

Cluttering up photos with other images also results in severe degradation.7 A few irrelevant letters added to the red area of a stop sign are easily ignored by h­ umans, but when an image altered in this way was presented to one deep learning system, it classified it as a speed limit sign. And ­there are similar real-­world examples, including autonomous naviga- P rob­lems with D eduction and I nduction 127 tion systems on self-­driving cars that have misclassified a school bus as a snowplow, and a turning truck as an overpass. Machine learning is inductive b­ ecause it acquires knowledge from observation of data. The technique known as deep learning is a type of machine learning—­a neural network—­that has shown much promise in recognizing objects in photos, boosting per­for­mance on autonomous vehicles, and playing seemingly difficult games.

Time-­series prediction has impor­ tant applications in complex tasks like medical diagnosis, factory planning, and stock prediction, among ­others. Supervised learning accounts for nearly all the major successes in machine learning to date, including image or voice recognition, autonomous navigation with self-­driving cars, and text classification and personalization strategies online. Unsupervised learning has the virtue of requiring significantly less data preparation, since labels ­aren’t added to training data by h­ umans. But as a direct consequence of this loss of a h­ uman “signal,” unsupervised systems lag far b­ ehind their supervised cousins on real-­world tasks.

“And the answer is, it’s still improving—­but we are getting to the point where we get less benefit than we did in the past.”13 As of this writing, Norvig’s cautionary comments are seven years old. The ImageNet competitions prob­ably ­can’t use more data—­the best systems are now 98 ­percent accurate (using the standard test mea­sure of getting a target label in a system’s top five predictions). But self-­driving cars, once thought to be around the corner, are still in a heavy research phase, and no doubt part of the prob­lem is the training data from labeled video feeds, which is not insufficient in volume but is inadequate to ­handle long tail prob­lems with aty­pi­cal driving scenarios that nonetheless must be factored in for safety.


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Open for Business Harnessing the Power of Platform Ecosystems by Lauren Turner Claire, Laure Claire Reillier, Benoit Reillier

Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, barriers to entry, basic income, benefit corporation, Blitzscaling, blockchain, carbon footprint, Chuck Templeton: OpenTable:, cloud computing, collaborative consumption, commoditize, crowdsourcing, data science, deep learning, Diane Coyle, Didi Chuxing, disintermediation, distributed ledger, driverless car, fake news, fulfillment center, future of work, George Akerlof, independent contractor, intangible asset, Internet of things, Jean Tirole, Jeff Bezos, Kickstarter, knowledge worker, Lean Startup, Lyft, Mark Zuckerberg, market design, Metcalfe’s law, minimum viable product, multi-sided market, Network effects, Paradox of Choice, Paul Graham, peer-to-peer lending, performance metric, Peter Thiel, platform as a service, price discrimination, price elasticity of demand, profit motive, ride hailing / ride sharing, Sam Altman, search costs, self-driving car, seminal paper, shareholder value, sharing economy, Silicon Valley, Skype, smart contracts, Snapchat, software as a service, Steve Jobs, Steve Wozniak, TaskRabbit, the long tail, The Market for Lemons, Tim Cook: Apple, transaction costs, two-sided market, Uber and Lyft, uber lyft, universal basic income, Y Combinator

If the future that GM is preparing for materializes soon, we will have the option to hail (or pre-book) a self-driving car from an app in just a few years’ time.14 This is not as far-fetched as many people think, and since the disruptive entry of Tesla, Uber and Google in the race for self-driving capabilities, the pressure 212 The future of platforms has been mounting for traditional car manufacturers to get their act together. It is relatively easy to imagine a platform model where one side is entirely automated. There may still be a market for hand-driven rides (like handmade suits) for many years to come, but self-driving cars certainly have the potential to replace cab – and truck – drivers within a generation.

Google is also behind the Android operating system, a suite of software products including the Chrome browser, associated Chrome laptop products and Android smartphones, as well as fibre access networks in the US.30 The company also acquired the mobile division of Motorola and its extensive patent portfolio in 2013. More recently, Google has entered the ‘Internet of things’ (IoT) market with the acquisition of Nest (temperature control) and Dropcam (video surveillance) as well as the launch of Google Home (voice activated assistant). Google is also very active in self-driving car technology, artificial intelligence and, through its ventures arm, an investor in some of the most promising start-ups in Silicon Valley (including Uber). Although these different activities may look disparate, they actually create a powerful ecosystem with complex linkages, as well as unique antitrust challenges.

Index Accor Group 3, 201, 211 add-on platforms 77, 88–9, 92, 102, 105 advertising 37, 43, 45, 67–8, 93, 97, 99–100, 142, 144, 183 AdWords (Google) 67, 69, 183, 184 Airbnb 1–2, 3, 4, 15, 24, 168, 188; brand 51–2, 153, 169–70; business model 45; credibility 158–9, 163; feedback 35, 52, 160, 179–80; insurance 164; market sizing 79; match 47, 52, 83; value propositions 83, 95, 123 Alibaba 1, 15, 28, 34, 123, 124, 132, 208 Amazon 8, 12, 13, 14, 15, 125; business model 48, 59, 63; conflict resolution 163; ecosystem 28, 57–8, 59–61, 62, 70; feedback 35, 160; home automation 212 Amazon FBA (Fulfilment by Amazon) 58, 61–2 Amazon Marketplace 26–7, 48, 57, 58, 59, 60, 61–2, 84, 111 Amazon Prime 58, 61, 62 Amazon Web Services (AWS) 58 analytics 50–1, 52, 110, 123, 162, 187, 208, 213; see also data capture Android operating system (Google) 58, 67, 185–6 anticompetitive behaviour 173, 182, 186 APIs (application programming interfaces) 52, 183, 197 Apple 5, 8, 13, 14, 28, 193; App Store 32, 38, 63, 64–6; business model 54, 63, 66; ecosystem 63–6, 70, 132; iPhone 63, 65, 123; iTunes 63, 65 application programming interfaces see APIs Ariba 96 Artfire 143 attract 6, 15, 24, 47, 167; ignition 75, 91–6; maturity 75, 121–2, 131; pre-launch 74; scaling-up 75, 105, 107, 110–14, 117 audience builders 24 AWS see Amazon Web Services balance 48, 50, 106, 111–12, 114, 117, 124, 144–7, 150 Bezos, J. 57, 58 big bang launch strategy see event strategy BlaBlaCar 7, 154, 157, 158, 159; insurance 164; pricing strategies 147, 150, 156; scaling-up 112, 115 BorrowMyDoggy 99 bottlenecks 51, 73, 75–6, 86, 98, 99, 100, 101, 108, 112, 116 bowling pin strategy 54, 95, 109 brand 14–15, 51, 76, 118, 153–70 bundling strategies 61, 131, 182 business architecture 22, 66, 74, 80, 91 business models 6–7, 11, 22, 27, 59, 62, 63, 68, 132; platform businesses 6–7, 11, 22, 23, 27–8, 41–54, 70, 87, 138, 146, 177, 199, 200, 212; traditional businesses 62, 73, 91, 193, 211 card platforms 6, 23, 80, 126 catalyst businesses 23–4 Chesky, B. 1, 2, 169 218 Index Choudary, S. 25 communications platforms see Snapchat; WeChat; WhatsApp communities 44–5, 51, 78, 94, 112, 118, 125, 170 compensation strategy see dollar strategy competing platforms 131, 140, 149 competition 22, 25, 121, 130–2, 140, 173, 193 competition authorities 122, 131, 173-5, 186, 197 complements 31, 38, 130, 131 conflict resolution 126, 163, 167 connect 4, 45–6, 62; ignition 98; maturity 97; pre-launch 85; scaling-up 115, 117 consistency 159–60, 203 contributions 78, 82-3, 94, 118 control 52, 53, 123, 140–1, 162 core transactions 49, 52, 85–6, 98–101, 125, 168 correction activities 162 Craigslist 34, 95, 167 credibility 156, 158–9, 195 critical mass 31, 35–6, 37, 46, 73, 80, 121 cultural meme 97 customer acquisition 93, 107, 127; see also platform participants customer experience 36, 48, 53; maturity 108, 111; pricing strategies 139, 140–1; scaling-up 162 data capture 187; see also analytics dating platforms 13, 36, 48, 50, 95, 166, 176 delay strategy 195 Deliveroo 95, 111, 133, 170, 208 demand coordinators 24–5 denial strategy 194 denigrate strategy 194 deter strategy 194–5 Didi Chuxing 15, 132 digital platforms 4, 5, 12, 17, 73, 157, 167 direct network effects 34–5 direct platforms 80 disintermediation 126, 147–8 dispute resolution see conflict resolution dollar strategy 196 dynamic capabilities 196, 199, 200, 201 eBay 4, 15, 22, 48, 49, 53–4; brand 169, 170; business model 21, 27; connect 4, 85; feedback 159, 160; ignition 54; network effects 33, 34; pricing strategies 129, 140, 144, 146; scaling-up 108, 112, 115, 208; value propositions 83, 112 economic value 4, 14, 197 economies of scale 31, 32–33 ecosystems 6, 13, 17, 26–8, 180–1, 186, 207, 211; Amazon 26–7, 57–8, 59–61, 62, 70; Apple 63–6, 70, 132; Facebook 28, 70; Google 66–7, 68–9, 70, 132, 180–1, 185–6; Microsoft 70, 130, 132 enablers 51, 52, 87; ignition 101; maturity 128; pre-launch 87; scaling-up 118 Etsy 84, 87, 94, 110, 132, 146, 210 Evans, P. 14, 24, 25 event strategy 96 ex ante regulation 173, 188 ex post regulation 173 externalities 31–3, 65, 175, 180 Facebook 8, 13, 14, 52, 124, 131, 163, 168; ecosystem 28, 70; ignition 95, 96; network effects 35; pricing strategies 149; scaling-up 107, 166; search results 124 fast follower strategy 199 fear barriers 168–9 feedback 32, 34–6, 47, 77, 98, 100, 101, 108, 110, 111, 125, 160, 165, 188 Fulfilment by Amazon see Amazon FBA gaming platforms 35, 69, 97, 145 Gawer, A. 14, 25 Gebbia, J. 1, 169 Google 7, 13, 14, 67, 163, 180–6; AdWords 67; Android operating system 67, 185; brand 169; business model 50–1, 68; ecosystem 66–7, 68–9, 70, 132, 180–1, 186; home automation 212; search results 125; YouTube 67, 69, 131, 182 Google+ 69, 182 Index governance 51, 76, 140, 165, 167; prelaunch 87; trust framework 155, 157, 162, 164–70 Gumtree 132, 143 Hailo (MyTaxi) 93, 185 HiGear 163 home automation 212 Honda 27 hotels 1, 3, 7, 79, 179–80 IBM 14, 26, 194 ignition 73, 74, 91–102; eBay 53–4; Facebook 95, 96 indirect network effects 34–5 indirect platforms 80–1 innovation 140, 170, 174; maturity 127, 128; regulation 174, 186, 199 Instagram 70, 99, 107, 110, 133 insurance 164 intervention 175 intimacy 156 IT infrastructure 52 key enablers 51, 52 Kickstarter 78, 109, 210 La Belle Asiette 93–4, 99 labour laws 177 Launchworks 21, 157, 206, 208 leakage 49, 126, 147–8 leverage 61, 62, 70, 88, 124–5 linear businesses 6, 12, 41–2, 44, 52 LinkedIn 49, 70, 100, 107, 143, 150, 159 liquidity 33, 52, 143; ignition 99, 101; scaling-up 117 listing fees 144 Lyft 15, 132, 174, 176, 202 management principles 6, 7, 207 management rules 7, 17 market failures 167, 175–6, 187 market makers 24–5 marketplace platforms 12, 15, 48, 26, 59, 61, 85 marketplaces 6, 26, 64, 49, 59, 84, 85, 208 219 market power 37, 173–6, 180–3, 186, 197 market sizing 79–80 Mastercard 5, 23, 26, 80, 134 match 75; ignition 97–101, 115; maturity 124; platform participants 47–8, 52, 84–5, 95; pre-launch 86; scaling-up 114–15 maturity 73, 75–6, 121–2, 127–8 media companies 25 membership fees 139, 143–4 meshed communities 94–5 Microsoft 13–14, 23, 28, 63, 65, 70, 125, 143; ecosystem 70, 130, 132; home automation 212 minimum viable product see MVP monetization 50, 67, 86, 100, 116, 142, 147 MSP (multisided platforms) 25, 46 multihoming 36–7, 149, 176, 177 multisided markets 5, 23–4, 46 multisided platforms see MSP MVP (minimum viable product) 91, 100–1 MyTaxi 93 negative externalities 32, 180 Netflix 26, 197 network effects 33–5, 35, 48, 52, 68, 99, 133, 176; positive 48, 115, 126, 133, 142, 149 networks 31, 33–5 North Star metric 86, 100, 119 on-boarding processes 107–8, 168 Onefinestay 200–1 online platforms 11, 50, 51, 162 OpenTable 93 operating systems 5, 6, 25, 34; Android 58, 67, 185–6 optimize 50, 76; ignition 100, 101; maturity 128, 130; pre-launch 74, 86; scaling-up 116 over-regulation 174 Parker, G. 23, 25–6 partners 45, 79, 128 payment platforms 6, 24, 48 PayPal 95, 131, 187 performance metrics 100, 117, 127 220 Index personalization 124 piggybacking 95 platform balance 111, 117, 128, 144–6 platform businesses 4, 5–7, 11–14, 205, 207, 212, 214; definitions 21, 24–8 platform business models 4–7, 21–2, 27, 45–7, 146, 199, 205, 207, 212, 214 platform development 73, 77, 88; ignition 73, 75–6, 91–101; maturity 73, 75–6, 122, 125, 126, 128, 130; pre-launch 50, 73, 74, 75–6, 77–8, 82–7, 87, 169; scaling-up 73, 75–6, 91, 105–18, 169, 176 platform disruption 193–9, 201–3, 210–11; traditional businesses 132, 193–4, 206, 198–201 platform ecosystems see ecosystems platform fit 75–6, 91, 92, 100, 105, 169 platform management 6–7, 17, 161, 168 platform owners 79, 84, 95, 100, 105 platform participants 35, 47, 48, 51, 78–80, 113, 142, 165; connect 98; ignition 91, 101; match 35, 84, 97; maturity 121–2; pre-launch 78–9, 86; trust framework 51, 158, 160–1 platforms 11–12 Pokémon Go 97 Porter, M. 6, 42–3 positive externalities 31, 32, 65 positive network effects: match 48; maturity 111, 114; pricing strategies 126, 142, 149; scaling-up 117 pre-launch 73, 74, 75–6, 77–8 premium services 142–3 price discrimination 137–8, 143 price elasticity 37, 137 pricing 50, 81, 116, 126–8, 137, 150 pricing friction 141–4 pricing models 139, 141–4, 146 pricing strategies 23, 138–9, 140, 145–50 producer acquisition 108–9 producer retention 108, 121 producers 78, 107–8 Quora 110, 159 Reddit 49, 78, 84, 94, 209, 210, 213 regulation 167, 173–9, 186–9 reliability 156 retention 47, 107–8, 111, 122 risk management 163 Rochet, J. 23–4 rocket model 46, 51, 73, 82, 116; ignition 73, 74, 91; maturity 73, 122; pre-launch 73, 74; scaling-up 73, 162 Ruby Lane 143, 166 SAP 123 scaling-up 73, 105–6, 116 Schmalensee, R. 24 search results 48, 50, 52, 67, 98, 114, 124, 180–1 self-driving cars 67, 211–12 sharing economy 8, 16, 164, 208–11 single homing 36–7 Snapchat 16, 110 Spotify 123 stakeholders 74, 79, 82, 210 start-ups 15 Stootie 98 strategic enablers 51, 101, 118 substitutes 38 talent platforms 205–6 Taobao 124–5 TaskRabbit 108, 148 taxi market 7, 24, 80, 93, 131, 174, 176, 178, 196 Tesco 27 Tinder 13, 96, 166 tipping point 36 Tirole, J. 7, 23–4 traditional businesses 4, 11, 13, 25, 41, 46, 79, 82, 137, 176; platform disruption 38, 193, 198 traditional business models 33 41, 53, 62, 132, 193, 211; ignition 91; maturity 132, 193 transactional platforms 85, 111 transaction fees 139, 143–4, 146 transaction metrics 111, 115, 116 Transport for London (TfL) 177–8 travel platforms 3, 11; see also Airbnb; Lyft; Uber Index trust 45, 49, 51, 84, 87, 99, 106, 115, 121, 130, 147, 153 trust framework 153, 155, 157, 165, 188 Twitter 69, 96, 159, 163 tying strategies 182–3 Uber 11, 15, 22, 52, 108, 177; business model 24, 79; price strategies 81, 140; regulation 140, 162, 167, 174, 177 UberEATS 133 under-regulation 174 unfair competition 3, 174, 180–1, 186 unicorns 15–16 Upwork 47, 88, 126, 159, 169, 206 user acquisition 93, 97, 107–10, 122; see also platform participants user retention 107–8, 111, 122 221 value chains 6, 42–3, 52–3, 202–3 value creation 11, 50, 78, 139, 174, 193, 211 value propositions 57, 63, 82, 112, 196, 197; maturity 112; pre-launch 82; scaling-up 112, 133, 142 Van Alstyne, M. 23, 25–6 VIP strategy 96 viral loops 110 Visa 5, 14, 23, 26, 80–1, 134 WeChat 6, 97, 132 WhatsApp 70, 110 YouTube (Google) 67–9, 131, 182, 183, 186 Zalando 12, 102, 200


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The Technology Trap: Capital, Labor, and Power in the Age of Automation by Carl Benedikt Frey

3D printing, AlphaGo, Alvin Toffler, autonomous vehicles, basic income, Bernie Sanders, Branko Milanovic, British Empire, business cycle, business process, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Charles Babbage, Clayton Christensen, collective bargaining, computer age, computer vision, Corn Laws, Cornelius Vanderbilt, creative destruction, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, demographic transition, desegregation, deskilling, Donald Trump, driverless car, easy for humans, difficult for computers, Edward Glaeser, Elon Musk, Erik Brynjolfsson, everywhere but in the productivity statistics, factory automation, Fairchild Semiconductor, falling living standards, first square of the chessboard / second half of the chessboard, Ford Model T, Ford paid five dollars a day, Frank Levy and Richard Murnane: The New Division of Labor, full employment, future of work, game design, general purpose technology, Gini coefficient, Great Leap Forward, Hans Moravec, high-speed rail, Hyperloop, income inequality, income per capita, independent contractor, industrial cluster, industrial robot, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of movable type, invention of the steam engine, invention of the wheel, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeremy Corbyn, job automation, job satisfaction, job-hopping, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kickstarter, Kiva Systems, knowledge economy, knowledge worker, labor-force participation, labour mobility, Lewis Mumford, Loebner Prize, low skilled workers, machine translation, Malcom McLean invented shipping containers, manufacturing employment, mass immigration, means of production, Menlo Park, minimum wage unemployment, natural language processing, new economy, New Urbanism, Nick Bostrom, Norbert Wiener, nowcasting, oil shock, On the Economy of Machinery and Manufactures, OpenAI, opioid epidemic / opioid crisis, Pareto efficiency, pattern recognition, pink-collar, Productivity paradox, profit maximization, Renaissance Technologies, rent-seeking, rising living standards, Robert Gordon, Robert Solow, robot derives from the Czech word robota Czech, meaning slave, safety bicycle, Second Machine Age, secular stagnation, self-driving car, seminal paper, Silicon Valley, Simon Kuznets, social intelligence, sparse data, speech recognition, spinning jenny, Stephen Hawking, tacit knowledge, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas Malthus, total factor productivity, trade route, Triangle Shirtwaist Factory, Turing test, union organizing, universal basic income, warehouse automation, washing machines reduced drudgery, wealth creators, women in the workforce, working poor, zero-sum game

The productivity slowdown appears structural and real. 78. Brynjolfsson, Rock, and Syverson, forthcoming, “Artificial Intelligence and the Modern Productivity Paradox,” 25. 79. C. F. Kerry and J. Karsten, 2017, “Gauging Investment in Self-Driving Cars,” Brookings Institution, October 16. https://www.brookings.edu/research/gauging-investment-in-self-driving-cars/. 80. Brynjolfsson, Rock, and Syverson, forthcoming, “Artificial Intelligence and the Modern Productivity Paradox,” 25. 81. N. F. Crafts and T. C. Mills, 2017, “Trend TFP Growth in the United States: Forecasts versus Outcomes” (Discussion Paper 12029, Centre for Economic Policy Research, London).

“News Conference 24.” https://www.jfklibrary.org/archives/other-resources/john-f-kennedy-press-conferences/news-conference-24. Kenworthy, L. 2012. “It’s Hard to Make It in America: How the United States Stopped Being the Land of Opportunity.” Foreign Affairs 91 (November/December): 97–109. Kerry, C. F., and J. Karsten. 2017. “Gauging Investment in Self-Driving Cars.” Brookings Institution, October 16. https://www.brookings.edu/research/gauging-investment-in-self-driving-cars/. Keynes, J. M. [1930] 2010. “Economic Possibilities for Our Grandchildren.” In Essays in Persuasion, 321–32. London: Palgrave Macmillan. Klein, M. 2007. The Genesis of Industrial America, 1870–1920. Cambridge: Cambridge University Press.

And unlike the situation in the days of the Industrial Revolution, workers in the developed world today have more political power than the Luddites did. In America, where Andrew Yang is already tapping into growing anxiety about automation, an overwhelming majority now favor policies to restrict it. The disruptive force of technology, Yang fears, could cause another wave of Luddite uprisings: “All you need is self-driving cars to destabilize society.… [W]e’re going to have a million truck drivers out of work who are 94 percent male, with an average level of education of high school or one year of college. That one innovation will be enough to create riots in the street. And we’re about to do the same thing to retail workers, call center workers, fast-food workers, insurance companies, accounting firms.”8 The point is not fatalism or pessimism.


pages: 304 words: 82,395

Big Data: A Revolution That Will Transform How We Live, Work, and Think by Viktor Mayer-Schonberger, Kenneth Cukier

23andMe, Affordable Care Act / Obamacare, airport security, Apollo 11, barriers to entry, Berlin Wall, big data - Walmart - Pop Tarts, Black Swan, book scanning, book value, business intelligence, business process, call centre, cloud computing, computer age, correlation does not imply causation, dark matter, data science, double entry bookkeeping, Eratosthenes, Erik Brynjolfsson, game design, hype cycle, IBM and the Holocaust, index card, informal economy, intangible asset, Internet of things, invention of the printing press, Jeff Bezos, Joi Ito, lifelogging, Louis Pasteur, machine readable, machine translation, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, obamacare, optical character recognition, PageRank, paypal mafia, performance metric, Peter Thiel, Plato's cave, post-materialism, random walk, recommendation engine, Salesforce, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, smart grid, smart meter, social graph, sparse data, speech recognition, Steve Jobs, Steven Levy, systematic bias, the scientific method, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, Thomas Davenport, Turing test, vertical integration, Watson beat the top human players on Jeopardy!

Instead, it’s about applying math to huge quantities of data in order to infer probabilities: the likelihood that an email message is spam; that the typed letters “teh” are supposed to be “the”; that the trajectory and velocity of a person jaywalking mean he’ll make it across the street in time—the self-driving car need only slow slightly. The key is that these systems perform well because they are fed with lots of data on which to base their predictions. Moreover, the systems are built to improve themselves over time, by keeping a tab on what are the best signals and patterns to look for as more data is fed in.

A single Google Street View drive amassed a myriad of discrete data streams at every moment. The extensibility comes in because Google applied the data not just for a primary use but for lots of secondary uses. For example, the GPS data it garnered improved the company’s mapping service and was indispensable for the functioning of its self-driving car. The extra cost of collecting multiple streams or many more data points in each stream is often low. So it makes sense to gather as much data as possible, as well as to make it extensible by considering potential secondary uses at the outset. That increases the data’s option value. The point is to look for “twofers”—where a single dataset can be used in multiple instances if it can be collected in a certain way.

Its stock market prospectus in 1997 described “collaborative filtering” before Amazon knew how it would work in practice or had enough data to make it useful. Both Google and Amazon span the categories, but their strategies differ. When Google first sets out to collect any sort of data, it has secondary uses in mind. Its Street View cars, as we have seen, collected GPS information not just for its map service but also to train self-driving cars. By contrast, Amazon is more focused on the primary use of data and only taps the secondary uses as a marginal bonus. Its recommendation system, for example, relies on clickstream data as a signal, but the company hasn’t used the information to do extraordinary things like predict the state of the economy or flu outbreaks.


pages: 304 words: 80,143

The Autonomous Revolution: Reclaiming the Future We’ve Sold to Machines by William Davidow, Michael Malone

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, AlphaGo, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, benefit corporation, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, Cambridge Analytica, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, deep learning, DeepMind, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, fake news, Filter Bubble, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, high-speed rail, holacracy, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, low interest rates, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, robo advisor, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Skinner box, Snapchat, speech recognition, streetcar suburb, Stuxnet, surveillance capitalism, synthetic biology, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, two and twenty, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, vertical integration, warehouse automation, zero day, zero-sum game, Zipcar

David Cardinal, “Ten Years After Their Debut, Autonomous Trucks Are Finally Hitting the Roads,” ExtremeTech, October 5, 2015, http://www.extremetech.com/extreme/215626-ten-years-after-their-debut-autonomous-trucks-are-finally-hitting-the-roads (accessed June 27, 2019). 18. Dan Fagella, “Self-Driving Car Timeline for 11 Top Automakers,” VentureBeat, June 4, 2017, https://venturebeat.com/2017/06/04/self-driving-car-timeline-for-11-top-automakers/ (accessed June 27, 2019). 19. “Number of Motor Vehicles Registered in the United States from 1990 to 2017,” Statista, www.statista.com/statistics/183505/number-of-vehicles-in-the-united-states-since-1990/ (accessed June 27, 2019); and Jared Green, “500 Million Reasons to Rethink the Parking Lot,” Grist, June 7, 2012, http://grist.org/cities/500-million-reasons-to-rethink-the-parking-lot/ (accessed June 27, 2019). 20.

It claims to operate from 4,700 cities, provide owners with liability insurance, and deliver cars directly to their renters.45 BlaBlaCar, a European service, allows its more than 35 million members to locate other members who are going where they want to so they can hitch a ride.46 Looming in the future, when the self-driving car arrives, are driverless types of Uber services. The vision is that you will be able to summon a car using your smartphone. It will pick you up, drive you to where you are going, and then speed away to pick up the next passenger. Car sharing is one of the growth industries of the future. GM estimates that 5 to 6 million people globally already share cars and that that number will grow to 20 to 30 million in the next few years.47 To capitalize on this trend, GM has launched its Maven car-sharing service, which allows part-time workers in the gig economy to rent a car when they need it to do such things as delivering groceries to paying customers.48 Maven competes with Mercedes’ Car2Go, which “allows customers to take cars one-way inside of a set perimeter and charges by the minute.”49 Our first thought is that these services compete with cabs and limousine services, but that may be overlooking the depth of the structural transformation.

“Truck Drivers in the USA,” All Trucking.com, http://www.alltrucking.com/faq/truck-drivers-in-the-usa/ (accessed June 27, 2019). 27. Alexis C. Madrigal, “Could Self-Driving Trucks Be Good for Truckers?,” The Atlantic, February 1, 2018, https://www.theatlantic.com/technology/archive/2018/02/uber-says-its-self-driving-trucks-will-be-good-for-truckers/551879/ (accessed on June 27, 2019); and Anika Balakrishman, “Self-Driving Cars Could Cost America’s Professional Drivers Up to 25,000 Jobs a Month, Goldman Sachs Says,” CNBC, May 22, 2017, https://www.cnbc.com/2017/05/22/goldman-sachs-analysis-of-autonomous-vehicle-job-loss.html (access June 27, 2019). 28. “Truck Drivers in the USA.” 29. Nicholas Carlson, “Revenue per Employee Charts Are a Fascinating Way to Judge the Health of Tech Companies,” Business Insider, April 9, 2015, http://www.businessinsider.com/revenue-per-employee-charts-are-a-fascinating-way-to-judge-the-health-of-tech-companies-2015-4 (accessed June 27, 2019). 30.


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The Decadent Society: How We Became the Victims of Our Own Success by Ross Douthat

Affordable Care Act / Obamacare, AI winter, Apollo 13, Bernie Sanders, bitcoin, Black Lives Matter, Boeing 747, Burning Man, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, centre right, Charlie Hebdo massacre, charter city, crack epidemic, CRISPR, crowdsourcing, David Graeber, Deng Xiaoping, deplatforming, Donald Trump, driverless car, East Village, Easter island, Elon Musk, fake news, Flynn Effect, Francis Fukuyama: the end of history, Francisco Pizarro, ghettoisation, gig economy, Golden age of television, green new deal, Haight Ashbury, helicopter parent, hive mind, Hyperloop, immigration reform, informal economy, intentional community, Intergovernmental Panel on Climate Change (IPCC), Islamic Golden Age, Jeff Bezos, Jeremy Corbyn, Joan Didion, Kevin Kelly, Kickstarter, knowledge worker, life extension, low interest rates, mass immigration, mass incarceration, means of production, megacity, meritocracy, microaggression, move fast and break things, multiplanetary species, Neal Stephenson, Neil Armstrong, New Journalism, Nicholas Carr, Norman Mailer, obamacare, Oculus Rift, open borders, opioid epidemic / opioid crisis, out of africa, Panopticon Jeremy Bentham, Paris climate accords, peak TV, Peter Thiel, plutocrats, pre–internet, private spaceflight, QAnon, quantitative easing, radical life extension, rent-seeking, Robert Bork, Robert Gordon, Ronald Reagan, secular stagnation, self-driving car, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Snapchat, Snow Crash, Social Justice Warrior, social web, Steve Bannon, Steve Jobs, Steven Pinker, technoutopianism, TED Talk, the built environment, The Rise and Fall of American Growth, Tyler Cowen, Tyler Cowen: Great Stagnation, wage slave, WeWork, women in the workforce, Y2K

So it sits there, widely regarded as one of the defining success stories of the Internet era, a unicorn unlike any other, with billions in losses and a plan to become profitable that involves vague promises to somehow monetize all its user data and a specific promise that its investment in a different new technology—the self-driving car, much ballyhooed but as yet not exactly real—will square the circle and make the math add up. That’s the story of Uber—so far. It isn’t a pure Instagram fantasy like the Fyre Festival or a naked fraud like Theranos; it managed to go public and maintain its outsize valuation, unlike its fellow money-losing unicorn WeWork, whose recent attempt at an IPO hurled it into crisis.

Or to use Cowen’s favored metaphor, if we have plucked most of the low-hanging fruit that the industrial revolution made possible to reach, there might still be a ladder that someone could invent that would make the higher branches suddenly easier to reach, and for all we know, that ladder might be being extended even now. In which case, we will look back on our present decadence as simply a lull—a period when innovation slowed temporarily before self-driving cars and CRISPR and nanotech and private spaceflight sent it surging forward once again. That possibility will be considered in more detail later. But the lull is still the multigenerational reality right now, and human history offers no reassurance that it will necessarily end. As Gordon and Cowen note, it’s the great surge of innovation in recent Western history that’s the historical anomaly, not the disappointing years since our great leap moonward.

Maybe we have simply been in a kind of bottleneck for the last few generations, achieving important scientific breakthroughs that don’t (yet) translate into society-altering changes. At a certain point, we’ll clear the bottleneck, and it will become clear that our era was a necessary prelude to renewed acceleration—eventually giving us self-driving cars courtesy of a finally profitable Uber, a Mars colony courtesy of the Elon Musk–Jeff Bezos space race, and radical life extension courtesy of Google’s longevity lab or some other zillionaire who can’t imagine shuffling off this mortal coil. All of this could happen on a scale that would be world altering without having the truly utopian scenarios come to pass.


pages: 306 words: 82,909

A Hacker's Mind: How the Powerful Bend Society's Rules, and How to Bend Them Back by Bruce Schneier

4chan, Airbnb, airport security, algorithmic trading, Alignment Problem, AlphaGo, Automated Insights, banking crisis, Big Tech, bitcoin, blockchain, Boeing 737 MAX, Brian Krebs, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computerized trading, coronavirus, corporate personhood, COVID-19, cryptocurrency, dark pattern, deepfake, defense in depth, disinformation, Donald Trump, Double Irish / Dutch Sandwich, driverless car, Edward Thorp, Elon Musk, fake news, financial innovation, Financial Instability Hypothesis, first-past-the-post, Flash crash, full employment, gig economy, global pandemic, Goodhart's law, GPT-3, Greensill Capital, high net worth, Hyman Minsky, income inequality, independent contractor, index fund, information security, intangible asset, Internet of things, Isaac Newton, Jeff Bezos, job automation, late capitalism, lockdown, Lyft, Mark Zuckerberg, money market fund, moral hazard, move fast and break things, Nate Silver, offshore financial centre, OpenAI, payday loans, Peter Thiel, precautionary principle, Ralph Nader, recommendation engine, ride hailing / ride sharing, self-driving car, sentiment analysis, Skype, smart cities, SoftBank, supply chain finance, supply-chain attack, surveillance capitalism, systems thinking, TaskRabbit, technological determinism, TED Talk, The Wealth of Nations by Adam Smith, theory of mind, TikTok, too big to fail, Turing test, Uber and Lyft, uber lyft, ubercab, UNCLOS, union organizing, web application, WeWork, When a measure becomes a target, WikiLeaks, zero day

Other examples include carefully placed, seemingly innocuous stickers affixed to a stop sign that fool an AI classifier into thinking it’s a speed-limit sign, or placement of stickers on a road that fool a self-driving car into swerving into oncoming traffic. These are all theoretical examples, and while researchers have succeeded in causing cars to fail in this manner in tests, as far as we know no one has actually crashed a self-driving car through adversarial ML. Adversarial ML doesn’t have to be malevolent, and it isn’t restricted to laboratory settings. Right now, there are adversarial ML projects trying to hack facial recognition systems so that protesters and others can congregate publicly without fear of being identified by police.

While this is fascinating work, encompassing fields from computer science to sociology to philosophy, its practical applications are probably decades away. I want to focus instead on specialized AI, because that’s what’s under development now. Specialized AI is designed for a specific task, such as the system that controls a self-driving car. It knows how to steer the vehicle, how to follow traffic laws, how to avoid getting into accidents, and what to do when something unexpected happens—like a child’s ball suddenly bouncing into the road. Specialized AI knows a lot and can make decisions based on that knowledge, but only in the limited domain of driving.

See wealth/power private equity, 101–2 Protestant Reformation, 72 psychotherapy, 217 pump-and-dump, 80–81 Quibi, 100–101 ranked-choice voting, 171 real estate hacks, 86–88 reciprocation, 217 recommendation engines, 236 Reconstruction, 161–62 red-teaming, 56, 77, 126–27, 149 Redeemers, 161–62 regulation accountability and, 68 banking, 74 financial exchange hacks and, 84 governance systems, 245–48 market hacks and, 94 real estate hacks and, 87–88 See also regulation avoidance regulation avoidance, 123–27 financial exchange hacks and, 82 gig economy and, 123–25, 264n jurisdictional rules and, 131 regulatory capture, 75–77, 91, 116–18 ride-sharing apps and, 123–24, 264n “too big to fail” hack and, 97 wealth/power advantages and, 121 Regulation Q, 74, 75 regulatory capture, 75–77, 91, 116–18 religious hacks, 71–72, 73, 85, 111, 139–40, 260n resilience, 28, 67–68 responsible disclosure, 89–90 rewards, 184, 186, 231–35, 240 ride-sharing apps, 99, 100, 101, 116, 123–25, 264n Riegle-Neal Interstate Banking and Branching Efficiency Act (1994), 75 risk analysis, 195–96 robotics, 208, 217–19, 222–23 See also AI hacking; AI systems Rodriguez, Alex, 170 Rodríguez, Jose, 170 Roombas, 217 Rosenblum, Jeremy, 126–27 rules, 18–19, 25, 232 Russell, Stuart, 233 Sahu, Lakhan, 170–71 Saunders, Don, 31 script kiddies, 22 secure systems design, 59, 85 Securities Act (1933), 82 Securities Exchange Act (1934), 80 Sedol, Lee, 212 segmentation, 60 self-driving cars, 209–10 SGT STAR, 188 shoplifting, 63, 68 SIM swapping, 191 simplicity, 59, 80 Siri, 217 skimming, 33 Smith, Adam, 93 social engineering, 191–92, 216 social media, 184–85, 186–87 soft money, 169 SoftBank, 99 SolarWinds, 54–55, 60, 145 South Carolina v. Katzenbach, 164 spam, 46–47 spear phishing, 192 Spectre, 48 sponsored content, 194 spoofing, 81, 82 sports hacks, 41–44, 46, 103, 259n Summers, Larry, 97 sumptuary laws, 110 supply chain attacks, 145 Susskind, Jamie, 248 Suzuki, Daichi, 42 systems additional for hacking defense, 54, 60 biological, 19–20 defined, 17–18, 19 hierarchy and, 200 multiple levels of, 32 norms and, 66–67 resilience in, 152 rigidity of, 27 rules and, 18–19 thinking based on, 20 TaskRabbit, 124 Tata, Anthony, 160 tax code bugs in, 14–15 complexity of, 13–14 See also tax hacks Tax Cuts and Jobs Act (2017), 14, 15–16, 129, 146–47, 149 tax hacks architecture and, 109 creative hackers and, 22 cum-ex trading, 104–5 de minimis rule and, 249 defenses against, 15–16, 51, 61 jurisdictional rules and, 128–31 morality and, 263n wealth/power advantages and, 120 tax havens, 128–31 Tay (chatbot), 210 technological change, 251–52 telephone hacks, 26–27, 46 Terminator, 243 terrorism, 196 Tetzel, Johann, 72, 260n Theranos, 101 Thiel, Peter, 3, 4 threat modeling, 62–63, 64–65, 96 title-only bills, 154 “too big to fail” hack, 95–98 travel hacks, 179–80 trespass law, 135–36 tribal courts, 113 tribalism, 196–97 Troubled Asset Relief Program, 96 Trump, Donald banking hacks and, 77 cognitive hacks and, 182 destruction as result of hacking and, 173 legislative process hacks and, 147 norms and, 66–67 payday loans and, 126 social media and, 185 tax hacks and, 105 trust hacking, 27, 191–94, 218 TurboTax, 190 turducken, 110, 263n Turkle, Sherry, 218–19 Twenty-Fourth Amendment, 164 Twitter, 81 typos, 84–85 Uber, 99, 100, 101, 116, 123, 125, 264n unemployment insurance, 132–33 United Nations Convention on the Law of the Sea (1994), 130 user interface design, 189–90 Vacancies Reform Act (1998), 160 variable rewards, 186 venture capital (VC), 99–101, 125 Violence Against Women Act (2013), 114 voice assistants, 217 Volcker Rule, 77 Volkswagen, 234 Voltaire, 172 voter eligibility hacks, 161–63 voter ID laws, 164–65 Voting Rights Act (1965), 164 vulnerabilities acceptance of, 16 AI ability to find, 229–30, 238–39 ATM hacks and, 31, 33, 34 bugs as, 14–15 hacking as parasitical and, 48, 49 hacking hierarchy and, 201 hacking life cycle and, 21 identifying, 56–57, 77–78, 237–38 legislative process hacks and, 147–48, 267n of AI systems, 4, 209–11, 226–27 real estate hacks and, 86 responsible disclosure, 89–90 secure systems design and, 59 zero-day, 90 See also patching Walker, Scott, 166–67 WannaCry, 50 Warner, Mark, 190 Watts, Duncan, 97 wealth/power access and, 22 administrative burdens and, 134 democratic growth and, 250 election hacks and, 168–71 hacking advantages of, 103–4, 119–22 hacking governance systems and, 248 hacking normalization and, 73, 104, 119, 120, 122 impact on vulnerability patches and, 24 market hacks and, 97 trust breakdown and, 251 West, Kanye, 170 Westphal, Paul, 41 WeWork, 100 WikiLeaks, 191 Wilson, Edward O., 251 Winston, Patrick, 206 Women, Infants, and Children (WIC) program, 134 work-to-rule, 115–16, 121 YouTube, 185, 236 Zelenskyy, Volodymyr, 193 zero-day vulnerabilities, 90 Zone of Death jurisdictional loophole, 112–13 Zuckerberg, Mark, 94 Zuckerman, Ethan, 183 ALSO BY BRUCE SCHNEIER We Have Root Click Here to Kill Everybody Data and Goliath Carry On Liars and Outliers Cryptography Engineering Schneier on Security Practical Cryptography Beyond Fear Secrets and Lies The Twofish Encryption Algorithm The Electronic Privacy Papers E-Mail Security Protect Your Macintosh Applied Cryptography Copyright © 2023 by Bruce Schneier All rights reserved First Edition For information about permission to reproduce selections from this book, write to Permissions, W.


pages: 222 words: 53,317

Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman

algorithmic trading, Anthropocene, Anton Chekhov, Apple II, Benoit Mandelbrot, Boeing 747, Chekhov's gun, citation needed, combinatorial explosion, Computing Machinery and Intelligence, Danny Hillis, data science, David Brooks, digital map, discovery of the americas, driverless car, en.wikipedia.org, Erik Brynjolfsson, Flash crash, friendly AI, game design, Google X / Alphabet X, Googley, Hans Moravec, HyperCard, Ian Bogost, Inbox Zero, Isaac Newton, iterative process, Kevin Kelly, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mandelbrot fractal, Minecraft, Neal Stephenson, Netflix Prize, Nicholas Carr, Nick Bostrom, Parkinson's law, power law, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman: Challenger O-ring, Second Machine Age, self-driving car, SimCity, software studies, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, superintelligent machines, synthetic biology, systems thinking, the long tail, Therac-25, Tyler Cowen, Tyler Cowen: Great Stagnation, urban planning, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Y2K

I’ve noticed that when faced with such massive complexity, we tend to respond at one of two extremes: either with fear in the face of the unknown, or with a reverential and unquestioning approach to technology. Fear is a natural response, given how often we are confronted with articles on such topics as the threat of killer machines, the dawn of superintelligent computers with powers far beyond our ken, or the question of whether we can program self-driving cars to avoid hitting jaywalkers. These are technologies so complex that even the experts don’t completely understand them, and they also happen to be quite formidable. This combination often leads us to approach them with alarm and worry. Even if we aren’t afraid of our technological systems, many of us still maintain an attitude of wariness and distaste toward the algorithms and technologies that surround us, particularly when we are confronted with their phenomenal power.

One need not always end up with messy code because the world is messy, but it does often happen. Fortunately, there are ways to mitigate it. See Steve McConnell, Code Complete: A Practical Handbook of Software Construction, 2nd ed. (Redmond, WA: Microsoft Press, 2004), 583. building a self-driving vehicle: The complexity of building self-driving cars was discussed by Google[x]’s “Captain of Moonshots” in his closing keynote address at South by Southwest Interactive (SXSW) 2015: Astro Teller, “How to Make Moonshots,” Backchannel, March 17, 2015, https://medium.com/backchannel/how-to-make-moonshots-65845011a277. the exceptions that nonetheless have to be dealt with: One solution is to use humans to manually troubleshoot, or at least hard-code, the exceptions.

isn’t the worst thing to tell someone: I am thankful for this insight, as well the insights related to limitative theorems, from discussion with folks from the Department of Philosophy at the University of Kansas. incomprehensible systems are the new reality: For example, just because we might not fully grasp all the details of a self-driving car, that doesn’t mean that it can’t be much safer than one driven by a person. And by the way, we already don’t really understand the car driven by a person, let alone the driver himself! the “unthinkable present”: Quoted in Carlin Romano, America the Philosophical (New York: Alfred A. Knopf, 2012), 501.


pages: 194 words: 56,074

Angrynomics by Eric Lonergan, Mark Blyth

AlphaGo, Amazon Mechanical Turk, anti-communist, Asian financial crisis, basic income, Ben Bernanke: helicopter money, Berlin Wall, Bernie Sanders, Big Tech, bitcoin, blockchain, Branko Milanovic, Brexit referendum, business cycle, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, collective bargaining, COVID-19, credit crunch, cryptocurrency, decarbonisation, deindustrialization, diversified portfolio, Donald Trump, Erik Brynjolfsson, Extinction Rebellion, fake news, full employment, gig economy, green new deal, Greta Thunberg, hiring and firing, Hyman Minsky, income inequality, income per capita, Jeremy Corbyn, job automation, labour market flexibility, liberal capitalism, lockdown, low interest rates, market clearing, Martin Wolf, Modern Monetary Theory, precariat, price stability, quantitative easing, Ronald Reagan, secular stagnation, self-driving car, Skype, smart grid, sovereign wealth fund, spectrum auction, The Future of Employment, The Great Moderation, The Spirit Level, universal basic income

The OECD then took it down to 9 per cent, as reality took hold.24 That is, once the hysteria died down, certain things started to become apparent. The first was that almost none of these technologies either exist or are deployable at scale. Self-driving cars and trucks are probably the most developed of these technologies, but they only exist in pilot schemes and are subject to innumerable legal and practical obstacles. Think about the following problem. How do you write a computer command in advance to tell the self-driving car to “always hit the car with fewer people in it” in order to minimize losses, and not get sued by the family of the fewer people in the car? Nonetheless, the constant drumbeat that, for example, in a decade “all truck-driving jobs will go the way of the gas-lighter” may be, in part, responsible for the fact that the US trucking industry was by around 2017 short of 100,000 drivers and the fleet was operating at 100 per cent capacity.

MARK: Here’s something that perhaps illustrates this tension for people who lived through the 2010s. Just as the global financial crisis really began to bite in Europe in 2010, the press everywhere suddenly became replete with stories about how pretty much all workers were shortly to be replaced by robots. Whether in the form of self-driving cars and trucks, drone delivery of goods, computer analysis of financial and legal data, blockchain and bitcoin, we were told over and over that no one was immune. Why? Because this time it was different – different in that AI and ML would combine to do things better than humans can do, and that such machines would get better at doing whatever they do better faster than we can catch up, hence the “race against the machine” and we were all going to lose.


pages: 202 words: 59,883

Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel

Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, driverless car, Edward Snowden, Edward Thorp, Elon Musk, factory automation, Filter Bubble, G4S, gamification, Google Earth, Google Glasses, Internet of things, job automation, John Markoff, Kickstarter, lifelogging, Marc Andreessen, Marc Benioff, Mars Rover, Menlo Park, Metcalfe’s law, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, TSMC, ubercab, urban planning, Zipcar

Similarly, significant innovations and accuracy improvements in voice recognition make systems like Apple’s Siri, Google Now and Google Voice Search possible. The foundation for the Age of Context—all of these technologies working together—is the cloud computing infrastructure, which continues to grow exponentially in capability and capacity. And it had better keep growing: A self-driving car, which we describe in Chapter 5, generates about 700 megabytes of data per second. We talked with GM, Ford, Toyota—and Google—about what would happen if every car had that technology. Well, for one thing, today’s cloud computing technology would melt down. Rackspace, a cloud hosting provider and Scoble’s employer, was the first and largest sponsor of this book.

Prior to CES, thousands of Northern California drivers had already been startled, while driving along public roads, to pass vehicles with odd spinning devices mounted on their roofs. These cars usually moved at precisely the speed limit and contained passengers. Normal enough, except that no one was behind the wheel. These were part of Google’s growing fleet of experimental self-driving cars. They employ short-range radar, laser beams and motion and 3D sensors. The technology allows the cars to discern what’s around them in all directions and decide what, if anything, to do about it. The rooftop spinners contain a new technology, called “lidar” (Laser Imaging Detection and Ranging).

Lien walked us through a multitude of issues that dampen our hopes that the self-driving objects we see in the future are closer than they appear. To get from a consumer exhibition to general use on public roads will require “a great many incremental steps” in technology refinement, user acceptance and cost, as well as institutional adjustments such as legislation, liability and mixed-use roadways. Lien predicts that self-driving cars will first be available “in urban scenarios, because the technology can understand terrain and traffic patterns more easily and cars move at lower speeds.” Industrywide, some cars have already instituted features that are helpful in urban settings: Automatic parking features use sensors to back into tight spots without curb scrape or bumper tapping, and traffic-jam assistance recognizes patterns and adjusts lanes or routes for the driver, thus burning less fuel.


pages: 207 words: 59,298

The Gig Economy: A Critical Introduction by Jamie Woodcock, Mark Graham

Airbnb, algorithmic management, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, British Empire, business process, business process outsourcing, Californian Ideology, call centre, collective bargaining, commoditize, corporate social responsibility, crowdsourcing, data science, David Graeber, deindustrialization, Didi Chuxing, digital divide, disintermediation, emotional labour, en.wikipedia.org, full employment, future of work, gamification, gender pay gap, gig economy, global value chain, Greyball, independent contractor, informal economy, information asymmetry, inventory management, Jaron Lanier, Jeff Bezos, job automation, knowledge economy, low interest rates, Lyft, mass immigration, means of production, Network effects, new economy, Panopticon Jeremy Bentham, planetary scale, precariat, rent-seeking, RFID, ride hailing / ride sharing, Ronald Reagan, scientific management, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, TaskRabbit, The Future of Employment, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, union organizing, women in the workforce, working poor, young professional

Within the platform, huge amounts of data are collected about the drivers and journeys. Uber knows where its drivers are, where they have been, the routes they have taken, the cost of each journey, and how it was rated by the passenger. Part of this hunger for data can be explained by Uber’s ambition to introduce self-driving cars.11,12 The huge quantities of data provide a training set that can be used to train artificial intelligence self-driving, meaning that the losses made in the short term could be offset by the potential for longerterm gains if Uber has the majority on self-driving vehicles.13 Anyone in doubt about the granularity of Uber’s data collection should note the so-called ‘god view’ that can be used to show all drivers and users in a city.

Tied up in the platform model is the capturing of data from workers and users, and the developing of ways to turn it into a productive resource. For example, with Uber, the actions of workers provide data that is used to further the short-term aims of the platform, while also developing the possibility to replace workers with even cheaper (and more docile) artificial intelligence in the form of self-driving cars. While, in many cases, this level of automation may seem relatively far off, it impacts on the strategy of the platform and also informs the perspective that they take towards workers: why offer a steady and secure employment contract if you would prefer these tasks were automated anyway? Automation is a concern that is increasingly on the policy agenda throughout the world.

With delivery work, some parts of the labour process have already been automated, through the use of GPS-assisted route planning and barcodes or radio-frequency identification (RFID) tagging for inventory management. The second is that in all of these cases, workers are contributing to datasets being used to train artificial replacements. The data generated by drivers contributes to the training sets for self-driving cars, while microwork allows for a much wider range of training data. Often workers will not be aware of the role they are playing, as the tasks are fractured and stripped of their meaning. Barriers to entry for workers Many platforms operate with limited barriers to entry for workers, in part because of the relatively low levels of formal training needed for workers to engage in the job.


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No Ordinary Disruption: The Four Global Forces Breaking All the Trends by Richard Dobbs, James Manyika

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, access to a mobile phone, additive manufacturing, Airbnb, Amazon Mechanical Turk, American Society of Civil Engineers: Report Card, asset light, autonomous vehicles, Bakken shale, barriers to entry, business cycle, business intelligence, carbon tax, Carmen Reinhart, central bank independence, circular economy, cloud computing, corporate governance, creative destruction, crowdsourcing, data science, demographic dividend, deskilling, digital capitalism, disintermediation, disruptive innovation, distributed generation, driverless car, Erik Brynjolfsson, financial innovation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Gini coefficient, global supply chain, global village, high-speed rail, hydraulic fracturing, illegal immigration, income inequality, index fund, industrial robot, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, inventory management, job automation, Just-in-time delivery, Kenneth Rogoff, Kickstarter, knowledge worker, labor-force participation, low interest rates, low skilled workers, Lyft, M-Pesa, machine readable, mass immigration, megacity, megaproject, mobile money, Mohammed Bouazizi, Network effects, new economy, New Urbanism, ocean acidification, oil shale / tar sands, oil shock, old age dependency ratio, openstreetmap, peer-to-peer lending, pension reform, pension time bomb, private sector deleveraging, purchasing power parity, quantitative easing, recommendation engine, Report Card for America’s Infrastructure, RFID, ride hailing / ride sharing, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, sovereign wealth fund, spinning jenny, stem cell, Steve Jobs, subscription business, supply-chain management, synthetic biology, TaskRabbit, The Great Moderation, trade route, transaction costs, Travis Kalanick, uber lyft, urban sprawl, Watson beat the top human players on Jeopardy!, working-age population, Zipcar

And in Europe, organizations such as Design for Manufacturing Forum connect industrial designers, engineers, and manufacturers with the fast-growing “maker” movement to create a decentralized, lean manufacturing ecosystem around urban clusters in cities like Rotterdam. Think of Cities as Laboratories Cities are demographic and political microcosms well suited for both private- and public-sector experimentation. Compared with their rural counterparts, city leaders often have greater license to experiment, be it in school reform or regulation of self-driving cars. Private- and public-sector leaders are increasingly collaborating on R&D to find innovative solutions to evolving city needs. As a result, cities are becoming increasingly important partners in innovation—particularly for companies that need to pilot new products and services in self-contained markets before rolling them out nationally.

In 2004, DARPA (Defense Advanced Research Projects Agency) funded the Grand Challenge, a competition that offered $1 million to the first driverless car that could drive 150 miles across the Mojave Desert. Nobody won the prize money; the best-performing car (from Carnegie Mellon) managed a little over 7 miles. Ten years later, Google’s fleet of self-driving cars has already logged 700,000 miles in city streets—with the only accident occurring when a human was operating one of the Toyota Prius cars. Today, new car models offer the latest advances in driver-assist systems, such as braking, parking, and collision avoidance. By 2025, the driverless revolution in ground and airborne vehicles could be well underway, especially if the regulatory framework keeps pace with the changes.

Policy makers who understand technology can harness it to improve societal outcomes in a range of ways—from providing health care, education, and other public services to improving productivity and making governance more transparent and accountable. In addition, governments need to constantly revise legal and regulatory frameworks to ensure their relevance. California legislators are now trying to prepare for advances in self-driving cars; officials from several state departments meet routinely to try to understand all of the ways in which the technology requires legislative changes—such as in liability insurance, drivers’ licenses, safety requirements, and infrastructure needs. They understand that the benefits of being early—especially the potential jobs that could be created in related businesses—are sufficiently large to offset the difficulties of being early.23 Governments around the world are also facing new challenges from increasing global connectivity in data and communication flows.


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Everything for Everyone: The Radical Tradition That Is Shaping the Next Economy by Nathan Schneider

1960s counterculture, Aaron Swartz, Adam Curtis, Affordable Care Act / Obamacare, Airbnb, altcoin, Amazon Mechanical Turk, antiwork, back-to-the-land, basic income, Berlin Wall, Bernie Sanders, bitcoin, Black Lives Matter, blockchain, Brewster Kahle, Burning Man, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, carbon tax, Clayton Christensen, collaborative economy, collective bargaining, commons-based peer production, Community Supported Agriculture, corporate governance, creative destruction, crowdsourcing, cryptocurrency, Debian, degrowth, disruptive innovation, do-ocracy, Donald Knuth, Donald Trump, Edward Snowden, Elon Musk, emotional labour, Ethereum, ethereum blockchain, Evgeny Morozov, Fairphone, Food sovereignty, four colour theorem, future of work, Gabriella Coleman, gentrification, gig economy, Google bus, holacracy, hydraulic fracturing, initial coin offering, intentional community, Internet Archive, Jeff Bezos, Jeremy Corbyn, jimmy wales, John Perry Barlow, joint-stock company, Joseph Schumpeter, Julian Assange, Kevin Roose, Kickstarter, low interest rates, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, mass immigration, means of production, Money creation, multi-sided market, Murray Bookchin, new economy, offshore financial centre, old-boy network, Peter H. Diamandis: Planetary Resources, Pier Paolo Pasolini, post-work, precariat, premature optimization, pre–internet, profit motive, race to the bottom, Richard Florida, Richard Stallman, ride hailing / ride sharing, Rutger Bregman, Salesforce, Sam Altman, Satoshi Nakamoto, self-driving car, shareholder value, sharing economy, Silicon Valley, Slavoj Žižek, smart contracts, Steve Bannon, Steve Jobs, Steve Wozniak, Stewart Brand, surveillance capitalism, tech worker, TED Talk, transaction costs, Turing test, Uber and Lyft, uber lyft, underbanked, undersea cable, universal basic income, Upton Sinclair, Vanguard fund, Vitalik Buterin, W. E. B. Du Bois, white flight, Whole Earth Catalog, WikiLeaks, women in the workforce, working poor, workplace surveillance , Y Combinator, Y2K, Zipcar

While I waited, I scrolled through the Twitter feed of Autocab International, the company that created Green Taxi’s app, “the No. 1 largest supplier of taxi booking and dispatch systems in the world.” Recent posts included pictures of a workshop held in the United Kingdom about how to beat Uber, as well as links to news articles reporting new tests of self-driving cars, self-driving trucks, self-driving mini-buses. Also, alongside a broken link: “Crisis management is our specialty.” While Green Taxi’s drivers scrambled to protect their livelihoods, Uber and Tesla and Google were tooling up to automate them. I asked Buni about this. He said, “We’re really trying to feed a family for the next day.

He said, “We’re really trying to feed a family for the next day. When it happens, we’ll make a plan”—that is, crisis management, for the foreseeable future. To that end, he and his crisis-ridden co-owners pooled more than $1.5 million to put one-third of Denver’s taxi industry under worker control. Self-driving cars hadn’t come to the city’s roads yet, but Wall Street’s anticipation of them was fueling investment in the big apps, which put pressure on the taxi market and motivated so many drivers to set off on their own. The disruption was already happening, and Green Taxi had been born of it. In the beginning, before Uber and Lyft and even checkered taxicabs, there was sharing.

The founders of both Savvy and Word Jammers live with chronic conditions, and their concern for the standards of platform work stem from experiences of being differently abled. They know, better than most, that the dominant online economy wasn’t designed with them in mind. Some platform users are already serving as trainers for their robot replacements. Uber drivers feed their data to the future self-driving cars, and Google’s algorithms learn every time a website asks us to identify the street signs in a reCAPTCHA quiz. Artificial intelligence should be a wonderful thing, but so far the bulk of it is being owned and controlled by a few big-data giants. That’s why a group of researchers in the United States and India has proposed “cooperative models for training artificial intelligence”—enabling trainers to receive benefits through shared ownership.20 Yet these are still only models, and they’re competing against up-and-running juggernauts.


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The End of College: Creating the Future of Learning and the University of Everywhere by Kevin Carey

Albert Einstein, barriers to entry, Bayesian statistics, behavioural economics, Berlin Wall, Blue Ocean Strategy, business cycle, business intelligence, carbon-based life, classic study, Claude Shannon: information theory, complexity theory, data science, David Heinemeier Hansson, declining real wages, deliberate practice, discrete time, disruptive innovation, double helix, Douglas Engelbart, Douglas Engelbart, Downton Abbey, Drosophila, Fairchild Semiconductor, Firefox, Frank Gehry, Google X / Alphabet X, Gregor Mendel, informal economy, invention of the printing press, inventory management, John Markoff, Khan Academy, Kickstarter, low skilled workers, Lyft, Marc Andreessen, Mark Zuckerberg, meta-analysis, natural language processing, Network effects, open borders, pattern recognition, Peter Thiel, pez dispenser, Recombinant DNA, ride hailing / ride sharing, Ronald Reagan, Ruby on Rails, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, social web, South of Market, San Francisco, speech recognition, Steve Jobs, technoutopianism, transcontinental railway, uber lyft, Vannevar Bush

He dressed in jeans and stylish T-shirts and married a beautiful professor of comparative literature who liked to tease him about his techno-utopian ways. In March of that year, Thrun was invited to TED (Technology, Education, Design), the annual festival of technologist self-congratulation, where he stood before a rapt audience and described how he and his colleagues at Google had built a self-driving car. Afterward, Thrun hung around the conference to watch the other presenters, including an energetic former hedge fund analyst named Salman Khan. Khan had computer science degrees from MIT and an MBA from Harvard, and had become recently famous for creating a series of instructional videos for elementary, middle, and high school children that had attracted millions of views on YouTube.

Then a New York Times reporter wrote a story about the course, and suddenly word shot around the world. Enrollment reached six figures and continued to climb. Sebastian Thrun had done nothing particularly interesting from an educational or technological perspective. He did not invent the college equivalent of a self-driving car. There were already thousands of lecture videos on YouTube and iTunes by 2011 and millions of students enrolled in online courses offered by accredited colleges and universities. Because it was huge and free, CS221 was soon described as a “massive open online course,” or MOOC. But Thrun hadn’t invented MOOCs, either; the term had first been used three years earlier to describe a course on the nature of learning taught at the University of Manitoba by a pair of Canadian professors named George Siemens and Stephen Downes.

To survive and prosper in the world with limited cognitive capacity, humans filter waves of constant sensory information through neural patterns—heuristics and mental shortcuts that our minds use to weigh the odds that what we are sensing is familiar and categorizable based on our past experience. Sebastian Thrun’s self-driving car does this with Bayesian statistics built into silicon and code, while the human mind uses electrochemical processes that we still don’t fully understand. But the underlying principle is the same: Based on the pattern of lines and shapes and edges, that is probably a boulder and I should drive around it.


Data Action: Using Data for Public Good by Sarah Williams

affirmative action, Amazon Mechanical Turk, Andrei Shleifer, augmented reality, autonomous vehicles, Brexit referendum, Cambridge Analytica, Charles Babbage, City Beautiful movement, commoditize, coronavirus, COVID-19, crowdsourcing, data acquisition, data is the new oil, data philanthropy, data science, digital divide, digital twin, Donald Trump, driverless car, Edward Glaeser, fake news, four colour theorem, global village, Google Earth, informal economy, Internet of things, Jane Jacobs, John Snow's cholera map, Kibera, Lewis Mumford, Marshall McLuhan, mass immigration, mass incarceration, megacity, military-industrial complex, Minecraft, neoliberal agenda, New Urbanism, Norbert Wiener, nowcasting, oil shale / tar sands, openstreetmap, place-making, precautionary principle, RAND corporation, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, Sidewalk Labs, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, Steven Levy, the built environment, The Chicago School, The Death and Life of Great American Cities, transatlantic slave trade, Uber for X, upwardly mobile, urban planning, urban renewal, W. E. B. Du Bois, Works Progress Administration

., Data Deprivation: Another Deprivation to End (Washington, DC: World Bank, 2015). 3 Ibid. 4 Data Revolution Group, “A World That Counts,” 2014, http://www.undatarevolution.org/wp-content/uploads/2014/11/A-World-That-Counts.pdf. 5 danah boyd and Kate Crawford, “Critical Questions for Big Data: Provocations for a Cultural, Technological, and Scholarly Phenomenon,” Information, Communication & Society 15, no. 5 (2012): 662–679. 6 Sean O’Kane, “Tesla and Waymo Are Taking Wildly Different Paths to Creating Self-Driving Cars,” Verge, April 19, 2018, https://www.theverge.com/transportation/2018/4/19/17204044/tesla-waymo-self-driving-car-data-simulation. 7 Sarah Williams, “Who Owns the City of the Future?” Cooper Hewitt Design Journal (Winter 2018): 8–10. 8 Jim Thatcher, David O’Sullivan, Dillon Mahmoudi, “Data Colonialism through Accumulation by Dispossession: New Metaphors for Daily Data,” Environment and Planning D: Society and Space 34, no. 6 (2016): 990–1006. 9 Nick Couldry, The Costs of Connection: How Data Is Colonizing Human Life and Appropriating It for Capitalism (Stanford, CA: Stanford University Press, 2019). 10 Greg Elmer, Ganaele Langlois, and Joanna Redden, Compromised Data: From Social Media to Big Data (New York: Bloomsbury Publishing, 2015). 11 Jo Bates, “The Strategic Importance of Information Policy for the Contemporary Neoliberal State: The Case of Open Government Data in the United Kingdom,” Government Information Quarterly 31 (2014): 388–395. 12 Thomas L.

Economist, November 13, 2014. https://www.economist.com/international/2014/11/13/off-the-map. Offenhuber, Dietmar, and Carlo Ratti. Waste Is Information: Infrastructure Legibility and Governance. Cambridge, MA: MIT Press, 2017. O’Kane, Sean. “Tesla and Waymo Are Taking Wildly Different Paths to Creating Self-Driving Cars.” The Verge, April 19, 2018. https://www.theverge.com/transportation/2018/4/19/17204044/tesla-waymo-self-driving-car-data-simulation. Olson, Donald R., Kevin J. Konty, Marc Paladini, Cecile Viboud, and Lone Simonsen. “Reassessing Google Flu Trends Data for Detection of Seasonal and Pandemic Influenza: A Comparative Epidemiological Study at Three Geographic Scales.”


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Aiming High: Masayoshi Son, SoftBank, and Disrupting Silicon Valley by Atsuo Inoue

Adam Neumann (WeWork), air freight, Apple II, bitcoin, Black Lives Matter, business climate, cloud computing, coronavirus, COVID-19, fixed income, game design, George Floyd, hive mind, information security, interest rate swap, Internet of things, Jeff Bezos, Kickstarter, Kōnosuke Matsushita, Larry Ellison, lateral thinking, Masayoshi Son, off grid, popular electronics, self-driving car, shareholder value, sharing economy, Silicon Valley, social distancing, SoftBank, Steve Ballmer, Steve Jobs, Steve Wozniak, TikTok, Vision Fund, WeWork

Ron Fisher offers his comments on the deal as well: ‘Arm and Nvidia combining their individual areas of expertise is going to usher in a new generation of performance and I think it was a very good decision. The solution they’ve come up with by combining the two types of processing will give rise to next-level platforms. One of the more obvious next-level applications is autonomous transportation (such as self-driving cars) but when combining the CPU and GPU pairing with robotics, AI, central processing and cloud computing, there is no doubt in my mind completely new technological platforms will start springing up, much like when the Intel 8088 and 8086 came out, sparking the creation of platforms which were used over decades.

He would also comment further on the value of the ecosystem created by the fusion between Arm and Nvidia, stating the two would be capable of creating the types of chips required to jump-start the AI revolution, leading to AI being capable of solving problems faced by humanity, such as AI-led drug development or self-driving cars, or robots with access to the cloud and AI. It would even be capable of developing its own computing platforms. Son would comment that the most important thing with these advances was their capacity for making major contributions to the future of humankind. Elsewhere in his comments he would state that the seeds sown with the SoftBank Vision Fund were starting to sprout and bear fruit.

Out of the 128 companies at least 15 to 20 are thought to be truly revolutionary, and their emergence and mass popularity will change people’s lives: companies specialising in new medicines to prevent cancer, businesses focusing on online education to completely change the way people learn, virtual kitchens, self-driving cars, driverless delivery vehicles. Misra also makes his expectations concerning the scope of the Vision Fund perfectly clear. ‘Where venture capitalists invest early, SVF will invest later, mid-stage and help those companies in a number of ways. SVF helps them in bank financing. SVF helps them hire people.


pages: 292 words: 94,660

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back by Jacob Ward

2021 United States Capitol attack, 4chan, Abraham Wald, AI winter, Albert Einstein, Albert Michelson, Amazon Mechanical Turk, assortative mating, autonomous vehicles, availability heuristic, barriers to entry, Bayesian statistics, Benoit Mandelbrot, Big Tech, bitcoin, Black Lives Matter, Black Swan, blockchain, Broken windows theory, call centre, Cass Sunstein, cloud computing, contact tracing, coronavirus, COVID-19, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, deep learning, Donald Trump, drone strike, endowment effect, George Akerlof, George Floyd, hindsight bias, invisible hand, Isaac Newton, Jeffrey Epstein, license plate recognition, lockdown, longitudinal study, Lyft, mandelbrot fractal, Mark Zuckerberg, meta-analysis, natural language processing, non-fungible token, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, QAnon, RAND corporation, Richard Thaler, Robert Shiller, selection bias, self-driving car, seminal paper, shareholder value, smart cities, social contagion, social distancing, Steven Levy, survivorship bias, TikTok, Turing test

This particular competition in Florida was an annual competition, a yearly federal goosing of the robotics industry into producing an all-in-one robot helper for disaster work. (Although of course a robot capable of these tasks could have other, more military purposes as well.) This same competition format previously produced a functional self-driving car, after several years during which the entrants careened off roadways and crashed into barriers. But this year it didn’t reach such heights with the robotic assistants that DARPA imagined someday putting out fires, handling nuclear material, or carrying lunch across a minefield. The trouble was that these robots were faced with human tasks, in human environments.

That process might take hours, days, maybe longer, but if no one had bothered to build a dog-versus-cow recognition system before, perhaps it would be the best route to distinguishing the animals from one another. Whatever AI we brought in, there are two major forces at work that guide the outcome of the process. The first is called the objective function. It’s what the human wants out of the project, whether that’s a self-driving car parked equidistant between two other cars, no more than six inches from the curb, or a cheeseburger parked between two buns, cooked medium rare. The objective function is the purpose toward which the whole system is striving, and writing it out clearly is the defining first task of any successful machine-learning system.

Ancient myths are full of badly composed objective functions: Pirithous wants Persephone, queen of the underworld, as his wife and winds up stuck in hell. Sibyl asks Apollo for as many years of life as grains of sand in her fist, but she forgets to ask for youth along with it and winds up shriveled in pain. If the self-driving car isn’t told it must park parallel to the curb, or if the burger bot doesn’t know the buns go on the outside, it can all go very wrong. The other major force at work is the sheer ruthless efficiency that we design any machine-learning system to use in pursuit of the objective function. After all, the purpose of these systems is to save humans the time and effort required to spot patterns in a vast field of data, and to get better and better at the process over time.


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Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"World Economic Forum" Davos, 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, Andy Rubin, AOL-Time Warner, artificial general intelligence, asset light, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, backtesting, barriers to entry, behavioural economics, bitcoin, blockchain, blood diamond, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, CRISPR, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, deep learning, DeepMind, Demis Hassabis, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, Evgeny Morozov, fake news, family office, fiat currency, financial innovation, general purpose technology, Geoffrey Hinton, George Akerlof, global supply chain, Great Leap Forward, Gregor Mendel, Hernando de Soto, hive mind, independent contractor, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, Jim Simons, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, Kiva Systems, law of one price, longitudinal study, low interest rates, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Marc Benioff, Mark Zuckerberg, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Mustafa Suleyman, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Project Xanadu, radical decentralization, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Robert Solow, Ronald Coase, Salesforce, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, synthetic biology, tacit knowledge, TaskRabbit, Ted Nelson, TED Talk, the Cathedral and the Bazaar, The Market for Lemons, The Nature of the Firm, the strength of weak ties, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, Two Sigma, two-sided market, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, ubercab, Vitalik Buterin, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

Google believes that because human inattention is a perennial problem, we need to be taken entirely out of the loop in driving. As Chris Urmson, the former head of the company’s self-driving car project, put it, “Conventional wisdom would say that we’ll just take these driver assistance systems and we’ll kind of push them and incrementally improve them, and over time, they’ll turn into self-driving cars. Well, I’m here to tell you that’s like me saying that if I work really hard at jumping, one day I’ll be able to fly. We actually need to do something a little different.” So the company is working to build 100% self-driving cars that require no contributions from humans—known in the industry as “level 5 autonomy.”

Some people will continue to self-select human-to-human interactions, but we believe virtualization is a long-term trend that will generally increase over time as machines gain more capabilities. Robotics is undergoing a “Cambrian Explosion” as machines learn to see, as well as by many other kinds of digital progress. Automatons of all kinds—robots, drones, self-driving cars, and so on—are becoming cheaper, more widely available, more capable, and more diverse all at the same time. Drivers of the robotic Cambrian Explosion include data, algorithms, networks, the cloud, and exponential improvements in hardware: DANCE. Robots and their kin will be increasingly used wherever work is dull, dirty, dangerous, and dear.

And for products with a demand curve that looks like the one in Figure 8, the biggest rectangle turns out to be a long, low one. The revenue-maximizing price, in other words, is surprisingly low. This appears to be the case for rides in cars within cities. As Uber has lowered prices, first with UberX and then with UberPool (and perhaps eventually with self-driving cars), demand has expanded greatly.** Uber very much wants to satisfy this demand by charging extremely low prices, since doing so will maximize its revenue. Figure 8 Most demand curves are not straight lines. Sometimes, they have this shape. But in two-sided markets, simply working its way down the demand curve is only a small part of the story.


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The New Digital Age: Transforming Nations, Businesses, and Our Lives by Eric Schmidt, Jared Cohen

access to a mobile phone, additive manufacturing, airport security, Amazon Mechanical Turk, Amazon Web Services, Andy Carvin, Andy Rubin, anti-communist, augmented reality, Ayatollah Khomeini, barriers to entry, bitcoin, borderless world, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, crowdsourcing, data acquisition, Dean Kamen, disinformation, driverless car, drone strike, Elon Musk, Evgeny Morozov, failed state, false flag, fear of failure, Filter Bubble, Google Earth, Google Glasses, Hacker Conference 1984, hive mind, income inequality, information security, information trail, invention of the printing press, job automation, John Markoff, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, Mary Meeker, means of production, military-industrial complex, MITM: man-in-the-middle, mobile money, mutually assured destruction, Naomi Klein, Nelson Mandela, no-fly zone, off-the-grid, offshore financial centre, Parag Khanna, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Singer: altruism, power law, Ray Kurzweil, RFID, Robert Bork, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, social graph, speech recognition, Steve Jobs, Steven Pinker, Stewart Brand, Stuxnet, Susan Wojcicki, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day

While some of the very exciting new possibilities in transportation, like supersonic tube commutes and suborbital space travel, are still far in the distance, ubiquitous self-driving cars are imminent. Google’s fleet of driverless cars, built by a team of Google and Stanford University engineers, has logged hundreds of thousands of miles without incident, and other models will soon join it on the road. Rather than replacing drivers altogether, the liminal step will be a “driver-assist” approach, where the self-driving option can be turned on, just as an airline captain turns on the autopilot. Government authorities are already well versed on self-driving cars and their potential—in 2012, Nevada became the first state to issue licenses to driverless cars, and later that same year California also affirmed their legality.

one of the lowest rates of literacy in the world: “Field Listing: Literacy,” CIA, World Fact Book, accessed October 11, 2012, https://www.cia.gov/library/publications/the-world-factbook/fields/2103.html#af. in 2012, Nevada became the first state to issue licenses to driverless cars: Chris Gaylord, “Ready for a Self-Driving Car? Check Your Driveway,” Christian Science Monitor, June 25, 2012, http://www.csmonitor.com/Innovation/Tech/2012/0625/Ready-for-a-self-driving-car-Check-your-driveway. California also affirmed their legality: James Temple, “California Affirms Legality of Driverless Cars,” The Tech Chronicles (blog), San Francisco Chronicle, September 25, 2012, http://blog.sfgate.com/techchron/2012/09/25/california-legalizes-driverless-cars/; Florida has passed a similar law.

Sarkozy, Nicolas satellite positioning Saud, Alwaleed bin Talal al- Saudi Arabia, 2.1, 2.2, 3.1, 4.1, 6.1 “Saudi People Demand Hamza Kashgari’s Execution, The” (Facebook group) Save the Children scale effects Schengen Agreement Scott-Railton, John search-engine optimization (SEO), n secession movements secure sockets layer (SSL) security, 2.1, 2.2, 2.3, 2.4 in autocracies censorship and company policy on, 2.1, 2.2 privacy vs., itr.1, 5.1, 5.2 in schools selective memory self-control self-driving cars, itr.1, 1.1, 1.2 September 11, 2001, terrorist attacks of, 3.1, 5.1 Serbia, 4.1, 6.1 servers Shafik, Ahmed shanzhai network, 1.1 sharia Shia Islam Shia uprising Shiites Shock Doctrine, The (Klein), 7.1n short-message-service (SMS) platform, 4.1, 7.1 Shukla, Prakash Sichuan Hongda SIM cards, 5.1, 5.2, 5.3, 6.1, 6.2, nts.1 Singapore, 2.1, 4.1 Singer, Peter, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8 singularity SkyGrabber Skype, 2.1, 2.2, 2.3, 3.1, 5.1 sleeping rhythms Slim Helú, Carlos smart phones, itr.1, 1.1, 1.2, 5.1, 5.2, 7.1 in failed states peer-to-peer capability on Snapchat Snoad, Nigel social networking, 2.1, 4.1, 5.1 social-networking profiles social prosthetics social robots “socioeconomically at risk” people Solidarity Somalia, 2.1, 5.1, 5.2, 5.3, 6.1n, 210, 7.1, 7.2, 7.3 Sony South Africa, 4.1, 7.1 South Central Los Angeles Southern African Development Community (SADC) South Korea, 3.1, 3.2 South Sudan Soviet Union, 4.1, 6.1 Spain Speak2Tweet Special Weapons Observation Reconnaissance Detection System (SWORDS), 6.1, 6.2 speech-recognition technology spoofing Spotify Sputnik spyware, 3.1, 6.1 Stanford University statecraft State Department, U.S., 5.1, 7.1 states: ambition of future of Storyful, n Strategic Arms Limitation Talks (SALT) Stuxnet worm, 3.1, 3.2 suborbital space travel Sudan suggestion engines Summit Against Violent Extremism Sunni Web supersonic tube commutes supplements supply chains Supreme Council of the Armed Forces (SCAF) surveillance cameras Sweden switches Switzerland synthetic skin grafts Syria, 2.1, 3.1, 4.1, 4.2 uprising in Syrian Telecommunications Establishment tablets, 1.1, 1.2, 7.1 holographic Tacocopter Tahrir Square, 4.1, 4.2, 4.3 Taiwan Taliban, 2.1, 5.1, 7.1 TALON Tanzania technology companies, 2.1, 3.1 Tehran Telecom Egypt telecommunications, reconstruction of telecommunications companies Télécoms Sans Frontières television terrorism, terrorists, 4.1, 5.1, con.1 chat rooms of connectivity and cyber, 3.1n, 153–5, 5.1 hacking by Thailand Thomson Reuters Foundation thought-controlled robotic motion 3-D printing, 1.1, 2.1, 2.2, 5.1 thumbprints Tiananmen Square protest, 3.1, 4.1 Tibet time zones tissue engineers to-do lists Tor service, 2.1, 2.2, 2.3, 3.1, 5.1n Total Information Awareness (TIA) trade transmission towers transparency, 2.1, 4.1 “trespass to chattels” tort, n Trojan horse viruses, 2.1, 3.1 tsunami Tuareg fighters Tumblr Tunisia, 4.1, 4.2, 4.3, 4.4, 4.5 Turkey, 3.1, 3.2, 4.1, 5.1, 6.1 Tutsis Twa Twitter, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 3.1, 3.2, 4.1, 4.2, 5.1, 5.2, 6.1, 7.1, 7.2, nts.1 Uganda Uighurs, 3.1, 6.1 Ukraine unemployment UNESCO World Heritage Centre unique identification (UID) program United Arab Emirates, 2.1, 2.2, 2.3 United Kingdom, 2.1, 2.2, 2.3, 3.1 United Nations, 4.1, 5.1, 6.1, 7.1 United Nations Security Council, 3.1n, 214, 7.1 United Russia party United States, 3.1, 3.2, 3.3, 4.1, 5.1, 7.1 engineering sector in United States Agency for International Development (USAID) United States Cyber Command (USCYBERCOM) unmanned aerial vehicles (UAVs), 6.1, 6.2, 6.3, 6.4, 6.5 Ürümqi riots user-generated content Ushahidi vacuuming, 1.1, 1.2 Valspar Corporation Venezuela, 2.1, 2.2, 6.1 verification video cameras video chats video games videos Vietcong Vietnam vigilantism violence virtual espionage virtual governance virtual identities, itr.1, 2.1, 2.2 virtual juvenile records virtual kidnapping virtual private networks (VPNs), 2.1, 3.1 virtual reality virtual statehood viruses vitamins Vodafone, 4.1, 7.1 Vodafone/Raya voice-over-Internet-protocol (VoIP) calls, 2.1, 5.1 voice-recognition software, 1.1, 2.1, 5.1 Voilà VPAA statute, n Walesa, Lech walled garden Wall Street Journal, 97 war, itr.1, itr.2, 6.1 decline in Wardak, Abdul Rahim warfare: automated remote warlords, 2.1, 2.2 Watergate Watergate break-in Waters, Carol weapons of mass destruction wearable technology weibos, 62 Wen Jiabao Wenzhou, China West Africa whistle-blowers whistle-blowing websites Who Controls the Internet?


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Human + Machine: Reimagining Work in the Age of AI by Paul R. Daugherty, H. James Wilson

3D printing, AI winter, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Robotics, augmented reality, autonomous vehicles, blockchain, business process, call centre, carbon footprint, circular economy, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, data science, deep learning, DeepMind, digital twin, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, fail fast, friendly AI, fulfillment center, future of work, Geoffrey Hinton, Hans Moravec, industrial robot, Internet of things, inventory management, iterative process, Jeff Bezos, job automation, job satisfaction, knowledge worker, Lyft, machine translation, Marc Benioff, natural language processing, Neal Stephenson, personalized medicine, precision agriculture, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, robotic process automation, Rodney Brooks, Salesforce, Second Machine Age, self-driving car, sensor fusion, sentiment analysis, Shoshana Zuboff, Silicon Valley, Snow Crash, software as a service, speech recognition, tacit knowledge, telepresence, telepresence robot, text mining, the scientific method, uber lyft, warehouse automation, warehouse robotics

Chapter 7 1.Shoshana Zuboff, In the Age of the Smart Machine: The Future of Work and Power (New York: Basic Books, 1989), 13. 2.Autoline Network, “The ART of Audi,” YouTube video, 1:04:45, August 22, 2014, https://youtu.be/Y6ymjyPryRo. 3.Sharon Gaudin, “New Markets Push Strong Growth in Robotics Industry,” ComputerWorld, February 26, 2016, http://www.computerworld.com/article/3038721/robotics/new-markets-push-strong-growth-in-robotics-industry.html. 4.Spencer Soper and Olivia Zaleski, “Inside Amazon’s Battle to Break into the $800 Billion Grocery Market,” Bloomberg, March 20, 2017, https://www.bloomberg.com/news/features/2017-03-20/inside-amazon-s-battle-to-break-into-the-800-billion-grocery-market. 5.Izzie Lapowski, “Jeff Bezos Defends the Fire Phone’s Flop and Amazon’s Dismal Earnings,” Wired, December 2, 2014, https://www.wired.com/2014/12/jeff-bezos-ignition-conference/. 6.Ben Fox Rubin, “Amazon’s Store of the Future Is Delayed. Now What?” CNET, June 20, 2017, www.cnet.com/news/amazon-go-so-far-is-a-no-show-now-what/. 7.Steven Overly, “The Big Moral Dilemma Facing Self-Driving Cars,” Washington Post, February 20, 2017, https://www.washingtonpost.com/news/innovations/wp/2017/02/20/the-big-moral-dilemma-facing-self-driving-cars/?utm_term=.e12ae9dedb61. 8.Matthew Hutson, “Why We Need to Learn to Trust Robots,” Boston Globe, January 25, 2015, https://www.bostonglobe.com/ideas/2015/01/25/why-need-learn-trust-robots/Nj6yQ5DSNsuTQlMcqnVQEI/story.html. 9.Aaron Timms, “Leda Braga: Machines Are the Future of Trading,” Institutional Investor, July 15, 2015, http://www.institutionalinvestor.com/article/3471429/banking-and-capital-markets-trading-and-technology/leda-braga-machines-are-the-future-of-trading.html. 10.Accenture Research Survey, January 2017; and Lee Rainie and Janna Anderson, “Code-Dependent: Pros and Cons of the Algorithm Age,” Pew Research, February 8, 2017, http://www.pewinternet.org/2017/02/08/code-dependent-pros-and-cons-of-the-algorithm-age/. 11.Jane Wakefield, “Microsoft Chatbot Is Taught to Swear on Twitter,” BBC, March 24, 2016, http://www.bbc.com/news/technology-35890188. 12.Craig Le Clair et al., “The Future of White-Collar Work: Sharing Your Cubicle with Robots,” Forrester, June 22, 2016. 13.Madeline Clare Elish, “The Future of Designing Autonomous Systems Will Involve Ethnographers,” Ethnography Matters, June 28, 2016, https://ethnographymatters.net/blog/2016/06/28/the-future-of-designing-autonomous-systems-will-involve-ethnographers/. 14.Madeleine Clare Elish, “Letting Autopilot Off the Hook,” Slate, June 16, 2016, www.slate.com/articles/technology/future_tense/2016/06/why_do_blame_humans_when_automation_fails.html. 15.Berkeley J.

Lanier, “Disturbing Trends in Physician Burnout and Satisfaction with Work-Life Balance,” Mayo Clinic Proceedings 90, no. 12 (December 2015): 1593–1596. 5.Wes Venteicher, “UPMC Turns to Artificial Intelligence to Ease Doctor Burnout,” TribLive, February 16, 2017, http://triblive.com/news/healthnow/11955589-74/burnout-doctors-microsoft. 6.Bob Rogers, “Making Healthcare More Human with Artificial Intelligence,” IT Peer Network at Intel, February 17, 2017, https://itpeernetwork.intel.com/making-healthcare-human-artificial-intelligence/. 7.Conner Dial, “Audi Makes Self-Driving Cars Seem Normal By Putting a T-Rex at the Wheel,” PSFK, September 16, 2016, https://www.psfk.com/2016/09/audi-t-rex-ad-campaign-makes-self-driving-vehicles-seem-normal.html. 8.“AI Summit New York,” AI Business, 2016, http://aibusiness.org/tag/ai-summit-new-york/. 9.Ibid. 10.Murray Shanahan, “The Frame Problem,” Stanford, February 23, 2004, https://plato.stanford.edu/entries/frame-problem/. 11.Manoj Sahi, “Sensabot Is the First Inspection Robot Approved for Use by Oil and Gas Companies,” Tractica, October 18, 2016, https://www.tractica.com/robotics/sensabot-is-the-first-inspection-robot-approved-for-use-by-oil-and-gas-companies/. 12.Author interview with Steve Schnur, December 7, 2016. 13.Author interview with Bill Ruh, April 11, 2017. 14.Shivon Zilis, “Machine Intelligence Will Let Us All Work Like CEOs,” Harvard Business Review, June 13, 2013, https://hbr.org/2016/06/machine-intelligence-will-let-us-all-work-like-ceos. 15.Julie Bort, “How Salesforce CEO Marc Benioff Uses Artificial Intelligence to End Internal Politics at Meetings,” Business Insider, May 18, 2017, www.businessinsider.com/benioff-uses-ai-to-end-politics-at-staff-meetings-2017-5. 16.


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50 Future Ideas You Really Need to Know by Richard Watson

23andMe, 3D printing, access to a mobile phone, Albert Einstein, Alvin Toffler, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, carbon credits, Charles Babbage, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, Dennis Tito, digital Maoism, digital map, digital nomad, driverless car, Elon Musk, energy security, Eyjafjallajökull, failed state, Ford Model T, future of work, Future Shock, gamification, Geoffrey West, Santa Fe Institute, germ theory of disease, global pandemic, happiness index / gross national happiness, Higgs boson, high-speed rail, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Mark Shuttleworth, Marshall McLuhan, megacity, natural language processing, Neil Armstrong, Network effects, new economy, ocean acidification, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, private spaceflight, profit maximization, RAND corporation, Ray Kurzweil, RFID, Richard Florida, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Skype, smart cities, smart meter, smart transportation, space junk, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, synthetic biology, tech billionaire, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Virgin Galactic, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional

In the UK, 13 percent of farmers are now using autosteering on farm machinery and we will soon see the development of automated, driverless vehicles that work, alone, out in the fields. From there it’s not much of a leap to semi-intelligent harvester robots or robots that can go off by themselves to look for weeds or sick animals. The idea of self-driving cars has been a staple of science fiction for many decades, but in reality we are likely to see self-driving farm machinery and military vehicles far sooner than we will see serious numbers of automated vehicles on public roads, largely because many of the safety and legal concerns that apply to road use do not apply quite so much on private farmland or battlefields.

the condensed idea Reinventing our wheels timeline 1769 First self-propelled mechanical vehicle 1885 Karl Benz invents the modern motorcar 1960s Personal jetpack technology becomes a reality 2004 China unveils a high-speed magnetic levitation train 2016 35 percent of cars now hybrids 2022 Self-driving cars start to appear in China and India 2039 High-speed rail networks link Europe with North Africa 2036 Solar-powered planes widely used in Africa and Australia 15 Extra-legal & feral slums According to a UN estimate, 1 in 7 people worldwide now live in slums and in many cases these slums, which are not regulated or sanctioned by law, are set to become major cities in the near future.

But in the future, machines with strong AI will be able to learn as they go and respond to unexpected events. The implications? Think of automated disease diagnosis and surgery, military planning and battle command, customer-service avatars, artificial creativity and autonomous robots that predict then respond to crime (a “Department of Future Crime”—see also Chapter 32 and Biocriminology). Self-driving cars Gone are the days when Google was just a search engine and cars needed a driver. Google’s autonomous car project, started by Sebastian Thrun of Stanford Artificial Intelligence Laboratory, uses a Toyota Prius equipped with sensors to follow a GPS route all by itself. A robotics scientist sits in the car, but doesn’t actually drive it.


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Like, Comment, Subscribe: Inside YouTube's Chaotic Rise to World Domination by Mark Bergen

23andMe, 4chan, An Inconvenient Truth, Andy Rubin, Anne Wojcicki, Big Tech, Black Lives Matter, book scanning, Burning Man, business logic, call centre, Cambridge Analytica, citizen journalism, cloud computing, Columbine, company town, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, cryptocurrency, data science, David Graeber, DeepMind, digital map, disinformation, don't be evil, Donald Trump, Edward Snowden, Elon Musk, fake news, false flag, game design, gender pay gap, George Floyd, gig economy, global pandemic, Golden age of television, Google Glasses, Google X / Alphabet X, Googley, growth hacking, Haight Ashbury, immigration reform, James Bridle, John Perry Barlow, Justin.tv, Kevin Roose, Khan Academy, Kinder Surprise, Marc Andreessen, Marc Benioff, Mark Zuckerberg, mass immigration, Max Levchin, Menlo Park, Minecraft, mirror neurons, moral panic, move fast and break things, non-fungible token, PalmPilot, paypal mafia, Peter Thiel, Ponzi scheme, QAnon, race to the bottom, recommendation engine, Rubik’s Cube, Salesforce, Saturday Night Live, self-driving car, Sheryl Sandberg, side hustle, side project, Silicon Valley, slashdot, Snapchat, social distancing, Social Justice Warrior, speech recognition, Stanford marshmallow experiment, Steve Bannon, Steve Jobs, Steven Levy, surveillance capitalism, Susan Wojcicki, systems thinking, tech bro, the long tail, The Wisdom of Crowds, TikTok, Walter Mischel, WikiLeaks, work culture

He asked if Google should look into selling YouTube or shutting it down. Such questions, Mehrotra would learn, were recurring at Google, only half in jest. Nearly every quarter its financial chief would eye a growing list of the company’s internet assets and science-fair projects—bottomless free web video, free global maps, and later self-driving cars and computerized eyeglasses—and wonder aloud how long they should remain funded. Google rarely pulled plugs abruptly. Instead, it usually let fading projects slowly die on their vines; eventually, the Google Video searchable TV database did just that. YouTube, however, had a turn of luck that helped it avoid that fate.

Everyone should be “uncomfortably excited,” should possess “a healthy disregard for the impossible.” The top Larry-ism: “10x.” As in, that thing you’re working on, why not make it ten times bigger? When Google’s new web browser, Chrome, failed to meet its usage goals, Page called for higher ones. When roboticists making self-driving cars readied vehicles that could handle contained settings like a college campus, Page ordered the vehicles to work on all roads. 10x! Inside YouTube, Page’s 10x decree prompted a dramatic overhaul of its business goals and operations. In a few short months, YouTube would lay the groundwork for an expansion in size and economic activity beyond what anyone imagined.

* * * • • • The company knew something was askew with its creator economy. But it had another corporate hubbub to deal with. That August in 2015, Larry Page had shocked the world (and most Googlers) by announcing the creation of Alphabet, a new holding company that would split his empire into several stand-alone businesses: one for Google, one for self-driving cars, one for smart thermostats, and so on. YouTube seemed like a natural splinter; it already operated with a different name and office. Leaders there considered plans to become a separate Alphabet unit, detached from Google. Wojcicki wanted to keep reporting to Page, who had appointed himself Alphabet CEO, rather than his successor at Google, Sundar Pichai.


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The Truth Machine: The Blockchain and the Future of Everything by Paul Vigna, Michael J. Casey

3D printing, additive manufacturing, Airbnb, altcoin, Amazon Web Services, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Big Tech, bitcoin, blockchain, blood diamond, Blythe Masters, business process, buy and hold, carbon credits, carbon footprint, cashless society, circular economy, cloud computing, computer age, computerized trading, conceptual framework, content marketing, Credit Default Swap, cross-border payments, crowdsourcing, cryptocurrency, cyber-physical system, decentralized internet, dematerialisation, disinformation, disintermediation, distributed ledger, Donald Trump, double entry bookkeeping, Dunbar number, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, failed state, fake news, fault tolerance, fiat currency, financial engineering, financial innovation, financial intermediation, Garrett Hardin, global supply chain, Hernando de Soto, hive mind, informal economy, information security, initial coin offering, intangible asset, Internet of things, Joi Ito, Kickstarter, linked data, litecoin, longitudinal study, Lyft, M-Pesa, Marc Andreessen, market clearing, mobile money, money: store of value / unit of account / medium of exchange, Network effects, off grid, pets.com, post-truth, prediction markets, pre–internet, price mechanism, profit maximization, profit motive, Project Xanadu, ransomware, rent-seeking, RFID, ride hailing / ride sharing, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, smart contracts, smart meter, Snapchat, social web, software is eating the world, supply-chain management, Ted Nelson, the market place, too big to fail, trade route, Tragedy of the Commons, transaction costs, Travis Kalanick, Turing complete, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, universal basic income, Vitalik Buterin, web of trust, work culture , zero-sum game

Nonetheless the capacity to incorporate IoT-specific protections into a permissionless blockchain is important because it opens up a much broader vision of an IoT future than one that is controlled by select IT companies. Consider one scenario that some envisage in an IoT world, where a self-driving car that needs to get somewhere in a hurry can make a small payment to another self-driving car to let it pass. As discussed, you’ll need a distributed trust system to verify the integrity of the transaction, which may involve a lot more information than just that of the money transfer before it can be processed—for example, you may need to know whether the overtaking car is certified as safe to drive at the faster speed, or whether one car’s software can be trusted not to infect the other with malware.

Not only is the data inaccessible to the wider community unless fees are paid, but mistrust of the monopoly can lead data providers to withhold information. A “global brain” can’t really come into existence in an economy dominated by the centralized trust model. Blockchain-based network designs probably won’t get the same attention in homeware magazines as smart doorknobs and self-driving cars, but they will be a fundamental backbone of the network computational capacity of an Internet of Things economy in which tens of billions of devices like doorknobs and cars are autonomously “talking” to and trading with each other. World Economic Forum founder Klaus Schwab says we’re moving into a “fourth industrial revolution,” not because one particular new line of products is coming but because a variety of technologies are combining to create whole new systems: mobile devices, sensors, nanotech processors, renewable energy, brain research, virtual reality, artificial intelligence, and so forth.

In the past, new technology has spurred a sufficiently healthy gain in the U.S. economy to foster new, higher-tech jobs that offset the losses of the lower-tech and typically lower-paying ones they replaced. Farmworkers became factory workers and factory workers became office workers. But this shift to decentralized trust, along with all other disruption coming from, you name it—self-driving cars, automated medicine, peer-to-peer credit, 3D printing, artificially intelligent writers—will be too big to keep up with. The idea that the office towers of New York and Chicago will be left half empty for decades is not unfeasible. “Software is eating the world,” as Marc Andreessen likes to say.


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Bezonomics: How Amazon Is Changing Our Lives and What the World's Best Companies Are Learning From It by Brian Dumaine

activist fund / activist shareholder / activist investor, AI winter, Airbnb, Amazon Robotics, Amazon Web Services, Atul Gawande, autonomous vehicles, basic income, Bernie Sanders, Big Tech, Black Swan, call centre, Cambridge Analytica, carbon tax, Carl Icahn, Chris Urmson, cloud computing, corporate raider, creative destruction, Danny Hillis, data science, deep learning, Donald Trump, Elon Musk, Erik Brynjolfsson, Fairchild Semiconductor, fake news, fulfillment center, future of work, gig economy, Glass-Steagall Act, Google Glasses, Google X / Alphabet X, income inequality, independent contractor, industrial robot, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kevin Roose, Lyft, Marc Andreessen, Mark Zuckerberg, military-industrial complex, money market fund, natural language processing, no-fly zone, Ocado, pets.com, plutocrats, race to the bottom, ride hailing / ride sharing, Salesforce, Sand Hill Road, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Steve Jobs, Stewart Brand, supply-chain management, TED Talk, Tim Cook: Apple, too big to fail, Travis Kalanick, two-pizza team, Uber and Lyft, uber lyft, universal basic income, warehouse automation, warehouse robotics, wealth creators, web application, Whole Earth Catalog, work culture

In the business arena, warehouse robots, scanners, and self-driving delivery vans also connect to the Internet, thanks to inexpensive sensors and smart algorithms. By 2022, there will be more than 29 billion connected devices worldwide, roughly four times the number of people in the world. Now tech giants such as Alibaba, JD.com, Tencent, and even Google’s parent, Alphabet—with its smart home devices and self-driving cars—are joining Amazon in its quest to infiltrate every corner of our lives with AI. This has dire implications for the global job market. As these companies automate their warehouses, use drones and self-driving trucks for delivery, many solid blue-collar jobs will disappear. Moreover, as Amazon and other global tech giants move into new industries, they’ll accelerate the digitization of health care, banking, and other sectors of the economy and have an even bigger impact on jobs.

Around the same time, Amazon led a $530 million investment round for Aurora, a Silicon Valley self-driving vehicle start-up founded by three stars of this emerging industry: Sterling Anderson, Drew Bagnell, and Chris Urmson. Anderson ran Tesla’s autopilot program, Bagnell headed the autonomy and perception team at Uber, and Urmson was the former head of Google’s self-driving project, which has morphed into one of the leading self-driving car companies: Waymo. Aurora will not build cars but is developing the AI brains behind autonomous vehicles and plans to partner with retailers like Amazon and major automakers to create state-of-the-art autonomous vehicles. Amazon is far from alone in the race for self-driving vehicles. According to the research firm CB Insights, at least forty-six companies around the world are working on self-driving vehicle technology.

As a first step, Bezos in 2014 hired Babak Parviz, an Iranian immigrant who previously headed Google X, a respected research facility (now a division of Alphabet called X) that worked on various moonshot projects, including kites that gather wind energy, the Google Glass virtual-reality headset, and self-driving cars—an initiative that eventually became the Alphabet subsidiary Waymo. Just as at Google, Parviz’s innovation lab at Amazon, which is named Grand Challenge, will have, as its name suggests, a broad mandate to take the long view and to tinker creatively on some of the world’s biggest problems. A job posting for the lab cited astronomer Carl Sagan: “Somewhere, something incredible is waiting to be known.”


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The Best Interface Is No Interface: The Simple Path to Brilliant Technology (Voices That Matter) by Golden Krishna

Airbnb, Bear Stearns, computer vision, crossover SUV, data science, en.wikipedia.org, fear of failure, impulse control, Inbox Zero, Internet Archive, Internet of things, Jeff Bezos, Jony Ive, Kickstarter, lock screen, Mark Zuckerberg, microdosing, new economy, Oculus Rift, off-the-grid, Paradox of Choice, pattern recognition, QR code, RFID, self-driving car, Silicon Valley, skeuomorphism, Skype, Snapchat, Steve Jobs, tech worker, technoutopianism, TED Talk, Tim Cook: Apple, Y Combinator, Y2K

.,” Bloomberg, May 28, 2013. http://www.bloomberg.com/visual-data/best-and-worst/biggest-increases-in-lobbying-in-u-dot-s-companies 2014 April–June (Q2) Lobbying (Millions $) Google, 5.03 Exxon Mobil, 2.80 Pfizer, 1.60 Lauren Hepler, “Google Drops $5M on Q2 2014 Lobbying: Self-Driving Cars, Health, Tax, Immigration,” Silicon Valley Business Journal, July 28, 2014. http://www.bizjournals.com/sanjose/news/2014/07/28/self-driving-cars-health-tech-immigration-google.html 6 Ashlee Vance, “This Tech Bubble Is Different,” Businessweek, April 14, 2011. http://www.businessweek.com/magazine/content/11_17/b4225060960537.htm Chapter 5 Addiction UX 1 “A whopping 96 percent of Google’s $37.9 billion 2011 revenue came from advertising . . .”

Automatic solutions sound so wonderful. Everything just magically gets done. And machines do it all on their own. They give us what we need, when we need it, and how we need it. The computer becomes a mind reader for our preferences. Science fiction becomes reality. Robot vacuums. Autopilot. Self-driving cars. Back to the Future power laces. A lot of NoUI thinking lends itself to making these kinds of automatic solutions. They aim to feel like pure delight. Like magic. Where technology should be in the twenty-first century. But when it comes to actually implementing an automatic solution, a lot of people get scared.


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The Self-Made Billionaire Effect: How Extreme Producers Create Massive Value by John Sviokla, Mitch Cohen

Bear Stearns, Blue Ocean Strategy, business cycle, Cass Sunstein, Colonization of Mars, corporate raider, Daniel Kahneman / Amos Tversky, driverless car, eat what you kill, Elon Musk, Frederick Winslow Taylor, game design, global supply chain, James Dyson, Jeff Bezos, John Harrison: Longitude, Jony Ive, loss aversion, Mark Zuckerberg, market design, megaproject, old-boy network, paper trading, RAND corporation, randomized controlled trial, Richard Thaler, risk tolerance, scientific management, self-driving car, Sheryl Sandberg, Silicon Valley, smart meter, Steve Ballmer, Steve Jobs, Steve Wozniak, tech billionaire, Tony Hsieh, Toyota Production System, Virgin Galactic, young professional

One example of this discomfort and its negative effects comes from an effort that has been getting a great deal of press in recent months: the self-driving car. Right now Google owns this space. Google is the brand that has been working on the technology, and there are Google leaders out there making it real in the marketplace and evangelizing its positive applications. Reportedly, the team that went to Sergey Brin and Larry Page for permission to work on the self-driving car was pushed to be more ambitious by the founding Producers—Page and Brin gave the development team the green light to make the car only if it would be able to travel a thousand miles in both highway and city contexts with limited GPS access, a challenge the car achieved in fifteen months.6 But Google’s prominence in this market raises a thorny question: Why aren’t leading car manufacturers pursuing that charge?

They have difficulty with the idea that their innovations—historically built for proprietary use—might reside in a competitor’s product; they can’t or won’t construct a narrative of change that shows employees, customers, and stakeholders the ways in which this new disruptive model is simply a natural evolution of what they have always done. Compare the way the automotive industry has recused itself from Inventive Execution on the self-driving car with the way the Producer Bill Gates inserted Microsoft into the Internet space. In 1995, Gates knew Microsoft was late to the Internet party, and that Netscape, Sun Microsystems, and other competitors had already taken over and defined the browser and net server markets which served as the gateways to defining how users store, find, organize, and use information.


Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig

3D printing, Airbnb, algorithmic trading, AlphaGo, Alvin Toffler, Amazon Web Services, anti-work, antiwork, artificial general intelligence, asset light, autonomous vehicles, basic income, behavioural economics, business cycle, cloud computing, collective bargaining, Computing Machinery and Intelligence, correlation does not imply causation, creative destruction, data is the new oil, data science, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, disintermediation, do what you love, Donald Trump, driverless car, Erik Brynjolfsson, fake news, feminist movement, Ford Model T, Frederick Winslow Taylor, future of work, Future Shock, general purpose technology, gig economy, global supply chain, income inequality, independent contractor, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, job polarisation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, Nick Bostrom, off grid, pattern recognition, post-work, Ronald Coase, scientific management, Second Machine Age, self-driving car, sharing economy, SoftBank, Steve Jobs, strong AI, tacit knowledge, technological determinism, technoutopianism, TED Talk, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

He argues that specific jobs or categories of work rarely become obsolete in their entirety—rather, they evolve as technology becomes available to automate particular elements of the work. In deciding whether automation creates or destroys jobs, the decisive factor is demand rather than technology. 1 Introduction 5 What are the advantages of technology? Often people say, ‘It is obvious that self-driving cars will reduce the numbers of road accidents. Automated diagnostic and treatment systems will reduce medical casualties and so on’. We know that argument, but will algorithmic trading increase the efficiency of financial markets, or render them more liable to crashes? So far, it seems the latter has been the case.

Artificial General Intelligence: Concept, State of the Art, and Future Prospects. Journal of Artificial General Intelligence, 5(1), 1–26. Hurst, D. (2018, February 6). Japan Lays Groundwork for Boom in Robot Carers. The Guardian. Price, R. (2019, April 12). Uber Says Its Future is Riding on the Success of Self-­ driving Cars, but Warns Investors That There’s a Lot That Can Go Wrong. Business Insider. Shead, S. (2016, April 4). There’s a Worldwide Shortage of the Board Game Go after Google’s Computer Beat the World Champ. Business Insider. Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., Hubert, T., Baker, L., Lai, M., Bolton, A., Chen, Y., Lillicrap, T., Hui, F., Sifre, L., van den Driessche, G., Graepel, T., & Hassabis, D. (2018).

Uber retorted that it would only be $429 million.7 The result of this worker pushback is that these very low margin businesses are going to become even more unprofitable in the future, and the business model is unlikely to expand much further. What is Uber’s plan? Here we see that even Uber doesn’t think the business model they pioneered is likely to succeed. They want to grow big—to monopolise taxi services. Yet their next goal is to replace drivers with self-driving cars and build a massive moat around their business that no one else can compete with. This is a major shift in the nature of their business as suddenly they are taking on the costs and responsibilities of an immense amount of fixed capital. We can see the shift by looking at a now famous quote from 2015: Uber, the world’s largest taxi company, owns no vehicles.


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Rush Hour: How 500 Million Commuters Survive the Daily Journey to Work by Iain Gately

Albert Einstein, Alvin Toffler, autonomous vehicles, Beeching cuts, blue-collar work, Boris Johnson, British Empire, business intelligence, business process, business process outsourcing, California high-speed rail, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Clapham omnibus, cognitive dissonance, congestion charging, connected car, corporate raider, DARPA: Urban Challenge, Dean Kamen, decarbonisation, Deng Xiaoping, Detroit bankruptcy, don't be evil, driverless car, Elon Musk, extreme commuting, Ford Model T, General Motors Futurama, global pandemic, Google bus, Great Leap Forward, Henri Poincaré, high-speed rail, Hyperloop, Jeff Bezos, lateral thinking, Lewis Mumford, low skilled workers, Marchetti’s constant, planned obsolescence, postnationalism / post nation state, Ralph Waldo Emerson, remote working, safety bicycle, self-driving car, Silicon Valley, social distancing, SpaceShipOne, stakhanovite, Steve Jobs, Suez crisis 1956, telepresence, Tesla Model S, Traffic in Towns by Colin Buchanan, urban planning, éminence grise

qt=qt0375405. 320 For Chunka Mui and driverless cars, see: http://www.forbes.com/sites/chunkamui/. 321 ‘typical commuter arteries’, Chris Knapman, ‘Large-scale trial of driverless cars to begin on public roads’, Daily Telegraph, 2 December 2013: http://www.telegraph.co.uk/motoring/news/10484839/Large-scale-trial-of-driverless-cars-to-begin-on-public- roads.html. 321 For KPMG and driverless cars, see ‘Self-driving cars: the next revolution’: https://www.kpmg.com/US/en/IssuesAndInsights/ArticlesPublications/Documents/self-driving-cars-next-revolution.pdf. 326 ‘Something suddenly falls off a truck ahead of the car. Can the system react faster’, Dan Neil, ‘Driverless Cars’. 328 A ‘bold commitment to modernisation’, Jim Pickard, ‘Mandelson fears HS2 will prove an “expensive mistake”’, Financial Times, 2 July 2013. 328 ‘live in a rail bubble’, Matt Ridley, ‘Hadrian’s wall was a marvellous mistake; so is HS2’, 26 July 2013: http://www.rationaloptimist.com/blog/hadrian%27s-wall-was-a-marvellous-mistake-so- is-hs2.aspx. 329 For ‘reached the end of its life’, see: http://www.businessweek.com/articles/2013-07-12/french-wreck-reveals-hidden-danger-in-its-vaunted-train-system. 331 For ‘barf ride’, see: http://pedestrianobservations.wordpress.com/2013/08/13/loopy-ideas-are-fine-if- youre-an-entrepreneur/. 333 For population estimates and the world’s carrying capacity, see World Population Monitoring 2001, United Nations, Department of Economic and Social Affairs, Population Division, New York, 2001, pp. 31ff. 333 ‘occasional vertical and random horizontal low speed vehicular or moving-belt travel’, John Heaver Fremlin, ‘How Many People Can the World Support?’

Traffic density could double or treble in rush hour, without creating the need to build any new roads. The vehicles could be fun to ride in, too. Instead of a row or two of forward-facing seats, there’d be space for beds, chandeliers, mini-bars, workstations, or a croupier and a roulette wheel. Commuting would become an adventure, or a form of luxury travel once again. Self-driving cars will also be wonderfully easy to use – the ultimate taxi service. You’ll be able to text for one, anywhere, anytime. Those with handicaps, alcoholics, and other classes of people currently disbarred from driving would also be empowered by the hands-free revolution. Elderly people who, whether through sight loss or growing immobility, are unable to drive themselves, will not have to lose the freedom to ride in a car that they have enjoyed all their lives.

In the opinion of Catherine Lovazzano, senior manager for consumer trends at Chrysler, the motorcar is no longer ‘the iconic freedom machine that it might have been for a baby boomer’. Generation Now, together with the Digital Natives in the age cohort beneath them, make up nearly half the population of America, and if the Natives are as apathetic as the Nows, then self-driving cars will have a giant market. A similar lack of interest in driving among the young is apparent throughout the West. In a 2012 feature, ‘Seeing the back of the car’, the UK news magazine The Economist noted that ‘All over the rich world, young people are getting their licences later than they used to… in Britain, Canada, France, Norway, South Korea and Sweden’ as well as in America.


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A World Without Work: Technology, Automation, and How We Should Respond by Daniel Susskind

"World Economic Forum" Davos, 3D printing, agricultural Revolution, AI winter, Airbnb, Albert Einstein, algorithmic trading, AlphaGo, artificial general intelligence, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, Big Tech, blue-collar work, Boston Dynamics, British Empire, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, computer age, computer vision, computerized trading, creative destruction, David Graeber, David Ricardo: comparative advantage, deep learning, DeepMind, Demis Hassabis, demographic transition, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, drone strike, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, fake news, financial innovation, flying shuttle, Ford Model T, fulfillment center, future of work, gig economy, Gini coefficient, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, income per capita, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Hargreaves, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kenneth Arrow, Kevin Roose, Khan Academy, Kickstarter, Larry Ellison, low skilled workers, lump of labour, machine translation, Marc Andreessen, Mark Zuckerberg, means of production, Metcalfe’s law, natural language processing, Neil Armstrong, Network effects, Nick Bostrom, Occupy movement, offshore financial centre, Paul Samuelson, Peter Thiel, pink-collar, precariat, purchasing power parity, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Gordon, Sam Altman, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, social intelligence, software is eating the world, sovereign wealth fund, spinning jenny, Stephen Hawking, Steve Jobs, strong AI, tacit knowledge, technological solutionism, TED Talk, telemarketer, The Future of Employment, The Rise and Fall of American Growth, the scientific method, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, Travis Kalanick, Turing test, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, upwardly mobile, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, wealth creators, working poor, working-age population, Y Combinator

We spend large parts of our lives trying to perfect whatever talents and abilities we have, and like the proverbial oil tanker we find it difficult to slow down and change course. Whenever I take a ride in a London taxi, for instance, I am in awe of the drivers: each one has spent years memorizing every street in London, all twenty-five thousand of them, building a legendary body of street smarts known as “the knowledge.” Worrying about their future in the age of self-driving cars, I wonder how they might have fared as doctors or lawyers if they had turned their remarkable memories to remembering symptoms and illnesses, or regulations and court cases, rather than destinations and routes. At this point, though, for older drivers a U-turn like this is likely to be a fantasy.

,” International Labour Office, Working Paper No. 13 (November 2016); Executive Office of the President, “Artificial Intelligence, Automation, and the Economy,” December 2016. 35.  Fergal O’Brien and Maciej Onoszko, “Tech Upheaval Means a ‘Massacre of the Dilberts’ BOE’s Carney Says,” Bloomberg, 13 April 2018. 36.  Scott Dadich, “Barack Obama, Neural Nets, Self-Driving Cars, and the Future of the World,” Wired, November 2016. 37.  UBS, “Intelligence Automation: A UBS Group Innovation White Paper” (2017); PwC, “Workforce of the Future: The Competing Forces Shaping 2030” (2018); Deloitte, “From Brawn to Brains: The Impact of Technology on Jobs in the UK” (2015). 38.  

., “Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks,” Nature 542 (2017): 115–18. 11.  See Jeff Reinke, “From Old Steel Mill to Autonomous Vehicle Test Track,” Thomas, 19 October 2017; Michael J. Coren, “Tesla Has 780 Million Miles of Driving Data, and Adds Another Million Every 10 Hours,” Quartz, 28 May 2016; and Alexis C. Madrigal, “Inside Waymo’s Secret World for Training Self-Driving Cars,” Atlantic, 23 August 2017. 12.  David McCandless, “Codebases: Millions of Lines of Code,” 24 September 2015, https://informationisbeautiful.net/visualizations/million-lines-of-code/ (accessed 25 April 2018). 13.  Michael J. Coren, “San Francisco Is Actually One of the Worst-Paying Places in the US for Software Engineers,” Quartz, 9 February 2017. 14.  


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Power and Progress: Our Thousand-Year Struggle Over Technology and Prosperity by Daron Acemoglu, Simon Johnson

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 4chan, agricultural Revolution, AI winter, Airbnb, airline deregulation, algorithmic bias, algorithmic management, Alignment Problem, AlphaGo, An Inconvenient Truth, artificial general intelligence, augmented reality, basic income, Bellingcat, Bernie Sanders, Big Tech, Bletchley Park, blue-collar work, British Empire, carbon footprint, carbon tax, carried interest, centre right, Charles Babbage, ChatGPT, Clayton Christensen, clean water, cloud computing, collapse of Lehman Brothers, collective bargaining, computer age, Computer Lib, Computing Machinery and Intelligence, conceptual framework, contact tracing, Corn Laws, Cornelius Vanderbilt, coronavirus, corporate social responsibility, correlation does not imply causation, cotton gin, COVID-19, creative destruction, declining real wages, deep learning, DeepMind, deindustrialization, Demis Hassabis, Deng Xiaoping, deskilling, discovery of the americas, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, energy transition, Erik Brynjolfsson, European colonialism, everywhere but in the productivity statistics, factory automation, facts on the ground, fake news, Filter Bubble, financial innovation, Ford Model T, Ford paid five dollars a day, fulfillment center, full employment, future of work, gender pay gap, general purpose technology, Geoffrey Hinton, global supply chain, Gordon Gekko, GPT-3, Grace Hopper, Hacker Ethic, Ida Tarbell, illegal immigration, income inequality, indoor plumbing, industrial robot, interchangeable parts, invisible hand, Isaac Newton, Jacques de Vaucanson, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, Johannes Kepler, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph-Marie Jacquard, Kenneth Arrow, Kevin Roose, Kickstarter, knowledge economy, labor-force participation, land reform, land tenure, Les Trente Glorieuses, low skilled workers, low-wage service sector, M-Pesa, manufacturing employment, Marc Andreessen, Mark Zuckerberg, megacity, mobile money, Mother of all demos, move fast and break things, natural language processing, Neolithic agricultural revolution, Norbert Wiener, NSO Group, offshore financial centre, OpenAI, PageRank, Panopticon Jeremy Bentham, paperclip maximiser, pattern recognition, Paul Graham, Peter Thiel, Productivity paradox, profit maximization, profit motive, QAnon, Ralph Nader, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Robert Bork, Robert Gordon, Robert Solow, robotic process automation, Ronald Reagan, scientific management, Second Machine Age, self-driving car, seminal paper, shareholder value, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, social intelligence, Social Responsibility of Business Is to Increase Its Profits, social web, South Sea Bubble, speech recognition, spice trade, statistical model, stem cell, Steve Jobs, Steve Wozniak, strikebreaker, subscription business, Suez canal 1869, Suez crisis 1956, supply-chain management, surveillance capitalism, tacit knowledge, tech billionaire, technoutopianism, Ted Nelson, TED Talk, The Future of Employment, The Rise and Fall of American Growth, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, theory of mind, Thomas Malthus, too big to fail, total factor productivity, trade route, transatlantic slave trade, trickle-down economics, Turing machine, Turing test, Twitter Arab Spring, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, union organizing, universal basic income, Unsafe at Any Speed, Upton Sinclair, upwardly mobile, W. E. B. Du Bois, War on Poverty, WikiLeaks, wikimedia commons, working poor, working-age population

Indeed, there is now a shortage of radiologists that is predicted to increase over the next decade.” On the diagnosis of diabetic retinopathy and the combination of AI algorithms and specialists, see Raghu, Blumer, Corrado, Kleinberg, Obermeyer, and Mullainathan (2019). On the wishes of Google’s chief of self-driving cars, see Fried (2015). For Elon Musk’s comments on self-driving cars, see Hawkins (2021). General AI Illusion. On superintelligence, see Bostrom (2017). On AlphaZero, see https://www.deepmind.com/blog/alphazero-shedding-new-light-on-chess-shogi-and-go. For an interesting critique of the current AI approach to intelligence, which also emphasizes the social and situational aspects of intelligence, see Larson (2021).

Princeton, NJ: Princeton University Press. Frey, Carl Benedikt, and Michael A. Osborne. 2013. “The Future of Employment: How Susceptible Are Jobs to Computerisation?” Mimeo. Oxford: Oxford Martin School. Fried, Ina. 2015. “Google Self-Driving Car Chief Wants Tech on the Market Within Five Years.” Vox, March 17. www.vox.com/2015/3/17/11560406/google-self-driving-car-chief-wants-tech-on-the-market-within-five. Friedman, Milton. 1970. “A Friedman Doctrine—the Social Responsibility of Business Is to Increase Its Profits.” New York Times, September 13. www.nytimes.com/1970/09/13/archives/a-friedman-doctrine-the-social-responsibility-of-business-is-to.html.

For example, state-of-the-art machine-learning algorithms can improve the diagnosis of diabetic retinopathy, which results from damage to blood vessels on the retina among diabetic patients. Nevertheless, accuracy increases significantly more when algorithms are used to identify difficult cases, which are then assigned to ophthalmologists for better diagnosis. The chief technology officer of Google’s self-driving car division confidently expected in 2015 that his then-eleven-year-old son would not need to get a driver’s license by the time he turned sixteen. In 2019 Elon Musk predicted that Tesla would have one million fully automated, driverless taxicabs on the streets by the end of 2020. These predictions have not come to pass for the same reason.


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Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari

23andMe, Aaron Swartz, agricultural Revolution, algorithmic trading, Anne Wojcicki, Anthropocene, anti-communist, Anton Chekhov, autonomous vehicles, behavioural economics, Berlin Wall, call centre, Chekhov's gun, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, DeepMind, Demis Hassabis, Deng Xiaoping, don't be evil, driverless car, drone strike, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, Great Leap Forward, Intergovernmental Panel on Climate Change (IPCC), invention of writing, invisible hand, Isaac Newton, job automation, John Markoff, Kevin Kelly, lifelogging, low interest rates, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Monkeys Reject Unequal Pay, mutually assured destruction, new economy, Nick Bostrom, pattern recognition, peak-end rule, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, The future is already here, The Future of Employment, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!, zero-sum game

Christopher Steiner, Automate This: How Algorithms Came to Rule Our World (New York: Penguin, 2012), 215; Tom Vanderbilt, ‘Let the Robot Drive: The Autonomous Car of the Future is Here’, Wired, 20 January 2012, accessed 21 December 2014, http://www.wired.com/2012/01/ff_autonomouscars/all/; Chris Urmson, ‘The Self-Driving Car Logs More Miles on New Wheels’, Google Official Blog, 7 August 2012, accessed 23 December 2014, http://googleblog.blogspot.hu/2012/08/the-self-driving-car-logs-more-miles-on.html; Matt Richtel and Conor Dougherty, ‘Google’s Driverless Cars Run into Problem: Cars with Drivers’, New York Times, 1 September 2015, accessed 2 September 2015, http://www.nytimes.com/2015/09/02/technology/personaltech/google-says-its-not-the-driverless-cars-fault-its-other-drivers.html?

And it is sobering to realise that, at least for armies and corporations, the answer is straightforward: intelligence is mandatory but consciousness is optional. Armies and corporations cannot function without intelligent agents, but they don’t need consciousness and subjective experiences. The conscious experiences of a flesh-and-blood taxi driver are infinitely richer than those of a self-driving car, which feels absolutely nothing. The taxi driver can enjoy music while navigating the busy streets of Seoul. His mind may expand in awe as he looks up at the stars and contemplates the mysteries of the universe. His eyes may fill with tears of joy when he sees his baby girl taking her very first step.

And the autonomous car will soon be able to do that far better than a human driver, even though it cannot enjoy music or be awestruck by the magic of existence. Indeed, if we forbid humans to drive taxis and cars altogether, and give computer algorithms monopoly over traffic, we can then connect all vehicles to a single network, and thereby make car accidents virtually impossible. In August 2015, one of Google’s experimental self-driving cars had an accident. As it approached a crossing and detected pedestrians wishing to cross, it applied its brakes. A moment later it was hit from behind by a sedan whose careless human driver was perhaps contemplating the mysteries of the universe instead of watching the road. This could not have happened if both vehicles were steered by interlinked computers.


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The Age of Spiritual Machines: When Computers Exceed Human Intelligence by Ray Kurzweil

Ada Lovelace, Alan Greenspan, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alvin Toffler, Any sufficiently advanced technology is indistinguishable from magic, backpropagation, Buckminster Fuller, call centre, cellular automata, Charles Babbage, classic study, combinatorial explosion, complexity theory, computer age, computer vision, Computing Machinery and Intelligence, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, Everything should be made as simple as possible, financial engineering, first square of the chessboard / second half of the chessboard, flying shuttle, fudge factor, functional programming, George Gilder, Gödel, Escher, Bach, Hans Moravec, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, John Gilmore, John Markoff, John von Neumann, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, Marshall McLuhan, Menlo Park, natural language processing, Norbert Wiener, optical character recognition, ought to be enough for anybody, pattern recognition, phenotype, punch-card reader, quantum entanglement, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Robert Metcalfe, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, social intelligence, speech recognition, Steven Pinker, Stewart Brand, stochastic process, Stuart Kauffman, technological singularity, Ted Kaczynski, telepresence, the medium is the message, The Soul of a New Machine, There's no reason for any individual to have a computer in his home - Ken Olsen, traveling salesman, Turing machine, Turing test, Whole Earth Review, world market for maybe five computers, Y2K

• Prediction: Around the year 2000, chips with more than a billion components will emerge.What Happened: We’re right on schedule. • Prediction: The technology for the “cybernetic chauffeur” (self-driving cars using special sensors in the roads) will become available by the end of the 1990s with implementation on major highways feasible during the first decade of the twenty-first century.What Happened: Self-driving cars are being tested in Los Angeles, London, Tokyo, and other cities. There were extensive successful tests on Interstate 15 in southern California during 1997. City planners now realize that automated driving technologies will greatly expand the capacity of existing roads.

Crystalline computing also refers to the possibility of growing computers as crystals. CSR See Continuous speech recognition. Cybernetic artist A computer program that is able to create original artwork in poetry, visual art, or music. Cybernetic artists will become increasingly commonplace starting in 2009. Cybernetic chauffeur Self-driving cars that use special sensors in the roads. Self driving cars are being experimented with in the late 1990s, with implementation on major highways feasible during the first decade of the twenty-first century. Cybernetic poet A computer program that is able to create original poetry. Cybernetics A term coined by Norbert Wiener to describe the “science of control and communication in animals and machines.”

City planners now realize that automated driving technologies will greatly expand the capacity of existing roads. Installing the requisite sensors on a highway costs only about $10,000 per mile, compared to $1 to $10 million per mile for building new highways. Automated highways and self-driving cars will also eliminate most accidents on these roads. The U.S. National Automated Highway System (NAHS) consortium is predicting implementation of these systems during the first decade of the twenty-first century.15 • Prediction: Continuous speech recognition (CSR) with large vocabularies for specific tasks will emerge in the early 1990s.What Happened: Whoops.


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To Be a Machine: Adventures Among Cyborgs, Utopians, Hackers, and the Futurists Solving the Modest Problem of Death by Mark O'Connell

"World Economic Forum" Davos, 3D printing, Ada Lovelace, AI winter, Airbnb, Albert Einstein, AlphaGo, Amazon Picking Challenge, artificial general intelligence, Bletchley Park, Boston Dynamics, brain emulation, Charles Babbage, clean water, cognitive dissonance, computer age, cosmological principle, dark matter, DeepMind, disruptive innovation, double helix, Edward Snowden, effective altruism, Elon Musk, Extropian, friendly AI, global pandemic, Great Leap Forward, Hans Moravec, impulse control, income inequality, invention of the wheel, Jacques de Vaucanson, John von Neumann, knowledge economy, Law of Accelerating Returns, Lewis Mumford, life extension, lifelogging, Lyft, Mars Rover, means of production, military-industrial complex, Nick Bostrom, Norbert Wiener, paperclip maximiser, Peter Thiel, profit motive, radical life extension, Ray Kurzweil, RFID, San Francisco homelessness, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Singularitarianism, Skype, SoftBank, Stephen Hawking, Steve Wozniak, superintelligent machines, tech billionaire, technological singularity, technoutopianism, TED Talk, The Coming Technological Singularity, Travis Kalanick, trickle-down economics, Turing machine, uber lyft, Vernor Vinge

This, in fact, is basically the first of Isaac Asimov’s famous Three Laws of Robotics, which states: “A robot may not injure a human being or, through inaction, allow a human being to come to harm.” But the reality is that we’re not quite as monomaniacally invested in the prevention of harm to our children as we imagine ourselves to be. A self-driving car that followed this instruction with absolute rigor would, for instance—given the nontrivial risk of getting into an accident on the way—decline to take your kids to the movies to see the latest computer-animated film about a young boy and his adventures with his robot pal. One potential approach, most prominently proposed by Stuart himself, was that rather than attempting to write these implicit values and trade-offs into an AI’s source code, the AI be programmed so that it learned by observing human behavior.

The car that got the farthest from the starting pistol made it just under seven and a half miles before finally coming to grief on a large rock, and DARPA declined to award its million-dollar prize. But when the race was held again the following year, five cars finished the route, and the winning team went on to form the nucleus of Google’s Self-Driving Car Project, under the auspices of which, even now, California’s roads were being successfully navigated by vehicles unguided by human hands, luxury ghostmobiles on the decaying highways, an advance guard of an automated future. Uber, the drive-sharing service that had seriously damaged the taxi sector in recent years, was already speaking openly about its plans to replace all of its drivers with automated cars as soon as the technology allowed.

“It’s really a matter of gathering as much data as possible about your life,” she told him, “and figuring out how you can use that data to optimize yourself as a person.” “Right,” said Tim. “Although I’d like to take the ‘person’ term completely out of the equation. People really suck at decisions. It’s like the whole self-driving car thing. People are like, ‘Oh, you can’t take humans out of the loop, I’m a human and I’m an awesome driver.’ And I’m like, no, man, you’re not an awesome driver. You’re a monkey, and monkeys suck at making decisions.” Anne emitted a perfunctory laugh. She seemed uncomfortable, and I wondered whether this discomfort was a reaction to the way in which Tim’s language seemed to lay bare the mechanistic principles of the QS movement, its view of the self as reducible to a set of facts and statistics that could be interpreted, and whose interpretation thereby informed the activity of the self, and thereby the generation of further data—the human being as a feedback loop of input and output.


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Measure What Matters: How Google, Bono, and the Gates Foundation Rock the World With OKRs by John Doerr

Abraham Maslow, Albert Einstein, Big Tech, Bob Noyce, cloud computing, collaborative editing, commoditize, crowdsourcing, data science, fail fast, Fairchild Semiconductor, Firefox, Frederick Winslow Taylor, Google Chrome, Google Earth, Google X / Alphabet X, Haight Ashbury, hockey-stick growth, intentional community, Jeff Bezos, job satisfaction, Khan Academy, knowledge worker, Mary Meeker, Menlo Park, meta-analysis, PageRank, Paul Buchheit, Ray Kurzweil, risk tolerance, Salesforce, scientific management, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Steven Levy, subscription business, Susan Wojcicki, web application, Yogi Berra, éminence grise

Thousand percent improvement requires rethinking problems, exploring what’s technically possible and having fun in the process. At Google, in line with Andy Grove’s old standard, aspirational OKRs are set at 60 to 70 percent attainment. In other words, performance is expected to fall short at least 30 percent of the time. And that’s considered success! Eric Schmidt, Larry Page, and Sergey Brin with Google’s first self-driving car, 2011—10x thinking in action! Google has had its share of colossal misfires, from Helpouts to Google Answers. Living in the 70 percent zone entails a liberal sprinkling of moonshots and a willingness to court failure. At the start of the period, not a single goal may look possible. And so the Googlers are pushed to ask harder questions: What radical, high-risk action needs to be considered?

And then after ten milliseconds of celebration we have to set ourselves another [set of] highly difficult-to-reach objectives and we have to meet them. And the reward of having met one of these challenging goals is that you get to play again. 13 Stretch: The Google Chrome Story Sundar Pichai CEO Stretch goals were beautifully defined by the leader of the Google X team that developed Project Loon and self-driving cars. Says Astro Teller: “ If you want your car to get fifty miles per gallon, fine. You can retool your car a little bit. But if I tell you it has to run on a gallon of gas for five hundred miles, you have to start over.” In 2008, Sundar Pichai was Google’s vice president of product development.

., 47 “rule of seven,” 14 Salesforce, 114 sandbagging, 34 , 78 , 181 , 259 –60 Sandberg, Sheryl, 54 n, 175 , 184 –85, 251 Sand Hill Unicorns, 81 , 81 –85, 82 , 84 , 87 San Francisco Marathon, 88 San Jose Hyatt House, 43 scaling, 113 –14 culture and, 220 –21 at Google, 3 , 11 MyFitnessPal story, 93 –94, 95 Nuna story, 72 Remind story, 63 –65 Zuma Pizza story, 203 scarcity, 161 Schmidt, Eric, 6 n, 11 , 13 , 15 , 140 , 146 , 251 , 252 scoring, 120 –22 assessment variations, 123 –24 Sculley, John, 248 Sears, 8 Seidman, Dov, 219 –21 self-actualization, 136 –37 self-assessments, 122 –24, 276 assessment variations, 123 –24 self-discipline, 30 , 31 , 202 –3 self-driving cars, 140 , 143 self-reflection, 124 –25 goal planning and, 183 , 269 Series A funding, 64 , 94 Series B funding, 64 Shriver, Bobby, 237 Sibyl, 161 silos, 12 , 25 , 102 –3, 275 Slack, 12 , 73 , 110 , 111 Slavitt, Andrew M., 76 Smith, Brad, 104 –5, 106 Smith, Jeff, 231 –32 “snippets,” 11 n Social+Capital Partnership, 64 stack rankings, 175 , 191 , 278 Staffa, Tim, 179 –80 Stanford University, 3 , 21 n, 144 , 152 Stonesifer, Patty, 126 –32, 127 stretch goals, 17 , 34 , 133 –71, 277 –78 Bono’s ONE Campaign story, 238 –39 daring to fail, 33 –34 Google Chrome story, 143 –53 gospel of 10x, 138 –40 need to stretch, 136 –38 stretch variables, 141 –42 YouTube story, 154 –71 structured goal setting, 9 –10, 134 , 203 , 215 subjective self-assessments, 122 –24, 276 suggestion boxes, 185 Sun Microsystems, 6 –7, 148 Superpower #1 (focus and commit to priorities), 16 –17, 47 –76, 274 care of key results, 50 communicating with clarity, 49 –50 less is more, 55 –57 Nuna story, 69 –76 pairing key results, 52 –54 the perfect and the good, 54 –55 Remind story, 58 –68 top-line goals, 48 –49 the what, how, when, 51 –52 Superpower #2 (align and connect for teamwork), 17 , 77 –112, 275 bottom-up OKRs, 86 –89 cascading OKRs, 79 –86 cross-functional coordination, 88 –89 Intuit story, 102 –12 MyFitnessPal story, 90 –101 Sand Hill Unicorns, 81 –85 Superpower #3 (track for accountability), 17 , 113 –32, 276 –77 Gates Foundation story, 126 –32 midlife tracking, 117 –19 OKR shepherds, 115 –16 reflection, 124 –25 scoring, 120 –22 self-assessment, 122 –24 setting up, 113 –15 Superpower #4 (stretch for amazing), 17 , 133 –71, 277 –78 Google Chrome story, 143 –53 gospel of 10x, 138 –40 need to stretch, 136 –38 stretch variables, 141 –42 YouTube story, 154 –71 Suzuki, Joseph, 205 –6 Taylor, Frederick Winslow, 24 teamwork, 12 , 178 , 275 .


pages: 260 words: 76,223

Ctrl Alt Delete: Reboot Your Business. Reboot Your Life. Your Future Depends on It. by Mitch Joel

3D printing, Amazon Web Services, augmented reality, behavioural economics, call centre, clockwatching, cloud computing, content marketing, digital nomad, do what you love, Firefox, future of work, gamification, ghettoisation, Google Chrome, Google Glasses, Google Hangouts, Khan Academy, Kickstarter, Kodak vs Instagram, Lean Startup, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Network effects, new economy, Occupy movement, place-making, prediction markets, pre–internet, QR code, recommendation engine, Richard Florida, risk tolerance, Salesforce, self-driving car, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, Steve Jobs, Steve Wozniak, TechCrunch disrupt, TED Talk, the long tail, Thomas L Friedman, Tim Cook: Apple, Tony Hsieh, vertical integration, white picket fence, WikiLeaks, zero-sum game

Instagram started out as a very derivative copy of foursquare before switching its focus to mobile photos with a social edge. Google continues to fascinate as the search engine expands into areas like online video (YouTube), mobile (Android and the Nexus line of devices), email services (Gmail), Web browsers (Google Chrome), online social networking (Google+), and beyond (self-driving cars and Google Glasses). Amazon continues to squiggle by pushing beyond selling books online into e-readers (Kindle), selling shoes (Zappos), offering cloud computing technology (Amazon Web Services), and beyond. When you actually start digging down deep into how these companies have evolved and stayed relevant, you won’t see business models that look like anything from the playbooks of Kodak or RIM.

This means that we can’t be afraid to have a more squiggly career path, and we also have to be more open to doing the big, big stuff (Steve Jobs would often talk about making a “dent in the universe”). You will hear Mark Zuckerberg talk about Facebook as the place to connect the world. Sergey Brin and Larry Page of Google often talk about Google’s mission to organize the world’s information and knowledge (and, with that, they squiggle to create self-driving cars!). It’s one thing to dream big. It’s another thing to think and do big. In this new world, the squiggle is about not being afraid of the big stuff within whatever industry you serve. If you’re not thinking about the bigger problems that face your industry, someone else is. Squiggle… uncover and go after the big ideas.

I can’t help but wonder and think about a time in the not-too-distant future when we’re no longer carrying these devices around but they are actually in us… a part of us (subdermal implants or brain-activity-activated… who knows?). As I was walking through the airport, I noticed that the current cover story for Wired magazine is all about self-driving cars. Such instances of science fiction catching up to reality get me excited. They get me thinking more about how much I love business, and it makes me hopeful that I’ll be privileged enough to be alive long enough to see how we innovate from this very powerful moment in time that we currently find ourselves in.


pages: 286 words: 79,305

99%: Mass Impoverishment and How We Can End It by Mark Thomas

"there is no alternative" (TINA), "World Economic Forum" Davos, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, additive manufacturing, Alan Greenspan, Albert Einstein, anti-communist, autonomous vehicles, bank run, banks create money, behavioural economics, bitcoin, business cycle, call centre, Cambridge Analytica, central bank independence, circular economy, complexity theory, conceptual framework, creative destruction, credit crunch, CRISPR, declining real wages, distributed ledger, Donald Trump, driverless car, Erik Brynjolfsson, eurozone crisis, fake news, fiat currency, Filter Bubble, full employment, future of work, Gini coefficient, gravity well, income inequality, inflation targeting, Internet of things, invisible hand, ITER tokamak, Jeff Bezos, jimmy wales, job automation, Kickstarter, labour market flexibility, laissez-faire capitalism, Larry Ellison, light touch regulation, Mark Zuckerberg, market clearing, market fundamentalism, Martin Wolf, Modern Monetary Theory, Money creation, money: store of value / unit of account / medium of exchange, Nelson Mandela, Nick Bostrom, North Sea oil, Occupy movement, offshore financial centre, Own Your Own Home, Peter Thiel, Piper Alpha, plutocrats, post-truth, profit maximization, quantitative easing, rent-seeking, Robert Solow, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, smart cities, Steve Jobs, The Great Moderation, The Wealth of Nations by Adam Smith, Tyler Cowen, warehouse automation, wealth creators, working-age population

Other problems which may benefit from this kind of high-power computing include mapping of entire proteins in the way that genes can be mapped today – or even entire genomes – and, of course, the development of full AI. Narrow AI for applications such as autonomous vehicles Proof of concept studies on self-driving cars have been underway for almost a decade and large-scale trials are now in progress. Google, for example, has more than twenty autonomous vehicles in the US, and NuTonomy has trials of taxis underway in Singapore. Many commentators believe that the first commercially available self-driving cars will hit the market before 2020.9 The heads of automakers General Motors and Nissan have both confirmed that they expect driverless cars on the roads by 2020.

I would pick out the following technologies for special attention, because they seem to me to be harbingers of what is just around the corner: • additive manufacturing – for example, 3-D printing; • nanotechnology – for example, new processes like genetic editing and new materials like graphene; • new computing approaches – for example quantum computing, new applications of narrow artificial intelligence (AI) such as self-driving cars, and even full artificial intelligence (AI) capable of solving any problem a human can solve; • clean energy – possibly even nuclear fusion. Most of these technologies will have entered the mainstream by 2040, so over the next thirty-five years, you can expect to feel their impact. ADDITIVE MANUFACTURING Traditionally, to manufacture a complex structure, you would start with a block and machine away the material you didn’t want.


The Smartphone Society by Nicole Aschoff

"Susan Fowler" uber, 4chan, A Declaration of the Independence of Cyberspace, Airbnb, algorithmic bias, algorithmic management, Amazon Web Services, artificial general intelligence, autonomous vehicles, barriers to entry, Bay Area Rapid Transit, Bernie Sanders, Big Tech, Black Lives Matter, blockchain, carbon footprint, Carl Icahn, Cass Sunstein, citizen journalism, cloud computing, correlation does not imply causation, crony capitalism, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, degrowth, Demis Hassabis, deplatforming, deskilling, digital capitalism, digital divide, do what you love, don't be evil, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Evgeny Morozov, fake news, feminist movement, Ferguson, Missouri, Filter Bubble, financial independence, future of work, gamification, gig economy, global value chain, Google Chrome, Google Earth, Googley, green new deal, housing crisis, income inequality, independent contractor, Jaron Lanier, Jeff Bezos, Jessica Bruder, job automation, John Perry Barlow, knowledge economy, late capitalism, low interest rates, Lyft, M-Pesa, Mark Zuckerberg, minimum wage unemployment, mobile money, moral panic, move fast and break things, Naomi Klein, Network effects, new economy, Nicholas Carr, Nomadland, occupational segregation, Occupy movement, off-the-grid, offshore financial centre, opioid epidemic / opioid crisis, PageRank, Patri Friedman, peer-to-peer, Peter Thiel, pets.com, planned obsolescence, quantitative easing, Ralph Waldo Emerson, RAND corporation, Ray Kurzweil, RFID, Richard Stallman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Sidewalk Labs, Silicon Valley, single-payer health, Skype, Snapchat, SoftBank, statistical model, Steve Bannon, Steve Jobs, surveillance capitalism, TaskRabbit, tech worker, technological determinism, TED Talk, the scientific method, The Structural Transformation of the Public Sphere, TikTok, transcontinental railway, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, upwardly mobile, Vision Fund, W. E. B. Du Bois, wages for housework, warehouse robotics, WikiLeaks, women in the workforce, yottabyte

Amazon alone employs more than 613,000 warehouse workers worldwide, and adds about 100,000 more temp workers during peaks.48 Jessica Bruder, in Nomadland: Surviving America in the Twenty-First Century, follows the lives of Amazon’s “CamperForce,” a large group of (mainly) retirees who can’t afford to retire, so they live in their RVs and other vehicles and find temporary work in the warehouses of “the everything store” during the holidays. When peak season is over, these “workampers” drive away in what Amazon executives proudly call a “tail light parade.”49 Log on to the Spare5 mobile task app on the long bus ride home, circle the road signs in a series of photographs for some self-driving-car start-up, and you’ve paid for that afternoon splurge on a triple macchiato at Starbucks. App jobs like this are appealing at first glance. There are few barriers to entry. You can work when you’re able, and the ads promise decent money. You just need a smartphone, often a car, and a willingness to work.

Google founders Sergey Brin and Larry Page created Alphabet in 2015 to organize their growing pile of tech companies—a conglomerate that, in addition to Google, includes companies focused on biotech (Calico), cybersecurity (Chronicle), wind power (Makani), and the life sciences (Verily). Add to that Waymo and Wing, which develop self-driving car and drone delivery technology, and DeepMind, Alphabet’s artificial intelligence subsidiary. Throw in venture capital and private equity firms (GV, Capital G), a tech incubator (Jigsaw), broadband and balloon internet providers (Google Fiber and Loon), an urban innovation organization (Sidewalk Labs), and a “semisecret” research and development facility called X Development, and one gets a sense of the growing reach of the behemoth that started with a search engine.

The fundamentals are in place for bounty that vastly exceeds anything we’ve ever seen before.19 Chris Anderson, the editor-in-chief of Wired magazine, predicted back in 2008 that we’d reached the “end of theory”—that our 24/7 connected lives would generate so much data that soon we wouldn’t even need theory anymore: “Correlation supersedes causation, and science can advance even without coherent models, unified theories, or really any mechanistic explanation at all.”20 With advancements in networks and computing we’ll generate more and more data, solving problems and fueling breakthroughs in medicine, science, agriculture, policing, and logistics. Self-driving cars and the ability of the Alpha Go Zero program to teach itself how to play the ancient and extremely difficult Chinese game of Go using only the game rules and reinforcement learning are just the beginning of a seismic shift rooted in the power of data. The motto of Alphabet subsidiary DeepMind encapsulates this vision of the future: “Solve intelligence and use that to solve everything else.”


pages: 161 words: 39,526

Applied Artificial Intelligence: A Handbook for Business Leaders by Mariya Yao, Adelyn Zhou, Marlene Jia

Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, artificial general intelligence, autonomous vehicles, backpropagation, business intelligence, business process, call centre, chief data officer, cognitive load, computer vision, conceptual framework, data science, deep learning, DeepMind, en.wikipedia.org, fake news, future of work, Geoffrey Hinton, industrial robot, information security, Internet of things, iterative process, Jeff Bezos, job automation, machine translation, Marc Andreessen, natural language processing, new economy, OpenAI, pattern recognition, performance metric, price discrimination, randomized controlled trial, recommendation engine, robotic process automation, Salesforce, self-driving car, sentiment analysis, Silicon Valley, single source of truth, skunkworks, software is eating the world, source of truth, sparse data, speech recognition, statistical model, strong AI, subscription business, technological singularity, The future is already here

When smoke levels reach a predefined level, the device will play an alarm sound until it is turned off manually. Similarly, the cruise control in your car uses a powered mechanism to control the throttle position in order to maintain a constant speed. You would never set your cruise control, take your hands off the wheel, and claim that you now have a self-driving car. Doing so would result in terrible outcomes. Yet most companies claiming to have AI are really just using Systems That Act, or rule-based mechanisms that are incapable of dynamic actions or decisions. Systems That Predict Systems That Predict are systems that are capable of analyzing data and using it to produce probabilistic predictions.

In sales, for example, machine learning approaches to lead scoring can perform better than rule-based or statistical methods. Once the machine has produced a prediction on the quality of a lead, the salesperson then applies human judgment to decide how to follow up. More complex systems, such as self-driving cars and industrial robotics, handle everything from gathering the initial data to executing the action resulting from its analysis. For example, an autonomous vehicle must turn video and sensor feeds into accurate predictions of the surrounding world and adjust its driving accordingly. Systems That Create We humans like to think we’re the only beings capable of creativity, but computers have been used for generative design and art for decades.


pages: 389 words: 112,319

Think Like a Rocket Scientist: Simple Strategies You Can Use to Make Giant Leaps in Work and Life by Ozan Varol

Abraham Maslow, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, Amazon Web Services, Andrew Wiles, Apollo 11, Apollo 13, Apple's 1984 Super Bowl advert, Arthur Eddington, autonomous vehicles, Ben Horowitz, Boeing 747, Cal Newport, Clayton Christensen, cloud computing, Colonization of Mars, dark matter, delayed gratification, different worldview, discovery of DNA, double helix, Elon Musk, fail fast, fake news, fear of failure, functional fixedness, Gary Taubes, Gene Kranz, George Santayana, Google Glasses, Google X / Alphabet X, Inbox Zero, index fund, Isaac Newton, it is difficult to get a man to understand something, when his salary depends on his not understanding it, James Dyson, Jeff Bezos, job satisfaction, Johannes Kepler, Kickstarter, knowledge worker, Large Hadron Collider, late fees, lateral thinking, lone genius, longitudinal study, Louis Pasteur, low earth orbit, Marc Andreessen, Mars Rover, meta-analysis, move fast and break things, multiplanetary species, Neal Stephenson, Neil Armstrong, Nick Bostrom, obamacare, Occam's razor, out of africa, Peter Pan Syndrome, Peter Thiel, Pluto: dwarf planet, private spaceflight, Ralph Waldo Emerson, reality distortion field, Richard Feynman, Richard Feynman: Challenger O-ring, Ronald Reagan, Sam Altman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Simon Singh, Skinner box, SpaceShipOne, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, subprime mortgage crisis, sunk-cost fallacy, TED Talk, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Tyler Cowen, Upton Sinclair, Vilfredo Pareto, We wanted flying cars, instead we got 140 characters, Whole Earth Catalog, women in the workforce, Yogi Berra

Taking a risk on big ideas—using a jetpack to land a Humvee on Mars or building a career that defies stereotypes—is easier when the potential reward is also big. The reward, in the case of Curiosity, “is that we have a Humvee driving around on Mars, exploring it, and unlocking the secrets of the solar system,” Waydo said. And the reward for Waydo? She helped put three rovers on Mars and later moved on to building self-driving cars—accomplishments that transcend Waydo to enrich every person touched by her skills. If you’re still having trouble activating those divergent-thinking muscles, even with the payoff in mind, the next section will give you a jetpack you can use to boost your own vision. Shocking the Brain There was a guy who became famous in the 1970s by lifting some weights.

She later joined Google as the director of consumer marketing for Europe, Middle East, and Africa.58 While she was at the top of her marketing game, a phone call from Teller changed everything. On the call, Teller walked Felten through the audacious projects that X was incubating—including self-driving cars and balloon-powered internet. She responded with questions that Teller hadn’t heard before: Is what you’re doing legal? Have you talked to any governments and regulators about it? Will you collaborate with other companies? Do you have a business plan?59 Teller had no answers. “Oh, no one’s really thinking about any of these problems,” he replied.

For example, they examined how the food industry makes snack chip bags and sausage casings.73 They eventually solved the problem and survived all other attempts by Xers to prove the project impossible. Projects like Loon that survive this rigorous de-risking process graduate from X—with employees getting actual diplomas—and become their own independent companies. X’s graduates include businesses that produce self-driving cars, autonomous drones, and contact lenses that measure glucose levels. These ideas all seemed like science fiction—until X struck the right balance between idealism and pragmatism, making them a reality. At a different company, SpaceX, two leaders represent these two perspectives of idealism and pragmatism.


pages: 409 words: 112,055

The Fifth Domain: Defending Our Country, Our Companies, and Ourselves in the Age of Cyber Threats by Richard A. Clarke, Robert K. Knake

"World Economic Forum" Davos, A Declaration of the Independence of Cyberspace, Affordable Care Act / Obamacare, air gap, Airbnb, Albert Einstein, Amazon Web Services, autonomous vehicles, barriers to entry, bitcoin, Black Lives Matter, Black Swan, blockchain, Boeing 737 MAX, borderless world, Boston Dynamics, business cycle, business intelligence, call centre, Cass Sunstein, cloud computing, cognitive bias, commoditize, computer vision, corporate governance, cryptocurrency, data acquisition, data science, deep learning, DevOps, disinformation, don't be evil, Donald Trump, Dr. Strangelove, driverless car, Edward Snowden, Exxon Valdez, false flag, geopolitical risk, global village, immigration reform, information security, Infrastructure as a Service, Internet of things, Jeff Bezos, John Perry Barlow, Julian Assange, Kubernetes, machine readable, Marc Benioff, Mark Zuckerberg, Metcalfe’s law, MITM: man-in-the-middle, Morris worm, move fast and break things, Network effects, open borders, platform as a service, Ponzi scheme, quantum cryptography, ransomware, Richard Thaler, Salesforce, Sand Hill Road, Schrödinger's Cat, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Skype, smart cities, Snapchat, software as a service, Steven Levy, Stuxnet, technoutopianism, The future is already here, Tim Cook: Apple, undersea cable, unit 8200, WikiLeaks, Y2K, zero day

“third-party control risk”: Warning letter to Abbott Laboratories from the Food and Drug Administration, April 12, 2017, www.fda.gov/iceci/enforcementactions/warningletters/2017/ucm552687.htm. issuing regulations requiring such assurances: Colin Dwyer, “Department of Transportation Rolls Out New Guidelines for Self-Driving Cars,” National Public Radio, September 12, 2017, www.npr.org/sections/the-two-way/2017/09/12/550533833/department-of-transportation-rolls-out-new-guidelines-for-self-driving-cars. Chapter 18: Derisking Ourselves We like ten-character passwords: “How to Choose a Password,” Office of Information Security, University of Cincinnati, accessed January 6, 2019, www.uc.edu/infosec/password/choosepassword.html.

In that approach, DHS would regulate only the sectors it already has responsibility for, such as the chemical, pipeline, and maritime industries, leaving other sector-specific agencies that understand their industries to regulate them. Although Clinton, Bush, and Obama eschewed, rejected, or declined to establish a federal cybersecurity regulatory regime, there is a mountain of cybersecurity regulation created by federal agencies. Banks, nuclear power plants, self-driving cars, hospitals, insurance companies, defense contractors, passenger aircraft, chemical plants, and dozens of other private-sector entities are all subject to cybersecurity regulation by a nearly indecipherable stream of agencies including the FTC, FAA, DHS, DoD, FERC, DOE, HHS, DOT, OCC, and on and on.

What kind of device qualifies as part of the IoT? Any electronic device that has some sort of computer chip or computing ability and is connected to a network that is in turn connected to the internet is part of the IoT. The places they are used vary enormously and include heart pacemakers, self-driving cars, safety monitors on refineries and chemical plants, surveillance cameras, subway cars and airport trams, drones, switches on electric power substations, robotic welders on assembly lines, sensors in Coke vending machines, office building HVAC sensors and controls, self-diagnostic elevators, and on and on.


pages: 677 words: 206,548

Future Crimes: Everything Is Connected, Everyone Is Vulnerable and What We Can Do About It by Marc Goodman

23andMe, 3D printing, active measures, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, algorithmic trading, Alvin Toffler, Apollo 11, Apollo 13, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, Bill Joy: nanobots, bitcoin, Black Swan, blockchain, borderless world, Boston Dynamics, Brian Krebs, business process, butterfly effect, call centre, Charles Lindbergh, Chelsea Manning, Citizen Lab, cloud computing, Cody Wilson, cognitive dissonance, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, data acquisition, data is the new oil, data science, Dean Kamen, deep learning, DeepMind, digital rights, disinformation, disintermediation, Dogecoin, don't be evil, double helix, Downton Abbey, driverless car, drone strike, Edward Snowden, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, Firefox, Flash crash, Free Software Foundation, future of work, game design, gamification, global pandemic, Google Chrome, Google Earth, Google Glasses, Gordon Gekko, Hacker News, high net worth, High speed trading, hive mind, Howard Rheingold, hypertext link, illegal immigration, impulse control, industrial robot, information security, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jaron Lanier, Jeff Bezos, job automation, John Harrison: Longitude, John Markoff, Joi Ito, Jony Ive, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, knowledge worker, Kuwabatake Sanjuro: assassination market, Large Hadron Collider, Larry Ellison, Laura Poitras, Law of Accelerating Returns, Lean Startup, license plate recognition, lifelogging, litecoin, low earth orbit, M-Pesa, machine translation, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, mobile money, more computing power than Apollo, move fast and break things, Nate Silver, national security letter, natural language processing, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, offshore financial centre, operational security, optical character recognition, Parag Khanna, pattern recognition, peer-to-peer, personalized medicine, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, printed gun, RAND corporation, ransomware, Ray Kurzweil, Recombinant DNA, refrigerator car, RFID, ride hailing / ride sharing, Rodney Brooks, Ross Ulbricht, Russell Brand, Salesforce, Satoshi Nakamoto, Second Machine Age, security theater, self-driving car, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, SimCity, Skype, smart cities, smart grid, smart meter, Snapchat, social graph, SoftBank, software as a service, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, subscription business, supply-chain management, synthetic biology, tech worker, technological singularity, TED Talk, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, the long tail, The Wisdom of Crowds, Tim Cook: Apple, trade route, uranium enrichment, Virgin Galactic, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, Wave and Pay, We are Anonymous. We are Legion, web application, Westphalian system, WikiLeaks, Y Combinator, you are the product, zero day

Though the evidence and my gut tell me there are significant bumps in the road ahead—ones that government and industry are not dedicating sufficient resources to address or combat—I want to believe in the techno-utopia promised to us by Silicon Valley. This book is the story of the society we are building with our technological tools and how these very same implements can be used against us. The more we plug our devices and our lives into the global information grid—whether via mobile phones, social networks, elevators, or self-driving cars—the more vulnerable we become to those who know how the underlying technologies work and how to exploit them to their advantage and to the detriment of the common man. Simply stated, when everything is connected, everyone is vulnerable. The technology we routinely accept into our lives with little or no self-reflection or examination may very well come back to bite us.

In what vision of the future, then, is it conceivable that we will have any clue how to protect the next fifty billion things to go online? Given our inability to secure today’s global information matrix, how might we ever protect a world in which every physical object, from pets to pacemakers to self-driving cars, is connected to the Net and hackable from anywhere on the planet? The obvious reality is that we cannot. The Internet of Things will become nothing more than the Internet of Things to be hacked, a cornucopia of malicious opportunity for those with the means and motivation to exploit our common technological insecurity.

In the same way both Crime, Inc. and crazed exes are targeting computers and mobile phones, it’s only logical that they will go after cars in the future too, bringing scenes like those in Stephen King’s 1983 horror thriller about a possessed car named Christine many steps closer to reality. Law enforcement officials clearly see the threat, and in July 2014 the FBI warned in an internal report that driverless cars could be used as “lethal weapons, with terrorists potentially packing explosives into a self-driving car aimed at a specific destination.” Autonomous vehicles could also potentially be turned off en masse, bringing traffic to a complete standstill in a city or country. To be certain, some of these vehicular attacks require a high degree of computer savvy to pull off, but as we have seen with other exploits, soon there will be point-and-click crimeware options for car hacking as well.


pages: 302 words: 85,877

Cult of the Dead Cow: How the Original Hacking Supergroup Might Just Save the World by Joseph Menn

"World Economic Forum" Davos, 4chan, A Declaration of the Independence of Cyberspace, Andy Rubin, Apple II, autonomous vehicles, Berlin Wall, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Cambridge Analytica, Chelsea Manning, Citizen Lab, commoditize, corporate governance, digital rights, disinformation, Donald Trump, dumpster diving, Edward Snowden, end-to-end encryption, fake news, Firefox, Gabriella Coleman, Google Chrome, Haight Ashbury, independent contractor, information security, Internet of things, Jacob Appelbaum, Jason Scott: textfiles.com, John Gilmore, John Markoff, John Perry Barlow, Julian Assange, Laura Poitras, machine readable, Mark Zuckerberg, military-industrial complex, Mitch Kapor, Mondo 2000, Naomi Klein, NSO Group, Peter Thiel, pirate software, pre–internet, Ralph Nader, ransomware, Richard Stallman, Robert Mercer, Russian election interference, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Skype, slashdot, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Stuxnet, tech worker, Whole Earth Catalog, WikiLeaks, zero day

“We’ve just owned everything that we are about and believe in,” O’Rourke said. Declining money from political action committees hurt, but Adam and Stamos’s fundraiser helped. Several who attended it went on to hold their own fundraising parties in a chain reaction. Across the country in Boston, cDc stalwart Sam Anthony, a Harvard doctoral candidate working to make self-driving cars safer, held a fundraiser for O’Rourke that likewise inspired additional East Coast donations. Though many others would also gravitate toward helping O’Rourke as he gained steam, won the 2018 primary, and drew almost even with Cruz in the polls, the early support in San Francisco and Boston was fitting.

Cofounder Brandon Brewer, once known as Sid Vicious, is as straight as it gets: senior vice president of real estate–services firm Republic Title, based in Fort Worth. Sam Anthony went to work as a programmer in a Harvard University lab, then started graduate school there, working on biological models for computation. He earned a PhD in 2018. Along the way, he cofounded a self-driving car technology company, Perceptive Automata. Autonomous vehicles “are super good at knowing where the road is, how fast the car is going, whether something’s a tree or a person,” Sam explained. “They’re miserably bad at solving the psychology problem of guessing what’s in a human’s head. The techniques we developed while I was doing my PhD are perfect for situations where you want machine learning to do something where humans are incredible.”

See Monsegur, Hector Sadofsky, Jason Scott, 38, 46–47, 58–59, 67, 69 Salon (online magazine), 151 San Antonio Express-News, 60 Sandberg, Sheryl, 200 San Francisco, California, 1–8, 22, 30, 65, 113, 123, 128, 155, 186 satellite communications, 61, 93, 95, 131, 177 Schneier, Bruce, 82–83, 155, 210 script kiddies, 64, 83, 122 secrecy, role of, 117–120, 134, 145, 171, 196 WikiLeaks, 142–151, 155–156, 158–159, 163, 166, 169–170, 192 “Secrets of the Little Blue Box” (Rosenbaum), 18 Secure Sockets Layer encryption, 127 security, 3–5, 43, 51, 84, 136, 182–184, 199 advisories to public, 56–57, 59, 61, 72–74, 108–110, 113, 193, 197 consulting, 56, 109–111 industry, 29–30, 79–80, 85, 171, 177–180, 197 self-driving car technology, 7, 175, 178, 186–187 Sendmail, 48, 56 Setec Astronomy, 65 sexism, in tech world, 158, 170, 193–194 sexual misconduct, in tech world, 150, 152–156, 193 Shadow Brokers, 167 Shea, Dylan (FreqOut), 49, 65 Sid Vicious. See Brewer, Brandon Signal, 152, 162, 171, 178 Silicon Valley, 3–4, 37, 121, 130, 133, 184–185, 191–192, 201–202 Sir Dystic (Josh Buchbinder), 63–68, 78, 81, 93, 186 Six/Four System, 128–129, 162 Slack, 193 Slashdot, 84 Slick, Grace, 22 smartphones, 124, 130–131, 163, 178 Smith, Val, 121, 177–178 Sneakers (film), 65, 109 Snowden, Edward, 138, 167, 172, 177, 181–182, 211 contents of leak, 4, 120, 137, 139, 151, 172 Freedom of the Press Foundation, 150–151, 155 ramifications of leak, 122, 153, 161, 196–198 Snyder, Window (Rosie the Riveter), 49–50, 63, 110–111, 121–123, 171 social engineering, 35, 141, 153 social media, 6, 192–195, 203 Facebook, 4–6, 152, 157–158, 190–196, 198–201, 211 Instagram, 192, 199–200 See also Twitter software, pirated, 15–16, 21, 30, 54, 68, 121 software flaws.


pages: 283 words: 81,376

The Doomsday Calculation: How an Equation That Predicts the Future Is Transforming Everything We Know About Life and the Universe by William Poundstone

Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, Arthur Eddington, Bayesian statistics, behavioural economics, Benoit Mandelbrot, Berlin Wall, bitcoin, Black Swan, conceptual framework, cosmic microwave background, cosmological constant, cosmological principle, CRISPR, cuban missile crisis, dark matter, DeepMind, digital map, discounted cash flows, Donald Trump, Doomsday Clock, double helix, Dr. Strangelove, Eddington experiment, Elon Musk, Geoffrey Hinton, Gerolamo Cardano, Hans Moravec, heat death of the universe, Higgs boson, if you see hoof prints, think horses—not zebras, index fund, Isaac Newton, Jaron Lanier, Jeff Bezos, John Markoff, John von Neumann, Large Hadron Collider, mandelbrot fractal, Mark Zuckerberg, Mars Rover, Neil Armstrong, Nick Bostrom, OpenAI, paperclip maximiser, Peter Thiel, Pierre-Simon Laplace, Plato's cave, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, ride hailing / ride sharing, Rodney Brooks, Ronald Reagan, Ronald Reagan: Tear down this wall, Sam Altman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Skype, Stanislav Petrov, Stephen Hawking, strong AI, tech billionaire, Thomas Bayes, Thomas Malthus, time value of money, Turing test

Should a self-driving vehicle swerve to save the life of its occupants, if that means killing a pedestrian? Is saving the life of a dog that darts into the street worth allowing a human passenger’s broken rib? Designers of self-driving cars are beginning to grapple with such issues. Human drivers hardly ever confront them: our reflexes are too slow to allow meaningful choices. (“It all happened so fast!”) The nearly instant reflexes of a self-driving car pose new ethical imponderables. Automotive engineers can try to interpolate what the driver (and society) would want the car to do. But the problem is immensely more challenging for AI coders on the verge of an intelligence explosion.

Even if AI were a genie capable of ending hunger or curing cancer, some people would have a problem with that. AI would need robust ways of dealing with a fact we all struggle with: you can’t please everyone. Twenty Questions AI should have an “off” switch. This is good practice for a carpet-cleaning robot or a self-driving car. But implementing an “off” switch isn’t so easy for advanced AI that perpetually redesigns itself. “How,” asked Yudkowsky, “do you encode the goal functions of an AI such that it has an ‘Off’ switch, and it wants there to be an ‘Off’ switch, and it won’t try to eliminate the ‘Off’ switch, and it will let you press the ‘Off’ switch, but it won’t jump ahead and press the ‘Off’ switch itself?


Know Thyself by Stephen M Fleming

Abraham Wald, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, autism spectrum disorder, autonomous vehicles, availability heuristic, backpropagation, citation needed, computer vision, confounding variable, data science, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, Dunning–Kruger effect, Elon Musk, Estimating the Reproducibility of Psychological Science, fake news, global pandemic, higher-order functions, index card, Jeff Bezos, l'esprit de l'escalier, Lao Tzu, lifelogging, longitudinal study, meta-analysis, mutually assured destruction, Network effects, patient HM, Pierre-Simon Laplace, power law, prediction markets, QWERTY keyboard, recommendation engine, replication crisis, self-driving car, side project, Skype, Stanislav Petrov, statistical model, theory of mind, Thomas Bayes, traumatic brain injury

In fact, a key problem with modern machine learning techniques is that they are often overconfident in the real world; they think they know the answer when they would be better off hedging their bets. This poses a serious problem for operating AI devices in novel environments—for instance, the software installed in self-driving cars can be fooled by inputs it has not encountered before or different lighting conditions, potentially leading to accidents.10 Another problem is that, once a neural network is trained, it is hard to know why it is doing what it is doing. As we have seen, modern AI is not usually set up with the goal of self-explanation.

It is likely that similar abstract self-beliefs will prove useful in guiding autonomous robots toward tasks that fit their niche—for instance, to allow a drone to know that it should seek out more parcel-delivery jobs rather than try to vacuum the floor. Let’s imagine what a future may look like in which we are surrounded by metacognitive machines. Self-driving cars could be engineered to glow gently in different colors, depending on how confident they were that they knew what to do next—perhaps a blue glow for when they are confident and yellow for when they are uncertain. These signals could be used by their human operators to take control in situations of high uncertainty and increase the humans’ trust that the car did know what it was doing at all other times.

That is our first scenario: building minimal forms of artificial metacognition and self-awareness into machines. This research is already well underway. But there is also a more ambitious alternative: augmenting machines with the biology of human self-awareness. Know Thy Robot Imagine that when we step into a self-driving car of the future, we simply hook it up to a brain-computer interface while we drive it around the block a few times. The signals streaming back from the car while we drive gradually lead to changes in neural representations in the PFC just as they have already been shaped by a variety of other tools we use.


pages: 300 words: 81,293

Supertall: How the World's Tallest Buildings Are Reshaping Our Cities and Our Lives by Stefan Al

3D printing, autonomous vehicles, biodiversity loss, British Empire, Buckminster Fuller, carbon footprint, Cesare Marchetti: Marchetti’s constant, colonial rule, computer vision, coronavirus, COVID-19, Deng Xiaoping, digital twin, Disneyland with the Death Penalty, Donald Trump, Easter island, Elisha Otis, energy transition, food miles, Ford Model T, gentrification, high net worth, Hyperloop, invention of air conditioning, Kickstarter, Lewis Mumford, Marchetti’s constant, megaproject, megastructure, Mercator projection, New Urbanism, plutocrats, plyscraper, pneumatic tube, ride hailing / ride sharing, Salesforce, self-driving car, Sidewalk Labs, SimCity, smart cities, smart grid, smart meter, social distancing, Steve Jobs, streetcar suburb, synthetic biology, Tacoma Narrows Bridge, the built environment, the High Line, transit-oriented development, Triangle Shirtwaist Factory, tulip mania, urban planning, urban sprawl, value engineering, Victor Gruen, VTOL, white flight, zoonotic diseases

Perhaps you can live a little farther away and buy a cheaper home? In other words, cities will increasingly sprawl out. The increase of telecommuting, accelerated by COVID-19, may have a similar impact. Workers may choose to live farther away when they require fewer commutes to their downtown office. Self-driving cars may have more positive implications on downtown cores. They can liberate a lot—a parking lot. When the car drops you off, it likely won’t be parking. It will pick up someone else, since many of us may be sharing cars. The obsolete city parking lot can then be repurposed. In New York, some predict this AV-reduced parking demand can free up roughly 900 city blocks, the size of six Central Parks.27 This means a lot of potential for parks, or for skyscrapers.

“Gloves to Muffle Stadium Applause,” South China Morning Post, October 13, 1994. 24.Jeffrey Hardwick, Mall Maker: Victor Gruen, Architect of an American Dream (Philadelphia: University of Pennsylvania Press, 2010), 216. 25.Yuko Okayasu, “Hong Kong Tops Global Ranking of Most Expensive Shopping Streets,” Cushman & Wakefield, November 14, 2019, accessed February 21, 2021, https://www.cushmanwakefield.com/. 26.Stefan Al, “Mall City: Hong Kong’s Dreamworlds of Consumption,” in Mall City (Honolulu: University of Hawaii Press, 2020), 1–20. 27.Nikolaus Lang et al., “Can Self-Driving Cars Stop the Urban Mobility Meltdown?” Boston Consulting Group, July 8, 2020, https://www.bcg.com. 28.Ben Hamilton-Baillie, “Towards Shared Space,” Urban Design International 13, no. 2 (2008): 130–38. 29.SpaceX, “Hyperloop Alpha,” August 12, 2013, accessed February 27, 2021, https://www.spacex.com.

See also rebar of Burj Khalifa, 67 compression strength in 1950s, 4 mass timber combined with, 47 rust (corrosion) of, 22, 34, 42 taller than steel-framed buildings, 35 residential towers, luxurious in England, 166, 171 in New York, 178–79, 197, 200–201, 202, 206 RHW.2, Vienna, 135 risk in building tall, 16–17, 271, 272 robotics, 139, 237, 263, 265, 271 Rockefeller Center, 188–89, 202 Rogers, Richard, 124–25, 163 Roman arches, 52, 54–56 Roman city planning, 151–52, 171 Roman concrete, 22, 25–28, 29, 33–34, 41, 55 Roman cooling systems, 118 roofs, 139, 240, 250–51, 262–63 Rudolph, Paul, 224 rust, 22, 34, 42 Safdie, Moshe, 254 Sagrada Familia, 32, 82 Saint-Denis, Church of, 56–57 Salesforce Tower, San Francisco, 10–11 sand in concrete, 25, 33 for Dubai real estate, 37 Sant’Elia, Antonio, 14, 96 Saskatchewan Conservation House, 133–34 Saudi Binladen Group, 68, 75 Scheeren, Ole, 77, 78 Seafront Towers, Shenzhen, 77 Seagram Building, 123, 123–24, 190–91 sea level rise, 271 Sears Tower, 65, 65–66, 206 Second Industrial Revolution, 156, 246–47 self-driving cars, 233, 235–36 sensors of conditioned air and electric light, 12 digital twin and, 140 structural stability and, 9 Seoul, Korea, 78, 249 setback regulations, New York, 184–86, 269 Shanghai Tower, 86, 86–87 double curtain wall, 126 elevator cables, 103, 108–9 elevators, 87, 100–101, 103, 104–5, 106, 107 structure of, 103 tuned mass damper, 74–75 twisted shape, 71–72 Shanghai World Financial Center, 72, 86, 102, 103 Shard, London, 145, 147, 148, 160–61, 162–63, 168, 169, 172 Shibam, Yemen, 128 Shukhov, Vladimir, 6–7, 10 Siemens, Werner von, 93, 218 silica fume, 33 Singapore building regulations, 254–55 digital twin, 264 food supply, 257–58 founding of, 251–52 greening of, 240, 252–53 home ownership in, 253–54 imagined future of, 265 parks in, 254, 257, 264 skyscrapers, 254–56, 259 strict behavior enforcement, 258–59 vertical farms, 240, 258 vertical gardens, 256 water supply, 253, 254, 255, 258 Skidmore, Owings & Merrill (SOM), 38, 47, 60 skybridges, 15, 76–77, 111, 173, 225 sky exposure planes, 184–85, 189 sky lobbies, 76, 77, 174 Skyscraper Curse, 207–8 skyscrapers.


pages: 606 words: 157,120

To Save Everything, Click Here: The Folly of Technological Solutionism by Evgeny Morozov

"World Economic Forum" Davos, 3D printing, algorithmic bias, algorithmic trading, Amazon Mechanical Turk, An Inconvenient Truth, Andrew Keen, augmented reality, Automated Insights, behavioural economics, Berlin Wall, big data - Walmart - Pop Tarts, Buckminster Fuller, call centre, carbon footprint, Cass Sunstein, choice architecture, citizen journalism, classic study, cloud computing, cognitive bias, creative destruction, crowdsourcing, data acquisition, Dava Sobel, digital divide, disintermediation, Donald Shoup, driverless car, East Village, en.wikipedia.org, Evgeny Morozov, Fall of the Berlin Wall, Filter Bubble, Firefox, Francis Fukuyama: the end of history, frictionless, future of journalism, game design, gamification, Gary Taubes, Google Glasses, Ian Bogost, illegal immigration, income inequality, invention of the printing press, Jane Jacobs, Jean Tirole, Jeff Bezos, jimmy wales, Julian Assange, Kevin Kelly, Kickstarter, license plate recognition, lifelogging, lolcat, lone genius, Louis Pasteur, machine readable, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, moral panic, Narrative Science, Nelson Mandela, Nicholas Carr, packet switching, PageRank, Parag Khanna, Paul Graham, peer-to-peer, Peter Singer: altruism, Peter Thiel, pets.com, placebo effect, pre–internet, public intellectual, Ray Kurzweil, recommendation engine, Richard Thaler, Ronald Coase, Rosa Parks, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, Slavoj Žižek, smart meter, social graph, social web, stakhanovite, Steve Jobs, Steven Levy, Stuxnet, surveillance capitalism, systems thinking, technoutopianism, TED Talk, the built environment, The Chicago School, The Death and Life of Great American Cities, the medium is the message, The Nature of the Firm, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, transaction costs, Twitter Arab Spring, urban decay, urban planning, urban sprawl, Vannevar Bush, warehouse robotics, WikiLeaks, work culture , Yochai Benkler

Their point is not that such liberation through technology is illusory or inconsequential but, rather, that such liberation never happens in a vacuum and may, all things considered, actually enslave. Yes, Google’s self-driving cars would make driving easier and perhaps even cut the number of deaths on the road, but a reasonable transportation system ought to pursue many other objectives. Would self-driving cars result in inferior public transportation as more people took up driving? Would it lead to even greater suburban sprawl as, now that they no longer had to drive, people could do e-mail during their commute and thus would tolerate spending more time in the car?

Even those who’ve never bothered to vote in the past are finally provided with the right incentives—naturally, as a part of an online game where they collect points for saving humanity—and so they rush to use their smartphones to “check in” at the voting booth. Thankfully, getting there is no longer a chore; self-driving cars have been invented for the purpose of getting people from place to place. Streets are clean and shiny; keeping them that way is also part of an elaborate online game. Appeals to civic duty and responsibility to fellow citizens have all but disappeared—and why wouldn’t they, when getting people to do things by leveraging their eagerness to earn points, badges, and virtual currencies is so much more effective?

See Perversity-futility-jeopardy triad Galileo Galison, Peter Galton, Francis Gambling addiction Game mechanics Games and gamification and humanitarianism and smartphones vs. reality Gamification and adversarial design and degrading environment, enjoyment in and efficiency vs. inefficiency and games and games vs. reality literature and motivation and rewards vs. citizenship Gardner, James Garland, David Gasto Público Bahiense (website) Gatekeepers Gates, Bill Gates, Kelly Gawker Gender discrimination Generativity theory Genetic engineering Gertner, Joe Ghonim, Wael Gillespie, Tarleton Global Integrity Godin, Benoit Google AdSense and advertising and algorithms and algorithms, and democracy and algorithms, neutrality and objectivity of and badges and citizenship and content business and ethics GPS-enabled Android phones and Huffington Post and information organization and legal challenges and mirror imagery and openness PageRank Places and predictive policing and privacy Project Glass goggles and scientific credentials and self-driving cars values and WiFi networks and Zagat Google+ Google Autocomplete Google Buzz Google News and badges Google Scholar Government and networks role of Government, US, and WikiLeaks GPS driving data GPS-enabled Android phones (Google) Grafton, Anthony Graham, Paul Grant, Ruth Green, Donald Green, Shane Greenwald, Glenn Guernica Gutenberg, Johannes Gutshot-detection systems Hanrahan, Nancy Harvey, David Hayek, Friedrich Heald, David Health and gamification monitoring device Heller, Nathaniel Hibbing, John Hierarchies, and networks Hieronymi, Pamela Highlighting and shading Hildebrandt, Mireille Hill, Kashmir Hirschman, Albert Historians, and Internet debate History as irrelevant of technology Hoffman, Reid Holiday, Ryan Holocaust Horkheimer, Max Howard Dean for Iowa Game Huffington Post, The Humanitarianism, and games Hunch.com Hypocrisy Illich, Ivan Image-recognition software Imperfection Impermium Incentives Information cascades theory Information consumption self-tracking of Information emperors Information industries and government history of Information organization Information-processing imperative Information reductionism Information technology InfoWorld (website) Innovation and justice and technology unintended consequences of Innovation talk Institutions, and networks Intel Intermediaries.


pages: 165 words: 45,397

Speculative Everything: Design, Fiction, and Social Dreaming by Anthony Dunne, Fiona Raby

3D printing, Adam Curtis, Anthropocene, augmented reality, autonomous vehicles, behavioural economics, Berlin Wall, Boeing 747, Buckminster Fuller, capitalist realism, Cass Sunstein, computer age, corporate governance, David Attenborough, en.wikipedia.org, Fall of the Berlin Wall, game design, General Motors Futurama, global village, Google X / Alphabet X, haute couture, Herman Kahn, intentional community, life extension, machine readable, Mark Zuckerberg, mouse model, New Urbanism, Peter Eisenman, RAND corporation, Richard Thaler, Ronald Reagan, self-driving car, Silicon Valley, social software, synthetic biology, systems thinking, technoutopianism, Wall-E

It is the most dystopian yet familiar of all the micro-kingdoms. Their main form of transport is the digicar, a development of electric self-drive cars being pioneered today by companies such as Google.27 The car has evolved from being a vehicle for navigating space and time, to being an interface for navigating tariffs and markets. Every square meter of road surface and every millisecond of access, at any moment, is monetized and optimized. Today, self-drive cars are presented as social spaces for relaxing commutes, but digicars are closer to economy airlines, offering the most basic but humane experience.


pages: 420 words: 135,569

Imaginable: How to See the Future Coming and Feel Ready for Anything―Even Things That Seem Impossible Today by Jane McGonigal

2021 United States Capitol attack, Airbnb, airport security, Alvin Toffler, augmented reality, autism spectrum disorder, autonomous vehicles, availability heuristic, basic income, biodiversity loss, bitcoin, Black Lives Matter, blockchain, circular economy, clean water, climate change refugee, cognitive bias, cognitive dissonance, Community Supported Agriculture, coronavirus, COVID-19, CRISPR, cryptocurrency, data science, decarbonisation, digital divide, disinformation, Donald Trump, drone strike, Elon Musk, fake news, fiat currency, future of work, Future Shock, game design, George Floyd, global pandemic, global supply chain, Greta Thunberg, income inequality, index card, Internet of things, Jane Jacobs, Jeff Bezos, Kickstarter, labor-force participation, lockdown, longitudinal study, Mason jar, mass immigration, meta-analysis, microbiome, Minecraft, moral hazard, open borders, pattern recognition, place-making, plant based meat, post-truth, QAnon, QR code, remote working, RFID, risk tolerance, School Strike for Climate, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, Silicon Valley startup, Snapchat, social distancing, stem cell, TED Talk, telepresence, telepresence robot, The future is already here, TikTok, traumatic brain injury, universal basic income, women in the workforce, work culture , Y Combinator

“There’s a reason why getting your first driver’s license is such an important rite of passage,” he said. “It marks the moment when a young person finally feels in control of their own life. That’s just not going to change.” I asked if they thought that the possibility of reducing motor vehicle fatalities would be a stronger motivator than being in charge of your life. What if self-driving cars were ultimately safer than people-driven cars? What if we could eliminate some of the nearly 1.5 million car-related fatalities that happen globally each year? No, they all agreed. Safety gains would never trump a sense of individual freedom and control. I shared a few other reasons I thought autonomous cars would become commonplace, but I didn’t get any traction.

And then as soon as I had a moment to myself, I pulled out the tiny notebook I always carry with me, and I wrote up everything I could remember about the conversation. I wanted to capture the conversation because I was fascinated by the certainty they expressed in rejecting this possible future. I didn’t take their opinions to be an official company position—and I later learned that there were already people within the company advocating for taking self-driving cars more seriously. But our conversation stood out to me as a clear example of how an idea can get labeled “unthinkable,” and how, once the idea is labeled, it is hard to get people to change their minds about it, even in a team tasked with innovation. Before I started training as a futurist, I was probably just as stubborn about my own views of the world.

People predict they would feel excited, nervous, awed, terrified, curious, nauseated, grateful, thrilled, confused, vigilant, asleep, free. The words they share run the full gamut of positive and negative emotions, with no consensus whatsoever. With this kind of insight into other people’s imagined futures, it becomes much harder to hold any strong opinion about self-driving cars too tightly. This is especially true if we try to understand—or better yet, have a conversation about—why exactly someone else might feel nervous about the ride while we would feel excited, why someone else might feel free during the ride while we might feel nauseated, why some people want this future and others don’t.


pages: 302 words: 95,965

How to Be the Startup Hero: A Guide and Textbook for Entrepreneurs and Aspiring Entrepreneurs by Tim Draper

3D printing, Airbnb, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, basic income, Berlin Wall, bitcoin, blockchain, Buckminster Fuller, business climate, carried interest, connected car, CRISPR, crowdsourcing, cryptocurrency, deal flow, Deng Xiaoping, discounted cash flows, disintermediation, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, fake news, family office, fiat currency, frictionless, frictionless market, growth hacking, high net worth, hiring and firing, initial coin offering, Jeff Bezos, Kickstarter, Larry Ellison, low earth orbit, Lyft, Mahatma Gandhi, Marc Benioff, Mark Zuckerberg, Menlo Park, Metcalfe's law, Metcalfe’s law, Michael Milken, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, Nelson Mandela, Network effects, peer-to-peer, Peter Thiel, pez dispenser, Ralph Waldo Emerson, risk tolerance, Robert Metcalfe, Ronald Reagan, Rosa Parks, Salesforce, Sand Hill Road, school choice, school vouchers, self-driving car, sharing economy, Sheryl Sandberg, short selling, Silicon Valley, Skype, smart contracts, Snapchat, sovereign wealth fund, stealth mode startup, stem cell, Steve Jobs, Steve Jurvetson, Tesla Model S, Twitter Arab Spring, Uber for X, uber lyft, universal basic income, women in the workforce, Y Combinator, zero-sum game

The school, Draper University of Heroes, has shaken up the education establishment by offering team-based learning, project-oriented experiences and survival training, and has transformed the lives of 1000 young people who have come from 68 different countries and started about 300 companies at this writing, one of which is a unicorn already. Since deciding to invest through my new vehicle, Draper Associates, I have had a few early successes. Twitch.TV, a company that created a platform for fans to watch people play video games, was sold to Amazon for $1 billion; Cruise Automation, the first independent self-driving car company was sold to General Motors for $1 billion; and my son Billy sourced a company that simplifies investing in the public markets called Robinhood, which just completed a venture round at a valuation over $1 billion. Billy and I were able to raise a $190 million fund and we are enjoying working together.

She made the first bold move to “make them fly” autonomously. Kyle is a Startup Hero. He took the first step and all the steps after that. He was willing to take long odds at the extraordinary outcome. So is the CEO of GM, who stuck her neck and professional reputation on the line to leap into the great unknown and invest in a self-driving car. Book One: The Startup Hero’s Pledge This book is written to help make you a Startup Hero. A Startup Hero needs a guide for when life gets complicated. I have written this pledge to help Startup Heroes get centered. A Startup Hero must also be flexible and willing to continue marching forward when things are not predictable.

If other scientists decide to follow suit and we get an open source outflowing of scientific ideas and collaboration, science could go through the same explosion that we have had in entrepreneurship in Silicon Valley, and human progress will again be accelerated. Look for industries to improve: healthcare, entertainment, real estate, insurance, fashion and banking. Look for new technologies to improve them with: the shared economy, social media, programmable stem cells, CRISPR, microsatellites, virtual reality, Bitcoin, solar economies, self-driving cars, electronic clothes, bioelectronics, robot brains, prosthetic limbs, Pokémon derivatives, offline massively open online courses (MOOCs), enterprise software, and multivariable authenticators. Put them together to approach or even invent new technology industries. Your imagination is unlimited.


pages: 323 words: 90,868

The Wealth of Humans: Work, Power, and Status in the Twenty-First Century by Ryan Avent

3D printing, Airbnb, American energy revolution, assortative mating, autonomous vehicles, Bakken shale, barriers to entry, basic income, Bernie Sanders, Big Tech, BRICs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collective bargaining, computer age, creative destruction, currency risk, dark matter, David Ricardo: comparative advantage, deindustrialization, dematerialisation, Deng Xiaoping, deskilling, disruptive innovation, Dissolution of the Soviet Union, Donald Trump, Downton Abbey, driverless car, Edward Glaeser, Erik Brynjolfsson, eurozone crisis, everywhere but in the productivity statistics, falling living standards, financial engineering, first square of the chessboard, first square of the chessboard / second half of the chessboard, Ford paid five dollars a day, Francis Fukuyama: the end of history, future of work, general purpose technology, gig economy, global supply chain, global value chain, heat death of the universe, hydraulic fracturing, income inequality, independent contractor, indoor plumbing, industrial robot, intangible asset, interchangeable parts, Internet of things, inventory management, invisible hand, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph-Marie Jacquard, knowledge economy, low interest rates, low skilled workers, lump of labour, Lyft, machine translation, manufacturing employment, Marc Andreessen, mass immigration, means of production, new economy, performance metric, pets.com, post-work, price mechanism, quantitative easing, Ray Kurzweil, rent-seeking, reshoring, rising living standards, Robert Gordon, Robert Solow, Ronald Coase, savings glut, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, single-payer health, software is eating the world, supply-chain management, supply-chain management software, tacit knowledge, TaskRabbit, tech billionaire, The Future of Employment, The Nature of the Firm, The Rise and Fall of American Growth, The Spirit Level, The Wealth of Nations by Adam Smith, trade liberalization, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Uber and Lyft, Uber for X, uber lyft, very high income, warehouse robotics, working-age population

Acemoglu, Daron ageing populations agency, concept of Airbnb Amazon American Medical Association (AMA) anarchism Andreessen, Marc Anglo-Saxon economies Apple the iPhone the iPod artisanal goods and services Atkinson, Anthony Atlanta, Georgia austerity policies automation in car plants fully autonomous trucks of ‘green jobs’ during industrial revolution installation work as resistant to low-pay as check on of menial/routine work self-driving cars and technological deskilling automobiles assembly-line techniques automated car plants and dematerialization early days of car industry fully autonomous trucks self-driving cars baseball Baumol, William Belgium Bernanke, Ben Bezos, Jeff black plague (late Middle Ages) Boston, Massachusetts Brazil BRIC era Bridgewater Associates Britain deindustrialization education in extensions of franchise in financial crisis (2008) Great Exhibition (London 1851) housing wealth in and industrial revolution Labour Party in liberalization in political fractionalization in real wages in social capital in surpassed by US as leading nation wage subsidies in Brontë, Charlotte Brynjolfsson, Erik bubbles, asset-price Buffalo Bill (William Cody) BuzzFeed Cairncross, Frances, The Death of Distance (1997) capital ‘deepening’ infrastructure investment investment in developing world career, concept of cars see automobiles Catalan nationalism Central African Republic central banks Chait, Jonathan Charlotte chemistry, industrial Chicago meat packers in nineteenth-century expansion of World’s Columbia Exposition (1893) China Deng Xiaoping’s reforms economic slow-down in era of rapid growth foreign-exchange reserves ‘green jobs’ in illiberal institutions in inequality in iPod assembly in technological transformation in wage levels in Chorus (content-management system) Christensen, Clayton Cisco cities artisanal goods and services building-supply restrictions growth of and housing costs and industrial revolution and information membership battles in rich/skilled and social capital clerical work climate change Clinton, Hillary Coase, Ronald Columbia University, School of Mines communications technology communism communities of affinity computing app-based companies capability thresholds cloud services cycles of experimentation desktop market disk-drive industry ‘enterprise software’ products exponential progress narrative as general purpose technology hardware and software infrastructure history of ‘Moore’s Law’ and productivity switches transistors vacuum tubes see also digital revolution; software construction industry regulations on Corbyn, Jeremy Corliss steam engine corporate power Cowen, Tyler craft producers Craigslist creative destruction the Crystal Palace, London Dalio, Ray Dallas, Texas debt deindustrialization demand, chronically weak dematerialization Detroit developing economies and capital investment and digital revolution era of rapid growth and industrialization pockets of wealth in and ‘reshoring’ phenomenon and sharp slowdown and social capital see also emerging economies digital revolution and agency and company cultures and developing economies and distance distribution of benefits of dotcom tech boom emergence of and global imbalances and highly skilled few and industrial institutions and information flows investment in social capital niche markets pace of change and paradox of potential productivity and output and secular stagnation start-ups and technological deskilling techno-optimism techno-pessimism as tectonic economic transformation and trading patterns web journalism see also automation; computing; globalization discrimination and exclusion ‘disruption’, phenomenon of distribution of wealth see inequality; redistribution; wealth and income distribution dotcom boom eBay economics, classical The Economist education in emerging economies during industrial revolution racial segregation in USA and scarcity see also university education electricity Ellison, Glenn Ellison, Sara Fisher emerging economies deindustrialization economic growth in education in foreign-exchange reserves growth in global supply chains highly skilled workers in see also developing economies employment and basic income policy cheap labour as boost to and dot.com boom in Europe and financial crisis (2008) ‘green jobs’ low-pay sector minimum wage impact niche markets in public sector ‘reshoring’ phenomenon as rising globally and social contexts and social membership as source of personal identity and structural change trilemma in USA see also labour; wages Engels, Friedrich environmental issues Etsy euro- zone Europe extreme populist politics liberalized economies political fractionalization in European Union Facebook face-recognition technology factors of production land see also capital; labour ‘Factory Asia’ factory work assembly-line techniques during industrial revolution family fascism Federal Reserve financial crisis (2008) financial markets cross-border capital flows in developing economies Finland firms and companies Coase’s work on core competencies culture of dark matter (intangible capital) and dematerialization and ‘disruption’ ‘firm-specific’ knowledge and information flows internal incentive structures pay of top executives shifting boundaries of social capital of and social wealth start-ups Ford, Martin, Rise of the Robots (2015) Ford Motor Company fracking France franchise, electoral Friedman, Milton Fukuyama, Francis Gates, Bill gender discrimination general purpose technologies enormous benefits from exponential progress and skilled labour supporting infrastructure and time lags see also digital revolution Germany ‘gig economy’ Glaeser, Ed global economy growth in supply chains imbalances lack of international cooperation savings glut and social consensus globalization hyperglobalization and secular stagnation and separatist movements Goldman Sachs Google Gordon, Robert Gothenburg, Sweden Great Depression Great Depression (1930s) Great Exhibition, London (1851) Great Recession Great Stagnation Greece ‘green jobs’ growth, economic battle over spoils of boom (1994-2005) and classical economists as consistent in rich countries decline of ‘labour share’ dotcom boom emerging economies gains not flowing to workers and industrial revolution Kaldor’s ‘stylized facts of’ and Keynes during liberal era pie metaphor in post-war period and quality of institutions and rich/elite cities rich-poor nation gap and skilled labour guilds Hansen, Alvin Hayes, Chris, The Twilight of the Elites healthcare and medicine hedge funds and private equity firms Holmes, Oliver Wendell Hong Kong housing in Bay-Area NIMBY campaigns against soaring prices pre-2008 crisis zoning and regulations Houston, Texas Huffington Post human capital Hungary IBM identity, personal immigration and ethno-nationalist separatism and labour markets in Nordic countries and social capital income distribution see inequality; redistribution; wealth and income distribution India Indonesia industrial revolution automation during and economic growth and growth of cities need for better-educated workers and productivity ‘second revolution’ and social change and wages and World’s Fairs inequality and education levels between firms and housing wealth during industrial revolution during liberal era between nations pay of top executives rise of in emerging economies and secular stagnation in Sweden wild contingency of wealth see also rich people; wealth and income distribution inflation in 1970s hyperinflation information technology see computing Intel interest rates International Space Station (ISS) iRobot ISIS Italy Jacksonville, Florida Jacquard, Joseph Marie Japan journalism Kaldor, Nicholas Keynes, John Maynard Kurzweil.

Ray labour abundance as good problem bargaining power cognitive but repetitive collective bargaining and demographic issues discrimination and exclusion global growth of workforce and immigration liberalization in 1970s/80s ‘lump of labour’ fallacy occupational licences organized and proximity reallocation to growing industries retraining and skill acquisition and scarcity and social value work as a positive good see also employment Labour Party, British land scarcity Latvia Le Pen, Jean-Marie Le Pen, Marine legal profession Lehman Brothers collapse (2008) Lepore, Jill liberalization, economic (from 1970s) Linkner, Josh, The Road to Reinvention London Lucas, Robert Lyft maker-taker distinction Malthus, Reverend Thomas Manchester Mandel, Michael Mankiw, Gregory marketing and public relations Marshall, Alfred Marx, Karl Mason, Paul, Postcapitalism (2015) McAfee, Andrew medicine and healthcare ‘mercantilist’ world Mercedes Benz Mexico Microsoft mineral industries minimum wage Mokyr, Joel Monroe, President James MOOCs (‘massive open online courses’) Moore, Gordon mortality rates Mosaic (web browser) music, digital nation states big communities of affinity inequality between as loci of redistribution and social capital nationalist and separatist movements Netherlands Netscape New York City Newsweek NIMBYism Nordic and Scandinavian economies North Carolina North Dakota Obama, Barack oil markets O’Neill, Jim Oracle Orbán, Viktor outsourcing Peretti, Jonah Peterson Institute for International Economics pets.com Philadelphia Centennial Fair (1876) Philippines Phoenix, Arizona Piketty, Thomas, Capital in the Twenty-First Century (2013) Poland political institutions politics fractionalization in Europe future/emerging narratives geopolitical forces human wealth narrative left-wing looming upheaval/conflict Marxism nationalist and separatist movements past unrest and conflict polarization in USA radicalism and extremism realignment revolutionary right-wing rise of populist outsiders and scarcity social membership battles Poor Laws, British print media advertising revenue productivity agricultural artisanal goods and services Baumol’s Cost Disease and cities and dematerialization and digital revolution and employment trilemma and financial crisis (2008) and Henry Ford growth data in higher education of highly skilled few and industrial revolution minimum wage impact paradox of in service sector and specialization and wage rates see also factors of production professional, technical or managerial work and education levels and emerging economies the highly skilled few and industrial revolution and ‘offshoring’ professional associations skilled cities professional associations profits Progressive Policy Institute property values proximity public spending Putnam, Robert Quakebot quantitative easing Race Against the Machine, Brynjolfsson and McAfee (2011) railways Raleigh, North Carolina Reagan, Ronald redistribution and geopolitical forces during liberal era methods of nation state as locus of as a necessity as politically hard and societal openness wealth as human rent, economic Republican Party, US ‘reshoring’ phenomenon Resseger, Matthew retail sector retirement age Ricardo, David rich people and maker-taker distinction wild contingency of wealth Robinson, James robots Rodrik, Dani Romney, Mitt rule of law Russia San Francisco San Jose Sanders, Bernie sanitation SAP Saudi Arabia savings glut, global ‘Say’s Law’ Scalia, Antonin Scandinavian and Nordic economies scarcity and labour political effects of Schleicher, David Schwartz, Anna scientists Scotland Sears Second World War secular stagnation global spread of possible solutions shale deposits sharing economies Silicon Valley Singapore skilled workers and education levels and falling wages the highly skilled few and industrial revolution ‘knowledge-intensive’ goods and services reshoring phenomenon technological deskilling see also professional, technical or managerial work Slack (chat service) Slate (web publication) smartphone culture Smith, Adam social capital and American Constitution baseball metaphor and cities ‘deepening’ definition/nature of and dematerialization and developing economies and erosion of institutions of firms and companies and good government and housing wealth and immigration and income distribution during industrial revolution and liberalization and nation-states productive application of and rich-poor nation gap and Adam Smith and start-ups social class conflict middle classes and NIMBYism social conditioning of labour force working classes social democratic model social reform social wealth and social membership software ‘enterprise software’ products supply-chain management Solow, Robert Somalia South Korea Soviet Union, dissolution of (1991) specialization Star Trek state, role of steam power Subramanian, Arvind suburbanization Sweden Syriza party Taiwan TaskRabbit taxation telegraphy Tesla, Nikola Thatcher, Margaret ‘tiger’ economies of South-East Asia Time Warner Toyota trade China as ‘mega-trader’ ‘comparative advantage’ theory and dematerialization global supply chains liberalization shaping of by digital revolution Adam Smith on trade unions transhumanism transport technology self-driving cars Trump, Donald Twitter Uber UK Independence Party United States of America (USA) 2016 Presidential election campaign average income Bureau of Labour Statistics (BLS) Constitution deindustrialization education in employment in ethno-nationalist diversity of financial crisis (2008) housing costs in housing wealth in individualism in industrialization in inequality in Jim Crow segregation labour scarcity in Young America liberalization in minimum wage in political polarization in post-crisis profit rates productivity boom of 1990s real wage data rising debt levels secular stagnation in shale revolution in social capital in and social wealth surpasses Britain as leading nation wage subsidies in university education advanced degrees downward mobility of graduates MOOCs (‘massive open online courses’) and productivity see also education urbanization utopias, post-work Victoria, Queen video-gamers Virginia, US state Volvo Vox wages basic income policy Baumol’s Cost Disease cheap labour and employment growth and dot.com boom and financial crisis (2008) and flexibility and Henry Ford government subsidies and housing costs and immigration and industrial revolution low-pay as check on automation minimum wage and productivity the ‘reservation wage’ as rising in China rising in emerging economies and scarcity in service sector and skill-upgrading approach stagnation of and supply of graduates Wandsworth Washington D.C.


pages: 326 words: 88,968

The Science and Technology of Growing Young: An Insider's Guide to the Breakthroughs That Will Dramatically Extend Our Lifespan . . . And What You Can Do Right Now by Sergey Young

23andMe, 3D printing, Albert Einstein, artificial general intelligence, augmented reality, basic income, Big Tech, bioinformatics, Biosphere 2, brain emulation, caloric restriction, caloric restriction, Charles Lindbergh, classic study, clean water, cloud computing, cognitive bias, computer vision, coronavirus, COVID-19, CRISPR, deep learning, digital twin, diversified portfolio, Doomsday Clock, double helix, Easter island, Elon Musk, en.wikipedia.org, epigenetics, European colonialism, game design, Gavin Belson, George Floyd, global pandemic, hockey-stick growth, impulse control, Internet of things, late capitalism, Law of Accelerating Returns, life extension, lockdown, Lyft, Mark Zuckerberg, meta-analysis, microbiome, microdosing, moral hazard, mouse model, natural language processing, personalized medicine, plant based meat, precision agriculture, radical life extension, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, self-driving car, seminal paper, Silicon Valley, stem cell, Steve Jobs, tech billionaire, TED Talk, uber lyft, ultra-processed food, universal basic income, Virgin Galactic, Vision Fund, X Prize

If so, blame the audacity of the concept or the scarcity of mainstream reporting about longevity science—but don’t blame the man. Kurzweil predicted, nearly a decade in advance, that world chess champion Garry Kasparov would be beaten by a computer like IBM’s Deep Blue. He predicted the widespread use of wireless communication and something like Google nearly twenty years before they came to be. He predicted self-driving cars, remote learning, cloud computing, smart watches, augmented reality, nano devices, robotic exoskeletons, and at least a hundred more innovations, often with eerily accurate timing. In tech circles, when Ray makes a prediction, many people now set their watch by it. Nobody laughs anymore. It is anybody’s guess what will become of longevity escape velocity, or when it may come to pass.

And of the more than one billion people worldwide living with hypertension, as many as half do not know about it.7 In fact, when you dig into the nearly sixty million lives lost around the globe each year, more than thirty million are from conditions that are restorable if caught early. Only one item on the World Health Organization’s top ten causes of death—road accidents—isn’t a partially or fully treatable condition8 (and that one will soon be eliminated by self-driving cars). The problem is—we just are not diagnosing people early enough. Diagnosing Accurately The second problem with diagnosis today comes when the sick do get checked but are given the wrong diagnosis. For a view into this troubling matter, Google Doug Lindsay. The St. Louis, Missouri, native suffers from an autonomic nervous system disorder known as autonomic dysfunction, which killed his mother and left him bedridden for eleven years.

If you must drink, use a ride-sharing app like Uber or Lyft, which studies show have reduced alcohol-related auto accidents by 25 to 35 percent since their launch.15 Consider using a blood alcohol concentration calculator app like MyLimit or even installing a smartphone breathalyzer like BacTrack. Soon, fully self-driving cars will drastically reduce road accidents. Until then, for heaven’s sake, slow down, never drink and drive, put your phone away, and buckle up! Obviously, I don’t recommend that you board up your windows and stay inside. You have to decide for yourself what level of calculated risk you are comfortable with.


pages: 200 words: 47,378

The Internet of Money by Andreas M. Antonopoulos

AltaVista, altcoin, bitcoin, blockchain, clean water, cognitive dissonance, cryptocurrency, disruptive innovation, Dogecoin, Ethereum, ethereum blockchain, financial exclusion, global reserve currency, information security, litecoin, London Interbank Offered Rate, Marc Andreessen, Oculus Rift, packet switching, peer-to-peer lending, Ponzi scheme, QR code, ransomware, reserve currency, Satoshi Nakamoto, self-driving car, skeuomorphism, Skype, smart contracts, the medium is the message, trade route, Tragedy of the Commons, underbanked, WikiLeaks, zero-sum game

Uber. Self-driving cars. What happens when you mash the three together? The self-owning car. A car that pays for its Toyota lease, its insurance, and its gas, by giving people rides. A car that is not owned by a corporation. A car that is a corporation. A car that is a shareholder and owner of its own corporation. A car that exists as an autonomous financial entity with no human ownership. This has never happened before, and that’s just the beginning. Audience member gasps: "Oh shit!" "Let’s take three radically disruptive technologies and mash them together. Bitcoin. Uber. Self-driving cars. What happens when you mash the three together?


pages: 197 words: 49,296

The Future We Choose: Surviving the Climate Crisis by Christiana Figueres, Tom Rivett-Carnac

3D printing, Airbnb, AlphaGo, Anthropocene, autonomous vehicles, Berlin Wall, biodiversity loss, carbon footprint, circular economy, clean water, David Attenborough, decarbonisation, DeepMind, dematerialisation, Demis Hassabis, disinformation, Donald Trump, driverless car, en.wikipedia.org, Extinction Rebellion, F. W. de Klerk, Fall of the Berlin Wall, Gail Bradbrook, General Motors Futurama, green new deal, Greta Thunberg, high-speed rail, income inequality, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, job automation, Lyft, Mahatma Gandhi, Marc Benioff, Martin Wolf, mass immigration, Mustafa Suleyman, Nelson Mandela, new economy, ocean acidification, plant based meat, post-truth, rewilding, ride hailing / ride sharing, self-driving car, smart grid, sovereign wealth fund, the scientific method, trade route, uber lyft, urban planning, urban sprawl, Yogi Berra

San Francisco Chronicle, July 10, 2017, https://www.sfchronicle.com/​opinion/​openforum/​article/​Are-we-ready-for-the-end-of-individual-car-11278535.php. 34. A great article and podcast on this can be found here: Hans-Werner Kaas, Detlev Mohr, and Luke Collins, “Self-Driving Cars and the Future of the Auto Sector,” McKinsey & Company, August 2016, https://www.mckinsey.com/​industries/​automotive-and-assembly/​our-insights/​self-driving-cars-and-the-future-of-the-auto-sector. 35. Rosie McCall, “Millions of Fossil Fuel Dollars Are Being Pumped into Anti-Climate Lobbying,” IFLScience, March 22, 2019, https://www.iflscience.com/​environment/​millions-of-fossil-fuel-dollars-are-being-pumped-into-anticlimate-lobbying/. 36.


pages: 360 words: 101,038

The Revenge of Analog: Real Things and Why They Matter by David Sax

Airbnb, barriers to entry, big-box store, call centre, cloud computing, creative destruction, death of newspapers, declining real wages, delayed gratification, dematerialisation, deskilling, Detroit bankruptcy, digital capitalism, digital divide, Elon Musk, Erik Brynjolfsson, game design, gentrification, hype cycle, hypertext link, informal economy, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Khan Academy, Kickstarter, knowledge economy, low cost airline, low skilled workers, mandatory minimum, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, military-industrial complex, Minecraft, new economy, Nicholas Carr, off-the-grid, One Laptop per Child (OLPC), PalmPilot, Paradox of Choice, Peter Thiel, Ponzi scheme, quantitative hedge fund, race to the bottom, Rosa Parks, Salesforce, Second Machine Age, self-driving car, Sheryl Sandberg, short selling, Silicon Valley, Silicon Valley startup, Skype, Snapchat, Steve Jobs, technoutopianism, TED Talk, the long tail, Travis Kalanick, Tyler Cowen, upwardly mobile, warehouse robotics, Whole Earth Catalog, work culture

What began on the factory floor has edged up to the cubicle and corner office, threatening not just warehouse workers and delivery drivers with robots and drones, but lawyers, radiologists, and newspaper reporters, whose jobs are increasingly being done by artificial intelligence software. In a few years, as you step into your self-driving car, what job will it take you to? If the answer is a job that can either be done by a computer with artificial intelligence or a robot equipped with one, you might want to think about a new career path. Futurists and technology leaders offer a sunnier view: all this displacement could simply be a way station to a period of technological bliss.

Although fundamentally not that different from printed correspondence courses, recorded audio- or videotape lectures, or other distance-learning initiatives of the past century, MOOC emerged as a new term in 2008, when advances in streaming video technology and cloud computing made it possible to deliver lectures online in real time. A number of universities, colleges, and other institutions had previously experimented with putting some courses, lectures, and even degrees online, but MOOC fever really hit in 2012. That was the year the artificial intelligence researcher Sebastian Thrun (creator of Google’s self-driving car) and his partner Peter Norvig (Google’s director of research) posted their introductory lecture on artificial intelligence at Stanford University online. Surprisingly, their lecture attracted more than 100,000 views, and the two launched the MOOC company Udacity, which along with its competitor Coursera and a handful of others promised to usher in a revolution in the way the world learned.

The most commonly floated solution to this, which is still in the early stages of research, is so-called analog computing. This would rely not on the exact binary calculations of 1’s and 0’s flowing through silicon chips, but on more approximate calculations, which recognize patterns while using far less energy. It is highly complex, futuristic stuff, but then again, so are self-driving cars. Many say that analog is nothing less than the future of computers. Silicon Valley is an idealistic place, far more so than other industries, such as finance or manufacturing. Although its technical roots lie in the postwar military/industrial complex, its soul and heart are intimately tied to the counterculture movement of the late 1960s and early 1970s.


pages: 326 words: 103,170

The Seventh Sense: Power, Fortune, and Survival in the Age of Networks by Joshua Cooper Ramo

air gap, Airbnb, Alan Greenspan, Albert Einstein, algorithmic trading, barriers to entry, Berlin Wall, bitcoin, Bletchley Park, British Empire, cloud computing, Computing Machinery and Intelligence, crowdsourcing, Danny Hillis, data science, deep learning, defense in depth, Deng Xiaoping, drone strike, Edward Snowden, Fairchild Semiconductor, Fall of the Berlin Wall, financial engineering, Firefox, Google Chrome, growth hacking, Herman Kahn, income inequality, information security, Isaac Newton, Jeff Bezos, job automation, Joi Ito, Laura Poitras, machine translation, market bubble, Menlo Park, Metcalfe’s law, Mitch Kapor, Morris worm, natural language processing, Neal Stephenson, Network effects, Nick Bostrom, Norbert Wiener, Oculus Rift, off-the-grid, packet switching, paperclip maximiser, Paul Graham, power law, price stability, quantitative easing, RAND corporation, reality distortion field, Recombinant DNA, recommendation engine, Republic of Letters, Richard Feynman, road to serfdom, Robert Metcalfe, Sand Hill Road, secular stagnation, self-driving car, Silicon Valley, Skype, Snapchat, Snow Crash, social web, sovereign wealth fund, Steve Jobs, Steve Wozniak, Stewart Brand, Stuxnet, superintelligent machines, systems thinking, technological singularity, The Coming Technological Singularity, The Wealth of Nations by Adam Smith, too big to fail, Vernor Vinge, zero day

The connected devices themselves are constantly improving too. Back in Baran’s day, dozens of scientists counted themselves lucky to share a single computer. A few decades later, the PC revolution gave everyone a machine. And now, of course, we each have many computers in our lives: phones, wired TVs, soon smart self-driving cars. Because of connection, we have access to thousands of such devices in data centers, a fusion of software and hardware and connection that we are starting to lean on as “everyware.” This now-commonplace magic was formalized in 1965 by Gordon Moore, one of the founding engineers at Intel, who noticed that since the introduction of integrated chips, in 1959, the number of transistors on each tiny chip had been doubling every two years.

If they can’t innovate fast enough to develop tools to manage massive data flows or are unable to absorb the best new technology, they will be the new divergence club. Fast networks will elude them. Self-defense will be impossible; their time will be as vulnerable to manipulation by enemies as the resources of Africa and Latin America were to colonialist plunder several hundred years ago. In the next decade, everything from self-driving cars to war-fighting robots will begin to become commonplace in the most advanced nations. Think of the efficiencies these will bring: cheaper logistics and transport in a world of self-handled and self-unloaded trucks. A country such as the United States, which might be a leader in adopting such systems, would outperform even further a nation such as Chile or Nigeria, which may wait years before automated logistics can be implemented.

.… Thus the first ultraintelligent machine is the last invention that man need ever make, provided that the machine is docile enough to tell us how to keep it under control.” Vinge labeled this instant “the singularity”: “It is a point,” he wrote, “where our models must be discarded.” The trivial version of this would be an age of autonomous armed drones, self-driving cars, and electrical grids that flipped nuclear plants on or off according to a logic only they understood. The more profound version, however, would be the arrival of AI that really did think and create and intuit tremors too subtle for the human mind to sense. Like so much of our connected age, such machines would arrive, Vinge felt, because we wanted and even needed them to achieve our dreams.


pages: 340 words: 97,723

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb

"Friedman doctrine" OR "shareholder theory", Ada Lovelace, AI winter, air gap, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Andy Rubin, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Bayesian statistics, behavioural economics, Bernie Sanders, Big Tech, bioinformatics, Black Lives Matter, blockchain, Bretton Woods, business intelligence, Cambridge Analytica, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, CRISPR, cross-border payments, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, disinformation, distributed ledger, don't be evil, Donald Trump, Elon Musk, fail fast, fake news, Filter Bubble, Flynn Effect, Geoffrey Hinton, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Herman Kahn, high-speed rail, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, machine translation, Mark Zuckerberg, Menlo Park, move fast and break things, Mustafa Suleyman, natural language processing, New Urbanism, Nick Bostrom, one-China policy, optical character recognition, packet switching, paperclip maximiser, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, Recombinant DNA, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, seminal paper, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, surveillance capitalism, technological singularity, The Coming Technological Singularity, the long tail, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

Here are just a few of our real-world outcomes: In 2016, an AI-powered security robot intentionally crashed into a 16-month-old child in a Silicon Valley mall.11 The AI system powering the Elite: Dangerous video game developed a suite of superweapons that the creators never imagined, wreaking havoc within the game and destroying the progress made by all the real human players.12 There are myriad problems when it comes to AI safety, some of which are big and obvious: self-driving cars have already run red lights and, in a few instances, killed pedestrians. Predictive policing applications continually mislabel suspects’ faces, landing innocent people in jail. There are an unknowable number of problems that escape our notice, too, because they haven’t affected us personally yet.

Right now, when you talk to your Alexa or Google Home, your voice is being recorded, parsed, and then transmitted to the cloud for a response—given the physical distance between you and the various data centers involved, it’s mind-blowing that Alexa can talk back within a second or two. As AI permeates more of our devices—in the form of smartphones with biometric sensors, security cameras that can lock onto our faces, cars that drive themselves, or precision robots capable of delivering medicine—a one- or two-second processing delay could lead to a catastrophic outcome. A self-driving car can’t ping up to the cloud for every single action because there are far too many sensors that would need to continually feed data up for processing. The only solution is to move the computing closer to the source of the data, which will reduce latency while also saving on bandwidth. This new kind of architecture is called “edge computing,” and it is the inevitable evolution of AI hardware and systems architecture.

See also Dartmouth Workshop Rongcheng, China, 81, 168 Rosenblatt, Frank, 32, 34, 41; Perception system, 32–33. See also Dartmouth Workshop Royal Dutch Shell company, 141–142 Rubin, Andy, 55 Rus, Daniela, 65 Russell, Bertrand: Principia Mathematica, 30–31 Ryder, Jon, 41 Safety issues, AI: robots, 58; self-driving cars, 58. See also Accidents and mistakes, AI Safety standards, AI: establishment of global, 251 Salieri, Antonio, 16 Scenario planning, 141; Royal Dutch Shell company use of, 141–142. See also Scenarios Scenarios, 141; as cognitive bias behavioral economics coping tool, 142; preferred outcomes and, 141; probability neglect and, 142; purpose of, 143.


pages: 173 words: 53,564

Fair Shot: Rethinking Inequality and How We Earn by Chris Hughes

"World Economic Forum" Davos, basic income, Donald Trump, effective altruism, Elon Musk, end world poverty, full employment, future of journalism, gig economy, high net worth, hockey-stick growth, income inequality, invisible hand, Jeff Bezos, job automation, knowledge economy, labor-force participation, Lyft, M-Pesa, Mark Zuckerberg, meta-analysis, new economy, oil rush, payday loans, Peter Singer: altruism, Potemkin village, precariat, randomized controlled trial, ride hailing / ride sharing, Ronald Reagan, Rutger Bregman, Second Machine Age, self-driving car, side hustle, side project, Silicon Valley, TaskRabbit, TED Talk, The Bell Curve by Richard Herrnstein and Charles Murray, traveling salesman, trickle-down economics, uber lyft, universal basic income, winner-take-all economy, working poor, working-age population, zero-sum game

The same goes with Google’s translation software or Amazon’s Echo devices, which are constantly incorporating feedback into their future performance plans. Tesla’s self-driving cars improve their driving ability by collecting, storing, and analyzing all of the driving data they receive while cars are on the road. These systems aren’t just automating processes: they are growing smarter over time. There is little doubt that artificial intelligence could destroy many jobs in the future, but I’m not sure they will. Self-driving cars could replace human drivers, and smart bots might replace personal assistants. The white-collar jobs of doctors and nurses, teachers, and lawyers might be radically reshaped with the introduction of smarter technologies.


pages: 499 words: 144,278

Coders: The Making of a New Tribe and the Remaking of the World by Clive Thompson

"Margaret Hamilton" Apollo, "Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Aaron Swartz, Ada Lovelace, AI winter, air gap, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, Andy Rubin, Asperger Syndrome, augmented reality, Ayatollah Khomeini, backpropagation, barriers to entry, basic income, behavioural economics, Bernie Sanders, Big Tech, bitcoin, Bletchley Park, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, Cambridge Analytica, cellular automata, Charles Babbage, Chelsea Manning, Citizen Lab, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crisis actor, crowdsourcing, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, deep learning, DeepMind, Demis Hassabis, disinformation, don't be evil, don't repeat yourself, Donald Trump, driverless car, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, fake news, false flag, Firefox, Frederick Winslow Taylor, Free Software Foundation, Gabriella Coleman, game design, Geoffrey Hinton, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, growth hacking, Guido van Rossum, Hacker Ethic, hockey-stick growth, HyperCard, Ian Bogost, illegal immigration, ImageNet competition, information security, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Ken Thompson, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microdosing, microservices, Minecraft, move 37, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Nick Bostrom, no silver bullet, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Oculus Rift, off-the-grid, OpenAI, operational security, opioid epidemic / opioid crisis, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, scientific management, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, systems thinking, TaskRabbit, tech worker, techlash, TED Talk, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WeWork, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

Facebook engineers had long been using many different styles of machine learning to help recognize faces in photos, filter stories in the News Feed, and predict whether users would click on an ad; it set up an experimental AI research lab, and soon Facebook was producing a deep-learning model that could recognize faces with 97.35 percent accuracy, 27 percent better than the state of the art (“closely approaching human-level performance,” as they noted.) Self-driving car programs around the world seized on deep learning to help teach cars to navigate roads. Uber uses it to predict where new rides will emerge. The National Cancer Institute is working on using it to detect cancer in CT scans. It’s even seeping into the world of culture: ByteDance, one of China’s hugest firms, uses neural nets to help curate news stories in its Toutiao news app, so successfully that users spend more than 74 minutes a day using it.

They’re fields that are newly technically challenging; serious machine-learning work requires some genuinely mathematical thinking (and, if you practice it at a high level, formal computer science education). And these coders know that these skills are the most lucrative, because they’re necessary for the hot venture-capital-soaked fields like robotics and self-driving cars. This is why thinkers like Marie Hicks, the academic who’s closely studied the history of women in coding, are unenthusiastic about the idea that boot camps will bring more women deeply into the field. Sure, they’ll get some work—but they’ll be limited as to how high they can rise by the industry’s belief that women innately aren’t suited to the “harder” coding jobs; hey, if they were, wouldn’t they already be getting hired for those top jobs?

“human-level performance,” as they noted: Steven Levy, “Inside Facebook’s AI Machine,” Wired, February 23, 2017, accessed August 19, 2018, https://www.wired.com/2017/02/inside-facebooks-ai-machine/; Yaniv Taigman, Ming Yang, Marc’Aurelio Ranzato, and Lior Wolf, “DeepFace: Closing the Gap to Human-Level Performance in Face Verification,” Conference on Computer Vision and Pattern Recognition (CVPR), June 24, 2014, accessed August 19, 2018, https://research.fb.com/publications/deepface-closing-the-gap-to-human-level-performance-in-face-verification. to navigate roads: Andrew J. Hawkins, “Inside Waymo’s Strategy to Grow the Best Brains for Self-driving Cars,” The Verge, May 9, 2018, accessed August 19, 2018, https://www.theverge.com/2018/5/9/17307156/google-waymo-driverless-cars-deep-learning-neural-net-interview. where new rides will emerge: Nikolay Laptev, Slawek Smyl, and Santhosh Shanmugam, “Engineering Extreme Event Forecasting at Uber with Recurrent Neural Networks,” Uber Engineering, June 9, 2017, accessed August 19, 2018, https://eng.uber.com/neural-networks.


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Terms of Service: Social Media and the Price of Constant Connection by Jacob Silverman

"World Economic Forum" Davos, 23andMe, 4chan, A Declaration of the Independence of Cyberspace, Aaron Swartz, Airbnb, airport security, Amazon Mechanical Turk, augmented reality, basic income, Big Tech, Brian Krebs, California gold rush, Californian Ideology, call centre, cloud computing, cognitive dissonance, commoditize, company town, context collapse, correlation does not imply causation, Credit Default Swap, crowdsourcing, data science, deep learning, digital capitalism, disinformation, don't be evil, driverless car, drone strike, Edward Snowden, Evgeny Morozov, fake it until you make it, feminist movement, Filter Bubble, Firefox, Flash crash, game design, global village, Google Chrome, Google Glasses, Higgs boson, hive mind, Ian Bogost, income inequality, independent contractor, informal economy, information retrieval, Internet of things, Jacob Silverman, Jaron Lanier, jimmy wales, John Perry Barlow, Kevin Kelly, Kevin Roose, Kickstarter, knowledge economy, knowledge worker, Larry Ellison, late capitalism, Laura Poitras, license plate recognition, life extension, lifelogging, lock screen, Lyft, machine readable, Mark Zuckerberg, Mars Rover, Marshall McLuhan, mass incarceration, meta-analysis, Minecraft, move fast and break things, national security letter, Network effects, new economy, Nicholas Carr, Occupy movement, off-the-grid, optical character recognition, payday loans, Peter Thiel, planned obsolescence, postindustrial economy, prediction markets, pre–internet, price discrimination, price stability, profit motive, quantitative hedge fund, race to the bottom, Ray Kurzweil, real-name policy, recommendation engine, rent control, rent stabilization, RFID, ride hailing / ride sharing, Salesforce, self-driving car, sentiment analysis, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, Snapchat, social bookmarking, social graph, social intelligence, social web, sorting algorithm, Steve Ballmer, Steve Jobs, Steven Levy, systems thinking, TaskRabbit, technological determinism, technological solutionism, technoutopianism, TED Talk, telemarketer, transportation-network company, Travis Kalanick, Turing test, Uber and Lyft, Uber for X, uber lyft, universal basic income, unpaid internship, women in the workforce, Y Combinator, yottabyte, you are the product, Zipcar

Google then had a challenge: how to get the hundreds of millions of people using its other products, like Search, Gmail, Docs, and YouTube, to embrace Google+. And it was a problem they desperately needed to solve, since Facebook’s huge, active membership and its use of Like buttons and other tracking tools meant that it had a glut of information that advertisers wanted. No matter its various ambitious projects (self-driving cars, mapping the world) or its widely used e-mail service, Google is, at heart, an advertising company—about 91 percent of its 2013 revenue came from its advertising arm, down from 95 percent the previous year. So how could Google both rope more users into Google+ and maintain its informational supremacy in the advertising arms race?

In the meantime, those whose sweat this industry still relies upon find themselves submitting to exploitative conditions, whether as a Foxconn worker in Shenzhen or a Postmates courier in San Francisco. As one Uber driver complained to a reporter: “We have a real person performing a function, not a Google automatic car. We have become the functional end of the app.” It might not be long before he is traded in for a self-driving car. They don’t need breaks, they don’t worry about safety conditions or unions, they don’t complain about wages. Compared to a human being, automatic cars are perfectly efficient. And who will employ him then? Who will be interested in someone who’s spent a few years bouncing between gray-market transportation facilitation services, distributed labor markets, and other hazy digital makework?

They are culture jammers par excellence. A collection of about twenty artists and pranksters, F.A.T. distributes all of their material free of copyright. Often, they produce 3-D models, instruction kits, masks, and other materials that they encourage people to disseminate and to make at home. F.A.T. has produced a fake Google self-driving car that they drove around New York City. They built a fake Google Street View car and took it around Berlin, where concern about Google’s privacy and surveillance practices runs high. (The car also appeared in New York.) They created BRICKiPhone, a functional case for the iPhone (“the most ubiquitous device of the yuppie class”), which turns the phone into a blocky, gray, late-eighties-style cell phone, complete with protruding black antenna.


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Data and Goliath: The Hidden Battles to Collect Your Data and Control Your World by Bruce Schneier

23andMe, Airbnb, airport security, AltaVista, Anne Wojcicki, AOL-Time Warner, augmented reality, behavioural economics, Benjamin Mako Hill, Black Swan, Boris Johnson, Brewster Kahle, Brian Krebs, call centre, Cass Sunstein, Chelsea Manning, citizen journalism, Citizen Lab, cloud computing, congestion charging, data science, digital rights, disintermediation, drone strike, Eben Moglen, Edward Snowden, end-to-end encryption, Evgeny Morozov, experimental subject, failed state, fault tolerance, Ferguson, Missouri, Filter Bubble, Firefox, friendly fire, Google Chrome, Google Glasses, heat death of the universe, hindsight bias, informal economy, information security, Internet Archive, Internet of things, Jacob Appelbaum, James Bridle, Jaron Lanier, John Gilmore, John Markoff, Julian Assange, Kevin Kelly, Laura Poitras, license plate recognition, lifelogging, linked data, Lyft, Mark Zuckerberg, moral panic, Nash equilibrium, Nate Silver, national security letter, Network effects, Occupy movement, operational security, Panopticon Jeremy Bentham, payday loans, pre–internet, price discrimination, profit motive, race to the bottom, RAND corporation, real-name policy, recommendation engine, RFID, Ross Ulbricht, satellite internet, self-driving car, Shoshana Zuboff, Silicon Valley, Skype, smart cities, smart grid, Snapchat, social graph, software as a service, South China Sea, sparse data, stealth mode startup, Steven Levy, Stuxnet, TaskRabbit, technological determinism, telemarketer, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, undersea cable, unit 8200, urban planning, Wayback Machine, WikiLeaks, workplace surveillance , Yochai Benkler, yottabyte, zero day

Much of that is automatically recorded: Nate Cardozo (11 Feb 2013), “Mandatory black boxes in cars raise privacy questions,” Electronic Frontier Foundation, https://www.eff.org/press/releases/mandatory-black-boxes-cars-raise-privacy-questions. A self-driving car: Lucas Mearian (23 Jul 2013), “Self-driving cars could create 1GB of data a second,” Computer World, http://www.computerworld.com/s/article/9240992/Self_driving_cars_could_create_1GB_of_data_a_second. Embedded in digital photos: Benjamin Henne, Maximilian Koch, and Matthew Smith (3–7 Mar 2014), “On the awareness, control and privacy of shared photo metadata,” Distributed Computing & Security Group, Leibniz University, presented at the Eighteenth International Conference for Financial Cryptography and Data Security, Barbados, http://ifca.ai/fc14/papers/fc14_submission_117.pdf. 15 If you upload the photo: This is a particularly creepy story about camera metadata.

Much of that is automatically recorded in a black box recorder, useful for figuring out what happened in an accident. There’s even a computer in each tire, gathering pressure data. Take your car into the shop, and the first thing the mechanic will do is access all that data to diagnose any problems. A self-driving car could produce a gigabyte of data per second. Snap a photo, and you’re at it again. Embedded in digital photos is information such as the date, time, and location—yes, many cameras have GPS—of the photo’s capture; generic information about the camera, lens, and settings; and an ID number of the camera itself.


Four Battlegrounds by Paul Scharre

2021 United States Capitol attack, 3D printing, active measures, activist lawyer, AI winter, AlphaGo, amateurs talk tactics, professionals talk logistics, artificial general intelligence, ASML, augmented reality, Automated Insights, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, bitcoin, Black Lives Matter, Boeing 737 MAX, Boris Johnson, Brexit referendum, business continuity plan, business process, carbon footprint, chief data officer, Citizen Lab, clean water, cloud computing, commoditize, computer vision, coronavirus, COVID-19, crisis actor, crowdsourcing, DALL-E, data is not the new oil, data is the new oil, data science, deep learning, deepfake, DeepMind, Demis Hassabis, Deng Xiaoping, digital map, digital rights, disinformation, Donald Trump, drone strike, dual-use technology, Elon Musk, en.wikipedia.org, endowment effect, fake news, Francis Fukuyama: the end of history, future of journalism, future of work, game design, general purpose technology, Geoffrey Hinton, geopolitical risk, George Floyd, global supply chain, GPT-3, Great Leap Forward, hive mind, hustle culture, ImageNet competition, immigration reform, income per capita, interchangeable parts, Internet Archive, Internet of things, iterative process, Jeff Bezos, job automation, Kevin Kelly, Kevin Roose, large language model, lockdown, Mark Zuckerberg, military-industrial complex, move fast and break things, Nate Silver, natural language processing, new economy, Nick Bostrom, one-China policy, Open Library, OpenAI, PalmPilot, Parler "social media", pattern recognition, phenotype, post-truth, purchasing power parity, QAnon, QR code, race to the bottom, RAND corporation, recommendation engine, reshoring, ride hailing / ride sharing, robotic process automation, Rodney Brooks, Rubik’s Cube, self-driving car, Shoshana Zuboff, side project, Silicon Valley, slashdot, smart cities, smart meter, Snapchat, social software, sorting algorithm, South China Sea, sparse data, speech recognition, Steve Bannon, Steven Levy, Stuxnet, supply-chain attack, surveillance capitalism, systems thinking, tech worker, techlash, telemarketer, The Brussels Effect, The Signal and the Noise by Nate Silver, TikTok, trade route, TSMC

These problems are common in image classification systems, and have resulted in increased scrutiny of high-consequence applications, such as law enforcement use of facial recognition systems. In some settings, these failures can have fatal consequences. Problems with computer vision and image classification have contributed to self-driving cars and Tesla cars on autopilot striking pedestrians, semitrailers, concrete barriers, fire trucks, and parked cars, leading to several fatal accidents. Military systems are not immune to these flaws. In fact, the first deployed Maven systems reportedly suffered similar challenges, with initially a very poor accuracy rate (60 percent) because the algorithm was trained using drone footage from a different region than the one in which it was deployed.

Adversarial attacks have been embedded into physical objects, such as a 3D-printed turtle that was subtly altered to fool an image classifier into misidentifying it as a rifle. Even more alarming than stickers on stop signs, in a real-world driving experiment researchers used black box methods to create a physical adversarial object that was able to evade detection by the laser-based detection systems used by self-driving cars. To the autonomous car’s sensors, the object simply wasn’t there. In theory, adversarial attacks could be used to subvert deployed AI systems in a variety of real-world settings. An individual wearing manipulated clothing—a hat, shirt, or glasses—could fool a facial recognition system into believing they are someone else.

Adversarial examples could be placed like cognitive land mines in the environment waiting for AI systems to run across them, altering their behavior. Adversarial stickers or patches could be used to hide objects from detectors or to overload detectors with false positives. Adversarial objects or stickers could cause self-driving cars to disobey traffic laws, collide with objects, or cause traffic jams. Some physical adversarial attacks involve changes that are big enough to be obvious to a human. For example, the manipulated glasses that fooled facial recognition systems consisted of large, multicolored frames that were hardly subtle.


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Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days by Jake Knapp, John Zeratsky, Braden Kowitz

23andMe, 3D printing, Airbnb, Anne Wojcicki, Apollo 13, Blue Bottle Coffee, cognitive load, fake news, Gene Kranz, Google Earth, Google Hangouts, Google X / Alphabet X, self-driving car, side project, Silicon Valley, Wall-E

We’ve used sprints to assess the viability of new businesses, to make the first version of new mobile apps, to improve products with millions of users, to define marketing strategies, and to design reports for medical tests. Sprints have been run by investment bankers looking for their next strategy, by the team at Google building the self-driving car, and by high school students working on a big math assignment. This book is a DIY guide for running your own sprint to answer your pressing business questions. On Monday, you’ll map out the problem and pick an important place to focus. On Tuesday, you’ll sketch competing solutions on paper.

., 169–70 finance experts, 34 Fitbit, 171 fitness training, automated, 171–74 FitStar sprint, 171–74, 189, 206 Flatiron Health sprint, 60–64, 76, 85, 88, 100–101, 153, 176, 224 Flickr, 143 focus, sprint process emphasis on, 32 Foundation Medicine sprint, 16, 176–77, 185 FoundationOne, 176 Freeman, James, 21–25, 30, 103 Gebbia, Joe, 210–11 genetic analysis, in cancer treatments, 176 George Mason University, 38 Getting Things Done (Allen), 108–9 Giarusso, Serah, 24, 103 Glitch (video game), 128–29, 143 Gmail, 2, 4 goals, ambitious, 229 goals, long-term, 55–57, 61, 67, 110, 138, 141, 147 dangerous assumptions and, 56–57 in Flatiron Health sprint, 62–63 Goldilocks quality, 170, 207 Gonzalez, Tony, 171–72 Google, 60 experimentation culture of, 1 self-driving car of, 16 Google Earth, 83 Google Forms, 121 Google Hangouts, 3 Google Search, 4 Google Ventures (GV), 4–6, 7, 12, 15, 16, 60, 85, 113, 130, 171, 176, 201, 231 Google X, 4 Grace, Merci, 130, 131, 143–44, 152, 156, 175, 216–17, 221, 222 Graco sprint, 27–28 Green, Bobby, 76, 85, 86 Grijalva, Dave, 171–74 Harry Potter and the Philosopher’s Stone (Rowling), 196, 196n heat map, in deciding process, 131, 132–35 high stakes, as challenge, 26 honesty, in deciding process, 139–40 hotels, guest satisfaction and, 10, 56 Howard, Ron, 53 How Might We notes, 68, 73–82, 110 in Blue Bottle sprint, 73–74 challenges and, 77–78 in Flatiron Health sprint, 76–78 maps and, 81–82 organizing, 79–80 prioritizing, 80–81 target and, 87 HTML, 184 Hurley, Chad, 6 IdeaPaint, 44 IDEO, 73 illusion, 165–66 see also façades Incredibles, The (film), 149 Indian Ocean, 84 industrial companies, sprints and, 27–28 Ingram, Alex, 62, 76 interruptions, productivity and, 38–39 Interviewer, 188, 190, 204–5, 217, 225 tips for, 212–15 interviews, 196–200, 201–15 being a good host in, 212 broken questions in, 214–15 context questions in, 202, 205–6 curiosity mindset in, 215 debriefing in, 202, 209–10 detailed tasks in, 202, 208–9 as emotional roller coaster for sprint team, 197 feedback in, 207 in FitStar sprint, 197, 206 in FitStar test, 208 five-act structure of, 202 ideal number of customers for, 197–99 introducing prototypes in, 202, 206–7 in One Medical sprint, 199–200 open-ended vs. leading questions in, 212–13 power of, 210–11 schedule of, 199 in Slack sprint, 217 team observation of, see interviews, learning from thinking aloud in, 207–8 welcome in, 202, 204–5 “why” questions in, 199–200 interviews, learning from: in Blue Bottle sprint, 223–24 in Flatiron Health sprint, 224 group note-taking in, 219–21 importance of real-time team observation in, 202–4, 218–19 looking for patterns in, 222 in Savioke sprint, 223 in Slack sprint, 220–21, 223 sprint questions and, 222–23 Invite Media, 60 iPads, 171–73, 178, 189 as banned from sprint room, 41 JavaScript, 184 Keynote, 171, 173, 175, 176, 177, 178, 184–85, 186 Knapp, Jake, 24, 27–28, 30, 47, 48, 60, 62, 76, 77, 85, 107n, 109 Kowitz, Braden, 5, 22, 23–24, 30, 43, 60, 76, 156, 216 Kranz, Gene, 53, 55, 85 Lachapelle, Serge, 3 Lancelotta, Mary Pat, 176 Landauer, Thomas K., 198n laptops, as banned from sprint room, 41 Lau, Tessa, 11, 12, 178 lean development, 17 learning, see interviews, learning from Lightning Demos, 96–101, 110 Lord of the Rings, The (Tolkien), 59, 60 Lowe, David, 27 McKinsey & Company, 230 Makers, 187, 188 mapping the problem, 16, 59–67, 110, 230 in Blue Bottle sprint, 23–24, 65, 66 division of labor and, 101–2 experts and, 69–70, 76, 77 in Flatiron Health sprint, 62–63 How Might We notes and, 81–82, 85 in Savioke sprint, 10, 64–65, 66 steps in, 66 as story, 65–66 target and, 84, 85–86 Margolis, Michael, 5, 12, 60, 62, 201–2, 203, 204, 206, 208, 209, 212, 214, 216, 217 Maris, Bill, 4–5 markers, dry-erase, 75 marketing experts, 34 Maser, Mike, 171–73 “Mathematical Model of the Finding of Usability Problems, A” (Nielsen and Landauer), 198n mechanics, of product or service, 70–71 Medium, 6 Medium sprint, 224 Meehan, Bryan, 22 meetings, frustrations of, 127–28, 230 Microsoft Word, 186 Mid-Ocean Ridge, 83–84, 87 “Mind Reader, The” (Blue Bottle solution sketch), 104–6, 115 Mission Control, 53–54, 225 momentum, regaining, 26 Move Loot sprint, 113 movies, façades in, 165–66, 173 My Neighbor Totoro (film), 98 NASA, 54 Nest, 16 Newton, Alice, 195–96 Newton, Nigel, 195–96 New York Times, 15, 130, 152, 153, 188 Nielsen, Jakob, 197–98, 198n no-devices rule, 41, 110 Note-and-Vote, 146–47 note-taking: on interviews, 219–21 sketching and, 109, 110 see also How Might We notes Ocean’s Eleven (film), 29–30, 36, 37, 225 office supplies, for sprint rooms, 45 One Medical Group sprint, 180–82, 185–86, 199 opening scene, 188 OstrichCo, 139–40 paper, for sprint rooms, 44 paper coffee filters, 95–96 patterns, in customer reactions to prototypes, 222 permission, Facilitators and, 89 personal trainers, 171 phones, as banned from sprint room, 41 Photoshop, 184 Pitt, Brad, 29, 36 Pixar, 149 plate tectonics, 84 PlayStation, 178 Porter, Josh, 89 Post-It notes, see sticky notes PowerPoint, 184, 186 previous efforts, see existing solutions priorities, setting, 54–55 “Priority Inbox” project, 2–3 Procter & Gamble, 73 productivity, interruptions and, 38–39 progress, rapid, from sprint process, 31 prototype mindset, 168–69, 230 prototypes, prototyping, 16, 60, 183–90 actors and scripts in, 186 appearance of reality in, 169–70 Asset Collector in, 188 in Blue Bottle sprint, 25, 28, 104–6 Brochure Façades in, 185 Deciders and, 31, 32 deciding on, see deciding as disposable, 169 division of labor in, 183, 187 façades and, see façades Facilitator and, 187 in FitStar sprint, 189 focus on learning from, 169 in Foundation Medicine sprint, 185 Goldilocks quality in, 170 in Graco sprint, 27–28 Interviewer in, 188, 190 Makers in, 187 mindset and, 168–69 in One Medical sprint, 199 picking right tools for, 183–86 in Priority Inbox sprint, 3 Rumbles and, 143–47 in Savioke sprint, 9, 10, 11–12, 185 sketching and, 104–6 in SquidCo sprint, 30–31 Stitcher in, 183, 187, 189 storyboard scenes and, 188, 189–90 trial run in, 183, 189–90 universal application of, 169 using existing objects or spaces in, 186 Writer in, 187–88 questions: in interviews, 212–14 obvious, Facilitators and, 90 questions, finding answers to, 138, 141, 147 in Blue Bottle sprint, 23 in FitStar sprint, 171 in Flatiron Health sprint, 62–63, 88 in Foundation Medicine sprint, 176–77 in Graco sprint, 27–28 and learning from interviews, 222–23 in One Medical sprint, 180 role of sprints in, 15, 16–17, 67 in Savioke sprint, 9, 10, 178 in Slack sprint, 175, 216–17, 222–23 Starting at the End and, 55–58 surface and, 28 see also How Might We notes reaction, feedback vs., 169–70 Relay robot, 7, 14, 56 eyes of, 97–98 guest satisfaction and, 10 guests’ responses to, 13 “personality” of, 11, 13, 71, 178, 179 risk-taking, 156, 166 robot helpers, human interaction with, 8–9, 10 Rogers, Jan, 46–47 Rogers, Loran, 46, 48 rooms, for sprints, 41–45 Rumbles, 143–47, 223 in Blue Bottle sprint, 146 Deciders in, 145, 146 fake brands in, 145–46 Note-and-Vote in, 146–47 single-prototype vs., 145, 147 in Slack sprint, 144, 145 Savioke Labs sprint, 7–15, 26, 33, 64, 66, 71, 119, 145, 153, 157, 178–79, 185, 223 better guest experience as goal of, 56, 84 schedule, clearing space for sprints in, 10, 39, 40–41 screener surveys, in recruiting test customers, 119–21 Scribe, in speed critique, 135–36 Seattle, Wash., 229 Sharpies, 75n simplicity, in maps, 66 sketching, 16, 60, 102, 103–18 abstract ideas and, 106–7 in Blue Bottle sprint, 24, 103–4, 108, 113 Crazy 8s exercise in, 109, 111–13 in Move Loot sprint, 113 prototypes and, 104–6 of rough ideas, 109, 111 solution sketches in, see solution sketches taking notes in, 109, 110 as working alone together, 107–9 Slack sprint, 129–31, 143–44, 149–58, 175, 216, 217, 220–21, 222, 223 expansion into new markets as challenge for, 129–30 Smithsonian Institute, 228 snacks, for sprints, 45 solution sketches, 109, 114–18 anonymity of, 114–15 in Blue Bottle sprint, 116–17 deciding on, see deciding as explanatory, 114 importance of words in, 115 maybe-laters in, 142, 155 single-scene, 114, 117 in Slack sprint, 130 sticky notes and, 114 storyboard format in, 114, 116 titles for, 115 winners in, 141–42 speed critique: in deciding process, 131, 135–37 Scribe in, 135–36 sprints: checklists for, 232–49 clearing calendars for, 10, 39, 40–41 concept of, 3 daily schedule in, 39, 40–41, 90–91 deciding process in, see deciding façades in, see façades as five-day process, 5–6, 9, 16, 40–41 frequently asked questions about, 251–57 learning from, see interviews, learning from no-devices rule in, 41, 110 origin of, 2–5 prototypes in, see prototypes, prototyping questions to be answered in, see questions, finding answers to; tests, real-world risk-taking in, 166 Rumbles in, 143–47 setting priorities in, 54–55 storyboards in, see storyboarding time allocation in, 38–41 timers for, 46–48 uncovering dangerous assumptions through, 56–57 universal application of, 229–30 versatility of, 5–6, 229–30 wide application of, 5–6 working alone together in, 107–9 work rooms for, 41–45 Squarespace, 186 SquidCo sprint, 30–31, 32, 139 Starting at the End, 5, 53–58 in Apollo 13 rescue, 53–54 in Blue Bottle sprint, 55–56, 57 in Flatiron Health sprint, 62–63 long-term goals and, 55–57, 61, 62–63, 67 questions to be answered in, 55–58, 62–63, 67 in Savioke sprint, 56 setting priorities in, 54–55 startups, 231 sprints and, 4–5, 15–16, 27 Starwood, 9 sticky notes: poster-size, 43, 44 solution sketches and, 114 see also How Might We notes Stitcher, 187, 189 storyboarding, 125, 148–58 “artist” for, 151, 154–55, 156 assigning prototyping tasks from, 188, 189–90 in Blue Bottle sprint, 153, 157, 188 competitors’ products in, 154 copywriting in, 155–56 Decider in, 156 detail in, 156 in Flatiron Health sprint, 153 maybe-laters in, 155 opening scene in, 152–53 resisting new ideas in, 155 risk-taking in, 156 in Savioke sprint, 153, 157 in Slack sprint, 149–53, 156 solution sketches as, 114, 116 test-time limits and, 157 story-centered design, 5 strategy, 70 straw polls, 87–88 in deciding process, 131, 138–40 successes, flawed, 223–24 supervotes, 143, 144 in deciding process, 131, 140–42, 143 surface, as contact point between product and customer, 28 target, 82, 83–88 in Blue Bottle sprint, 84–85, 101 Decider and, 31, 32, 85–88 in Flatiron Health sprint, 85–87, 88 How Might We notes and, 87 key customers in, 85–86 key event in, 85–86 maps and, 84, 85–86 in Savioke sprint, 84 straw polls and, 87–88 Tcho, 97 team processes, 1 teams, 29–37, 218 in Blue Bottle sprint, 22–24, 33 challenges and, 68 choosing members of, 33, 34–36 Deciders in, see Deciders division of labor in, 101–2 experts and, see Ask the Experts Facilitators in, see Facilitators ideal size of, 33 interviews observed by, see interviews, learning from in Ocean’s Eleven, 29–30 in Savioke sprint, 9–11, 33 in SquidCo sprint, 30–31 troublemakers in, 35 tech/logistic experts, 34 “Tenacious Tour, The” (Slack solution sketch), 144, 175, 217, 220–21, 222 tests, real-world, 5, 16, 231 in Blue Bottle sprint, 25 competitors’ products in, 154 Deciders and, 31, 32 in FitStar sprint, 173–74 in Graco sprint, 27–28 interview in, see interviews recruiting customers for, 119–23, 197 in Savioke sprint, 10, 11–13, 15 time units in, 157 Tharp, Marie, 83–84 3D printing, 27, 185, 186 tight deadlines, 109 time, allocation of, for sprints, 38–41 timers, in deciding process, 136, 138 Time Timers, 46–48 Tolkien, J.


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Predictive Analytics: The Power to Predict Who Will Click, Buy, Lie, or Die by Eric Siegel

Alan Greenspan, Albert Einstein, algorithmic trading, Amazon Mechanical Turk, Apollo 11, Apple's 1984 Super Bowl advert, backtesting, Black Swan, book scanning, bounce rate, business intelligence, business process, butter production in bangladesh, call centre, Charles Lindbergh, commoditize, computer age, conceptual framework, correlation does not imply causation, crowdsourcing, dark matter, data is the new oil, data science, driverless car, en.wikipedia.org, Erik Brynjolfsson, Everything should be made as simple as possible, experimental subject, Google Glasses, happiness index / gross national happiness, information security, job satisfaction, Johann Wolfgang von Goethe, lifelogging, machine readable, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, mass immigration, Moneyball by Michael Lewis explains big data, Nate Silver, natural language processing, Netflix Prize, Network effects, Norbert Wiener, personalized medicine, placebo effect, prediction markets, Ray Kurzweil, recommendation engine, risk-adjusted returns, Ronald Coase, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, Shai Danziger, software as a service, SpaceShipOne, speech recognition, statistical model, Steven Levy, supply chain finance, text mining, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Davenport, Turing test, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra, zero-sum game

Colin Farrell’s Department of Justice agent confronts Cruise, and the two brutes stand off, mano a mano. “You ever get any false positives?” accuses Farrell. A false positive, aka false alarm, is when a model incorrectly predicts yes, when the correct answer is no. It says you’re guilty, convicting you of a crime you didn’t (or in this case, won’t) commit. As self-driving cars emerge from Google and BMW and begin to hit the streets, a new cultural acceptance of machine risk will emerge as well. The world will see automobile collision casualty rates decrease overall, and eventually, among waves of ire and protest, learn to accept that on some occasions the computer is to blame for an accidental death.

See crime fighting and fraud detection frequency Freud, Sigmund Friedman, Jerome friendships, predicting Fukuman, Audrey Fulcher, Christopher fund-raising, predicting in Furnas, Alexander future, views on human nature and knowing about predictions for 2020 uncertainty of G Galileo generalization paradox Ghani, Rayid Gilbert, Eric Gimpert, Ben Gladwell, Malcolm GlaxoSmithKline (UK) Gmail Goethe, Johann Wolfgang von Goldbloom, Anthony Gondek, David Google ineffective ads, predicting mouse clicks, measuring for predictions privacy policies Schmidt, Eric searches for playing Jeopardy! self-driving cars spam filtering Google Adwords Google Flu Trends Google Glass Google Page Rank government data storage by fraud detection for invoices PA for public access to data GPS data grades, predicting Granger, Clive grant awards, predicting Greenspan, Alan Grockit Groundhog Day (film) Grundhoefer, Michael H hackers, predicting Halder, Gitali HAL (intelligent computer) Hansell, Saul happiness, social effect and Harbor Sweets Harcourt, Bernard Harrah’s Las Vegas Harris, Jeanne Harvard Medical School Harvard University Hastings, Reed healthcare death predictions in health risks, predicting hospital admissions, predicting influenza, predicting medical research, predicting in medical treatments, risks for wrong predictions in medical treatments, testing persuasion in PA for personalized medicine, uplift modeling applications for health insurance companies, PA for Hebrew University Heisenberg, Werner Karl Helle, Eva Helsinki Brain Research Centre Hennessey, Kathleen Heraclitus Heritage Health Prize Heritage Provider Network Hewlett Foundation Hewlett-Packard (HP) employee data used by financial savings and benefits of PA Global Business Services (GBS) quitting and Flight Risks, predicting sales leads, predicting turnover rates at warranty claims and fraud detection High Anxiety (film) HIV progression, predicting HIV treatments, uplift modeling for Hollifield, Stephen Holmes, Sherlock hormone replacement, coronary disease and hospital admissions, predicting Hotmail.com House (TV show) “How Companies Learn Your Secrets” (Duhigg) Howe, Jeff HP.

See PA (predictive analytics) Predictive Analytics World (PAW) conferences predictive models defined marketing models overlearning and assuming response modeling response uplift modeling univariate vs. multivariate See also ensemble models predictive models, launching about action and decision making causality and deployment phase Elder’s success in going live machine learning and building observation and personalization and risks in uplift modeling predictive technology See also machine learning predictor variables pregnancy and birth, predicting customer pregnancy and buying behavior premature births prejudice, risk of PREMIER Bankcard privacy Google policies on insight vs. intrusion regarding predicted consumer data and profiling customers Progressive Insurance psychology emotions, cause and effect of Freud on emotions predictive analysis in schizophrenia, predicting psychopathy, predicting Psych (TV show) purchases, predicting Q Quadstone R Radcliffe, Nicholas Radica Games Ralph’s random forests Rebellion Research recency recidivism prediction for law enforcement recommendation systems Reed Elsevier reliability modeling REO Speedwagon (band) response modeling drawbacks of examples of targeted marketing with response rates response uplift modeling retail websites, behavior on retirement, health and Richmond (VA) Police Department Rio Salado Community College Riskprediction.org.uk risk management risk scores Risky Business (film) Robin, Leo Romney, Mitt Royal Astronomy Society R software Russell, Bertrand Rutter, Brad S Saaf, Randy safety and efficiency, PA for Safeway sales leads, predicting Salford Systems Salsburg, David Santa Cruz (CA) Police Department sarcasm, in reviews Sartre, Jean-Paul SAS satellites, predicting fault in satisficing Schamberg, Lisa schizophrenia, predicting Schlitz, Don Schmidt, Eric Schwartz, Ari Science magazine security levels, predicting self-driving cars Selfridge, Oliver Semisonic (band) sepsis, predicting Sessions, Roger Shakespeare, William Shaw, George Bernard Shearer, Colin shopping habits, predicting sickness, predicting Siegel, Eric silence, concept of Silver, Nate Simpsons, The (TV show) Siri Sisters of Mercy Health Systems small business credit risks Smarr, Larry smoking and smokers health problems and causation for motion disorders and social effect and quitting SNTMNT Sobel, David social computing social effect social media networks data glut on happiness as contagious on healthcare LinkedIn PA for spam filtering on Twitter viral tweets and posts on YouTube See also Facebook sociology, uplift modeling applications for SpaceShipOne spam filtering Spider-Man (film) sporting events, crime rates and sports cars Sprint SPSS staff behavior.


pages: 409 words: 105,551

Team of Teams: New Rules of Engagement for a Complex World by General Stanley McChrystal, Tantum Collins, David Silverman, Chris Fussell

Airbus A320, Albert Einstein, Apollo 11, Atul Gawande, autonomous vehicles, bank run, barriers to entry, Black Swan, Boeing 747, butterfly effect, call centre, Captain Sullenberger Hudson, Chelsea Manning, clockwork universe, crew resource management, crowdsourcing, driverless car, Edward Snowden, Flash crash, Frederick Winslow Taylor, global supply chain, Henri Poincaré, high batting average, Ida Tarbell, information security, interchangeable parts, invisible hand, Isaac Newton, Jane Jacobs, job automation, job satisfaction, John Nash: game theory, knowledge economy, Mark Zuckerberg, Mohammed Bouazizi, Nate Silver, Neil Armstrong, Pierre-Simon Laplace, pneumatic tube, radical decentralization, RAND corporation, scientific management, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, supply-chain management, systems thinking, The Wealth of Nations by Adam Smith, urban sprawl, US Airways Flight 1549, vertical integration, WikiLeaks, zero-sum game

UT professor Peter Stone, one of the leaders of the project, notes that 25 percent of accidents and 33 percent of the thirty-three thousand auto deaths each year in America occur at intersections, and 95 percent are attributable to “human error.” Early trials suggest that self-driving cars could save ten thousand deaths a year while making commutes faster and more comfortable. But for all the statistics and trials, the UT simulation still doesn’t look right. A computer simulation on how intersections could change in the future due to the innovation of self-driving cars. It looks unnatural because we have a strongly ingrained idea of how traffic should look, and it is governed by a mechanical rhythm of stops, starts, and turns.

Human interaction—not just in the context of management—is changing tremendously. The University of Texas at Austin computer simulation on the future of automotive traffic provides a perfect reflection of these shifts. The program illustrates how a four-way intersection might look in an urban landscape dominated by self-driving cars communicating in real time. And watching it somehow just feels wrong. The intersection is huge—a ten-lane highway crossing over another ten-lane highway—but there are no traffic lights, no stop signs, and seemingly no sense of order. Vehicles don’t queue up based on direction of travel to wait their turn before migrating to the other side en masse.


The Smart Wife: Why Siri, Alexa, and Other Smart Home Devices Need a Feminist Reboot by Yolande Strengers, Jenny Kennedy

active measures, Amazon Robotics, Anthropocene, autonomous vehicles, Big Tech, Boston Dynamics, cloud computing, cognitive load, computer vision, Computing Machinery and Intelligence, crowdsourcing, cyber-physical system, data science, deepfake, Donald Trump, emotional labour, en.wikipedia.org, Evgeny Morozov, fake news, feminist movement, game design, gender pay gap, Grace Hopper, hive mind, Ian Bogost, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jeff Bezos, John Markoff, Kitchen Debate, knowledge economy, Masayoshi Son, Milgram experiment, Minecraft, natural language processing, Network effects, new economy, pattern recognition, planned obsolescence, precautionary principle, robot derives from the Czech word robota Czech, meaning slave, self-driving car, Shoshana Zuboff, side hustle, side project, Silicon Valley, smart grid, smart meter, social intelligence, SoftBank, Steve Jobs, surveillance capitalism, systems thinking, technological solutionism, technoutopianism, TED Talk, Turing test, Wall-E, Wayback Machine, women in the workforce

,” PLoS ONE 13, no. 7 (2018): e0201581. 59. Jennifer Rhee, The Robotic Imaginary: The Human and the Price of Dehumanized Labor (Minneapolis: University of Minnesota Press, 2018). 60. Julia Carrie Wong, “Rage against the Machine: Self-Driving Cars Attacked by Angry Californians,” Guardian, March 6, 2018, https://www.theguardian.com/technology/2018/mar/06/california-self-driving-cars-attacked. 61. “Rage against the Machine,” Sydney Morning Herald, July 26, 2003, https://www.smh.com.au/technology/rage-against-the-machine-20030726-gdh5sc.html. 62. Stanley Milgram, “Behavioral Study of Obedience,” Journal of Abnormal and Social Psychology 67, no. 4 (October 1963): 371–378; Christoph Bartneck and Jun Hu, “Exploring the Abuse of Robots,” Interaction Studies 9, no. 3 (2008): 415–433. 63.

For those not familiar with this cult manga and anime series originating in the 1950s (the decade responsible for much of the smart wife’s prototyping), Astro Boy is a powerful “roboy” created by the head of the Ministry of Science, Dr. Tenma, to replace his son Tobio (or Toby), who died in a self-driving car accident. Tenma ultimately rejects Astro Boy, realizing that he could never replace his son. After a cruel beginning, Astro Boy is adopted by another man and the new head of the Ministry of Science, Professor Ochanomizu, who realizes Astro Boy’s amazing powers, skills, and ability to experience human emotions.


pages: 1,331 words: 163,200

Hands-On Machine Learning With Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurélien Géron

AlphaGo, Amazon Mechanical Turk, Anton Chekhov, backpropagation, combinatorial explosion, computer vision, constrained optimization, correlation coefficient, crowdsourcing, data science, deep learning, DeepMind, don't repeat yourself, duck typing, Elon Musk, en.wikipedia.org, friendly AI, Geoffrey Hinton, ImageNet competition, information retrieval, iterative process, John von Neumann, Kickstarter, machine translation, natural language processing, Netflix Prize, NP-complete, OpenAI, optical character recognition, P = NP, p-value, pattern recognition, pull request, recommendation engine, self-driving car, sentiment analysis, SpamAssassin, speech recognition, stochastic process

Convolutional neural networks (CNNs) emerged from the study of the brain’s visual cortex, and they have been used in image recognition since the 1980s. In the last few years, thanks to the increase in computational power, the amount of available training data, and the tricks presented in Chapter 11 for training deep nets, CNNs have managed to achieve superhuman performance on some complex visual tasks. They power image search services, self-driving cars, automatic video classification systems, and more. Moreover, CNNs are not restricted to visual perception: they are also successful at other tasks, such as voice recognition or natural language processing (NLP); however, we will focus on visual applications for now. In this chapter we will present where CNNs came from, what their building blocks look like, and how to implement them using TensorFlow.

In this chapter we will first explain what Reinforcement Learning is and what it is good at, and then we will present two of the most important techniques in deep Reinforcement Learning: policy gradients and deep Q-networks (DQN), including a discussion of Markov decision processes (MDP). We will use these techniques to train a model to balance a pole on a moving cart, and another to play Atari games. The same techniques can be used for a wide variety of tasks, from walking robots to self-driving cars. Learning to Optimize Rewards In Reinforcement Learning, a software agent makes observations and takes actions within an environment, and in return it receives rewards. Its objective is to learn to act in a way that will maximize its expected long-term rewards. If you don’t mind a bit of anthropomorphism, you can think of positive rewards as pleasure, and negative rewards as pain (the term “reward” is a bit misleading in this case).

Pac-Man, (c) Go player, (d) thermostat, (e) automatic trader5 Note that there may not be any positive rewards at all; for example, the agent may move around in a maze, getting a negative reward at every time step, so it better find the exit as quickly as possible! There are many other examples of tasks where Reinforcement Learning is well suited, such as self-driving cars, placing ads on a web page, or controlling where an image classification system should focus its attention. Policy Search The algorithm used by the software agent to determine its actions is called its policy. For example, the policy could be a neural network taking observations as inputs and outputting the action to take (see Figure 16-2).


pages: 434 words: 114,583

Faster, Higher, Farther: How One of the World's Largest Automakers Committed a Massive and Stunning Fraud by Jack Ewing

"RICO laws" OR "Racketeer Influenced and Corrupt Organizations", 1960s counterculture, Asilomar, asset-backed security, Bear Stearns, Berlin Wall, business logic, cognitive dissonance, collapse of Lehman Brothers, corporate governance, crossover SUV, Fall of the Berlin Wall, financial engineering, Ford Model T, full employment, hiring and firing, independent contractor, Kaizen: continuous improvement, McMansion, military-industrial complex, self-driving car, short selling, short squeeze, Silicon Valley, sovereign wealth fund, Steve Jobs, subprime mortgage crisis

Winterkorn alluded to the Silicon Valley companies that were beginning to take an interest in the car business—a reference to Google, which was devoting some of its huge financial resources to develop cars that could drive themselves, and to Apple, said to have assembled a large team to design an electric car. Volkswagen, too, would develop self-driving cars, he said, and they would be “not just for the upper class, but for everyone.” What’s more, all Volkswagen Group vehicles would soon be smartphones on wheels. (The statement made at least a few well-informed listeners chuckle. A few days earlier, Müller, the Porsche chief executive, had told an auto magazine that his vehicles would not be smartphones on wheels.)

Dozens of workers in their white uniforms crowded around the black sedan for a last group photo. While probably no one but Ferdinand Piëch would rue the unpopular luxury Volkswagen, the cuts came as the auto industry faced new competition from Silicon Valley. Apple was rumored to be working on its own car project. Google was investing massively in self-driving cars. At auto shows, the talk was of a move to battery power. Though the number of electric cars on the roads was still tiny, executives worried that a shift away from internal combustion engines to batteries would make them vulnerable to new competitors, perhaps from China. Volkswagen would have less money to respond to all these changes.

., 229 Schröder, Gerhard, 95, 101, 163 Schuster, Helmuth, 102–4, 106 SCR (selective catalytic reduction) systems; See also BlueMotion emissions technology; BlueTec emissions technology Audi’s undermining of, 127 and CARB tests, 173, 176, 177 and defeat device, 227, 246 and EA 288 engine, 180 falling price of, 209 ICCT test, 167 and urea tank, 167 SEAT, 97, 158 self-driving cars, 204, 222 self-expression, cars as form of, 146 sex scandal, 102–7 shared platform, See platform strategy shareholders, VW, 26, 58–59, 244 share prices declines (2015), 189 declines (early 2000s), 130–31 following EPA charges against VW, 212 under Pischetsrieder, 110 Porsche, 96 Porsche-VW takeover battle, 138–40 short-selling, 138–41 short squeeze, 139–40, 142 Siemens bribery scandal, 257–58 Silicon Valley, 222 Skoda, 6, 48, 102–4, 158 Skoda Octavia, 53 slave labor, 12–14 smog, NOx and, 2, 160, 168–69 Snap-On, 70 Social Democratic Party, 26, 49, 164 software, 120, 226–28; See also defeat device software updates, 182–84, 224, 244 soot particles, 43–44, 115, 159 Sorrell, William, 248 South Korea, 245 sovereign wealth funds, 143 Soviet Union, 17 special settlement master, 233 Speer, Albert, 17 sport utility vehicles (SUVs), 94–96 Stadler, Rupert, 257, 272, 273 Stalin, Joseph, 6 Standard & Poor’s, 219 “Statement of Facts,” 269 Steiner, Rudolf, 129 Steinkühler, Franz, 49 stock market, 96 stock options, 133, 136, 137 Strategy 2018, 150–51, 188 stretch goals, 151 Stumpf, John, 262 subprime mortgage crisis, 136 Sudetenland, 6 Sullivan & Cromwell, 229–30, 235 Super Bowl, 145 supervisory board (Audi), 45–46 supervisory board (VW) executive committee awareness of emissions problem, 271 failure to sanction managers for emissions cheating, 256–57 and internal investigation, 216 Piëch as member after retirement, 97, 157 Piëch’s attempt to oust Winterkorn, 187 Piëch’s elevation to VW CEO, 49 Porsche-Piëch family’s influence on, 264 Porsche’s attempted VW acquisition, 135–36 Porsche’s position on VW board, 133 VW’s acquisition of Porsche, 143–44 worker participation in, 57 supply chain, as VW weakness, 50–51 synthetic shares, 137 Tatra, 9 tax credits, 147 TDI (turbocharged direct injection), 44–45, 55, 116, 128, 146, 181 “Think Small” ad campaign, 34–35 Thiruvengadam, Arvind, 1, 167, 169, 171, 172 Thomas, Sven, 239 Thompson, Greg, 79–80, 166, 167, 172, 174 three-liter diesel engine and “acoustic function,” 123, 128, 246 defeat device in, 152, 158 EPA/CARB questions about, 183–84 EPA’s second notice of violation, 217 exclusion from first US settlement, 238 notice of violation for cars with, 217 SCR technology, 180 settlement for US and Canada, 266 US charges against designer of, 246–47 Tiercelet, France, iron mine, 15 Tiger Tank, 10–11 Time magazine, 265 Tolischus, Otto D., 9 Toyota as competitor in early 1990s, 50–51 EA 189 engine as part of VW’s strategy against, 107 European market share, 161 former employees at Porsche, 52 hybrid technology, 2–3, 116, 146; See also Toyota Prius surpassed by VW as world’s largest carmaker, 187–88, 206 VW’s efficiency gap with, 50–51, 58, 94, 110, 188, 219 VW’s sales competition with, 156 Toyota Prius, 107, 116, 146, 207, 208 Transport Select Committee, House of Commons, 231 truck engine emissions cheating scandal, 72–74, 76, 78, 125, 163 trust fund, 236 Tuch, Frank, 223 turbochargers, 44 Type 166 Schwimmwagen, 10 Umweltbundesamt, 162–64 unemployment benefits, 101 unemployment rate, German, 101, 102 United States Audi defeat device revelations, 267–68 Clean Air Act Amendments (1990), 67 EA 189 as key to VW market in, 116–17, 119 EA 189 development, 107–8, 119–20 environmental rule enforcement, 165 fleet average fuel economy milestones, 131 legal ramifications of emissions violations, 225–31 NOx regulations, 165 penalties for emissions violations, 122, 155, 156, 163 state lawsuits, 245–47 TDI Club, 45 truck engine defeat device scandal, 163 VW plant in, 148–49 VW sales (early 1970s), 36 VW sales (early 1990s), 47 VW sales (late 1990s), 55 VW sales (2014), 206 VW sales after cheating revelations, 217–18 VW’s “clean diesel” campaign, 145–58 VW’s declining fortunes in early 1990s, 47, 48, 50 VW’s early success in, 34–35 VW’s missteps in, 115–16 VW’s responsibility for diesel pollution, 253 and Winterkorn’s sales ambitions for VW, 112–13 unit injector (Pumpe Düse), 116–17 University of Virginia Center for Alternative Fuels, Engines, and Emissions, See Center for Alternative Fuels, Engines, and Emissions (CAFEE) urea solution, 177, 180–81, 192; See also BlueTec emissions technology urea tank, 113, 127–28, 152–53, 183 U.S.


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The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age by Robert Wachter

activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, AI winter, Airbnb, Atul Gawande, Captain Sullenberger Hudson, Checklist Manifesto, Chuck Templeton: OpenTable:, Clayton Christensen, cognitive load, collapse of Lehman Brothers, computer age, creative destruction, crowdsourcing, deep learning, deskilling, disruptive innovation, driverless car, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Firefox, Frank Levy and Richard Murnane: The New Division of Labor, general purpose technology, Google Glasses, human-factors engineering, hype cycle, Ignaz Semmelweis: hand washing, Internet of things, job satisfaction, Joseph Schumpeter, Kickstarter, knowledge worker, lifelogging, Marc Benioff, medical malpractice, medical residency, Menlo Park, minimum viable product, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, peer-to-peer, personalized medicine, pets.com, pneumatic tube, Productivity paradox, Ralph Nader, RAND corporation, Richard Hendricks, Robert Solow, Salesforce, Second Machine Age, self-driving car, seminal paper, Silicon Valley, Silicon Valley startup, six sigma, Skype, Snapchat, software as a service, Steve Jobs, Steven Levy, TED Talk, The future is already here, the payments system, The Wisdom of Crowds, Thomas Bayes, Toyota Production System, Uber for X, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, Yogi Berra

McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies (New York: W. W. Norton, 2014). While the car is an astounding achievement, there still are a few things it cannot do, described in R. Sorokanich, “Six Simple Things Google’s Self-Driving Car Still Can’t Handle,” August 30, 2014, available at http://gizmodo.com/6-simplethings-googles-self-driving-car-still-cant-han-1628040470. 94 “Executing a left turn across oncoming traffic” F. Levy and R. J. Murnane, The New Division of Labor: How Computers Are Creating the Next Job Market (Princeton, NJ: Princeton University Press, 2004). 94 “Just as factory jobs were eliminated” The story of Watson’s defeat of the Jeopardy champions is described in S.

The juice bar is a busy congregational space, as are the small glasswalled rectangular conference rooms, in which one twentysomething, seated on a couch, is listening to another map out a company’s can’t-miss strategy on a dry-erase board. If one half expects to see a horse-drawn carriage from the window of David Blumenthal’s Fifth Avenue office in Manhattan, one half expects to see a self-driving car outside the offices of these Silicon Valley start-ups. The sense of limitless possibilities is palpable when you enter this world, but the hype can border on the farcical. In the “Health 2.0” office near San Francisco’s CalTrain station, a London-born healthcare impresario named Matthew Holt and his staff spend their days analyzing healthcare IT start-ups for a series of publications and conferences that they produce.


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Always Day One: How the Tech Titans Plan to Stay on Top Forever by Alex Kantrowitz

accounting loophole / creative accounting, Albert Einstein, AltaVista, Amazon Robotics, Amazon Web Services, Andy Rubin, anti-bias training, augmented reality, Automated Insights, autonomous vehicles, Bernie Sanders, Big Tech, Cambridge Analytica, Clayton Christensen, cloud computing, collective bargaining, computer vision, Donald Trump, drone strike, Elon Musk, fake news, Firefox, fulfillment center, gigafactory, Google Chrome, growth hacking, hive mind, income inequality, Infrastructure as a Service, inventory management, iterative process, Jeff Bezos, job automation, Jony Ive, Kiva Systems, knowledge economy, Lyft, Mark Zuckerberg, Menlo Park, new economy, Nick Bostrom, off-the-grid, Peter Thiel, QR code, ride hailing / ride sharing, robotic process automation, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, SoftBank, Steve Ballmer, Steve Jobs, Steve Wozniak, super pumped, tech worker, Tim Cook: Apple, uber lyft, warehouse robotics, wealth creators, work culture , zero-sum game

When you build an autonomous driving vehicle, the software inside is much more important than how the car looks, similar to a smart speaker. But design frustrated its engineers by issuing burdensome top-down edicts instead of listening to what would be best for the project. Design, for instance, tried to hide the car’s sensors, ugly appendages that make your typical self-driving car look like a rolling submarine. But by burying them, design obstructed the sensors’ view, limiting the data they could collect and forcing the engineers into suboptimal workarounds. Design also put its hands on the wheel. After assigning groups to work on the design of the car with and without a steering wheel, design removed the wheel completely, creating further technical challenges for the team now tasked with building for full autonomy.

When Linda arrives at the office, she’s approached by a consultant who tells her she’s going to wear a recording device at all times. The company has already automated her entire department, so she understands what’s coming. One month later, after the device has fully recorded her work, Linda’s self-driving car plunges into a lake. Dealing with the loss, her family sits down to review the recording device’s footage, and they’re struck by what they see. Linda’s husband, who’s questioned her intelligence, watches her brilliant, creative performance at work—the reason she was hard to automate—and becomes emotional.


pages: 205 words: 71,872

Whistleblower: My Journey to Silicon Valley and Fight for Justice at Uber by Susan Fowler

"Susan Fowler" uber, Airbnb, Albert Einstein, Big Tech, Burning Man, cloud computing, data science, deep learning, DevOps, Donald Trump, Elon Musk, end-to-end encryption, fault tolerance, Grace Hopper, Higgs boson, Large Hadron Collider, Lyft, Maui Hawaii, messenger bag, microservices, Mitch Kapor, Richard Feynman, ride hailing / ride sharing, self-driving car, Silicon Valley, TechCrunch disrupt, Travis Kalanick, Uber for X, uber lyft, work culture

It was clear that Kalanick wanted to send a message: he was taking this seriously—so seriously that anyone involved in what had happened, anyone responsible for the story that was now being repeated by every major news outlet across the globe, would be fired. Three days later, The New York Times published its own damning account of Uber’s culture. The day after that, Waymo, a subsidiary of Google that was developing self-driving cars, sued Uber for patent infringement and trade secret theft. Less than a week later, a video leaked of Travis Kalanick berating an Uber driver. And that was only the beginning. By the time I found myself across the table from President Obama’s attorney general, the public consensus was that something was very wrong with Uber, but nobody was quite sure of the extent of the problem or who should be held responsible for it.

In the wake of this report, two of Uber’s earliest investors—Mitch Kapor and Freada Kapor Klein—penned an open letter to Uber’s board and investors that was picked up by multiple media outlets; in the letter, they said that they had known about Uber’s culture for a long time and had been trying to quietly change it from the inside. Waymo, a Google subsidiary that was developing self-driving cars, sued Uber for patent infringement and trade secret theft, and a video leaked of Travis Kalanick berating an Uber driver. There was something validating about all the stories that were finally coming out about problems at Uber, because they proved to the world that my mistreatment and the mistreatment of my colleagues weren’t isolated incidents and that, as I knew all too well, Uber’s mistreatment of employees and egregious corporate behavior weren’t limited to sexual harassment.


pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, algorithmic bias, algorithmic management, AlphaGo, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, Black Lives Matter, blockchain, Boston Dynamics, business intelligence, business process, Californian Ideology, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, circular economy, cloud computing, Cody Wilson, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, CRISPR, cryptocurrency, David Graeber, deep learning, DeepMind, dematerialisation, digital map, disruptive innovation, distributed ledger, driverless car, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, fulfillment center, gentrification, global supply chain, global village, Goodhart's law, Google Glasses, Herman Kahn, Ian Bogost, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, Jacob Silverman, James Watt: steam engine, Jane Jacobs, Jeff Bezos, Jeff Hawkins, job automation, jobs below the API, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, Kiva Systems, late capitalism, Leo Hollis, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Nick Bostrom, Occupy movement, Oculus Rift, off-the-grid, PalmPilot, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, printed gun, proprietary trading, RAND corporation, recommendation engine, RFID, rolodex, Rutger Bregman, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Shenzhen special economic zone , Sidewalk Labs, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, Tony Fadell, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, vertical integration, Vitalik Buterin, warehouse robotics, When a measure becomes a target, Whole Earth Review, WikiLeaks, women in the workforce

Bush,” New York Times, October 17, 2004. 73.Alex Tabarrok, “The Rise of Opaque Intelligence,” Marginal Revolution, February 20, 2015. 74.Travis Mannon, “Facebook Outreach Tool Ignores Black Lives Matter,” Intercept, June 9, 2016. 75.This is easier to do than it is to explain. See rednuht.org/genetic_cars_2/. 76.August C. Bourré, Comment, Speedbird blog, May 28, 2014, speedbird.wordpress.com/2014/05/28/weighing-the-pros-and-cons-of-driverless-cars/#comment-23389. 77.David Z. Morris, “Trains and Self-Driving Cars, Headed for a (Political) Collision,” Fortune, November 2, 2014. 9Artificial intelligence 1.Jeff Hawkins, keynote speech, “Why Can’t a Computer Be More Like a Brain? How a New Theory of Neocortex Will Lead to Truly Intelligent Machines,” O’Reilly Emerging Technology Conference 2007, San Diego, CA, March 27, 2007. 2.The Next Rembrandt project, nextrembrandt.com. 3.David Silver et al., “Mastering the Game of Go with Deep Neural Networks and Tree Search,” Nature, Volume 529, Issue 7587, pp. 484–9, January 28, 2016. 4.Younggil An and David Ormerod, Relentless: Lee Sedol vs Gu Li, Go Game Guru, 2016. 5.Nature Video, “The Computer That Mastered Go,” January 27, 2016, YouTube.com. 6.Ormerod, David.

“Alphago Shows Its True Strength in 3rd Victory Against Lee Sedol,” Go Game Guru, March 12, 2016, gogameguru.com. 7.Yaskawa Electric Corporation, “YASKAWA BUSHIDO PROJECT: Industrial Robot vs Sword Master,” June 4, 2015, YouTube.com. 8.See Machii’s official website at http://nihontou.jp/syuushinryuu/intro.htm. 9.Cade Metz, “The Sadness and Beauty of Watching Google’s AI Play Go,” Wired, March 11, 2016. 10.Jo Liss, tweet, December 8, 2015, twitter.com/jo_liss/status/674332649226436613 11.Hector J. Levesque, Ernest Davis and Leora Morgenstern, “The Winograd Schema Challenge,” Proceedings of the Thirteenth International Conference on Principles of Knowledge Representation and Reasoning, 2012, aaai.org/ocs/index.php/KR/KR12/paper/download/4492/4924. 12.Cara McGoogan, “Uber’s Self-Driving Cars Labelled ‘Not Ready for Streets’ After They Are Found to Cut Across Cycle Lanes,” Telegraph, December 20, 2016. 13.Timothy A. Salthouse, “When Does Age-Related Cognitive Decline Begin?,” Neurobiology of Aging, April 2009, Volume 30, Issue 4, pp. 507–14. 10Radical technologies 1.Bruce Sterling and Jon Lebkowsky, “Topic 487: State of the World 2016,” The WELL, January 3, 2016, well.com. 2.Mark Bergen, “Nest CEO Tony Fadell Went to Google’s All-Hands Meeting to Defend Nest.

,” Neurobiology of Aging, April 2009, Volume 30, Issue 4, pp. 507–14. 10Radical technologies 1.Bruce Sterling and Jon Lebkowsky, “Topic 487: State of the World 2016,” The WELL, January 3, 2016, well.com. 2.Mark Bergen, “Nest CEO Tony Fadell Went to Google’s All-Hands Meeting to Defend Nest. Here’s What He Said,” Recode, April 13, 2016. f 3.Brad Stone and Jack Clark, “Google Puts Boston Dynamics Up for Sale in Robotics Retreat,” Bloomberg Technology, March 17, 2016. 4.John Markoff, “Latest to Quit Google’s Self-Driving Car Unit: Top Roboticist,” New York Times, August 5, 2016. 5.Mark Harris, “Secretive Alphabet Division Funded by Google Aims to Fix Public Transit in US,” Guardian, June 27, 2016. 6.Siimon Reynolds, “Why Google Glass Failed: A Marketing Lesson,” Forbes, February 5, 2015. 7.Rajat Agrawal, “Why India Rejected Facebook’s ‘Free’ Version of the Internet,” Mashable, February 9, 2016. 8.Mark Zuckerberg, “The technology behind Aquila,” Facebook, July 21, 2016, facebook.com/notes/mark-zuckerberg/the-technology-behind-aquila/10153916136506634/. 9.Mari Saito, “Exclusive: Amazon Expanding Deliveries by Its ‘On-Demand’ Drivers,” Reuters, February 8, 2016. 10.Alan Boyle, “First Amazon Prime Airplane Debuts in Seattle After Secret Night Flight,” GeekWire, August 4, 2016. 11.Farhad Manjoo, “Think Amazon’s Drone Delivery Idea Is a Gimmick?


pages: 402 words: 126,835

The Job: The Future of Work in the Modern Era by Ellen Ruppel Shell

"Friedman doctrine" OR "shareholder theory", 3D printing, Abraham Maslow, affirmative action, Affordable Care Act / Obamacare, Airbnb, airport security, Albert Einstein, AlphaGo, Amazon Mechanical Turk, basic income, Baxter: Rethink Robotics, big-box store, blue-collar work, Buckminster Fuller, call centre, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collective bargaining, company town, computer vision, corporate governance, corporate social responsibility, creative destruction, crowdsourcing, data science, deskilling, digital divide, disruptive innovation, do what you love, Donald Trump, Downton Abbey, Elon Musk, emotional labour, Erik Brynjolfsson, factory automation, follow your passion, Frederick Winslow Taylor, future of work, game design, gamification, gentrification, glass ceiling, Glass-Steagall Act, hiring and firing, human-factors engineering, immigration reform, income inequality, independent contractor, industrial research laboratory, industrial robot, invisible hand, It's morning again in America, Jeff Bezos, Jessica Bruder, job automation, job satisfaction, John Elkington, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kickstarter, knowledge economy, knowledge worker, Kodak vs Instagram, labor-force participation, low skilled workers, Lyft, manufacturing employment, Marc Andreessen, Mark Zuckerberg, means of production, move fast and break things, new economy, Norbert Wiener, obamacare, offshore financial centre, Paul Samuelson, precariat, Quicken Loans, Ralph Waldo Emerson, risk tolerance, Robert Gordon, Robert Shiller, Rodney Brooks, Ronald Reagan, scientific management, Second Machine Age, self-driving car, shareholder value, sharing economy, Silicon Valley, Snapchat, Steve Jobs, stock buybacks, TED Talk, The Chicago School, The Theory of the Leisure Class by Thorstein Veblen, Thomas L Friedman, Thorstein Veblen, Tim Cook: Apple, Uber and Lyft, uber lyft, universal basic income, urban renewal, Wayback Machine, WeWork, white picket fence, working poor, workplace surveillance , Y Combinator, young professional, zero-sum game

People have always shifted away from work better done by machines, but the economic principle of “comparative advantage” predicts that humans will maintain an edge in fields where they are the least disadvantaged. Under this logic, technology will not displace us but set us free to do less dangerous, more challenging things, essentially the very things that make humans human. For example, in 2016, the National Highway Traffic Safety Administration officially recognized “software” as a driver of self-driving cars, thereby putting the nation’s 4.1 million paid motor-vehicle operators—drivers of taxis, trucks, buses, and Uber—on notice. But under the rubric of competitive advantage, this will not simply unemploy people but free them to fill new roles—for example, to invent new sorts of engines or design new sorts of fenders or tackle other challenges better suited to uniquely human capabilities.

Robots, she said, would soon weave themselves into the fabric of everyday life and become “indistinguishable” from that fabric. As an example, she cited a robot maid “waking up” in the morning, sensing its owner’s desire for coffee or orange juice, and—noting that there was none at hand—jumping into a self-driving car to the grocery store, where it would be waited on by other robots. Humans who desired to get their own coffee or juice could of course do so, Rus said, but that seemed unlikely, because robots—not other people—would man the store, allowing little opportunity for human interaction, and therefore little incentive to endure the inconvenience.

The company invests in such a broad array of endeavors that even its employees have difficulty keeping track: a browser called Chrome, a smartphone operating system called Android, a suite of cloud computing platforms called Google Cloud Platform, a video-sharing platform called YouTube, and online services that include Google Maps, Gmail, and Google Docs. Alphabet, Google’s parent company, is a force in the self-driving car realm, and its investment arm, GV, has a piece of more than three hundred other companies, including Uber. All this is mind-bogglingly impressive, but incomplete, as it neglects the segment of Google business that generates the vast bulk of its revenue stream. Roughly 90 percent of Google’s revenue comes from advertising, more than three-quarters of it plastered across the company’s own websites.


pages: 510 words: 120,048

Who Owns the Future? by Jaron Lanier

3D printing, 4chan, Abraham Maslow, Affordable Care Act / Obamacare, Airbnb, augmented reality, automated trading system, barriers to entry, bitcoin, Black Monday: stock market crash in 1987, book scanning, book value, Burning Man, call centre, carbon credits, carbon footprint, cloud computing, commoditize, company town, computer age, Computer Lib, crowdsourcing, data science, David Brooks, David Graeber, delayed gratification, digital capitalism, digital Maoism, digital rights, Douglas Engelbart, en.wikipedia.org, Everything should be made as simple as possible, facts on the ground, Filter Bubble, financial deregulation, Fractional reserve banking, Francis Fukuyama: the end of history, Garrett Hardin, George Akerlof, global supply chain, global village, Haight Ashbury, hive mind, if you build it, they will come, income inequality, informal economy, information asymmetry, invisible hand, Ivan Sutherland, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Perry Barlow, Kevin Kelly, Khan Academy, Kickstarter, Kodak vs Instagram, life extension, Long Term Capital Management, machine translation, Marc Andreessen, Mark Zuckerberg, meta-analysis, Metcalfe’s law, moral hazard, mutually assured destruction, Neal Stephenson, Network effects, new economy, Norbert Wiener, obamacare, off-the-grid, packet switching, Panopticon Jeremy Bentham, Peter Thiel, place-making, plutocrats, Ponzi scheme, post-oil, pre–internet, Project Xanadu, race to the bottom, Ray Kurzweil, rent-seeking, reversible computing, Richard Feynman, Ronald Reagan, scientific worldview, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, smart meter, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, synthetic biology, tech billionaire, technological determinism, Ted Nelson, The Market for Lemons, Thomas Malthus, too big to fail, Tragedy of the Commons, trickle-down economics, Turing test, Vannevar Bush, WikiLeaks, zero-sum game

We kill each other in car accidents so frequently that the toll has become a more deadly problem than wars or terrorism. It’s one of our biggest sources of death and pain. A team of Google and Stanford researchers has famously demonstrated cars that are quite effective at driving themselves. (They are not alone; similar developments are occurring around the world.) The motivations for developing self-driving cars are so extraordinarily powerful that it’s hard to imagine stronger ones. Results from experiments thus far indicate that it is unlikely robots will ever drive as badly as people. My mother died in a car accident. What could be more compelling? But there’s more. Stoplights would generally go away.

However, the chosen route might be peculiar. Maybe the taxi lingers in front of billboards along the way, or forces you to a particular convenience store if you need to pick up something, or whatever scam would come about in a Siren Server–driven car. But one thing we can guess even at this early date is that self-driving cars will depend on cloud data about streets, pedestrians, and everything else that can affect a trip. That information will be renewed constantly, with every single ride. Will the rider be compensated beyond a free ride for helping to generate this information? To do otherwise would be considered accounting fraud in a humanistic information economy.

The age of silver bullets seems to have retired around the time networking got good and data became big. And yet, the future hasn’t vanished completely. My daughter, who turned six as I finished this book, asks me: “Will I learn to drive, or will cars drive themselves?” In ten years, I imagine, self-driving cars will be familiar, but probably not yet ubiquitous. But it’s at least possible that learning to drive will start to feel anachronistic to my daughter and her friends, instead of a beckoning rite of passage. Driving for her might be like writing in longhand. Will she ever wear the same dress twice as an adult?


pages: 468 words: 124,573

How to Build a Billion Dollar App: Discover the Secrets of the Most Successful Entrepreneurs of Our Time by George Berkowski

Airbnb, Amazon Web Services, Andy Rubin, barriers to entry, Black Swan, business intelligence, call centre, crowdsourcing, deal flow, Dennis Tito, disruptive innovation, Dunbar number, en.wikipedia.org, game design, Google Glasses, Google Hangouts, Google X / Alphabet X, growth hacking, iterative process, Jeff Bezos, Jony Ive, Kickstarter, knowledge worker, Lean Startup, loose coupling, Marc Andreessen, Mark Zuckerberg, Mary Meeker, minimum viable product, MITM: man-in-the-middle, move fast and break things, Network effects, Oculus Rift, Paul Graham, QR code, Ruby on Rails, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, SoftBank, software as a service, software is eating the world, Steve Jobs, Steven Levy, subscription business, TechCrunch disrupt, Travis Kalanick, two-pizza team, ubercab, Y Combinator

.’, article and video interview on FirstRound.com, firstround.com/article/how-dave-goldberg-of-surveymonkey-built-a-billion-dollar-business-and-still-gets-home-by-5-30. 4 Ibid. 5 Mike Rose, ‘Supercell’s Secret Sauce’, article on Gamasutra.com, 7 December 2012, www.gamasutra.com/view/feature/183064/supercells_secret_sauce.php. 6 Ibid. 7 Alyson Shontell and Andrea Huspeni, ‘15 Incredible Employee Perks That Will Make You Wish You Worked at a Startup’, article on BusinessInsider.com, 31 May 2012, www.BusinessInsider.com/killer-startup-perks-2012-5. 8 Heather Leonard, ‘Facebook Generates Over $1 Million in Revenue Per Employee’, article on BusinessInsider.com, 19 March 2013, www.BusinessInsider.com/facebook-has-high-revenue-per-employee-2013-3. 9 Megan Rose Dickey, ‘“Clash of Clans” Maker Had a Monster Year in 2013: Revenue Increased Nearly Ninefold’, article on BusinessInsider.com, 12 February 2014, www.BusinessInsider.com/gaming-startup-supercell-2013-revenue-2014-2. 10 Steven Levy, ‘Google’s Larry Page on Why Moon Shots Matter’, article on Wired.com, 17 January 2013, www.wired.com/business/2013/01/ff-qa-larry-page/all/. 11 Peter Murray, ‘Google’s Self-Driving Car Passes 300,000 Miles’, article on Forbes.com, 15 August 2012, www.forbes.com/sites/singularity/2012/08/15/googles-self-driving-car-passes-300000-miles/. 12 For more information about Project Loon, visit www.google.com/loon/. 13 ‘Google X’, entry on Wikipedia, en.wikipedia.org/wiki/Google_X. Chapter 38: Advice from Billion-Dollar CEOs 1 Will Oremus, ‘Google’s Big Break’, article on Slate.com, 13 October 2013, www.slate.com/articles/business/when_big_businesses_were_small/2013/10/google_s_big_break_how_bill_gross_goto_com_inspired_the_adwords_business.html. 2 Ibid. 3 ‘Drew Houston’s Morph from Hacker to Hyper-Growth CEO’, article on FirstRound.com, www.firstround.com/article/Drew-Houstons-morph-from-hacker-to-hyper-growth-CEO. 4 Peter Kafka, ‘Larry Page on Speed: “There are no companies that have good slow decisions”’, article on AllThingsD.com, 27 September 2011, allthingsd.com/20110927/larry-page-on-speed-there-are-no-companies-that-have-good-slow-decisions/. 5 Glen Cathey, ‘LinkedIn Traffic Statistics and User Demographics 2013’, article on BooleanBlackBelt.com, 24 July 2013, booleanblackbelt.com/2013/07/linkedin-traffic-statistics-and-user-demographics-2013/. 6 Juhana Hietala, ‘Rovio Mobile Company Presentation – Dynamic World of Mobile Game Business’, 1 April 2005, www.soberit.hut.fi/T-76.640/Slides/T-76.640_Rovio2005_04_01HUT.pdf. 7 ‘The 30 Best Pieces of Advice for Entrepreneurs’, article on FirstRound.com, firstround.com/article/30-Best-Pieces#ixzz2pRF5EZ8a. 8 Ibid. 9 ‘Drew Houston’s Morph from Hacker to Hyper-Growth CEO’, op. cit. 10 Ibid. 11 ‘The 30 Best Pieces of Advice for Entrepreneurs’, op. cit. 12 Ibid. 13 Eric Savitz, ‘Jack Dorsey: Leadership Secrets of Twitter and Square’, article for Forbes, 5 November 2012 issue, www.forbes.com/sites/ericsavitz/2012/10/17/jack-dorsey-the-leadership-secrets-of-twitter-and-square/. 14 ‘The 30 Best Pieces of Advice for Entrepreneurs’, op. cit.

Companies that don’t have robust business models will not be able to invest in these kinds of activities, which will make it increasingly harder for them to retain the best people, who in turn, once salary is taken care of, will be looking for a job with meaning. And that comes from a company that has a culture of pure innovation and solving meaningful problems. Google X is the division of Google that is home to the company’s moonshots. Since 2010 it has delivered a variety of seemingly impossible fantasies, such as the self-driving car (which has travelled over 500,000 km without a single accident11), Google Glass (a wearable computer with an optical head-mounted display), Project Loon (which provides rural Internet connectivity via high-altitude autonomous balloons12) and more than 100 other projects.13 So, when you think about the future of your app, it’s important to think about how big your ambition and vision are – and how you are going to take people on that journey.


pages: 257 words: 80,100

Time Travel: A History by James Gleick

Ada Lovelace, Albert Einstein, Albert Michelson, Arthur Eddington, augmented reality, butterfly effect, Charles Babbage, crowdsourcing, Doomsday Book, Eddington experiment, index card, Isaac Newton, John von Neumann, luminiferous ether, Marshall McLuhan, Norbert Wiener, pattern recognition, Plato's cave, pneumatic tube, Richard Feynman, Schrödinger's Cat, self-driving car, Stephen Fry, Stephen Hawking, telepresence, The future is already here, time dilation, Wayback Machine, wikimedia commons

Warp drive and wormholes notwithstanding, we seem to have given up on populating the galaxy. Nanorobots. Remote-control warfare. The internet in your contact lens or brain implant. Self-driving cars, a comedown, somehow, from i futuristi and their fearsome roaring racing machines. The aesthetic of futurism changed, too, without anyone issuing a manifesto—from big and bold, primary colors and metallic shine to grim, dank rot and ruins. Genetic engineering and/or species extinctions. Is that all the future we have to look forward to? Nanobots and self-driving cars? Credit 14.1 Card produced c. 1900 by Hildebrands chocolate company If we lack space travel, we do have telepresence.


pages: 293 words: 78,439

Dual Transformation: How to Reposition Today's Business While Creating the Future by Scott D. Anthony, Mark W. Johnson

activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Andy Rubin, Apollo 13, asset light, autonomous vehicles, barriers to entry, behavioural economics, Ben Horowitz, Big Tech, blockchain, business process, business process outsourcing, call centre, Carl Icahn, Clayton Christensen, cloud computing, commoditize, corporate governance, creative destruction, crowdsourcing, death of newspapers, disintermediation, disruptive innovation, distributed ledger, diversified portfolio, driverless car, Internet of things, invention of hypertext, inventory management, Jeff Bezos, job automation, job satisfaction, Joseph Schumpeter, Kickstarter, late fees, Lean Startup, long term incentive plan, Lyft, M-Pesa, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Minecraft, obamacare, Parag Khanna, Paul Graham, peer-to-peer lending, pez dispenser, recommendation engine, Salesforce, self-driving car, shareholder value, side project, Silicon Valley, SimCity, Skype, software as a service, software is eating the world, Steve Jobs, subscription business, the long tail, the market place, the scientific method, Thomas Kuhn: the structure of scientific revolutions, transfer pricing, uber lyft, Watson beat the top human players on Jeopardy!, Y Combinator, Zipcar

Over the next decade Google invested to further develop the technology behind self-driving cars and to change local regulations to welcome autonomous cars. In 2014 it introduced a new car with no wheels and no pedals. In August 2016, Singapore’s first autonomous taxi debuted on the roads of a cluster of buildings with far-out names like Fusionopolis. Scenes in movies with legions of driverless cars—such as I, Robot and Minority Report—increasingly seem less like science fiction and more like a preview of the next decade. And, of course, that’s to say nothing of the rise of electric vehicles. The rise of self-driving cars will have systemwide effects.


pages: 245 words: 72,893

How Democracy Ends by David Runciman

barriers to entry, basic income, Bernie Sanders, Big Tech, bitcoin, blockchain, Brexit referendum, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, centre right, crowdsourcing, cuban missile crisis, disinformation, Dominic Cummings, Donald Trump, Dr. Strangelove, Edward Snowden, fake news, first-past-the-post, Francis Fukuyama: the end of history, full employment, Internet of things, Jeremy Corbyn, Jon Ronson, Joseph Schumpeter, Kickstarter, Large Hadron Collider, loss aversion, Mahatma Gandhi, Mark Zuckerberg, money: store of value / unit of account / medium of exchange, mutually assured destruction, Network effects, Nick Bostrom, Norman Mailer, opioid epidemic / opioid crisis, Panopticon Jeremy Bentham, Paris climate accords, Peter Thiel, post-truth, power law, precautionary principle, quantitative easing, Russell Brand, self-driving car, Sheryl Sandberg, Silicon Valley, Steve Bannon, Steven Pinker, the long tail, The Wisdom of Crowds, Travis Kalanick, universal basic income, Yogi Berra

What the historian David Edgerton calls ‘the shock of the old’ applies to digital technology as much as to any other kind of technology – change rarely happens as fast as we think.49 It takes place in a landscape where most objects are still the familiar ones. A world that is about to herald the arrival of self-driving cars also contains more bicycles than ever before. We tend to overstate how quickly technological transformation will make itself clear to us. This is especially true of people with a vested interest in making it happen. They want it to happen now. We remain some way off from the long-promised dawn of machines with minds of their own.

The danger of unintelligent machines is that, as they grow in power and usefulness, they lure intelligent human beings into relying on them for too much. Machine learning currently allows computers to mine vast amounts of data for insights that no human could match, picking up the rules of the game as they go along. These are not intelligent insights – they lack depth, nuance and emotional resonance. Yet it is machine learning that enables self-driving cars to travel the roads more safely and reliably than any human-driven automobile could. It is machine learning that tells Google what you are searching for before you have quite realised it yourself. Without knowing what they are doing, machines can navigate the world we have built more successfully than we can.


pages: 268 words: 76,702

The System: Who Owns the Internet, and How It Owns Us by James Ball

"World Economic Forum" Davos, behavioural economics, Big Tech, Bill Duvall, bitcoin, blockchain, Cambridge Analytica, Chelsea Manning, cryptocurrency, digital divide, don't be evil, Donald Trump, Douglas Engelbart, Edward Snowden, en.wikipedia.org, fake news, financial engineering, Firefox, Frank Gehry, Internet of things, invention of movable type, Jeff Bezos, jimmy wales, John Gilmore, John Perry Barlow, Julian Assange, Kickstarter, Laura Poitras, Leonard Kleinrock, lock screen, Marc Andreessen, Mark Zuckerberg, Menlo Park, military-industrial complex, Minecraft, Mother of all demos, move fast and break things, Network effects, Oculus Rift, packet switching, patent troll, Peter Thiel, pre–internet, ransomware, RFC: Request For Comment, risk tolerance, Ronald Reagan, Rubik’s Cube, self-driving car, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, Snapchat, Steve Crocker, Stuxnet, surveillance capitalism, systems thinking, The Chicago School, the long tail, undersea cable, uranium enrichment, WikiLeaks, yield management, zero day

In many of the 5G specs today, they talk about something called network slicing,’ says Marby. Network slicing would essentially allow 5G to be broken up, or ‘sliced’, into smaller networks optimised for different apps or different types of content – designed to work ideally for video streaming, self-driving cars, industrial control information or some other form of content. This has practical uses, but could easily be overextended to allow for the kind of charging by type of content that net neutrality was designed to prevent – if someone who stood to profit from it could say that video really does have technical needs different from those of instant messaging, they could maybe justify ‘slicing’ it, and then charging separately.

We have come to see that, almost always, online power reflects offline power, and in the offline world at least, the era of the USA as the world’s only superpower is coming to an end – and it’s China that is rising fastest to join it as a second superpower. Huawei is not China’s only tech behemoth. China has its own version of Amazon, eBay and Paypal, largely merged within the same company, Alibaba. It has its own search engine rival to Google, Baidu, which like Google is also diversifying into AI, self-driving cars, ad platforms and more. Its WhatsApp rival WeChat is owned by a company called TenCent, which is now investing in global social networking apps. The Chinese tech giants are no longer content to operate solely in China. The USA certainly exploited the soft power and intelligence advantages of its global domination of the internet and its largest companies.


pages: 232 words: 72,483

Immortality, Inc. by Chip Walter

23andMe, Airbnb, Albert Einstein, Arthur D. Levinson, bioinformatics, Buckminster Fuller, cloud computing, CRISPR, data science, disintermediation, double helix, Elon Musk, Isaac Newton, Jeff Bezos, Larry Ellison, Law of Accelerating Returns, life extension, Menlo Park, microbiome, mouse model, pattern recognition, Peter Thiel, phenotype, radical life extension, Ray Kurzweil, Recombinant DNA, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Snapchat, South China Sea, SpaceShipOne, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, TED Talk, Thomas Bayes, zero day

IBM’s Deep Blue computer defeated Garry Kasparov, the chess world champion, in one of the highest profile competitions ever. The world gasped. Kurzweil also foresaw the explosive growth of the internet in the early 1990s, when the world’s total population of users was a mere 2.6 million. In 2017, that number would clock in at 3.7 billion, more than a thousandfold increase. Smartphones, cloud computing, and self-driving cars were also among his predictions. Not that he was always right, but he clearly foresaw something in this idea of digitizing the human genome, and all the exponential business that went with it. In his efforts to predict the future, Kurzweil had turned to Moore’s law. Gordon Moore was one of the founders of NM Electronics, which later became the Silicon Valley juggernaut Intel Corporation.

They feel that way because of something called recency bias, the sense that a new thing is quickly perceived as old hat because it’s become so indispensable. Think of fax machines, microwaves, streaming television, and car doors that open with a gesture. The race for immortality will behave very much the same way, Kurzweil says. Costs will start high, and the idea of living radically long will look as cockamamy as mobile phones, or self-driving cars. But then costs will plummet. And when they do, that is when they will actually work—because history shows that the only people who pay through the nose for technology that doesn’t work are the wealthy. They are the early adopters because they can afford to be. But they are also the only ones who lower the costs of new technology enough that the rest of us can afford them.


pages: 256 words: 73,068

12 Bytes: How We Got Here. Where We Might Go Next by Jeanette Winterson

"Margaret Hamilton" Apollo, "World Economic Forum" Davos, 3D printing, Ada Lovelace, Airbnb, Albert Einstein, Alignment Problem, Amazon Mechanical Turk, Anthropocene, Apollo 11, Apple's 1984 Super Bowl advert, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Big Tech, bitcoin, Bletchley Park, blockchain, Boston Dynamics, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Charles Babbage, computer age, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cryptocurrency, dark matter, Dava Sobel, David Graeber, deep learning, deskilling, digital rights, discovery of DNA, Dominic Cummings, Donald Trump, double helix, driverless car, Elon Musk, fake news, flying shuttle, friendly AI, gender pay gap, global village, Grace Hopper, Gregor Mendel, hive mind, housing crisis, Internet of things, Isaac Newton, Jacquard loom, James Hargreaves, Jeff Bezos, Johannes Kepler, John von Neumann, Joseph-Marie Jacquard, Kickstarter, Large Hadron Collider, life extension, lockdown, lone genius, Mark Zuckerberg, means of production, microdosing, more computing power than Apollo, move fast and break things, natural language processing, Nick Bostrom, Norbert Wiener, off grid, OpenAI, operation paperclip, packet switching, Peter Thiel, pink-collar, Plato's cave, public intellectual, QAnon, QWERTY keyboard, Ray Kurzweil, rewilding, ride hailing / ride sharing, Rutger Bregman, Sam Altman, self-driving car, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Skype, Snapchat, SoftBank, SpaceX Starlink, speech recognition, spinning jenny, stem cell, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, superintelligent machines, surveillance capitalism, synthetic biology, systems thinking, tech billionaire, tech worker, TED Talk, telepresence, telepresence robot, TikTok, trade route, Turing test, universal basic income, Virgin Galactic, Watson beat the top human players on Jeopardy!, women in the workforce, Y Combinator

A robot with a map of your kitchen wouldn’t ‘know’ why a table is where it is – and it will be confused if the table moves. Statistical knowledge is not the same thing as general understanding. Machine learning can get around this by throwing more data at the problem (train your AI on bigger data-sets), but we aren’t solving the underlying issue. This is still narrow AI. That’s why it is so hard to design self-driving cars in open environments. If something random happens (and let’s face it, humans and animals can be pretty random out on the road), the system stalls. Even a perfect 3D mapping of an environment, using sensors and lasers, will still produce errors, if there is no general understanding. And right now, there isn’t.

As technology advances, customised robots based on your favourite cartoon character will be available. What they do will be programmable – my Frogbot will tell stories. Your Frogbot will sing. Linking the programmes links the robots, so that children can share their friend. For adults the range will be unlimited. A helperbot can guide you round the shops, just as a self-driving car guides itself round town. Mobility scooters will chat with you as you ride along, and if your friend is nearby, your scooter will ‘know’. * * * Anything we start talking to develops into a relationship. If people can form a bond with the fish in their fish tank – and they do – forming a bond with a non-bio helper won’t be a problem


pages: 515 words: 126,820

Blockchain Revolution: How the Technology Behind Bitcoin Is Changing Money, Business, and the World by Don Tapscott, Alex Tapscott

"World Economic Forum" Davos, Airbnb, altcoin, Alvin Toffler, asset-backed security, autonomous vehicles, barriers to entry, behavioural economics, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, Bretton Woods, business logic, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, carbon footprint, clean water, cloud computing, cognitive dissonance, commoditize, commons-based peer production, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crowdsourcing, cryptocurrency, currency risk, decentralized internet, digital capitalism, disintermediation, disruptive innovation, distributed ledger, do well by doing good, Donald Trump, double entry bookkeeping, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ethereum, ethereum blockchain, failed state, fiat currency, financial innovation, Firefox, first square of the chessboard, first square of the chessboard / second half of the chessboard, future of work, Future Shock, Galaxy Zoo, general purpose technology, George Gilder, glass ceiling, Google bus, GPS: selective availability, Hacker News, Hernando de Soto, Higgs boson, holacracy, income inequality, independent contractor, informal economy, information asymmetry, information security, intangible asset, interest rate swap, Internet of things, Jeff Bezos, jimmy wales, Kickstarter, knowledge worker, Kodak vs Instagram, Lean Startup, litecoin, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, microcredit, mobile money, money market fund, Neal Stephenson, Network effects, new economy, Oculus Rift, off grid, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, performance metric, Peter Thiel, planetary scale, Ponzi scheme, prediction markets, price mechanism, Productivity paradox, QR code, quantitative easing, radical decentralization, ransomware, Ray Kurzweil, renewable energy credits, rent-seeking, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Salesforce, Satoshi Nakamoto, search costs, Second Machine Age, seigniorage, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, smart grid, Snow Crash, social graph, social intelligence, social software, standardized shipping container, Stephen Hawking, Steve Jobs, Steve Wozniak, Stewart Brand, supply-chain management, systems thinking, TaskRabbit, TED Talk, The Fortune at the Bottom of the Pyramid, The Nature of the Firm, The Soul of a New Machine, The Wisdom of Crowds, transaction costs, Turing complete, Turing test, Tyler Cowen, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, unorthodox policies, vertical integration, Vitalik Buterin, wealth creators, X Prize, Y2K, Yochai Benkler, Zipcar

He also expects to see bitcoin applications in the Metaverse (a virtual world) where you can convert bitcoin into Kongbucks and hire Hiro Protagonist to hack you some data.20 Or jack yourself into the OASIS (a world of multiple virtual utopias) where you actually do discover the Easter egg, win Halliday’s estate, license OASIS’s virtual positioning rights to Google, and buy a self-driving car to navigate Toronto.21 And, of course, there’s the Internet of Things, where we register our devices, assign them an identity (Intel is already doing this), and coordinate payment among them using bitcoin rather than multiple fiat currencies. “You can define all these new business cases that you want to do, and have it interoperate within the network, and use the network infrastructure without having to bootstrap a new blockchain, just for yourself,” said Hill. 22 Unlike fiat currency, each bitcoin is divisible to eight decimal places.

All the available vehicles start automatically posting offers, which Melissa’s node ranks and presents to her based on her selection criteria. Melissa factors in how much she’s willing to pay for faster routes (e.g., higher-priced toll lanes). Meanwhile John, unlike most users, is a SUber vehicle owner and as his self-driving car is taking him to work, it identifies all the parking options, both public and privately owned, selects a space, and reserves and pays for it through an autonomous parking marketplace. Because John’s predetermined parameters always include seeking the cheapest available spot within a ten-minute walk of his destination, he almost always goes with his car’s first choice.

All this runs on a distributed peer-to-peer platform—connecting multiple apps—so no centralized company is mediating the orders or taking part of the fee. There is no surge pricing and no unexpected fees. What is striking about this proposed model is not the driverless vehicles, because self-driving cars will be commonplace—probably sooner rather than later. Rather, the cars could be fully autonomous agents that earn their own fares, pay for their own fuel and repair, get their own auto insurance, negotiate liability in collisions, and operate (“drive”) without outside human control, except when they need to take some entity—maybe a human being—to court.


pages: 464 words: 127,283

Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia by Anthony M. Townsend

1960s counterculture, 4chan, A Pattern Language, Adam Curtis, air gap, Airbnb, Amazon Web Services, anti-communist, Apple II, Bay Area Rapid Transit, Big Tech, bike sharing, Boeing 747, Burning Man, business process, call centre, carbon footprint, charter city, chief data officer, clean tech, clean water, cloud computing, company town, computer age, congestion charging, congestion pricing, connected car, crack epidemic, crowdsourcing, DARPA: Urban Challenge, data acquisition, Deng Xiaoping, digital divide, digital map, Donald Davies, East Village, Edward Glaeser, Evgeny Morozov, food desert, game design, garden city movement, General Motors Futurama, gentrification, Geoffrey West, Santa Fe Institute, George Gilder, ghettoisation, global supply chain, Grace Hopper, Haight Ashbury, Hedy Lamarr / George Antheil, Herman Kahn, hive mind, Howard Rheingold, interchangeable parts, Internet Archive, Internet of things, Jacquard loom, Jane Jacobs, Jevons paradox, jitney, John Snow's cholera map, Joi Ito, Khan Academy, Kibera, Kickstarter, knowledge worker, Lewis Mumford, load shedding, lolcat, M-Pesa, machine readable, Mark Zuckerberg, megacity, megaproject, messenger bag, mobile money, mutually assured destruction, new economy, New Urbanism, Norbert Wiener, Occupy movement, off grid, One Laptop per Child (OLPC), openstreetmap, packet switching, PalmPilot, Panopticon Jeremy Bentham, Parag Khanna, patent troll, Pearl River Delta, place-making, planetary scale, popular electronics, power law, RFC: Request For Comment, RFID, ride hailing / ride sharing, Robert Gordon, scientific management, self-driving car, sharing economy, Shenzhen special economic zone , Silicon Valley, SimCity, Skype, smart cities, smart grid, smart meter, social graph, social software, social web, SpaceShipOne, special economic zone, Steve Jobs, Steve Wozniak, Stuxnet, supply-chain management, technoutopianism, Ted Kaczynski, telepresence, The Death and Life of Great American Cities, too big to fail, trade route, Twitter Arab Spring, Tyler Cowen, Tyler Cowen: Great Stagnation, undersea cable, Upton Sinclair, uranium enrichment, urban decay, urban planning, urban renewal, Vannevar Bush, working poor, working-age population, X Prize, Y2K, zero day, Zipcar

When congestion is reduced due to the new capacity, the opportunity cost of driving falls, spurring drivers who would never have ventured onto the previously clogged road to sally forth. Over the coming decades, we’ll witness just such a process play out as automated vehicles take to the road. So far, the excitement over innovations like Google’s self-driving car has been about safety and convenience. You’ll be able to surf the net during your commute. You’ll never have to worry about your drunken teenager wrapping the family sedan around a telephone pole. But the even greater economic potential of self-driving cars is that they could potentially double road capacity by reducing spacing between cars and jams caused by a whole host of idiosyncratic human behaviors. If that spurs people who would have stayed home to take new trips, we’ll have to double fuel economy just to hold even.

., 62 Gettys, Jim, 266 Gibson, William, 119 GigaOM, 38 Gig.U, 289 Gilbertson, Nate, 159 Gilder, George, 6 Gilliam, Terry, 258 Gilman, Howard, 20–21 Giuliani, Rudolph “Rudy,” 205 Glaeser, Ed, 159–60, 278 GLONASS, 265 Goldman, Greg, 194–98 Goldsmith, Stephen, 205–7 Goldstein, Brett, 211 GOODBUILDINGS, 229 Google, 125, 134, 146, 157–58, 242, 272 Flu Trends of, 157 self-driving car of, 317–18 Google Maps, 200 Gordon, Robert, 280 Gottmann, Jean, 160–61 “Gov 2.0,” 237–38, 241 Gowalla, 146–47 GPS, 68, 163, 186–87, 207, 265, 272, 306 headset for blind using, 244 Gray Area Foundation for the Arts, 226–28, 230 Greenfield, Adam, 113, 303 Greenhill, Andrew, 237–38 Green Metropolis (Owen), 278 Greenwich Village, 102–4 Grossman, Nick, 158–59 Haamer, Veljo, 133–34 hackers, 119–26, 145, 153, 158, 227–30, 292, 301, 320 for open-source and free-wireless, 223 of the smart city, 227 Hagen, Erica, 186, 188 Hahn, Jury, 301–2 Hamas, 233, 273 Harrelson, Chris, 204 Harris, Josh, 121 Harrison, Colin, 64–65, 68, 69, 72, 84–85, 88, 269, 299 Harvey, Adam, 14 Haselmayer, Sascha, 243–48 HBO, 132 Hebbert, Frank, 307 Heeks, Richard, 175–76, 180, 188, 192 Heiferman, Scott, 159 Heisenberg, Werner, 88 Herron, Ron, 20 Hickenlooper, John, 206 Hills Are Evil!


pages: 72 words: 21,361

Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy by Erik Brynjolfsson

Abraham Maslow, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, business cycle, business process, call centre, combinatorial explosion, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, driverless car, easy for humans, difficult for computers, Erik Brynjolfsson, factory automation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, general purpose technology, hiring and firing, income inequality, intangible asset, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, Kickstarter, knowledge worker, Loebner Prize, low skilled workers, machine translation, minimum wage unemployment, patent troll, pattern recognition, Paul Samuelson, Ray Kurzweil, rising living standards, Robert Gordon, Robert Solow, self-driving car, shareholder value, Skype, the long tail, too big to fail, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, wealth creators, winner-take-all economy, zero-sum game

Although multiplying five-digit numbers is an unnatural and difficult skill for the human mind to master, the visual cortex routinely does far more complex mathematics each time it detects an edge or uses parallax to locate an object in space. Machine computation has surpassed humans in the first task but not yet in the second one. As digital technologies continue to improve, we are skeptical that even these skills will remain bastions of human exceptionalism in the coming decades. The examples in Chapter 2 of Google’s self-driving car and IBM’s Watson point to a different path going forward. The technology is rapidly emerging to automate truck driving in the coming decade, just as scheduling truck routes was increasingly automated in the last decade. Likewise, the high end of the skill spectrum is also vulnerable, as we see in the case of e-discovery displacing lawyers and, perhaps, in a Watson-like technology, displacing human medical diagnosticians.


pages: 305 words: 79,303

The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World by Scott Galloway

"Susan Fowler" uber, activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Robotics, Amazon Web Services, Apple II, autonomous vehicles, barriers to entry, Ben Horowitz, Bernie Sanders, Big Tech, big-box store, Bob Noyce, Brewster Kahle, business intelligence, California gold rush, Cambridge Analytica, cloud computing, Comet Ping Pong, commoditize, cuban missile crisis, David Brooks, Didi Chuxing, digital divide, disintermediation, don't be evil, Donald Trump, Elon Musk, fake news, follow your passion, fulfillment center, future of journalism, future of work, global supply chain, Google Earth, Google Glasses, Google X / Alphabet X, Hacker Conference 1984, Internet Archive, invisible hand, Jeff Bezos, Jony Ive, Khan Academy, Kiva Systems, longitudinal study, Lyft, Mark Zuckerberg, meta-analysis, Network effects, new economy, obamacare, Oculus Rift, offshore financial centre, passive income, Peter Thiel, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, risk tolerance, Robert Mercer, Robert Shiller, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Snapchat, software is eating the world, speech recognition, Stephen Hawking, Steve Ballmer, Steve Bannon, Steve Jobs, Steve Wozniak, Stewart Brand, supercomputer in your pocket, Tesla Model S, the long tail, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, vertical integration, warehouse automation, warehouse robotics, Wayback Machine, Whole Earth Catalog, winner-take-all economy, working poor, you are the product, young professional

Facebook faces a similar question. The prime material—the oil—for Facebook is the billions of identities it is following and getting to know in ever-greater detail. The easy money is on the sure things in its people portfolio. By comparison, virtual reality goggles, curing death, laying fiber, self-driving cars, and other business opportunities represent much longer odds. If people make it clear, with their clicks, likes, and postings, that they hate certain things and love others, those people are easy to sell to. Clear as day. Easy as oil in Arabia. If I go into Facebook and click on an article about Bernie Sanders and “love” one about Chuck Schumer, the machine, expending almost no energy, can throw me in a bucket of liberal die-hards.

Can a car service really justify Uber’s $70 billion private-market valuation? Doubtful. But Uber is more than just a car service. In fact, taxis are to Uber what books were to Amazon. It’s a real business, and one Uber can do quite well with, but it’s only the camel’s nose under the tent. The real prize is leveraging its massive driver network (and soon, its massive self-driving car network). In California, Uber trialed UberFRESH, a food delivery service. In Manhattan, it trialed UberRUSH, a package courier. In Washington, D.C., it started UberEssentials, an online ordering and delivery service of grocery store essentials.26 The firm appears to be building a vascular (last-mile) system for global business—that is, taking the “blood” of commerce to the “organs” of business, globally.


pages: 301 words: 85,263

New Dark Age: Technology and the End of the Future by James Bridle

AI winter, Airbnb, Alfred Russel Wallace, AlphaGo, Anthropocene, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, Boeing 747, British Empire, Brownian motion, Buckminster Fuller, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, coastline paradox / Richardson effect, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dr. Strangelove, drone strike, Edward Snowden, Eyjafjallajökull, Fairchild Semiconductor, fake news, fear of failure, Flash crash, fulfillment center, Google Earth, Greyball, Haber-Bosch Process, Higgs boson, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, ITER tokamak, James Bridle, John von Neumann, Julian Assange, Kickstarter, Kim Stanley Robinson, Large Hadron Collider, late capitalism, Laura Poitras, Leo Hollis, lone genius, machine translation, mandelbrot fractal, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, security theater, self-driving car, Seymour Hersh, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, warehouse robotics, WikiLeaks

But we are no longer in the industrial age, and, according to Piketty, any belief that technological progress will lead to ‘the triumph of human capital over financial capital and real estate, capable managers over fat cat stockholders, and skill over nepotism’ is ‘largely illusory’.17 Technology is in fact a key driver of inequality across many sectors. The relentless progress of automation – from supermarket checkouts to trading algorithms, factory robots to self-driving cars – increasingly threatens human employment across the board. There is no safety net for those whose skills are rendered obsolete by machines; and even those who programme the machines are not immune. As the capabilities of machines increase, more and more professions are under attack, with artificial intelligence augmenting the process.

Their customers are in turn further alienated; the whole system contributing to the offshoring of tax revenues, the decline of public transport services, and the class divisions and congestion of city streets. And, like Amazon and most other digitally driven businesses, Uber’s ultimate goal is to replace its human workers entirely with machines. It has its own self-driving car program, and its chief product officer, asked about the company’s long-term viability when so many of its employees were dissatisfied, responded simply, ‘Well, we’re just going to replace them all with robots.’ What happens to the Amazon workers eventually happens to everyone. Technological opacity is also wielded by corporations against the wider population, and against the planet.


pages: 303 words: 81,071

Infinite Detail by Tim Maughan

3D printing, augmented reality, bitcoin, Black Lives Matter, Buckminster Fuller, Burning Man, cognitive dissonance, driverless car, fake news, Free Software Foundation, friendly fire, gentrification, global supply chain, hydroponic farming, Internet of things, Mason jar, messenger bag, off grid, Panamax, post-Panamax, ransomware, RFID, rolling blackouts, security theater, self-driving car, Skype, smart cities, South China Sea, surveillance capitalism, the built environment, urban decay, urban planning

Plus he’s poured years of work into security systems to protect them from exactly this. They’re getting a hammering, though—just glancing at his diagnostics software he can see unprecedented levels of network traffic trying to break in, and he doesn’t need to check IP or MAC addresses to know where it’s coming from. Spex, self-driving cars, smart lightbulbs, toys, fridges, security cameras—it’s coming from everywhere, everything and anything with a connection is pumping data into the network, flooding it. It’s not being targeted at the Croft, either, it’s being targeted at everything. He’s seen this before, in the countless analyses he’s read of all the major outages over the last few weeks, starting with Times Square: something is spreading, hijacking any and all Internet-connected devices it finds, and as it does, it floods the network with data—a distributed-denial-of-service attack without a specified target, apparently aimed at bringing the whole connected world to its knees.

It’s lit only by the rapidly fading daylight and the glow of fires—part of Cabot Circus seems to be ablaze. The streets are full of people, walking, running, shouting. Some gather in groups, some sit on the ground, looking concussed, confused. Others are trying to make their way around the vehicles that jam up the roads, self-driving cars and cabs and buses that have all ground to a halt, their passengers shouting for help or smashing windows to free themselves. There’s a constant ambient soundscape of breaking glass, chanting, and police sirens that reverberates up the architecture to where they’re watching. “Fucking hell,” says College.


pages: 276 words: 81,153

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives by David Sumpter

affirmative action, algorithmic bias, AlphaGo, Bernie Sanders, Brexit referendum, Cambridge Analytica, classic study, cognitive load, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data science, DeepMind, Demis Hassabis, disinformation, don't be evil, Donald Trump, Elon Musk, fake news, Filter Bubble, Geoffrey Hinton, Google Glasses, illegal immigration, James Webb Space Telescope, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, Nick Bostrom, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, post-truth, power law, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, social contagion, speech recognition, statistical model, Stephen Hawking, Steve Bannon, Steven Pinker, TED Talk, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

Their offices were at a well-appointed address on Buckingham Palace Road, where there were large Lego structures in the lobby and fridges stuffed full of health drinks and superfoods. The ‘Googlers’, which was how they referred to themselves, were clearly very proud of their surroundings. I asked some of the Googlers what the company was up to now. I had heard about the self-driving cars, Google Glass and contact lenses, the drones delivering packages to our door, and the idea of injecting nanoparticles into our bodies to detect disease, and I wanted to know more about the rumours. But the Googlers were cagey. After a run of bad publicity about the company becoming an over-creative hub for crazy ideas, the policy was to stop telling the outside world too much about what it was up to.

Without a bit of hype early on, DeepMind might not have acquired the resources to solve some of these important problems. Elon Musk hasn’t toned down his rhetoric. He appears to be adopting a deliberate position of continually hyping AI to push many of his super-ambitious projects, including self-driving cars. These long-term projects can only succeed if customers are willing to buy into the idea that purchasing the latest Tesla car is a step towards a fantastic future. It is often a far-off dream that drives our desire to find things out about the world. It isn’t money per se that motivates the people at DeepMind or Tesla.


pages: 296 words: 86,610

The Bitcoin Guidebook: How to Obtain, Invest, and Spend the World's First Decentralized Cryptocurrency by Ian Demartino

3D printing, AltaVista, altcoin, bitcoin, Bitcoin Ponzi scheme, blockchain, buy low sell high, capital controls, cloud computing, Cody Wilson, corporate governance, crowdsourcing, cryptocurrency, decentralized internet, distributed ledger, Dogecoin, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, fiat currency, Firefox, forensic accounting, global village, GnuPG, Google Earth, Haight Ashbury, initial coin offering, Jacob Appelbaum, Kevin Kelly, Kickstarter, litecoin, M-Pesa, Marc Andreessen, Marshall McLuhan, Oculus Rift, peer-to-peer, peer-to-peer lending, Ponzi scheme, prediction markets, printed gun, QR code, ransomware, Ross Ulbricht, Salesforce, Satoshi Nakamoto, self-driving car, selling pickaxes during a gold rush, Skype, smart contracts, Steven Levy, the medium is the message, underbanked, WikiLeaks, Zimmermann PGP

It would be up to the owners of the network to put into the initial program how the network plans on turning a profit—but with a much lower overhead than traditional businesses, it might not be that difficult. Another idea is publicly run, self-driving cars acting as a decentralized, Uber-like platform. A group of people could raise money by selling a coin, then use that money to buy a fleet of self-driving cars, design an app that calls the cars, and let them loose on a metropolitan area. In the future, people might have multiple DACs that they own a small part of, and they might receive some residual income from them on a regular basis.


pages: 83 words: 23,805

City 2.0: The Habitat of the Future and How to Get There by Ted Books

active transport: walking or cycling, Airbnb, Albert Einstein, big-box store, carbon footprint, clean tech, cognitive load, collaborative consumption, crowdsourcing, demand response, food desert, high-speed rail, housing crisis, Induced demand, Internet of things, Jane Jacobs, jitney, Kibera, Kickstarter, Kitchen Debate, McMansion, megacity, New Urbanism, openstreetmap, ride hailing / ride sharing, self-driving car, sharing economy, Silicon Valley, smart cities, smart grid, TED Talk, the built environment, The Death and Life of Great American Cities, urban planning, urban renewal, urban sprawl, walkable city, Zipcar

This is why it’s incredibly useful to have sensors embedded within the tools and objects we swap out frequently, like our phones and cars. In fact, much of the progress being made toward the Internet of Things has occurred in transportation. In the very near future, cars will communicate with other cars to improve the safety and flow of traffic. Google’s self-driving car is one high-profile example, but another project, run by the U.S. National Highway Traffic Safety Administration (NHTSA), is a more likely predictor of where this concept could go. The NHTSA recently launched a yearlong test enabling 2,800 cars in Ann Arbor, Mich., to effectively communicate directly with one another.


pages: 372 words: 89,876

The Connected Company by Dave Gray, Thomas Vander Wal

A Pattern Language, Alan Greenspan, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Atul Gawande, Berlin Wall, business cycle, business process, call centre, Clayton Christensen, commoditize, complexity theory, creative destruction, David Heinemeier Hansson, digital rights, disruptive innovation, en.wikipedia.org, factory automation, folksonomy, Googley, index card, industrial cluster, interchangeable parts, inventory management, Jeff Bezos, John Markoff, Kevin Kelly, loose coupling, low cost airline, market design, minimum viable product, more computing power than Apollo, power law, profit maximization, Richard Florida, Ruby on Rails, Salesforce, scientific management, self-driving car, shareholder value, side project, Silicon Valley, skunkworks, software as a service, South of Market, San Francisco, Steve Jobs, Steven Levy, Stewart Brand, subscription business, systems thinking, tacit knowledge, The Wealth of Nations by Adam Smith, Tony Hsieh, Toyota Production System, two-pizza team, Vanguard fund, web application, WikiLeaks, work culture , Zipcar

FORD “Ford announced recently that they are creating an open platform that will allow tinkerers and developers to electronically ‘hot-rod’ their cars.” From “Hack your car with OpenXC, a platform for modding Ford car computers,” by Dean Takahashi, VentureBeat, September 12, 2011, http://www.venturebeat.com/2011/09/12/hack-your-car-with-openxc-platform-for-modding-ford-car-computers/. SELF-DRIVING CARS “Google is working on cars that will drive themselves.” From “Google Cars Drive Themselves, in Traffic,” by John Markoff, The New York Times, October 9, 2010. JOB GROWTH “Job growth will be led by health care…” From “Occupational Outlook Handbook: 2010-20 Projections,” Bureau of Labor Statistics, March 29, 2012, http://www.bls.gov/ooh/About/Projections-Overview.htm.

, How IBM Rediscovered Customers, How Profits Can Destroy Your Company American International Group (AIG), Too Much Autonomy–Failure to Invest in the Platform, Failure to Invest in the Platform ant colony, Strategy by Discovery AOL (company), The Platform Ashby’s Law, The Law of Requisite Variety Asplund, Jim, Promoters and Detractors ATM revolt (Bank of America), The ATM Revolt attractors, Attractors, Attractors attrition warfare, Three Types of Strategy authoritarian power structure, People Resist Being Controlled Autodesk software company, Disrupting Desktop Software at Autodesk–Network Weaving, Network Weaving automobiles, Urbanization–Urbanization, Urbanization, Urbanization, Urbanization, Urbanization, Urbanization self-driving cars, Urbanization services for, Urbanization urban populations and, Urbanization–Urbanization, Urbanization, Urbanization, Urbanization avatars, A Product is a Service Avatar products as, A Product is a Service Avatar B back stage, Front Stage and Back Stage–Balancing the Front Stage and the Back Stage, Balancing the Front Stage and the Back Stage–Making Platform Decisions, Balancing the Front Stage and the Back Stage, Balancing the Front Stage and the Back Stage, Making Platform Decisions about, Front Stage and Back Stage–Balancing the Front Stage and the Back Stage, Balancing the Front Stage and the Back Stage balancing with front stage, Balancing the Front Stage and the Back Stage–Making Platform Decisions, Balancing the Front Stage and the Back Stage, Making Platform Decisions Bain & Company, Promoters and Detractors, The Net Promoter Score Bank of America, The ATM Revolt Barabás, Albert-László, Scale-free Networks Basecamp application, You don’t have to be Big Bass, Carl, Disrupting Desktop Software at Autodesk Beer, Stafford, Purpose Sets the Context for Organizations to Learn behaviorist philosophy, The Company as a Machine Beinhocker, Eric D., Let a Thousand Flowers Bloom Best Buy (company), Whole Foods, an Agile Team of Agile Teams betweenness measure in networks, Power in Networks–Control, Control, Control Bezos, Jeff, Products as Job Descriptions, Amazon is Podular–A Podular System Trades Flexibility for Consistency, Amazon is Podular, Amazon is Podular, A Podular System Trades Flexibility for Consistency, Level One: How Entrepreneurs Learn, Balancing the Front Stage and the Back Stage, Small Bets: Set a Low Bar for Initial Experimentation, Moral Authority, Attractors Amazon and, Amazon is Podular–A Podular System Trades Flexibility for Consistency, Amazon is Podular, Amazon is Podular, A Podular System Trades Flexibility for Consistency appreciation shown by, Attractors as entrepreneur, Level One: How Entrepreneurs Learn moral authority and, Moral Authority on innovation, Small Bets: Set a Low Bar for Initial Experimentation on Kindle, Products as Job Descriptions on Zappos, Balancing the Front Stage and the Back Stage blind alleys, Small Bets: Set a Low Bar for Initial Experimentation blitzkrieg, Three Types of Strategy boiled frog effect, It Won’t be Easy boundary-setting in companies, Balance the Individual Freedom with the Common Good Boyd, John, Three Types of Strategy, Moral Authority, Customers First Brand, Stuart, Pace Layers Branson, Richard, Level One: How Entrepreneurs Learn Brickhouse innovation studio, Failure to Invest in the Platform Brogan, Chris, Network Weaving brokerage, defined, Small Worlds Brown, John Seely, Return on Assets is Dwindling Building Collaboration Services, Disrupting Desktop Software at Autodesk Bureau of Labor Statistics, Urbanization Burger King (company), Adaptive Moves Can Create Opportunities for Others Burt, Ron, Small Worlds Buzzsaw (company), Disrupting Desktop Software at Autodesk C Carlzon, Jan, Moments of Truth Carroll, Dave, Cascading Effects Can be Initiated by Customers–Cascading Effects Can be Initiated by Senior Executives, Cascading Effects Can be Initiated by Customers, Cascading Effects Can be Initiated by Senior Executives Carroll, Lewis, The Red Queen Race Castain, Eric, Be Connectable to Everything Channel Marketing Corp, Big Bets: The Responsibility of Senior Leaders chaos monkey, Netflix, a City of Services Christensen, Clayton, Purpose Sets the Context for Organizations to Learn Christian, Kristen, The ATM Revolt cities, Complex Adaptive Systems–The Long-lived Company, Complex Adaptive Systems, The Long-lived Company, What is a Platform?


pages: 295 words: 89,430

Small Data: The Tiny Clues That Uncover Huge Trends by Martin Lindstrom

autonomous vehicles, Berlin Wall, big-box store, correlation does not imply causation, driverless car, Edward Snowden, Fall of the Berlin Wall, land reform, Mikhail Gorbachev, Murano, Venice glass, Richard Florida, rolodex, self-driving car, Skype, Snapchat, Steve Jobs, Steven Pinker, too big to fail, urban sprawl

Thing is, it wasn’t because customers were dissatisfied with the bank or its customer service. No: most were getting a divorce, which explained why they were shifting around their assets.14 A parallel small data study could have figured this out in a day or less. Then there are the issues facing Google’s new self-driving cars, most of which it seems can be credited to the mismatch between technology and humanity. According to the New York Times, last year as one of Google’s new cars approached a crosswalk, it did as it was supposed to and came to a complete stop. The pedestrian in front crossed the street safely, at which point the Google car was rammed from behind by a second non-Google automobile.

See also ethnography; Subtext Research Apple, 49, 59, 96, 112, 138, 172–4, 178, 180, 203 Arons, Marc de Swaan, 13 aspiration, 4, 29, 34, 60, 81, 131–7, 180, 196 and Brazil, 122, 126–35, 140–1, 144–5 and fashion, 128–9, 150–2, 155–6, 158 Australia, 15, 75, 104–5, 107–8, 115, 131, 142, 176, 180–1 Austria, 142, 151, 154, 156, 158, 168 automobiles BMW Mini Cooper, ix, 199 Chevrolet, 138 and China, 172, 179–80, 199–205 and Germany, 173, 200–202 and identity, 180 and Saudi Arabia, 35 self-driving cars, 216 and the United States, 61, 63, 200–202 beauty, 29, 77, 80, 100, 145, 177. See also cosmetics; fashion bicycling, 105, 171–2, 214 big data, vii-viii, 2–3, 12–14, 73, 160, 212–16 Boas, Franz, 11 brands and building, 1–2, 4, 14, 67, 112, 173 and aspiration, 131–7 brand ambassadors, 108, 115, 145, 191 brand loyalty, 107, 113, 163, 213 country branding, 57, 60, 175–7 definition of brand, 211 and desire, 9–11 destinations, 173–7 fear of losing branded identities, 210–11 globalization of, 147–8 and Kulturbrille (culture glasses), 11 and religion, 138–9 and Somatic Marker Hypothesis, 65–6 See also specific brands; Subtext Research Brasil Kirin, 121–3, 138.


pages: 442 words: 94,734

The Art of Statistics: Learning From Data by David Spiegelhalter

Abraham Wald, algorithmic bias, Anthropocene, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, confounding variable, correlation coefficient, correlation does not imply causation, dark matter, data science, deep learning, DeepMind, Edmond Halley, Estimating the Reproducibility of Psychological Science, government statistician, Gregor Mendel, Hans Rosling, Higgs boson, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, seminal paper, sparse data, speech recognition, statistical model, sugar pill, systematic bias, TED Talk, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Two Sigma

For example, the likes and dislikes of an online customer, or whether that object in a robot’s vision is a child or a dog. Prediction: to tell us what is going to happen. For example, what the weather will be next week, what a stock price might do tomorrow, what products that customer might buy, or whether that child is going to run out in front of our self-driving car. Although these tasks differ in whether they are concerned with the present or the future, they both have the same underlying nature: to take a set of observations relevant to a current situation, and map them to a relevant conclusion. This process has been termed predictive analytics, but we are verging into the territory of artificial intelligence (AI), in which algorithms embodied in machines are used either to carry out tasks that would normally require human involvement, or to provide expert-level advice to humans.

Bayesian learning is also now seen as a fundamental process of human awareness of the environment, in that we have prior expectations about what we will see in any context, and then only need to take notice of unexpected features in our vision which are then used to update our current perceptions. This is the idea behind the so-called Bayesian Brain.6 The same learning procedures have been implemented in self-driving cars, which have a probabilistic ‘mental map’ of their surroundings that is constantly being updated by recognition of traffic lights, people, other cars, and so on: ‘In essence, a robot car “thinks” of itself as a blob of probability, traveling down a Bayesian road.’7 These problems are about estimating quantities that describe the world, but using Bayesian methods for assessing scientific hypotheses remains more controversial.


pages: 295 words: 90,821

Fully Grown: Why a Stagnant Economy Is a Sign of Success by Dietrich Vollrath

active measures, additive manufacturing, American Legislative Exchange Council, barriers to entry, business cycle, Capital in the Twenty-First Century by Thomas Piketty, central bank independence, creative destruction, Deng Xiaoping, endogenous growth, falling living standards, hiring and firing, income inequality, intangible asset, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, labor-force participation, light touch regulation, low skilled workers, manufacturing employment, old age dependency ratio, patent troll, Peter Thiel, profit maximization, rising living standards, Robert Gordon, Robert Solow, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, tacit knowledge, The Rise and Fall of American Growth, total factor productivity, women in the workforce, working-age population

Advances in solar-cell and battery technologies contribute to productivity growth by lowering costs and allowing firms and individuals to substitute for other sources of energy. The examples go on and on. But be careful to keep technological change and productivity growth as separate concepts in your head. The slowdown in productivity growth does not mean that we have become less inventive or capable. From the other side, advances in exciting new technologies—self-driving cars, genetic editing, biofuel—don’t necessarily mean anything for the productivity growth rate. I have no clue what future technologies will look like or what their fundamental impact on human welfare will be. Maybe we are about to enter some kind of techno-utopia. Or perhaps Skynet will kill us all.

If any of this is true, then it would allow us to be more optimistic about the future growth rate of productivity, although it would still be unwise to assume anything. I think a similar sentiment is warranted with respect to technological change in general. There are, without a doubt, an incredible number of new and improved technologies arriving every day. Self-driving cars, gene editing, low-cost solar panels, more efficient batteries, biofuel, quantum computers, 3-D printing of metals, artificial intelligence, and on and on and on. Any, or all, of these could generate profound changes in how we live and how we produce the goods and services that go into GDP. That said, it isn’t obvious that we’ll see profound effects on the growth rate of the economy.


pages: 384 words: 93,754

Green Swans: The Coming Boom in Regenerative Capitalism by John Elkington

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, agricultural Revolution, Anthropocene, anti-fragile, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, Berlin Wall, bitcoin, Black Swan, blockchain, Boeing 737 MAX, Boeing 747, Buckminster Fuller, business cycle, Cambridge Analytica, carbon footprint, carbon tax, circular economy, Clayton Christensen, clean water, cloud computing, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, CRISPR, crowdsourcing, David Attenborough, deglobalization, degrowth, discounted cash flows, distributed ledger, do well by doing good, Donald Trump, double entry bookkeeping, drone strike, Elon Musk, en.wikipedia.org, energy transition, Extinction Rebellion, Future Shock, Gail Bradbrook, Geoffrey West, Santa Fe Institute, George Akerlof, global supply chain, Google X / Alphabet X, green new deal, green transition, Greta Thunberg, Hans Rosling, hype cycle, impact investing, intangible asset, Internet of things, invention of the wheel, invisible hand, Iridium satellite, Jeff Bezos, John Elkington, Jony Ive, Joseph Schumpeter, junk bonds, Kevin Kelly, Kickstarter, M-Pesa, Marc Benioff, Mark Zuckerberg, Martin Wolf, microplastics / micro fibres, more computing power than Apollo, move fast and break things, Naomi Klein, Nelson Mandela, new economy, Nikolai Kondratiev, ocean acidification, oil shale / tar sands, oil shock, opioid epidemic / opioid crisis, placebo effect, Planet Labs, planetary scale, plant based meat, plutocrats, Ponzi scheme, radical decentralization, Ralph Nader, reality distortion field, Recombinant DNA, Rubik’s Cube, Salesforce, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, smart cities, smart grid, sovereign wealth fund, space junk, Steven Pinker, Stewart Brand, supply-chain management, synthetic biology, systems thinking, The future is already here, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Tim Cook: Apple, urban planning, Whole Earth Catalog

Their goal is “to produce a 10x impact on the world’s most intractable problems, not just 10% improvements.”70 “X is perhaps the only enterprise on the planet where regular investigation into the absurd is not just permitted but encouraged, and even required,” reported The Atlantic magazine. “X has quietly looked into space elevators and cold fusion. It has tried, and abandoned, projects to design hoverboards with magnetic levitation and to make affordable fuel from seawater. It has tried—and succeeded, in varying measures—to build self-driving cars, make drones that deliver aerodynamic packages, and design contact lenses that measure glucose levels in a diabetic person’s tears.”71 I recently met up again with X’s Sarah Hunter, this time in London. She was responsible for X’s public policy team, helping governments and policy makers around the world understand new technologies and their impacts.

,” BBC News, May 17, 2019. See also: https://www.bbc.co.uk/news/resources/idt-sh/boeing_two_deadly_crashes. 10.Henry Grabar, “The Crash of the Boeing 737 Max Is a Warning to Drivers, Too,” Slate, March 12, 2019. See also: https://slate.com/technology/2019/03/boeing-737-max-crashes-automation-self-driving-cars-surprise.html. 11.John Gapper, “Boeing’s Hubris Blinded It to a Lurking Danger,” Financial Times, April 11, 2019. 12.Jared Diamond, Collapse: How Societies Choose to Fail or Succeed. New York: Viking Press, 2005. See also: https://en.wikipedia.org/wiki/Collapse:_How_Societies_Choose_to_Fail_or_Succeed. 13.Jared Diamond, Upheaval: How Nations Cope With Crisis And Change.


pages: 404 words: 92,713

The Art of Statistics: How to Learn From Data by David Spiegelhalter

Abraham Wald, algorithmic bias, Antoine Gombaud: Chevalier de Méré, Bayesian statistics, Brexit referendum, Carmen Reinhart, Charles Babbage, complexity theory, computer vision, confounding variable, correlation coefficient, correlation does not imply causation, dark matter, data science, deep learning, DeepMind, Edmond Halley, Estimating the Reproducibility of Psychological Science, government statistician, Gregor Mendel, Hans Rosling, Higgs boson, Kenneth Rogoff, meta-analysis, Nate Silver, Netflix Prize, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, publication bias, randomized controlled trial, recommendation engine, replication crisis, self-driving car, seminal paper, sparse data, speech recognition, statistical model, sugar pill, systematic bias, TED Talk, The Design of Experiments, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Thomas Malthus, Two Sigma

For example, the likes and dislikes of an online customer, or whether that object in a robot’s vision is a child or a dog. • Prediction: to tell us what is going to happen. For example, what the weather will be next week, what a stock price might do tomorrow, what products that customer might buy, or whether that child is going to run out in front of our self-driving car. Although these tasks differ in whether they are concerned with the present or the future, they both have the same underlying nature: to take a set of observations relevant to a current situation, and map them to a relevant conclusion. This process has been termed predictive analytics, but we are verging into the territory of artificial intelligence (AI), in which algorithms embodied in machines are used either to carry out tasks that would normally require human involvement, or to provide expert-level advice to humans.

Bayesian learning is also now seen as a fundamental process of human awareness of the environment, in that we have prior expectations about what we will see in any context, and then only need to take notice of unexpected features in our vision which are then used to update our current perceptions. This is the idea behind the so-called Bayesian Brain.6 The same learning procedures have been implemented in self-driving cars, which have a probabilistic ‘mental map’ of their surroundings that is constantly being updated by recognition of traffic lights, people, other cars, and so on: ‘In essence, a robot car “thinks” of itself as a blob of probability, traveling down a Bayesian road.’7 These problems are about estimating quantities that describe the world, but using Bayesian methods for assessing scientific hypotheses remains more controversial.


pages: 339 words: 92,785

I, Warbot: The Dawn of Artificially Intelligent Conflict by Kenneth Payne

Abraham Maslow, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asperger Syndrome, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Black Lives Matter, Bletchley Park, Boston Dynamics, classic study, combinatorial explosion, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cuban missile crisis, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, disinformation, driverless car, drone strike, dual-use technology, Elon Musk, functional programming, Geoffrey Hinton, Google X / Alphabet X, Internet of things, job automation, John Nash: game theory, John von Neumann, Kickstarter, language acquisition, loss aversion, machine translation, military-industrial complex, move 37, mutually assured destruction, Nash equilibrium, natural language processing, Nick Bostrom, Norbert Wiener, nuclear taboo, nuclear winter, OpenAI, paperclip maximiser, pattern recognition, RAND corporation, ransomware, risk tolerance, Ronald Reagan, self-driving car, semantic web, side project, Silicon Valley, South China Sea, speech recognition, Stanislav Petrov, stem cell, Stephen Hawking, Steve Jobs, strong AI, Stuxnet, technological determinism, TED Talk, theory of mind, TikTok, Turing machine, Turing test, uranium enrichment, urban sprawl, V2 rocket, Von Neumann architecture, Wall-E, zero-sum game

The Artificial Intelligence of today would astonish a visitor from the recent past. The science fiction writer Arthur C. Clarke wrote in the 1970s that any sufficiently advanced technology is indistinguishable from magic. That might well be the experience of someone catapulted forward in time half a century from then to today. Here we are, on the cusp of self-driving cars, where the smartphone in everyone’s pocket responds to voice commands by pulling up useful information from the ether. That’s precisely the sort of magic that Douglas Adams imagined in his bestselling 1970s novel The Hitchhiker’s Guide to the Galaxy. It’s now almost a decade since an AI called Watson thrashed humans at Jeopardy, the popular television general knowledge quiz.

A-10 Warthog abacuses Abbottabad, Pakistan Able Archer (1983) acoustic decoys acoustic torpedoes Adams, Douglas Aegis combat system Aerostatic Corps affective empathy Affecto Afghanistan agency aircraft see also dogfighting; drones aircraft carriers algorithms algorithm creation Alpha biases choreography deep fakes DeepMind, see DeepMind emotion recognition F-117 Nighthawk facial recognition genetic selection imagery analysis meta-learning natural language processing object recognition predictive policing alien hand syndrome Aliens (1986 film) Alpha AlphaGo Altered Carbon (television series) Amazon Amnesty International amygdala Andropov, Yuri Anduril Ghost anti-personnel mines ants Apple Aristotle armour arms races Army Research Lab Army Signal Corps Arnalds, Ólafur ARPA Art of War, The (Sun Tzu) art Artificial Intelligence agency and architecture autonomy and as ‘brittle’ connectionism definition of decision-making technology expert systems and feedback loops fuzzy logic innateness intelligence analysis meta-learning as ‘narrow’ needle-in-a-haystack problems neural networks reinforcement learning ‘strong AI’ symbolic logic and unsupervised learning ‘winters’ artificial neural networks Ashby, William Ross Asimov, Isaac Asperger syndrome Astute class boats Atari Breakout (1976) Montezuma’s Revenge (1984) Space Invaders (1978) Athens ATLAS robots augmented intelligence Austin Powers (1997 film) Australia authoritarianism autonomous vehicles see also drones autonomy B-21 Raider B-52 Stratofortress B2 Spirit Baby X BAE Systems Baghdad, Iraq Baidu balloons ban, campaigns for Banks, Iain Battle of Britain (1940) Battle of Fleurus (1794) Battle of Midway (1942) Battle of Sedan (1940) batwing design BBN Beautiful Mind, A (2001 film) beetles Bell Laboratories Bengio, Yoshua Berlin Crisis (1961) biases big data Bin Laden, Osama binary code biological weapons biotechnology bipolarity bits Black Lives Matter Black Mirror (television series) Blade Runner (1982 film) Blade Runner 2049 (2017 film) Bletchley Park, Buckinghamshire blindness Blunt, Emily board games, see under games boats Boden, Margaret bodies Boeing MQ-25 Stingray Orca submarines Boolean logic Boston Dynamics Bostrom, Nick Boyd, John brain amygdala bodies and chunking dopamine emotion and genetic engineering and language and mind merge and morality and plasticity prediction and subroutines umwelts and Breakout (1976 game) breathing control brittleness brute force Buck Rogers (television series) Campaign against Killer Robots Carlsen, Magnus Carnegie Mellon University Casino Royale (2006 film) Castro, Fidel cat detector centaur combination Central Intelligence Agency (CIA) centre of gravity chaff Challenger Space Shuttle disaster (1986) Chauvet cave, France chemical weapons Chernobyl nuclear disaster (1986) chess centaur teams combinatorial explosion and creativity in Deep Blue game theory and MuZero as toy universe chicken (game) chimeras chimpanzees China aircraft carriers Baidu COVID-19 pandemic (2019–21) D-21 in genetic engineering in GJ-11 Sharp Sword nuclear weapons surveillance in Thucydides trap and US Navy drone seizure (2016) China Lake, California Chomsky, Noam choreography chunking Cicero civilians Clarke, Arthur Charles von Clausewitz, Carl on character on culmination on defence on genius on grammar of war on materiel on nature on poker on willpower on wrestling codebreaking cognitive empathy Cold War (1947–9) arms race Berlin Crisis (1961) Cuban Missile Crisis (1962) F-117 Nighthawk Iran-Iraq War (1980–88) joint action Korean War (1950–53) nuclear weapons research and SR-71 Blackbird U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) VRYAN Cole, August combinatorial creativity combinatorial explosion combined arms common sense computers creativity cyber security games graphics processing unit (GPU) mice Moore’s Law symbolic logic viruses VRYAN confirmation bias connectionism consequentialism conservatism Convention on Conventional Weapons ConvNets copying Cormorant cortical interfaces cost-benefit analysis counterfactual regret minimization counterinsurgency doctrine courageous restraint COVID-19 pandemic (2019–21) creativity combinatorial exploratory genetic engineering and mental disorders and transformational criminal law CRISPR, crows Cruise, Thomas Cuban Missile Crisis (1962) culmination Culture novels (Banks) cyber security cybernetics cyborgs Cyc cystic fibrosis D-21 drones Damasio, Antonio dance DARPA autonomous vehicle research battlespace manager codebreaking research cortical interface research cyborg beetle Deep Green expert system programme funding game theory research LongShot programme Mayhem Ng’s helicopter Shakey understanding and reason research unmanned aerial combat research Dartmouth workshop (1956) Dassault data DDoS (distributed denial-of-service) dead hand system decision-making technology Deep Blue deep fakes Deep Green DeepMind AlphaGo Atari playing meta-learning research MuZero object recognition research Quake III competition (2019) deep networks defence industrial complex Defence Innovation Unit Defence Science and Technology Laboratory defence delayed gratification demons deontological approach depth charges Dionysus DNA (deoxyribonucleic acid) dodos dogfighting Alpha domains dot-matrix tongue Dota II (2013 game) double effect drones Cormorant D-21 GJ-11 Sharp Sword Global Hawk Gorgon Stare kamikaze loitering munitions nEUROn operators Predator Reaper reconnaissance RQ-170 Sentinel S-70 Okhotnik surveillance swarms Taranis wingman role X-37 X-47b dual use technology Eagleman, David early warning systems Echelon economics Edge of Tomorrow (2014 film) Eisenhower, Dwight Ellsberg, Daniel embodied cognition emotion empathy encryption entropy environmental niches epilepsy epistemic community escalation ethics Asimov’s rules brain and consequentialism deep brain stimulation and deontological approach facial recognition and genetic engineering and golden rule honour hunter-gatherer bands and identity just war post-conflict reciprocity regulation surveillance and European Union (EU) Ex Machina (2014 film) expert systems exploratory creativity extra limbs Eye in the Sky (2015 film) F-105 Thunderchief F-117 Nighthawk F-16 Fighting Falcon F-22 Raptor F-35 Lightning F/A-18 Hornet Facebook facial recognition feedback loops fighting power fire and forget firmware 5G cellular networks flow fog of war Ford forever wars FOXP2 gene Frahm, Nils frame problem France Fukushima nuclear disaster (2011) Future of Life Institute fuzzy logic gait recognition game theory games Breakout (1976) chess, see chess chicken Dota II (2013) Go, see Go Montezuma’s Revenge (1984) poker Quake III (1999) Space Invaders (1978) StarCraft II (2010) toy universes zero sum games gannets ‘garbage in, garbage out’ Garland, Alexander Gates, William ‘Bill’ Gattaca (1997 film) Gavotti, Giulio Geertz, Clifford generalised intelligence measure Generative Adversarial Networks genetic engineering genetic selection algorithms genetically modified crops genius Germany Berlin Crisis (1961) Nuremburg Trials (1945–6) Russian hacking operation (2015) World War I (1914–18) World War II (1939–45) Ghost in the Shell (comic book) GJ-11 Sharp Sword Gladwell, Malcolm Global Hawk drone global positioning system (GPS) global workspace Go (game) AlphaGo Gödel, Kurt von Goethe, Johann golden rule golf Good Judgment Project Google BERT Brain codebreaking research DeepMind, see DeepMind Project Maven (2017–) Gordievsky, Oleg Gorgon Stare GPT series grammar of war Grand Challenge aerial combat autonomous vehicles codebreaking graphics processing unit (GPU) Greece, ancient grooming standard Groundhog Day (1993 film) groupthink guerilla warfare Gulf War First (1990–91) Second (2003–11) hacking hallucinogenic drugs handwriting recognition haptic vest hardware Harpy Hawke, Ethan Hawking, Stephen heat-seeking missiles Hebrew Testament helicopters Hellfire missiles Her (2013 film) Hero-30 loitering munitions Heron Systems Hinton, Geoffrey Hitchhiker’s Guide to the Galaxy, The (Adams) HIV (human immunodeficiency viruses) Hoffman, Frank ‘Holeshot’ (Cole) Hollywood homeostasis Homer homosexuality Hongdu GJ-11 Sharp Sword honour Hughes human in the loop human resources human-machine teaming art cyborgs emotion games King Midas problem prediction strategy hunter-gatherer bands Huntingdon’s disease Hurricane fighter aircraft hydraulics hypersonic engines I Robot (Asimov) IARPA IBM identity Iliad (Homer) image analysis image recognition cat detector imagination Improbotics nformation dominance information warfare innateness intelligence analysts International Atomic Energy Agency International Criminal Court international humanitarian law internet of things Internet IQ (intelligence quotient) Iran Aegis attack (1988) Iraq War (1980–88) nuclear weapons Stuxnet attack (2010) Iraq Gulf War I (1990–91) Gulf War II (2003–11) Iran War (1980–88) Iron Dome Israel Italo-Turkish War (1911–12) Jaguar Land Rover Japan jazz JDAM (joint directed attack munition) Jeopardy Jobs, Steven Johansson, Scarlett Johnson, Lyndon Joint Artificial Intelligence Center (JAIC) de Jomini, Antoine jus ad bellum jus in bello jus post bellum just war Kalibr cruise missiles kamikaze drones Kasparov, Garry Kellogg Briand Pact (1928) Kennedy, John Fitzgerald KGB (Komitet Gosudarstvennoy Bezopasnosti) Khrushchev, Nikita kill chain King Midas problem Kissinger, Henry Kittyhawk Knight Rider (television series) know your enemy know yourself Korean War (1950–53) Kratos XQ-58 Valkyrie Kubrick, Stanley Kumar, Vijay Kuwait language connectionism and genetic engineering and natural language processing pattern recognition and semantic webs translation universal grammar Law, Jude LeCun, Yann Lenat, Douglas Les, Jason Libratus lip reading Litvinenko, Alexander locked-in patients Lockheed dogfighting trials F-117 Nighthawk F-22 Raptor F-35 Lightning SR-71 Blackbird logic loitering munitions LongShot programme Lord of the Rings (2001–3 film trilogy) LSD (lysergic acid diethylamide) Luftwaffe madman theory Main Battle Tanks malum in se Manhattan Project (1942–6) Marcus, Gary Maslow, Abraham Massachusetts Institute of Technology (MIT) Matrix, The (1999 film) Mayhem McCulloch, Warren McGregor, Wayne McNamara, Robert McNaughton, John Me109 fighter aircraft medical field memory Merkel, Angela Microsoft military industrial complex Mill, John Stuart Milrem mimicry mind merge mind-shifting minimax regret strategy Minority Report (2002 film) Minsky, Marvin Miramar air base, San Diego missiles Aegis combat system agency and anti-missile gunnery heat-seeking Hellfire missiles intercontinental Kalibr cruise missiles nuclear warheads Patriot missile interceptor Pershing II missiles Scud missiles Tomahawk cruise missiles V1 rockets V2 rockets mission command mixed strategy Montezuma’s Revenge (1984 game) Moore’s Law mosaic warfare Mueller inquiry (2017–19) music Musk, Elon Mutually Assured Destruction (MAD) MuZero Nagel, Thomas Napoleon I, Emperor of the French Napoleonic France (1804–15) narrowness Nash equilibrium Nash, John National Aeronautics and Space Administration (NASA) National Security Agency (NSA) National War College natural language processing natural selection Nature navigation computers Nazi Germany (1933–45) needle-in-a-haystack problems Netflix network enabled warfare von Neumann, John neural networks neurodiversity nEUROn drone neuroplasticity Ng, Andrew Nixon, Richard normal accident theory North Atlantic Treaty Organization (NATO) North Korea nuclear weapons Cuban Missile Crisis (1962) dead hand system early warning systems F-105 Thunderchief and game theory and Hiroshima and Nagasaki bombings (1945) Manhattan Project (1942–6) missiles Mutually Assured Destruction (MAD) second strike capability submarines and VRYAN and in WarGames (1983 film) Nuremburg Trials (1945–6) Obama, Barack object recognition Observe Orient Decide and Act (OODA) offence-defence balance Office for Naval Research Olympic Games On War (Clausewitz), see Clausewitz, Carl OpenAI optogenetics Orca submarines Ottoman Empire (1299–1922) pain Pakistan Palantir Palmer, Arnold Pandemonium Panoramic Research Papert, Seymour Parkinson’s disease Patriot missile interceptors pattern recognition Pearl Harbor attack (1941) Peloponnesian War (431–404 BCE) Pentagon autonomous vehicle research codebreaking research computer mouse development Deep Green Defence Innovation Unit Ellsberg leaks (1971) expert system programme funding ‘garbage in, garbage out’ story intelligence analysts Project Maven (2017–) Shakey unmanned aerial combat research Vietnam War (1955–75) perceptrons Perdix Pershing II missiles Petrov, Stanislav Phalanx system phrenology pilot’s associate Pitts, Walter platform neutrality Pluribus poker policing polygeneity Portsmouth, Hampshire Portuguese Man o’ War post-traumatic stress disorder (PTSD) Predator drones prediction centaur teams ‘garbage in, garbage out’ story policing toy universes VRYAN Prescience principles of war prisoners Project Improbable Project Maven (2017–) prosthetic arms proximity fuses Prussia (1701–1918) psychology psychopathy punishment Putin, Vladimir Pyeongchang Olympics (2018) Qinetiq Quake III (1999 game) radar Rafael RAND Corporation rational actor model Rawls, John Re:member (Arnalds) Ready Player One (Cline) Reagan, Ronald Reaper drones reciprocal punishment reciprocity reconnaissance regulation ban, campaigns for defection self-regulation reinforcement learning remotely piloted air vehicles (RPAVs) revenge porn revolution in military affairs Rid, Thomas Robinson, William Heath Robocop (1987 film) Robotics Challenge robots Asimov’s rules ATLAS Boston Dynamics homeostatic Shakey symbolic logic and Rome Air Defense Center Rome, ancient Rosenblatt, Frank Royal Air Force (RAF) Royal Navy RQ-170 Sentinel Russell, Stuart Russian Federation German hacking operation (2015) Litvinenko murder (2006) S-70 Okhotnik Skripal poisoning (2018) Ukraine War (2014–) US election interference (2016) S-70 Okhotnik SAGE Said and Done’ (Frahm) satellite navigation satellites Saudi Arabia Schelling, Thomas schizophrenia Schwartz, Jack Sea Hunter security dilemma Sedol, Lee self-actualisation self-awareness self-driving cars Selfridge, Oliver semantic webs Shakey Shanahan, Murray Shannon, Claude Shogi Silicon Valley Simon, Herbert Single Integrated Operations Plan (SIOP) singularity Siri situational awareness situationalist intelligence Skripal, Sergei and Yulia Slaughterbots (2017 video) Slovic, Paul smartphones Smith, Willard social environments software Sophia Sorcerer’s Apprentice, The (Goethe) South China Sea Soviet Union (1922–91) aircraft Berlin Crisis (1961) Chernobyl nuclear disaster (1986) Cold War (1947–9), see Cold War collapse (1991) Cuban Missile Crisis (1962) early warning systems Iran-Iraq War (1980–88) Korean War (1950–53) nuclear weapons radar technology U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) VRYAN World War II (1939–45) Space Invaders (1978 game) SpaceX Sparta Spike Firefly loitering munitions Spitfire fighter aircraft Spotify Stanford University Stanley Star Trek (television series) StarCraft II (2010 game) stealth strategic bombing strategic computing programme strategic culture Strategy Robot strategy Strava Stuxnet sub-units submarines acoustic decoys nuclear Orca South China Sea incident (2016) subroutines Sukhoi Sun Tzu superforecasting surveillance swarms symbolic logic synaesthesia synthetic operation environment Syria Taliban tanks Taranis drone technological determinism Tempest Terminator franchise Tesla Tetlock, Philip theory of mind Threshold Logic Unit Thucydides TikTok Tomahawk cruise missiles tongue Top Gun (1986 film) Top Gun: Maverick (2021 film) torpedoes toy universes trade-offs transformational creativity translation Trivers, Robert Trump, Donald tumours Turing, Alan Twitter 2001: A Space Odyssey (1968 film) Type-X Robotic Combat Vehicle U2 incident (1960) Uber Uexküll, Jacob Ukraine ultraviolet light spectrum umwelts uncanny valley unidentified flying objects (UFOs) United Kingdom AI weapons policy armed force, size of Battle of Britain (1940) Bletchley Park codebreaking Blitz (1940–41) Cold War (1947–9) COVID-19 pandemic (2019–21) DeepMind, see DeepMind F-35 programme fighting power human rights legislation in Litvinenko murder (2006) nuclear weapons principles of war Project Improbable Qinetiq radar technology Royal Air Force Royal Navy Skripal poisoning (2018) swarm research wingman concept World War I (1914–18) United Nations United States Afghanistan War (2001–14) Air Force Army Research Lab Army Signal Corps Battle of Midway (1942) Berlin Crisis (1961) Bin Laden assassination (2011) Black Lives Matter protests (2020) centaur team research Central Intelligence Agency (CIA) Challenger Space Shuttle disaster (1986) Cold War (1947–9), see Cold War COVID-19 pandemic (2019–21) Cuban Missile Crisis (1962) culture cyber security DARPA, see DARPA Defense Department drones early warning systems F-35 programme Gulf War I (1990–91) Gulf War II (2003–11) IARPA Iran Air shoot-down (1988) Korean War (1950–53) Manhattan Project (1942–6) Marines Mueller inquiry (2017–19) National Security Agency National War College Navy nuclear weapons Office for Naval Research Patriot missile interceptor Pearl Harbor attack (1941) Pentagon, see Pentagon Project Maven (2017–) Rome Air Defense Center Silicon Valley strategic computing programme U2 incident (1960) Vienna Summit (1961) Vietnam War (1955–75) universal grammar Universal Schelling Machine (USM) unmanned aerial vehicles (UAVs), see drones unsupervised learning utilitarianism UVision V1 rockets V2 rockets Vacanti mouse Valkyries Van Gogh, Vincent Vietnam War (1955–75) Vigen, Tyler Vincennes, USS voice assistants VRYAN Wall-e (2008 film) WannaCry ransomware War College, see National War College WarGames (1983 film) warrior ethos Watson weapon systems WhatsApp Wiener, Norbert Wikipedia wingman role Wittgenstein, Ludwig World War I (1914–18) World War II (1939–45) Battle of Britain (1940) Battle of Midway (1942) Battle of Sedan (1940) Bletchley Park codebreaking Blitz (1940–41) Hiroshima and Nagasaki bombings (1945) Pearl Harbor attack (1941) radar technology V1 rockets V2 rockets VRYAN and Wrangham, Richard Wright brothers WS-43 loitering munitions Wuhan, China X-37 drone X-drone X-rays YouTube zero sum games


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Boundless: The Rise, Fall, and Escape of Carlos Ghosn by Nick Kostov

"World Economic Forum" Davos, airport security, bitcoin, business logic, collapse of Lehman Brothers, corporate governance, COVID-19, cryptocurrency, Donald Trump, glass ceiling, Google Earth, Les Trente Glorieuses, lockdown, Masayoshi Son, offshore financial centre, rolodex, self-driving car, Silicon Valley, the payments system

Still, using a Dutch-registered company tied to Nissan sounded promising, and an opportunity to do just that soon presented itself. In the summer of 2010, Nissan executives were debating the idea of creating a special fund aimed at investing in innovative startups. Ghosn had expressed concerns that Nissan risked falling behind in the race to develop electric vehicles and self-driving cars if it didn’t keep track of disruptive projects being thought up in places like Silicon Valley. Ghosn wanted the idea of an internal venture capital fund to be put on a fast track. In September, he called Kelly and the company’s chief financial officer into his office to outline a plan. Kelly was tasked with writing a proposal for approval by the executive committee.

” * * * Beating Toyota, Volkswagen, and GM to the top of the automotive leaderboard was just the start. In September, Ghosn told the world that the Alliance wasn’t content to sell 10 million cars a year; it wanted to sell 14 million. Ghosn reasoned that with the advent of electric vehicles and self-driving cars, only the biggest companies would have the cash to survive and prosper. If the Alliance could gain an edge in the early chase for breakthroughs, Ghosn thought, the gap would only continue to widen. He set targets for Renault, Mitsubishi, and Nissan to hit those sales numbers by 2022. Saikawa was uneasy about Ghosn’s aggressive sales targets, fearing they would further erode the profit margin at Nissan, which had already missed its sales targets a few months earlier.


pages: 769 words: 169,096

Order Without Design: How Markets Shape Cities by Alain Bertaud

autonomous vehicles, call centre, colonial rule, congestion charging, congestion pricing, creative destruction, cross-subsidies, Deng Xiaoping, discounted cash flows, Donald Trump, Edward Glaeser, en.wikipedia.org, extreme commuting, garden city movement, gentrification, Google Earth, Great Leap Forward, Jane Jacobs, job satisfaction, Joseph Schumpeter, land tenure, manufacturing employment, market design, market fragmentation, megacity, microapartment, new economy, New Urbanism, openstreetmap, Pearl River Delta, price mechanism, rent control, Right to Buy, Ronald Coase, self-driving car, Shenzhen special economic zone , Silicon Valley, special economic zone, the built environment, trade route, transaction costs, transit-oriented development, trickle-down economics, urban planning, urban sprawl, zero-sum game

There are only two ways to decrease the street area consumed by moving cars: the first would be to decrease the width of vehicles so that two vehicles fit in the width of one lane (e.g., a motorcycle); the second would be to decrease safely the 2-second reaction time by using technology like self-driving cars. We will explore these possibilities later on. For vehicles moving on a road, the consumption of street area per passenger is therefore dependent on four parameters: the length of the vehicle, the reaction time to ensure a safe distance between vehicles, the speed of the vehicle, and the number of passengers.

Emerging new personal mobility vehicles, such as the Toyota i-Road, are examples of a possible replacement for the traditional car that would provide more mobility for less road space and less energy, pollution, and GHG emissions per kilometer. 6.  Finally, the possibility of sharing small self-driving vehicles on demand could provide a very efficient alternative in the future for many suburb-to-suburb trips. Self-driving cars would have three important advantages over traditional cars. First, they would save street space by being able to run closer to one another without requiring the 2-second reaction time that human drivers require; that would save about 65 percent of road space at speeds of about 60 km/h. Second, they would dramatically reduce accidents and, therefore, the unpredictability of road commuting times.

Designing New Land Use Regulations and Auditing Existing Regulations The necessity of living and working in close proximity requires rules that will minimize friction. Because the economic and technological environment is changing constantly, the rules have to be constantly adapted to the new environment. For instance, the introduction of self-driving cars in cities in the near future will require new regulations, just as new regulations were required when cars replaced horse carts as a main mode of urban transport. Past regulations also have to be periodically reviewed for their relevance. As described in chapter 7, the shadows cast by very tall buildings were a major issue for the first part of the twentieth century, until indoor lighting and air conditioning became efficient and cheap.


pages: 374 words: 97,288

The End of Ownership: Personal Property in the Digital Economy by Aaron Perzanowski, Jason Schultz

3D printing, Airbnb, anti-communist, barriers to entry, behavioural economics, bitcoin, blockchain, carbon footprint, cloud computing, conceptual framework, crowdsourcing, cryptocurrency, Donald Trump, Eben Moglen, Edward Snowden, en.wikipedia.org, endowment effect, Firefox, Free Software Foundation, general purpose technology, gentrification, George Akerlof, Hush-A-Phone, independent contractor, information asymmetry, intangible asset, Internet Archive, Internet of things, Isaac Newton, it's over 9,000, loss aversion, Marc Andreessen, means of production, minimum wage unemployment, new economy, Open Library, Paradox of Choice, peer-to-peer, price discrimination, Richard Thaler, ride hailing / ride sharing, rolodex, self-driving car, sharing economy, Silicon Valley, software as a service, software patent, software studies, speech recognition, Steve Jobs, subscription business, telemarketer, the long tail, The Market for Lemons, Tony Fadell, transaction costs, winner-take-all economy

It relies less—or at least less obviously—on DRM and the threat of DMCA liability, and more on the appeal of new product features, and in particular those found in the smart devices that make up the so-called Internet of Things (IoT). IoT has become something of a buzzword, intended to cover a range of devices from smartphones and networked thermostats to self-driving cars and wearable technology. These products generally combine embedded software, network connectivity, microscopic sensors, and large-scale data analytics. In essence, they are computers. As Chief Justice John Roberts recently wrote about mobile phones: “The term ‘cell phone’ is itself misleading shorthand; many of these devices are in fact minicomputers that also happen to have the capacity to be used as a telephone.

What’s more, Nest planned to exercise its software-enabled remote control over the devices to render them entirely inoperable. After a May 15 software update, it explained, “The Revolv app won’t open and the hub won’t work.”9 Alphabet, Google’s recently-created parent company, which has its sights set on the self-driving car and medical device markets, decided it was within its rights to reduce a device that consumers bought to nothing more than an overpriced paperweight. Consider that before you buy a Google car. In this chapter, we look at a small sampling of IoT devices across a wide range of sectors and consider their consequences for ownership and consumer welfare more broadly.


Future Files: A Brief History of the Next 50 Years by Richard Watson

Abraham Maslow, Albert Einstein, bank run, banking crisis, battle of ideas, Black Swan, call centre, carbon credits, carbon footprint, carbon tax, cashless society, citizen journalism, commoditize, computer age, computer vision, congestion charging, corporate governance, corporate social responsibility, deglobalization, digital Maoism, digital nomad, disintermediation, driverless car, epigenetics, failed state, financial innovation, Firefox, food miles, Ford Model T, future of work, Future Shock, global pandemic, global supply chain, global village, hive mind, hobby farmer, industrial robot, invention of the telegraph, Jaron Lanier, Jeff Bezos, knowledge economy, lateral thinking, linked data, low cost airline, low skilled workers, M-Pesa, mass immigration, Northern Rock, Paradox of Choice, peak oil, pensions crisis, precautionary principle, precision agriculture, prediction markets, Ralph Nader, Ray Kurzweil, rent control, RFID, Richard Florida, self-driving car, speech recognition, synthetic biology, telepresence, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Turing test, Victor Gruen, Virgin Galactic, white flight, women in the workforce, work culture , Zipcar

Hence better vehicular access (doors) and better forward, backward and side vision will become increasingly important engineering elements and testing of older drivers will eventually become commonplace globally. Automotive and Transport 165 Ultimately, though, the solution to both older and younger driver safety will be to take the necessity of driving away altogether. Along with flying cars, self-driving cars have been a feature of the sci-fi future for decades. They first appeared in the 1950s, although the idea never really progressed beyond the concept stage for a number of legal, social and technical reasons. Nevertheless, General Motors claims that it is building such a car and that it could be introduced as early as 2008.

A 311 Index ‘O’ Garage 170 3D printers 56 accelerated education 57 accidents 159, 161–6, 173, 246 ACNielsen 126 adaptive cruise control 165 Adeg Aktiv 50+ 208 advertising 115–16, 117, 119 Africa 70, 89, 129, 174, 221, 245, 270, 275, 290, 301 ageing 1, 10, 54, 69, 93, 139, 147–8, 164, 188, 202, 208, 221, 228–9, 237, 239, 251, 261, 292, 295, 297–8 airborne networks 56 airlines 272 allergies 196–7, 234, 236 Alliance Against Urban 4x4s 171 alternative energy 173 alternative futures viii alternative medicine 244–5 alternative technology 151 amateur production 111–12 Amazon 32, 113–14, 121 American Apparel 207 American Express 127–8 androids 55 Angola 77 anti-ageing drugs 231, 237 anti-ageing foods 188 anti-ageing surgery 2, 237 antibiotics 251 anxiety 10, 16, 30, 32, 36, 37, 128, 149, 179, 184, 197, 199, 225, 228, 243, 251, 252, 256, 263, 283–4, 295–6, 300, 301, 305 Apple 61, 115, 121, 130, 137–8, 157 Appleyard, Bryan 79 Argentina 210 Armamark Corporation 193 artificial intelliegence 22, 40, 44, 82 131, 275, 285–6, 297, 300 Asda 136, 137 Asia 11, 70, 78, 89, 129, 150, 174, 221, 280, 290, 292 Asimov, Isaac 44 Asos.com 216 asthma 235 auditory display software 29 Australia 20–21, 72–3, 76, 92, 121, 145, 196, 242, 246, 250, 270, 282 Austria 208 authenticity 32, 37, 179, 194, 203–11 authoritarianism 94 automated publishing machine (APM) 114 automation 292 automotive industry 154–77 B&Q 279 baby boomers 41, 208 bacterial factories 56 Bahney, Anna 145 Bahrain 2 baking 27, 179, 195, 199 Bangladesh 2 bank accounts, body double 132 banknotes 29, 128 banks 22, 123, 135–8, 150, 151 virtual 134 Barnes and Noble 114 bartering 151 BBC 25, 119 Become 207 Belgium 238 313 314 benriya 28 Berlusconi, Silvio 92 Best Buy 223 biofuel 64 biomechatronics 56 biometric identification 28, 35, 52, 68, 88, 132 bionic body parts 55 Biosphere Expeditions 259 biotechnology 40, 300 blended families 20 blogs 103, 107, 109, 120 Blurb 113 BMW 289 board games 225 body double bank accounts 132 body parts bionic 55 replacement 2, 188, 228 Bolivia 73 Bollywood 111 books 29, 105, 111–25 boomerang kids 145 brain transplants 231 brain-enhancing foods 188 Brazil 2, 84, 89, 173, 247, 254, 270, 290 Burger King 184 business 13, 275–92 Bust-Up 189 busyness 27, 195, 277 Calvin, Bill 45 Canada 63, 78, 240 cancer 251 car sharing 160, 169, 176 carbon credits 173 carbon footprints 255 carbon taxes 76, 172 cars classic 168–9 driverless 154–5 flying 156, 165 hydrogen-powered 12, 31, 157, 173 pay-as-you-go 167–8 self-driving 165 cascading failure 28 cash 126–7, 205 cellphone payments 129, 213 cellphones 3, 25, 35, 51, 53, 120, 121, FUTURE FILES 129, 156, 161, 251 chicken, Christian 192 childcare robots 57 childhood 27, 33–4, 82–3 children’s database 86 CHIME nations (China, India, Middle East) 2, 10, 81 China 2, 10, 11, 69–72, 75–81, 88, 92–3, 125, 137, 139–40, 142, 151, 163, 174–5, 176, 200, 222, 228, 247, 260, 270–71, 275, 279, 295, 302 choice 186–7 Christian chicken 192 Christianity, muscular 16, 73 Chrysler 176 cinema 110–11, 120 Citibank 29, 128 citizen journalism 103–4, 108 City Car Club 168 Clarke, Arthur C. 58–9 Clarke’s 187 classic cars 168–9 climate change 4, 11, 37, 43, 59, 64, 68, 74, 77–9, 93, 150, 155, 254, 257, 264, 298–9 climate-controlled buildings 254, 264 cloning 38 human 23, 249 CNN 119 coal 176 Coca-Cola 78, 222–3 co-creation 111–12, 119 coins 29, 128, 129 collective intelligence 45–6 Collins, Jim 288 comfort eating 200 Comme des Garçons 216 community 36 compassion 120 competition in financial services 124–5 low-cost 292 computers disposable 56 intelligent 23, 43 organic 56 wearable 56, 302 computing 3, 33, 43, 48, 82 connectivity 3, 10, 11, 15, 91, 120, Index 233, 261, 275–6, 281, 292, 297, 299 conscientious objection taxation 86 contactless payments 123, 150 continuous partial attention 53 control 36, 151, 225 convenience 123, 178–9, 184, 189, 212, 223, 224 Coren, Stanley 246 corporate social responsibility 276, 282, 298 cosmetic neurology 250 Costa Rica 247 Craig’s List 102 creativity 11, 286; see also innovation credit cards 141–3, 150 crime 86–9 forecasting 86–7 gene 57, 86 Croatia 200 Crowdstorm 207 Cuba 75 cultural holidays 259, 273 culture 11, 17–37 currency, global 127, 151 customization 56, 169, 221–2, 260 cyberterrorism 65, 88–9 Cyc 45 cynicism 37 DayJet 262 death 237–9 debt 123–4, 140–44, 150 defense 63, 86 deflation 139 democracy 94 democratization of media 104, 108, 113 demographics 1, 10, 21, 69, 82, 93, 202, 276, 279–81, 292, 297–8 Denmark 245 department stores 214 deregulation 11, 3 Destiny Health 149 detox 200 Detroit Project 171 diagnosis 232 remote 228 digital downloads 121 evaporation 25 315 immortality 24–5 instant gratification syndrome 202 Maoism 47 money 12, 29, 123, 126–7, 129, 132, 138, 150, 191 nomads 20, 283 plasters 241 privacy 25, 97, 108 readers 121 digitalization 37, 292 Dinner by Design 185 dirt holidays 236 discount retailers 224 Discovery Health 149 diseases 2, 228 disintegrators 57 Disney 118–19 disposable computers 56 divorce 33, 85 DNA 56–7, 182 database 86 testing, compulsory 86 do-it-yourself dinner shops 185–6 dolls 24 doorbells 32 downshifters 20 Dream Dinners 185 dream fulfillment 148 dressmaking 225 drink 178–200 driverless cars 154–5 drugs anti-ageing 231, 237 performance-improving 284–5 Dubai 264, 267, 273 dynamic pricing 260 E Ink 115 e-action 65 Earthwatch 259 Eastern Europe 290 eBay 207 e-books 29, 37, 60, 114, 115, 302 eco-luxe resorts 272 economic collapse 2, 4, 36, 72, 221, 295 economic protectionism 10, 15, 72, 298 economy travel 272 316 Ecuador 73 education 15, 18, 82–5, 297 accelerated 57 lifelong learning 290 Egypt 2 electricity shortages 301 electronic camouflage 56 electronic surveillance 35 Elephant 244 email 18–19, 25, 53–4, 108 embedded intelligence 53, 154 EMF radiation 251 emotional capacity of robots 40, 60 enclosed resorts 273 energy 72, 75, 93 alternative 173 nuclear 74 solar 74 wind 74 enhancement surgery 249 entertainment 34, 121 environment 4, 10, 11, 14, 64, 75–6, 83, 93, 155, 171, 173, 183, 199, 219–20, 252, 256–7, 271, 292, 301 epigenetics 57 escapism 16, 32–3, 121 Estonia 85, 89 e-tagging 129–30 e-therapy 242 ethical bankruptcy 35 ethical investing 281 ethical tourism 259 ethics 22, 24, 41, 53, 78, 86, 132, 152, 194, 203, 213, 232, 238, 249–50, 258, 276, 281–2, 298–9 eugenics 252 Europe 11, 70, 72, 81, 91, 141, 150, 174–5, 182, 190, 192, 209 European Union 15, 139 euthanasia 238, 251 Everquest 33 e-voting 65 experience 224 extended financial families 144 extinction timeline 9 Facebook 37, 97, 107 face-recognition doors 57 fakes 32 family 36, 37 FUTURE FILES family loans 145 fantasy-related industries 32 farmaceuticals 179, 182 fast food 178, 183–4 fat taxes 190 fear 10, 34, 36, 38, 68, 150, 151, 305 female-only spaces 210–11, 257 feminization 84 financial crisis 38, 150–51, 223, 226, 301 financial services 123–53, 252 trends 123–5 fish farming 181 fixed-price eating 200 flashpacking 273 flat-tax system 85–6 Florida, Richard 36, 286, 292 flying cars 165 food 69–70, 72, 78–9, 162, 178–201 food anti-ageing 188 brain-enhancing 188 fast 178, 183–4 functional 179 growing your own 179, 192, 195 history 190–92 passports 200 slow 178, 193 tourism 273 trends 178–80 FoodExpert ID 182 food-miles 178, 193, 220 Ford 169, 176, 213, 279–80 forecasting 49 crime 86–7 war 49 Forrester Research 132 fractional ownership 168, 175, 176, 225 France 103, 147, 170, 189, 198, 267 Friedman, Thomas 278–9, 292 FriendFinder 32 Friends Reunited 22 frugality 224 functional food 179 Furedi, Frank 68 gaming 32–3, 70, 97, 111–12, 117, 130, 166, 262 Gap 217 Index gardening 27, 148 gas 176 GE Money 138, 145 gendered medicine 244–5 gene silencing 231 gene, crime 86 General Motors 157, 165 Generation X 41, 281 Generation Y 37, 41, 97, 106, 138, 141–2, 144, 202, 208, 276, 281, 292 generational power shifts 292 Genes Reunited 35 genetic enhancement 40, 48 history 35 modification 31, 182 testing 221 genetics 3, 10, 45, 251–2 genomic medicine 231 Germany 73, 147, 160, 170, 204–5, 216–17, 261, 267, 279, 291 Gimzewski, James 232 glamping 273 global currency 127 global warming 4, 47, 77, 93, 193, 234 globalization 3, 10, 15–16, 36–7, 63–7, 72–3, 75, 81–2, 88, 100, 125, 139, 143, 146, 170, 183, 189, 193–5, 221, 224, 226, 233–4, 247–8, 263, 275, 278–80, 292, 296, 299 GM 176 Google 22, 61, 121, 137, 293 gout 235 government 14, 18, 36, 63–95, 151 GPS 3, 15, 26, 50, 88, 138, 148, 209, 237, 262, 283 Grameen Bank 135 gravity tubes 57 green taxes 76 Greenpeace 172 GRIN technologies (genetics, robotics, internet, nanotechnology) 3, 10, 11 growing your own food 178, 192, 195 Gucci 221 Gulf States 125, 260, 268 H&M 217 habitual shopping 212 Handy, Charles 278 317 Happily 210 happiness 63–4, 71–2, 146, 260 health 15, 82, 178–9, 199 health monitoring 232, 236, 241 healthcare 2, 136, 144, 147–8, 154, 178–9, 183–4, 189–91, 228–53, 298; see also medicine trends 214–1534–7 Heinberg, Richard 74 Helm, Dieter 77 Heritage Foods 195 hikikomori 18 hive mind 45 holidays 31, 119; see also tourism holidays at home 255 cultural 259 dirt 236 Hollywood 33, 111–12 holographic displays 56 Home Equity Share 145 home baking 225 home-based microgeneration 64 home brewing 225 honesty 152 Hong Kong 267 hospitals 228, 241–3, 266 at home 228, 238, 240–42 hotels 19, 267 sleep 266 human cloning 23, 249 Hungary 247 hybrid humans 22 hydrogen power 64 hydrogen-powered cars 12, 31, 157, 173 Hyperactive Technologies 184 Hyundai 170 IBM 293 identities, multiple 35, 52 identity 64, 71 identity theft 88, 132 identity verification, two-way 132 immigration 151–2, 302 India 2, 10, 11, 70–72, 76, 78–9, 81, 92, 111, 125, 135, 139, 163, 174–5, 176, 247, 249–50, 254, 260, 270, 275, 279, 302 indirect taxation 86 318 individualism 36 Indonesia 2, 174 industrial robots 42 infinite content 96–7 inflation 151 information overlead 97, 120, 159, 285; see also too much information innovation 64, 81–2, 100, 175, 222, 238, 269, 277, 286–8, 291, 297, 299 innovation timeline 8 instant gratification 213 insurance 123, 138, 147–50, 154, 167, 191, 236, 250 pay-as-you-go 167 weather 264 intelligence 11 embedded 53, 154 implants 229 intelligent computers 23, 43 intelligent night vision 162–3 interaction, physical 22, 25, 97, 110, 118, 133–4, 215, 228, 243, 276, 304 interactive media 97, 105 intergenerational mortgages 140, 144–5 intermediaries 123, 135 internet 3, 10, 11, 17–18, 25, 68, 103, 108, 115–17, 124, 156, 240–41, 261, 270, 283, 289, 305 failure 301 impact on politics 93–4 sensory 56 interruption science 53 iPills 240 Iran 2, 69 Ishiguro, Hiroshi 55 Islamic fanaticism 16 Italy 92, 170, 198–9 iTunes 115, 130; see also Apple Japan 1, 18, 26, 28–9, 54–5, 63, 80–81, 114, 121, 128–9, 132, 140, 144–5, 147, 174, 186, 189, 192, 196, 198, 200, 209–10, 223, 240, 260, 264, 271, 279, 291 jetpacks 60 job security 292 journalism 96, 118 journalism, citizen 103–4, 107 joy-makers 57 FUTURE FILES Kaboodle 207 Kapor, Mitchell 45 Kenya 128 keys 28–9 Kindle 60, 121 Kramer, Peter 284 Kuhn, Thomas 281 Kurzweil, Ray 45 Kuwait 2 labor migration 290–91 labor shortages 3, 80–81, 289–90 Lanier, Jaron 47 laser shopping 212 leisure sickness 238 Let’s Dish 185 Lexus 157 libraries 121 Libya 73 life-caching 24, 107–8 lighting 158, 160 Like.com 216 limb farms 249 limited editions 216–17 live events 98, 110, 304 localization 10, 15–16, 116, 128, 170, 178, 189, 193, 195, 215, 220, 222–3, 224, 226, 255, 270, 297 location tagging 88 location-based marketing 116 longevity 188–9, 202 Longman, Philip 71 low cost 202, 219–22 luxury 202, 221, 225, 256, 260, 262, 265–6, 272 machinamas 112 machine-to-machine communication 56 marketing 115–16 location-based 116 now 116 prediction 116 Marks & Spencer 210 Maslow, Abraham 305–6 masstigue 223 materialism 37 Mayo Clinic 243 McDonald’s 130, 168, 180, 184 McKinsey 287 Index meaning, search for 16, 259, 282, 290, 305–6 MECU 132 media 96–122 democratization of 104, 108, 115 trends 96–8 medical outsourcing 247–8 medical tourism 2, 229, 247 medicine 188, 228–53; see also healthcare alternative 243–4 gendered 244–5 genomic 231 memory 229, 232, 239–40 memory loss 47 memory pills 231, 240 memory recovery 2, 228–9, 239 memory removal 29–30, 29, 240 Menicon 240 mental health 199 Meow Mix 216 Merriman, Jon 126 metabolomics 56 meta-materials 56 Metro 204–5 Mexico 2 micromedia 101 micro-payments 130, 150 Microsoft 137, 147, 293 Middle East 10, 11, 70, 81, 89, 119, 125, 129, 139, 174–5, 268, 301 migration 3, 11, 69–70, 78, 82, 234, 275, 290–91 boomerang 20 labor 290–91 Migros 215 military recruitment 69 military vehicles 158–9 mind-control toys 38 mindwipes 57 Mitsubishi 198, 279 mobile payments 123, 150 Modafinil 232 molecular biology 231 monetization 118 money 123–52 digital 12, 29, 123, 126–7, 129, 132, 138, 150, 191 monitoring, remote 154, 168, 228, 242 monolines 135, 137 319 mood sensitivity 41, 49, 154, 158, 164, 187–8 Morgan Stanley 127 mortality bonds 148 Mozilla Corp. 289 M-PESA 129 MTV 103 multigenerational families 20 multiple identities 35, 52 Murdoch, Rupert 109 muscular Christianity 16, 73 music industry 121 My-Food-Phone 242 MySpace 22, 25, 37, 46, 97, 107, 113 N11 nations (Bangladesh, Egypt, Indonesia, Iran, South Korea, Mexico, Nigeria, Pakistan, Philippines, Turkey, Vietnam) 2 nanoelectronics 56 nanomedicine 32 nanotechnology 3, 10, 23, 40, 44–5, 50, 157, 183, 232, 243, 286, 298 napcaps 56 narrowcasting 109 NASA 25, 53 nationalism 16, 70, 72–3, 139, 183, 298, 302 natural disasters 301 natural resources 2, 4, 11, 64, 298–9 Nearbynow 223 Nestlé 195 Netherlands 238 NetIntelligence 283 networkcar.com 154 networks 28, 166, 288 airborne 56 neural nets 49 neuronic whips 57 neuroscience 33, 48 Neville, Richard 58–9 New Economics Foundation 171 New Zealand 265, 269 newspapers 29, 102–9, 117, 119, 120 Nigeria 2, 73 Nike 23 nimbyism 63 no-frills 224 Nokia 61, 105 Norelift 189 320 Northern Rock 139–40 Norwich Union 167 nostalgia 16, 31–2, 51, 169–70, 179, 183, 199, 203, 225, 303 now marketing 116 nuclear annihilation 10, 91 nuclear energy 74 nutraceuticals 179, 182 Obama, Barack 92–3 obesity 75, 190–92, 199, 250–51 oceanic thermal converters 57 oil 69, 72–3, 93, 151, 174, 176, 272, 273, 301 Oman 2, 270 online relationships 38 organic computers 56 organic food 200, 226 osteoporosis 235 outsourcing 224, 292 Pakistan 2 pandemics 4, 10, 16, 59, 72, 128, 232, 234, 272, 295–7, 301 paper 37 parasite singles 145 passwords 52 pictorial 52 pathogens 233 patient simulators 247 patina 31 patriotism 63, 67, 299 pay-as-you-go cars 167–8 pay-as-you-go insurance 167 payments cellphone 129, 213 contactless 123, 150 micro- 130, 150 mobile 123, 150 pre- 123, 150 PayPal 124, 137 Pearson, Ian 44 performance-improving drugs 284–5 personal restraint 36 personal robots 42 personalization 19, 26, 56, 96–8, 100, 102–3, 106, 108–9, 120, 138, 149, 183, 205–6, 223, 244–5, 262, 267, 269 Peru 73 FUTURE FILES Peters, Tom 280 Pharmaca 244 pharmaceuticals 2, 33, 228, 237 Philippines 2, 212, 290 Philips 114 Philips, Michael 232–3 photographs 108 physical interaction 22, 25, 97, 110, 118, 133–4, 215, 228, 243, 276, 304 physicalization 96–7, 101–2, 106, 110, 120 pictorial passwords 52 piggy banks 151 Pink, Daniel 285 plagiarism 83 polarization 15–16, 285 politics 37, 63–95, 151–2 regional 63 trends 63–5 pop-up retail 216, 224 pornography 31 portability 178, 183–4 power shift eastwards 2, 10–11, 81, 252 Prada 205–6, 216 precision agriculture 181–2 precision healthcare 234–7 prediction marketing 116 predictions 37, 301–2 premiumization 223 pre-payments 123, 150 privacy 3, 15, 41, 50, 88, 154, 165–7, 205, 236, 249, 285, 295 digital 25, 97, 108 Procter & Gamble 105, 280 product sourcing 224 Prosper 124, 135 protectionism 67, 139, 156, 220, 226, 301 economic 10, 15, 72, 299 provenance 178, 193, 226 proximity indicators 32 PruHealth 149 psychological neoteny 52 public ownership 92 public transport 171 purposeful shopping 212 Qatar 2 quality 96–7, 98, 101, 109 Index quantum mechanics 56 quantum wires 56 quiet materials 56 radiation, EMF 251 radio 117 randominoes 57 ranking 34, 83, 109, 116, 134, 207 Ranking Ranqueen 186 reality mining 51 Really Cool Foods 185 rebalancing 37 recession 139–40, 202, 222 recognition 36, 304 refrigerators 197–8 refuge 121 regeneration 233 regional food 200 regional politics 63 regionality 178, 192–3 regulation 124, 137, 143 REI 207 Reid, Morris 90 relationships, online 38 religion 16, 58 remote diagnosis 228 remote monitoring 154, 168, 228, 242 renting 225 reputation 34–5 resistance to technology 51 resorts, enclosed 273 resource shortages 11, 15, 146, 155, 178, 194, 254, 300 resources, natural 2, 4, 11, 64, 73–4, 143, 298–9 respect 36, 304 restaurants 186–8 retail 20–21, 202–27, 298 pop-up 216, 224 stealth 215 theater 214 trends 202–3 Revkin, Andy 77 RFID 3, 24, 50, 121, 126, 149, 182, 185, 192, 196, 205 rickets 232 risk 15, 124, 134, 138, 141, 149–50, 162, 167, 172, 191, 265, 299–300, 303 Ritalin 232 321 road pricing 166 Robertson, Peter 49 robogoats 55 robot department store 209 Robot Rules 44 robotic assistants 54, 206 concierges 268 financial advisers 131–2 lobsters 55 pest control 57 soldiers 41, 55, 60 surgery 35, 41, 249 robotics 3, 10, 41, 44–5, 60, 238, 275, 285–6, 292, 297 robots 41, 54–5, 131, 237, 249 childcare 57 emotional capacity of 40, 60 industrial 42 personal 42 security 209 therapeutic 41, 54 Russia 2, 69, 72, 75, 80, 89, 92–3, 125, 174, 232, 254, 270, 295, 302 safety 32, 36, 151, 158–9, 172–3, 182, 192, 196 Sainsbury’s 215 Salt 187 sanctuary tourism 273 satellite tracking 166–7 Saudi Arabia 2, 69 Schwartz, Barry 186 science 13, 16, 40–62, 300 interruption 53 trends 40–42 scramble suits 57 scrapbooking 25, 108, 225 Sears Roebuck 137 seasonality 178, 193–4 second-hand goods 224 Second Life 133, 207–8 securitization 124, 140 security 16, 31, 151 security robots 209 self-driving cars 165 self-medication 242 self-publishing 103, 113–14 self-reliance 35, 75 self-repairing roads 57 322 self-replicating machines 23, 44 Selfridges 214 sensor motes 15, 50, 196 sensory internet 56 Sharia-based investment 125 Shop24 209 shopping 202–27 habitual 212 laser 212 malls 211–5 purposeful 212 slow 213 social 207 Shopping 2.0 224 short-wave scalpels 57 silicon photonics 56 simplicity 169–70, 179, 186, 202, 218, 224, 226, 272 Singapore 241 single-person households 19–20, 202–3, 208–9, 221, 244, 298, 304 skills shortage 293, 302 sky shields 57 sleep 159–60, 188, 228, 231, 246–7, 265 sleep debt 96, 266 sleep hotels 266 sleep surrogates 57 slow food 178, 193 slow shopping 213 slow travel 273 smart devices 26–7, 28, 32, 35, 44, 50, 56, 57, 164, 206, 207 smart dust 3, 15, 50, 196 smartisans 20 Smartmart 209 snakebots 55 social networks 97, 107, 110, 120, 133, 217, 261 social shopping 207 society 13, 15–16, 17–37 trends 15–16 Sodexho 193 solar energy 74 Sony 114, 121 South Africa 84, 149, 242 South America 82, 270 South Korea 2, 103, 128–9 space ladders 56 space mirrors 47 space tourism 271, 273 FUTURE FILES space tugs 57 speed 164, 202, 209, 245, 296–7 spirituality 16, 22, 282, 298, 306 spot knowledge 47 spray-on surgical gloves 57 St James’s Ethics Centre 282 stagflation 139 starch-based plastics 64 stealth retail 215 stealth taxation 86 Sterling, Bruce 55 storytelling 203 Strayer, David 161 street signs 162–3 stress 32, 96, 235, 243, 245–6, 258–9, 265, 257–9, 275, 277, 283–5 stress-control clothing 57 stupidity 151, 302 Stylehive 207 Sudan 73 suicide tourism 236 Super Suppers 185 supermarkets 135–6, 184–6, 188, 191–2, 194, 202–3, 212, 215, 218–19, 224, 229 surgery 2, 31 anti-ageing 2, 237 enhancement 249 Surowiecki, James 45 surveillance 35, 41 sustainability 4, 37, 74, 181, 193–5, 203, 281, 288, 298–9 Sweden 84 swine flu 38, 251, 272 Switzerland 168, 210, 215 synthetic biology 56 Taco Bell 184 Tactical Numerical Deterministic Model 49 tagging, location 86, 88 Taiwan 81 talent, war for 275, 279, 293; see also labor shortages Target 216 Tasmania 267 Tata Motors 174, 176 taxation 85–6, 92, 93 carbon 76, 172 conscientious objection 86 Index fat 190 flat 85–6 green 76 indirect 86 stealth 86 Tchibo 217 technology 3, 14–16, 18, 22, 26, 28, 32, 37, 40–62, 74–5, 82–3, 96, 119, 132, 147–8, 154, 157, 160, 162, 165–7, 178, 182, 195–8, 208, 221, 229, 237, 242–3, 249, 256, 261, 265–6, 268, 275–6, 280, 283–4, 292, 296–7, 300 refuseniks 30, 51, 97 trends 40–42 telemedicine 228, 238, 242 telepathy 29 teleportation 56 television 21, 96, 108, 117, 119 terrorism 67, 91, 108, 150, 262–3, 267, 272, 295–6, 301 Tesco 105, 135–6, 185, 206, 215, 219, 223 Thailand 247, 290 therapeutic robots 41, 54 thermal imaging 232 things that won’t change 10, 303–6 third spaces 224 ThisNext 207 thrift 224 Tik Tok Easy Shop 209 time scarcity 30, 96, 102, 178, 184–6, 218, 255 time shifting 96, 110, 116 time stamps 50 timeline, extinction 9 timeline, innovation 8 timelines 7 tired all the time 246 tobacco industry 251 tolerance 120 too much choice (TMC) 29, 202, 218–19 too much information (TMI) 29, 51, 53, 202, 229; see also information overload tourism 254–74 cultural 273 ethical 259 food 273 323 local 273 medical 2, 229, 247 sanctuary 273 space 271, 273 suicide 238 tribal 262 Tourism Concern 259 tourist quotas 254, 271 Toyota 48–9, 157 toys, mind-control 38 traceability 195 trading down 224 transparency 3, 15, 143, 152, 276, 282, 299 transport 15, 154–77, 298 public 155, 161 trends 154–6 transumerism 223 travel 2, 3, 11, 148, 254–74 economy 272 luxury 272 slow 273 trends 254–6 trend maps 6–7 trends 1, 5–7, 10, 13 financial services 123–5 food 178–80 healthcare 228–9 media 96–8 politics 63–5 retail 202–3 science and technology 40–42 society 15–16 transport 154–6 travel 254–6 work 275–7 tribal tourism 262 tribalism 15–16, 63, 127–8, 183, 192, 220, 260 trust 82, 133, 137, 139, 143, 192, 203, 276, 282–3 tunnels 171 Turing test 45 Turing, Alan 44 Turkey 2, 200, 247 Twitter 60, 120 two-way identity verification 132 UAE 2 UFOs 58 324 UK 19–20, 72, 76, 84, 86, 90–91, 100, 102–3, 105, 128–9, 132, 137, 139–42, 147–9, 150, 163, 167–8, 170–71, 175, 185, 195–6, 199, 200, 206, 210, 214–16, 238, 259, 267–8, 278–9, 284, 288 uncertainty 16, 30, 34, 52, 172, 199, 246, 263, 300, 303 unemployment 151 Unilever 195 University of Chicago 245–6 urban rental companies 176 urbanization 11, 18–19, 78, 84, 155, 233 Uruguay 200 US 1, 11, 19–21, 23, 55–6, 63, 67, 69, 72, 75, 77, 80–83, 86, 88–90, 92, 104–5, 106, 121, 129–33, 135, 139–42, 144, 147, 149, 150, 151, 162, 167, 169–71, 174, 185, 190–3, 195, 205–6, 209, 211, 213, 216, 218, 220, 222–3, 237–8, 240–8, 250, 260, 262, 267–8, 275, 279–80, 282–4, 287, 291 user-generated content (UGC) 46, 97, 104, 289 utility 224 values 36, 152 vending machines 209 Venezuela 69, 73 verbal signatures 132 VeriChip 126 video on demand 96 Vietnam 2, 290 Vino 100 113 Virgin Atlantic 261 virtual adultery 33 banks 134 economy 130–31 protests 65 reality 70 sex 32 stores 206–8 vacations 32, 261 worlds 157, 213, 255, 261, 270, 305 Vocation Vacations 259–60 Vodafone 137 voice recognition 41 voice-based internet search 56 voicelifts 2, 237 FUTURE FILES Volkswagen 175 voluntourism 259 Volvo 164 voting 3, 68, 90–91 Walgreens 244 Wal-Mart 105, 136–7, 215, 219–20, 223, 244, 282 war 68–9, 72 war for talent 275, 279; see also labor shortages war forecasting 49 water 69–70, 74, 77–9, 199 wearable computers 55 weather 64 weather insurance 264 Web 2.0 93, 224 Weinberg, Peter 125 wellbeing 2, 183, 188, 199 white flight 20 Wikipedia 46, 60, 104 wild swimming 273 Wilson, Edward O. 74 wind energy 74 wine producers 200 wisdom of idiots 47 Wizard 145 work 275–94 trends 275–94 work/life balance 64, 71, 260, 277, 289, 293 worldphone 19 xenophobia 16, 63 YouTube 46, 103, 107, 112 Zara 216–17 Zipcar 167 Zopa 124, 134


pages: 347 words: 97,721

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

"World Economic Forum" Davos, AI winter, Amazon Robotics, Andy Kessler, Apollo Guidance Computer, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, behavioural economics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, content marketing, dark matter, data science, David Brooks, deep learning, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, driverless car, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, financial engineering, fixed income, flying shuttle, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, independent contractor, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, Kiva Systems, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, machine translation, Mark Zuckerberg, Narrative Science, natural language processing, Nick Bostrom, Norbert Wiener, nuclear winter, off-the-grid, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, robo advisor, robotic process automation, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, tacit knowledge, tech worker, TED Talk, the long tail, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

Law, regulations, and union contracts can strictly limit a firm’s use of promising technologies—and even the sense that such rules might be imposed can put a damper on development. It’s important to note, however, that the same rules that constrain automation in your industry can end up serving you well, if your own strategy aims for augmentation. For example, developers of automated transportation solutions—think self-driving cars—face something between a thicket and a morass of regulations. Although it’s now pretty clear that the technical capabilities for driverless cars, trucks, and golf carts are already mastered or masterable, it’s not at all obvious when the regulatory structure will allow them on highways and fairways.

Plenty of people have pointed out that the laws are problematic, because social situations are complex. Legendary investor Warren Buffett, for example, raised a common question about autonomous vehicles during a forum hosted by the National Automobile Dealers Association. What if, he asked, a toddler runs into the street in front of a self-driving car, and the robot’s only option not to hit that child is to swerve into the path of an oncoming vehicle with four people in it? After that split-second decision is made and fatal accident results, said Buffett, “I am not sure who gets sued.” More deeply, “[I]t will be interesting to know who programs that computer and what their thoughts are about the values of human lives and things.”


Falter: Has the Human Game Begun to Play Itself Out? by Bill McKibben

"Hurricane Katrina" Superdome, 23andMe, Affordable Care Act / Obamacare, Airbnb, Alan Greenspan, American Legislative Exchange Council, An Inconvenient Truth, Anne Wojcicki, Anthropocene, Apollo 11, artificial general intelligence, Bernie Sanders, Bill Joy: nanobots, biodiversity loss, Burning Man, call centre, Cambridge Analytica, carbon footprint, carbon tax, Charles Lindbergh, clean water, Colonization of Mars, computer vision, CRISPR, David Attenborough, deep learning, DeepMind, degrowth, disinformation, Donald Trump, double helix, driverless car, Easter island, Edward Snowden, Elon Musk, ending welfare as we know it, energy transition, Extinction Rebellion, Flynn Effect, gigafactory, Google Earth, Great Leap Forward, green new deal, Greta Thunberg, Hyperloop, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), James Bridle, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, Kim Stanley Robinson, life extension, light touch regulation, Mark Zuckerberg, mass immigration, megacity, Menlo Park, moral hazard, Naomi Klein, Neil Armstrong, Nelson Mandela, Nick Bostrom, obamacare, ocean acidification, off grid, oil shale / tar sands, paperclip maximiser, Paris climate accords, pattern recognition, Peter Thiel, plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, Robert Mercer, Ronald Reagan, Sam Altman, San Francisco homelessness, self-driving car, Silicon Valley, Silicon Valley startup, smart meter, Snapchat, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, supervolcano, tech baron, tech billionaire, technoutopianism, TED Talk, The Wealth of Nations by Adam Smith, traffic fines, Tragedy of the Commons, Travis Kalanick, Tyler Cowen, urban sprawl, Virgin Galactic, Watson beat the top human players on Jeopardy!, Y Combinator, Y2K, yield curve

It’s why Amazon knows the thing you want to buy next, and it’s how Siri sort of responds to your queries, and it’s why your new car knows to slow down if another car pulls in front of you. When the fully self-driving car finally arrives in your driveway, that will be weak AI to the max: thousands of sensors deployed to perform a specific task better than you can do it. You’ll be able to drink IPAs for hours at your local tavern, and the self-driving car will take you home—and it may well be able to recommend precisely which IPAs you’d like best. But it won’t be able to carry on an interesting discussion about whether this is the best course for your life.


pages: 351 words: 100,791

The World Beyond Your Head: On Becoming an Individual in an Age of Distraction by Matthew B. Crawford

airport security, behavioural economics, Cass Sunstein, choice architecture, collateralized debt obligation, creative destruction, David Brooks, delayed gratification, dematerialisation, deskilling, digital Maoism, Google Glasses, hive mind, index card, informal economy, Jaron Lanier, large denomination, new economy, new new economy, Norman Mailer, online collectivism, Plato's cave, plutocrats, precautionary principle, Richard Thaler, Rodney Brooks, scientific management, self-driving car, Silicon Valley, Silicon Valley ideology, Stanford marshmallow experiment, tacit knowledge, the built environment, the scientific method, The Wisdom of Crowds, theory of mind, Walter Mischel, winner-take-all economy

If you have ever listened to the NPR show Car Talk and heard people mimicking the sounds their cars make when they are misbehaving in some way, then you have some idea of the role played by sound in our ongoing monitoring of our cars, which we become aware of only when there is a new sound, indicating a problem. 11. Then again, it is said that we live at the end of history, so maybe we needn’t fret about any of this. “In the future” (as Conan O’Brien used to say), we will be ferried around by Google’s self-driving cars, wearing Google Glass goggles and who knows what all. The goggles will give us something exciting to watch, like Grand Theft Auto, and we will be given a steering wheel that shakes realistically as we execute brilliant evasive maneuvers. We will make vroom vroom sounds with our mouths to preserve that “sense of involvement,” and arrive at our destination in a mood of triumph.

Sara (chimpanzee) Sartre, Jean-Paul SATs savings rate Schüll, Natasha Dow Schutz, Alfred Schweitzer, Albert science under communism scientific method scientific revolution screw transform seat belts secondary qualities Seeman, Axel self brain-centered perspective on cognitive extension and coherent in conflict with world consciousness and contingencies and in dismantling inherited cultural jigs environment and fantasy of unencumbered fragility of freedom of heteronomy vs. and inheritance of past maintaining of as result of historical polemics right vs. left politics and scientific method and situated statistical in West self-absorption self-awareness self-control self-criticism self-discipline self-doubt self-driving cars self-knowledge self-motion self-protection “Self-Reliance” (Emerson) self-responsibility self-sufficiency self-understanding Sennett, Richard sensorimotor contingency sensorimotor experience Seoul sequences Sesame Street sex, Kinsey Reports on sex addicts Sexual Behavior studies (Kinsey) Shapiro, Lawrence Sharon (gambler) Sheba (chimpanzee) Shop Class as Soulcraft (Crawford) short-order cooks background jig of jigging of environment by kitchen-self relationship of as “machine” silence, importance of Simmel, Georg situatedness situated self affordances and situation defining features of pragmatic criteria of propositional knowledge and role of risk in comprehending role of skill in comprehending 60 Minutes skateboarders, world-historical significance of skepticism skill acquisition of and perception of affordances skilled action, contingent facts in skilled practices see also specific skilled practices Sleeper (film) slot machines autoplay see also machine gambling smart technology Smith, Christian “smooth coping” social capital social engineering gambling addiction as nudging and social media social mobility social nature Socrates South Korea sovereign, relationship of citizens to sovereignty, absolute Soviet Union five-year plan in spatial categories speed, judgment and Spiegel, Bernt spontaneous encounters Springsteen, Bruce stackable self Stahl, Lesley state of nature steering head bearings stereotyping stimulation Stoicism stories Stowe, Doug Stradivari, Antonio subjectivism Kant and submission suicide Sunstein, Cass supermarket Supersizing the Mind (Clark) suppression of environment suspension Sweden symbolic representation tacit knowledge tactical flight suit Tagliapietra, Lino Tannenberg, David tannic acid tapered roll bearings taxes, taxation filing of gambling as Taylor, Charles Taylor, George reverse engineering by Taylor and Boody Tea Party technology automaticity and television children’s and orienting response in public spaces Terror (French Revolution) texting Thaler, Richard Thatcher, Margaret “thing in itself” things attention structured by encountering of thought Descartes’s belief in certainty of explicit thrift tipping Tocqueville, Alexis de toddlers mastery of body gained by will of “To Each His Own” (Fleming) tools totalitarianism Toyota recall (2008) toys agency and traction traders tradition as authority vs. self-responsibility traffic lights transvestites Trilling, Lionel truth as representation standard for TRW Turkle, Sherry Two Treatises of Government (Locke) United Kingdom United States apprenticeships criticized in belief in meritocracy of massification in science in social mobility in Upper Half of the Motorcycle, The (Spiegel) value ventilation systems Verizon Vico, Giambattista video gambling violins virtual reality virtual reel mapping, in slot machines virtual stops, in slot machines visual demand visual perception von Hebenstreit, Benedikt walking Wallace, David Foster walls, colored Wampole, Christy “war of all against all” (Hobbes) water wealth Weariness of the Self, The (Ehrenberg) Weber, Max weight lifting Weil, Simone welfare reform Whitman, Walt Who Owns the Future?


pages: 398 words: 105,032

Soonish: Ten Emerging Technologies That'll Improve And/or Ruin Everything by Kelly Weinersmith, Zach Weinersmith

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 23andMe, 3D printing, Airbnb, Alvin Roth, Apollo 11, augmented reality, autonomous vehicles, connected car, CRISPR, data science, disinformation, double helix, Elon Musk, en.wikipedia.org, Google Glasses, hydraulic fracturing, industrial robot, information asymmetry, ITER tokamak, Kickstarter, low earth orbit, market design, megaproject, megastructure, microbiome, moral hazard, multiplanetary species, orbital mechanics / astrodynamics, personalized medicine, placebo effect, printed gun, Project Plowshare, QR code, Schrödinger's Cat, self-driving car, Skype, space junk, stem cell, synthetic biology, Tunguska event, Virgin Galactic

A very specific example—if you have a plane and you have programmable materials in the wings, you may not want to just give over total freedom to allow the wings to come up with solutions as you’re flying. There’s a lot of fear in terms of that space, like how do we ensure that? How do we guarantee that this is going to happen? What happens if there’s failure and the material’s at fault? Who’s blamed for it when you give agency to materials?” The field of self-driving cars is already dealing with the “Who’s at fault when a machine messes up?” question. But at least with cars, the space of possible problems is pretty limited. If thinking objects are ubiquitous, figuring out blame is going to be really complicated. There are some frightening military applications as well, depending on whether you’re the country with control of the programmable swarm or not.

., 154 sanitation, 157 satellites, 20, 34, 41, 47 Schalk, Gerwin, 313 Schall, Gerhard, 177 Schrödinger’s cat, 329 Schrödinger’s Killer App (Dowling), 330n Schwenk, Kurt, 187 See No Evil, Hear No Evil (film), 328n seizures, 300, 301, 302 Select Sires, Incorporated, 197n self-driving cars, 123 Sensorama, 168 Shapiro, Beth, 222, 223–24 Shotwell, Gwynne, 19 Shtetl-Optimized (blog), 330n Siberia, 224 sickle cell amenia, 237 Silberg, Joff, 210–11, 218–19 silicon, 52, 54 Silver, Pamela, 204, 205–6, 208–10, 219 Simberg, Rand, 44 Skylon, 22 Skype, 314 Skywalker, Luke (char.), 324 Slingatron, 25–26 slums, 157 smallpox, 216, 217 Smart Helmet, 179 “smart homes,” 111 smartphones, 169 smell, sense of, 174–75, 186–89, 334 Smith, Noah, 153n, 154 snakes, 187 social media, 248, 250 privacy issues of, 180–81 software, 102, 104–5, 124 hacking of, 122 solar flares, 60 solar panels, 58 cost of, 320 solar photovoltaic cells, 92, 208 solar power, space-based, 319–21 solar wind, 37 Solid Freeform Fabrication Symposium, 162 solid rocket boosters, 39 solid tumors, 238, 240–41 Solomon, Scott, 200n sound, speed of, 21 South Africa, 48 Southern California, University of, 145, 308 Soviet Union, 38, 58, 99, 100, 135 space cannon, 23–26 space debris, 39–40 space elevators, 31–38, 39, 41, 42–43, 314, 320 spaceflight, 13–50 air-breathing rockets and spaceplanes for, 19–24 benefits of, 41–45 concerns about, 38–40 cost of, 41, 44–45 present cost of, 13–14 reusable rockets for, 18–19 space elevators and tethers for, 31–38 starting at high altitude, 29–30 spaceplanes, 19–24, 39 space settlements, 40 Space Shuttle, U.S., 18, 39 space tethers, 31–38 space tourism, 42 space travel: fusion energy in, 94 supergun for, 23–26 SpaceX, 8n, 18–19, 30 spatial resolution, 288, 289, 292–93 spearmint, 334 spinal damage, 312 Sputnik, 39 SR-71 spy plane, 21 Starbucks, 180 Star Trek franchise, 34, 86 Star Wars franchise, 78n, 82, 263 steam turbine, 76 stem cells, 263, 272–73 Stevens Institute of Technology, 92, 122 STL-file, 267 storytelling, 178 stratospheric spaceport, 29–30 straw, reconfigurable, 103–4 stress, 246 stroke, 247 strong nuclear force, 77 strontium-90 (Sr-90), 99 Stuttgart, University of, 104, 143 S-type (stony) asteroids, 53, 54 sugar molecules, 210 sugar sintering, 270–71 sun, 59, 78 Sung, Cynthia, 108, 119, 127 superconducting levitation, 326–27 superconducting quantum interference device (SQUID), 4, 6, 290 superconductors, 4–6 room-temperature, 325–28 supergun, 46–50 supersonic ramjet (“scramjet”), 21–22, 26, 126 Sure Shot Cattle Company, 197n surgery, 185–86 Surrey, University of, 122 swarm bots, 119–20, 121–22 SWARMORPH project, 113–15 swarm robots, 149–53 switchgrass, 209–10 Switzerland, 22n SYMBRION, 115 Syn 3.0, 215 synthetic biology, 190–225 benefits of, 220–21 concerns about, 216–19 environmental monitoring by, 210–12 fuel production by, 208–10 generalizing of, 212–14 grassroots approach to, 216 “Synthetic Biology for Recycling Human Waste into Food, Nutraceuticals, and Materials: Closing the Loop for Long-Term Space Travel” project, 160 synthetic materials, 101–2 syphilis, 230n Syria, 156 Systems & Materials Research Consultancy, 159 T cells, 242–43 technology, 3–4 asteroid-moving, 67 contingent nature of development of, 3–7 discontinuous leaps in, 2 Telegraph, 183 Teller, Edward, 98 temporal resolution, 288, 292–93 Terminator (film), 103 termites, 120, 149, 150–51 terrorism, 36, 38, 217 Tethers Unlimited, 63 tetracycline, 200 theft, 130 3D printers, 144–49, 151–52, 259 prosthetics and, 322 3D printing, 125, 152 of food, 159–63 of organs, see bioprinting software for, 267 3554 Amun, 53 Throw Trucks with Your Mind (game), 312 thyroid, 60 Tibbits, Skylar, 103–5, 118, 123, 126 titanium, 35 “tokamak” configuration, 88, 92 tornados, 25 touch, sense of, 175 Tourette’s syndrome, 301 transcranial magnetic stimulation, 302, 304 transfer RNA, 193–94, 195 Transformers series, 102 The Tree of Life (Web site), 234n tritium, 74, 77n, 91 tumor cells, 205 tumors, 290 “Tunable Protein Piston That Breaks Membranes to Release Encapsulated Cargo, A” (Silver, et al.), 206 “Tunguska event” (1908), 67 turbofan engine, 20–21, 22 Turner, Ron, 35, 36, 37 23andMe, 251, 252 Twitter, 20n, 187, 250 Two and a Half Men (TV show), 310 Type II superconductors, 326 Umbrellium (Haque Design + Research), 111 Underground Railroad, 178 UN-Habitat, 157 Unilateral Forced Nostril Breathing (UFNB), 189 United Nations, 96 United States, 39, 135–36 Universal Semen Sales, Inc., 197n uranium, 58 U.S.


pages: 343 words: 102,846

Trees on Mars: Our Obsession With the Future by Hal Niedzviecki

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, Airbnb, Albert Einstein, Alvin Toffler, Amazon Robotics, anti-communist, big data - Walmart - Pop Tarts, big-box store, business intelligence, Charles Babbage, Colonization of Mars, computer age, crowdsourcing, data science, David Brooks, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Flynn Effect, Ford Model T, Future Shock, Google Glasses, hive mind, Howard Zinn, if you build it, they will come, income inequality, independent contractor, Internet of things, invention of movable type, Jaron Lanier, Jeff Bezos, job automation, John von Neumann, knowledge economy, Kodak vs Instagram, life extension, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Neil Armstrong, One Laptop per Child (OLPC), Peter H. Diamandis: Planetary Resources, Peter Thiel, Pierre-Simon Laplace, Ponzi scheme, precariat, prediction markets, Ralph Nader, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, rising living standards, Robert Solow, Ronald Reagan, Salesforce, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, TaskRabbit, tech worker, technological singularity, technological solutionism, technoutopianism, Ted Kaczynski, TED Talk, Thomas L Friedman, Tyler Cowen, Uber and Lyft, uber lyft, Virgin Galactic, warehouse robotics, working poor

Adam Fisher, “Google’s Road Map to Global Domination,” The New York Times, December 11, 2013, sec. Magazine, http://www.nytimes.com/2013/12/15/magazine/googles-plan-for-global-domination-dont-ask-why-ask-where.html. 20. Alexis C. Madrigal, “The Trick That Makes Google’s Self-Driving Cars Work,” The Atlantic, May 15, 2014, http://www.theatlantic.com/technology/archive/2014/05/all-the-world-a-track-the-trick-that-makes-googles-self-driving-cars-work/370871/. 21. Mayer-Scho¨nberger and Cukier, Big Data, 57. 22. “The Pregnancy Is Gone, but the Promotions Keep Coming,” Motherlode Blog, accessed February 4, 2014, http://parenting.blogs.nytimes.com/2014/02/02/the-pregnancy-is-gone-but-the-promotions-keep-coming/. 23.


pages: 331 words: 104,366

Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins by Garry Kasparov

3D printing, Ada Lovelace, AI winter, Albert Einstein, AlphaGo, AltaVista, Apple Newton, barriers to entry, Berlin Wall, Bletchley Park, business process, call centre, Charles Babbage, Charles Lindbergh, clean water, computer age, cotton gin, Daniel Kahneman / Amos Tversky, David Brooks, DeepMind, Donald Trump, Douglas Hofstadter, driverless car, Drosophila, Elon Musk, Erik Brynjolfsson, factory automation, Freestyle chess, gamification, Gödel, Escher, Bach, Hans Moravec, job automation, Ken Thompson, Leonard Kleinrock, low earth orbit, machine translation, Max Levchin, Mikhail Gorbachev, move 37, Nate Silver, Nick Bostrom, Norbert Wiener, packet switching, pattern recognition, Ray Kurzweil, Richard Feynman, rising living standards, rolodex, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, Skype, speech recognition, stem cell, Stephen Hawking, Steven Pinker, technological singularity, The Coming Technological Singularity, The Signal and the Noise by Nate Silver, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero-sum game

This growth of machines from chess beginners to Grandmasters is also a progression that is being repeated by countless AI projects around the world. AI products tend to evolve from laughably weak to interesting but feeble, then to artificial but useful, and finally to transcendent and superior to human. We see this path with speech recognition and speech synthesis, with self-driving cars and trucks, and with virtual assistants like Apple’s Siri. There is always a tipping point at which they go from amusing diversions to essential tools. Then there comes another shift, when a tool becomes something more, something more powerful than even its creators had in mind. Often this is the result of a combining of technologies over time, as in the case of the Internet, which is really a half-dozen different layers of technology working together.

DARPA never completely gave up on AI, and even had room in its budget for a little chess. If you check the fine print of the scholarly papers on Hans Berliner’s machine HiTech at Carnegie Mellon, you can see it was partly funded by a DARPA grant in the 1980s. More recently, DARPA has funded contests for self-driving cars and other “practical AI” tech. Using the development of chess machines as a model, DARPA has proposed tournament competitions to develop autonomous network defense. In true Darwinian fashion, focusing on competition over basic research was bad for true AI, but very good at producing better and better chess machines.


pages: 382 words: 105,819

Zucked: Waking Up to the Facebook Catastrophe by Roger McNamee

"Susan Fowler" uber, "World Economic Forum" Davos, 4chan, Albert Einstein, algorithmic trading, AltaVista, Amazon Web Services, Andy Rubin, barriers to entry, Bernie Sanders, Big Tech, Bill Atkinson, Black Lives Matter, Boycotts of Israel, Brexit referendum, Cambridge Analytica, carbon credits, Cass Sunstein, cloud computing, computer age, cross-subsidies, dark pattern, data is the new oil, data science, disinformation, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Electric Kool-Aid Acid Test, Elon Musk, fake news, false flag, Filter Bubble, game design, growth hacking, Ian Bogost, income inequality, information security, Internet of things, It's morning again in America, Jaron Lanier, Jeff Bezos, John Markoff, laissez-faire capitalism, Lean Startup, light touch regulation, Lyft, machine readable, Marc Andreessen, Marc Benioff, Mark Zuckerberg, market bubble, Max Levchin, Menlo Park, messenger bag, Metcalfe’s law, minimum viable product, Mother of all demos, move fast and break things, Network effects, One Laptop per Child (OLPC), PalmPilot, paypal mafia, Peter Thiel, pets.com, post-work, profit maximization, profit motive, race to the bottom, recommendation engine, Robert Mercer, Ronald Reagan, Russian election interference, Sand Hill Road, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software is eating the world, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Stewart Brand, subscription business, TED Talk, The Chicago School, The future is already here, Tim Cook: Apple, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, vertical integration, WikiLeaks, Yom Kippur War

What I have in mind is an independent company that represents the interest of users at login, providing the minimum information required for each transaction. With each new generation of technology, entrepreneurs and engineers have an opportunity to profit from designing products that serve rather than exploit the needs of their users. Virtual reality, artificial intelligence, self-driving cars, and the Internet of Things (IoT)—smart speakers and web-enabled televisions, automobiles, and appliances—all present opportunities to create bicycles for the mind. Unfortunately, I see no evidence yet that the designers in those categories are thinking that way. The term you hear instead is “Big Data,” which is code for extracting value rather than creating it.

The platforms have been able to acquire promising startups in adjacent categories—as Facebook had done with Instagram and WhatsApp—converting potential competitors into extensions of their monopoly. In addition, Facebook and Google have gotten footholds in many promising new categories—ranging from virtual reality to AI to self-driving cars—in their pre-market stages. Engagement by the platforms has validated new categories, but in all probability, it has also distorted them, changing the incentives for market participants. It is hard to imagine that Facebook’s purchase of and commitment to the Oculus virtual-reality platform did not discourage investment in alternative hardware platforms.


pages: 374 words: 111,284

The AI Economy: Work, Wealth and Welfare in the Robot Age by Roger Bootle

"World Economic Forum" Davos, 3D printing, agricultural Revolution, AI winter, Albert Einstein, AlphaGo, Alvin Toffler, anti-work, antiwork, autonomous vehicles, basic income, Ben Bernanke: helicopter money, Bernie Sanders, Bletchley Park, blockchain, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, Carl Icahn, Chris Urmson, computer age, Computing Machinery and Intelligence, conceptual framework, corporate governance, correlation does not imply causation, creative destruction, David Ricardo: comparative advantage, deep learning, DeepMind, deindustrialization, Demis Hassabis, deskilling, Dr. Strangelove, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, facts on the ground, fake news, financial intermediation, full employment, future of work, Future Shock, general purpose technology, Great Leap Forward, Hans Moravec, income inequality, income per capita, industrial robot, Internet of things, invention of the wheel, Isaac Newton, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, job automation, job satisfaction, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, Kevin Kelly, license plate recognition, low interest rates, machine translation, Marc Andreessen, Mark Zuckerberg, market bubble, mega-rich, natural language processing, Network effects, new economy, Nicholas Carr, Ocado, Paul Samuelson, Peter Thiel, Phillips curve, positional goods, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, ride hailing / ride sharing, rising living standards, road to serfdom, Robert Gordon, Robert Shiller, Robert Solow, Rutger Bregman, Second Machine Age, secular stagnation, self-driving car, seminal paper, Silicon Valley, Silicon Valley billionaire, Simon Kuznets, Skype, social intelligence, spinning jenny, Stanislav Petrov, Stephen Hawking, Steven Pinker, synthetic biology, technological singularity, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, trade route, universal basic income, US Airways Flight 1549, Vernor Vinge, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, wealth creators, winner-take-all economy, world market for maybe five computers, Y2K, Yogi Berra

The state of California has recently approved new rules allowing driverless cars to operate without a human driver sitting behind the wheel. In the UK the Chancellor of the Exchequer, Philip Hammond, told the BBC that he aimed to have “fully driverless cars” in use by 2021. About 50 companies, including Alphabet, Apple, Ford, GM, Toyota, and Uber, are already testing self-driving cars in California. Indeed, more than a hundred trials of autonomous vehicles are currently taking place around the world. Moreover, according to the companies developing them, the performance of self-driven cars is already impressive and is improving all the time. All these companies have invested huge sums and clearly believe that driverless vehicles are the future.

A more serious test would be to put these cars through their paces in London, Moscow, or Istanbul – in February. The degree of human intervention Legislators, courts, and insurance companies are having to deal with some very tricky issues created by driverless vehicles. Under new UK legislation, drivers of self-driving cars must not take their hands off the wheel for more than a minute. And in April 2018 a motorist was banned from driving after being caught on the M1 motorway in the passenger seat, with the driving seat vacant as the AI “drove” the car.10 The British government is planning to scrap the requirement for a “safety driver” to enable advanced trials on public roads of fully automated vehicles by the end of 2019.


pages: 363 words: 109,077

The Raging 2020s: Companies, Countries, People - and the Fight for Our Future by Alec Ross

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, Affordable Care Act / Obamacare, air gap, air traffic controllers' union, Airbnb, Albert Einstein, An Inconvenient Truth, autonomous vehicles, barriers to entry, benefit corporation, Bernie Sanders, Big Tech, big-box store, British Empire, call centre, capital controls, clean water, collective bargaining, computer vision, coronavirus, corporate governance, corporate raider, COVID-19, deep learning, Deng Xiaoping, Didi Chuxing, disinformation, Dissolution of the Soviet Union, Donald Trump, Double Irish / Dutch Sandwich, drone strike, dumpster diving, employer provided health coverage, Francis Fukuyama: the end of history, future of work, general purpose technology, gig economy, Gini coefficient, global supply chain, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, high-speed rail, hiring and firing, income inequality, independent contractor, information security, intangible asset, invisible hand, Jeff Bezos, knowledge worker, late capitalism, low skilled workers, Lyft, Marc Andreessen, Marc Benioff, mass immigration, megacity, military-industrial complex, minimum wage unemployment, mittelstand, mortgage tax deduction, natural language processing, Oculus Rift, off-the-grid, offshore financial centre, open economy, OpenAI, Parag Khanna, Paris climate accords, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, Robert Bork, rolodex, Ronald Reagan, Salesforce, self-driving car, shareholder value, side hustle, side project, Silicon Valley, smart cities, Social Responsibility of Business Is to Increase Its Profits, sovereign wealth fund, sparse data, special economic zone, Steven Levy, stock buybacks, strikebreaker, TaskRabbit, tech bro, tech worker, transcontinental railway, transfer pricing, Travis Kalanick, trickle-down economics, Uber and Lyft, uber lyft, union organizing, Upton Sinclair, vertical integration, working poor

For instance, when Google received €3.96 from the Ascani family, it likely would have avoided paying a dime to the Italian government. As soon as the transaction was completed, that money departed the country. The payment did not go directly to Google LLC, the corporation headquartered in Silicon Valley, nor to Alphabet Inc., the company that owns Google and its various side projects for self-driving cars, drone delivery, biotechnology, and the like. Instead, the Ascanis’ €3.96 worked its way through a chain of corporate entities scattered across three different countries, none of which played any role in getting Marco his belt. The reason Google and other multinational firms shuffle their money around the map in this way is to minimize their taxes, allowing them to generate billions of dollars from customers around the globe without giving a cut to the countries where they do business.

But artificial intelligence is a general-purpose technology with both national security applications and completely benign commercial uses. A computer vision algorithm can be trained to spot enemy combatants on a battlefield, but it can also be used to tag friends in social media posts and power self-driving cars. AI takes on the values and intentions of its human masters. The same AI-enabled facial recognition technology that can identify known terrorism suspects can just as easily profile and track members of an ethnic minority. The technology is also imperfect. The accuracy of artificial intelligence depends on the quality of the data used to train it, and it is not always clear how the software reaches a particular conclusion.


pages: 611 words: 188,732

Valley of Genius: The Uncensored History of Silicon Valley (As Told by the Hackers, Founders, and Freaks Who Made It Boom) by Adam Fisher

adjacent possible, Airbnb, Albert Einstein, AltaVista, An Inconvenient Truth, Andy Rubin, AOL-Time Warner, Apple II, Apple Newton, Apple's 1984 Super Bowl advert, augmented reality, autonomous vehicles, Bill Atkinson, Bob Noyce, Brownian motion, Buckminster Fuller, Burning Man, Byte Shop, circular economy, cognitive dissonance, Colossal Cave Adventure, Computer Lib, disintermediation, Do you want to sell sugared water for the rest of your life?, don't be evil, Donald Trump, Douglas Engelbart, driverless car, dual-use technology, Dynabook, Elon Musk, Fairchild Semiconductor, fake it until you make it, fake news, frictionless, General Magic , glass ceiling, Hacker Conference 1984, Hacker Ethic, Henry Singleton, Howard Rheingold, HyperCard, hypertext link, index card, informal economy, information retrieval, Ivan Sutherland, Jaron Lanier, Jeff Bezos, Jeff Rulifson, John Markoff, John Perry Barlow, Jony Ive, Kevin Kelly, Kickstarter, knowledge worker, Larry Ellison, life extension, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, Maui Hawaii, Menlo Park, Metcalfe’s law, Mondo 2000, Mother of all demos, move fast and break things, Neal Stephenson, Network effects, new economy, nuclear winter, off-the-grid, PageRank, Paul Buchheit, paypal mafia, peer-to-peer, Peter Thiel, pets.com, pez dispenser, popular electronics, quantum entanglement, random walk, reality distortion field, risk tolerance, Robert Metcalfe, rolodex, Salesforce, self-driving car, side project, Silicon Valley, Silicon Valley startup, skeuomorphism, skunkworks, Skype, Snow Crash, social graph, social web, South of Market, San Francisco, Startup school, Steve Jobs, Steve Jurvetson, Steve Wozniak, Steven Levy, Stewart Brand, Susan Wojcicki, synthetic biology, Ted Nelson, telerobotics, The future is already here, The Hackers Conference, the long tail, the new new thing, Tim Cook: Apple, Tony Fadell, tulip mania, V2 rocket, We are as Gods, Whole Earth Catalog, Whole Earth Review, Y Combinator

Steve Wozniak: Science fiction leads to real products, but first you’ve got to deal with the laws of physics, and ask, “What’s it going to cost?” John Giannandrea: I’m fond of this book by Arthur C. Clarke, Profiles of the Future. He splits the book into two sections. One section is things that will happen, like self-driving cars. The other is things that will be surprising if it happens, like time travel. And the thing is the kind of people who worked at General Magic or Netscape would be like, “Show me the technical detail that makes this possible—and sign me up!” John Battelle: They were these really smart engineering types who had nonstandard political views.

It’s not that it’s going to be humanlike but faster. It’s an alien intelligence—and that turns out to be its chief benefit. The reason why we employ an AI to drive our cars is because they’re not driving it like humans. Jim Clark: It’s bound to happen, the robotic driving of cars. Nolan Bushnell: Self-driving cars are going to change everything and help cities to literally become gardens because streets, in some ways, kind of go away. Jaron Lanier: Once we have automated transportation you might stop thinking about home in the same way. The idea is that everybody would be in self-driving RVs forever, so there would just be like this constant streaming of people living a mobile lifestyle going from here to there all over the world.

That could actually be really nice, because what we do these days is we spend hours a day moving kids around from this lesson to that school to this soccer thing or whatever and it’s kind of an insane way to live. That makes no sense to anybody. And so I could imagine something that’s actually pretty nice coming together. I like that vision a lot. Scott Hassan: I like the concept of self-driving cars, but I worry that our legal system can’t really handle them. The problem with autonomous cars is that it’s the manufacturer who is driving that car. Kevin Kelly: Humans should not be allowed to drive! We’re just terrible drivers. In the last twelve months humans killed one million other humans driving.


pages: 121 words: 36,908

Four Futures: Life After Capitalism by Peter Frase

Aaron Swartz, Airbnb, Anthropocene, basic income, bitcoin, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, carbon tax, congestion pricing, cryptocurrency, deindustrialization, do what you love, Dogecoin, Donald Shoup, Edward Snowden, emotional labour, Erik Brynjolfsson, Ferguson, Missouri, fixed income, full employment, future of work, green new deal, Herbert Marcuse, high net worth, high-speed rail, income inequality, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), iterative process, Jevons paradox, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kim Stanley Robinson, litecoin, mass incarceration, means of production, military-industrial complex, Occupy movement, pattern recognition, peak oil, plutocrats, post-work, postindustrial economy, price mechanism, private military company, Ray Kurzweil, Robert Gordon, Second Machine Age, self-driving car, sharing economy, Silicon Valley, smart meter, TaskRabbit, technoutopianism, The future is already here, The Future of Employment, Thomas Malthus, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, Wall-E, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, Wolfgang Streeck

But only recently have they begun to challenge the areas in which humans excel: fine-grained motor skills and the navigation of a complex physical terrain. The US Department of Defense is now developing computer-controlled sewing machines so as to avoid sourcing its uniforms from China.9 Until just the past few years, self-driving cars were regarded as well beyond the scope of our technical ability. Now the combination of sensor technology and comprehensive map databases is making it a reality in such projects as the Google self-driving fleet. Meanwhile a company called Locus Robotics has launched a robot that can process orders in giant warehouses, potentially replacing the workers for Amazon and other companies who currently toil in often brutal conditions.10 Automation continues to proceed even in agriculture, which once consumed the largest share of human labor but now makes up a tiny fraction of employment, especially in the United States and other rich countries.


pages: 373 words: 112,822

The Upstarts: How Uber, Airbnb, and the Killer Companies of the New Silicon Valley Are Changing the World by Brad Stone

Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Andy Kessler, autonomous vehicles, Ben Horowitz, Benchmark Capital, Boris Johnson, Burning Man, call centre, Chuck Templeton: OpenTable:, collaborative consumption, data science, Didi Chuxing, Dr. Strangelove, driverless car, East Village, fake it until you make it, fixed income, gentrification, Google X / Alphabet X, growth hacking, Hacker News, hockey-stick growth, housing crisis, inflight wifi, Jeff Bezos, John Zimmer (Lyft cofounder), Justin.tv, Kickstarter, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Menlo Park, Mitch Kapor, Necker cube, obamacare, PalmPilot, Paul Graham, peer-to-peer, Peter Thiel, power law, race to the bottom, rent control, ride hailing / ride sharing, Ruby on Rails, San Francisco homelessness, Sand Hill Road, self-driving car, semantic web, sharing economy, side project, Silicon Valley, Silicon Valley startup, Skype, SoftBank, South of Market, San Francisco, Startup school, Steve Jobs, TaskRabbit, tech bro, TechCrunch disrupt, Tony Hsieh, transportation-network company, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, Y Combinator, Y2K, Zipcar

They have attained these heights despite the fact that their businesses own little in the way of physical assets. Airbnb can be considered the biggest hotel company on the planet, yet it possesses no actual hotel rooms. Uber is among the world’s largest car services, yet it doesn’t employ any professional drivers or own any vehicles (save for a small, experimental fleet of self-driving cars). They are the ultimate twenty-first-century internet businesses, bringing not only new opportunities but new kinds of risks, often poorly understood, to those who provide and utilize their services. Uber, as the world well knows, allows anyone to summon a vehicle with ease, track its progress on a virtual map, and then ride with a driver whose reliability is illustrated by a one-to five-star rating.

Krane had orchestrated an experience that would blow Kalanick’s mind. When Uber’s CEO came down to the lobby, a prototype driverless car from the Google X lab idled in front of the hotel, waiting to ferry him to Mountain View. Sitting in the front seat was a Google engineer who could answer all his questions. It was Kalanick’s first ride in a self-driving car on real roads. At the Google campus, Kalanick met with Page, Google senior lawyer David Drummond, and Krane’s boss at GV at the time, Bill Maris. Page assured Kalanick that the companies could work together to develop Google Maps, which Uber relied on for navigation in its apps, but he didn’t say much or stay very long.


pages: 437 words: 113,173

Age of Discovery: Navigating the Risks and Rewards of Our New Renaissance by Ian Goldin, Chris Kutarna

"World Economic Forum" Davos, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, Airbnb, Albert Einstein, AltaVista, Asian financial crisis, asset-backed security, autonomous vehicles, banking crisis, barriers to entry, battle of ideas, Bear Stearns, Berlin Wall, bioinformatics, bitcoin, Boeing 747, Bonfire of the Vanities, bread and circuses, carbon tax, clean water, collective bargaining, Colonization of Mars, Credit Default Swap, CRISPR, crowdsourcing, cryptocurrency, Dava Sobel, demographic dividend, Deng Xiaoping, digital divide, Doha Development Round, double helix, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, epigenetics, experimental economics, Eyjafjallajökull, failed state, Fall of the Berlin Wall, financial innovation, full employment, Galaxy Zoo, general purpose technology, Glass-Steagall Act, global pandemic, global supply chain, Higgs boson, Hyperloop, immigration reform, income inequality, indoor plumbing, industrial cluster, industrial robot, information retrieval, information security, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invention of the printing press, Isaac Newton, Islamic Golden Age, Johannes Kepler, Khan Academy, Kickstarter, Large Hadron Collider, low cost airline, low skilled workers, Lyft, Mahbub ul Haq, Malacca Straits, mass immigration, Max Levchin, megacity, Mikhail Gorbachev, moral hazard, Nelson Mandela, Network effects, New Urbanism, non-tariff barriers, Occupy movement, On the Revolutions of the Heavenly Spheres, open economy, Panamax, Paris climate accords, Pearl River Delta, personalized medicine, Peter Thiel, post-Panamax, profit motive, public intellectual, quantum cryptography, rent-seeking, reshoring, Robert Gordon, Robert Metcalfe, Search for Extraterrestrial Intelligence, Second Machine Age, self-driving car, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart grid, Snapchat, special economic zone, spice trade, statistical model, Stephen Hawking, Steve Jobs, Stuxnet, synthetic biology, TED Talk, The Future of Employment, too big to fail, trade liberalization, trade route, transaction costs, transatlantic slave trade, uber lyft, undersea cable, uranium enrichment, We are the 99%, We wanted flying cars, instead we got 140 characters, working poor, working-age population, zero day

Now is the time to turn the tap back on, and get a leg up on over-cautious competitors who lack your insight into the bigger picture. Companies and entrepreneurs that are showing the way include: IBM, which in 2014 announced a five-year plan to bet 10 percent of its net income on post-silicon computer chips;30 Google (Alphabet), whose recent long-term bets include a new quantum artificial intelligence lab, self-driving cars and research into anti-aging drugs;31 and Elon Musk, a co-founder of PayPal whose moon shots include SpaceX (a space transport firm whose eventual goal is to colonize Mars) and Tesla (whose diverse aims include the mass-market adoption of electric cars, household battery packs to store renewable energy, and a 600-mile-per-hour hyperloop to transport people between Los Angeles and San Francisco).

See also trade Free Trade Area of the Association of Southeast Asian Nations (ASEAN), 25 Galaxy Zoo, 148 Galileo Galilei, 107, 134, 237 Gates, Bill, 36 Gemma, Reinerus, 61 genetics gene therapy, 119–20, 158 genome sequencing costs, 117 history of, 114 Human Genome Project, 117, 238 and paradigm shifts, 114–21, 129–31 and race, 236–7 synthetic biology, 120 and technology, 116–21, 129–31, 148, 161, 165, 183 genius challenges of, 163–8 collective genius, 132–9 and contact points, 137–9 and courage, 242–3 and diversity, 238 and doubt, 150–5 and economic data, 155–6 and education, 135, 263–6 embracing, 238–45 and failure, 240–2 and fast-flowing ideas, 134–9 formula for flourishing, 132 and hope, 155–63 impacts of, 155–9 mitigating risk, 251–2 and new maps, 251–2 and patronage, 239–51 and place, 245–51 and policy, 243–5, 259–62 and risk-taking, 135–6 and technology, 136–7 and virtue, 256–66 See also paradigm shifts al-Ghazali, 69 GitHub, 35 Giving Pledge, 262 globalization, 5, 195, 219, 231, 259 Google search trends, 5 Google (Alphabet), 59, 198 cloud storage, 33 Google Translate, 146 high-altitude balloons, 96 Quantum Artificial Intelligence Lab, 126, 243 search engine, 156, 159 self-driving car, 167, 243 Gorbachev, Mikhail, 21–2 Gordon, Robert, 151 Grove, Andy, 157 Guicciardini, Francesco, 164 gunpowder, 2, 10, 19, 60, 74, 164, 168, 194 Gutenberg, Johann, 1, 7, 11, 25–6, 39, 80, 133–4, 136, 143, 150, 156, 167, 229 Gutenberg moment, 30–3, 35 Hanny’s Voorwerp, 148 Hawking, Stephen, 141, 156 health and medicine aging, 7, 113, 119, 153, 162 child mortality, 84, 119 genetics, 114–21, 129–31, 148, 153, 157, 161, 163, 165, 183, 236–7 life expectancy, 4, 7, 76, 82, 84, 88, 101, 153, 158 life expectancy at birth vs.


pages: 390 words: 109,870

Radicals Chasing Utopia: Inside the Rogue Movements Trying to Change the World by Jamie Bartlett

Andrew Keen, back-to-the-land, Bernie Sanders, bitcoin, Black Lives Matter, blockchain, blue-collar work, Boris Johnson, brain emulation, Californian Ideology, centre right, clean water, climate change refugee, cryptocurrency, digital rights, Donald Trump, drone strike, Elon Musk, energy security, Ethereum, ethereum blockchain, Evgeny Morozov, failed state, gig economy, hydraulic fracturing, income inequality, intentional community, Intergovernmental Panel on Climate Change (IPCC), Jaron Lanier, Jeremy Corbyn, job automation, John Markoff, John Perry Barlow, Joseph Schumpeter, Kickstarter, life extension, military-industrial complex, Nick Bostrom, Occupy movement, off grid, Overton Window, Peter Thiel, post-industrial society, post-truth, postnationalism / post nation state, precariat, QR code, radical life extension, Ray Kurzweil, RFID, Rosa Parks, Ross Ulbricht, Satoshi Nakamoto, self-driving car, Silicon Valley, Silicon Valley startup, Skype, smart contracts, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, systems thinking, technoutopianism, the long tail, Tragedy of the Commons

Elon Musk, the billionaire Silicon Valley entrepreneur, declared AI to be comparable to summoning the Devil and donated $10 million to research to make sure the super-machines of the future will be kind to us. Stephen Hawking said ‘the development of artificial intelligence could spell the end of the human race.’ Either way, and of more immediate concern, AI—or the ability of machines to replicate human decision-making—could leave millions without jobs.29 Google’s self-driving cars will replace drivers, drones will replace warehouse workers, machine-learning algorithms will undertake some (although not all) the work of lawyers and doctors. What will we do if half the population—especially the middle class tax payers—are laid off work? Of course, workforces change, and it’s also possible that the gains of AI-produced wealth are distributed fairly amongst populations and free up people from repetitive tasks for more exciting and fulfilling roles.

See Radio Frequency Identification Richards, Bill, 106 right wing, 309 See also far right Robinson, Tommy, 49, 50, 56–60, 76, 79–80 arrests of, 87 demonstration in Copenhagen led by, 67–70 EDL started by, 53–54, 161 home town context for attitudes of, 77–78 Muslim stories framed by, 72–74 Muslim taxi driver and, 82 after Pegida-UK failure, 91 Pegida-UK leadership and, 61–66 police attitude toward, 87–88 speech in Copenhagen, 74 violent past of, 61–62 robots, 18, 29 Rohic, Milan, 56, 57–61, 88 Rome, Italy, female mayor of, 174 Rothery, Tina Louise, 255–258, 262, 263 roulette, 9–10, 14 Rousseau, 180 Russia, 311 Sabir, Rizwaan, 136–137 Safeguarding Children Board, 138 Sanders, Bernie, 178–179, 306 science regenerative, 33 transhumanism and, 31–32 Science for the Masses, 26 secret service, 283 self-driving cars, 36 self-focusing, 22 Serbia Croatian war of independence from, 269 Gornja Siga and, 267 serotonin, psilocybin mimicking of, 118 Sharia Watch, 62 Sharp, Mark, 78 Shirky, Clay, 160 Sinclair, David, 32 Slavonja, 270 Smith, Ifhat, 146 social licence, 243–244 social media, 27–28, 177–178 populism and, 180 Socrates, 131 Solar Village, in Tamera commune, 206–208, 209 South Sudan, 278 Spain, 306 spirituality, survey on religion and, 122–123 statecraft, Liberland, 278–284 Stone, Mark, 237–238 stoning, 63, 145 Stroukal, Dominik, 277–278 subculture, activist, 234, 242–243, 244, 266 Syria, 129–130, 141, 144 Syrian refugees, German acceptance of, 52–53 Tamera commune (in Portugal), Introduction Week at, 192, 214, 215 autonomy goal of, 225–226 children in, 195 communal living issues and, 217, 218–219 creator of, 193–194 decision-making body, 196 Duhm’s influence on, 218 eco-villages and, 204–205 Energy Power Greenhouse, 207–208 EU study on, 208–209 failure and success of, 223–224, 225–227 free love lifestyle and philosophy, 195, 198, 219, 220 God Point meeting, 211–213 healing biotope goal of, 190 Introduction Week, 200–203 lack of smartphones in, 224–225 Love School, 199 meals, 196, 199 men’s discussion group during, 220–221 number of residents, 191 post-visit assessment of, 217–218 requirements for permanent residence in, 192 Ring of Power, 189, 190, 226 Solar Village, 206–208, 209 spiritual work of, 210 typical day in, 222 visitor area, 190–191, 197 water-retention landscape in, 208–209 wild boar problem, 201–202 See also Duhm, Dieter Tarkowski Tempelhof, Susanne, 270–271, 285–294, 296, 299 on democracy, 292 taxation, voluntary, 275 technologies artificial intelligence, 36–38 bio-hacking, 22 exaggerated claims about, 37–38 Grinders and, 23–24 life extension and transhumanist, 32–34 mobile, 26–27 as transhumanist focus, 10–11 Tehachapi (bio-hacking lab town), 21–22 Terra Deva, 200, 202 terra nullius, 268, 278 terrorism conveyor-belt model of radicalisation and, 137–138 factors leading to, 134–135 right-wing extremists attacks of, 148 See also counter-terrorism strategies; Islamist radicalism; Prevent Thiel, Peter, 12 third parties, 41 The Time Machine (Wells), 38 Toynbee, Polly, 79 Transatlantic Trade and Investment Partnership (TTIP), 169 transhumanism Gyurko’s mass appeal goal for, 15–16 immortality quest of, 10–11, 30–31 Las Vegas setting for, 29 life extension and, 12 modern roots of, 11 movement, 12 philosophy, 11–13 problem with, 36–40 religious undertone in, 35–36 roulette scenario and, 9–10, 14 science and, 31–32 technology focus of, 10–11 wager of, 13, 14, 15 See also presidential campaign tour, Gyurko Transhumanist Bill of Rights, 14, 16, 46 Transhumanist Party, 30, 43, 44–45 transhumanists bio-hackers and, 26 as Californians, 35 tropical rainforests, 232 Trump, Donald, 5, 7, 80–81, 147–148, 306 digital politics and, 179–180 free media coverage given to, 180 Keystone XL veto overturned by, 264 Muslim ban ordered by, 92 Muslim focus of, 149 Pegida-UK and, 91–93 TTIP.


pages: 421 words: 110,406

Platform Revolution: How Networked Markets Are Transforming the Economy--And How to Make Them Work for You by Sangeet Paul Choudary, Marshall W. van Alstyne, Geoffrey G. Parker

3D printing, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, Apple's 1984 Super Bowl advert, autonomous vehicles, barriers to entry, Benchmark Capital, big data - Walmart - Pop Tarts, bitcoin, blockchain, business cycle, business logic, business process, buy low sell high, chief data officer, Chuck Templeton: OpenTable:, clean water, cloud computing, connected car, corporate governance, crowdsourcing, data acquisition, data is the new oil, data science, digital map, discounted cash flows, disintermediation, driverless car, Edward Glaeser, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, financial innovation, Free Software Foundation, gigafactory, growth hacking, Haber-Bosch Process, High speed trading, independent contractor, information asymmetry, Internet of things, inventory management, invisible hand, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, Kevin Roose, Khan Academy, Kickstarter, Lean Startup, Lyft, Marc Andreessen, market design, Max Levchin, Metcalfe’s law, multi-sided market, Network effects, new economy, PalmPilot, payday loans, peer-to-peer lending, Peter Thiel, pets.com, pre–internet, price mechanism, recommendation engine, RFID, Richard Stallman, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Salesforce, Satoshi Nakamoto, search costs, self-driving car, shareholder value, sharing economy, side project, Silicon Valley, Skype, smart contracts, smart grid, Snapchat, social bookmarking, social contagion, software is eating the world, Steve Jobs, TaskRabbit, The Chicago School, the long tail, the payments system, Tim Cook: Apple, transaction costs, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, winner-take-all economy, zero-sum game, Zipcar

Even more remarkably, the changes that Uber has already brought are probably just the opening salvo in a barrage of further disruptions that may ultimately transform the entire transportation sector. Combining the platform model with another technology that is rapidly moving from the drawing board to the showroom—the self-driving car—will improve Uber’s already stellar economic model and could lead to a series of cascading impacts that extend beyond the taxi industry. One futurist foresees a time when millions of people will eschew car ownership altogether, instead relying on an instantly deployable fleet of driverless Uber vehicles to take them wherever they want to go at a cost of around fifty cents per mile.

., 245–46 rate of conversion to sale, 197 ratings, 157–58, 265 razors-and-blades strategy, 109–10 Real Audio, 222 real estate market, 9, 12, 62, 124, 237, 277, 282 RealNetworks, 222 real-time processing, 247, 252–53 recipients, 100, 101, 104, 105 recruiters, 50, 51, 119, 218–19 redBus, 73, 95 Reddit, 5, 36, 47, 93, 173 Regulation 2.0, 253–56 regulatory capture, 235–37, 257 RelayRides, 9, 10, 67, 230 research and development (R & D), 14, 33, 275 reservations, 8–9, 90, 95, 101, 137, 142, 194 resources: allocation of, 6, 15, 70–71, 199, 200, 298–99 control of, 208–9, 212, 227 intensive use of, 263–64, 278, 289 model based on, 208–10, 213, 216 restaurants, 36, 37, 76, 90, 91, 95, 101, 113, 120, 142, 170, 194, 259 retail industry, 12, 63, 77, 82–83, 85, 89, 111, 123–24, 141, 145, 157–58, 204–7, 240–49, 251, 264 revenue grabs, 121, 157–58 rewards (incentives), 82, 101, 102, 166, 173–74, 182, 227 R/GA, 76 ride-sharing services, 2, 9, 12, 16–18, 25, 30, 36, 37, 49–50, 60–62, 67, 115, 175, 190, 227, 231, 233, 250–54, 258–59, 264, 278, 287, 297 Ries, Eric, 199, 201–2 Rifkin, Jeremy, 286 Roman military campaigns, 183, 237 Roth, Alvin, 164, 171 royalties, 72, 122 Rudder, Christian, 26–27 Sacks, David, 17, 18 Safaricom, 277–78 safety net, 280–81, 288 Saks Fifth Avenue, 275 sales conversion rate, 191–92 Salesforce, x, 55, 145, 245–46, 267 sales forces, 42–44, 73–74, 91, 125, 145 sales tax, 248–49 same-side effects, 29–32, 34, 298 Samsung, xi, 86, 137, 270–71, 295 San Francisco, 1–2, 18, 61, 233, 278, 281–83 SAP, vii, x, 155, 173–75, 216, 219, 241 scrapers (automated software), 91–92, 107 search engine optimization, 120–21, 145, 191, 297 search engines, 24–25, 40, 120–21, 145, 190, 191, 197–98, 215, 216, 242, 297 Sears, Roebuck, 207 seeding strategy, 18, 92–93, 105 self-driving cars, 62 self-governance, 176–80, 182, 246, 253–56 self-serve advertising, 131, 133–34 semiconductor industry, 225 senders, 100, 101–2, 105 sensor data, 246, 286 service interfaces, 176–78, 221 Shapiro, Carl, 19, 240–41 shared model, 137, 138, 140–41, 154–55 shareholders, 11, 164 sharing economy, 10, 298–99 Shleifer, Andrei, 236–37, 238 shopping malls, 123–24 ShopRunner, 206–7 ShopThis!


pages: 385 words: 112,842

Arriving Today: From Factory to Front Door -- Why Everything Has Changed About How and What We Buy by Christopher Mims

air freight, Airbnb, Amazon Robotics, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, big-box store, blue-collar work, Boeing 747, book scanning, business logic, business process, call centre, cloud computing, company town, coronavirus, cotton gin, COVID-19, creative destruction, data science, Dava Sobel, deep learning, dematerialisation, deskilling, digital twin, Donald Trump, easy for humans, difficult for computers, electronic logging device, Elon Musk, Frederick Winslow Taylor, fulfillment center, gentrification, gig economy, global pandemic, global supply chain, guest worker program, Hans Moravec, heat death of the universe, hive mind, Hyperloop, immigration reform, income inequality, independent contractor, industrial robot, interchangeable parts, intermodal, inventory management, Jacquard loom, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kaizen: continuous improvement, Kanban, Kiva Systems, level 1 cache, Lewis Mumford, lockdown, lone genius, Lyft, machine readable, Malacca Straits, Mark Zuckerberg, market bubble, minimum wage unemployment, Nomadland, Ocado, operation paperclip, Panamax, Pearl River Delta, planetary scale, pneumatic tube, polynesian navigation, post-Panamax, random stow, ride hailing / ride sharing, robot derives from the Czech word robota Czech, meaning slave, Rodney Brooks, rubber-tired gantry crane, scientific management, self-driving car, sensor fusion, Shenzhen special economic zone , Shoshana Zuboff, Silicon Valley, six sigma, skunkworks, social distancing, South China Sea, special economic zone, spinning jenny, standardized shipping container, Steve Jobs, supply-chain management, surveillance capitalism, TED Talk, the scientific method, Tim Cook: Apple, Toyota Production System, traveling salesman, Turing test, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, vertical integration, warehouse automation, warehouse robotics, workplace surveillance

The three remaining cameras on the robot are “time-of-flight” cameras that can individually perceive depth in a very different way: by sending out a pulse of LED or laser light and measuring the time it takes for it to return to the camera. A laser-based time-of-flight camera is in fact a form of solid-state lidar, but the resolution of time-of-flight cameras has historically been much lower than what can be achieved with the lidar that goes on self-driving cars and trucks. The robot also uses ultrasonic sensors that send out a high-frequency pulse of sound and “listen” for its return. It’s the same principle as echolocation in a bat or dolphin, only it’s achieved in silicon. What all these sensors have in common is that, thanks to their use in many common devices other than robots, they are widely available and relatively cheap.

The same is true of the “brain” of the Starship robot, which is a mobile application-optimized AMD Ryzen processor. Designed for laptops and other devices, it’s significantly less powerful than a desktop computer, gaming laptop, or the kind of server-level equipment used on autonomous cars. To cram object detection, navigation, SLAM, GPS, IMU navigation, and the like—all the things a self-driving car relies on, more or less—into such a tiny and energy-efficient package is by now a greater feat of engineering than autonomous navigation itself. Starship’s robots are driving themselves with the computing power of an iPhone. All of that economizing, all of that cramming of its systems into a package of technology that is good enough today and will only become cheaper with time, until the day it’s almost disposably cheap, is what it takes to eke out a profit on two-dollar deliveries.


pages: 720 words: 197,129

The Innovators: How a Group of Inventors, Hackers, Geniuses and Geeks Created the Digital Revolution by Walter Isaacson

1960s counterculture, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, Alvin Toffler, Apollo Guidance Computer, Apple II, augmented reality, back-to-the-land, beat the dealer, Bill Atkinson, Bill Gates: Altair 8800, bitcoin, Bletchley Park, Bob Noyce, Buckminster Fuller, Byte Shop, c2.com, call centre, Charles Babbage, citizen journalism, Claude Shannon: information theory, Clayton Christensen, commoditize, commons-based peer production, computer age, Computing Machinery and Intelligence, content marketing, crowdsourcing, cryptocurrency, Debian, desegregation, Donald Davies, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, driverless car, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, en.wikipedia.org, eternal september, Evgeny Morozov, Fairchild Semiconductor, financial engineering, Firefox, Free Software Foundation, Gary Kildall, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Haight Ashbury, Hans Moravec, Howard Rheingold, Hush-A-Phone, HyperCard, hypertext link, index card, Internet Archive, Ivan Sutherland, Jacquard loom, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, John von Neumann, Joseph-Marie Jacquard, Leonard Kleinrock, Lewis Mumford, linear model of innovation, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Menlo Park, Mitch Kapor, Mother of all demos, Neil Armstrong, new economy, New Journalism, Norbert Wiener, Norman Macrae, packet switching, PageRank, Paul Terrell, pirate software, popular electronics, pre–internet, Project Xanadu, punch-card reader, RAND corporation, Ray Kurzweil, reality distortion field, RFC: Request For Comment, Richard Feynman, Richard Stallman, Robert Metcalfe, Rubik’s Cube, Sand Hill Road, Saturday Night Live, self-driving car, Silicon Valley, Silicon Valley startup, Skype, slashdot, speech recognition, Steve Ballmer, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, Stewart Brand, Susan Wojcicki, technological singularity, technoutopianism, Ted Nelson, Teledyne, the Cathedral and the Bazaar, The Coming Technological Singularity, The Nature of the Firm, The Wisdom of Crowds, Turing complete, Turing machine, Turing test, value engineering, Vannevar Bush, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, Whole Earth Review, wikimedia commons, William Shockley: the traitorous eight, Yochai Benkler

Among the papers they produced (along with another graduate student, Craig Silverstein, who would become the first hire when they founded Google) were two on market basket analysis, a technique that assesses to what extent a consumer who buys items A and B is more or less likely also to buy items C and D.141 From that Brin became interested in ways to analyze patterns from the data trove on the Web. With Winograd’s help, Page began casting around for a dissertation topic. He considered close to a dozen ideas, including one on how to design self-driving cars, as Google would later do. Eventually he homed in on studying how to assess the relative importance of different sites on the Web. His method came from growing up in an academic environment. One criterion that determines the value of a scholarly paper is how many other researchers cite it in their notes and bibliography.

Presper, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10, ref11, ref12, ref13, ref14, ref15, ref16, ref17, ref18 patents sought by, ref1, ref2 and public display of ENIAC, ref1 and storage of programs in ENIAC, ref1, ref2 von Neumann accused of stealing ideas by, ref1 Eckert-Mauchly Computer Corporation, ref1 Edison, Thomas, ref1, ref2 EDSAC, ref1 EDVAC, ref1, ref2, ref3, ref4 Edwards, Dan, ref1, ref2 Edwards, Elwood, ref1 Einstein, Albert, ref1, ref2, ref3, ref4, ref5, ref6, ref7 Eisenhower, Dwight, ref1, ref2, ref3, ref4, ref5, ref6 electrical circuits, ref1, ref2, ref3 needed to break German codes, ref1, ref2 electricity, ref1 Electric Kool-Aid Acid Test, The (Wolfe), ref1, ref2 Electronic Discrete Variable Automatic Calculator, see EDVAC Electronic Engineering Times, ref1 Electronic News, ref1, ref2 Electronics, ref1 Electronics Magazine, ref1 electrons, ref1, ref2, ref3, ref4 Elkind, Jerry, ref1, ref2 Elwell, Cyril, ref1 email, ref1 Emsworth, Lord, ref1 Encyclopedia Britannica, ref1 Engelbart, Doug, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10, ref11, ref12, ref13, ref14, ref15, ref16, ref17, ref18, ref19, ref20 on human-machine interaction, ref1, ref2, ref3, ref4, ref5, ref6, ref7 English, Bill, ref1, ref2, ref3 ENIAC, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9 decimal system used by, ref1 as first modern computer, ref1, ref2 hydrogen bomb equations worked out by, ref1 patents for work on, ref1, ref2, ref3, ref4 public unveiling of, ref1 speed of, ref1, ref2 storage of programs in, ref1, ref2 update of, ref1 women as programmers of, ref1, ref2 Enigma, ref1 Enlightenment, ref1 Enquire, ref1 Enquire Within Upon Everything, ref1, ref2, ref3, ref4 Entscheidungsproblem, ref1, ref2, ref3, ref4 Esquire, ref1, ref2, ref3 Estridge, Don, ref1 Eternal September, ref1, ref2 Ethernet, ref1, ref2n, ref3 Euclidean geometry, ref1 Eudora, ref1 Evans, David, ref1, ref2 Evans, Kent, ref1, ref2 EvHead, ref1 Excite, ref1, ref2 Expensive Planetarium, ref1 Eyser, George, ref1 Facebook, ref1, ref2, ref3, ref4 Fairchild, Sherman, ref1, ref2 Fairchild Camera and Instrument, ref1, ref2, ref3 Fairchild Semiconductor, ref1, ref2, ref3, ref4 formation of, ref1, ref2 microchips sold to weapons makers by, ref1 Noyce’s resignation from, ref1 Farnsworth, Philo, ref1 Federal Communications Commission, ref1 Felsenstein, Lee, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10, ref11 Ferranti, ref1 Ferranti Mark I, ref1 Ferrucci, David, ref1 Feynman, Richard, ref1 file sharing, ref1 Filo, David, ref1 Firefox, ref1, ref2, ref3 “First Draft of a Report on the EDVAC, by John von Neumann,” ref1 Fischer, Dave, ref1 Flowers, Tommy, ref1, ref2, ref3, ref4 influence of, ref1 “Fool on the Hill, The,” ref1 formal systems of mathematics, ref1 Fortran, ref1, ref2, ref3, ref4 Fortune, ref1, ref2, ref3 Frankenstein, or The Modern Prometheus (Shelley), ref1, ref2, ref3 Franklin, Benjamin, ref1, ref2, ref3, ref4 Frankston, Bob, ref1 Free Software Foundation, ref1 Free Speech Movement, ref1, ref2, ref3 French, Gordon, ref1, ref2 French Revolution, ref1 Fuchs, Klaus, ref1, ref2 Fulghum, Robert, ref1 Fuller, Buckminster, ref1, ref2, ref3, ref4 Fylstra, Dan, ref1 Galaxy Games, ref1 Gale, Grant, ref1 Galison, Peter, ref1 GameLine, ref1, ref2 Garcia, Jerry, ref1 Gates, Bill, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10, ref11, ref12, ref13, ref14, ref15, ref16, ref17, ref18 Allen’s disputes with, ref1, ref2, ref3, ref4, ref5 background of, ref1 BASIC for Altair designed by, ref1, ref2 BASIC learned by, ref1 belief of, in future of personal computer, ref1 copyright issues and, ref1, ref2, ref3 8008 language written by, ref1 electronic grid work of, ref1 Evans’s death and, ref1, ref2 at Harvard, ref1, ref2 innovator personality of, ref1 Jobs’s dispute with, ref1 Lakeside Programming Group formed by, ref1 operating system and, ref1, ref2 payroll program written by, ref1, ref2 PDP-10 work of, ref1 programming’s importance seen by, ref1 on reverse-engineering brain, ref1 Gates, Mary, ref1 Gatlinburg conference, ref1, ref2, ref3 General Electric (GE), ref1, ref2 General Post Office, ref1 general-purpose machines, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10 Engelbert’s foreseeing of, ref1 see also memex General Relativity, ref1, ref2 geometry, ref1 germanium, ref1 Germany, codes of, ref1, ref2, ref3 Gertner, Jon, ref1, ref2 Gibson, William, ref1 Gingrich, Newt, ref1 Ginsberg, Allen, ref1 GNU, ref1 GNU/Linux, ref1, ref2, ref3, ref4, ref5, ref6 Go, ref1 Gödel, Escher, Bach (Hofstadter), ref1 Gödel, Kurt, ref1, ref2, ref3, ref4, ref5 gold, ref1 Goldberg, Adele, ref1 Goldstine, Adele, ref1, ref2, ref3, ref4 Goldstine, Herman, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 von Neumann’s first meeting with, ref1 Google, ref1, ref2, ref3, ref4, ref5, ref6, ref7 creation of, ref1, ref2, ref3 lawsuits of, ref1 page ranks of, ref1 self-driving cars of, ref1 Google Glass, ref1 Gopher, ref1 Gore, Al, ref1, ref2 Gore Act (1991), ref1, ref2, ref3 government funding, ref1, ref2, ref3 see also ARPANET Graetz, Martin, ref1, ref2 Gran Trak 10, ref1 graphic user interface, ref1, ref2 Grateful Dead, ref1, ref2, ref3 “Great Conversation, The” (Cerf), ref1 Greeks, ref1 Greening of America, The (Reich), ref1 Greig, Woronzow, ref1 Grinnell College, ref1, ref2, ref3 Grove, Andy, ref1, ref2, ref3, ref4, ref5 management techniques of, ref1, ref2 hackers, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 Hackers (Levy), ref1, ref2 Hafner, Katie, ref1, ref2, ref3 Haggerty, Pat, ref1, ref2, ref3 idea for calculator of, ref1 Hall, Justin, ref1, ref2, ref3, ref4, ref5, ref6 halting problem, ref1 Hambrecht & Quist, ref1, ref2 harmonic synthesizer, ref1 Hartree, Douglas, ref1 Harvard University, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 Hayden, Stone & Co., ref1, ref2 Hayes Smartmodem, ref1 Heart, Frank, ref1 “Heath Robinson,” ref1 Heinlein, Robert, ref1, ref2 Hells Angel, ref1 Hennessy, John, ref1 Herschel, John, ref1 Hertzfeld, Andy, ref1 Herzfeld, Charles, ref1, ref2, ref3 Hewlett, William, ref1, ref2, ref3, ref4 Hewlett-Packard, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 High Performance Computing Act (1991), ref1, ref2 Higinbotham, William, ref1 Hilbert, David, ref1, ref2, ref3, ref4, ref5, ref6 Hiltzik, Michael, ref1 Hingham Institute Study Group, ref1 hippies, ref1, ref2, ref3, ref4, ref5 Hiroshima, ref1n His Majesty’s Government Code and Cypher School, ref1 Hitler, Adolf, ref1, ref2 Hoddeson, Lillian, ref1 Hodges, Andrew, ref1 Hoefler, Don, ref1 Hoerni, Jean, ref1, ref2, ref3, ref4 Hoff, Ted, ref1, ref2 Hofstadter, Douglas, ref1 Holberton, Betty Snyder, see Snyder, Betty Hollerith, Herman, ref1, ref2 Homebrew Computer Club, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10 Home Terminal Club, ref1 Honeywell, ref1, ref2, ref3, ref4 Hoover Dam, ref1 Hopper, Grace, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 communication skills of, ref1, ref2 on ENIAC’s lack of programmability, ref1 hired at Eckert-Mauchley, ref1 subroutines perfected by, ref1 Hopper, Vincent, ref1 HotWired, ref1 HotWired.com, ref1 Hourihan, Meg, ref1 House, David, ref1 House of Lords, ref1, ref2 Huffington, Arianna, ref1 Huffington Post, ref1 Human Brain Project, ref1 Human-Computer Interaction Group, ref1 human-machine interaction, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10, ref11, ref12, ref13, ref14, ref15, ref16 Hush-A-Phone case, ref1 hydrogen bomb, ref1, ref2 HyperCard, ref1 hypertext, ref1, ref2 limitation of, ref1 Hypertext Markup Language (HTML), ref1, ref2, ref3, ref4 Hypertext Transfer Protocol (HTTP), ref1 IAS Machine, ref1 IBM, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8, ref9, ref10, ref11, ref12, ref13, ref14, ref15, ref16, ref17 dress code at, ref1 founding of, ref1 Gates’s deal with, ref1 Jobs’s criticism of, ref1 Mark I history of, ref1, ref2 Mark I of, ref1, ref2, ref3, ref4 IBM 704, ref1 IBM 1401, ref1 Idea Factory, The (Gertner), ref1 Illich, Ivan, ref1, ref2 imitation game, ref1 incompleteness theorem, ref1, ref2 indeterminacy, ref1 individualism, ref1 Industrial Revolution, ref1, ref2, ref3, ref4, ref5 two grand concepts of, ref1 Infocast, ref1 Information Processing Techniques Office (IPTO), ref1, ref2, ref3, ref4, ref5 Information Sciences, Inc.

Jude (Jude Milhon), ref1, ref2 Sams, Jack, ref1 Samson, Peter, ref1, ref2, ref3 Sanders, Bob, ref1 San Francisco Chronicle, ref1 Sanger, Larry, ref1, ref2, ref3, ref4 Sarofim, Fayez, ref1 SATNET, ref1 Say Everything (Rosenberg), ref1 Scantlebury, Roger, ref1, ref2, ref3 Schreyer, Helmut, ref1, ref2 “Science, the Endless Frontier” (Bush), ref1n Scientific American, ref1 Scientific and Advanced Technology Act (1992), ref1 Scientific Data System, ref1 Scientific Memoirs, ref1, ref2, ref3 Scientific Revolution, ref1, ref2 Scripting News, ref1 search engines, ref1, ref2, ref3 Searle, John, ref1, ref2 Sears, ref1 Seattle Computer Products, ref1 self-driving cars, ref1 Semi-Automatic Ground Environment (SAGE), ref1, ref2, ref3, ref4 semiconductivity theory, ref1 semiconductor amplifier, ref1 semiconductors, ref1, ref2 Sendall, Mike, ref1 Sequoia Capital, ref1n, ref2 Seva Foundation, ref1 Shannon, Claude, ref1, ref2, ref3, ref4, ref5, ref6, ref7, ref8 Shapin, Steven, ref1 Shaw, Artie, ref1 Shelley, Mary, ref1, ref2, ref3 Shelley, Percy Bysshe, ref1 Shirky, Clay, ref1, ref2 Shockley, William, ref1, ref2, ref3, ref4, ref5, ref6 acoustic delay line developed by, ref1 Bardeen and Brattain’s dispute with, ref1, ref2, ref3 credit for transistor taken by, ref1, ref2 IQ of, ref1 midlife crisis of, ref1 Nobel Prize won by, ref1, ref2 poor leadership skills of, ref1, ref2, ref3 racial views of, ref1, ref2 replacement for vacuum tube sought by, ref1, ref2 researchers recruited to company by, ref1, ref2, ref3 semiconductor amplifier idea of, ref1 solid-state studied by, ref1 in World War II, ref1 Shockley Semiconductor Laboratory, ref1, ref2, ref3, ref4, ref5, ref6 Shockley replaced at, ref1, ref2 Siemens, ref1 Signals and Power Subcommittee, ref1 silicon, ref1, ref2, ref3, ref4, ref5, ref6 Silverstein, Craig, ref1 Simon, Leslie, ref1 Simonyi, Charles, ref1 singularity, ref1 Siri, ref1 Sketchpad, ref1 “Sketchpad: A Man-Machine Graphical Communications System” (Sutherland), ref1 slide rules, ref1, ref2, ref3 Smalltalk, ref1 Smarter Than You Think (Thompson), ref1 Smith, Adam, ref1 Smith, E.


Innovation and Its Enemies by Calestous Juma

3D printing, additive manufacturing, agricultural Revolution, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, behavioural economics, big-box store, biodiversity loss, business cycle, Cass Sunstein, classic study, clean water, collective bargaining, colonial rule, computer age, creative destruction, CRISPR, Daniel Kahneman / Amos Tversky, deskilling, disruptive innovation, driverless car, electricity market, energy transition, Erik Brynjolfsson, fail fast, financial innovation, global value chain, Honoré de Balzac, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of movable type, invention of the printing press, Joseph Schumpeter, knowledge economy, loss aversion, Marc Andreessen, means of production, Menlo Park, mobile money, New Urbanism, Nicholas Carr, pensions crisis, phenotype, precautionary principle, Ray Kurzweil, Recombinant DNA, refrigerator car, Second Machine Age, self-driving car, smart grid, smart meter, stem cell, Steve Jobs, synthetic biology, systems thinking, tacit knowledge, technological singularity, The Future of Employment, Thomas Kuhn: the structure of scientific revolutions, Travis Kalanick

The developing world has the potential to access more scientific and technical knowledge than the more advanced countries had in their early stages of industrialization. The pace at which latecomer economies such as China have been able to leapfrog in certain technologies underscores the possibilities.6 There are growing concerns over the implications of these developments for employment. Self-driving cars will restructure transportation through new ownership patterns, insurance arrangements, and business models. Computer-aided diagnosis, robotic surgery, and myriad medical devices are already changing the role of doctors and how medical care is provided.7 Artificial intelligence and computer algorithms are influencing the way basic decisions are made.

See AquAdvantage salmon Salt, use in ice cooling, 177 Sandoz company, 232–233 Schools, computers in, 41 Schultz, Theodore William, 95, 116 Schumpeter, Joseph on consumers’ tastes, changes in, 45 creative destruction, concept of, 16–17, 19, 39, 42, 47, 121, 129, 139, 280, 309 economic development, application of complex systems thinking to, 27 on economic gains from innovation, 203 on equilibrium, 27 on entrepreneurs, 258 innovation, taxonomy of, 175 on leadership, 282 railroads, characterization of impact of, 122 on resistance to innovation, 1, 96 social transformation and, 16–23 on technological innovation, 225, 293–294 Schuylkill River, ice from, 181 Science advisory bodies, 286–287 science-based approval processes, need for, 277–278 science-based regulation, 236–244, 277 scientific and technical knowledge, developing world’s access to, 13 scientific information, democratization of, 313 scientific research, dynamics of success in, 327n115 scientific uncertainty, about new technologies, 120, 239–240 scientists, communication by, 312–313 Science advice importance of, 174–175 scientific advisory bodies, importance of, 7 scientific and technical advice, structures for, 287–288, 306 Scott, Leon, 207 Scribes, 71, 77 Seatbelts, social norms on, 33 Seattle, frozen pack laboratory in, 195 SEC (Securities Exchange Commission), 274–275 Second-generation biotechnology, 253–254 Secularization, of the Ottoman Empire, 91 Securities Exchange Commission (SEC), 274–275 Security, as grand challenge, 12 Sedgwick, William, 187 Seed sector, 30, 243 Selective breeding, fish farming and, 262–263 Self-driving cars, impact of, 13 Self-organizing systems, 6, 28 Selim I, Sultan, 68 Selim II, Sultan, 51 Selim III, Sultan, 93 Senate, US, 196, 215 Senefelder, Alois, 92 Shams (Syrian businessman), 51 Ships, 195, 295–296 Silent Spring (Carson), 14, 224–225, 231 Singers, prominence of, vs. bands, 219 Single path dependence, as limitation on innovation, 250 Single studies, false balance of, vs. evidence, 249–250 Singularity.


pages: 480 words: 123,979

Dawn of the New Everything: Encounters With Reality and Virtual Reality by Jaron Lanier

4chan, air gap, augmented reality, back-to-the-land, Big Tech, Bill Atkinson, Buckminster Fuller, Burning Man, carbon footprint, cloud computing, collaborative editing, commoditize, Computer Lib, cosmological constant, creative destruction, crowdsourcing, deep learning, Donald Trump, Douglas Engelbart, Douglas Hofstadter, El Camino Real, Elon Musk, fake news, Firefox, game design, general-purpose programming language, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, Howard Rheingold, hype cycle, impulse control, information asymmetry, intentional community, invisible hand, Ivan Sutherland, Jaron Lanier, John Gilmore, John Perry Barlow, John von Neumann, Kevin Kelly, Kickstarter, Kuiper Belt, lifelogging, mandelbrot fractal, Mark Zuckerberg, Marshall McLuhan, Menlo Park, military-industrial complex, Minecraft, Mitch Kapor, Mondo 2000, Mother of all demos, Murray Gell-Mann, Neal Stephenson, Netflix Prize, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, pattern recognition, Paul Erdős, peak TV, Plato's cave, profit motive, Project Xanadu, quantum cryptography, Ray Kurzweil, reality distortion field, recommendation engine, Richard Feynman, Richard Stallman, Ronald Reagan, self-driving car, Silicon Valley, Silicon Valley startup, Skinner box, Skype, Snapchat, stem cell, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Stewart Brand, systems thinking, technoutopianism, Ted Nelson, telemarketer, telepresence, telepresence robot, Thorstein Veblen, Turing test, Vernor Vinge, Whole Earth Catalog, Whole Earth Review, WikiLeaks, wikimedia commons

Another example is UC Santa Barbara’s Allosphere, a spherical CAVE with a catwalk suspended in the center.5 My guess is that there will be a lot of VR in self-driving cars. It’s almost intolerably boring to be in one, and we’ll all be stuck in them for hours. The interior of a car is small enough to instrument without much trouble, but large enough to solve the duplex problem, which will be explained shortly. You can even cancel out the motion of the road to counter car sickness. VR and self-driving cars are a perfect match, even better than drive-time radio was with cars you had to drive. I wonder if people without property will spend a lot of time in VR, being driven from place to place because it’s cheaper than standing still.


pages: 424 words: 119,679

It's Better Than It Looks: Reasons for Optimism in an Age of Fear by Gregg Easterbrook

affirmative action, Affordable Care Act / Obamacare, air freight, Alan Greenspan, Apollo 11, autonomous vehicles, basic income, Bernie Madoff, Bernie Sanders, Black Lives Matter, Boeing 747, Branko Milanovic, Brexit referendum, business cycle, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, clean tech, clean water, coronavirus, Crossrail, David Brooks, David Ricardo: comparative advantage, deindustrialization, Dissolution of the Soviet Union, Donald Trump, driverless car, Elon Musk, Exxon Valdez, factory automation, failed state, fake news, full employment, Gini coefficient, Google Earth, Home mortgage interest deduction, hydraulic fracturing, Hyperloop, illegal immigration, impulse control, income inequality, independent contractor, Indoor air pollution, interchangeable parts, Intergovernmental Panel on Climate Change (IPCC), invisible hand, James Watt: steam engine, labor-force participation, liberal capitalism, longitudinal study, Lyft, mandatory minimum, manufacturing employment, Mikhail Gorbachev, minimum wage unemployment, Modern Monetary Theory, obamacare, oil shale / tar sands, Paul Samuelson, peak oil, plant based meat, plutocrats, Ponzi scheme, post scarcity, purchasing power parity, quantitative easing, reserve currency, rising living standards, Robert Gordon, Ronald Reagan, self-driving car, short selling, Silicon Valley, Simon Kuznets, Slavoj Žižek, South China Sea, Steve Wozniak, Steven Pinker, supervolcano, The Chicago School, The Rise and Fall of American Growth, the scientific method, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, transaction costs, Tyler Cowen, uber lyft, universal basic income, War on Poverty, Washington Consensus, We are all Keynesians now, WikiLeaks, working poor, Works Progress Administration

The three-car suburban family—about one-third of American households own at least three vehicles—will become a one-car family, the vehicle driving itself to wherever the next pickup or drop-off is required by a family member. Offices, schools, and entertainment destinations won’t need parking lots by the doors: cars will drop off their masters and mistresses, continue on to remote parking facilities, then come back when summoned. Groups of friends will get together to purchase one shared self-driving car, rather than each person owning a car: all members of the group will save on car and insurance expenses, resulting in another increase in standards of living. The horsepower arms race will end, since a car that refuses to violate the speed limit—this is going to please some people while driving others to distraction—would not benefit from prodigious power output.

See specific topics Nike, 253, 256 Nixon, Richard, 54–55, 179, 256–257 nuclear power, 226–227 nuclear weapons, 125–126, 160–161, 277–280 Obama, Barack, 62, 109, 131 anecdotes of, 219 declinism and, 200–201, 221 Dodd-Frank Act and, 92–93 on drone aircrafts, 159 fuel-economy regulatory regime and, 147–148 on infrastructure, 94 national debt and, 97, 100 ObamaCare and, 29, 220, 249 on tax, 254 ObamaCare, 29, 220, 249 obesity, 5, 26, 35 optimism, 283–285 ozone, 48, 49–50, 62 Paine, Lincoln, 80 Paine, Thomas, 256 Panasonic, 68–69 Paris Agreement, 239, 243 The Passing of the Great Race (Grant), 197, 198 Piketty, Thomas, 84–85 Pinker, Steven, 120, 137, 138–139 Plank, Terry Ann, 278 Plato, 202–203 pollution, 26, 30, 59 See also air pollution Prince William Sound, 43 The Promised Land (Lemann), 71 public health, 27 ability to pay and, 29 carbon dioxide and, 62 deindustrialization and, 29 flu pandemics and, 28 health care and, 29, 40, 101–102, 220, 247–248, 249 inequality and healthcare, 247–248 longevity and, 30–31 mosquitoes and, 39 ObamaCare and, 29, 220, 249 pollution reduction and, 30 sanitation infrastructure and, 29–30 racism, 223, 259–260, 266 law enforcement and, 113–114 refugees and, 197 slavery and, 174–175, 191 Radelet, Steven, 20 Reagan, Ronald, 206–207, 209, 273 Reilly, Bill, 45–46 religion, 222, 282 resource consumption, 280 resource depletion fossil fuels and, 52–53, 54, 55–57 immensity of geology and, 54 market forces and, 51–52 price controls and, 54–55 uninterrupted trends and, 51 in US and European Union, 51–52 Resources for the Future, 45, 46 Ricardo, David, 134 Rose, Reginald, 197–199 Rubenstein, David, 271 Russia. See specific topics Sagan, Carl, 140–141 Sanders, Bernie, 78, 79, 96 as declinist, 84, 201–202 on middle class, 86–87 on tax, 255 Sanger-Katz, Margot, 35 The Sea and Civilization (Paine, L.), 80 Sedjo, Roger, 46–47 self-driving cars and, 152–154 Shah, Rajiv, 4, 10 Shakya, Holly, 213 Sharkey, Patrick, 224–225 siege mentality, 283–284 Siegel, Rebecca, 24 Silberstein, Steve, 190 Sivak, Michael, 147–148, 149 slavery, 174–175, 191 smartphones, 52, 212 smog. See air pollution social media. See Facebook and social media Social Security, 36, 87, 98, 102, 185, 273 Soviet Union.


pages: 452 words: 126,310

The Case for Space: How the Revolution in Spaceflight Opens Up a Future of Limitless Possibility by Robert Zubrin

Ada Lovelace, Albert Einstein, anthropic principle, Apollo 11, battle of ideas, Boeing 747, Charles Babbage, Charles Lindbergh, Colonization of Mars, complexity theory, cosmic microwave background, cosmological principle, Dennis Tito, discovery of DNA, double helix, Elon Musk, en.wikipedia.org, flex fuel, Francis Fukuyama: the end of history, gravity well, if you build it, they will come, Internet Archive, invisible hand, ITER tokamak, James Webb Space Telescope, Jeff Bezos, Johannes Kepler, John von Neumann, Kim Stanley Robinson, Kuiper Belt, low earth orbit, Mars Rover, Mars Society, Menlo Park, more computing power than Apollo, Naomi Klein, nuclear winter, ocean acidification, off grid, out of africa, Peter H. Diamandis: Planetary Resources, Peter Thiel, place-making, Pluto: dwarf planet, private spaceflight, Recombinant DNA, rising living standards, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, SoftBank, SpaceX Starlink, Strategic Defense Initiative, Stuart Kauffman, telerobotics, Thomas Malthus, three-masted sailing ship, time dilation, transcontinental railway, uranium enrichment, Virgin Galactic, Wayback Machine

While this does not matter for applications like one-way radio or TV broadcasting, it poses serious problems for two-way communication. For long-distance telephone calls, the quarter-second signal time delay each way can be quite annoying. For systems attempting to remotely control machinery under dynamic conditions (for example, self-driving cars or aircraft), it could potentially be catastrophic. If the satellites could orbit lower, say 1,200 kilometers, the time delay could be cut thirtyfold and the transmission power each way reduced a thousandfold. But satellites at that height orbit the Earth every two hours and can only been seen from the ground when they are relatively close by.

For example, such augmented communication constellations could enable low-cost wristwatch-sized communication devices that would be able to access on a real-time interactive basis all the storehouses of human knowledge from anywhere in the world. In addition, they would enable their users to communicate very high volumes of data—including voice, video, and music—either to each other or to the system's central libraries. They will not only make possible the global coordination of billions of self-driving cars but provide the kind of previously impossible automated air traffic control that could make mass use of private aircraft (finally, flying cars!) a reality as well. The practical value of such systems is obvious, but their implications go far beyond the practical into the social and historical.


pages: 316 words: 117,228

The Code of Capital: How the Law Creates Wealth and Inequality by Katharina Pistor

Andrei Shleifer, Asian financial crisis, asset-backed security, barriers to entry, Bear Stearns, Bernie Madoff, Big Tech, bilateral investment treaty, bitcoin, blockchain, Bretton Woods, business cycle, business process, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, collateralized debt obligation, colonial rule, conceptual framework, Corn Laws, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, digital rights, Donald Trump, double helix, driverless car, Edward Glaeser, Ethereum, ethereum blockchain, facts on the ground, financial innovation, financial intermediation, fixed income, Francis Fukuyama: the end of history, full employment, global reserve currency, Gregor Mendel, Hernando de Soto, income inequality, initial coin offering, intangible asset, investor state dispute settlement, invisible hand, joint-stock company, joint-stock limited liability company, Joseph Schumpeter, Kenneth Rogoff, land reform, land tenure, London Interbank Offered Rate, Long Term Capital Management, means of production, money market fund, moral hazard, offshore financial centre, phenotype, Ponzi scheme, power law, price mechanism, price stability, profit maximization, railway mania, regulatory arbitrage, reserve currency, Robert Solow, Ronald Coase, Satoshi Nakamoto, secular stagnation, self-driving car, seminal paper, shareholder value, Silicon Valley, smart contracts, software patent, sovereign wealth fund, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, time value of money, too big to fail, trade route, Tragedy of the Commons, transaction costs, Wolfgang Streeck

And Google has not shied away from using trade secrecy law to restrain former employees, thereby undermining one of the greatest comparative advantages of Silicon Valley’s legal landscape: the non-enforceability of non-compete clauses.78 When information technology first came of age, other technology companies, such as IBM along Route 128 in Massachusetts, were invoking these rules to keep know-how in house but were soon outcompeted by Silicon Valley with its free-wheeling start-up culture. It did not stay this way. Google recently sued Uber after one of its prized employees switched sides, claiming that he had appropriated e n c Los i n g n at U r e ’ s co d e 131 trade secrets for self-driving cars of one or more of the company’s subsidiaries.79 The civil case was settled, but criminal proceedings continued and Google cooperated with the authorities.80 The pattern should be familiar by now: The former disrupters of existing law or technology learn quickly that only by invoking legal protection of their own (often the same protection they only recently fought) can they protect their own gains.

AnnaLee Saxenian, Regional Advantage: Culture and Competition in Silicon Valley and Route 128 (Cambridge, MA: Harvard University Press, 1994). 79. The case, Waymo LLC v. Ueber Techs Inc., was filed in 2017, but settled in February 2018 only a few days into the trial. Ueber paid Waymo 0.34 percent of its equity, worth $245 million, and promised not to use Waymo technology for self-driving cars. Daisuke Wakabayashi, “Ueber and Waymo settle Trade Secrecy Suit over Driverless Car,” February 9, 2018, available online at www .nytimes.com. 80. Charles Duhigg, “Stop Thief,” New Yorker, October 22, 2018, 50–61, p. 61, quoting a spokesperson for Waymo, the Google subsidiary: “We comply with law-enforcement requests where there is a valid legal process, and this case is no exception.” 81.


pages: 1,261 words: 294,715

Behave: The Biology of Humans at Our Best and Worst by Robert M. Sapolsky

autism spectrum disorder, autonomous vehicles, behavioural economics, Bernie Madoff, biofilm, blood diamond, British Empire, Broken windows theory, Brownian motion, car-free, classic study, clean water, cognitive dissonance, cognitive load, corporate personhood, corporate social responsibility, Daniel Kahneman / Amos Tversky, delayed gratification, desegregation, different worldview, domesticated silver fox, double helix, Drosophila, Edward Snowden, en.wikipedia.org, epigenetics, Flynn Effect, framing effect, fudge factor, George Santayana, global pandemic, Golden arches theory, Great Leap Forward, hiring and firing, illegal immigration, impulse control, income inequality, intentional community, John von Neumann, Loma Prieta earthquake, long peace, longitudinal study, loss aversion, Mahatma Gandhi, meta-analysis, microaggression, mirror neurons, Mohammed Bouazizi, Monkeys Reject Unequal Pay, mouse model, mutually assured destruction, Nelson Mandela, Network effects, nocebo, out of africa, Peter Singer: altruism, phenotype, Philippa Foot, placebo effect, publication bias, RAND corporation, risk tolerance, Rosa Parks, selective serotonin reuptake inhibitor (SSRI), self-driving car, Silicon Valley, Skinner box, social contagion, social distancing, social intelligence, Stanford marshmallow experiment, Stanford prison experiment, stem cell, Steven Pinker, strikebreaker, theory of mind, Tragedy of the Commons, transatlantic slave trade, traveling salesman, trickle-down economics, trolley problem, twin studies, ultimatum game, Walter Mischel, wikimedia commons, zero-sum game, zoonotic diseases

Alabama, 171, 589 mimicry, 390 empathic, 102, 522–24 mirror neurons and, see mirror neurons minimal group paradigm, 389–91 Minsky, Marvin, 603, 605 mirror neurons and supposed functions, 166n, 180n, 536–41 autism and, 539–40 empathy and, 540–41 social interactions and, 538–39 Mischel, Walter, 186–87 Mitchell, David, 657 M’Naghten, Daniel, 586–87, 598 Mogil, Jeffrey, 133, 524, 544 mole rats, 120, 352 Moniz, Egas, 9 Money, John, 215 monkeys, 4, 35, 36, 47, 48, 50–51, 55, 67, 68, 70, 71, 73–74, 82, 104, 109–10, 123, 148, 172, 221, 429, 535, 557 baboons, 17, 123, 131–32, 162, 172, 191–92, 196, 207, 295, 303, 337, 338, 429, 648–52, 648, 650 “Garbage Dump” troop of, 648–50, 649 hierarchies and, 426–27, 427, 428, 436–39, 442, 455 deception in, 513 “executive,” stress in, 436 Harlow’s experiments with, 189–90, 190, 192 kinship understanding in, 337–38 langurs and competitive infanticide, 334–35 moral judgments in, 484–85, 487 sex differences in behaviors of, 213–14, 214 social rank and, 433, 434 tamarins, 110, 213, 355, 357 monoamine oxidase-A (MAO-A), 251–55, 257, 264, 605 monogamy, 339, 366 morality and moral decisions, 478–520 in animals, 484–87 applying science of, 504–20 automaticity and, 50 in children, 181–85 reasoning in, 182–83 competition and, 495–500 consequentialism and, 504–7, 520 context in, 488–503 cultural, 275, 493–503 framing, 491–92 language, 491 proximity, 491 special circumstances, 492–93 cooperation and, 495–500, 508–9 cultural differences and, 275 deontology and, 504, 505, 520 disgust and, 398, 454, 561–65 doing the harder thing when it’s the correct thing to do, 45, 47–48, 50, 51, 55, 56, 63, 64, 74, 75, 92, 130, 134, 513, 515, 614 dumbfounding in, 483 honesty and duplicity and, 512–20 in infants, 483–84 internal motives and external actions in, 493 intuition in, 478, 479, 481–83, 507–8 “me vs. us” and “us vs. them” in, 508–12 obedience and, 471, 473 see also obedience and conformity political orientation and, 449–50 punishment and, see punishment reasoning in, 169, 478–81, 487–88, 507–8, 542 in adolescents, 167–69 in children, 182–83 in infants, 483–84 runaway trolley problem (killing one person to save five) and, 55, 56, 58–59, 117, 482, 488–91, 505–7 self-driving cars and, 612n saving person vs. dog, 368, 371 and sins of commission vs. omission, 490 and tragedy of the commons vs. tragedy of commonsense morality, 508–11, 533 universals of, 494–95 utilitarianism and, 505–7 virtue ethics and, 504, 520 Moral Life of Children, The (Coles), 181n Moral Origins: The Evolution of Virtue, Altruism, and Shame (Boehm), 323 Moral Politics: How Liberals and Conservatives Think (Lakoff), 558 Moral Tribes: Emotion, Reason, and the Gap Between Us and Them (Greene), 508–9 Mormons, 367 Morozov, Pavlik, 368–69, 487 Morse, Stephen, 598–600 Moscone, George, 92n Mother Teresa, 535 motivation, “you must be so smart” vs.

., 298 Parkinson’s disease, 693 Parks, Rosa, 652 Pascual-Leone, Alvaro, 146 pastoralism, 282–83, 288, 379 religion and, 283, 304 pattern separation, 149 Pavlovian conditioning, 37n Paxton, Joseph, 518–19 peace: peaceology, 647 see also war and peace Pearl Harbor, 653–55 pedophilia, 597, 598 Peel, Robert, 586–87 peers, 164–67, 204 periaqueductal gray (PAG), 41, 42, 59, 527 Perkins, Marlin, 332, 426, 427 Perry, Gina, 466 personality traits, 439 compliance and, 473–74 genes and, 236 repressive, 63n perspective taking, 419, 522, 523, 617 Peterson, Dale, 316 Phelps, Elizabeth, 67, 85 phenylketonuria, 246 pheromones, 84, 90, 340 phobias and amygdala, 36 Piaget, Jean, 176–78, 181, 479 Pinker, Steven, 306–9, 311–15, 319, 321, 541, 616–20 pituitary gland, 99, 108, 125, 621, 708–9 Planet of the Apes, 387, 388 Plants and transposable genetic elements, 232 play, 204–5 playback experiments, 338, 352, 428 Plomin, Robert, 239 PMC (premotor cortex), 47, 166–68, 535–36, 540 PMDD (premenstrual dysphoric disorder), 121–22 PMS (premenstrual syndrome), 121–24 Poe, Edgar Allan, 62 political orientations, 88, 90, 444–55, 476–77 biology and, 452–55 genetic influences on, 455 genetics as viewed via, 224n, 237n implicit factors underlying, 446–52 affective psychological differences, 450–52 intellectual style, 447–49 intelligence, 446–47 moral cognition, 449–50 internal consistency of, 445–46 right-wing authoritarianism (RWA), 422, 446–47, 450, 451 polygamy, 339, 366 polygraph tests, 516–17 populations: density of, 297–99 heterogeneity of, 299–301 size of, 296–97 Porter, John, 632–33 post-traumatic stress disorder (PTSD), 34, 78, 150, 152, 153, 195, 645, 646, 656n poverty, 195–96, 249, 294, 295, 441, 576, 614 prairie voles, 110–11, 113n, 116, 229, 525–26, 530 Predictably Irrational (Ariely), 491 prefrontal cortex (PFC), 46–51, 54–61, 63, 65, 72, 74, 75, 79, 85, 88, 103, 129, 130, 157, 168, 416, 433, 434, 460, 535, 545 empathy and, 527, 531 moral decision making and, 479–82, 487–89, 492, 505–7, 513, 515–19 punishment and, 609–10 prehistoric and indigenous cultures, 305–26, 307, 310, 318, 320, 322 prejudice: racial, 89, 392, 416 see also Us/Them dichotomies premenstrual dysphoric disorder (PMDD), 121–22 premenstrual syndrome (PMS), 121–24 premotor cortex (PMC), 47, 166–68, 535–36, 540 Price, Tavin, 555 Prinz, Jesse, 546 prisoners, 464, 468 IRA, 468, 555 judicial decisions on, 448, 449, 566, 583, 643 Prisoner’s Dilemma (PD), 92, 116, 345–46, 372, 393, 557, 633, 634 prison experiments: BBC, 467–68 Stanford, 461, 463–68 progesterone, 117–19, 124, 158, 211, 708 prostate gland, 329 proteins, 709, 711–17 amino acids in, 712 DNA as blueprint for, 712–14 shape of, 711–12 Provance, Samuel, 652 Prozac, 694 psilocybin, 693 psychopaths, 44, 54, 97 PTSD (post-traumatic stress disorder), 34, 78, 150, 152, 153, 195, 645, 646, 656n puberty, 158–59 see also adolescence Punisher’s Brain, The: The Evolution of Judge and Jury (Hoffman), 609 punishment, 2, 38, 39, 56, 57, 62, 66, 182–83, 297, 484, 609–11, 635–37 antisocial, 272, 292–93, 496, 497 free-riding, 496, 497 third-party, 297, 636 purity, hygiene, and moral judgment, 563–65 quantum mechanics, 583n Rabin, Yitzhak, 576–77, 670 race, 406–8, 425 faces and, 85–87, 89, 391–92, 398, 408–9, 418–19, 614, 628–29 stereotypes and racism, 89, 392, 416 Raine, Adrian, 54 Rakic, Pasko, 147–50 Ramachandran, Vilayanur, 541, 594–95 Ramón y Cajal, Santiago, 681, 684, 688 Ramses II, 366–67 Rand, David, 511 Rapoport, Anatol, 346 rats, 37, 71, 82, 103, 127, 142, 146, 148, 151, 193, 205 population density and, 298 ravens, 428 Rayburn, Sam, 515n Reagan, Ronald, 587 reappraisal strategies, 60–61, 160, 453 reasoning, 617 moral, 169, 478–81, 487–88, 507–8, 542 in adolescents, 167–69 in children, 182–83 in infants, 483–84 see also morality and moral decisions see also cognition reciprocal altruism, 324, 342–54, 372–73, 499, 547 indirect, 324 in single-cell amoeba, 344n red blood cells, 680, 680, 681 reciprocity, 15, 523 reconciliation, 3, 15, 18, 525, 614, 637–42, 670 truth and reconciliation commissions (TRC), 638–39, 642 Red Queen scenario, 344 Reicher, Stephen, 467–68 religion, 88, 304–5, 547, 553, 617, 621–26 aggression and, 624–26, 625 atheism and, 626 Bible in, 11, 624–25, 625, 660 commonalities in, 621–22 and fairness and punishment, 498–99 in hunter-gatherer societies, 297, 623 Islam, 372, 396n, 553–54, 624, 626 pastoralism and, 283, 304 Theory of Mind and, 622 violence and, 624 Religion, Brain and Behavior, 622 Reparations for slavery, 638, 640 repressive personalities, 63n reputation, 95, 106, 393, 548, 634, 635 revenge, 15, 501 reward, 39–40, 65–70, 548 adolescence and, 162–64, í163 anticipation of, 70–73, 70, 72 arbitrary markers and, 391 pursuit of, 73–76, 75 Ricard, Matthieu, 544–45 rice farming and culture, 278–79, 278, 281 rights revolution, 617 right-wing authoritarianism (RWA), 422, 446–47, 450, 451 risk taking, 103, 131 (see also dopamine, and DRD4) in adolescence, 160–64 Rivers, Mendel, 658n Rizzolatti, Giacomo, 535, 538–39 RNA, 225, 226, 230, 233, 713–14 Robinson, Peter, 577 Roof, Dylann, 641 Roosevelt, Franklin Delano, 640 Roper v. Simmons, 170–71, 589, 590, 592 Rosenberg, Julius and Ethel, 396 Rousseau, Jean-Jacques, 305, 309, 325, 616 Rozin, Paul, 399, 562 Rudolph, Wilma, 596 runaway trolley problem (killing one person to save five), 55, 56, 58–59, 117, 482, 488–91, 505–7 self-driving cars and, 612n Russell, Jeffrey, 606 Rwanda, 570 genocide in, 571–72, 573, 619 Hutu and Tutsi tribes in, 372, 469, 570–73 Sabah, Nayirah al-, and supposed atrocities during the Gulf War, 632–33 sacred values, in conflict resolution, 575–79, 643–44 Sahlins, Marshall, 318 Saleh, Ali Abdullah, 653 Samoans, 122 Sandusky, Jerry, 597 Sandy Hook Elementary School massacre, 561 San Francisco earthquake (1989), 301 Santayana, George, 669–70 Saud, King, 367 Saypol, Irving, 396 Scalia, Antonin, 590 scapegoating, 531 schadenfreude, 15, 413 Schiller, Friedrich, 443 schizophrenia, 234, 235, 239, 582, 586, 593, 607 Schultz, Wolfram, 68, 71 Science, 133, 246–47, 251, 266, 278, 300n, 313, 322, 495, 524, 546, 549, 574–75, 636 Scientific American, 298 selective serotonin reuptake inhibitors, 694 self-confidence, 102–3, 237 Semai, 313, 502n Semang, 317, 318 sensorimotor contagion, 86, 395, 522 sensory stimuli, 6–7, 15, 81–98 amygdala and, 40–41 in animals, 83–84 auditory, 6, 83–84, 89 cultural differences in processing, 276 haptic (touch), 565–66 hormones and, see hormones interoceptive information, 90–92, 528, 529, 566 real vs. metaphorical sensation, 565–68 and sensitivity of sensory organs, 96–97 subliminal and unconscious, 84–90, 93–96 language, 92–93 temperature, 566 visual, 6, 84 Sepoy Mutiny, 391n September 11 attacks, 619 Seromba, Athanase, 572 serotonin, 134, 692 aggression and, 76–77, 250–55 genes and, 227, 246, 250–55, 264 psilocybin and, 693 selective serotonin reuptake inhibitors, 694 SES, see socioeconomic status sex, 11, 39, 43, 65–66, 95 oxytocin and, 110 sex differences, 266 cultural, 272 dimorphic, 366 and hormones in prenatal environment, 211–19 math skills and, 266–67, 267, 406 obedience and, 474 in monkey behaviors, 213–14, 214 transgender individuals and, 215n sexual selection, 330–31 Seyfarth, Robert, 337–38 shame, 502–3 Shariff, Azim, 623 Shepher, Joseph, 371 Sherman, Marshall, 554 Shermer, Michael, 495 Shweder, Richard, 271, 494 Sigmund, Karl, 350 Silkwood, Karen, 652 Simpson, O.

., 354–58, 360, 383 Toxoplasma gondii, 151, 219 trade, 620–21 tragedy of the commons vs. tragedy of commonsense morality, 508–11, 533 transcription factor (TF), 226–29, 233 transgender individuals, 215n Treachery of Images, The (Magritte), 556–57, 556 Trench Warfare: 1914–1918 (Ashworth), 665, 666 Trip to the Moon, A, 398 Trivers, Robert, 344, 384 trolley problem (killing one person to save five), 55, 56, 58–59, 117, 482, 488–91, 505–7 self-driving cars and, 612n trust, 112–13, 116, 292, 496 chimpanzees and, 393 truth and reconciliation commissions (TRC), 638–39, 642 tryptophan hydroxylase (TH), 251 Tsai, Jeanne, 275 Tunisia and the Arab Spring, 652–53 Turchin, Peter, 291 Tutsi and Hutu tribes, 372, 469, 570–73 Tutu, Desmond, 639 Tversky, Amos, 93 Twinkie defense, 92n twins, 336, 717 studies of, 234–41 Tylor, Edward, 269 UCLA, 502–3 Uganda, 414 Ultimatum Game, 38–39, 106, 486, 497, 498, 500, 610, 635 unconscious and subliminal cuing, 84–90, 93–96 language, 92–93 United States: ethnicity in, 395 individualism in, 277 regionalism in, 288 South in, 181n, 207, 284–88, 501 urban living, 296, 298–99 Us and Them: The Science of Identity (Berreby), 399 Us/Them dichotomies, 387–424, 425, 478, 493, 526 in children, 391–92 conformity and, 470 converts and, 397 and discrepancies between what people claim to believe and how they act, 416–18 disgust and, 398–99 elimination of, 423 empathy and, 532–35 essentialism and, 399, 407, 423 individuation vs., 420–21 frontal cortex and, 416–17 hierarchies and, 421–22, 425 honorable enemy phenomenon and, 414, 415 Implicit Association Test (IAT) and, 116, 388, 389, 416, 582 magical contagion and, 403 manipulation of, 418–22, 469 by changing rank ordering of categories, 419–20 by contact, 420, 626–30 by cuing and priming, 418–19 to decrease implicit biases, 419, 643 essentialism vs. individuation in, 420–21 hierarchies in, 421–22 minimal, arbitrary groupings in, 389–91, 393 oxytocin and, 116–17, 389 race and, 406–8 self-hating and, 415 strength of, 388–93 Them in, 398–405 dehumanization and pseudospeciation of, 372, 570, 572–73, 574, 632–33 different feelings about different types of, 410–11 individual vs. group interactions with, 404–5 oxytocin and, 116–17, 135, 614 religion and, 624 thoughts vs. feelings about, 400–404 uniquely human realms of, 405–18 Us in, 393–97 multiple categories of, 405–10, 491 warmth and competence categories in, 410–15, 522 values, sacred, in conflict resolution, 575–79, 643–44 vasopressin, see oxytocin and vasopressin Vietnam War, 415, 624, 647, 664 My Lai Massacre in, 464, 655–58, 657, 658 Viljoen, Constand and Abraham Viljoen, 578, 670 violence, 2–4, 11, 15 in adolescence, 170–71 in American South, 285–88, 286, 501 context of, 3 in crime waves of 1970s and 1980s, 311 crowding and, 298–99 culture and, 272 decline in, 306, 615–21 fear and, 44 frontal cortex and, 54 genes and, 224 hot-blooded vs. cold-blooded, 18 in hunter-gatherer societies, 319–25, 322 income inequality and, 295 incompetence at and aversion to, 644–47 intrafamily, 369–70 media, 198, 206–7 observing in childhood, 197–98 in prehistoric and indigenous cultures, 306–15 psychopathic, 44 religion and, 624 reproductive success and, 367 serotonin and, 76–77 temperature and, 303 testosterone and, 170 see also aggression virtue ethics, 504, 520 visual spectrum, 6 visual stimuli, 6, 84 voles, 110–11, 113n, 116, 229, 525–26, 530 Voltaire, 383 Von Frisch, Karl, 83n Von Neumann, John, 345 voting, 237, 403, 442–44, 451 Wall, Patrick, 699 Wallace, Alfred Russel, 230n Wallen, Kim, 215 war and peace, 614–70 Civil War, 409, 662 Battle of Gettysburg, 554, 644 collective power and, 662–68 contact and, 420, 626–30 decline in violence, 306, 615–21 fraternizing between enemy soldiers, 662 Golden Arches theory of peace, 620 individuals making a difference, 652–61 religion and, 621–26 trade and, 620–21 Vietnam War, 415, 624, 647, 664 My Lai Massacre in, 464, 655–58, 657, 658 World War I, 394–95, 414, 619–21, 662–68, 670 Christmas truce in, 410, 663–65, 663, 667 Live and Let Live truces in, 665–67 propaganda posters in, 667 World War II, 202, 308, 404, 410, 413, 618, 619, 645–47 Japanese in, 413, 640, 653–55, 668, 669 War Before Civilization: The Myth of the Peaceful Savage (Keeley), 306 Washington, Booker T., 642 Watergate, 652 Watson, James, 714 Watson, John, 8–9, 82 weaning conflict, 358 weather, 302–3 Wegner, Daniel, 62 Wellesley effect, 11, 90 Wendorf, Fred, 308 Westermarck effect, 371 Weyer, Johann, 584 Wheeler, Mary, 408 White, Dan, 92n Whiten, Andrew, 458 Whitman, Charles, 33 Who’s in Charge?


pages: 158 words: 46,353

Future War: Preparing for the New Global Battlefield by Robert H. Latiff

Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, Berlin Wall, Boeing 747, CRISPR, cyber-physical system, Danny Hillis, defense in depth, drone strike, dual-use technology, Elon Musk, failed state, friendly fire, Howard Zinn, Internet of things, low earth orbit, military-industrial complex, Nicholas Carr, orbital mechanics / astrodynamics, post-truth, precautionary principle, Recombinant DNA, self-driving car, Seymour Hersh, South China Sea, Stephen Hawking, Stewart Brand, Strategic Defense Initiative, Stuxnet, synthetic biology, VTOL, Wall-E

In health care, computers and algorithms make excellent diagnoses, and robots perform exquisite surgeries, displacing highly skilled doctors. Police departments now depend heavily on data mining and predictive analytics to solve and prevent crimes, eliminating the need for human deductive reasoning. Self-driving cars are on the horizon. In war, that most human of endeavors, we are designing machines to handle every phase of conflict, including finding an enemy, tracking them, identifying their capabilities, targeting them with an appropriate weapon, and destroying them. The issue is the speed at which this is taking place, and how little debate or understanding will go into it.


pages: 474 words: 130,575

Surveillance Valley: The Rise of the Military-Digital Complex by Yasha Levine

23andMe, activist fund / activist shareholder / activist investor, Adam Curtis, Airbnb, AltaVista, Amazon Web Services, Anne Wojcicki, anti-communist, AOL-Time Warner, Apple's 1984 Super Bowl advert, bitcoin, Black Lives Matter, borderless world, Boston Dynamics, British Empire, Californian Ideology, call centre, Charles Babbage, Chelsea Manning, cloud computing, collaborative editing, colonial rule, company town, computer age, computerized markets, corporate governance, crowdsourcing, cryptocurrency, data science, digital map, disinformation, don't be evil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Dr. Strangelove, drone strike, dual-use technology, Edward Snowden, El Camino Real, Electric Kool-Aid Acid Test, Elon Musk, end-to-end encryption, fake news, fault tolerance, gentrification, George Gilder, ghettoisation, global village, Google Chrome, Google Earth, Google Hangouts, Greyball, Hacker Conference 1984, Howard Zinn, hypertext link, IBM and the Holocaust, index card, Jacob Appelbaum, Jeff Bezos, jimmy wales, John Gilmore, John Markoff, John Perry Barlow, John von Neumann, Julian Assange, Kevin Kelly, Kickstarter, Laura Poitras, life extension, Lyft, machine readable, Mark Zuckerberg, market bubble, Menlo Park, military-industrial complex, Mitch Kapor, natural language processing, Neal Stephenson, Network effects, new economy, Norbert Wiener, off-the-grid, One Laptop per Child (OLPC), packet switching, PageRank, Paul Buchheit, peer-to-peer, Peter Thiel, Philip Mirowski, plutocrats, private military company, RAND corporation, Ronald Reagan, Ross Ulbricht, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, Snapchat, Snow Crash, SoftBank, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Susan Wojcicki, Telecommunications Act of 1996, telepresence, telepresence robot, The Bell Curve by Richard Herrnstein and Charles Murray, The Hackers Conference, Tony Fadell, uber lyft, vertical integration, Whole Earth Catalog, Whole Earth Review, WikiLeaks

Stanford was the epicenter of the Bay Area dot-com boom when a young Larry Page parachuted right into the vortex. Page started the computer science PhD program at Stanford in the autumn of 1995. He was in his element and immediately started scratching around for a research topic worthy of a dissertation. He toyed with various ideas, including a self-driving car, which Google would later get into in a heavy way. Eventually, he settled on Internet search.19 In the mid-1990s, the Internet was growing exponentially. The landscape was chaotic: a jumble of random websites, personal webpages, university sites, news sites, and corporate properties. Pages were popping up all over the place.

It could be hard to keep track of them all: Gmail, Google Docs, Google Drive, Google Maps, Android, Google Play, Google Cloud, YouTube, Google Translate, Google Hangouts, Google Chrome, Google+, Google Sites, Google Developer, Google Voice, Google Analytics, Android TV. It blasted beyond pure Internet services and delved into fiber-optic telecommunication systems, tablets, laptops, home security cameras, self-driving cars, shopping delivery, robots, electric power plants, life extension technology, cyber security, and biotech. The company even launched a powerful in-house investment bank that now rivals Wall Street companies, investing money in everything from Uber to obscure agricultural crop monitoring start-ups, ambitious human DNA sequencing companies like 23andME, and a secretive life extension research center called Calico.88 No matter what service it deployed or what market it entered, surveillance and prediction were cooked into the business.


pages: 742 words: 137,937

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

23andMe, 3D printing, Abraham Maslow, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, Blue Ocean Strategy, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, Computing Machinery and Intelligence, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, death of newspapers, disintermediation, Douglas Hofstadter, driverless car, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, Filter Bubble, full employment, future of work, Garrett Hardin, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, Large Hadron Collider, lifelogging, lump of labour, machine translation, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, Nick Bostrom, optical character recognition, Paul Samuelson, personalized medicine, planned obsolescence, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, Susan Wojcicki, tacit knowledge, TED Talk, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, transaction costs, Turing test, Two Sigma, warehouse robotics, Watson beat the top human players on Jeopardy!, WikiLeaks, world market for maybe five computers, Yochai Benkler, young professional

Another is provided by Walmart, which analysed the buying habits of its customers prior to hurricanes and found not just that flashlights were in greater demand but so too were Pop-Tarts; and this insight enabled them to stock up accordingly when the next storm came round. Natural language translation systems and self-driving cars are also said to operate on the back of Big Data techniques.41 While there are many ways in which Big Data is valuable,42 most specialists in the field would agree with Mayer-Schönberger and Cukier that, ‘[a]t its core, big data is about predictions … it’s about applying math to huge quantities of data in order to infer probabilities … these systems perform well because they are fed with lots of data on which to base their predictions’.43 More extravagantly, Eric Siegel, a computer scientist, goes further when he speaks of ‘computers automatically developing new knowledge and capabilities by furiously feeding on modern society’s greatest and most potent unnatural resource: data’.44 If we combine these views of Big Data, we can see its promise for the professions—as a way of making predictions and as a way of generating new knowledge.

They argued that computers had caused ‘a major upheaval in the nature of human work’, and that they would continue replacing people in ‘an ever widening range of tasks … the list becomes longer each year’.54 But they stopped short of declaring that computers would replace all jobs. One task that they thought was beyond their reach was driving. They said it was ‘hard to imagine’ that truck-drivers would ever be computerized. Is it not remarkable, therefore, that Google has developed a small fleet of self-driving cars just one decade later? In ten years robots have moved ‘from making cars to driving them’.55 By 2014, Google’s vehicles had travelled almost 700,000 miles, with only one incident (said to be caused by a car driven by a human being). In the United States legislation has been passed in four states and in Washington, DC, allowing driverless cars.56 By 2020 most major car manufacturers also expect to be selling autonomous vehicles.


pages: 370 words: 129,096

Elon Musk: Tesla, SpaceX, and the Quest for a Fantastic Future by Ashlee Vance

addicted to oil, Burning Man, clean tech, digital map, El Camino Real, Elon Musk, fail fast, Ford Model T, gigafactory, global supply chain, Great Leap Forward, high-speed rail, Hyperloop, industrial robot, Jeff Bezos, Kickstarter, Kwajalein Atoll, Larry Ellison, low earth orbit, Mark Zuckerberg, Mars Society, Maui Hawaii, Max Levchin, Menlo Park, Mercator projection, military-industrial complex, money market fund, multiplanetary species, off-the-grid, optical character recognition, orbital mechanics / astrodynamics, PalmPilot, paypal mafia, performance metric, Peter Thiel, pneumatic tube, pre–internet, risk tolerance, Ronald Reagan, Sand Hill Road, Scaled Composites, self-driving car, side project, Silicon Valley, Silicon Valley startup, Solyndra, Steve Jobs, Steve Jurvetson, technoutopianism, Tesla Model S, Tony Fadell, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, vertical integration, Virgin Galactic, We wanted flying cars, instead we got 140 characters, X Prize

Tony Fadell, the former Apple executive credited with bringing the iPod and iPhone to market, has characterized the smartphone as representative of a type of super-cycle in which hardware and software have reached a critical point of maturity. Electronics are good and cheap, while software is more reliable and sophisticated. Their interplay is now resulting in science fiction–worthy ideas we were promised long ago becoming a reality. Google has its self-driving cars and has acquired dozens of robotics companies as it looks to merge code and machine. Fadell’s company Nest has its intelligent thermostats and smoke alarms. General Electric has jet engines packed full of sensors taught to proactively report possible anomalies to its human mechanics. And a host of start-ups have begun infusing medical devices with powerful software to help people monitor and analyze their bodies and diagnose conditions.

“He’s kind of homeless, which I think is sort of funny,” Page said. “He’ll e-mail and say, ‘I don’t know where to stay tonight. Can I come over?’ I haven’t given him a key or anything yet.” Google has invested more than just about any other technology company into Musk’s sort of moon-shot projects: self-driving cars, robots, and even a cash prize to get a machine onto the moon cheaply. The company, however, operates under a set of constraints and expectations that come with employing tens of thousands of people and being analyzed constantly by investors. It’s with this in mind that Page sometimes feels a bit envious of Musk, who has managed to make radical ideas the basis of his companies.


pages: 496 words: 131,938

The Future Is Asian by Parag Khanna

3D printing, Admiral Zheng, affirmative action, Airbnb, Amazon Web Services, anti-communist, Asian financial crisis, asset-backed security, augmented reality, autonomous vehicles, Ayatollah Khomeini, barriers to entry, Basel III, bike sharing, birth tourism , blockchain, Boycotts of Israel, Branko Milanovic, British Empire, call centre, capital controls, carbon footprint, cashless society, clean tech, clean water, cloud computing, colonial rule, commodity super cycle, computer vision, connected car, corporate governance, CRISPR, crony capitalism, cross-border payments, currency peg, death from overwork, deindustrialization, Deng Xiaoping, Didi Chuxing, Dissolution of the Soviet Union, Donald Trump, driverless car, dual-use technology, energy security, European colonialism, factory automation, failed state, fake news, falling living standards, family office, financial engineering, fixed income, flex fuel, gig economy, global reserve currency, global supply chain, Great Leap Forward, green transition, haute couture, haute cuisine, illegal immigration, impact investing, income inequality, industrial robot, informal economy, initial coin offering, Internet of things, karōshi / gwarosa / guolaosi, Kevin Kelly, Kickstarter, knowledge worker, light touch regulation, low cost airline, low skilled workers, Lyft, machine translation, Malacca Straits, Marc Benioff, Mark Zuckerberg, Masayoshi Son, megacity, megaproject, middle-income trap, Mikhail Gorbachev, money market fund, Monroe Doctrine, mortgage debt, natural language processing, Netflix Prize, new economy, off grid, oil shale / tar sands, open economy, Parag Khanna, payday loans, Pearl River Delta, prediction markets, purchasing power parity, race to the bottom, RAND corporation, rent-seeking, reserve currency, ride hailing / ride sharing, Ronald Reagan, Salesforce, Scramble for Africa, self-driving car, Shenzhen special economic zone , Silicon Valley, smart cities, SoftBank, South China Sea, sovereign wealth fund, special economic zone, stem cell, Steve Jobs, Steven Pinker, supply-chain management, sustainable-tourism, synthetic biology, systems thinking, tech billionaire, tech worker, trade liberalization, trade route, transaction costs, Travis Kalanick, uber lyft, upwardly mobile, urban planning, Vision Fund, warehouse robotics, Washington Consensus, working-age population, Yom Kippur War

Bike stations and dockless biking have been pioneered by companies such as Mobike and Ofo, which have spread from China across Asia and into Europe. As Asian cities prepare for driverless cars and buses on their street, policy makers, regulators, urban planners, and insurance companies are developing new frameworks to govern them. Even the Western firms ranked most likely to get self-driving cars onto the road first—including Ford, Renault, Daimler, Volkswagen, and BMW—will be looking to do so in Asia. In South Korea, Hyundai and Kia have partnered with Cisco Systems and other US IT companies to advance connected car communications. Baidu’s open-source approach to driverless-car software development, called Apollo, has lured Intel, Daimler, and Ford to contribute resources.

Google has invested more than $500 million in the Chinese e-commerce company JD.com and has opened an AI research center in Beijing. The United States’ leading high-performance graphics chip maker, NVIDIA, has partnered with Baidu to enhance the company’s efforts to deliver cloud-based services for home assistants and self-driving cars. At the same time, both Baidu and Tencent have funded AI labs in the United States, while Chinese investors more broadly have poured about $700 million into more than fifty AI start-ups in the United States—all of which want to advance their applications in Asia’s largest markets. All of this helps to explain why, according to the Asian Development Bank, AI is creating far more jobs in Asia than it is destroying.


pages: 462 words: 129,022

People, Power, and Profits: Progressive Capitalism for an Age of Discontent by Joseph E. Stiglitz

affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, AlphaGo, antiwork, barriers to entry, basic income, battle of ideas, behavioural economics, Berlin Wall, Bernie Madoff, Bernie Sanders, Big Tech, business cycle, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, carried interest, central bank independence, clean water, collective bargaining, company town, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crony capitalism, DeepMind, deglobalization, deindustrialization, disinformation, disintermediation, diversified portfolio, Donald Trump, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, fake news, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, Firefox, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, George Akerlof, gig economy, Glass-Steagall Act, global macro, global supply chain, greed is good, green new deal, income inequality, information asymmetry, invisible hand, Isaac Newton, Jean Tirole, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John von Neumann, Joseph Schumpeter, labor-force participation, late fees, low interest rates, low skilled workers, Mark Zuckerberg, market fundamentalism, mass incarceration, meta-analysis, minimum wage unemployment, moral hazard, new economy, New Urbanism, obamacare, opioid epidemic / opioid crisis, patent troll, Paul Samuelson, pension reform, Peter Thiel, postindustrial economy, price discrimination, principal–agent problem, profit maximization, purchasing power parity, race to the bottom, Ralph Nader, rent-seeking, Richard Thaler, Robert Bork, Robert Gordon, Robert Mercer, Robert Shiller, Robert Solow, Ronald Reagan, Savings and loan crisis, search costs, secular stagnation, self-driving car, shareholder value, Shoshana Zuboff, Silicon Valley, Simon Kuznets, South China Sea, sovereign wealth fund, speech recognition, Steve Bannon, Steve Jobs, surveillance capitalism, TED Talk, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, too big to fail, trade liberalization, transaction costs, trickle-down economics, two-sided market, universal basic income, Unsafe at Any Speed, Upton Sinclair, uranium enrichment, War on Poverty, working-age population, Yochai Benkler

We can make machines that not only perform programmed tasks better than humans but also learn better, at least in certain domains. Thus machines can outperform people in many key jobs. Better education and job training for workers may be a short-term palliative for many, but computers can and are replacing radiologists, so not even a doctor’s degree provides a safe harbor. It is anticipated that within a few years, self-driving cars and trucks will replace drivers; if true, this is of especial concern, because truck driving today represents a very large source of employment for men who have a high school diploma or less. The worry is that these labor-replacing machines will drive down wages, especially of low-skilled workers, and increase unemployment.

United States, 133 science and collective judgments, 262–63n20 Enlightenment and, 10–12 opposition to, 20 replacement with ideology, 20 as shared value, 228 as social enterprise, 262n18 and standard of living, 263n22 Trump’s attack on, xvii, 16–17 and wealth of a nation, xiv sea level rise, 207 secular stagnation, 120 securitization of mortgages, 217 self-driving cars, 118 self-interest, 19–20, 113 selfishness, 30, 240 Senate, 6, 159–60 senior citizens, 181–82 service sector, 122–23 share buybacks, 109 shareholder interests, 102 shareholder value maximization, 112 share of capital, 53 Shelby County v. Holder, 342–43n44 Sherman Antitrust Act (1890), 68 Shiller, Robert, 63–64 shortsightedness, 104–5 short-term interests, 84, 112 short-term stock trading, 207 Silicon Valley, 16, 117 Sinclair, Upton, 144 single payer system, 214 skill-biased technological changes, 304n19 slavery, 161, 271n3 small and medium-sized enterprises (SMEs), 102, 105–6 Smith, Adam, 152 on collusion among businesses, 51, 66 and Enlightenment, 10 and limits of markets, 24 and moral sentiments, 229 Wealth of Nations, 8–9 Snowden, Edward, 127 Social Darwinism, 309–10n42 social insurance programs, 141, 189 “socialist market economy with Chinese characteristics,” 95 social justice government involvement in economy and, 142 and intergenerational transmission of advantage/disadvantage, 199–201 and labor market, 197–99 restoring, 197–201 social media, 96, 131–36; See also Facebook social protection, 188–91 government and, 231 unemployment insurance, 189–90 universal basic income, 190–91 Social Security, 13, 142, 189, 210, 214–16, 242 Social Security Administration, 217 Social Security Trust Fund, 216 society, economic behavior and, 30 soft power, 29, 235–36 solar panels, Chinese, 91–92 Solow, Robert, 263n22 special interests, 146; See also lobbyists specialization, 9 SpeechNow.org v.


pages: 197 words: 49,240

Melting Pot or Civil War?: A Son of Immigrants Makes the Case Against Open Borders by Reihan Salam

Affordable Care Act / Obamacare, Bonfire of the Vanities, charter city, delayed gratification, Donald Trump, driverless car, Edward Glaeser, gentrification, ghettoisation, guest worker program, illegal immigration, immigration reform, income inequality, income per capita, industrial robot, interchangeable parts, job automation, low skilled workers, low-wage service sector, mass immigration, megacity, new economy, obamacare, open borders, open immigration, race to the bottom, self-driving car, Shenzhen special economic zone , Silicon Valley, special economic zone, two tier labour market, upwardly mobile, urban decay, working poor

Under these circumstances, it’s not just offshoring that might go into reverse. The same could happen to laborsaving technologies of all sorts. Think back to the relationship between gas prices and the prevalence of gas-guzzling cars. If low-skill labor were sufficiently abundant, no one would bother to work on self-driving cars and delivery drones, as chauffeurs and bicycle messengers could be had at cut-rate prices. Indeed, in the absence of low-skill immigration, many of today’s low-wage jobs—in agriculture, garment manufacturing, meatpacking, and retail—would already be done by machines or by workers overseas. Consider the case made by Lant Pritchett, a senior fellow at the Center for Global Development and a leading advocate of open borders immigration policies.


pages: 165 words: 50,798

Intertwingled: Information Changes Everything by Peter Morville

A Pattern Language, Airbnb, Albert Einstein, Arthur Eddington, augmented reality, Bernie Madoff, bike sharing, Black Swan, business process, Cass Sunstein, cognitive dissonance, collective bargaining, Computer Lib, disinformation, disruptive innovation, folksonomy, holacracy, index card, information retrieval, Internet of things, Isaac Newton, iterative process, Jane Jacobs, Jeff Hawkins, John Markoff, Kanban, Lean Startup, Lyft, messenger bag, minimum viable product, Mother of all demos, Nelson Mandela, Paul Graham, peer-to-peer, Project Xanadu, quantum entanglement, RFID, Richard Thaler, ride hailing / ride sharing, Schrödinger's Cat, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley startup, single source of truth, source of truth, Steve Jobs, Stewart Brand, systems thinking, Ted Nelson, the Cathedral and the Bazaar, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, theory of mind, uber lyft, urban planning, urban sprawl, Vannevar Bush, vertical integration, zero-sum game

An industry that is not just “sustainable,” but enhances the world.clx A decade later, not only are we not there yet, but we may be traveling in the opposite direction. Are the espoused values of tricksters clouding our vision? What are the real assumptions, beliefs, and values of Silicon Valley? What is the theory of the world behind self-driving cars, wearable devices, ingestible sensors, clones, drones, and the Singularity? Of course, we shouldn’t be too hard on the technologists, since we won’t even have a future unless we invent ourselves out of the box. The root of our problem is on the opposite coast. Our federal government is corrupt and riddled with tricksters who are neither wise nor noble.


Moon Rush: The New Space Race by Leonard David

agricultural Revolution, Apollo 11, Apollo 13, Colonization of Mars, cuban missile crisis, dark pattern, data acquisition, Donald Trump, driverless car, Elon Musk, financial engineering, Google X / Alphabet X, gravity well, Jeff Bezos, Late Heavy Bombardment, life extension, low earth orbit, Mars Society, multiplanetary species, Neil Armstrong, out of africa, self-driving car, Silicon Valley, telepresence, telerobotics, Virgin Galactic

We do not need it to go anywhere, in fact, argues Zubrin. “It is true that one could operate rovers on the lunar surface from orbit, but the argument that it is worth the expense of such a station in order to eliminate the two-second time delay involved in controlling them from Earth is absurd. We are on the verge of having self-driving cars on Earth that can handle traffic conditions in New York City and Los Angeles. There’s a lot less traffic on the Moon,” says Zubrin. But for many space planners, the Gateway is the way to go. It offers the ability to regain and sustain deep-space exploration beyond the historical but short-duration undertaking accomplished with the Apollo missions.


pages: 173 words: 55,328

Last Best Hope: America in Crisis and Renewal by George Packer

affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, anti-bias training, anti-communist, Berlin Wall, Bernie Sanders, Big Tech, BIPOC, Black Lives Matter, blue-collar work, Branko Milanovic, British Empire, business cycle, Capital in the Twenty-First Century by Thomas Piketty, collective bargaining, coronavirus, COVID-19, crony capitalism, defund the police, deindustrialization, desegregation, disinformation, Donald Trump, failed state, fake news, Fall of the Berlin Wall, Ferguson, Missouri, fulfillment center, full employment, George Floyd, ghettoisation, gig economy, glass ceiling, informal economy, Jeff Bezos, knowledge economy, liberal capitalism, lockdown, Lyft, Mark Zuckerberg, mass immigration, meritocracy, minimum wage unemployment, new economy, Norman Mailer, obamacare, off-the-grid, postindustrial economy, prosperity theology / prosperity gospel / gospel of success, QAnon, ride hailing / ride sharing, road to serfdom, Ronald Reagan, school vouchers, self-driving car, Silicon Valley, social distancing, Social Justice Warrior, Steve Bannon, too big to fail, Triangle Shirtwaist Factory, Uber and Lyft, uber lyft, Upton Sinclair, white flight, working poor, young professional

The winners in Smart America—connected by airplane, Internet, and investments to the rest of the globe—have lost the capacity and the need for a national identity, which is why they can’t grasp its importance for others. Their passionate loyalty, the one that gives them a particular identity, goes to their family. The rest is diversity and efficiency, heirloom tomatoes and self-driving cars. They don’t see the point of patriotism. In 2004 the Harvard political scientist Samuel Huntington published his last book, Who Are We? It was a cry of alarm about the demise of American identity under globalization. The New Yorker gave it a withering review for raising a panic about something obsolete: “If the world is becoming more porous, more transnational, more tuned to the same economic, social, and informational frequency—if the globe is more global, which means more Americanized—then the need for national cultural homogeneity is lesser, not greater.


pages: 573 words: 157,767

From Bacteria to Bach and Back: The Evolution of Minds by Daniel C. Dennett

Ada Lovelace, adjacent possible, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, Andrew Wiles, Bayesian statistics, bioinformatics, bitcoin, Bletchley Park, Build a better mousetrap, Claude Shannon: information theory, computer age, computer vision, Computing Machinery and Intelligence, CRISPR, deep learning, disinformation, double entry bookkeeping, double helix, Douglas Hofstadter, Elon Musk, epigenetics, experimental subject, Fermat's Last Theorem, Gödel, Escher, Bach, Higgs boson, information asymmetry, information retrieval, invention of writing, Isaac Newton, iterative process, John von Neumann, language acquisition, megaproject, Menlo Park, Murray Gell-Mann, Necker cube, Norbert Wiener, pattern recognition, phenotype, Richard Feynman, Rodney Brooks, self-driving car, social intelligence, sorting algorithm, speech recognition, Stephen Hawking, Steven Pinker, strong AI, Stuart Kauffman, TED Talk, The Wealth of Nations by Adam Smith, theory of mind, Thomas Bayes, trickle-down economics, Turing machine, Turing test, Watson beat the top human players on Jeopardy!, Y2K

It is just another level of generate and test, in a game that could hardly be more abstract and insulated from real-world noise and its attendant concerns, but AlphaGo is learning to make “intuitive” judgments about situations that have few of the hard-edged landmarks that computer programs excel at sorting through. With the self-driving car almost ready for mass adoption—a wildly optimistic prospect that not many took seriously only a few years ago—will the self-driving scientific exploration vehicle be far behind? So practical, scientific, and aesthetic judgment may soon be off-loaded or outsourced to artificial agents. If Susan Blackmore is right, this abdication or alienation of human judgment is already being pioneered in the digital world of popular music and Internet memes—tremes, in her new terminology (see ch. 11, p. 237).

., 61–63, 203, 206, 222, 287, 337, 350, 354, 355–56, 366, 367, 412 scientific information, 113–14 scientism, 11 Seabright, Paul, 408–9 seagulls, 83 Searle, John R., 95, 364–65, 366 Secret of Our Success, The (Heinrich), 305 Seedol, Lee, 391 self: as end-user, 345, 346–53 sense of, see sense of self self-criticism, 341 self-deception, 342 self-domestication, 122, 197 self-driving cars, 392 Selfish Gene, The (Dawkins), 205–6 self-justification, 341 self-maintenance, self-repair: AI and, 158, 159 in living organisms, 6, 18, 26 self-monitoring, 390 in communication, 342 talking to oneself as, 296–98 self-monitoring, of machines, 65 as step toward consciousness, 69 self-protection, as fundamental reason for behavior, 343–44 Selfridge, Oliver, 201, 342 Sellars, Wilfrid, 41, 61–62, 63, 219, 314n, 409, 412 semantic information, 112, 128 as “a difference that makes a difference,” 116–17, 118n, 120, 125, 411 asymmetric, 114–15 design improvement and, 119 as design worth getting, 5, 115, 126–27, 128, 206, 411 digitization of, 226–27 economic, see economic information information theory and, 108–9 as information worth stealing, 228 memes as, 206, 211 semantic information as misinformation and disinformation, 116, 117–18, 128, 131, 206–7 ontologies and, 125 scientific, 113–14 Shannon information vs., 108, 113, 130–31 storage of, 108–9 unwanted, 115–16 useful, 117–18, 119–21 “useless,” 118, 126–27 use of term, 107 see also affordances semantic information, extraction of, 3, 74, 85 accumulated knowledge and, 122–23 Bayesian models of, 167–68 brain as organ for, 150–51, 157 evolution and, 118–19 human competences and, 135 see also learning semantic information, transmission of, 106–7, 108–9, 412 beneficiaries in, 117–18 deception in, 127 DNA and, 123–24, 125 language and, 96 light and, 119–20 signal and noise in, 127–28 semantic information, value of: as confirmable but not measurable, 128 legal protection of, 128–34, 136 semantics, children’s acquisition of, 194–95 sense of self: as applied to one’s view of others, 345 in humans, 344 in nonhuman species, 343 see also consciousness serendipity, in software, 47 Seung, Sebastian, 162 sexism, 22, 23–24 sexual selection, 134 Shakespeare, William, 77, 227, 324 Shannon, Claude, 116, 151, 162–63 information theory of, 106, 107–9, 113, 124, 129, 157–58, 411 Shannon information, 5, 108, 109, 111, 117, 129–30, 136, 165 Shazam (app), 185n shell game, 306–7 shibboleth test, 330 Siegel, Lee, 318 signal, information transmission and, 108, 111, 124, 127–28, 136 Simon, Herbert, 153 Sims, Karl, 385 Skinner, B.


pages: 501 words: 145,943

If Mayors Ruled the World: Dysfunctional Nations, Rising Cities by Benjamin R. Barber

"World Economic Forum" Davos, Aaron Swartz, Affordable Care Act / Obamacare, American Legislative Exchange Council, Berlin Wall, bike sharing, borderless world, Boris Johnson, Bretton Woods, British Empire, car-free, carbon footprint, Cass Sunstein, Celebration, Florida, classic study, clean water, congestion pricing, corporate governance, Crossrail, crowdsourcing, David Brooks, desegregation, Detroit bankruptcy, digital divide, digital Maoism, digital rights, disinformation, disintermediation, edge city, Edward Glaeser, Edward Snowden, Etonian, Evgeny Morozov, failed state, Fall of the Berlin Wall, feminist movement, Filter Bubble, gentrification, George Gilder, ghettoisation, global pandemic, global village, Hernando de Soto, Howard Zinn, illegal immigration, In Cold Blood by Truman Capote, income inequality, informal economy, information retrieval, Jane Jacobs, Jaron Lanier, Jeff Bezos, Lewis Mumford, London Interbank Offered Rate, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, megacity, microcredit, Mikhail Gorbachev, mortgage debt, mutually assured destruction, new economy, New Urbanism, Nicholas Carr, Norman Mailer, nuclear winter, obamacare, Occupy movement, off-the-grid, Panopticon Jeremy Bentham, Peace of Westphalia, Pearl River Delta, peer-to-peer, planetary scale, plutocrats, Prenzlauer Berg, profit motive, Ralph Waldo Emerson, RFID, Richard Florida, Ronald Reagan, self-driving car, Silicon Valley, SimCity, Skype, smart cities, smart meter, Steve Jobs, Stewart Brand, technological determinism, technological solutionism, TED Talk, Telecommunications Act of 1996, The Death and Life of Great American Cities, The Fortune at the Bottom of the Pyramid, The future is already here, The Wealth of Nations by Adam Smith, Tobin tax, Tony Hsieh, trade route, UNCLOS, UNCLOS, unpaid internship, urban sprawl, Virgin Galactic, War on Poverty, zero-sum game

Electronic (principally wireless) sensors are, for example, introducing a valuable new layer of automation to city sustainability and efficiency in transportation and energy. Sensors that facilitate the “platooning” (efficient spacing) of vehicles and the democratization of parking information and other measures that reduce engine idling are surely useful innovations. A number of American states, including California and Nevada, are experimenting with “self-driving cars” that are far more energy efficient than traditional vehicles and are intended primarily for cities. Smart thermostats save electricity and make heating and air-conditioning more efficient. Smart sensors of every kind are being used in cities across the world, including Cairo, Dubai, Kochi, Málaga, Malta, Yokohama, Songdo, and Southampton.

See Virtues and vices of cities vs. countryside Rustbelt cities, 186, 223 Sandig, Jochen, 278–279 San Francisco and gay marriage, 167 Santa Monica as rebel town, 324 Santander, Spain, smart sensors in, 261 São Paulo business revival, 223 Sassen, Saskia, 10, 16, 65–66, 116, 248 Scavengers, 231 Schmidt, Eric, 241 Scholz, Olaf, 109 Schuster, Wolfgang: on democracy, 8, 84; on jobs, 213; on networks, 169; on parliament of mayors, 337, 338, 344, 353; profile, 103–105 Sea level rise, 130 Seastead, 16 Seattle, plastic grocery bag ban, 149 “Seaworlds,” 16 Second Life (video game), 261, 391n39 Secularism, 70 Security, 121–130, 160, 202–204 Segregation, 187–192 Selebi, Jackie, 126 “Self-driving cars,” 261 Self-sufficiency, 60, 63–64, 321–325 Self-Sufficient City Contest, 18 Seoul. See Park Won-soon September 11, 2001, terrorist attacks, 107 Serra, Artur, 241 Service centers, 16 Service jobs, 221–222 Service Learning movement, 395n5 Shaw, George Bernard, 272–273, 365n21 Shklar, Judith N., 197–198 Shopping malls, 44–45, 47, 276 Singapore: as city-state, 8–9, 11; mitigation of inequality in, 234, 386n38; natural networks in, 113; parks in, 47; reinvention of, 222.


pages: 559 words: 155,372

Chaos Monkeys: Obscene Fortune and Random Failure in Silicon Valley by Antonio Garcia Martinez

Airbnb, airport security, always be closing, Amazon Web Services, Big Tech, Burning Man, business logic, Celtic Tiger, centralized clearinghouse, cognitive dissonance, collective bargaining, content marketing, corporate governance, Credit Default Swap, crowdsourcing, data science, deal flow, death of newspapers, disruptive innovation, Dr. Strangelove, drone strike, drop ship, El Camino Real, Elon Musk, Emanuel Derman, Fairchild Semiconductor, fake it until you make it, financial engineering, financial independence, Gary Kildall, global supply chain, Goldman Sachs: Vampire Squid, Hacker News, hive mind, How many piano tuners are there in Chicago?, income inequality, industrial research laboratory, information asymmetry, information security, interest rate swap, intermodal, Jeff Bezos, Kickstarter, Malcom McLean invented shipping containers, Marc Andreessen, Mark Zuckerberg, Maui Hawaii, means of production, Menlo Park, messenger bag, minimum viable product, MITM: man-in-the-middle, move fast and break things, Neal Stephenson, Network effects, orbital mechanics / astrodynamics, Paul Graham, performance metric, Peter Thiel, Ponzi scheme, pre–internet, public intellectual, Ralph Waldo Emerson, random walk, Reminiscences of a Stock Operator, Ruby on Rails, Salesforce, Sam Altman, Sand Hill Road, Scientific racism, second-price auction, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, Social Justice Warrior, social web, Socratic dialogue, source of truth, Steve Jobs, tech worker, telemarketer, the long tail, undersea cable, urban renewal, Y Combinator, zero-sum game, éminence grise

What are the costliest Google keywords among relatively high-volume keywords? The ranking changes, but the top ten is always composed of some combination of “insurance,” “loans,” “mortgage,” “classes,” “credit,” “lawyer,” and so on. These are Google’s moneymakers, which pay for the Android phones, the Chrome browser, the self-driving cars, the flying Wi-Fi balloons, and whatever weird, geeky, philanthropic shit the company is up to recently. Think about this in the context of more traditional industries for a moment. Chain restaurants like McDonald’s have a best-performing outlet in a particularly busy high-rent district. Automakers have a particularly popular, bestselling model like the Ford Fusion or the Chevy Impala that makes their quarter.

Like “scuba,” “radar,” and “laser,” “Kitten” was originally an acronym, whose origins had been more or less forgotten; the name now simply referred to the current state of Facebook’s topic-extraction technology. Topic extraction is one of those critical but unsexy artificial-intelligence challenges that underlie huge pieces of Internet technology (e.g., Google Search), but never receive the attention of sexy initiatives like self-driving cars. In essence, it’s a programmatic way of mapping the convoluted parlance of human texts like messages, webpages, or social media posts into a dictionary of semantic categories. For example, your status update of “Tiger really managed to hit that birdie in the US Open” would be automatically mapped to the categories “Tiger Woods,” “Golf,” and “US Open.”


pages: 565 words: 151,129

The Zero Marginal Cost Society: The Internet of Things, the Collaborative Commons, and the Eclipse of Capitalism by Jeremy Rifkin

3D printing, active measures, additive manufacturing, Airbnb, autonomous vehicles, back-to-the-land, benefit corporation, big-box store, bike sharing, bioinformatics, bitcoin, business logic, business process, Chris Urmson, circular economy, clean tech, clean water, cloud computing, collaborative consumption, collaborative economy, commons-based peer production, Community Supported Agriculture, Computer Numeric Control, computer vision, crowdsourcing, demographic transition, distributed generation, DIY culture, driverless car, Eben Moglen, electricity market, en.wikipedia.org, Frederick Winslow Taylor, Free Software Foundation, Garrett Hardin, general purpose technology, global supply chain, global village, Hacker Conference 1984, Hacker Ethic, industrial robot, informal economy, information security, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Isaac Newton, James Watt: steam engine, job automation, John Elkington, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Julian Assange, Kickstarter, knowledge worker, longitudinal study, low interest rates, machine translation, Mahatma Gandhi, manufacturing employment, Mark Zuckerberg, market design, mass immigration, means of production, meta-analysis, Michael Milken, mirror neurons, natural language processing, new economy, New Urbanism, nuclear winter, Occupy movement, off grid, off-the-grid, oil shale / tar sands, pattern recognition, peer-to-peer, peer-to-peer lending, personalized medicine, phenotype, planetary scale, price discrimination, profit motive, QR code, RAND corporation, randomized controlled trial, Ray Kurzweil, rewilding, RFID, Richard Stallman, risk/return, Robert Solow, Rochdale Principles, Ronald Coase, scientific management, search inside the book, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, smart cities, smart grid, smart meter, social web, software as a service, spectrum auction, Steve Jobs, Stewart Brand, the built environment, the Cathedral and the Bazaar, the long tail, The Nature of the Firm, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas L Friedman, too big to fail, Tragedy of the Commons, transaction costs, urban planning, vertical integration, warehouse automation, Watson beat the top human players on Jeopardy!, web application, Whole Earth Catalog, Whole Earth Review, WikiLeaks, working poor, Yochai Benkler, zero-sum game, Zipcar

Joann Muller, “With Driverless Cars, Once Again It Is California Leading the Way,” Forbes, September 26, 2012, http://www.forbes.com/sites/joannmuller/2012/09/26/with-driverless-cars -once-again-it-is-california-leading-the-way/ (accessed June 2, 2013). 20. Chris Urmson, “The Self-Driving Car Logs More Miles on New Wheels,” Google Blog, August 7, 2012, http://googleblog.blogspot.com/2012/08/the-self-driving-car-logs-more-miles-on.html (accessed June 2, 2013). 21. Mary Slosson, “Google Gets First Self-Driven Car License in Nevada,” Reuters, May 8, 2012, http://www.reuters.com/article/2012/05/08/uk-usa-nevada-google-idUSLNE84701320120508 (accessed June 3, 2013). 22.


pages: 470 words: 148,730

Good Economics for Hard Times: Better Answers to Our Biggest Problems by Abhijit V. Banerjee, Esther Duflo

3D printing, accelerated depreciation, affirmative action, Affordable Care Act / Obamacare, air traffic controllers' union, Airbnb, basic income, behavioural economics, Bernie Sanders, Big Tech, business cycle, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, carbon tax, Cass Sunstein, charter city, company town, congestion pricing, correlation does not imply causation, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, decarbonisation, Deng Xiaoping, Donald Trump, Edward Glaeser, en.wikipedia.org, endowment effect, energy transition, Erik Brynjolfsson, experimental economics, experimental subject, facts on the ground, fake news, fear of failure, financial innovation, flying shuttle, gentrification, George Akerlof, Great Leap Forward, green new deal, high net worth, immigration reform, income inequality, Indoor air pollution, industrial cluster, industrial robot, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), Jane Jacobs, Jean Tirole, Jeff Bezos, job automation, Joseph Schumpeter, junk bonds, Kevin Roose, labor-force participation, land reform, Les Trente Glorieuses, loss aversion, low skilled workers, manufacturing employment, Mark Zuckerberg, mass immigration, middle-income trap, Network effects, new economy, New Urbanism, no-fly zone, non-tariff barriers, obamacare, off-the-grid, offshore financial centre, One Laptop per Child (OLPC), open economy, Paul Samuelson, place-making, post-truth, price stability, profit maximization, purchasing power parity, race to the bottom, RAND corporation, randomized controlled trial, restrictive zoning, Richard Thaler, ride hailing / ride sharing, Robert Gordon, Robert Solow, Ronald Reagan, Savings and loan crisis, school choice, Second Machine Age, secular stagnation, self-driving car, shareholder value, short selling, Silicon Valley, smart meter, social graph, spinning jenny, Steve Jobs, systematic bias, Tax Reform Act of 1986, tech worker, technology bubble, The Chicago School, The Future of Employment, The Market for Lemons, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, total factor productivity, trade liberalization, transaction costs, trickle-down economics, Twitter Arab Spring, universal basic income, urban sprawl, very high income, War on Poverty, women in the workforce, working-age population, Y2K

Their current technological backwardness could just be a symptom of their lack of capital. Finally, and this might be the hardest piece to wrap one’s head around, countries on the way to the balanced growth path could actually be upgrading their technologies faster than those already there. Of course, the most showy breakthroughs, the self-driving cars and 3D printers of the day, will always be in the more advanced countries, but most technology upgrading is just moving from day-before-yesterday’s technology to yesterday’s. This is typically easier than pushing the frontier, precisely because it has already been done and we know exactly how to do it.

Bill Gates has recommended it.23 In 2017 the European Parliament considered, but ultimately voted down, a proposed “robot tax,” citing concern over stifling innovation.24 Around the same time, however, South Korea announced the world’s first robot tax. The Korean plan reduces tax subsidies for businesses investing in automation and combines it with a tax on outsourcing, so that the tax on robots does not lead to outsourcing.25 The problem is that while it is easy to ban self-driving cars (whether or not it’s a good idea), most robots do not look like R2-D2 in Star Wars. They are typically embedded inside machines that will still have human operators, just fewer of them; how does the regulator decide where the machine stops and the robot begins? A robot tax would likely lead companies to find new ways around it, further distorting the economy.


pages: 1,034 words: 241,773

Enlightenment Now: The Case for Reason, Science, Humanism, and Progress by Steven Pinker

3D printing, Abraham Maslow, access to a mobile phone, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Albert Einstein, Alfred Russel Wallace, Alignment Problem, An Inconvenient Truth, anti-communist, Anton Chekhov, Arthur Eddington, artificial general intelligence, availability heuristic, Ayatollah Khomeini, basic income, Berlin Wall, Bernie Sanders, biodiversity loss, Black Swan, Bonfire of the Vanities, Brexit referendum, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, carbon tax, Charlie Hebdo massacre, classic study, clean water, clockwork universe, cognitive bias, cognitive dissonance, Columbine, conceptual framework, confounding variable, correlation does not imply causation, creative destruction, CRISPR, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, decarbonisation, degrowth, deindustrialization, dematerialisation, demographic transition, Deng Xiaoping, distributed generation, diversified portfolio, Donald Trump, Doomsday Clock, double helix, Eddington experiment, Edward Jenner, effective altruism, Elon Musk, en.wikipedia.org, end world poverty, endogenous growth, energy transition, European colonialism, experimental subject, Exxon Valdez, facts on the ground, fake news, Fall of the Berlin Wall, first-past-the-post, Flynn Effect, food miles, Francis Fukuyama: the end of history, frictionless, frictionless market, Garrett Hardin, germ theory of disease, Gini coefficient, Great Leap Forward, Hacker Conference 1984, Hans Rosling, hedonic treadmill, helicopter parent, Herbert Marcuse, Herman Kahn, Hobbesian trap, humanitarian revolution, Ignaz Semmelweis: hand washing, income inequality, income per capita, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), invention of writing, Jaron Lanier, Joan Didion, job automation, Johannes Kepler, John Snow's cholera map, Kevin Kelly, Khan Academy, knowledge economy, l'esprit de l'escalier, Laplace demon, launch on warning, life extension, long peace, longitudinal study, Louis Pasteur, Mahbub ul Haq, Martin Wolf, mass incarceration, meta-analysis, Michael Shellenberger, microaggression, Mikhail Gorbachev, minimum wage unemployment, moral hazard, mutually assured destruction, Naomi Klein, Nate Silver, Nathan Meyer Rothschild: antibiotics, negative emissions, Nelson Mandela, New Journalism, Norman Mailer, nuclear taboo, nuclear winter, obamacare, ocean acidification, Oklahoma City bombing, open economy, opioid epidemic / opioid crisis, paperclip maximiser, Paris climate accords, Paul Graham, peak oil, Peter Singer: altruism, Peter Thiel, post-truth, power law, precautionary principle, precision agriculture, prediction markets, public intellectual, purchasing power parity, radical life extension, Ralph Nader, randomized controlled trial, Ray Kurzweil, rent control, Republic of Letters, Richard Feynman, road to serfdom, Robert Gordon, Rodney Brooks, rolodex, Ronald Reagan, Rory Sutherland, Saturday Night Live, science of happiness, Scientific racism, Second Machine Age, secular stagnation, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Kuznets, Skype, smart grid, Social Justice Warrior, sovereign wealth fund, sparse data, stem cell, Stephen Hawking, Steve Bannon, Steven Pinker, Stewart Brand, Stuxnet, supervolcano, synthetic biology, tech billionaire, technological determinism, technological singularity, Ted Kaczynski, Ted Nordhaus, TED Talk, The Rise and Fall of American Growth, the scientific method, The Signal and the Noise by Nate Silver, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, total factor productivity, Tragedy of the Commons, union organizing, universal basic income, University of East Anglia, Unsafe at Any Speed, Upton Sinclair, uranium enrichment, urban renewal, W. E. B. Du Bois, War on Poverty, We wanted flying cars, instead we got 140 characters, women in the workforce, working poor, World Values Survey, Y2K

This is the danger that we will be subjugated, intentionally or accidentally, by artificial intelligence (AI), a disaster sometimes called the Robopocalypse and commonly illustrated with stills from the Terminator movies. As with Y2K, some smart people take it seriously. Elon Musk, whose company makes artificially intelligent self-driving cars, called the technology “more dangerous than nukes.” Stephen Hawking, speaking through his artificially intelligent synthesizer, warned that it could “spell the end of the human race.”19 But among the smart people who aren’t losing sleep are most experts in artificial intelligence and most experts in human intelligence.20 The Robopocalypse is based on a muzzy conception of intelligence that owes more to the Great Chain of Being and a Nietzschean will to power than to a modern scientific understanding.21 In this conception, intelligence is an all-powerful, wish-granting potion that agents possess in different amounts.

The observation of a 1965 report from NASA still holds: “Man is the lowest-cost, 150-pound, nonlinear, all-purpose computer system which can be mass-produced by unskilled labor.”32 Driving a car is an easier engineering problem than unloading a dishwasher, running an errand, or changing a diaper, and at the time of this writing we’re still not ready to loose self-driving cars on city streets.33 Until the day when battalions of robots are inoculating children and building schools in the developing world, or for that matter building infrastructure and caring for the aged in ours, there will be plenty of work to be done. The same kind of ingenuity that has been applied to the design of software and robots could be applied to the design of government and private-sector policies that match idle hands with undone work.34 * * * If not robots, then what about hackers?

Hanson, “I Still Don’t Get Foom,” Humanity+, July 29, 2014, http://hplusmagazine.com/2014/07/29/i-still-dont-get-foom/; Hanson & Yudkowsky 2008. See also Kelly 2017, and notes 26 and 27 above. 31. Quoted in J. Bohannon, “Fears of an AI Pioneer,” Science, July 17, 2016. 32. Quoted in Brynjolfsson & McAfee 2015. 33. Self-driving cars not quite ready: Brooks 2016. 34. Robots and jobs: Brynjolfsson & McAfee 2016; see also chapter 9, notes 67 and 68. 35. The bet is registered on the “Long Bets” Web site, http://longbets.org/9/. 36. Improving computer security: Schneier 2008; B. Schneier, “Lessons from the Dyn DDoS Attack,” Schneier on Security, Nov. 1, 2016, https://www.schneier.com/essays/archives/2016/11/lessons_from_the_dyn.html. 37.


pages: 903 words: 235,753

The Stack: On Software and Sovereignty by Benjamin H. Bratton

1960s counterculture, 3D printing, 4chan, Ada Lovelace, Adam Curtis, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Robotics, Amazon Web Services, Andy Rubin, Anthropocene, augmented reality, autonomous vehicles, basic income, Benevolent Dictator For Life (BDFL), Berlin Wall, bioinformatics, Biosphere 2, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, capitalist realism, carbon credits, carbon footprint, carbon tax, carbon-based life, Cass Sunstein, Celebration, Florida, Charles Babbage, charter city, clean water, cloud computing, company town, congestion pricing, connected car, Conway's law, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, digital capitalism, digital divide, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Douglas Engelbart, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, Ethereum, ethereum blockchain, Evgeny Morozov, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, fulfillment center, functional programming, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, high-speed rail, Hyperloop, Ian Bogost, illegal immigration, industrial robot, information retrieval, Intergovernmental Panel on Climate Change (IPCC), intermodal, Internet of things, invisible hand, Jacob Appelbaum, James Bridle, Jaron Lanier, Joan Didion, John Markoff, John Perry Barlow, Joi Ito, Jony Ive, Julian Assange, Khan Academy, Kim Stanley Robinson, Kiva Systems, Laura Poitras, liberal capitalism, lifelogging, linked data, lolcat, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megaproject, megastructure, Menlo Park, Minecraft, MITM: man-in-the-middle, Monroe Doctrine, Neal Stephenson, Network effects, new economy, Nick Bostrom, ocean acidification, off-the-grid, offshore financial centre, oil shale / tar sands, Oklahoma City bombing, OSI model, packet switching, PageRank, pattern recognition, peak oil, peer-to-peer, performance metric, personalized medicine, Peter Eisenman, Peter Thiel, phenotype, Philip Mirowski, Pierre-Simon Laplace, place-making, planetary scale, pneumatic tube, post-Fordism, precautionary principle, RAND corporation, recommendation engine, reserve currency, rewilding, RFID, Robert Bork, Sand Hill Road, scientific management, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, skeuomorphism, Slavoj Žižek, smart cities, smart grid, smart meter, Snow Crash, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, synthetic biology, TaskRabbit, technological determinism, TED Talk, the built environment, The Chicago School, the long tail, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, undersea cable, universal basic income, urban planning, Vernor Vinge, vertical integration, warehouse automation, warehouse robotics, Washington Consensus, web application, Westphalian system, WikiLeaks, working poor, Y Combinator, yottabyte

It is still geared toward staging and accelerating cognitive accomplishment, or as Heatherwick says in the project's video, “what is the best possible environment we can make, to invent, engineer and most importantly, make ideas happen and go out into the world?”56 Strategies for that staging move past the open-plan faux warehouse, “our self-driving car team, for example, has very different needs when it comes to office space from our Search engineers,” Google executive, Daniel Radcliffe explains. Others are more circumspect about the “Googledome,” arguing that it is either a wasteful expenditure or that its success will further gentrify the area, making it all but unlivable for anyone but the elect.57 It is too early in the project to say anything definite about is success or failure on specific terms, but we can read in the choice to keep (at least parts of) the campus open to publics including retailers and non-employee pedestrians that Google wishes for its footprint to be more than a high-performance vitrine for its human resources, and more like a spatial platform that draws surplus value from and provides lesser surplus value to those who come.

Prototypes to date have mostly used a customized Prius, though the company recently announced plans to work with auto manufacturers to build autonomous vehicles to Google's own specifications, and some early products could be commercially available in a few years, if some very wicked problems can be worked out first. On these see Lee Gomes, “Hidden Obstacles for Google's Self-Driving Cars,” MIT Technology Review, August 28, 2014. 58.  Levy again: “Why is OpenFlow so advantageous to a company like Google? In the traditional model you can think of routers as akin to taxicabs getting passengers from one place to another. If a street is blocked, the taxi driver takes another route—but the detour may be time-consuming.

It is interesting to note that the transposition of human labor into simple puzzle solving is taken by some as straightforward market efficiency and not as a transformation of humans into diminished automatons, whereas other Stack technologies that may ultimately allow for greater individual pleasure and safety are seen as affronts to the dignity of Creation. I recently heard Joi Ito, director of the MIT Media Lab say, “Google didn't just design a self-driving car. They designed a driver.” This focuses attention on the hardware-data-Cloud path dynamic that comes into play as the car navigates the City layer, partially or fully autonomous from human passenger intention. Among the most interesting features of what we call today the “driverless car” (“horseless carriage”) is how it decenters the agency and authority of the human pilot from the cockpit and disperses it into ambient networks operating at multiple scales.


pages: 223 words: 58,732

The Retreat of Western Liberalism by Edward Luce

"World Economic Forum" Davos, 3D printing, affirmative action, Airbnb, Alan Greenspan, basic income, Berlin Wall, Bernie Sanders, Boris Johnson, Branko Milanovic, bread and circuses, Bretton Woods, Brexit referendum, business cycle, call centre, carried interest, centre right, Charles Lindbergh, cognitive dissonance, colonial exploitation, colonial rule, computer age, corporate raider, cuban missile crisis, currency manipulation / currency intervention, disinformation, Dissolution of the Soviet Union, Doha Development Round, Donald Trump, double entry bookkeeping, driverless car, Erik Brynjolfsson, European colonialism, everywhere but in the productivity statistics, Evgeny Morozov, fake news, Fall of the Berlin Wall, Francis Fukuyama: the end of history, future of work, gentrification, George Santayana, gig economy, Gini coefficient, global pandemic, global supply chain, Great Leap Forward, illegal immigration, imperial preference, income inequality, independent contractor, informal economy, Internet of things, Jaron Lanier, knowledge economy, lateral thinking, Les Trente Glorieuses, liberal capitalism, Marc Andreessen, Mark Zuckerberg, Martin Wolf, mass immigration, means of production, meritocracy, microaggression, Monroe Doctrine, moral panic, more computing power than Apollo, mutually assured destruction, new economy, New Urbanism, Norman Mailer, offshore financial centre, one-China policy, opioid epidemic / opioid crisis, Peace of Westphalia, Peter Thiel, plutocrats, precariat, purchasing power parity, reserve currency, reshoring, Richard Florida, Robert Gordon, Robert Solow, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Skype, Snapchat, software is eating the world, South China Sea, Steve Bannon, Steve Jobs, superstar cities, telepresence, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thomas L Friedman, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, unpaid internship, Washington Consensus, We are the 99%, We wanted flying cars, instead we got 140 characters, white flight, World Values Survey, Yogi Berra

Is progress built ‘one brick at a time, calloused hand by calloused hand’?4 That is surely so. Reform is the fruit of painstaking effort. The civil rights victories of the 1960s were won by courageous people who were prepared to risk their lives. But I have grave doubts about history’s long arc. History is not some self-driving car taking humanity to a pre-set destination. Whichever human is behind the wheel must ensure the others stay in the car. Telling some of the passengers they have no business in the driver’s seat because they are clueless about the destination will sooner or later result in a crash. ‘Take back control’ was the chant of Brexiteers and Trump voters alike.


The Non-Tinfoil Guide to EMFs by Nicolas Pineault

Albert Einstein, en.wikipedia.org, Ignaz Semmelweis: hand washing, Internet of things, off-the-grid, precautionary principle, self-driving car, Silicon Valley, Skype, smart cities, smart grid, smart meter

At this point, I’ll see that he’s clearly had enough doom and gloom for today — and won’t even talk about the fact that things will likely get way worse in the next few years. © 2017 N&G Media Inc. 195 I won’t even tell him that while the imminent rollout of the next-generation 5G cellular network will enable incredible technological advances like self-driving cars,526 smart cities filled with billions of sensors forming what’s called “The Internet Of Things” (IoT)527 and make clean energy cheaper than coal528 — it’s also going to increase the levels of EMF radiation we’re exposed to by orders of magnitude.529 I won’t tell him that while users will be busy enjoying the incredible download speeds 5G will bring to the table (up to 50X faster than the current 4G/LTE), and while the industry will make trillions in profits, 5G technology will also require installing millions of new cellular antennas — possibly one at every street corner,530 and on most traffic light poles.531 And I won’t even tell him that most people working for any industry closely benefiting from a quick rollout of 5G will likely send me hate mail for having the insolence to “slow down human progress”, and accuse me of being a quack — staying completely blind even when faced with the almost-overwhelming scientific evidence showing that non-ionizing radiation is making people sick.


pages: 204 words: 58,565

Keeping Up With the Quants: Your Guide to Understanding and Using Analytics by Thomas H. Davenport, Jinho Kim

behavioural economics, Black-Scholes formula, business intelligence, business process, call centre, computer age, correlation coefficient, correlation does not imply causation, Credit Default Swap, data science, en.wikipedia.org, feminist movement, Florence Nightingale: pie chart, forensic accounting, global supply chain, Gregor Mendel, Hans Rosling, hypertext link, invention of the telescope, inventory management, Jeff Bezos, Johannes Kepler, longitudinal study, margin call, Moneyball by Michael Lewis explains big data, Myron Scholes, Netflix Prize, p-value, performance metric, publish or perish, quantitative hedge fund, random walk, Renaissance Technologies, Robert Shiller, self-driving car, sentiment analysis, six sigma, Skype, statistical model, supply-chain management, TED Talk, text mining, the scientific method, Thomas Davenport

The availability of all this data means that virtually every business or organizational activity can be viewed as a big data problem or initiative. Manufacturing, in which most machines already have one or more microprocessors, is increasingly a big-data environment. Consumer marketing, with myriad customer touchpoints and clickstreams, is already a big data problem. Google has even described its self-driving car as a big-data project. CEOs like Gary Loveman at Caesars Entertainment (he’s known for saying, “Do we think, or do we know?”), Jeff Bezos at Amazon (“We never throw away data”), and Reid Hoffman at LinkedIn (“Web 3.0 is about data”) are publicly on record that analytical thinking and decision making is a route to organizational success and personal fortune.


pages: 223 words: 60,909

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Wachter-Boettcher

"Susan Fowler" uber, Abraham Maslow, Airbnb, airport security, algorithmic bias, AltaVista, big data - Walmart - Pop Tarts, Big Tech, Black Lives Matter, data science, deep learning, Donald Trump, fake news, false flag, Ferguson, Missouri, Firefox, Grace Hopper, Greyball, Hacker News, hockey-stick growth, independent contractor, job automation, Kickstarter, lifelogging, lolcat, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microaggression, move fast and break things, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, pattern recognition, Peter Thiel, real-name policy, recommendation engine, ride hailing / ride sharing, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, Tactical Technology Collective, TED Talk, Tim Cook: Apple, Travis Kalanick, upwardly mobile, Wayback Machine, women in the workforce, work culture , zero-sum game

It knows that diverse teams perform better. It also knows it needs programmers and designers—badly enough to pay six-figure starting salaries to twenty-two-year-old computer science graduates. Why, then, aren’t things getting better, faster? How can the industry that put a powerful computer in my pocket and self-driving cars on the street not be able to figure out how to get more diverse candidates into its companies? Well, I’ll tell you the secret. It’s because tech doesn’t really want to—or at least, not as much as it wants something else: lack of oversight. Consider the entire concept of the “tech industry.”


Demystifying Smart Cities by Anders Lisdorf

3D printing, artificial general intelligence, autonomous vehicles, backpropagation, behavioural economics, Big Tech, bike sharing, bitcoin, business intelligence, business logic, business process, chief data officer, circular economy, clean tech, clean water, cloud computing, computer vision, Computing Machinery and Intelligence, congestion pricing, continuous integration, crowdsourcing, data is the new oil, data science, deep learning, digital rights, digital twin, distributed ledger, don't be evil, Elon Musk, en.wikipedia.org, facts on the ground, Google Glasses, hydroponic farming, income inequality, information security, Infrastructure as a Service, Internet of things, Large Hadron Collider, Masdar, microservices, Minecraft, OSI model, platform as a service, pneumatic tube, ransomware, RFID, ride hailing / ride sharing, risk tolerance, Salesforce, self-driving car, smart cities, smart meter, software as a service, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Stuxnet, Thomas Bayes, Turing test, urban sprawl, zero-sum game

The engines were eventually changed to gasoline and electrical power, but the essence is that the industrial revolution boosted human physical power beyond what any single human or group of humans would be capable of. Similarly, AI promises to boost human mental powers. It promises to perform a number of tasks in a human way with superior performance. Current examples include self-driving cars, diagnostic aids for doctors, recommendations on music and films, and so on. All of these things have been performed by humans, but AI would be able to take them over with superior performance leaving humans to focus on other more interesting or worthwhile tasks. This is similar to the industrial revolution where machines would take over the hard, physical labor making it possible for humans to do something different instead.


pages: 196 words: 61,981

Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside by Xiaowei Wang

4chan, AI winter, Amazon Web Services, artificial general intelligence, autonomous vehicles, back-to-the-land, basic income, Big Tech, bitcoin, blockchain, business cycle, cloud computing, Community Supported Agriculture, computer vision, COVID-19, cryptocurrency, data science, deep learning, Deng Xiaoping, Didi Chuxing, disruptive innovation, Donald Trump, drop ship, emotional labour, Ethereum, ethereum blockchain, Francis Fukuyama: the end of history, Garrett Hardin, gig economy, global pandemic, Great Leap Forward, high-speed rail, Huaqiangbei: the electronics market of Shenzhen, China, hype cycle, income inequality, informal economy, information asymmetry, Internet Archive, Internet of things, job automation, Kaizen: continuous improvement, Kickstarter, knowledge worker, land reform, Marc Andreessen, Mark Zuckerberg, Menlo Park, multilevel marketing, One Laptop per Child (OLPC), Pearl River Delta, peer-to-peer lending, precision agriculture, QR code, ride hailing / ride sharing, risk tolerance, Salesforce, Satoshi Nakamoto, scientific management, self-driving car, Silicon Valley, Snapchat, SoftBank, software is eating the world, surveillance capitalism, TaskRabbit, tech worker, technological solutionism, the long tail, TikTok, Tragedy of the Commons, universal basic income, vertical integration, Vision Fund, WeWork, Y Combinator, zoonotic diseases

These non-Western theories have been marginalized throughout time by the forces of imperialism and colonialism. Ingredients dong quai (Angelica sinensis) | 9 g goji berries | 9 g ginger, cut into coarse slices | 16 g whole red dates, chopped | 12 soy milk | 2,000 ml uncooked white rice | 200 g dried apricots, diced | 100 g While companies in the West promised self-driving cars and fully sentient machines by 2020, neural networks used in AI are still constrained by a number of factors, including the specificity of training data for AI models, which is said to create a “generalization problem”: an inability to adapt to unseen new data. For example, AI models trained to perform facial recognition can classify well-lit images with great accuracy, but have a difficult time classifying faces if the photos are obscured, occluded, or shown in different lighting conditions than the images on which the AI model was trained.


pages: 254 words: 61,387

This Could Be Our Future: A Manifesto for a More Generous World by Yancey Strickler

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, Abraham Maslow, accelerated depreciation, Adam Curtis, basic income, benefit corporation, Big Tech, big-box store, business logic, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cognitive dissonance, corporate governance, Daniel Kahneman / Amos Tversky, data science, David Graeber, Donald Trump, Doomsday Clock, Dutch auction, effective altruism, Elon Musk, financial independence, gender pay gap, gentrification, global supply chain, Hacker News, housing crisis, Ignaz Semmelweis: hand washing, invention of the printing press, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Nash: game theory, Joi Ito, Joseph Schumpeter, Kickstarter, Kōnosuke Matsushita, Larry Ellison, Louis Pasteur, Mark Zuckerberg, medical bankruptcy, Mr. Money Mustache, new economy, Oculus Rift, off grid, offshore financial centre, Parker Conrad, Ralph Nader, RAND corporation, Richard Thaler, Ronald Reagan, Rutger Bregman, self-driving car, shareholder value, Silicon Valley, Simon Kuznets, Snapchat, Social Responsibility of Business Is to Increase Its Profits, Solyndra, stem cell, Steve Jobs, stock buybacks, TechCrunch disrupt, TED Talk, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Travis Kalanick, Tyler Cowen, universal basic income, white flight, Zenefits

The people in these generations have a tremendous opportunity—I would even say responsibility—to think carefully about where they want to lead us. Thirty years is not that far away. It will be here faster than we think. Three centuries ago, people lived as aristocrats or subjects. The idea of a person as an individual with rights was like the self-driving car of 2016: cool in theory, but far from an everyday reality. The idea that the rich would willfully share power was unthinkable. You had to petition the House of Lords to start a company. Children were expected to work long hours of hard labor. Then new ideas developed and spread for how the world could work.


pages: 201 words: 60,431

Long Game: How Long-Term Thinker Shorthb by Dorie Clark

3D printing, autonomous vehicles, Big Tech, Blue Ocean Strategy, buy low sell high, cognitive load, corporate social responsibility, COVID-19, crowdsourcing, delayed gratification, digital nomad, driverless car, Elon Musk, fail fast, Google X / Alphabet X, hedonic treadmill, Jeff Bezos, knowledge worker, lake wobegon effect, Lean Startup, lockdown, minimum viable product, passive income, pre–internet, rolodex, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, solopreneur, Stanford marshmallow experiment, Steven Levy, the strength of weak ties, Walter Mischel, zero-sum game

I’d be happy to spend a few hours on it a week’—it’s hard [for them] to say no.” That’s your opening, he says. “And then, if there’s a fit, naturally over time, you get invited to more meetings, you’re in the circle a little bit more, you get entrusted with things.” That’s how Adam first got involved with X. A colleague had landed a job working on the self-driving car project, and Adam was desperate to get involved. “When I say ‘an opportunity arose,’” he recalls, “it’s a very fancy way of saying I begged: Could I help? I was extremely interested in the future of mobility and what they were doing. It was exciting.” He spent several months volunteering on a research project to better understand how customers learn about and adopt new technologies.


pages: 192 words: 63,813

The End of Astronauts: Why Robots Are the Future of Exploration by Donald Goldsmith, Martin Rees

Apollo 11, Biosphere 2, blockchain, Colonization of Mars, cosmic abundance, crewed spaceflight, Donald Trump, Elon Musk, en.wikipedia.org, gravity well, hydroponic farming, Isaac Newton, James Webb Space Telescope, Jeff Bezos, Johannes Kepler, Kuiper Belt, low earth orbit, Menlo Park, microplastics / micro fibres, Neil Armstrong, operation paperclip, Peter H. Diamandis: Planetary Resources, place-making, Planet Labs, planetary scale, Ronald Reagan, satellite internet, self-driving car, South China Sea, SpaceX Starlink, Stephen Hawking, UNCLOS, V2 rocket, Virgin Galactic, Yogi Berra

However, predictions of the most significant improvements coming from artificial intelligence during the next de­cade or so show a significant overlap between terrestrial and lunar tasks. Transportation on Earth seems likely to receive the greatest impact from AI, as the replacement of our existing road vehicles with self-­driving cars and trucks improves the safety of highway travel, f­ ree from accidents caused by h ­ uman distraction and poor judgment. Excessively dangerous activities such as firefighting and under­g round mining w ­ ill be largely performed by robots. Their ability to judge and to navigate complex situations w ­ ill mimic what robots on the moon must apply on a larger scale of operation.


pages: 526 words: 160,601

A Generation of Sociopaths: How the Baby Boomers Betrayed America by Bruce Cannon Gibney

1960s counterculture, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, AlphaGo, American Society of Civil Engineers: Report Card, Bear Stearns, Bernie Madoff, Bernie Sanders, Black Lives Matter, bond market vigilante , book value, Boston Dynamics, Bretton Woods, business cycle, buy and hold, carbon footprint, carbon tax, Charles Lindbergh, classic study, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate personhood, Corrections Corporation of America, currency manipulation / currency intervention, Daniel Kahneman / Amos Tversky, dark matter, DeepMind, Deng Xiaoping, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, ending welfare as we know it, equal pay for equal work, failed state, financial deregulation, financial engineering, Francis Fukuyama: the end of history, future of work, gender pay gap, gig economy, Glass-Steagall Act, Haight Ashbury, Higgs boson, high-speed rail, Home mortgage interest deduction, Hyperloop, illegal immigration, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), invisible hand, James Carville said: "I would like to be reincarnated as the bond market. You can intimidate everybody.", Jane Jacobs, junk bonds, Kitchen Debate, labor-force participation, Long Term Capital Management, low interest rates, Lyft, Mark Zuckerberg, market bubble, mass immigration, mass incarceration, McMansion, medical bankruptcy, Menlo Park, Michael Milken, military-industrial complex, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, Neil Armstrong, neoliberal agenda, Network effects, Nixon triggered the end of the Bretton Woods system, obamacare, offshore financial centre, oil shock, operation paperclip, plutocrats, Ponzi scheme, price stability, prosperity theology / prosperity gospel / gospel of success, quantitative easing, Ralph Waldo Emerson, RAND corporation, rent control, ride hailing / ride sharing, risk tolerance, Robert Shiller, Ronald Reagan, Rubik’s Cube, Savings and loan crisis, school choice, secular stagnation, self-driving car, shareholder value, short selling, side project, Silicon Valley, smart grid, Snapchat, source of truth, stem cell, Steve Jobs, Stewart Brand, stock buybacks, survivorship bias, TaskRabbit, The Wealth of Nations by Adam Smith, Tim Cook: Apple, too big to fail, War on Poverty, warehouse robotics, We are all Keynesians now, white picket fence, Whole Earth Catalog, women in the workforce, Y2K, Yom Kippur War, zero-sum game

Then again, the possibility of a rogue supercomputer is not zero, though it remains distant. * Full disclosure: I invested in DeepMind personally in its earlier years; the company was then acquired by Google, in which I now hold stock. Wall Street has long dismissed Google’s side projects like self-driving cars and AI as money sinks, but Google has a thoughtful plan and one you may not be fully comfortable with. Google (in the verb sense; may as well start there) “self-driving car,” “AlphaGo,” and “Android Marketshare” and you’ll get a sense for the future Google might have in mind. You can add in Boston Dynamics +Atlas +Google, and you might get a sense of Google’s terminal ambitions, even if it ultimately ditches Boston Dynamics in favor of other robotics companies


pages: 665 words: 159,350

Shape: The Hidden Geometry of Information, Biology, Strategy, Democracy, and Everything Else by Jordan Ellenberg

Albert Einstein, AlphaGo, Andrew Wiles, autonomous vehicles, British Empire, Brownian motion, Charles Babbage, Claude Shannon: information theory, computer age, coronavirus, COVID-19, deep learning, DeepMind, Donald Knuth, Donald Trump, double entry bookkeeping, East Village, Edmond Halley, Edward Jenner, Elliott wave, Erdős number, facts on the ground, Fellow of the Royal Society, Geoffrey Hinton, germ theory of disease, global pandemic, government statistician, GPT-3, greed is good, Henri Poincaré, index card, index fund, Isaac Newton, Johannes Kepler, John Conway, John Nash: game theory, John Snow's cholera map, Louis Bachelier, machine translation, Mercator projection, Mercator projection distort size, especially Greenland and Africa, Milgram experiment, multi-armed bandit, Nate Silver, OpenAI, Paul Erdős, pets.com, pez dispenser, probability theory / Blaise Pascal / Pierre de Fermat, Ralph Nelson Elliott, random walk, Rubik’s Cube, self-driving car, side hustle, Snapchat, social distancing, social graph, transcontinental railway, urban renewal

That class of strategies is just not big enough to, for instance, tell you which images are cats. For that, you have to venture into the wild world of the nonlinear. DX21 The biggest thing going on right now in machine learning is the technique called deep learning. It powers AlphaGo, the computer that beat Lee Se-dol, it powers Tesla’s fleet of sort-of-self-driving cars, and it powers Google Translate. It is sometimes presented as a kind of oracle, offering superhuman insight automatically and at scale. Another name for the technique, neural networks, makes it sound as if the method is somehow capturing the workings of the human brain itself. But no. As Broussard said, it’s just math.

Common Cause, 384–85, 402, 405, 406, 408 rumors, 233–34 Russian Orthodox Church, 85 Salisbury Cathedral, 350–51 sampling, 70–74 Sanskrit poetry, 236–37, 236n, 268, 322 Sargent, John Singer, 59 Sartorius von Waltershausen, Wolfgang, 46 satire, 411–13 Savilian Professor of Geometry, 323 scale changes, 202 Scarpetta, Sergio, 142 Schachtner, Patty, 381–82 Schaeffer, Jonathan, 98–99, 138, 139–42, 140n Schimel, Brad, 380–81 Schubfachprinzip (“chest-of-drawers principle”), 273–74 Schwarzenegger, Arnold, 408 Science and Hypothesis (Poincaré), 83 science fiction, 183n Scientific American, 325n scoring functions, 164 scronch geometry, 55–56, 61–63 Second Congressional District of Wisconsin, 349–50 second law of motion, 238–39, 239n second law of thermodynamics, 331 selectivity, 37 self-driving cars, 177–78, 204–5 self-education, 210 self-evident truths, 13n Selfridge, Oliver, 204 September 11, 2001 terrorist attacks, 220 seven-shuffle theorem, 331, 393, 399 Seventh Congressional District of Pennsylvania (Goofy Kicking Donald Duck), 366, 366–67 Shamir, Adi, 135 Shannon, Claude, 93, 96, 128 Shaw v.


pages: 558 words: 175,965

When the Heavens Went on Sale: The Misfits and Geniuses Racing to Put Space Within Reach by Ashlee Vance

"Peter Beck" AND "Rocket Lab", 3D printing, Airbnb, autonomous vehicles, barriers to entry, Biosphere 2, bitcoin, Burning Man, Charles Lindbergh, cloud computing, Colonization of Mars, COVID-19, cryptocurrency, deepfake, disinformation, Elon Musk, Ernest Rutherford, fake it until you make it, Google Earth, hacker house, Hyperloop, intentional community, Iridium satellite, James Webb Space Telescope, Jeff Bezos, Kwajalein Atoll, lockdown, low earth orbit, Maui Hawaii, McMansion, Menlo Park, Mikhail Gorbachev, new economy, off-the-grid, overview effect, Peter Thiel, Planet Labs, private spaceflight, Rainbow Mansion, risk tolerance, Ronald Reagan, self-driving car, side project, Silicon Valley, Silicon Valley startup, skunkworks, SoftBank, South China Sea, South of Market, San Francisco, SpaceX Starlink, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steve Wozniak, Strategic Defense Initiative, synthetic biology, tech billionaire, TikTok, Virgin Galactic

Though the navy once used it for all sorts of missions, the airfield had been neglected for many years like the rest of the base and used in more recent times mostly for weird one-off projects. Parts of The Matrix, for example, were filmed there, and so, too, were experiments done by the MythBusters. I’d been to Nimitz on a couple of previous occasions to take part in experimental runs of self-driving cars, during which I had strapped myself into a robot going sixty miles per hour. Adam London and Chris Thompson led much of the static fire test. Surprising no one, the dragging of the mobile launcher and the rocket out to the tarmac was as much of an experiment as the test itself and took a number of hours.

It began by hiring a slew of executives from big-name companies in Silicon Valley. Most bizarrely, it named Benjamin Lyon as chief engineer. Lyon came from Apple, which, as of this writing, does not make rockets. His experience revolved around developing track pads for laptops, iPhones, and allegedly Apple’s struggling and secretive self-driving car program. Kemp argued that Lyon brought fresh perspective and the knowledge of what it took to make industrial-grade products to Astra. In other conversations, people told me that Lyon had been hired to make Astra’s investors feel good. He’d built up a strong reputation in the Valley, and the board thought he might check some of Kemp’s more brazen impulses.


pages: 391 words: 71,600

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone by Satya Nadella, Greg Shaw, Jill Tracie Nichols

3D printing, AlphaGo, Amazon Web Services, anti-globalists, artificial general intelligence, augmented reality, autonomous vehicles, basic income, Bretton Woods, business process, cashless society, charter city, cloud computing, complexity theory, computer age, computer vision, corporate social responsibility, crowdsourcing, data science, DeepMind, Deng Xiaoping, Donald Trump, Douglas Engelbart, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, fulfillment center, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, growth hacking, hype cycle, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, late capitalism, Mars Rover, Minecraft, Mother of all demos, Neal Stephenson, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Robert Solow, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, Snow Crash, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, subscription business, TED Talk, telepresence, telerobotics, The Rise and Fall of American Growth, The Soul of a New Machine, Tim Cook: Apple, trade liberalization, two-sided market, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, young professional, zero-sum game

As the digital transformation automates many tasks formerly handled by people, workers need the skills that will enable them to become managers of the new automated tools. Just as workers wielding shovels gave way to workers capable of driving bulldozers, societies now need people with the skills to manage fleets of automated bulldozers, self-driving cars, and drones. To this end, government must demonstrate empathy for all of its constituents, and work to create a more knowledge-based economy. The pathway to new technologies requires a parallel investment in skills development—making sure people have the requisite skills to participate in an increasingly digital society, one that depends on smart devices and online services.


pages: 222 words: 70,132

Move Fast and Break Things: How Facebook, Google, and Amazon Cornered Culture and Undermined Democracy by Jonathan Taplin

"Friedman doctrine" OR "shareholder theory", "there is no alternative" (TINA), 1960s counterculture, affirmative action, Affordable Care Act / Obamacare, Airbnb, AlphaGo, Amazon Mechanical Turk, American Legislative Exchange Council, AOL-Time Warner, Apple's 1984 Super Bowl advert, back-to-the-land, barriers to entry, basic income, battle of ideas, big data - Walmart - Pop Tarts, Big Tech, bitcoin, Brewster Kahle, Buckminster Fuller, Burning Man, Clayton Christensen, Cody Wilson, commoditize, content marketing, creative destruction, crony capitalism, crowdsourcing, data is the new oil, data science, David Brooks, David Graeber, decentralized internet, don't be evil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Dynabook, Edward Snowden, Elon Musk, equal pay for equal work, Erik Brynjolfsson, Fairchild Semiconductor, fake news, future of journalism, future of work, George Akerlof, George Gilder, Golden age of television, Google bus, Hacker Ethic, Herbert Marcuse, Howard Rheingold, income inequality, informal economy, information asymmetry, information retrieval, Internet Archive, Internet of things, invisible hand, Jacob Silverman, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John Perry Barlow, John von Neumann, Joseph Schumpeter, Kevin Kelly, Kickstarter, labor-force participation, Larry Ellison, life extension, Marc Andreessen, Mark Zuckerberg, Max Levchin, Menlo Park, Metcalfe’s law, military-industrial complex, Mother of all demos, move fast and break things, natural language processing, Network effects, new economy, Norbert Wiener, offshore financial centre, packet switching, PalmPilot, Paul Graham, paypal mafia, Peter Thiel, plutocrats, pre–internet, Ray Kurzweil, reality distortion field, recommendation engine, rent-seeking, revision control, Robert Bork, Robert Gordon, Robert Metcalfe, Ronald Reagan, Ross Ulbricht, Sam Altman, Sand Hill Road, secular stagnation, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Skinner box, smart grid, Snapchat, Social Justice Warrior, software is eating the world, Steve Bannon, Steve Jobs, Stewart Brand, tech billionaire, techno-determinism, technoutopianism, TED Talk, The Chicago School, the long tail, The Market for Lemons, The Rise and Fall of American Growth, Tim Cook: Apple, trade route, Tragedy of the Commons, transfer pricing, Travis Kalanick, trickle-down economics, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, unpaid internship, vertical integration, We are as Gods, We wanted flying cars, instead we got 140 characters, web application, Whole Earth Catalog, winner-take-all economy, women in the workforce, Y Combinator, you are the product

So assuming one could argue that Google is a monopoly and needs to enter into a consent decree, would the Bell Labs model work? If Google were required to license every patent it owns for a nominal fee to any American company that asks for it, it would have to license its search algorithms, Android patents, self-driving car patents, smart-thermostat patents, advertising-exchange patents, Google Maps patents, Google Now patents, virtual-reality patents, and thousands of others. What is clear from the Bell Labs model is that such a solution actually benefits innovation in general. The availability of the Bell Labs transistor patents allowed the rise of Texas Instruments, Fairchild Semiconductor, and Intel.


pages: 281 words: 71,242

World Without Mind: The Existential Threat of Big Tech by Franklin Foer

artificial general intelligence, back-to-the-land, Berlin Wall, big data - Walmart - Pop Tarts, Big Tech, big-box store, Buckminster Fuller, citizen journalism, Colonization of Mars, computer age, creative destruction, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, don't be evil, Donald Trump, Double Irish / Dutch Sandwich, Douglas Engelbart, driverless car, Edward Snowden, Electric Kool-Aid Acid Test, Elon Musk, Evgeny Morozov, Fall of the Berlin Wall, Filter Bubble, Geoffrey Hinton, global village, Google Glasses, Haight Ashbury, hive mind, income inequality, intangible asset, Jeff Bezos, job automation, John Markoff, Kevin Kelly, knowledge economy, Law of Accelerating Returns, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, means of production, move fast and break things, new economy, New Journalism, Norbert Wiener, off-the-grid, offshore financial centre, PageRank, Peace of Westphalia, Peter Thiel, planetary scale, Ray Kurzweil, scientific management, self-driving car, Silicon Valley, Singularitarianism, software is eating the world, Steve Jobs, Steven Levy, Stewart Brand, strong AI, supply-chain management, TED Talk, the medium is the message, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Thomas L Friedman, Thorstein Veblen, Upton Sinclair, Vernor Vinge, vertical integration, We are as Gods, Whole Earth Catalog, yellow journalism

Algorithms can be gorgeous expressions of logical thinking, not to mention a source of ease and wonder. They can track down copies of obscure nineteenth-century tomes in a few milliseconds; they put us in touch with long-lost elementary school friends; they enable retailers to deliver packages to our doors in a flash. Very soon, they will guide self-driving cars and pinpoint cancers growing in our innards. But to do all these things, algorithms are constantly taking our measure. They make decisions about us and on our behalf. The problem is that when we outsource thinking to machines, we are really outsourcing thinking to the organizations that run the machines


pages: 276 words: 64,903

Built for Growth: How Builder Personality Shapes Your Business, Your Team, and Your Ability to Win by Chris Kuenne, John Danner

Airbnb, Amazon Web Services, asset light, Benchmark Capital, Berlin Wall, Bob Noyce, business climate, business logic, call centre, cloud computing, disruptive innovation, don't be evil, Fairchild Semiconductor, Fall of the Berlin Wall, Gordon Gekko, Jeff Bezos, Kickstarter, Larry Ellison, Lean Startup, Mark Zuckerberg, pattern recognition, risk tolerance, Sand Hill Road, self-driving car, Silicon Valley, solopreneur, Steve Jobs, Steve Wozniak, sugar pill, super pumped, supply-chain management, systems thinking, TED Talk, work culture , zero-sum game

But that’s the crusade Google’s cofounders, Larry Page and Sergey Brin, embarked on in 1998. Since then, Google has redefined how we use the web, redesigned concepts of the workplace, and refined its business model—separating its wildly successful advertising business from its “moon shot” initiatives like self-driving cars. The open, shoot-for-the-moon culture of Google and its parent company, Alphabet, reflects the comparably creative but looser management approach that characterizes the Crusader personality. Both of Google’s businesses are essentially search engines: one for what is, and the other for what might be.


pages: 256 words: 67,563

Explaining Humans: What Science Can Teach Us About Life, Love and Relationships by Camilla Pang

autism spectrum disorder, backpropagation, bioinformatics, Brownian motion, correlation does not imply causation, data science, deep learning, driverless car, frictionless, job automation, John Nash: game theory, John von Neumann, Kickstarter, Nash equilibrium, neurotypical, phenotype, random walk, self-driving car, stem cell, Stephen Hawking

A bit like some of your maths textbooks, in which you could look up the answer at the back of the book, and the tricky part was working out how to get there. It’s supervised because, as the programmer, you know what the answers should be. Your challenge is how to get an algorithm to always reach the right answer from a wide variety of potential inputs. How, for instance, can you ensure an algorithm in a self-driving car will always recognize the difference between red and green on a traffic light, or what a pedestrian looks like? How do you guarantee that the algorithm you use to help diagnose cancer screens can correctly identify a tumour? This is classification, one of the main uses of supervised learning, in which you are essentially trying to get the algorithm to correctly label something, and to prove (and over time improve) its reliability for doing this in all sorts of real-world situations.


Survival of the Friendliest: Understanding Our Origins and Rediscovering Our Common Humanity by Brian Hare, Vanessa Woods

autism spectrum disorder, Cass Sunstein, cognitive bias, desegregation, domesticated silver fox, Donald Trump, drone strike, income inequality, Jane Jacobs, Law of Accelerating Returns, meta-analysis, microbiome, Milgram experiment, Nelson Mandela, New Urbanism, nuclear winter, out of africa, phenotype, Ray Kurzweil, Richard Florida, Ronald Reagan, selective serotonin reuptake inhibitor (SSRI), self-driving car, smart cities, social intelligence, Stanford marshmallow experiment, stem cell, Steven Pinker, The Death and Life of Great American Cities, theory of mind, Tim Cook: Apple, trade route, W. E. B. Du Bois, white flight, zero-sum game

And sometimes it’s even part of the problem.” Because technology is, and always has been, a double-edged sword. The projectile weapon that we used to cooperatively hunt mammoths could also be used to kill our fellow humans. Nuclear power could be a critical solution to our energy crisis if we manage not to start a nuclear war. Self-driving cars will save a hundred thousand lives a year, until terrorists hijack the network and kill a hundred thousand people in a series of crashes. The Internet was an amazing tool for human advancement, until foreign governments used it to sway democratic elections. In order for technology to be used as a force for good, it needs to be developed in anticipation of the best and worst of human nature, but it almost never is.


pages: 225 words: 70,590

Curbing Traffic: The Human Case for Fewer Cars in Our Lives by Chris Bruntlett, Melissa Bruntlett

15-minute city, An Inconvenient Truth, autonomous vehicles, bike sharing, BIPOC, car-free, coronavirus, COVID-19, emotional labour, en.wikipedia.org, global pandemic, green new deal, Jane Jacobs, lockdown, Lyft, microplastics / micro fibres, New Urbanism, post-work, RAND corporation, ride hailing / ride sharing, self-driving car, social distancing, streetcar suburb, the built environment, Uber and Lyft, uber lyft, urban planning, white flight, working-age population, World Values Survey

“If you are in a car, with this traffic light issue, you’re never in a prisoner’s dilemma, because this situation is solved by an external algorithm,” reveals Te Brömmelstroet. And that state of affairs will only be worsened by the (seemingly inevitable) introduction of autonomous cars: “Imagine all of us being in self-driving vehicles. The algorithm of the self-driving car solves all of these conflicts by itself. Imagine doing that for a year, and what that would do to your sense of trust of others.” On a smaller scale, this is already happening with ride-hailing services, such as Uber and Lyft, gradually diminishing our capacity to trust, and willingness to go out of our way to help one another.


pages: 225 words: 70,180

Humankind: Solidarity With Nonhuman People by Timothy Morton

a long time ago in a galaxy far, far away, Anthropocene, capitalist realism, David Brooks, Georg Cantor, gravity well, Ian Bogost, invisible hand, means of production, megacity, microbiome, mirror neurons, Oklahoma City bombing, phenotype, planetary scale, Plato's cave, Richard Feynman, self-driving car, Silicon Valley, Slavoj Žižek, trolley problem, Turing test, wage slave, zero-sum game

The future is foreclosed. An algorithm is an automated past: past “squared” if you like, because appearance is already the past. “The tradition of dead generations weighs like a nightmare on the brains of the living.”22 To run a society (or anything) purely in an algorithmic mode is to be caught in the past. Self-driving cars will be programmed to save the driver or save the pedestrians if there’s an accident: each mode will represent a past state of human style—driving will be caught in the past. PTSD is evidently automated human behavior resulting from a trauma that ripped a hole in the victim’s psyche. The PTSD victim is caught in the past to the power of two.


One Billion Americans: The Case for Thinking Bigger by Matthew Yglesias

Affordable Care Act / Obamacare, airport security, assortative mating, Big Tech, Boeing 737 MAX, Boris Johnson, British Empire, business logic, carbon footprint, carbon tax, classic study, collective bargaining, Colonization of Mars, congestion charging, congestion pricing, coronavirus, COVID-19, cross-subsidies, deindustrialization, demographic transition, Diane Coyle, Donald Trump, drive until you qualify, Edward Glaeser, Elon Musk, gentrification, global pandemic, Greta Thunberg, high-speed rail, housing crisis, illegal immigration, immigration reform, income inequality, Induced demand, industrial cluster, Kowloon Walled City, low interest rates, mandatory minimum, mass immigration, Mercator projection, minimum wage unemployment, moral panic, New Urbanism, open borders, open immigration, plutocrats, purchasing power parity, race to the bottom, secular stagnation, selective serotonin reuptake inhibitor (SSRI), self-driving car, Silicon Valley, social distancing, superstar cities, tech worker, the built environment, Thomas Malthus, transit-oriented development, white flight, working-age population, Yogi Berra

Hiring and training more staff to safely supervise little kids and to provide older ones with safe and edifying things to do after school and during the summer months is not an entirely trivial problem, and it can’t be done overnight. But the country is not running out of able-bodied people who could do this work. Indeed, there is widespread anxiety that automation in the form of self-checkout machines and self-driving cars might lead to mass unemployment of people who lack specialized skills. This worry strikes me as overblown. But at a minimum there’s no reason to think we’d be facing a social crisis if a growing child-care and education workforce started pulling labor out of low-wage retail jobs. Most likely the big companies will find technological substitutes.


pages: 829 words: 187,394

The Price of Time: The Real Story of Interest by Edward Chancellor

"World Economic Forum" Davos, 3D printing, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, asset allocation, asset-backed security, assortative mating, autonomous vehicles, balance sheet recession, bank run, banking crisis, barriers to entry, Basel III, Bear Stearns, Ben Bernanke: helicopter money, Bernie Sanders, Big Tech, bitcoin, blockchain, bond market vigilante , bonus culture, book value, Bretton Woods, BRICs, business cycle, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, carried interest, cashless society, cloud computing, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, commodity super cycle, computer age, coronavirus, corporate governance, COVID-19, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, cryptocurrency, currency peg, currency risk, David Graeber, debt deflation, deglobalization, delayed gratification, Deng Xiaoping, Detroit bankruptcy, distributed ledger, diversified portfolio, Dogecoin, Donald Trump, double entry bookkeeping, Elon Musk, equity risk premium, Ethereum, ethereum blockchain, eurozone crisis, everywhere but in the productivity statistics, Extinction Rebellion, fiat currency, financial engineering, financial innovation, financial intermediation, financial repression, fixed income, Flash crash, forward guidance, full employment, gig economy, Gini coefficient, Glass-Steagall Act, global reserve currency, global supply chain, Goodhart's law, Great Leap Forward, green new deal, Greenspan put, high net worth, high-speed rail, housing crisis, Hyman Minsky, implied volatility, income inequality, income per capita, inflation targeting, initial coin offering, intangible asset, Internet of things, inventory management, invisible hand, Japanese asset price bubble, Jean Tirole, Jeff Bezos, joint-stock company, Joseph Schumpeter, junk bonds, Kenneth Rogoff, land bank, large denomination, Les Trente Glorieuses, liquidity trap, lockdown, Long Term Capital Management, low interest rates, Lyft, manufacturing employment, margin call, Mark Spitznagel, market bubble, market clearing, market fundamentalism, Martin Wolf, mega-rich, megaproject, meme stock, Michael Milken, Minsky moment, Modern Monetary Theory, Mohammed Bouazizi, Money creation, money market fund, moral hazard, mortgage debt, negative equity, new economy, Northern Rock, offshore financial centre, operational security, Panopticon Jeremy Bentham, Paul Samuelson, payday loans, peer-to-peer lending, pensions crisis, Peter Thiel, Philip Mirowski, plutocrats, Ponzi scheme, price mechanism, price stability, quantitative easing, railway mania, reality distortion field, regulatory arbitrage, rent-seeking, reserve currency, ride hailing / ride sharing, risk free rate, risk tolerance, risk/return, road to serfdom, Robert Gordon, Robinhood: mobile stock trading app, Satoshi Nakamoto, Satyajit Das, Savings and loan crisis, savings glut, Second Machine Age, secular stagnation, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, South Sea Bubble, Stanford marshmallow experiment, Steve Jobs, stock buybacks, subprime mortgage crisis, Suez canal 1869, tech billionaire, The Great Moderation, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Thorstein Veblen, Tim Haywood, time value of money, too big to fail, total factor productivity, trickle-down economics, tulip mania, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, Walter Mischel, WeWork, When a measure becomes a target, yield curve

Theranos bled investors of more than $1 billion before going bankrupt – a unicorn-sized loss.36 Other unicorns engaged in ‘narrative construction’. Uber didn’t see itself for what it was – an online cab-hailing business – but as an integral part of the new ‘gig economy’, whose software platform could be leveraged to enter myriad other business lines. Uber planned flying taxis and self-driving cars, which, by removing pesky driver costs, would put it on a path to eventual profitability. Founded in 2010, over the following years Uber raised $20 billion from investors. In the four years prior to its 2019 stock market launch, losses totalled more than $14 billion. Uber was often described as a ‘disruptive’ business.

As one tech analyst commented: ‘The rise in unprofitable IPOs reflects the general preference in both public and private markets for growth over profitability.’18 Silicon Valley’s unicorns attracted higher valuations at each funding round, even as losses outpaced sales. A fortunate few, such as Uber and Lyft, made it to the public markets, where they jostled for attention with another company that had long promised, or rather over-promised, the imminent arrival of self-driving cars. In 2017, the market capitalization of Elon Musk’s Tesla Inc. accelerated past General Motors.19 Three years later, Tesla was valued at more than Toyota, even though the Japanese car maker produced over twenty times as many vehicles. For this valuation to hold, Musk would have to produce millions more cars every year, and to achieve that Tesla required a great deal of investment.


pages: 254 words: 76,064

Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe

3D printing, air gap, Albert Michelson, AlphaGo, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Black Swan, Bletchley Park, blockchain, Burning Man, business logic, buy low sell high, Claude Shannon: information theory, cloud computing, commons-based peer production, Computer Numeric Control, conceptual framework, CRISPR, crowdsourcing, cryptocurrency, data acquisition, deep learning, DeepMind, Demis Hassabis, digital rights, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, Ford Model T, frictionless, game design, Gerolamo Cardano, informal economy, information security, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, move 37, Nate Silver, Network effects, neurotypical, Oculus Rift, off-the-grid, One Laptop per Child (OLPC), PalmPilot, pattern recognition, peer-to-peer, pirate software, power law, pre–internet, prisoner's dilemma, Productivity paradox, quantum cryptography, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, synthetic biology, technological singularity, technoutopianism, TED Talk, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, the strength of weak ties, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, Two Sigma, universal basic income, unpaid internship, uranium enrichment, urban planning, warehouse automation, warehouse robotics, Wayback Machine, WikiLeaks, Yochai Benkler

Rather than completely retooling, as Ford needed to do when it replaced the Model T with the Model A, an engaged community can redesign its solutions in real time, or something close to it. Of course, codesign is not the only way of creating systems-oriented solutions, nor is the Media Lab the only organization working toward incorporating this principle into its work. In describing its self-driving car, Google has emphasized that the car itself is merely an object—the artificial intelligence that drives it is the system, and it must mesh seamlessly into the other systems it touches. As such, its sensors and software are being designed to work with existing road infrastructure and to solve common problems such as drunk driving and transport for people with mobility challenges.


pages: 267 words: 72,552

Reinventing Capitalism in the Age of Big Data by Viktor Mayer-Schönberger, Thomas Ramge

accounting loophole / creative accounting, Air France Flight 447, Airbnb, Alvin Roth, Apollo 11, Atul Gawande, augmented reality, banking crisis, basic income, Bayesian statistics, Bear Stearns, behavioural economics, bitcoin, blockchain, book value, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Cass Sunstein, centralized clearinghouse, Checklist Manifesto, cloud computing, cognitive bias, cognitive load, conceptual framework, creative destruction, Daniel Kahneman / Amos Tversky, data science, Didi Chuxing, disruptive innovation, Donald Trump, double entry bookkeeping, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, flying shuttle, Ford Model T, Ford paid five dollars a day, Frederick Winslow Taylor, fundamental attribution error, George Akerlof, gig economy, Google Glasses, Higgs boson, information asymmetry, interchangeable parts, invention of the telegraph, inventory management, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, job satisfaction, joint-stock company, Joseph Schumpeter, Kickstarter, knowledge worker, labor-force participation, land reform, Large Hadron Collider, lone genius, low cost airline, low interest rates, Marc Andreessen, market bubble, market design, market fundamentalism, means of production, meta-analysis, Moneyball by Michael Lewis explains big data, multi-sided market, natural language processing, Neil Armstrong, Network effects, Nick Bostrom, Norbert Wiener, offshore financial centre, Parag Khanna, payday loans, peer-to-peer lending, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price mechanism, purchasing power parity, radical decentralization, random walk, recommendation engine, Richard Thaler, ride hailing / ride sharing, Robinhood: mobile stock trading app, Sam Altman, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, Snapchat, statistical model, Steve Jobs, subprime mortgage crisis, Suez canal 1869, tacit knowledge, technoutopianism, The Future of Employment, The Market for Lemons, The Nature of the Firm, transaction costs, universal basic income, vertical integration, William Langewiesche, Y Combinator

Zetsche is a tall, slim man with a background in electrical engineering. He is easy to recognize, thanks to his bushy mustache and his sense of humor. Confronted with potential disruption by new competitors such as Tesla and its Chinese counterparts, facing game-changing advances in technology, such as self-driving cars, and finding himself up against novel business models, such as ride-hailing services, the Daimler head ordered his organization to shift into start-up mode. Copying the cellular organizational structure of successful Internet titans such as Google, Zetsche explained, would mean that Daimler will “supplement the hierarchical-management pyramid with cross-functional and interdisciplinary groups and eventually replace them.”


pages: 294 words: 77,356

Automating Inequality by Virginia Eubanks

autonomous vehicles, basic income, Black Lives Matter, business process, call centre, cognitive dissonance, collective bargaining, correlation does not imply causation, data science, deindustrialization, digital divide, disruptive innovation, Donald Trump, driverless car, Elon Musk, ending welfare as we know it, experimental subject, fake news, gentrification, housing crisis, Housing First, IBM and the Holocaust, income inequality, job automation, mandatory minimum, Mark Zuckerberg, mass incarceration, minimum wage unemployment, mortgage tax deduction, new economy, New Urbanism, payday loans, performance metric, Ronald Reagan, San Francisco homelessness, self-driving car, sparse data, statistical model, strikebreaker, underbanked, universal basic income, urban renewal, W. E. B. Du Bois, War on Poverty, warehouse automation, working poor, Works Progress Administration, young professional, zero-sum game

But in the last decade, Pittsburgh has seen a wave of young college graduates flocking to the region for jobs in the health professions, higher education, technology, and the arts. What was once Steel City now houses an estimated 1,600 technology companies, including a 450-employee office of Google and Uber’s robotic self-driving car division. Marc Cherna, director of the Allegheny County Department of Human Services, arrived in February 1996 to run what was then known as Children and Youth Services (CYS) in the wake of two very public scandals. In the first, known as the “Baby Byron” case, a white foster family, the Derzacks, refused to return an African American toddler, Byron Griffin, to the agency so he could be reunited with his mother.


pages: 255 words: 78,207

Web Scraping With Python: Collecting Data From the Modern Web by Ryan Mitchell

AltaVista, Amazon Web Services, Apollo 13, cloud computing, Computing Machinery and Intelligence, data science, en.wikipedia.org, Firefox, Guido van Rossum, information security, machine readable, meta-analysis, natural language processing, optical character recognition, random walk, self-driving car, Turing test, web application

We can detect that redirect in a clever way by “watching” an element in the DOM when the page initially loads, then repeatedly calling that element until Selenium throws a StaleElementReferenceException; that is, the element is no longer attached to the page’s DOM and the site has redirected: from selenium import webdriver import time from selenium.webdriver.remote.webelement import WebElement from selenium.common.exceptions import StaleElementReferenceException def waitForLoad(driver): elem = driver.find_element_by_tag_name("html") count = 0 while True: count += 1 if count > 20: print("Timing out after 10 seconds and returning") return time.sleep(.5) try: elem == driver.find_element_by_tag_name("html") except StaleElementReferenceException: return driver = webdriver.PhantomJS(executable_path='<Path to Phantom JS>') driver.get("http://pythonscraping.com/pages/javascript/redirectDemo1.html") waitForLoad(driver) print(driver.page_source) 158 | Chapter 10: Scraping JavaScript This script checks the page every half second, with a timeout of 10 seconds, although the times used for the checking time and timeout can be easily adjusted up or down as needed. Handling Redirects | 159 CHAPTER 11 Image Processing and Text Recognition From Google’s self-driving cars to vending machines that recognize counterfeit cur‐ rency, machine vision is a huge field with far-reaching goals and implications. In this chapter, we will focus on one very small aspect of the field: text recognition, specifi‐ cally how to recognize and use text-based images found online by using a variety of Python libraries.


pages: 268 words: 74,724

Who Needs the Fed?: What Taylor Swift, Uber, and Robots Tell Us About Money, Credit, and Why We Should Abolish America's Central Bank by John Tamny

Airbnb, Alan Greenspan, Apollo 13, bank run, Bear Stearns, Bernie Madoff, bitcoin, Bretton Woods, business logic, buy and hold, Carl Icahn, Carmen Reinhart, corporate raider, correlation does not imply causation, cotton gin, creative destruction, Credit Default Swap, crony capitalism, crowdsourcing, Donald Trump, Downton Abbey, Fairchild Semiconductor, fiat currency, financial innovation, Fractional reserve banking, full employment, George Gilder, Glass-Steagall Act, Home mortgage interest deduction, Jeff Bezos, job automation, Joseph Schumpeter, junk bonds, Kenneth Rogoff, Kickstarter, Larry Ellison, liquidity trap, low interest rates, Mark Zuckerberg, market bubble, Michael Milken, Money creation, money market fund, moral hazard, mortgage tax deduction, NetJets, offshore financial centre, oil shock, peak oil, Peter Thiel, Phillips curve, price stability, profit motive, quantitative easing, race to the bottom, Ronald Reagan, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Solyndra, Steve Jobs, The Wealth of Nations by Adam Smith, too big to fail, Travis Kalanick, Uber for X, War on Poverty, yield curve

This is particularly true in the age of the TSA. What’s exciting is that we don’t have to worry about how private flight will be turned into a common good; we can just wait for entrepreneurs to deliver it. Odds are the wait won’t be a long one. And with more and more talk about the promise of self-driving cars, can self-flying jets be too far off? There are many arguments for reducing government meddling in the economy and the allocation of credit, but private flight is perhaps one of the more visibly appealing of them. If government is consuming less of the economy’s resources, then entrepreneurs will have more credit to access and utilize in their attempts to turn the luxury that is private flight into a common good.


pages: 273 words: 72,024

Bitcoin for the Befuddled by Conrad Barski

Airbnb, AltaVista, altcoin, bitcoin, blockchain, buttonwood tree, cryptocurrency, Debian, en.wikipedia.org, Ethereum, ethereum blockchain, fiat currency, Isaac Newton, MITM: man-in-the-middle, money: store of value / unit of account / medium of exchange, Network effects, node package manager, p-value, peer-to-peer, price discovery process, QR code, radical decentralization, Satoshi Nakamoto, self-driving car, SETI@home, software as a service, the payments system, Yogi Berra

Usually, his bracelet would now dispense 20 satoshis to the winning bidder as a reward; however, because the bracelet calculates that Crowley has missed his bus, it draws upon a 100 satoshi insurance pool from an escrow account that the winning alarm-clock bidder had to set up as part of the bidding process. As a result, the winner loses money on today’s bid (the programmer has some algorithm debugging to do). With the satoshis from the escrow account, the bracelet starts an impromptu Bitcoin auction with all nearby parked, self-driving cars to determine if any are willing to rent to Crowley. After entering the winning car, Crowley is off to work. Today, Crowley’s real estate client is buying a house. Ever since the 2023 Digital Real Estate Reform Act, all houses are managed by simply tracking ownership of a single, specific satoshi assigned to each property.


pages: 202 words: 72,857

The Wealth Dragon Way: The Why, the When and the How to Become Infinitely Wealthy by John Lee

8-hour work day, Abraham Maslow, Albert Einstein, barriers to entry, Bernie Madoff, butterfly effect, buy low sell high, California gold rush, Donald Trump, financial independence, gentrification, high net worth, high-speed rail, intangible asset, Kickstarter, low interest rates, Mark Zuckerberg, Maslow's hierarchy, multilevel marketing, negative equity, passive income, payday loans, reality distortion field, self-driving car, Snapchat, Stephen Hawking, Steve Jobs, stocks for the long run, stocks for the long term, Tony Hsieh, Y2K

Documentary filmmakers sit in boats in shark-infested seas or lie amongst ravenous lions in order to bring us never-before-seen footage. It's the same in business. People risk their money investing in technology, spending huge sums on research and development, and going through successes and failures, so that we can enjoy new innovations such as smartphones and (coming sooner than you think) self-driving cars. In Without Risk There's No Reward, Bob Mayer tells many anecdotes to show how his booming property business could not have been built without taking huge risks. Step two of your plan should be to create a passive income from rental income and through trading the money markets, with a possible long-term view to creating a passive income from a business.


pages: 278 words: 70,416

Smartcuts: How Hackers, Innovators, and Icons Accelerate Success by Shane Snow

3D printing, Airbnb, Albert Einstein, Apollo 11, attribution theory, augmented reality, barriers to entry, conceptual framework, correlation does not imply causation, David Heinemeier Hansson, deliberate practice, disruptive innovation, Elon Musk, fail fast, Fellow of the Royal Society, Filter Bubble, Ford Model T, Google X / Alphabet X, hive mind, index card, index fund, Isaac Newton, job satisfaction, Khan Academy, Kickstarter, lateral thinking, Law of Accelerating Returns, Lean Startup, Mahatma Gandhi, meta-analysis, Neil Armstrong, pattern recognition, Peter Thiel, popular electronics, Ray Kurzweil, Richard Florida, Ronald Reagan, Ruby on Rails, Saturday Night Live, self-driving car, seminal paper, Sheryl Sandberg, side project, Silicon Valley, social bookmarking, Steve Jobs, superconnector, vertical integration

Teller is the goatee-and-ponytailed head of a rather secret Google laboratory in California called Google[x]. He holds a PhD in artificial intelligence. Teller’s job is to dream big. 10x big. Google’s founders have endowed him with an engineer-filled building and a mandate to blow their minds. His team has built self-driving cars, augmented reality glasses, and WiFi balloons meant to roam the stratosphere. He’s hired some brilliant minds onto his team, but that’s not the secret of their success. The secret sounds a bit crazy. Says Teller, “It’s often easier to make something 10 times better than it is to make it 10 percent better.”


pages: 271 words: 77,448

Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will by Geoff Colvin

Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Black Swan, call centre, capital asset pricing model, commoditize, computer age, corporate governance, creative destruction, deskilling, driverless car, en.wikipedia.org, flying shuttle, Freestyle chess, future of work, Google Glasses, Grace Hopper, Hans Moravec, industrial cluster, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, Marc Andreessen, meta-analysis, Narrative Science, new economy, rising living standards, self-driving car, sentiment analysis, Silicon Valley, Skype, social intelligence, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, theory of mind, Tim Cook: Apple, transaction costs, Tyler Cowen

But now he and others began seeing a new possibility: Capital can substitute for labor, period. Summers explained, “That is, you can take some of the stock of machines and, by designing them appropriately, you can have them do exactly what labor did before.” The key word is “exactly.” A Google self-driving car doesn’t complement anybody’s work because nobody operates it at all. The company produced a version that doesn’t have a steering wheel, brake pedal, or accelerator, and it’s designed to transport even blind or other disabled people. So it doesn’t make drivers, even a shrunken population of them, more productive.


pages: 238 words: 73,824

Makers by Chris Anderson

3D printing, Airbnb, Any sufficiently advanced technology is indistinguishable from magic, Apple II, autonomous vehicles, barriers to entry, Buckminster Fuller, Build a better mousetrap, business process, carbon tax, commoditize, company town, Computer Numeric Control, crowdsourcing, dark matter, David Ricardo: comparative advantage, deal flow, death of newspapers, dematerialisation, digital capitalism, DIY culture, drop ship, Elon Musk, factory automation, Firefox, Ford Model T, future of work, global supply chain, global village, hockey-stick growth, hype cycle, IKEA effect, industrial robot, interchangeable parts, Internet of things, inventory management, James Hargreaves, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, Kickstarter, Lean Startup, manufacturing employment, Mark Zuckerberg, means of production, Menlo Park, Neal Stephenson, Network effects, planned obsolescence, private spaceflight, profit maximization, QR code, race to the bottom, Richard Feynman, Ronald Coase, Rubik’s Cube, Scaled Composites, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, South of Market, San Francisco, SpaceShipOne, spinning jenny, Startup school, stem cell, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, supply-chain management, the long tail, The Nature of the Firm, The Wealth of Nations by Adam Smith, TikTok, Tragedy of the Commons, transaction costs, trickle-down economics, vertical integration, Virgin Galactic, Whole Earth Catalog, X Prize, Y Combinator

The front of its website features not products but its blog, with chatty tutorials and videos from its employees. Its forums are full of customers helping one another. Every year Sparkfun throws an autonomous vehicle competition, featuring a live band playing robot-themed songs of its own composition, and lots of kids chasing self-driving cars (I’ve been competing in the aerial category every year since it started—no wins yet). At Maker festivals around the country, Sparkfun engineers teach people how to solder, which is actually a lot more fun than it may sound. Sparkfun’s employees are young, passionate, and appear to totally love their jobs.


Hands-On Machine Learning With Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Geron

AlphaGo, Amazon Mechanical Turk, Bayesian statistics, centre right, combinatorial explosion, constrained optimization, correlation coefficient, crowdsourcing, data science, deep learning, DeepMind, duck typing, en.wikipedia.org, Geoffrey Hinton, iterative process, Netflix Prize, NP-complete, optical character recognition, P = NP, p-value, pattern recognition, performance metric, recommendation engine, self-driving car, SpamAssassin, speech recognition, statistical model

We will use K-Means, but feel free to experiment with other clustering algorithms. Using clustering for image segmentation Image segmentation is the task of partitioning an image into multiple segments. In semantic segmentation, all pixels that are part of the same object type get assigned to the same segment. For example, in a self-driving car’s vision system, all pixels that are part of a pedestrian’s image might be assigned to the “pedestrian” segment (there would just be one segment containing all the pedestrians). In instance segmentation, all pixels that are part of the same individual object are assigned to the same segment. In this case there would be a different segment for each pedestrian.


pages: 280 words: 76,638

Rebel Ideas: The Power of Diverse Thinking by Matthew Syed

adjacent possible, agricultural Revolution, Alfred Russel Wallace, algorithmic bias, behavioural economics, Bletchley Park, Boeing 747, call centre, Cass Sunstein, classic study, cognitive load, computer age, crowdsourcing, cuban missile crisis, deep learning, delayed gratification, drone strike, Elon Musk, Erik Brynjolfsson, Fairchild Semiconductor, fake news, Ferguson, Missouri, Filter Bubble, Firefox, invention of writing, James Dyson, Jeff Bezos, knowledge economy, lateral thinking, market bubble, mass immigration, microbiome, Mitch Kapor, persistent metabolic adaptation, Peter Thiel, post-truth, Richard Thaler, Ronald Reagan, Second Machine Age, self-driving car, seminal paper, Silicon Valley, social intelligence, Steve Jobs, Steve Wozniak, Stuart Kauffman, tech worker, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, traveling salesman, vertical integration

We might call these ‘rebel combinations’: merging the old with the new, the alien and the familiar, the outside and the inside, the yin and the yang. This trend is not slowing up but accelerating in the computer age, with its vast networks. Think of Waze. This is classically recombinant, combining a location sensor, data transmission device, GPS system and social network. Or take Waymo, the self-driving car technology company, which brings together the internal combustion engine, fast computation, a new generation of sensors, extensive map and street information, and many other technologies.14 Indeed, almost all tech innovations connect disparate ideas, minds, concepts, technologies, data-sets and more.


pages: 250 words: 79,360

Escape From Model Land: How Mathematical Models Can Lead Us Astray and What We Can Do About It by Erica Thompson

Alan Greenspan, Bayesian statistics, behavioural economics, Big Tech, Black Swan, butterfly effect, carbon tax, coronavirus, correlation does not imply causation, COVID-19, data is the new oil, data science, decarbonisation, DeepMind, Donald Trump, Drosophila, Emanuel Derman, Financial Modelers Manifesto, fudge factor, germ theory of disease, global pandemic, hindcast, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, implied volatility, Intergovernmental Panel on Climate Change (IPCC), John von Neumann, junk bonds, Kim Stanley Robinson, lockdown, Long Term Capital Management, moral hazard, mouse model, Myron Scholes, Nate Silver, Neal Stephenson, negative emissions, paperclip maximiser, precautionary principle, RAND corporation, random walk, risk tolerance, selection bias, self-driving car, social distancing, Stanford marshmallow experiment, statistical model, systematic bias, tacit knowledge, tail risk, TED Talk, The Great Moderation, The Great Resignation, the scientific method, too big to fail, trolley problem, value at risk, volatility smile, Y2K

In 2020, a video uploaded to Facebook by a British tabloid newspaper including clips of Black men was automatically labelled with the category ‘primates’. These offensively inadequate models are then incorporated into even more consequential applications. Whether it is a mobile phone that cannot detect the face of the user or self-driving cars that are unable to recognise a human pedestrian, there are many reasons to be appalled by the continued appearance of this kind of mistake in the context of the rapid development of real-world artificially ‘intelligent’ autonomous systems. With larger and larger data sets and aspirations to more widespread use, the prospect of only removing this kind of error by hand after the event is infeasible and unacceptable.


The Code: Silicon Valley and the Remaking of America by Margaret O'Mara

A Declaration of the Independence of Cyberspace, accounting loophole / creative accounting, affirmative action, Airbnb, Alan Greenspan, AltaVista, Alvin Toffler, Amazon Web Services, An Inconvenient Truth, AOL-Time Warner, Apple II, Apple's 1984 Super Bowl advert, autonomous vehicles, back-to-the-land, barriers to entry, Ben Horowitz, Berlin Wall, Big Tech, Black Lives Matter, Bob Noyce, Buckminster Fuller, Burning Man, business climate, Byte Shop, California gold rush, Californian Ideology, carried interest, clean tech, clean water, cloud computing, cognitive dissonance, commoditize, company town, Compatible Time-Sharing System, computer age, Computer Lib, continuous integration, cuban missile crisis, Danny Hillis, DARPA: Urban Challenge, deindustrialization, different worldview, digital divide, Do you want to sell sugared water for the rest of your life?, don't be evil, Donald Trump, Doomsday Clock, Douglas Engelbart, driverless car, Dynabook, Edward Snowden, El Camino Real, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Fairchild Semiconductor, Frank Gehry, Future Shock, Gary Kildall, General Magic , George Gilder, gig economy, Googley, Hacker Ethic, Hacker News, high net worth, hockey-stick growth, Hush-A-Phone, immigration reform, income inequality, industrial research laboratory, informal economy, information retrieval, invention of movable type, invisible hand, Isaac Newton, It's morning again in America, Jeff Bezos, Joan Didion, job automation, job-hopping, John Gilmore, John Markoff, John Perry Barlow, Julian Assange, Kitchen Debate, knowledge economy, knowledge worker, Larry Ellison, Laura Poitras, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, Mary Meeker, mass immigration, means of production, mega-rich, Menlo Park, Mikhail Gorbachev, military-industrial complex, millennium bug, Mitch Kapor, Mother of all demos, move fast and break things, mutually assured destruction, Neil Armstrong, new economy, Norbert Wiener, old-boy network, Palm Treo, pattern recognition, Paul Graham, Paul Terrell, paypal mafia, Peter Thiel, pets.com, pirate software, popular electronics, pre–internet, prudent man rule, Ralph Nader, RAND corporation, Richard Florida, ride hailing / ride sharing, risk tolerance, Robert Metcalfe, ROLM, Ronald Reagan, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, shareholder value, Sheryl Sandberg, side hustle, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, skunkworks, Snapchat, social graph, software is eating the world, Solyndra, speech recognition, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Strategic Defense Initiative, supercomputer in your pocket, Susan Wojcicki, tacit knowledge, tech billionaire, tech worker, technoutopianism, Ted Nelson, TED Talk, the Cathedral and the Bazaar, the market place, the new new thing, The Soul of a New Machine, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas L Friedman, Tim Cook: Apple, Timothy McVeigh, transcontinental railway, Twitter Arab Spring, Uber and Lyft, uber lyft, Unsafe at Any Speed, upwardly mobile, Vannevar Bush, War on Poverty, Wargames Reagan, WarGames: Global Thermonuclear War, We wanted flying cars, instead we got 140 characters, Whole Earth Catalog, WikiLeaks, William Shockley: the traitorous eight, work culture , Y Combinator, Y2K

This also was a reminder of the Pentagon spending still lurking behind the Valley’s entrepreneurial audacity, for a DARPA “Grand Challenge” competition a decade earlier had revved up the race to bring driverless vehicles to market. As ever, the Valley’s next generation was helped along by the military’s willingness to make far-out bets. See Alex Davies, “Inside the Races that Jump-Started the Self-Driving Car,” Wired, November 10, 2017, https://www.wired.com/story/darpa-grand-urban-challenge-self-driving-car/, archived at https://perma.cc/EWN5-8XCD. 2. Tiernan Ray and Alex Eule, “John Doerr on Leadership, Education, Google, and AI,” Barron’s, May 5, 2018, https://www.barrons.com/articles/john-doerr-on-leadership-education-google-and-ai-1525478401, archived at https://perma.cc/S2W5-5GMY [inactive]; James Morra, “Groq to reveal potent artificial intelligence chip next year,” ee News: Europe, November 17, 2017, http://www.eenewseurope.com/news/groq-reveal-potent-artificial-intelligence-chip-next-year, archived at https://perma.cc/FQ3G-YAEK. 3.


pages: 289 words: 87,292

The Strange Order of Things: The Biological Roots of Culture by Antonio Damasio

Albert Einstein, algorithmic bias, biofilm, business process, CRISPR, Daniel Kahneman / Amos Tversky, double helix, Gordon Gekko, invention of the wheel, invention of writing, invisible hand, job automation, mental accounting, meta-analysis, microbiome, Nick Bostrom, Norbert Wiener, pattern recognition, Peter Singer: altruism, planetary scale, post-truth, profit motive, Ray Kurzweil, Richard Feynman, self-driving car, Silicon Valley, Steven Pinker, Stuart Kauffman, Thomas Malthus

It will not be difficult, even without feelings, for so-called humanlike robots to play and win many sorts of games, or to talk as well as HAL seemed to talk in 2001, or to serve as helpful human companions, although one shudders a bit at the prospect of a society that needs robots as companions. Are there not enough unemployed to fill those jobs after self-driving cars and trucks took away their livelihood? I can see humanlike robots predict the weather, operate heavy machinery, and perhaps even turn against us. But it will take a while until they really feel, and until then the simulation of humanity will be just that, a simulation. Back to Mortality While we wait for the promised and touted singularities, we might as well deal seriously with two of the largest medical problems anywhere in the world: drug addictions and pain management.


pages: 307 words: 82,680

A Pelican Introduction: Basic Income by Guy Standing

"World Economic Forum" Davos, anti-fragile, bank run, basic income, behavioural economics, Bernie Sanders, Bertrand Russell: In Praise of Idleness, Black Lives Matter, Black Swan, Boris Johnson, British Empire, carbon tax, centre right, collective bargaining, cryptocurrency, David Graeber, declining real wages, degrowth, deindustrialization, Donald Trump, Elon Musk, Fellow of the Royal Society, financial intermediation, full employment, future of work, gig economy, Gunnar Myrdal, housing crisis, hydraulic fracturing, income inequality, independent contractor, intangible asset, Jeremy Corbyn, job automation, job satisfaction, Joi Ito, labour market flexibility, land value tax, libertarian paternalism, low skilled workers, lump of labour, Marc Benioff, Mark Zuckerberg, Martin Wolf, mass immigration, mass incarceration, moral hazard, Nelson Mandela, nudge theory, offshore financial centre, open economy, Panopticon Jeremy Bentham, Paul Samuelson, plutocrats, precariat, quantitative easing, randomized controlled trial, rent control, rent-seeking, Salesforce, Sam Altman, self-driving car, shareholder value, sharing economy, Silicon Valley, sovereign wealth fund, Stephen Hawking, The Future of Employment, universal basic income, Wolfgang Streeck, women in the workforce, working poor, Y Combinator, Zipcar

Washington, DC: National League of Cities Center for City Solutions and Applied Research. 6. M. Bittman (2015), ‘Why not Utopia?’, New York Times, Sunday Review, 20 March. 7. H. Koch and J. Quoos (2017), ‘Schwab: “Gewinner müssen mit Verlierern solidarisch sein” ’, Hamburger Abendblatt, 9 January. 8. S. Dadich (2016), ‘Barack Obama, neural nets, self-driving cars and the future of the world’, Wired, October. https://www.wired.com/2016/10/president-obama-mit-joi-ito-interview/. 9. The best general treatment is Caputo, Basic Income Guarantee and Politics. 10. V. Taylor et al. (2002), Report of the Commission on the Comprehensive Reform of Social Security.


pages: 294 words: 80,084

Tomorrowland: Our Journey From Science Fiction to Science Fact by Steven Kotler

adjacent possible, Albert Einstein, Alexander Shulgin, autonomous vehicles, barriers to entry, Biosphere 2, Burning Man, carbon footprint, carbon tax, Colonization of Mars, crowdsourcing, Dean Kamen, Dennis Tito, epigenetics, gravity well, Great Leap Forward, haute couture, Helicobacter pylori, interchangeable parts, Kevin Kelly, life extension, Louis Pasteur, low earth orbit, North Sea oil, Oculus Rift, off-the-grid, oil shale / tar sands, peak oil, personalized medicine, Peter H. Diamandis: Planetary Resources, private spaceflight, RAND corporation, Ray Kurzweil, Richard Feynman, Ronald Reagan, self-driving car, SpaceShipOne, stem cell, Stephen Hawking, Stewart Brand, synthetic biology, theory of mind, Virgin Galactic, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, WikiLeaks

Initially, offerings were simple. We crowdsourced the design of T-shirts (Threadless.com) and the writing of encyclopedias (Wikipedia.com), but it didn’t take long for the trend to start making inroads into the harder sciences. Pretty soon, the hunt for extraterrestrial life, the development of self-driving cars, and the folding of enzymes into new and novel proteins were being done this way. With the fundamental tools of genetic manipulation — tools that cost millions of dollars not ten years ago — dropping precipitously in price, the crowdsourced design of biological agents was just the next logical step.


pages: 293 words: 81,183

Doing Good Better: How Effective Altruism Can Help You Make a Difference by William MacAskill

barriers to entry, basic income, behavioural economics, Black Swan, Branko Milanovic, Cal Newport, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, clean water, corporate social responsibility, correlation does not imply causation, Daniel Kahneman / Amos Tversky, David Brooks, Edward Jenner, effective altruism, en.wikipedia.org, end world poverty, experimental subject, follow your passion, food miles, immigration reform, income inequality, index fund, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, job automation, job satisfaction, Lean Startup, M-Pesa, mass immigration, meta-analysis, microcredit, Nate Silver, Peter Singer: altruism, power law, public intellectual, purchasing power parity, quantitative trading / quantitative finance, randomized controlled trial, self-driving car, Skype, Stanislav Petrov, Steve Jobs, Steve Wozniak, Steven Pinker, The Future of Employment, The Wealth of Nations by Adam Smith, Tyler Cowen, universal basic income, William MacAskill, women in the workforce

For example, before the advent of alarm clocks, people called knocker uppers were employed to knock on the windows of sleeping people in the morning, so they could get to work on time. Similarly, computers have decreased the need for jobs that involve basic number crunching; refrigerators have decreased the need for milkmen; robotic assemblers have decreased the need for assembly-line workers. The technology for self-driving cars is already here, so it may be unwise to become a taxi or a truck driver because there is a good chance that this industry will become automated over the next couple of decades. Improvements in technology are reducing demand for clerks and secretaries. In general, jobs that require social skills (like public relations), creativity (like fashion design), or precise perception and manipulation (like boilermaking) are the least likely to become automated.


pages: 283 words: 85,824

The People's Platform: Taking Back Power and Culture in the Digital Age by Astra Taylor

"World Economic Forum" Davos, A Declaration of the Independence of Cyberspace, Aaron Swartz, Alan Greenspan, American Legislative Exchange Council, Andrew Keen, AOL-Time Warner, barriers to entry, Berlin Wall, big-box store, Brewster Kahle, business logic, Californian Ideology, citizen journalism, cloud computing, collateralized debt obligation, Community Supported Agriculture, conceptual framework, content marketing, corporate social responsibility, creative destruction, cross-subsidies, crowdsourcing, David Brooks, digital capitalism, digital divide, digital Maoism, disinformation, disintermediation, don't be evil, Donald Trump, Edward Snowden, Evgeny Morozov, Fall of the Berlin Wall, Filter Bubble, future of journalism, Gabriella Coleman, gentrification, George Gilder, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, Internet Archive, Internet of things, invisible hand, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Perry Barlow, Julian Assange, Kevin Kelly, Kickstarter, knowledge worker, Laura Poitras, lolcat, Mark Zuckerberg, means of production, Metcalfe’s law, Naomi Klein, Narrative Science, Network effects, new economy, New Journalism, New Urbanism, Nicholas Carr, oil rush, peer-to-peer, Peter Thiel, planned obsolescence, plutocrats, post-work, power law, pre–internet, profit motive, recommendation engine, Richard Florida, Richard Stallman, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, slashdot, Slavoj Žižek, Snapchat, social graph, Steve Jobs, Stewart Brand, technological solutionism, technoutopianism, TED Talk, the long tail, trade route, Tragedy of the Commons, vertical integration, Whole Earth Catalog, WikiLeaks, winner-take-all economy, Works Progress Administration, Yochai Benkler, young professional

It makes devices, offers cloud computing services, and has begun to produce its own content, starting various publishing imprints before expanding to feature film production.32 Google is taking a similar approach, having expanded from search into content, operating system design, gadget manufacturing, retail, “smart” appliances, robotics, self-driving cars, debit cards, and fiber broadband service in select communities.” More troublingly, at least for those who believed the Internet upstarts would inevitably vanquish the establishment dinosaurs, are the ways the new and old players have melded. Condé Nast bought Reddit, Fox has a stake in Vice Media, Time Warner bet on Maker Studios (which is behind some of YouTube’s biggest stars), Apple works intimately with Hollywood and AT&T, Facebook joined forces with Microsoft and the major-label-backed Spotify, and Twitter is trumpeting its utility to television programmers.


pages: 282 words: 81,873

Live Work Work Work Die: A Journey Into the Savage Heart of Silicon Valley by Corey Pein

"World Economic Forum" Davos, 23andMe, 4chan, affirmative action, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Anne Wojcicki, artificial general intelligence, bank run, barriers to entry, Benevolent Dictator For Life (BDFL), Bernie Sanders, Big Tech, bitcoin, Bitcoin Ponzi scheme, Build a better mousetrap, California gold rush, cashless society, colonial rule, computer age, cryptocurrency, data is the new oil, deep learning, digital nomad, disruptive innovation, Donald Trump, Douglas Hofstadter, driverless car, Elon Musk, Evgeny Morozov, Extropian, fail fast, fake it until you make it, fake news, gamification, gentrification, gig economy, Google bus, Google Glasses, Google X / Alphabet X, Greyball, growth hacking, hacker house, Hacker News, hive mind, illegal immigration, immigration reform, independent contractor, intentional community, Internet of things, invisible hand, Isaac Newton, Jeff Bezos, job automation, Kevin Kelly, Khan Academy, Larry Ellison, Law of Accelerating Returns, Lean Startup, life extension, Lyft, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Menlo Park, minimum viable product, move fast and break things, mutually assured destruction, Neal Stephenson, obamacare, Parker Conrad, passive income, patent troll, Patri Friedman, Paul Graham, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, platform as a service, plutocrats, Ponzi scheme, post-work, public intellectual, Ray Kurzweil, regulatory arbitrage, rent control, RFID, Robert Mercer, rolodex, Ronald Reagan, Ross Ulbricht, Ruby on Rails, Sam Altman, Sand Hill Road, Scientific racism, self-driving car, selling pickaxes during a gold rush, sharing economy, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Singularitarianism, Skype, Snapchat, Social Justice Warrior, social software, software as a service, source of truth, South of Market, San Francisco, Startup school, stealth mode startup, Steve Bannon, Steve Jobs, Steve Wozniak, TaskRabbit, tech billionaire, tech bro, tech worker, TechCrunch disrupt, technological singularity, technoutopianism, telepresence, too big to fail, Travis Kalanick, tulip mania, Tyler Cowen, Uber for X, uber lyft, ubercab, unit 8200, upwardly mobile, Vernor Vinge, vertical integration, Virgin Galactic, X Prize, Y Combinator, Zenefits

My fellow passengers boozed it up the whole way, of course. The transit plaza teemed with more drunken, lanyard-wearing princelings and princesses. As I approached the taxi stand, two white Google cars pulled up simultaneously to an intersection. One was a Street View surveillance vehicle. The other had a label on the side that said SELF-DRIVING CAR, so it must’ve been a prototype. I took an old-fashioned taxi that got lost on the way to my new home. When I arrived, the host, Jeannie, was out wandering the unlit streets looking for her cat. It being late, she showed me outside to the tent straightaway. I tiptoed around fallen seeds and dried pine needles in the dark summer night.


pages: 324 words: 86,056

The Socialist Manifesto: The Case for Radical Politics in an Era of Extreme Inequality by Bhaskar Sunkara

Affordable Care Act / Obamacare, agricultural Revolution, Bernie Sanders, British Empire, business climate, business cycle, capital controls, centre right, Charles Lindbergh, collective bargaining, Deng Xiaoping, deskilling, Donald Trump, equal pay for equal work, fake news, false flag, feminist movement, Ferguson, Missouri, Francis Fukuyama: the end of history, full employment, gig economy, Great Leap Forward, Gunnar Myrdal, happiness index / gross national happiness, high-speed rail, Honoré de Balzac, income inequality, inventory management, Jeremy Corbyn, labor-force participation, land reform, land value tax, Mark Zuckerberg, means of production, Meghnad Desai, Mikhail Gorbachev, Neil Kinnock, new economy, Occupy movement, postindustrial economy, precariat, race to the bottom, Ralph Waldo Emerson, self-driving car, Silicon Valley, SimCity, single-payer health, Steve Bannon, telemarketer, The Wealth of Nations by Adam Smith, too big to fail, union organizing, Upton Sinclair, urban renewal, We are all Keynesians now, We are the 99%

Reformat your CV, and work on that handshake. Your factory is being outsourced? Stop whining, and take a coding class. As one popular libertarian title puts it, You’re Broke Because You Want to Be. Today, the masters of Silicon Valley are presenting a new vision of the future—space travel, 3-D printing, artificial intelligence, self-driving cars. But without the promise of mass employment to go along with all that disruption, they summon more concern that robots are coming to steal our jobs than awe. We all can’t just go to an “innovation clinic” and launch our own app. This hasn’t stopped the new captains of industry from trying to expand their appeal into the political realm.


pages: 302 words: 84,881

The Digital Party: Political Organisation and Online Democracy by Paolo Gerbaudo

Airbnb, barriers to entry, basic income, Bernie Sanders, bitcoin, Californian Ideology, call centre, Cambridge Analytica, centre right, creative destruction, crowdsourcing, data science, digital capitalism, digital divide, digital rights, disintermediation, disruptive innovation, Donald Trump, Dunbar number, Edward Snowden, end-to-end encryption, Evgeny Morozov, feminist movement, gig economy, industrial robot, Jaron Lanier, Jeff Bezos, Jeremy Corbyn, jimmy wales, Joseph Schumpeter, Mark Zuckerberg, Network effects, Occupy movement, offshore financial centre, oil shock, post-industrial society, precariat, Ralph Waldo Emerson, Richard Florida, Richard Stallman, Ruby on Rails, self-driving car, Silicon Valley, Skype, Slavoj Žižek, smart cities, Snapchat, social web, software studies, Stewart Brand, technological solutionism, technoutopianism, the long tail, Thomas L Friedman, universal basic income, vertical integration, Vilfredo Pareto, WikiLeaks

This trend is epitomised by the rapid growth of causalised workers such as call centre workers, riders for delivery companies such as Deliveroo, Uber drivers or warehouse workers as those of Amazon104 among many other typical profiles of the so-called ‘gig economy’.105 These figures can be considered as part of the ‘precariat’, an emerging class which, in his General Theory of the Precariat, Italian activist and theorist Alex Foti describes as ‘the underpaid, underemployed, underprotected, overeducated, and overexploited’.106 What is more, many fear the job-destroying avalanche of the incoming second automation revolution, with robots predicted to eliminate many manual jobs such as drivers substituted by self-driving cars, and artificial intelligence threatening to destroy clerical jobs, such as those in the legal and accounting sectors. This ambiguous nature of the digital revolution provides useful insights to make sense of the support base of digital parties, which, as we shall see, centres on digitally savvy millennials living in urban areas.


pages: 292 words: 85,151

Exponential Organizations: Why New Organizations Are Ten Times Better, Faster, and Cheaper Than Yours (And What to Do About It) by Salim Ismail, Yuri van Geest

23andMe, 3D printing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, anti-fragile, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, behavioural economics, Ben Horowitz, bike sharing, bioinformatics, bitcoin, Black Swan, blockchain, Blue Ocean Strategy, book value, Burning Man, business intelligence, business process, call centre, chief data officer, Chris Wanstrath, circular economy, Clayton Christensen, clean water, cloud computing, cognitive bias, collaborative consumption, collaborative economy, commoditize, corporate social responsibility, cross-subsidies, crowdsourcing, cryptocurrency, dark matter, data science, Dean Kamen, deep learning, DeepMind, dematerialisation, discounted cash flows, disruptive innovation, distributed ledger, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fail fast, game design, gamification, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, hiring and firing, holacracy, Hyperloop, industrial robot, Innovator's Dilemma, intangible asset, Internet of things, Iridium satellite, Isaac Newton, Jeff Bezos, Joi Ito, Kevin Kelly, Kickstarter, knowledge worker, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, lifelogging, loose coupling, loss aversion, low earth orbit, Lyft, Marc Andreessen, Mark Zuckerberg, market design, Max Levchin, means of production, Michael Milken, minimum viable product, natural language processing, Netflix Prize, NetJets, Network effects, new economy, Oculus Rift, offshore financial centre, PageRank, pattern recognition, Paul Graham, paypal mafia, peer-to-peer, peer-to-peer model, Peter H. Diamandis: Planetary Resources, Peter Thiel, Planet Labs, prediction markets, profit motive, publish or perish, radical decentralization, Ray Kurzweil, recommendation engine, RFID, ride hailing / ride sharing, risk tolerance, Ronald Coase, Rutger Bregman, Salesforce, Second Machine Age, self-driving car, sharing economy, Silicon Valley, skunkworks, Skype, smart contracts, Snapchat, social software, software is eating the world, SpaceShipOne, speech recognition, stealth mode startup, Stephen Hawking, Steve Jobs, Steve Jurvetson, subscription business, supply-chain management, synthetic biology, TaskRabbit, TED Talk, telepresence, telepresence robot, the long tail, Tony Hsieh, transaction costs, Travis Kalanick, Tyler Cowen, Tyler Cowen: Great Stagnation, uber lyft, urban planning, Virgin Galactic, WikiLeaks, winner-take-all economy, X Prize, Y Combinator, zero-sum game

Sustainable production and logistics Greener and more self-sufficient production driven by robo-transport, sensors, AI, flexible solar panels and perovskite solar cells. Nanomaterials (graphene) that can be added to buildings, vehicles, machines and equipment. Transformation in Logistics (road, water and air transport). Autonomous transport and delivery Leveraging autonomous vehicles (e.g., Google’s self-driving car) and drones (e.g., Matternet) for the transport and delivery of supplies and products, especially in remote areas. Full supply chain tracking/monitoring Internet of Things sensors used to monitor the entire supply chain. Location, status, preservation and safety of most substances can be monitored (chemical substance traces, pollution, quality of life).


pages: 252 words: 80,636

Bureaucracy by David Graeber

a long time ago in a galaxy far, far away, Affordable Care Act / Obamacare, airport security, Albert Einstein, Alvin Toffler, banking crisis, barriers to entry, borderless world, Bretton Woods, British Empire, collateralized debt obligation, Columbine, conceptual framework, Corn Laws, David Graeber, Future Shock, George Gilder, High speed trading, hiring and firing, junk bonds, Kitchen Debate, late capitalism, Lewis Mumford, means of production, music of the spheres, Neal Stephenson, new economy, obamacare, Occupy movement, Oklahoma City bombing, Parkinson's law, Peter Thiel, planetary scale, pneumatic tube, post-work, price mechanism, Ronald Reagan, self-driving car, Silicon Valley, South Sea Bubble, stock buybacks, technological determinism, transcontinental railway, union organizing, urban planning, zero-sum game

The Internet does provide opportunities for collaboration and dissemination that may eventually help break us through the wall, as well. Where will the breakthrough come? We can’t know. Over the last couple years, since the first version of this essay saw print, there has been a whole spate of new possibilities: 3-D printing, advances in materials technologies, self-driving cars, a new generation of robots, and as a result, a new spate of discussion of robot factories and the end of work. There are hints, too, of impending conceptual breakthroughs in physics, biology, and other sciences, made all the more difficult because of the absolute institutional lock of existing orthodoxies, but which might well have profound technological implications as well.


pages: 294 words: 81,292

Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

AI winter, air gap, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, Bletchley Park, brain emulation, California energy crisis, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, Computing Machinery and Intelligence, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, dual-use technology, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Hacker News, Hans Moravec, Isaac Newton, Jaron Lanier, Jeff Hawkins, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, machine translation, mutually assured destruction, natural language processing, Neil Armstrong, Nicholas Carr, Nick Bostrom, optical character recognition, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, precautionary principle, prisoner's dilemma, Ray Kurzweil, Recombinant DNA, Rodney Brooks, rolling blackouts, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Jurvetson, Steve Wozniak, strong AI, Stuxnet, subprime mortgage crisis, superintelligent machines, technological singularity, The Coming Technological Singularity, Thomas Bayes, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day

So it seemed inconceivable that Google did not have AGI in mind. Then, about a month after my last correspondence with Freidenfelds, The New York Times broke a story about Google X. Google X was a stealth company. The secret Silicon Valley laboratory was initially headed by AI expert and developer of Google’s self-driving car, Sebastian Thrun. It is focused on one hundred “moon-shot” projects such as the Space Elevator, which is essentially a scaffolding that would reach into space and facilitate the exploration of our solar system. Also onboard at the stealth facility is Andrew Ng, former director of Stanford University’s Artificial Intelligence Lab, and a world-class roboticist.


pages: 309 words: 79,414

Going Dark: The Secret Social Lives of Extremists by Julia Ebner

23andMe, 4chan, Airbnb, anti-communist, anti-globalists, augmented reality, Ayatollah Khomeini, Bellingcat, Big Tech, bitcoin, blockchain, Boris Johnson, Cambridge Analytica, citizen journalism, cognitive dissonance, Comet Ping Pong, crisis actor, crowdsourcing, cryptocurrency, deepfake, disinformation, Donald Trump, Dunning–Kruger effect, Elon Musk, fake news, false flag, feminist movement, game design, gamification, glass ceiling, Google Earth, Greta Thunberg, information security, job satisfaction, Mark Zuckerberg, mass immigration, Menlo Park, Mikhail Gorbachev, Network effects, off grid, OpenAI, Overton Window, pattern recognition, pre–internet, QAnon, RAND corporation, ransomware, rising living standards, self-driving car, Silicon Valley, Skype, Snapchat, social intelligence, Social Justice Warrior, SQL injection, Steve Bannon, Steve Jobs, Transnistria, WikiLeaks, zero day

Most recently, hacks of political institutions have gone beyond this: they have entailed not just financial losses, but, more worryingly, a loss of trust – in democratic processes as well as in political representatives. And while past hacks may have led to millions of dollars or files being lost, future hacks could lead to millions of lives being lost. If terrorists continue upgrading their cyber skills, there is a real risk of more sophisticated hacks targeting power plants or self-driving cars. At this point in time, though, it is the hybrid threats we should be worried about: the mix of cyber and real-world terrorism. 12 Gamified Terrorism: Within the Sub-cultures behind the New Zealand Attack My heart is racing and I feel sick as I leave the office. I can still see the men and women collapsing one by one as they are hit by the rain of bullets, and I can hear those gunshots fired with the semi-automatic rifle.


pages: 283 words: 85,906

The Clock Mirage: Our Myth of Measured Time by Joseph Mazur

Albert Einstein, Alfred Russel Wallace, Arthur Eddington, computer age, Credit Default Swap, Danny Hillis, Drosophila, Eratosthenes, Henri Poincaré, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, Isaac Newton, Jeff Bezos, job automation, Lewis Mumford, Mark Zuckerberg, mass immigration, Pepto Bismol, quantum entanglement, self-driving car, seminal paper, Stephen Hawking, time dilation, twin studies

Once I get beyond the nauseatingly painful gradual acceleration to get up to goal speed, and then cruise at that speed at a comfortable constant velocity, I will hardly sense moving. After the joy of peeking at a few dwarf stars and returning to earth, time will have moved on by a hundred years. I’d likely be amazed to see self-driving cars levitating on roads that don’t look at all like roads. I might see a few very old people jogging to work with prosthetic legs, see brainwave bonnets on sidewalk lampposts for getting five-minute naps to make up for no nighttime sleep or vending machines where one can get a caffeine jolt pumped directly to the basal ganglia area of the brain.


pages: 297 words: 83,528

The Startup Wife by Tahmima Anam

Anthropocene, Black Lives Matter, cryptocurrency, DeepMind, driverless car, family office, glass ceiling, Greta Thunberg, high net worth, index card, lockdown, microdosing, nudge theory, post-truth, Rubik’s Cube, self-driving car, Sheryl Sandberg, side project, Stanford marshmallow experiment, stealth mode startup, TED Talk, the High Line, TikTok

I knew this was coming—we can’t live on his allowance forever, and we don’t have the money to launch—but neither of us knows how to do this, not even Jules, whose amniotic fluid was probably flecked with gold. I’ve gone as far as leafing through copies of Harvard Business Review, and subscribing to daily updates from various websites that promise to tell me how to do it, but all I can see is funding for self-driving cars, for putting stuff up in the cloud like it’s one giant safe-deposit box in the sky, and subscriptions to everything from dye-free tampons to vegan protein powder. There’s fintech, biotech, oiltech, real estate, bento boxes of skin care, and tiffin carriers of meat-free protein. There are no funds for quasi-religious platforms.


pages: 251 words: 80,831

Super Founders: What Data Reveals About Billion-Dollar Startups by Ali Tamaseb

"World Economic Forum" Davos, 23andMe, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Anne Wojcicki, asset light, barriers to entry, Ben Horowitz, Benchmark Capital, bitcoin, business intelligence, buy and hold, Chris Wanstrath, clean water, cloud computing, coronavirus, corporate governance, correlation does not imply causation, COVID-19, cryptocurrency, data science, discounted cash flows, diversified portfolio, Elon Musk, Fairchild Semiconductor, game design, General Magic , gig economy, high net worth, hiring and firing, index fund, Internet Archive, Jeff Bezos, John Zimmer (Lyft cofounder), Kickstarter, late fees, lockdown, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Max Levchin, Mitch Kapor, natural language processing, Network effects, nuclear winter, PageRank, PalmPilot, Parker Conrad, Paul Buchheit, Paul Graham, peer-to-peer lending, Peter Thiel, Planet Labs, power law, QR code, Recombinant DNA, remote working, ride hailing / ride sharing, robotic process automation, rolodex, Ruby on Rails, Salesforce, Sam Altman, Sand Hill Road, self-driving car, shareholder value, sharing economy, side hustle, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, SoftBank, software as a service, software is eating the world, sovereign wealth fund, Startup school, Steve Jobs, Steve Wozniak, survivorship bias, TaskRabbit, telepresence, the payments system, TikTok, Tony Fadell, Tony Hsieh, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, web application, WeWork, work culture , Y Combinator

Those subsectors were followed by network-management software (like Palo Alto Networks, which creates firewalls for networks, among other products), database management (like MongoDB, a document-oriented database company), automation/workflow software (like UiPath, a company automating manual tasks for enterprise clients), automotive (like Cruise, a self-driving car company acquired by General Motors), and biotechnology (like Indigo Ag, an agriculture company that works with plant microbes, aiming to improve yields). Business and productivity software was the largest subsector among billion-dollar startups, followed by social/consumer software, application software, and e-commerce.


pages: 422 words: 86,414

Hands-On RESTful API Design Patterns and Best Practices by Harihara Subramanian

blockchain, business logic, business process, cloud computing, continuous integration, create, read, update, delete, cyber-physical system, data science, database schema, DevOps, disruptive innovation, domain-specific language, fault tolerance, information security, Infrastructure as a Service, Internet of things, inventory management, job automation, Kickstarter, knowledge worker, Kubernetes, loose coupling, Lyft, machine readable, microservices, MITM: man-in-the-middle, MVC pattern, Salesforce, self-driving car, semantic web, single page application, smart cities, smart contracts, software as a service, SQL injection, supply-chain management, web application, WebSocket

There are novel use cases being unearthed and rolled out to enhance the proliferation and penetration of the IoT paradigm. Due to the multiplicity and heterogeneity of IoT devices, the operational, management, and security complexities of IoT devices are being deployed in homes, hotels, hospitals, retail stores, manufacturing floors, railway stations, restaurants, and self-driving cars. Nowadays, sensors and actuators have become the eyes and ears of every digital application these days. Every device is becoming computational, communicative, perceptive, and active. There are edge and fog devices with sufficient power to form ad hoc clouds to capture, store, process, and analyze real-time, time-series, and streaming data to extract actionable insights that can be looped back to devices and people to make intelligent decisions in time and to engage in correct and relevant activities with all the clarity and confidence.


pages: 801 words: 209,348

Americana: A 400-Year History of American Capitalism by Bhu Srinivasan

activist fund / activist shareholder / activist investor, American ideology, AOL-Time Warner, Apple II, Apple's 1984 Super Bowl advert, bank run, barriers to entry, Bear Stearns, Benchmark Capital, Berlin Wall, blue-collar work, Bob Noyce, Bonfire of the Vanities, British Empire, business cycle, buy and hold, California gold rush, Carl Icahn, Charles Lindbergh, collective bargaining, commoditize, Cornelius Vanderbilt, corporate raider, cotton gin, cuban missile crisis, Deng Xiaoping, diversification, diversified portfolio, Douglas Engelbart, Fairchild Semiconductor, financial innovation, fixed income, Ford Model T, Ford paid five dollars a day, global supply chain, Gordon Gekko, guns versus butter model, Haight Ashbury, hypertext link, Ida Tarbell, income inequality, information security, invisible hand, James Watt: steam engine, Jane Jacobs, Jeff Bezos, John Markoff, joint-stock company, joint-stock limited liability company, junk bonds, Kickstarter, laissez-faire capitalism, Louis Pasteur, Marc Andreessen, Menlo Park, Michael Milken, military-industrial complex, mortgage debt, mutually assured destruction, Norman Mailer, oil rush, peer-to-peer, pets.com, popular electronics, profit motive, punch-card reader, race to the bottom, refrigerator car, risk/return, Ronald Reagan, Sand Hill Road, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley startup, Steve Ballmer, Steve Jobs, Steve Wozniak, strikebreaker, Ted Nelson, The Death and Life of Great American Cities, the new new thing, The Predators' Ball, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, trade route, transcontinental railway, traveling salesman, Upton Sinclair, Vannevar Bush, Works Progress Administration, zero-sum game

In prime time on ABC, Americans could tune in to catch the animated cartoon The Jetsons, about a space-age family who lived with their housekeeping robot, Rosie, and dog, Astro. A couple of years later, Desilu, I Love Lucy’s production company, premiered Star Trek on CBS. Stanley Kubrick’s 2001: A Space Odyssey had a near-omniscient computer named Hal manipulating its astronauts. By the midsixties, the concepts of artificial intelligence and self-driving cars were no longer in the realm of magic or science fiction—they were seen as the logical, inevitable outcome of the American trajectory. Modernity and sophistication looked like American Airlines. A leader in the fast-growing field of jet travel, the airline brought in IBM to develop flight plans.

America had been the world’s top oil producer from the first drilling in Pennsylvania in 1859 until the early seventies, remained in the top three, then climbed back to the top with new methods of drilling. Boeing still made planes. Its pharmaceutical companies and medical device makers were among the best in the world. John Deere and Caterpillar remained leaders in farm and construction equipment. And it dominated in technology, shaping the world’s future in terms of what came next, like self-driving cars and trucks. For many, the lingering American discontent had roots in nostalgia about some former era of glory—but the thirties had the Depression; the forties, World War II; the idyllic fifties, over thirty thousand battlefield deaths in the Korean War and schoolchildren doing drills to prepare for a nuclear war; the sixties, Vietnam and three political assassinations; the seventies, the oil crisis, economic malaise, and high inflation; the eighties, the fear of AIDS, street crime, and paranoia about Japan.


Americana by Bhu Srinivasan

activist fund / activist shareholder / activist investor, American ideology, AOL-Time Warner, Apple II, Apple's 1984 Super Bowl advert, bank run, barriers to entry, Bear Stearns, Benchmark Capital, Berlin Wall, blue-collar work, Bob Noyce, Bonfire of the Vanities, British Empire, business cycle, buy and hold, California gold rush, Carl Icahn, Charles Lindbergh, collective bargaining, commoditize, Cornelius Vanderbilt, corporate raider, cotton gin, cuban missile crisis, Deng Xiaoping, diversification, diversified portfolio, Douglas Engelbart, Fairchild Semiconductor, financial innovation, fixed income, Ford Model T, Ford paid five dollars a day, global supply chain, Gordon Gekko, guns versus butter model, Haight Ashbury, hypertext link, Ida Tarbell, income inequality, information security, invisible hand, James Watt: steam engine, Jane Jacobs, Jeff Bezos, John Markoff, joint-stock company, joint-stock limited liability company, junk bonds, Kickstarter, laissez-faire capitalism, Louis Pasteur, Marc Andreessen, Menlo Park, Michael Milken, military-industrial complex, mortgage debt, mutually assured destruction, Norman Mailer, oil rush, peer-to-peer, pets.com, popular electronics, profit motive, punch-card reader, race to the bottom, refrigerator car, risk/return, Ronald Reagan, Sand Hill Road, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley startup, Steve Ballmer, Steve Jobs, Steve Wozniak, strikebreaker, Ted Nelson, The Death and Life of Great American Cities, the new new thing, The Predators' Ball, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, trade route, transcontinental railway, traveling salesman, Upton Sinclair, Vannevar Bush, Works Progress Administration, zero-sum game

In prime time on ABC, Americans could tune in to catch the animated cartoon The Jetsons, about a space-age family who lived with their housekeeping robot, Rosie, and dog, Astro. A couple of years later, Desilu, I Love Lucy’s production company, premiered Star Trek on CBS. Stanley Kubrick’s 2001: A Space Odyssey had a near-omniscient computer named Hal manipulating its astronauts. By the midsixties, the concepts of artificial intelligence and self-driving cars were no longer in the realm of magic or science fiction—they were seen as the logical, inevitable outcome of the American trajectory. Modernity and sophistication looked like American Airlines. A leader in the fast-growing field of jet travel, the airline brought in IBM to develop flight plans.

America had been the world’s top oil producer from the first drilling in Pennsylvania in 1859 until the early seventies, remained in the top three, then climbed back to the top with new methods of drilling. Boeing still made planes. Its pharmaceutical companies and medical device makers were among the best in the world. John Deere and Caterpillar remained leaders in farm and construction equipment. And it dominated in technology, shaping the world’s future in terms of what came next, like self-driving cars and trucks. For many, the lingering American discontent had roots in nostalgia about some former era of glory—but the thirties had the Depression; the forties, World War II; the idyllic fifties, over thirty thousand battlefield deaths in the Korean War and schoolchildren doing drills to prepare for a nuclear war; the sixties, Vietnam and three political assassinations; the seventies, the oil crisis, economic malaise, and high inflation; the eighties, the fear of AIDS, street crime, and paranoia about Japan.


pages: 285 words: 86,853

What Algorithms Want: Imagination in the Age of Computing by Ed Finn

Airbnb, Albert Einstein, algorithmic bias, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, bitcoin, blockchain, business logic, Charles Babbage, Chuck Templeton: OpenTable:, Claude Shannon: information theory, commoditize, Computing Machinery and Intelligence, Credit Default Swap, crowdsourcing, cryptocurrency, data science, DeepMind, disruptive innovation, Donald Knuth, Donald Shoup, Douglas Engelbart, Douglas Engelbart, Elon Musk, Evgeny Morozov, factory automation, fiat currency, Filter Bubble, Flash crash, game design, gamification, Google Glasses, Google X / Alphabet X, Hacker Conference 1984, High speed trading, hiring and firing, Ian Bogost, industrial research laboratory, invisible hand, Isaac Newton, iterative process, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, Just-in-time delivery, Kickstarter, Kiva Systems, late fees, lifelogging, Loebner Prize, lolcat, Lyft, machine readable, Mother of all demos, Nate Silver, natural language processing, Neal Stephenson, Netflix Prize, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, PageRank, peer-to-peer, Peter Thiel, power law, Ray Kurzweil, recommendation engine, Republic of Letters, ride hailing / ride sharing, Satoshi Nakamoto, self-driving car, sharing economy, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, SimCity, Skinner box, Snow Crash, social graph, software studies, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, supply-chain management, tacit knowledge, TaskRabbit, technological singularity, technological solutionism, technoutopianism, the Cathedral and the Bazaar, The Coming Technological Singularity, the scientific method, The Signal and the Noise by Nate Silver, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, traveling salesman, Turing machine, Turing test, Uber and Lyft, Uber for X, uber lyft, urban planning, Vannevar Bush, Vernor Vinge, wage slave

As Siva Vaidhyanathan wrote of Google in 2011: “there has never been a company with explicit ambitions to connect individual minds with information on a global—in fact universal—scale.”24 Google’s immensity and deep imbrication in the core structures of algorithmic culture have also fueled global ambitions. The company’s X Lab dedicates itself entirely to considering “moonshot” ideas that offer radical solutions or exponential improvements to current challenges, and they are the intellectual force behind high-risk ventures such as Google Glass, the self-driving car, and Project Loon, an effort to deliver Internet service to remote areas via high-altitude balloons. Astro Teller, the lab’s captain of moonshots, has encouraged a culture of rapid prototyping and early failure points to try out new ideas.25 Since the company acquired the artificial intelligence research group DeepMind in 2014, Google has also made a string of breathtaking announcements about advances in machine learning.


pages: 356 words: 91,157

The New Urban Crisis: How Our Cities Are Increasing Inequality, Deepening Segregation, and Failing the Middle Class?and What We Can Do About It by Richard Florida

affirmative action, Airbnb, back-to-the-city movement, basic income, Bernie Sanders, bike sharing, blue-collar work, business climate, Capital in the Twenty-First Century by Thomas Piketty, clean water, Columbine, congestion charging, creative destruction, David Ricardo: comparative advantage, declining real wages, deindustrialization, Donald Trump, East Village, edge city, Edward Glaeser, failed state, Ferguson, Missouri, gentrification, Gini coefficient, Google bus, high net worth, high-speed rail, income inequality, income per capita, industrial cluster, informal economy, Jane Jacobs, jitney, Kitchen Debate, knowledge economy, knowledge worker, land value tax, low skilled workers, Lyft, megacity, megaproject, Menlo Park, mortgage tax deduction, Nate Silver, New Economic Geography, new economy, New Urbanism, occupational segregation, off-the-grid, opioid epidemic / opioid crisis, Paul Graham, plutocrats, RAND corporation, rent control, rent-seeking, restrictive zoning, Richard Florida, rising living standards, Ronald Reagan, secular stagnation, self-driving car, Silicon Valley, SimCity, sovereign wealth fund, streetcar suburb, superstar cities, tech worker, the built environment, The Chicago School, The Death and Life of Great American Cities, the High Line, The Rise and Fall of American Growth, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, trickle-down economics, Tyler Cowen, Uber and Lyft, uber lyft, universal basic income, upwardly mobile, urban decay, urban planning, urban renewal, urban sprawl, white flight, young professional

It is time to level the playing field for mass transit by reducing the outright subsidy we give to the car in the form of roads and highways. Cities in other parts of the world, including London, have begun to institute congestion charges, which make drivers pay for their use of busy roads to help alleviate traffic, sprawl, and pollution. New developments like self-driving cars, electric vehicles, and on-demand digital delivery systems, such as Uber and Lyft, will certainly play a big role in the city of the future. But we still need mass transit to provide the connective fiber that will increase clustering and enable the development of a larger number of dense, mixed-use clustered neighborhoods that are affordable to more people.


pages: 346 words: 89,180

Capitalism Without Capital: The Rise of the Intangible Economy by Jonathan Haskel, Stian Westlake

23andMe, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, Albert Einstein, Alvin Toffler, Andrei Shleifer, bank run, banking crisis, Bernie Sanders, Big Tech, book value, Brexit referendum, business climate, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, carbon credits, cloud computing, cognitive bias, computer age, congestion pricing, corporate governance, corporate raider, correlation does not imply causation, creative destruction, dark matter, Diane Coyle, Donald Trump, Douglas Engelbart, Douglas Engelbart, driverless car, Edward Glaeser, Elon Musk, endogenous growth, Erik Brynjolfsson, everywhere but in the productivity statistics, Fellow of the Royal Society, financial engineering, financial innovation, full employment, fundamental attribution error, future of work, gentrification, gigafactory, Gini coefficient, Hernando de Soto, hiring and firing, income inequality, index card, indoor plumbing, intangible asset, Internet of things, Jane Jacobs, Jaron Lanier, Jeremy Corbyn, job automation, Kanban, Kenneth Arrow, Kickstarter, knowledge economy, knowledge worker, laissez-faire capitalism, liquidity trap, low interest rates, low skilled workers, Marc Andreessen, Mother of all demos, Network effects, new economy, Ocado, open economy, patent troll, paypal mafia, Peter Thiel, pets.com, place-making, post-industrial society, private spaceflight, Productivity paradox, quantitative hedge fund, rent-seeking, revision control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Robert Solow, Ronald Coase, Sand Hill Road, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, software patent, sovereign wealth fund, spinning jenny, Steve Jobs, sunk-cost fallacy, survivorship bias, tacit knowledge, tech billionaire, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, total factor productivity, TSMC, Tyler Cowen, Tyler Cowen: Great Stagnation, urban planning, Vanguard fund, walkable city, X Prize, zero-sum game

Even if these forms of education end up being paid for by the adult students themselves rather than by taxpayers, the research to develop new models that work seems like a worthy goal of public policy. Government funding can also help reduce coordination problems that may hold businesses back from investing. Suppose there are big economic gains to be had from developing self-driving cars and reconfiguring our cities around them (fewer car accidents, more productive commutes, freeing up parking spaces for redevelopment, and so on). But realizing these benefits requires a lot of investments to be made together (driverless car technology, urban design, new insurance policies, and so on); it may well be that no company is willing to make investments on its own unless it knows that others will make complementary ones.


pages: 400 words: 88,647

Frugal Innovation: How to Do Better With Less by Jaideep Prabhu Navi Radjou

3D printing, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, barriers to entry, Baxter: Rethink Robotics, behavioural economics, benefit corporation, Bretton Woods, business climate, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, circular economy, cloud computing, collaborative consumption, collaborative economy, Computer Numeric Control, connected car, corporate social responsibility, creative destruction, crowdsourcing, disruptive innovation, driverless car, Elon Musk, fail fast, financial exclusion, financial innovation, gamification, global supply chain, IKEA effect, income inequality, industrial robot, intangible asset, Internet of things, job satisfaction, Khan Academy, Kickstarter, late fees, Lean Startup, low cost airline, M-Pesa, Mahatma Gandhi, Marc Benioff, megacity, minimum viable product, more computing power than Apollo, new economy, payday loans, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, planned obsolescence, precision agriculture, race to the bottom, reshoring, risk tolerance, Ronald Coase, Salesforce, scientific management, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, six sigma, smart grid, smart meter, software as a service, standardized shipping container, Steve Jobs, supply-chain management, tacit knowledge, TaskRabbit, TED Talk, The Fortune at the Bottom of the Pyramid, the long tail, The Nature of the Firm, Tony Fadell, transaction costs, Travis Kalanick, unbanked and underbanked, underbanked, value engineering, vertical integration, women in the workforce, work culture , X Prize, yield management, Zipcar

Contributors could either tinker with concept designs available on Volkswagen’s website or submit their own original designs. The winning idea came from Wan Jia, a student from Chengdu, who dreamed up a two-person, emissions-free hover car that uses magnetic levitation. Volkswagen then brought to life Jia’s imaginary car by making a short video that shows her ecstatic parents proudly flying in the self-driving car that their daughter designed (the video has been viewed over 7.5 million times on YouTube). Validators Validators are customers who do not create anything themselves but help to validate new product ideas and prototypes. These customers can reduce thousands of potential options and features to a few that matter most to the majority of customers.


pages: 330 words: 91,805

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism by Robin Chase

Airbnb, Amazon Web Services, Andy Kessler, Anthropocene, Apollo 13, banking crisis, barriers to entry, basic income, Benevolent Dictator For Life (BDFL), bike sharing, bitcoin, blockchain, Burning Man, business climate, call centre, car-free, carbon tax, circular economy, cloud computing, collaborative consumption, collaborative economy, collective bargaining, commoditize, congestion charging, creative destruction, crowdsourcing, cryptocurrency, data science, deal flow, decarbonisation, different worldview, do-ocracy, don't be evil, Donald Shoup, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, Eyjafjallajökull, Ferguson, Missouri, Firefox, Free Software Foundation, frictionless, Gini coefficient, GPS: selective availability, high-speed rail, hive mind, income inequality, independent contractor, index fund, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Kinder Surprise, language acquisition, Larry Ellison, Lean Startup, low interest rates, Lyft, machine readable, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, off-the-grid, openstreetmap, optical character recognition, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, Post-Keynesian economics, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Salesforce, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, smart cities, smart grid, Snapchat, sovereign wealth fund, Steve Crocker, Steve Jobs, Steven Levy, TaskRabbit, The Death and Life of Great American Cities, The Future of Employment, the long tail, The Nature of the Firm, Tragedy of the Commons, transaction costs, Turing test, turn-by-turn navigation, Uber and Lyft, uber lyft, vertical integration, Zipcar

There is no economic rationale to have cars that are used 5 percent of the time taking up valuable and heavily subsidized real estate for parking the other 95 percent of the time. Once vehicles are shared, we only need about a tenth of the number we currently have. The move to shared-cars-only cities is almost certainly inevitable, particularly when the self-driving car arrives. Technology makes it simple, the economics of pay per use are preferable to consumers, and cities will increasingly require it because of parking space constraints. Back in the mid-2000s, I couldn’t figure out why the car companies didn’t see this (today, they all do). Why weren’t they adapting faster?


pages: 293 words: 90,714

Copenhagenize: The Definitive Guide to Global Bicycle Urbanism by Mikael Colville-Andersen

active transport: walking or cycling, Airbnb, Albert Einstein, autonomous vehicles, bike sharing, business cycle, car-free, congestion charging, corporate social responsibility, Donald Trump, Edward Snowden, Enrique Peñalosa, functional fixedness, gamification, if you build it, they will come, Induced demand, intermodal, Jane Jacobs, Johann Wolfgang von Goethe, Kickstarter, Mahatma Gandhi, megaproject, meta-analysis, neurotypical, out of africa, place-making, Ralph Waldo Emerson, safety bicycle, self-driving car, sharing economy, smart cities, starchitect, transcontinental railway, urban planning, urban sprawl, Yogi Berra

Add to this the concerted effort being made to hype electric vehicles and autonomous vehicles as the next big thing that will change the world. The former only eliminates one aspect of the problem—emissions. The latter brings new problems with it. I recall reading a quote on Twitter that “In Amsterdam, a Google self-driving car would park itself after a few minutes and start crying.” Both of them still occupy an arrogant amount of urban space. I spoke at the State of Design Festival in Melbourne a few years back. The event was book-ended by two keynote speakers. The American, Chris Bangle, who is the former head of design at BMW, would kick it off, and I would wrap it up a few days later.


pages: 304 words: 91,566

Bitcoin Billionaires: A True Story of Genius, Betrayal, and Redemption by Ben Mezrich

airport security, Albert Einstein, bank run, Ben Horowitz, Big Tech, bitcoin, Bitcoin Ponzi scheme, blockchain, Burning Man, buttonwood tree, cryptocurrency, East Village, El Camino Real, Elon Musk, fake news, family office, fault tolerance, fiat currency, financial engineering, financial innovation, game design, information security, Isaac Newton, junk bonds, Marc Andreessen, Mark Zuckerberg, Max Levchin, Menlo Park, Metcalfe’s law, Michael Milken, new economy, offshore financial centre, paypal mafia, peer-to-peer, Peter Thiel, Ponzi scheme, proprietary trading, QR code, Ronald Reagan, Ross Ulbricht, Sand Hill Road, Satoshi Nakamoto, Savings and loan crisis, Schrödinger's Cat, self-driving car, Sheryl Sandberg, side hustle, side project, Silicon Valley, Skype, smart contracts, South of Market, San Francisco, Steve Jobs, Susan Wojcicki, transaction costs, Virgin Galactic, zero-sum game

The phrase sounded space age, sci-fi, to Cameron, but he knew it was truly the next step in the nearly instant economy that Bitcoin allowed; basically, it referred to programmed transactions between banks or individuals that could be self-validating and perfectly efficient; smart contracts that could be set in place to occur automatically, without any middlemen or oversight. For instance, self-driving cars and autonomous agents of the future would exchange value back and forth, perhaps while changing lanes in real time, paying for faster rates of travel—but they wouldn’t be doing it via wires, ACH, or credits cards, which were too slow and costly; they would have to use crypto. Machines couldn’t open accounts at Wells Fargo, but they could plug into protocols exchanging bitcoin.


pages: 375 words: 88,306

The Sharing Economy: The End of Employment and the Rise of Crowd-Based Capitalism by Arun Sundararajan

"World Economic Forum" Davos, additive manufacturing, Airbnb, AltaVista, Amazon Mechanical Turk, asset light, autonomous vehicles, barriers to entry, basic income, benefit corporation, bike sharing, bitcoin, blockchain, book value, Burning Man, call centre, Carl Icahn, collaborative consumption, collaborative economy, collective bargaining, commoditize, commons-based peer production, corporate social responsibility, cryptocurrency, data science, David Graeber, distributed ledger, driverless car, Eben Moglen, employer provided health coverage, Erik Brynjolfsson, Ethereum, ethereum blockchain, Frank Levy and Richard Murnane: The New Division of Labor, future of work, general purpose technology, George Akerlof, gig economy, housing crisis, Howard Rheingold, independent contractor, information asymmetry, Internet of things, inventory management, invisible hand, job automation, job-hopping, John Zimmer (Lyft cofounder), Kickstarter, knowledge worker, Kula ring, Lyft, Marc Andreessen, Mary Meeker, megacity, minimum wage unemployment, moral hazard, moral panic, Network effects, new economy, Oculus Rift, off-the-grid, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, peer-to-peer rental, profit motive, public intellectual, purchasing power parity, race to the bottom, recommendation engine, regulatory arbitrage, rent control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Ross Ulbricht, Second Machine Age, self-driving car, sharing economy, Silicon Valley, smart contracts, Snapchat, social software, supply-chain management, TaskRabbit, TED Talk, the long tail, The Nature of the Firm, total factor productivity, transaction costs, transportation-network company, two-sided market, Uber and Lyft, Uber for X, uber lyft, universal basic income, Vitalik Buterin, WeWork, Yochai Benkler, Zipcar

As illustrated, most of the population actually uses an owned car less than 10% of its life, which means that a vast majority of cars are parked on the street or in a garage for over 90% of their lives. (As I point out in the Introduction, this seems true in Manhattan as well.) And Californians actually use their cars more intensively than the average US resident does. Figure 5.1 Vehicle usage in the United States (compiled from NHTS data as of 2009). We may not need to wait for self-driving cars to see a digitally induced economic revolution in the auto and transportation sector. The range of new peer-to-peer models—Uber to get a driven car on-demand, Lyft to see who else is driving your route, Getaround to see whose car in your neighborhood might be available for you to drive by yourself, BlaBlaCar to get a ride to another city—have already started to increase the impact of the global automobile stock.


pages: 374 words: 89,725

A More Beautiful Question: The Power of Inquiry to Spark Breakthrough Ideas by Warren Berger

Airbnb, carbon footprint, Clayton Christensen, clean water, disruptive innovation, fail fast, fake it until you make it, fear of failure, food desert, Google X / Alphabet X, Isaac Newton, Jeff Bezos, jimmy wales, Joi Ito, Kickstarter, late fees, Lean Startup, Marc Benioff, Mark Zuckerberg, minimum viable product, new economy, Paul Graham, Peter Thiel, Ray Kurzweil, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Stanford marshmallow experiment, Stephen Hawking, Steve Jobs, Steven Levy, TED Talk, Thomas L Friedman, Toyota Production System, Watson beat the top human players on Jeopardy!, Y Combinator, Zipcar

But students find it really liberating to have a teacher say, ‘I don’t know the answer—so let’s figure this out together.’” Is it possible the kind of Socratic teaching that Deresiewicz’s professor did could make a comeback in the online world? That’s what Sebastian Thrun is hoping. Thrun, known for developing Google’s self-driving car and other tech breakthroughs, says he was never comfortable asking disruptive questions in his native Germany but found a much more receptive environment in Silicon Valley. While working at Google he also taught at Stanford University; in 2011, an artificial intelligence course he co-taught was offered online, and Thrun was surprised to see that tens of thousands signed up for it.


pages: 293 words: 88,490

The End of Theory: Financial Crises, the Failure of Economics, and the Sweep of Human Interaction by Richard Bookstaber

asset allocation, bank run, Bear Stearns, behavioural economics, bitcoin, business cycle, butterfly effect, buy and hold, capital asset pricing model, cellular automata, collateralized debt obligation, conceptual framework, constrained optimization, Craig Reynolds: boids flock, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, dark matter, data science, disintermediation, Edward Lorenz: Chaos theory, epigenetics, feminist movement, financial engineering, financial innovation, fixed income, Flash crash, geopolitical risk, Henri Poincaré, impact investing, information asymmetry, invisible hand, Isaac Newton, John Conway, John Meriwether, John von Neumann, Joseph Schumpeter, Long Term Capital Management, margin call, market clearing, market microstructure, money market fund, Paul Samuelson, Pierre-Simon Laplace, Piper Alpha, Ponzi scheme, quantitative trading / quantitative finance, railway mania, Ralph Waldo Emerson, Richard Feynman, risk/return, Robert Solow, Saturday Night Live, self-driving car, seminal paper, sovereign wealth fund, the map is not the territory, The Predators' Ball, the scientific method, Thomas Kuhn: the structure of scientific revolutions, too big to fail, transaction costs, tulip mania, Turing machine, Turing test, yield curve

We are not stepping into a smooth, equilibrium sort of world where the agents and institutions will behave the same way in the future as they have in the past, or even where the agents in our financial system will be those of the past. We have had one hundred fifty years of neoclassical economics. We need to look elsewhere if we are going to be successful in understanding and containing complex, dynamic financial crises. If we build a highway for self-driving cars, we don’t need to use an agent-based model (so long as there are no human drivers or pedestrians on it). If we construct a network of distributed databases sharing and updating information, we don’t need to use an agent-based model. We don’t need an agent-based model for a mechanical, predetermined world.


Gods and Robots: Myths, Machines, and Ancient Dreams of Technology by Adrienne Mayor

AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, Asilomar, autonomous vehicles, caloric restriction, caloric restriction, classic study, deep learning, driverless car, Elon Musk, industrial robot, Islamic Golden Age, Jacquard loom, life extension, Menlo Park, Nick Bostrom, Panopticon Jeremy Bentham, popular electronics, self-driving car, Silicon Valley, Stephen Hawking, Thales and the olive presses, Thales of Miletus, theory of mind, TikTok, Turing test

AI can also be classified by types: Type I machines are reactive, acting on what they have been programmed to perceive at the present, with no memory or ability to learn from past experience (examples include IBM’s Deep Blue chess computer, Google’s AlphaGo, and the ancient bronze robot Talos and the self-moving tripods in the Iliad). Type II AI machines have limited capacity to make memories and can add observations to their preprogrammed representations of the world (examples: self-driving cars, chatbots, and Hephaestus’s automated bellows). Type III, as yet undeveloped, would possess theory of mind and the ability to anticipate others’ expectations or desires (fictional examples: Star Wars’ C-3PO, Hephaestus’s Golden Servants, the Phaeacian ships). Type IV AI of the future would possess theory of mind as well as self-awareness (fictional examples include Tik-Tok in John Sladek’s 1983 novel and Eva in the 2015 film Ex Machina).


pages: 340 words: 90,674

The Perfect Police State: An Undercover Odyssey Into China's Terrifying Surveillance Dystopia of the Future by Geoffrey Cain

airport security, Alan Greenspan, AlphaGo, anti-communist, Bellingcat, Berlin Wall, Black Lives Matter, Citizen Lab, cloud computing, commoditize, computer vision, coronavirus, COVID-19, deep learning, DeepMind, Deng Xiaoping, Edward Snowden, European colonialism, fake news, Geoffrey Hinton, George Floyd, ghettoisation, global supply chain, Kickstarter, land reform, lockdown, mass immigration, military-industrial complex, Nelson Mandela, Panopticon Jeremy Bentham, pattern recognition, phenotype, pirate software, post-truth, purchasing power parity, QR code, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Right to Buy, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, South China Sea, speech recognition, TikTok, Tim Cook: Apple, trade liberalization, trade route, undersea cable, WikiLeaks

Instead, the software could connect the dots on its own when looking at vast datasets, and then learn from that data. The software could then improve its algorithm for the task its creators built it for.4 With fewer humans supervising and limiting the software, businesses could deploy AI for far broader uses. Deep neural nets could control self-driving cars, help physicians diagnose patients, and detect credit card fraud.5 Until 2012, the idea of creating a deep neural network that could impact the market was dismissed as quackery. No matter how many times they tried, the computer scientists at Microsoft Research Asia, and other emerging companies, were hitting a wall.


pages: 324 words: 89,875

Modern Monopolies: What It Takes to Dominate the 21st Century Economy by Alex Moazed, Nicholas L. Johnson

3D printing, Affordable Care Act / Obamacare, Airbnb, altcoin, Amazon Web Services, Andy Rubin, barriers to entry, basic income, bitcoin, blockchain, book value, Chuck Templeton: OpenTable:, cloud computing, commoditize, connected car, disintermediation, driverless car, fake it until you make it, future of work, gig economy, hockey-stick growth, if you build it, they will come, information asymmetry, Infrastructure as a Service, intangible asset, Internet of things, invisible hand, jimmy wales, John Gruber, Kickstarter, Lean Startup, Lyft, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Marshall McLuhan, means of production, Metcalfe’s law, money market fund, multi-sided market, Network effects, PalmPilot, patent troll, peer-to-peer lending, Peter Thiel, pets.com, platform as a service, power law, QWERTY keyboard, Ray Kurzweil, ride hailing / ride sharing, road to serfdom, Robert Metcalfe, Ronald Coase, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, Skype, Snapchat, social graph, software as a service, software is eating the world, source of truth, Startup school, Steve Jobs, TaskRabbit, technological determinism, the medium is the message, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, vertical integration, white flight, winner-take-all economy, Y Combinator

Its one glaring hole has been a social networking platform, which explains its repeat failed attempts at building one (e.g., Orkut, Google Wave, and Google+). Google has also experimented with services marketplaces, including Google Express, and is toying with the idea of an eventual Uber competitor that uses self-driving cars. Similarly, Apple has iTunes, iOS and the App Store, Apple Pay, and iMessage, among others. And although Facebook started as a social networking platform, it has expanded to include content platforms (Instagram and Facebook Pages), messaging platforms (Messenger and WhatsApp), and development platforms, as well as a number of experiments with product and services marketplaces.


pages: 312 words: 92,131

Beginners: The Joy and Transformative Power of Lifelong Learning by Tom Vanderbilt

AlphaGo, crowdsourcing, DeepMind, deliberate practice, Downton Abbey, Dunning–Kruger effect, fake it until you make it, functional fixedness, future of work, G4S, global supply chain, IKEA effect, Khan Academy, Kickstarter, lateral thinking, Maui Hawaii, meta-analysis, mirror neurons, performance metric, personalized medicine, quantum entanglement, randomized controlled trial, Rubik’s Cube, self-driving car, side hustle, Silicon Valley, Skype, Socratic dialogue, spaced repetition, Steve Jobs, zero-sum game

Beginner drivers are told to stop at red lights; beginning chess players learn to “always” do this or that (for example, don’t move your knight to the edge of the board). But what if a driver comes to an intersection where the red light has malfunctioned? (This was a classic problem in the “beginner” stages of self-driving cars.) What if your chess opponent responds to your textbook move with something unorthodox? Beginners judge their performance, the Dreyfuses suggested, by how well they follow rules. On the surfboard, I was trying to follow a set of fundamental rules, without otherwise paying attention to what was going on in the real world.


Upstream: The Quest to Solve Problems Before They Happen by Dan Heath

"Hurricane Katrina" Superdome, Affordable Care Act / Obamacare, airport security, Albert Einstein, bank run, British Empire, Buckminster Fuller, call centre, cloud computing, cognitive dissonance, colonial rule, correlation does not imply causation, cuban missile crisis, en.wikipedia.org, epigenetics, food desert, high-speed rail, Housing First, illegal immigration, Internet of things, mandatory minimum, millennium bug, move fast and break things, Nick Bostrom, payday loans, Ralph Nader, RAND corporation, randomized controlled trial, self-driving car, Skype, Snapchat, subscription business, systems thinking, urban planning, Watson beat the top human players on Jeopardy!, Y2K

Rather, thousands of people—auto safety experts and transportation engineers and Mothers Against Drunk Driving volunteers—tweaked the system so that millions of other people could be safer. They shaped the water. And they shape it still: Despite the success, there are still more than 37,000 people who die annually from car crashes in the US. Someday, self-driving cars might come close to eliminating those fatalities. In the meantime, there are countless tweaks being made every week to help fallible human drivers. On sharp curves where accidents tend to happen, transportation departments have begun to install high friction surface treatments (HFSTs)—overlays of ultra-rough material superglued to existing roads.


pages: 285 words: 91,144

App Kid: How a Child of Immigrants Grabbed a Piece of the American Dream by Michael Sayman

airport security, augmented reality, Bernie Sanders, Big Tech, Cambridge Analytica, data science, Day of the Dead, fake news, Frank Gehry, Google bus, Google Chrome, Google Hangouts, Googley, hacker house, imposter syndrome, Khan Academy, Marc Benioff, Mark Zuckerberg, Menlo Park, microaggression, move fast and break things, Salesforce, San Francisco homelessness, self-driving car, Sheryl Sandberg, Silicon Valley, skeuomorphism, Snapchat, Steve Jobs, tech worker, the High Line, TikTok, Tim Cook: Apple

She’d taken off her pumps to fit her legs under the table but still looked pinched and uncomfortable in her gray wool pantsuit. Technically, not all of us were “Nooglers,” as they called first-week Google employees. Google was one of several companies owned by Alphabet, our parent corporation. Back in 2015, Google was pursuing so many projects—self-driving cars, longevity research, urban innovation, and so on—that the founders, Sergey Brin and Larry Page, decided to spin off each non-Internet division into its own company. They created Alphabet to hold them all, including Google, the biggest. I could not believe I worked at Google now! Everything I’d ever learned—how to code, program, design—I’d taught myself through Google.


pages: 277 words: 91,698

SAM: One Robot, a Dozen Engineers, and the Race to Revolutionize the Way We Build by Jonathan Waldman

Burning Man, computer vision, Ford paid five dollars a day, glass ceiling, helicopter parent, Hyperloop, industrial robot, information security, James Webb Space Telescope, job automation, Lean Startup, minimum viable product, off grid, Ralph Nader, Ralph Waldo Emerson, Ronald Reagan, self-driving car, Silicon Valley, stealth mode startup, Steve Jobs, Strategic Defense Initiative, strikebreaker, union organizing, Yogi Berra

By the time the workday was over, long into overtime territory, SAM had put down all of 108 bricks, which was only a fraction of what even the laziest human bricklayer typically laid. For Scott, the number was hard to digest. For eight years, he’d dreamed of a machine so dominant, so refined and widely dispersed, that it would render today’s antiquated hand-laying technique obsolete. In many ways, such a shift was to be more significant than the much heralded move to self-driving cars, because horseless carriages have been evolving for a hundred years, thanks to armies of engineers and billions of R&D dollars. Bricklaying hasn’t changed since man crawled out of the muck. As ever, laying bricks requires hard work and a lot of time. Old-timers know that bricklayers lift the equivalent of a Ford truck every few days, basically trading a body for a paycheck.


pages: 318 words: 91,957

The Man Who Broke Capitalism: How Jack Welch Gutted the Heartland and Crushed the Soul of Corporate America—and How to Undo His Legacy by David Gelles

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 3D printing, accounting loophole / creative accounting, Adam Neumann (WeWork), air traffic controllers' union, Alan Greenspan, Andrei Shleifer, Bear Stearns, benefit corporation, Bernie Sanders, Big Tech, big-box store, Black Monday: stock market crash in 1987, Boeing 737 MAX, call centre, carbon footprint, Carl Icahn, collateralized debt obligation, Colonization of Mars, company town, coronavirus, corporate governance, corporate raider, corporate social responsibility, COVID-19, Credit Default Swap, credit default swaps / collateralized debt obligations, disinformation, Donald Trump, financial deregulation, financial engineering, fulfillment center, gig economy, global supply chain, Gordon Gekko, greed is good, income inequality, inventory management, It's morning again in America, Jeff Bezos, junk bonds, Kaizen: continuous improvement, Kickstarter, Lean Startup, low interest rates, Lyft, manufacturing employment, Mark Zuckerberg, Michael Milken, Neil Armstrong, new economy, operational security, profit maximization, profit motive, public intellectual, QAnon, race to the bottom, Ralph Nader, remote working, Robert Bork, Ronald Reagan, Rutger Bregman, self-driving car, shareholder value, side hustle, Silicon Valley, six sigma, Social Responsibility of Business Is to Increase Its Profits, Steve Ballmer, stock buybacks, subprime mortgage crisis, TaskRabbit, technoutopianism, Travis Kalanick, Uber and Lyft, uber lyft, warehouse robotics, Watson beat the top human players on Jeopardy!, We are the 99%, WeWork, women in the workforce

When he asked the business leaders of different units what the purpose of the company was, he said, their answer was always about delivering for shareholders. “When you tried to get anything done, you would feel the imprint of Welch,” he said. “The impact that man left was profoundly deep.” For years, Dignan burrowed into GE, encouraging Immelt to pursue 3D printing, self-driving cars, anything that might give the company relevance in the years ahead. Dignan kept telling Immelt and the board what any student of business understands: there would be up-front costs, but that if the investments paid off, the rewards could be enormous. “This is going to hurt before it gets better,” he would tell them.


pages: 778 words: 239,744

Gnomon by Nick Harkaway

"Margaret Hamilton" Apollo, Albert Einstein, back-to-the-land, banking crisis, behavioural economics, Burning Man, choice architecture, clean water, cognitive dissonance, false flag, fault tolerance, fear of failure, Future Shock, gravity well, Great Leap Forward, high net worth, impulse control, Isaac Newton, Khartoum Gordon, lifelogging, neurotypical, off-the-grid, pattern recognition, place-making, post-industrial society, Potemkin village, precautionary principle, Richard Feynman, Scramble for Africa, self-driving car, side project, Silicon Valley, skeuomorphism, skunkworks, the market place, trade route, Tragedy of the Commons, urban planning, urban sprawl

You painted The Lion in Space.’ She stopped, suddenly concerned. ‘Can you do it? I mean, can you still paint? Did you burn out?’ An intimate question from a professional colleague. A daring one from a granddaughter. A good one. ‘No,’ I said. ‘I faded away.’ Colson offered to take me home in the self-driving car, but in the end I got in a taxi because I needed very much to collect myself and get sensible. Annie had quite adroitly doubled my oblique family repair work back upon me in a style of manipulation that I recognised as my own and Michael’s mixed. I didn’t want to default on grandfatherly assistance or fatherly amends, so I had at least to think it over, even if my first reaction was to run a mile from such a strange, enormous undertaking.

Whatever private discussion they were having seemed to be over. ‘Well, all right,’ I said. ‘That’s a nice historical background. For today, your advice is?’ ‘To explore your options. See what may be negotiated before things deteriorate. To consider what you most wish to achieve.’ And with that, the meeting was over. We all got into one of the self-driving cars and let it take us away. ‘What was that about?’ I asked Colson, after a long silence during which no one spoke. ‘The nice lady is doing her best for us,’ Colson said. ‘But she’s being fucked with and she’s a little bit scared on her own account.’ ‘Lindsey?’ ‘Yes. Did you see the little squit at the end of the table taking notes?’


pages: 825 words: 228,141

MONEY Master the Game: 7 Simple Steps to Financial Freedom by Tony Robbins

"World Economic Forum" Davos, 3D printing, active measures, activist fund / activist shareholder / activist investor, addicted to oil, affirmative action, Affordable Care Act / Obamacare, Albert Einstein, asset allocation, backtesting, Bear Stearns, behavioural economics, bitcoin, Black Monday: stock market crash in 1987, buy and hold, Carl Icahn, clean water, cloud computing, corporate governance, corporate raider, correlation does not imply causation, Credit Default Swap, currency risk, Dean Kamen, declining real wages, diversification, diversified portfolio, Donald Trump, estate planning, fear of failure, fiat currency, financial independence, fixed income, forensic accounting, high net worth, index fund, Internet of things, invention of the wheel, it is difficult to get a man to understand something, when his salary depends on his not understanding it, Jeff Bezos, John Bogle, junk bonds, Kenneth Rogoff, lake wobegon effect, Lao Tzu, London Interbank Offered Rate, low interest rates, Marc Benioff, market bubble, Michael Milken, money market fund, mortgage debt, Neil Armstrong, new economy, obamacare, offshore financial centre, oil shock, optical character recognition, Own Your Own Home, passive investing, profit motive, Ralph Waldo Emerson, random walk, Ray Kurzweil, Richard Thaler, risk free rate, risk tolerance, riskless arbitrage, Robert Shiller, Salesforce, San Francisco homelessness, self-driving car, shareholder value, Silicon Valley, Skype, Snapchat, sovereign wealth fund, stem cell, Steve Jobs, subscription business, survivorship bias, tail risk, TED Talk, telerobotics, The 4% rule, The future is already here, the rule of 72, thinkpad, tontine, transaction costs, Upton Sinclair, Vanguard fund, World Values Survey, X Prize, Yogi Berra, young professional, zero-sum game

But you’re going to get an insider’s look at what’s coming from some of the most brilliant minds of our time. We’ll hear from my friends Ray Kurzweil, the Edison of our age, and Peter Diamandis, creator of the X Prize, about new technologies coming online: 3-D printers that will transform your personal computer into a manufacturing plant, self-driving cars, exoskeletons that enable paraplegics to walk, artificial limbs grown from single cells—innovations that will dramatically change our lives for the better in the very near future. I’m hoping this will inspire you, and also show you that even if you somehow screw up and don’t get your financial act together, you’ll still have a better quality of life.

He created the first digital music. If you’ve ever dictated an email to Siri or other voice-to-text systems, that’s because of Ray. I remember meeting Ray Kurzweil nearly 20 years ago and listening with amazement as he described the future. It seemed like magic then, but it’s all real now. Self-driving cars. A computer that could beat the world’s greatest chess master. He had already invented an optical character-recognition system to create the first reading machine for the blind—Stevie Wonder was his first customer. Now he wanted to help blind people read street signs and navigate cities without help, and go into restaurants and order off the menu using a little device the size of a pack of cigarettes.


The New Harvest: Agricultural Innovation in Africa by Calestous Juma

agricultural Revolution, Albert Einstein, barriers to entry, bioinformatics, business climate, carbon footprint, clean water, colonial rule, conceptual framework, creative destruction, CRISPR, double helix, electricity market, energy security, energy transition, export processing zone, global value chain, high-speed rail, impact investing, income per capita, industrial cluster, informal economy, Intergovernmental Panel on Climate Change (IPCC), Joseph Schumpeter, knowledge economy, land tenure, M-Pesa, microcredit, mobile money, non-tariff barriers, off grid, out of africa, precautionary principle, precision agriculture, Recombinant DNA, rolling blackouts, search costs, Second Machine Age, self-driving car, Silicon Valley, sovereign wealth fund, structural adjustment programs, supply-chain management, synthetic biology, systems thinking, total factor productivity, undersea cable

This is referred to as the evolution of technologies.2 While it is easy to be intimidated by the pace of technological growth, it could also be a means to achieve a society of abundance, where all people have access basic needs, education, freedom, and good health.3 Exponential growth in knowledge is also one of the main criteria for what some have called the new machine age, a time when digital technologies can create self-driving cars and contribute to solve some of the world’s most pressing challenges.4 African countries can utilize the large aggregation of knowledge and know-how that has been amassed globally in their efforts to improve their access to and use of the most cuttingedge technology. While Africa is currently lagging in the Advances in Science, Technology, and Engineering 41 utilization and accumulation of technology, its countries have the ability not only to catch up to industrial leaders but also to attain their own level of research growth.


pages: 360 words: 100,991

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence by Richard Yonck

3D printing, AI winter, AlphaGo, Apollo 11, artificial general intelligence, Asperger Syndrome, augmented reality, autism spectrum disorder, backpropagation, Berlin Wall, Bletchley Park, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, crowdsourcing, deep learning, DeepMind, Dunning–Kruger effect, Elon Musk, en.wikipedia.org, epigenetics, Fairchild Semiconductor, friendly AI, Geoffrey Hinton, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta-analysis, Metcalfe’s law, mirror neurons, Neil Armstrong, neurotypical, Nick Bostrom, Oculus Rift, old age dependency ratio, pattern recognition, planned obsolescence, pneumatic tube, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, SoftBank, software as a service, SQL injection, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, TED Talk, telepresence, telepresence robot, The future is already here, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, Two Sigma, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

Today, instead of tales told around a campfire or in a temple, our stories are increasingly told between the covers of a book or on the screen of a movie theater, television, or smartphone. It’s no coincidence that the more our myths and stories have used technology as a medium, the more they have also turned to it as primary subject matter. The narratives of our ancestors had little cause to ponder space travel or self-driving cars or biotechnology run amok. They most certainly didn’t need to contemplate a world filled with artificial intelligence. Today, we do. The past two centuries have seen a growing exploration of technology’s ongoing impact through many different mediums of fiction. Automation, robotics, and artificial intelligence have all seen an evolution in books and movies that often mirrors and sometimes anticipates the changes occurring in the real world.


pages: 441 words: 96,534

Streetfight: Handbook for an Urban Revolution by Janette Sadik-Khan

autonomous vehicles, bike sharing, Boris Johnson, business cycle, call centre, car-free, carbon footprint, clean water, congestion charging, congestion pricing, Cornelius Vanderbilt, crowdsourcing, digital map, Donald Shoup, edge city, Edward Glaeser, en.wikipedia.org, Enrique Peñalosa, fixed-gear, gentrification, high-speed rail, Hyperloop, Induced demand, Jane Jacobs, Lewis Mumford, Loma Prieta earthquake, Lyft, megaproject, New Urbanism, off-the-grid, place-making, self-driving car, sharing economy, the built environment, The Death and Life of Great American Cities, the High Line, transportation-network company, Uber and Lyft, uber lyft, urban decay, urban planning, urban renewal, urban sprawl, walkable city, white flight, Works Progress Administration, Zipcar

The major near-term goal is establishing licensing and liability requirements that promote safe street operation (obeying speed limits, yielding to pedestrians) on test vehicles and the early prototypes that will be on the market. The new street code for cities must also address parking policies, such as dedicated on- and off-street spaces for shared vehicles. And with the advent of self-driving cars, cities must be prepared for an increase in the demand for curb space for pickup and drop-offs. Finally, as self-driving fleets evolve, cities will need to invest in updated signs, infrastructure, and mapping technology to accommodate the new way of getting around and dedicate funding to adapt cities’ roadways.


Cataloging the World: Paul Otlet and the Birth of the Information Age by Alex Wright

1960s counterculture, Ada Lovelace, barriers to entry, British Empire, business climate, business intelligence, Cape to Cairo, card file, centralized clearinghouse, Charles Babbage, Computer Lib, corporate governance, crowdsourcing, Danny Hillis, Deng Xiaoping, don't be evil, Douglas Engelbart, Douglas Engelbart, Electric Kool-Aid Acid Test, European colonialism, folksonomy, Frederick Winslow Taylor, Great Leap Forward, hive mind, Howard Rheingold, index card, information retrieval, invention of movable type, invention of the printing press, Jane Jacobs, John Markoff, Kevin Kelly, knowledge worker, Law of Accelerating Returns, Lewis Mumford, linked data, Livingstone, I presume, lone genius, machine readable, Menlo Park, military-industrial complex, Mother of all demos, Norman Mailer, out of africa, packet switching, pneumatic tube, profit motive, RAND corporation, Ray Kurzweil, scientific management, Scramble for Africa, self-driving car, semantic web, Silicon Valley, speech recognition, Steve Jobs, Stewart Brand, systems thinking, Ted Nelson, The Death and Life of Great American Cities, the scientific method, Thomas L Friedman, urban planning, Vannevar Bush, W. E. B. Du Bois, Whole Earth Catalog

E ­ veryone 297 C ATA L O G I N G T H E WO R L D was busy carving one stone here and another stone there, with some invisible architect getting everything to fit. The mood was playful, yet there was a palpable reverence in the air.” 2 That spirit of reverence underlies much of what Google has tried to accomplish—not just delivering search results, but creating self-driving cars, space elevators, humanitarian tools for crisis response, and a scientific platform for environmental forecasting, as well as any number of unknown classified projects going on at any given time in the company’s secretive X Lab, under the close supervision of co-founder Sergey Brin. But many contemporary critics have questioned how such a lofty-sounding mission can square with the commercial imperatives of a publicly traded, for-profit enterprise.


pages: 368 words: 96,825

Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler

3D printing, additive manufacturing, adjacent possible, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, Boston Dynamics, Charles Lindbergh, cloud computing, company town, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deal flow, deep learning, dematerialisation, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Exxon Valdez, fail fast, Fairchild Semiconductor, fear of failure, Firefox, Galaxy Zoo, Geoffrey Hinton, Google Glasses, Google Hangouts, gravity well, hype cycle, ImageNet competition, industrial robot, information security, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, OpenAI, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, Scaled Composites, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, Stuart Kauffman, superconnector, Susan Wojcicki, synthetic biology, technoutopianism, TED Talk, telepresence, telepresence robot, Turing test, urban renewal, Virgin Galactic, Wayback Machine, web application, X Prize, Y Combinator, zero-sum game

Already, we’ve seen the first wave of this in the smart assembly lines and supply chains (what’s technically called process optimization) that have enabled things like just-in-time delivery. With the smart grid for energy and the smart grid for water—what’s technically called resource consumption optimization—we’re seeing the second wave. Next up is the automation and control of far more complex autonomous systems—such as self-driving cars. There are even further opportunities in finding simpler ways to connect decision makers to sensor data in real time. The aforementioned plants that tweet their owners when they need watering were an early (2010) iteration of this sector. A more contemporary example (2013) is the Washington, DC-based start-up SmartThings, a company that CNN called “a digital maestro for every object in the home.”21 SmartThings makes an interface that can recognize over a thousand smart household objects, from temperature sensors that control the thermostat to door and windows sensors that tell you if you left something unlocked to ways to have appliances automatically shut off before you go to bed.


Calling Bullshit: The Art of Scepticism in a Data-Driven World by Jevin D. West, Carl T. Bergstrom

airport security, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Andrew Wiles, Anthropocene, autism spectrum disorder, bitcoin, Charles Babbage, cloud computing, computer vision, content marketing, correlation coefficient, correlation does not imply causation, crowdsourcing, cryptocurrency, data science, deep learning, deepfake, delayed gratification, disinformation, Dmitri Mendeleev, Donald Trump, Elon Musk, epigenetics, Estimating the Reproducibility of Psychological Science, experimental economics, fake news, Ford Model T, Goodhart's law, Helicobacter pylori, Higgs boson, invention of the printing press, John Markoff, Large Hadron Collider, longitudinal study, Lyft, machine translation, meta-analysis, new economy, nowcasting, opioid epidemic / opioid crisis, p-value, Pluto: dwarf planet, publication bias, RAND corporation, randomized controlled trial, replication crisis, ride hailing / ride sharing, Ronald Reagan, selection bias, self-driving car, Silicon Valley, Silicon Valley startup, social graph, Socratic dialogue, Stanford marshmallow experiment, statistical model, stem cell, superintelligent machines, systematic bias, tech bro, TED Talk, the long tail, the scientific method, theory of mind, Tim Cook: Apple, twin studies, Uber and Lyft, Uber for X, uber lyft, When a measure becomes a target

It takes a set of numerical values as inputs, and then spits out either a 0 or a 1. The numerical inputs might be the pixel values of a chest X-ray; the output, whether a patient has pneumonia. Connect enough of these perceptrons together in the right ways, and you can build a chess-playing computer, a self-driving car, or an algorithm that translates speech in real time like Douglas Adams’s Babel Fish. You don’t hear the term “perceptron” often these days, but these circuits are the building blocks for the convolutional neural networks and deep learning technologies that appear in headlines daily. The same old magic is still selling tickets.


pages: 393 words: 102,801

Welcome to Britain: Fixing Our Broken Immigration System by Colin Yeo;

barriers to entry, Boris Johnson, Brexit referendum, British Empire, coronavirus, COVID-19, Donald Trump, G4S, illegal immigration, immigration reform, Jeremy Corbyn, low skilled workers, lump of labour, open immigration, post-war consensus, self-driving car, Shamima Begum, Skype, Socratic dialogue

Junior officials were expected to act like robots, merely checking whether the documents submitted by an applicant met precise predefined criteria and, if so, issuing a visa. In a way, the Home Office could be accused of being too far ahead of its time. Today, technology entrepreneurs are using artificial intelligence to recognise everyday objects, utilising vast sets of data and complex detection and perception technology. Self-driving cars require computers to interpret, classify and predict the behaviour of a bewildering array of real-life objects. The Home Office tried something similar with no prior experience, no artificial intelligence, no data and no computers all the way back in 2006. Unsurprisingly, the project failed.


pages: 418 words: 102,597

Being You: A New Science of Consciousness by Anil Seth

AlphaGo, artificial general intelligence, augmented reality, backpropagation, carbon-based life, Claude Shannon: information theory, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, correlation does not imply causation, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Drosophila, en.wikipedia.org, Filter Bubble, GPT-3, GPT-4, John Markoff, longitudinal study, Louis Pasteur, mirror neurons, Neil Armstrong, Nick Bostrom, Norbert Wiener, OpenAI, paperclip maximiser, pattern recognition, Paul Graham, Pierre-Simon Laplace, planetary scale, Plato's cave, precautionary principle, Ray Kurzweil, self-driving car, speech recognition, stem cell, systems thinking, technological singularity, TED Talk, telepresence, the scientific method, theory of mind, Thomas Bayes, TikTok, Turing test

Fig. 23: Eight faces. These people are not real. — The rapid rise of AI – whatever mixture of hype and reality it is fuelled by – has sparked a resurgent and necessary discussion of ethics. Many ethical concerns have to do with the economic and societal consequences of near-future technologies like self-driving cars and automated factory workers, where significant disruption is inevitable.† There are legitimate worries about delegating decision-making capability to artificial systems, the inner workings of which may be susceptible to all kinds of bias and caprice, and which may remain opaque – not only to those affected, but also to those who designed them.


pages: 329 words: 100,162

Hype: How Scammers, Grifters, and Con Artists Are Taking Over the Internet―and Why We're Following by Gabrielle Bluestone

Adam Neumann (WeWork), Airbnb, Bellingcat, Bernie Madoff, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Burning Man, cashless society, coronavirus, COVID-19, Donald Trump, driverless car, Elon Musk, fake it until you make it, financial thriller, forensic accounting, gig economy, global pandemic, growth hacking, high net worth, hockey-stick growth, hype cycle, Hyperloop, Kevin Roose, lock screen, lockdown, Lyft, Mark Zuckerberg, Masayoshi Son, Mason jar, Menlo Park, Multics, Naomi Klein, Netflix Prize, NetJets, Peter Thiel, placebo effect, post-truth, RFID, ride hailing / ride sharing, Russell Brand, Sand Hill Road, self-driving car, Silicon Valley, Snapchat, social distancing, SoftBank, Steve Jobs, tech billionaire, tech bro, TikTok, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, uber lyft, unpaid internship, upwardly mobile, Vision Fund, WeWork

In 2019, for example, The Drive calculated that Tesla had “three times as many OSHA violations as the 10 largest US plants combined,”49 making a job with Musk more dangerous than working in a slaughterhouse or a sawmill, according to a 2017 Los Angeles Times report.50Nor have his problems been limited to the factory floor: he recently had to pay out $20 million for tweeting that he had secured private funding to take Tesla private at $420 a share, when in fact he had not.51 This is a guy who sells an $8,000 “self-driving” car software that requires an active driver behind the wheel to operate. And still three people have died from crashes stemming from the software, with at least ten other nonfatal crashes also under investigation by the National Highway Traffic Safety Administration.52 Still, it’s no coincidence that, alongside a disregard for local laws, the vast majority of wealth created over the last few decades has been concentrated in the tech industry, starting with the rise of Silicon Valley venture capital that set the stage for today’s obscene IPOs.


pages: 346 words: 97,890

The Road to Conscious Machines by Michael Wooldridge

Ada Lovelace, AI winter, algorithmic bias, AlphaGo, Andrew Wiles, Anthropocene, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, basic income, Bletchley Park, Boeing 747, British Empire, call centre, Charles Babbage, combinatorial explosion, computer vision, Computing Machinery and Intelligence, DARPA: Urban Challenge, deep learning, deepfake, DeepMind, Demis Hassabis, don't be evil, Donald Trump, driverless car, Elaine Herzberg, Elon Musk, Eratosthenes, factory automation, fake news, future of work, gamification, general purpose technology, Geoffrey Hinton, gig economy, Google Glasses, intangible asset, James Watt: steam engine, job automation, John von Neumann, Loebner Prize, Minecraft, Mustafa Suleyman, Nash equilibrium, Nick Bostrom, Norbert Wiener, NP-complete, P = NP, P vs NP, paperclip maximiser, pattern recognition, Philippa Foot, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Silicon Valley, Stephen Hawking, Steven Pinker, strong AI, technological singularity, telemarketer, Tesla Model S, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, trolley problem, Turing machine, Turing test, universal basic income, Von Neumann architecture, warehouse robotics

The Trolley Problem has risen rapidly in prominence recently because of the imminent arrival of driverless cars. Pundits were quick to point out that driverless cars might well find themselves in a situation like the Trolley Problem, and AI software would then be called upon to make an impossible choice. ‘Self-driving cars are already deciding who to kill’ ran one Internet headline in 2016.8 There was a flurry of anguished online debate, and several philosophers of my acquaintance were surprised and flattered to discover that there was suddenly an attentive audience for their opinions on what had hitherto been a rather obscure problem in the philosophy of ethics.


pages: 337 words: 96,666

Practical Doomsday: A User's Guide to the End of the World by Michal Zalewski

accounting loophole / creative accounting, AI winter, anti-communist, artificial general intelligence, bank run, big-box store, bitcoin, blockchain, book value, Buy land – they’re not making it any more, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carrington event, clean water, coronavirus, corporate governance, COVID-19, cryptocurrency, David Graeber, decentralized internet, deep learning, distributed ledger, diversification, diversified portfolio, Dogecoin, dumpster diving, failed state, fiat currency, financial independence, financial innovation, fixed income, Fractional reserve banking, Francis Fukuyama: the end of history, Haber-Bosch Process, housing crisis, index fund, indoor plumbing, information security, inventory management, Iridium satellite, Joan Didion, John Bogle, large denomination, lifestyle creep, mass immigration, McDonald's hot coffee lawsuit, McMansion, medical bankruptcy, Modern Monetary Theory, money: store of value / unit of account / medium of exchange, moral panic, non-fungible token, nuclear winter, off-the-grid, Oklahoma City bombing, opioid epidemic / opioid crisis, paperclip maximiser, passive investing, peak oil, planetary scale, ransomware, restrictive zoning, ride hailing / ride sharing, risk tolerance, Ronald Reagan, Satoshi Nakamoto, Savings and loan crisis, self-driving car, shareholder value, Silicon Valley, supervolcano, systems thinking, tech worker, Ted Kaczynski, TED Talk, Tunguska event, underbanked, urban sprawl, Wall-E, zero-sum game, zoonotic diseases

With that in mind, and now that we have the taxonomy of the challenges sketched out, let’s dive into some of the investment strategies that can help protect your savings down the line. If you approach a financial advisor and ask them what to do with your savings, their first question will be about your investment objective—expecting to hear that you want to retire early, send your children to a posh college, or make a lot of money on self-driving cars. But their first real question will be about your risk tolerance. If you’re risk-averse, they’ll keep your account mostly in cash or government bonds, but if you tell them you want to get rich quick, they’ll recommend putting a good chunk of your money into stocks that historically showed high volatility, with the implication that higher payoffs might result down the line.* For the purpose of safeguarding rainy-day funds, I believe this thinking is flawed; risk has many dimensions.


pages: 331 words: 95,582

Golden Gates: Fighting for Housing in America by Conor Dougherty

Airbnb, bank run, basic income, Bay Area Rapid Transit, Bernie Sanders, Big Tech, big-box store, business logic, California gold rush, carbon footprint, commoditize, death of newspapers, desegregation, do-ocracy, don't be evil, Donald Trump, edge city, Edward Glaeser, El Camino Real, emotional labour, fixed income, fixed-gear, gentrification, Golden Gate Park, Google bus, Haight Ashbury, Home mortgage interest deduction, housing crisis, illegal immigration, income inequality, Joan Didion, Marc Andreessen, Marc Benioff, mass immigration, new economy, New Urbanism, passive income, Paul Buchheit, Peter Thiel, rent control, rent-seeking, Richard Florida, Ronald Reagan, Salesforce, San Francisco homelessness, self-driving car, sharing economy, side hustle, side project, Silicon Valley, single-payer health, software is eating the world, South of Market, San Francisco, The Rise and Fall of American Growth, universal basic income, urban planning, urban renewal, vertical integration, white flight, winner-take-all economy, working poor, Y Combinator, Yom Kippur War, young professional

Having spent most of her life in Philadelphia, a city with blocks of empty property that had lost about half a million people from its 1950 peak, she knew what a troubled city looked like and was not going to blame anyone for moving to a region that to an outsider felt like an economic wonderland. It had been only a few years since the Great Recession. Most of America was still grappling with what the pundits called “a jobless recovery” (whatever that meant), while the Bay Area lived in a bubble of exuberance and self-satisfaction. Google had just revealed its self-driving car project, Facebook was gearing up to go public, and the venture capitalist Marc Andreessen was coining the phrase “software is eating the world.” Sonja thought it was exciting to live in a place with so much optimism and easy employment, and when she heard people complain about how San Francisco was being murdered by runaway growth, she regarded them as ingrates who didn’t know or didn’t care what places like Philly and St.


pages: 431 words: 107,868

The Great Race: The Global Quest for the Car of the Future by Levi Tillemann

Affordable Care Act / Obamacare, An Inconvenient Truth, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, banking crisis, Bear Stearns, car-free, carbon footprint, clean tech, creative destruction, decarbonisation, deindustrialization, demand response, Deng Xiaoping, Donald Trump, driverless car, electricity market, Elon Musk, en.wikipedia.org, energy security, factory automation, Fairchild Semiconductor, Ford Model T, foreign exchange controls, gigafactory, global value chain, high-speed rail, hydrogen economy, index card, Intergovernmental Panel on Climate Change (IPCC), joint-stock company, Joseph Schumpeter, Kanban, Kickstarter, manufacturing employment, market design, megacity, Nixon shock, obamacare, off-the-grid, oil shock, planned obsolescence, Ralph Nader, RFID, rolodex, Ronald Reagan, Rubik’s Cube, self-driving car, shareholder value, Shenzhen special economic zone , short squeeze, Silicon Valley, Silicon Valley startup, skunkworks, smart cities, Solyndra, sovereign wealth fund, special economic zone, Steve Jobs, Tesla Model S, too big to fail, Unsafe at Any Speed, zero-sum game, Zipcar

For academics and policy makers it was suddenly legitimate, reasonable, and even necessary to start pondering the implications of a future with cars that drove themselves and what this might mean for automakers, cities, and countries around the world. Different people used different terminologies with various meanings to describe these robotic cars—self-driving cars, robot cars, automated vehicles, and autonomous vehicles, to name a few. However, the ultimate goal was generally understood to be a car that could drive itself. Although theirs was not the first autonomous car, Google broke the logjam. And that is why one might say that automation is not simply the next frontier, but the finishing line in the Great Race.


pages: 345 words: 104,404

Pandora's Brain by Calum Chace

AI winter, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Bletchley Park, brain emulation, Extropian, friendly AI, hive mind, lateral thinking, machine translation, mega-rich, Nick Bostrom, precautionary principle, Ray Kurzweil, self-driving car, Silicon Valley, Singularitarianism, Skype, speech recognition, stealth mode startup, Stephen Hawking, strong AI, technological singularity, theory of mind, Turing test, Wall-E

When you watch those videos of the latest military-grade robots on YouTube, or when you ask your smartphone a question, do you detect anything like a conscious mind? The AI community said we would have them by now, along with flying cars and personal jetpacks.’ Matt raised his hands. ‘Come off it, Carl. It’s ridiculous to say that AI has made no progress. Self-driving cars are legal on public roads in parts of the US, and they will be legal over here soon too. Computers can recognise faces as well as you and I can: a lot of people said that would be in the ‘too-hard’ box for decades. Real-time machine translation is getting seriously impressive. This is all driven by the hugely increased processing power at researchers’ disposal, so they are going back to their original goal of developing a human-level intelligence which will pass a robust version of the Turing Test.


pages: 391 words: 105,382

Utopia Is Creepy: And Other Provocations by Nicholas Carr

Abraham Maslow, Air France Flight 447, Airbnb, Airbus A320, AltaVista, Amazon Mechanical Turk, augmented reality, autonomous vehicles, Bernie Sanders, book scanning, Brewster Kahle, Buckminster Fuller, Burning Man, Captain Sullenberger Hudson, centralized clearinghouse, Charles Lindbergh, cloud computing, cognitive bias, collaborative consumption, computer age, corporate governance, CRISPR, crowdsourcing, Danny Hillis, data science, deskilling, digital capitalism, digital map, disruptive innovation, Donald Trump, driverless car, Electric Kool-Aid Acid Test, Elon Musk, Evgeny Morozov, factory automation, failed state, feminist movement, Frederick Winslow Taylor, friendly fire, game design, global village, Google bus, Google Glasses, Google X / Alphabet X, Googley, hive mind, impulse control, indoor plumbing, interchangeable parts, Internet Archive, invention of movable type, invention of the steam engine, invisible hand, Isaac Newton, Jeff Bezos, jimmy wales, Joan Didion, job automation, John Perry Barlow, Kevin Kelly, Larry Ellison, Lewis Mumford, lifelogging, lolcat, low skilled workers, machine readable, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Max Levchin, means of production, Menlo Park, mental accounting, natural language processing, Neal Stephenson, Network effects, new economy, Nicholas Carr, Nick Bostrom, Norman Mailer, off grid, oil shale / tar sands, Peter Thiel, plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, Republic of Letters, robot derives from the Czech word robota Czech, meaning slave, Ronald Reagan, scientific management, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley ideology, Singularitarianism, Snapchat, social graph, social web, speech recognition, Startup school, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, technoutopianism, TED Talk, the long tail, the medium is the message, theory of mind, Turing test, Tyler Cowen, Whole Earth Catalog, Y Combinator, Yochai Benkler

From Politico Magazine 2015 WHY ROBOTS WILL ALWAYS NEED US “HUMAN BEINGS ARE ASHAMED to have been born instead of made,” wrote the philosopher Günther Anders in 1956. Our shame has only deepened as our machines have grown more adept. Every day we’re reminded of the superiority of computers. Self-driving cars are immune to distractions and road rage. Automatic trains don’t speed out of control. Factory robots don’t goof off. Algorithms don’t suffer the cognitive biases that can cloud the judgments of doctors, accountants, and lawyers. Computers work with a speed and precision that make us look like bumbling slackers.


pages: 565 words: 122,605

The Human City: Urbanism for the Rest of Us by Joel Kotkin

"World Economic Forum" Davos, Alvin Toffler, autonomous vehicles, birth tourism , blue-collar work, British Empire, carbon footprint, Celebration, Florida, citizen journalism, colonial rule, crony capitalism, deindustrialization, demographic winter, Deng Xiaoping, Downton Abbey, edge city, Edward Glaeser, financial engineering, financial independence, Frank Gehry, gentrification, Gini coefficient, Google bus, housing crisis, illegal immigration, income inequality, informal economy, intentional community, Jane Jacobs, labor-force participation, land reform, Lewis Mumford, life extension, market bubble, mass immigration, McMansion, megacity, megaproject, microapartment, new economy, New Urbanism, Own Your Own Home, peak oil, pensions crisis, Peter Calthorpe, post-industrial society, RAND corporation, Richard Florida, rising living standards, Ronald Reagan, Salesforce, Seaside, Florida, self-driving car, Shenzhen was a fishing village, Silicon Valley, starchitect, Stewart Brand, streetcar suburb, Ted Nelson, the built environment, trade route, transit-oriented development, upwardly mobile, urban planning, urban renewal, urban sprawl, Victor Gruen, Whole Earth Catalog, women in the workforce, young professional

“You’ll Never Guess the City Where Private School Is Most Common,” Time, http://time.com/money/3105112/private-school-enrollment-cities-highest/. DAVIES, Alan. (2012, March 20). “Is suburban living a neurotic condition?,” Crikey, http://blogs.crikey.com.au/theurbanist/2012/03/20/is-suburban-living-a-neurotic-condition/. DAVIES, Alex. (2015, March 9). “Self-Driving Cars Will Make Us Want Fewer Cars,” Wired, http://www.wired.com/2015/03/the-economic-impact-of-autonomous-vehicles/. DAVIS, Bob and PAGE, Jeremy. (2011, March 7). “China’s Focus Turns to its Poor,” Wall Street Journal, http://www.wsj.com/articles/SB10001424052748703362804576184364247082474. de BARY, William Theodore, CHAN, Wing-Tsit and WATSON, Burton. (1960).


pages: 371 words: 108,317

The Inevitable: Understanding the 12 Technological Forces That Will Shape Our Future by Kevin Kelly

A Declaration of the Independence of Cyberspace, Aaron Swartz, AI winter, Airbnb, Albert Einstein, Alvin Toffler, Amazon Web Services, augmented reality, bank run, barriers to entry, Baxter: Rethink Robotics, bitcoin, blockchain, book scanning, Brewster Kahle, Burning Man, cloud computing, commoditize, computer age, Computer Lib, connected car, crowdsourcing, dark matter, data science, deep learning, DeepMind, dematerialisation, Downton Abbey, driverless car, Edward Snowden, Elon Musk, Filter Bubble, Freestyle chess, Gabriella Coleman, game design, Geoffrey Hinton, Google Glasses, hive mind, Howard Rheingold, index card, indoor plumbing, industrial robot, Internet Archive, Internet of things, invention of movable type, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Perry Barlow, Kevin Kelly, Kickstarter, lifelogging, linked data, Lyft, M-Pesa, machine readable, machine translation, Marc Andreessen, Marshall McLuhan, Mary Meeker, means of production, megacity, Minecraft, Mitch Kapor, multi-sided market, natural language processing, Netflix Prize, Network effects, new economy, Nicholas Carr, off-the-grid, old-boy network, peer-to-peer, peer-to-peer lending, personalized medicine, placebo effect, planetary scale, postindustrial economy, Project Xanadu, recommendation engine, RFID, ride hailing / ride sharing, robo advisor, Rodney Brooks, self-driving car, sharing economy, Silicon Valley, slashdot, Snapchat, social graph, social web, software is eating the world, speech recognition, Stephen Hawking, Steven Levy, Ted Nelson, TED Talk, The future is already here, the long tail, the scientific method, transport as a service, two-sided market, Uber for X, uber lyft, value engineering, Watson beat the top human players on Jeopardy!, WeWork, Whole Earth Review, Yochai Benkler, yottabyte, zero-sum game

In the next 10 years, 99 percent of the artificial intelligence that you will interact with, directly or indirectly, will be nerdly narrow, supersmart specialists. In fact, robust intelligence may be a liability—especially if by “intelligence” we mean our peculiar self-awareness, all our frantic loops of introspection and messy currents of self-consciousness. We want our self-driving car to be inhumanly focused on the road, not obsessing over an argument it had with the garage. The synthetic Dr. Watson at our hospital should be maniacal in its work, never wondering whether it should have majored in finance instead. What we want instead of conscious intelligence is artificial smartness.


pages: 379 words: 108,129

An Optimist's Tour of the Future by Mark Stevenson

23andMe, Albert Einstein, Alvin Toffler, Andy Kessler, Apollo 11, augmented reality, bank run, Boston Dynamics, carbon credits, carbon footprint, carbon-based life, clean water, computer age, decarbonisation, double helix, Douglas Hofstadter, Dr. Strangelove, Elon Musk, flex fuel, Ford Model T, Future Shock, Great Leap Forward, Gregor Mendel, Gödel, Escher, Bach, Hans Moravec, Hans Rosling, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of agriculture, Isaac Newton, Jeff Bezos, Kevin Kelly, Law of Accelerating Returns, Leonard Kleinrock, life extension, Louis Pasteur, low earth orbit, mutually assured destruction, Naomi Klein, Nick Bostrom, off grid, packet switching, peak oil, pre–internet, private spaceflight, radical life extension, Ray Kurzweil, Richard Feynman, Rodney Brooks, Scaled Composites, self-driving car, Silicon Valley, smart cities, social intelligence, SpaceShipOne, stem cell, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, synthetic biology, TED Talk, the scientific method, Virgin Galactic, Wall-E, X Prize

Today she’s leading a charge for the renewable energy industry in New Zealand that could have international repercussions – initiatives that are undoubtedly good for the planet whether you worry about climate change or not. The guard takes my blog address and promises to buy the book. ‘Don’t forget to mention those self-driving cars!’ he says. Vicki meets me at my hotel the next morning to drive us to breakfast. Although to say one ‘meets’ Vicki is something of an understatement. Vicki meets me in the same way a tornado ‘meets’ the air. You are instantly swept up, and it’s invigorating. The first thing you notice is she cannot stop laughing.


pages: 392 words: 108,745

Talk to Me: How Voice Computing Will Transform the Way We Live, Work, and Think by James Vlahos

Albert Einstein, AltaVista, Amazon Mechanical Turk, Amazon Web Services, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Big Tech, Cambridge Analytica, Chuck Templeton: OpenTable:, cloud computing, Colossal Cave Adventure, computer age, deep learning, DeepMind, Donald Trump, Elon Musk, fake news, Geoffrey Hinton, information retrieval, Internet of things, Jacques de Vaucanson, Jeff Bezos, lateral thinking, Loebner Prize, machine readable, machine translation, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Mark Zuckerberg, Menlo Park, natural language processing, Neal Stephenson, Neil Armstrong, OpenAI, PageRank, pattern recognition, Ponzi scheme, randomized controlled trial, Ray Kurzweil, Ronald Reagan, Rubik’s Cube, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Levy, TechCrunch disrupt, Turing test, Watson beat the top human players on Jeopardy!

For the first edition of the annually recurring contest, more than one hundred university teams applied to compete, and Amazon selected the fifteen squads whose proposals seemed the most promising. If any team actually succeeded, its members would snare academic glory and the promise of brilliant future careers. (Consider that alums of the DARPA Grand Challenges, an early set of autonomous-vehicle competitions, went on to run the self-driving-car divisions of Google, Ford, Uber, and General Motors.) The victors would also walk away with the Alexa Prize itself—a $1 million purse. The Alexa Prize is not the only contest that tries to squeeze more humanlike rapport out of the world’s chatbots; recall the Loebner Prize, the one that Mauldin entered, from chapter 4.


pages: 401 words: 109,892

The Great Reversal: How America Gave Up on Free Markets by Thomas Philippon

airline deregulation, Amazon Mechanical Turk, Amazon Web Services, Andrei Shleifer, barriers to entry, Big Tech, bitcoin, blockchain, book value, business cycle, business process, buy and hold, Cambridge Analytica, carbon tax, Carmen Reinhart, carried interest, central bank independence, commoditize, crack epidemic, cross-subsidies, disruptive innovation, Donald Trump, driverless car, Erik Brynjolfsson, eurozone crisis, financial deregulation, financial innovation, financial intermediation, flag carrier, Ford Model T, gig economy, Glass-Steagall Act, income inequality, income per capita, index fund, intangible asset, inventory management, Jean Tirole, Jeff Bezos, Kenneth Rogoff, labor-force participation, law of one price, liquidity trap, low cost airline, manufacturing employment, Mark Zuckerberg, market bubble, minimum wage unemployment, money market fund, moral hazard, natural language processing, Network effects, new economy, offshore financial centre, opioid epidemic / opioid crisis, Pareto efficiency, patent troll, Paul Samuelson, price discrimination, profit maximization, purchasing power parity, QWERTY keyboard, rent-seeking, ride hailing / ride sharing, risk-adjusted returns, Robert Bork, Robert Gordon, robo advisor, Ronald Reagan, search costs, Second Machine Age, self-driving car, Silicon Valley, Snapchat, spinning jenny, statistical model, Steve Jobs, stock buybacks, supply-chain management, Telecommunications Act of 1996, The Chicago School, the payments system, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, transaction costs, Travis Kalanick, vertical integration, Vilfredo Pareto, warehouse automation, zero-sum game

As the GAFAMs’ dominant positions became more obvious, and amid a string of scandals related to their treatment of users’ data, they began to attract more regulatory scrutiny. Amazon’s lobbying increased after its acquisition of grocery chain Whole Foods. Facebook has been embroiled in a string of data privacy scandals, one of them involving the firm Cambridge Analytica. Waymo, Google’s self-driving car unit, faces potential liability issues and other concerns. Google, Twitter, and Facebook are also involved in the targeting of their users by Russian agents during the 2016 campaign. Generally, companies exert influence in Washington for one of four main reasons. The first two reasons are related to benefits they expect to receive thanks to their lobbying efforts.


pages: 353 words: 106,704

Choked: Life and Breath in the Age of Air Pollution by Beth Gardiner

barriers to entry, Boris Johnson, call centre, carbon footprint, clean water, connected car, Crossrail, deindustrialization, Donald Trump, Elon Musk, epigenetics, Exxon Valdez, failed state, Hyperloop, index card, Indoor air pollution, Mahatma Gandhi, megacity, meta-analysis, rolling blackouts, Ronald Reagan, self-driving car, Silicon Valley, Skype, statistical model, Steve Jobs, TED Talk, white picket fence

This is an idea I’ve heard before, proffered by many who hope to loosen cities’ gridlock, clean their air, and ease pressure on the climate. We’ll be less likely, the thinking goes, to buy our own cars than to pay for whatever form of transportation we need when we need it—a shared bike at the end of a train ride, a self-driving car home from the supermarket. And indeed, the millennial generation, at least in wealthier nations, seems less interested in driving than its elders. In both America and Britain, the proportion of young people holding driver’s licenses has declined steadily since the 1990s.5 “If you own a car, it stands [idle] 23 hours a day,” Welke says.


pages: 407 words: 104,622

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Alan Greenspan, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, Bear Stearns, beat the dealer, behavioural economics, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Black Monday: stock market crash in 1987, blockchain, book value, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Cambridge Analytica, Carl Icahn, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, data science, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, financial engineering, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, Jim Simons, John Meriwether, John Nash: game theory, John von Neumann, junk bonds, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, Michael Milken, Monty Hall problem, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, Neil Armstrong, obamacare, off-the-grid, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, proprietary trading, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Bannon, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine, Two Sigma

Prosecutors later charged a trader operating out of his West London home for manipulating a stock-market-index futures contract, laying the groundwork for the decline.11 To some, the sudden downturn, which was accompanied by little news to explain the move, suggested the rise of the machine had ushered in a new era of risk and volatility. Automated trading by computers is a scary concept for many, much as airplanes flown by autopilot and self-driving cars can frighten, despite evidence that those machines improve safety. There’s reason to believe computer traders can amplify or accelerate existing trends. Author and former risk manager Richard Bookstaber has argued that risks today are significant because the embrace of quant models is “system-wide across the investment world,” suggesting that future troubles for these investors would have more impact than in the past.12 As more embrace quantitative trading, the very nature of financial markets could change.


pages: 379 words: 109,223

Frenemies: The Epic Disruption of the Ad Business by Ken Auletta

"World Economic Forum" Davos, Airbnb, Alvin Toffler, AOL-Time Warner, barriers to entry, Bernie Sanders, bike sharing, Boris Johnson, Build a better mousetrap, Burning Man, call centre, Cambridge Analytica, capitalist realism, carbon footprint, cloud computing, commoditize, connected car, content marketing, corporate raider, crossover SUV, data science, digital rights, disintermediation, Donald Trump, driverless car, Elon Musk, fake news, financial engineering, forensic accounting, Future Shock, Google Glasses, Internet of things, Jeff Bezos, Kevin Roose, Khan Academy, Lyft, Mark Zuckerberg, market design, Mary Meeker, Max Levchin, Menlo Park, move fast and break things, Naomi Klein, NetJets, Network effects, pattern recognition, pets.com, race to the bottom, Richard Feynman, ride hailing / ride sharing, Salesforce, Saturday Night Live, self-driving car, sharing economy, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Snapchat, Steve Ballmer, Steve Jobs, surveillance capitalism, Susan Wojcicki, The Theory of the Leisure Class by Thorstein Veblen, three-martini lunch, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, Upton Sinclair, éminence grise

The e-mail invite said PetChatz was located in CES Booth 82646, which was in Hall G of the Sands. But a trip to the floor found Booth 82646 empty. One objective of CES is to manufacture buzz. In 2016, virtual reality was the much-hyped new new thing, which would be supplanted at CES 2017 by artificial intelligence, and AI-centric products like self-driving cars and Amazon’s Alexa. In previous years, drones, Google Glass, and 4K TVs had their moment. Writing about CES 2016, Farhad Manjoo of the New York Times observed, “If news from CES feels especially desultory this year, it might not be the show that’s at fault. Instead, blame the tech cycle. We’re at a weird moment in the industry: The best new stuff is not all that cool, and the coolest stuff”—AI, virtual reality, the Internet of things, drones—“isn’t quite ready


pages: 344 words: 104,522

Woke, Inc: Inside Corporate America's Social Justice Scam by Vivek Ramaswamy

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 2021 United States Capitol attack, activist fund / activist shareholder / activist investor, affirmative action, Airbnb, Amazon Web Services, An Inconvenient Truth, anti-bias training, Bernie Sanders, Big Tech, BIPOC, Black Lives Matter, carbon footprint, clean tech, cloud computing, contact tracing, coronavirus, corporate governance, corporate social responsibility, COVID-19, critical race theory, crony capitalism, cryptocurrency, defund the police, deplatforming, desegregation, disinformation, don't be evil, Donald Trump, en.wikipedia.org, Eugene Fama: efficient market hypothesis, fudge factor, full employment, George Floyd, glass ceiling, global pandemic, green new deal, hiring and firing, Hyperloop, impact investing, independent contractor, index fund, Jeff Bezos, lockdown, Marc Benioff, Mark Zuckerberg, microaggression, military-industrial complex, Network effects, Parler "social media", plant based meat, Ponzi scheme, profit maximization, random walk, ride hailing / ride sharing, risk-adjusted returns, Robert Bork, Robinhood: mobile stock trading app, Ronald Reagan, Salesforce, self-driving car, shareholder value, short selling, short squeeze, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, single source of truth, Snapchat, social distancing, Social Responsibility of Business Is to Increase Its Profits, source of truth, sovereign wealth fund, Susan Wojcicki, the scientific method, Tim Cook: Apple, too big to fail, trade route, transcontinental railway, traveling salesman, trickle-down economics, Vanguard fund, Virgin Galactic, WeWork, zero-sum game

Uber CEO Dara Khosrowshahi retreated from his initially critical stance in 2018. During a November 2019 interview, he said the murder of Khashoggi was “a mistake” but that Uber had “made mistakes too, with self-driving”—drawing an equivalence between a targeted assassination of a journalist and a pedestrian accident involving a self-driving car.47 McKinsey & Company went back to charging a small fortune for advising clients on how to do business in the Kingdom of Saudi Arabia. The cottage industry built around opening up personal connections in the Middle East was soon humming once again. To be sure, most business leaders still haven’t forgotten about the Khashoggi episode.


pages: 460 words: 107,454

Stakeholder Capitalism: A Global Economy That Works for Progress, People and Planet by Klaus Schwab, Peter Vanham

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, air traffic controllers' union, Anthropocene, Apple II, Asian financial crisis, Asperger Syndrome, basic income, Berlin Wall, Big Tech, biodiversity loss, bitcoin, Black Lives Matter, blockchain, blue-collar work, Branko Milanovic, Bretton Woods, British Empire, business process, capital controls, Capital in the Twenty-First Century by Thomas Piketty, car-free, carbon footprint, carbon tax, centre right, clean tech, clean water, cloud computing, collateralized debt obligation, collective bargaining, colonial rule, company town, contact tracing, contact tracing app, Cornelius Vanderbilt, coronavirus, corporate governance, corporate social responsibility, COVID-19, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, cuban missile crisis, currency peg, cyber-physical system, decarbonisation, demographic dividend, Deng Xiaoping, Diane Coyle, digital divide, don't be evil, European colonialism, Fall of the Berlin Wall, family office, financial innovation, Francis Fukuyama: the end of history, future of work, gender pay gap, general purpose technology, George Floyd, gig economy, Gini coefficient, global supply chain, global value chain, global village, Google bus, green new deal, Greta Thunberg, high net worth, hiring and firing, housing crisis, income inequality, income per capita, independent contractor, industrial robot, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, Khan Academy, Kickstarter, labor-force participation, lockdown, low interest rates, low skilled workers, Lyft, manufacturing employment, Marc Benioff, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Martin Wolf, means of production, megacity, microplastics / micro fibres, Mikhail Gorbachev, mini-job, mittelstand, move fast and break things, neoliberal agenda, Network effects, new economy, open economy, Peace of Westphalia, Peter Thiel, precariat, Productivity paradox, profit maximization, purchasing power parity, race to the bottom, reserve currency, reshoring, ride hailing / ride sharing, Ronald Reagan, Salesforce, San Francisco homelessness, School Strike for Climate, self-driving car, seminal paper, shareholder value, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, Simon Kuznets, social distancing, Social Responsibility of Business Is to Increase Its Profits, special economic zone, Steve Jobs, Steve Wozniak, synthetic biology, TaskRabbit, The Chicago School, The Future of Employment, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, the scientific method, TikTok, Tim Cook: Apple, trade route, transfer pricing, Uber and Lyft, uber lyft, union organizing, universal basic income, War on Poverty, We are the 99%, women in the workforce, working poor, working-age population, Yom Kippur War, young professional, zero-sum game

The Fourth Industrial Revolution Even as many technologies of the Third Industrial Revolution are still playing out in the market, we have entered a Fourth Industrial Revolution. As I wrote back in 2016: This Fourth Industrial Revolution is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres. Already, artificial intelligence is all around us, from self-driving cars and drones to virtual assistants and software that translate or invest. Impressive progress has been made in AI in recent years, driven by exponential increases in computing power and by the availability of vast amounts of data, from software used to discover new drugs to algorithms used to predict our cultural interests.


pages: 460 words: 107,454

Stakeholder Capitalism: A Global Economy That Works for Progress, People and Planet by Klaus Schwab

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, 3D printing, additive manufacturing, agricultural Revolution, air traffic controllers' union, Anthropocene, Apple II, Asian financial crisis, Asperger Syndrome, basic income, Berlin Wall, Big Tech, biodiversity loss, bitcoin, Black Lives Matter, blockchain, blue-collar work, Branko Milanovic, Bretton Woods, British Empire, business process, capital controls, Capital in the Twenty-First Century by Thomas Piketty, car-free, carbon footprint, carbon tax, centre right, clean tech, clean water, cloud computing, collateralized debt obligation, collective bargaining, colonial rule, company town, contact tracing, contact tracing app, Cornelius Vanderbilt, coronavirus, corporate governance, corporate social responsibility, COVID-19, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, cuban missile crisis, currency peg, cyber-physical system, decarbonisation, demographic dividend, Deng Xiaoping, Diane Coyle, digital divide, don't be evil, European colonialism, Fall of the Berlin Wall, family office, financial innovation, Francis Fukuyama: the end of history, future of work, gender pay gap, general purpose technology, George Floyd, gig economy, Gini coefficient, global supply chain, global value chain, global village, Google bus, green new deal, Greta Thunberg, high net worth, hiring and firing, housing crisis, income inequality, income per capita, independent contractor, industrial robot, intangible asset, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, joint-stock company, Joseph Schumpeter, Kenneth Rogoff, Khan Academy, Kickstarter, labor-force participation, lockdown, low interest rates, low skilled workers, Lyft, manufacturing employment, Marc Benioff, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Martin Wolf, means of production, megacity, microplastics / micro fibres, Mikhail Gorbachev, mini-job, mittelstand, move fast and break things, neoliberal agenda, Network effects, new economy, open economy, Peace of Westphalia, Peter Thiel, precariat, Productivity paradox, profit maximization, purchasing power parity, race to the bottom, reserve currency, reshoring, ride hailing / ride sharing, Ronald Reagan, Salesforce, San Francisco homelessness, School Strike for Climate, self-driving car, seminal paper, shareholder value, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, Simon Kuznets, social distancing, Social Responsibility of Business Is to Increase Its Profits, special economic zone, Steve Jobs, Steve Wozniak, synthetic biology, TaskRabbit, The Chicago School, The Future of Employment, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, the scientific method, TikTok, Tim Cook: Apple, trade route, transfer pricing, Uber and Lyft, uber lyft, union organizing, universal basic income, War on Poverty, We are the 99%, women in the workforce, working poor, working-age population, Yom Kippur War, young professional, zero-sum game

The Fourth Industrial Revolution Even as many technologies of the Third Industrial Revolution are still playing out in the market, we have entered a Fourth Industrial Revolution. As I wrote back in 2016: This Fourth Industrial Revolution is characterized by a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres. Already, artificial intelligence is all around us, from self-driving cars and drones to virtual assistants and software that translate or invest. Impressive progress has been made in AI in recent years, driven by exponential increases in computing power and by the availability of vast amounts of data, from software used to discover new drugs to algorithms used to predict our cultural interests.


pages: 363 words: 109,834

The Crux by Richard Rumelt

activist fund / activist shareholder / activist investor, air gap, Airbnb, AltaVista, AOL-Time Warner, Bayesian statistics, behavioural economics, biodiversity loss, Blue Ocean Strategy, Boeing 737 MAX, Boeing 747, Charles Lindbergh, Clayton Christensen, cloud computing, cognitive bias, commoditize, coronavirus, corporate raider, COVID-19, creative destruction, crossover SUV, Crossrail, deep learning, Deng Xiaoping, diversified portfolio, double entry bookkeeping, drop ship, Elon Musk, en.wikipedia.org, financial engineering, Ford Model T, Herman Kahn, income inequality, index card, Internet of things, Jeff Bezos, Just-in-time delivery, Larry Ellison, linear programming, lockdown, low cost airline, low earth orbit, Lyft, Marc Benioff, Mark Zuckerberg, Masayoshi Son, meta-analysis, Myron Scholes, natural language processing, Neil Armstrong, Network effects, packet switching, PageRank, performance metric, precision agriculture, RAND corporation, ride hailing / ride sharing, Salesforce, San Francisco homelessness, search costs, selection bias, self-driving car, shareholder value, sharing economy, Silicon Valley, Skype, Snapchat, social distancing, SoftBank, software as a service, statistical model, Steve Ballmer, Steve Jobs, stochastic process, Teledyne, telemarketer, TSMC, uber lyft, undersea cable, union organizing, vertical integration, WeWork

This experience seems to have pushed change at GM. Barra killed the matrix and flattened the organization. She fired fifteen employees for cause. She has put together a team of executives at the top who would model problem-solving behavior. GM has dropped brands and whole divisions. It has new electric cars and is researching self-driving cars. The company has returned to profit. After the 2009 bankruptcy, General Motors dropped four famous (North American) brands: Saturn, Hummer, Pontiac, and Saab. Since the ignition-switch crisis, CEO Barra had gotten the company out of Western Europe, Russia, South Africa, and India. It stopped assembling cars in Australia and Indonesia.


pages: 394 words: 112,770

Fire and Fury: Inside the Trump White House by Michael Wolff

Affordable Care Act / Obamacare, barriers to entry, Bernie Sanders, Biosphere 2, Carl Icahn, centre right, disinformation, disintermediation, Donald Trump, drone strike, Edward Snowden, Elon Musk, fake news, false flag, forensic accounting, illegal immigration, impulse control, Jeff Bezos, Jeffrey Epstein, obamacare, open immigration, opioid epidemic / opioid crisis, Paris climate accords, Peter Thiel, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ronald Reagan, Russian election interference, Saturday Night Live, self-driving car, Sheryl Sandberg, Silicon Valley, single-payer health, Steve Bannon, Travis Kalanick, WikiLeaks, zero-sum game

He was voluble, open, and expansive, a charmer and an international player, a canny salesman rather than a remote, taciturn grandee. He had seized the economic portfolio and was pursuing a vision—quite a Trumpian vision—to out-Dubai Dubai and diversify the economy. His would be a new, modern—well, a bit more modern—kingdom (yes, women would soon be allowed to drive—so thank God self-driving cars were coming!). Saudi leadership was marked by age, traditionalism, relative anonymity, and careful consensus thinking. The Saudi royal family, on the other hand, whence the leadership class comes, was often marked by excess, flash, and the partaking of the joys of modernity in foreign ports.


pages: 405 words: 117,219

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence by George Zarkadakis

3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, animal electricity, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, Bletchley Park, British Empire, business process, carbon-based life, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, complexity theory, Computing Machinery and Intelligence, continuous integration, Conway's Game of Life, cosmological principle, dark matter, data science, deep learning, DeepMind, dematerialisation, double helix, Douglas Hofstadter, driverless car, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, Hans Moravec, income inequality, index card, industrial robot, intentional community, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, Kickstarter, liberal capitalism, lifelogging, machine translation, millennium bug, mirror neurons, Moravec's paradox, natural language processing, Nick Bostrom, Norbert Wiener, off grid, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, Plato's cave, post-industrial society, power law, precautionary principle, prediction markets, Ray Kurzweil, Recombinant DNA, Rodney Brooks, Second Machine Age, self-driving car, seminal paper, Silicon Valley, social intelligence, speech recognition, stem cell, Stephen Hawking, Steven Pinker, Strategic Defense Initiative, strong AI, Stuart Kauffman, synthetic biology, systems thinking, technological singularity, The Coming Technological Singularity, The Future of Employment, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

Dick. 1989: Tim Berners-Lee invents the World Wide Web. 1990: Seiji Ogawa presents the first fMRI machine. 1993: Rodney Brooks and others start the MIT Cog Project, an attempt to build a humanoid robot child in five years. 1997: Deep Blue defeats Garry Kasparov at chess. 2000: Cynthia Breazeal at MIT describes Kismet, a robot with a face that simulates expressions. 2004: DARPA launches the Grand Challenge for autonomous vehicles. 2009: Google builds the self-driving car. 2011: IBM’s Watson wins the TV game show Jeopardy!. 2014: Google buys UK company Deep Mind for $650 million. 2014: Eugene Goostman, a computer program that simulates a thirteen-year-old boy, passes the Turing Test. 2014: Estimated number of robots in the world reaches 8.6 million.1 2015: Estimated number of PCs in the world reaches two billion.2 NOTES Introduction 1PCs (‘Personal computers’) started becoming widely available in the early 1980s: IBM 5150 in 1981, Commodore PET in 1983.


Succeeding With AI: How to Make AI Work for Your Business by Veljko Krunic

AI winter, Albert Einstein, algorithmic trading, AlphaGo, Amazon Web Services, anti-fragile, anti-pattern, artificial general intelligence, autonomous vehicles, Bayesian statistics, bioinformatics, Black Swan, Boeing 737 MAX, business process, cloud computing, commoditize, computer vision, correlation coefficient, data is the new oil, data science, deep learning, DeepMind, en.wikipedia.org, fail fast, Gini coefficient, high net worth, information retrieval, Internet of things, iterative process, job automation, Lean Startup, license plate recognition, minimum viable product, natural language processing, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, six sigma, smart cities, speech recognition, statistical model, strong AI, tail risk, The Design of Experiments, the scientific method, web application, zero-sum game

In other cases, data must be analyzed in real time as it’s arriving into the system. (This is called streaming analytics.) NOTE An important consideration in the application of a Sense/Analyze/React loop is the question of who or what is reacting. It could be the system itself in some automated fashion. (That’s what a self-driving car [38] does.) Or, based on the results of the analysis, it could be a human. The latter case is much more common today within an enterprise use of data science. The Sense/Analyze/React loop is widely applicable The Sense/Analyze/React loop is applicable across many scales. It could be applied on the level of a single device (as in the case of smart thermometers like Nest [36] and ecobee [37]), a business process, multiple departments, the whole enterprise, a smart city, or an even larger geographical area.


pages: 382 words: 114,537

On the Clock: What Low-Wage Work Did to Me and How It Drives America Insane by Emily Guendelsberger

Adam Curtis, Affordable Care Act / Obamacare, Airbnb, Amazon Picking Challenge, autism spectrum disorder, basic income, behavioural economics, Bernie Sanders, call centre, Capital in the Twenty-First Century by Thomas Piketty, cognitive dissonance, company town, David Attenborough, death from overwork, deskilling, do what you love, Donald Trump, Erik Brynjolfsson, Ford Model T, Ford paid five dollars a day, Frederick Winslow Taylor, fulfillment center, future of work, hive mind, housing crisis, independent contractor, Jeff Bezos, Jessica Bruder, job automation, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, Jon Ronson, karōshi / gwarosa / guolaosi, Kiva Systems, late capitalism, Lean Startup, market design, McDonald's hot coffee lawsuit, McJob, Minecraft, Nicholas Carr, Nomadland, obamacare, opioid epidemic / opioid crisis, Panopticon Jeremy Bentham, pattern recognition, precariat, Richard Thaler, San Francisco homelessness, scientific management, Second Machine Age, security theater, self-driving car, Silicon Valley, Silicon Valley startup, speech recognition, TaskRabbit, tech worker, The Future of Employment, The Wealth of Nations by Adam Smith, Tony Hsieh, Toyota Production System, Travis Kalanick, union organizing, universal basic income, unpaid internship, Upton Sinclair, wage slave, working poor

She then invents birth control pills, the microprocessor, personal computers, cell phones, the Big Mac, and the internet. Five seconds to midnight, Wanda’s gotten herself a laptop, wireless internet, a smartphone, Google, Facebook, YouTube, and Amazon. At one second to midnight, Wanda’s made self-driving cars, Tinder, and commercial space flight. The society Wanda’s built in your backyard is, for this single second, identical to the one you live in right now. By the time the clock finishes striking midnight, Wanda will have technology you can’t even imagine. Wanda spent almost the entire week evolving to better fit her environment with what seemed like agonizing slowness.


Visual Thinking: The Hidden Gifts of People Who Think in Pictures, Patterns, and Abstractions by Temple Grandin, Ph.D.

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, a long time ago in a galaxy far, far away, air gap, Albert Einstein, American Society of Civil Engineers: Report Card, Apollo 11, Apple II, ASML, Asperger Syndrome, autism spectrum disorder, autonomous vehicles, Black Lives Matter, Boeing 737 MAX, Captain Sullenberger Hudson, clean water, cloud computing, computer vision, Computing Machinery and Intelligence, coronavirus, cotton gin, COVID-19, defense in depth, Drosophila, Elon Musk, en.wikipedia.org, GPT-3, Gregor Mendel, Greta Thunberg, hallucination problem, helicopter parent, income inequality, industrial robot, invention of movable type, Isaac Newton, James Webb Space Telescope, John Nash: game theory, John von Neumann, Jony Ive, language acquisition, longitudinal study, Mark Zuckerberg, Mars Rover, meta-analysis, Neil Armstrong, neurotypical, pattern recognition, Peter Thiel, phenotype, ransomware, replication crisis, Report Card for America’s Infrastructure, Robert X Cringely, Saturday Night Live, self-driving car, seminal paper, Silicon Valley, Skinner box, space junk, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Tacoma Narrows Bridge, TaskRabbit, theory of mind, TikTok, twin studies, unpaid internship, upwardly mobile, US Airways Flight 1549, warehouse automation, warehouse robotics, web application, William Langewiesche, Y Combinator

It did. If the computer was hacked, manufacturing could come to a halt. In, say, a transportation system, with human lives at stake, such an oversight could create enormous vulnerability. What if a hacker took control and commanded electric trains to crash into each other? For the same reason, self-driving cars must be hacker-proof, with a mechanical kill switch accessible to the driver in an emergency and not connected to the internet. After the computer is disabled, the car should have a mechanical emergency brake and be steerable, so the driver can get it off the road. I think we’ve become so reliant on computers and blindly trusting of them that we no longer see the inherent dangers.


pages: 412 words: 116,685

The Metaverse: And How It Will Revolutionize Everything by Matthew Ball

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", 3D printing, Airbnb, Albert Einstein, Amazon Web Services, Apple Newton, augmented reality, Big Tech, bitcoin, blockchain, business process, call centre, cloud computing, commoditize, computer vision, COVID-19, cryptocurrency, deepfake, digital divide, digital twin, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, game design, gig economy, Google Chrome, Google Earth, Google Glasses, hype cycle, intermodal, Internet Archive, Internet of things, iterative process, Jeff Bezos, John Gruber, Kevin Roose, Kickstarter, lockdown, Mark Zuckerberg, Metcalfe’s law, Minecraft, minimum viable product, Neal Stephenson, Network effects, new economy, non-fungible token, open economy, openstreetmap, pattern recognition, peer-to-peer, peer-to-peer model, Planet Labs, pre–internet, QR code, recommendation engine, rent control, rent-seeking, ride hailing / ride sharing, Robinhood: mobile stock trading app, satellite internet, self-driving car, SETI@home, Silicon Valley, skeuomorphism, Skype, smart contracts, Snapchat, Snow Crash, social graph, social web, SpaceX Starlink, Steve Ballmer, Steve Jobs, thinkpad, TikTok, Tim Cook: Apple, TSMC, undersea cable, Vannevar Bush, vertical integration, Vitalik Buterin, Wayback Machine, Y2K

Culturally, at least, the idea of collectively shared but privately owned infrastructure is already well understood. Anyone who installs solar panels at their home can sell excess power to their local grid (and, indirectly, to their neighbor). Elon Musk touts a future in which your Tesla earns you rent as a self-driving car when you’re not using it yourself—better than just being parked in your garage for 99% of its life. As early as the 1990s programs emerged for distributed computing using everyday consumer hardware. One of the most famous examples is the University of California, Berkeley’s SETI@HOME, wherein consumers would volunteer use of their home computers to power the search for alien life.


pages: 370 words: 112,809

The Equality Machine: Harnessing Digital Technology for a Brighter, More Inclusive Future by Orly Lobel

2021 United States Capitol attack, 23andMe, Ada Lovelace, affirmative action, Airbnb, airport security, Albert Einstein, algorithmic bias, Amazon Mechanical Turk, augmented reality, barriers to entry, basic income, Big Tech, bioinformatics, Black Lives Matter, Boston Dynamics, Charles Babbage, choice architecture, computer vision, Computing Machinery and Intelligence, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, crowdsourcing, data science, David Attenborough, David Heinemeier Hansson, deep learning, deepfake, digital divide, digital map, Elon Musk, emotional labour, equal pay for equal work, feminist movement, Filter Bubble, game design, gender pay gap, George Floyd, gig economy, glass ceiling, global pandemic, Google Chrome, Grace Hopper, income inequality, index fund, information asymmetry, Internet of things, invisible hand, it's over 9,000, iterative process, job automation, Lao Tzu, large language model, lockdown, machine readable, machine translation, Mark Zuckerberg, market bubble, microaggression, Moneyball by Michael Lewis explains big data, natural language processing, Netflix Prize, Network effects, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, occupational segregation, old-boy network, OpenAI, openstreetmap, paperclip maximiser, pattern recognition, performance metric, personalized medicine, price discrimination, publish or perish, QR code, randomized controlled trial, remote working, risk tolerance, robot derives from the Czech word robota Czech, meaning slave, Ronald Coase, Salesforce, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, social distancing, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, surveillance capitalism, tech worker, TechCrunch disrupt, The Future of Employment, TikTok, Turing test, universal basic income, Wall-E, warehouse automation, women in the workforce, work culture , you are the product

Our explorations throughout the chapters of this book have shown us that the answer to whether we should be excited or alarmed by our newfound capabilities to know, detect, analyze, interpret, predict, and enhance begins and concludes with the ends to which we’re employing our newfound superpowers. What seemed like science fiction a few decades ago now seems natural to us—smartphones, wearable digital devices that can read our bioprocesses, chatbots, virtual personal assistants, robot surgeons, self-driving cars, replicas of ourselves, humanoid friends. The unprecedented acceleration in digital capabilities and their integration into our lives mean that, inevitably, the key question is how technology can help address the challenges of power and inequality—challenges to our very humanity—that lie at the heart of our society.


pages: 288 words: 16,556

Finance and the Good Society by Robert J. Shiller

Alan Greenspan, Alvin Roth, bank run, banking crisis, barriers to entry, Bear Stearns, behavioural economics, benefit corporation, Bernie Madoff, buy and hold, capital asset pricing model, capital controls, Carmen Reinhart, Cass Sunstein, cognitive dissonance, collateralized debt obligation, collective bargaining, computer age, corporate governance, Daniel Kahneman / Amos Tversky, democratizing finance, Deng Xiaoping, diversification, diversified portfolio, Donald Trump, Edward Glaeser, eurozone crisis, experimental economics, financial engineering, financial innovation, financial thriller, fixed income, full employment, fundamental attribution error, George Akerlof, Great Leap Forward, Ida Tarbell, income inequality, information asymmetry, invisible hand, John Bogle, joint-stock company, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, land reform, loss aversion, Louis Bachelier, Mahatma Gandhi, Mark Zuckerberg, market bubble, market design, means of production, microcredit, moral hazard, mortgage debt, Myron Scholes, Nelson Mandela, Occupy movement, passive investing, Ponzi scheme, prediction markets, profit maximization, quantitative easing, random walk, regulatory arbitrage, Richard Thaler, Right to Buy, road to serfdom, Robert Shiller, Ronald Reagan, selection bias, self-driving car, shareholder value, Sharpe ratio, short selling, Simon Kuznets, Skype, social contagion, Steven Pinker, tail risk, telemarketer, Thales and the olive presses, Thales of Miletus, The Market for Lemons, The Theory of the Leisure Class by Thorstein Veblen, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, Vanguard fund, young professional, zero-sum game, Zipcar

Financial Crisis Inquiry Commission, in its nal 2011 report, described the boom as “madness,”2 but, whatever it was, it was not for the most part criminal. And, pursuing this highway metaphor a bit further, we may suggest that automotive designers would best stay focused on how new technology can help us better manage vehicular tra c, with improved cruise control, external electronic feedback to cars, and ultimately even self-driving cars—complex new systems that will enable people to reach their travel destinations more easily and more safely. If that’s the future for our highways, something like it should be the future for our financial institutions as well. All of these protest movements are only the most manifest signs of discontent that have been discernible in conversations and blogs ever since the nancial crisis began.


pages: 494 words: 116,739

Geek Heresy: Rescuing Social Change From the Cult of Technology by Kentaro Toyama

Abraham Maslow, Albert Einstein, Apollo 11, behavioural economics, Berlin Wall, Bernie Madoff, blood diamond, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cognitive dissonance, commoditize, computer vision, conceptual framework, delayed gratification, digital divide, do well by doing good, Edward Glaeser, Edward Jenner, en.wikipedia.org, end world poverty, epigenetics, Erik Brynjolfsson, Evgeny Morozov, Francis Fukuyama: the end of history, fundamental attribution error, gamification, germ theory of disease, global village, Hans Rosling, happiness index / gross national happiness, income inequality, invention of the printing press, invisible hand, Isaac Newton, Khan Academy, Kibera, knowledge worker, Larry Ellison, Lewis Mumford, liberation theology, libertarian paternalism, longitudinal study, M-Pesa, Mahatma Gandhi, Mark Zuckerberg, means of production, microcredit, mobile money, Neil Armstrong, Nelson Mandela, Nicholas Carr, North Sea oil, One Laptop per Child (OLPC), Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, post-industrial society, Powell Memorandum, randomized controlled trial, rent-seeking, RFID, Richard Florida, Richard Thaler, school vouchers, self-driving car, Sheryl Sandberg, Silicon Valley, Simon Kuznets, Stanford marshmallow experiment, Steve Jobs, Steven Pinker, technological determinism, technological solutionism, technoutopianism, TED Talk, The Fortune at the Bottom of the Pyramid, the long tail, Twitter Arab Spring, Upton Sinclair, Walter Mischel, War on Poverty, winner-take-all economy, World Values Survey, Y2K

The flaw is not in either technology or technocracy, per se, but in our misguided, overly optimistic beliefs about what kinds of social change they will accomplish. It hasn’t yet been a century since Asimov imagined his first fictional robots, but robots are already current news: Google has prototyped a self-driving car; software bots manipulate online product ratings; Amazon proposes delivery by automated quad-copter. These robots are designed for profit, not human betterment. Technology doesn’t bootstrap an ethical outlook on its own. Ultimately, people govern technology. Any progress worthy of the name requires progress in human heart, mind, and will.


pages: 448 words: 117,325

Click Here to Kill Everybody: Security and Survival in a Hyper-Connected World by Bruce Schneier

23andMe, 3D printing, air gap, algorithmic bias, autonomous vehicles, barriers to entry, Big Tech, bitcoin, blockchain, Brian Krebs, business process, Citizen Lab, cloud computing, cognitive bias, computer vision, connected car, corporate governance, crowdsourcing, cryptocurrency, cuban missile crisis, Daniel Kahneman / Amos Tversky, David Heinemeier Hansson, disinformation, Donald Trump, driverless car, drone strike, Edward Snowden, Elon Musk, end-to-end encryption, fault tolerance, Firefox, Flash crash, George Akerlof, incognito mode, industrial robot, information asymmetry, information security, Internet of things, invention of radio, job automation, job satisfaction, John Gilmore, John Markoff, Kevin Kelly, license plate recognition, loose coupling, market design, medical malpractice, Minecraft, MITM: man-in-the-middle, move fast and break things, national security letter, Network effects, Nick Bostrom, NSO Group, pattern recognition, precautionary principle, printed gun, profit maximization, Ralph Nader, RAND corporation, ransomware, real-name policy, Rodney Brooks, Ross Ulbricht, security theater, self-driving car, Seymour Hersh, Shoshana Zuboff, Silicon Valley, smart cities, smart transportation, Snapchat, sparse data, Stanislav Petrov, Stephen Hawking, Stuxnet, supply-chain attack, surveillance capitalism, The Market for Lemons, Timothy McVeigh, too big to fail, Uber for X, Unsafe at Any Speed, uranium enrichment, Valery Gerasimov, Wayback Machine, web application, WikiLeaks, Yochai Benkler, zero day

Lorenzo Franceschi-Bicchierai (28 Mar 2017), “Apple just banned the app that tracks U.S. drone strikes again,” Vice Motherboard, https://motherboard.vice.com/en_us/article/538kan/apple-just-banned-the-app-that-tracks-us-drone-strikes-again. 60“content that ridicules public figures”: Jason Grigsby (19 Apr 2010), “Apple’s policy on satire: 16 apps rejected for ‘ridiculing public figures,’” Cloudfour, https://cloudfour.com/thinks/apples-policy-on-satire-16-rejected-apps. 60in 2017, Apple removed security apps: Telegraph Reporters (31 Jul 2017), “Apple removes VPN apps used to evade China’s internet censorship,” Telegraph, http://www.telegraph.co.uk/technology/2017/07/31/apple-removes-vpn-apps-used-evade-chinas-internet-censorship. 60Google has also banned an app: AdNauseam (5 Jan 2017), “AdNauseam banned from the Google Web Store,” https://adnauseam.io/free-adnauseam.html. 61“Some of us have pledged our allegiance”: Bruce Schneier (26 Nov 2012), “When it comes to security, we’re back to feudalism,” Wired, https://www.wired.com/2012/11/feudal-security. 61Companies owning fleets of autonomous cars: Judith Donath (16 Nov 2017), “Uber-FREE: The ultimate advertising experience,” Medium, https://medium.com/@judithd/the-future-of-self-driving-cars-and-of-advertising-will-be-promoted-rides-free-transportation-b5f7acd702d4. 62Because the machines use software: After years of refusing to allow consumers to use refillable pods, Keurig now allows consumers to use any coffee they want, as long as they buy a special add-on. Alex Hern (11 May 2015), “Keurig takes steps towards abandoning coffee-pod DRM,” Guardian, https://www.theguardian.com/technology/2015/may/11/keurig-takes-steps-towards-abandoning-coffee-pod-drm. 62HP printers no longer allow: Brian Barrett (23 Sep 2016), “HP has added DRM to its ink cartridges.


pages: 481 words: 120,693

Plutocrats: The Rise of the New Global Super-Rich and the Fall of Everyone Else by Chrystia Freeland

"World Economic Forum" Davos, activist fund / activist shareholder / activist investor, Alan Greenspan, Albert Einstein, algorithmic trading, assortative mating, banking crisis, barriers to entry, Basel III, battle of ideas, Bear Stearns, behavioural economics, Bernie Madoff, Big bang: deregulation of the City of London, Black Monday: stock market crash in 1987, Black Swan, Boris Johnson, Branko Milanovic, Bretton Woods, BRICs, Bullingdon Club, business climate, call centre, carried interest, Cass Sunstein, Clayton Christensen, collapse of Lehman Brothers, commoditize, conceptual framework, corporate governance, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Deng Xiaoping, disruptive innovation, don't be evil, double helix, energy security, estate planning, experimental subject, financial deregulation, financial engineering, financial innovation, Flash crash, Ford Model T, Frank Gehry, Gini coefficient, Glass-Steagall Act, global village, Goldman Sachs: Vampire Squid, Gordon Gekko, Guggenheim Bilbao, haute couture, high net worth, income inequality, invention of the steam engine, job automation, John Markoff, joint-stock company, Joseph Schumpeter, knowledge economy, knowledge worker, liberation theology, light touch regulation, linear programming, London Whale, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, Max Levchin, Mikhail Gorbachev, Moneyball by Michael Lewis explains big data, NetJets, new economy, Occupy movement, open economy, Peter Thiel, place-making, plutocrats, Plutonomy: Buying Luxury, Explaining Global Imbalances, postindustrial economy, Potemkin village, profit motive, public intellectual, purchasing power parity, race to the bottom, rent-seeking, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, self-driving car, seminal paper, Sheryl Sandberg, short selling, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Simon Kuznets, sovereign wealth fund, starchitect, stem cell, Steve Jobs, TED Talk, the long tail, the new new thing, The Spirit Level, The Wealth of Nations by Adam Smith, Tony Hsieh, too big to fail, trade route, trickle-down economics, Tyler Cowen: Great Stagnation, wage slave, Washington Consensus, winner-take-all economy, zero-sum game

Google’s company motto is “Don’t be evil,” and at a recent company conference, Larry Page, Google’s cofounder and now its CEO, said earnestly that one of Google’s greatest accomplishments was to save lives—thanks to the search engine, for instance, people can type in their symptoms, learn immediately they are having a heart attack, and get life-saving help sooner than they would have otherwise. The self-driving car, one of Page’s pet projects, would eventually, he argued, save more lives than any political, social, or humanitarian effort. “It’s not possible in tech to frame your ambitions aside from those who are making the world a better place,” Eric Schmidt, former CEO of Google, told me. “I think it has a lot to do with the way Silicon Valley was formed and the university culture.


pages: 382 words: 120,064

Bank 3.0: Why Banking Is No Longer Somewhere You Go but Something You Do by Brett King

3D printing, Abraham Maslow, additive manufacturing, Airbus A320, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apollo 11, Apollo 13, Apollo Guidance Computer, asset-backed security, augmented reality, barriers to entry, behavioural economics, bitcoin, bounce rate, business intelligence, business process, business process outsourcing, call centre, capital controls, citizen journalism, Clayton Christensen, cloud computing, credit crunch, crowdsourcing, disintermediation, en.wikipedia.org, fixed income, George Gilder, Google Glasses, high net worth, I think there is a world market for maybe five computers, Infrastructure as a Service, invention of the printing press, Jeff Bezos, jimmy wales, Kickstarter, London Interbank Offered Rate, low interest rates, M-Pesa, Mark Zuckerberg, mass affluent, Metcalfe’s law, microcredit, mobile money, more computing power than Apollo, Northern Rock, Occupy movement, operational security, optical character recognition, peer-to-peer, performance metric, Pingit, platform as a service, QR code, QWERTY keyboard, Ray Kurzweil, recommendation engine, RFID, risk tolerance, Robert Metcalfe, self-driving car, Skype, speech recognition, stem cell, telepresence, the long tail, Tim Cook: Apple, transaction costs, underbanked, US Airways Flight 1549, web application, world market for maybe five computers

A computing device with the power of our current iPhone would fit inside a “nano-robot” computer the size of a blood cell in two or three decades’ time. What does that mean for medical sciences? What will it mean when the device we carry around in our pocket is more powerful than the most advanced supercomputer available today? How will such technologies impact our life? Self-driving cars, computer-based personal assistants that can predict and anticipate our needs or manage our calendar without needing to ask us any questions; holographic telepresence when we’re away from our loved ones; computers built into everything, from the paint we put on our walls, the clothes to jewellery we wear, to sensors in our bathrooms that can monitor our health based on our morning’s ablutions . . .


pages: 428 words: 121,717

Warnings by Richard A. Clarke

"Hurricane Katrina" Superdome, active measures, Albert Einstein, algorithmic trading, anti-communist, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, Bear Stearns, behavioural economics, Bernie Madoff, Black Monday: stock market crash in 1987, carbon tax, cognitive bias, collateralized debt obligation, complexity theory, corporate governance, CRISPR, cuban missile crisis, data acquisition, deep learning, DeepMind, discovery of penicillin, double helix, Elon Musk, failed state, financial thriller, fixed income, Flash crash, forensic accounting, friendly AI, Hacker News, Intergovernmental Panel on Climate Change (IPCC), Internet of things, James Watt: steam engine, Jeff Bezos, John Maynard Keynes: Economic Possibilities for our Grandchildren, knowledge worker, Maui Hawaii, megacity, Mikhail Gorbachev, money market fund, mouse model, Nate Silver, new economy, Nicholas Carr, Nick Bostrom, nuclear winter, OpenAI, pattern recognition, personalized medicine, phenotype, Ponzi scheme, Ray Kurzweil, Recombinant DNA, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Ronald Reagan, Sam Altman, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, smart grid, statistical model, Stephen Hawking, Stuxnet, subprime mortgage crisis, tacit knowledge, technological singularity, The Future of Employment, the scientific method, The Signal and the Noise by Nate Silver, Tunguska event, uranium enrichment, Vernor Vinge, WarGames: Global Thermonuclear War, Watson beat the top human players on Jeopardy!, women in the workforce, Y2K

Thus, a botnet could infect an IV pump and use it to spew spam around the world, monopolizing the processor while degrading the functioning of the pump. Despite repeated warnings from Weiss and others, nothing seems to slow industry’s mad dash toward connecting everything. The latest arena is automobiles. Long before the self-driving car craze, cars were already becoming part of the IoT. The academic expert we know in that arena is Stefan Savage, who hangs his hat at the beautiful campus of the University of California at San Diego. He famously demonstrated to a conference how to hack a Corvette. As Wired reported, Stefan used “a 2-inch-square gadget that’s designed to be plugged into cars’ and trucks’ dashboards and used by insurance firms and trucking fleets to monitor vehicles’ location, speed and efficiency.


pages: 482 words: 121,173

Tools and Weapons: The Promise and the Peril of the Digital Age by Brad Smith, Carol Ann Browne

"World Economic Forum" Davos, Affordable Care Act / Obamacare, AI winter, air gap, airport security, Alan Greenspan, Albert Einstein, algorithmic bias, augmented reality, autonomous vehicles, barriers to entry, Berlin Wall, Big Tech, Bletchley Park, Blitzscaling, Boeing 737 MAX, business process, call centre, Cambridge Analytica, Celtic Tiger, Charlie Hebdo massacre, chief data officer, cloud computing, computer vision, corporate social responsibility, data science, deep learning, digital divide, disinformation, Donald Trump, Eben Moglen, Edward Snowden, en.wikipedia.org, Hacker News, immigration reform, income inequality, Internet of things, invention of movable type, invention of the telephone, Jeff Bezos, Kevin Roose, Laura Poitras, machine readable, Mark Zuckerberg, minimum viable product, national security letter, natural language processing, Network effects, new economy, Nick Bostrom, off-the-grid, operational security, opioid epidemic / opioid crisis, pattern recognition, precision agriculture, race to the bottom, ransomware, Ronald Reagan, Rubik’s Cube, Salesforce, school vouchers, self-driving car, Sheryl Sandberg, Shoshana Zuboff, Silicon Valley, Skype, speech recognition, Steve Ballmer, Steve Jobs, surveillance capitalism, tech worker, The Rise and Fall of American Growth, Tim Cook: Apple, Wargames Reagan, WikiLeaks, women in the workforce

But it was a technical advance in touch-based screens and Jobs’s vision for integrating everything with a clean design that led to a rapid explosion of smartphones around the world. AI likely will be both similar and different. There is good reason to believe that we’re reaching a liftoff point for many AI scenarios, like the use of a computer to take an order at a drive-through. But more complex tasks in which errors can result in injury or death—like a self-driving car—may well require considerably more time. As a result, we’re likely to see not a single transition across the entire economy or even for a single technology, but rather successive waves and ripples in different sectors. This may characterize technology and societal change over the next two or three decades.


Text Analytics With Python: A Practical Real-World Approach to Gaining Actionable Insights From Your Data by Dipanjan Sarkar

bioinformatics, business intelligence, business logic, computer vision, continuous integration, data science, deep learning, Dr. Strangelove, en.wikipedia.org, functional programming, general-purpose programming language, Guido van Rossum, information retrieval, Internet of things, invention of the printing press, iterative process, language acquisition, machine readable, machine translation, natural language processing, out of africa, performance metric, premature optimization, recommendation engine, self-driving car, semantic web, sentiment analysis, speech recognition, statistical model, text mining, Turing test, web application

With the resurgence in popularity of neural networks and advances made in computer architecture, we now have deep learning and artificial intelligence evolving rapidly to make some efforts into trying to engineer machines into learning, perceiving, understanding, and performing actions on their own. You may have seen or heard several of these efforts, such as self-driving cars, computers beating experienced players in games like chess and Go, and the proliferation of chatbots on the Internet. In Chapters 4–6, we have looked at various computational, language processing, and ML techniques to classify, cluster, and summarize text. Back in Chapter 3 we developed certain methods and programs to analyze and understand text syntax and structure.


pages: 433 words: 127,171

The Grid: The Fraying Wires Between Americans and Our Energy Future by Gretchen Bakke

addicted to oil, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, back-to-the-land, big-box store, Buckminster Fuller, demand response, dematerialisation, distributed generation, electricity market, energy security, energy transition, full employment, Gabriella Coleman, illegal immigration, indoor plumbing, Internet of things, Kickstarter, laissez-faire capitalism, Menlo Park, Neal Stephenson, Negawatt, new economy, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off grid, off-the-grid, post-oil, profit motive, rolling blackouts, Ronald Reagan, self-driving car, Silicon Valley, smart grid, smart meter, the built environment, too big to fail, Twitter Arab Spring, vertical integration, washing machines reduced drudgery, Whole Earth Catalog

A second, increasingly popular means for translating interests of different kinds into a single system is to rely upon a platform—an integrative computer program rather than a gadget. In order to help ensure that our grid is wrenched out of its current workings, this platform would need to be open to all the strange sorts of things people are dreaming up and building today (from vehicle to grid-enabled self-driving car pods to real live nanogrids) and to the boring old stuff we’re stuck with for the moment (like natural gas combustion plants and old coal or nuclear), and also to the desires and activities of regular people. All without letting the basic structures of the grid get too rotten or out of date.


The Book of Why: The New Science of Cause and Effect by Judea Pearl, Dana Mackenzie

affirmative action, Albert Einstein, AlphaGo, Asilomar, Bayesian statistics, computer age, computer vision, Computing Machinery and Intelligence, confounding variable, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, driverless car, Edmond Halley, Elon Musk, en.wikipedia.org, experimental subject, Great Leap Forward, Gregor Mendel, Isaac Newton, iterative process, John Snow's cholera map, Loebner Prize, loose coupling, Louis Pasteur, Menlo Park, Monty Hall problem, pattern recognition, Paul Erdős, personalized medicine, Pierre-Simon Laplace, placebo effect, Plato's cave, prisoner's dilemma, probability theory / Blaise Pascal / Pierre de Fermat, randomized controlled trial, Recombinant DNA, selection bias, self-driving car, seminal paper, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steve Jobs, strong AI, The Design of Experiments, the scientific method, Thomas Bayes, Turing test

The owl can be a good hunter without understanding why the rat always goes from point A to point B. Some readers may be surprised to see that I have placed present-day learning machines squarely on rung one of the Ladder of Causation, sharing the wisdom of an owl. We hear almost every day, it seems, about rapid advances in machine learning systems—self-driving cars, speech-recognition systems, and, especially in recent years, deep-learning algorithms (or deep neural networks). How could they still be only at level one? The successes of deep learning have been truly remarkable and have caught many of us by surprise. Nevertheless, deep learning has succeeded primarily by showing that certain questions or tasks we thought were difficult are in fact not.


pages: 483 words: 143,123

The Frackers: The Outrageous Inside Story of the New Billionaire Wildcatters by Gregory Zuckerman

activist fund / activist shareholder / activist investor, addicted to oil, Alan Greenspan, American energy revolution, Asian financial crisis, Bakken shale, Bear Stearns, Bernie Sanders, Buckminster Fuller, Carl Icahn, corporate governance, corporate raider, credit crunch, energy security, Exxon Valdez, Great Leap Forward, housing crisis, hydraulic fracturing, Kickstarter, LNG terminal, man camp, margin call, Maui Hawaii, North Sea oil, oil rush, oil shale / tar sands, oil shock, peak oil, Peter Thiel, reshoring, self-driving car, Silicon Valley, sovereign wealth fund, Steve Jobs, Timothy McVeigh, urban decay

Any slowing of global energy demand will bring benefits to the environment and put pressure on prices. Alternative-fuel vehicles, such as all-electric cars and hybrids, are also gaining popularity. Brokerage firm Raymond James says electric vehicles could claim 1 percent of the market in 2013, a share likely to keep rising. Any widespread embrace of self-driving cars could cripple oil demand. George Mitchell’s son, Todd, says his father’s work will have had a negative impact on the world if it forestalls progress on renewable energy, instead of giving innovators time to improve wind, solar, and other cleaner energy sources. “I think that it will be clear in decades or more that extracting hydrocarbons from tight shale formations blew up all previous assumptions about the availability and economics of oil and gas development,” Mitchell says.


pages: 515 words: 143,055

The Attention Merchants: The Epic Scramble to Get Inside Our Heads by Tim Wu

1960s counterculture, Aaron Swartz, Affordable Care Act / Obamacare, AltaVista, Andrew Keen, anti-communist, AOL-Time Warner, Apple II, Apple's 1984 Super Bowl advert, barriers to entry, Bob Geldof, borderless world, Brownian motion, Burning Man, Cass Sunstein, citizen journalism, colonial rule, content marketing, cotton gin, data science, do well by doing good, East Village, future of journalism, George Gilder, Golden age of television, Golden Gate Park, Googley, Gordon Gekko, Herbert Marcuse, housing crisis, informal economy, Internet Archive, Jaron Lanier, Jeff Bezos, jimmy wales, John Perry Barlow, Live Aid, Mark Zuckerberg, Marshall McLuhan, McMansion, mirror neurons, Nate Silver, Neal Stephenson, Network effects, Nicholas Carr, Pepsi Challenge, placebo effect, Plato's cave, post scarcity, race to the bottom, road to serfdom, Saturday Night Live, science of happiness, self-driving car, side project, Silicon Valley, Skinner box, slashdot, Snapchat, Snow Crash, Steve Jobs, Steve Wozniak, Steven Levy, Ted Nelson, telemarketer, the built environment, The Chicago School, the scientific method, The Structural Transformation of the Public Sphere, Tim Cook: Apple, Torches of Freedom, Upton Sinclair, upwardly mobile, Virgin Galactic, Wayback Machine, white flight, Yochai Benkler, zero-sum game

Google had, in fact, laid bare what had originally been so miraculous about the attention merchant model—getting something truly desirable at no apparent cost. For what really seemed like nothing, the public got the best search ever designed and, in time, other goodies as well, like free email with unlimited storage, peerless maps, the world’s libraries, and even research devoted to exciting innovations like self-driving cars. Of course, there was, as there always is, a quid pro quo: in its ripest state, the buying public was exposed to sales pitches; which might prove useful but then again might not. Google also began to collect a lot of information about a lot of people. Nevertheless, Page, who had the most qualms about advertising, told Wired’s Steven Levy that he’d begun to feel that AdWords was a good and just innovation.


pages: 430 words: 135,418

Power Play: Tesla, Elon Musk, and the Bet of the Century by Tim Higgins

air freight, asset light, autonomous vehicles, big-box store, call centre, Colonization of Mars, coronavirus, corporate governance, COVID-19, Donald Trump, electricity market, Elon Musk, family office, Ford Model T, gigafactory, global pandemic, Henry Ford's grandson gave labor union leader Walter Reuther a tour of the company’s new, automated factory…, Jeff Bezos, Jeffrey Epstein, junk bonds, Larry Ellison, low earth orbit, Lyft, margin call, Mark Zuckerberg, Masayoshi Son, Menlo Park, Michael Milken, paypal mafia, ride hailing / ride sharing, Sand Hill Road, self-driving car, Sheryl Sandberg, short selling, side project, Silicon Valley, Silicon Valley startup, skunkworks, SoftBank, Solyndra, sovereign wealth fund, stealth mode startup, Steve Jobs, Steve Jurvetson, Tesla Model S, Tim Cook: Apple, Travis Kalanick, Uber for X, uber lyft, vertical integration

* * * — Musk was working just as steadily to change the perception of Autopilot. It had attracted a lot of attention when it was announced in 2014 and rolled out in earnest in late 2015, further burnishing Tesla’s credentials as a car company of the future. It had been used by Musk as an example of how the automaker was on the road to deploying fully self-driving cars, the kind of technology that was gaining increased attention in Silicon Valley with advances made by Google and others. Tesla’s underlying technology came from a parts supplier named Mobileye, which had developed a camera system to identify objects on the roadway. Tesla’s team had worked to push the boundaries of the system through clever software programming.


pages: 460 words: 130,820

The Cult of We: WeWork, Adam Neumann, and the Great Startup Delusion by Eliot Brown, Maureen Farrell

"World Economic Forum" Davos, activist fund / activist shareholder / activist investor, Adam Neumann (WeWork), Airbnb, AOL-Time Warner, asset light, Bear Stearns, Bernie Madoff, Burning Man, business logic, cloud computing, coronavirus, corporate governance, COVID-19, Didi Chuxing, do what you love, don't be evil, Donald Trump, driverless car, East Village, Elon Musk, financial engineering, Ford Model T, future of work, gender pay gap, global pandemic, global supply chain, Google Earth, Gordon Gekko, greed is good, Greensill Capital, hockey-stick growth, housing crisis, index fund, Internet Archive, Internet of things, Jeff Bezos, John Zimmer (Lyft cofounder), Larry Ellison, low interest rates, Lyft, Marc Benioff, Mark Zuckerberg, Masayoshi Son, Maui Hawaii, Network effects, new economy, PalmPilot, Peter Thiel, pets.com, plant based meat, post-oil, railway mania, ride hailing / ride sharing, Robinhood: mobile stock trading app, rolodex, Salesforce, San Francisco homelessness, Sand Hill Road, self-driving car, sharing economy, Sheryl Sandberg, side hustle, side project, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, SoftBank, software as a service, sovereign wealth fund, starchitect, Steve Jobs, subprime mortgage crisis, super pumped, supply chain finance, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, vertical integration, Vision Fund, WeWork, women in the workforce, work culture , Y Combinator, Zenefits, Zipcar

Investors ignored the fact that the company was losing money on every ride and focused instead on the hope that Uber was going to completely replace personal car ownership. It would be the go-to courier for everything from packages to laundry, rivaling FedEx and Amazon. It would decrease congestion (it increased it), make its own self-driving car technology (it’s lagging), and spit out profits (it hasn’t). Those who missed out on pumping money into Uber were desperate to find the next one. A whole breed of startups were branded the Uber of X—on-demand services that arrive within minutes after the touch of an app. There were at least three on-demand car valets, an Uber for laundry called Washio, even an Uber for cookies called Doughbies.


Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps by Valliappa Lakshmanan, Sara Robinson, Michael Munn

A Pattern Language, Airbnb, algorithmic trading, automated trading system, business intelligence, business logic, business process, combinatorial explosion, computer vision, continuous integration, COVID-19, data science, deep learning, DevOps, discrete time, en.wikipedia.org, Hacker News, industrial research laboratory, iterative process, Kubernetes, machine translation, microservices, mobile money, natural language processing, Netflix Prize, optical character recognition, pattern recognition, performance metric, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, sentiment analysis, speech recognition, statistical model, the payments system, web application

Embeddings Hashed Feature Neutral Class Multimodal Input Transfer Learning Two-Phase Predictions Cascade Windowed Inference Computer Vision Computer vision is the broad parent name for AI that trains machines to understand visual input, such as images, videos, icons, and anything where pixels might be involved. Computer vision models aim to automate any task that might rely on human vision, from using an MRI to detect lung cancer to self-driving cars. Some classical applications of computer vision are image classification, video motion analysis, image segmentation, and image denoising. Reframing Neutral Class Multimodal Input Transfer Learning Embeddings Multilabel Cascade Two-Phase Predictions Predictive Analytics Predictive modeling uses historical data to find patterns and determine the likelihood of a certain event occurring in the future.


pages: 491 words: 141,690

The Controlled Demolition of the American Empire by Jeff Berwick, Charlie Robinson

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, airport security, Alan Greenspan, American Legislative Exchange Council, American Society of Civil Engineers: Report Card, bank run, barriers to entry, Berlin Wall, Bernie Sanders, Big Tech, big-box store, bitcoin, Black Lives Matter, bread and circuses, Bretton Woods, British Empire, call centre, carbon credits, carbon footprint, carbon tax, Cass Sunstein, Chelsea Manning, clean water, cloud computing, cognitive dissonance, Comet Ping Pong, coronavirus, Corrections Corporation of America, COVID-19, crack epidemic, crisis actor, crony capitalism, cryptocurrency, dark matter, deplatforming, disinformation, Donald Trump, drone strike, Edward Snowden, Elon Musk, energy transition, epigenetics, failed state, fake news, false flag, Ferguson, Missouri, fiat currency, financial independence, George Floyd, global pandemic, global supply chain, Goldman Sachs: Vampire Squid, illegal immigration, Indoor air pollution, information security, interest rate swap, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Jeff Bezos, Jeffrey Epstein, Julian Assange, Kickstarter, lockdown, Mahatma Gandhi, mandatory minimum, margin call, Mark Zuckerberg, mass immigration, megacity, microapartment, Mikhail Gorbachev, military-industrial complex, new economy, no-fly zone, offshore financial centre, Oklahoma City bombing, open borders, opioid epidemic / opioid crisis, pill mill, planetary scale, plutocrats, Ponzi scheme, power law, pre–internet, private military company, Project for a New American Century, quantitative easing, RAND corporation, reserve currency, RFID, ride hailing / ride sharing, Saturday Night Live, security theater, self-driving car, Seymour Hersh, Silicon Valley, smart cities, smart grid, smart meter, Snapchat, social distancing, Social Justice Warrior, South China Sea, stock buybacks, surveillance capitalism, too big to fail, unpaid internship, urban decay, WikiLeaks, working poor

They do generate some revenue, but for the most part, are considered to be money- losing operations for the government, and thus do not receive the financial help that they desperately require to maintain them. Roads: D The most obvious fail of American infrastructure are the crumbling and potholed roads that keep expanding, yet always seem to be filled beyond capacity with cars and trucks, starting and stopping for miles and miles. Self-driving cars boast that they will remove inefficiencies associated with human driving and decision making, but that still does not address the problem of too many cars occupying too few spaces. Each year Americans waste $160 billion in fuel and time by sitting in traffic, and 20% of all roads in the country are considered to be in poor condition and require replacement.


pages: 524 words: 130,909

The Contrarian: Peter Thiel and Silicon Valley's Pursuit of Power by Max Chafkin

3D printing, affirmative action, Airbnb, anti-communist, bank run, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Black Monday: stock market crash in 1987, Blitzscaling, Boeing 747, borderless world, Cambridge Analytica, charter city, cloud computing, cognitive dissonance, Cornelius Vanderbilt, coronavirus, COVID-19, Credit Default Swap, cryptocurrency, David Brooks, David Graeber, DeepMind, digital capitalism, disinformation, don't be evil, Donald Trump, driverless car, Electric Kool-Aid Acid Test, Elon Musk, Ethereum, Extropian, facts on the ground, Fairchild Semiconductor, fake news, Ferguson, Missouri, Frank Gehry, Gavin Belson, global macro, Gordon Gekko, Greyball, growth hacking, guest worker program, Hacker News, Haight Ashbury, helicopter parent, hockey-stick growth, illegal immigration, immigration reform, Internet Archive, Jeff Bezos, John Markoff, Kevin Roose, Kickstarter, Larry Ellison, life extension, lockdown, low interest rates, Lyft, Marc Andreessen, Mark Zuckerberg, Maui Hawaii, Max Levchin, Menlo Park, military-industrial complex, moral panic, move fast and break things, Neal Stephenson, Nelson Mandela, Network effects, off grid, offshore financial centre, oil shale / tar sands, open borders, operational security, PalmPilot, Paris climate accords, Patri Friedman, paypal mafia, Peter Gregory, Peter Thiel, pets.com, plutocrats, Ponzi scheme, prosperity theology / prosperity gospel / gospel of success, public intellectual, QAnon, quantitative hedge fund, quantitative trading / quantitative finance, randomized controlled trial, regulatory arbitrage, Renaissance Technologies, reserve currency, ride hailing / ride sharing, risk tolerance, Robinhood: mobile stock trading app, Ronald Reagan, Sam Altman, Sand Hill Road, self-driving car, sharing economy, Sheryl Sandberg, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Silicon Valley startup, skunkworks, social distancing, software is eating the world, sovereign wealth fund, Steve Bannon, Steve Jobs, Steven Levy, Stewart Brand, surveillance capitalism, TaskRabbit, tech billionaire, tech worker, TechCrunch disrupt, techlash, technology bubble, technoutopianism, Ted Kaczynski, TED Talk, the new new thing, the scientific method, Tim Cook: Apple, transaction costs, Travis Kalanick, Tyler Cowen, Uber and Lyft, uber lyft, Upton Sinclair, Vitalik Buterin, We wanted flying cars, instead we got 140 characters, Whole Earth Catalog, WikiLeaks, William Shockley: the traitorous eight, Y Combinator, Y2K, yellow journalism, Zenefits

These were perhaps the necessary moral compromises made by any real disrupter—and no different from the growth hacking at PayPal, or the privacy violations at Facebook, or the lies that Thiel and his peers had told throughout their careers to hasten the advent of the future. “It almost doesn’t matter if you agree with it or not, he was right,” said former Thiel Fellow Austin Russell, now the CEO of Luminar, which makes sensors for self-driving cars. “If you really want to change the world, you have to have a seat at the table.” Indeed, perhaps if only to placate his friends, Thiel sought to put some distance between himself and Trump, explaining the endorsement as a practical matter of allying with the likely winner—as Austin Russell had.


pages: 573 words: 142,376

Whole Earth: The Many Lives of Stewart Brand by John Markoff

A Pattern Language, air freight, Anthropocene, Apple II, back-to-the-land, Benoit Mandelbrot, Bernie Madoff, Beryl Markham, Big Tech, Bill Atkinson, Biosphere 2, Brewster Kahle, Buckminster Fuller, Burning Man, butterfly effect, Claude Shannon: information theory, cloud computing, complexity theory, computer age, Computer Lib, computer vision, Danny Hillis, decarbonisation, demographic transition, disinformation, Douglas Engelbart, Douglas Engelbart, Dynabook, El Camino Real, Electric Kool-Aid Acid Test, en.wikipedia.org, experimental subject, feminist movement, Fillmore Auditorium, San Francisco, Filter Bubble, game design, gentrification, global village, Golden Gate Park, Hacker Conference 1984, Hacker Ethic, Haight Ashbury, Herman Kahn, housing crisis, Howard Rheingold, HyperCard, intentional community, Internet Archive, Internet of things, Jane Jacobs, Jaron Lanier, Jeff Bezos, John Gilmore, John Markoff, John Perry Barlow, Kevin Kelly, Kickstarter, knowledge worker, Lao Tzu, Lewis Mumford, Loma Prieta earthquake, Marshall McLuhan, megacity, Menlo Park, Michael Shellenberger, microdosing, Mitch Kapor, Morris worm, Mother of all demos, move fast and break things, New Urbanism, Norbert Wiener, Norman Mailer, North Sea oil, off grid, off-the-grid, paypal mafia, Peter Calthorpe, Ponzi scheme, profit motive, public intellectual, Ralph Nader, RAND corporation, Ray Kurzweil, Richard Stallman, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, South of Market, San Francisco, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, systems thinking, technoutopianism, Ted Nelson, Ted Nordhaus, TED Talk, The Death and Life of Great American Cities, The Hackers Conference, Thorstein Veblen, traveling salesman, Turing test, upwardly mobile, Vernor Vinge, We are as Gods, Whole Earth Catalog, Whole Earth Review, young professional

(happening), 94 Wiener, Norbert, 160, 169, 222, 226, 273 Wiesner, Jerome, 272 Wilkinson, Lawrence, 295, 301, 302 Williams, Cecil, 137 Willis, Garry, 200 Wolfe, Tom, 5, 88, 111, 121, 125, 170, 212, 304–5 Wolpman, Jim, 157 Women Building the Earth for the Children’s Sake, 41 Woodstock, 128, 181, 188 woolly mammoth, 359, 360 World Future Society, 231 World’s Fair (1964; New York), 96, 113 “World War IV,” 149, 211 World Wide Web, 151, 172, 230, 279, 292, 314, 330 World Without Mind (Foer), 5–6 Wozniak, Steve, 25, 252, 268–69, 325 Wright, Frank Lloyd, 305, 307, 319 X Xerces blue butterfly, 359, 361 Xerox Palo Alto Research Center, see PARC Y Year with Swollen Appendices, A (Eno), 306 Yucca Mountain, 335–36 Z Zen Buddhism, 33, 77, 98, 194, 225–26 SB and, 207–8, 210, 218, 225 see also San Francisco Zen Center Zihuatanejo, Mexico, Leary/Alpert project in, 89 Zoloft, 338 Zomeworks, 203 Zuni Indians, 100 A B C D E F G H I J K L M N O P Q R S T U V W X Y Z About the Author John Markoff was one of a team of New York Times reporters who won the 2013 Pulitzer Prize for Explanatory Reporting. He has covered Silicon Valley since 1977, wrote the first account of the World Wide Web in 1993, and broke the story of Google’s self-driving car in 2010. He is the author of five books, including What the Dormouse Said: How the Sixties Counterculture Shaped the Personal Computer Industry and Machines of Loving Grace: The Quest for Common Ground Between Humans and Robots. What’s next on your reading list? Discover your next great read!


AI 2041 by Kai-Fu Lee, Chen Qiufan

3D printing, Abraham Maslow, active measures, airport security, Albert Einstein, AlphaGo, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Cambridge Analytica, carbon footprint, Charles Babbage, computer vision, contact tracing, coronavirus, corporate governance, corporate social responsibility, COVID-19, CRISPR, cryptocurrency, DALL-E, data science, deep learning, deepfake, DeepMind, delayed gratification, dematerialisation, digital map, digital rights, digital twin, Elon Musk, fake news, fault tolerance, future of work, Future Shock, game design, general purpose technology, global pandemic, Google Glasses, Google X / Alphabet X, GPT-3, happiness index / gross national happiness, hedonic treadmill, hiring and firing, Hyperloop, information security, Internet of things, iterative process, job automation, language acquisition, low earth orbit, Lyft, Maslow's hierarchy, mass immigration, mirror neurons, money: store of value / unit of account / medium of exchange, mutually assured destruction, natural language processing, Neil Armstrong, Nelson Mandela, OpenAI, optical character recognition, pattern recognition, plutocrats, post scarcity, profit motive, QR code, quantitative easing, Richard Feynman, ride hailing / ride sharing, robotic process automation, Satoshi Nakamoto, self-driving car, seminal paper, Silicon Valley, smart cities, smart contracts, smart transportation, Snapchat, social distancing, speech recognition, Stephen Hawking, synthetic biology, telemarketer, Tesla Model S, The future is already here, trolley problem, Turing test, uber lyft, universal basic income, warehouse automation, warehouse robotics, zero-sum game

Another important consideration is that we shouldn’t think of AVs simply as upgraded cars, but as part of a full smart-city infrastructure, the kind of interconnected technology infrastructure depicted in the story. On the way to realizing that vision, AV—as “The Holy Driver” suggests—will disrupt many professions and industries, and raise significant ethical and legal issues. Let’s dig into these issues in more detail. WHAT IS AN AUTONOMOUS VEHICLE? At its most basic, an AV, or self-driving car, is a computer-controlled vehicle that drives itself. Humans take about forty-five hours to learn how to drive, so it is a complicated task. Human driving involves perception (watching our surroundings and listening for sounds), navigation and planning (associating our surroundings to locations on a map, and getting us from point A to point B), inference (predicting the intent and the action of pedestrians and other drivers), decision-making (applying rules of the road to situations), and controlling the vehicle (translating intent to turning the steering wheel, stepping on the brake, and so on).


pages: 575 words: 140,384

It's Not TV: The Spectacular Rise, Revolution, and Future of HBO by Felix Gillette, John Koblin

activist fund / activist shareholder / activist investor, Airbnb, Amazon Web Services, AOL-Time Warner, Apollo 13, Big Tech, bike sharing, Black Lives Matter, Burning Man, business cycle, call centre, cloud computing, coronavirus, corporate governance, COVID-19, data science, disruptive innovation, Dissolution of the Soviet Union, Donald Trump, Elon Musk, Erlich Bachman, Exxon Valdez, fake news, George Floyd, Jeff Bezos, Keith Raniere, lockdown, Menlo Park, multilevel marketing, Nelson Mandela, Netflix Prize, out of africa, payday loans, peak TV, period drama, recommendation engine, Richard Hendricks, ride hailing / ride sharing, risk tolerance, Robert Durst, Ronald Reagan, Saturday Night Live, self-driving car, shareholder value, Sheryl Sandberg, side hustle, Silicon Valley, Silicon Valley startup, Stephen Hawking, Steve Jobs, subscription business, tech billionaire, TechCrunch disrupt, TikTok, Tim Cook: Apple, traveling salesman, unpaid internship, upwardly mobile, urban decay, WeWork

Mobile upload and download speeds were poised to get much faster. Soon, wireless networks would be behaving more like the seamless internet connections inside office buildings. Everything would be instantaneous, no delays. Stankey said that 5G was on the verge of ushering in a wave of new mobile technologies. For example, autonomous self-driving cars. It wouldn’t be long before commuters in a city like Los Angeles would be sitting in the backseat of their cars watching premium video on the way to work instead of driving, he said. Over the next four years, individuals would be consuming, on average, an additional hour to an hour and a half of video each day.


pages: 554 words: 149,489

The Content Trap: A Strategist's Guide to Digital Change by Bharat Anand

Airbnb, Alan Greenspan, An Inconvenient Truth, AOL-Time Warner, Benjamin Mako Hill, Bernie Sanders, Clayton Christensen, cloud computing, commoditize, correlation does not imply causation, creative destruction, crowdsourcing, death of newspapers, disruptive innovation, Donald Trump, driverless car, electricity market, Eyjafjallajökull, fulfillment center, gamification, Google Glasses, Google X / Alphabet X, information asymmetry, Internet of things, inventory management, Jean Tirole, Jeff Bezos, John Markoff, Just-in-time delivery, Kaizen: continuous improvement, Khan Academy, Kickstarter, late fees, managed futures, Mark Zuckerberg, market design, Minecraft, multi-sided market, Network effects, post-work, price discrimination, publish or perish, QR code, recommendation engine, ride hailing / ride sharing, Salesforce, selection bias, self-driving car, shareholder value, Shenzhen special economic zone , Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, social graph, social web, special economic zone, Stephen Hawking, Steve Jobs, Steven Levy, Stuart Kauffman, the long tail, Thomas L Friedman, transaction costs, two-sided market, ubercab, vertical integration, WikiLeaks, winner-take-all economy, zero-sum game

Car companies are used to investing in engines and transmissions, looking to grab differentiation there. But some of them, including BMW, have begun licensing their technologies to others. Their reasoning? That superiority in hardware will be short-lived, even superfluous, as the shift toward electric cars (inspired by Tesla), self-driving cars (inspired by Google), and ride sharing (inspired by Uber) shifts the locus of differentiation toward sensors, controls, and software. It’s a familiar story: As competition moves from products to connected portfolios, it pays to know whose complement you are—or, to put it another way, which business you are really in.


pages: 519 words: 142,646

Track Changes by Matthew G. Kirschenbaum

active measures, Alvin Toffler, Apollo 11, Apple II, Apple's 1984 Super Bowl advert, Bill Gates: Altair 8800, Buckminster Fuller, Charles Babbage, commoditize, computer age, Computer Lib, corporate governance, David Brooks, dematerialisation, Donald Knuth, Douglas Hofstadter, Dynabook, East Village, en.wikipedia.org, feminist movement, forensic accounting, future of work, Future Shock, Google Earth, Gödel, Escher, Bach, Haight Ashbury, HyperCard, Jason Scott: textfiles.com, Joan Didion, John Markoff, John von Neumann, Kickstarter, low earth orbit, machine readable, machine translation, mail merge, Marshall McLuhan, Mother of all demos, Neal Stephenson, New Journalism, Norman Mailer, off-the-grid, pattern recognition, pink-collar, planned obsolescence, popular electronics, Project Xanadu, RAND corporation, rolodex, Ronald Reagan, scientific management, self-driving car, Shoshana Zuboff, Silicon Valley, social web, Stephen Fry, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, systems thinking, tacit knowledge, technoutopianism, Ted Nelson, TED Talk, text mining, thinkpad, Turing complete, Vannevar Bush, Whole Earth Catalog, Y2K, Year of Magical Thinking

“Computers also amplify creative imagination,” we read just after the urgent prognostications about civil liberties.58 Computers, they assert, will be used by writers and artists, by journalists and doctors, by account executives and their secretaries, and by architects and government clerks. We find early anticipations of assistive and adaptive applications for the disabled, such as screen readers and self-driving cars. Computers can be used to control the environment in one’s house, and even (with “appropriate sensors”) can be used for pest control. Machine translation, electronic voting, and sensors to detect spoiled food or contaminated water are also all on offer. Nonetheless, Herbert and Barnard are careful to emphasize, computers are only tools.


pages: 523 words: 143,139

Algorithms to Live By: The Computer Science of Human Decisions by Brian Christian, Tom Griffiths

4chan, Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, algorithmic bias, algorithmic trading, anthropic principle, asset allocation, autonomous vehicles, Bayesian statistics, behavioural economics, Berlin Wall, Big Tech, Bill Duvall, bitcoin, Boeing 747, Charles Babbage, cognitive load, Community Supported Agriculture, complexity theory, constrained optimization, cosmological principle, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, David Sedaris, delayed gratification, dematerialisation, diversification, Donald Knuth, Donald Shoup, double helix, Dutch auction, Elon Musk, exponential backoff, fault tolerance, Fellow of the Royal Society, Firefox, first-price auction, Flash crash, Frederick Winslow Taylor, fulfillment center, Garrett Hardin, Geoffrey Hinton, George Akerlof, global supply chain, Google Chrome, heat death of the universe, Henri Poincaré, information retrieval, Internet Archive, Jeff Bezos, Johannes Kepler, John Nash: game theory, John von Neumann, Kickstarter, knapsack problem, Lao Tzu, Leonard Kleinrock, level 1 cache, linear programming, martingale, multi-armed bandit, Nash equilibrium, natural language processing, NP-complete, P = NP, packet switching, Pierre-Simon Laplace, power law, prediction markets, race to the bottom, RAND corporation, RFC: Request For Comment, Robert X Cringely, Sam Altman, scientific management, sealed-bid auction, second-price auction, self-driving car, Silicon Valley, Skype, sorting algorithm, spectrum auction, Stanford marshmallow experiment, Steve Jobs, stochastic process, Thomas Bayes, Thomas Malthus, Tragedy of the Commons, traveling salesman, Turing machine, urban planning, Vickrey auction, Vilfredo Pareto, Walter Mischel, Y Combinator, zero-sum game

The good news is that the lack of centralized coordination is making your commute at most only 33% worse. On the other hand, if you’re hoping that networked, self-driving autonomous cars will bring us a future of traffic utopia, it may be disheartening to learn that today’s selfish, uncoordinated drivers are already pretty close to optimal. It’s true that self-driving cars should reduce the number of road accidents and may be able to drive more closely together, both of which would speed up traffic. But from a congestion standpoint, the fact that anarchy is only 4/3 as congested as perfect coordination means that perfectly coordinated commutes will only be 3/4 as congested as they are now.


pages: 497 words: 150,205

European Spring: Why Our Economies and Politics Are in a Mess - and How to Put Them Right by Philippe Legrain

3D printing, Airbnb, Alan Greenspan, Asian financial crisis, bank run, banking crisis, barriers to entry, Basel III, battle of ideas, Berlin Wall, Big bang: deregulation of the City of London, book value, Boris Johnson, Bretton Woods, BRICs, British Empire, business cycle, business process, capital controls, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, Carmen Reinhart, Celtic Tiger, central bank independence, centre right, clean tech, collaborative consumption, collapse of Lehman Brothers, collective bargaining, corporate governance, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Crossrail, currency manipulation / currency intervention, currency peg, debt deflation, Diane Coyle, disruptive innovation, Downton Abbey, Edward Glaeser, Elon Musk, en.wikipedia.org, energy transition, eurozone crisis, fear of failure, financial deregulation, financial engineering, first-past-the-post, Ford Model T, forward guidance, full employment, Gini coefficient, global supply chain, Great Leap Forward, Growth in a Time of Debt, high-speed rail, hiring and firing, hydraulic fracturing, Hyman Minsky, Hyperloop, immigration reform, income inequality, interest rate derivative, Intergovernmental Panel on Climate Change (IPCC), Irish property bubble, James Dyson, Jane Jacobs, job satisfaction, Joseph Schumpeter, Kenneth Rogoff, Kickstarter, labour market flexibility, labour mobility, land bank, liquidity trap, low interest rates, margin call, Martin Wolf, mittelstand, moral hazard, mortgage debt, mortgage tax deduction, North Sea oil, Northern Rock, offshore financial centre, oil shale / tar sands, oil shock, open economy, peer-to-peer rental, price stability, private sector deleveraging, pushing on a string, quantitative easing, Richard Florida, rising living standards, risk-adjusted returns, Robert Gordon, savings glut, school vouchers, self-driving car, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart grid, smart meter, software patent, sovereign wealth fund, Steve Jobs, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, too big to fail, total factor productivity, Tyler Cowen, Tyler Cowen: Great Stagnation, working-age population, Zipcar

Using nitrates as fertilisers helped us feed billions but their overuse pollutes the water; perhaps the solution will be genetic engineering to enable plants to fix more of their own nitrates or bacteria that convert nitrates into nitrogen at more efficient rates. Fossil fuels have made possible all the comforts of modern life; now we need clean energy to limit climate change. Above all, we don’t know what we don’t know and have no idea what the future holds. Only a decade ago, even technologists thought self-driving cars were scarcely on the horizon, yet Google has successfully developed them. Future advances in computing, biotech, nanotechnology or something else entirely are likely to surprise us.710 We have surely only scratched the surface of what is possible. Astronomy, nanochemistry and genetic engineering are advancing in leaps and bounds.


pages: 513 words: 152,381

The Precipice: Existential Risk and the Future of Humanity by Toby Ord

3D printing, agricultural Revolution, Albert Einstein, Alignment Problem, AlphaGo, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, availability heuristic, biodiversity loss, Columbian Exchange, computer vision, cosmological constant, CRISPR, cuban missile crisis, decarbonisation, deep learning, DeepMind, defense in depth, delayed gratification, Demis Hassabis, demographic transition, Doomsday Clock, Dr. Strangelove, Drosophila, effective altruism, Elon Musk, Ernest Rutherford, global pandemic, Goodhart's law, Hans Moravec, Herman Kahn, Higgs boson, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, James Watt: steam engine, Large Hadron Collider, launch on warning, Mark Zuckerberg, Mars Society, mass immigration, meta-analysis, Mikhail Gorbachev, mutually assured destruction, Nash equilibrium, Nick Bostrom, Norbert Wiener, nuclear winter, ocean acidification, OpenAI, p-value, Peter Singer: altruism, planetary scale, power law, public intellectual, race to the bottom, RAND corporation, Recombinant DNA, Ronald Reagan, self-driving car, seminal paper, social discount rate, Stanislav Petrov, Stephen Hawking, Steven Pinker, Stewart Brand, supervolcano, survivorship bias, synthetic biology, tacit knowledge, the scientific method, Tragedy of the Commons, uranium enrichment, William MacAskill

This burst of progress via deep learning is fueling great optimism about what may soon be possible. There is tremendous growth in both the number of researchers and the amount of venture capital flowing into AI.83 Entrepreneurs are scrambling to put each new breakthrough into practice: from simultaneous translation, personal assistants and self-driving cars to more concerning areas like improved surveillance and lethal autonomous weapons. It is a time of great promise but also one of great ethical challenges. There are serious concerns about AI entrenching social discrimination, producing mass unemployment, supporting oppressive surveillance, and violating the norms of war.


We Are the Nerds: The Birth and Tumultuous Life of Reddit, the Internet's Culture Laboratory by Christine Lagorio-Chafkin

"Friedman doctrine" OR "shareholder theory", 4chan, Aaron Swartz, Airbnb, Amazon Web Services, Bernie Sanders, big-box store, bitcoin, blockchain, Brewster Kahle, Burning Man, compensation consultant, crowdsourcing, cryptocurrency, data science, David Heinemeier Hansson, digital rights, disinformation, Donald Trump, East Village, eternal september, fake news, game design, Golden Gate Park, growth hacking, Hacker News, hiring and firing, independent contractor, Internet Archive, Jacob Appelbaum, Jeff Bezos, jimmy wales, Joi Ito, Justin.tv, Kickstarter, Large Hadron Collider, Lean Startup, lolcat, Lyft, Marc Andreessen, Mark Zuckerberg, medical residency, minimum viable product, natural language processing, Palm Treo, Paul Buchheit, Paul Graham, paypal mafia, Peter Thiel, plutocrats, QR code, r/findbostonbombers, recommendation engine, RFID, rolodex, Ruby on Rails, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, semantic web, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, slashdot, Snapchat, Social Justice Warrior, social web, South of Market, San Francisco, Startup school, Stephen Hawking, Steve Bannon, Steve Jobs, Steve Wozniak, Streisand effect, technoutopianism, uber lyft, Wayback Machine, web application, WeWork, WikiLeaks, Y Combinator

(No, no, and no: Kan could simply point the camera to the ceiling when he deemed necessary.) Building their camera rig system, which would transmit a continuous video signal to the Internet, was an undergrad they’d found on an MIT listserv, Kyle Vogt. Vogt was a robotics-obsessed engineer who’d already experimented with self-driving cars and had competed on the TV show BattleBots. He flew to San Francisco after his fall semester ended, to crash on the couch and finish up the hardware during winter break. With him, Vogt had brought a camera prototype. It was a black device a bit smaller than a soda can, with a headset that could perch above Kan’s right ear.


Lifespan: Why We Age—and Why We Don't Have To by David A. Sinclair, Matthew D. Laplante

Albert Einstein, Albert Michelson, Anthropocene, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, Atul Gawande, basic income, Berlin Wall, Bernie Sanders, biofilm, Biosphere 2, blockchain, British Empire, caloric restriction, caloric restriction, carbon footprint, Charles Babbage, Claude Shannon: information theory, clean water, creative destruction, CRISPR, dark matter, dematerialisation, discovery of DNA, double helix, Drosophila, Easter island, Edward Jenner, en.wikipedia.org, epigenetics, experimental subject, Fall of the Berlin Wall, Fellow of the Royal Society, global pandemic, Grace Hopper, helicopter parent, income inequality, invention of the telephone, Isaac Newton, John Snow's cholera map, Kevin Kelly, Khan Academy, labor-force participation, life extension, Louis Pasteur, McMansion, Menlo Park, meta-analysis, microbiome, mouse model, mutually assured destruction, Paul Samuelson, personalized medicine, phenotype, Philippa Foot, placebo effect, plutocrats, power law, quantum entanglement, randomized controlled trial, Richard Feynman, ride hailing / ride sharing, self-driving car, seminal paper, Skype, stem cell, Stephen Hawking, Steven Pinker, TED Talk, the scientific method, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Tim Cook: Apple, Tragedy of the Commons, trolley problem, union organizing, universal basic income, WeWork, women in the workforce, zero-sum game

Both times, he suffered through six months of corneal stitches that felt like “branches” in his eyes, but his vision was saved. The fact that Nick now literally sees the world through others’ eyes is an amusing topic for dinner conversation that belies the true depth of our family’s gratefulness to his deceased donors. Now, as we rapidly approach the era of self-driving cars—a technological and social paradigm shift that almost every expert expects will rapidly reduce car crashes—we need to confront an important question: Where will the organs come from? The geneticist Luhan Yang and her former mentor Professor George Church in my department at Harvard Medical School had just discovered how to gene edit mammalian cells when they began working to edit out genes in pigs.


pages: 467 words: 149,632

If Then: How Simulmatics Corporation Invented the Future by Jill Lepore

A Declaration of the Independence of Cyberspace, Alvin Toffler, anti-communist, Apollo 11, Buckminster Fuller, Cambridge Analytica, company town, computer age, coronavirus, cuban missile crisis, data science, desegregation, don't be evil, Donald Trump, Dr. Strangelove, Elon Musk, fake news, game design, George Gilder, Grace Hopper, Hacker Ethic, Howard Zinn, index card, information retrieval, Jaron Lanier, Jeff Bezos, Jeffrey Epstein, job automation, John Perry Barlow, land reform, linear programming, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, mass incarceration, Maui Hawaii, Menlo Park, military-industrial complex, New Journalism, New Urbanism, Norbert Wiener, Norman Mailer, packet switching, Peter Thiel, profit motive, punch-card reader, RAND corporation, Robert Bork, Ronald Reagan, Rosa Parks, self-driving car, Silicon Valley, SimCity, smart cities, social distancing, South China Sea, Stewart Brand, technoutopianism, Ted Sorensen, Telecommunications Act of 1996, urban renewal, War on Poverty, white flight, Whole Earth Catalog

Commentators accused the Trump campaign of using a “weaponized AI propaganda machine,” describing a new and “nearly impenetrable voter manipulation machine.”18 New? Hardly. Simulmatics invented that machine in 1959. In twenty-first-century Silicon Valley, the meaninglessness of the past and the uselessness of history became articles of faith, gleefully performed arrogance. “The only thing that matters is the future,” said the Google and Uber self-driving car designer Anthony Levandowski in 2018. “I don’t even know why we study history. It’s entertaining, I guess—the dinosaurs and the Neanderthals and the Industrial Revolution and stuff like that. But what already happened doesn’t really matter. You don’t need to know history to build on what they made.


pages: 524 words: 154,652

Blood in the Machine: The Origins of the Rebellion Against Big Tech by Brian Merchant

"World Economic Forum" Davos, Ada Lovelace, algorithmic management, Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, basic income, Bernie Sanders, Big Tech, big-box store, Black Lives Matter, Cambridge Analytica, Charles Babbage, ChatGPT, collective bargaining, colonial rule, commoditize, company town, computer age, computer vision, coronavirus, cotton gin, COVID-19, cryptocurrency, DALL-E, decarbonisation, deskilling, digital rights, Donald Trump, Edward Jenner, Elon Musk, Erik Brynjolfsson, factory automation, flying shuttle, Frederick Winslow Taylor, fulfillment center, full employment, future of work, George Floyd, gig economy, gigafactory, hiring and firing, hockey-stick growth, independent contractor, industrial robot, information asymmetry, Internet Archive, invisible hand, Isaac Newton, James Hargreaves, James Watt: steam engine, Jeff Bezos, Jessica Bruder, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Roose, Kickstarter, Lyft, Mark Zuckerberg, Marshall McLuhan, means of production, military-industrial complex, move fast and break things, Naomi Klein, New Journalism, On the Economy of Machinery and Manufactures, OpenAI, precariat, profit motive, ride hailing / ride sharing, Sam Bankman-Fried, scientific management, Second Machine Age, self-driving car, sharing economy, Silicon Valley, sovereign wealth fund, spinning jenny, Steve Jobs, Steve Wozniak, super pumped, TaskRabbit, tech billionaire, tech bro, tech worker, techlash, technological determinism, Ted Kaczynski, The Future of Employment, The Wealth of Nations by Adam Smith, Thomas Malthus, Travis Kalanick, Uber and Lyft, uber lyft, union organizing, universal basic income, W. E. B. Du Bois, warehouse automation, warehouse robotics, working poor, workplace surveillance

While the factory was degrading the cloth workers’ livelihood, it was creating new, appalling conditions for the next generation of machine workers. Cartwright’s loom, poised to enable such mass accumulation, didn’t replace the human, of course—it just let boys like Blincoe do the work. These cascading consequences of automation, unintended or otherwise, persist. We might marvel at the progress of, say, the self-driving car, but its autonomous navigation requires the labor of numerous invisible workers who do the thankless, drudgery-filled toil, often for very low wages, of labeling image after image to make the datasets the algorithm needs in order to operate. From Amazon’s Mechanical Turk to refugee camps in Europe, workers are paid pennies to sort endless reams of data, the raw materials for computer vision programs and self-driving vehicles.


Lonely Planet Sri Lanka by Lonely Planet

British Empire, car-free, carbon footprint, clean water, colonial rule, digital map, European colonialism, land tenure, Mahatma Gandhi, megaproject, off grid, off-the-grid, period drama, place-making, ride hailing / ride sharing, selective serotonin reuptake inhibitor (SSRI), self-driving car, special economic zone, spice trade, trade route, urban sprawl

Recommended companies with drivers include the following (there are many more; the Lonely Planet Thorn Tree forum is a good source of driver recommendations): Ancient Lanka (%077 727 2780; www.ancientlanka.com) Let's Go Lanka (%077 630 2070; www.letsgolanka.com) Self-Drive Hire Colombo-based company Shineway Rent a Car ( GOOGLE MAP ; %071 278 9323; http://rentalcarsrilanka.com; 45/15 Nawala Rd, Narahenpita, Col 5) offers self-drive car hire. You'll find other local firms as well as very small operations in tourist towns. You can usually hire a car for about US$30 per day with 100km of included kilometres. But it is still uncommon to see visitors driving themselves in Sri Lanka. Motorbike rentals run about Rs 1500 per day across the country.


pages: 568 words: 164,014

Dawn of the Code War: America's Battle Against Russia, China, and the Rising Global Cyber Threat by John P. Carlin, Garrett M. Graff

1960s counterculture, A Declaration of the Independence of Cyberspace, Aaron Swartz, air gap, Andy Carvin, Apple II, Bay Area Rapid Transit, bitcoin, Brian Krebs, business climate, cloud computing, cotton gin, cryptocurrency, data acquisition, Deng Xiaoping, disinformation, driverless car, drone strike, dual-use technology, eat what you kill, Edward Snowden, fake news, false flag, Francis Fukuyama: the end of history, Hacker Ethic, information security, Internet of things, James Dyson, Jeff Bezos, John Gilmore, John Markoff, John Perry Barlow, Ken Thompson, Kevin Roose, Laura Poitras, Mark Zuckerberg, Menlo Park, millennium bug, Minecraft, Mitch Kapor, moral hazard, Morris worm, multilevel marketing, Network effects, new economy, Oklahoma City bombing, out of africa, packet switching, peer-to-peer, peer-to-peer model, performance metric, RAND corporation, ransomware, Reflections on Trusting Trust, Richard Stallman, Robert Metcalfe, Ronald Reagan, Saturday Night Live, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley startup, Skype, Snapchat, South China Sea, Steve Crocker, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Stuxnet, The Hackers Conference, Tim Cook: Apple, trickle-down economics, Wargames Reagan, Whole Earth Catalog, Whole Earth Review, WikiLeaks, Y2K, zero day, zero-sum game

In the years since, Pittsburgh developed a whole ecosystem of key cyber resources, including the National Cyber Forensics and Training Alliance (NCFTA), which since its own founding in 1997 emerged as perhaps law enforcement’s most important clearinghouse for computer crime. NCFTA was part of the city’s dramatic transformation from a dying steel town to a thriving, hip technology hub—the very city, years later, where Uber would first deploy its self-driving cars. On paper, according to government charts at least, public–private information sharing is supposed to be the purview of groups like the Department of Homeland Security’s National Cybersecurity and Communications Integration Center, a 700-person, billion-dollar-a-year, state-of-the-art operation based in Arlington, Virginia.


pages: 569 words: 165,510

There Is Nothing for You Here: Finding Opportunity in the Twenty-First Century by Fiona Hill

2021 United States Capitol attack, active measures, Affordable Care Act / Obamacare, algorithmic bias, barriers to entry, Berlin Wall, Bernie Sanders, Big Tech, Black Lives Matter, blue-collar work, Boris Johnson, Brexit referendum, British Empire, business climate, call centre, collective bargaining, company town, coronavirus, COVID-19, crony capitalism, cuban missile crisis, David Brooks, deindustrialization, desegregation, digital divide, disinformation, Dissolution of the Soviet Union, Donald Trump, Fall of the Berlin Wall, financial independence, first-past-the-post, food desert, gender pay gap, gentrification, George Floyd, glass ceiling, global pandemic, Great Leap Forward, housing crisis, illegal immigration, imposter syndrome, income inequality, indoor plumbing, industrial cluster, industrial research laboratory, informal economy, Jeff Bezos, Jeremy Corbyn, Kickstarter, knowledge economy, lockdown, low skilled workers, Lyft, Martin Wolf, mass immigration, meme stock, Mikhail Gorbachev, new economy, oil shock, opioid epidemic / opioid crisis, Own Your Own Home, Paris climate accords, pension reform, QAnon, ransomware, restrictive zoning, ride hailing / ride sharing, Right to Buy, Ronald Reagan, self-driving car, Silicon Valley, single-payer health, statistical model, Steve Bannon, The Chicago School, TikTok, transatlantic slave trade, Uber and Lyft, uber lyft, University of East Anglia, urban decay, urban planning, Washington Consensus, WikiLeaks, Winter of Discontent, women in the workforce, working poor, Yom Kippur War, young professional

It coordinates with Michigan’s state and local governments to attract new business and investment to the region and produces reports on improving the business climate, as well as informing programs on workforce development. The DRP helps big companies in the Detroit region, such as the Ford Motor Company and General Motors, connect with smaller firms in the new automotive and “mobility technology” sector (self-driving cars and electric vehicles) to create a business network focused on developing technology clusters. In 2020 alone the DRP’s model of bringing together private companies and local government officials brought over $450 million in new investment into the Detroit region and created 1,745 new jobs. In the Lehigh Valley, where Dad might have ended up back in the 1960s had he actually emigrated, the city of Bethlehem set up a similar public-private partnership, the Lehigh Valley Economic Development Corporation.


Termites of the State: Why Complexity Leads to Inequality by Vito Tanzi

accounting loophole / creative accounting, Affordable Care Act / Obamacare, Alan Greenspan, Andrei Shleifer, Andrew Keen, Asian financial crisis, asset allocation, barriers to entry, basic income, behavioural economics, bitcoin, Black Swan, Bretton Woods, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Cass Sunstein, central bank independence, centre right, clean water, crony capitalism, David Graeber, David Ricardo: comparative advantage, deindustrialization, Donald Trump, Double Irish / Dutch Sandwich, experimental economics, financial engineering, financial repression, full employment, George Akerlof, Gini coefficient, Gunnar Myrdal, high net worth, hiring and firing, illegal immigration, income inequality, indoor plumbing, information asymmetry, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Jean Tirole, John Maynard Keynes: Economic Possibilities for our Grandchildren, Kenneth Arrow, Kenneth Rogoff, knowledge economy, labor-force participation, libertarian paternalism, Long Term Capital Management, low interest rates, market fundamentalism, means of production, military-industrial complex, moral hazard, Naomi Klein, New Urbanism, obamacare, offshore financial centre, open economy, Pareto efficiency, Paul Samuelson, Phillips curve, price stability, principal–agent problem, profit maximization, pushing on a string, quantitative easing, rent control, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Solow, Ronald Coase, Ronald Reagan, Second Machine Age, secular stagnation, self-driving car, Silicon Valley, Simon Kuznets, synthetic biology, The Chicago School, The Great Moderation, The Market for Lemons, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, transfer pricing, Tyler Cowen: Great Stagnation, universal basic income, unorthodox policies, urban planning, very high income, Vilfredo Pareto, War on Poverty, Washington Consensus, women in the workforce

Ch apter 12 Termites in Regulatory Activities While one may venture a guess about limits to how much governments should spend, it is difficult, or impossible, to set limits to the number, or the use, of regulations in today’s economies, in part because those limits would need to be adjusted frequently to accommodate technological and other changes. For example, the introduction of cars required many new regulations and so did that of planes. Regulations significantly reduced the number of car accidents over the years. Self-driving cars will require new, different regulations. There are growing complaints about the number of regulations in today’s world that some, and especially representatives of enterprises, libertarian economists, and conservative politicians, see as excessive. At the same time there are complaints from other groups that various activities are not regulated enough, and abuses, presumably due to the absence of regulations, are reported with increasing frequency.


pages: 626 words: 181,434

I Am a Strange Loop by Douglas R. Hofstadter

Albert Einstein, Andrew Wiles, Benoit Mandelbrot, Brownian motion, Charles Babbage, double helix, Douglas Hofstadter, Georg Cantor, Gödel, Escher, Bach, Hans Moravec, Isaac Newton, James Watt: steam engine, John Conway, John von Neumann, language acquisition, mandelbrot fractal, pattern recognition, Paul Erdős, place-making, probability theory / Blaise Pascal / Pierre de Fermat, publish or perish, random walk, Ronald Reagan, self-driving car, Silicon Valley, telepresence, Turing machine

If we once again postulate the idea of obtaining nutrition by carrying out certain remote actions, and if we add back the ability to control distant motion by means of a joystick or even by certain brain events, then things really start to shimmer in uncertainty. For surely a mobile robot is not where the radio-connected computer that is controlling it happens to be sitting. A robot might be strolling about on the moon while its computerized guidance system was in some earthbound laboratory. Or a self-driving car like Stanley could be crossing the Nevada desert, and its computer control system might be on board or might be located in a lab in California, connected by radio. But would we even care where the computer was? Why should we care where it is located? A robot, we feel, is where its body is. And so when my brain can switch at will (using the fancy glasses described above) between inhabiting any one of a hundred different bodies — or worse yet, when it can inhabit several bodies at the same time, processing different kinds of input from all of them at once (perhaps visual input from one, sonic from another, tactile from a third) — then where I am becomes extremely ill-defined.


pages: 706 words: 202,591

Facebook: The Inside Story by Steven Levy

active measures, Airbnb, Airbus A320, Amazon Mechanical Turk, AOL-Time Warner, Apple's 1984 Super Bowl advert, augmented reality, Ben Horowitz, Benchmark Capital, Big Tech, Black Lives Matter, Blitzscaling, blockchain, Burning Man, business intelligence, Cambridge Analytica, cloud computing, company town, computer vision, crowdsourcing, cryptocurrency, data science, deep learning, disinformation, don't be evil, Donald Trump, Dunbar number, East Village, Edward Snowden, El Camino Real, Elon Musk, end-to-end encryption, fake news, Firefox, Frank Gehry, Geoffrey Hinton, glass ceiling, GPS: selective availability, growth hacking, imposter syndrome, indoor plumbing, information security, Jeff Bezos, John Markoff, Jony Ive, Kevin Kelly, Kickstarter, lock screen, Lyft, machine translation, Mahatma Gandhi, Marc Andreessen, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, natural language processing, Network effects, Oculus Rift, operational security, PageRank, Paul Buchheit, paypal mafia, Peter Thiel, pets.com, post-work, Ray Kurzweil, recommendation engine, Robert Mercer, Robert Metcalfe, rolodex, Russian election interference, Salesforce, Sam Altman, Sand Hill Road, self-driving car, sexual politics, Sheryl Sandberg, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, skeuomorphism, slashdot, Snapchat, social contagion, social graph, social software, South of Market, San Francisco, Startup school, Steve Ballmer, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, surveillance capitalism, tech billionaire, techlash, Tim Cook: Apple, Tragedy of the Commons, web application, WeWork, WikiLeaks, women in the workforce, Y Combinator, Y2K, you are the product

He was a serious musician, playing multiple instruments but particularly excelling as a jazz pianist. Stanford brought out his geek side, and he majored in symbolic systems—a cult program whose alumni include Reid Hoffman and Google’s Marissa Mayer—and took courses with world-renowned masters of AI. He was around the AI lab when it won the DARPA self-driving car challenge. After graduating in 2004, he decided to take a year off before grad school, traveling the country and doing computer consulting. When he returned from his trek, he was back at Stanford and living in one of a group of houses in Palo Alto known as the Grateful Dead Houses, run by a landlord who was a serious fan of the band.


pages: 1,380 words: 190,710

Building Secure and Reliable Systems: Best Practices for Designing, Implementing, and Maintaining Systems by Heather Adkins, Betsy Beyer, Paul Blankinship, Ana Oprea, Piotr Lewandowski, Adam Stubblefield

air gap, anti-pattern, barriers to entry, bash_history, behavioural economics, business continuity plan, business logic, business process, Cass Sunstein, cloud computing, cognitive load, continuous integration, correlation does not imply causation, create, read, update, delete, cryptocurrency, cyber-physical system, database schema, Debian, defense in depth, DevOps, Edward Snowden, end-to-end encryption, exponential backoff, fault tolerance, fear of failure, general-purpose programming language, Google Chrome, if you see hoof prints, think horses—not zebras, information security, Internet of things, Kubernetes, load shedding, margin call, microservices, MITM: man-in-the-middle, NSO Group, nudge theory, operational security, performance metric, pull request, ransomware, reproducible builds, revision control, Richard Thaler, risk tolerance, self-driving car, single source of truth, Skype, slashdot, software as a service, source of truth, SQL injection, Stuxnet, the long tail, Turing test, undersea cable, uranium enrichment, Valgrind, web application, Y2K, zero day

As with certifications, we recommend considering a candidate’s academic achievements in the context of their practical experience and your organization’s needs. For example, you might want to bring on an experienced professional as your first security hire, and then hire early career talent once the team is established and can offer mentorship. Alternatively, if your organization is working on a niche technical problem (such as securing self-driving cars), a new PhD graduate with deep knowledge in that specific research area but little work experience might fit the role nicely. Integrating Security into the Organization Knowing when to start working on security is more of an art than a science. Opinions on this topic are plentiful and varied.


pages: 935 words: 197,338

The Power Law: Venture Capital and the Making of the New Future by Sebastian Mallaby

"Susan Fowler" uber, 23andMe, 90 percent rule, Adam Neumann (WeWork), adjacent possible, Airbnb, Apple II, barriers to entry, Ben Horowitz, Benchmark Capital, Big Tech, bike sharing, Black Lives Matter, Blitzscaling, Bob Noyce, book value, business process, charter city, Chuck Templeton: OpenTable:, Clayton Christensen, clean tech, cloud computing, cognitive bias, collapse of Lehman Brothers, Colonization of Mars, computer vision, coronavirus, corporate governance, COVID-19, cryptocurrency, deal flow, Didi Chuxing, digital map, discounted cash flows, disruptive innovation, Donald Trump, Douglas Engelbart, driverless car, Dutch auction, Dynabook, Elon Musk, Fairchild Semiconductor, fake news, family office, financial engineering, future of work, game design, George Gilder, Greyball, guns versus butter model, Hacker Ethic, Henry Singleton, hiring and firing, Hyperloop, income inequality, industrial cluster, intangible asset, iterative process, Jeff Bezos, John Markoff, junk bonds, Kickstarter, knowledge economy, lateral thinking, liberal capitalism, Louis Pasteur, low interest rates, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, Marshall McLuhan, Mary Meeker, Masayoshi Son, Max Levchin, Metcalfe’s law, Michael Milken, microdosing, military-industrial complex, Mitch Kapor, mortgage debt, move fast and break things, Network effects, oil shock, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, plant based meat, plutocrats, power law, pre–internet, price mechanism, price stability, proprietary trading, prudent man rule, quantitative easing, radical decentralization, Recombinant DNA, remote working, ride hailing / ride sharing, risk tolerance, risk/return, Robert Metcalfe, ROLM, rolodex, Ronald Coase, Salesforce, Sam Altman, Sand Hill Road, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley startup, Skype, smart grid, SoftBank, software is eating the world, sovereign wealth fund, Startup school, Steve Jobs, Steve Wozniak, Steven Levy, super pumped, superconnector, survivorship bias, tech worker, Teledyne, the long tail, the new new thing, the strength of weak ties, TikTok, Travis Kalanick, two and twenty, Uber and Lyft, Uber for X, uber lyft, urban decay, UUNET, vertical integration, Vilfredo Pareto, Vision Fund, wealth creators, WeWork, William Shockley: the traitorous eight, Y Combinator, Zenefits

BACK TO NOTE REFERENCE 84 Michael Ewens and Joan Farre-Mensa, “The Deregulation of the Private Equity Markets and the Decline in IPOs,” Review of Financial Studies 33, no. 12 (Dec. 2020): 5463–509. BACK TO NOTE REFERENCE 85 Heather Somerville, “Toyota to Invest $500 Million in Uber for Self-Driving Cars,” Reuters, Aug. 27, 2018. BACK TO NOTE REFERENCE 86 Sam Nussey, “SoftBank’s Son Admits Mistakes After Vision Fund’s $8.9 Billion Loss,” Reuters, Nov. 6, 2019. BACK TO NOTE REFERENCE 87 Arash Massoudi and Kana Inagaki, “SoftBank Imposes New Standards to Rein In Start-Up Founders,” Financial Times, Nov. 4, 2019.


Seeking SRE: Conversations About Running Production Systems at Scale by David N. Blank-Edelman

Affordable Care Act / Obamacare, algorithmic trading, AlphaGo, Amazon Web Services, backpropagation, Black Lives Matter, Bletchley Park, bounce rate, business continuity plan, business logic, business process, cloud computing, cognitive bias, cognitive dissonance, cognitive load, commoditize, continuous integration, Conway's law, crowdsourcing, dark matter, data science, database schema, Debian, deep learning, DeepMind, defense in depth, DevOps, digital rights, domain-specific language, emotional labour, en.wikipedia.org, exponential backoff, fail fast, fallacies of distributed computing, fault tolerance, fear of failure, friendly fire, game design, Grace Hopper, imposter syndrome, information retrieval, Infrastructure as a Service, Internet of things, invisible hand, iterative process, Kaizen: continuous improvement, Kanban, Kubernetes, loose coupling, Lyft, machine readable, Marc Andreessen, Maslow's hierarchy, microaggression, microservices, minimum viable product, MVC pattern, performance metric, platform as a service, pull request, RAND corporation, remote working, Richard Feynman, risk tolerance, Ruby on Rails, Salesforce, scientific management, search engine result page, self-driving car, sentiment analysis, Silicon Valley, single page application, Snapchat, software as a service, software is eating the world, source of truth, systems thinking, the long tail, the scientific method, Toyota Production System, traumatic brain injury, value engineering, vertical integration, web application, WebSocket, zero day

For this last bullet point, it can be somewhat of a difficult situation for the reliability organization to say they’re responsible for improving availability, but at the same time don’t have total control over the factors that impact availability. A lot of people aren’t comfortable with saying they’re responsible for improving something they don’t necessarily have absolute control over. At a company where you ship a product to the field (for example, self-driving car software), this model might not work. You wouldn’t want to wait and say, “Wow, look, that car drove off the road. That was an error. Who wrote that? Well, let’s get it right next year.” In contrast, the space we operate in allows a fair amount of room to leverage context over control, as we don’t put lives at risk and can make changes quickly to the aggregate product environment.


Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

active measures, Amazon Web Services, billion-dollar mistake, bitcoin, blockchain, business intelligence, business logic, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, data science, database schema, deep learning, DevOps, distributed ledger, Donald Knuth, Edward Snowden, end-to-end encryption, Ethereum, ethereum blockchain, exponential backoff, fake news, fault tolerance, finite state, Flash crash, Free Software Foundation, full text search, functional programming, general-purpose programming language, Hacker News, informal economy, information retrieval, Internet of things, iterative process, John von Neumann, Ken Thompson, Kubernetes, Large Hadron Collider, level 1 cache, loose coupling, machine readable, machine translation, Marc Andreessen, microservices, natural language processing, Network effects, no silver bullet, operational security, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, SQL injection, statistical model, surveillance capitalism, systematic bias, systems thinking, Tragedy of the Commons, undersea cable, web application, WebSocket, wikimedia commons

Responsibility and accountability Automated decision making opens the question of responsibility and accountability [87]. If a human makes a mistake, they can be held accountable, and the person affec‐ ted by the decision can appeal. Algorithms make mistakes too, but who is accounta‐ ble if they go wrong [88]? When a self-driving car causes an accident, who is responsible? If an automated credit scoring algorithm systematically discriminates against people of a particular race or religion, is there any recourse? If a decision by your machine learning system comes under judicial review, can you explain to the judge how the algorithm made its decision?


pages: 788 words: 223,004

Merchants of Truth: The Business of News and the Fight for Facts by Jill Abramson

"World Economic Forum" Davos, 23andMe, 4chan, Affordable Care Act / Obamacare, Alexander Shulgin, Apple's 1984 Super Bowl advert, barriers to entry, Bernie Madoff, Bernie Sanders, Big Tech, Black Lives Matter, Cambridge Analytica, Charles Lindbergh, Charlie Hebdo massacre, Chelsea Manning, citizen journalism, cloud computing, commoditize, content marketing, corporate governance, creative destruction, crowdsourcing, data science, death of newspapers, digital twin, diversified portfolio, Donald Trump, East Village, Edward Snowden, fake news, Ferguson, Missouri, Filter Bubble, future of journalism, glass ceiling, Google Glasses, haute couture, hive mind, income inequality, information asymmetry, invisible hand, Jeff Bezos, Joseph Schumpeter, Khyber Pass, late capitalism, Laura Poitras, Marc Andreessen, Mark Zuckerberg, move fast and break things, Nate Silver, new economy, obamacare, Occupy movement, Paris climate accords, performance metric, Peter Thiel, phenotype, pre–internet, race to the bottom, recommendation engine, Robert Mercer, Ronald Reagan, Saturday Night Live, self-driving car, sentiment analysis, Sheryl Sandberg, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, skunkworks, Snapchat, social contagion, social intelligence, social web, SoftBank, Steve Bannon, Steve Jobs, Steven Levy, tech billionaire, technoutopianism, telemarketer, the scientific method, The Wisdom of Crowds, Tim Cook: Apple, too big to fail, vertical integration, WeWork, WikiLeaks, work culture , Yochai Benkler, you are the product

., Mental Health Week), the viral video personalities who worked in BuzzFeed’s Hollywood studio would be able to contribute some comic relief to the conversation. After unveiling HIVE to his employees, Peretti wanted to step back and explain how the new tool fit into his broader vision for BuzzFeed, which he did by way of analogy: “Self-driving cars are starting to get better than humans at driving. Humans crash a lot more. The reason is that you only have your own experience when you drive. If you’ve never had a ball roll out into the street, or driven on an icy patch, you don’t know how to do that.” That was the limitation on human expertise: it was necessarily confined to the lived experience of an individual.


pages: 1,237 words: 227,370

Designing Data-Intensive Applications: The Big Ideas Behind Reliable, Scalable, and Maintainable Systems by Martin Kleppmann

active measures, Amazon Web Services, billion-dollar mistake, bitcoin, blockchain, business intelligence, business logic, business process, c2.com, cloud computing, collaborative editing, commoditize, conceptual framework, cryptocurrency, data science, database schema, deep learning, DevOps, distributed ledger, Donald Knuth, Edward Snowden, end-to-end encryption, Ethereum, ethereum blockchain, exponential backoff, fake news, fault tolerance, finite state, Flash crash, Free Software Foundation, full text search, functional programming, general-purpose programming language, Hacker News, informal economy, information retrieval, Infrastructure as a Service, Internet of things, iterative process, John von Neumann, Ken Thompson, Kubernetes, Large Hadron Collider, level 1 cache, loose coupling, machine readable, machine translation, Marc Andreessen, microservices, natural language processing, Network effects, no silver bullet, operational security, packet switching, peer-to-peer, performance metric, place-making, premature optimization, recommendation engine, Richard Feynman, self-driving car, semantic web, Shoshana Zuboff, social graph, social web, software as a service, software is eating the world, sorting algorithm, source of truth, SPARQL, speech recognition, SQL injection, statistical model, surveillance capitalism, systematic bias, systems thinking, Tragedy of the Commons, undersea cable, web application, WebSocket, wikimedia commons

Responsibility and accountability Automated decision making opens the question of responsibility and accountability [87]. If a human makes a mistake, they can be held accountable, and the person affected by the decision can appeal. Algorithms make mistakes too, but who is accountable if they go wrong [88]? When a self-driving car causes an accident, who is responsible? If an automated credit scoring algorithm systematically discriminates against people of a particular race or religion, is there any recourse? If a decision by your machine learning system comes under judicial review, can you explain to the judge how the algorithm made its decision?


The Art of SEO by Eric Enge, Stephan Spencer, Jessie Stricchiola, Rand Fishkin

AltaVista, barriers to entry, bounce rate, Build a better mousetrap, business intelligence, cloud computing, content marketing, dark matter, en.wikipedia.org, Firefox, folksonomy, Google Chrome, Google Earth, hypertext link, index card, information retrieval, Internet Archive, Larry Ellison, Law of Accelerating Returns, linked data, mass immigration, Metcalfe’s law, Network effects, optical character recognition, PageRank, performance metric, Quicken Loans, risk tolerance, search engine result page, self-driving car, sentiment analysis, social bookmarking, social web, sorting algorithm, speech recognition, Steven Levy, text mining, the long tail, vertical integration, Wayback Machine, web application, wikimedia commons

For example, Google and NASA are working on new networking protocols that can work with the long latency times and low bandwidth in space. Google is also pursuing alternative energy initiatives (http://www.google.com/green/), which clearly goes beyond its mission statement. An example of such an initiative is its investments in self-driving cars. In addition, Google has ventures in office productivity software with Google Docs. These two initiatives have little to do with SEO, but they do speak to how Google is trying to expand its reach. Another potential future involves Google becoming a more general-purpose pattern-matching and searching engine.


The Rough Guide to Sri Lanka by Rough Guides

active transport: walking or cycling, British Empire, citizen journalism, clean water, country house hotel, European colonialism, flag carrier, gentrification, land reform, self-driving car, spice trade, upwardly mobile, urban sprawl

Bike, motorbike and car rental Alma Tours (217 Lewis Place; 031 487 3624, almatours65@yahoo.com), Yellow Fleet Bike Tours (077 776 5919) and Pick & Go (077 646 4346, pickandgotravels.com) have a range of scooters and larger motorbikes for rent for around $10–25/day. Alma Tours and Pick & Go also rent out mountain bikes (Rs.500/day), self-drive cars and minivans (€30–40/day) and even tuktuks. Internet Sunrise Internet Café between Lords and Sherry Land (daily 8.30am–1pm & 3–9pm; Rs.330/hr) has a handful of machines. Swimming pools Non-guests can use the pools at the Paradise Beach Hotel, Camelot Beach Hotel and the Jetwing Beach and Jetwing Blue hotels.


The Rough Guide to Egypt (Rough Guide to...) by Dan Richardson, Daniel Jacobs

Bletchley Park, British Empire, call centre, colonial rule, disinformation, Easter island, Eratosthenes, European colonialism, glass ceiling, haute cuisine, Khartoum Gordon, Kickstarter, lateral thinking, Livingstone, I presume, satellite internet, self-driving car, sexual politics, Skype, spice trade, Suez canal 1869, Suez crisis 1956, sustainable-tourism, three-masted sailing ship, trade route, Wall-E, Yom Kippur War

Car rental Renting a car pays obvious dividends if you are pushed for time or plan to visit remote sites, but whether you’d want to drive yourself is another matter – it’s not much more expensive to hire a car and driver. Branches of Misr Travel, and numerous local tour agencies, can fix you up with one, or you can charter a taxi. If you bring your own vehicle, you are required to re-export it when you leave – even if it gets wrecked. A self-drive car can be rented through one of the international franchise chains, or a local firm (addresses are given in the guide). It’s worth shopping around as rates and terms vary considerably. At the cheaper end, you can get a car with unlimited mileage for around £50/$75 a day. Most companies require a hefty deposit, and not all accept credit cards.