self-driving car

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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?

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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.

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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.

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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].

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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.

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.”

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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.

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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?

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.

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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.

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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.

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.

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[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.

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[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.

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.

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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.

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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?”

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.

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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.

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.), 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.

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.

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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.

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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.

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.

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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.”

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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.

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.

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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.

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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.

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/.

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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.

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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.

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“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.

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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).

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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’.

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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.

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.

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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?

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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.

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.

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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.

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* * * 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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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“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.

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.

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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.

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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.

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.

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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.”

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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.

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.

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., “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.”

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.

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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.

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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.

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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.

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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).

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.

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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.

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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.

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.

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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.

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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?

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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?

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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.: 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?

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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.

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.

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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/.

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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.

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.

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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.

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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?

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?

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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.

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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.

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.

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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.

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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.

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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.

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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?”

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.

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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.

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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.

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.

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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.

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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.

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.

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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?

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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.

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?

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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).

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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.

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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.

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(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.

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.

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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.

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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.

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.

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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?”

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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.

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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.

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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.

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.

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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.

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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.”

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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.

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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.

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?

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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.

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“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.”

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.

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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.

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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.

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.

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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.

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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.

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.

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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?

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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.

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.

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“‘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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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“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

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.

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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.

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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.

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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.

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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.

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.

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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.”

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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

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.

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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.”

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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.

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.

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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.

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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.

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