self-driving car

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pages: 472 words: 80,835

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

3D printing, Airbnb, airport security, Albert Einstein, autonomous vehicles, big-box store, butterfly effect, call centre, car-free, Cesare Marchetti: Marchetti’s constant, Chris Urmson, commoditize, computer vision, congestion charging, connected car, DARPA: Urban Challenge, deskilling, disruptive innovation, edge city, Elon Musk, en.wikipedia.org, future of work, invention of the wheel, Just-in-time delivery, 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, technoutopianism, the built environment, Thorstein Veblen, traffic fines, transit-oriented development, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, Unsafe at Any Speed, urban planning, urban sprawl, 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?

Shladover, University of California, Berkeley October 2, 2013 [286] http://www.forbes.com/sites/neilhowe/2016/09/15/driverless-cars-unsafe-at-any-speed/ [287] https://newsroom.statefarm.com/download/234883/allstates2015-16deerstats-finalpdf.pdf [288] https://patents.google.com/patent/US9176500B1/ [289] http://www.bloomberg.com/news/articles/2016-09-13/robot-car-ethics-need-urgent-review-in-society-bill-ford-says [290] https://www.inverse.com/article/20716-germany-outlines-three-laws-of-robotics-for-self-driving-cars [291] http://moralmachine.mit.edu/ [292] https://www.researchgate.net/publication/301293464_The_Social_Dilemma_of_Autonomous_Vehicles [293] http://science.sciencemag.org/content/352/6293/1573 [294] https://www.wired.com/2016/06/self-driving-cars-will-power-kill-wont-conscience/ [295] Autonomes Fahren, https://link.springer.com/chapter/10.1007/978-3-662-48847-8_5/fulltext.html [296] http://www-nrd.nhtsa.dot.gov/Pubs/812102.pdf [297] http://www-nrd.nhtsa.dot.gov/pubs/812115.pdf [298] https://www.wired.com/2016/06/self-driving-cars-will-power-kill-wont-conscience/ [299] https://www.theguardian.com/technology/2016/aug/22/self-driving-cars-moral-dilemmas [300] http://wardsauto.com/autonomous-vehicles/google-self-driving-car-passes-woman-wheelchair-chasing-duck-test [301] https://www.1843magazine.com/technology/driving-lessons [302] Fighting Traffic: The Dawn of the Motor Age in the American City, Peter Norton [303] Nicholas Felton, NYT, 2008 [304] https://www.gov.uk/government/publications/driverless-vehicles-impacts-on-traffic-flow [305] http://www.mckinsey.com/industries/automotive-and-assembly/our-insights/disruptive-trends-that-will-transform-the-auto-industry [306] Rise of the Robots: Technology and the Threat of a Jobless Future Hardcover – 2015 [307] https://www.selectusa.gov/automotive-industry-united-states [308] http://www.bls.gov/ooh/transportation-and-material-moving/taxi-drivers-and-chauffeurs.htm [309] http://www.bls.gov/iag/tgs/iagauto.htm [310] http://www.roadandtrack.com/new-cars/future-cars/news/a28053/no-self-driving-porsches/ [311] http://www.ic3.gov/media/2016/160317.aspx [312] https://www.ft.com/content/8eff8fbe-d6f0-11e6-944b-e7eb37a6aa8e [313] https://www.transportation.gov/briefing-room/federal-automated-vehicles-policy-september-2016 [314] https://www.theguardian.com/cities/2015/apr/28/end-of-the-car-age-how-cities-outgrew-the-automobile [315] http://apps.who.int/gho/data/node.main.A995 [316] http://www.worldbank.org/en/news/feature/2016/05/05/transforming-the-worlds-mobility---its-time-for-action [317] http://www.bbc.com/news/magazine-37362728 [318] http://earlyindications.blogspot.ie/2016/08/early-indications-august-2016-next-car.html [319] http://communicationtheory.org/technological-determinism/ [320] http://www.newsweek.com/when-will-we-know-self-driving-cars-are-safe-501270 [321] http://www.consumerreports.org/autonomous-driving/with-autonomous-cars-how-safe-is-safe-enough/ [322] https://one.nhtsa.gov/nhtsa/av/av-policy.html [323] https://www.nhtsa.gov/staticfiles/rulemaking/pdf/Automated_Vehicles_Policy.pdf [324] http://www.post-gazette.com/opinion/Op-Ed/2016/09/19/Barack-Obama-Self-driving-yes-but-also-safe/stories/201609200027 [325] http://www.popsci.com/cars/article/2013-09/google-self-driving-car#page-4 [326] https://isearch.nhtsa.gov/files/Google%20--%20compiled%20response%20to%2012%20Nov%20%2015%20interp%20request%20--%204%20Feb%2016%20final.htm [327] Paul Scullion, safety manager at the Association of Global Automakers [328] http://www.mercurynews.com/2016/09/02/driving-regulation-how-lyft-works-to-shape-ride-hailing-legislation/ [329] https://www.dmv.ca.gov/portal/dmv/detail/vr/autonomous/testing [330] http://money.cnn.com/2017/01/10/technology/new-york-self-driving-cars-ridesharing/index.html [331] https://www.wired.com/2016/05/detroit-wants-go-spot-self-driving-tech-big-automakers/ [332] http://imgur.com/a/gNXJj [333] http://fortune.com/2015/12/02/somerville-driverless-car/ [334] https://en.wikipedia.org/wiki/International_Driving_Permit#The_1949_convention [335] http://www.unece.org/info/media/presscurrent-press-h/transport/2016/unece-paves-the-way-for-automated-driving-by-updating-un-international-convention/doc.html [336] http://www.highwaycode.info/rule/160 [337] http://www.highwaycode.info/rule/126 [338] https://www.gov.uk/government/uploads/system/uploads/attachment_data/file/581577/pathway-to-driverless-cars-consultation-response.pdf [339] http://www.statisticbrain.com/driving-citation-statistics/ [340] http://www.telegraph.co.uk/news/uknews/road-and-rail-transport/11566026/How-is-your-driving-Half-of-drivers-admit-breaking-traffic-laws.html [341] https://www.brookings.edu/wp-content/uploads/2016/06/desouza.pdf [342] http://chicago.suntimes.com/news/7/71/1345175/judge-declares-red-light-speed-cam-tickets-void-city-violated-due-process [343] https://www.congress.gov/bill/115th-congress/senate-bill/680 [344] https://www.ftc.gov/system/files/attachments/press-releases/ftc-nhtsa-conduct-workshop-june-28-privacy-security-issues-related-connected-automated-vehicles/notice_connected_cars_workshop_with_nhtsa_1.pdf [345] https://autoalliance.org/connected-cars/automotive-privacy-2/principles/ [346] https://www.nhtsa.gov/technology-innovation/automated-vehicles [347] https://www.dmv.ca.gov/portal/wcm/connect/58a64bed-2bf3-449f-a270-822c208bb7f0/Volvo.pdf?

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, augmented reality, autonomous vehicles, blockchain, call centre, carbon footprint, cleantech, computer vision, conceptual framework, connected car, crowdsourcing, cyber-physical system, DARPA: Urban Challenge, data acquisition, demand response, digital map, disruptive innovation, Elon Musk, fault tolerance, fear of failure, global supply chain, industrial cluster, intermodal, Internet of things, Jeff Bezos, 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, uber lyft, upwardly mobile, urban planning, Zipcar

This platform has sufficient processing power to support deep learning, sensor fusion and surround vision, all of which are key elements for a self-driving car. It also announced that its PX2 would be used as a standard computer in the Roborace series for self-driving race. Nvidia has also already built autonomous test vehicles, which have only been driven on test routes to date. Meanwhile, it has been licensed by the State of California Department of Motor Vehicles to use these vehicles on public roads in California. Microsoft has for some time been working on a project with Volvo involving the use of its HoloLens technology for autonomous vehicles. 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.

Many other car manufacturers such as Ford, General Motors, BMW, Toyota, Kia, Nissan and Volkswagen are working on prototypes of self-driving cars or have already tested them in road traffic. The CTOs of many car manufacturers and technology companies agree that achieving the first 90 per cent of autonomous driving is nothing special. It’s the last 10 per cent cially in urban areas in the most challenging traffic situations and espethat makes the difference. That’s why the vehicles have to be tested in as many traffic situations as possible so that experience is gained on their reactions. A similar argument is presented by Jen-Hsun Huang, CEO of Nvidia, who demands accuracy of 99.999999 per cent in the development of autonomous cars, whereby the last percentage point can only be achieved at very great expense. Toyota and Rand Corporation have published calculations of the number of miles self-driving cars have to be tested before they can be assessed as roadworthy because the algorithms required for driverless cars undergo self-learning in multiple road traffic situations.

This project brought the breakthrough; self-driving cars became reality and the science fiction novels and films had fulfilled their purpose [78]. At this point, car manufacturers and their suppliers as well as technology companies started developing the technol- ogy required for autonomous driving (see Box 5.1). Some of the highlights of these developments will be described using Audi and Mercedes as examples. Autonomous Driving 42 Box 5.1. Statement by Jan Becker Jan Becker, Senior Director, Faraday Future The development of autonomous vehicles requires the collaboration of companies with various skills. The Urban Challenge marked the transition from academic research to industrial development. Google started to work on driverless cars in 2008 and two years later officially announced its self-driving-car programme.


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, congestion charging, crowdsourcing, cryptocurrency, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, future of work, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, 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, 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, Network effects, new economy, obamacare, Occupy movement, Oculus Rift, off grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, RFID, ride hailing / ride sharing, Robert Metcalfe, 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, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, TaskRabbit, technological singularity, telemarketer, telepresence, telepresence robot, Tesla Model S, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks

Taking all of this into account, it turns out that machine intelligence is going to be able to demonstrate fairly quickly that it is better and safer at driving than us humans. 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.”

Instead of asking for their own car when they reach driving age, teens are now asking their parents for an Uber account.6 So this is not just a factor of electric, self-driving cars; the sharing economy is already starting to shift behaviour towards dramatically different vehicle ownership models. Children who have grown up with parents who use Uber or ride-sharing services will do the maths and find that it is cheaper to not own their own vehicle in an average city with good public transportation and an autonomous vehicle network. For those with a commute, this is where the Mercedes vision of the self-driving car gives us a glimpse of the near future. Realising that a self-driving car does not need to be optimised for driving, the interior space could instead be used for entertainment, eating your breakfast on the way to the office, as an office itself or just as an extension of your personal space. How will users of self-driving cars personalise their vehicles?

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. During the journey, the car is alerted that it will be required again in approximately 6 hours.


pages: 245 words: 83,272

Artificial Unintelligence: How Computers Misunderstand the World by Meredith Broussard

1960s counterculture, A Declaration of the Independence of Cyberspace, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, autonomous vehicles, availability heuristic, barriers to entry, Bernie Sanders, bitcoin, Buckminster Fuller, Chris Urmson, Clayton Christensen, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, digital map, disruptive innovation, Donald Trump, Douglas Engelbart, easy for humans, difficult for computers, Electric Kool-Aid Acid Test, Elon Musk, Firefox, gig economy, global supply chain, Google Glasses, Google X / Alphabet X, Hacker Ethic, Jaron Lanier, Jeff Bezos, John von Neumann, Joi Ito, Joseph-Marie Jacquard, life extension, Lyft, Mark Zuckerberg, mass incarceration, Minecraft, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Nate Silver, natural language processing, PageRank, 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, speech recognition, statistical model, Steve Jobs, Steven Levy, Stewart Brand, Tesla Model S, the High Line, The Signal and the Noise by Nate Silver, theory of mind, Travis Kalanick, Turing test, Uber for X, uber lyft, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, women in the workforce

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. Looking at what worked—and what didn’t—during the first decade of autonomous vehicles is a cautionary tale about how technochauvinism can lead to magical thinking about technology and can create a public health hazard.

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. Internet pioneer Jaron Lanier warned of the economic consequences in an interview: The way self-driving cars work is big data. It’s not some brilliant artificial brain that knows how to drive a car.

The updating of the input that is needed is more valuable, per bit, than we imagine it would be today.23 Lanier is describing a world in which vehicle safety could depend on monetized data—a dystopia in which the best data goes to the people who can afford to pay the most for it. 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. Some luxury cars now have a parallel-parking feature to help the driver get into a tight space.


pages: 477 words: 75,408

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

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, 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, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, 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, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, 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. “Self-driving cars” is the name we are stuck with for the time being, but it is all clunk and no click. At the end of the 19th century it was becoming obvious that horseless carriages were here to stay, and needed a shorter name.

[clxxxi] Perhaps we will contract the phrase “autonomous vehicle”, and call them “autos”. Some people are going to hate self-driving cars, whatever they are called: petrol-heads like Jeremy Clarkson are unlikely to be enthusiastic about the objects of their devotion being replaced by machines with all the romance of a horizontal elevator. Some people are already describing a person who has been relegated from driver to chaperone as a “meat puppet”.[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.

They struggle with heavy rain or snow, they can get confused by potholes or debris obstructing the road, and they cannot always discern between a pedestrian and a policeman indicating for the vehicle to stop. A self-driving car which travelled 3,400 miles from San Francisco to New York in March 2015 did 99% of the driving itself, but that means it had to hand over to human occupants for 1% of the journey.[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.


pages: 181 words: 52,147

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

23andMe, 3D printing, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, Bernie Sanders, bitcoin, blockchain, clean water, correlation does not imply causation, distributed ledger, Donald Trump, double helix, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Google bus, Hyperloop, income inequality, Internet of things, job automation, Kevin Kelly, Khan Academy, Kickstarter, Law of Accelerating Returns, license plate recognition, life extension, longitudinal study, Lyft, M-Pesa, Menlo Park, microbiome, mobile money, new economy, personalized medicine, phenotype, precision agriculture, RAND corporation, Ray Kurzweil, recommendation engine, Ronald Reagan, Second Machine Age, self-driving car, Silicon Valley, Skype, smart grid, stem cell, Stephen Hawking, Steve Wozniak, Stuxnet, supercomputer in your pocket, Tesla Model S, 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

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

.), 28 June 2014, http://www.telegraph.co.uk/finance/newsbysector/banksandfinance/10933273/Addison-Lee-owner-flags-sale.html (accessed 21 October 2016). 3. Johana Bhuiyan, “Why Uber has to be first to market with self-driving cars,” Recode 29 September 2016, http://www.recode.net/2016/9/29/12946994/why-uber-has-to-be-first-to-market-with-self-driving-cars (accessed 21 October 2016). 4. Alison Griswold, “Uber wants to replace its drivers with robots. So much for that ‘new economy’ it was building,” Slate 2 February 2015, http://www.slate.com/blogs/moneybox/2015/02/02/uber_self_driving_cars_autonomous_taxis_aren_t_so_good_for_contractors_in.html (accessed 21 October 2016). 5. Ray Kurzweil, How to Create a Mind: The Secret of Human Thought Revealed, New York: Viking, 2012. 6. Ray Kurzweil, “The law of accelerating returns,” Kurzweil Accelerating Intelligence 7 March 2001, http://www.kurzweilai.net/the-law-of-accelerating-returns (accessed 21 October 2016). 7.

The single-axis controller, a core component of most robots’ inner workings, has fallen in price from $1,000 to $10. The price of critical sensors for navigation and obstacle avoidance has fallen from $5,000 to less than $100. And the software—the A.I. that I described in Chapter 5—is advancing on a similar exponential curve. In the DARPA Grand Challenge of 2004 for autonomous vehicles, no self-driving car came close to finishing the course. Just eleven years later, self-driving cars are legal in more than a dozen states and are a common sight on the streets of the Bay Area of San Francisco. Incidentally, three teams, with three different designs, completed DARPA’s 2015 Challenge course. In voice recognition, robots are already close to attaining the capabilities of C-3PO. Apple, Amazon, and Google do decent jobs of translating speech to text, even in noisy environments.


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Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, Bernie Sanders, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, dark matter, Douglas Hofstadter, Elon Musk, en.wikipedia.org, 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, Mark Zuckerberg, natural language processing, Norbert Wiener, ought to be enough for anybody, 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, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

The next chapter explores some of the formidable challenges of balancing the benefits of AI with the risks of its unreliability and misuse. 7 On Trustworthy and Ethical AI Imagine yourself in a self-driving car, late at night, after the office Christmas party. It’s dark out, and snow is falling. “Car, take me home,” you say, tired and a little tipsy. You lean back, gratefully allowing your eyes to close as the car starts itself up and pulls into traffic. All good, but how safe should you feel? 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.

With the proliferation of deep-learning systems in real-world applications, companies are finding themselves in need of new labeled data sets for training deep neural networks. Self-driving cars are a noteworthy example. These cars need sophisticated computer vision in order to recognize lanes in the road, traffic lights, stop signs, and so on, and to distinguish and track different kinds of potential obstacles, such as other cars, pedestrians, bicyclists, animals, traffic cones, knocked-over garbage cans, tumbleweeds, and anything else that you might not want your car to hit. Self-driving cars need to learn what these various objects look like—in sun, rain, snow, or fog, day or night—and which objects are likely to move and which will stay put. Deep learning has helped make this task possible, at least in part, but deep learning, as always, requires a profusion of training examples. Self-driving car companies collect these training examples from countless hours of video taken by cameras mounted on actual cars driving in traffic on highways and city streets.

The trolley problem has recently reemerged as part of the media’s coverage of self-driving cars,20 and the question of how an autonomous vehicle should be programmed to deal with such problems has become a central talking point in discussions on AI ethics. Many AI ethics thinkers have pointed out that the trolley problem itself, in which the driver has only two horrible options, is a highly contrived scenario that no real-world driver will ever encounter. But the trolley problem has become a kind of symbol for asking about how we should program self-driving cars to make moral decisions on their own. 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.


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Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, 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, zero-sum game, Zipcar

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. MARTIN FORD: I know you’re on the board of directors of the self-driving car company Drive.ai. Do you have an estimate for when their technology will be in general use? ANDREW NG: They’re driving round in Texas right now. Let’s see, what time is it? Someone’s just taken one and gone for lunch. The important thing is how mundane that is. Someone’s just gone out for lunch, like any normal day, and they’ve done it by getting in a self-driving car. MARTIN FORD: How do you feel about the progress you’ve seen in self-driving cars so far?

MARTIN FORD: How do you feel about the progress you’ve seen in self-driving cars so far? How has it compared with your expectations? 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.

A while back I co-authored a Wired article talking about Train Terrain (https://www.wired.com/2016/03/self-driving-cars-wont-work-change-roads-attitudes/) about how I think self-driving cars might roll out. We’ll need infrastructure changes, and societal and legal changes, before we’ll see mass adoption of self-driving cars. I have been fortunate to have seen the self-driving industry evolve for over 20 years now. As an undergraduate at Carnegie Mellon in the late ‘90s, I did a class with Dean Pomerleau working on their autonomous car project that steered the vehicle based an input video image. The technology was great, but it wasn’t ready for its time. Then at Stanford, I was a peripheral part of the DARPA Urban Challenge in 2007. 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.


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

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

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

., “Autonomous Driving in Urban Environments: Boss and the Urban Challenge,” Journal of Field Robotics 25 (9) (2008): 426–464. 12. “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.

They ask, “Will there ever be an operating system that can handle a car as well as a person can?” A more interesting question is to ask is, “Why weren’t driverless cars invented years ago?” Notes 1. Ashlee Vance, “The First Person to Hack the iPhone Built a Self-Driving Car. In His Garage,” Bloomberg Business, December 16, 2015; Tesla Motors, “Correction to article: The First Person to Hack the iPhone Built a Self-Driving Car,”https://www.teslamotors.com/support/correction-article-first-person-hack-iphone-built-self-driving-car 2. “Right-of-Way Rules,” California Driver Handbook— Laws and Rules of the Road, California DMV, https://www.dmv.ca.gov/portal/dmv/detail/pubs/hdbk/right_of_way 3. Luke Fletcher, Seth Teller, Edwin Olson, David Moore, Yoshiaki Kuwata, Jonathan How, John Leonard, Isaac Miller, Mark Campbell, Dan Huttenlocher, Aaron Nathan, and Frank-Robert Kline, “The MIT–Cornell Collision and Why It Happened,” Springer Tracts in Advanced Robotics 56:509–548. 4.


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, Elon Musk, high net worth, RAND corporation, ride hailing / ride sharing, self-driving car, Silicon Valley, smart cities, Tesla Model S, Travis Kalanick, wikimedia commons, Zipcar

Hook and T. Bradshaw. 2017. Driverless cars inspire a new gold rush in California. 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). By Diablanco (own work) [CC BY-SA 3.0 (https://creative​com​ m​ons​​.org/licenses/by-sa/3.0/deed.en)], via Wikimedia Commons.

On the day I was working on this chapter, my eye was caught by a headline on the ­Mirror’s website that read ‘Domino’s launches ROBOT pizza deliveries in Europe’. 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’. I’ll short cut the process and save them the bother of continuing the experiment: I would guess that people living on the ninth floor of a New York apartment block or ordering a pizza to settle down to watch a football game would not be pleased at having to rush out to pick up their pizza from a car that might be parked far down the street, especially if it were raining or snowing.

The top five manufacturers spent $46 billion on R&D in 2015: an 8 per cent increase year on year. 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. In June 2017 the VentureBeat website helpfully pulled together the promises of 56 What can cars do now?


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

Amazon Web Services, basic income, clean water, cloud computing, computer vision, digital map, en.wikipedia.org, full employment, income inequality, job automation, knowledge worker, low earth orbit, mutually assured destruction, Occupy movement, Search for Extraterrestrial Intelligence, self-driving car, Stephen Hawking, 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. The only problem at this moment (2015) is that all of the equipment for a self-driving car is fairly expensive. As in, more expensive than the car itself.

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? Instead of cameras, a self-driving car uses different sensors to detect the world around it.

Imagine a search engine like Google, but you can ask it any question you like in English and get a great answer immediately. There once was a day when the idea of a self-driving car operating on city streets seemed way off in the future. How could a computer possibly handle the vagaries of pedestrians, wildlife, drunk drivers, weather, etc.? Then one day in 2012 Google announced that it had a self-driving system that had logged 100,000 accident-free miles on normal roads in normal traffic. No longer were self-driving cars the stuff of SciFi novels – they were here driving around amongst us. One day, as far as the general public knew, there were zero real self-driving cars on normal roadways. The next day we discovered that Google had made self-driving cars a fait accompli. The exact same thing will happen with machine consciousness. One day it will seem impossible that a computer could think and talk and act just like a normal human being.


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Super Pumped: The Battle for Uber by Mike Isaac

"side hustle", 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, Burning Man, call centre, Chris Urmson, Chuck Templeton: OpenTable:, citizen journalism, Clayton Christensen, cloud computing, corporate governance, creative destruction, don't be evil, Donald Trump, Elon Musk, family office, gig economy, Google Glasses, Google X / Alphabet X, high net worth, Jeff Bezos, John Markoff, Kickstarter, Lyft, Marc Andreessen, Mark Zuckerberg, mass immigration, Menlo Park, Mitch Kapor, money market fund, moral hazard, move fast and break things, move fast and break things, Network effects, new economy, off grid, peer-to-peer, pets.com, Richard Florida, ride hailing / ride sharing, Sand Hill Road, self-driving car, shareholder value, side project, Silicon Valley, Silicon Valley startup, skunkworks, Snapchat, software as a service, software is eating the world, South China Sea, South of Market, San Francisco, sovereign wealth fund, special economic zone, Steve Jobs, TaskRabbit, the payments system, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, ubercab, union organizing, upwardly mobile, Y Combinator

But Kalanick had one very important requirement: Uber wouldn’t only play defense. 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. The technologists, venture capitalists, and journalists were buzzing with excitement.

Chapter 24: NO ONE STEALS FROM LARRY PAGE 232 first consumer-ready version: John Markoff, “No Longer a Dream: Silicon Valley Takes on the Flying Car,” New York Times, April 24, 2017, https://www.nytimes.com/2017/04/24/technology/flying-car-technology.html. 233 “a new way forward in mobility”: Daisuke Wakabayashi, “Google Parent Company Spins Off Self-Driving Car Business,” New York Times, December 13, 2016, https://www.nytimes.com/2016/12/13/technology/google-parent-company-spins-off-waymo-self-driving-car-business.html. 233 suing him months ago: Biz Carson, “Google Secretly Sought Arbitration Against Its Former Self-Driving Guru Months Before the Uber Lawsuit,” Business Insider, March 29, 2017, https://www.businessinsider.com/google-filed-against-anthony-levandowski-in-arbitration-before-uber-lawsuit-2017-3. 233 old Google workplace accounts: Waymo LLC v. Uber Technologies. 234 downloaded proprietary information: Waymo LLC v. Uber Technologies. 234 “Otto and Uber have taken”: Daisuke Wakabayashi and Mike Isaac, “Google Self-Driving Car Unit Accuses Uber of Using Stolen Technology,” New York Times, February 23, 2017, https://www.nytimes.com/2017/02/23/technology/google-self-driving-waymo-uber-otto-lawsuit.html. 236 Kalanick had announced his hire: Mike Isaac and Daisuke Wakabayashi, “Uber Hires Google’s Former Head of Search, Stoking a Rivalry,” New York Times, January 20, 2017, https://www.nytimes.com/2017/01/20/technology/uber-amit-singhal-google.html?

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.


pages: 190 words: 62,941

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

"side hustle", Airbnb, always be closing, Amazon Web Services, autonomous vehicles, Ayatollah Khomeini, business process, Chuck Templeton: OpenTable:, cognitive dissonance, corporate governance, DARPA: Urban Challenge, Donald Trump, Elon Musk, gig economy, Golden Gate Park, Google X / Alphabet X, information retrieval, Jeff Bezos, Lyft, Marc Andreessen, Mark Zuckerberg, megacity, Menlo Park, new economy, pattern recognition, price mechanism, ride hailing / ride sharing, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, South of Market, San Francisco, sovereign wealth fund, statistical model, Steve Jobs, TaskRabbit, Tony Hsieh, transportation-network company, Travis Kalanick, turn-by-turn navigation, Uber and Lyft, Uber for X, uber lyft, ubercab, young professional

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. 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.

So suffice it to say they paid keen attention to scientific breakthrough prizes that engaged the computer science departments from which they continued to recruit as well as the government agency that sponsored the competitions. 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. Google also bought a small company called 510 Systems, founded by an engineer named Anthony Levandowski, that specialized in self-driving technology.

Google also bought a small company called 510 Systems, founded by an engineer named Anthony Levandowski, that specialized in self-driving technology. The Google effort was a classic skunk works project: a seemingly goofy, behind-the-scenes science experiment that Google trotted out for the public often enough to get a positive public-relations glow. In 2013, however, Thrun dropped out of the self-driving car game by leaving Google to start a completely different venture, the online education company Udacity. He committed to remaining a Google adviser for two years, effectively keeping him away from working with any other self-driving car entity. At the same time, he and Kalanick saw each other socially, and the subject of autonomous vehicles was a frequent topic of conversation. “Travis started asking me a lot of questions,” says Thrun. “He became very concerned that self-driving taxis were a threat to Uber.


pages: 307 words: 88,180

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

AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, business cycle, cloud computing, commoditize, computer vision, corporate social responsibility, creative destruction, crony capitalism, Deng Xiaoping, deskilling, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, full employment, future of work, gig economy, Google Chrome, happiness index / gross national happiness, if you build it, they will come, ImageNet competition, 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, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, new economy, 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, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, Y Combinator

There will be circumstances that force an autonomous vehicle to make agonizing ethical decisions, like whether to veer right and have a 55 percent chance of killing two people or veer left and have a 100 percent chance of killing one person. 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. They may well lead American politicians, ever fearful of interest groups and attack ads, to pump the brakes on widespread self-driving vehicle deployment.

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. In China, there’s a sense that everything can change—including current roads.

When it comes to the core technology needed for self-driving cars, American companies remain two to three years ahead of China. In technology timelines, that’s light-years of distance. Part of that stems from the relative importance of elite expertise in fourth-wave AI: safety issues and sheer complexity make autonomous vehicles a much tougher engineering nut to crack. It’s a problem that requires a core team of world-class engineers rather than just a broad base of good ones. This tilts the playing field back toward the United States, where the best engineers from around the globe still cluster at companies like Google. Silicon Valley companies also have a substantial head start on research and development, a product of the valley’s proclivity for moonshot projects. Google began testing its self-driving cars as early as 2009, and many of its engineers went on to found early self-driving startups.


pages: 343 words: 91,080

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

"side hustle", Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, basic income, big-box store, call centre, cashless society, Cass Sunstein, choice architecture, collaborative economy, collective bargaining, creative destruction, crowdsourcing, disruptive innovation, don't be evil, Donald Trump, en.wikipedia.org, future of work, gender pay gap, gig economy, Google Chrome, income inequality, information asymmetry, Jaron Lanier, 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, Ralph Waldo Emerson, regulatory arbitrage, ride hailing / ride sharing, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, social software, stealth mode startup, Steve Jobs, strikebreaker, TaskRabbit, 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, Zipcar

For context: Uber provided these comments to me and my coauthor Ryan Calo as part of our fact-checking effort in preparation for our law review article, “The Taking Economy: Uber, Information and Power.” 14. Sam Levin, “Uber Admits to Self-Driving Car ‘Problem’ in Bike Lanes as Safety Concerns Mount,” The Guardian, December 19, 2016, www.theguardian.com/technology/2016/dec/19/uber-self-driving-cars-bike-lanes-safety-san-francisco. 15. Julia Carrie Wong, “California Threatens Legal Action against Uber Unless It Halts Self-Driving Cars,” The Guardian, December 16, 2016, www.theguardian.com/technology/2016/dec/16/uber-defies-california-self-driving-cars-san-francisco. 16. Mike Isaac (@Mikeisaac) wrote, “The state attorney generals office is not happy with Uber.” Twitter, December 16, 2016, https://twitter.com/MikeIsaac/status/809936567078965248. 17. Marisa Kendall, “Uber Sends Self-Driving Cars to Arizona after Failed San Francisco Pilot,” Mercury News, December 23, 2016, www.mercurynews.com/2016/12/22/uber-ships-self-driving-cars-to-arizona-after-failed-san-francisco-pilot/. 18.

Yet when the Department of Motor Vehicles developed a license for the budding self-driving cars developed by companies like Uber, Google, Lyft, and others, Uber refused to cooperate, contradicting its stated rhetoric. Instead, it debuted its self-driving cars without licenses in the streets of San Francisco. When the DMV and State Attorney General Kamala Harris threatened legal action, Uber initially refused to back down. 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.

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. The attorney general’s office wrote to Uber in response: “We are asking Uber to adhere to California law and immediately remove its ‘self-driving’ vehicles from the state’s roadways until Uber complies with all applicable statutes and regulations[,] . . . until it obtains the appropriate permit, as 20 other companies have done.”16 Rather than change its approach, Uber packed up its self-driving cars and delivered them to Arizona, with the expectation that regulatory requirements there would be minimal.17 (In March 2018, Arizona suspended Uber’s self-driving car tests after one of them struck and killed a woman as she walked her bicycle across the street in Tempe, Arizona.)18 Uber’s seemingly disingenuous protest against regulations highlights the power of technology companies to ignore the clear intentions of legal authorities.


pages: 307 words: 90,634

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

Airbnb, Albert Einstein, augmented reality, autonomous vehicles, barriers to entry, basic income, Bay Area Rapid Transit, Ben Horowitz, business climate, car-free, carbon footprint, Chris Urmson, Clayton Christensen, cleantech, Colonization of Mars, connected car, crony capitalism, Deng Xiaoping, disruptive innovation, Donald Trump, Elon Musk, Google Glasses, Hyperloop, Internet of things, Jeff Bezos, John Markoff, low earth orbit, Lyft, Marc Andreessen, margin call, Mark Zuckerberg, 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, South China Sea, special economic zone, stealth mode startup, Steve Jobs, Tesla Model S, Tim Cook: Apple, Uber and Lyft, uber lyft, universal basic income, urban planning, urban sprawl, 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. A more efficient distribution of bodies per vehicle through ride-sharing would improve traffic and speed up commutes.

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. The city government was “very enthusiastic” about the project, he said. And besides, even if governments were slow to embrace the technology, the advent of self-driving cars was inevitable. “That’s the mega trend. It’s definitely coming.” In Wang’s opinion, software will be the most important car spec of the future.

Shipping software start-up Flexport, backed by Google Ventures, wants to be the “Uber of the oceans.” 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. That October, a self-driving truck controlled by Otto’s technology and sponsored by Budweiser delivered a load of beer from Fort Collins, Colorado, to Colorado Springs—a 120-mile trip on Interstate 25.


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

AI winter, Albert Einstein, algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, 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, delayed gratification, discovery of DNA, Donald Trump, Douglas Engelbart, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, 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, John Conway, John Markoff, John von Neumann, Mark Zuckerberg, Minecraft, natural language processing, Netflix Prize, Norbert Wiener, orbital mechanics / astrodynamics, PageRank, pattern recognition, prediction markets, randomized controlled trial, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, 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!, X Prize, Yogi Berra

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

Waymo, the self-driving spin-off from Google, has invested $1 billion over 8 years and has constructed a secretive testing facility in California’s central valley with a 91-acre fake town, including fake bicycle riders and fake auto breakdowns.6 The goal is to broaden the training data to include special and unusual circumstances, called edge cases. Rare driving events that occur on highways often lead to accidents. The difference with self-driving cars is that when one car experiences a rare event, the learning experience will propagate to all other self-driving cars, a form of collective intelligence. Many similar test facilities are being constructed by other self-driving car companies. These create new jobs that did not exist before, and new supply chains for the sensors and lasers that are needed to guide the cars.7 Self-driving cars are just the most visible manifestation of a major shift in an economy being driven by information technology (IT). Information flows through the Internet like water through city pipes. Information accumulates in massive data centers run by Google, Amazon, Microsoft, and other IT companies that require so much electrical power that they need to be located near hydroelectric plants, and streaming information generates so much heat that it needs rivers to supply the coolant.

The same learning algorithm can be used to solve many difficult problems; its solutions are much less labor intensive than writing a different program for every problem. 4 Chapter 1 Learning How to Drive The $2 million cash prize for the Defense Advanced Research Projects Agency (DARPA) Grand Challenge in 2005 was won by Stanley, a self-driving car instrumented by Sebastian Thrun’s group at Stanford, who taught it how to navigate across the desert in California using machine learning. The 132-mile course had narrow tunnels and sharp turns, including Beer Bottle Pass, a winding mountain road with a sheer drop-off on one side and a rock face on the other (figure 1.1). 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.


pages: 416 words: 112,268

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

3D printing, Ada Lovelace, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Alfred Russel Wallace, Andrew Wiles, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, augmented reality, autonomous vehicles, basic income, blockchain, brain emulation, Cass Sunstein, Claude Shannon: information theory, complexity theory, computer vision, connected car, crowdsourcing, Daniel Kahneman / Amos Tversky, delayed gratification, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ernest Rutherford, Flash crash, full employment, future of work, Gerolamo Cardano, 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, Mark Zuckerberg, Nash equilibrium, Norbert Wiener, NP-complete, openstreetmap, P = NP, Pareto efficiency, Paul Samuelson, Pierre-Simon Laplace, positional goods, probability theory / Blaise Pascal / Pierre de Fermat, profit maximization, RAND corporation, random walk, Ray Kurzweil, recommendation engine, RFID, Richard Thaler, ride hailing / ride sharing, Robert Shiller, Robert Shiller, 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, Thales of Miletus, The Future of Employment, Thomas Bayes, Thorstein Veblen, transport as a service, Turing machine, Turing test, universal basic income, uranium enrichment, Von Neumann architecture, Wall-E, Watson beat the top human players on Jeopardy!, web application, zero-sum game

One of the most common patterns involves omitting something from the objective that you do actually care about. 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. It may even push other cars out of the way to gain a few seconds in the scrum at the airport terminal.

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. The human understands this signal and goes ahead, confident that there will be no collision.

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? No. There are several breakthroughs that have to happen before we have anything resembling machines with superhuman intelligence.


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

4chan, Affordable Care Act / Obamacare, Airbnb, Alvin Roth, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, barriers to entry, basic income, Bernie Madoff, Bernie Sanders, Bill Joy: nanobots, bitcoin, blockchain, Bretton Woods, Brewster Kahle, British Empire, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, Chuck Templeton: OpenTable:, Clayton Christensen, clean water, cloud computing, cognitive dissonance, collateralized debt obligation, commoditize, computer vision, corporate governance, corporate raider, creative destruction, crowdsourcing, Danny Hillis, data acquisition, deskilling, DevOps, Donald Davies, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Firefox, Flash crash, full employment, future of work, George Akerlof, gig economy, glass ceiling, Google Glasses, Gordon Gekko, gravity well, greed is good, Guido van Rossum, High speed trading, hiring and firing, Home mortgage interest deduction, Hyperloop, income inequality, 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 Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, Khan Academy, Kickstarter, knowledge worker, Kodak vs Instagram, Lao Tzu, Larry Wall, Lean Startup, Leonard Kleinrock, Lyft, Marc Andreessen, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, McMansion, microbiome, microservices, minimum viable product, mortgage tax deduction, move fast and break things, move fast and break things, Network effects, new economy, Nicholas Carr, obamacare, Oculus Rift, packet switching, PageRank, pattern recognition, Paul Buchheit, peer-to-peer, peer-to-peer model, Ponzi scheme, 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, 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, strong AI, TaskRabbit, telepresence, the built environment, 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, transaction costs, transcontinental railway, transportation-network company, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, ubercab, universal basic income, US Airways Flight 1549, VA Linux, 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

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. 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.

Siri’s responses, in a pleasing female voice, were the stuff of science fiction. 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.

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. More likely, the right answer will be to tune their mathematical models and algorithms to find the optimum mix of human and machine, in the same way that the electrical grid relies on coal, natural gas, or nuclear power for “base load” while meeting peak daytime demand with renewables.


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

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, augmented reality, autonomous vehicles, Brixton riot, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, Douglas Hofstadter, Elon Musk, Firefox, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta analysis, meta-analysis, 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, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche

.’ , YouTube, 22 Jan. 2014, https://www.youtube.com/watch?v=FaBJ5sPPmcI. 8. Alex Davies, ‘An oral history of the DARPA Grand Challenge, the grueling robot race that launched the self-driving car’, Wired, 8 March 2017, https://www.wired.com/story/darpa-grand-challenge-2004-oral-history/. 9. ‘Desert race too tough for robots’, BBC News, 15 March, 2004, http://news.bbc.co.uk/1/hi/technology/3512270.stm. 10. Davies, ‘An oral history of the DARPA Grand Challenge’. 11. Denise Chow, ‘DARPA and drone cars: how the US military spawned self-driving car revolution’, LiveScience, 21 March 2014, https://www.livescience.com/44272-darpa-self-driving-car-revolution.html. 12. Joseph Hooper, ‘From Darpa Grand Challenge 2004 DARPA’s debacle in the desert’, Popular Science, 4 June 2004, https://www.popsci.com/scitech/article/2004-06/darpa-grand-challenge-2004darpas-debacle-desert. 13.

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

Jason Kottke, Mercedes’ Solution to the Trolley Problem, Kottke.org, 24 Oct. 2016, https://kottke.org/16/10/mercedes-solution-to-the-trolley-problem. 35. Jean-François Bonnefon, Azim Shariff and Iyad Rahwan (2016), ‘The social dilemma of autonomous vehicles’, Science, vol. 35, 24 June 2016, DOI 10.1126/science.aaf2654; https://arxiv.org/pdf/1510.03346.pdf. 36. All quotes from Paul Newman are from private conversation. 37. Naaman Zhou, ‘Volvo admits its self-driving cars are confused by kangaroos’, Guardian, 1 July 2017, https://www.theguardian.com/technology/2017/jul/01/volvo-admits-its-self-driving-cars-are-confused-by-kangaroos. 38. All quotes from Jack Stilgoe are from private conversation. 39. Jeff Sabatini, ‘The one simple reason nobody is talking realistically about driverless cars’, Car and Driver, Oct. 2017, https://www.caranddriver.com/features/the-one-reason-nobody-is-talking-realistically-about-driverless-cars-feature. 40.


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

"Robert Solow", 3D printing, additive manufacturing, Affordable Care Act / Obamacare, AI winter, algorithmic trading, Amazon Mechanical Turk, artificial general intelligence, assortative mating, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Bernie Madoff, Bill Joy: nanobots, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chris Urmson, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, computer age, creative destruction, debt deflation, deskilling, disruptive innovation, diversified portfolio, Erik Brynjolfsson, factory automation, financial innovation, Flash crash, Fractional reserve banking, Freestyle chess, full employment, 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, knowledge worker, labor-force participation, liquidity trap, low skilled workers, low-wage service sector, Lyft, 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, Plutocrats, post scarcity, precision agriculture, price mechanism, Ray Kurzweil, rent control, rent-seeking, reshoring, RFID, Richard Feynman, Rodney Brooks, Sam Peltzman, secular stagnation, self-driving car, Silicon Valley, 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, Thomas L Friedman, too big to fail, Tyler Cowen: Great Stagnation, uber lyft, union organizing, Vernor Vinge, very high income, Watson beat the top human players on Jeopardy!, women in the workforce

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. Information on collision avoidance systems can be found at http://www.iihs.org/iihs/topics/t/crash-avoidance-technologies/qanda. 15. As quoted in Burkhard Bilger, “Auto Correct: Has the Self-Driving Car at Last Arrived?,” New Yorker, November 25, 2013, http://www.newyorker.com/reporting/2013/11/25/131125fa_fact_bilger?

Perhaps the most important thing to understand about a future in which your car is fully autonomous is that it probably won’t be your car. 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. Rather than spending 90 percent or more of their time parked, cars will see much higher utilization rates.

To avoid a feeling of being closed in, virtual windows could be mounted on the dividing walls; high resolution screens would display images captured by cameras mounted on the exterior of the car. By the time self-driving cars are in routine operation, the hardware to accomplish all this will be remarkably inexpensive. The vehicle would stop, a green light would flash on one of the doors, and you would get in and ride to your destination just as if you were traveling alone. You’d be sharing the vehicle, but riding in your own virtual commuter pod. Other vehicles might be designed to carry groups (or more sociable solo travelers), or perhaps the barriers could slide away upon mutual consent.* Then, again, the commuter pod might not need to be “virtual.” In May 2014, Google announced that the next phase of its research into self-driving cars would focus on the development of two-passenger electric vehicles with a top speed of 25 miles per hour and specifically geared toward urban environments.


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

"Robert Solow", 3D printing, Ada Lovelace, AI winter, Airbnb, artificial general intelligence, augmented reality, barriers to entry, basic income, bitcoin, blockchain, brain emulation, Buckminster Fuller, cloud computing, computer age, computer vision, correlation does not imply causation, credit crunch, cryptocurrency, cuban missile crisis, dematerialisation, discovery of the americas, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, everywhere but in the productivity statistics, Flash crash, friendly AI, Google Glasses, hedonic treadmill, 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, Mahatma Gandhi, means of production, mutually assured destruction, Nicholas Carr, pattern recognition, peer-to-peer, peer-to-peer model, Peter Thiel, Ray Kurzweil, 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, The Future of Employment, theory of mind, Turing machine, Turing test, universal basic income, Vernor Vinge, wage slave, Wall-E, zero-sum game

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. 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.

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. In 2014, Los Angeles generated $160m from parking violations, much of which could have to come from somewhere else in future.) The second wave of automation forecast by the Oxford Martin School report will affect jobs in the heartland of the middle and upper-middle class: professional occupations like medicine and the law, managerial jobs, and even in the arts.

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. Significant industries where Google has engaged by acquiring include smartphones (Android, Motorola), voice over IP telephony (GrandCentral, Phonetic Arts), intelligent house management (Nest Labs, Dropcam and Revolv), robotics (eight robot manufacturers acquired in 2013 alone), publishing (reCAPTCHA and eBook Technologies), banking (TxVia), music (Songza), and drones (Titan Aerospace).


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

Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, 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, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

Instead, the algorithm sees lots of examples from each category (fraudulent, not fraudulent), and it finds the patterns that distinguish one from the other. In AI, the role of the programmer isn’t to tell the algorithm what to do. 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.

Then a Bayesian search firm was hired, a map of probabilities was drawn up, and the plane was found within one week of undersea search.19 Moreover, the main idea of Bayes’s rule—updating your prior knowledge in light of new evidence—applies everywhere, not least behind the wheel of a self-driving car. Biologists use it to help understand the role of our genes in explaining cancer. Astronomers use it to find planets orbiting other stars on the outer fringes of our galaxy. It’s been used to detect doping at the Olympics, to filter spam from your in-box, and to help quadriplegics control robot arms directly with their minds, just like Luke Skywalker.20 And as you’ve seen, it’s essential for navigating the treacherous landscapes of health care and finance. So Bayes’s rule is much more than just a principle for finding what has been lost. Yes, it helped find the Scorpion, and it helps self-driving cars find themselves on the road. But it can also help you find wisdom in confronting the flood of information you face every day

Those words are sent to a search engine—itself a huge pipeline of algorithms that processes the query and sends back an answer. 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. First, these algorithms typically deal with probabilities rather than certainties.


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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, computerized trading, David Brooks, deliberate practice, deskilling, digital map, Douglas Engelbart, drone strike, Elon Musk, Erik Brynjolfsson, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, global supply chain, Google Glasses, Google Hangouts, High speed trading, indoor plumbing, industrial robot, Internet of things, 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, Lyft, 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, 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, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, TaskRabbit, technoutopianism, The Wealth of Nations by Adam Smith, turn-by-turn navigation, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche

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?,” Spiegel Online, July 31, 2009, spiegel.de/international/world/the-computer-vs-the-captain-will-increasing-automation-make-jets-less-safe-a-639298.html. 6.See Adam Fisher, “Inside Google’s Quest to Popularize Self-Driving Cars,” Popular Science, October 2013. 7.Tosha B. Weeterneck et al., “Factors Contributing to an Increase in Duplicate Medication Order Errors after CPOE Implementation,” Journal of the American Medical Informatics Association 18 (2011): 774–782. 8.Sergey V.

NOTES Introduction: ALERT FOR OPERATORS 1.Federal Aviation Administration, SAFO 13002, January 4, 2013, faa.gov/other_visit/aviation_industry/airline_operators/airline_safety/safo/all_safos/media/2013/SAFO13002.pdf. 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. Schweizer et al., “Brain Activity during Driving with Distraction: An Immersive fMRI Study,” Frontiers in Human Neuroscience, February 28, 2013, frontiersin.org/Human_Neuroscience/10.3389/fnhum.2013.00053/full. 6.N.

Outfitted with laser range-finders, radar and sonar transmitters, motion detectors, video cameras, and GPS receivers, the car can sense its surroundings in minute detail. It can see where it’s going. 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.


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

Air France Flight 447, autonomous vehicles, Captain Sullenberger Hudson, Charles Lindbergh, Chris Urmson, digital map, disruptive innovation, drone strike, en.wikipedia.org, Erik Brynjolfsson, fudge factor, index card, John Markoff, low earth orbit, Mars Rover, ride hailing / ride sharing, Ronald Reagan, self-driving car, Silicon Valley, telepresence, telerobotics, trade route, US Airways Flight 1549, William Langewiesche, zero-sum game

“We want to make cars that are better than drivers”: Burkhard Bilger, “Auto Correct,” The New Yorker, November 25, 2013, http://www.newyorker.com/reporting/2013/11/25/131125fa_fact_bilger?currentPage=all. “without traffic accidents or congestion”: Sebastian Thrun, “Self-Driving Cars Can Save Lives, and Parking Spaces,” New York Times, December 5, 2011, http://www.nytimes.com/2011/12/06/science/sebastian-thrun-self-driving-cars-can-save-lives-and-parking-spaces.html. Sebastian Thrun, “What We’re Driving At,” Google official blog, http://googleblog.blogspot.com/2010/10/what-were-driving-at.html, accessed July 10, 2014. John Markoff, “A Trip in a Self-Driving Car Now Seems Routine,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/13/a-trip-in-a-self-driving-car-now-seems-routine, accessed July 10, 2014. John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html.

John Markoff, “Police, Pedestrians and the Social Ballet of Merging: The Real Challenges for Self-Driving Cars,” Bits Blog, http://bits.blogs.nytimes.com/2014/05/29/police-bicyclists-and-pedestrians-the-real-challenges-for-self-driving-cars/, accessed July 10, 2014. We know that driverless cars will be susceptible: John Markoff, “Collision in the Making Between Self-Driving Cars and How the World Works,” New York Times, January 23, 2012, http://www.nytimes.com/2012/01/24/technology/googles-autonomous-vehicles-draw-skepticism-at-legal-symposium.html. Will Knight, “Proceed with Caution toward the Self-Driving Car,” MIT Technology Review, April 16, 2013, http://www.technologyreview.com/review/513531/proceed-with-caution-toward-the-self-driving-car/. M. L. Cummings and Jason Ryan, “Shared Authority Concerns in Automated Driving Applications,” Journal of Ergonomics, S3:001. doi:10.4172/2165-7556.S3-001 how will they rush into the loop quickly enough: Bianca Bosker, “No One Understands the Scariest, Most Dangerous Part of a Self-Driving Car: Us,” Huffington Post, September 16, 2013, accessed July 10, 2014.

., “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. He put a video camera on the dashboard of his car: John Leonard, “Conversations on Autonomy,” presentation, MIT, March 13, 2014. 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.


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

3D printing, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Andrei Shleifer, autonomous vehicles, barriers to entry, call centre, cashless society, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, disruptive innovation, Donald Trump, Erik Brynjolfsson, full employment, future of work, George Akerlof, gig economy, global supply chain, hiring and firing, income inequality, information asymmetry, invisible hand, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kickstarter, low skilled workers, Lyft, 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, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, Simon Singh, software as a service, Steve Jobs, TaskRabbit, 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, 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.

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? As the Financial Times concludes, ‘this sort of think- ing fundamentally mis-assesses the economics of the car market’.76 Workers, too, suffer from the gig economy’s threat to innovation.

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. As long as the routine nature of the task is central to its automation, however, technology is likely to advance much more rapidly in other sectors of the economy.14 Consider legal discovery and due diligence as an example: once the preserve of well-paid junior lawyers, locked away for weeks on end to wade through crates of documents, it has quickly become dominated by language- and pattern-recognition software.15 For now, it seems more likely that automation will drive further gig- economy growth.


pages: 386 words: 113,709

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

1960s counterculture, Airbus A320, airport security, augmented reality, autonomous vehicles, Bernie Sanders, Boeing 737 MAX, British Empire, Burning Man, call centre, collective bargaining, crony capitalism, deskilling, digital map, don't be evil, Donald Trump, Elon Musk, en.wikipedia.org, Fellow of the Royal Society, gig economy, Google Earth, hive mind, income inequality, informal economy, Internet of things, Jane Jacobs, labour mobility, Lyft, Network effects, New Journalism, New Urbanism, Nicholas Carr, Ponzi scheme, 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, technoutopianism, the built environment, The Death and Life of Great American Cities, the High Line, too big to fail, traffic fines, Travis Kalanick, 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?hp&action=click&pgtype=Homepage&clickSource=story-heading&module=first-column-region&region=top-news &WT.nav=top-news&_r=0.

hp&action=click&pgtype=Homepage&clickSource=story-heading&module=first-column-region&region=top-news &WT.nav=top-news&_r=0. Mike Isaac, “Uber Defies California Regulators with Self-Driving Car Service,” New York Times, December 16, 2016, https://www.nytimes.com/2016/12/16/technology/uber-defies-california-regulators-with-self-driving-car-service.html. 9.John Harris, “With Trump and Uber, the Driverless Future Could Turn into a Nightmare,” Guardian, December 16, 2016, https://www.theguardian.com/commentisfree/2016/dec/16/trump-uber-driverless-future-jobs-go. 10.These are the findings of the city’s transport department as characterized by Nicole Gelinas in “Why Uber’s Investors May Lose Their Lunch,” New York Post, December 26, 2017, available at https://www.manhattan-institute.org/html/why-ubers-investors-may-lose-their-lunch-10847.html. 11.

The boosters of driverless cars are unimpressed with pleasure as an ideal and suspicious of individual judgment. 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. Or by the portioning out of scarce road surface, with preference given to driverless cars.


pages: 688 words: 147,571

Robot Rules: Regulating Artificial Intelligence by Jacob Turner

Ada Lovelace, Affordable Care Act / Obamacare, AI winter, algorithmic trading, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, autonomous vehicles, Basel III, bitcoin, blockchain, brain emulation, Clapham omnibus, cognitive dissonance, corporate governance, corporate social responsibility, correlation does not imply causation, crowdsourcing, distributed ledger, don't be evil, Donald Trump, easy for humans, difficult for computers, effective altruism, Elon Musk, financial exclusion, financial innovation, friendly fire, future of work, hive mind, Internet of things, iterative process, job automation, John Markoff, John von Neumann, Loebner Prize, medical malpractice, Nate Silver, natural language processing, nudge unit, obamacare, off grid, pattern recognition, Peace of Westphalia, race to the bottom, Ray Kurzweil, 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, 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.

See, for example, Joel Achenbach, “Driverless Cars Are Colliding with the Creepy Trolley Problem”, Washington Post, 29 December 2015, https://​www.​washingtonpost.​com/​news/​innovations/​wp/​2015/​12/​29/​will-self-driving-cars-ever-solve-the-famous-and-creepy-trolley-problem/​?​utm_​term=​.​30f91abdad96, accessed 1 June 2018; Jean-François Bonnefon, Azim Shariff, and Iyad Rahwan, “The Social Dilemma of Autonomous Vehicles”, Cornell University Library Working Paper, 4 July 2016, https://​arxiv.​org/​abs/​1510.​03346, accessed 1 June 2018. 107The scenario involving a criminal pedestrian was posed by researchers at MIT, in their “Moral Machine” game, which is described by its designers as “A platform for gathering a human perspective on moral decisions made by machine intelligence, such as self-driving cars. We show you moral dilemmas, where a driverless car must choose the lesser of two evils, such as killing two passengers or five pedestrians.

See also the decision of the majority in R (Nicklinson) v. 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.


pages: 533

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

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, 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, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, 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 Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

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

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

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


pages: 345 words: 75,660

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

"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

So if yogurt demand was suddenly shifting with demographics or some other fad, new data will help you improve the algorithm. 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. Silke Januszewski Forbes and Mara Lederman, “Adaptation and Vertical Integration in the Airline Industry,” American Economic Review 99, no. 5 (December 2009): 1831–1849. 3.

That other drivers could show up with “The Knowledge” on their phones and predictions of the fastest routes meant they could provide equivalent service. 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? Until now, we’ve been a little imprecise about what judgment actually is. To explain it, we introduce a decision-making tool: the decision tree.2 It is especially useful for decisions under uncertainty, when you are not sure what will happen if you make a particular choice.

Of course, because experimentation necessarily means making what you will later regard as mistakes, experiments also have costs. 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. For instance, before Google acquired it, the Israeli startup Waze generated accurate traffic maps by tracking the routes drivers chose.


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

Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic trading, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, book scanning, borderless world, call centre, cellular automata, Claude Shannon: information theory, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, drone strike, Elon Musk, Flash crash, friendly AI, game design, global village, Google X / Alphabet X, 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, Marc Andreessen, Mark Zuckerberg, Menlo Park, natural language processing, Norbert Wiener, out of africa, PageRank, pattern recognition, Ray Kurzweil, recommendation engine, remote working, RFID, 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, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, too big to fail, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

In the end, the car drove 2,797 miles coast-to-coast from Pittsburgh, Pennsylvania to San Diego, California – including a crossing of the Hoover Dam carried out autonomously. In one memorable highlight, a Businessweek reporter who was covering the event was pulled over by a Kansas State Trooper. Pomerleau and Jochem sailed by in their self-driving car, hands exaggeratedly off the steering wheel. It would be another fifteen years, until October 2010, before Google announced its own self-driving car initiative. However, thanks to his groundbreaking work in neural nets, Dean Pomerleau had proved his point. Welcome to Deep Learning The next significant advance for neural networks took place in the mid-2000s. In 2005, Geoff Hinton was working at the University of Toronto, having recently returned from setting up the Gatsby Computational Neuroscience Unit at University College London.

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. We are already starting to see how this may work. In early 2015, the Google-owned traffic app Waze teamed up with the city of Boston to reduce local traffic.

In most cases, investment firms trained separate networks for different stocks, with human traders then deciding which to invest in. 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. In 1969, John McCarthy came closest to describing modern self-driving vehicles when he wrote an essay with the provocative title, ‘Computer-Controlled Cars’.


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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, Chris Urmson, collaborative consumption, commoditize, crowdsourcing, DARPA: Urban Challenge, dematerialisation, Elon Musk, en.wikipedia.org, Google Hangouts, Induced demand, intermodal, invention of the printing press, jitney, John Markoff, labor-force participation, lifelogging, Lyft, means of production, megacity, Menlo Park, Network effects, Occam's razor, oil shock, place-making, post-work, Ray Kurzweil, rent-seeking, ride hailing / ride sharing, Robert Gordon, self-driving car, sharing economy, Silicon Valley, Skype, smart cities, technological singularity, Tesla Model S, the built environment, 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?op=1 700,000 http://googleblog.blogspot.co.uk/2014/04/the-latest-chapter-for-self-driving-car.html Nearly a million http://googleblog.blogspot.com/2015/05/self-driving-vehicle-prototypes-on-road.html 1,498,21 Miles in Autonomous Mode https://static.googleusercontent.com/media/www.google.com/en//selfdrivingcar/files/reports/report-0316.pdf 158 Alexis Madrigal @ The Atlantic: How Google Builds Its Maps—and What It Means for the Future of Everything – Technology : http://www.theatlantic.com/technology/print/2012/09/how-google-builds-its-maps-and-what-it-means-for-the-future-of-everything/261913/ 159 Strengths of using maps include a better understanding of the environment.

In this race, Carnegie Mellon took first (4 hours) and Stanford was second (4.5 hours). 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).

Eventually it will lead, as newer and more capable AVs are used more frequently than non-AV vehicles. 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. Automated vehicles are probably already legal in most US states (New York requires hands on the wheel),167 so the burden of proof is on those who want to slow them down.


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, disruptive innovation, Edward Snowden, Erik Brynjolfsson, intermodal, Internet of things, Jeff Bezos, knowledge worker, lifelogging, Mark Zuckerberg, move fast and break things, 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

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

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

Her notes suggest that insurance will never be the same after massive data, and neither will the experience of traveling to learn about the coming changes. (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. He is in charge of facilities and energy management for Bathworks’s twenty office campuses and facilities around the country.


pages: 245 words: 64,288

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, 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, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, 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, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, technological singularity, Turing test, Vernor Vinge, 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. By utilising neural networks and other sophisticated machine-learning algorithms, an immense quantity of data, and thanks to the power of exponentially-increasing technologies that made computation cheaper and faster, as well as sensors, GPS, and laser systems, Google now had a working prototype of a car that drives without the need for a human driver.

Traffic congestions decreased, the number of accidents fell significantly. 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?” dominate the news arena. The families of the victims are interviewed on national TV, their pain and anger fermented the hatred towards machines that had been dormant up until then.

I cannot wait to finally get one of those” - said one of the people I interviewed - “It is pretty obvious that human drivers are going to disappear very soon”. 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. After a few years of research and development, sometimes even just a few months, they have a working prototype.


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

23andMe, affirmative action, algorithmic trading, Alvin Roth, Bayesian statistics, bitcoin, cloud computing, computer vision, crowdsourcing, Edward Snowden, Elon Musk, Filter Bubble, general-purpose programming language, 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, speech recognition, statistical model, Stephen Hawking, superintelligent machines, telemarketer, Turing machine, two-sided market, Vilfredo Pareto

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. 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.

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. From the Moral Machine project at MIT.

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. So while you might have an incentive to disregard Maxwell’s recommendation of a slower route on any given trip (which you might well discover by using Google Maps to see your selfish best-response route), you know that over time you will benefit from following Maxwell’s suggestions (as long as others do as well).


pages: 389 words: 119,487

21 Lessons for the 21st Century by Yuval Noah Harari

1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon-based life, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, deglobalization, Donald Trump, failed state, 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, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-work, purchasing power parity, race to the bottom, RAND corporation, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, transatlantic slave trade, 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. Which means that when designing their self-driving car, Toyota or Tesla will be transforming a theoretical problem in the philosophy of ethics into a practical problem of engineering.

Rather, individual humans are likely to be replaced by an integrated network. 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. When two such vehicles approach the same junction, they are not really two separate entities – they are part of a single algorithm.

For example, suppose two kids chasing a ball jump right in front of a self-driving car. Based on its lightning calculations, the algorithm driving the car concludes that the only way to avoid hitting the two kids is to swerve into the opposite lane, and risk colliding with an oncoming truck. The algorithm calculates that in such a case there is a 70 per cent chance that the owner of the car – who is fast asleep in the back seat – would be killed. What should the algorithm do?16 Philosophers have been arguing about such ‘trolley problems’ for millennia (they are called ‘trolley problems’ because the textbook examples in modern philosophical debates refer to a runaway trolley car racing down a railway track, rather than to a self-driving car).17 Up till now, these arguments have had embarrassingly little impact on actual behaviour, because in times of crisis humans all too often forget about their philosophical views and follow their emotions and gut instincts instead.


pages: 590 words: 152,595

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

active measures, Air France Flight 447, algorithmic trading, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, brain emulation, Brian Krebs, cognitive bias, computer vision, cuban missile crisis, dark matter, DARPA: Urban Challenge, DevOps, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, fault tolerance, Flash crash, Freestyle chess, friendly fire, IFF: identification friend or foe, ImageNet competition, Internet of things, Johann Wolfgang von Goethe, John Markoff, Kevin Kelly, Loebner Prize, loose coupling, Mark Zuckerberg, moral hazard, mutually assured destruction, Nate Silver, 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, Stuxnet, superintelligent machines, Tesla Model S, The Signal and the Noise by Nate Silver, theory of mind, Turing test, universal basic income, Valery Gerasimov, Wall-E, William Langewiesche, Y2K, zero day

The furthest any car got was 7.4 miles, only 5 percent of the way through the course. 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. From 2013 to 2015, DARPA held a Robotics Challenge to advance the field of humanoid robotics, running robots through a set of tasks simulating humanitarian relief and disaster response.

Senior Russian military commanders envision that in the near future a “fully robotized unit will be created, capable of independently conducting military operations,” while the U.S. 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? New types of AI like deep learning neural networks have shown startling advances in visual object recognition, facial recognition, and sensing human emotions.

Davis resets the battlefield for Round 2 and the swarms return to their respective corners. 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. Unshackled from the physiological limits of humans, uninhabited (“unmanned”) vehicles can be made smaller, lighter, faster, and more maneuverable.


pages: 252 words: 78,780

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

Airbnb, Amazon Web Services, Apple II, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, business process, call centre, Clayton Christensen, clean water, collective bargaining, corporate governance, corporate social responsibility, creative destruction, cryptocurrency, David Heinemeier Hansson, Donald Trump, Elon Musk, Ethereum, ethereum blockchain, full employment, future of work, gig economy, Gordon Gekko, greed is good, hiring and firing, housing crisis, income inequality, informal economy, Jeff Bezos, job automation, job satisfaction, job-hopping, John Gruber, Joseph Schumpeter, Kevin Kelly, knowledge worker, Lean Startup, loose coupling, Lyft, Marc Andreessen, Mark Zuckerberg, McMansion, Menlo Park, Milgram experiment, minimum viable product, Mitch Kapor, move fast and break things, move fast and break things, new economy, Panopticon Jeremy Bentham, Paul Graham, paypal mafia, Peter Thiel, plutocrats, Plutocrats, precariat, RAND corporation, remote working, RFID, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Ruby on Rails, Sam Altman, Sand Hill Road, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, six sigma, Skype, Social Responsibility of Business Is to Increase Its Profits, software is eating the world, Stanford prison experiment, stem cell, Steve Jobs, Steve Wozniak, Stewart Brand, TaskRabbit, 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, Whole Earth Catalog, Y Combinator, young professional

Tesla’s soaring stock is held up mostly by hype. CEO Elon Musk is a masterful marketer, a genius at generating buzz. 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. In ten years, people won’t buy cars based on which model has the most horsepower or the nicest leather seats.

New Yorker, October 12, 2015. https://www.newyorker.com/magazine/2015/10/12/the-network-man. Miller, Michael E. “‘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. Osnos, Evan.

Fields has announced plans to bulldoze parts of the drab old Ford campus and build a new campus that looks like the Googleplex. 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 Ford execs wear jeans and give casual talks about coping with change and disruption. Dan Ariely, a famous TED Talk guy, gives a TED-style talk.


pages: 301 words: 89,076

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

agricultural Revolution, Airbnb, AltaVista, Amazon Web Services, augmented reality, autonomous vehicles, basic income, 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, David Ricardo: comparative advantage, declining real wages, deindustrialization, deskilling, Donald Trump, Douglas Hofstadter, Downton Abbey, Elon Musk, Erik Brynjolfsson, facts on the ground, future of journalism, future of work, George Gilder, Google Glasses, Google Hangouts, hiring and firing, impulse control, income inequality, industrial robot, intangible asset, Internet of things, invisible hand, James Watt: steam engine, Jeff Bezos, job automation, knowledge worker, laissez-faire capitalism, low skilled workers, 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, 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, Ronald Reagan, 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, TaskRabbit, telepresence, telepresence robot, telerobotics, Thomas Malthus, trade liberalization, universal basic income

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. Many of today’s most innovative products, apps and systems, including Uber, Airbnb, and Upwork.com are mostly mash-ups of existing digital components.

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

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


pages: 413 words: 119,587

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

"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, 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, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

Hagerty, “A Roboticist’s Trip from Mines to the Moon,” Wall Street Journal, July 2, 2011, http://www.wsj.com/articles/SB10001424052702304569504576405671616928518. 4.John Markoff, “The Creature That Lives in Pittsburgh,” New York Times, April 21, 1991, http://www.nytimes.com/1991/04/21/business/the-creature-that-lives-in-pittsburgh.html. 5.John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html?pagewanted=all. 6.“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.

The philosophers convinced him that there were real limits to the capabilities of intelligent machines. 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. More recently at Stanford Winograd helped create an academic program focusing on “Liberation Technologies,” which studies the construction of computerized systems based on human-centered values.

Vehicles tipped over, drove in circles, and ignominiously knocked down fences. 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. Stanley crested a rise in the desert and plunged smartly into a swale.


pages: 424 words: 114,905

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

23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, augmented reality, autonomous vehicles, bioinformatics, blockchain, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, conceptual framework, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, David Brooks, digital twin, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fault tolerance, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, microbiome, natural language processing, new economy, Nicholas Carr, nudge unit, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, speech recognition, Stephen Hawking, text mining, the scientific method, Tim Cook: Apple, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

As you might anticipate, companies are not enthusiastic about government regulation; many firms, including Microsoft and Google, have set up their own internal ethics boards, arguing that regulatory involvement might be counterproductive, delaying the adoption of self-driving cars over fringe issues when it already seems clear that autonomous vehicles will reduce traffic fatalities overall. But we don’t think of it in the big picture way. More than 1.25 million people are killed by human drivers each year, most by human error, but we as a society don’t bat an eye at the situation.62 The introduction of computers into the mix sets up a cognitive bias, not acknowledging the net benefit. When a self-driving car kills a person, there’s an outcry over the dangers of self-driving cars. The first fatality of a pedestrian hit by a driverless car occurred in an Uber program in Arizona in 2018. The car’s algorithm detected a pedestrian crossing the road in the dark but did not stop, and the human backup driver did not react because she trusted the car too much.63 Here, ironically, I would question the ethics of the company, rather than AI per se, for prematurely pushing the program forward without sufficient testing and human driver backup.

That is a long way off, estimated to be decades, if ever attainable.59 Level 4 means the car is autonomous in most conditions, without the possibility for human backup. 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. The integrated, multitasking deep learning tracks other cars, pedestrians, and lane markings.

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


pages: 116 words: 31,356

Platform Capitalism by Nick Srnicek

3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, collaborative economy, collective bargaining, deindustrialization, deskilling, disintermediation, future of work, gig economy, Infrastructure as a Service, Internet of things, Jean Tirole, Jeff Bezos, knowledge economy, knowledge worker, liquidity trap, 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, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, software as a service, TaskRabbit, the built environment, total factor productivity, two-sided market, Uber and Lyft, Uber for X, uber lyft, unconventional monetary instruments, unorthodox policies, 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. The trend to convergence is igniting international competition as well: intense struggles occur in India and China over who will dominate the ride-sharing industry (Uber, Didi, Lyft) and who will dominate e-commerce (Amazon, Alibaba, Flipkart).

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. In the early years of the century it was hardly clear, however, that data would become the raw material to jumpstart a major shift in capitalism.9 The incipient efforts by Google simply used data to draw advertising revenues away from traditional media outlets like newspapers and television.

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


pages: 242 words: 73,728

Give People Money by Annie Lowrey

"Robert Solow", affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, airport security, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, clean water, collective bargaining, computer age, crowdsourcing, cryptocurrency, deindustrialization, desegregation, Donald Trump, Edward Glaeser, Elon Musk, ending welfare as we know it, everywhere but in the productivity statistics, full employment, gender pay gap, 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, mortgage tax deduction, new economy, obamacare, Peter Thiel, post scarcity, post-work, Potemkin village, precariat, randomized controlled trial, ride hailing / ride sharing, Robert Bork, Ronald Reagan, Sam Altman, self-driving car, Silicon Valley, single-payer health, Steve Jobs, TaskRabbit, 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, 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

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. * * * 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.

But what is so great about having a self-driving car if you have no job, your neighbor has no job, and your town is slashing the school budget for the third time in four years? What if there is no need for humans, because the robots have gotten so good? Detroit again offers a pretty good encapsulation of the argument. Cars are undergoing a profound technological shift, transforming from mechanical gadgets to superpowered computers with the potential to revolutionize every facet of transit. Billions of dollars are being spent to rush driverless vehicles into the hands of consumers and businesses. Yet the total employment gains from this revolutionary technology amount to perhaps a few tens of thousands of jobs. Robots are designing and building these new self-driving cars, not just driving them. That same dynamic is writ large around the country.

Add in school bus drivers, municipal bus drivers, cross-country bus drivers, delivery drivers, limo drivers, cabdrivers, long-haul truckers, and port workers. 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. Elon Musk of Tesla, for instance, has argued that the large-scale automation of the transportation sector is imminent.


pages: 268 words: 75,850

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

3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, commoditize, computer age, death of newspapers, deferred acceptance, disruptive innovation, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kodak vs Instagram, lifelogging, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, Panopticon Jeremy Bentham, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, 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. Using satellite feeds, or cameras designed to automatically detect and read road signs, vehicles could be forced to conform to speed limits.

“Do Robots Dream of Electric Laws: An Experiment in the Law as Algorithm,” March 29, 2013. rumint.org/gregconti/publications/201303_AlgoLaw.pdf. 43 Reiser, Stanley. 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. “Penal Code.” New Scientist, September 7, 2013. 48 Hook, P.

Both at law and in the research “laboratory,” the technology of the blood level sample and the Breathalyzer meant a definitive and easily validated measure of the amount of alcohol in the blood and, consequently, an accentuated law enforcement and a higher expectancy of convictions.44 In other words, the arrival of the Breathalyzer turned a person’s ability to drive after several drinks from abstract “standard” into concrete “rule” in the eyes of the law. 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.


pages: 23 words: 5,264

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

AltaVista, 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.

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


pages: 332 words: 100,601

Rebooting India: Realizing a Billion Aspirations by Nandan Nilekani

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

http://articles.economictimes.indiatimes.com/2014-06-04/news/50330087_1_skymet-monsoon-arrival-imd 15. DARPA Urban Challenge 2005. 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. ‘Coming to a street near you’. Economist. http://www.economist.com/news/business-and-finance/21618531-making-autonomous-vehicles-reality-coming-street-near-you 18.

Private weather forecasters like Skymet and the Bangalore-based Citizen Weather Network are entering a domain that was until recently the exclusive preserve of the Indian Meteorological Department (IMD).13 The holy grail of weather forecasting in India is predicting the onset of the annual monsoon season, and Skymet has already clashed with the IMD by releasing monsoon predictions and analyses that differ from the IMD’s interpretation.14 Much like GPS, a more recent instance of a military technology being opened to the public is that of autonomous vehicles. Through DARPA—coincidentally the agency that also birthed the internet—the US government has been funding such endeavours for over a decade.15 The underlying technology has now entered the commercial space; Google is testing self-driving cars using its Google Chauffeur platform, Uber has just announced an academic collaboration with Carnegie Mellon University to ‘develop driverless car and mapping technology’, and Apple is reportedly investigating technologies for building electric and self-driving cars.16 While we may not see a fleet of self-driving cars taking over our streets in the near future, it’s worthwhile to consider that various US state governments are already starting to pass laws that permit driverless cars to operate on state roads.17 Once again, government regulations need to anticipate innovation by keeping a close eye on emerging trends and assessing their potential impact and chances of widespread adoption.

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


pages: 285 words: 58,517

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

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

With the increased prominence of intangible assets, tangible assets are declining proportionally. 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. The hope is that an asset-light strategy will enable greater market flexibility and focus on the core business, which is property management and not real estate.

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. Whereas the first nine principles emphasize specific shifts that network orchestrators make in order to better enable their outward-looking, co-creative business models, the final principle is about your own openness to making these shifts and to taking in and adapting to new information in general—whether it’s from your customers, employee groups, or the market.

Open organizations don’t need to own everything and keep it within their walls; they can access assets that exist outside the organization. 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. As Page said in the Alphabet announcement, “We’ve long believed that over time companies tend to get comfortable doing the same thing, just making incremental changes.


pages: 237 words: 64,411

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

Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Bob Noyce, Brian Krebs, business cycle, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, 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, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, The Future of Employment, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

The split-second decisions these contraptions will have to make pose ethical questions that have bedeviled deep thinkers for millennia. 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. What should “one per customer” mean when a robot is the customer, and I own a whole fleet of them?

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

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


pages: 339 words: 88,732

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

"Robert Solow", 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, 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, British Empire, business cycle, business intelligence, business process, call centre, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, digital map, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, 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, global village, 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, 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, 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, pattern recognition, Paul Samuelson, payday loans, post-work, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Rodney Brooks, Ronald Reagan, 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 Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen: Great Stagnation, Vernor Vinge, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

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

Murnane, The New Division of Labor: How Computers Are Creating the Next Job Market (Princeton, NJ: Princeton University Press, 2004). 2. Michael Polanyi, The Tacit Dimension (Chicago, IL: University of Chicago Press, 2009), p. 4. 3. Joseph Hooper, “DARPA’s Debacle in the Desert,” Popular Science, June 4, 2004, http://www.popsci.com/scitech/article/2004-06/darpa-grand-challenge-2004darpas-debacle-desert. 4. Mary Beth Griggs, “4 Questions About Google’s Self-Driving Car Crash,” Popular Mechanics, August 11, 2011, http://www.popularmechanics.com/cars/news/indus try/4-questions-about-googles-self-driving-car-crash; John Markoff, “Google Cars Drive Themselves, in Traffic,” New York Times, October 9, 2010, http://www.nytimes.com/2010/10/10/science/10google.html. 5. Ernest Hemingway, The Sun Also Rises (New York: HarperCollins, 2012), p. 72. 6. Levy and Murnane, The New Division of Labor, p. 29. 7. “Siri Is Actually Incredibly Useful Now,” Gizmodo, accessed August 4, 2013, http://gizmodo.com/5917461/siri-is-better-now. 8.

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


pages: 339 words: 94,769

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

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, Danny Hillis, David Graeber, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, finite state, friendly AI, future of work, Geoffrey West, Santa Fe Institute, gig economy, income inequality, industrial robot, information retrieval, invention of writing, James Watt: steam engine, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Loebner Prize, market fundamentalism, Marshall McLuhan, Menlo Park, Norbert Wiener, optical character recognition, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, telemarketer, telerobotics, the scientific method, theory of mind, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, zero-sum game

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

We’re now in the deep-learning era, which is delivering on many of the early AI promises but in a way that’s considered hard to understand, with consequences ranging from intellectual to existential threats. Each of these stages was heralded as a revolutionary advance over the limitations of its predecessors, yet all effectively do the same thing: They make inferences from observations. How these approaches relate can be understood by how they scale—that is, how their performance depends on the difficulty of the problem they’re addressing. Both a light switch and a self-driving car must determine their operators’ intentions, but the former has just two options to choose from, whereas the latter has many more. The AI-boom phases have started with promising examples in limited domains; the bust phases came with the failure of those demonstrations to handle the complexity of less-structured, practical problems. Less apparent is the steady progress we’ve made in mastering scaling.

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


pages: 195 words: 52,701

Better Buses, Better Cities by Steven Higashide

Affordable Care Act / Obamacare, autonomous vehicles, business process, congestion charging, decarbonisation, Elon Musk, Hyperloop, income inequality, intermodal, jitney, Lyft, mass incarceration, Pareto efficiency, performance metric, place-making, self-driving car, Silicon Valley, six sigma, smart cities, transportation-network company, Uber and Lyft, Uber for X, uber lyft, urban planning, urban sprawl, walkable city, white flight, young professional

Joey Garrison, “Nashville Transit Referendum: 6 Reasons Why It Lost Big.” The Tennessean, May 2 2018. https://​www.tennessean.com/​story/​news/​2018/​05/​02/​nashville-transit-referendum-6-reasons-why-lost-big/​571782002/ 3. Bryan Salesky, “A Decade after DARPA: Our View on the State of the Art in Self-Driving Cars.” Medium, October 16, 2017. https://​medium.com/​self-driven/​a-decade-after-darpa-our-view-on-the-state-of-the-art-in-self-driving-cars-3e8698e6afe8 4. Aarian Marshall, “After Peak Hype, Self-Driving Cars Face the Trough of Disillusionment.” WIRED, December 29, 2017. https://​www.wired.com/​story/​self-driving-cars-challenges/ 5. Shara Tibken, “Waymo CEO: Autonomous Cars Won’t Ever Be Able to Drive in All Conditions.” CNET, November 13, 2018. https://​www.cnet.com/​news/​alphabet-google-waymo-ceo-john-krafcik-autonomous-cars-wont-ever-be-able-to-drive-in-all-conditions/ 6.

The evidence suggests that will happen only if cities buy the snake oil. Distraction and Hype Those who are closest to autonomous vehicle technology say it will be a long time, if ever, before fully autonomous vehicles can coexist on streets with pedestrians and human drivers. In a 2017 blog post, the CEO of Argo AI, which has received $1 billion from Ford to work on self-driving technology, wrote that “those who think self-driving cars will be ubiquitous on city streets . . . in a few years are not well connected to the state of the art or committed to the safe deployment of the technology.”3 That same year, a prominent investor in autonomous vehicle startups concluded that “autonomous technology is currently where computing was in the 60s.”4 Waymo, a subsidiary of Alphabet, Google’s parent company, is considered (along with General Motors) one of the leaders in the autonomous vehicle industry.


pages: 300 words: 76,638

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

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

Self-driving trucks successfully made deliveries in Nevada and Colorado in 2017. Rio Tinto has 73 autonomous mining trucks hauling iron ore 24 hours a day in Australia. Europe saw its first convoys of self-driving trucks cross the continent in 2016. In 2016 Uber bought the self-driving truck company Otto for $680 million and now employs 500 engineers to perfect the technology. Google spun off its self-driving car company Waymo, which is working on self-driving trucks with the big truck manufacturers Daimler and Volvo. Jim Scheinman, a venture capitalist at Maven Ventures who has backed startups in both autonomous trucks and cars, says that self-driving trucks will arrive significantly before cars because highway driving is so much easier. Highways, the domain of semi trucks, are much less complex than urban areas, with fewer intersections and clearer road markings.

Department of Transportation is throwing its full support behind development of autonomous vehicles as a way to improve safety on our roadways.” In 2016 the trucking industry spent $9.1 million on lobbying, and the Ohio government has already committed $15 million to set up a 35-mile stretch of highway outside Columbus for testing self-driving trucks. Arizona, California, and Nevada have begun allowing self-driving car trials in their states, and others will follow. Will truckers and the industry fight back? Back in the 1950s, truckers were highly unionized, with the Teamsters being legendary in their aggressiveness. Today, only about 13 percent of U.S. truckers are unionized, and 90 percent of the trucking industry is made up of small businesses with 10 or fewer trucks. About 10 percent of truck drivers—350,000—are solo owner operators who own their own trucks; the trucking companies have been pushing drivers to buy or lease their own trucks to reduce overhead.

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


pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Robert Solow", autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Bretton Woods, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, David Ricardo: comparative advantage, decarbonisation, dematerialisation, Donald Trump, double helix, Elon Musk, energy transition, Erik Brynjolfsson, financial independence, Francis Fukuyama: the end of history, future of work, G4S, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, liberal capitalism, low earth orbit, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private space industry, Productivity paradox, profit motive, race to the bottom, RFID, rising living standards, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, stem cell, Stewart Brand, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, working-age population

Register, 23 January 2015. Autonomous Vehicles Balakrishnan, Anita. ‘Drivers Could Lose up to 25,000 Jobs per Month when Self-Driving Cars Hit, Goldman Sachs Says’. CNBC, 22 May 2017. Bomey, Nathan. ‘US Vehicle Deaths Topped 40,000 in 2017, National Safety Council Estimates’. USA Today, 15 February 2018. Darter, Michael. ‘DARPA’s Debacle in the Desert’. Popular Science, 4 June 2004. Dillow, Clay. ‘Revealed: Google’s Car Fleet Has Been Driving around Unmanned for 140,000 Miles Already’. Popular Science, 11 October 2010. Ford, Martin. The Rise of the Robots: Technology and the Threat of Mass Unemployment. Oneworld, 2017. Marshall, Aarian. ‘As Uber Flails, Its Self-driving Car Research Rolls On’. Wired, 23 June 2017. Thrun, Sebastian. ‘What We’re Driving At’. Official Google Blog, 9 October 2010.

The ‘winner’, built by Carnegie Mellon University, was only able to successfully navigate 5 per cent of the route. While the challenge had been ambitious – after all, the point was to stretch the entrants’ abilities – few thought it would descend into such farce. One observer even labelled the episode ‘the debacle in the desert’. To any reasonable person the possibility of autonomous vehicles seemed decades away. And yet, just six years later in 2010, Google announced their self-driving cars had ‘logged in over 140,000 miles’ with seven test vehicles completing over 1,000 miles each without any human intervention – including difficult terrain like San Francisco’s notoriously steep Lombard Street. Since then the likes of Apple, Tesla and Uber have entered the game, not to mention the older incumbents of the automobile industry. By 2016 Uber’s then-CEO Travis Kalanick was clear about the importance of self-driving vehicles for any transport company: ‘It starts with understanding that the world is going to go self-driving and autonomous … what would happen if we weren’t a part of that future?

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


pages: 396 words: 117,149

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

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

Satellites, DNA sequencers, and particle accelerators probe nature in ever-finer detail, and learning algorithms turn the torrents of data into new scientific knowledge. Companies know their customers like never before. The candidate with the best voter models wins, like Obama against Romney. Unmanned vehicles pilot themselves across land, sea, and air. No one programmed your tastes into the Amazon recommendation system; a learning algorithm figured them out on its own, by generalizing from your past purchases. Google’s self-driving car taught itself how to stay on the road; no engineer wrote an algorithm instructing it, step-by-step, how to get from A to B. No one knows how to program a car to drive, and no one needs to, because a car equipped with a learning algorithm picks it up by observing what the driver does. Machine learning is something new under the sun: a technology that builds itself. Ever since our remote ancestors started sharpening stones into tools, humans have been designing artifacts, whether they’re hand built or mass produced.

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

“All humans are mortal” is a piece of knowledge. Riding a bicycle is a skill. In machine learning, knowledge is often in the form of statistical models, because most knowledge is statistical: all humans are mortal, but only 4 percent are Americans. Skills are often in the form of procedures: if the road curves left, turn the wheel left; if a deer jumps in front of you, slam on the brakes. (Unfortunately, as of this writing Google’s self-driving cars still confuse windblown plastic bags with deer.) Often, the procedures are quite simple, and it’s the knowledge at their core that’s complex. If you can tell which e-mails are spam, you know which ones to delete. If you can tell how good a board position in chess is, you know which move to make (the one that leads to the best position). Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more.


pages: 215 words: 59,188

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

agricultural Revolution, augmented reality, autonomous vehicles, blood diamonds, corporate governance, Deng Xiaoping, Donald Trump, Elon Musk, failed state, financial independence, gender pay gap, gig economy, Gini coefficient, high net worth, income inequality, index fund, industrial robot, Internet of things, invisible hand, job-hopping, Julian Assange, life extension, Lyft, M-Pesa, Mahatma Gandhi, manufacturing employment, mega-rich, megacity, Minecraft, mobile money, natural language processing, Nelson Mandela, plutocrats, Plutocrats, price mechanism, purchasing power parity, ransomware, reshoring, ride hailing / ride sharing, Ronald Coase, self-driving car, Silicon Valley, Snapchat, South China Sea, speech recognition, stem cell, supply-chain management, transaction costs, Uber and Lyft, uber lyft, undersea cable, US Airways Flight 1549, WikiLeaks

What people want at the end of life How China reduced its air pollution Why forests are spreading in the rich world The Arctic could be ice-free by 2040, 30 years sooner than expected Why there’s something in the water in New Zealand Measures to discourage smoking are spreading around the world Why “gene drives” have yet to be deployed in the wild Why it is so hard to fix India’s sanitation problems Why some deadly diseases are hard to eradicate Why China is sick of foreign waste Why are wolves coming back in France? Why biggest isn’t fastest in the animal kingdom Geek speak: getting technical What is a brain-computer interface? The link between video games and unemployment What do robots do all day? Why 5G might be both faster and slower than previous wireless technologies Mobile phones are more common than electricity in much of sub-Saharan Africa Why self-driving cars will mostly be shared, not owned How ride-hailing apps reduce drink-driving What is augmented reality? Why we’re still waiting for the space elevator How astronomers spotted the first interstellar asteroid Why drones could pose a greater risk to aircraft than birds What is the point of spam e-mail? Why the police should wear body cameras Why tech giants are laying their own undersea cables Game theory: sport and leisure Why tennis players grunt Why board games are so popular in Nigeria How drones can keep beaches safe from sharks How football transfers work How St Louis became America’s chess capital What does “digitally remastering” a film really mean?

One example would be real-time virtual- or augmented-reality streaming. At the Olympics, for example, many contestants were followed by 360-degree video cameras. At special venues sports fans could don virtual-reality goggles to put themselves right into the action. 5G is also supposed to become the connective tissue for the internet of things, interconnecting everything from smartphones and wireless sensors to industrial robots and self-driving cars. This will be made possible by a technique called “network slicing”, which allows operators to create bespoke networks that give each set of devices exactly the kind of connectivity they need to job a particular job. Despite its versatility, it is not clear how quickly 5G will take off. The biggest brake will be economic. When the GSMA, an industry group, asked 750 telecoms bosses in 2017 about the most salient impediment to delivering 5G, more than half cited the lack of a clear business case.

Mobile-money services, which enable people to send cash straight from their phones, have in effect created personal bank accounts that people can carry in their pockets. By one estimate, the M-Pesa mobile-money system alone lifted about 2% of Kenyan households out of poverty between 2008 and 2014. Technology cannot solve all of Africa’s problems, but it can help with some of them. Why self-driving cars will mostly be shared, not owned When will you be able to buy a driverless car that will work anywhere? This commonly asked question contains three assumptions: that autonomous vehicles (AVs) will resemble cars; that people will buy them; and that they will be capable of working on all roads in all conditions. All three of those assumptions may be wrong. Although today’s experimental vehicles are modified versions of ordinary cars, with steering wheels that eerily turn by themselves, future AVs will have no steering wheel or pedals and will come in all sorts of shapes and sizes; pods capable of carrying six or eight people may prove to be the most efficient design.


pages: 349 words: 95,972

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

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

Sarah O’Connor, “Leave the Robotic Jobs to Robots and Improve Humans’ Lives,” Financial Times, January 5, 2016, https://next.ft.com/content/da557b66-b09c-11e5-993b-c425a3d2b65a. 18. Klein, Streetlights and Shadows, pp. 123–124. 19. “Will Self-Driving Cars Spell the End of the American Road Trip?” 99% Invisible (podcast), available on The Eye: Slate’s Design Blog, July 3, 2015, http://www.slate.com/blogs/the_eye/2015/07/03/self_driving_cars_and_the_paradox_of_automation_from_99_invisible.html. Raj Rajkumar’s comments below are from the same podcast. 20. Jack Stewart, “What May Be Self-Driving Cars’ Biggest Problem,” BBC Future, August 25, 2015, http://www.bbc.com/future/story/20150824-what-may-be-self-driving-cars-biggest-problem. 21. Cited in Langewiesche, “The Human Factor.” 22. M. L. Cummings, C. Mastracchio, K. M. Thornburg, and A. Mkrtchyan, “Boredom and Distraction in Multiple Unmanned Vehicle Supervisory Control,” Interacting with Computers 25, no. 1 (2013), pp. 34–47, http://hdl.handle.net/1721.1/86942. 23.

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


pages: 185 words: 43,609

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

Airbnb, Albert Einstein, Andrew Wiles, Andy Kessler, Berlin Wall, cleantech, cloud computing, crony capitalism, discounted cash flows, diversified portfolio, don't be evil, Elon Musk, eurozone crisis, income inequality, Jeff Bezos, Lean Startup, life extension, lone genius, Long Term Capital Management, Lyft, Marc Andreessen, Mark Zuckerberg, minimum viable product, Nate Silver, Network effects, new economy, paypal mafia, Peter Thiel, pets.com, profit motive, Ralph Waldo Emerson, Ray Kurzweil, self-driving car, shareholder value, Silicon Valley, Silicon Valley startup, Singularitarianism, software is eating the world, Steve Jobs, strong AI, Ted Kaczynski, Tesla Model S, uber lyft, Vilfredo Pareto, working poor

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

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

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


pages: 361 words: 81,068

The Internet Is Not the Answer by Andrew Keen

"Robert Solow", 3D printing, A Declaration of the Independence of Cyberspace, Airbnb, AltaVista, Andrew Keen, augmented reality, Bay Area Rapid Transit, Berlin Wall, bitcoin, Black Swan, Bob Geldof, Burning Man, Cass Sunstein, citizen journalism, Clayton Christensen, clean water, cloud computing, collective bargaining, Colonization of Mars, computer age, connected car, creative destruction, cuban missile crisis, David Brooks, disintermediation, disruptive innovation, Donald Davies, Downton Abbey, Edward Snowden, Elon Musk, Erik Brynjolfsson, Fall of the Berlin Wall, Filter Bubble, Francis Fukuyama: the end of history, Frank Gehry, Frederick Winslow Taylor, frictionless, full employment, future of work, gig economy, global village, Google bus, Google Glasses, Hacker Ethic, happiness index / gross national happiness, income inequality, index card, informal economy, information trail, Innovator's Dilemma, Internet of things, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joi Ito, Joseph Schumpeter, Julian Assange, Kevin Kelly, Kickstarter, Kodak vs Instagram, Lean Startup, libertarian paternalism, lifelogging, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Metcalfe’s law, move fast and break things, move fast and break things, Nate Silver, Nelson Mandela, Network effects, new economy, Nicholas Carr, nonsequential writing, Norbert Wiener, Norman Mailer, Occupy movement, packet switching, PageRank, Panopticon Jeremy Bentham, Paul Graham, peer-to-peer, peer-to-peer rental, Peter Thiel, plutocrats, Plutocrats, Potemkin village, precariat, pre–internet, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Robert Metcalfe, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Silicon Valley ideology, Skype, smart cities, Snapchat, social web, South of Market, San Francisco, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, TaskRabbit, Ted Nelson, telemarketer, The Future of Employment, the medium is the message, the new new thing, Thomas L Friedman, Travis Kalanick, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, urban planning, Vannevar Bush, Whole Earth Catalog, WikiLeaks, winner-take-all economy, working poor, Y Combinator

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

Thanks to cloud computing, robotics, Facebook, Google, LinkedIn, Twitter, the iPad, and cheap Internet-enabled smartphones, Friedman says, “the world has gone from connected to hyper-connected.”13 Runciman, Lanchester, and Friedman are all describing the same great economic, cultural, and, above all, intellectual transformation. “The Internet,” Joi Ito, the director of the MIT Media Lab, notes, “is not a technology; it’s a belief system.”14 Everything and everyone are being connected in a network revolution that is radically disrupting every aspect of today’s world. Education, transportation, health care, finance, retail, and manufacturing are now being reinvented by Internet-based products such as self-driving cars, wearable computing devices, 3-D printers, personal health monitors, massive open online courses (MOOCs), peer-to-peer services like Airbnb and Uber, and currencies like Bitcoin. Revolutionary entrepreneurs like Sean Parker and Kevin Systrom are building this networked society on our behalf. They haven’t asked our permission, of course. But then the idea of consent is foreign, even immoral, to many of these architects of what the Columbia University historian Mark Lilla calls our “libertarian age.”

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


pages: 345 words: 84,847

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

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

When our grandparents were young, they didn’t envision their libraries would evaporate into zeroes and ones in the cloud, that cures would come from injecting new genes into their bloodstreams, or that they would walk around with small rectangles in their pockets that ping them from space satellites while they’re anywhere in the world. Likewise, it’s hard for us to imagine that some decades from now, our children may have their own self-driving cars. Your six-year-old child will be able to commute to school on her own: just strap her in and wave goodbye. Meanwhile, in case of an emergency, your own self-driving car could be turned into an ambulance: if your heart starts beating irregularly, the car’s built-in biological monitoring can detect it and reroute to the nearest hospital. And there’s no reason why you have to be the only one in the car. You could be picked up in a self-driving car and get a manipedi or a dental appointment while moving to your next destination: offices can be entirely mobile. Once a car is truly self-driving, there’s really no reason for it to have front-facing seats and a steering wheel: it could just as easily look like a living room with couches, or a speeding Jacuzzi.

From our daily activities to our schools to our companies, we are all riding arm-in-arm into a future that compels a constant remodeling of the world. In recent decades, the world has found itself transitioning from a manufacturing economy to an information economy. But that is not where this road ends. As computers become better at digesting mountains of data, people are being freed up to work on other tasks. We’re already seeing the first glimpses of this new model: the creativity economy. Synthetic biologist, app developer, self-driving car designer, quantum computer designer, multimedia engineer – these are positions that didn’t exist when most of us were in school, and they represent the vanguard of what’s coming. When you grab your morning coffee ten years from now, you may be walking into a job that looks very different from the one you’re working now. For these reasons, corporate boardrooms everywhere are scrambling to figure out how to keep up, because the technologies and processes of running a company are constantly changing.

The consultants aren’t wrong, it’s simply that the details of their advice don’t matter. It’s not always about the particular solution, but instead about the variation. Why do humans adapt to everything around us so quickly? It’s because of a phenomenon known as repetition suppression. When your brain gets used to something, it displays less and less of a response each time it sees it. Imagine, for example, that you come across a new object – say, a self-driving car. The first time you see it, your brain shows a large response. It’s absorbing something new and registering it. The second time you see it, your brain shows slightly less response. It doesn’t care quite as much about it, because it’s not quite as novel. The third time: less response again. The fourth time: even less. Repetition suppression in action.2 The more familiar something is, the less neural energy we spend on it.


pages: 481 words: 125,946

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

agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, basic income, bitcoin, blockchain, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, discrete time, Douglas Engelbart, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, functional fixedness, global pandemic, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, Johannes Kepler, John Markoff, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Satyajit Das, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, social intelligence, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

And the operation would have to survive the hazards of detection, betrayal, stings, blunders, and bad luck. In theory it could happen, but we have more pressing things to worry about. Once we put aside the sci-fi disaster plots, the possibility of advanced artificial intelligence is exhilarating—not just for the practical benefits, like the fantastic gains in safety, leisure, and environment-friendliness of self-driving cars but also for the philosophical possibilities. The computational Theory of Mind has never explained the existence of consciousness in the sense of first-person subjectivity (though it’s perfectly capable of explaining the existence of consciousness in the sense of accessible and reportable information). One suggestion is that subjectivity is inherent to any sufficiently complicated cybernetic system.

Should we worry that we’re building systems whose increasingly accurate decisions are based on incomprehensible foundations? First, and most simply, it matters because we regularly find ourselves in everyday situations where we need to know why. Why was I denied a loan? Why was my account blocked? Why did my condition suddenly get classified as “severe”? And sometimes we need to know why in cases where the machine made a mistake. Why did the self-driving car abruptly go off the road? It’s hard to troubleshoot problems when you don’t understand why they’re happening. There are deeper troubles, too; to talk about them, we need to understand more about how these algorithms work. They’re trained on massive quantities of data and they’re remarkably good at picking up on the subtle patterns these data contain. We know, for example, how to build systems that can look at millions of identically structured loan applications from the past, all encoded the same way, and start to identify the recurring patterns in the loans that—in retrospect—were the right ones to grant.

Conceptually, autonomous or artificial intelligence systems can develop in two ways: either as an extension of human thinking or as radically new thinking. Call the first “Humanoid Thinking,” or Humanoid AI, and the second “Alien Thinking,” or Alien AI. Almost all AI today is Humanoid Thinking. We use AI to solve problems too difficult, time-consuming, or boring for our limited brains to process: electrical-grid balancing, recommendation engines, self-driving cars, face recognition, trading algorithms, and the like. These artificial agents work in narrow domains with clear goals their human creators specify. Such AI aims to accomplish human objectives—often better, with fewer cognitive errors, distractions, outbursts of bad temper, or processing limitations. In a couple of decades, AI agents might serve as virtual insurance sellers, doctors, psychotherapists, and maybe even virtual spouses and children.


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

3D printing, 9 dash line, activist fund / activist shareholder / activist investor, addicted to oil, Admiral Zheng, Albert Einstein, American energy revolution, Asian financial crisis, autonomous vehicles, Ayatollah Khomeini, Bakken shale, Bernie Sanders, BRICs, British Empire, coronavirus, COVID-19, Covid-19, decarbonisation, Deng Xiaoping, disruptive innovation, distributed generation, Donald Trump, Edward Snowden, Elon Musk, energy security, energy transition, failed state, gig economy, global pandemic, global supply chain, hydraulic fracturing, Indoor air pollution, Intergovernmental Panel on Climate Change (IPCC), inventory management, James Watt: steam engine, Kickstarter, LNG terminal, Lyft, Malacca Straits, Malcom McLean invented shipping containers, Masdar, mass incarceration, megacity, Mikhail Gorbachev, mutually assured destruction, new economy, off grid, oil rush, oil shale / tar sands, oil shock, open economy, paypal mafia, peak oil, pension reform, price mechanism, purchasing power parity, RAND corporation, rent-seeking, ride hailing / ride sharing, Ronald Reagan, self-driving car, Silicon Valley, smart cities, South China Sea, sovereign wealth fund, supply-chain management, trade route, Travis Kalanick, Uber and Lyft, uber lyft, ubercab, UNCLOS, UNCLOS, uranium enrichment, women in the workforce

Sometime later, Thrun received an email from Page, who said he was having problems with a robot that he had built to enable him to attend meetings at Google without being physically present. The robot wasn’t working. Thrun met up with him in a parking lot. Page opened up the trunk of his car and pulled out the robot for Thrun to examine. Thrun quickly pulled together a team, and the robot was fixed.5 The decisive race was two years later—that 2007 Grand Challenge on that deserted Air Force base in Victorville, California. An empty desert was one thing. But could a self-driving car navigate the streets of an American city, even if it was a ghost city? Eleven teams made it into the 2007 competition, but once again it was Carnegie Mellon versus Stanford. Stanford’s team was back with a Volkswagen named “Junior,” after Leland Stanford Jr., for whom the university was named. Thrun was part of the Stanford team, although by this time he was already working at Google. CMU’s entry was a Chevy Tahoe that was named “Boss” in honor of General Motors’ Charles “Boss” Kettering, the inventor of the electric ignition in 1911, who had gone on to run GM research for more than a quarter century.

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

A host of other obstacles will have to be addressed. Reliability has to be assured. What happens if a vehicle goes to the wrong destination? Or if there is construction or an accident en route? Or weather causes a malfunction? Or the car has to make a decision whether to hit a person or crash on the side of the road? Since people are handing over control to a machine, in order to instill confidence these self-driving cars will have to function at much higher levels of performance than cars driven by humans. While millions of miles have now been driven by test driverless cars, humans drive more than eight billion miles every day in the United States. Will some groups create havoc by hacking the software in tens of thousands of cars? In response, in addition to the obvious emphasis on cybersecurity, the concept of “graceful degradation” in the face of hacking is also being quietly tested—a sort of glide path to a minimal level of functionality, which would allow the driver to take over.


pages: 364 words: 99,897

The Industries of the Future by Alec Ross

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

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

And it turns out that the development of a driverless car is deeply personal. As Sebastian Thrun explained in a TED talk, his best friend was killed in a car accident, spurring his personal crusade to innovate the car accident out of existence: “I decided I’d dedicate my life to saving 1 million people every year.” Google has hired the former deputy director of the National Highway Traffic Safety Administration, Ron Medford, to be its director of safety for self-driving cars. Medford explained that Americans collectively drive approximately 3 trillion miles per year, and more than 30,000 people die in the process. Worldwide, those statistics are enormous; approximately 1.3 million people die every year in car crashes. Google, of course, also has an interest in allowing consumers to have more time on their hands—quite literally, to have their hands free. The average American spends 18.5 hours a week driving, and Europeans spend about half that.

More to the point, even if passengers end up preferring robot drivers to humans, what happens to the human taxi driver who loses his job because service industry jobs are at risk in the next wave of innovation as never before? This isn’t just about taxi drivers; the delivery driver may be replaced by Amazon’s airborne delivery drones or automated delivery trucks. UPS and Google are also testing their own versions of the delivery drone. Two and a half million people in the United States make their living from driving trucks, taxis, or buses, and all of them are vulnerable to displacement by self-driving cars. It’s hard to wrap your head around all the changes this might mean. I met the CEO of a company that develops high-tech access control systems (like the new parking garage system at the airport that tells you how many open spaces are available on each floor) and asked him what worries him about the future. He cited a disruption that I’d never considered before: what driverless cars might mean for parking garages.


pages: 380 words: 109,724

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

"side hustle", accounting loophole / creative accounting, Airbnb, AltaVista, autonomous vehicles, banking crisis, barriers to entry, Bernie Madoff, Bernie Sanders, bitcoin, book scanning, Brewster Kahle, Burning Man, call centre, cashless society, cleantech, cloud computing, cognitive dissonance, Colonization of Mars, computer age, corporate governance, creative destruction, Credit Default Swap, cryptocurrency, data is the new oil, death of newspapers, Deng Xiaoping, disintermediation, don't be evil, Donald Trump, drone strike, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Etonian, Filter Bubble, future of work, game design, gig economy, global supply chain, Gordon Gekko, greed is good, income inequality, informal economy, information asymmetry, intangible asset, Internet Archive, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, Kenneth Rogoff, life extension, light touch regulation, Lyft, Mark Zuckerberg, Marshall McLuhan, Martin Wolf, Menlo Park, move fast and break things, move fast and break things, Network effects, new economy, offshore financial centre, PageRank, patent troll, paypal mafia, Peter Thiel, pets.com, price discrimination, profit maximization, race to the bottom, recommendation engine, ride hailing / ride sharing, Robert Bork, Sand Hill Road, search engine result page, self-driving car, shareholder value, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, smart cities, Snapchat, South China Sea, sovereign wealth fund, Steve Jobs, Steven Levy, subscription business, supply-chain management, TaskRabbit, Telecommunications Act of 1996, The Chicago School, the new new thing, Tim Cook: Apple, too big to fail, Travis Kalanick, trickle-down economics, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, WikiLeaks, zero-sum game

The first was during a meeting at the Uber headquarters in Boston, attended by a couple dozen of the company’s full-time employees, most of whom were young, elite college–educated, hoodie-wearing techies who looked up to Travis like a god. The energy in the room, the excitement to simply be in the same space with the uber-Bro, was visceral. We all enjoyed the high-end snacks in the company canteen while Kalanick was peppered with questions about his career history, Uber’s new ventures into self-driving cars, and whether the company would ever consider augmenting its already hefty pay and benefits (self-driving-car engineers in the Valley can make around $2 million) by handing out perks like subsidized MBAs, as other larger tech firms do. “Oooh, it’s getting hot in here,” he quipped, to laughs and earnest nods of agreement around the room. But in another session, a somewhat different view of the company emerged. Uber had rented a large auditorium space near the waterfront, and was feting a carefully chosen group of top revenue-generating drivers.

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

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


pages: 246 words: 68,392

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

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

EPILOGUE 1   Press release. Workers and US Government Cheated Out of Billions in Stolen Wages and Lost Tax Revenue. National Employment Law Project. February 19, 2014. 2   Newton, Casey. Uber Will Eventually Replace All of Its Drivers with Self-Driving Cars. The Verge. May 28, 2014. https://hbr.org/2017/07/lots-of-employees-get-misclassified-as-contractors-heres-why-it-matters. 3   Kessler, Sarah. A Timeline of When Self-Driving Vehicles Will Be on the Road, According to the People Making Them. Quartz. March 29, 2017. https://qz.com/943899/a-timeline-of-when-self-driving-cars-will-be-on-the-road-according-to-the-people-making-them/. 4   McKinsey Global Institute. What the Future of Work Will Mean for Jobs, Skills, and Wages. November 2017. https://www.mckinsey.com/global-themes/future-of-organizations-and-work/what-the-future-of-work-will-mean-for-jobs-skills-and-wages. 5   Lynley, Matthew.

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

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


pages: 240 words: 65,363

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

Albert Einstein, Anton Chekhov, autonomous vehicles, Barry Marshall: ulcers, call centre, Cass Sunstein, colonial rule, Edward Glaeser, Everything should be made as simple as possible, food miles, Gary Taubes, income inequality, Internet Archive, Isaac Newton, medical residency, Metcalfe’s law, microbiome, prediction markets, randomized controlled trial, Richard Thaler, Scramble for Africa, self-driving car, Silicon Valley, Tony Hsieh, transatlantic slave trade, éminence grise

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

If you make an argument that promises all benefits and no costs, your opponent will never buy it—nor should he. Panaceas are almost nonexistent. If you paper over the shortcomings of your plan, that only gives your opponent reason to doubt the rest of it. Let’s say you’ve become a head-over-heels advocate for a new technology you think will change the world. Your argument goes like this: The era of the self-driving car—a.k.a. the driverless car, or autonomous vehicle—is just around the corner, and we should embrace it as vigorously as possible. It will save millions of lives and improve just about every facet of our society and economy. You could go on and on. You could talk about how the toughest challenge—the technology itself—has largely been conquered. Nearly every major automaker in the world, as well as Google, has successfully tested cars that use an onboard computer, GPS, cameras, radar, laser scanners, and actuators to do everything a human driver can do—but better.

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


pages: 340 words: 92,904

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

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

In the view of former PRT advocate Alain Kornhauser, who is now convinced of the practicality of street-useful driverless cars, the beauty of these technological improvements is that, because they increase driving safety, they even have the potential to be self-financing: so long as collision avoidance and other autonomous driving modules cost less than the potential liability from future accidents, it will be in the interest of automobile insurance companies to pay for them. Even better: so long as more autonomy equals more safety, there is no point where the cost of the technology exceeds its added value. The most prominent player in the world of autonomous driving, though, isn’t Allstate or Geico. It isn’t Mercedes-Benz or Ford, or even Tesla. It’s Google. The Google Self-Driving Car is a project that the Internet giant saw as a natural outgrowth of its existing mapping software, particularly the technology from Google Street View, which stitches together panoramic photos of more than five million miles of roads in more than forty countries. Google’s versions of the driverless car—refitted Toyotas, Audis, and Lexuses—combine real-time access to all that data with a laser rangefinder that creates and refreshes three-dimensional maps of the area immediately around the car.

Transit riders use more than 20 percent more calories than drivers on a per-trip basis, which gives buses, subways, and streetcars a giant health advantage over cars. In fact, after five years of taking transit, the obese percentage of a given population—those with a Body Mass Index greater than 30—drops by more than half. And, as long as cities create plazas and piazzas where cars are banned but not people, self-driving cars offer no advantage, even without recognizing the mathematical impossibility of moving thousands of people through a city center in single-occupant vehicles. This doesn’t mean there isn’t a place for cars, with or without laser-rangefinders and GPS mapping. In less dense parts of cities, suburbs, and rural areas, all the safety aspects developed by automated cars make sense. In some ways, the driverless car is a natural next step following all the technological and demographic changes that contributed to the original Millennial-led driving revolution that is the subject of this book, especially the information oversupply that made smartphones into a tool for transportation planning.

A group of simulated driverless cars negotiating a typical urban intersection at the same (slow) acceleration of a commuter train increases the time needed to cross the intersection by anywhere from 36 percent to more than 2,000 percent. If you want to browse the Internet while commuting, and still want to get to work on time, trains look like a much better option. There are other reasons to be suspicious of the brave new world represented by Google’s self-driving cars and others of similar ambition. On a purely personal level, I’m a little taken aback by the promise that autonomous vehicles will be able to collect you at your front door and deposit you at the front door of a supermarket or shopping mall—or even at your desk or workstation—without your feet ever touching the ground. In the Disney movie Wall-E, spaceship-bound refugees from an Earth destroyed by environmental catastrophe are so well cared for by their robot transportation devices that hardly anyone even stands up anymore, with the result that the universe’s entire remaining population of Homo sapiens is morbidly obese.


pages: 501 words: 114,888

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

Ada Lovelace, additive manufacturing, Airbnb, Albert Einstein, Amazon Mechanical Turk, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, blood diamonds, Burning Man, call centre, cashless society, Charles Lindbergh, Clayton Christensen, clean water, cloud computing, Colonization of Mars, computer vision, creative destruction, crowdsourcing, cryptocurrency, Dean Kamen, delayed gratification, dematerialisation, digital twin, disruptive innovation, Edward Glaeser, Edward Lloyd's coffeehouse, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, Ethereum, ethereum blockchain, experimental economics, food miles, game design, Geoffrey West, Santa Fe Institute, gig economy, Google X / Alphabet X, gravity well, hive mind, housing crisis, Hyperloop, indoor plumbing, industrial robot, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invention of the telegraph, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, Joseph Schumpeter, Kevin Kelly, Kickstarter, late fees, Law of Accelerating Returns, life extension, lifelogging, loss aversion, Lyft, M-Pesa, Mary Lou Jepsen, mass immigration, megacity, meta analysis, meta-analysis, microbiome, mobile money, multiplanetary species, Narrative Science, natural language processing, Network effects, new economy, New Urbanism, Oculus Rift, out of africa, packet switching, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, QR code, RAND corporation, Ray Kurzweil, RFID, Richard Feynman, Richard Florida, ride hailing / ride sharing, risk tolerance, Satoshi Nakamoto, Second Machine Age, self-driving car, Silicon Valley, Skype, smart cities, smart contracts, smart grid, Snapchat, sovereign wealth fund, special economic zone, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, supercomputer in your pocket, supply-chain management, technoutopianism, Tesla Model S, Tim Cook: Apple, transaction costs, Uber and Lyft, uber lyft, unbanked and underbanked, underbanked, urban planning, Watson beat the top human players on Jeopardy!, We wanted flying cars, instead we got 140 characters, X Prize

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

Instead, Amazon Prime was born, and today, 100 million Prime members later, that zany idea accounts for a significant portion of the company’s bottom line. Next, Holden went to another startup, Groupon—which is hard to remember as a disruptive enterprise today, but was then part of the first wave of “power to the people” internet companies. From there, he went to Uber, where, despite the turmoil the company experienced, Holden strung together a series of unlikely wins: UberPool, Uber Eats, and, most recently, Uber’s self-driving car program. So when he proposed an even zanier product line—that Uber take to the skies—it wasn’t all that surprising that the company’s leadership took him seriously. And for good reason. The theme of the second annual Uber Elevate wasn’t actually flying cars. The cars have already arrived. Instead, the theme of the second Uber Elevate was the path to scale. And the more critical point: That path is a lot shorter than many suspect.

By the middle of 2019, dozens of vehicles had logged millions of miles on California roads. Traditional automotive players like BMW, Mercedes and Toyota were competing for this emerging market with tech giants like Apple, Google (via Waymo), Uber, and Tesla, trying out different designs, gathering data, and honing neural networks. Out of these, Waymo seems well positioned for early market dominance. Formerly Google’s self-driving car project, Waymo began its work in 2009 by hiring Sebastian Thrun, the Stanford professor who won the DARPA Grand Challenge. Thrun helped develop the AI system that would become the brains behind Waymo’s self-driving fleet. About ten years later, in March 2018, Waymo purchased that fleet, buying twenty thousand sporty, self-driving Jaguars for its forthcoming ride-hailing service. With this many cars, Waymo intends to deliver a million trips per day in 2020 (this might be ambitious but Uber currently delivers 15 million rides a day).


pages: 237 words: 74,109

Uncanny Valley: A Memoir by Anna Wiener

autonomous vehicles, back-to-the-land, basic income, blockchain, Burning Man, call centre, charter city, cloud computing, cognitive bias, cognitive dissonance, commoditize, crowdsourcing, cryptocurrency, Extropian, future of work, Golden Gate Park, housing crisis, Jane Jacobs, job automation, knowledge worker, Lean Startup, means of production, medical residency, new economy, New Urbanism, passive income, pull request, rent control, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, social web, South of Market, San Francisco, special economic zone, technoutopianism, telepresence, telepresence robot, union organizing, universal basic income, unpaid internship, urban planning, urban renewal, women in the workforce, Y2K, young professional

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

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

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


pages: 269 words: 70,543

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

Airbnb, augmented reality, autonomous vehicles, blockchain, call centre, cashless society, Chuck Templeton: OpenTable:, cloud computing, computer vision, connected car, corporate governance, cryptocurrency, data is the new oil, Deng Xiaoping, digital map, disruptive innovation, Donald Trump, El Camino Real, Elon Musk, family office, fear of failure, glass ceiling, global supply chain, income inequality, industrial robot, Internet of things, invention of movable type, Jeff Bezos, Kickstarter, knowledge worker, Lyft, Mark Zuckerberg, megacity, Menlo Park, money market fund, Network effects, new economy, peer-to-peer lending, personalized medicine, Peter Thiel, QR code, RFID, ride hailing / ride sharing, Sand Hill Road, self-driving car, sharing economy, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart transportation, Snapchat, social graph, software as a service, South China Sea, sovereign wealth fund, speech recognition, stealth mode startup, Steve Jobs, supply-chain management, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, urban planning, winner-take-all economy, Y Combinator, young professional

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

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

This no doubt conjures up images of Big Brother watching all that goes on, not to mention an episode of Black Mirror. But it’s not just China. The New York City Police Department is reportedly monitoring citizens using cameras and facial recognition software developed in China, from SenseTime partner Hikvision.1 In the United States, tech giants Google, Microsoft, Amazon, Facebook, and IBM dominate AI for many futuristic and practical uses. Google self-driving cars are being tested on California’s Highway 101; Facebook spins out posts based on deep learning of content preferences; Amazon’s Alexa powers lights, TVs, and speakers by voice activation; and Microsoft’s Azure relies on cognitive computing for speech and language applications, while IBM Watson’s AI-based computer system increases productivity and improves customer service for call centers, production lines, and warehouses.


pages: 372 words: 101,174

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

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

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

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

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


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The Age of Surveillance Capitalism by Shoshana Zuboff

Amazon Web Services, Andrew Keen, augmented reality, autonomous vehicles, barriers to entry, Bartolomé de las Casas, Berlin Wall, bitcoin, blockchain, blue-collar work, book scanning, Broken windows theory, California gold rush, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, choice architecture, citizen journalism, cloud computing, collective bargaining, Computer Numeric Control, computer vision, connected car, corporate governance, corporate personhood, creative destruction, cryptocurrency, dogs of the Dow, don't be evil, Donald Trump, Edward Snowden, en.wikipedia.org, Erik Brynjolfsson, facts on the ground, Ford paid five dollars a day, future of work, game design, Google Earth, Google Glasses, Google X / Alphabet X, hive mind, impulse control, income inequality, Internet of things, invention of the printing press, invisible hand, Jean Tirole, job automation, Johann Wolfgang von Goethe, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, knowledge economy, linked data, longitudinal study, low skilled workers, Mark Zuckerberg, market bubble, means of production, multi-sided market, Naomi Klein, natural language processing, Network effects, new economy, Occupy movement, off grid, PageRank, Panopticon Jeremy Bentham, pattern recognition, Paul Buchheit, performance metric, Philip Mirowski, precision agriculture, price mechanism, profit maximization, profit motive, recommendation engine, refrigerator car, RFID, Richard Thaler, ride hailing / ride sharing, Robert Bork, Robert Mercer, Second Machine Age, self-driving car, sentiment analysis, shareholder value, Shoshana Zuboff, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, slashdot, smart cities, Snapchat, social graph, social web, software as a service, speech recognition, statistical model, Steve Jobs, Steven Levy, structural adjustment programs, The Future of Employment, The Wealth of Nations by Adam Smith, Tim Cook: Apple, two-sided market, union organizing, Watson beat the top human players on Jeopardy!, winner-take-all economy, Wolfgang Streeck

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

Google was also among the wealthiest of all registered lobbyists in the EU, second only to a lobbying group that represents a confederation of European corporations.108 The firm also learned to engineer sophisticated lobbying efforts at the state level, primarily geared to fight back any proposed legislation that would augment privacy and curtail behavioral surplus operations. For example, Google won the right to put its self-driving cars on the road—anticipated as important supply chains—after enlisting Obama officials to lobby state regulators for key legislation.109 Both Google and Facebook currently lead aggressive state-level lobbying campaigns aimed at repelling or weakening statutes to regulate biometric data and protect privacy. As one report put it, “They want your body.”110 In the fourth arena of fortifications the corporation learned to infiltrate and influence academic research and civil society advocacy in ways that softened or in some cases thwarted the examination of its practices.

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


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

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

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

You carry in you pocket a phone that tracks where you are and thus who you are with 24 hours per day. Computers note your licence plate when you drive down the road, and much of your day to day communication is via computer networks that are carefully monitored. The computers that do this are locked away in secure data centres so you personally cannot turn them any of them off. More directly, robots in many shapes and sizes will soon be leaving the factory. Initially, there will be self driving cars and automated cleaners, fruit pickers, and systems for maintaining racks of computers in data centres. Computers already fly military drones and the military is investing heavily in semi-autonomous robot soldiers. By the time computers become truly intelligent they will be in a good position to directly control the physical world. Powerful people are not powerful due to their personal physical strength.

Robots leaving the factory The most significant change that is likely to be seen over the next ten years is the practical application of robots that are working outside of carefully structured factory environments. The earliest have been the automated vacuum cleaners, the better of which actively map out the rooms that are cleaning. Probably the most significant in the short term will be autonomous, self-driving cars. Huge trucks have been autonomously driving around mine sites for several years. Mercedes already ships driver assist technology that senses other cars, while BMW expects to move their completely automatic freeway driving system into production by 2020. The Google driverless car has received considerable attention, but all vehicle manufactures have invested in the technology. The initial focus is on just assisting human drivers, but fully autonomous or partially remotely controlled cars are likely to be in production by 2025.


pages: 97 words: 31,550

Money: Vintage Minis by Yuval Noah Harari

23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, European colonialism, Flash crash, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, pattern recognition, Ponzi scheme, self-driving car, telemarketer, The Future of Employment, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Watson beat the top human players on Jeopardy!, zero-sum game

But in the twenty-first century this is becoming an urgent political and economic issue. And it is sobering to realise that, at least for armies and corporations, the answer is straightforward: intelligence is mandatory but consciousness is optional. Armies and corporations cannot function without intelligent agents, but they don’t need consciousness and subjective experiences. The conscious experiences of a flesh-and-blood taxi driver are infinitely richer than those of a self-driving car, which feels absolutely nothing. The taxi driver can enjoy music while navigating the busy streets of Seoul. His mind may expand in awe as he looks up at the stars and contemplates the mysteries of the universe. His eyes may fill with tears of joy when he sees his baby girl taking her very first step. But the system doesn’t need all that from a taxi driver. All it really wants is to bring passengers from point A to point B as quickly, safely and cheaply as possible.

But cars nevertheless replaced horses because they were superior in the handful of tasks that the system really needed. Taxi drivers are highly likely to go the way of horses. Indeed, if we forbid humans to drive not only taxis but vehicles altogether, and give computer algorithms a monopoly over traffic, we can then connect all vehicles to a single network, thereby rendering car accidents far less likely. In August 2015 one of Google’s experimental self-driving cars had an accident. As it approached a crossing and detected pedestrians wishing to cross, it applied its brakes. A moment later it was hit from behind by a sedan whose careless human driver was perhaps contemplating the mysteries of the universe instead of watching the road. This could not have happened if both vehicles had been guided by interlinked computers. The controlling algorithm would have known the position and intentions of every vehicle on the road, and would not have allowed two of its marionettes to collide.

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


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

activist fund / activist shareholder / activist investor, Airbnb, Amazon Web Services, autonomous vehicles, bitcoin, blockchain, Bob Noyce, business intelligence, Chuck Templeton: OpenTable:, cloud computing, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, database schema, discounted cash flows, Elon Musk, Firefox, forensic accounting, George Gilder, global pandemic, Google Hangouts, Google X / Alphabet X, hydraulic fracturing, Hyperloop, inventory management, Isaac Newton, Jeff Bezos, Joi Ito, Khan Academy, late fees, Lean Startup, Lyft, M-Pesa, Marc Andreessen, margin call, Mark Zuckerberg, minimum viable product, move fast and break things, move fast and break things, Network effects, Oculus Rift, oil shale / tar sands, Paul Buchheit, Paul Graham, Peter Thiel, pre–internet, recommendation engine, ride hailing / ride sharing, Sam Altman, Sand Hill Road, Saturday Night Live, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart grid, social graph, software as a service, software is eating the world, speech recognition, stem cell, Steve Jobs, subscription business, Tesla Model S, thinkpad, transaction costs, transport as a service, Travis Kalanick, Uber for X, uber lyft, web application, winner-take-all economy, Y Combinator, yellow journalism

Google has since used the financial power of its gross margins to place big bets that other companies might shy away from, such as investing in Android and Chrome, two products that were going up against dominant competitors (Apple’s iOS in mobile phone software and Microsoft and Firefox in Web browsers). Google has also used its margins to fund radical experiments like X (formerly Google X) and Waymo (self-driving cars). These bets may or may not pay off, but even if they fail, Google’s margins give it the ability to recover quickly and keep going. Network Effects Google has leveraged network effects quite a bit in its major business lines, though not, ironically enough, in its core search product! The mobile traffic app Waze is a classic example of a direct network effect. Waze harnesses each user’s location to create a more accurate model of traffic conditions, while also letting drivers easily report events such as traffic accidents, speed traps, and stopped cars on the side of the road.

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

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


pages: 247 words: 81,135

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

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

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

When this happens, the possibilities of dashboard technology will no longer be restricted by safe driving practices. Given we’re talking about years, rather than decades, car companies should probably prepare for the inevitable now. A world of entirely new revenue streams awaits the auto industry if they follow the playbook already evidenced in both media evolution and personal computing technology. All they need is to have the courage to let other people get involved. The technology for safe self-driving cars already exists. From products to platforms Being able to thrive going forward is about removing the finality that comes with the launch mentality: not assuming that a product is finished when we deliver it to the market. Brands that survive the current reconfiguration of economics will understand that a product or service is a continuum of development, a continuum that people take from the company and invent the next stages of.

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


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

agricultural Revolution, Airbnb, Albert Einstein, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, Asperger Syndrome, Baxter: Rethink Robotics, bitcoin, blockchain, business process, call centre, clean water, creative destruction, crowdsourcing, Donald Trump, Douglas Engelbart, Douglas Engelbart, drone strike, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, experimental economics, Exxon Valdez, future of work, Galaxy Zoo, gig economy, happiness index / gross national happiness, industrial robot, Internet of things, invention of the telegraph, inventory management, invisible hand, Jeff Rulifson, jimmy wales, job automation, John Markoff, Joi Ito, Joseph Schumpeter, Kenneth Arrow, knowledge worker, longitudinal study, Lyft, Marshall McLuhan, Occupy movement, Pareto efficiency, pattern recognition, prediction markets, price mechanism, Ray Kurzweil, Rodney Brooks, Ronald Coase, Second Machine Age, self-driving car, Silicon Valley, slashdot, social intelligence, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, technological singularity, The Nature of the Firm, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Tim Cook: Apple, transaction costs, Travis Kalanick, Uber for X, uber lyft, Vernor Vinge, Vilfredo Pareto, Watson beat the top human players on Jeopardy!

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

Because they do all these other tasks without any attention from you, they are really some combination of tools and assistants. As we move further along the continuum toward greater machine control, Google Assistant and Amazon’s Alexa are examples of automated systems that strive to be assistants rather than just tools, especially when they do things like volunteering information you never asked for—such as reminding you that you need to leave for the airport now to make your flight. Similarly, a fully self-driving car would be a clear example of an assistant. Like a human taxi (or Uber) driver, this automated assistant will take a great deal of initiative to navigate through traffic to the destination you specify. Another example of an automated assistant is the software used by the online clothing retailer Stitch Fix to help its human stylists recommend items to customers.3 Stitch Fix customers first fill out detailed questionnaires about their style, size, and price preferences.

Human doctors make the final decisions about how to treat the patient, but their automated medical assistant helps them take into account vast amounts of medical literature and come up with diagnoses they might never have even considered. Peers Some of the most intriguing uses of computers will involve roles where the machines operate as peers of the humans more than as assistants or tools, even in cases where there isn’t much actual artificial intelligence being used. In many cases, this will happen because a program that acts as an assistant for one human acts as a peer of another. For example, if you are riding in a self-driving car, and I am driving myself on the same road, then your driving assistant is my driving peer. If you are a stock trader, you may already be transacting with someone else’s automated program trading system without even knowing it. And if you are bidding in an eBay auction, you may be competing with someone else who uses an automated “sniping” assistant that is programmed to outbid you in the last few seconds of an auction.


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

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

Sullivan, “Google’s New Driverless Car Has No Brakes or Steering Wheel,” Washington Post, May 28, 2014, http://www.washingtonpost.com/news/morning-mix/wp/2014/05/28/googles-new-driverless-car-has-no-brakes-or-steering-wheel//?print=1. 29. R. Lawler, “Google X Built a Fully Self-Driving Car from Scratch, Sans Steering Wheel and Pedals,” TechCrunch, May 27, 2014, http://techcrunch.com/2014/05/27/google-x-introduces-a-fully-self-driving-car-sans-steering-wheel-and-pedals/. 30. L. Gannes, “Google’s New Self-Driving Car Ditches the Steering Wheel,” Recode, May 27, 2014, http://recode.net/2014/05/27/googles-new-self-driving-car-ditches-the-steering-wheel/. 31. R. W. Lucky, “The Drive for Driverless Cars,” IEEE Spectrum, June 26, 2014, http://spectrum.ieee.org/computing/embedded-systems/the-drive-for-driverless-cars. 32. C. Smith, “‘I No Longer Have to Go to See the Doctor’: How the Patient Portal is Changing Medical Practice,” Journal of Participatory Medicine 6 (2014): e6. 33.

Sarasohn-Kahn, “Why Having Access to Your Health Information Matters,” Healthcare DIY, March 1, 2014, http://healthcarediy.com/technology/your-medical-records-are-your-medical-records/. 24. B. Dolan and A. Pai, “In-Depth: Providers’ Inevitable Acceptance of Patient Generated Health Data,” MobiHealthNews, March 21, 2014, http://mobihealthnews.com/31268/in-depth-providers-inevitable-acceptance-of-patient-generated-health-data/. 25. J. Markoff, “A Trip in a Self-Driving Car Now Seems Routine,” New York Times, May 13, 2014, http://bits.blogs.nytimes.com/2014/05/13/a-trip-in-a-self-driving-car-now-seems-routine/?smid=tw-nytimesbits. 26. A. Salkever, “What Google’s Driverless Car Future Might Really Look Like,” Read Write, May 28, 2014, http://readwrite.com/2014/05/28/googles-driverless-car-future?awesm=readwr.it_p20r-awesm=~oFWmYrlzbbCpi0. 27. C. C. Miller, “When Driverless Cars Break the Law,” New York Times, May 14, 2014, http://www.nytimes.com/2014/05/14/upshot/when-driverless-cars-break-the-law.html. 28.

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


Virtual Competition by Ariel Ezrachi, Maurice E. Stucke

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

., citing Benjamin Shiller, “First-Degree Price Discrimination Using Big Data” (2014), http://benjaminshiller.com /images/First_Degree_PD_Using _Big _Data_Apr_8,_2014.pdf. For a discussion of the “nowcasting radar,” see Stucke and Grunes, Big Data and Competition Policy. Yoko Kubota, “Toyota Aims to Make Self-Driving Cars by 2020,” Wall Street Journal, October 6, 2015, http://www.wsj.com/articles/toyota-aims-to-make -self-driving-cars-by-2020 -1444136396; Yoko Kubota, “Behind Toyota’s Late Shift into Self-Driving Cars,” Wall Street Journal, January 12, 2016, 338 25. 26. 27. 28. 29. 30. 31. 32. Notes to Pages 239–240 http://www.wsj.com/articles/behind-toyotas-late-shift-into-self-driving-cars -1452649436 (“In the battle for global pre-eminence, traditional car makers fear soft ware makers will steal the auto’s soul and profitability, putting incumbents in a similar position to Chinese factories making smartphones for global brands.”).

Nathaniel Mott, “Uber Should Fear the Company Formerly Known as Google,” Gigaom (August 11, 2015), https://gigaom.com/2015/08/11/uber-vs -alphabet-google/. Weinberger, “Microsoft Could See an Opportunity to Poke Google in the Eye with Uber Investment.” Douglas MacMillan, “GM Invests $500 Million in Lyft, Plans System for Self-Driving Cars,” Wall Street Journal, January 4, 2016, http://www.wsj.com /article _email/gm-invests-500-million-in-lyft-plans-system-for-self-driving -cars-1451914204-lMyQjAxMTI2NTA2NDEwODQyWj. Coupons.com, Form 10-K for 2014 (2014), 17; Yelp Inc., Form 10-Q for the Quarterly Period Ended June 30, 2015 (2015), 33, http://www.sec.gov /Archives/edgar/data/1345016/000120677415002479/yelp_10q.htm. “The number of people who access information about local businesses through mobile devices, including smartphones, tablets and handheld computers, has increased dramatically over the past few years and is expected to continue to increase.

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


pages: 305 words: 93,091

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

4chan, big-box store, bitcoin, blockchain, connected car, crowdsourcing, Edward Snowden, en.wikipedia.org, Firefox, Google Chrome, Google Earth, Internet of things, Kickstarter, license plate recognition, Mark Zuckerberg, MITM: man-in-the-middle, pattern recognition, ransomware, Ross Ulbricht, self-driving car, Silicon Valley, Skype, Snapchat, speech recognition, Tesla Model S, web application, WikiLeaks, zero day, Zimmermann PGP

If you think the way Tesla and Uber are tracking every ride you take is scary, then self-driving cars will be even scarier. Like the personal surveillance devices we keep in our pockets—our cell phones—self-driving cars will need to keep track of where we want to go and perhaps even know where we are at a given moment in order to be always at the ready. The scenario proposed by Google and others is that cities will no longer need parking lots or garages—your car will drive around until it is needed. Or perhaps cities will follow the on-demand model, in which private ownership is a thing of the past and everyone shares whatever car is nearby. Just as our cell phones are less like copper-wire phones than they are like traditional PCs, self-driving cars will also be a new form of computer. They’ll be self-contained computing devices, able to make split-second autonomous decisions while driving in case they are cut off from their network communications.

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


pages: 294 words: 96,661

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

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

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

Back in 1962, a NASA rocket that cost a bit under a billion dollars in today’s money exploded in flight due to a single hyphen missing from deep within its code. In another example, a European rocket exploded in flight with $7 billion worth of loss because a 64-bit number was too large to convert to a 16-bit number, causing both a metaphoric and a literal crash. While expensive, these disasters were at least well contained. Imagine a similar problem affecting the self-driving car network, the power grid, or—gasp—your company’s payroll system. I point these issues out not to suggest that we should rethink our march toward a more mechanical future. Machines are, on the whole, more reliable than people in what they do. However, generally speaking, machine failures have a larger potential to cascade. Digital systems are generally more brittle than analog ones. Delete a word from The Great Gatsby and you still have a masterpiece.

They surely would not stop until the very oceans themselves are drained. Mickey cannot stop his creation. Lucky for him, a deus ex machina in the form of the sorcerer, awakened by the commotion, comes down and puts a stop to the whole affair, retrieving his hat from a contrite apprentice. 13 * * * The Human Brain We’ve thoroughly explored the world of narrow AI. Narrow AI powers the self-driving car, the thermostat that learns the temperatures you prefer, and the spam filter of your email folder. Yes, these are technical marvels, but don’t ask any of them what you should get your spouse for Christmas. Artificial general intelligence (AGI), on the other hand, is an intelligence that is at least as smart as you and me. You could ask it to do anything, including a task that it had never been programmed to do, and it would figure out how to perform such a task, and then go and try to do it.


pages: 357 words: 95,986

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

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

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

These are tasks that computers are perfectly suited to accomplish once a programmer has created the appropriate software, leading to a drastic reduction in the numbers of routine manual and cognitive jobs over the past four decades.22 The result has been a polarisation of the labour market, since many middle-wage, mid-skilled jobs are routine, and therefore subject to automation.23 Across both North America and Western Europe, the labour market is now characterised by a predominance of workers in low-skilled, low-wage manual and service jobs (for example, fast-food, retail, transport, hospitality and warehouse workers), along with a smaller number of workers in high-skilled, high-wage, non-routine cognitive jobs.24 The most recent wave of automation is poised to change this distribution of the labour market drastically, as it comes to encompass every aspect of the economy: data collection (radio-frequency identification, big data); new kinds of production (the flexible production of robots,25 additive manufacturing,26 automated fast food); services (AI customer assistance, care for the elderly); decision-making (computational models, software agents); financial allocation (algorithmic trading); and especially distribution (the logistics revolution, self-driving cars,27 drone container ships and automated warehouses).28 In every single function of the economy – from production to distribution to management to retail – we see large-scale tendencies towards automation.29 This latest wave of automation is predicated upon algorithmic enhancements (particularly in machine learning and deep learning), rapid developments in robotics and exponential growth in computing power (the source of big data) that are coalescing into a ‘second machine age’ that is transforming the range of tasks that machines can fulfil.30 It is creating an era that is historically unique in a number of ways. New pattern-recognition technologies are rendering both routine and non-routine tasks subject to automation: complex communication technologies are making computers better than humans at certain skilled-knowledge tasks, and advances in robotics are rapidly making technology better at a wide variety of manual-labour tasks.31 For instance, self-driving cars involve the automation of non-routine manual tasks, and non-routine cognitive tasks such as writing news stories or researching legal precedents are now being accomplished by robots.32 The scope of these developments means that everyone from stock analysts to construction workers to chefs to journalists is vulnerable to being replaced by machines.33 Workers who move symbols on a screen are as at risk as those moving goods around a warehouse.

While full automation of the economy is presented here as an ideal and a demand, in practice it is unlikely to be fully achieved.45 In certain spheres, human labour is likely to continue for technical, economic and ethical reasons. On a technical level, machines today remain worse than humans at jobs involving creative work, highly flexible work, affective work and most tasks relying on tacit rather than explicit knowledge.46 The engineering problems involved in automating these tasks appear insurmountable for the next two decades (though similar claims were made about self-driving cars ten years ago), and a programme of full automation would aim to invest research money into overcoming these limits. A second barrier to full automation occurs for economic reasons: certain tasks can already be completed by machines, but the cost of the machines exceeds the cost of the equivalent labour.47 Despite the efficiency, accuracy and productivity of machine labour, capitalism prefers to make profits, and therefore uses human labour whenever it is cheaper than capital investment.


pages: 239 words: 70,206

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

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

In an immense economy, like that of the United States, with its gross domestic product of $17 trillion, an amalgam of factors affects performance, including business cycles, financial crises, and demographic trends, not just technology. But Brynjolfsson sees a pattern playing out with big data that is comparable to past technologies. Innovations that have been percolating for years in research labs are making their way into products. An industry or two leads the way, like online advertising, and showcase projects point toward the future, like IBM’s Watson or Google’s self-driving cars (robotic incarnations of big data). Enthusiasm fans investment by companies and start-ups. But a broad-based payoff has not yet emerged. Debate rages between the techno-optimists and the pessimists. In his office, I ask Brynjolfsson to describe the steps that led him to become a big-data believer. He starts by observing that the data groundwork has been laid in the steady digitization of business in recent years.

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

People check in on temperatures and energy savings nearly twice a day on average, from smartphones and Web apps. The human users are interested partners and can override the machine, but most of the time they let the Nest algorithms take over. The issue of when to trust the machine—a mechanical one or a virtual one, a software algorithm—is going to play out repeatedly in the future. Appeals to efficiency alone will not carry the day. Advocates for self-driving cars marshal safety statistics and logical-sounding arguments to push their case—about accident rates and the human foibles of drowsiness, distractedness, and drunkenness. Those arguments help, but they do not speak to the issues of trust and comfort with the machines. People are not aggregates; we all experience the world as individuals. So declaring that something will be good for the population, on average, isn’t entirely persuasive.


pages: 138 words: 40,787

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

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

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

Many cars have drive by wire implemented, whereby the steering wheel is not mechanically connected to the wheels anymore, but controls a motor, which controls the wheels. In addition, modern cars are filled with sensors: detecting light for the mirrors and headlights and rain for the windshield wipers, tire pressure monitors, accelerometers, gyroscopes, and compasses. Going further down this path, the digitalization of mechanics in cars allows for the arrival of driverless cars or self-driving cars, which Google has successfully tested over the past few years. But connectivity offers even more: communication between cars to optimize traffic flow and make better decisions on behalf of the driver. Volvo, for example, has successfully demonstrated road trains as part of the EU’s SARTRE (Safe Road Trains for the Environment) Project, which has several cars following one another in a platoon formation; the lead car has a professional driver taking responsibility for the platoon, while following vehicles operate in a semi-autonomous mode, reducing the distance between the vehicles, and reducing drag and fuel consumption, while getting to their destination faster.23 You may be familiar with the crowd-sourced navigation application Waze, which is one of the most accurate personal navigation applications today because it uses real-time traffic and construction information provided by users.


pages: 179 words: 43,441

The Fourth Industrial Revolution by Klaus Schwab

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

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

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


pages: 387 words: 119,409

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

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

Most mobile phones and tablets rely on Google’s free, open-sourcei operating system, Android, which didn’t exist in the market before 2007. More than fifty billion apps have been downloaded from the Google Play store. Chrome, launched as a safer, faster, and open-source Web browser in 2008, has over 750 million active users and has grown into an operating system powering “Chromebook” laptops.10 And Google is just beginning to explore what is possible, from self-driving cars to Project Loon, which aims to provide Internet access by balloon to the hardest-to-reach parts of the globe. From wearable computing products like Google Glass, which blends the Web and the world in a tiny lens that sits above your right eye (we’re working on a version for lefties), to the Google Smart Contact Lens, a contact lens that doubles as a blood glucose monitor for people with diabetes.

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

Historically, they enjoy a last gasp of play and freedom between the crush of the juku and the monotony of the career of a sarariman (“salaryman”—the nomenclature for the expected rule-following, slow, tenure-based progression that characterized Japanese careers in the past). Japanese college grades are virtually useless as a hiring signal, but knowing which college someone attended is helpful, at least for hiring new graduates. Our professional recruiters are also familiar with many jobs across Google, no small feat considering our business currently includes search, self-driving cars, futuristic glasses, fiber-based Internet services, manufacturing, video studios, and venture capital! This is important, because when someone applies to a job at your company, they don’t know everything your company does. In fact, most large companies have distinct recruiting teams for different divisions. Someone rejected for a product management job in one division might have been great for a marketing job in another division, but won’t be considered for that job because the recruiters in the two divisions don’t talk.


pages: 602 words: 177,874

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

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

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

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

At first it moves up very gradually, then it starts to slope higher as innovations build on innovations that have come before, and then it starts to soar straight to the sky. What would be on that line? Think of the introduction of the printing press, the telegraph, the manual typewriter, the Telex, the mainframe computer, the first word processors, the PC, the Internet, the laptop, the mobile phone, search, mobile apps, big data, virtual reality, human-genome sequencing, artificial intelligence, and the self-driving car. A thousand years ago, Teller explained, that curve representing scientific and technological progress rose so gradually that it could take one hundred years for the world to look and feel dramatically different. For instance, it took centuries for the longbow to go from development into military use in Europe in the late thirteenth century. If you lived in the twelfth century, your basic life was not all that different than if you lived in the eleventh century.


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

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

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

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

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


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

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

The bottom line is that mismeasurement might be large, but it is not sufficiently large to account for the productivity slowdown. The productivity slowdown appears structural and real. 78. Brynjolfsson, Rock, and Syverson, forthcoming, “Artificial Intelligence and the Modern Productivity Paradox,” 25. 79. C. F. Kerry and J. Karsten, 2017, “Gauging Investment in Self-Driving Cars,” Brookings Institution, October 16. https://www.brookings.edu/research/gauging-investment-in-self-driving-cars/. 80. Brynjolfsson, Rock, and Syverson, forthcoming, “Artificial Intelligence and the Modern Productivity Paradox,” 25. 81. N. F. Crafts and T. C. Mills, 2017, “Trend TFP Growth in the United States: Forecasts versus Outcomes” (Discussion Paper 12029, Centre for Economic Policy Research, London). Their findings are consistent with the observation of Eric Bartelsman that productivity forecasts perform “horribly, with forecast standard errors being larger than ranges that would be useful for policy purposes” (2013, “ICT, Reallocation and Productivity” [Brussels: European Commission, Directorate-General for Economic and Financial Affairs]). 82.

“News Conference 24.” https://www.jfklibrary.org/archives/other-resources/john-f-kennedy-press-conferences/news-conference-24. Kenworthy, L. 2012. “It’s Hard to Make It in America: How the United States Stopped Being the Land of Opportunity.” Foreign Affairs 91 (November/December): 97–109. Kerry, C. F., and J. Karsten. 2017. “Gauging Investment in Self-Driving Cars.” Brookings Institution, October 16. https://www.brookings.edu/research/gauging-investment-in-self-driving-cars/. Keynes, J. M. [1930] 2010. “Economic Possibilities for Our Grandchildren.” In Essays in Persuasion, 321–32. London: Palgrave Macmillan. Klein, M. 2007. The Genesis of Industrial America, 1870–1920. Cambridge: Cambridge University Press. Kleiner, M. M. 2011. “Occupational Licensing: Protecting the Public Interest or Protectionism?”

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


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

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, agricultural Revolution, Airbnb, American Society of Civil Engineers: Report Card, Automated Insights, autonomous vehicles, basic income, bitcoin, blockchain, blue-collar work, Bob Noyce, business process, call centre, cashless society, citizen journalism, Clayton Christensen, collaborative consumption, collaborative economy, collective bargaining, creative destruction, crowdsourcing, cryptocurrency, disintermediation, disruptive innovation, distributed ledger, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, Francis Fukuyama: the end of history, Geoffrey West, Santa Fe Institute, gig economy, Gini coefficient, Hyperloop, income inequality, industrial robot, Internet of things, invention of agriculture, invention of movable type, invention of the printing press, invisible hand, Jane Jacobs, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, license plate recognition, Lyft, Mark Zuckerberg, mass immigration, Network effects, new economy, peer-to-peer lending, QWERTY keyboard, ransomware, Richard Florida, Robert Gordon, Ronald Reagan, Second Machine Age, self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Simon Kuznets, Snapchat, speech recognition, Stuxnet, TaskRabbit, The Death and Life of Great American Cities, The Rise and Fall of American Growth, the scientific method, trade route, Turing test, Uber and Lyft, uber lyft, universal basic income, uranium enrichment, urban planning, zero day, zero-sum game, Zipcar

“Employment Status of the Civilian Population by Sex and Age,” Bureau of Labor Statistics, http://www.bls.gov/news.release/empsit.t01.htm (accessed on June 27, 2019); and “Databases, Tables & Calculators by Subject,” Bureau of Labor Statistics, https://data.bls.gov/timeseries/LNS11000000. 17. David Cardinal, “Ten Years After Their Debut, Autonomous Trucks Are Finally Hitting the Roads,” ExtremeTech, October 5, 2015, http://www.extremetech.com/extreme/215626-ten-years-after-their-debut-autonomous-trucks-are-finally-hitting-the-roads (accessed June 27, 2019). 18. Dan Fagella, “Self-Driving Car Timeline for 11 Top Automakers,” VentureBeat, June 4, 2017, https://venturebeat.com/2017/06/04/self-driving-car-timeline-for-11-top-automakers/ (accessed June 27, 2019). 19. “Number of Motor Vehicles Registered in the United States from 1990 to 2017,” Statista, www.statista.com/statistics/183505/number-of-vehicles-in-the-united-states-since-1990/ (accessed June 27, 2019); and Jared Green, “500 Million Reasons to Rethink the Parking Lot,” Grist, June 7, 2012, http://grist.org/cities/500-million-reasons-to-rethink-the-parking-lot/ (accessed June 27, 2019). 20.

Getaround allows neighbors to rent cars from other neighbors by the hour, while a competing service, Turo, focuses on longer-term rentals.44 Turo’s website claims that owners can cover their monthly car payments by renting their cars for as few as nine days a month. It claims to operate from 4,700 cities, provide owners with liability insurance, and deliver cars directly to their renters.45 BlaBlaCar, a European service, allows its more than 35 million members to locate other members who are going where they want to so they can hitch a ride.46 Looming in the future, when the self-driving car arrives, are driverless types of Uber services. The vision is that you will be able to summon a car using your smartphone. It will pick you up, drive you to where you are going, and then speed away to pick up the next passenger. Car sharing is one of the growth industries of the future. GM estimates that 5 to 6 million people globally already share cars and that that number will grow to 20 to 30 million in the next few years.47 To capitalize on this trend, GM has launched its Maven car-sharing service, which allows part-time workers in the gig economy to rent a car when they need it to do such things as delivering groceries to paying customers.48 Maven competes with Mercedes’ Car2Go, which “allows customers to take cars one-way inside of a set perimeter and charges by the minute.”49 Our first thought is that these services compete with cabs and limousine services, but that may be overlooking the depth of the structural transformation.

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


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

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

Though it is described as part of the branch of computer science called artificial intelligence, and more specifically, an area called machine learning, this characterization is misleading. Big data is not about trying to “teach” a computer to “think” like humans. Instead, it’s about applying math to huge quantities of data in order to infer probabilities: the likelihood that an email message is spam; that the typed letters “teh” are supposed to be “the”; that the trajectory and velocity of a person jaywalking mean he’ll make it across the street in time—the self-driving car need only slow slightly. The key is that these systems perform well because they are fed with lots of data on which to base their predictions. Moreover, the systems are built to improve themselves over time, by keeping a tab on what are the best signals and patterns to look for as more data is fed in. In the future—and sooner than we may think—many aspects of our world will be augmented or replaced by computer systems that today are the sole purview of human judgment.

Its controversial Street View cars cruised around snapping pictures of houses and roads, but also gobbling up GPS data, checking mapping information, and even sucking in wifi network names (and, perhaps illegally, the content that flowed over open wireless networks). A single Google Street View drive amassed a myriad of discrete data streams at every moment. The extensibility comes in because Google applied the data not just for a primary use but for lots of secondary uses. For example, the GPS data it garnered improved the company’s mapping service and was indispensable for the functioning of its self-driving car. The extra cost of collecting multiple streams or many more data points in each stream is often low. So it makes sense to gather as much data as possible, as well as to make it extensible by considering potential secondary uses at the outset. That increases the data’s option value. The point is to look for “twofers”—where a single dataset can be used in multiple instances if it can be collected in a certain way.

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


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

Affordable Care Act / Obamacare, AI winter, Bernie Sanders, bitcoin, Burning Man, Capital in the Twenty-First Century by Thomas Piketty, centre right, charter city, crack epidemic, crowdsourcing, David Graeber, Deng Xiaoping, Donald Trump, East Village, Elon Musk, Flynn Effect, Francis Fukuyama: the end of history, Francisco Pizarro, ghettoisation, gig economy, Haight Ashbury, helicopter parent, hive mind, Hyperloop, immigration reform, informal economy, Intergovernmental Panel on Climate Change (IPCC), Islamic Golden Age, Jeff Bezos, Joan Didion, Kevin Kelly, Kickstarter, knowledge worker, life extension, mass immigration, mass incarceration, means of production, megacity, move fast and break things, move fast and break things, multiplanetary species, New Journalism, Nicholas Carr, Norman Mailer, obamacare, Oculus Rift, open borders, out of africa, Panopticon Jeremy Bentham, Peter Thiel, plutocrats, Plutocrats, pre–internet, QAnon, quantitative easing, rent-seeking, Robert Bork, Robert Gordon, Ronald Reagan, secular stagnation, self-driving car, Silicon Valley, Silicon Valley ideology, Snapchat, social web, Steve Jobs, Steven Pinker, technoutopianism, the built environment, The Rise and Fall of American Growth, Tyler Cowen: Great Stagnation, wage slave, women in the workforce, Y2K

And in that time, it has solved exactly none of the problems that would have prevented a company that needed to make a profit from building such a large user base: it has no obvious competitive advantages besides the huge investor subsidy; the technology it uses is hardly proprietary or complex; its rival in disruption controls 30 percent of the market, even as the legacy players are still very much alive; and all of its paths to reduce its losses—charging higher prices, paying its workers less—would destroy the advantages that it has built. So it sits there, widely regarded as one of the defining success stories of the Internet era, a unicorn unlike any other, with billions in losses and a plan to become profitable that involves vague promises to somehow monetize all its user data and a specific promise that its investment in a different new technology—the self-driving car, much ballyhooed but as yet not exactly real—will square the circle and make the math add up. That’s the story of Uber—so far. It isn’t a pure Instagram fantasy like the Fyre Festival or a naked fraud like Theranos; it managed to go public and maintain its outsize valuation, unlike its fellow money-losing unicorn WeWork, whose recent attempt at an IPO hurled it into crisis. But like them, it is, for now, an example of a major twenty-first-century company invented entirely out of surplus, less economically efficient so far than the rivals it is supposed to leapfrog, sustained by investors who believe its promises in defiance of the existing evidence, floated by the hope that with enough money and market share, you can will a profitable company into existence, and goldwashed by an “Internet company” identity that obscures the weakness of its real-world fundamentals.

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

One can be skeptical of this utopianism and still see it as a possible seedbed for some of the technological breakthroughs that Kelly’s correspondents envision. Maybe we have simply been in a kind of bottleneck for the last few generations, achieving important scientific breakthroughs that don’t (yet) translate into society-altering changes. At a certain point, we’ll clear the bottleneck, and it will become clear that our era was a necessary prelude to renewed acceleration—eventually giving us self-driving cars courtesy of a finally profitable Uber, a Mars colony courtesy of the Elon Musk–Jeff Bezos space race, and radical life extension courtesy of Google’s longevity lab or some other zillionaire who can’t imagine shuffling off this mortal coil. All of this could happen on a scale that would be world altering without having the truly utopian scenarios come to pass. Terraforming Mars and becoming a multiplanetary species may be unattainable for now—but just going to Mars would be a bigger leap for mankind than anything we’ve accomplished since Neil Armstrong.


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Overcomplicated: Technology at the Limits of Comprehension by Samuel Arbesman

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

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

Systems we build to reflect the world: That the complexity of the world is reflected in the complexity of our systems is also discussed in Vikram Chandra, Geek Sublime: The Beauty of Code, the Code of Beauty (Minneapolis: Graywolf Press, 2014). One need not always end up with messy code because the world is messy, but it does often happen. Fortunately, there are ways to mitigate it. See Steve McConnell, Code Complete: A Practical Handbook of Software Construction, 2nd ed. (Redmond, WA: Microsoft Press, 2004), 583. building a self-driving vehicle: The complexity of building self-driving cars was discussed by Google[x]’s “Captain of Moonshots” in his closing keynote address at South by Southwest Interactive (SXSW) 2015: Astro Teller, “How to Make Moonshots,” Backchannel, March 17, 2015, https://medium.com/backchannel/how-to-make-moonshots-65845011a277. the exceptions that nonetheless have to be dealt with: One solution is to use humans to manually troubleshoot, or at least hard-code, the exceptions.

shifting the car into neutral: “Customer FAQs Regarding the Sticking Accelerator Pedal and Floor Mat Pedal Entrapment Recalls,” Toyota Pressroom, accessed April 27, 2015, http://pressroom.toyota.com/article_print.cfm?article_id=1861. isn’t the worst thing to tell someone: I am thankful for this insight, as well the insights related to limitative theorems, from discussion with folks from the Department of Philosophy at the University of Kansas. incomprehensible systems are the new reality: For example, just because we might not fully grasp all the details of a self-driving car, that doesn’t mean that it can’t be much safer than one driven by a person. And by the way, we already don’t really understand the car driven by a person, let alone the driver himself! the “unthinkable present”: Quoted in Carlin Romano, America the Philosophical (New York: Alfred A. Knopf, 2012), 501. Index The page numbers in this index refer to the printed version of this book.


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

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

When we talk about “the system knowing about you,” that knowledge depends on machine learning and database computation breakthroughs that couldn’t be imagined when Microsoft researcher Jim Gray turned on Microsoft’s first terabyte database back in December 1997. Similarly, significant innovations and accuracy improvements in voice recognition make systems like Apple’s Siri, Google Now and Google Voice Search possible. The foundation for the Age of Context—all of these technologies working together—is the cloud computing infrastructure, which continues to grow exponentially in capability and capacity. And it had better keep growing: A self-driving car, which we describe in Chapter 5, generates about 700 megabytes of data per second. We talked with GM, Ford, Toyota—and Google—about what would happen if every car had that technology. Well, for one thing, today’s cloud computing technology would melt down. Rackspace, a cloud hosting provider and Scoble’s employer, was the first and largest sponsor of this book. Since 2009, it has funded Scoble to travel the world interviewing hundreds of entrepreneurs and innovators.

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

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


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

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

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

Tied up in the platform model is the capturing of data from workers and users, and the developing of ways to turn it into a productive resource. For example, with Uber, the actions of workers provide data that is used to further the short-term aims of the platform, while also developing the possibility to replace workers with even cheaper (and more docile) artificial intelligence in the form of self-driving cars. While, in many cases, this level of automation may seem relatively far off, it impacts on the strategy of the platform and also informs the perspective that they take towards workers: why offer a steady and secure employment contract if you would prefer these tasks were automated anyway? Automation is a concern that is increasingly on the policy agenda throughout the world. For example, in an influential study, Frey and Osborne (2017) analysed the susceptibility of 702 different occupations to computerization.

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


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

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

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

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

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


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

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

He dressed in jeans and stylish T-shirts and married a beautiful professor of comparative literature who liked to tease him about his techno-utopian ways. In March of that year, Thrun was invited to TED (Technology, Education, Design), the annual festival of technologist self-congratulation, where he stood before a rapt audience and described how he and his colleagues at Google had built a self-driving car. Afterward, Thrun hung around the conference to watch the other presenters, including an energetic former hedge fund analyst named Salman Khan. Khan had computer science degrees from MIT and an MBA from Harvard, and had become recently famous for creating a series of instructional videos for elementary, middle, and high school children that had attracted millions of views on YouTube. The videos became the basis for Khan’s hugely popular education web site, Khan Academy.

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

In describing how the brain reacts to surprise, Lue said that “everything is a function of risk and opportunity.” To survive and prosper in the world with limited cognitive capacity, humans filter waves of constant sensory information through neural patterns—heuristics and mental shortcuts that our minds use to weigh the odds that what we are sensing is familiar and categorizable based on our past experience. Sebastian Thrun’s self-driving car does this with Bayesian statistics built into silicon and code, while the human mind uses electrochemical processes that we still don’t fully understand. But the underlying principle is the same: Based on the pattern of lines and shapes and edges, that is probably a boulder and I should drive around it. That is probably a group of three young women eating lunch at a table near the sushi bar and I should pay them no mind.


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

1960s counterculture, Affordable Care Act / Obamacare, Airbnb, altcoin, Amazon Mechanical Turk, back-to-the-land, basic income, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Brewster Kahle, Burning Man, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, Clayton Christensen, collaborative economy, collective bargaining, Community Supported Agriculture, corporate governance, creative destruction, crowdsourcing, cryptocurrency, Debian, disruptive innovation, do-ocracy, Donald Knuth, Donald Trump, Edward Snowden, Elon Musk, Ethereum, ethereum blockchain, Food sovereignty, four colour theorem, future of work, gig economy, Google bus, hydraulic fracturing, Internet Archive, Jeff Bezos, jimmy wales, joint-stock company, Joseph Schumpeter, Julian Assange, Kickstarter, Lyft, M-Pesa, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, mass immigration, means of production, multi-sided market, new economy, offshore financial centre, old-boy network, Peter H. Diamandis: Planetary Resources, post-work, precariat, premature optimization, pre–internet, profit motive, race to the bottom, Richard Florida, Richard Stallman, ride hailing / ride sharing, Sam Altman, Satoshi Nakamoto, self-driving car, shareholder value, sharing economy, Silicon Valley, Slavoj Žižek, smart contracts, Steve Jobs, Steve Wozniak, Stewart Brand, transaction costs, Turing test, Uber and Lyft, uber lyft, underbanked, undersea cable, universal basic income, Upton Sinclair, Vanguard fund, white flight, Whole Earth Catalog, WikiLeaks, women in the workforce, working poor, Y Combinator, Y2K, Zipcar

Recent posts included pictures of a workshop held in the United Kingdom about how to beat Uber, as well as links to news articles reporting new tests of self-driving cars, self-driving trucks, self-driving mini-buses. Also, alongside a broken link: “Crisis management is our specialty.” While Green Taxi’s drivers scrambled to protect their livelihoods, Uber and Tesla and Google were tooling up to automate them. I asked Buni about this. He said, “We’re really trying to feed a family for the next day. When it happens, we’ll make a plan”—that is, crisis management, for the foreseeable future. To that end, he and his crisis-ridden co-owners pooled more than $1.5 million to put one-third of Denver’s taxi industry under worker control. Self-driving cars hadn’t come to the city’s roads yet, but Wall Street’s anticipation of them was fueling investment in the big apps, which put pressure on the taxi market and motivated so many drivers to set off on their own.

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

Marshall Brain, Manna: Two Views of Humanity’s Future (2012), marshallbrain.com/manna1.htm; for another perspective on parallels between basic income and venture capital, see Steve Randy Waldman, “VC for the People” (April 16, 2014), interfluidity.com/v2/5066.html. 16. Matt Zwolinski, Michael Huemer, Jim Manzi, and Robert H. Frank, “Basic Income and the Welfare State,” Cato Unbound (August 2014); Noah Gordon, “The Conservative Case for a Guaranteed Basic Income,” Atlantic (August 6, 2014). 17. In Scott Dadich, “Barack Obama, Neural Nets, Self-Driving Cars, and the Future of the World,” Wired (November 2016), Obama said, “Whether a universal income is the right model—is it gonna be accepted by a broad base of people?—that’s a debate that we’ll be having over the next ten or twenty years.” On universal dividends funded through common goods, see, for example, Peter Barnes, With Liberty and Dividends for All: How to Save Our Middle Class When Jobs Don’t Pay Enough (Berrett-Koehler Publishers, 2014); for the Oregon case, see Nathan Schneider, “Soon, Oregon Polluters May Have to Pay Residents for Changing the Climate,” YES!


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

access to a mobile phone, additive manufacturing, airport security, Amazon Mechanical Turk, Amazon Web Services, anti-communist, augmented reality, Ayatollah Khomeini, barriers to entry, bitcoin, borderless world, call centre, Chelsea Manning, citizen journalism, clean water, cloud computing, crowdsourcing, data acquisition, Dean Kamen, drone strike, Elon Musk, failed state, fear of failure, Filter Bubble, Google Earth, Google Glasses, hive mind, income inequality, information trail, invention of the printing press, job automation, John Markoff, Julian Assange, Khan Academy, Kickstarter, knowledge economy, Law of Accelerating Returns, market fundamentalism, means of production, MITM: man-in-the-middle, mobile money, mutually assured destruction, Naomi Klein, Nelson Mandela, offshore financial centre, Parag Khanna, peer-to-peer, peer-to-peer lending, personalized medicine, Peter Singer: altruism, Ray Kurzweil, RFID, Robert Bork, self-driving car, sentiment analysis, Silicon Valley, Skype, Snapchat, social graph, speech recognition, Steve Jobs, Steven Pinker, Stewart Brand, Stuxnet, The Wisdom of Crowds, upwardly mobile, Whole Earth Catalog, WikiLeaks, young professional, zero day

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

distributing preloaded tablets to primary-age kids: David Talbot, “Given Tablets but No Teachers, Ethiopian Children Teach Themselves,” Technology Review, October 29, 2012, http://www.technologyreview.com/news/506466/given-tablets-but-no-teachers-ethiopian-children-teach-themselves/. one of the lowest rates of literacy in the world: “Field Listing: Literacy,” CIA, World Fact Book, accessed October 11, 2012, https://www.cia.gov/library/publications/the-world-factbook/fields/2103.html#af. in 2012, Nevada became the first state to issue licenses to driverless cars: Chris Gaylord, “Ready for a Self-Driving Car? Check Your Driveway,” Christian Science Monitor, June 25, 2012, http://www.csmonitor.com/Innovation/Tech/2012/0625/Ready-for-a-self-driving-car-Check-your-driveway. California also affirmed their legality: James Temple, “California Affirms Legality of Driverless Cars,” The Tech Chronicles (blog), San Francisco Chronicle, September 25, 2012, http://blog.sfgate.com/techchron/2012/09/25/california-legalizes-driverless-cars/; Florida has passed a similar law. See Joann Muller, “With Driverless Cars, Once Again It Is California Leading the Way,” Forbes, September 26, 2012, http://www.forbes.com/sites/joannmuller/2012/09/26/with-driverless-cars-once-again-it-is-california-leading-the-way/.

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