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The Economic Singularity: Artificial intelligence and the death of capitalism by Calum Chace

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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, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, 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, universal basic income, Vernor Vinge, working-age population, Y Combinator, young professional

In deep learning, the algorithms operate in several layers, each layer processing data from previous ones and passing the output up to the next layer. The output is not necessarily binary, just on or off: it can be weighted. The number of layers can vary too, with anything above ten layers seen as very deep learning – although in December 2015 a Microsoft team won the ImageNet competition with a system which employed a massive 152 layers.[lxvi] Deep learning, and especially artificial neural nets (ANNs), are in many ways a return to an older approach to AI which was explored in the 1960s but abandoned because it proved ineffective. While Good Old-Fashioned AI held sway in most labs, a small group of pioneers known as the Toronto mafia kept faith with the neural network approach.

In December 2015, Microsoft's chief speech scientist Xuedong Huang noted that speech recognition has improved 20% a year consistently for the last 20 years. He predicted that computers would be as good as humans at understanding human speech within five years. Geoff Hinton – the man whose team won the landmark 2012 ImageNet competition – went further. In May 2015 he said that he expects machines to demonstrate common sense within a decade. Common sense can be described as having a mental model of the world which allows you to predict what will happen if certain actions are taken. Professor Murray Shanahan of Imperial College uses the example of throwing a chair from a stage into an audience: humans would understand that members of the audience would throw up their hands to protect themselves, but some damage would probably be caused, and certainly some upset.


pages: 368 words: 96,825

Bold: How to Go Big, Create Wealth and Impact the World by Peter H. Diamandis, Steven Kotler

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3D printing, additive manufacturing, Airbnb, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, cloud computing, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dematerialisation, deskilling, Elon Musk, en.wikipedia.org, Exxon Valdez, fear of failure, Firefox, Galaxy Zoo, Google Glasses, Google Hangouts, Google X / Alphabet X, gravity well, ImageNet competition, industrial robot, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta analysis, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, technoutopianism, telepresence, telepresence robot, Turing test, urban renewal, web application, X Prize, Y Combinator, zero-sum game

Fifty thousand different traffic signs are used—signs obscured by long distances, by trees, by the glare of sunlight. In 2011, for the first time, a machine-learning algorithm bested its makers, achieving a 0.5 percent error rate, compared to 1.2 percent for humans.32 Even more impressive were the results of the 2012 ImageNet Competition, which challenged algorithms to look at one million different images—ranging from birds to kitchenware to people on motor scooters—and correctly slot them into a thousand unique categories. Seriously, it’s one thing for a computer to recognize known objects (zip codes, traffic signs), but categorizing thousands of random objects is an ability that is downright human.

., 15, 17, 18, 19, 20, 21 structure of, 21 see also entrepreneurs, exponential; specific exponential entrepreneurs and organizations Exponential Organizations (ExO) (Ismail), xiv, 15 extrinsic rewards, 78, 79 Exxon Valdez, 250 FAA (Federal Aviation Administration), 110, 111, 261 Facebook, 14, 16, 88, 128, 173, 182, 185, 190, 195, 196, 202, 212, 213, 217, 218, 224, 233, 234, 236, 241 facial recognition software, 58 Fairchild Semiconductor, 4 Falcon launchers, 97, 119, 122, 123 false wins, 268, 269, 271 Fast Company, 5, 248 Favreau, Jon, 117 feedback, feedback loops, 28, 77, 83, 84, 120, 176, 180 in crowdfunding campaigns, 176, 180, 182, 185, 190, 199, 200, 202, 209–10 triggering flow with, 86, 87, 90–91, 92 Festo, 61 FeverBee (blog), 233 Feynman, Richard, 268, 271 Firefox Web browser, 11 first principles, 116, 120–21, 122, 126 Fiverr, 157 fixed-funding campaigns, 185–86, 206 “flash prizes,” 250 Flickr, 14 flow, 85–94, 109, 278 creative triggers of, 87, 93 definition of, 86 environmental triggers of, 87, 88–89 psychological triggers of, 87, 89–91, 92 social triggers of, 87, 91–93 Flow Genome Project, xiii, 87, 278 Foldit, 145 Forbes, 125 Ford, Henry, 33, 112–13 Fortune, 123 Fossil Wrist Net, 176 Foster, Richard, 14–15 Foundations (Rose), 120 Fowler, Emily, 299n Foxconn, 62 Free (Anderson), 10–11 Freelancer.com, 149–51, 156, 158, 163, 165, 195, 207 Friedman, Thomas, 150–51 Galaxy Zoo, 220–21, 228 Gartner Hype Cycle, 25–26, 25, 26, 29 Gates, Bill, 23, 53 GEICO, 227 General Electric (GE), 43, 225 General Mills, 145 Gengo.com, 145 Genius, 161 genomics, x, 63, 64–65, 66, 227 Georgia Tech, 197 geostationary satellite, 100 Germany, 55 Get a Freelancer (website), 149 Gigwalk, 159 Giovannitti, Fred, 253 Gmail, 77, 138, 163 goals, goal setting, 74–75, 78, 79, 80, 82–83, 84, 85, 87, 137 in crowdfunding campaigns, 185–87, 191 moonshots in, 81–83, 93, 98, 103, 104, 110, 245, 248 subgoals in, 103–4, 112 triggering flow with, 89–90, 92, 93 Godin, Seth, 239–40 Google, 11, 14, 47, 50, 61, 77, 80, 99, 128, 134, 135–39, 167, 195, 208, 251, 286n artificial intelligence development at, 24, 53, 58, 81, 138–39 autonomous cars of, 43–44, 44, 136, 137 eight innovation principles of, 84–85 robotics at, 139 skunk methodology used at, 81–84 thinking-at-scale strategies at, 136–38 Google Docs, 11 Google Glass, 58 Google Hangouts, 193, 202 Google Lunar XPRIZE, 139, 249 Googleplex, 134 Google+, 185, 190, 202 GoogleX, 81, 82, 83, 139 Google Zeitgeist, 136 Gossamer Condor, 263 Gou, Terry, 62 graphic designers, in crowdfunding campaigns, 193 Green, Hank, 180, 200 Grepper, Ryan, 210, 211–13 Grishin, Dmitry, 62 Grishin Robotics, 62 group flow, 91–93 Gulf Coast oil spill (2010), 250, 251, 253 Gulf of Mexico, 250, 251 hackathons, 159 hacker spaces, 62, 64 Hagel, John, III, 86, 106–7 HAL (fictional AI system), 52, 53 Hallowell, Ned, 88 Hariri, Robert, 65, 66 Harrison, John, 245, 247, 267 Hawking, Stephen, 110–12 Hawley, Todd, 100, 103, 104, 107, 114n Hayabusa mission, 97 health care, x, 245 AI’s impact on, 57, 276 behavior tracking in, 47 crowdsourcing projects in, 227, 253 medical manufacturing in, 34–35 robotics in, 62 3–D printing’s impact on, 34–35 Heath, Dan and Chip, 248 Heinlein, Robert, 114n Hendy, Barry, 12 Hendy’s law, 12 HeroX, 257–58, 262, 263, 265, 267, 269, 299n Hessel, Andrew, 63, 64 Hinton, Geoffrey, 58 Hoffman, Reid, 77, 231 Hollywood, 151–52 hosting platforms, 20–21 Howard, Jeremy, 54 Howe, Jeff, 144 Hseih, Tony, 80 Hughes, Jack, 152, 225–27, 254 Hull, Charles, 29–30, 32 Human Longevity, Inc. (HLI), 65–66 Hyatt Hotels Corporation, 20 IBM, 56, 57, 59, 76 ImageNet Competition (2012), 55 image recognition, 55, 58 Immelt, Jeff, 225 incentive competitions, xiii, 22, 139, 148, 152–54, 159, 160, 237, 240, 242, 243–73 addressing market failures with, 264–65, 269, 272 back-end business models in, 249, 265, 268 benefits of, 258–61 case studies of, 250–58 collaborative spirit in, 255, 260–61 crowdsourcing in designing of, 257–58 factors influencing success of, 245–47 false wins in, 268, 269, 271 “flash prizes” in, 250 global participation in, 267 innovation driven by, 245, 247, 248, 249, 252, 258–59, 260, 261 intellectual property (IP) in, 262, 267–68, 271 intrinsic rewards in, 254, 255 judging in, 273 key parameters for designing of, 263–68 launching of new industries with, 260, 268, 272 Master Team Agreements in, 273 media exposure in, 265, 266, 272, 273 MTP and passion as important in, 248, 249, 255, 263, 265, 270 operating costs of, 271, 272–73 principal motivators in, 254, 262–63 purses in, 265, 266, 270, 273 reasons for effectiveness of, 247–49 risk taking in, 247, 248–49, 261, 270 setting rules in, 263, 268, 269, 271, 273 small teams as ideal in, 262 step-by-step guide to, 269–73 telegenic finishes in, 266, 272, 273 time limits in, 249, 267, 271–72 XPRIZE, see XPRIZE competitions Indian Motorcycle company, 222 Indian Space Research Organization, 102 Indiegogo, 145, 173, 175, 178, 179, 184, 185–86, 187, 190, 199, 205, 206, 257 infinite computing, 21, 24, 41, 48–52, 61, 66 entrepreneurial opportunities and, 50–52 information: crowdsourcing platforms in gathering of, 145–46, 154–56, 157, 159–60, 220–21, 228 in data-driven crowdfunding campaigns, 207–10, 213 networks and sensors in garnering of, 42–43, 44, 47, 48, 256 science, 64 see also data mining Inman, Matthew, 178, 192, 193, 200 innovation, 8, 30, 56, 137, 256 companies resistant to, xi, 9–10, 12, 15, 23, 76 crowdsourcing and, see crowdsourcing as disruptive technology, 9–10 feedback loops in fostering of, 28, 77, 83, 84, 86, 87, 90–91, 92, 120, 176 Google’s eight principles of, 84–85 incentive competitions in driving of, 245, 247, 248, 249, 252, 258–59, 260, 261 infinite computing as new approach to, 51 power of constraints and, 248–49, 259 rate of, in online communities, 216, 219, 224, 225, 228, 233, 237 setting big goals for, 74–75, 78, 79, 80, 82–83, 84, 85, 87, 89–90, 92, 93, 103 skunk methodology in fostering of, 71–87, 88; see also skunk methodology inPulse, 176, 200 Instagram, 15–16, 16 insurance companies, 47 Intel, 7 intellectual property (IP), 262, 267–68, 271 INTELSAT, 102 Intel Science and Engineering Fair, 65 International Manufacturing Technology Show, 33 International Space Station (ISS), 35–36, 37, 97, 119 International Space University (ISU), 96, 100–104, 107–8 Founding Conference of, 102, 103 Internet, 8, 14, 39, 41, 45, 49, 50, 117, 118, 119, 132, 136, 143, 144, 153, 154, 163, 177, 207, 208, 209, 212, 216, 217, 228 building communities on, see communities, online crowd tools on, see crowdfunding, crowdfunding campaigns; crowdsourcing development of, 27 explosion of connectivity to, 42, 46, 46, 146, 147, 245 mainstreaming of, 27, 32, 33 reputation economics and, 217–19, 230, 232, 236–37 Internet-of-Things (IoT), 46, 47, 53 intrinsic rewards, 79, 254, 255 Invisalign, 34–35 iPads, 42, 57, 167 iPhones, 12, 42, 62, 176 iPod, 17, 18, 178 iRobot, 60 Iron Man, 52–53, 117 Ismail, Salim, xiv, 15, 77, 92 isolation, innovation and, 72, 76, 78, 79, 81–82, 257 Japan Aerospace Exploration Agency, 97 JARVIS (fictional AI system), 52–53, 58, 59, 146 Jeopardy, 56, 57 Jet Propulsion Laboratory (JPL), 99 Jobs, Steve, xiv, 23, 66–67, 72, 89, 111, 123 Johnson, Carolyn, 227 Johnson, Clarence “Kelly,” 71, 74, 75 skunk work rules of, 74, 75–76, 77, 81, 84, 247 Joy, Bill, 216, 256 Jumpstart Our Business Startups (JOBS) Act (2012), 171, 173 Kaggle, 160, 161 Kahneman, Daniel, 78, 121 Kaku, Michio, 49 Kauffman, Stuart, 276 Kaufman, Ben, 17–20 Kay, Alan, 114n Kemmer, Aaron, 35, 36, 37 Kickstarter, 145, 171, 173, 175, 176, 179–80, 182, 184, 190, 191, 193, 195, 197, 200, 205, 206 Kindle, 132 Kiva.org, 144–45, 172 Klein, Candace, 19–20, 171 Klein, Joshua, 217–18, 221 Klout, 218 Kodak Corporation, 4–8, 9–10, 11, 12, 20 Apparatus Division of, 4 bankruptcy of, 10, 16 digital camera developed by, 4–5, 5, 9 as innovation resistant, 9–10, 12, 15, 76 market dominance of, 5–6, 13–14 Kotler, Steven, xi, xiii, xv, 87, 279 Krieger, Mike, 15 Kubrick, Stanley, 52 Kurzweil, Ray, 53, 54, 58, 59 language translators, 137–38 crowdsourcing projects of, 145, 155–56 Latham, Gary, 74–75, 103 Law of Niches, 221, 223, 228, 231 leadership: importance of vision in, 23–24 moral, 274–76 Lean In (Sandberg), 217 Lean In circles, 217, 237 LEAP airplane, 34 LendingClub, 172 LeNet 5, 54, 55 Let’s Build a Goddamn Tesla Museum, see Tesla Museum campaign Levy, Steven, 138 Lewicki, Chris, 99, 179, 202, 203–4 Lichtenberg, Byron K., 102, 114n Licklider, J.