hype cycle

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Green Swans: The Coming Boom in Regenerative Capitalism by John Elkington

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, agricultural Revolution, Anthropocene, anti-fragile, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, Berlin Wall, bitcoin, Black Swan, blockchain, Boeing 737 MAX, Boeing 747, Buckminster Fuller, business cycle, Cambridge Analytica, carbon footprint, carbon tax, circular economy, Clayton Christensen, clean water, cloud computing, corporate governance, corporate social responsibility, correlation does not imply causation, creative destruction, CRISPR, crowdsourcing, David Attenborough, deglobalization, degrowth, discounted cash flows, distributed ledger, do well by doing good, Donald Trump, double entry bookkeeping, drone strike, Elon Musk, en.wikipedia.org, energy transition, Extinction Rebellion, Future Shock, Gail Bradbrook, Geoffrey West, Santa Fe Institute, George Akerlof, global supply chain, Google X / Alphabet X, green new deal, green transition, Greta Thunberg, Hans Rosling, hype cycle, impact investing, intangible asset, Internet of things, invention of the wheel, invisible hand, Iridium satellite, Jeff Bezos, John Elkington, Jony Ive, Joseph Schumpeter, junk bonds, Kevin Kelly, Kickstarter, M-Pesa, Marc Benioff, Mark Zuckerberg, Martin Wolf, microplastics / micro fibres, more computing power than Apollo, move fast and break things, Naomi Klein, Nelson Mandela, new economy, Nikolai Kondratiev, ocean acidification, oil shale / tar sands, oil shock, opioid epidemic / opioid crisis, placebo effect, Planet Labs, planetary scale, plant based meat, plutocrats, Ponzi scheme, radical decentralization, Ralph Nader, reality distortion field, Recombinant DNA, Rubik’s Cube, Salesforce, self-driving car, shareholder value, sharing economy, Sheryl Sandberg, Silicon Valley, smart cities, smart grid, sovereign wealth fund, space junk, Steven Pinker, Stewart Brand, supply-chain management, synthetic biology, systems thinking, The future is already here, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Tim Cook: Apple, urban planning, Whole Earth Catalog

See also change process stages; Future-Fit change approach; technology allowing to flourish, 166 Anthropocenic route, taking, 230–234 antibiotics through lens of, 108 assets with characteristics of, 73 be a leader, not an algorithm, 223–230 Black Swans starting off as, 182 business models with characteristics of, 53 calories through lens of, 102 capitalism with characteristics of, 202–208 carbon economy through lens of, 111 defined, 9, 22, 167 democracy with characteristics of, 208–213 different thinking, need for, 23–27 early sharing of content about, 199–201 exponential leaders, 236–242 future, differing views of, 190–193 global grand challenges approach, 186–187 governance with characteristics of, 71 gradual, then sudden evolution of, 76 growth with characteristics of, 57–58 historical, 43 impact with characteristics of, 64 liability with characteristics of, 68 losing control, risk of, 193–197 materiality with characteristics of, 69–70 overview, 1–3, 22–23, 219–223 as parallel reality with Black Swans, 9–10 plastics through lens of, 97 profitability with characteristics of, 55 purpose with characteristics of, 50 push and pull in evolution of, 189–190 recent examples, 42 reinventing everything, 197–199 space junk through eyes of, 116 spotting, 254–256 sustainability with characteristics of, 213–218 systemic change overview, 201–202 three horizons, two scenarios, 2000-2100, 38–39 and triple bottom line concept, 12–13 U-bend, unclogging, 234–236 value with characteristics of, 61 Green Swans (film), 9–10, 248 Green Transition Scoreboard, 233 growth, 47, 55–58 The Guardian (newspaper), 96 H Haan, Nick, 200, 222 Hamid, Mohsin, 109, 110 Harvard Business Review (HBR), 32, 155–158 Haut, Sonja, 253 Hawken, Paul, 141, 142, 232 Hemingway, Ernest, 79 Hichens, Robert, 198–199 Hill-Landolt, Julian, 229–230 Hippocratic Oath, 108 Hoffman, Donald, 27 Hofstetter, Dominic, 220 Holocene epoch, 86 Honda, 135 horseshoe crabs, 231–232 How Adam Smith Can Change Your Life (Roberts), 80 The Human Planet (Lewis and Maslin), 29 Humanitarians, exponential leaders as, 238 humor, 120–121 Hunter, Sarah, 240 Hurd, Nick, 212–213 Hutton, Will, 196 Hwang Sang-ki, 126 Hyatt, John Wesley, 94 hydropower, 201 Hype Cycle, Gartner, 173–175 I Ibbitson, John, 222–223 Ignatius, Adi, 155–156 illusion of control, 44 impact, 47, 61–64, 256 impact investing, 63, 64 Impossible Foods, 233 incremental change, 34, 35f, 57, 233–234 India, 82 Indonesia, 220–221 industrial revolutions, 175–176 industry federations and associations, 132 inflated expectations, in Gartner Hype Cycle, 174 influencing activities, 145–146 information, role in economy, 190, 192 Innovation Trigger stage, Gartner Hype Cycle, 174 innovations, in three horizons framework, 38–39 Innovators, exponential leaders as, 238 Institute for Energy Economics and Financial Analysis, 242 Institute for Transformative Technologies (ITT), 184–185 insulin, 156–157 insurance industry, 136–137 intangible assets, 72 integrated business models, 52 Interface, 142 intergenerational transfer of wealth, 201 International Accounting Standards Board, 58 International Finance Corporation, 119 international OTA, need for, 183, 185 International Renewable Energy Agency (IRENA), 133 International Space Station (ISS), 111 internet, 173, 175, 192 Internet of Things (IoT), 177 investors/investing, 63, 64, 162, 205–208, 242–244 invisible hand, 18, 25, 85 Ipsos, 219 Israel, 171 Ive, Jony, 213–214 Iversen, Torben, 212 ivory, 93 J Jackson, Clive, 245–246 Jackson, Tim, 56 Jakarta, Indonesia, 220–221 Japan, 135 Johnson, Nicholas, 113 Johnson & Johnson, 13 Johnson County, Indiana, 126–127 joint and several liability, 66 Jørgensen, Lars Fruergaard, 158–159 journalists, 227 JPMorgan, 142–143 Just, Inc., 233 JWT, 140–141 K Kahneman, Daniel, 204 Kaiser Permanente, 255 Kelly, Kevin, 36 Kelly, Marjorie, 205 Kendall, Geoff, 159–164 Kerr, Andrew, 201 Kessler, Donald, 112 Kingston, Phil, 189 Klee, Louis, 109 Klimenko, Svetlana, 207–208 Kondratiev, Nikolai, 203 Kramer, Mark, 59 Kuhn, Thomas, 41, 121–122, 123, 191, 230 L Langer, Ellen, 44 Lawrence Berkeley National Lab, 184–185 Layton, David, 190–191 leaded gasoline, 172 leaders, 223–230, 236–242.

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Before we consider how that might best be done, though, it is worth underscoring just how unusual the current moment is in technology’s evolution. RIDING HYPE CYCLES I have long been fascinated by technology, writing many books about it. I edited a newsletter, Biotechnology Bulletin, for fifteen years, enabling me to visit almost a hundred firms involved in genetic engineering in Europe, Japan, and North America during the peak of the first biotechnology boom. If I learned any one thing over that time, it is that new technologies trigger feeding frenzies that typically follow a standard arc like that visualized in the famous Gartner Hype Cycle. This, Gartner tells us, evolves through five main stages: 1.Innovation Trigger: At this point we see some form of breakthrough in a key technology, accompanied by one or more initial product launches, sparking growing media and public interest.

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See also change process stages; Future-Fit change approach Paradise, California, 137 Patriotic Millionaires, 132 Peak of Inflated Expectations stage, Gartner Hype Cycle, 174 Pearl, Morris, 132 penicillin, 103–104 People, Planet & Profit (3Ps), 30, 54 Perez, Carlota, 203, 235 PG&E power utility, 137 pharmaceutical industry, 231–232 Phelan, Ryan, 231–232 philanthropy, misuse of, 13 Piketty, Thomas, 60 Pinker, Steven, 28 placebo buttons, 43–46, 54 Plan B work, 234 Planet Labs, 114, 115 plant-based meat, 233 Plastic: A Toxic Love Story (Freinkel), 92–93 A Plastic Ocean (film), 92 plastics, as wicked problem, 92–97 Plateau of Productivity stage, Gartner Hype Cycle, 175 Pliny the Elder, 39 politics. See also democracy activism in, 227–228 breakers in, 221 different thinking, need for, 23–27 in future, 224 future-fit, 163–164 with Green Swan characteristics, 208–213 and need for systemic change, 14–15 underperformance in, 128 Polman, Paul, 19, 131 Polos, Stephen, 215 polymers industry, 92–97 Pope Francis, 128 population decline, 210–211, 222–223 Porter, Michael, 59, 150, 150f Positive Pursuits element, Future-Fit approach, 163 The Power of Unreasonable People (Elkington and Hartigan), 11 precision medicine, 179 predatory delay, 67 “Pre-Science” stage, in paradigm shift, 121–122, 123 product liability, 66 professional media, 227 profit(s), 27, 30–33, 47, 53–55, 128–129 Project Breakthrough, 34–36 Project Drawdown, 141, 232 Prosperity (Mayer), 49–50 Prosperity without Growth (Jackson), 56 psychoanalyzing business terms.

Bitcoin: The Future of Money? by Dominic Frisby

3D printing, Alan Greenspan, altcoin, bank run, banking crisis, banks create money, barriers to entry, bitcoin, Bitcoin Ponzi scheme, blockchain, capital controls, Chelsea Manning, cloud computing, computer age, cryptocurrency, disintermediation, Dogecoin, Ethereum, ethereum blockchain, fiat currency, financial engineering, fixed income, friendly fire, game design, Hacker News, hype cycle, Isaac Newton, John Gilmore, Julian Assange, land value tax, litecoin, low interest rates, M-Pesa, mobile money, Money creation, money: store of value / unit of account / medium of exchange, Occupy movement, Peter Thiel, Ponzi scheme, prediction markets, price stability, printed gun, QR code, quantitative easing, railway mania, Ronald Reagan, Ross Ulbricht, Satoshi Nakamoto, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, Stephen Hawking, Steve Jobs, Ted Nelson, too big to fail, transaction costs, Turing complete, Twitter Arab Spring, Virgin Galactic, Vitalik Buterin, War on Poverty, web application, WikiLeaks

Only a handful of the companies and altcoins that have sprung up will make it, but those that do could dominate the space for many years to come, just as those few rail, car and dotcom companies that survived did. Beware of the hype cycle There is a cycle that a new technology passes through as it goes from conception to widespread adoption. The research company Gartner has dubbed it the ‘hype cycle’. It has five phases: the technology trigger, the peak of inflated expectations, the trough of disappointment, the slope of enlightenment and the plateau of productivity. In the first phase the new technology is invented.

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A great deal of not particularly rewarding hard work, time and investment lies ahead. Forget the ideas men – now we need the water-carriers. Suddenly, the excitement has gone. Negative press starts to creep in. Now there are more sellers than buyers. Investment is harder to come by. Many companies start going bust. People are losing money. The hype cycle has reversed and we have descended into the ‘trough of disappointment.’ This was the internet between 2000 and 2003. But now that the hot money has left, we can move into phase four. The incompetent or fraudulent companies have died. The sector has been purged. Most of those that remain are serious players.

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Investors now demand better practice and the survivors deliver it. They release the second and third generation products, and they work quite well. More and more people start to use the technology and it is finally finding mainstream adoption. This was the internet in 2004. It climbed the ‘Slope of Enlightenment’, the fourth phase of the hype cycle, and entered the ‘Plateau of Productivity’ – phase five – which is where the likes of Google, Amazon and eBay are today. Of course, cycles like this are arbitrary. Reality is never quite so simple. But it’s easy to make the case that crypto-currencies in late 2013 reached a ‘peak of inflated expectations’.

Geek Sublime: The Beauty of Code, the Code of Beauty by Vikram Chandra

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Apple II, barriers to entry, Berlin Wall, Big Tech, British Empire, business process, Californian Ideology, Charles Babbage, conceptual framework, create, read, update, delete, crowdsourcing, don't repeat yourself, Donald Knuth, East Village, European colonialism, finite state, Firefox, Flash crash, functional programming, glass ceiling, Grace Hopper, Hacker News, haute couture, hype cycle, iterative process, Jaron Lanier, John von Neumann, land reform, London Whale, Norman Mailer, Paul Graham, pink-collar, revision control, Silicon Valley, Silicon Valley ideology, Skype, Steve Jobs, Steve Wozniak, supercomputer in your pocket, synthetic biology, tech worker, the Cathedral and the Bazaar, theory of mind, Therac-25, Turing machine, wikimedia commons, women in the workforce

“Household Data Annual Averages: Employed Persons by Detailed Occupation, Sex, Race, and Hispanic or Latino Ethnicity.” Bureau of Labor Statistics, United States Department of Labor, 2012. http://www.bls.gov/opub/ee/2013/cps/annavg11_2012.pdf. “How SQLite Is Tested.” Sqlite.org. Accessed February 3, 2013. http://www.sqlite.org/testing.html. “Hype Cycle Research Methodology.” Gartner.com. Accessed February 3, 2013. http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp. “iGEM 2012 HS Is Officially Over!” Igem.org. Accessed February 3, 2013. http://2012hs.igem.org/Main_Page. “The IBM 650 Magnetic Drum Calculator.” Columbia University Computing History. Accessed August 27, 2013. http://www.columbia.edu/cu/computinghistory/650.html.

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Institute Archives and Special Collections, MIT Libraries, Cambridge, MA Figure 5.1: Rules from the Ashtadhyayi (Vedic Literature Collection, Maharishi University of Management) Figure 6.1: Dependency diagram (TheDailyWTF, www.thedailywtf.com) Figure 6.2: “Hello, world!” in brainfuck Figure 6.3: “Hello, world!” in Malbolge Figure 6.4: Gartner, Inc.’s Hype Cycle (Jeremy Kemp, Wikimedia Commons) ACKNOWLEDGMENTS This project has been supported by the University of California, Berkeley. Thanks to Martin Howard for the images of the LEGO logic gates (http://www.randomwraith.com/logic.html); and to Alex Papadimoulis of TheDailyWTF.com for the dependency diagram.

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A naive outsider might wonder if the quality of a language matters a little, just a teeny bit at least, but in the real world fashion trumps all.19 In respect to programming languages and techniques, the programming industry has now been through many cycles of faith and disillusionment, and many of its members have acquired a sharp, necessary cynicism. “Hype Cycle”—a phrase coined by the analysts at Gartner, Inc.—adroitly captures the up-and-down fortunes of many a tech fad. 20 The tools and processes used to manage all this complexity engender another layer of complexity. All but the simplest programs must be written by teams of programmers, each working on a small portion of the system.

The Digital Doctor: Hope, Hype, and Harm at the Dawn of Medicine’s Computer Age by Robert Wachter

activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, AI winter, Airbnb, Atul Gawande, Captain Sullenberger Hudson, Checklist Manifesto, Chuck Templeton: OpenTable:, Clayton Christensen, cognitive load, collapse of Lehman Brothers, computer age, creative destruction, crowdsourcing, deep learning, deskilling, disruptive innovation, driverless car, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Firefox, Frank Levy and Richard Murnane: The New Division of Labor, general purpose technology, Google Glasses, human-factors engineering, hype cycle, Ignaz Semmelweis: hand washing, Internet of things, job satisfaction, Joseph Schumpeter, Kickstarter, knowledge worker, lifelogging, Marc Benioff, medical malpractice, medical residency, Menlo Park, minimum viable product, natural language processing, Network effects, Nicholas Carr, obamacare, pattern recognition, peer-to-peer, personalized medicine, pets.com, pneumatic tube, Productivity paradox, Ralph Nader, RAND corporation, Richard Hendricks, Robert Solow, Salesforce, Second Machine Age, self-driving car, seminal paper, Silicon Valley, Silicon Valley startup, six sigma, Skype, Snapchat, software as a service, Steve Jobs, Steven Levy, TED Talk, The future is already here, the payments system, The Wisdom of Crowds, Thomas Bayes, Toyota Production System, Uber for X, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, Yogi Berra

David, “The Dynamo and the Computer: An Historical Perspective on the Modern Productivity Paradox,” American Economic Review Papers and Proceedings 1:355–361 (1990). 246 Robert Wah … told me what happened Interview of Wah by the author, August 4, 2014. 246 In 1995, the consulting group Gartner coined Gartner Hype Cycle, available at http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp. 247 One widely cited 2005 RAND study R. Hillestad, J. Bigelow, A. Bower, et al., “Can Electronic Medical Record Systems Transform Health Care? Potential Health Benefits, Savings, and Costs,” Health Affairs 24:1103–1117 (2005). 247 Bob Kocher, the Obama advisor Interview of Kocher by the author, June 19, 2014. 247 President-elect Obama, who, in a January 2009 speech, declared Remarks of President-elect Barack Obama, January 8, 2009, available at http://change.gov/newsroom/entry/presidentelect_obama_speaks_on_the_need_for_urgent_action_on_an_american_r/. 247 In 2014 … another RAND team reviewed the literature S.

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“The receptionist checked the patient in electronically, and then turned around and put a chart-sized piece of cardboard in the chart rack. That’s how wedded we are to our paper-based processes.” Appreciating the need to address work flow and culture does not get kludgy technology off the hook. In 1995, the consulting group Gartner coined the term Hype Cycle to describe the trajectory of many technologies. The cycle commences with the introduction of a new technology. Then the hype begins, with a “peak of inflated expectations” preceding a swift descent into the dreaded “trough of disillusionment.” Some technologies die there; the ones destined to be successful climb the “slope of enlightenment” to reach their final stage: a “plateau of productivity.”

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Part of what is so impressive about the modern generation of consumer-directed IT tools— Google search, the iPhone and iPad, Facebook—is that they moved through the stages so rapidly, with barely any trough. Could it be that technology designers have become so skilled that, in today’s world, the Hype Cycle is either avoided or markedly foreshortened? Perhaps, but not in healthcare. Part of the hype in health IT was that we’d save boatloads of cash. This was a particularly attractive selling point for EHRs, since healthcare now consumes nearly one in five U.S. dollars, leading to a widespread consensus that if we don’t get healthcare costs under control, they will smother the rest of the economy.

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

3D printing, additive manufacturing, adjacent possible, Airbnb, Amazon Mechanical Turk, Amazon Web Services, Apollo 11, augmented reality, autonomous vehicles, Boston Dynamics, Charles Lindbergh, cloud computing, company town, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, data science, deal flow, deep learning, dematerialisation, deskilling, disruptive innovation, driverless car, Elon Musk, en.wikipedia.org, Exxon Valdez, fail fast, Fairchild Semiconductor, fear of failure, Firefox, Galaxy Zoo, Geoffrey Hinton, Google Glasses, Google Hangouts, gravity well, hype cycle, ImageNet competition, industrial robot, information security, Internet of things, Jeff Bezos, John Harrison: Longitude, John Markoff, Jono Bacon, Just-in-time delivery, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, Lean Startup, life extension, loss aversion, Louis Pasteur, low earth orbit, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Mars Rover, meta-analysis, microbiome, minimum viable product, move fast and break things, Narrative Science, Netflix Prize, Network effects, Oculus Rift, OpenAI, optical character recognition, packet switching, PageRank, pattern recognition, performance metric, Peter H. Diamandis: Planetary Resources, Peter Thiel, pre–internet, Ray Kurzweil, recommendation engine, Richard Feynman, ride hailing / ride sharing, risk tolerance, rolodex, Scaled Composites, self-driving car, sentiment analysis, shareholder value, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart grid, SpaceShipOne, stem cell, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, Stuart Kauffman, superconnector, Susan Wojcicki, synthetic biology, technoutopianism, TED Talk, telepresence, telepresence robot, Turing test, urban renewal, Virgin Galactic, Wayback Machine, web application, X Prize, Y Combinator, zero-sum game

Back in the late 1960s, when folks like writer Stewart Brand (who coined the term personal computer) first started discussing the idea of the PC, it was with an incredible amount of “change the world” fervor.3 Then the machines actually arrived, and all most people could do was play Pong. This was the trough of disillusionment, cultural deception at its finest. But imagine being able to take your knowledge of what computers can do today back to the early 1980s—what bold entrepreneurial business opportunities might this have unlocked for you? Hype Cycle Indicators Gartner Hype Cycle Indicators Source: www.gartner.com Recognizing when a technology is exiting the trough of disillusionment and beginning to rise up the slope of enlightenment is critical for entrepreneurs. Reading an exponential curve like a road map, experts watch for a number of indicators—the development of best practices, supplier proliferation, secondary financings, among others.

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Thus each of these technologies hover on the verge of widespread adoption, and for those exponential entrepreneurs able to stay ahead of this curve, the opportunities are considerable. The Hype Curve and the User Interface If we want to stay ahead of this curve, it helps to understand a little more about the nature of exponential deception. That starts with understanding the powerful biases that inform the Gartner Hype Cycle (see below). After a novel technology is introduced and begins gaining momentum, we tend to envision it in its final form—seriously overinflating our expectations for both its developmental timetable and its short-term potential. Invariably, when these technologies fail to live up to the initial hype—usually in that gap between deception and disruption on our list of the Six Ds—public sentiment for the technology falls into the trough of disillusionment.

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., 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.

Cryptoassets: The Innovative Investor's Guide to Bitcoin and Beyond: The Innovative Investor's Guide to Bitcoin and Beyond by Chris Burniske, Jack Tatar

Airbnb, Alan Greenspan, altcoin, Alvin Toffler, asset allocation, asset-backed security, autonomous vehicles, Bear Stearns, bitcoin, Bitcoin Ponzi scheme, blockchain, Blythe Masters, book value, business cycle, business process, buy and hold, capital controls, carbon tax, Carmen Reinhart, Clayton Christensen, clean water, cloud computing, collateralized debt obligation, commoditize, correlation coefficient, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, disintermediation, distributed ledger, diversification, diversified portfolio, Dogecoin, Donald Trump, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, fiat currency, financial engineering, financial innovation, fixed income, Future Shock, general purpose technology, George Gilder, Google Hangouts, high net worth, hype cycle, information security, initial coin offering, it's over 9,000, Jeff Bezos, Kenneth Rogoff, Kickstarter, Leonard Kleinrock, litecoin, low interest rates, Marc Andreessen, Mark Zuckerberg, market bubble, money market fund, money: store of value / unit of account / medium of exchange, moral hazard, Network effects, packet switching, passive investing, peer-to-peer, peer-to-peer lending, Peter Thiel, pets.com, Ponzi scheme, prediction markets, quantitative easing, quantum cryptography, RAND corporation, random walk, Renaissance Technologies, risk free rate, risk tolerance, risk-adjusted returns, Robert Shiller, Ross Ulbricht, Salesforce, Satoshi Nakamoto, seminal paper, Sharpe ratio, Silicon Valley, Simon Singh, Skype, smart contracts, social web, South Sea Bubble, Steve Jobs, transaction costs, tulip mania, Turing complete, two and twenty, Uber for X, Vanguard fund, Vitalik Buterin, WikiLeaks, Y2K

The realm of public blockchains and their native assets is most relevant to the innovative investor, as private blockchains have not yielded an entirely new asset class that is investable to the public. WHERE IS BLOCKCHAIN TECHNOLOGY IN THE HYPE CYCLE? By now it will be clear to the innovative investor that the blockchain technology space is still working itself out and will continue to do so for years to come. Captivating technologies have a gravitational pull that brings in new minds with varied perspectives and that will push the boundaries of the technology. The progression of a new technology, and the way it evolves as it gains mental mindshare, is at the core of Gartner’s Hype Cycle for Emerging Technologies (Gartner is a leading technology research and advisory firm),17 which displays five common stages of technology.18 • Innovation Trigger • Peak of Inflated Expectations • Trough of Disillusionment • Slope of Enlightenment • Plateau of Productivity First is the Innovation Trigger that brings the technology into the world.

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When enough people have given up, but the loyal keep working in dedication, the technology begins to rise again, this time not with the irrational exuberance of its early years, but instead with a sustained release of improvements and productivity. Over time the technology matures, ultimately becoming a steady platform in the Plateau of Productivity that provides a base on which to build other technologies. While it’s hard to predict where blockchain technology currently falls on Gartner’s Hype Cycle (these things are always easier in retrospect), we would posit that Bitcoin is emerging from the Trough of Disillusionment. At the same time, blockchain technology stripped of native assets (private blockchain) is descending from the Peak of Inflated Expectations, which it reached in the summer of 2016 just before The DAO hack occurred (which we will discuss in detail in Chapter 5).

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The computers are not technically miners because they are not minting any new assets and they are not paid directly for their work. 16. http://www.nyu.edu/econ/user/jovanovi/JovRousseauGPT.pdf. 17. http://www.gartner.com/newsroom/id/3412017. 18. http://www.gartner.com/technology/research/methodologies/hype-cycle.jsp. Chapter 4 1. Network value = (units of the asset outstanding) × ($ value per asset). This is often referred to as the market capitalization of an asset on many current resources, but the authors prefer this term as more accurately conveying the total value of a cryptoasset. 2. https://coinmarketcap.com/. 3. http://cryptome.org/jya/digicrash.htm. 4.

Infonomics: How to Monetize, Manage, and Measure Information as an Asset for Competitive Advantage by Douglas B. Laney

3D printing, Affordable Care Act / Obamacare, banking crisis, behavioural economics, blockchain, book value, business climate, business intelligence, business logic, business process, call centre, carbon credits, chief data officer, Claude Shannon: information theory, commoditize, conceptual framework, crowdsourcing, dark matter, data acquisition, data science, deep learning, digital rights, digital twin, discounted cash flows, disintermediation, diversification, en.wikipedia.org, endowment effect, Erik Brynjolfsson, full employment, hype cycle, informal economy, information security, intangible asset, Internet of things, it's over 9,000, linked data, Lyft, Nash equilibrium, Neil Armstrong, Network effects, new economy, obamacare, performance metric, profit motive, recommendation engine, RFID, Salesforce, semantic web, single source of truth, smart meter, Snapchat, software as a service, source of truth, supply-chain management, tacit knowledge, technological determinism, text mining, uber lyft, Y2K, yield curve

(ref: “Applied Infonomics: Designing for Optimal Marginal Utility in a Digital World,” Douglas Laney, Dale Kutnick and Saul Brand, Gartner, 16 December 2016, www.gartner.com/document/3546617). 5 Douglas Laney, and Michael Smith, “How CIOs and CDOs Can Use Infonomics to Identify, Justify and Fund Initiatives,” Gartner, 29 March 2016, www.gartner.com/document/3267517. 6 The standard yield curve plots the yields or interest rates for bonds or other debt instruments of differing contract or maturity lengths. 7 “Gartner Hype Cycle: Interpreting the Hype,” Gartner, accessed 09 February 2017, www.gartner.com/technology/research/methodologies/hype-cycle.jsp. 8 Douglas Laney, and Michael Patrick Moran, “Toolkit: Enterprise Information Management Maturity Self-Assessment,” Gartner, 13 June 2016, www.gartner.com/document/3344417. Chapter 13 Infonomics Trends There’s no doubt that infonomics as a concept and a set of disciplines is in its infancy.

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., IV 224 fraud and risk, identifying and reducing 44–5 Freedom of Information Act (FOIA) 17, 45–6 Friedman, Milton 128–9 Friedman, Ted 247, 268n12 fundamental valuation models, information assets 251–5 Gaia hypothesis 144n5 Ganschow, Karen 54 Gartner: enterprise information management (EIM) maturity model 108–11, 297–302; financial valuation models 251, 255–6; fundamental valuation models 251–5; Hype Cycle 281, 284n7; information asset valuation models 250; information value models 262; Magic Quadrants 68 Geis, Alex 33 General Data Protection Regulations (GDPR) 240n24 generally accepted accounting principles (GAAP) 21, 116, 217, 245 generally accepted information principles 116–19, 120n13; assumptions 117; constraints 117–18; principles 118–19 Georgia Aquarium 29–30 geographic 18, 35, 96, 235 Gledhill, David 43 goods and services, bartering 37–8 Google 11, 21, 47, 76, 211, 213, 217n2 governance: applied asset management 188–9; challenges and remedies 187; data entry 187; governance, risk and compliance (GRC) 91–2, 152; information 188, 234; information management 186–9; information management challenges 299–300; proving benefits of information 264 government 9, 17, 22–3, 41, 45–8, 61, 64, 95–6, 105, 115, 193, 206, 223–5, 237, 276, 286 Grayson-Rizzuto, Kimberly 39 Grossman, Larry 46 Hadoop 41 Hamilton, Stuart 114 Hawthorne effect 243, 268n4 Hawthorne Works 243 Health and Human Services Department 44 HealthMap 95–6 HERE Life 38 Hershberger, John 111 Higgins, Mike 97 Hillard, Rob 148, 272 Hogan, Tom 107 Holloway, Todd 32 Horrisberger, Jim 83 House of Cards (TV series) 59 Hubbard, Douglas 260 Human Capital (Becker) 128 human capital management 165–8, 184 Hutton, James 144n7 Hype Cycle 281, 284n7 IBM 87, 91, 115, 148 IMDB 34 Indigenous Land Corporation 63 indirect information monetization 68–9 industry average 283 Infinity Property and Casualty 94 infodiversity 180 infonomics 272, 285–6; concept of 3; definition 9; future of 292–5; improving information yield 281–4; information pricing and elasticity 275–6; information-related trends 286–91; managing information 106; marginal utility of information 276–9; opportunity cost for information choices 279–80; production possibilities of information 280; supply and demand of information 274–5 Informatica 163 information: accountants and 227–30; accounting for 214–17; as asset 2, 205–7; asset realization 207–8; business models and profitability 24–6; characteristics of 18–26; control of 228–9, 233; data vs 25–6, 26n4; digitalization of 288; economic alternatives for 13–14; getting more than cash for 14–16; as liability 216; liquidity of 20–1; monetizing, managing and measuring 9–11; multimedia 2; opportunity cost for information 279–80; pricing elasticity 275–6; probable economic value of 229–30; real world evidence of economic value of 210–13; replicability 23; reusable nature of 19; as second language 143–4; stop giving it away 18; supply and demand of 274–5; taxing situation 21–2; thinking beyond 16–17; transferability 23–4; uncovering hidden treasures 17–18; value of 208–10; see also ownership Information Age 3, 95, 136, 149, 160, 216 informationalize 36 informationalized product 75 information as a second language (ISL) 143–4, 192 information asset management (IAM) 107, 176; information yield 281–4; unified approach to 169–70; vision 176–7; see also applied asset management information assets 59–66; commercial data 63; commercial general liability (CGL) 241; dark data 62–3; financial valuation models 249, 255–60; fundamental valuation models 251–6; inventory 60; measuring 242–6; new supply chain model for 128–31; operational data 61; privacy and security 291; public data 64; social media data 64–5; valuation models 249–60; web content 65–6 information curation 17 information ecosystem: classic ecosystem entities concepts 135; ecosystem entities 135; ecosystem features of 136; ecosystem influences 137; ecosystem management 137–8; ecosystem processes 136–7; lessons from sustainability 138–43; preparing for 131–8; recycle 142–3; reduce 140–1; refuse 139–40; remove 143; repurpose 141–2; reuse 141; role of information in 133–5 information keiretsus 132 information lifecycle: expense 267; process challenges 301–2 information management 105–8; barriers to asset management 114–16; challenges and principles 119; cultural attitudes about 114; future of infonomics 292; generally accepted information principles 116–19; governance challenges 299–300; impediments to maturity 111–14; information metrics challenges 299; infrastructure challenges 302; leadership 112; levels of information maturity 109–11; maturity model 108–11, 174; monetization success 74; monetization to 99–100; people-related challenges 300–1; priority control 113–14; process challenges 301–2; resources 113–14; strategy challenges 298; vision challenges 297–8 information measurement, future of infonomics 294–5 information owners 222; see also ownership information ownership 222, 226–8, 232–4 information performance gap 262 information product management 56–9 information property rights 303–5; rulings affirming 303–4; rulings denying 304–5; see also information ownership; ownership information security 244 information supply chain (ISC) 8, 119; activities 131; metrics for 126–7; model for information assets 128–31; preparing for information ecosystem 131–8; scenarios 126; SCOR (Supply Chain Operations Reference) model 124–5; see also information ecosystem Information Technology Infrastructure Library (ITIL) 151–2 information valuation models 263–7; benefits of information governance 264; expanded revenue 266; innovation and digitalization 265; monetization and analytics 265–6; prioritizing IAM investments 264; reducing information lifecycle expense 267 information vision gap 262 information yield 281–4; concept 281; curve 281 infosavvy 3, 11; chief data officer (CDO) 199–200; growing market valuations 245; investors prizing, companies 211–13; roles for organization 198–200 infrastructure 12, 40–1, 47, 108, 139, 150–1, 192, 196–8, 267, 286, 290–1, 299, 302; information 290–1; information management 196; information management challenges 302 innovation 3, 26, 31, 46, 76, 97–9, 107, 113, 161, 236, 246, 265, 287, 289, 298, 301 innovation and digitalization, value 265 Instagram 34 Institute of Electrical and Electronics Engineers (IEEE) 147 intangible assets 168–9 integrity 248 intellectual property (IP) 62, 116, 128, 130, 168, 176, 181, 230–1, 288 International Accounting Standards Board (IASB) 214 International Accounting Standards (IAS) 214–15, 217 International Astronomical Union 148 International Federation of Library Association and Institutions (IFLA) 157 International Financial Reporting Standards (IFRS) 214–15; criteria 219n19 International Organization for Standardization (ISO): ISO 8000 170n2; ISO 15489–1:2016 152, 170n8, 171n10; ISO 19770–1 149; ISO 19770–2 149; ISO 19770–3 150; ISO 19770–4 150; ISO 30300:2011 170n9; ISO 55001 158; ISO/IEC 20000 170n7; ISO/IEC 27001 147, 170n3; IT asset management (ITAM) 149–50; IT service management (ITSM) 150–1 intrinsic value of information (IVI) 251–2 Intuit’s TurboTax 36 inventory, information asset 60 investor awareness, monetization success 76 IT asset management (ITAM): International Organization for Standardization (ISO) standards 149–50 IT service management (ITSM) 150–1; information strategy 180–1 J.D.

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., IV 224 fraud and risk, identifying and reducing 44–5 Freedom of Information Act (FOIA) 17, 45–6 Friedman, Milton 128–9 Friedman, Ted 247, 268n12 fundamental valuation models, information assets 251–5 Gaia hypothesis 144n5 Ganschow, Karen 54 Gartner: enterprise information management (EIM) maturity model 108–11, 297–302; financial valuation models 251, 255–6; fundamental valuation models 251–5; Hype Cycle 281, 284n7; information asset valuation models 250; information value models 262; Magic Quadrants 68 Geis, Alex 33 General Data Protection Regulations (GDPR) 240n24 generally accepted accounting principles (GAAP) 21, 116, 217, 245 generally accepted information principles 116–19, 120n13; assumptions 117; constraints 117–18; principles 118–19 Georgia Aquarium 29–30 geographic 18, 35, 96, 235 Gledhill, David 43 goods and services, bartering 37–8 Google 11, 21, 47, 76, 211, 213, 217n2 governance: applied asset management 188–9; challenges and remedies 187; data entry 187; governance, risk and compliance (GRC) 91–2, 152; information 188, 234; information management 186–9; information management challenges 299–300; proving benefits of information 264 government 9, 17, 22–3, 41, 45–8, 61, 64, 95–6, 105, 115, 193, 206, 223–5, 237, 276, 286 Grayson-Rizzuto, Kimberly 39 Grossman, Larry 46 Hadoop 41 Hamilton, Stuart 114 Hawthorne effect 243, 268n4 Hawthorne Works 243 Health and Human Services Department 44 HealthMap 95–6 HERE Life 38 Hershberger, John 111 Higgins, Mike 97 Hillard, Rob 148, 272 Hogan, Tom 107 Holloway, Todd 32 Horrisberger, Jim 83 House of Cards (TV series) 59 Hubbard, Douglas 260 Human Capital (Becker) 128 human capital management 165–8, 184 Hutton, James 144n7 Hype Cycle 281, 284n7 IBM 87, 91, 115, 148 IMDB 34 Indigenous Land Corporation 63 indirect information monetization 68–9 industry average 283 Infinity Property and Casualty 94 infodiversity 180 infonomics 272, 285–6; concept of 3; definition 9; future of 292–5; improving information yield 281–4; information pricing and elasticity 275–6; information-related trends 286–91; managing information 106; marginal utility of information 276–9; opportunity cost for information choices 279–80; production possibilities of information 280; supply and demand of information 274–5 Informatica 163 information: accountants and 227–30; accounting for 214–17; as asset 2, 205–7; asset realization 207–8; business models and profitability 24–6; characteristics of 18–26; control of 228–9, 233; data vs 25–6, 26n4; digitalization of 288; economic alternatives for 13–14; getting more than cash for 14–16; as liability 216; liquidity of 20–1; monetizing, managing and measuring 9–11; multimedia 2; opportunity cost for information 279–80; pricing elasticity 275–6; probable economic value of 229–30; real world evidence of economic value of 210–13; replicability 23; reusable nature of 19; as second language 143–4; stop giving it away 18; supply and demand of 274–5; taxing situation 21–2; thinking beyond 16–17; transferability 23–4; uncovering hidden treasures 17–18; value of 208–10; see also ownership Information Age 3, 95, 136, 149, 160, 216 informationalize 36 informationalized product 75 information as a second language (ISL) 143–4, 192 information asset management (IAM) 107, 176; information yield 281–4; unified approach to 169–70; vision 176–7; see also applied asset management information assets 59–66; commercial data 63; commercial general liability (CGL) 241; dark data 62–3; financial valuation models 249, 255–60; fundamental valuation models 251–6; inventory 60; measuring 242–6; new supply chain model for 128–31; operational data 61; privacy and security 291; public data 64; social media data 64–5; valuation models 249–60; web content 65–6 information curation 17 information ecosystem: classic ecosystem entities concepts 135; ecosystem entities 135; ecosystem features of 136; ecosystem influences 137; ecosystem management 137–8; ecosystem processes 136–7; lessons from sustainability 138–43; preparing for 131–8; recycle 142–3; reduce 140–1; refuse 139–40; remove 143; repurpose 141–2; reuse 141; role of information in 133–5 information keiretsus 132 information lifecycle: expense 267; process challenges 301–2 information management 105–8; barriers to asset management 114–16; challenges and principles 119; cultural attitudes about 114; future of infonomics 292; generally accepted information principles 116–19; governance challenges 299–300; impediments to maturity 111–14; information metrics challenges 299; infrastructure challenges 302; leadership 112; levels of information maturity 109–11; maturity model 108–11, 174; monetization success 74; monetization to 99–100; people-related challenges 300–1; priority control 113–14; process challenges 301–2; resources 113–14; strategy challenges 298; vision challenges 297–8 information measurement, future of infonomics 294–5 information owners 222; see also ownership information ownership 222, 226–8, 232–4 information performance gap 262 information product management 56–9 information property rights 303–5; rulings affirming 303–4; rulings denying 304–5; see also information ownership; ownership information security 244 information supply chain (ISC) 8, 119; activities 131; metrics for 126–7; model for information assets 128–31; preparing for information ecosystem 131–8; scenarios 126; SCOR (Supply Chain Operations Reference) model 124–5; see also information ecosystem Information Technology Infrastructure Library (ITIL) 151–2 information valuation models 263–7; benefits of information governance 264; expanded revenue 266; innovation and digitalization 265; monetization and analytics 265–6; prioritizing IAM investments 264; reducing information lifecycle expense 267 information vision gap 262 information yield 281–4; concept 281; curve 281 infosavvy 3, 11; chief data officer (CDO) 199–200; growing market valuations 245; investors prizing, companies 211–13; roles for organization 198–200 infrastructure 12, 40–1, 47, 108, 139, 150–1, 192, 196–8, 267, 286, 290–1, 299, 302; information 290–1; information management 196; information management challenges 302 innovation 3, 26, 31, 46, 76, 97–9, 107, 113, 161, 236, 246, 265, 287, 289, 298, 301 innovation and digitalization, value 265 Instagram 34 Institute of Electrical and Electronics Engineers (IEEE) 147 intangible assets 168–9 integrity 248 intellectual property (IP) 62, 116, 128, 130, 168, 176, 181, 230–1, 288 International Accounting Standards Board (IASB) 214 International Accounting Standards (IAS) 214–15, 217 International Astronomical Union 148 International Federation of Library Association and Institutions (IFLA) 157 International Financial Reporting Standards (IFRS) 214–15; criteria 219n19 International Organization for Standardization (ISO): ISO 8000 170n2; ISO 15489–1:2016 152, 170n8, 171n10; ISO 19770–1 149; ISO 19770–2 149; ISO 19770–3 150; ISO 19770–4 150; ISO 30300:2011 170n9; ISO 55001 158; ISO/IEC 20000 170n7; ISO/IEC 27001 147, 170n3; IT asset management (ITAM) 149–50; IT service management (ITSM) 150–1 intrinsic value of information (IVI) 251–2 Intuit’s TurboTax 36 inventory, information asset 60 investor awareness, monetization success 76 IT asset management (ITAM): International Organization for Standardization (ISO) standards 149–50 IT service management (ITSM) 150–1; information strategy 180–1 J.D.

The Fourth Industrial Revolution by Klaus Schwab

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

It is already being used in a range of applications, from making wind turbines to toys. Over time, 3D printers will overcome the obstacles of speed, cost and size, and become more pervasive. Gartner has developed a “Hype Cycle” chart (Figure VI) showing the various stages of different 3D printing capabilities and their market impact, and plotting most business uses of the technology as entering the “slope of enlightenment”.95 Figure VI: Hype Cycle for 3D Printing Source: Gartner (July 2014) Positive impacts – Accelerated product development – Reduction in the design-to-manufacturing cycle – Easily manufactured intricate parts (not possible or difficult to do earlier) – Rising demand for product designers – Educational institutions using 3D printing to accelerate learning and understanding – Democratized power of creation/manufacturing (both limited only by the design) – Traditional mass manufacturing responding to the challenge by finding ways to reduce costs and the size of minimum runs – Growth in open-source “plans” to print a range of objects – Birth of a new industry supplying printing materials – Rise in entrepreneurial opportunities in the space96 – Environmental benefits from reduced transportation requirements Negative impacts – Growth in waste for disposal, and further burden on the environment – Production of parts in the layer process that are anisotropic, i.e. their strength is not the same in all directions, which could limit the functionality of parts – Job losses in a disrupted industry – Primacy of intellectual property as a source of value in productivity – Piracy – Brand and product quality Unknown, or cuts both ways – Potential that any innovation can be instantly copied The shift in action An example of 3D printing for manufacturing has been recently covered by FORTUNE: “General Electric’s Leap jet engine is not only one of the company’s bestsellers, it’s going to incorporate a fuel nozzle produced entirely through additive manufacturing.

How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester

"Friedman doctrine" OR "shareholder theory", "World Economic Forum" Davos, asset allocation, Basel III, behavioural economics, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamond, Bretton Woods, BRICs, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, commoditize, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, fear index, financial engineering, financial innovation, Flash crash, forward guidance, Garrett Hardin, Gini coefficient, Glass-Steagall Act, global reserve currency, high net worth, High speed trading, hindsight bias, hype cycle, income inequality, inflation targeting, interest rate swap, inverted yield curve, Isaac Newton, Jaron Lanier, John Perry Barlow, joint-stock company, joint-stock limited liability company, junk bonds, Kodak vs Instagram, Kondratiev cycle, Large Hadron Collider, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, low interest rates, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, negative equity, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Nixon triggered the end of the Bretton Woods system, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Ponzi scheme, precautionary principle, proprietary trading, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Right to Buy, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, sovereign wealth fund, Steve Jobs, survivorship bias, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, Tragedy of the Commons, trickle-down economics, two and twenty, Two Sigma, Tyler Cowen, Washington Consensus, wealth creators, working poor, yield curve

There’s a Big Issue seller near where I live who holds out a copy with the line “last one”; when he sells it, he waits for the customer to walk away, then reaches into his bag and pulls out another “last one.” That is bullshit, and relatively harmless—I say “relatively” rather than “wholly” because once you’ve fallen for the line, and then seen through it, it tends to diminish your trust in Big Issue sellers. The “hype cycle” around new inventions involves a near-ritualized early period of puffing, boosterism, and bullshit: as John Perry Barlow, songwriter for the Grateful Dead, once brilliantly put it, “bullshit is the grease for the skids on which we ride into the future.” (I like that line because it is both an example of bullshit and a great explanation of it.)

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The hot waitress index is a joking variation on that: it suggests that the better an economy is doing, good-looking women get better and better work—what the girls in Lena Dunham’s Girls refer to as “pretty girl jobs,” as gallery receptionists and suchlike. When times are harder, the girls who would otherwise get pretty girl jobs instead end up working as waitresses. So the worse the economy is doing, the hotter the waitresses. hype cycle A term coined by the research firm Gartner to describe the process in which a new invention or technology is hugely hyped when it arrives; then found not to live up to the hype; then, when the hype has quieted down and you’re no longer hearing so much about it, the thing gradually starts getting better and begins to do the things it was supposed to do when it was first hyped.

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uid=3738032&uid=2&uid=4&sid=21102724064461. 41See https://www.cia.gov/library/publications/the-world-factbook/geos/xx.html. 42Available at www.transparency.org/. 43Graham, The Intelligent Investor: A Book of Practical Counsel (New York: Harper & Row, 1973), p. 277. 44See www.wired.com/business/2012/08/ff_wallstreet_trading/all/. 45See www.capgemini.com/resources/world-wealth-report-2010. 46Jaron Lanier, Who Owns the Future? (Allen Lane: London, 2013), p. xii. 47See www.gartner.com/technology/research/methodologies/hype-cycle.jsp. 48See cdn.budgetresponsibility.independent.gov.uk/2013-FSR_OBR_web.pdf. 49John Maynard Keynes, The Economic Consequences of the Peace (London: Macmillan, 1919), p. 118. 50See www.forbes.com/sites/luisakroll/2011/04/22/just-how-rich-is-queen-elizabeth-and-her-family/ and www.guardian.co.uk/artanddesign/2006/apr/20/art.monarchy. 51Here’s the actual napkin: web.archive.org/web/20110503200219/http://www.polyconomics.com/gallery/Napkin003.jpg. 52See www.cdc.gov/nchs/data/hus/hus12.pdf#017. 53See media.bloomberg.com/bb/avfile/rJ5Q_k_NsIk8. 54Available at www.marxists.org/archive/marx/works/1852/18th-brumaire/. 55The study, called “When Choice Is Demotivating,” is available at www.columbia.edu/~ss957/articles/Choice_is_Demotivating.pdf. 56See www.theguardian.com/commentisfree/2013/jul/30/obama-grand-bargain-speech-middle-class. 57At www.un.org/millenniumgoals/poverty.shtml. 58See www.businessinsider.com/most-miserable-countries-in-the-world-2013-2?

The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences by Rob Kitchin

Bayesian statistics, business intelligence, business process, cellular automata, Celtic Tiger, cloud computing, collateralized debt obligation, conceptual framework, congestion charging, corporate governance, correlation does not imply causation, crowdsourcing, data science, discrete time, disruptive innovation, George Gilder, Google Earth, hype cycle, Infrastructure as a Service, Internet Archive, Internet of things, invisible hand, knowledge economy, Large Hadron Collider, late capitalism, lifelogging, linked data, longitudinal study, machine readable, Masdar, means of production, Nate Silver, natural language processing, openstreetmap, pattern recognition, platform as a service, recommendation engine, RFID, semantic web, sentiment analysis, SimCity, slashdot, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart grid, smart meter, software as a service, statistical model, supply-chain management, technological solutionism, the scientific method, The Signal and the Noise by Nate Silver, transaction costs

Such was its prevalence and associated boosterism that Gartner’s had already declared by January 2013 that it had moved along the hype cycle from ‘peak of inflated expectation’ to ‘trough of disillusionment’ (Sicular 2013), with some evangelists already declaring ‘big data’ dead as a meaningful term, having become too wideranging and woolly in definition (e.g., de Goes 2013), some early adopters struggling to convert investment into return, and others voicing scepticism as to its potential benefits. Nonetheless, business, government and research funders have largely remained firm in their conviction that big data is set to rise back up the hype cycle’s ‘slope of enlightenment’ to the ‘plateau of productivity’, and, what’s more, it is set to alter fundamentally how science and business are conducted (Sicular 2013; see Chapters 7 and 8).

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Index A/B testing 112 abduction 133, 137, 138–139, 148 accountability 34, 44, 49, 55, 63, 66, 113, 116, 165, 171, 180 address e-mail 42 IP 8, 167, 171 place 8, 32, 42, 45, 52, 93, 171 Web 105 administration 17, 30, 34, 40, 42, 56, 64, 67, 87, 89, 114–115, 116, 124, 174, 180, 182 aggregation 8, 14, 101, 140, 169, 171 algorithm 5, 9, 21, 45, 76, 77, 83, 85, 89, 101, 102, 103, 106, 109, 111, 112, 118, 119, 122, 125, 127, 130, 131, 134, 136, 142, 146, 154, 160, 172, 177, 179, 181, 187 Amazon 72, 96, 131, 134 Anderson, C. 130, 135 Andrejevic, M. 133, 167, 178 animation 106, 107 anonymity 57, 63, 79, 90, 92, 116, 167, 170, 171, 172, 178 apophenia 158, 159 Application Programming Interfaces (APIs) 57, 95, 152, 154 apps 34, 59, 62, 64, 65, 78, 86, 89, 90, 95, 97, 125, 151, 170, 174, 177 archive 21, 22, 24, 25, 29–41, 48, 68, 95, 151, 153, 185 archiving 23, 29–31, 64, 65, 141 artificial intelligence 101, 103 Acxiom 43, 44 astronomy 34, 41, 72, 97 ATM 92, 116 audio 74, 77, 83 automatic meter reading (AMR) 89 automatic number plate recognition (ANPR) 85, 89 automation 32, 51, 83, 85, 87, 89–90, 98, 99, 102, 103, 118, 127, 136, 141, 146, 180 Ayasdi 132, 134 backup 29, 31, 40, 64, 163 barcode 74, 85, 92, Bates, J. 56, 61, 62, 182 Batty, M. 90, 111, 112, 140 Berry, D. 134, 141 bias 13, 14, 19, 28, 45, 101, 134–136, 153, 154, 155, 160 Big Brother 126, 180 big data xv, xvi, xvii, 2, 6, 13, 16, 20, 21, 27–29, 42, 46, 67–183, 186, 187, 188, 190, 191, 192 analysis 100–112 characteristics 27–29, 67–79 enablers 80–87 epistemology 128–148 ethical issues 165–183 etymology 67 organisational issues 160–163 rationale 113–127 sources 87–99 technical issues 149–160 biological sciences 128–129, 137 biometric data 8, 84, 115 DNA 8, 71, 84 face 85, 88, 105 fingerprints 8, 9, 84, 87, 88, 115 gait 85, 88 iris 8, 84, 88 bit-rot 20 blog 6, 95, 170 Bonferroni principle 159 born digital 32, 46, 141 Bowker, G. 2, 19, 20, 22, 24 Borgman, C. 2, 7, 10, 20, 30, 37, 40, 41 boyd, D. 68, 75, 151, 152, 156, 158, 160, 182 Brooks, D. 130, 145 business 1, 16, 42, 45, 56, 61, 62, 67, 79, 110, 113–127, 130, 137, 149, 152, 161, 166, 172, 173, 187 calculative practices 115–116 Campbell’s Law 63, 127 camera 6, 81, 83, 87, 88, 89, 90, 107, 116, 124, 167, 178, 180 capitalism 15, 16, 21, 59, 61, 62, 86, 95, 114, 119–123, 126, 136, 161, 184, 186 capta 2 categorization 6, 8, 12, 19, 20, 102, 106, 176 causation 130, 132, 135, 147 CCTV 87, 88, 180 census 17, 18, 19, 22, 24, 27, 30, 43, 54, 68, 74, 75, 76, 77, 87, 102, 115, 157, 176 Centro De Operações Prefeitura Do Rio 124–125, 182 CERN 72, 82 citizen science 97–99, 155 citizens xvi, 45, 57, 58, 61, 63, 71, 88, 114, 115, 116, 126, 127, 165, 166, 167, 174, 176, 179, 187 citizenship 55, 115, 170, 174 classification 6, 10, 11, 23, 28, 104, 105, 157, 176 clickstream 43, 92, 94, 120, 122, 154, 176 clustering 103, 104, 105, 106, 110, 122 Codd, E. 31 competitiveness xvi, 16, 114, computation 2, 4, 5, 6, 29, 32, 68, 80, 81–82, 83, 84, 86, 98, 100, 101, 102, 110, 129, 136, 139–147, 181 computational social science xiv, 139–147, 152, 186 computing cloud xv, 81, 86 distributed xv, 37, 78, 81, 83, 98 mobile xv, 44, 78, 80, 81, 83, 85, 139 pervasive 81, 83–84, 98, 124 ubiquitous 80, 81, 83–84, 98, 100, 124, 126 confidence level 14, 37, 133, 153, 160 confidentiality 8, 169, 175 control creep 126, 166, 178–179 cookies 92, 119, 171 copyright 16, 30, 40, 49, 51, 54, 96 correlation 105, 110, 130, 131, 132, 135, 145, 147, 157, 159 cost xv, 6, 11, 16, 27, 31, 32, 37, 38, 39, 40, 44, 52, 54, 57, 58, 59, 61, 66, 80, 81, 83, 85, 93, 96, 100, 116, 117, 118, 120, 127, 150 Crawford, K. 68, 75, 135, 151, 152, 155, 156, 158, 160, 182 credit cards 8, 13, 42, 44, 45, 85, 92, 167, 171, 176 risk 42, 63, 75, 120, 176, 177 crime 55, 115, 116, 123, 175, 179 crowdsourcing 37, 73, 93, 96–97, 155, 160 Cukier, K. 68, 71, 72, 91, 114, 128, 153, 154, 161, 174 customer relationship management (CRM) 42, 99, 117–118, 120, 122, 176 cyber-infrastructure 33, 34, 35, 41, 186 dashboard 106, 107, 108 data accuracy 12, 14, 110, 153, 154, 171 administrative 84–85, 89, 115, 116, 125, 150, 178 aggregators see data brokers amplification 8, 76, 99, 102, 167 analogue 1, 3, 32, 83, 88, 140, 141 analytics 42, 43, 63, 73, 80, 100–112, 116, 118, 119, 120, 124, 125, 129, 132, 134, 137, 139, 140, 145, 146, 149, 151, 159, 160, 161, 176, 179, 186, 191 archive see archive assemblage xvi, xvii, 2, 17, 22, 24–26, 66, 80, 83, 99, 117, 135, 139, 183, 184–192 attribute 4, 8–9, 31, 115, 150 auditing 33, 40, 64, 163 authenticity 12, 153 automated see automation bias see bias big see big data binary 1, 4, 32, 69 biometric see biometric data body 177–178, 187 boosterism xvi, 67, 127, 187, 192 brokers 42–45, 46, 57, 74, 75, 167, 183, 186, 187, 188, 191 calibration 13, 20 catalogue 32, 33, 35 clean 12, 40, 64, 86, 100, 101, 102, 152, 153, 154, 156 clearing house 33 commodity xvi, 4, 10, 12, 15, 16, 41, 42–45, 56, 161 commons 16, 42 consolidators see data brokers cooked 20, 21 corruption 19, 30 curation 9, 29, 30, 34, 36, 57, 141 definition 1, 2–4 deluge xv, 28, 73, 79, 100, 112, 130, 147, 149–151, 157, 168, 175 derived 1, 2, 3, 6–7, 8, 31, 32, 37, 42, 43, 44, 45, 62, 86, 178 deserts xvi, 28, 80, 147, 149–151, 161 determinism 45, 135 digital 1, 15, 31, 32, 67, 69, 71, 77, 82, 85, 86, 90, 137 directories 33, 35 dirty 29, 154, 163 dive 64–65, 188 documentation 20, 30, 31, 40, 64, 163 dredging 135, 147, 158, 159 dump 64, 150, 163 dynamic see dynamic data enrichment 102 error 13, 14, 44, 45, 101, 110, 153, 154, 156, 169, 175, 180 etymology 2–3, 67 exhaust 6–7, 29, 80, 90 fidelity 34, 40, 55, 79, 152–156 fishing see data dredging formats xvi, 3, 5, 6, 9, 22, 25, 30, 33, 34, 40, 51, 52, 54, 65, 77, 102, 153, 156, 157, 174 framing 12–26, 133–136, 185–188 gamed 154 holding 33, 35, 64 infrastructure xv, xvi, xvii, 2, 21–24, 25, 27–47, 52, 64, 102, 112, 113, 128, 129, 136, 140, 143, 147, 148, 149, 150, 156, 160, 161, 162, 163, 166, 184, 185, 186, 188, 189, 190, 191, 192 integration 42, 149, 156–157 integrity 12, 30, 33, 34, 37, 40, 51, 154, 157, 171 interaction 43, 72, 75, 85, 92–93, 94, 111, 167 interoperability 9, 23, 24, 34, 40, 52, 64, 66, 156–157, 163, 184 interval 5, 110 licensing see licensing lineage 9, 152–156 linked see linked data lost 5, 30, 31, 39, 56, 150 markets xvi, 8, 15, 25, 42-45, 56, 59, 75, 167, 178 materiality see materiality meta see metadata mining 5, 77, 101, 103, 104–106, 109, 110, 112, 129, 132, 138, 159, 188 minimisation 45, 171, 178, 180 nominal 5, 110 ordinal 5, 110 open see open data ontology 12, 28, 54, 150 operational 3 ownership 16, 40, 96, 156, 166 preparation 40, 41, 54, 101–102 philosophy of 1, 2, 14, 17–21, 22, 25, 128–148, 185–188 policy 14, 23, 30, 33, 34, 37, 40, 48, 64, 160, 163, 170, 172, 173, 178 portals 24, 33, 34, 35 primary 3, 7–8, 9, 50, 90 preservation 30, 31, 34, 36, 39, 40, 64, 163 protection 15, 16, 17, 20, 23, 28, 40, 45, 62, 63, 64, 167, 168–174, 175, 178, 188 protocols 23, 25, 30, 34, 37 provenance 9, 30, 40, 79, 153, 156, 179 qualitative 4–5, 6, 14, 146, 191 quantitative 4–5, 14, 109, 127, 136, 144, 145, 191 quality 12, 13, 14, 34, 37, 40, 45, 52, 55, 57, 58, 64, 79, 102, 149, 151, 152–156, 157, 158 raw 1, 2, 6, 9, 20, 86, 185 ratio 5, 110 real-time 65, 68, 71, 73, 76, 88, 89, 91, 99, 102, 106, 107, 116, 118, 121, 124, 125, 139, 151, 181 reduction 5, 101–102 representative 4, 8, 13, 19, 21, 28 relational 3, 8, 28, 44, 68, 74–76, 79, 84, 85, 87, 88, 99, 100, 119, 140, 156, 166, 167, 184 reliability 12, 13–14, 52, 135, 155 resellers see data brokers resolution 7, 26, 27, 28, 68, 72, 73–74, 79, 84, 85, 89, 92, 133–134, 139, 140, 150, 180 reuse 7, 27, 29, 30, 31, 32, 39, 40, 41, 42, 46, 48, 49–50, 52, 56, 59, 61, 64, 102, 113, 163 scaled xvi, xvii 32, 100, 101, 112, 138, 149, 150, 163, 186 scarcity xv, xvi, 28, 80, 149–151, 161 science xvi, 100–112, 130, 137–139, 148, 151, 158, 160–163, 164, 191 secondary 3, 7–8 security see security selection 101, 176 semi-structured 4, 5–6, 77, 100, 105 sensitive 15, 16, 45, 63, 64, 137, 151, 167, 168, 171, 173, 174 shadow 166–168, 177, 179, 180 sharing 9, 11, 20, 21, 23, 24, 27, 29–41, 48–66, 80, 82, 95, 113, 141, 151, 174, 186 small see small data social construction 19–24 spatial 17, 52, 63, 68, 73, 75, 84–85, 88–89 standards xvi, 9, 14, 19, 22, 23, 24, 25, 31, 33, 34, 38, 40, 52, 53, 64, 102, 153, 156, 157 storage see storage stranded 156 structures 4, 5–6, 12, 21, 23, 30, 31, 40, 51, 68, 77, 86, 103, 106, 156 structured 4, 5–6, 11, 32, 52, 68, 71, 75, 77, 79, 86, 88, 105, 112, 163 tertiary 7–8, 9, 27, 74 time-series 68, 102, 106, 110 transient 6–7, 72, 150 transactional 42, 43, 71, 72, 74, 75, 85, 92, 93–94, 120, 122, 131, 167, 175, 176, 177 uncertainty see uncertainty unstructured 4, 5–6, 32, 52, 68, 71, 75, 77, 86, 100, 105, 112, 140, 153, 157 validity 12, 40, 72, 102, 135, 138, 154, 156, 158 variety 26, 28, 43, 44, 46, 68, 77, 79, 86, 139, 140, 166, 184 velocity 26, 28, 29, 68, 76–77, 78, 79, 86, 88, 102, 106, 112. 117, 140, 150, 153, 156, 184 veracity 13, 79, 102, 135, 152–156, 157, 163 volume 7, 26, 27, 28, 29, 32, 46, 67, 68, 69–72, 74, 76, 77, 78, 79, 86, 102, 106, 110, 125, 130, 135, 140, 141, 150, 156, 166, 184 volunteered 87, 93–98, 99, 155 databank 29, 34, 43 database NoSQL 6, 32, 77, 78, 86–87 relational 5, 6, 8, 32–33, 43, 74–75, 77, 78, 86, 100, 105 data-driven science 133, 137–139, 186 data-ism 130 datafication 181 dataveillance 15, 116, 126, 157, 166–168, 180, 181, 182, 184 decision tree 104, 111, 122, 159, deconstruction 24, 98, 126, 189–190 decontextualisation 22 deduction 132, 133, 134, 137, 138, 139, 148 deidentification 171, 172, 178 democracy 48, 55, 62, 63, 96, 117, 170 description 9, 101, 104, 109, 143, 147, 151, 190 designated community 30–31, 33, 46 digital devices 13, 25, 80, 81, 83, 84, 87, 90–91, 167, 174, 175 humanities xvi, 139–147, 152, 186 object identifier 8, 74 serendipity 134 discourse 15, 20, 55, 113–114, 117, 122, 127, 192 discursive regime 15, 20, 24, 56, 98, 113–114, 116, 123, 126, 127, 190 disruptive innovation xv, 68, 147, 184, 192 distributed computing xv, 37, 78, 81, 83, 98 sensors 124, 139, 160 storage 34, 37, 68, 78, 80, 81, 85–87, 97 division of labour 16 Dodge, M. 2, 21, 68, 73, 74, 76, 83, 84, 85, 89, 90, 92, 93, 96, 113, 115, 116, 124, 154, 155, 167, 177, 178, 179, 180, 189 driver’s licence 45, 87, 171 drone 88, Dublin Core 9 dynamic data xv, xvi, 76–77, 86, 106, 112 pricing 16, 120, 123, 177 eBureau 43, 44 ecological fallacy 14, 102, 135, 149, 158–160 Economist, The 58, 67, 69, 70, 72, 128 efficiency 16, 38, 55, 56, 59, 66, 77, 93, 102, 111, 114, 116, 118, 119, 174, 176 e-mail 71, 72–73, 82, 85, 90, 93, 116, 174, 190 empiricism 129, 130–137, 141, 186 empowerment 61, 62–63, 93, 115, 126, 165 encryption 171, 175 Enlightenment 114 Enterprise Resource Planning (ERP) 99, 117, 120 entity extraction 105 epistemology 3, 12, 19, 73, 79, 112, 128–148, 149, 185, 186 Epsilon 43 ethics 12, 14–15, 16, 19, 26, 30, 31, 40, 41, 64, 73, 99, 128, 144, 151, 163, 165–183, 186 ethnography 78, 189, 190, 191 European Union 31, 38, 45, 49, 58, 59, 70, 157, 168, 173, 178 everyware 83 exhaustive 13, 27, 28, 68, 72–73, 79, 83, 88, 100, 110, 118, 133–134, 140, 150, 153, 166, 184 explanation 101, 109, 132, 133, 134, 137, 151 extensionality 67, 78, 140, 184 experiment 2, 3, 6, 34, 75, 78, 118, 129, 131, 137, 146, 150, 160 Facebook 6, 28, 43, 71, 72, 77, 78, 85, 94, 119, 154, 170 facts 3, 4, 9, 10, 52, 140, 159 Fair Information Practice Principles 170–171, 172 false positive 159 Federal Trade Commission (FTC) 45, 173 flexibility 27, 28, 68, 77–78, 79, 86, 140, 157, 184 Flickr 95, 170 Flightradar 107 Floridi, L. 3, 4, 9, 10, 11, 73, 112, 130, 151 Foucault, M. 16, 113, 114, 189 Fourth paradigm 129–139 Franks, B. 6, 111, 154 freedom of information 48 freemium service 60 funding 15, 28, 29, 31, 34, 37, 38, 40, 41, 46, 48, 52, 54–55, 56, 57–58, 59, 60, 61, 65, 67, 75, 119, 143, 189 geographic information systems 147 genealogy 98, 127, 189–190 Gitelman, L. 2, 19, 20, 21, 22 Global Positioning System (GPS) 58, 59, 73, 85, 88, 90, 121, 154, 169 Google 32, 71, 73, 78, 86, 106, 109, 134, 170 governance 15, 21, 22, 23, 38, 40, 55, 63, 64, 66, 85, 87, 89, 117, 124, 126, 136, 168, 170, 178–182, 186, 187, 189 anticipatory 126, 166, 178–179 technocratic 126, 179–182 governmentality xvi, 15, 23, 25, 40, 87, 115, 127, 168, 185, 191 Gray, J. 129–130 Guardian, The 49 Gurstein, M. 52, 62, 63 hacking 45, 154, 174, 175 hackathon 64–65, 96, 97, 188, 191 Hadoop 87 hardware 32, 34, 40, 63, 78, 83, 84, 124, 143, 160 human resourcing 112, 160–163 hype cycle 67 hypothesis 129, 131, 132, 133, 137, 191 IBM 70, 123, 124, 143, 162, 182 identification 8, 44, 68, 73, 74, 77, 84–85, 87, 90, 92, 115, 169, 171, 172 ideology 4, 14, 25, 61, 113, 126, 128, 130, 134, 140, 144, 185, 190 immutable mobiles 22 independence 3, 19, 20, 24, 100 indexical 4, 8–9, 32, 44, 68, 73–74, 79, 81, 84–85, 88, 91, 98, 115, 150, 156, 167, 184 indicator 13, 62, 76, 102, 127 induction 133, 134, 137, 138, 148 information xvii, 1, 3, 4, 6, 9–12, 13, 23, 26, 31, 33, 42, 44, 45, 48, 53, 67, 70, 74, 75, 77, 92, 93, 94, 95, 96, 100, 101, 104, 105, 109, 110, 119, 125, 130, 138, 140, 151, 154, 158, 161, 168, 169, 171, 174, 175, 184, 192 amplification effect 76 freedom of 48 management 80, 100 overload xvi public sector 48 system 34, 65, 85, 117, 181 visualisation 109 information and communication technologies (ICTs) xvi, 37, 80, 83–84, 92, 93, 123, 124 Innocentive 96, 97 INSPIRE 157 instrumental rationality 181 internet 9, 32, 42, 49, 52, 53, 66, 70, 74, 80, 81, 82, 83, 86, 92, 94, 96, 116, 125, 167 of things xv, xvi, 71, 84, 92, 175 intellectual property rights xvi, 11, 12, 16, 25, 30, 31, 40, 41, 49, 50, 56, 62, 152, 166 Intelius 43, 44 intelligent transportation systems (ITS) 89, 124 interoperability 9, 23, 24, 34, 40, 52, 64, 66, 149, 156–157, 163, 184 interpellation 165, 180, 188 interviews 13, 15, 19, 78, 155, 190 Issenberg, S. 75, 76, 78, 119 jurisdiction 17, 25, 51, 56, 57, 74, 114, 116 Kafka 180 knowledge xvii, 1, 3, 9–12, 19, 20, 22, 25, 48, 53, 55, 58, 63, 67, 93, 96, 110, 111, 118, 128, 130, 134, 136, 138, 142, 159, 160, 161, 162, 187, 192 contextual 48, 64, 132, 136–137, 143, 144, 187 discovery techniques 77, 138 driven science 139 economy 16, 38, 49 production of 16, 20, 21, 24, 26, 37, 41, 112, 117, 134, 137, 144, 184, 185 pyramid 9–10, 12, situated 16, 20, 28, 135, 137, 189 Latour, B. 22, 133 Lauriault, T.P. 15, 16, 17, 23, 24, 30, 31, 33, 37, 38, 40, 153 law of telecosm 82 legal issues xvi, 1, 23, 25, 30, 31, 115, 165–179, 182, 183, 187, 188 levels of measurement 4, 5 libraries 31, 32, 52, 71, 141, 142 licensing 14, 25, 40, 42, 48, 49, 51, 53, 57, 73, 96, 151 LIDAR 88, 89, 139 linked data xvii, 52–54, 66, 156 longitudinal study 13, 76, 140, 149, 150, 160 Lyon, D. 44, 74, 87, 167, 178, 180 machine learning 5, 6, 101, 102–104, 106, 111, 136, 188 readable 6, 52, 54, 81, 84–85, 90, 92, 98 vision 106 management 62, 88, 117–119, 120, 121, 124, 125, 131, 162, 181 Manovich, L. 141, 146, 152, 155 Manyika, J. 6, 16, 70, 71, 72, 104, 116, 118, 119, 120, 121, 122, 161 map 5, 22, 24, 34, 48, 54, 56, 73, 85, 88, 93, 96, 106, 107, 109, 115, 143, 144, 147, 154, 155–156, 157, 190 MapReduce 86, 87 marginal cost 11, 32, 57, 58, 59, 66, 151 marketing 8, 44, 58, 73, 117, 119, 120–123, 131, 176 marketisation 56, 61–62, 182 materiality 4, 19, 21, 24, 25, 66, 183, 185, 186, 189, 190 Mattern, S. 137, 181 Mayer-Schonberger, V. 68, 71, 72, 91, 114, 153, 154, 174 measurement 1, 3, 5, 6, 10, 12, 13, 15, 19, 23, 69, 97, 98, 115, 128, 166 metadata xvi, 1, 3, 4, 6, 8–9, 13, 22, 24, 29, 30, 31, 33, 35, 40, 43, 50, 54, 64, 71, 72, 74, 78, 85, 91, 93, 102, 105, 153, 155, 156 methodology 145, 158, 185 middleware 34 military intelligence 71, 116, 175 Miller, H.J. xvi, 27, 100, 101, 103, 104, 138, 139, 159 Minelli, M. 101, 120, 137, 168, 170, 171, 172, 174, 176 mixed methods 147, 191 mobile apps 78 computing xv, 44, 78, 80, 81, 83, 85, 139 mapping 88 phones 76, 81, 83, 90, 93, 151, 168, 170, 175 storage 85 mode of production 16 model 7, 11, 12, 24, 32, 37, 44, 57, 72, 73, 101, 103, 105, 106, 109, 110–112, 119, 125, 129, 130, 131, 132, 133, 134, 137, 139, 140, 144, 145, 147, 158–159, 166, 181 agent-based model 111, business 30, 54, 57–60, 61, 95, 118, 119, 121 environmental 139, 166 meteorological 72 time-space 73 transportation 7 modernity 3 Moore’s Law 81, moral philosophy 14 Moretti, F. 141–142 museum 31, 32, 137 NASA 7 National Archives and Records Administration (NARA) 67 National Security Agency (NSA) 45, 116 natural language processing 104, 105 near-field communication 89, 91 neoliberalism 56, 61–62, 126, 182 neural networks 104, 105, 111 New Public Management 62, non-governmental organisations xvi, 43, 55, 56, 73, 117 non-excludable 11, 151 non-rivalrous 11, 57, 151 normality 100, 101 normative thinking 12, 15, 19, 66, 99, 127, 144, 182, 183, 187, 192 Obama, B. 53, 75–76, 78, 118–119 objectivity 2, 17, 19, 20, 62, 135, 146, 185 observant participation 191 oligopticon 133, 167, 180 ontology 3, 12, 17–21, 22, 28, 54, 79, 128, 138, 150, 156, 177, 178, 184, 185 open data xv, xvi, xvii, 2, 12, 16, 21, 25, 48–66, 97, 114, 124, 128, 129, 140, 149, 151, 163, 164, 167, 186, 187, 188, 190, 191, 192 critique of 61–66 economics of 57–60 rationale 54–56 Open Definition 50 OpenGovData 50, 51 Open Knowledge Foundation 49, 52, 55, 58, 189, 190 open science 48, 72, 98 source 48, 56, 60, 87, 96 OpenStreetMap 73, 93, 96, 154, 155–156 optimisation 101, 104, 110–112, 120, 121, 122, 123 Ordnance Survey 54, 57 Organization for Economic Cooperation and Development (OECD) 49, 50, 59 overlearning 158, 159 panoptic 133, 167, 180 paradigm 112, 128–129, 130, 138, 147, 148, 186 participant observation 190, 191 participation 48, 49, 55, 66, 82, 94, 95, 96, 97–98, 126, 155, 165, 180 passport 8, 45, 84, 87, 88, 115 patent 13, 16, 41, 51 pattern recognition 101, 104–106, 134, 135 personally identifiable information 171 philanthropy 32, 38, 58 philosophy of science 112, 128–148, 185–188 phishing 174, 175 phone hacking 45 photography 6, 43, 71, 72, 74, 77, 86, 87, 88, 93, 94, 95, 105, 115, 116, 141, 155, 170 policing 80, 88, 116, 124, 125, 179 political economy xvi, 15–16, 25, 42–45, 182, 185, 188, 191 Pollock, R. 49, 54, 56, 57 58, 59 positivism 129, 136–137, 140, 141, 144, 145, 147 post-positivism 140, 144, 147 positionality 135, 190 power/knowledge 16, 22 predictive modelling 4, 7, 12, 34, 44, 45, 76, 101, 103, 104, 110–112, 118, 119, 120, 125, 132, 140, 147, 168, 179 profiling 110–112, 175–178, 179, 180 prescription 101 pre-analytical 2, 3, 19, 20, 185 pre-analytics 101–102, 112 pre-factual 3, 4, 19, 185 PRISM 45, 116 privacy 15, 28, 30, 40, 45, 51, 57, 63, 64, 96, 117, 163, 165, 166, 168–174, 175, 178, 182, 187 privacy by design 45, 173, 174 probability 14, 110, 153, 158 productivity xvi, 16, 39, 55, 66, 92, 114, 118 profiling 12, 42–45, 74, 75, 110–112, 119, 166, 168, 175–178, 179, 180, 187 propriety rights 48, 49, 54, 57, 62 prosumption 93 public good 4, 12, 16, 42, 52, 56, 58, 79, 97 –private partnerships 56, 59 sector information (PSI) 12, 48, 54, 56, 59, 61, 62 quantified self 95 redlining 176, 182 reductionism 73, 136, 140, 142, 143, 145 regression 102, 104, 105, 110, 111, 122 regulation xvi, 15, 16, 23, 25, 40, 44, 46, 83, 85, 87, 89–90, 114, 115, 123, 124, 126, 168, 174, 178, 180, 181–182, 187, 192 research design 7, 13, 14, 77–78, 98, 137–138, 153, 158 Renaissance xvi, 129, 141 repository 29, 33, 34, 41 representativeness 13, 14, 19, 21 Resource Description Framework (RDF) 53, 54 remote sensing 73–74, 105 RFID 74, 85, 90, 91, 169 rhetorical 3, 4, 185 right to be forgotten 45, 172, 187 information (RTI) 48, 62 risk 16, 44, 58, 63, 118, 120, 123, 132, 158, 174, 176–177, 178, 179, 180 Rosenberg, D. 1, 3 Ruppert, E. 22, 112, 157, 163, 187 sampling 13, 14, 27, 28, 46, 68, 72, 73, 77, 78, 88, 100, 101, 102, 120, 126, 133, 138, 139, 146, 149–150, 152, 153, 154, 156, 159 scale of economy 37 scanners 6, 25, 29, 32, 83, 85, 88, 89, 90, 91, 92, 175, 177, 180 science xvi, 1, 2, 3, 19, 20, 29, 31, 34, 37, 46, 65, 67, 71, 72, 73, 78, 79, 97, 98, 100, 101, 103, 111, 112, 128–139, 140, 147, 148, 150, 158, 161, 165, 166, 181, 184, 186 scientific method 129, 130, 133, 134, 136, 137–138, 140, 147, 148, 186 security data 28, 33, 34, 40, 45, 46, 51, 57, 126, 157, 166, 169, 171, 173, 174–175, 182, 187 national 42, 71, 88, 116–117, 172, 176, 178, 179 private 99, 115, 118, 151 social 8, 32, 45, 87, 115, 171 segmentation 104, 105, 110, 119, 120, 121, 122, 176 semantic information 9, 10, 11, 105, 157 Web 49, 52, 53, 66 sensors xv, 6, 7, 19, 20, 24, 25, 28, 34, 71, 76, 83, 84, 91–92, 95, 124, 139, 150, 160 sentiment analysis 105, 106, 121, Siegel, E. 103, 110, 111, 114, 120, 132, 158, 176, 179 signal 9, 151, 159 Silver, N. 136, 151, 158 simulation 4, 32, 37, 101, 104, 110–112, 119, 129, 133, 137, 139, 140 skills 37, 48, 52, 53, 57, 63, 94, 97, 98, 112, 149, 160–163, 164 small data 21, 27–47, 68, 72, 75, 76, 77, 79, 100, 103, 110, 112, 146, 147, 148, 150, 156, 160, 166, 184, 186, 188, 191 smart cards 90 cities 91, 92, 99, 124–125, 181–182 devices 83 metering 89, 123, 174 phones 81, 82, 83, 84, 90, 94, 107, 121, 155, 170, 174 SmartSantander 91 social computing xvi determinism 144 media xv, 13, 42, 43, 76, 78, 90, 93, 94–95, 96, 105, 119, 121, 140, 150, 151, 152, 154, 155, 160, 167, 176, 180 physics 144 security number 8, 32, 45, 87, 115, 171 sorting 126, 166, 168, 175–178, 182 sociotechnical systems 21–24, 47, 66, 183, 185, 188 software 6, 20, 32, 34, 40, 48, 53, 54, 56, 63, 80, 83, 84, 86, 88, 96, 132, 143, 160, 161, 163, 166, 170, 172, 175, 177, 180, 189 Solove, D. 116, 120, 168, 169, 170, 172, 176, 178, 180 solutionism 181 sousveillance 95–96 spatial autocorrelation 146 data infrastructure 34, 35, 38 processes 136, 144 resolution 149 statistics 110 video 88 spatiality 17, 157 Star, S.L. 19, 20, 23, 24 stationarity 100 statistical agencies 8, 30, 34, 35, 115 geography 17, 74, 157 statistics 4, 8, 13, 14, 24, 48, 77, 100, 101, 102, 104, 105, 109–110, 111, 129, 132, 134, 135, 136, 140, 142, 143, 145, 147, 159 descriptive 4, 106, 109, 147 inferential 4, 110, 147 non-parametric 105, 110 parametric 105, 110 probablistic 110 radical 147 spatial 110 storage 31–32, 68, 72, 73, 78, 80, 85–87, 88, 100, 118, 161, 171 analogue 85, 86 digital 85–87 media 20, 86 store loyalty cards 42, 45, 165 Sunlight Foundation 49 supervised learning 103 Supply Chain Management (SCM) 74, 99, 117–118, 119, 120, 121 surveillance 15, 71, 80, 83, 87–90, 95, 115, 116, 117, 123, 124, 151, 165, 167, 168, 169, 180 survey 6, 17, 19, 22, 28, 42, 68, 75, 77, 87, 115, 120 sustainability 16, 33, 34, 57, 58, 59, 61, 64–66, 87, 114, 123–124, 126, 155 synchronicity 14, 95, 102 technological handshake 84, 153 lock-in 166, 179–182 temporality 17, 21, 27, 28, 32, 37, 68, 75, 111, 114, 157, 160, 186 terrorism 116, 165, 179 territory 16, 38, 74, 85, 167 Tesco 71, 120 Thrift, N. 83, 113, 133, 167, 176 TopCoder 96 trading funds 54–55, 56, 57 transparency 19, 38, 44, 45, 48–49, 55, 61, 62, 63, 113, 115, 117, 118, 121, 126, 165, 173, 178, 180 trust 8, 30, 33, 34, 40, 44, 55, 84, 117, 152–156, 163, 175 trusted digital repository 33–34 Twitter 6, 71, 78, 94, 106, 107, 133, 143, 144, 146, 152, 154, 155, 170 uncertainty 10, 13, 14, 100, 102, 110, 156, 158 uneven development 16 Uniform Resource Identifiers (URIs) 53, 54 United Nations Development Programme (UNDP) 49 universalism 20, 23, 133, 140, 144, 154, 190 unsupervised learning 103 utility 1, 28, 53, 54, 55, 61, 63, 64–66, 100, 101, 114, 115, 134, 147, 163, 185 venture capital 25, 59 video 6, 43, 71, 74, 77, 83, 88, 90, 93, 94, 106, 141, 146, 170 visual analytics 106–109 visualisation 5, 10, 34, 77, 101, 102, 104, 106–109, 112, 125, 132, 141, 143 Walmart 28, 71, 99, 120 Web 2.0 81, 94–95 Weinberger, D. 9, 10, 11, 96, 97, 132, 133 White House 48 Wikipedia 93, 96, 106, 107, 143, 154, 155 Wired 69, 130 wisdom 9–12, 114, 161 XML 6, 53 Zikopoulos, P.C. 6, 16, 68, 70, 73, 76, 119, 151

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

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

Cuban, who lives and works in the heart of Silicon Valley, began as a hopeful evangelist for education technology, but slowly turned into one of ed tech’s most prominent skeptics after witnessing, time and again, the failure of ed tech to deliver on its promises. He calls it the hype cycle. “There is this pattern of extreme claims for transformation, and then a kind of bumpy landing and disappointment.” Why does this happen, over and over again, without the technology industry, the educational institutions, and other stakeholders learning from their mistakes? It is not as though the evidence is lacking, or industry leaders lack the ability to learn from mistakes. Rather, Cuban attributes the persistence of ed tech’s hype cycle to deeply held values around technology and innovation. “In this culture, like other developed cultures, technology is seen as an unadulterated good,” he said.

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See e-readers digital Sabbath, xii digital special effects, 72 digital studios, 23–24, 25, 26 digital synthesizers, 23 digital technology impact of, views on, xiii, xv, 239, 240 increasing role of, analog’s rise amidst, xiv–xv See also specific digital things, processes, and ideas digital technology industry, 161–166, 177, 207, 208, 226 digital technology workplaces conducting work in, 207, 208, 221–224 meditation and thinking in, 205–207, 208–211 meetings and communication in, 219–221 physical workspace in, 211–217 digital textbooks, 189–190 digital utopia, xvi, xvii, 178, 184 digital watches, 168 digital work standard narrative on, 154, 155, 160 value gap involving, 160, 161, 171 Discogs, 12, 20–21, 137 disruption, xv, xvii, xviii, 31, 91, 103, 153, 162, 164, 168, 175, 177, 179, 189, 200, 201, 208, 210, 238 distance-learning initiatives, previous, 201 Doeblin, Chris, 132, 140–141, 142, 144, 146–147, 148 Dollar Shave Club, 137 Donors Choose, 191 DOS systems, 65 Drift (magazine), 105 DriveThruCards, 91 drones, 163, 187, 234 Duke University, 183 Dund Gol, 15 Dundas Central Public School, 196 Dungeons and Dragons, 78, 82, 85, 98 dysphoria, 238 Earley, Matt, 17 early childhood education (ECE), 180–182 Eastman Kodak, 55, 71 See also Kodak eBay, 12, 107, 124, 146, 238 eBooks, 124, 126, 142, 143, 187, 189 Ecojot journals, 126 e-commerce disadvantages of, 129, 130, 132, 134, 135–136 growth of, 124, 126 profitability in, issue of, 125, 133, 136 sales from, by brick-and-mortar stores, 136–137 economic inequality, 161, 165, 166 Economist, The (magazine), 45, 110, 111, 155 ed tech appropriate use of, advantages in, 182–183, 198 attraction of, to politicians/policymakers, 186 costs of, 186–187 greatest promises and failures of, 201–203 hype cycle of, 179 spending on, 177, 179, 187, 191–192 student preferences regarding, 188–189 and ways in which it falls short, 183–187, 189–190, 203, 204 Edison, Thomas, 179 education in early childhood, 180–182 more promising tools for affecting change in, 189, 190–192 new approaches to teaching in, 192–201 previous technologies predicted to change, 178–179 as a relationship, learning anchored in, 203–204 visualizing the future of, integrative approach to, 175, 176–177 education philanthropy, 177, 184, 191 educational inequality, ed tech failing to address, 183–184, 185 8 mm film, 56, 71 8-track tapes, 9 Elvis, 5, 22 e-mail, x, 30, 33, 44, 46, 210, 219, 220, 224, 233, 234, 237 eMarketer, 136 EMI, 25 empathy, defined, 193 Empathy Toy, 193–196, 198 Encore, 127 Endicott Books, 141 Enjoy, 139 e-readers, 108, 124, 130, 142, 143, 228, 231 Essen Spiel, 77, 95 Etsy, 126, 145, 146, 164 Evernote, 46–47, 221, 222–223 Exploding Kittens, 92 Fab.com, 133 Facebook, 12, 39–40, 46, 80, 91, 94, 110, 137, 154, 155, 161, 162, 163, 165, 170, 199, 206, 211, 215–217, 224, 234, 236, 238 factory jobs.

Digital Transformation at Scale: Why the Strategy Is Delivery by Andrew Greenway,Ben Terrett,Mike Bracken,Tom Loosemore

Airbnb, behavioural economics, bitcoin, blockchain, butterfly effect, call centre, chief data officer, choice architecture, cognitive dissonance, cryptocurrency, data science, Diane Coyle, en.wikipedia.org, fail fast, G4S, hype cycle, Internet of things, Kevin Kelly, Kickstarter, loose coupling, M-Pesa, machine readable, megaproject, minimum viable product, nudge unit, performance metric, ransomware, robotic process automation, Silicon Valley, social web, The future is already here, the long tail, the market place, The Wisdom of Crowds, work culture

As the consequences of not doing them are minor and largely invisible to start with, people generally believe that it will stay that way. It becomes even easier for a large business or government administration to ignore hard yet necessary tasks if they can find something else that has the characteristics of work, while being much more comfortable to sink time into. Fortunately, the technology hype cycle is ready to provide a stream of distractions. All too often, the word digital is conflated with whatever technology fad has made it into the colour supplements this month. Blockchain. Artificial intelligence. The Internet of Things and connected devices. Robotic Process Automation. The captains of industry, ministers and senior officials who read colour supplements during their brief periods of down time see these exciting things and commission policy papers to unpick their potential effect on the organisations they run.

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Resisting the hype Every business strategy presentation for the last two years (and the next three) will have a slide that says something like: ‘AI, blockchain, Internet of Things – what should we do?’ For most organisations, this discussion is a little premature. Even allowing for the fact these technology breakthroughs are near the top of their hype cycle at the time of writing, we are not saying that they are unimportant for large organisations, public or private. Far from it. We’re confident that artificial intelligence, connected devices and advances in cryptography will change the world, in predictable and unexpected ways. There are many excellent books, talks and blogs that do these subjects far better justice than we have the space to here.

Hit Makers: The Science of Popularity in an Age of Distraction by Derek Thompson

Airbnb, Albert Einstein, Alexey Pajitnov wrote Tetris, always be closing, augmented reality, Clayton Christensen, data science, Donald Trump, Downton Abbey, Ford Model T, full employment, game design, Golden age of television, Gordon Gekko, hindsight bias, hype cycle, indoor plumbing, industrial cluster, information trail, invention of the printing press, invention of the telegraph, Jeff Bezos, John Snow's cholera map, Kevin Roose, Kodak vs Instagram, linear programming, lock screen, Lyft, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, Mary Meeker, Menlo Park, Metcalfe’s law, Minecraft, Nate Silver, Network effects, Nicholas Carr, out of africa, planned obsolescence, power law, prosperity theology / prosperity gospel / gospel of success, randomized controlled trial, recommendation engine, Robert Gordon, Ronald Reagan, Savings and loan crisis, Silicon Valley, Skype, Snapchat, social contagion, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, subscription business, TED Talk, telemarketer, the medium is the message, The Rise and Fall of American Growth, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, Vilfredo Pareto, Vincenzo Peruggia: Mona Lisa, women in the workforce

That means purposefully making products that will be fashionable or functional for only a limited time in order to encourage repeat shopping trips. Across the economy, companies realized that they could engineer turnover and multiply sales by constantly changing the colors, shapes, and styles of their goods. Technology enabled choices and choices created fashion—that perpetual hype cycle where designs and colors and behaviors appear suddenly cool and then suddenly anachronistic. It was an age of new things, a birth of American neophilia. Artists, once silenced by the whir of assembly belts churning out identical products, played a starring role in the new mass production economy.

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News still carries influence—in the 1930s, the name Franklin soared in popularity while Adolf vanished—but there is nothing like direct advertising for names. No organization or company benefits from more sons named Michael, Noah, or Dmitri. The weird thing about first names is that, even though they’re free and infinite, they follow the same hot-and-cold “hype cycle” of many other products that do have finite choices, diverse prices, and lots of advertising. Just like clothes, first names are a fashion. Some names are cool today (Emily) while some once popular names now sound out-of-date (Ethel), even though the names Emily and Ethel are as Emilyish and Ethelian as they’ve always been.

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Within a few years, on-the-nose social media messages were mostly condemned as embarrassing and forced, like a dad misquoting a new teen movie in an attempt to look hip. Advertising firms are still catching up to the fact that the fashion cycle of slang moves faster than their copy desks. Clothing, once a ritual, is now the definitive fashion. First names, once a tradition, now follow the hype cycle of fashion lines. Communication, too, is now coming to resemble the hallmarks of a fashion, where choices emerge and preferences change, sometimes with seeming arbitrariness, as people discover new, more convenient, and more fun ways to say hello. INTERLUDE A Brief History of Teens The teenager is one of the more unusual inventions of the twentieth century.

The Charisma Machine: The Life, Death, and Legacy of One Laptop Per Child by Morgan G. Ames

"World Economic Forum" Davos, 1960s counterculture, 4chan, A Declaration of the Independence of Cyberspace, Benjamin Mako Hill, British Empire, Burning Man, Cass Sunstein, clean water, commoditize, computer age, digital divide, digital rights, Evgeny Morozov, fail fast, Firefox, Free Software Foundation, Gabriella Coleman, game design, Hacker Conference 1984, Hacker Ethic, hype cycle, informal economy, Internet of things, John Markoff, Joi Ito, Khan Academy, Marshall McLuhan, Mary Lou Jepsen, Minecraft, new economy, One Laptop per Child (OLPC), Peter Thiel, placebo effect, Potemkin village, RFID, Richard Stallman, ride hailing / ride sharing, side project, Silicon Valley, Silicon Valley ideology, SimCity, smart cities, Steve Jobs, Steven Levy, Stewart Brand, technological determinism, technological solutionism, technoutopianism, TED Talk, The Hackers Conference, Travis Kalanick

The hacker identity that resulted was one that was still deeply influenced by privilege, infrastructure, and proximity to cosmopolitan cores and international networks. Even though anthropology has deconstructed notions of center and periphery, the technological elite in Paraguay reinscribed their centrality and the peripherality of those they sought to help. 6 Performing Development The peak of the hype cycle was ... obviously deeply ideological, and interesting as such. But it also registered something real about the performative efficacy of what one might call “technological charisma.” That is to say, the hype was not just empty; rather, it brought about its own concrete social effects “on the ground.”

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Anthropologist William Mazzarella has described performances such as this as “beautiful balloons” that captivate—until they deflate under the weight of broken promises. And just as this book does, he has connected this captivation with a kind of charisma: This charismatic moment, at the peak of the hype cycle, opened up new possibilities. As the moment passed, and gravity began to win, disillusionment set in. The erstwhile beautiful balloon, having apparently moved from hype to habit, remains more enigmatic than it might appear. Much of the air that has gone out of it was, in any case, stale: the exhaust fumes of nationalism, paternalist politics, and profit seeking ...

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The performance that the visit elicited could help the project survive, even as it hid the ongoing work needed to do so; it showed teachers and students what success looked like, even as it concealed what would be required to achieve success in the long term; and it suggested that any obstacles encountered—breakage, disinterest, “little toys,” drained batteries, missing software, infrastructural deficiencies, unrealistic labor expectations, design flaws, media corporations, language barriers—were merely small speedbumps on the road to laptop-driven, child-led progress. I, like Mazzarella, was fascinated by what he has called the “hype cycle,” not only because of its “inflated, excessive tone” but because of the “collective desire” it demonstrated.28 This operates simultaneously on the planes of the ideological and the material—one does not preclude the other, as the quote that opens this chapter also attests. But where, really, have these charismatic performances left Caacupé, Paraguay Educa, and One Laptop per Child?

The Coming Wave: Technology, Power, and the Twenty-First Century's Greatest Dilemma by Mustafa Suleyman

"World Economic Forum" Davos, 23andMe, 3D printing, active measures, Ada Lovelace, additive manufacturing, agricultural Revolution, AI winter, air gap, Airbnb, Alan Greenspan, algorithmic bias, Alignment Problem, AlphaGo, Alvin Toffler, Amazon Web Services, Anthropocene, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, ASML, autonomous vehicles, backpropagation, barriers to entry, basic income, benefit corporation, Big Tech, biodiversity loss, bioinformatics, Bletchley Park, Blitzscaling, Boston Dynamics, business process, business process outsourcing, call centre, Capital in the Twenty-First Century by Thomas Piketty, ChatGPT, choice architecture, circular economy, classic study, clean tech, cloud computing, commoditize, computer vision, coronavirus, corporate governance, correlation does not imply causation, COVID-19, creative destruction, CRISPR, critical race theory, crowdsourcing, cryptocurrency, cuban missile crisis, data science, decarbonisation, deep learning, deepfake, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, disinformation, drone strike, drop ship, dual-use technology, Easter island, Edward Snowden, effective altruism, energy transition, epigenetics, Erik Brynjolfsson, Ernest Rutherford, Extinction Rebellion, facts on the ground, failed state, Fairchild Semiconductor, fear of failure, flying shuttle, Ford Model T, future of work, general purpose technology, Geoffrey Hinton, global pandemic, GPT-3, GPT-4, hallucination problem, hive mind, hype cycle, Intergovernmental Panel on Climate Change (IPCC), Internet Archive, Internet of things, invention of the wheel, job automation, John Maynard Keynes: technological unemployment, John von Neumann, Joi Ito, Joseph Schumpeter, Kickstarter, lab leak, large language model, Law of Accelerating Returns, Lewis Mumford, license plate recognition, lockdown, machine readable, Marc Andreessen, meta-analysis, microcredit, move 37, Mustafa Suleyman, mutually assured destruction, new economy, Nick Bostrom, Nikolai Kondratiev, off grid, OpenAI, paperclip maximiser, personalized medicine, Peter Thiel, planetary scale, plutocrats, precautionary principle, profit motive, prompt engineering, QAnon, quantum entanglement, ransomware, Ray Kurzweil, Recombinant DNA, Richard Feynman, Robert Gordon, Ronald Reagan, Sam Altman, Sand Hill Road, satellite internet, Silicon Valley, smart cities, South China Sea, space junk, SpaceX Starlink, stealth mode startup, stem cell, Stephen Fry, Steven Levy, strong AI, synthetic biology, tacit knowledge, tail risk, techlash, techno-determinism, technoutopianism, Ted Kaczynski, the long tail, The Rise and Fall of American Growth, Thomas Malthus, TikTok, TSMC, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, warehouse robotics, William MacAskill, working-age population, world market for maybe five computers, zero day

We must begin to suggest what to do if it looks like there is a real risk that technology fails us. What’s required is a societal and political response, not merely individual efforts, but it needs to begin with my peers and me. Some will argue this is all overblown. That change is far more incremental. That it is just another turn of the hype cycle. That systems for coping with crises and change are actually quite robust. That my view of human nature is far too dark. That humanity’s record is, well, so far, so good. History is full of false prophets and doomsayers proven wrong. Why should this time be different? Pessimism aversion is an emotional response, an ingrained gut refusal to accept the possibility of seriously destabilizing outcomes.

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This kind of power is even harder to centralize and oversee; this wave is not just a deepening and acceleration of history’s pattern, then, but also a sharp break from it. Not everyone agrees these technologies are either as locked on or as consequential as I think they are. Skepticism and pessimism aversion are not unreasonable responses, given there is much uncertainty. Each technology is subject to a vicious hype cycle, each is uncertain in development and reception, each is surrounded by challenges technical, ethical, and social. None is complete. There are certain to be setbacks, and many of the harms—and indeed benefits—are still unclear. But each is also growing more concrete, developed, and capable by the day.

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Many who ridicule overly optimistic technologists stick to writing theoretical oversight frameworks or op-eds calling for regulation. If you believe technology is important and powerful, and you follow the implications of these critiques, such responses are clearly inadequate. Even the critics duck the true reality in front of them. Indeed, at times shrill criticism just becomes part of the same hype cycle as technology itself. Credible critics must be practitioners. Building the right technology, having the practical means to change its course, not just observing and commenting, but actively showing the way, making the change, effecting the necessary actions at source, means critics need to be involved.

Hit Refresh: The Quest to Rediscover Microsoft's Soul and Imagine a Better Future for Everyone by Satya Nadella, Greg Shaw, Jill Tracie Nichols

3D printing, AlphaGo, Amazon Web Services, anti-globalists, artificial general intelligence, augmented reality, autonomous vehicles, basic income, Bretton Woods, business process, cashless society, charter city, cloud computing, complexity theory, computer age, computer vision, corporate social responsibility, crowdsourcing, data science, DeepMind, Deng Xiaoping, Donald Trump, Douglas Engelbart, driverless car, Edward Snowden, Elon Musk, en.wikipedia.org, equal pay for equal work, everywhere but in the productivity statistics, fault tolerance, fulfillment center, Gini coefficient, global supply chain, Google Glasses, Grace Hopper, growth hacking, hype cycle, industrial robot, Internet of things, Jeff Bezos, job automation, John Markoff, John von Neumann, knowledge worker, late capitalism, Mars Rover, Minecraft, Mother of all demos, Neal Stephenson, NP-complete, Oculus Rift, pattern recognition, place-making, Richard Feynman, Robert Gordon, Robert Solow, Ronald Reagan, Salesforce, Second Machine Age, self-driving car, side project, Silicon Valley, Skype, Snapchat, Snow Crash, special economic zone, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, subscription business, TED Talk, telepresence, telerobotics, The Rise and Fall of American Growth, The Soul of a New Machine, Tim Cook: Apple, trade liberalization, two-sided market, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, young professional, zero-sum game

HoloLens provides access to mixed reality in which the users can navigate both their current location—interact with people in the same room—and a remote environment while also manipulating holograms and other digital objects. Analysts at Gartner Inc., the technology research firm, have made an art from the study of the hype cycles and arcs followed by new technologies as they move from invention to widespread adoption (or demise), and believe virtual reality technologies are likely five to ten years away from mainstream adoption. Just getting to the starting line proved difficult for us. My colleague Alex Kipman had been perfecting a prototype of HoloLens for some time.

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Popular Mechanics, September 6, 2016, http://www.popularmechanics.com/technology/a22384/hololens-ar-breakthrough-awards/. Aukstakalnis, Steve. Practical Augmented Reality. A Guide to the Technologies, Applications, and Human Factors for AR and VR. Boston: Addison-Wesley, 2016. Grunwald, Martin. Human Haptic Perception: Basics and Applications. Boston: Birkhauser, 2008. Gartner, Hype Cycle for Emerging Technologies, 2016, G00299893 Aaronson, Scott. Quantum Computing Since Democritus. Cambridge: Cambridge University Press, 2013. Linn, Allison. “Microsoft Doubles Down on Quantum Computing Bet.” Next at Microsoft Blog, November 20, 2016. https://blogs.microsoft.com/next/2016/11/20/microsoft-doubles-quantum-computing-bet/.

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

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

Gilder’s Law As with Gordon Moore, there is a strange parallel between George Gilder the man and the law he named after himself. In 1989, Glider predicted that data transmission rates would grow three times faster than computer power. This prediction went through a massive hype cycle—a bit like Gilder himself. The two stories are surprisingly intertwined. The technology breakthrough that triggered the hype cycle was the commercial viability of fiber optic cables. These promised vastly faster transmission rates. The innovation was oversold at first, largely by Gilder himself. This fostered overinflated expectations that became part of the “dot com” bubble of the late 1990s.

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

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

The great fragmentation Note Recommended books and documentaries Index Advert End User License Agreement List of Tables Chapter 6 Table 6.1: Chapter 19 Table 19.1: List of Illustrations Chapter 4 Figure 4.1 life in boxes Figure 4.2 a remote control Chapter 6 Figure 6.1 the behaviour of cities has evolved as people are self-organising themselves into groups around passions Chapter 8 Figure 8.1 photo of Vasilii Racovitsa Chapter 14 Figure 14.1 expectation and utility curve: a personal variation on Gartner’s hype cycle* Chapter 17 Figure 17.1 anyone can get into space Figure 17.2 the pitch tweet Figure 17.3 the hourglass launch strategy PREFACE Over the past 10 years I've been a keen observer of the business landscape. I had an inkling something was going on when I left corporate life for the first time in 2005.

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But this doesn’t mean the predictions of what it may become were inaccurate. It was more a reflection of the diffusion of innovation rather than of it disappearing. Most disruptive innovations and technologies go through similar trajectories (see figure 14.1). Figure 14.1 expectation and utility curve: a personal variation on Gartner’s hype cycle (The underlying concept for this was conceived by Gartner, Inc.). The truth is we’re still hurtling towards a gamified future of commerce. We’re all still playing the games right now, but like many aspects of commerce, we go deep into the wormhole before we realise it. Gamification not only becomes possible in a connected and social world, it’s inevitable.

Ludicrous: The Unvarnished Story of Tesla Motors by Edward Niedermeyer

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

The challenge of self-driving car development was not simply convincing the public that a car could “drive itself,” which could theoretically be achieved with as little hardware as a brick placed on the accelerator, but by making a car drive itself safely. As this reality sank in, the rest of the autonomous-drive space dialed back expectations. In the summer of 2018, the research and advisory company Gartner released its latest “hype cycle” analysis, which showed that autonomous-drive technology had passed peak hype and was entering the “trough of disillusionment.” Increasingly, the entire goal of a Level 5 vehicle, capable of full autonomy anywhere, was being dismissed in favor of Level 4 vehicles, which operate fully autonomously but only in a limited “geofenced” area.

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Valuewalk, August 17, 2015. https://www.valuewalk.com/2015/08/tesla-motors-morgan-stanley-raises-price-target/ 173the on-demand mobility market could be worth $2 trillion: Arjun Kharpal. “Tesla could be worth ‘multiples’ of current $50 billion market cap by 2020, fund manager says.” CNBC, May 19, 2017. https://www.cnbc.com/2017/05/19/tesla-stock-valuation-driverless-taxi.html 175Gartner released its latest “hype cycle” analysis: Mike Ramsey. “Autonomous Vehicles Fall Into the Trough of Disillusionment . . . But That’s Good.” Forbes, August 14, 2018. https://www.forbes.com/sites/enroute/2018/08/14/autonomous-vehicles-fall-into-the-trough-of-disillusionment-but-thats-good/#3b5a3c6e7b5a 176John Krafcik publicly admitted that autonomous vehicles might never work in all locations: Sam Abuelsamid.

Architects of Intelligence by Martin Ford

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

DAPHNE KOLLER: I think it’s important to recognize that we never said this is going to put universities out of business. There were other people who said that, but we never endorsed that and we didn’t think it was a good idea. In some ways, the typical Gartner hype cycle of MOOCs was compressed. People made these extreme comments, in 2012 it was, “MOOCs are going to put universities out of business,” then 12 months later it was “universities are still here, so obviously MOOCs have failed.” Both of those comments are ridiculous extremes of the hype cycle. I think that we actually have done a lot for people who don’t normally have access to that level of education. About 25% of Coursera learners don’t have degrees, and about 40% of Coursera learners are in developing economies.

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How seriously do you take that, and is it something we should worry about? China does have a different system, a more authoritarian system, and a much bigger population which means more data to train algorithms on and less restrictions regarding privacy and so forth. Are we at risk of falling behind in AI leadership? FEI-FEI LI: Right now, we’re living in a major hype-cycle of modern physics and how that can transform technology, whether it’s nuclear technology, or electrical technology. One hundred years later, will we ask ourselves the question: which person owned modern physics? Will we try to name the company or country that owned modern physics and everything after the industrial revolution?

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

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

Those technologies which do cross the chasm are then adopted by the “early majority”, then the “late majority”, and finally by the “laggards”. But by the time the early majority is getting on board the hype is already ancient history, and people are already taking for granted the improvement to their lives. The hype cycle has run its course. The hedonic treadmill is a name for the fact that most people have a fairly constant level of happiness (hedonic level), and that when something significant in our life changes – for good or bad – we quickly adjust and return to our previous level. When we look ahead to an anticipated event we often believe that it will change our lives permanently, and that we will feel happier – or less happy – forever afterwards.

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

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

Additive manufacturing and 3D printing To the general population, the promise of anything we want printed right before our eyes vanished with the first wave of 3D printers that only printed crude versions of knickknacks. That image, of a printer slowly layering plastic into a rudimentary product, has been etched into our minds because the reality was so far away from the promise. Many of us, me included, dismissed a world where anything could be printed in our living rooms as a faraway dream, and the hype cycle of additive manufacturing ended. But it was really only starting. The relentless march of technology innovation continued, and today the state of additive manufacturing is vastly different. Now commercially viable for a wide range of applications, the industry is moving fast, having surpassed $7.3 billion in 2017, according to Wohlers Associates.26 Although not yet seen by the public commercially, it is just starting to reach a tipping point.

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

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

I met with start-up founders and engineers in Silicon Valley who showed me how new advances in fields like deep learning were helping them build all kinds of world-improving tools: algorithms that could increase farmers’ crop yields, software that would help hospitals run more efficiently, self-driving cars that could shuttle us around while we took naps and watched Netflix. This was the euphoric peak of the AI hype cycle, a time when all of the American tech giants—Google, Facebook, Apple, Amazon, Microsoft—were pouring billions of dollars into developing new AI products and shoving machine learning algorithms into as many of their apps as possible. They wrote blank checks to their AI research teams, and poached professors and grad students out of top computer science departments with frankly hilarious job offers.

Blockchain Chicken Farm: And Other Stories of Tech in China's Countryside by Xiaowei Wang

4chan, AI winter, Amazon Web Services, artificial general intelligence, autonomous vehicles, back-to-the-land, basic income, Big Tech, bitcoin, blockchain, business cycle, cloud computing, Community Supported Agriculture, computer vision, COVID-19, cryptocurrency, data science, deep learning, Deng Xiaoping, Didi Chuxing, disruptive innovation, Donald Trump, drop ship, emotional labour, Ethereum, ethereum blockchain, Francis Fukuyama: the end of history, Garrett Hardin, gig economy, global pandemic, Great Leap Forward, high-speed rail, Huaqiangbei: the electronics market of Shenzhen, China, hype cycle, income inequality, informal economy, information asymmetry, Internet Archive, Internet of things, job automation, Kaizen: continuous improvement, Kickstarter, knowledge worker, land reform, Marc Andreessen, Mark Zuckerberg, Menlo Park, multilevel marketing, One Laptop per Child (OLPC), Pearl River Delta, peer-to-peer lending, precision agriculture, QR code, ride hailing / ride sharing, risk tolerance, Salesforce, Satoshi Nakamoto, scientific management, self-driving car, Silicon Valley, Snapchat, SoftBank, software is eating the world, surveillance capitalism, TaskRabbit, tech worker, technological solutionism, the long tail, TikTok, Tragedy of the Commons, universal basic income, vertical integration, Vision Fund, WeWork, Y Combinator, zoonotic diseases

Projects like the failed One Laptop per Child, by Nicholas Negroponte, or the Hole in the Wall project exude this techno-optimistic belief—if you can give a laptop to a child or put a computer in an Indian slum, children will teach themselves linear algebra and become the next Bill Gates. We now know this is a myth inflated by a hype cycle. A whole support system of teachers, peers, and family is a stronger influence than a laptop or computer screen. But part of me—the American part of me—wants to believe the narrative about individualistic passion overcoming everything, including a lack of formal schooling or connections. Sun Wei grins at my question and gives me a pitying look.

The History of the Future: Oculus, Facebook, and the Revolution That Swept Virtual Reality by Blake J. Harris

"World Economic Forum" Davos, 4chan, airport security, Anne Wojcicki, Apollo 11, Asian financial crisis, augmented reality, barriers to entry, Benchmark Capital, Bernie Sanders, bitcoin, call centre, Carl Icahn, company town, computer vision, cryptocurrency, data science, disruptive innovation, Donald Trump, drone strike, Elon Musk, fake news, financial independence, game design, Grace Hopper, hype cycle, illegal immigration, invisible hand, it's over 9,000, Ivan Sutherland, Jaron Lanier, Jony Ive, Kickstarter, Marc Andreessen, Mark Zuckerberg, Menlo Park, Minecraft, move fast and break things, Neal Stephenson, Network effects, Oculus Rift, off-the-grid, Peter Thiel, QR code, sensor fusion, Sheryl Sandberg, side project, Silicon Valley, SimCity, skunkworks, Skype, slashdot, Snapchat, Snow Crash, software patent, stealth mode startup, Steve Jobs, unpaid internship, white picket fence

And even if you were lucky enough to make that list, the devkits themselves were usually incredibly expensive1—for example, early devkits for Sony’s PlayStation 3 sold for nearly $20,0002. So what Iribe was proposing was uncommon in the gaming space. But it actually made a whole lot of sense, as both a way to tap into the booming indie development space, and also a way to try and spare Oculus from the hype cycles that have plagued VR headsets of the past. Because, at least in rhetoric, what they’d be selling wasn’t a headset. It was a devkit—just a devkit. That would keep user expectations low and also buy Oculus time to ramp up to a consumer product. “I love it,” Mitchell told Iribe. “We should totally do devkits.”

…

In response to this wonderful news, fans freak out about the possibilities. Excitement builds—Anything? Anything!—until one of the megafans finally gets his hands on the game, installs it, and is greeted by this message: WELCOME TO THE TUTORIAL . . . YOU CAN’T DO ANYTHING. Mitchell laughed. “Yeah, that pretty much sums up the hype cycle for every game.” “But we can’t do that,” Luckey said. “I don’t want to do that. Not just because it’s a shitty thing to do, but because that would be bad for VR.” This was not the first time Mitchell had heard Luckey make a comment like this—about something being “good” or “bad” for virtual reality—and he found it utterly charming.

Editing Humanity: The CRISPR Revolution and the New Era of Genome Editing by Kevin Davies

23andMe, Airbnb, Anne Wojcicki, Apple's 1984 Super Bowl advert, Asilomar, bioinformatics, California gold rush, clean water, coronavirus, COVID-19, CRISPR, crowdsourcing, discovery of DNA, disinformation, Doomsday Clock, double helix, Downton Abbey, Drosophila, Edward Jenner, Elon Musk, epigenetics, fake news, Gregor Mendel, Hacker News, high-speed rail, hype cycle, imposter syndrome, Isaac Newton, John von Neumann, Kickstarter, life extension, Mark Zuckerberg, microbiome, Mikhail Gorbachev, mouse model, Neil Armstrong, New Journalism, ocean acidification, off-the-grid, personalized medicine, Peter Thiel, phenotype, QWERTY keyboard, radical life extension, RAND corporation, Recombinant DNA, rolodex, scientific mainstream, Scientific racism, seminal paper, Shenzhen was a fishing village, side project, Silicon Valley, Silicon Valley billionaire, Skype, social distancing, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, synthetic biology, TED Talk, the long tail, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, traumatic brain injury, warehouse automation

As one gene therapy expert said: “We underestimated the fact that it took billions of years for the viruses to learn to live in us—and we were hoping to do it in a five-year grant cycle.”31 There was also the complication of our immune system, which is designed to combat foreign agents such as viruses. The human body isn’t going to automatically give billions of recombinant viruses a pass just because they mean well. It has been a long haul back to respectability and success for gene augmentation therapy. The roller-coaster ride follows the Gartner Hype Cycle: the inflated expectations of the 1990s, the trough—or abyss—of disillusionment at the turn of the century; followed by the slope of enlightenment. What’s been holding up the field is not a lack of suitable targets—we have an encyclopedic catalogue of thousands of eligible Mendelian genetic diseases—but the capability of delivering the therapeutic gene safely and effectively.

…

., 158 Eugenics and Health Protection Law, 267 Eugenics Education Society, 341 European Patent Office (EPO), 184, 189 Evans, John, 330, 359, 360 Evans, Martin, 98, 107 Evolution hierarchical evolution, 207 microbial evolution, 38 natural evolution, 29 views on, 15, 291 volitional evolution, 337–360 Exonics, 175–177 Extinction, 6, 279, 283–288 “Extraordinary Guangdong,” 265 Extraordinary Measures, 163 F Face transplant, 360 Facial features, 357 Fan, Christina, 208 Fantastic Voyage, 4 Farm aid, 301–320 Fauci, Anthony, 96 Feinstein, Dianne, 258 Felipe II, King, 30 Ferny Fertility Clinic, 340 Ferrell, Ryan, 193, 195, 197–203, 211, 228, 230, 239–240, 257 Fire, Andy, 50 Firestein, Stuart, 72 Fischer, Alain, 111–112, 144, 146, 161 Fleay, David, 286 Fleming, Alexander, 7, 92 Flies, 298–299 Flotte, Terry, 164 Fonfara, Ines, 64 Fong, Mei, 268, 275 Food and Drug Administration (FDA), 119, 122, 144, 150–151, 155, 159–162, 165, 212–214, 239, 258, 264, 267, 273, 315–317, 345, 363–365 Forbes, 89 Ford, Harrison, 163 Forever Fix, The, 147 Foy, Shaun, 170 Francis, Pope, 13–14 Francis Crick Institute, 222, 234 Frangoul, Haydar, 170 Franklin, Rosalind, 7, 47, 60, 92, 110, 123, 125, 126, 132 Fraser, Brendan, 163 Fraser, Claire, 32 Fremaux, Christophe, 44 Friedmann, Theodore (Ted), 135, 140–141, 146 Frost, Lucy, 162 Fu, Ying-Hui, 346 Fungus, 67, 287, 311–312 FUT2, 343–344 “Future, Human, Nature: Reading, Writing, Revolution,” 221 “Future of Man, The,” 132 G Gabriel, Meabh, 13 Gabriel, Peter, 13 Gabriel, Stacey, 94 Gaiman, Neil, 1 Game of Thrones, 244, 346 Gantz, Valentino, 293 Gao, Guangping, 145 Gartner Hype Cycle, 144–145 Gasiunas, Giedrius, 69 Gates, Bill, 97, 171, 293, 299–300, 353 Gates, Henry “Skip,” 353 Gates Foundation, 158, 293 Gattaca, 355 Gaudelli, Nicole, 325–326, 328–329, 331 Geisinger healthcare, 230 Gelsinger, Jesse, 112, 141–144, 146, 149, 159, 164, 227, 259, 267, 293 Gelsinger, Paul, 141–144, 164 GenBank, 35 Gendaq, 110 Gene, The (book), xi, xviii, 143 Gene, The (documentary), xi, 144, 354–355 Gene mapping, xiii–xiv, 15–18, 91, 123, 138, 348 Gene Sequencing Center, 209 Gene surgery, 25, 199, 204, 247 Gene therapy beginnings of, xi, xvi, 135–138 challenges with, 141–178 cost of, 159–161 father of, 151 germline and, 254, 359–360 problems with, 141–145 retinal gene therapy, 148–151 stem cells and, 119, 136, 144, 181 trials, 111–112, 116, 133–151, 159, 253 “Gene Therapy for Genetic Disease?

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

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

[clx] http://www.ft.com/cms/s/0/b33d75fe-cc5a-11e5-be0b-b7ece4e953a0.html#axzz3znOxP8QH [clxi] http://www.kitguru.net/peripherals/anton-shilov/gartner-two-million-vr-headsets-to-be-sold-in-2016/ [clxii] http://www.digi-capital.com/news/2015/04/augmentedvirtual-reality-to-hit-150-billion-disrupting-mobile-by-2020/#.VoV65vmLRD8 [clxiii] http://uk.businessinsider.com/virtual-reality-on-gartner-hype-cycle-2015-8 [clxiv] http://techcrunch.com/2016/01/30/how-the-growth-of-mixed-reality-will-change-communication-collaboration-and-the-future-of-the-workplace/ [clxv] The games industry is much bigger than Hollywood if you stop measuring move income at the box office. If you add in DVD and other “windows”, plus merchandising, it is hard to say. https://www.quora.com/Who-makes-more-money-Hollywood-or-the-video-game-industry [clxvi] https://versions.killscreen.com/we-should-be-talking-about-torture-in-vr/ [clxvii] http://www.tomdispatch.com/post/175822/tomgram%3A_crump_and_harwood%2C_the_net_closes_around_us/ [clxviii] https://www.washingtonpost.com/local/public-safety/the-new-way-police-are-surveilling-you-calculating-your-threat-score/2016/01/10/e42bccac-8e15-11e5-baf4-bdf37355da0c_story.html [clxix] http://www.newyorker.com/tech/elements/little-brother-is-watching-you [clxx] http://www.wired.com/2014/03/going-tracked-heres-way-embrace-surveillance/ [clxxi] https://www.washingtonpost.com/news/the-switch/wp/2016/03/28/mass-surveillance-silences-minority-opinions-according-to-study/ [clxxii] http://www.bbc.co.uk/news/world-asia-china-34592186 [clxxiii] http://www.computerworld.com/article/2990203/security/aclu-orwellian-citizen-score-chinas-credit-score-system-is-a-warning-for-americans.html [clxxiv] http://www.theguardian.com/technology/2015/oct/06/peeple-ratings-app-removes-contentious-features-boring [clxxv] https://www.technologyreview.com/s/601294/microsoft-and-google-want-to-let-artificial-intelligence-loose-on-our-most-private-data/?

Makers by Chris Anderson

3D printing, Airbnb, Any sufficiently advanced technology is indistinguishable from magic, Apple II, autonomous vehicles, barriers to entry, Buckminster Fuller, Build a better mousetrap, business process, carbon tax, commoditize, company town, Computer Numeric Control, crowdsourcing, dark matter, David Ricardo: comparative advantage, deal flow, death of newspapers, dematerialisation, digital capitalism, DIY culture, drop ship, Elon Musk, factory automation, Firefox, Ford Model T, future of work, global supply chain, global village, hockey-stick growth, hype cycle, IKEA effect, industrial robot, interchangeable parts, Internet of things, inventory management, James Hargreaves, James Watt: steam engine, Jeff Bezos, job automation, Joseph Schumpeter, Kickstarter, Lean Startup, manufacturing employment, Mark Zuckerberg, means of production, Menlo Park, Neal Stephenson, Network effects, planned obsolescence, private spaceflight, profit maximization, QR code, race to the bottom, Richard Feynman, Ronald Coase, Rubik’s Cube, Scaled Composites, self-driving car, Sheryl Sandberg, side project, Silicon Valley, Silicon Valley startup, Skype, slashdot, South of Market, San Francisco, SpaceShipOne, spinning jenny, Startup school, stem cell, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, supply-chain management, the long tail, The Nature of the Firm, The Wealth of Nations by Adam Smith, TikTok, Tragedy of the Commons, transaction costs, trickle-down economics, vertical integration, Virgin Galactic, Whole Earth Catalog, X Prize, Y Combinator

Everybody speaks the same language of digital manufacturing. It’s as simple as that. It just took common platforms to make the dream of hyperefficient B2B online marketplaces a reality. This is the way all successful technological revolutions work. The Gartner Group describes this boom-bust-boom trajectory as the “Hype Cycle” of tech-driven change. After the “Peak of Inflated Expectations,” there is the “Trough of Disillusionment.” Then comes the “Slope of Enlightenment,” and finally the “Plateau of Productivity.” We’ve been through the first three already. Now we’re enjoying the last. By the time a business process is too boring to comment on, it’s probably starting to actually work.

Devil's Bargain: Steve Bannon, Donald Trump, and the Storming of the Presidency by Joshua Green

4chan, Affordable Care Act / Obamacare, Ayatollah Khomeini, Bernie Sanders, Biosphere 2, Black Lives Matter, business climate, Cambridge Analytica, Carl Icahn, centre right, Charles Lindbergh, coherent worldview, collateralized debt obligation, conceptual framework, corporate raider, crony capitalism, currency manipulation / currency intervention, data science, Donald Trump, Dr. Strangelove, fake news, Fractional reserve banking, Glass-Steagall Act, Goldman Sachs: Vampire Squid, Gordon Gekko, guest worker program, hype cycle, illegal immigration, immigration reform, Jim Simons, junk bonds, liberation theology, low skilled workers, machine translation, Michael Milken, Nate Silver, Nelson Mandela, nuclear winter, obamacare, open immigration, Peace of Westphalia, Peter Thiel, quantitative hedge fund, Renaissance Technologies, Robert Mercer, Ronald Reagan, Silicon Valley, social intelligence, speech recognition, Steve Bannon, urban planning, vertical integration

“While I’m not at this time a candidate for the presidency,” Trump announced grandly at CPAC, “I will decide by June whether or not I will become one.” Reporters soon realized that his announcement timeline just happened to coincide with sweeps week and dismissed it as a ratings stunt, the usual Trump-presidential-hype cycle cranking up again. That’s why it didn’t register as particularly significant when Trump, in the same speech, deployed a curious line of attack against Obama, one previously confined mostly to the fever swamps of far-right websites. “Our current president came out of nowhere. Came out of nowhere,” Trump said, shaking his head.

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

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

While there is much optimism around the future potential benefits of self-driving cars, there’s a mixture of uncertainty, pessimism and even fear around the timing of the transition. Sceptics compare driverless car technology with Zeno’s dichotomy paradox:[6] every leap will take us halfway to our destination without ever reaching it. Some pundits believe we are nearing the infamous “Peak of Inflated Expectations” in the Gartner Hype Cycle (see below), and a long let-down is ahead before the technology eventually meets our expectations. I believe we are entering a foreseeable phase of negativity for driverless cars as people look beyond their initial allure, identify the problems and then set about proposing and implementing solutions for these problems, both real and imagined.

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

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

This overturns centuries of established practices and challenges our most basic understanding of how to make decisions and comprehend reality. Big data marks the beginning of a major transformation. Like so many new technologies, big data will surely become a victim of Silicon Valley’s notorious hype cycle: after being feted on the cover of magazines and at industry conferences, the trend will be dismissed and many of the data-smitten startups will flounder. But both the infatuation and the damnation profoundly misunderstand the importance of what is taking place. Just as the telescope enabled us to comprehend the universe and the microscope allowed us to understand germs, the new techniques for collecting and analyzing huge bodies of data will help us make sense of our world in ways we are just starting to appreciate.

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

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

This explains, for example, why progress toward fully autonomous self-driving cars has not lived up to some of the more exuberant early predictions. As these limitations came into focus toward the end of the decade, there was a gnawing fear that the field had once again gotten over its skis and that the hype cycle had driven expectations to unrealistic levels. In the tech media and on social media, one of the most terrifying phrases in the field of artificial intelligence—“AI winter”—was making a reappearance. In a January 2020 interview with the BBC, Yoshua Bengio said that “AI’s abilities were somewhat overhyped… by certain companies with an interest in doing so.”14 A large share of this concern came to bear on the industry that was, as we saw in Chapter 3, at the absolute summit of all the accumulated hype: self-driving cars.

Stealing Fire: How Silicon Valley, the Navy SEALs, and Maverick Scientists Are Revolutionizing the Way We Live and Work by Steven Kotler, Jamie Wheal

"World Economic Forum" Davos, 3D printing, Abraham Maslow, Alexander Shulgin, Alvin Toffler, augmented reality, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Burning Man, Colonization of Mars, crowdsourcing, David Brooks, delayed gratification, disruptive innovation, driverless car, Electric Kool-Aid Acid Test, Elon Musk, en.wikipedia.org, Future Shock, Hacker News, high batting average, hive mind, How many piano tuners are there in Chicago?, hype cycle, Hyperloop, impulse control, independent contractor, informal economy, Jaron Lanier, John Markoff, John Perry Barlow, Kevin Kelly, Larry Ellison, lateral thinking, Mason jar, Maui Hawaii, McMansion, means of production, Menlo Park, meta-analysis, microdosing, military-industrial complex, mirror neurons, music of the spheres, off-the-grid, pattern recognition, Peter Thiel, PIHKAL and TIHKAL, prosperity theology / prosperity gospel / gospel of success, Ray Kurzweil, ride hailing / ride sharing, risk tolerance, science of happiness, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, Steve Jobs, synthetic biology, TED Talk, time dilation, Tony Hsieh, urban planning, Virgin Galactic

And, for what it’s worth, we believe there is a comparable, though far less sensational risk to living a suburban life of quiet desperation and dying prematurely of a lifestyle disease without having once tasted what these athletes always live (and only sometimes die) for. Overhyped Sensor Tech: In several places, we highlight the accelerating potential of smart sensors and wearables to give us more feedback on our bodies and brains. In the past few years, the hype cycle has claimed several high-profile victims as the Federal Trade Commission, Food and Drug Administration, and class-action suits have clamped down on Nike, Apple, and Lumosity for claiming benefits or accuracy they could not deliver. In our research, we generally break down wearable tech into three buckets.

How Music Got Free: The End of an Industry, the Turn of the Century, and the Patient Zero of Piracy by Stephen Witt

4chan, Alan Greenspan, AOL-Time Warner, autism spectrum disorder, barriers to entry, Berlin Wall, big-box store, cloud computing, collaborative economy, company town, crowdsourcing, Eben Moglen, game design, hype cycle, Internet Archive, invention of movable type, inventory management, iterative process, Jason Scott: textfiles.com, job automation, late fees, mental accounting, moral panic, operational security, packet switching, pattern recognition, peer-to-peer, pirate software, reality distortion field, Ronald Reagan, security theater, sharing economy, side project, Silicon Valley, software patent, Stephen Fry, Steve Jobs, Tipper Gore, zero day

Pitchfork, Rolling Stone, and even The New Yorker all called it one of 2006’s best releases—establishment accolades that would have been unthinkable for Wayne just two or three years earlier. By leaking his own stuff first, Wayne had rebooted his career. As Jay-Z and Eminem were complaining about the leakers, Wayne was embracing them. Better than any artist before him, he leveraged the Internet hype cycle to his own advantage. His boast of “best rapper alive” started to get taken seriously. But the mp3 revolution was not yet complete: the 2005 model iPod, at $300 retail, was still a luxury good, and most of Wayne’s younger urban fan base couldn’t afford it. They were still in the compact disc era, and Drama was serving them by producing and distributing the mix CDs wholesale on dedicated burners in his Atlanta offices.

Bad Data Handbook by Q. Ethan McCallum

Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable:, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, data science, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, functional programming, Gini coefficient, hype cycle, illegal immigration, iterative process, labor-force participation, loose coupling, machine readable, natural language processing, Netflix Prize, One Laptop per Child (OLPC), power law, quantitative trading / quantitative finance, recommendation engine, selection bias, sentiment analysis, SQL injection, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application

.; Page, L. 1998. “The anatomy of a large-scale hypertextual Web search engine.” Computer Networks and ISDN Systems 30: 107–117 Chapter 14. Myths of Cloud Computing Steve Francia Myths are an important and natural part of the emergence of any new technology, product, or idea as identified by the hype cycle. Like any myth, technology myths originate in a variety of ways, each revealing intriguing aspects of the human psyche. Some myths come from early adopters, whose naive excitement and need to defend their higher risk decision introduce hopeful, yet mistaken myths. Others come from vendors who, with eagerness, over-promise to their customers.

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

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

That’s one of the reasons that understanding does not obey Moore’s Law: Knowledge is acquired by formulating explanations and testing them against reality, not by running an algorithm faster and faster. Devouring the information on the Internet will not confer omniscience either: Big Data is still finite data, and the universe of knowledge is infinite. A third reason to be skeptical of a sudden AI takeover is that it takes too seriously the inflationary phase in the AI hype cycle in which we are living today. Despite the progress in machine learning, particularly multilayered artificial neural networks, current AI systems are nowhere near achieving general intelligence (if that concept is even coherent). Instead, they are restricted to problems that consist of mapping well-defined inputs to well-defined outputs in domains where gargantuan training sets are available, in which the metric for success is immediate and precise, in which the environment doesn’t change, and in which no stepwise, hierarchical, or abstract reasoning is necessary.

Junk DNA: A Journey Through the Dark Matter of the Genome by Nessa Carey

dark matter, discovery of DNA, double helix, Downton Abbey, Drosophila, epigenetics, Higgs boson, hype cycle, Kickstarter, mouse model, phenotype, placebo effect, stem cell, Stephen Hawking, Steve Jobs

The same amount of money could have funded at least 600 average-sized single research grants focusing on investigation of individual hypotheses. Choosing how to distribute funding is a balancing act, and at these levels of funding it is guaranteed to create division and concern. A company called Gartner created a graphic that shows how new technologies are perceived. It is known as the Hype Cycle. At first everyone is very excited – ‘the peak of inflated expectations’. When the new tech fails to transform everything about your life there is a crash leading to the ‘trough of disillusionment’. Eventually, everyone settles down, there is a steady growth in rational understanding and finally a productive plateau is reached.

Hype: How Scammers, Grifters, and Con Artists Are Taking Over the Internet―and Why We're Following by Gabrielle Bluestone

Adam Neumann (WeWork), Airbnb, Bellingcat, Bernie Madoff, Bernie Sanders, Big Tech, bitcoin, Black Lives Matter, Burning Man, cashless society, coronavirus, COVID-19, Donald Trump, driverless car, Elon Musk, fake it until you make it, financial thriller, forensic accounting, gig economy, global pandemic, growth hacking, high net worth, hockey-stick growth, hype cycle, Hyperloop, Kevin Roose, lock screen, lockdown, Lyft, Mark Zuckerberg, Masayoshi Son, Mason jar, Menlo Park, Multics, Naomi Klein, Netflix Prize, NetJets, Peter Thiel, placebo effect, post-truth, RFID, ride hailing / ride sharing, Russell Brand, Sand Hill Road, self-driving car, Silicon Valley, Snapchat, social distancing, SoftBank, Steve Jobs, tech billionaire, tech bro, TikTok, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, uber lyft, unpaid internship, upwardly mobile, Vision Fund, WeWork

But by filtering his scam through a network of trusted influencers on social media, which caught the attention of legacy news outlets more than happy to cover things superficially from an entertainment angle, McFarland had been able to wash the stench of fraud from his scheme, resulting in a pure hype cycle that bypassed any need for proof of concept. And with a millennial army willing to follow these influential generals from the screen to the literal ends of the earth, McFarland was able to convince a series of experienced investors to keep funding the scam—at least until it all fell apart in real time, where else, but on social media.

We Are Electric: Inside the 200-Year Hunt for Our Body's Bioelectric Code, and What the Future Holds by Sally Adee

air gap, airport security, anesthesia awareness, animal electricity, biofilm, colonial rule, computer age, COVID-19, CRISPR, discovery of DNA, double helix, Elon Musk, epigenetics, experimental subject, Fellow of the Royal Society, hype cycle, impulse control, informal economy, Internet Archive, invention of the telegraph, Isaac Newton, Kickstarter, lockdown, mass immigration, meta-analysis, microbiome, microdosing, multilevel marketing, New Journalism, Norbert Wiener, Peter Thiel, placebo effect, randomized controlled trial, seminal paper, Silicon Valley, Silicon Valley startup, stealth mode startup, stem cell, synthetic biology, TED Talk, the long tail, the scientific method, Tragedy of the Commons, traumatic brain injury

(Part of that story concerns a patenting misfire.) Nobody has rice-sized implants routing their nervous signals around the body. Galvani Biosciences is still plugging away, but with replication results that don’t make any headlines. Now, part of this is just the inevitable rollercoaster of the hype cycle. First, you get a big splashy announcement of a new possibility and everyone is very excited. Then the grind of basic research sets in, and there’s a long trough of disillusionment because the new hot devices aren’t ready immediately. Eventually, positive results start to emerge from the long tail of clinical research, and slowly the once-hyped revolution is integrated into routine care at your doctor’s office, and fades into the background of everyday life.

Traffic: Genius, Rivalry, and Delusion in the Billion-Dollar Race to Go Viral by Ben Smith

2021 United States Capitol attack, 4chan, Affordable Care Act / Obamacare, AOL-Time Warner, behavioural economics, Bernie Sanders, Big Tech, blockchain, Cambridge Analytica, citizen journalism, COVID-19, cryptocurrency, data science, David Brooks, deplatforming, Donald Trump, drone strike, fake news, Filter Bubble, Frank Gehry, full stack developer, future of journalism, hype cycle, Jeff Bezos, Kevin Roose, Larry Ellison, late capitalism, lolcat, Marc Andreessen, Mark Zuckerberg, Menlo Park, moral panic, obamacare, paypal mafia, Peter Thiel, post-work, public intellectual, reality distortion field, Robert Mercer, Sand Hill Road, Saturday Night Live, sentiment analysis, side hustle, Silicon Valley, Silicon Valley billionaire, skunkworks, slashdot, Snapchat, social web, Socratic dialogue, SoftBank, Steve Bannon, Steven Levy, subscription business, tech worker, TikTok, traveling salesman, WeWork, WikiLeaks, young professional, Zenefits

It went unsaid that they’d be an obvious buyer of BuzzFeed one day—the VCs could, we assumed, check that out with Zuckerberg. How news reporting fit into the picture was a little hazy, but—Valleywag aside—it was still an era when journalists turned investors like Andreessen into heroes, and one of Andreessen Horowitz’s strengths was its mastery of the hype cycle. I got the sense that the investors believed—though it was never formally stated—that BuzzFeed would be one more friendly platform for founders. Because Andreessen was a man of big bets, he made this one. His firm led an investment round of $50 million that August, valuing BuzzFeed at $850 million and bragging in a press release that the company was already profitable.

Human Frontiers: The Future of Big Ideas in an Age of Small Thinking by Michael Bhaskar

"Margaret Hamilton" Apollo, 3D printing, additive manufacturing, AI winter, Albert Einstein, algorithmic trading, AlphaGo, Anthropocene, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, barriers to entry, basic income, behavioural economics, Benoit Mandelbrot, Berlin Wall, Big bang: deregulation of the City of London, Big Tech, Bletchley Park, blockchain, Boeing 747, brain emulation, Brexit referendum, call centre, carbon tax, charter city, citizen journalism, Claude Shannon: information theory, Clayton Christensen, clean tech, clean water, cognitive load, Columbian Exchange, coronavirus, cosmic microwave background, COVID-19, creative destruction, CRISPR, crony capitalism, cyber-physical system, dark matter, David Graeber, deep learning, DeepMind, deindustrialization, dematerialisation, Demis Hassabis, demographic dividend, Deng Xiaoping, deplatforming, discovery of penicillin, disruptive innovation, Donald Trump, double entry bookkeeping, Easter island, Edward Jenner, Edward Lorenz: Chaos theory, Elon Musk, en.wikipedia.org, endogenous growth, energy security, energy transition, epigenetics, Eratosthenes, Ernest Rutherford, Eroom's law, fail fast, false flag, Fellow of the Royal Society, flying shuttle, Ford Model T, Francis Fukuyama: the end of history, general purpose technology, germ theory of disease, glass ceiling, global pandemic, Goodhart's law, Google Glasses, Google X / Alphabet X, GPT-3, Haber-Bosch Process, hedonic treadmill, Herman Kahn, Higgs boson, hive mind, hype cycle, Hyperloop, Ignaz Semmelweis: hand washing, Innovator's Dilemma, intangible asset, interchangeable parts, Internet of things, invention of agriculture, invention of the printing press, invention of the steam engine, invention of the telegraph, invisible hand, Isaac Newton, ITER tokamak, James Watt: steam engine, James Webb Space Telescope, Jeff Bezos, jimmy wales, job automation, Johannes Kepler, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Kevin Kelly, Kickstarter, knowledge economy, knowledge worker, Large Hadron Collider, liberation theology, lockdown, lone genius, loss aversion, Louis Pasteur, Mark Zuckerberg, Martin Wolf, megacity, megastructure, Menlo Park, Minecraft, minimum viable product, mittelstand, Modern Monetary Theory, Mont Pelerin Society, Murray Gell-Mann, Mustafa Suleyman, natural language processing, Neal Stephenson, nuclear winter, nudge unit, oil shale / tar sands, open economy, OpenAI, opioid epidemic / opioid crisis, PageRank, patent troll, Peter Thiel, plutocrats, post scarcity, post-truth, precautionary principle, public intellectual, publish or perish, purchasing power parity, quantum entanglement, Ray Kurzweil, remote working, rent-seeking, Republic of Letters, Richard Feynman, Robert Gordon, Robert Solow, secular stagnation, shareholder value, Silicon Valley, Silicon Valley ideology, Simon Kuznets, skunkworks, Slavoj Žižek, sovereign wealth fund, spinning jenny, statistical model, stem cell, Steve Jobs, Stuart Kauffman, synthetic biology, techlash, TED Talk, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, TikTok, total factor productivity, transcontinental railway, Two Sigma, Tyler Cowen, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, We wanted flying cars, instead we got 140 characters, When a measure becomes a target, X Prize, Y Combinator

Gene editing is ready to change the world, but a combination of over-regulation in some territories, patent wars and scientific nationalism weighs down on the field. Meanwhile VR has been around for years, but to date no one has found a fit between the product and its market. Perhaps it stands poised to launch a bright multiverse of possibility; perhaps most of us are content with TV. Major tools are subject to a vicious, recurring hype cycle. And does the financialised and post-Covid world have the stomach for breakthroughs’ ever-increasing cost? To date the US government has spent billions on the National Ignition Facility, a laser research institute, at Lawrence Livermore National Laboratory with, in the words of one commentator, ‘no tangible results in sight’.61 The public and government appetite for such expenditures, epitomised by CERN's new $27 billion particle accelerator, is shaky.

The Metaverse: And How It Will Revolutionize Everything by Matthew Ball

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", 3D printing, Airbnb, Albert Einstein, Amazon Web Services, Apple Newton, augmented reality, Big Tech, bitcoin, blockchain, business process, call centre, cloud computing, commoditize, computer vision, COVID-19, cryptocurrency, deepfake, digital divide, digital twin, disintermediation, don't be evil, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, game design, gig economy, Google Chrome, Google Earth, Google Glasses, hype cycle, intermodal, Internet Archive, Internet of things, iterative process, Jeff Bezos, John Gruber, Kevin Roose, Kickstarter, lockdown, Mark Zuckerberg, Metcalfe’s law, Minecraft, minimum viable product, Neal Stephenson, Network effects, new economy, non-fungible token, open economy, openstreetmap, pattern recognition, peer-to-peer, peer-to-peer model, Planet Labs, pre–internet, QR code, recommendation engine, rent control, rent-seeking, ride hailing / ride sharing, Robinhood: mobile stock trading app, satellite internet, self-driving car, SETI@home, Silicon Valley, skeuomorphism, Skype, smart contracts, Snapchat, Snow Crash, social graph, social web, SpaceX Starlink, Steve Ballmer, Steve Jobs, thinkpad, TikTok, Tim Cook: Apple, TSMC, undersea cable, Vannevar Bush, vertical integration, Vitalik Buterin, Wayback Machine, Y2K

By the end of the decade, we’ll agree the Metaverse has arrived* and it will be worth many trillions. The question of exactly when it started and how much revenue it generates will remain uncertain. Before getting to that point, we will exit the current phase of hype and probably enter and then exit another one, too. The hype cycle will be caused by at least three factors: the reality that many companies will over-promise what sort of Metaverse experiences will be possible and when; the difficulty of overcoming key technical barriers; and the fact that, even when those barriers are overcome, it will take time to figure out exactly what companies should build “in the Metaverse.”

The Future of Technology by Tom Standage

air freight, Alan Greenspan, barriers to entry, business process, business process outsourcing, call centre, Clayton Christensen, computer vision, connected car, corporate governance, creative destruction, disintermediation, disruptive innovation, distributed generation, double helix, experimental economics, financial engineering, Ford Model T, full employment, hydrogen economy, hype cycle, industrial robot, informal economy, information asymmetry, information security, interchangeable parts, job satisfaction, labour market flexibility, Larry Ellison, Marc Andreessen, Marc Benioff, market design, Menlo Park, millennium bug, moral hazard, natural language processing, Network effects, new economy, Nicholas Carr, optical character recognition, PalmPilot, railway mania, rent-seeking, RFID, Salesforce, seminal paper, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, six sigma, Skype, smart grid, software as a service, spectrum auction, speech recognition, stem cell, Steve Ballmer, Steve Jurvetson, technological determinism, technology bubble, telemarketer, transcontinental railway, vertical integration, Y2K

At the time the index was announced, every company in it got a boost, but six months later share prices were down by a quarter. The same thing happened to the shares in a nanotechnology index launched by Punk Ziegel, another investment bank. At kpcb, Mr Khosla is worried about indices. “When companies like Merrill Lynch start having a nanotechnology index, I think that is getting into the hype cycle for which a lot of people got into a lot of trouble during the internet bubble.” When people start getting interested, he says, fund managers decide that some proportion of their investment should be in nanotechnology. A bubble gets going when everybody starts piling in, trying to buy stocks that do not exist, he adds.

Dawn of the New Everything: Encounters With Reality and Virtual Reality by Jaron Lanier

4chan, air gap, augmented reality, back-to-the-land, Big Tech, Bill Atkinson, Buckminster Fuller, Burning Man, carbon footprint, cloud computing, collaborative editing, commoditize, Computer Lib, cosmological constant, creative destruction, crowdsourcing, deep learning, Donald Trump, Douglas Engelbart, Douglas Hofstadter, El Camino Real, Elon Musk, fake news, Firefox, game design, general-purpose programming language, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Hacker Ethic, Hans Moravec, Howard Rheingold, hype cycle, impulse control, information asymmetry, intentional community, invisible hand, Ivan Sutherland, Jaron Lanier, John Gilmore, John Perry Barlow, John von Neumann, Kevin Kelly, Kickstarter, Kuiper Belt, lifelogging, mandelbrot fractal, Mark Zuckerberg, Marshall McLuhan, Menlo Park, military-industrial complex, Minecraft, Mitch Kapor, Mondo 2000, Mother of all demos, Murray Gell-Mann, Neal Stephenson, Netflix Prize, Network effects, new economy, Nick Bostrom, Norbert Wiener, Oculus Rift, pattern recognition, Paul Erdős, peak TV, Plato's cave, profit motive, Project Xanadu, quantum cryptography, Ray Kurzweil, reality distortion field, recommendation engine, Richard Feynman, Richard Stallman, Ronald Reagan, self-driving car, Silicon Valley, Silicon Valley startup, Skinner box, Skype, Snapchat, stem cell, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Stewart Brand, systems thinking, technoutopianism, Ted Nelson, telemarketer, telepresence, telepresence robot, Thorstein Veblen, Turing test, Vernor Vinge, Whole Earth Catalog, Whole Earth Review, WikiLeaks, wikimedia commons

Sherlock Holmes has been known to use them as well, at least in his Cumberbatch incarnation. 7.   Conspicuously underrepresented in this list of VR apps is help for clients with disabilities. We actually did a lot with gloves for sign language, therapy for aphasia patients, and so on, but I have grown tired of the PR hype cycle around VR and disabilities, so lately I prefer to just act and not talk so much about it. The hype comes so easily, like a drug, that it can actually be an impediment to funders and organizations following through well enough to make a difference. 8.   As long as I’m mentioning Randy, I have to also mention his PhD adviser, Andy van Dam.

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

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

He did, however, remain a staunch critic of the basic idea of software agents, and pointed out that aircraft cockpit designers had for decades tried and failed to use speech recognition to control airplanes. When Siri was introduced in 2010, the “Internet of Things” was approaching the peak in the hype cycle. This had originally been Xerox PARC’s next big idea after personal computing. In the late 1980s PARC computer scientist Mark Weiser had predicted that as microprocessor cost, size, and power collapsed, it would be possible to discreetly integrate computer intelligence into everyday objects. He called this “UbiComp” or ubiquitous computing.

CTOs at Work by Scott Donaldson, Stanley Siegel, Gary Donaldson

Amazon Web Services, Andy Carvin, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, do what you love, domain-specific language, functional programming, glass ceiling, Hacker News, hype cycle, Neil Armstrong, orbital mechanics / astrodynamics, pattern recognition, Pluto: dwarf planet, QR code, Richard Feynman, Ruby on Rails, Salesforce, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, systems thinking, thinkpad, web application, zero day, zero-sum game

Siegel: Okay, let's shift gears. Are there any big technology projects on the horizon now? Alving: One area of focus is on the cloud. When we look at the transformation that's taking place in cloud, we recognize there's a lot of hype. People have been talking about cloud for a couple of years now, and it's gone through its hype cycle and it sort of remains all things to all people. Then people say, “Wait, does it really mean anything to me?” There's no doubt the cloud is coming, for all the reasons that people talk about, including the financial imperative, because it's part of the solution to the budget issues that people are facing: we need a different paradigm for who owns, who operates, and who uses IT, and that's what the cloud offers.

Science Fictions: How Fraud, Bias, Negligence, and Hype Undermine the Search for Truth by Stuart Ritchie

Albert Einstein, anesthesia awareness, autism spectrum disorder, Bayesian statistics, Black Lives Matter, Carmen Reinhart, Cass Sunstein, Charles Babbage, citation needed, Climatic Research Unit, cognitive dissonance, complexity theory, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, data science, deindustrialization, Donald Trump, double helix, en.wikipedia.org, epigenetics, Estimating the Reproducibility of Psychological Science, fake news, Goodhart's law, Growth in a Time of Debt, Helicobacter pylori, Higgs boson, hype cycle, Kenneth Rogoff, l'esprit de l'escalier, Large Hadron Collider, meta-analysis, microbiome, Milgram experiment, mouse model, New Journalism, ocean acidification, p-value, phenotype, placebo effect, profit motive, publication bias, publish or perish, quantum entanglement, race to the bottom, randomized controlled trial, recommendation engine, rent-seeking, replication crisis, Richard Thaler, risk tolerance, Ronald Reagan, Scientific racism, selection bias, Silicon Valley, Silicon Valley startup, social distancing, Stanford prison experiment, statistical model, stem cell, Steven Pinker, TED Talk, Thomas Bayes, twin studies, Tyler Cowen, University of East Anglia, Wayback Machine

* * * At any given time, there’s usually an ‘emerging’ field that’s subject to the worst hype. Typically, a few publications with easy-to-grasp results in big-name journals get picked up by the media, public interest intensifies, and scientists in the field develop a kind of recklessness, feeding the hype cycle with careless and overblown statements. Then big claims fail to replicate in later experiments, the furore dies away and normal science resumes. Ultra-hyped fields include stem cells, genetics, epigenetics, machine learning and brain imaging; for the past few years, a strong contender for the ‘most hyped’ award has been research on the microbiome – the countless millions of microbes that inhabit our bodies.71 Thanks to the hype, the microbiome has been targeted by a plethora of products and treatments.

The Rise and Fall of Nations: Forces of Change in the Post-Crisis World by Ruchir Sharma

"World Economic Forum" Davos, Asian financial crisis, backtesting, bank run, banking crisis, Berlin Wall, Bernie Sanders, BRICs, business climate, business cycle, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, centre right, colonial rule, commodity super cycle, corporate governance, creative destruction, crony capitalism, currency peg, dark matter, debt deflation, deglobalization, deindustrialization, demographic dividend, demographic transition, Deng Xiaoping, Doha Development Round, Donald Trump, driverless car, Edward Glaeser, Elon Musk, eurozone crisis, failed state, Fall of the Berlin Wall, falling living standards, financial engineering, Francis Fukuyama: the end of history, Freestyle chess, Gini coefficient, global macro, Goodhart's law, guns versus butter model, hiring and firing, hype cycle, income inequality, indoor plumbing, industrial robot, inflation targeting, Internet of things, Japanese asset price bubble, Jeff Bezos, job automation, John Markoff, Joseph Schumpeter, junk bonds, Kenneth Rogoff, Kickstarter, knowledge economy, labor-force participation, Larry Ellison, lateral thinking, liberal capitalism, low interest rates, Malacca Straits, Mark Zuckerberg, market bubble, Mary Meeker, mass immigration, megacity, megaproject, Mexican peso crisis / tequila crisis, middle-income trap, military-industrial complex, mittelstand, moral hazard, New Economic Geography, North Sea oil, oil rush, oil shale / tar sands, oil shock, open immigration, pattern recognition, Paul Samuelson, Peter Thiel, pets.com, plutocrats, Ponzi scheme, price stability, Productivity paradox, purchasing power parity, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, Ronald Coase, Ronald Reagan, savings glut, secular stagnation, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Simon Kuznets, smart cities, Snapchat, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Steve Jobs, tacit knowledge, tech billionaire, The Future of Employment, The Wisdom of Crowds, Thomas Malthus, total factor productivity, trade liberalization, trade route, tulip mania, Tyler Cowen: Great Stagnation, unorthodox policies, Washington Consensus, WikiLeaks, women in the workforce, work culture , working-age population

Meanwhile, Turkey’s per capita income would triple over the course of the decade, making it the world’s tenth-fastest-growing economy in the 2000s. Elie Wiesel, the writer and Holocaust survivor, said the opposite of love is not hate; it is indifference. This observation applies well to understanding the hype cycle. The question to ask of any country: How is it portrayed by the global media? The longer an economic boom lasts, the more credible a country’s track record appears to the media and the more warmly they embrace it as the economy of the future. The more this love deepens, the more alarmed I get. As we have seen, long runs of sustained growth are rare.

The Singularity Is Near: When Humans Transcend Biology by Ray Kurzweil

additive manufacturing, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anthropic principle, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, backpropagation, Benoit Mandelbrot, Bill Joy: nanobots, bioinformatics, brain emulation, Brewster Kahle, Brownian motion, business cycle, business intelligence, c2.com, call centre, carbon-based life, cellular automata, Charles Babbage, Claude Shannon: information theory, complexity theory, conceptual framework, Conway's Game of Life, coronavirus, cosmological constant, cosmological principle, cuban missile crisis, data acquisition, Dava Sobel, David Brooks, Dean Kamen, digital divide, disintermediation, double helix, Douglas Hofstadter, en.wikipedia.org, epigenetics, factory automation, friendly AI, functional programming, George Gilder, Gödel, Escher, Bach, Hans Moravec, hype cycle, informal economy, information retrieval, information security, invention of the telephone, invention of the telescope, invention of writing, iterative process, Jaron Lanier, Jeff Bezos, job automation, job satisfaction, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, lifelogging, linked data, Loebner Prize, Louis Pasteur, mandelbrot fractal, Marshall McLuhan, Mikhail Gorbachev, Mitch Kapor, mouse model, Murray Gell-Mann, mutually assured destruction, natural language processing, Network effects, new economy, Nick Bostrom, Norbert Wiener, oil shale / tar sands, optical character recognition, PalmPilot, pattern recognition, phenotype, power law, precautionary principle, premature optimization, punch-card reader, quantum cryptography, quantum entanglement, radical life extension, randomized controlled trial, Ray Kurzweil, remote working, reversible computing, Richard Feynman, Robert Metcalfe, Rodney Brooks, scientific worldview, Search for Extraterrestrial Intelligence, selection bias, semantic web, seminal paper, Silicon Valley, Singularitarianism, speech recognition, statistical model, stem cell, Stephen Hawking, Stewart Brand, strong AI, Stuart Kauffman, superintelligent machines, technological singularity, Ted Kaczynski, telepresence, The Coming Technological Singularity, Thomas Bayes, transaction costs, Turing machine, Turing test, two and twenty, Vernor Vinge, Y2K, Yogi Berra

—RODNEY BROOKS, DIRECTOR OF THE MIT AI LAB161 I still run into people who claim that artificial intelligence withered in the 1980s, an argument that is comparable to insisting that the Internet died in the dot-com bust of the early 2000s.162 The bandwidth and price-performance of Internet technologies, the number of nodes (servers), and the dollar volume of e-commerce all accelerated smoothly through the boom as well as the bust and the period since. The same has been true for AI. The technology hype cycle for a paradigm shift—railroads, AI, Internet, telecommunications, possibly now nanotechnology—typically starts with a period of unrealistic expectations based on a lack of understanding of all the enabling factors required. Although utilization of the new paradigm does increase exponentially, early growth is slow until the knee of the exponential-growth curve is realized.