Narrative Science

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pages: 484 words: 104,873

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

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

In 2010, the Northwestern University researchers who oversaw the team of computer science and journalism students who worked on StatsMonkey raised venture capital and founded a new company, Narrative Science, Inc., to commercialize the technology. The company hired a team of top computer scientists and engineers; then it tossed out the original StatsMonkey computer code and built a far more powerful and comprehensive artificial intelligence engine that it named “Quill.” Narrative Science’s technology is used by top media outlets, including Forbes, to produce automated articles in a variety of areas, including sports, business, and politics. The company’s software generates a news story approximately every thirty seconds, and many of these are published on widely known websites that prefer not to acknowledge their use of the service. At a 2011 industry conference, Wired writer Steven Levy prodded Narrative Science co-founder Kristian Hammond into predicting the percentage of news articles that would be written algorithmically within fifteen years.

At a 2011 industry conference, Wired writer Steven Levy prodded Narrative Science co-founder Kristian Hammond into predicting the percentage of news articles that would be written algorithmically within fifteen years. His answer: over 90 percent.2 Narrative Science has its sights set on far more than just the news industry. Quill is designed to be a general-purpose analytical and narrative-writing engine, capable of producing high-quality reports for both internal and external consumption across a range of industries. Quill begins by collecting data from a variety of sources, including transaction databases, financial and sales reporting systems, websites, and even social media. It then performs an analysis designed to tease out the most important and interesting facts and insights. Finally, it weaves all this information into a coherent narrative that the company claims measures up to the efforts of the best human analysts.

Finally, it weaves all this information into a coherent narrative that the company claims measures up to the efforts of the best human analysts. Once it’s configured, the Quill system can generate business reports nearly instantaneously and deliver them continuously—all without human intervention.3 One of Narrative Science’s earliest backers was In-Q-Tel, the venture capital arm of the Central Intelligence Agency, and the company’s tools will likely be used to automatically transform the torrents of raw data collected by the US intelligence community into an easily understandable narrative format. The Quill technology showcases the extent to which tasks that were once the exclusive province of skilled, college-educated professionals are vulnerable to automation. Knowledge-based work, of course, typically calls upon a wide range of capabilities. Among other things, an analyst may need to know how to retrieve information from a variety of systems, perform statistical or financial modeling, and then write understandable reports and presentations.


pages: 271 words: 77,448

Humans Are Underrated: What High Achievers Know That Brilliant Machines Never Will by Geoff Colvin

Ada Lovelace, autonomous vehicles, Baxter: Rethink Robotics, Black Swan, call centre, capital asset pricing model, commoditize, computer age, corporate governance, creative destruction, deskilling, en.wikipedia.org, Freestyle chess, future of work, Google Glasses, Grace Hopper, industrial cluster, industrial robot, interchangeable parts, job automation, knowledge worker, low skilled workers, Marc Andreessen, meta analysis, meta-analysis, Narrative Science, new economy, rising living standards, self-driving car, sentiment analysis, Silicon Valley, Skype, social intelligence, Steve Jobs, Steve Wozniak, Steven Levy, Steven Pinker, theory of mind, Tim Cook: Apple, transaction costs

A company called Narrative Science makes software that writes articles that would not strike most people as computer-written. It focused first on events embodying lots of data: ball games and corporate earnings announcements. The software became increasingly sophisticated at going beyond the facts and figures—for example, figuring out the most important play in a game or identifying the best angle for the article: a come-from-behind win, say, or a hero player. Then the developers taught the software different writing styles, which customers could choose from a menu. Next, it learned to understand more than just numerical data, reading relevant material to create context for the article. A number of media companies, including Yahoo and Forbes, publish articles from Narrative Science, though some of the company’s customers don’t want to be identified and don’t tell readers which articles are computer-written.

A number of media companies, including Yahoo and Forbes, publish articles from Narrative Science, though some of the company’s customers don’t want to be identified and don’t tell readers which articles are computer-written. In mid-2014, the Associated Press assigned computers to write all its articles about corporate earnings announcements. Then Narrative Science realized that maybe the real money wasn’t in producing journalism at all (they could have asked any journalist about that) but in generating the writing that companies use internally, the countless reports and analyses that influence business decisions. So it arranged its technology to gather broad classes of data, including unstructured data like social media posts, on any given topic or problem, and to analyze it deeply, looking for trends, correlations, unusual events, and more. The software uses that data to “make judgments and draw conclusions,” the company says; it can also make recommendations.

So while software doesn’t assign the same grades as people, neither do people assign the same grades as other people. And if you look at a large group of grades assigned to the same work by people and by software , you can’t tell which is which. Two points to draw from this: One, the software is getting rapidly better. The people are not. Two, education as currently conceived is becoming really weird. After all, the report-writing software developed by Narrative Science and other companies is easily adapted to other markets, such as students’ papers. So we now have essay-grading software and essay-writing software, both of which are improving. What happens next is obvious. The writing software gets optimized to please the grading software. Every essay gets an A, and neither the student nor the teacher has anything to do with it. But not much education is necessarily going on, which poses problems for both student and teacher.


pages: 477 words: 75,408

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

3D printing, additive manufacturing, agricultural Revolution, AI winter, Airbnb, artificial general intelligence, augmented reality, autonomous vehicles, banking crisis, basic income, Baxter: Rethink Robotics, Berlin Wall, Bernie Sanders, bitcoin, blockchain, call centre, Chris Urmson, congestion charging, credit crunch, David Ricardo: comparative advantage, Douglas Engelbart, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Flynn Effect, full employment, future of work, gender pay gap, gig economy, Google Glasses, Google X / Alphabet X, ImageNet competition, income inequality, industrial robot, Internet of things, invention of the telephone, invisible hand, James Watt: steam engine, Jaron Lanier, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, lifelogging, lump of labour, Lyft, Marc Andreessen, Mark Zuckerberg, Martin Wolf, McJob, means of production, Milgram experiment, Narrative Science, natural language processing, new economy, Occupy movement, Oculus Rift, PageRank, pattern recognition, post scarcity, post-industrial society, post-work, precariat, prediction markets, QWERTY keyboard, railway mania, RAND corporation, Ray Kurzweil, RFID, Rodney Brooks, Sam Altman, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, software is eating the world, speech recognition, Stephen Hawking, Steve Jobs, TaskRabbit, technological singularity, The Future of Employment, Thomas Malthus, transaction costs, Tyler Cowen: Great Stagnation, Uber for X, uber lyft, universal basic income, Vernor Vinge, working-age population, Y Combinator, young professional

Sometimes accused of being conspiracies against lay people, these are protected occupations, with demanding entry requirements and restrictions on the number of trainees who can join the professions each year. They have commanded prestige and high salaries, but that may be about to change. Journalists Nuance, the company behind Swedbank's Nina call centre AI, offers services for journalists, helping them create interviews and articles faster. But Narrative Science, a company established in Chicago in 2010, has an AI system which writes articles without human help. Called Quill, it already produces thousands of articles every day on finance and sports for outlets like Forbes and Associated Press (AP).[ccxxiii] Most readers cannot identify which articles are written by Quill and which by human journalists, and Quill is much faster. Quill starts with data – graphs, tables and spreadsheets.

News services like AP increased the daily quota of articles for each journalist, cut back the number of journalists they employed, and reduced the number of articles they produced on, for instance, the quarterly earnings reports of particular companies. Quill and similar services have enabled them to reverse that decline. AP now produces articles on the quarterly reports of medium-sized companies that it gave up covering in such detail years ago. Kristian Hammond, founder of Narrative Science, forecast in 2014 that in a decade, 90% of all newspaper articles would be written by AIs. However, he argued that the number of journalists would remain stable, while the volume of articles increased sharply. Eventually, articles could become tailored for particular audiences, and ultimately for each of us individually. For instance, an announcement by a research organisation that inflating your car tyre correctly could reduce your spend on petrol by 7% could be tailored – perhaps with the help of your Digital Personal Assistant - to take into account your particular car, the number of miles you drive each week, and even your style of driving.

According to Samuel Nessbaum of Wellpoint, a private healthcare company, Watson’s diagnostic accuracy rate for lung cancer is 90%, which compares favourably with 50% for human physicians.[ccxli] IBM has come in for criticism for pretending that Watson is a unitary system rather than a kludge of different systems which can be mixed and matched according to need. It is also accused of scaling back its ambition by tackling much smaller projects than the “moonshots” it was originally earmarked for, like curing cancer.[ccxlii] Kris Hammond, the founder of Narrative Science whom we met when discussing journalists, says that “everybody thought [winning Jeopardy] was ridiculously impossible, [but now] it feels like they're putting a lot of things under the Watson brand name – but it isn't Watson.”[ccxliii] In March 2016, DeepMind founder Demis Hassabis went as far as to say that Watson is essentially an expert system as opposed to deep learning one.[ccxliv] IBM is unfazed by this kind of criticism.


pages: 300 words: 76,638

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

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

About 10 percent of truck drivers… own their own trucks: Owner Operator Independent Drivers Association, https://www.ooida.com/MediaCenter/trucking-facts.asp. … about 5 percent of Gulf War veterans… worked in transportation in 2012: Linda Longton, “Fit for Duty: Vets Find New Life in Trucking,” Overdrive, August 9, 2012. CHAPTER 6: WHITE-COLLAR JOBS WILL DISAPPEAR, TOO … Narrative Science produces thousands of earnings previews and stock updates…: Joe Fassler, “Can the Computers at Narrative Science Replace Paid Writers?” The Atlantic, April 12, 2012. … Moore’s Law, which states that computing power grows exponentially…: Annie Sneed, “Moore’s Law Keeps Going, Defying Expectations,” Scientific America, May 19, 2015. People didn’t think that Moore’s Law could hold for the past 50 years…: Russ Juskalian, “Practical Quantum Computers: Advances at Google, Intel, and Several Research Groups Indicate That Computers with Previously Unimaginable Power Are Finally within Reach,” MIT Technical Review, 2017.

The company has been profitable for the last eight quarters, and for the last four, profit has risen year-over-year by an average of 16%. The biggest boost for the company came in the third quarter, when profit jumped by 32%. Notice anything off about the piece? The prose isn’t going to win any awards. But it’s perfectly understandable. As it turns out, the article was written by AI. A company called Narrative Science produces thousands of earnings previews and stock updates for Forbes and recaps of sports stories for fantasy sports sites in real time. The company’s bots won’t be winning any Pulitzers in investigative reporting, but in the coming years, the quality of AI-produced writing will go from acceptable to very good—and those journalists who write routine stories like this will find their jobs increasingly at risk.


pages: 339 words: 88,732

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

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

SCIgen’s authors wrote that, “Our aim here is to maximize amusement, rather than coherence,” and after reading the abstract of “Towards the Simulation of E-commerce” it’s hard to argue with them:37 Recent advances in cooperative technology and classical communication are based entirely on the assumption that the Internet and active networks are not in conflict with object-oriented languages. In fact, few information theorists would disagree with the visualization of DHTs that made refining and possibly simulating 8 bitarchitectures a reality, which embodies the compelling principles of electrical engineering.38 Recent developments make clear, though, that not all computer-generated prose is nonsensical. Forbes.com has contracted with the company Narrative Science to write the corporate earnings previews that appear on the website. These stories are all generated by algorithms without human involvement. And they’re indistinguishable from what a human would write: Forbes Earning Preview: H.J. Heinz A quality first quarter earnings announcement could push shares of H.J. Heinz (HNZ) to a new 52-week high as the price is just 49 cents off the milestone heading into the company’s earnings release on Wednesday, August 29, 2012.

“SoLoMo,” Schott’s Vocab Blog, http://schott.blogs.nytimes.com/2011/02/22/solomo/ (accessed June 23, 2013). 37. “SCIgen – An Automatic CS Paper Generator,” accessed September 14, 2013, http://pdos.csail.mit.edu/scigen/. 38 Herbert Schlangemann, “Towards the Simulation of E-commerce,” in Proceedings of the 2008 International Conference on Computer Science and Software Engineering, vol. 5, CSSE 2008 (Washington, D.C.: IEEE Computer Society, 2008), 1144–47, doi:10.1109/CSSE.2008.1. 39. Narrative Science, “Forbes Earnings Preview: H.J. Heinz,” August 24, 2012, http://www.forbes.com/sites/narrativescience/2012/08/24/forbes-earnings-preview-h-j-heinz-3/. 40. “How Stereolithography 3-D Layering Works,” HowStuffWorks, http://computer.howstuffworks.com/stereolith.htm (accessed August 4, 2013). 41. Claudine Zap, “3D Printer Could Build a House in 20 Hours,” August 10, 2012, http://news.yahoo.com/blogs/sideshow/3d-printer-could-build-house-20-hours-224156687.html; see also Samantha Murphy, “Woman Gets Jawbone Made By 3D Printer,” February 6, 2012, http://mashable.com/2012/02/06/3d-printer-jawbone/; “Great Ideas Soar Even Higher with 3D Printing,” 2013, http://www.stratasys.com/resources/case-studies/aerospace/nasa-mars-rover.

., robot use by Minsky, Marvin MIT, Computer Science and Artificial Intelligence Lab at Mitchell, Tom Mitra, Sugata MITx Monster.com Montessori, Maria Monthly Labor Review Moore, Gordon Moore’s Law in business in computing persistence of spread of Moravec, Hans Moravec’s paradox Morris, Ian mortgages Mullis, Kary multidimensional poverty index Munster, Gene Murnane, Richard Murray, Charles music, digitization of Nader, Ralph Narrative Science NASA National Academy of Sciences National Association of Realtors National Bureau of Economic Research National Review Nature of Technology, The (Arthur) Neiman, Brent New Digital Age, The (Schmidt and Cohen) New Division of Labor, The (Levy and Murnane) Newell, Al new growth theory New York Times Next Convergence, The (Spence) Nike Nixon, Richard Nordhaus, William numbers: development of large Occupy movement oDesk Oh, Joo Hee Olshansky, S.


pages: 337 words: 103,522

The Creativity Code: How AI Is Learning to Write, Paint and Think by Marcus Du Sautoy

3D printing, Ada Lovelace, Albert Einstein, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Claude Shannon: information theory, computer vision, correlation does not imply causation, crowdsourcing, data is the new oil, Donald Trump, double helix, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, mandelbrot fractal, Minecraft, music of the spheres, Narrative Science, natural language processing, Netflix Prize, PageRank, pattern recognition, Paul Erdős, Peter Thiel, random walk, Ray Kurzweil, recommendation engine, Rubik’s Cube, Second Machine Age, Silicon Valley, speech recognition, Turing test, Watson beat the top human players on Jeopardy!, wikimedia commons

You could probably cover about 1000 companies during a year, but that meant so many other companies that people might be interested in were not reported on. Journalists in the office dreaded being chosen to write these stories. They were the bane of any reporter’s existence. So there are few journalists crying over Associated Press’s decision to enlist machines to help tell these stories. Algorithms like Wordsmith, created by Automated Insights, or Narrative Science’s Quill are now helping to churn out data-driven stories that match the dry efficiency of many of the articles that humans used to have to produce for the Associated Press. Most times you will know only when you come to the bottom of the article that a machine wrote the piece. The algorithms are freeing the journalists to write about the bigger picture. Data-mining algorithms are also increasingly useful to businesses producing the numbers reported on at Associated Press.

In one instance a report on the George Washington University sports website had failed to celebrate the remarkable achievement of the pitcher of the opposition team to pitch a perfect game – pitching to twenty-seven batsmen over nine innings and ensuring that none of them got even to first base. The journalists declared that this was the sort of rare event that an algorithm could never be programmed to report on. It turned out the article was actually written by a human who probably supported the home baseball team that had suffered the humiliating defeat and had buried the achievement in the penultimate paragraph. The team at Narrative Science were interested to take the data from the game to see what their algorithm would make of it. Here is the beginning of the article generated just from the numerical data it was given: Tuesday was a great day for W. Roberts, as the junior pitcher threw a perfect game to carry Virginia to a 2–0 victory over George Washington at Davenport Field. Twenty-seven Colonials came to the plate and the Virginia pitcher vanquished them all, pitching a perfect game.

.: A Song of Ice and Fire/Game of Thrones 56, 120 Martinez, David 123 Maslon LLP 109 Massive Attack 226–8, 229 Mathematical Society of France 279 mathematics: AI and proving mathematical theorems 233–53; AI as threat to job of mathematician 5–7, 17, 43, 151–5, 233; algorithms, development of and 44–65, 45, 50, 51, 52, 58, 59, 60, 63; art and see art; art of 150–68; birth of 44–5; chess and see chess; complexity of, increasing 176–85; computers as partners in proving deep theorems 169–85; creativity and 3–4, 5, 7, 10, 12, 14, 15–16, 17, 18, 98, 150–2, 181–2; drugs and 181–2; Go and see Go; language and 269, 276, 278, 279–80, 284, 289, 291–3, 297; limits of human 176–80; music and see musical composition; narrative art of 241–53, 250; origins of 155–68; pattern recognition and 20–1, 155–6; proof, mathematical game of 152–5; proof, narrative quality of 245–50; proof, origins of 161–8; proof, social context of 182–3; pure and applied, separation of 182; recommender algorithms and 84–6, 85, 86, 89–90; surprise, element of and 248–50, 250; tales, generating new mathematical 291–3 Mayans 157 McCarthy, John 24 McEwan, Ian 306 McHugh, Tommy 133, 134 medieval polyphony 187, 189 MENACE (algorithm) 24 Messiaen, Olivier 90; Quartet for the End of Time 205 Métamatics 119 metric spaces 240–1 Metropolitan Police, British 77 Michie, Donald 2, 24 Microsoft 72, 73, 127, 131; Kinect/Xbox 72–6, 79, 81–2; Microsoft Research Cambridge 174–6; Rembrandt project 127–32 Millennium Prize Problems 152, 172 Minsky, Marvin 2 Mitchell, Kerry: ‘Fractal Art Manifesto’ 114 Mitsuku (chatbot) 258–9, 260 Mizar Mathematical Library 236–41, 244, 246, 253 Modus Ponens 162 Modus Tollens 162–3 Monbiot, George: Out of the Wreckage 296 Monet, Claude 10, 138 Monster Symmetry Group 10, 177 Morris, Desmond 107 Mozart, Wolfgang Amadeus 2, 3, 5, 10, 13, 194, 197, 198, 200, 227, 230, 231, 280; Musikalisches Würfelspiel 194–5 Muggleton, Stephen 291 Murray, Sean 116 muses 13–14 musical composition 185, 186–233; algorithms and composition, correlation between 186–9; Bach as first musical coder 189–94, 195, 197, 198, 200, 201, 205 see also Bach, Johann Sebastian; DeepBach and 207–12; Emmy and 195–207, 197, 199; MduS and 186–8; mathematics and 186–212, 214–18, 216, 217, 221, 222, 223, 230; Mozart’s Musikalisches Würfelspiel and 194–5; songwriting 213–32 see also songwriting; Turing Test and 200–2, 220–1 Musil, Robert 276 Musk, Elon 25 Namagiri 14 Nam June Paik 119 NaNoGenMo (National Novel Generation Month) 282–3 Narrative Science 293, 295 Naruto (macaque) 108–9 National Novel Writing Month 282 Nature 28, 152 Neanderthals 104, 231 Nees, Georg 110, 111–12, 113, 114, 117, 126 Nekrasov, Pavel 215, 217 Netflix 44, 83, 91, 135, 286; prize challenge 83–9, 85, 86, 91 neural networks 24, 27, 33, 68–70, 68, 70, 93–4, 272–3 new/novelty, creativity and 3, 4, 7–8, 12, 13, 16, 17, 40–3, 102–3, 109, 138–41, 140, 167–8, 238–9, 291–3, 299, 301 Newton, Isaac 92, 171, 239 New York Times, The 29, 139 Nielsen, Frank 210 Nietzsche, Friedrich 169 Nobel Prize 16, 57, 179 No-Free Lunch Theorem 95 No Man’s Sky (game) 116 Norton, Simon 18 number theory 4, 11, 14 Oates, Joyce Carol 15 Obrist, Hans-Ulrich 102, 106, 146, 147, 148 Odd Order Theorem 175 OKCupid 57 On-Line Encyclopaedia of Integer Sequences 291–2 Orwell, George 303 Osborn, Alex 301 Oulipo (Ouvroir de littérature potentielle) 278–80 over-fitting 74–6, 75 Oxford University 19, 53–4, 110, 155, 171, 181, 234, 235 Pachet, François 210, 214, 218–24, 225 Pacific Journal of Math 175 Page, Larry 48–9, 51–2, 57 ‘Painting Fool, The’ 119–22, 200, 291 Paleolithic flutes 231 Parker, Charlie 218, 222–3 Pask, Gordon 119 pattern recognition 6, 20–1, 99–101, 155–6, 186–7 Peña, Javier López 55 pendulum, chaotic 123–5 People for the Ethical Treatment of Animals (PETA) 108–9 perceptron 68–70, 68, 70 Perelman, Grigori 11, 152 Philips Company 119 Picasso, Pablo 5, 9, 11, 13, 111, 135, 136–7, 138–9, 142, 222; Les Demoiselles d’Avignon 138–9 Pissarro, Camille 10, 138 Pixar 115, 116, 124 place value system 157–8 Plato 13–14, 105 PlayStation 4 116 Pleiades 156 Poincaré Conjecture 11, 152 Poincaré, Henri 11, 150, 152, 244–5, 250 polis 166 ‘Pollockizer, The’ 124 Pollock, Jackson 117–19, 148, 302; MduS attempts to fake work 123–5; No. 5, 1948 123 prime numbers 11, 44, 53, 154, 164, 165, 166–7, 175, 178, 205, 239, 245–6, 247–8, 249, 251, 277, 285, 292 proairetic code 251–2 probability 27, 37, 71, 82, 91–2, 96, 101, 182, 214–18, 219, 229, 252, 270, 284 Proceedings of the Natural Academy of Sciences 57 profnath 62–5 prolation canon 187, 206 Propp, Vladimir 290 PropperWryter 290 Pushkin, Alexander 265; Eugene Onegin 214, 217–18 quadratic equations 75, 159–60, 161 quantum physics 53, 92, 112–13, 227–8, 235 Queneau, Raymond 278, 279; 100,000,000,000,000 Poems 279–80 Quill 293 Ramanujan 14 Raskin, Jef 117 Rayner, Alex 145 recommender algorithms 44, 79–80, 81–90, 85, 86, 91 Reddit 54 Redmond, Michael 38 refactorable numbers 292–3 Reflection (app) 229 reinforcement learning 27, 96–7 Rembrandt van Rijn 3, 106, 126–31, 132, 143, 151, 301; AI attempts to recreate works of 127–32; Tobit and Anna 130–1 Renoir, Pierre-Auguste 10, 122 Rescue on Fractals (game) 115–16 Richter, Gerhard: 4900 Farben 99–103, 106, 146, 155 Riedl, Mark 286, 287, 306 Riemann Hypothesis 178 robots 32, 71, 94, 119, 129, 262, 271–3 Rogers, Carl 255; ‘Towards a Theory of Creativity’ 301–2 Roman Empire 157 Romantic movement, musical 12, 13 Rosenblatt, Frank 24 Roth, Alvin 57 Royal Society 9, 233; Computing Laboratory 277 Rutgers University 132–3, 138, 139 Rutter, Brad 261, 262 Saleh, Babek 134 Samuel, Arthur 24 Scape (app) 229 scenius 15 Scheherazade-IF 286–8, 306 Schoenberg, Arnold 11, 190, 205, 223 Schöffer, Nicholas: CYSP 1 118–19 Schwartz, Oscar 282 Scriabin, Alexander 199, 199 Scrubs (TV series) 284 Searle, John 164, 273–5 Sedol, Lee 22, 30, 32, 33–40, 97, 131, 219–20 Seeker, The (algorithmic novel) 282–3, 305 Seinfeld (TV series) 284 Serpentine Gallery, London 99–102, 105, 106, 146, 147, 155 Shakespeare, William 5, 16, 127; As You Like It 303; Othello 3, 23 Shalosh B.


pages: 606 words: 157,120

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

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

Thus, the language, for example, might reflect what the site can guess about the education level of the reader (Economist-like vocabulary for the educated few; New York Post–like vocabulary for the uneducated masses). Or perhaps a story about Angelina Jolie might end with a reference to her film about Bosnia (if you are into international news) or some gossipy tidbit about her life with Brad Pitt (if you are into Hollywood affairs). Many firms—with names like Automated Insights and Narrative Science—already employ algorithms to produce stories automatically. The next logical step—and probably a very lucrative one—will be to target such stories to individual readers, giving us, essentially, a new generation of content farms that can produce stories on demand tailored for particular users. The implications of such shifts for our public life are profound: the kind of personalization described above might destroy the opportunities for solidarity and informed debate that occur when the entire polis has access to the same stories.

But such statistics enhance many other things beyond reading—including Amazon’s ability to engage in the kind of meme-driven publishing that knows the audience better than it knows itself and can pander, perhaps even subliminally, to its every whim. Nothing prevents Jeff Bezos from taking such knowledge and churning out books automatically, bypassing the authors completely and offering such a personalized offering—pushing all the right emotional and intellectual buttons for each reader—so that no bought book goes unread. A growing number of newspapers and magazines already turn to companies like Narrative Science to supply them with articles—mostly about sports and finance—produced by algorithms. There’s no reason to believe that Amazon can’t do this job better—and in long form. Smaller start-ups already cull and sell books that are written without any human involvement (and, of course, they are mostly sold on Amazon.com). If one thinks that the goal of literature is to maximize the well-being of memes or to ensure that all readers are satisfied (and why wouldn’t they be, given that the books they read already reflect their subconscious inclinations and preferences?)

., 124. 162 “lots of firms are beginning to create”: ibid., 124–125. 162 “be nimble in the use of data”: ibid., 125. 162 “We are entering a world”: ibid., 7. 163 already employ algorithms to produce stories automatically: for more on this, see my Slate column: Evgeny Morozov, “A Robot Stole My Pulitzer,” Slate, March 19, 2012, http://www.slate.com/articles/technology/future_tense/2012/03/narrative_science_robot_journalists_customized_news_and_the_danger_to_civil_discourse_.html. 163 “I often wonder how many people”: Katy Waldman, “Popping the Myth of the Filter Bubble,” Slate, April 13, 2012, http://www.slate.com/articles/news_and_politics/intelligence_squared/2012/04/the_next_slate_intelligence_squared_debate_is_april_17_why_jacob_weisberg_rejects_the_idea_that_the_internet_is_closing_our_minds_in_politics_.single.html . 163 “beneficial inefficiency” that accompanied: David Karpf, The MoveOn Effect: The Unexpected Transformation of American Political Advocacy, 1st ed.


pages: 245 words: 64,288

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

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

Somehow your mind has already internalised this fact, and it is starting to reinforce it. If you go back and read them again, I am sure you can spot the flaw right away. It is like with subliminal messages, once you are aware of them, they do not work anymore. Sorry to disappoint, but you have just been trolled.77 The correct answer is in fact b), that is the computer generated article78. If you have been tricked, do not feel bad. Narrative Science and other companies have many customers in the big media industry that make use of this technology already. We just did not notice. The list of such media firms is secret, but we know they are there, because the companies that created these intelligent algorithms have rounded up several million dollars in a very short time. As of now the software is mainly used for sports, finance, business, market, and real estate reporting.

University of Southern California School of Engineering. http://viterbi.usc.edu/news/news/2008/caterpillar-inc-funds.htm 75 Colloquium with Behrokh Khoshnevis, 2009. Massachusetts Institute of Technology. http://www.media.mit.edu/node/2277 76 GSP-09 Team Project: ACASA, 2009. YouTube. http://www.youtube.com/watch?v=172Wne1t_2Q 77 Problem? http://www.urbandictionary.com/define.php?term=trolling 78 Are Sportswriters Really Necessary? Narrative Science’s software takes sports stats and spits out articles, Justin Bachman, 2010. Newsweek. http://www.businessweek.com/magazine/content/10_19/b4177037188386.htm 79 Garry Kasparov vs. Deep Blue, Frederic Friedel. Daily Chess Columns. http://www.chessbase.com/columns/column.asp?pid=146 80 In computer science, brute-force search or exhaustive search, also known as generate and test, is a trivial but very general problem-solving technique that consists of systematically enumerating all possible candidates for the solution and checking whether each candidate satisfies the problem’s statement.


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

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

Of course, if the primary output of a big data analysis is an automated decision, there is no need for visualization. Computers would prefer to get their inputs in numbers, not pictures! Another possibility for the applications layer is to create an automated narrative in textual format. Users of big data often talk about “telling a story with data,” but they don’t often enough employ ­narrative (rather than graphic images) to do so. This approach, used by such companies as Narrative Sciences and Automated Insights, creates a story from raw data. Automated narrative was initially used by these companies to write journalistic accounts of sporting contests, but it is also being used for financial data, marketing data, and many other types. Its proponents don’t argue that it will win the Nobel Prize in Literature, but they do think such tools are quite good at telling stories built around data—in some cases, better than humans.

., 16, 41, 66 Karu, Zoher, 143 Keeping Up with the Quants (Davenport and Kim), 93 Klamka, Jake, 104 Kyruus, 161, 162, 168 large companies action plan for Analytics 3.0 for ­managers in, 204 automating existing processes in, 190–193 big data objectives in, 178–180 big data’s value proposition in, 187 big data used in, 175–176 chief analytics officer role in, 202 company case studies in, 178, 181, 183, 186–187, 187–188, 192, 196, 198 data scientists and teams in, 201 historical context for analytics and big data in, 194–197 Index.indd 223 integrated and embedded models in, 199–200 hybrid technology models in, 200–201 integrating organizational structures and skills in, 182–185 managers’ views of big data in, 176–177 multiple data types in, 197–199 prescriptive analytics used in, 202–203 return on investment in, 188–189, 190f speed of technologies and methods in, 199 leadership, 139–143, 151 Library of Congress, 1 life-cycle management, 129 LinkedIn, 16, 65, 82, 83, 92, 104, 127, 146, 148, 153, 155, 157, 158–159, 160–161, 164, 165 People You May Know (PYMK) ­feature of, 23–24, 140–141, 148, 158 Lockheed Martin, 78 Louisiana State University, 102 machine learning, 4t, 29, 88, 96, 102, 110–111, 113, 114t, 118, 124, 183, 199 Macy’s, 63–64, 179, 183 Macys.com, 63, 182, 183 management big data technology perspective of, 15–18 big data usage and changes in, 27–28 leadership in big data initiatives and, 139–143, 151 new roles in, 141–143 managers action plans for, 30, 57, 84, 112, 134, 151–152, 173, 204 big data skills for, 106–110 in large companies, 176–177 retraining of, 112 visual analytics and, 109 manufacturing, 8t, 52–53, 56, 77, 193, 197 MapReduce framework, 29, 89, 114t, 116, 122, 123, 127f, 132, 148, 157, 199 marketing automated narrative for, 126 banking and, 44, 49, 55, 109 big data strategy and, 5, 8t, 66, 69, 71, 193 B2B firms and, 45–46 Caesars Entertainment and, 179 03/12/13 2:04 PM 224 Index marketing (continued) data-based products and services for, 75, 79, 92, 163, 171, 182 LinkedIn’s use of, 158–159 managerial roles for, 141–142 organizational structure and, 15, 18 retail and, 37–38, 63, 71, 183, 192 sources of data for, 50–51 targeting offers to, 27, 55, 63–64, 65, 67, 72, 79, 107, 108–109, 128, 142, 144, 179, 180, 197 Massachusetts Institute of Technology (MIT), 102, 142, 202, 206 massively parallel processing (MPP), 189, 195, 208 Matters Corp, 69 Mayer, Marissa, 166 Mayo Clinic, 181 McAfee, Andy, 27, 206 McGraw-Hill, 143 McKinsey, 185 media and entertainment firms, 5, 42, 44, 48–49, 54, 179–180 medical record systems, 9, 43, 44–45, 72, 121–122, 156, 181 MetaScale, 192 Me-trics, 13 Microsoft, 14, 37, 163 Microsoft Hadoop, 115 Microsoft Windows Azure, 163. See also Windows Azure military, big data use in, 19 Mint website, 142 MIT, 102, 142, 202, 206 modeling, 41, 62, 63–64, 69, 86, 94, 96, 98, 109–110, 113, 115, 118, 124, 129f, 131f, 146, 184, 195, 197, 199–200, 202 motivation of data scientists, 106 Mu Sigma, 104 MyZeo, 12 Naidoo, Allen, 121–122 Narrative Sciences, 126 National Security Agency, 19 natural language processing (NLP), 45, 67, 96, 114t, 181, 184 Netflix, 16, 42, 48–49, 66 Netflix Prize, 16, 22, 66 Neustar, 47, 78–79 Neustar Labs, University of Illinois, 79 Index.indd 224 new product development big data opportunities in, 23–26 big data strategy in, 65–66 data scientists and, 16, 18, 20, 24, 61–62, 65, 66, 71, 79–80, 106, 161 NewVantage Partners, 7, 177 New York Times, 94 New York University, 102 Nike, 12 Nike+ shoe, 12 North Carolina State University, 102 Northwestern University, 102 Norvig, Peter, 23 NoSQL, 98, 181 Novartis, 54, 66 Obama 2012 presidential campaign, 143, 202 objective in big data strategy cost reduction and, 60–63 developing, 60 internal business decision support and, 67–70 large companies and, 178–180 new product development and, 65–66 time reduction and, 63–65 online analytical processing, 10, 10t online firms action plan for managers and, 173 big data usage in, 153–154 lessons for what not to do from, 167–172 lessons learned from, 154–167 open-source computing, 76, 114t, 115, 118, 120–121, 123, 124, 142, 148, 160–161, 163–164, 208 Opera Solutions, 101 Operating Analytics, 170 Optimizely, 165 Optum, 155–156, 181 Oracle, 14, 117 Orange (mobile telecom firm), 168 organizational structure big data technology and, 15 culture for big data in, 147–149 data scientists and, 16, 61, 82, 140, 141, 142, 152, 153, 158, 173, 180, 187, 202, 207, 209 embedding big data culture in, 149–151 enterprise focus in, 138–139 new senior management roles in, 141–143, 202 orientation toward big data in, 18–22, 26 03/12/13 2:04 PM Index  225 ORION project (UPS), 178 overachievers, 42, 42t, 46 Palo Alto Networks, 104 Parks, Roger, 11 Patil, DJ, 92–93 PayPal, 140 Pegasystems, 150, 168, 169 Pentland, Alex (Sandy), 53 People You May Know (PYMK) feature of LinkedIn, 23–24, 140–141, 148, 158 PepsiCo, 46 personal analytics, 12–13, 45 personal monitoring devices, 12–13, 45 pets, data from, 13, 37–38 pharmaceutical industry, 43, 46, 54, 66, 126, 162 Phoenix Suns, 196 phone data, 13, 47, 51, 53, 78, 86, 122, 127, 196 physicians’ notes, 45, 72, 126, 162 Pig scripting language, 89, 114, 114t, 116, 123, 148, 157, 160, 163, 184 Pinterest, 11 Pivotal Chorus, 160 platform infrastructure, in big data stack, 119t, 120–121 PNC Bank, 108–109 point-of-sale systems, 44, 46 Portillo, Dan, 104 Porway, Jake, 89–90 privacy issues, 27, 42, 168 Procter & Gamble (P&G), 42, 46, 54, 182, 200 product development.


pages: 742 words: 137,937

The Future of the Professions: How Technology Will Transform the Work of Human Experts by Richard Susskind, Daniel Susskind

23andMe, 3D printing, additive manufacturing, AI winter, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, Andrew Keen, Atul Gawande, Automated Insights, autonomous vehicles, Big bang: deregulation of the City of London, big data - Walmart - Pop Tarts, Bill Joy: nanobots, business process, business process outsourcing, Cass Sunstein, Checklist Manifesto, Clapham omnibus, Clayton Christensen, clean water, cloud computing, commoditize, computer age, Computer Numeric Control, computer vision, conceptual framework, corporate governance, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, death of newspapers, disintermediation, Douglas Hofstadter, en.wikipedia.org, Erik Brynjolfsson, Filter Bubble, full employment, future of work, Google Glasses, Google X / Alphabet X, Hacker Ethic, industrial robot, informal economy, information retrieval, interchangeable parts, Internet of things, Isaac Newton, James Hargreaves, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, knowledge economy, lifelogging, lump of labour, Marshall McLuhan, Metcalfe’s law, Narrative Science, natural language processing, Network effects, optical character recognition, Paul Samuelson, personalized medicine, pre–internet, Ray Kurzweil, Richard Feynman, Second Machine Age, self-driving car, semantic web, Shoshana Zuboff, Skype, social web, speech recognition, spinning jenny, strong AI, supply-chain management, telepresence, The Future of Employment, the market place, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, transaction costs, Turing test, Watson beat the top human players on Jeopardy!, WikiLeaks, young professional

Journalists can manually sift through social media looking for breaking news or popular stories, or use computerized systems like Storyful.216 They can secure help with their copy-editing from apps like Grammarly, and with note-taking from Evernote.217 And, as noted, some tasks are no longer undertaken by people at all. In 2014 Associated Press started to use algorithms developed by Automated Insights to computerize the production of several hundred formerly handcrafted earnings reports, producing fifteen times as many as before.218 Forbes now provides similarly for earnings reports and sport, using algorithms developed by Narrative Science.219 The Los Angeles Times uses an algorithm called ‘Quakebot’ (which is currently followed by 95,600 people on Twitter) to monitor the US Geological Survey for earthquake alerts, and automatically to compose articles if an event takes place.220 Users can struggle to tell the difference.221 2.6. Management consulting In 2013 Clayton Christensen claimed, in a Harvard Business Review article, ‘Consulting on the Cusp of Disruption’, that change was ‘inevitable’ in consulting, and that those who had traditionally helped others in management difficulties were themselves ‘being upended’.222 Duff McDonald concluded his book The Firm with the observation that consulting was more ‘hotly contested than it has ever been’.223 Lucy Kellaway suggested in the Financial Times that, ‘fifty years hence, McKinsey won’t exist’.224 Christopher McKenna called the industry ‘the world’s newest profession’, whose status as a profession is ambiguous, and whose future remains uncertain.225 As Christensen notes, the consulting business model has changed little in the past 100 years.

., ‘News Video on the Web’, Pew Research Center, 26 Mar. 2014 <http://www.journalism.org> (accessed 8 March 2014). 213 Nicholas Negroponte, Being Digital (1995), 152–4. 214 The New York Times Innovation Report, retrieved from Jason Abbruzzese, ‘The Full New York Times Innovation Report’, Mashable, 16 May 2014 <http://mashable.com> (accessed 8 March 2015). 215 Ravi Somaiya, ‘How Facebook is Changing the Way Its Users Consume Journalism’, 26 Oct. 2014 <http://www.nytimes.com> (accessed 8 March 2015). 216 <http://www.storyful.com> (accessed 8 March 2015). 217 <https://www.grammarly.com>, <https://www.evernote.com>. 218 Paul Colford, ‘A Leap Forward in Quarterly Earnings Stories’, The Definitive Source blog at Associated Press, 30 June 2014 <http://blog.ap.org/2014/06/30/a-leap-forward-in-quarterly-earnings-stories/> (accessed 8 March). 219 <http://www.forbes.com> See e.g. ‘Earnings Increase Expected for Dick’s Sporting Goods’, Forbes, 3 Feb. 2015. The author on that piece is ‘Narrative Science’, an algorithm. (accessed 27 March 2015). 220 Timothy Aeppel, ‘This Wasn’t Written by an Algorithm, But More and More Is’, Wall Street Journal, 15 Dec. 2014 <http://www.wsj.com> (accessed 8 March). 221 Christer Clerwall, ‘Enter the Robot Journalist’, Journalism Practice, 8: 5 (2014), 519–31. 222 Clayton Christensen, Dina Wang, and Derek van Bever, ‘Consulting on the Cusp of Disruption’, Harvard Business Review, Oct. 2013 <https://hbr.org> (accessed 8 March 2015). 223 Duff McDonald, The Firm: The Story of McKinsey and Its Secret Influence on American Business (2013), 325. 224 Lucy Kellaway, ‘McKinsey’s airy platitudes bode ill for its next half-century’, The Financial Times, 14 Sept. 2014 <http://www.ft.com/> (accessed 8 March 2015). 225 Christopher D.

Morozov, Evgeny, To Save Everything, Click Here (New York: PublicAffairs, 2013). Mountain, Darryl, ‘Disrupting Conventional Law Firm Business Models Using Document Assembly’, International Journal of Law and Information Technology, 15: 2 (2007), 170–91. Mumford, Lewis, Technics and Civilization (Chicago: University of Chicago Press, 2010). Nagel, Thomas, Equality and Partiality (New York: Oxford University Press, 1991). Narrative Science, ‘Earnings Increase Expected for Dick’s Sporting Goods’, Forbes, 3 Feb. 2015 <http://www.forbes.com> (accessed 27 March 2015). NASA, ‘3D Printing: Food in Space’, 23 May 2013 <http://www.nasa.gov/directorates/spacetech/home/feature_3d_food.html#.VP18-N5mjww> (accessed 9 March 2015). Negroponte, Nicholas, Being Digital (London: Hodder & Stoughton, 1995). Neville, Sarah, ‘Hospital Takes the Pulse of Nursing by Video’, Financial Times, 5 Oct. 2014 <http://www.ft.com/> (accessed 6 March 2015).


pages: 317 words: 84,400

Automate This: How Algorithms Came to Rule Our World by Christopher Steiner

23andMe, Ada Lovelace, airport security, Al Roth, algorithmic trading, backtesting, big-box store, Black-Scholes formula, call centre, cloud computing, collateralized debt obligation, commoditize, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, Donald Trump, Douglas Hofstadter, dumpster diving, Flash crash, G4S, Gödel, Escher, Bach, High speed trading, Howard Rheingold, index fund, Isaac Newton, John Markoff, John Maynard Keynes: technological unemployment, knowledge economy, late fees, Marc Andreessen, Mark Zuckerberg, market bubble, medical residency, money market fund, Myron Scholes, Narrative Science, PageRank, pattern recognition, Paul Graham, Pierre-Simon Laplace, prediction markets, quantitative hedge fund, Renaissance Technologies, ride hailing / ride sharing, risk tolerance, Robert Mercer, Sergey Aleynikov, side project, Silicon Valley, Skype, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, transaction costs, upwardly mobile, Watson beat the top human players on Jeopardy!, Y Combinator

But perhaps his theory was simply ninety years early. Since the end of the recession in June 2009, according to Brynjolfsson and McAfee, corporations have spent 26 percent more on technology and software but haven’t raised their payrolls at all. In 2011, the S&P 500 logged record profits of nearly $1 trillion, a feat that didn’t take many extra people to pull off. Writers aren’t safe either. Narrative Science, a company in my hometown of Evanston, Illinois, has attracted $6 million of venture capital for its bots that transform raw stats into styled, grammatically correct, and clever news stories. The sports Web site belonging to the Big Ten Network uses the technology to post articles within a minute after a game is over. Its algorithms suck in the box score and identify the most important parts of the game and then write a story built around those moments, just as a journalist would.

., 192–93 minor-league statistics, baseball, 141 MIT, 24, 73, 128, 160, 179, 188, 217 Mocatta & Goldsmid, 20 Mocatta Group, 20, 21–25, 31 model building, predictive, 63 modifiers, 71 Boolean, 72–73 Mojo magazine, 110 Moneyball (Lewis), 141 money markets, 214 money streams, present value of future, 57 Montalenti, Andrew, 200–201 Morgan Stanley, 116, 128, 186, 191, 200–201, 204 mortgage-backed securities, 203 mortgages, 57 defaults on, 65 quantitative, 202 subprime, 65, 202, 216 Mosaic, 116 movies, algorithms and, 75–76 Mozart, Wolfgang Amadeus, 77, 89, 90, 91, 96 MP3 sharing, 83 M Resort Spa, sports betting at, 133–35 Mubarak, Hosni, 140 Muller, Peter, 128 music, 214 algorithms in creation of, 76–77, 89–103 decoding Beatles’, 70, 103–11 disruptors in, 102–3 homogenization or variety in, 88–89 outliers in, 102 predictive algorithms for success of, 77–89 Music X-Ray, 86–87 Musikalisches Würfelspiel, 91 mutual funds, 50 MyCityWay, 200 Najarian, John A., 119 Naples, 121 Napoleon I, emperor of France, 121 Napster, 81 Narrative Science, 218 NASA: Houston mission control of, 166, 175 predictive science at, 61, 164, 165–72, 174–77, 180, 194 Nasdaq, 177 algorithm dominance of, 49 Peterffy and, 11–17, 32, 42, 47–48, 185 terminals of, 14–17, 42 trading method at, 14 National Heart, Lung, and Blood Institute, 159 Nationsbank, Chicago Research and Trading Group bought by, 46 NBA, 142–43 Neanderthals, human crossbreeding with, 161 Nebraska, 79–80, 85 Netflix, 112, 207 Netherlands, 121 Netscape, 116, 188 Nevermind, 102 New England Patriots, 134 New Jersey, 115, 116 Newsweek, 126 Newton, Isaac, 57, 58, 59, 64, 65 New York, N.Y., 122, 130, 192, 201–2, 206 communication between markets in Chicago and, 42, 113–18, 123–24 financial markets in, 20, 198 high school matching algorithm in, 147–48 McCready’s move to, 85 Mocatta’s headquarters in, 26 Peterffy’s arrival in, 19 tech startups in, 210 New York Commodities Exchange (NYCE), 26 New Yorker, 156 New York Giants, 134 New York Knicks, 143 New York magazine, 34 New York State, health department of, 160 New York Stock Exchange (NYSE), 3, 38–40, 44–45, 49, 83, 123, 184–85 New York Times, 123, 158 New York University, 37, 132, 136, 201, 202 New Zealand, 77, 100, 191 Nietzsche, Friedrich, 69 Nirvana, 102 Nixon, Richard M., 140, 165 Nobel Prize, 23, 106 North Carolina, 48, 204 Northwestern University, 145, 186 Kellogg School of Management at, 10 Novak, Ben, 77–79, 83, 85, 86 NSA, 137 NuclearPhynance, 124 nuclear power, 139 nuclear weapons, in Iran, 137, 138–39 number theory, 65 numerals: Arabic-Indian, 56 Roman, 56 NYSE composite index, 40, 41 Oakland Athletics, 141 Obama, Barack, 46, 218–19 Occupy Wall Street, 210 O’Connor & Associates, 40, 46 OEX, see S&P 100 index Ohio, 91 oil prices, 54 OkCupid, 144–45 Olivetti home computers, 27 opera, 92, 93, 95 Operation Match, 144 opinions-driven people, 173, 174, 175 OptionMonster, 119 option prices, probability and statistics in, 27 options: Black-Scholes formula and, 23 call, 21–22 commodities, 22 definition of, 21 pricing of, 22 put, 22 options contracts, 30 options trading, 36 algorithms in, 22–23, 24, 114–15 Oregon, University of, 96–97 organ donor networks: algorithms in, 149–51, 152, 214 game theory in, 147–49 oscilloscopes, 32 Outkast, 102 outliers, 63 musical, 102 outputs, algorithmic, 54 Pacific Exchange, 40 Page, Larry, 213 PageRank, 213–14 pairs matching, 148–51 pairs trading, 31 Pakistan, 191 Pandora, 6–7, 83 Papanikolaou, Georgios, 153 Pap tests, 152, 153–54 Parham, Peter, 161 Paris, 56, 59, 121 Paris Stock Exchange, 122 Parse.ly, 201 partial differential equations, 23 Pascal, Blaise, 59, 66–67 pathologists, 153 patient data, real-time, 158–59 patterns, in music, 89, 93, 96 Patterson, Nick, 160–61 PayPal, 188 PCs, Quotron data for, 33, 37, 39 pecking orders, social, 212–14 Pennsylvania, 115, 116 Pennsylvania, University of, 49 pension funds, 202 Pentagon, 168 Perfectmatch.com, 144 Perry, Katy, 89 Persia, 54 Peru, 91 Peterffy, Thomas: ambitions of, 27 on AMEX, 28–38 automated trading by, 41–42, 47–48, 113, 116 background and early career of, 18–20 Correlator algorithm of, 42–45 early handheld computers developed by, 36–39, 41, 44–45 earnings of, 17, 37, 46, 48, 51 fear that algorithms have gone too far by, 51 hackers hired by, 24–27 independence retained by, 46–47 on index funds, 41–46 at Interactive Brokers, 47–48 as market maker, 31, 35–36, 38, 51 at Mocatta, 20–28, 31 Nasdaq and, 11–18, 32, 42, 47–48, 185 new technology innovated by, 15–16 options trading algorithm of, 22–23, 24 as outsider, 31–32 profit guidelines of, 29 as programmer, 12, 15–16, 17, 20–21, 26–27, 38, 48, 62 Quotron hack of, 32–35 stock options algorithm as goal of, 27 Timber Hill trading operation of, see Timber Hill traders eliminated by, 12–18 trading floor methods of, 28–34 trading instincts of, 18, 26 World Trade Center offices of, 11, 39, 42, 43, 44 Petty, Tom, 84 pharmaceutical companies, 146, 155, 186 pharmacists, automation and, 154–56 Philips, 159 philosophy, Leibniz on, 57 phone lines: cross-country, 41 dedicated, 39, 42 phones, cell, 124–25 phosphate levels, 162 Physicians’ Desk Reference (PDR), 146 physicists, 62, 157 algorithms and, 6 on Wall Street, 14, 37, 119, 185, 190, 207 pianos, 108–9 Pincus, Mark, 206 Pisa, 56 pitch, 82, 93, 106 Pittsburgh International Airport, security algorithm at, 136 Pittsburgh Pirates, 141 Pius II, Pope, 69 Plimpton, George, 141–42 pneumonia, 158 poetry, composed by algorithm, 100–101 poker, 127–28 algorithms for, 129–35, 147, 150 Poland, 69, 91 Polyphonic HMI, 77–79, 82–83, 85 predictive algorithms, 54, 61, 62–65 prescriptions, mistakes with, 151, 155–56 present value, of future money streams, 57 pressure, thriving under, 169–70 prime numbers, general distribution pattern of, 65 probability theory, 66–68 in option prices, 27 problem solving, cooperative, 145 Procter & Gamble, 3 programmers: Cope as, 92–93 at eLoyalty, 182–83 Peterffy as, 12, 15–16, 17, 20–21, 26–27, 38, 48, 62 on Wall Street, 13, 14, 24, 46, 47, 53, 188, 191, 203, 207 programming, 188 education for, 218–20 learning, 9–10 simple algorithms in, 54 Progress Energy, 48 Project TACT (Technical Automated Compatibility Testing), 144 proprietary code, 190 proprietary trading, algorithmic, 184 Prussia, 69, 121 PSE, 40 pseudocholinesterase deficiency, 160 psychiatry, 163, 171 psychology, 178 Pu, Yihao, 190 Pulitzer Prize, 97 Purdue University, 170, 172 put options, 22, 43–45 Pythagorean algorithm, 64 quadratic equations, 63, 65 quants (quantitative analysts), 6, 46, 124, 133, 198, 200, 202–3, 204, 205 Leibniz as, 60 Wall Street’s monopoly on, 183, 190, 191, 192 Queen’s College, 72 quizzes, and OkCupid’s algorithms, 145 Quotron machine, 32–35, 37 Rachmaninoff, Sergei, 91, 96 Radiohead, 86 radiologists, 154 radio transmitters, in trading, 39, 41 railroad rights-of-way, 115–17 reactions-based people, 173–74, 195 ReadyForZero, 207 real estate, 192 on Redfin, 207 recruitment, of math and engineering students, 24 Redfin, 192, 206–7, 210 reflections-driven people, 173, 174, 182 refraction, indexes of, 15 regression analysis, 62 Relativity Technologies, 189 Renaissance Technologies, 160, 179–80, 207–8 Medallion Fund of, 207–8 retirement, 50, 214 Reuter, Paul Julius, 122 Rhode Island hold ‘em poker, 131 rhythms, 82, 86, 87, 89 Richmond, Va., 95 Richmond Times-Dispatch, 95 rickets, 162 ride sharing, algorithm for, 130 riffs, 86 Riker, William H., 136 Ritchie, Joe, 40, 46 Rochester, N.Y., 154 Rolling Stones, 86 Rondo, Rajon, 143 Ross, Robert, 143–44 Roth, Al, 147–49 Rothschild, Nathan, 121–22 Royal Society, London, 59 RSB40, 143 runners, 39, 122 Russia, 69, 193 intelligence of, 136 Russian debt default of 1998, 64 Rutgers University, 144 Ryan, Lee, 79 Saint Petersburg Academy of Sciences, 69 Sam Goody, 83 Sandberg, Martin (Max Martin), 88–89 Sandholm, Tuomas: organ donor matching algorithm of, 147–51 poker algorithm of, 128–33, 147, 150 S&P 100 index, 40–41 S&P 500 index, 40–41, 51, 114–15, 218 Santa Cruz, Calif., 90, 95, 99 satellites, 60 Savage Beast, 83 Saverin, Eduardo, 199 Scholes, Myron, 23, 62, 105–6 schools, matching algorithm for, 147–48 Schubert, Franz, 98 Schwartz, Pepper, 144 science, education in, 139–40, 218–20 scientists, on Wall Street, 46, 186 Scott, Riley, 9 scripts, algorithms for writing, 76 Seattle, Wash., 192, 207 securities, 113, 114–15 mortgage-backed, 203 options on, 21 Securities and Exchange Commission (SEC), 185 semiconductors, 60, 186 sentence structure, 62 Sequoia Capital, 158 Seven Bridges of Königsberg, 69, 111 Shannon, Claude, 73–74 Shuruppak, 55 Silicon Valley, 53, 81, 90, 116, 188, 189, 215 hackers in, 8 resurgence of, 198–211, 216 Y Combinator program in, 9, 207 silver, 27 Simons, James, 179–80, 208, 219 Simpson, O.


pages: 291 words: 81,703

Average Is Over: Powering America Beyond the Age of the Great Stagnation by Tyler Cowen

Amazon Mechanical Turk, Black Swan, brain emulation, Brownian motion, business cycle, Cass Sunstein, choice architecture, complexity theory, computer age, computer vision, computerized trading, cosmological constant, crowdsourcing, dark matter, David Brooks, David Ricardo: comparative advantage, deliberate practice, Drosophila, en.wikipedia.org, endowment effect, epigenetics, Erik Brynjolfsson, eurozone crisis, experimental economics, Flynn Effect, Freestyle chess, full employment, future of work, game design, income inequality, industrial robot, informal economy, Isaac Newton, Johannes Kepler, John Markoff, Khan Academy, labor-force participation, Loebner Prize, low skilled workers, manufacturing employment, Mark Zuckerberg, meta analysis, meta-analysis, microcredit, Myron Scholes, Narrative Science, Netflix Prize, Nicholas Carr, P = NP, pattern recognition, Peter Thiel, randomized controlled trial, Ray Kurzweil, reshoring, Richard Florida, Richard Thaler, Ronald Reagan, Silicon Valley, Skype, statistical model, stem cell, Steve Jobs, Turing test, Tyler Cowen: Great Stagnation, upwardly mobile, Yogi Berra

These car robots don’t look like something from The Jetsons; the driverless features on these cars are a bunch of sensors, wires, and software. This technology works. There is now a joke that “a modern textile mill employs only a man and a dog—the man to feed the dog, and the dog to keep the man away from the machines.” Software is also encroaching upon journalism. One experiment found that the intelligent mechanized analysis of Narrative Science, a start-up from Illinois, can do a passable job of taking statistics and writing up descriptions of sporting events, company financial reports, and macroeconomic data. These programs won’t soon be at the frontier of creative journalism, but they may soon be generating a lot of run-of-the-mill news for purposes of search and storage. They also may take away some jobs: Should the local newspaper really send a reporter down to that minor league baseball game?

See also artificial intelligence (AI) Mechanical Turk, 148–49 mechanization, 126–27 media, 146 median incomes, 38, 52, 60, 253 Medicaid, 234–39, 250 medical diagnosis, 87–89, 128–29 Medicare, 232–35, 237–38, 242 Medication Adherence Scores, 124 Mediterranean Europe, 174–75 memory, 151–55 meritocracy, 189–90, 230–31 meta-rationality, 82, 115 meta-studies, 224–25 Mexico, 168, 171, 177, 242–43 microcredit, 222–23 microeconomics, 212, 225 “micro-intelligibility,” 219 mid-wage occupations, 38 military, 29, 57 Millennium Prize Problems, 207–8 minimum wage, 59, 60 modes of employment, 35–36 monetarist theory, 226 MOOCs (massive open online courses), 180 Moonwalking with Einstein (Foer), 152 Moore’s law, 10, 15–16 moral issues, 26, 130–31 morale in the workplace, 30, 36 Mormon Church, 197 Morphy, Paul, 106 motivation, 197–202, 203 movie ratings, 121 Moxon’s Master, 134 Mueller, Andreas, 59 multinational corporations, 164 Murray, Charles, 231, 249 music, 146–47, 158 Myspace, 42, 209 mysticism, 153 Nakamura, Hikaru, 80 Narrative Science, 8–9 natural gas production, 177 natural language, 7, 119, 140–41 Naum (chess program), 72 negotiations in business, 12–13, 73 Netflix, 9 Nevada, 8 The New York Times, 11–12 Newton, Isaac, 153 Ng, Jennifer Hwee Kwoon, 89 Nickel, Arno, 81 Nielsen, Dagh, 80 Nobel Prizes, 187, 216 non-tradeable sectors, 176 North American Free Trade Agreement (NAFTA), 8 Northeast US, 241 “nudge” concept, 105 Obama healthcare reform, 237–38 Occupy Wall Street, 230, 251, 253, 256 O’Daniel, Karrah, 96 offshoring, 175.


pages: 368 words: 96,825

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

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

They can extract relevant concepts—like documents relevant to social protest in the Middle East—even in the absence of specific terms, and deduce patterns of behavior that would have eluded lawyers examining millions of documents.” In our third human-skill category—writing—a January 2014 Deloitte University Press report35 explains that AI is making a dent here too. “Intelligent automation, though still rapidly developing, has matured to the point where it has penetrated nearly every sector of the economy. [In the writing category], Credit Suisse uses a technology from Narrative Science to analyze millions of data points on thousands of companies and automatically write English research reports that assess company expectations, upside, and risk. The reports help analysts, bankers, and investors make long-term investment decisions and has tripled the volume of reports produced while improving their quality and consistency compared with analyst-written reports.” Integrating knowledge, our fourth skill, represents the much more complex ability to pull together information from many sources and reach accurate conclusions.

., 258 loss aversion, 121 Louis Pasteur Université, 104 Lovins, Amory, 222 MacCready, Paul, 263 McDowell, Mike, 291n machine learning, 54–55, 58, 66, 85, 137, 167, 216 see also artificial intelligence (AI) Macintosh computer, 72 McKinsey & Company, 245 McLucas, John, 102 Macondo Prospect, 250 macrotasks, crowdsourcing of, 156, 157–58 Made in Space, 36–37 Made to Stick: Why Some Ideas Survive and Others Die (Heath and Heath), 248 MakerBot printers, 39 Makers (Doctorow), 38 MakieLabs, 39 manufacturing, 33, 41 biological, 63–64 digital, 33 in DIY communities, 223–25 robotics in, 62 subtractive vs. additive, 29–30, 31 3–D printing’s impact on, 30, 31, 34–35 Marines, US, 222 Markoff, John, 56 Mars missions, 99, 118–19, 128 Mars Oasis project, 118 Maryland, University of, 74 Maryniak, Gregg, 244 Mashable, 238 massively transformative purpose (MTP), 215, 221, 230, 231, 233, 240, 242, 274 in incentive competitions, 249, 255, 263, 265, 270 mastery, 79, 80, 85, 87, 92 materials, in crowdfunding campaigns, 195 Maven Research, 145 Maxwell, John, 114n Mead, Margaret, 247 Mechanical Turk, 157 meet-ups, 237 Menlo Ventures, 174 message boards, 164 Mexican entrepreneurs, 257–58 Michigan, University of, 135, 136 microfactories, 224, 225 microlending, 172 microprocessors, 49, 49 Microsoft, 47, 50, 99 Microsoft Windows, 27 Microsoft Word, 11 microtasks, crowdsourcing of, 156–57, 166 Mightybell, 217, 233 Migicovsky, Eric, 175–78, 186, 191, 193, 198, 199, 200, 206, 209 Millington, Richard, 233 Mims, Christopher, 290n MIT, 27, 60, 100, 101, 103, 291n mobile devices, 14, 42, 42, 46, 46, 47, 49, 124, 125, 135, 146, 163, 176 see also smartphones Modernizing Medicine, 57 monetization: in incentive competitions, 263 of online communities, 241–42 Montessori education, 89 moonshot goals, 81–83, 93, 98, 103, 104, 110, 245, 248 Moore, Gordon, 7 Moore’s Law, 6–7, 9, 12, 31, 64 Mophie, 18 moral leadership, 274–76 Morgan Stanley, 122, 132 Mosaic, 27, 32, 33, 57 motivation, science of, 78–80, 85, 87, 92, 103 incentive competitions and, 148, 254, 255, 262–63 Murphy’s Law, 107–8 Museum of Flight (Seattle), 205 music industry, 11, 20, 124, 125, 127, 161 Musk, Elon, xiii, 73, 97, 111, 115, 117–23, 128, 134, 138, 139, 167, 223 thinking-at-scale strategies of, 119–23, 127 Mycoskie, Blake, 80 Mycroft, Frank, 180 MySQL, 163 Napoléon I, Emperor of France, 245 Napster, 11 Narrative Science, 56 narrow framing, 121 NASA, 96, 97, 100, 102, 110, 123, 221, 228, 244 Ames Research Center of, 58 Jet Propulsion Laboratory (JPL) of, 99 Mars missions of, 99, 118 National Collegiate Athletic Association (NCAA), 226 National Institutes of Health, 64, 227 National Press Club, 251 navigation, in online communities, 232 Navteq, 47 Navy Department, US, 72 NEAR Shoemaker mission, 97 Netflix, 254, 255 Netflix Prize, 254–56 Netscape, 117, 143 networks and sensors, x, 14, 21, 24, 41–48, 42, 45, 46, 66, 275 information garnered by, 42–43, 44, 47, 256 in robotics, 60, 61 newcomer rituals, 234 Newman, Tom, 268 New York Times, xii, 56, 108, 133, 145, 150, 155, 220 Nickell, Jake, 143, 144 99designs, 145, 158, 166, 195 Nivi, Babak, 174 Nokia, 47 Nordstrom, 72 Nye, Bill, 180, 200, 207 “Oatmeal, the” (web comic), 178, 179, 193, 196, 200 Oculus Rift, 182 O’Dell, Jolie, 238–39 oil-cleanup projects, 247, 250–53, 262, 263, 264 Olguin, Carlos, 65 1Qbit, 59 operational assets, crowdsourcing of, 158–60 Orteig Prize, 244, 245, 259, 260, 263 Oxford Martin School, 62 Page, Carl, 135 Page, Gloria, 135 Page, Larry, xiii, 53, 74, 81, 84, 99, 100, 115, 126, 128, 134–39, 146 thinking-at-scale strategies of, 136–38 PageRank algorithm, 135 parabolic flights, 110–12, 123 Paramount Pictures, 151 Parliament, British, 245 passion, importance of, 106–7, 113, 116, 119–20, 122, 125, 134, 174, 180, 183, 184, 248, 249 in online communities, 224, 225, 228, 231, 258 PayPal, 97, 117–18, 167, 201 PC Tools, 150 Pebble Watch campaign, 174, 175–78, 179, 182, 186, 187, 191, 200, 206, 208, 209, 210 pitch video in, 177, 198, 199 peer-to-peer (P2P) lending, 172 Pelton, Joseph, 102 personal computers (PCs), 26, 76 Peter’s Laws, 108–14 PHD Comics, 200 philanthropic prizes, 267 photography, 3–6, 10, 15 demonetization of, 12, 15 see also digital cameras; Kodak Corporation Pink, Daniel, 79 Pishevar, Shervin, 174 pitch videos, 177, 180, 192, 193, 195, 198–99, 203, 212 Pivot Power, 19 Pixar, 89, 111 Planetary Resources, Inc., 34, 95, 96, 99, 109, 172, 175, 179, 180, 186, 189–90, 193, 195, 201–3, 221, 228, 230 Planetary Society, 190, 200 Planetary Vanguards, 180, 201–3, 212, 230 PlanetLabs, 286n +Pool, 171 Polaroid, 5 Polymath Project, 145 Potter, Gavin, 255–56 premium memberships, 242 PricewaterhouseCoopers, 146 Prime Movers, The (Locke), 23 Princeton University, 128–29, 222 Prius, 221 probabilistic thinking, 116, 121–22, 129 process optimization, 48 Project Cyborg, 65 psychological tools, of entrepreneurs, 67, 115, 274 goal setting in, 74–75, 78, 79, 80, 82–83, 84, 85, 87, 89–90, 92, 93, 103–4, 112, 137, 185–87 importance of, 73 line of super-credibility and, 96, 98–99, 98, 100, 101–2, 107, 190, 203, 266, 272 passion as important in, 106–7, 113, 116, 119–20, 122, 125, 134, 174, 249, 258 Peter’s Laws in, 108–14 and power of constraints, 248–49 rapid iteration and, 76, 77, 78, 79–80, 83–84, 85, 86, 120, 126, 133–34, 236 risk management and, see risk management science of motivation and, 78–80, 85, 87, 92, 103, 254, 255 in skunk methodology, 71–87, 88; see also skunk methodology staging of bold ideas and, 103–4, 107 for thinking at scale, see scale, thinking at triggering flow and, 85–94, 109 public relations managers, in crowdfunding campaigns, 193–94 purpose, 79, 85, 87, 116, 119–20 in DIY communities, see massively transformative purpose (MTP) Qualcomm Tricorder XPRIZE, 253 Quirky, 18–20, 21, 66, 161 Rackspace, 50, 257 Rally Fighter, 224, 225 rapid iteration, 76, 77, 78, 79–80, 83–84, 85, 86, 236 feedback loops in, 77, 83, 84, 86, 87, 90–91, 92, 120 in thinking at scale, 116, 126, 133–34 rating systems, 226, 232, 236–37, 240 rationally optimistic thinking, 116, 136–37 Ravikant, Naval, 174 Raytheon, 72 re:Invent 2012, 76–77 reCAPTCHA, 154–55, 156, 157 registration, in online communities, 232 Reichental, Avi, 30–32, 35 Rensselaer Polytechnic Institute, 4 reputation economics, 217–19, 230, 232, 236–37 Ressi, Adeo, 118 ReverbNation, 161 reward-based crowdfunding, 173, 174–80, 183, 185, 186–87, 195, 205, 207 case studies in, 174–80 designing right incentives for affiliates in, 200 early donor engagement in, 203–5 fundraising targets in, 186–87, 191 setting of incentives in, 189–91, 189 telling meaningful story in, 196–98 trend surfing in, 208 upselling in, 207, 208–9 see also crowdfunding, crowdfunding campaigns rewards, extrinsic vs. intrinsic, 78–79 Rhodin, Michael, 56 Richards, Bob, 100, 101–2, 103, 104 Ridley, Matt, 137 risk management, 76–77, 82, 83, 84, 86, 103, 109, 116, 121 Branson’s strategies for, 126–27 flow and, 87, 88, 92, 93 incentive competitions and, 247, 248–49, 261, 270 in thinking at scale, 116, 121–22, 126–27, 137 Robinson, Mark, 144 Robot Garden, 62 robotics, x, 22, 24, 35, 41, 59–62, 63, 66, 81, 135, 139 entrepreneurial opportunities in, 60, 61, 62 user interfaces in, 60–61 Robot Launchpad, 62 RocketHub, 173, 175, 184 Rogers, John “Jay,” 33, 38, 222–25, 231, 238, 240 Roomba, 60, 66 Rose, Geordie, 58 Rose, Kevin, 120 Rosedale, Philip, 144 Russian Federal Space Agency, 102 Rutan, Burt, 76, 96, 112, 127, 269 San Antonio Mix Challenge, 257–58 Sandberg, Sheryl, 217, 237 Santo Domingo, Dominican Republic, 3 Sasson, Steven, 4–5, 5, 6, 9 satellite technology, 14, 36–37, 44, 100, 127, 275, 286n scale, thinking at, xiii, 20–21, 116, 119, 125–28, 148, 225, 228, 243, 257 Bezos’s strategies for, 128, 129, 130–33 Branson’s strategies for, 125–27 in building online communities, 232–33 customer-centric approach in, 116, 126, 128, 130, 131–32, 133 first principles in, 116, 120–21, 122, 126, 138 long-term thinking and, 116, 128, 130–31, 132–33, 138 Musk’s strategies for, 119–23, 127 Page’s strategies for, 136–38 passion and purpose in, 116, 119–20, 122, 125, 134 probabilistic thinking and, 116, 121–22, 129 rapid iteration in, 116, 126, 133–34 rationally optimistic thinking and, 116, 136–37 risk management in, 116, 121–22, 126–27, 137 Scaled Composites, 262 Schawinski, Kevin, 219–21 Schmidt, Eric, 99, 128, 251 Schmidt, Wendy, 251, 253 Schmidt Family Foundation, 251 science of motivation, 78–80, 85, 87, 92, 103 incentive competitions and, 148, 254, 255, 262–63 Screw It, Let’s Do It (Branson), 125 Scriptlance, 149 Sealed Air Corporation, 30–31 Second Life, 144 SecondMarket, 174 “secrets of skunk,” see skunk methodology Securities and Exchange Commission (SEC), US, 172 security-related sensors, 43 sensors, see networks and sensors Shapeways.com, 38 Shingles, Marcus, 159, 245, 274–75 Shirky, Clay, 215 ShotSpotter, 43 Simply Music, 258 Singh, Narinder, 228 Singularity University (SU), xi, xii, xiv, 15, 35, 37, 53, 61, 73, 81, 85, 136, 169, 278, 279 Six Ds of Exponentials, 7–15, 8, 17, 20, 25 deception phase in, 8, 9, 10, 24, 25–26, 29, 30, 31, 41, 59, 60 dematerialization in, 8, 10, 11–13, 14, 15, 20–21, 66 democratization in, 8, 10, 13–15, 21, 33, 51–52, 59, 64–65, 276 demonetization in, 8, 10–11, 14, 15, 52, 64–65, 138, 163, 167, 223 digitalization in, 8–9, 10 disruption phase in, 8, 9–10, 20, 24, 25, 29, 32, 33–35, 37, 38, 39, 256; see also disruption, exponential Skonk Works, 71, 72 skunk methodology, 71–87, 88 goal setting in, 74–75, 78, 79, 80, 82–83, 84, 85, 87, 103 Google’s use of, 81–84 isolation in, 72, 76, 78, 79, 81–82, 257 “Kelly’s rules” in, 74, 75–76, 77, 81, 84, 247 rapid iteration approach in, 76, 77, 78, 79–80, 83–84, 85, 86 risk management in, 76–77, 82, 83, 84, 86, 87, 88 science of motivation and, 78–80, 85, 87, 92 triggering flow with, 86, 87 Skunk Works, 72, 75 Skybox, 286n Skype, 11, 13, 167 Sloan Digital Sky Survey, 219–20 Small Business Association, US, 169 smartphones, x, 7, 12, 14, 15, 42, 135, 283n apps for, 13, 13, 15, 16, 28, 47, 176 information gathering with, 47 SmartThings, 48 smartwatches, 176–77, 178, 191, 208 software development, 77, 144, 158, 159, 161, 236 in exponential communities, 225–28 SolarCity, 111, 117, 119, 120, 122 Space Adventures Limited, 96, 291n space exploration, 81, 96, 97–100, 115, 118, 119, 122, 123, 134, 139, 230, 244 asteroid mining in, 95–96, 97–99, 107, 109, 179, 221, 276 classifying of galaxies and, 219–21, 228 commercial tourism projects in, 96–97, 109, 115, 119, 125, 127, 244, 246, 261, 268 crowdfunding campaigns for, see ARKYD Space Telescope campaign incentive competitions in, 76, 96, 109, 112, 115, 127, 134, 139, 246, 248–49, 260, 261, 262, 263, 264, 265, 266, 267, 268, 269 International Space University and, 96, 100–104, 107–8 Mars missions in, 99, 118–19, 128 see also aerospace industry Space Fair, 291n “space selfie,” 180, 189–90, 196, 208 SpaceShipOne, 96, 97, 127, 269 SpaceShipTwo, 96–97 SpaceX, 34, 111, 117, 119, 122, 123 Speed Stick, 152, 154 Spiner, Brent, 180, 200, 207 Spirit of St.


pages: 347 words: 97,721

Only Humans Need Apply: Winners and Losers in the Age of Smart Machines by Thomas H. Davenport, Julia Kirby

AI winter, Andy Kessler, artificial general intelligence, asset allocation, Automated Insights, autonomous vehicles, basic income, Baxter: Rethink Robotics, business intelligence, business process, call centre, carbon-based life, Clayton Christensen, clockwork universe, commoditize, conceptual framework, dark matter, David Brooks, deliberate practice, deskilling, digital map, disruptive innovation, Douglas Engelbart, Edward Lloyd's coffeehouse, Elon Musk, Erik Brynjolfsson, estate planning, fixed income, follow your passion, Frank Levy and Richard Murnane: The New Division of Labor, Freestyle chess, game design, general-purpose programming language, global pandemic, Google Glasses, Hans Lippershey, haute cuisine, income inequality, index fund, industrial robot, information retrieval, intermodal, Internet of things, inventory management, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joi Ito, Khan Academy, knowledge worker, labor-force participation, lifelogging, longitudinal study, loss aversion, Mark Zuckerberg, Narrative Science, natural language processing, Norbert Wiener, nuclear winter, pattern recognition, performance metric, Peter Thiel, precariat, quantitative trading / quantitative finance, Ray Kurzweil, Richard Feynman, risk tolerance, Robert Shiller, Robert Shiller, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, six sigma, Skype, social intelligence, speech recognition, spinning jenny, statistical model, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, superintelligent machines, supply-chain management, transaction costs, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, Works Progress Administration, Zipcar

In insurance claims, for example, “auto-adjudication” can automatically evaluate and approve up to 75 percent of claims. Human claims adjusters are left to approve only the most challenging ones. 9. It involves the creation of data-based narratives. Jobs involving the narrative description of data and analysis were once the province of humans, but automated systems are already beginning to take them over. In journalism, companies like Automated Insights and Narrative Science are already creating data-intensive content. Sports and financial reporting are already at some risk, although the automation of these domains is on the margins thus far—high school and fantasy sports, and earnings reports for small companies. Other companies, like AnalytixInsight, create investment analysis narratives on more than 40,000 public companies with its CapitalCube service. The job at risk in this case is that of investment analyst.

See health care and medicine Memorial Sloan Kettering Cancer Center, 46, 209, 215, 217 Mendez, Ray, 170 Mendonca, George, 170 Mercedes, 213 Microsoft “Bob,” 196 Mindell, David, 67 mining automation, 201–3 Mitler, Lesley, 150 Mohr, Catherine, 40 Momentum Machines, 205 Moody, Paul, 132–33 Morris, Errol, 170 Mukhamedshina, Irina, 162 Munich Re and Swiss Re, 83 Murnane, Richard, 27, 63 music, 24, 126 Musk, Elon, 225, 246, 248 MYCIN expert system, 46 Myers, Justin, 98, 222 Nadel, Edward, 132, 146 Narain, Niven, 60 Narrative Science, 22 National Trust for Historic Preservation, 240 Nature of Expertise, The (Glaser and Chi, eds.), 163 Nayar, Vineet, 204 NBA, 116–17 New Division of Labor, The (Levy and Murnane), 27 Newton, Isaac, 165 New York Federal Reserve Bank, 90 New York Stock Exchange, 11–12, 18 Nicita, Camille, 62–63 Nokia, 239 Nordfors, David, 248 Northeastern University, 232 NYU Langone Medical Center, 138 Obama, Barack, 95 Office, The (TV show), 109–10 office workers, 3, 157, 187, 217, 239 Off the Grid News, 110, 111 Oracle, 133 Orellana, Marco, 202 Oremus, Will, 127 Organisation for Economic Co-operation and Development (OECD), 27 Osindero, Simon, 126 Oxford study, U.S. jobs at risk, 2, 30 Painting Fool, 125 Palmer, Shelly, 234 Parikh, Jay, 206–7, 211 Partners HealthCare, 66 Patil, D.


pages: 396 words: 117,149

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

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

The common theme is that narrowly defined tasks are easily learned from data, but tasks that require a broad combination of skills and knowledge aren’t. Most of your brain is devoted to vision and motion, which is a sign that walking around is much more complex than it seems; we just take it for granted because, having been honed to perfection by evolution, it’s mostly done subconsciously. The company Narrative Science has an AI system that can write pretty good summaries of baseball games, but not novels, because—pace George F. Will—there’s a lot more to life than to baseball games. Speech recognition is hard for computers because it’s hard to fill in the blanks—literally, the sounds speakers routinely elide—when you have no idea what the person is talking about. Algorithms can predict stock fluctuations but have no clue how they relate to politics.

See Markov logic networks (MLNs) Moby Dick (Melville), 72 Molecular biology, data and, 14 Moneyball (Lewis), 39 Mooney, Ray, 76 Moore’s law, 287 Moravec, Hans, 288 Muggleton, Steve, 80 Multilayer perceptron, 108–111 autoencoder, 116–118 Bayesian, 170 driving a car and, 113 Master Algorithm and, 244 NETtalk system, 112 reinforcement learning and, 222 support vector machines and, 195 Music composition, case-based reasoning and, 199 Music Genome Project, 171 Mutation, 124, 134–135, 241, 252 Naïve Bayes classifier, 151–153, 171, 304 Bayesian networks and, 158–159 clustering and, 209 Master Algorithm and, 245 medical diagnosis and, 23 relational learning and, 228–229 spam filters and, 23–24 text classification and, 195–196 Narrative Science, 276 National Security Agency (NSA), 19–20, 232 Natural selection, 28–29, 30, 52 as algorithm, 123–128 Nature Bayesians and, 141 evolutionaries and, 137–142 symbolists and, 141 Nature (journal), 26 Nature vs. nurture debate, machine learning and, 29, 137–139 Neal, Radford, 170 Nearest-neighbor algorithms, 24, 178–186, 202, 306–307 dimensionality and, 186–190 Negative examples, 67 Netflix, 12–13, 183–184, 237, 266 Netflix Prize, 238, 292 Netscape, 9 NETtalk system, 112 Network effect, 12, 299 Neumann, John von, 72, 123 Neural learning, fitness and, 138–139 Neural networks, 99, 100, 112–114, 122, 204 convolutional, 117–118, 302–303 Master Algorithm and, 240, 244, 245 reinforcement learning and, 222 spin glasses and, 102–103 Neural network structure, Baldwin effect and, 139 Neurons action potentials and, 95–96, 104–105 Hebb’s rule and, 93–94 McCulloch-Pitts model of, 96–97 processing in brain and, 94–95 See also Perceptron Neuroscience, Master Algorithm and, 26–28 Newell, Allen, 224–226, 302 Newhouse, Neil, 17 Newman, Mark, 160 Newton, Isaac, 293 attribute selection, 189 laws of, 4, 14, 15, 46, 235 rules of induction, 65–66, 81, 82 Newtonian determinism, Laplace and, 145 Newton phase of science, 39–400 New York Times (newspaper), 115, 117 Ng, Andrew, 117, 297 Nietzche, Friedrich, 178 NIPS.


pages: 501 words: 114,888

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

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

The AI responds by reading 120,000 books in half a second and answers by providing quotes from them. The upgrade here is that the answers are based on authorial intent and not merely keywords. Plus, the AI seems to have a sense of humor. Asking: “Where is heaven?” for example, produces: “Heaven, as a place, for humans, so it seems, cannot be found in Mesopotamia,” which is from the Early History of Heaven by J. Edward Wright. On the writing front, companies such as Narrative Science now use AI to write magazine-quality prose without any help from a human journalist. Forbes runs their business reports, dozens of daily papers run their baseball stories. Similarly, Gmail’s Smart Compose feature no longer simply suggests words and their correct spelling, now it blurts out whole phrases as you type. Other AIs are generating entire books. In the 2017 competition for Japan’s national literary prize, an AI-written novel made it to the final round of judging.

., 73 Kenya, 192 Kenyon, Larry, 70 Kernel, 81, 256 Kibar, Osman, 177 Kickstarter, 74, 109 Kim, Peter, 161 Kimmelman, Michael, 232 Kindred, 108 Kitkit School, 146 Kitty Hawk, 5 Kiva Systems, 108 knowledge, integration of, AI and, 35–36 Kodak, 126 Kotler, Steven, 264 Kroger stores, 107 Kurzweil, Ray, 8, 12, 29, 76, 82, 91, 173 labor, human vs. robotic, 108 Lahtela, Petteri, 41 Lancet, 226 land ownership, blockchain and, 58 Lanier, Jaron, 50 LA Times, 67 “Law for Restoration of the Professional Civil Service” (Germany), 238 “Law of Accelerating Returns,” 8, 12, 29 Leap Motion, 52 Lemonade, 186–87 lending, peer-to-peer, 74, 194–95 Leroy Merlin, 110 leveraged assets, 84 Levine, Mark, 241 LG, 51, 139 LIDAR, 43, 185 Lieber, Charles, deep brain stimulation research of, 253–55, 256 Lifekind, 132 lifespan, extension of, see longevity Light Field Lab, 133–34 LightStage, 133, 134 Lightwave, 137 listening, AI and, 35 lithium-ion batteries, 62, 219–20, 222 Littlewood, John, 80 Liu, David, 67 Lloyd, Edward, 182–83 Lloyd’s of London, 183 loans, micro-, 191–92, 195 locked in syndrome, 141 logistics, robots and, 108 London College of Fashion, 114 longevity, 87–91, 169–79 anti-aging compounds and, 175 average human lifespan and, 173 convergence and, 169, 173, 179 “escape velocity” for, 173 genetics and, 172–73 innovation and, 169 regenerating medicines and, 176–77 senolytic therapies and, 175–76 stem cells and, 90 young blood transfusions and, 90, 178–79 see also aging Long Now Foundation, 232 Lonsdale, Joe, 17 looking, AI and, 34–35 Lovelace, Ada, 87–88 Lovelace, Gunnar, 190 Lowe’s Home Improvement, 107 lung disease, 151–52 Lyft, 195–96, 234 Lyrebird, 121 machine learning, 10, 34 Macintosh, 70–71 McKinsey Quarterly, 83 McNierney, Ed, 145–46 Made in Space, 53 Mad Men (TV show), 117 Magic Leap, 139, 149–50 mail-order business, birth of, 95–96 malaria, 160 Mall of America, 112 malls, 112, 115 Manning, Richard, 202 Mansfield, Mike, 73 manufacturing: impact of 3–D printing on, 54 zero-waste, 226 Marillion, 74 Mars, colonization of, 252 MashUp Machine, 34 Massachusetts Institute of Technology (MIT), 144, 220 Materials Genome Initiative, 62–63 materials science, 10, 11, 17, 61–63 meat, cultured, 206–8 media: passive vs. active, 130–31 print, 138 medial prefrontal cortex, 21–22 medicine, see healthcare memory, brain implants and, 82 Memphis Meats, 208 Menabrea, Luigi, 88 Messina, Jim, 17 metformin, 175 microbots, 162 micro-loans, 191–92, 195 Microsoft, 33, 114 Microsoft HoloLens, 52 Middleton, Daniel, 129 Mighty Building, 55 migrations: climate change and, 211, 241–42 in human history, 237–40 as innovation accelerant, 238–40 interplanetary, 249–53 from physical to virtual reality, 245–49 urban relocation and, 243–45 military drones, increasing demand for, 10 Ministry of Supply, 109 Minnesota, University of, 163 Mission: Impossible (film), 121 mitochondrial dysfunction, 171 Mitra, Sugata, 145 MIT Review, 145, 146 mobile finance, 190–91 mobile health, 157–58 mobile phones, as quasi-currency, 191–92 mobility, and economic paradigm shifts, 98 Moment, The (film), 141 money: uses of, 189–90 see also cryptocurrencies; finance industry Moon, as space colonization platform, 251 Moore, Gordon, 7 Moore’s Law, 7–8, 28–31, 74 Morgan (film), 130 Morse, Samuel, 38 Mosaic, 32 Moser, Petra, 238–39 Mote Tropical Research Laboratory, 225 motion transfer, 131 movies, participatory, 135 M-Pesa, 192 mPower network, 40 MRI (magnetic resonance imaging), portable, 157 multiple world models, 86 Musk, Elon, 40, 81, 146, 161, 219, 231 Boring Company and, 18–19 brain-computer interfaces and, 255–56, 257–58 Hyperloop and, 16 space colonization and, 20–21, 250, 251–54 Myanmar, 226–27 My Drunk Kitchen (YouTube program), 128 Myers, Norman, 241 Naam, Ramez, 216, 218, 220 Nakamoto, Satoshi (pseudonym), 57 Nalamasu, Omkaram, 63 Nano Dimension, 54 nanotechnology, self-replicating, 63–64, 231 Nanticoke Generating Station, 216–17 Narrative Science, 35 NASA, 6, 233 Mars mission of, 160–61 National Institute on Aging, 179 National Institutes of Health, All of Us project of, 159 National Resources Defense Council, 203 Nebula Genomics, 159 Nefertari, tomb of, 147–48 Negroponte, Nicholas, Ethiopian self-teaching experiment of, 144–46 NeoSensory, 134 Netflix, 125–26 Netherlands, 232 networks, 39–41, 82–83 5G, 39–40, 119, 149 history of, 37–39 “Internet of Things,” 42–43, 104–5 new business models and, 84–87 and nurturing of genius, 80–81 ubiquity of, 39 user-friendly interfaces for, 38 Neuralink, 81, 256, 258 neural networks, 33, 34 neurobiology, innovation and, 81 neuro-modulation, 253–55 neurophysiology, 136 Nevermind (video game), 137 New Balance, 109 New Glenn rocket, 251 New Story, 55 New York Times, 105, 232 nine-dot problem, 81 Nintendo, 52, 140 Nokia, 100–101 norepinephrine, 247 Norway, electric cars in, 221 Novartis, 175 Nuro, 107 nutrient sensing, 171 O3B satellite network, 40 Obama, Barack, 62, 122 Oceanix City, 200 oceans, biodiversity crisis in, 223–24, 225 Oculus Rift, 51 O’Hagan, Ellie Mae, 242 OLEDS (organic light emitting diodes), 139 Omni Processor, 214 Onebillion, 146 O’Neill, Gerard K., 250, 251 One Laptop per Child, 145–46 Oneweb, 40 Opener, 5 OpenGov, 235 Openwater, 157 optical sensors, 43 Organovo, 55 organ transplants, 153–54, 175 Otoy, 133, 134 Oura ring, 42, 44 Outside, 241 overfishing, 223, 225 Ovid, 178 oxytocin, 247 Page, Larry, 5 Panasonic, 222 parabiosis, 178 parking spaces, repurposing of, 16 Parkinson’s disease, deep brain stimulation and, 253–54 Partnership for a New American Economy, 239 Paul, Logan, 129 PayPal, 252 Peabody Coal, 216 Pebble Time, 74 Peele, Jordan, 122 peer-to-peer lending, 74 Peleg, Danit, 109 Pepper (humanoid robot), 107 Perfect Day Foods, 208 perovskite, 63 photosynthesis, 202 physical world, boundaries between digital world and, 118–20 Picard, Rosalind, 137 Pichai, Sundar, 101, 102 Pine, Joseph, 111 Pinker, Steven, 262 Pinterest, 119 Pishevar, Shervin, 17 placental cells, 163–65 Plastic Bank, 85 Plenty Unlimited Inc., 204–5 podcasts, 127 Pokémon Go, 52, 140 pollution, 212, 223, 226 population growth, 213 Porsche, 222 possessions, experiences vs., 112 Postal Service, US, 96–97, 98 poverty, declining rate of, 262 Pratt, Gill, 46 Prellis Biologics, 55 presence, in VR, 50 prevention, existential risks and, 232–33 Prime Air, 107 Prime Wardrobe, 114 print media, 138 productivity: cities and, 244 technology and, 229 product reallocation, 239 Progressive Insurance, 187–88 Project Kuiper, 40 Project Loon, 39–40 proteins: aging and, 170–71 folding in, 167 proteostasis, loss of, 170–71 PTSD, VR and, 148–49 public institutions, impact of exponential technologies on, 23 Pulier, Eric, 59 pulmonary hypertension, 151–52 Qualcomm, 40 Qualcomm Tricorder XPRIZE, 157 quantum computing, 8, 27–32 drug development and, 30, 167 QuantumScape, 222 Quartz, 228 Quayside, 235 qubits (quantum bits), 27–28, 30 R3, 193 radio, 138–39 railways, time standardization and, 96 Ramanujan, Srinivasa, 79–80 Ramchurn, Richard, 141 rapamycin, 174–75 Rea, Andrew, 128 reading, AI and, 35 real estate industry, 181 convergence and, 196–200 Reebok, 109 reforestation, drones and, 224, 227 regenerative medicines, 176–77 Renault, 219 renewable energy, 78, 214, 215–18 convergence and, 217–18 see also specific technologies ridesharing: autonomous cars and, 14–16, 19 car ownership vs., 14–15, 26 flying cars and, 19 Ridley, Matt, 82 Rifkin, Jeremy, 97, 100 Rigetti, Chad, 28, 30 Rigetti Computing, 28, 30, 32 RIPE Project, 203 Ripple, 193 “Rising Billion,” 99–100 Rizzo, Skip, 148–49 Robbins, Tony, 132, 148 robots, robotics, 45–48, 136 as avatars, 25–26 convergence and, 48 demonetization and, 78 disaster mitigation and, 45–46 food industry and, 205 home delivery and, 106–7 human collaboration with, 47, 162, 229 in-store, 107 micro-, 162 surgery and, 161–62 unemployment and, 22, 69, 229 warehouse logistics and, 108 in workplace, 47–48 rockets: intercontinental travel on, 20, 26 interplanetary travel on, 20, 251, 253 Roebuck, Alvah Curtis, 96 Romkey, John, 42 Rose, Geordie, 29–30 Rosedale, Philip, 134, 147–48 Rose’s Law, 30, 31 Rothblatt, Jenesis, 152–53 Rothblatt, Martine, 151–54, 173 Rural Free Delivery Act (1896), 97 Russell, Bertrand, 80 Rustagi, Kevin, 108–9 R.


pages: 179 words: 43,441

The Fourth Industrial Revolution by Klaus Schwab

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

Sophisticated algorithms can create narratives in any style appropriate to a particular audience. The content is so human-sounding that a recent quiz by The New York Times showed that when reading two similar pieces, it is impossible to tell which one has been written by a human writer and which one is the product of a robot. The technology is progressing so fast that Kristian Hammond, co-founder of Narrative Science, a company specializing in automated narrative generation, forecasts that by the mid-2020s, 90% of news could be generated by an algorithm, most of it without any kind of human intervention (apart from the design of the algorithm, of course).24 In such a rapidly evolving working environment, the ability to anticipate future employment trends and needs in terms of the knowledge and skills required to adapt becomes even more critical for all stakeholders.


pages: 270 words: 79,992

The End of Big: How the Internet Makes David the New Goliath by Nicco Mele

4chan, A Declaration of the Independence of Cyberspace, Airbnb, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple's 1984 Super Bowl advert, barriers to entry, Berlin Wall, big-box store, bitcoin, business climate, call centre, Cass Sunstein, centralized clearinghouse, Chelsea Manning, citizen journalism, cloud computing, collaborative consumption, collaborative editing, commoditize, creative destruction, crony capitalism, cross-subsidies, crowdsourcing, David Brooks, death of newspapers, disruptive innovation, Donald Trump, Douglas Engelbart, Douglas Engelbart, en.wikipedia.org, Exxon Valdez, Fall of the Berlin Wall, Filter Bubble, Firefox, global supply chain, Google Chrome, Gordon Gekko, Hacker Ethic, Jaron Lanier, Jeff Bezos, jimmy wales, John Markoff, Julian Assange, Kevin Kelly, Khan Academy, Kickstarter, Lean Startup, Mark Zuckerberg, minimum viable product, Mitch Kapor, Mohammed Bouazizi, Mother of all demos, Narrative Science, new economy, Occupy movement, old-boy network, peer-to-peer, period drama, Peter Thiel, pirate software, publication bias, Robert Metcalfe, Ronald Reagan, Ronald Reagan: Tear down this wall, sharing economy, Silicon Valley, Skype, social web, Steve Jobs, Steve Wozniak, Stewart Brand, Stuxnet, Ted Nelson, Telecommunications Act of 1996, telemarketer, The Wisdom of Crowds, transaction costs, uranium enrichment, Whole Earth Catalog, WikiLeaks, Zipcar

On the contrary, we see an impetus, if anything, to turn journalistic writing into a commodity increasingly devoid of moral content. The Huffington Post, heralded as a successful online news business built on online advertising, uses a system called Blogsmith to manage and monitor publishing. Blogsmith tracks the amount of time a writer spends composing a piece and then compares it with the advertising revenue it generates once published. New outlets like Narrative Science have algorithms that “write” news stories, especially formulaic stories like reporting on sports events and financial news. Robots writing the news! As much as I love robots, I can’t see one winning the Goldsmith Award any time soon. We need people—specifically, dedicated, professional reporters—to do that. Steven Johnson has argued that journalism is evolving from a hierarchical system rooted in newspapers to a more distributed ecosystem with a multitude of players, including both professionals and amateurs.


pages: 244 words: 81,334

Picnic Comma Lightning: In Search of a New Reality by Laurence Scott

4chan, Airbnb, airport security, augmented reality, Berlin Wall, Bernie Sanders, Boris Johnson, clean water, colonial rule, cryptocurrency, dematerialisation, Donald Trump, Elon Musk, housing crisis, Internet of things, Joan Didion, job automation, late capitalism, Mark Zuckerberg, Narrative Science, Productivity paradox, QR code, ride hailing / ride sharing, Saturday Night Live, sentiment analysis, Silicon Valley, Skype, Slavoj Žižek, Snapchat, Y2K

There remains the perpetual issue of resemblance, of how the story matches the reality of what actually happened. Murdoch isn’t willing to accept that there is no reality beyond that which the story itself describes. For her, the extent to which the comfort of making form out of rubble ‘involves offences against truth is a problem any artist must face’. The story in itself is not the truth. We seem to be innately hospitable to the goings-on in the outside world when it is structured as narrative. Science is beginning to understand the physiological mechanisms for our love of stories. Paul Zak’s work combines neurology and economics, in order to understand how storytelling can be used to build successful businesses. His findings show that ‘stories that are personal and emotionally compelling engage more of the brain, and thus are better remembered, than simply stating a set of facts’. Zak’s research into the biochemical effects of stories has consistently revealed that hearing stories about the adventures of particular individuals causes us to produce oxytocin, a hormone related to feelings of security, calm and interpersonal bonding.


pages: 283 words: 85,824

The People's Platform: Taking Back Power and Culture in the Digital Age by Astra Taylor

A Declaration of the Independence of Cyberspace, American Legislative Exchange Council, Andrew Keen, barriers to entry, Berlin Wall, big-box store, Brewster Kahle, citizen journalism, cloud computing, collateralized debt obligation, Community Supported Agriculture, conceptual framework, corporate social responsibility, creative destruction, cross-subsidies, crowdsourcing, David Brooks, digital Maoism, disintermediation, don't be evil, Donald Trump, Edward Snowden, Fall of the Berlin Wall, Filter Bubble, future of journalism, George Gilder, Google Chrome, Google Glasses, hive mind, income inequality, informal economy, Internet Archive, Internet of things, invisible hand, Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Julian Assange, Kevin Kelly, Kickstarter, knowledge worker, Mark Zuckerberg, means of production, Metcalfe’s law, Naomi Klein, Narrative Science, Network effects, new economy, New Journalism, New Urbanism, Nicholas Carr, oil rush, peer-to-peer, Peter Thiel, plutocrats, Plutocrats, post-work, pre–internet, profit motive, recommendation engine, Richard Florida, Richard Stallman, self-driving car, shareholder value, sharing economy, Silicon Valley, Silicon Valley ideology, slashdot, Slavoj Žižek, Snapchat, social graph, Steve Jobs, Stewart Brand, technoutopianism, trade route, Whole Earth Catalog, WikiLeaks, winner-take-all economy, Works Progress Administration, young professional

Quotes from an interview with the author except for this one, which is from Justin Cox, “Documenting a Bin Laden Ex-Confidante: Q&A with Filmmaker Laura Poitras,” TheHill.com, July 13, 2010, http://thehill.com/capital-living/cover-stories/108553-documenting-a-bin-laden-ex-confidante-qaa-with-filmmaker-laura-poitras#ixzz2YfhpMdXu. 2. The other person Snowden contacted was the journalist Glenn Greenwald of the Guardian, with whom Poitras collaborated. 3. That start-up is Narrative Science, a computer program that generates sports stories. Janet Paskin, “The Future of Journalism?,” Columbia Journalism Review (November/December 2010): 10. 4. John Markoff, “Armies of Expensive Lawyers, Replaced by Cheaper Software,” New York Times, March 5, 2011, A1. 5. See Janice Gross Stein’s book based on her Massey Lecture: Janice Gross Stein, The Cult of Efficiency (Toronto: House of Anansi Press, 2002). 6.