Robert Mercer

34 results back to index


pages: 407 words: 104,622

The Man Who Solved the Market: How Jim Simons Launched the Quant Revolution by Gregory Zuckerman

affirmative action, Affordable Care Act / Obamacare, Albert Einstein, Andrew Wiles, automated trading system, backtesting, Bayesian statistics, beat the dealer, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, blockchain, Brownian motion, butter production in bangladesh, buy and hold, buy low sell high, Claude Shannon: information theory, computer age, computerized trading, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversified portfolio, Donald Trump, Edward Thorp, Elon Musk, Emanuel Derman, endowment effect, Flash crash, George Gilder, Gordon Gekko, illegal immigration, index card, index fund, Isaac Newton, John Meriwether, John Nash: game theory, John von Neumann, Loma Prieta earthquake, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, Mark Zuckerberg, More Guns, Less Crime, Myron Scholes, Naomi Klein, natural language processing, obamacare, p-value, pattern recognition, Peter Thiel, Ponzi scheme, prediction markets, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, Robert Mercer, Ronald Reagan, self-driving car, Sharpe ratio, Silicon Valley, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, Steve Jobs, stochastic process, the scientific method, Thomas Bayes, transaction costs, Turing machine

Michael Coleman, “Influential Conservative Is Sandia, UNM Grad,” Albuquerque Journal, November 5, 2017, https://www.abqjournal.com/1088165/influential-conservative-is-sandia-unm-grad-robert-mercer-trump-fundraiser-breitbart-investor-has-nm-roots.html. 4. Robert Mercer, “A Computational Life” (speech, Association for Computational Linguistics Lifetime Achievement Award, Baltimore, Maryland, June 25, 2014), http://techtalks.tv/talks/closing-session/60532. 5. Stephen Miller, “Co-Inventor of Money-Market Account Helped Serve Small Investors’ Interest,” Wall Street Journal, August 16, 2008, https://www.wsj.com/articles/SB121884007790345601. 6. Feng-Hsiung Hsu, Behind Deep Blue: Building the Computer That Defeated the World Chess Champion (Princeton, NJ: Princeton University Press, 2002). Chapter Ten 1. Peter Brown and Robert Mercer, “Oh, Yes, Everything’s Right on Schedule, Fred” (lecture, Twenty Years of Bitext Workshop, Empirical Methods in Natural Language Processing Conference, Seattle, Washington, October 2013), http://cs.jhu.edu/~post/bitext.

He was aware of the accomplishments of the computer giant’s speech-recognition group and thought their work bore similarity to what Renaissance was doing. In early 1993, Patterson sent separate letters to Peter Brown and Robert Mercer, deputies of the group, inviting them to visit Renaissance’s offices to discuss potential positions. Brown and Mercer both reacted the exact same way—depositing Patterson’s letter in the closest trash receptacle. They’d reconsider after experiencing family upheaval, laying the groundwork for dramatic change at Jim Simons’s company, and the world as a whole. * * * = Robert Mercer’s lifelong passion had been sparked by his father. A brilliant scientist with a dry wit, Thomas Mercer was born in Victoria, British Columbia, later becoming a world expert on aerosols, the tiny particles suspended in the atmosphere that both contribute to air pollution and cool the earth by blocking the sun.

Ryan Avent, “If It Works, Bet It,” Economist, June 14, 2010, https://www.economist.com/free-exchange/2010/06/14/if-it-works-bet-it. 5. James Simons, “My Life in Mathematics” (lecture, International Congress of Mathematics, Seoul, South Korea, August 13, 2014), https://www.youtube.com/watch?v=RP1ltutTN_4. 6. John Marzulli, “Hedge Fund Hotshot Robert Mercer Files Lawsuit over $2M Model Train, Accusing Builder of Overcharge,” New York Daily News, March 31, 2009, https://www.nydailynews.com/news/hedge-fund-hotshot-robert-mercer-files-lawsuit-2m-model-train-accusing-builder-overcharge-article-1.368624. 7. Patterson and Strasburg, “Pioneering Fund Stages Second Act.” 8. Joshua Green, Devil’s Bargain: Steve Bannon, Donald Trump, and the Storming of the Presidency (New York: Penguin Press, 2017). 9. Mider, “Ted Cruz?” 10.


pages: 296 words: 78,112

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

4chan, Affordable Care Act / Obamacare, Ayatollah Khomeini, Bernie Sanders, business climate, centre right, Charles Lindbergh, coherent worldview, collateralized debt obligation, conceptual framework, corporate raider, crony capitalism, currency manipulation / currency intervention, Donald Trump, Fractional reserve banking, Goldman Sachs: Vampire Squid, Gordon Gekko, guest worker program, illegal immigration, immigration reform, liberation theology, low skilled workers, Nate Silver, Nelson Mandela, nuclear winter, obamacare, Peace of Westphalia, Peter Thiel, quantitative hedge fund, Renaissance Technologies, Robert Mercer, Ronald Reagan, Silicon Valley, social intelligence, speech recognition, urban planning

The film debuted during the Cannes Film Festival,* on the French Riviera, where Rebekah Mercer entertained guests, including Bannon, aboard the family’s 203-foot luxury super yacht, Sea Owl. The fourth Mercer-funded outfit was a business after Robert Mercer’s own heart, the U.S. offshoot of a British data analytics company, Strategic Communication Laboratories, that advised foreign governments and militaries on influencing elections and public opinion using the tools of psychological warfare. The American affiliate of SCL, of which Robert Mercer became principal owner, was christened Cambridge Analytica. (Bannon, too, took an ownership stake and a seat on the company’s board.) The purpose of acquiring a major stake in a data company was to equip the Mercer network with the kind of state-of-the-art technology that had been glaringly absent from Mitt Romney’s campaign.

He was dressed up as one of his favorite movie characters of all-time, Brigadier General Frank Savage, the tough-as-nails commander, played by Gregory Peck, who takes over a demoralized World War II bombing unit and whips them into fighting shape in the 1949 classic Twelve O’Clock High. Ordinarily, Bannon wasn’t big into cosplay. But this was a special occasion: the annual Christmas party thrown by the reclusive billionaire Robert Mercer, an eccentric computer scientist who was co-CEO of the fabled quantitative hedge fund Renaissance Technologies. As introverted and private as Bannon was voluble and outspoken, Mercer was nonetheless a man of ardent passions. He collected machine guns and owned the gas-operated AR-18 assault rifle that Arnold Schwarzenegger wielded in The Terminator. He had built a $2.7 million model train set equipped with a miniature video camera to allow operators to experience the view from inside the cockpit of his toy engine.

Jack Hanna, the khaki-clad celebrity zookeeper, came wandering by (the Mercers gave $100,000 to his zoo). But the evening’s buzz was all about politics. With the presidential election less than a year away, Rebekah Mercer, who was dressed as Rita Hayworth, stood to be a figure of consequence. Texas senator Ted Cruz, dressed as Winston Churchill, was especially solicitous of her. As everyone gathered on the lush grounds of Robert Mercer’s estate was keenly aware, the Mercer family had given away more than $77 million to conservative politicians and organizations since 2008. You didn’t have to be a brilliant scientist to see the joy Bob Mercer derived from his annual Christmas pageant, or to understand that anyone hoping to curry favor with him would be wise to play along. This is how it came to be that adults who never imagined themselves dressing up in costumes—adults like Steve Bannon—wound up hunting for just the right period-appropriate accoutrements to make a positive impression.


pages: 174 words: 56,405

Machine Translation by Thierry Poibeau

AltaVista, augmented reality, call centre, Claude Shannon: information theory, cloud computing, combinatorial explosion, crowdsourcing, easy for humans, difficult for computers, en.wikipedia.org, Google Glasses, information retrieval, Internet of things, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, natural language processing, Necker cube, Norbert Wiener, RAND corporation, Robert Mercer, Skype, speech recognition, statistical model, technological singularity, Turing test, wikimedia commons

Unlike a translation memory, which can be relatively small, automatic processing presumes the availability of an enormous amount of data. Robert Mercer, one of the pioneers of statistical translation,1 proclaimed: “There is no data like more data.” In other words, for Mercer as well as followers of the statistical approach, the best strategy for developing a system consists in accumulating as much data as possible. These data must be representative and diversified, but as these are qualitative criteria that are difficult to evaluate, it is the quantitative criterion that continues to prevail. In fact, it has been proven that the systems’ performance regularly improves as more bi-texts are available to develop it. “There is no data like more data.” [Robert Mercer] Availability of Parallel Corpora There are two major sources of bi-texts: on the one hand, corpora already available for two or more languages; the bi-texts may be aligned or not.

“Example-based machine translation.” Machine translation 14 (2): 113–157. Nano Gough and Andy Way (2004). “Robust large-scale EBMT with marker-based segmentation.” Proceedings of the Tenth International Conference on Theoretical and Methodological Issues in Machine Translation, 95–104. Baltimore, MD. Peter Brown, John Cocke, Stephen Della Pietra, Vincent Della Pietra, Frederick Jelinek, Robert Mercer, and Paul Roossin (1988). “A statistical approach to language translation.” In Proceedings of the Twelfth Conference on Computational Linguistics, Vol. 1, 71–76. Association for Computational Linguistics, Stroudsburg, PA. http://dx.doi.org/10.3115/991635.991651/. Peter F. Brown, John Cocke, Stephen A. Della Pietra, Vincent J. Della Pietra, Frederick Jelinek, John D. Lafferty, Robert L. Mercer, and Paul S.

See also Post-edition Mass-market applications of machine translation. See Machine translation market Mass media, 239 Mathematical model of communication. See Model of communication Meaning, 8, 15, 17–21, 34, 52–55, 64–67, 70–71, 171, 176–179, 182, 186–187, 193, 252 Mechanical brain, 45–46 Mechanical Translation (journal), 62 Mel’čuk, Igor, 69 Memorandum, Warren Weaver’s, 50, 52–59 Mercer, Robert, 93, 94, 166, 216, 258 Mersel, Jules, 76 Metal. See Machine translation systems Metaphysics, 179 Météo. See Machine translation systems Meteor. See Evaluation measure and test Michigan University, 81 Microsoft, 227–229, 240, 248–250 MIT, 60–62 Mobile application, 229, 232, 236, 240–243, 250, 256 Mobile device, 227, 236, 237, 240. See also Mobile phone Mobile Internet, 240, 249. See also Mobile phone Mobile phone, 232, 236, 240–243, 250, 256 Model of communication, 52, 55–56, 144 Morphologically-rich languages, 165, 211–218, 263 Morphology, 14, 51, 165, 214–216, 263.


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

Speech recognition software—the ability of computers to capture and translate exactly what humans say—was a lost cause for decades. The software that did exist for the purpose was buggy and often wildly inaccurate. But in the early 1990s, two scientists at IBM’s research center dove into computerized speech recognition and translation, a field that had long failed to produce anything robust enough to be used in everyday situations. Peter Brown and Robert Mercer started by working on programs that translated one language to another, starting with French to English. Most hackers working on the problem up to that point knew both languages and wrote programs that translated words directly: the English ham is, in French, jambon; cheese is, of course, fromage; and so on. But all languages are rife with exceptions, strange rules, and counterintuitive idioms and phrase that severely complicate writing translation algorithms.

His showbiz career also led him to the movies; he starred in 1974’s The Legend of Hillbilly John. When his Hollywood luck ran out, Capers spent the requisite years in school to become a psychiatrist. He met Kahler in San Diego, where he proved a quick learner of the theory Kahler, McGuire, and NASA had developed. 5. Sebastian Mallaby, More Money Than God: Hedge Funds and the Making of a New Elite (New York: Penguin Press, 2010). 6. Peter Brown, Robert Mercer, Stephen Della Pietra, and Vincent J. Della Pietra, “The Mathematics of Statistical Machine Translation: Parameter Estimation,” Journal of Computational Linguistics 19, no. 2 (1993): 263–311. 7. Ingfei Chen, “Scientist at Work: Nick Patterson,” New York Times, December 12, 2006. CHAPTER 8: WALL STREET VERSUS SILICON VALLEY 1. Rana Foroohar, “Wall Street: Aiding the Economic Recovery, or Strangling It?”

., 217–18 McCartney, Paul, 104, 105, 107 “In My Life” claimed by, 110–11 as math savant, 103 McCready, Mike, 78–83, 85–89 McGuire, Terry, 145, 168–72, 174–76 machine-learning algorithms, 79, 100 Magnetar Capital, 3–4, 10 Mahler, Gustav, 98 Major Market Index, 40, 41 Making of a Fly, The (Lawrence), prices of, 1–2 Malyshev, Mikhail, 190 management consultants, 189 margin, trading with, 51 market cap, price swings and, 49 market makers: bids and offers by, 35–36 Peterffy as, 31, 35–36, 38, 51 market risk, 66 Maroon 5, 85 Marseille, 147, 149 Marshall, Andrew, 140 Martin, George, 108–10 Martin, Max (Martin Sandberg), 88–89 math: behind algorithms, 6, 53 education in, 218–20 mathematicians: algorithms and, 6, 71 online, 53 on Wall Street, 13, 23, 24, 27, 71, 179, 185, 201–3 Mattingly, Ken, 167 MBAs: eLoyalty’s experience with, 187 Peterffy’s refusal to hire, 47 MDCT scans, 154 measurement errors, distribution of, 63 medical algorithms, 54, 146 in diagnosis and testing, 151–56, 216 in organ sharing, 147–51 patient data and home monitoring in, 158–59 physicians’ practice and, 156–62 medical residencies, game theory and matching for, 147 medicine, evidence-based, 156 Mehta, Puneet, 200, 201 melodies, 82, 87, 93 Mercer, Robert, 178–80 Merrill Lynch, 191, 192, 200 Messiah, 68 metal: trading of, 27 volatility of, 22 MGM, 135 Miami University, 91 Michigan, 201 Michigan, University of, 136 Microsoft, 67, 124, 209 microwaves, 124 Midas (algorithm), 134 Miller, Andre, 143 mind-reading bots, 178, 181–83 Minneapolis, Minn., 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: 276 words: 81,153

Outnumbered: From Facebook and Google to Fake News and Filter-Bubbles – the Algorithms That Control Our Lives by David Sumpter

affirmative action, Bernie Sanders, correlation does not imply causation, crowdsourcing, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Google Glasses, illegal immigration, Jeff Bezos, job automation, Kenneth Arrow, Loebner Prize, Mark Zuckerberg, meta analysis, meta-analysis, Minecraft, Nate Silver, natural language processing, Nelson Mandela, p-value, prediction markets, random walk, Ray Kurzweil, Robert Mercer, selection bias, self-driving car, Silicon Valley, Skype, Snapchat, speech recognition, statistical model, Stephen Hawking, Steven Pinker, The Signal and the Noise by Nate Silver, traveling salesman, Turing test

He claimed that he could use ‘hundreds and thousands of individual data points on our target audiences to understand exactly which messages are going to appeal to which audiences’ and implied that the methods he had described were being used by the Trump campaign. The origins of Cambridge Analytica has all the ingredients of a modern conspiracy story. It involves Ted Cruz, Donald Trump, data security, the psychology of personality, Facebook, underpaid Mechanical Turk workers, big data, Cambridge University academics, right-wing populist Steve Bannon who sits on the board, right-wing financier Robert Mercer who is one of its biggest investors, one-time national security advisor Michael Flynn who has acted as consultant, and (in some less reliable versions of the story) Russian-sponsored trolls. I can imagine it as a film with Jesse Eisenberg playing a psychologist who gradually uncovers the true motives of the company he works for: to manipulate our every emotion for political means. Looked at in this way, it is a frightening story.

‘Inferring negative emotion from mouse cursor movements.’ 12 Hehman, E., Stolier, R. M. and Freeman, J. B. 2015. ‘Advanced mouse-tracking analytic techniques for enhancing psychological science.’ Group Processes & Intergroup Relations 18, no. 3: 384–401. Chapter 5 : Cambridge Hyperbolytica 1 The most recent article had been the subject of a legal challenge by the company: www.theguardian.com/technology/2017/may/14/robert-mercer-cambridge-analytica-leave-eu-referendum-brexit-campaigns 2 This is, of course, an example of one such binary statement. They are hard to avoid. 3 https://d25d2506sfb94s.cloudfront.net/cumulus_uploads/document/0q7lmn19of/TimesResults_160613_EUReferendum_W_Headline.pdf 4 The opinion poll does not have the exact age of the people interviewed, so in fitting the model I assumed that each person had the median reported age.

Candid here, here Fair Housing Act (US) here fairness here fake news here, here, here feedback loops here MacronLeaks here post-truth world here, here, here false negatives here, here false positives here, here, here, here Fark here Feedly here Feller, Avi here Fergus, Rob here Ferrara, Emilio here filter bubbles here, here, here FiveThirtyEight here, here, here, here Flipboard here Flynn, Michael here football here, here robot players here, here Fortunato, Santo here, here Fowler, James here Franks, Nigel here Frostbite here Future of Life Institute here, here Gates, Bill here Gelade, Garry here gender bias here, here, here GloVe (global vectors for word representation) here Genter, Katie here Gentzkow, Matthew here, here Geoengineering Watch here, here Glance, Natalie here GloVe (global vectors for word representation) here Go here, here, here, here Goel, Sharad here Google here, here, here, here, here, here, here, here, here, here, here, here, here, here, here, here artificial intelligence (AI) here, here, here black hats here, here, here DeepMind here, here, here, here, here, here, here, here ‘Don’t be evil’ here Google autocomplete here, here Google News here Google Scholar here, here, here, here Google Search here Google+ here personalised adverts here, here, here, here SharedCount here Gore, Al here Grammatas, Angela here, here Guardian here, here, here, here, here, here, here, here, here, here, here, here, here, here Guardian US here, here h-index here, here Häggström, Olle here, here, here Here Be Dragons here Hassabis, Demis here, here, here Hawking, Stephen here, here, here He, Kaiming here Her here Higginson, Andrew here Hinton, Geoffrey here HotUKDeal here Huckfeldt, Bob here, here, here, here Huffington Post here, here, here Independent here Instagram here Internet here, here, here, here Internet service providers (ISPs) here Intrade here Ishiguro, Kazuo Never Let Me Go here iTunes here, here James Webb Sapce Telescope here Jie, Ke here job matching here Johansson, Joakim here, here Journal of Spatial Science here Kaminski, Juliane here Kasparov, Garry here, here Keith, David here Kerry, John here Keuschnigg, Marc here Kleinberg, Jon here Kluemper, Donald here Kogan, Alex here, here, here Kosinski, Michal here, here, here, here, here, here, here Kramer, Adam here, here Krizhevsky, Alex here Kulsrestha, Juhi here Kurzweil, Ray here Labour Party here, here Momentum here Lake, Brenden here language here Laue, Tim here Le Comber, Steve here Le Cun, Yan here Le Pen, Marine here Le, Quoc here Lerman, Kristina here, here, here Levin, Simon here Libratus here LinkedIn here, here, here, here literature here logic gates here Luntz, Frank here Machine Bias here Macron, Emmanuel here Major League Soccer (MLS) here, here Mandela effect here, here Mandela, Nelson here Martin, Erik here matchmaking here mathematics here, here assessing bias here mathematical models here, here, here power laws here Matrix, The here May, Lord Robert here McDonald, Glenn here, here Mechanical Turk here, here, here, here, here Medium here Mercer, Robert here Microsoft here, here, here, here, here, here Mikolov, Tomas here, here Minecraft here Mosseri, Adam here, here, here Mrsic-Flogel, Thomas here Ms Pac-Man here, here, here Munafò, Marcus here Musk, Elon here, here, here myPersonality project here National Health Service (NHS) here, here National Women’s Soccer League (NWSL) here, here Nature here, here, here Natusch, Waffles Pi here Netflix here neural networks here, here convolutional neural networks here limitations here recurrent neural networks here New York Times here, here, here, here, here, here, here, here The Upshot here, here news aggregators here Nix, Alexander here, here, here, here Noiszy here Northpointe here, here, here, here O’Neil, Cathy here Weapons of Math Destruction here Obama, Barack here, here Observer here online data collection here, here gender bias here preventing here principal component analysis (PCA) here online help services here OpenWorm here Overwatch here, here Pasquale, Frank The Black Box Society here, here Paul, Jake here, here, here, here Pennington, Jeffrey here personality analysis here Big Five here, here, here, here PewDiePie here Pierson, Emma here Pittsburgh Post-Gazette here political blogs here political discussions here, here, here PolitiFact here polls here, here, here, here Popular Mechanics here post-truth world here, here, here power laws here Pratt, Stephen here, here PredictIt here, here, here, here, here, here Prince here principal component analysis (PCA) here categorising personalities here COMPAS algorithm here probability distributions here ProPublica here, here, here, here, here, here Pundit here Q*bert 214, here Qualtrics here racial bias here, here, here, here, here GloVe (global vectors for word representation) here randomness here Reddit here, here, here, here, here regression models here, here Republican Party here, here, here, here, here RiceGum here, here Richardson, Kathleen here Road Runner here Robotank here, here robots here, here, here, here, here, here Russian interference here, here, here Salganik, Matthew here, here Sanders, Bernie here Scholz, Monika here Science here SCL here, here search histories here Silver, David here Silver, Nate here, here, here The Signal and the Noise here Silverman, Craig here Simonyan, Karen here singularity hypothesis here Skeem, Jen here Sky Sports here slime moulds (Physarum polycephulum) here, here, here Snapchat here Snopes here social feedback here Space Invaders here, here, here, here Spotify here, here, here, here, here, here, here Stack Exchange here StarCraft here statistics here, here, here, here, here regression models here, here Stillwell, David here, here Sullivan, Andrew here, here Sumpter, David Soccermatics here, here, here, here, here, here, here Sun, The here superforecasters here, here superintelligence here, here Szorkovszky, Alex here, here, here, here, here, here Taleb, Nassim here, here, here Tegmark, Max here, here, here, here Telegraph here, here, here, here Tesla here, here, here, here Tetlock, Philip here, here Texas, Virgil here, here, here The Gateway here TIDAL here Times, The here, here Tinder here, here, here Tolstoy, Leo here, here, here Anna Karenina here trolls here true positives here, here Trump, Donald here, here, here, here, here, here election campaign here, here, here, here, here, here election outcome here, here, here Twitter here, here TUI here, here Turing, Alan here Twitter here, here, here, here, here, here, here, here, here, here, here, here, here, here MacronLeaks here Tyson, Gareth here van Seijen, Harm here, here Vinyals, Oriol here vloggers here voter analysis here, here, here Wall Street Journal here Ward, Ashley here Washington Post here, here, here, here Watts, Duncan here, here Which?


pages: 382 words: 105,819

Zucked: Waking Up to the Facebook Catastrophe by Roger McNamee

4chan, Albert Einstein, algorithmic trading, AltaVista, Amazon Web Services, barriers to entry, Bernie Sanders, Boycotts of Israel, Cass Sunstein, cloud computing, computer age, cross-subsidies, data is the new oil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Electric Kool-Aid Acid Test, Elon Musk, Filter Bubble, game design, income inequality, Internet of things, Jaron Lanier, Jeff Bezos, John Markoff, laissez-faire capitalism, Lean Startup, light touch regulation, Lyft, Marc Andreessen, Mark Zuckerberg, market bubble, Menlo Park, Metcalfe’s law, minimum viable product, Mother of all demos, move fast and break things, move fast and break things, Network effects, paypal mafia, Peter Thiel, pets.com, post-work, profit maximization, profit motive, race to the bottom, recommendation engine, Robert Mercer, Ronald Reagan, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, Skype, Snapchat, social graph, software is eating the world, Stephen Hawking, Steve Jobs, Steven Levy, Stewart Brand, The Chicago School, Tim Cook: Apple, two-sided market, Uber and Lyft, Uber for X, uber lyft, Upton Sinclair, WikiLeaks, Yom Kippur War

The Guardian story opened with a bang: The data analytics firm that worked with Donald Trump’s election team and the winning Brexit campaign harvested millions of Facebook profiles of US voters, in one of the tech giant’s biggest ever data breaches, and used them to build a powerful software program to predict and influence choices at the ballot box. A whistleblower has revealed to the Observer how Cambridge Analytica—a company owned by the hedge fund billionaire Robert Mercer, and headed at the time by Trump’s key adviser Steve Bannon—used personal information taken without authorisation in early 2014 to build a system that could profile individual US voters, in order to target them with personalised political advertisements. Christopher Wylie, who worked with a Cambridge University academic to obtain the data, told the Observer: “We exploited Facebook to harvest millions of people’s profiles.

In the world of market research, there is considerable doubt about how well psychographics work in their current form, but that issue did not prevent Cambridge Analytica from finding clients, mostly on the far right. To serve the US market, SCL needed to obey federal election laws. It created a US affiliate staffed by US citizens and legal residents. Reports indicated that Cambridge Analytica took a casual approach to regulations. The team of Robert Mercer and Steve Bannon financed and organized Cambridge Analytica, with Alexander Nix as CEO. The plan was to get into the market within a few months, test capabilities during the 2014 US midterm elections, and, if successful, transform American politics in 2016. To be confident that their models would work, Nix and his team needed a ton of data. They needed to create a giant data set of US voters in a matter of months and turned to Kogan to get one.

Kaiser’s experience in the Obama campaign appealed to Nix, who made a case that the next big market opportunity would be to help Republicans catch up to the Democrats in data analytics. Kaiser transferred into Cambridge Analytica and went to work bringing in clients. Her early clients were in Africa, but in 2015 she and Nix shifted their focus to the United States in anticipation of the presidential election cycle. Kaiser asserted that Nix was not a political ideologue—unlike his patrons Robert Mercer and Steve Bannon—and hoped to create a “famous advertising company in the US market.” As quoted in The Guardian: “Corporations like Google, Facebook, Amazon, all of these large companies, are making tens or hundreds of billions of dollars off of monetising people’s data,” Kaiser says. “I’ve been telling companies and governments for years that data is probably your most valuable asset. Individuals should be able to monetise their own data—that’s their own human value—not to be exploited.”


pages: 399 words: 114,787

Dark Towers: Deutsche Bank, Donald Trump, and an Epic Trail of Destruction by David Enrich

Affordable Care Act / Obamacare, anti-globalists, Asian financial crisis, banking crisis, Berlin Wall, buy low sell high, collateralized debt obligation, commoditize, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, Donald Trump, East Village, estate planning, Fall of the Berlin Wall, financial innovation, forensic accounting, high net worth, housing crisis, interest rate derivative, interest rate swap, Jeffrey Epstein, London Interbank Offered Rate, Lyft, Mikhail Gorbachev, NetJets, obamacare, offshore financial centre, post-materialism, Ralph Waldo Emerson, Renaissance Technologies, risk tolerance, Robert Mercer, rolodex, sovereign wealth fund, too big to fail, transcontinental railway, yield curve

Since the late 1990s, Deutsche had been peddling products to hedge funds, including the enormous Renaissance Technologies, that helped them avoid taxes. Founded by a former government code-breaker, Renaissance specialized in using computer programs to scout out tiny market inefficiencies that could be exploited. The firm recruited engineers and mathematicians, including an IBM programmer named Robert Mercer, a right-wing zealot who once noted that he enjoyed spending time with cats more than with people. Mercer eventually rose to the top of Renaissance, helping it become one of the world’s most successful hedge funds. Renaissance was always looking for a new, sharper edge, and that’s where Deutsche came in. The bank hatched a plan in which Renaissance parked billions of dollars of securities and other assets with Deutsche.

After the meeting, as the attorneys were in a Heathrow lounge awaiting their flight back to New York, Bill phoned one of them. He peppered the lawyer with questions about what he needed to do next. The way Broeksmit talked, he seemed to be bracing for a world of shit to land on him at any moment. The Senate’s report was unveiled with fanfare in July 2014, paired with congressional hearings—just as the co-head of Renaissance, Robert Mercer, was beginning to bankroll a series of right-wing initiatives, such as Breitbart News, aimed at upending the Western political order. Senator Carl Levin, the chairman of the investigations committee, convened the first public hearing at 9:30 one July morning in the Hart Senate Office Building. Levin and Roach had plotted lines of questioning with the witnesses. One of them was Deutsche’s Satish Ramakrishna.

Simpson texted Val an American Express card number to book plane tickets and asked him to start searching for some specific topics. “Any Russia stuff at all,” Simpson requested. He added that he was eager for emails or documents related to Renaissance Technologies—the huge hedge fund that Deutsche had worked with to help save it billions in taxes. Simpson was especially curious about any materials on Renaissance’s enigmatic leader, Robert Mercer, who along with his daughter Rebekah had become a leading financier of Trump, Steve Bannon, and Breitbart News. “Be safe and I will see you tomorrow,” Simpson signed off. The weather in Saint Thomas was balmy, and Val and Glenn alternated between sifting through Bill’s files in a hotel suite and sitting at a picnic table on the beach, drinking beers and smoking cigarettes. Simpson was slightly manic, chattering constantly about Trump and Fusion’s financial struggles and the high likelihood that, at that very moment, they were under government surveillance.


pages: 301 words: 85,263

New Dark Age: Technology and the End of the Future by James Bridle

AI winter, Airbnb, Alfred Russel Wallace, Automated Insights, autonomous vehicles, back-to-the-land, Benoit Mandelbrot, Bernie Sanders, bitcoin, British Empire, Brownian motion, Buckminster Fuller, Capital in the Twenty-First Century by Thomas Piketty, carbon footprint, cognitive bias, cognitive dissonance, combinatorial explosion, computer vision, congestion charging, cryptocurrency, data is the new oil, Donald Trump, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, drone strike, Edward Snowden, fear of failure, Flash crash, Google Earth, Haber-Bosch Process, hive mind, income inequality, informal economy, Internet of things, Isaac Newton, John von Neumann, Julian Assange, Kickstarter, late capitalism, lone genius, mandelbrot fractal, meta analysis, meta-analysis, Minecraft, mutually assured destruction, natural language processing, Network effects, oil shock, p-value, pattern recognition, peak oil, recommendation engine, road to serfdom, Robert Mercer, Ronald Reagan, self-driving car, Silicon Valley, Silicon Valley ideology, Skype, social graph, sorting algorithm, South China Sea, speech recognition, Spread Networks laid a new fibre optics cable between New York and Chicago, stem cell, Stuxnet, technoutopianism, the built environment, the scientific method, Uber for X, undersea cable, University of East Anglia, uranium enrichment, Vannevar Bush, WikiLeaks

As one Russian activist described it, ‘The point is to spoil it, to create the atmosphere of hate, to make it so stinky that normal people won’t want to touch it.’23 Unidentified forces have influenced other elections too, each laced with conspiracy and paranoia. In the run-up to the EU referendum in the United Kingdom, a fifth of the electorate believed that the poll would be rigged in collusion with the security services.24 Leave campaigners advised voters to take pens with them to vote, in order to ensure pencil votes weren’t erased.25 In the aftermath, attention focused on the work of Cambridge Analytica – a company owned by Robert Mercer, former AI engineer, hedge fund billionaire and Donald Trump’s most powerful supporter. Cambridge Analytica’s employees have described what they do as ‘psychological warfare’ – leveraging vast amounts of data in order to target and persuade voters. And of course it turned out that the election really was rigged by the security services, in the way that rigging actually happens: the board and staff of Cambridge Analytica, which ‘donated’ its services to the Leave campaign, includes former British military personnel – notably the former director of psychological operations for British forces in Afghanistan.26 In both the EU referendum and the US election, military contractors used military intelligence technologies to influence democratic elections in their own countries.

., 11, 249 ‘low-hanging fruit,’ 93–4 M Macedonia, 233–4 machine learning algorithms, 222 machine thought, 146 machine translation, 147 magnetism, 77 Malaysian Airlines, 66 manganese noodles, 163–4 Manhattan Project, 24–30, 248 Mara, Jane Muthoni, 170 Mark I Perceptron, 136–8, 137 Maslow’s hierarchy of needs, 128–9 Matthews, James Tilly, 208–10, 209 Mauro, Ian, 199 McCarthy, Joe, 205 McGovern, Thomas, 57–8 McKay Brothers, 107, 110 memex, 24 Mercer, Robert, 236 Merkel, Angela, 174 metalanguage, 3, 5 middens, 56 migrated archive, 170–1 Minds, 150 miniaturisation principle, 81 Mirai, 129 mobile phones, 126 The Modern Prometheus (Shelley), 201 monoculture, 55–6 Moore, Gordon, 80, 80, 83 Moore’s law, 80–3, 92–4 Mordvintsev, Alexander, 154 Morgellons, 211, 214 Morrison, Grant The Invisibles, 196–7 Morton, Timothy, 73, 194 Mount Tambora, eruption of, 201 Moynihan, Daniel Patrick, 169 Munch, Edvard The Scream, 202 Mutua, Ndiku, 170 N NarusInsight, 177 NASA Ames Advanced Concepts Flight Simulator, 42 Natanz Nuclear Facility, 129 National Centre for Atmospheric Science, 68–9 National Geospatial-Intelligence Agency, 243 National Health Service (NHS), 110 National Mining Association, 64 National Reconnaissance Office, 168, 243 National Security Agency (NSA), 167, 174, 177–8, 183, 242–3, 249–50 National Security Strategy, 59 natural gas, 48 neoliberalism, 138–9 network, 5, 9 networks, 249 Newton, Isaac, 78 NewYorkTimesPolitics.com, 221 New York World’s Fair, 30–1 NHS (National Health Service), 110 9/11 terrorist attacks, 203–4, 206 ‘Nine Eyes,’ 174 1984 (Orwell), 242 NORAD (North American Air Defense Command), 33 North American Air Defense Command (NORAD), 33 ‘The Nor’ project, 104 Not Aviation, 190–1 NSA (National Security Agency), 167, 174, 177–8, 183, 242–3, 249–50 nuclear fusion, 97–8, 100 nuclear warfare, 28 Numerical Prediction (Richardson), 45 Nyingi, Wambugu Wa, 170 Nzili, Paulo Muoka, 170 O Obama, Barack, 180, 206, 231 Official Secrets Act, 189 Omori, Fusakichi, 145 Omori’s Law, 145 Operation Castle, 97 Operation Legacy, 171–2 Optic Nerve programme, 174 Optometrist Algorithm, 99–101, 160 O’Reilly, James, 185–6 Orwell, George 1984, 242 ‘Outline of Weather Proposal’ (Zworykin), 25–6 P Paglen, Trevor, 144 ‘paranoid style,’ 205–6 Patriot Act, 178 Penrose, Roger, 20 Perceptron, 136–8, 137 permafrost, 47–9, 56–7 p-hacking, 89–91 Phillippi, Harriet Ann, 165 photophone, 19–20 Pichai, Sundar, 139 Piketty, Thomas Capital in the Twenty-First Century, 112 Pincher, Chapman, 175–6 Pitt, William, 208 Plague-Cloud, 195, 202 Poitras, Laura, 175 Polaroid, 143 ‘predictive policing’ systems, 144–6 PredPol software, 144, 146 Priestley, Joseph, 78, 208, 209 prion diseases, 50, 50–1 PRISM operation, 173 product spam, 125–6 Project Echelon, 190 Prometheus, 132–4, 198 psychogeography, 103 public key cryptography, 167–8 pure language, 156 Putin, Vladimir, 235 Pynchon, Thomas Gravity’s Rainbow, 128 Q Qajaa, 56, 57 quality control failure of, 92–3 in science, 91 Quidsi, 113–4 R racial profiling, 143–4 racism, 143–4 ‘radiation cats,’ 251 raw computing, 82–3 Reagan, Ronald, 36–7 Reed, Harry, 29 refractive index of the atmosphere, 62 Regin malware, 175 replicability, 88–9 Reproducibility Project, 89 resistance, modes of, 120 Reuter, Paul, 107 Review Group on Intelligence and Communications Technologies, 181 Richardson, Lewis Fry, 20–1, 29, 68 Numerical Prediction, 45 Weather Prediction by Numerical Process, 21–3 Richardson number, 68 The Road to Serfdom (Hayek), 139 Robinson, Kim Stanley Aurora, 128 robots, workers vs., 116 ‘Rogeting,’ 88 Romney, Mitt, 206–7 Rosenblatt, Frank, 137 Roy, Arundhati, 250 Royal Aircraft Establishment, 188–9 Ruskin, John, 17–20, 195, 202 Rwanda, 243, 244, 245 S Sabetta, 48 SABRE (Semi-Automated Business Research Environment), 35, 38 SAGE (Semi-Automatic Ground Environment), 33, 34, 35 Samsung, 127 Scheele, Carl Wilhelm, 78 Schmidt, Eric, 241–5 The Scream (Munch), 202 Sedol, Lee, 149, 157–8 seed banks, 52–6 Seed Vault, 55 seismic sensors, 48 self-excitation, 145 ‘semantic analyser,’ 177 Semi-Automated Business Research Environment (SABRE), 35, 38 Semi-Automatic Ground Environment (SAGE), 33, 34, 35 semiconductors, 82 The Sensory Order: An Inquiry into the Foundations of Theoretical Psychology (Hayek), 138–9 Shelley, Mary Frankenstein, 201 The Modern Prometheus, 201 SIGINT Seniors Europe, 174 simulation, conflating approximation with, 34–5 Singapore Exchange, 122–3 smart products, 127–8, 131 Smith, Robert Elliott, 152 smoking gun, 183–4, 186 Snowden, Edward, 173–5, 178 software about, 82–3 AlphaGo, 149, 156–8 Assistant, 152 AutoAwesome, 152 DeepFace, 140 Greyball programme, 119, 120 Hippo programme, 32 How-Old.net facial recognition programme, 141 Optic Nerve programme, 174 PredPol, 144, 146 Translate, 146 Solnit, Rebecca, 11–2 solutionism, 4 space telescopes, 168–9 speed of light, 107 Spread Networks, 107 SSEC (IBM Selective Sequence Electronic Calculator), 30, 30–2, 31, 146 Stapel, Diederik, 87–8 Stapledon, Olaf, 20 steam engines, 77 Stellar Wind, 176 Stewart, Elizabeth ‘Betsy,’ 30–1, 31 Steyerl, Hito, 126 stock exchanges, 108 ‘The Storm-Cloud of the Nineteenth Century’ lecture series, 17–9 Stratus homogenitus, 195–6 studios, 130 Stuxnet, 129–30 surveillance about, 243–4 complicity in, 185 computational excesses of, 180–1 devices for, 104 Svalbard archipelago, 51–2, 54 Svalbard Global Seed Vault, 52–3 Svalbard Treaty (1920), 52 Swiss National Bank, 123 Syed, Omar, 158–9 systemic literacy, 5–6 T Taimyr Peninsula, 47–8 Targeted Individuals, 210–1 The Task of the Translator (Benjamin), 147, 155–6 TCP (Transmission Control Protocol), 79 technology acceleration of, 2 complex, 2–3 opacity of, 119 Teletubbies, 217 television, children’s, 216–7 Tesco Clubcard, 245 thalidomide, 95 Thatcher, Margaret, 177 theory of evolution, 78 thermal power plants, 196 Three Guineas (Woolf), 12 Three Laws of Robotics (Asimov), 157 Tillmans, Wolfgang, 71 tools, 13–4 To Photograph the Details of a Dark Horse in Low Light exhibition, 143 totalitarianism, collectivism vs., 139 Toy Freaks, 225–6 transistors, 79, 80 Translate software, 146 translation algorithms, 84 Transmission Control Protocol (TCP), 79 Tri Alpha Energy, 98–101 Trinity test, 25 trolling, 231 Trump, Donald, 169–70, 194–5, 206, 207, 236 trust, science and, 91 trusted source, 220 Tuktoyaktuk Peninsula, 49 turbulence, 65–9 tyranny of techne, 132 U Uber, 117–9, 127 UberEats app, 120–1 unboxing videos, 216, 219 United Airlines, 66–7 Uniting and Strengthening America by Fulfilling Rights and Ending Eavesdropping, Dragnet-collection and Online Monitoring Act (USA FREEDOM Act), 178 USA FREEDOM Act (2015), 178 US Drug Efficacy Amendment (1962), 95 V van Helden, Albert, 102 Veles, objectification of, 235 Verizon, 173 VHF omnidirectional radio range (VOR) installations, 104 Vigilant Telecom, 110–1 Volkswagen, 119–20 von Neumann, John about, 25 ‘Can We Survive Technology?


pages: 282 words: 81,873

Live Work Work Work Die: A Journey Into the Savage Heart of Silicon Valley by Corey Pein

23andMe, 4chan, affirmative action, Affordable Care Act / Obamacare, Airbnb, Amazon Mechanical Turk, Anne Wojcicki, artificial general intelligence, bank run, barriers to entry, Benevolent Dictator For Life (BDFL), Bernie Sanders, bitcoin, Build a better mousetrap, California gold rush, cashless society, colonial rule, computer age, cryptocurrency, data is the new oil, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Extropian, gig economy, Google bus, Google Glasses, Google X / Alphabet X, hacker house, hive mind, illegal immigration, immigration reform, Internet of things, invisible hand, Isaac Newton, Jeff Bezos, job automation, Kevin Kelly, Khan Academy, Law of Accelerating Returns, Lean Startup, life extension, Lyft, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Menlo Park, minimum viable product, move fast and break things, move fast and break things, mutually assured destruction, obamacare, passive income, patent troll, Paul Graham, peer-to-peer lending, Peter H. Diamandis: Planetary Resources, Peter Thiel, platform as a service, plutocrats, Plutocrats, Ponzi scheme, post-work, Ray Kurzweil, regulatory arbitrage, rent control, RFID, Robert Mercer, rolodex, Ronald Reagan, Ross Ulbricht, Ruby on Rails, Sam Altman, Sand Hill Road, Scientific racism, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, Skype, Snapchat, social software, software as a service, source of truth, South of Market, San Francisco, Startup school, stealth mode startup, Steve Jobs, Steve Wozniak, TaskRabbit, technological singularity, technoutopianism, telepresence, too big to fail, Travis Kalanick, tulip mania, Uber for X, uber lyft, ubercab, upwardly mobile, Vernor Vinge, X Prize, Y Combinator

“It was late at night. I quickly typed it off,” he told the blogger Tyler Cowen in a 2015 interview. All the same, Thiel said, “You could never disown anything that you’ve written.” * * * The rise of the neoreactionaries was not exclusively a coup orchestrated from above with the help of powerful, well-connected hyperlibertarians like Thiel, Patri Friedman, and Trump’s campaign financier, the tech billionaire Robert Mercer. It was also a movement from below, embraced by thousands—and eventually perhaps by millions—of disaffected young people. While the neoreactionaries expounded at tiresome length about their aims, they revealed their individual motivations only in glimpses. Justine Tunney, the Google engineer–cum–Moldbug booster, provided one such peek inside the neoreactionary mind. In a 2014 blog post addressing “Silicon Valley and geeks in general,” Tunney called for a new “Nerd Nationalism” motivated primarily by personal resentment.

See PewDiePie Klein, Michael Klein, Roxanne Kleiner Perkins Kurzweil, Ray Laborize Land, Nick Lee, Rhoda Lifeboat Foundation Lifehacker Lifograph LinkedIn Lockheed Lockheed Martin Lombardi, Steven Lucas, George Luckey, Palmer Lyft Machine Intelligence Research Institute MacLeod, Ken Marshall, Brad Mason, Andrew McCauley, Raymond MCI Communications Mechanical Turk Meetup.com Mercer, Robert Microsoft Millionaires Society Miner, Bob Mishra, Pankaj Modi, Narendra Moldbug, Mencius. See Yarvin, Curtis Guy Monkeywrench International Moore, Gordon More, Max More Right Moritz, Michael Morozov, Evgeny Mossberg, Walt Muck Rack Musk, Elon Myers, PZ MySocialPetwork.com Nail, Rob NASA National Review National Science Foundation NerdWallet Netflix Netscape Newbridge Capital Newsweek New Yorker New York Times Nike Nimoy, Leonard Nissan Northrup Grumman Obama, Barack Odierno, Raymond Omni OpenBazaar Operation SLOG Oracle Othman, Ghazi Ben Outbrain Page, Larry Palantir Pando Pandora Patchwork Paul, Terry PayPal Paytm Pelosi, Nancy PepsiCo Petbu PewDiePie PharmaBot Plouffe, David Polous, James Poole, Chris Prabhakar, Hitha Procter & Gamble Product Hunt Quinn, Zoe Rand, Ayn Reagan, Ronald Reddit RentAFriend.com Reuters Rodger, Elliot Roof, Dylann Runway Sacks, David SAIC Samsung Sanders, Bernie San Francisco Chronicle San Jose Mercury News Sarkeesian, Anita SBIR Scalia, Antonin Schmidt, Eric Schulte, Todd Seasteading Institute SENS Research Foundation Sequoia Capital SF Weekly Shockley, William Silicon Valley Index Silk Road Singularity Singularity Is Near, The (Kurzweil) Singularity Summit Singularity University Sjöblad, Hannes Skinner, B.


pages: 307 words: 88,180

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

AI winter, Airbnb, Albert Einstein, algorithmic trading, artificial general intelligence, autonomous vehicles, barriers to entry, basic income, business cycle, cloud computing, commoditize, computer vision, corporate social responsibility, creative destruction, crony capitalism, Deng Xiaoping, deskilling, Donald Trump, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, full employment, future of work, gig economy, Google Chrome, happiness index / gross national happiness, if you build it, they will come, ImageNet competition, income inequality, informal economy, Internet of things, invention of the telegraph, Jeff Bezos, job automation, John Markoff, Kickstarter, knowledge worker, Lean Startup, low skilled workers, Lyft, mandatory minimum, Mark Zuckerberg, Menlo Park, minimum viable product, natural language processing, new economy, pattern recognition, pirate software, profit maximization, QR code, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, risk tolerance, Robert Mercer, Rodney Brooks, Rubik’s Cube, Sam Altman, Second Machine Age, self-driving car, sentiment analysis, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, Skype, special economic zone, speech recognition, Stephen Hawking, Steve Jobs, strong AI, The Future of Employment, Travis Kalanick, Uber and Lyft, uber lyft, universal basic income, urban planning, Y Combinator

Using internet AI, Alibaba can recommend products you’re more likely to buy, Google can target you with ads you’re more likely to click on, and YouTube can suggest videos that you’re more likely to watch. Adopting those same methods in a different context, a company like Cambridge Analytica used Facebook data to better understand and target American voters during the 2016 presidential campaign. Revealingly, it was Robert Mercer, founder of Cambridge Analytica, who reportedly coined the famous phrase, “There’s no data like more data.” ALGORITHMS AND EDITORS First-wave AI has given birth to entirely new, AI-driven internet companies. China’s leader in this category is Jinri Toutiao (meaning “today’s headlines”; English name: “ByteDance”). Founded in 2012, Toutiao is sometimes called “the BuzzFeed of China” because both sites serve as hubs for timely viral stories.

See also algorithms, AI chips and, 96 data and, 56 deep learning as part of, 6, 94 economy driven by, 25, 84, 91, 94–95 social investment stipend and, 221–22 machine reading, 161 machine translation, 104, 161 Manhattan Project, 85 Manpower, 47–48 market-driven startups, 26–27, 45 mass entrepreneurship and mass innovation, 54, 62–68, 99 McAfee, Andrew, 148–49, 150 McCarthy, John, 7 McKinsey Global Institute, 159–60 medical diagnosis, 113–15, 167, 195, 211. See also healthcare Meituan (Groupon clone), 23–24, 46–49, 72 Meituan Dianping, 49, 69, 70, 72 Mercer, Robert, 108 Messenger, 70 Mi AI speaker, 127 micro-finance, 112–13, 138 Microsoft AI chips and, 96 antitrust policy and, 28 China at time of founding of, 33 as dominant AI player, 83, 91 Face++ and, 90 Lee at, 28, 33, 105, 184 speech recognition, 93 Tencent and, 93 top researchers at, 93 Microsoft Research, 91 Microsoft Research Asia (formerly Microsoft Research China), 89–90, 105 Middle East, 137, 139, 169 mini-iPhones, 32 Minsky, Marvin, 7 mission-driven startups, 26, 45 MIT, 30 Mobike, 77–78 mobile payments, 16, 54–55, 60–61, 73–78, 79, 110 Momenta, 135 monopolies, 20, 96, 168–69, 170–71, 172, 229 Moravec, Hans, 166 Moravec’s Paradox, 166–67 Musical.ly, 109 Musk, Elon, 49, 131, 141 N Nanjing, China, 99 narrow AI, 10, 142 National Aeronautics and Space Administration (NASA), 3 natural-language processing, 105, 108, 115 Netherlands, 229 neural networks approach to AI, 7, 8–10, 89 new world order, 18–19, 20–21, 138–39 Ng, Andrew, 13, 44, 88, 93, 113–14, 144 99 Taxi, 137 Nixon, Richard, 207 North Africa, 138 Nuance, 105 Nuomi (group buying affiliate), 48–49 Nvidia, 96, 97, 135 O Obama, Barack, 97–98, 100, 104 object recognition, 9, 90, 94, 117.


pages: 706 words: 202,591

Facebook: The Inside Story by Steven Levy

active measures, Airbnb, Airbus A320, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, augmented reality, Ben Horowitz, blockchain, Burning Man, business intelligence, cloud computing, computer vision, crowdsourcing, cryptocurrency, don't be evil, Donald Trump, East Village, Edward Snowden, El Camino Real, Elon Musk, Firefox, Frank Gehry, glass ceiling, indoor plumbing, Jeff Bezos, John Markoff, Jony Ive, Kevin Kelly, Kickstarter, Lyft, Mahatma Gandhi, Marc Andreessen, Mark Zuckerberg, Menlo Park, Metcalfe’s law, MITM: man-in-the-middle, move fast and break things, move fast and break things, natural language processing, Network effects, Oculus Rift, PageRank, Paul Buchheit, paypal mafia, Peter Thiel, pets.com, post-work, Ray Kurzweil, recommendation engine, Robert Mercer, Robert Metcalfe, rolodex, Sam Altman, Sand Hill Road, self-driving car, sexual politics, Shoshana Zuboff, side project, Silicon Valley, Silicon Valley startup, slashdot, Snapchat, social graph, social software, South of Market, San Francisco, Startup school, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, Tim Cook: Apple, web application, WikiLeaks, women in the workforce, Y Combinator, Y2K

Not long after, Wylie met hard-core conservative warrior Steve Bannon, then editing the notoriously partisan right-wing news site Breitbart. Somehow the gay nerd and the proto–white nationalist bonded. “It felt like we were flirting,” Wylie would later write about their data-wonky intellectual jam sessions. Soon they were hatching a plan for SCL to enter America. Bannon set up a meeting with a wealthy funder of right-wing causes named Robert Mercer. Before making his fortune in hedge funds, Mercer had been a celebrated IBM researcher, so SCL’s promise to change voting behavior resonated with him. He agreed to fund the subsidiary. In December 2013, “Cambridge Analytica” was registered in Delaware. The name came from Bannon, who liked the implication that it was involved with the university. Cambridge Analytica began devising a plan to sell services to Republican candidates, with the flagship project a Wylie plan named Project Ripon.

Davies contacted SCL and asked about its relationship with Kogan, but got no answer. (Later, a Cambridge Analytica executive would explain that the team was headed to a party in Washington, DC, and the random person left in the office hung up on Davies.) He put the story aside. But in the fall of 2015, Davies came across a Politico article that explained the relationship between SCL and Cambridge Analytica, the connection to Robert Mercer, and that the Ted Cruz presidential campaign was using the data. Davies dug back into the documents and in spare time from his researching duties, put together the story: how Kogan had gathered the data for a research project and then, violating Facebook’s standards, sold it to Cambridge Analytica. The Cruz campaign insisted that all was kosher. “My understanding is all the information is acquired legally and ethically with the permission of the users when they sign up to Facebook,” said a spokesperson.

A feature writer and investigative journalist known for deep dives into her topics, often with a participatory twist (like working in an Amazon warehouse), Cadwalladr had become fascinated with what she perceived as the pernicious influence of big tech companies. In 2016, she began investigating Cambridge Analytica. She wrote a series of articles about the company—its involvement in Brexit, its methods, its ties to Robert Mercer and the ultraconservative movement that had backed Trump. And the Facebook data that Kogan had been called out for in December 2015. She lit on Wylie as the key to the story. When she first contacted him in March 2017, he was wary, but eventually he handed over documents that helped inform her stories. But Cadwalladr wanted him. If Wylie cooperated fully, and told the Cambridge story from his point of view, it would be more compelling.


Falter: Has the Human Game Begun to Play Itself Out? by Bill McKibben

23andMe, Affordable Care Act / Obamacare, Airbnb, American Legislative Exchange Council, Anne Wojcicki, artificial general intelligence, Bernie Sanders, Bill Joy: nanobots, Burning Man, call centre, carbon footprint, Charles Lindbergh, clean water, Colonization of Mars, computer vision, David Attenborough, Donald Trump, double helix, Edward Snowden, Elon Musk, ending welfare as we know it, energy transition, Flynn Effect, Google Earth, Hyperloop, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), Jane Jacobs, Jaron Lanier, Jeff Bezos, job automation, life extension, light touch regulation, Mark Zuckerberg, mass immigration, megacity, Menlo Park, moral hazard, Naomi Klein, Nelson Mandela, obamacare, off grid, oil shale / tar sands, pattern recognition, Peter Thiel, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, Ray Kurzweil, Robert Mercer, Ronald Reagan, Sam Altman, self-driving car, Silicon Valley, Silicon Valley startup, smart meter, Snapchat, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, supervolcano, technoutopianism, The Wealth of Nations by Adam Smith, traffic fines, Travis Kalanick, urban sprawl, Watson beat the top human players on Jeopardy!, Y Combinator, Y2K, yield curve

In fact, he promised, “everybody’s going to be taken care of much better than they’re taken care of now.” But as we now know, Trump was simply bringing something new to the game: not clever messaging, but brazen lying. And arriving in Washington with no existing ideology except feeding his narcissism and enriching his family, Trump proved the perfect president finally to enact the full government-hating agenda. Robert Mercer, who’d funded not only Trump’s campaign but also Cambridge Analytica, the source of so much Facebook skullduggery, was a key figure—and a classic Randian. As one colleague explained, “Bob believes that human beings have no inherent value other than how much money they make. A cat has value, he’s said, because it provides pleasure to humans. But if someone is on welfare[,] he has negative value.

Koch, Mary Koch, William “Bill” Kodas, Michael Kona Korea Krueger, Alan Kumkum Bhagya (soap opera) Kurzweil, Fredric Kurzweil, Ray Kyoto Protocol labor law labor unions Lahore, Pakistan laissez-faire Lanier, Jaron Las Vegas lead poisoning Leap Manifesto Lear, Norman Leary, Timothy Lee, Kai-fu LeFevre, Robert leukemia leverage Lewis, Seko Serge Lexington, Battle of libertarianism Libertarian Party life expectancy lightning strikes limestone limits Limits to Growth, The (Meadows) Lindbergh, Charles lobster fisheries Locklear, Samuel Lomé, Togo London Los Angeles Los Angeles Times Louisiana Lovelock, James Lowndes County, Alabama Luntz, Frank Lyme disease Machine Intelligence Research Institute MacLean, Nancy Maduro malaria Mallory, George Maltese Falcon (yacht) Mann, Michael Manson, Charles manufacturing MAOA gene variant marine species Maris, Bill markets marlin Mars Marsh, George Perkins Marshall Islands mass extinctions Matchright maturity Mauryan Empire Mayans Mayer, Jane McArthur Forest Fire Danger Index McCain, John Medicaid Medicare Megafire (Kodas) Mehlman, Maxwell Mekong Delta meltwater pulse 1A Mercer, Robert Merkle, Ralph Merritt Island Wildlife Refuge methane Mexico Miami Beach Microsoft migration Mill, John Stuart Miller, Dean Milner, Yuri Milosevic, Slobodan Minsky, Marvin Mises, Ludwig von Mississippi Delta MIT Technology Review Mongolia Monsanto Montgomery Bus Boycott Mont Pelerin movement Montreal Montreal Protocol More, Max mortality Moses, Robert Mount Kenya MSTN gene Muir, John Mumbai Murdoch, Rupert Musk, Elon Nabokov, Vladimir nanobot blood cells National Academy of Medicine National Academy of Sciences National Aeronautics and Space Administration (NASA) National Cancer Institute National Coal Association National Energy Policy Act (proposed) National Geographic National Governors Association National Journal National Oceanic and Atmospheric Administration (NOAA) national parks and monuments Native Americans natural gas Nature Nawabshah, Pakistan Nazi Germany Nectome neoliberalism Neolithic period Nepal Netherlands New Deal NewsCorp New York New Yorker New York Times Magazine Nietzsche, Friedrich Niviana, Aka Nixon, Richard Nokia nonviolence North America North American Free Trade Agreement (NAFTA) North Carolina North Korea Norway Novartis nuclear power Nuclear Test Ban Treaty nuclear weapons nutrition Obama, Barack Obamacare Objectivist oceans.


pages: 349 words: 98,868

Nervous States: Democracy and the Decline of Reason by William Davies

active measures, Affordable Care Act / Obamacare, Amazon Web Services, bank run, banking crisis, basic income, business cycle, Capital in the Twenty-First Century by Thomas Piketty, citizen journalism, Climategate, Climatic Research Unit, Colonization of Mars, continuation of politics by other means, creative destruction, credit crunch, decarbonisation, deindustrialization, discovery of penicillin, Dominic Cummings, Donald Trump, drone strike, Elon Musk, failed state, Filter Bubble, first-past-the-post, Frank Gehry, gig economy, housing crisis, income inequality, Isaac Newton, Jeff Bezos, Johannes Kepler, Joseph Schumpeter, knowledge economy, loss aversion, low skilled workers, Mahatma Gandhi, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, Mont Pelerin Society, mutually assured destruction, Northern Rock, obamacare, Occupy movement, pattern recognition, Peace of Westphalia, Peter Thiel, Philip Mirowski, planetary scale, post-industrial society, quantitative easing, RAND corporation, Ray Kurzweil, Richard Florida, road to serfdom, Robert Mercer, Ronald Reagan, sentiment analysis, Silicon Valley, Silicon Valley startup, smart cities, statistical model, Steve Jobs, the scientific method, Turing machine, Uber for X, universal basic income, University of East Anglia, Valery Gerasimov, We are the 99%, WikiLeaks, women in the workforce, zero-sum game

Later in his life, Hayek was entirely open about his sympathy for intergenerational inequalities, hinting at a eugenicist justification for inheritance: there are some socially valuable qualities which will be rarely acquired in a single generation but which will generally be formed only by the continuous efforts of two or three. . . . Granted this, it would be unreasonable to deny that a society is likely to get a better elite if ascent is not limited to one generation, if individuals are not deliberately made to start from the same level.27 In our new age of extreme personal wealth, billionaire owners of private companies such as the Koch brothers or Robert Mercer, the hedge-fund billionaire who has backed various populist and alt-right campaigns including Breitbart media, have huge political autonomy, without needing to be public about how they’re using it. Facebook and Google are now listed on the stock market, yet their founders retain majority shareholding rights. The family becomes the most important political and economic institution for these new oligarchs, and will ensure that extremes of inequality outlive them.

., Martin Luther, 21, 224 knowledge economy, 84, 85, 88, 151–2, 217 known knowns, 132, 138 Koch, Charles and David, 154, 164, 174 Korean War (1950–53), 178 Kraepelin, Emil, 139 Kurzweil, Ray, 183–4 Labour Party, 5, 6, 65, 80, 81, 221 Lagarde, Christine, 64 Le Bon, Gustave, 8–12, 13, 15, 16, 20, 24, 25, 38 Le Pen, Marine, 27, 79, 87, 92, 101–2 Leadbeater, Charles, 84 Leeds, West Yorkshire, 85 Leicester, Leicestershire, 85 Leviathan (Hobbes), 34, 39, 45 liberal elites, 20, 58, 88, 89, 161 libertarianism, 15, 151, 154, 158, 164, 173, 196, 209, 226 Liberty Fund, 158 Libya, 143 lie-detection technology, 136 life expectancy, 62, 68–71, 72, 92, 100–101, 115, 224 Lindemann, Frederick Alexander, 1st Viscount Cherwell, 138 Lloyds Bank, 29 London, England bills of mortality, 68–71, 75, 79–80, 81, 89, 127 Blitz (1940–41), 119, 143, 180 EU referendum (2016), 85 Great Fire (1666), 67 Grenfell Tower fire (2017), 10 and gross domestic product (GDP), 77, 78 housing crisis, 84 insurance sector, 59 knowledge economy, 84 life expectancy, 100 newspapers, early, 48 Oxford Circus terror scare (2017), ix–x, xiii, 41 plagues, 67–71, 75, 79–80, 81, 89, 127 Unite for Europe march (2017), 23 London School of Economics (LSE), 160 loss aversion, 145 Louis XIV, King of France, 73, 127 Louisiana, United States, 151, 221 Ludwig von Mises Institute, 154 MacLean, Nancy, 158 Macron, Emmanuel, 33 mainstream media, 197 “Make America Great Again,” 76, 145 Manchester, England, 85 Mann, Geoff, 214 maps, 182 March For Our Lives (2018), 21 March for Science (2017), 23–5, 27, 28, 210, 211 marketing, 14, 139–41, 143, 148, 169 Mars, 175, 226 Marxism, 163 Massachusetts Institute of Technology (MIT), 179 Mayer, Jane, 158 McCarthy, Joseph, 137 McGill Pain Questionnaire, 104 McKibben, William “Bill,” 213 Megaface, 188–9 memes, 15, 194 Menger, Carl, 154 mental illness, 103, 107–17, 139 mercenaries, 126 Mercer, Robert, 174, 175 Mexico, 145 Million-Man March (1995), 4 mind-reading technology, 136 see also telepathy Mirowski, Philip, 158 von Mises, Ludwig, 154–63, 166, 172, 173 Missing Migrants Project, 225 mobilization, 5, 7, 126–31 and Corbyn, 81 and elections, 81, 124 and experts, 27–8 and Internet, 15 and Le Bon’s crowd psychology, 11, 12, 16, 20 and loss, 145 and Napoleonic Wars, xv, 127–30, 141, 144 and Occupy movement, 5 and populism, 16, 22, 60 and violence, opposition to, 21 Moniteur Universel, Le, 142 monopoly on violence, 42 Mont Pelerin Society, 163, 164 moral emotion, 21 morphine, 105 multiculturalism, 84 Murs, Oliver “Olly,” ix Musk, Elon, 175, 176, 178, 183, 226 Nanchang, Jiangxi, 13 Napoleonic Wars (1803–15), 126–30 chappe system, 129, 182 and conscription, 87, 126–7, 129 and disruption, 170–71, 173, 174, 175, 226 and great leader ideal, 146–8 and intelligence, 134 and mobilization, xv, 126–30, 141, 144 and nationalism, 87, 128, 129, 144, 183, 211 and propaganda, 142 Russia, invasion of (1812), 128, 133 Spain, invasion of (1808), 128 National Aeronautics and Space Administration (NASA), 23, 175 National Audit Office (NAO), 29–30 national citizenship, 71 National Defense Research Committee, 180 National Health Service (NHS), 30, 93 National Park Service, 4 National Security Agency (NSA), 152 national sovereignty, 34, 53 nationalism, 87, 141, 210–12 and conservatism, 144 and disempowerment, 118–19 and elites, 22–3, 60–61, 145 ethnic, 15 and health, 92, 211–12, 224 and imagined communities, 87 and inequality, 78 and loss, 145 and markets, 167 and promises, 221 and resentment, 145, 197, 198 and war, 7, 20–21, 118–19, 143–6, 210–11 nativism, 61 natural philosophy, 35–6 nature, 86 see also environment Nazi Germany (1933–45), 137, 138, 154 Netherlands, 48, 56, 129 Neurable, 176 neural networking, 216 Neuralink, 176 neurasthenia, 139 Neurath, Otto, 153–4, 157, 160 neurochemistry, 108, 111, 112 neuroimaging, 176–8, 181 Nevada, United States, 194 new atheism, 209 New Orleans, Louisiana, 151 New Right, 164 New York, United States and climate change, 205 and gross domestic product (GDP), 78 housing crisis, 84 JFK Airport terror scare (2016), x, xiii, 41 knowledge economy, 84 September 11 attacks (2001), 17, 18 New York Times, 3, 27, 85 newspapers, 48, 71 Newton, Isaac, 35 Nietzsche, Friedrich, 217 Nixon, Robert, 206 no-platforming, 22, 208 Nobel Prize, 158–9 non-combatants, 43, 143, 204 non-violence, 224 North Atlantic Treaty Organization (NATO), 123, 145, 214 North Carolina, United States, 84 Northern Ireland, 43, 85 Northern League, 61 Northern Rock, 29 Norwich, Norfolk, 85 nostalgia, xiv, 143, 145, 210, 223 “Not in my name,” 27 nuclear weapons, 132, 135, 137, 180, 183, 192, 196, 204 nudge techniques, 13 Obama, Barack, 3, 24, 76, 77, 79, 158, 172 Obamacare, 172 objectivity, xiv, 13, 75, 136, 223 and crowd-based politics, 5, 7, 24–5 and death, 94 and Descartes, 37 and experts, trust in, 28, 32, 33, 51, 53, 64, 86, 89 and Hayek, 163, 164, 170 and markets, 169, 170 and photography, 8 and Scientific Revolution, 48, 49 and statistics, 72, 74, 75, 82, 88 and telepathic communication, 179 and war, 58, 125, 134, 135, 136, 146 Occupy movement, 5, 10, 24, 61 Oedipus complex, 109 Office for National Statistics, 63, 133 Ohio, United States, 116 oil crisis (1973), 166 “On Computable Numbers” (Turing), 181 On War (Clausewitz), 130 Open Society and Its Enemies, The (Popper), 171 opiates, 105, 116, 172–3 opinion polling, 65, 80–81, 191 Orbán, Viktor, 87, 146 Organisation for Economic Co-operation and Development (OECD), 72 Oxford, Oxfordshire, 85 Oxford Circus terror scare (2017), ix–x, xiii, 41 Oxford University, 56, 151 OxyContin, 105, 116 pacifism, 8, 20, 44, 151 pain, 102–19, 172–3, 224 see also chronic pain painkillers, 104, 105, 116, 172–3 Palantir, 151, 152, 175, 190 parabiosis, 149 Paris climate accord (2015), 205, 207 Paris Commune (1871), 8 Parkland attack (2018), 21 Patriot Act (2001), 137 Paul, Ronald, 154 PayPal, 149 Peace of Westphalia (1648), 34, 53 peer reviewing, 48, 139, 195, 208 penicillin, 94 Pentagon, 130, 132, 135, 136, 214, 216 pesticides, 205 Petty, William, 55–9, 67, 73, 85, 167 pharmacology, 142 Pielke Jr., Roger, 24, 25 Piketty, Thomas, 74 Pinker, Stephen, 207 plagues, 56, 67–71, 75, 79–80, 81, 89, 95 pleasure principle, 70, 109, 110, 224 pneumonia, 37, 67 Podemos, 5, 202 Poland, 20, 34, 60 Polanyi, Michael, 163 political anatomy, 57 Political Arithmetick (Petty), 58, 59 political correctness, 20, 27, 145 Popper, Karl, 163, 171 populism xvii, 211–12, 214, 220, 225–6 and central banks, 33 and crowd-based politics, 12 and democracy, 202 and elites/experts, 26, 33, 50, 152, 197, 210, 215 and empathy, 118 and health, 99, 101–2, 224–5 and immediate action, 216 in Kansas (1880s), 220 and markets, 167 and private companies, 174 and promises, 221 and resentment, 145 and statistics, 90 and unemployment, 88 and war, 148, 212 Porter, Michael, 84 post-traumatic stress disorder (PTSD), 111–14, 117, 209 post-truth, 167, 224 Potsdam Conference (1945), 138 power vs. violence, 19, 219 predictive policing, 151 presidential election, US (2016), xiv and climate change, 214 and data, 190 and education, 85 and free trade, 79 and health, 92, 99 and immigration, 79, 145 and inequality, 76–7 and Internet, 190, 197, 199 “Make America Great Again,” 76, 145 and opinion polling, 65, 80 and promises, 221 and relative deprivation, 88 and Russia, 199 and statistics, 63 and Yellen, 33 prisoners of war, 43 promises, 25, 31, 39–42, 45–7, 51, 52, 217–18, 221–2 Propaganda (Bernays), 14–15 propaganda, 8, 14–16, 83, 124–5, 141, 142, 143 property rights, 158, 167 Protestantism, 34, 35, 45, 215 Prussia (1525–1947), 8, 127–30, 133–4, 135, 142 psychiatry, 107, 139 psychoanalysis, 107, 139 Psychology of Crowds, The (Le Bon), 9–12, 13, 15, 16, 20, 24, 25 psychosomatic, 103 public-spending cuts, 100–101 punishment, 90, 92–3, 94, 95, 108 Purdue, 105 Putin, Vladimir, 145, 183 al-Qaeda, 136 quality of life, 74, 104 quantitative easing, 31–2, 222 quants, 190 radical statistics, 74 RAND Corporation, 183 RBS, 29 Reagan, Ronald, 15, 77, 154, 160, 163, 166 real-time knowledge, xvi, 112, 131, 134, 153, 154, 165–70 Reason Foundation, 158 Red Vienna, 154, 155 Rees-Mogg, Jacob, 33, 61 refugee crisis (2015–), 60, 225 relative deprivation, 88 representative democracy, 7, 12, 14–15, 25–8, 61, 202 Republican Party, 77, 79, 85, 154, 160, 163, 166, 172 research and development (R&D), 133 Research Triangle, North Carolina, 84 resentment, 5, 226 of elites/experts, 32, 52, 61, 86, 88–9, 161, 186, 201 and nationalism/populism, 5, 144–6, 148, 197, 198 and pain, 94 Ridley, Matt, 209 right to remain silent, 44 Road to Serfdom, The (Hayek), 160, 166 Robinson, Tommy, ix Roosevelt, Franklin Delano, 52 Royal Exchange, 67 Royal Society, 48–52, 56, 68, 86, 133, 137, 186, 208, 218 Rumsfeld, Donald, 132 Russian Empire (1721–1917), 128, 133 Russian Federation (1991–) and artificial intelligence, 183 Gerasimov Doctrine, 43, 123, 125, 126 and information war, 196 life expectancy, 100, 115 and national humiliation, 145 Skripal poisoning (2018), 43 and social media, 15, 18, 199 troll farms, 199 Russian Revolution (1917), 155 Russian SFSR (1917–91), 132, 133, 135–8, 155, 177, 180, 182–3 safe spaces, 22, 208 Sands, Robert “Bobby,” 43 Saxony, 90 scarlet fever, 67 Scarry, Elaine, 102–3 scenting, 135, 180 Schneier, Bruce, 185 Schumpeter, Joseph, 156–7, 162 Scientific Revolution, 48–52, 62, 66, 95, 204, 207, 218 scientist, coining of term, 133 SCL, 175 Scotland, 64, 85, 172 search engines, xvi Second World War, see World War II securitization of loans, 218 seismology, 135 self-employment, 82 self-esteem, 88–90, 175, 212 self-harm, 44, 114–15, 117, 146, 225 self-help, 107 self-interest, 26, 41, 44, 61, 114, 141, 146 Semi-Automatic Ground Environment (SAGE), 180, 182, 200 sentiment analysis, xiii, 12–13, 140, 188 September 11 attacks (2001), 17, 18 shell shock, 109–10 Shrecker, Ted, 226 Silicon Fen, Cambridgeshire, 84 Silicon Valley, California, xvi, 219 and data, 55, 151, 185–93, 199–201 and disruption, 149–51, 175, 226 and entrepreneurship, 149–51 and fascism, 203 and immortality, 149, 183–4, 224, 226 and monopolies, 174, 220 and singularity, 183–4 and telepathy, 176–8, 181, 185, 186, 221 and weaponization, 18, 219 singularity, 184 Siri, 187 Skripal poisoning (2018), 43 slavery, 59, 224 smallpox, 67 smart cities, 190, 199 smartphone addiction, 112, 186–7 snowflakes, 22, 113 social indicators, 74 social justice warriors (SJWs), 131 social media and crowd psychology, 6 emotional artificial intelligence, 12–13, 140–41 and engagement, 7 filter bubbles, 66 and propaganda, 15, 18, 81, 124 and PTSD, 113 and sentiment analysis, 12 trolls, 18, 20–22, 27, 40, 123, 146, 148, 194–8, 199, 209 weaponization of, 18, 19, 22, 194–5 socialism, 8, 20, 154–6, 158, 160 calculation debate, 154–6, 158, 160 Socialism (Mises), 160 Society for Freedom in Science, 163 South Africa, 103 sovereignty, 34, 53 Soviet Russia (1917–91), 132, 133, 135–8, 177, 180, 182–3 Spain, 5, 34, 84, 128, 202 speed of knowledge, xvi, 112, 124, 131, 134, 136, 153, 154, 165–70 Spicer, Sean, 3, 5 spy planes, 136, 152 Stalin, Joseph, 138 Stanford University, 179 statactivism, 74 statistics, 62–91, 161, 186 status, 88–90 Stoermer, Eugene, 206 strong man leaders, 16 suicide, 100, 101, 115 suicide bombing, 44, 146 superbugs, 205 surveillance, 185–93, 219 Sweden, 34 Switzerland, 164 Sydenham, Thomas, 96 Syriza, 5 tacit knowledge, 162 talking cure, 107 taxation, 158 Tea Party, 32, 50, 61, 221 technocracy, 53–8, 59, 60, 61, 78, 87, 89, 90, 211 teenage girls, 113, 114 telepathy, 39, 176–9, 181, 185, 186 terrorism, 17–18, 151, 185 Charlottesville attack (2017), 20 emergency powers, 42 JFK Airport terror scare (2016), x, xiii, 41 Oxford Circus terror scare (2017), ix–x, xiii, 41 September 11 attacks (2001), 17, 18 suicide bombing, 44, 146 vehicle-ramming attacks, 17 war on terror, 131, 136, 196 Thames Valley, England, 85 Thatcher, Margaret, 154, 160, 163, 166 Thiel, Peter, 26, 149–51, 153, 156, 174, 190 Thirty Years War (1618–48), 34, 45, 53, 126 Tokyo, Japan, x torture, 92–3 total wars, 129, 142–3 Treaty of Westphalia (1648), 34, 53 trends, xvi, 168 trigger warnings, 22, 113 trolls, 18, 20–22, 27, 40, 123, 146, 148, 194–8, 199, 209 Trump, Donald, xiv and Bannon, 21, 60–61 and climate change, 207 and education, 85 election campaign (2016), see under presidential election, US and free trade, 79 and health, 92, 99 and immigration, 145 inauguration (2017), 3–5, 6, 9, 10 and inequality, 76–7 “Make America Great Again,” 76, 145 and March for Science (2017), 23, 24, 210 and media, 27 and opinion polling, 65, 80 and Paris climate accord, 207 and promises, 221 and relative deprivation, 88 and statistics, 63 and Yellen, 33 Tsipras, Alexis, 5 Turing, Alan, 181, 183 Twitter and Corbyn’s rallies, 6 and JFK Airport terror scare (2016), x and Oxford Circus terror scare (2017), ix–x and Russia, 18 and sentiment analysis, 188 and trends, xvi and trolls, 194, 195 Uber, 49, 185, 186, 187, 188, 191, 192 UK Independence Party, 65, 92, 202 underemployment, 82 unemployment, 61, 62, 72, 78, 81–3, 87, 88, 203 United Kingdom austerity, 100 Bank of England, 32, 33, 64 Blitz (1940–41), 119, 143, 180 Brexit (2016–), see under Brexit Cameron government (2010–16), 33, 73, 100 Center for Policy Studies, 164 Civil Service, 33 climate-gate (2009), 195 Corbyn’s rallies, 5, 6 Dunkirk evacuation (1940), 119 education, 85 financial crisis (2007–9), 29–32, 100 first past the post, 13 general election (2015), 80, 81 general election (2017), 6, 65, 80, 81, 221 Grenfell Tower fire (2017), 10 gross domestic product (GDP), 77, 79 immigration, 63, 65 Irish hunger strike (1981), 43 life expectancy, 100 National Audit Office (NAO), 29 National Health Service (NHS), 30, 93 Office for National Statistics, 63, 133 and opiates, 105 Oxford Circus terror scare (2017), ix–x, xiii, 41 and pain, 102, 105 Palantir, 151 Potsdam Conference (1945), 138 quantitative easing, 31–2 Royal Society, 138 Scottish independence referendum (2014), 64 Skripal poisoning (2018), 43 Society for Freedom in Science, 163 Thatcher government (1979–90), 154, 160, 163, 166 and torture, 92 Treasury, 61, 64 unemployment, 83 Unite for Europe march (2017), 23 World War II (1939–45), 114, 119, 138, 143, 180 see also England United Nations, 72, 222 United States Bayh–Dole Act (1980), 152 Black Lives Matter, 10, 225 BP oil spill (2010), 89 Bush Jr. administration (2001–9), 77, 136 Bush Sr administration (1989–93), 77 Bureau of Labor, 74 Central Intelligence Agency (CIA), 3, 136, 151, 199 Charlottesville attack (2017), 20 Civil War (1861–5), 105, 142 and climate change, 207, 214 Clinton administration (1993–2001), 77 Cold War, see Cold War Defense Advanced Research Projects Agency (DARPA), 176, 178 Defense Intelligence Agency, 177 drug abuse, 43, 100, 105, 115–16, 131, 172–3 education, 85 Federal Bureau of Investigation (FBI), 137 Federal Reserve, 33 Fifth Amendment (1789), 44 financial crisis (2007–9), 31–2, 82, 158 first past the post, 13 Government Accountability Office, 29 gross domestic product (GDP), 75–7, 82 health, 92, 99–100, 101, 103, 105, 107, 115–16, 158, 172–3 Heritage Foundation, 164, 214 Iraq War (2003–11), 74, 132 JFK Airport terror scare (2016), x, xiii, 41 Kansas populists (1880s), 220 libertarianism, 15, 151, 154, 158, 164, 173 life expectancy, 100, 101 March For Our Lives (2018), 21 March for Science (2017), 23–5, 27, 28, 210 McCarthyism (1947–56), 137 Million-Man March (1995), 4 National Aeronautics and Space Administration (NASA), 23, 175 National Defense Research Committee, 180 National Park Service, 4 National Security Agency (NSA), 152 Obama administration (2009–17), 3, 24, 76, 77, 79, 158 Occupy Wall Street (2011), 5, 10, 61 and opiates, 105, 172–3 and pain, 103, 105, 107, 172–3 Palantir, 151, 152, 175, 190 Paris climate accord (2015), 205, 207 Parkland attack (2018), 21 Patriot Act (2001), 137 Pentagon, 130, 132, 135, 136, 214, 216 presidential election (2016), see under presidential election, US psychiatry, 107, 111 quantitative easing, 31–2 Reagan administration (1981–9), 15, 77, 154, 160, 163, 166 Rumsfeld’s “unknown unknowns” speech (2002), 132 Semi-Automatic Ground Environment (SAGE), 180, 182, 200 September 11 attacks (2001), 17, 18 Tea Party, 32, 50, 61, 221 and torture, 93 Trump administration (2017–), see under Trump, Donald unemployment, 83 Vietnam War (1955–75), 111, 130, 136, 138, 143, 205 World War I (1914–18), 137 World War II (1939–45), 137, 180 universal basic income, 221 universities, 151–2, 164, 169–70 University of Cambridge, 84, 151 University of Chicago, 160 University of East Anglia, 195 University of Oxford, 56, 151 University of Vienna, 160 University of Washington, 188 unknown knowns, 132, 133, 136, 138, 141, 192, 212 unknown unknowns, 132, 133, 138 “Use of Knowledge in Society, The” (Hayek), 161 V2 flying bomb, 137 vaccines, 23, 95 de Vauban, Sébastien Le Prestre, Marquis de Vauban, 73 vehicle-ramming attacks, 17 Vesalius, Andreas, 96 Vienna, Austria, 153–5, 159 Vietnam War (1955–75), 111, 130, 136, 138, 143, 205 violence vs. power, 19, 219 viral marketing, 12 virtual reality, 183 virtue signaling, 194 voice recognition, 187 Vote Leave, 50, 93 Wainright, Joel, 214 Wales, 77, 90 Wall Street, New York, 33, 190 War College, Berlin, 128 “War Economy” (Neurath), 153–4 war on drugs, 43, 131 war on terror, 131, 136, 196 Watts, Jay, 115 weaponization, 18–20, 22, 26, 75, 118, 123, 194, 219, 223 weapons of mass destruction, 132 wearable technology, 173 weather control, 204 “What Is An Emotion?”


pages: 394 words: 112,770

Fire and Fury: Inside the Trump White House by Michael Wolff

Affordable Care Act / Obamacare, barriers to entry, Bernie Sanders, centre right, disintermediation, Donald Trump, drone strike, Edward Snowden, Elon Musk, forensic accounting, illegal immigration, impulse control, Jeff Bezos, Jeffrey Epstein, obamacare, Peter Thiel, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ronald Reagan, Saturday Night Live, self-driving car, Silicon Valley, single-payer health, Travis Kalanick, WikiLeaks, zero-sum game

His partner in this enterprise was David Bossie, the far-right pamphleteer and congressional committee investigator into the Clintons’ Whitewater affair, who would join him as deputy campaign manager on the Trump campaign. Bannon met Breitbart News founder Andrew Breitbart at a screening of one of the Bannon-Bossie documentaries In the Face of Evil (billed as “Ronald Reagan’s crusade to destroy the most tyrannical and depraved political systems the world has ever known”), which in turn led to a relationship with the man who offered Bannon the ultimate opportunity: Robert Mercer. * * * In this regard, Bannon was not so much an entrepreneur of vision or even business discipline, he was more simply following the money—or trying to separate a fool from his money. He could not have done better than Bob and Rebekah Mercer. Bannon focused his entrepreneurial talents on becoming courtier, Svengali, and political investment adviser to father and daughter. Theirs was a consciously quixotic mission.

., 74 Manigault, Omarosa, 109 Mar-a-Lago, 4, 69, 99, 106, 159, 189, 193–94, 210, 228, 248–49 Marcus, Bernie, 309 Mattis, James, 4, 21, 103, 109, 188, 264–65, 288, 296, 304–5 May, Theresa, 258 McCain, John, 112, 306 McCarthy, Joe, 73 McConnell, Mitch, 32, 117, 301–2 McCormick, John, 167 McGahn, Don, 95, 212–14, 217 McLaughlin, John, 10 McMaster, H. R., 109, 176, 185, 188–93, 211, 235, 258, 263–68, 276–77, 288–89, 298–99, 304–5 McNerney, Jim, 88 Meadows, Mark, 161, 163, 171 Medicare, 165 Melton, Carol, 78 Mensch, Louise, 160 Mercer, Rebekah, 12, 58–59, 121, 127, 135, 139, 177–80, 201, 208, 309 Mercer, Robert, 12, 58–59, 112, 177–80, 201, 309 Mexico, 39, 62, 77, 93, 228 Middle East, 29, 70, 140, 145, 157, 190, 211, 224–33, 242, 264 Mighty Ducks, The (TV show), 56 military contractors, 265, 267 Miller, Jason, 234, 237–38, 299 Miller, Stephen, 61, 64–65, 89, 133, 148, 209, 213, 229, 258, 307 Mnuchin, Steve, 13, 133, 290, 296, 304 Mohammed bin Nayef, crown prince of Saudi Arabia (MBN), 228, 231 Mohammed bin Salman, crown prince of Saudi Arabia (MBS), 224–31 Moore, Roy, 302–4 Morgan, Piers, 22 Morning Joe (TV show), 32, 66–67, 121, 189, 247–48 MSNBC, 66, 106, 247 Ms.


pages: 419 words: 119,476

Posh Boys: How English Public Schools Ruin Britain by Robert Verkaik

accounting loophole / creative accounting, Alistair Cooke, banking crisis, Berlin Wall, Boris Johnson, British Empire, Brixton riot, Dominic Cummings, Donald Trump, Etonian, G4S, gender pay gap, God and Mammon, income inequality, Khartoum Gordon, Kickstarter, knowledge economy, Livingstone, I presume, loadsamoney, mega-rich, Neil Kinnock, offshore financial centre, old-boy network, place-making, plutocrats, Plutocrats, Robert Gordon, Robert Mercer, school vouchers, The Bell Curve by Richard Herrnstein and Charles Murray, trade route, traveling salesman, unpaid internship

Another significant Remain funder was Mark Coombs (Dulwich), who donated £750,000. Billionaire hedge-fund owner and former futures trader David Harding (Pangbourne) donated £3.5 million to the same campaign. Before the country went to the polls, the Leave campaigners had one more card to play in the battle to persuade the British people to support Brexit. Some believe it may have even been a decisive factor. In the summer of 2015, US billionaire Robert Mercer, a close friend of Donald Trump and an investor in alt-right media company Breitbart News, introduced Farage to a data company set up by two Old Etonian brothers who had cut their teeth on controversial military style ‘psy-ops’ which they ran in election campaigns in the developing world.15 Nigel and Alex Oakes were colourful businessmen with a special interest in psychological profiling. Alex Oakes was a close school contemporary of David Cameron, while his older brother was an ex-boyfriend of Lady Helen Windsor and a former executive of the Tories’ favourite advertising agency, Saatchi & Saatchi.

INDEX Abbott, Diane 181 Abromovich, Roman 197 abuse 207–20, 254 Adams’ Grammar School 172–3, 184 Addrison, John 210 Adonis, Andrew 240–1, 333 Africa 53–4, 314–15 Ahmad, Muhammad 37, 38–9 Aitken, Jonathan 255 Aldridge, Sir Rod 318 Alexander, Danny 148 Alfred the Great, King 14 Allan, Tim 169 Alpha Plus 316–18 Ametistova, Ekaterina 200–1 Ampleforth College 219, 237, 250–1 Anderson, Bruce 146 Andersdon, Dr Eric 105, 106, 115, 137 Anne, HRH Princess 110 Anthony, Vivian 108–9 Apostles Club 306 aristocracy 18, 22, 28 Arkwright, Richard 32 Arnold, Matthew 66 Arnold, Thomas 31, 67 Aspinall, John 156 Asquith, Herbert 64 al-Assad, Bashar 128, 196, 202 Attlee, Clement 87, 88, 89, 99, 180 Augustine, St 13–14 Australia 283 Baddiel, David 263–5 Bailey, Mark 227 Balfour, Arthur 33 Ball, Peter 216–19 banking 295–9 Banks, Arron 163, 164, 165 Bannon, Steve 164, 282 Barrington, Robert 202 Barton, Laura 305 Barttelot, Sir Brian 38, 40–1 Barttelot, Maj Edmund Musgrave 38–40 Barttelot, Sir Walter 40–1, 60–1 Bash camps 212–13, 216 BBC 298–300 beatings 21, 30, 211, 213–14, 215 Beckett, Andy 304–5 Bedales 182–3 Beefsteak Club 291–2 Bellak, Benjamin 138 Benn, Melissa 255, 268, 323–4, 339 Benn, Tony 96–7, 174, 175, 176, 180, 255–6 Bennett, Alan 1, 336–7 Bentham, Jeremy 66 Berezovsky, Boris 199 Beveridge, William 89 Blackadder Goes Forth (TV show) 62 Blair, Tony 103–6, 107, 109, 111, 179, 180–1 Blunkett, David 106–8, 109, 111 Blunt, Anthony 133 Bo Xilai 198 Board of Education 68–9, 69 ‘Boarding School Syndrome’ 270–1 Boarding Schools Corporation 98 Boer War 40, 52–4 Bonar Law, Andrew 64 Borwick, Tom 165 Bracken, Brendan 85–6 Brexit 127, 151, 161–3, 164–70; see also Farage, Nigel Bridgeman, Luke 292–3 British Army 36, 40–2, 55–64, 72–3, 224, 225 British Empire 30, 32, 33–5, 36–7, 38–40, 42–3, 44–5 and Second World War 74–5 British Expeditionary Force (BEF) 52 Brodie, Stanley 116 Brooke, Rupert 62 Brook’s 291 Brooks, Charlie 147 Brougham, Henry 29 Brown, Gordon 107, 287 Bruce, Charles 136 Brunel, Isambard Kingdom 32 Bryant, Chris 128 Buchanan, Mike 119 Buffet, Warren 329 Bullingdon Club 137, 141–2, 306 bullying 271, 273 Burgess, Guy 133 bursaries 226–35, 321–2, 332 Butler, Rab 77, 78–9, 81, 82 Butler, Robin, Lord 193 Byers, Stephen 107–8 Cable, Vince 167 Caldicott prep school 209–10, 215, 219 Callaghan, James 99, 180 Cambridge Analytica (CA) 164–6 Cambridge, HRH Catherine, Duchess of 191 Cambridge Spy Ring 133 Cambridge University 1, 25 Cameron, David 6, 105, 133–8, 139, 140, 274 and ‘big society’ 313, 315 and Conservative Party 143, 144–8 and Eton 119, 190 and EU Referendum 127, 161, 162, 166–7, 169, 170 and government 148–54 and Oxford 141–2, 143 and psychology 271, 273 and Russia 132 camps 212–16 Canning, George 33 Card, Tim 61 Cardigan, James Brudenell, 7th Earl of 56 Carey, George, Lord 217 Carpenter, John 17 Carswell, Douglas 161 Cash, Bill 161 Castle House Preparatory School 172 Catling, Susan 95, 96 Chakrabarti, Shami 181–3 Chamberlain, Neville 72 chantry schools 15, 24, 26 Charitable Uses Act (1601) 109 Charities Act (2006) 114–16, 223 charity 17, 47, 88–9, 114–21, 341 and law 109–10, 111 Charles, HRH Prince of Wales 105, 110, 143, 216, 217, 283 Charterhouse School 19–20, 195–6, 233, 257 China 197–8, 200, 203, 204 Christianity 13–14, 15, 24, 215–16 and muscular 35–8, 44, 67, 211 church, the 2, 13–15, 24, 211–19 Churchill, Winston 40, 72, 74, 78, 84, 286 and Bracken 86 and Eden 91 and Harrow 33, 77, 82–3 City, the 4, 292–3, 295–7 City of London School 17, 181 Clarendon, George Villiers, 4th Earl of 48, 49–51 Clark, Alan 62 Clark, Ross 194 Clarke, Kenneth 146, 147 class 22, 65–6, 86–7, 95, 237–8, 315–16 and the army 55–6 and government 91–3 and grammar schools 67–8 and the media 298–300 classics 30–1 Clegg, Nick 147–8, 181, 209–10, 327–8 Clifford, Adm Sir Augustus 34 Clive, Robert 42–3 Comenius, John 27 comprehensive schools 94–5, 255–6 Conrad, Joseph 39–40 conscription 52–3 Conservative Party 69, 86, 90, 92, 99–100, 101–2 and Cameron 143, 144–8 Cook, Henry 169 Cooke, Alistair 143 Coombs, Mark 163 Corbet, Richard 23 Corbyn, Ben 178 Corbyn, Jeremy 17, 121, 171–5, 176–80, 186, 314 Corbyn, Sebastian 178 corporal punishment 21 Cranmer, Thomas 11–12, 14 Crawford, David Lindsay, 27th Earl of 59–60 Crimean War 56 Cromwell, Oliver 27 Crosland, Anthony 95–6, 97, 255 Cruz, Ted 164 Cumberbatch, Benedict 252 Cummings, Dominic 165–6 Cust, Sir Lionel Henry 222 Czerin, Peter 144 Dacre, Paul 147 Daffarn, Edward 318 Dalton, Hugh 180 Damasio, Antonio 276 D’Ancona, Matthew 144 Darwin, Charles 30–1 Davidson, Jim 261–3, 266–7 Davison, Dick 108 De Carvalho, Alexander 168–9 De Freitas, Geoffrey 89 Deakin, Chloe 157, 158 Dean, Victoria 169 Debrett’s 289 Derham, Patrick 120 Dimbleby, David 149, 251 Disraeli, Benjamin 42 Doggart, Simon 215 donations 228–9 Dorries, Nadine 250–1 Dowding, Hugh 83 Duff, Grant 47–9, 50 Duffell, Nick 271, 272–3, 276–7, 284 Dulwich College 90, 156–61, 181–2, 192, 248, 330–1 and overseas franchises 203, 204 and sponsorships 239–42 Duncan, Alan 167 Eden, Anthony 64, 90–2 Edinburgh Academy 47 Edmiston, Robert, Lord 163 education 11–12, 13–15, 27, 321–4, 327–9 and Ragged Schools 37 and reforms 65–6 and rights 337–8 and Scotland 43–4 see also grammar schools; public schools; state schools Education Acts: 1902: 69 1918: 65 1944: 82, 87, 93 Education Review Group 117 Edward III, King 16 Edward IV, King 26 11-plus exam 82, 93 Elizabeth I, Queen 18, 19 Elizabeth II, Queen 133 Elliott, Matthew 165 Emms, David 157 employment 341–3 End of the ‘Old School Tie’, The (Worsley) 75–6 Endowed Schools Commission 50 English Civil War 27 entry requirements 18–19 Establishment, the 125–6 Eton College 3, 17, 19, 22, 204, 278–9 and admission 189–90 and alumni 33, 140–1 and bursaries 228–9, 230 and Cameron 119, 136–8, 140 and charity 99, 115, 221–3, 235–6 and Eden 91, 92 and exams 257 and fees 113, 188–9, 227 and foundation boys 50 and Goldsmith 155 and government 134 and grants 238 and international students 198–9 and Leggatt 293 and masters 46–7 and Oxbridge 25, 301 and poor boys 28–9 and Putin 127–32 and reforms 26–7 and Rifles 52, 53, 59 and sponsorships 242 and sport 35 and town 187–8 and Wellington 33, 41–2 see also Old Etonians EU Referendum 127, 150–1, 161–3, 164–70 Evans, Chris 298–9 exams 82, 93, 257, 267–8 Fabian Society 180 fagging 22, 29, 98, 104 faith schools 180–1 Fallon, Michael 62 Faraday, Michael 32 Farage, Nigel 156–9, 160–1, 162, 163, 167, 169, 248 and the establishment 283 and psychology 273 and Trump 282 Farr, Clarissa 278 Farron, Tim 153 fascism 157, 158 federations 238 fees 13, 19, 67, 69, 111–13, 194 and Eton 188–9 and subsidies 221–2, 224–35, 245–9 and university 260–1 Fettes College 103–6, 211 Finkelstein, Daniel 146 Finland 268, 344 First World War 54–64, 65–6 Fisher, Herbert 68–9 Fleming, David Pinkerton, Lord 79–81, 90 Fletcher, David 200, 201 Fletcher, Frank 68, 69 Foot, Michael 106, 180 Fox, Edward 156 Fox, Laurence 252–3 France 268–9 Fraser, Giles 211, 216 free scholars 16, 17, 18, 23, 50–1 Freemasons 193 French, John 61 French Revolution 28 Freud, Matthew 147 Gaitskell, Hugh 180 Galsworthy, John 66–7 Gascoigne, Michael 104 Gates, Bill 329 Geelong Grammar School 283 GEMS Education 111, 113–14, 204–6 gender pay gap 298, 299 gentlemen’s clubs 143–4, 169, 291–2 Germany 196, 268–9, 344 Ghosh, Helen 149 Gibb, Dame Moira 217, 218 Gill, Ameet 168 Girls’ Public Days School Trust 68 girls’ schools 229 Girls’ Schools Association 108 Gladstone, William 33, 37, 48 Goldsmith, James 155–6 Goodall, Lewis 298–9 Goodhart, David 320–1 Gordon, Gen Charles 36–8, 53 Gordonstoun 110, 211 Goschen, Giles, Viscount 135–6 Gove, Michael 144, 149, 161, 282, 322 and Brexit 166, 168 and psychology 273 and sponsorships 238 and subsidies 247–9 government 4–5, 6, 148–54 Gracie, Carrie 298 grammar schools 14, 15, 24, 30, 44, 56–7 and grants 93–4, 100–1 and Labour 183–5 and reforms 49–50, 67–8 Granville, Granville George Leveson-Gower, 2nd Earl of 48 Gray, Herbert Branston 45 Grayling, Chris 161 Great Depression 69–70 Green, Francis 307 Green, Michael 145 Greenwood, David 208, 219 Gregory, Pope 13 Grenfell Tower 316, 317–19 Greville, Fulke 22 Guppy, Darius 289–90 Guru-Murthy, Krishnan 234–5 Haberdashers’ Aske’s 263–5, 300 Haig, Gen Douglas 61 Haileybury College 88, 89 Haldane, Richard Burdon 53 Halfon, Robert 249, 311, 333, 336 Halls, Andrew 194 Hammond, Richard 67 Hancock, Matt 342 Hannan, Daniel 161 Hanson, David 219 Harding, David 163, 168 Hardman, Robert 144 Hargreaves, Peter 163 Harman, Harriet 181 Harrison, Rupert 144, 153, 275 Harrow School 17, 23, 24, 29, 31–2, 68 and alumni 33, 34, 252–3 and bursaries 233–4 and Churchill 83 and foundation boys 50 and overseas franchises 203 and soldiers 52, 53 Hart, Basil Liddel 62 Hasan, Mehdi 327 Hastings, Max 277 Hastings, Warren 43 Haynes, Tim 227 Headmasters’ and Headmistresses’ Conference (HMC) 51, 119 Healey, Denis 175, 176 Heart of Darkness (Conrad) 39–40 Heath, Edward 99, 287 Heath, Lt Gen Sir Lewis ‘Piggy’ Macclesfield 74 Heatherdown 135–6 Henderson, Simon 235 Hendon County Grammar School 183–5 Henry, Hugh 210 Henry VI, King 19, 26, 221, 301 Henry VIII, King 18, 26 Henty, G.A. 53 Heseltine, Michael 145, 167 Higgins, Matthew James 46–7 Hillman, Nick 99, 100, 255, 302–3 Hilton, Steve 144, 153 Hitler, Adolf 3, 73 Hobsbawm, Eric 34 Hoey, Kate 161 Hogg, Charlotte 295–6 Hogg, Dame Mary 294 Hogg, Douglas 294–5 Hogg, Quintin 97, 294, 295 Hogg, Sarah 294–5 Holle, Arnold 196 homosexuality 35–6 Hong Chin 201 Hosking, Jeremy 163 Howard, Adam 283–4 Howard, Michael 146 Howard, Nicholas 152 Howe, Geoffrey 101–2 Howell, Steve 183–5 Hughes, Billy 90 Hughes, Thomas 35 Huhne, Chris 148 Hunt, Jeremy 271, 273 Hurtwood House 225 Hutton, Will 274 Huxley, Thomas 66 Ibori, James 201 immigration 156, 157, 162, 168, 264 imperialism 33–4, 53–4; see also British Empire Independent Inquiry into Child Abuse 210–11 Independent Schools Council (ISC) 110, 113, 116, 120, 288, 325–7 and charity 223, 224, 226 and sponsorships 238 India 42–3, 53 industrial revolution 32 inequality 306–12, 314–16, 321–4, 327–9 initiation ceremonies 21 international students 196–202 internships 341–2 Iremonger, William 91 Itoje, Maro 234 Iwerne Trust 212, 214 Jameson, James Sligo 39 Japan 73–4 Jardine, Cassandra 94–5 Johnson, Boris 6, 31, 128, 142, 150–1, 272 and Brexit 162, 166, 167, 168, 169 and bursaries 321–2 and the Establishment 125–6 and Eton 136, 190 and Guppy 289–90 and psychology 271, 273, 276–7 Johnson, Jo 149 Johnson, Stanley 292 Jones, Owen 274–5, 328 Jonson, Ben 23 journalism 274–5, 297–8 Journey’s End (Sherriff) 58 judiciary 5, 292–3 Keir Hardie, James 180 Kensington Aldridge Academy (KAA) 318–19 KGB 132, 133 King Edward’s School 68, 93–4 Kingston Grammar School 56, 57 Kinnock, Neil 180 Kipling, Rudyard 53, 64 Kitchener, Gen Herbert 54–5, 57 Korski, Daniel 168–9 Kynaston, David 328–9, 337 Labour Party 6, 69, 86–8, 99, 100–1 and Corbyn 171, 174–5, 176–80 and education 180–6, 328–9 see also New Labour Lammy, David 303–4, 343 Lamont, Norman 143, 145 Lampl, Sir Peter 116 Landale, James 150 language 20–1, 277 Lansman, Jon 175–6, 178–9 Lansman, Max 178 Latin 14, 30 Laws, David 148 Leach, Arthur 14, 27 Leach, Sir John 109–10 league tables 267–8 Leanders, Rocky 214–15 Leather, Suzi 114–15, 117 Leggatt, George 292–3 Lenon, Barnaby 226–7, 228, 233–4, 258–61, 279, 303, 325–7 Leonard, Richard 186 Leslie, Chris 179 Letwin, Oliver 148, 149, 271 Levellers 27 Lewis, Sir George 48–9 Li Wei Gao 201 Liddle, Rod 299–300 Lineker, Gary 195, 298–9 literacy 14, 15, 43 Little, Steven 231–2 Little, Tony 190, 193–4, 198, 199, 205–6, 278–9 and assisted places 226, 230, 235 and parents 256 Litvinenko, Alexander 128 Livingstone, David 43, 44 Llewellyn, Ed 144, 148, 152 Lloyd George, David 65 local education authorities (LEAs) 80–1, 89–90, 98 Lockwood, Chris 144 London 316–19, 334; see also City, the London Oratory 180–1 Loom of Youth, The (Waugh) 63, 70 Lyon, John 17 Macdonald, Ramsay 180 McDonnell, John 174–5, 178–9, 186 McGovern, Steph 298 McKenna, Alison 116 Maclean, Donald 133 Macmillan, Harold 92 McNeil, Rosamund 120 Madders, Justin 185, 311–12 Made in Chelsea (TV show) 325 Magnitsky, Sergei 128 Major, John 321 Major, Lee Elliott 305 Mallinckrodt, Edward 135 Manchester Grammar School 27–8, 68 Mandelson, Peter 88, 183 Marathon Asset Management 292–3 Marlborough College 52, 55, 79, 192, 232 Marshall, Patrick 209 Marshall, Sir Paul 167–8 Marxism 177–8 Mason, A.E.W. 53 Masonic lodges 145, 193 May, Theresa 69, 118–19, 121, 127, 129 and internships 341–2 and ‘shared society’ 313–14, 322–3 and sponsorships 243 Meacher, Michael 176 media 297–300 Mercer, Robert 163, 164 Merchant Taylors’ School 17, 21, 28, 42–3, 140, 300–1 Merivale, Charles 22–3 Middleton, Kate, see Cambridge, Duchess of Milburn, Alan 315, 336 Military Cross 59 Millar, Fiona 109, 185–6, 324 Millfield School 247–8 Milne, Seumas 17, 177–9 Milton, John 27 Mitchell, Andrew 237, 271 Momentum 175–7, 178–9 monasteries 14, 15, 18, 24, 25, 26–7 money-laundering 201–2 Montgomery, Bernard 83 Moore, Thomas 42 morality 273–4 Morrison, Herbert 88 Mosley, Oswald 143, 158, 159 Mount, Ferdinand 139, 143 Mount, Harry 328 Mulcaster, Richard 20 Mumsnet 258 Murdoch, Rupert 147, 282–3 Murray, Andrew 178–9 Murray, Charles 334 Murray, Laura 178 Nash, Eric ‘Bash’ 212–13 Nash, Paul 62 National Front 157, 158 Neile, Richard 23 Nelson, Lord Horatio 44 New Labour 105, 106–7, 111 New York Military Academy (NYMA) 280–2 Newbolt, Sir Henry 55 Newmark, Brooks 292 Newsom, Sir John 97, 246 Newsome, David 273 newspapers 46–7, 297–8 Nix, Alexander 164, 165 non-cognitive skills 276 North Foreland Lodge 110 north–south divide 310–11 Norwood, Cyril 67, 70 Notting Hill Prep 316–18 Nyachuru, Guide 215 Oakes, Alex 163, 164 Oakes, Nigel 163–4 O’Brien, James 237, 250–1 Odey, Crispin 163, 167, 193 O’Dowda, Brendan 198 Office of Fair Trading (OFT) 112, 113 Officer Training Corps (OTC) 52, 53, 55, 62 old boys’ networks 21–2, 289–91 Old Etonians (OEs) 136, 140–1, 149, 192, 224, 228–9 Oldfield, Bruce 68 oligarchs 129–30, 140, 194, 197, 199, 202 Olympic Games 36 Onyeama, Dillibe 254 Operation Winthorpe 209 Organ, Bill 111–12 Orwell, George 3, 74, 76, 77, 254 and democracy 286, 309 Osborne, George 6, 144, 146, 147, 148, 153 and Brexit 162 and politics 274–5 and psychology 273 overseas franchises 202–6, 329 Oxbridge 1–2, 5, 264–5, 279, 300–6, 342–3; see also Cambridge University; Oxford University Oxford University 2, 16, 17, 18, 25, 107 and Cameron 141–2 and Union 125–6 Pakenham, Frank 180 Palmerston, Lord 33, 48 parents 194–6, 251–6, 257–8, 261–3, 265–7 and failure 278 and rights 337–8 Parker, Peter 62–3 Parris, Matthew 306, 314–15 Pasha, Emin 39 Patel, Priti 162 Patrick, Andrew 277 Paxman, Jeremy 223–4, 273 pay 298–9, 306–7 Peasants’ Revolt 16 Peat, Sir Michael 205 Peel, Robert 33 Percival, Arthur Ernest 73–4 Perry, Tom 210 Philby, Kim 133 Piers Gaveston Club 137, 141, 142–3 Pitt the Elder, William 28 Plato 313 Pleming, Richard 195 politics 91–3, 271–3, 274–5, 303–5; see also Conservative Party; government; Labour Party poor, the 16–17, 19–20, 22, 24, 28–9 and subsidised places 221–2, 224–7 Portillo, Michael 146 Portland Communications 169 ‘posh bashing’ 252–3 Powell, Enoch 93, 156–7 Powell, Hugh 138–40 prefects 21 Price, Leolin 115–16 Priestley, J.B. 76–7 private education, see public schools Profumo, John 92 property 310 psychology 270–3, 275–7 Public School Lodges’ Council 145, 193 public schools 2–7, 66–7, 258–61, 286–9, 324–5 and abolition 336–44 and abuse 207–20 and actors 252–3 and alumni 1–2, 140 and assisted places 87–8, 90, 101, 321–2, 329–33 and beginnings 15–20 and Brexit 161–2, 163, 165–6, 167, 170 and British Empire 33–4, 41, 42–3, 44–5 and business rates 243–4 and charity 88–9, 107–11, 114–21, 221–35 and class 22–4 and criticism 46–7 and demand 70–1 and entitlement 283–5 and espionage 132–3 and Europe 268–9 and facilities 193–4 and fees 111–14, 245–9 and funding 68–70 and government 91–3 and inequality 306–9 and international students 196–202 and Labour Party 180–3, 185–6 and London 316–18 and the media 297–300 and networks 21–2, 191–3, 289–91 and overseas franchises 202–6 and Oxbridge 300–6 and parents 194–6, 251–2, 253–6, 257–8, 261–3, 265–7 and psychology 270–3, 275–7 and reforms 25–7, 29–32, 47–51, 79–82, 95–100 and revolts 27–9 and Second World War 75–9 and slang 20–1 and society 334–6 and soldiers 52–64 and state schools 236–43, 326–7 Public Schools Act (1868) 51 Public Schools Commission 97–100 Puritans 27 Putin, Vladimir 127–32, 133, 154 Pyper, Mark 110 Queen’s Scholarship 19 Raab, Dominic 322 racism 156, 157, 162 Rae, John 101, 274, 302 Ragged School movement 29, 37, 38 Ranger, Terence 34 Rawls, John 5 Ray, Christopher 115 Reay, Diane 268, 269, 284–5, 335 Reckless, Mark 161 Redwood, John 161 Rees-Mogg, Jacob 31, 154, 161, 193, 251, 282 Referendum Party 155, 156 Reform Act (1832) 47 Reformation, the 26 Remain Vote 162, 163, 166, 168 Renton, Alex 219–20, 254 Repton School 302–3 Reznikov, Peter 131 Rhodes, Cecil 33, 43 Rich, Richard 11–12, 14 Richards, Amy 169 Richardson, Ed 197 Ripon Grammar School 67–8 Roberts, Frederick, Field Marshal Lord 52–3 Rock, Patrick 151–2 Roman Empire 13 Romilly, Peter 135 Rooney, Wayne 191 Rothermere, Jonathan Harmsworth, Lord 147 royal family 133, 134 Royal Military Academy Sandhurst 36, 38, 40, 56 Royal Military Academy Woolwich 36, 56 Royal Navy 44, 73 rugby 35 Rugby School 28, 31, 52, 53, 73 Ruskin, John 66 Russia 127–34, 139–40, 199–200, 202 Ruston, Mark 214 Sainsbury, David 163 St Paul’s School 14, 17, 18, 209, 227 Sandel, Michael J. 315–16 Sandhurst, see Royal Military Academy Sandhurst Sansom-Mallett, David 209 Sassoon, Siegfried 62 Sawar, Anas 186 Schaverien, Joy 270–1 Schellenberg, Walter Friedrich 3 Schneider, James 17, 177 scholarships 226–8, 240 School Teachers Superannuation Act (1918) 68 science 30 Scotland 43–4, 47, 186, 211, 341 Second World War 3, 40–1, 72–9, 82–4, 86–7 secondary schools 82, 90, 94–5 Sedbergh School 85 segregation 316 Seldon, Sir Anthony 192, 230, 242, 261, 331–3 serfdom 15 Sevenoaks School 111–12 sexual assault 207–20 Shaw, George Bernard 66 Shawcross, Hartley 99 Shawcross, William 117 Sherborne School 55, 70 Sherriff, Robert 56–7, 58 Shevkunov, Father Tikhon 130–1 Shrewsbury School 21–2, 30, 58 Shrosbree, Colin 31 Sidney, Sir Philip 21–2 Singapore 73–5 Sked, Alan 155 Smith, Ian Duncan 146, 161 Smith, Zadie 328 Smyth, John 211–12, 213–15, 216, 219 Soames, Nicholas 167 social media 165, 166 social mobility 93–4, 196, 311, 315, 321–2, 330–3 and Commission 336 socialism 86–7, 88, 95–6, 177–8 Socrates 313 song schools 14, 15 Spence, Dr Joseph 159, 160, 204, 241–2, 330–1 Spencer, Charles, 9th Earl 317 Spencer, Herbert 66 Spender, Stephen 70 Spielman, Amanda 252 spies 132–3 sponsorships 238–43 sport 20, 35–6, 233–4, 236–8 Stanley, Henry Morton 39, 40 Starkie, James 169 state schools 2, 6, 68, 83–4, 149, 318–20 and business rates 244 and Europe 268–9 and exams 257 and funds 265, 267 and Oxbridge 301–2 and parents 255–6 and public schools 120, 236–43, 326–7 Stephenson, George 32 Stephenson, Paul 168 Stewart, Rory 292 Stoics Club 142 Stowe School 233 Strachey, Lytton 38 Strategic Communication Laboratories (SCL) 164 Sudan 37, 38–40 Suez Crisis 91–2 super-rich 196–7 Sutton, Thomas 19, 233 Sutton Trust 116, 287, 296, 297, 303 Sweden 344 Taunton Commission 50 Tawney, R.H. 66, 89 taxation 243, 244–7, 248–9, 338–9; see also VAT teachers 257, 340 Thatcher, Margaret 93, 100, 101–2, 136, 138–9, 323 Thorn, John 213, 214 Timothy, Nick 121, 326 Titus Trust 215–16 Tom Brown’s School Days (Hughes) 35 Trades Union Congress 81 Transparency International 201–2 Trump, Donald 127, 163, 164, 280–2, 329 Turner, Andrew 233 Uber 151 UK Independence Party (UKIP) 155, 156, 157, 161 Ukraine 127, 128, 139–40 Ummuna, Chuka 179 United States of America 84, 164, 229, 280–2, 329 universities 260–1, 306, 308, 342–3; see also Oxbridge Utley, Tom 265–6 Vaizey, Ed 99 VAT (value added tax) 69, 107, 121, 183, 243, 247 Vereker, John 72–3 Victoria Cross (VC) 58–9 Villiers, Barbara 91 Villiers, Theresa 161–2 Viner, Katharine 67 Vote Leave 161–3, 164–6, 167–8 Vunipola, Billy 234 Wade, Rebekah 147 Waldegrave, William 342 Wang Sicong 198 Warre, Edmond 53 Warre-Dymond, Capt Godfrey 58 Warren, Justice 116 Wasserman, Gordon, Lord 102 Waterloo, Battle of 33, 42 Watson, Andrew 213 Waugh, Alec 55, 58, 59, 63, 67, 70, 254 wealth gap 309–10 Webb, Sidney 66 Welby, Justin, Archbishiop of Canterbury 79, 193, 212, 214, 216 Weller, Paul 136, 251–2 Wellington, Arthur Wellesley, Duke of 33, 41–2, 53 Wellington College 242 Westminster, Gerald Grosvenor, 6th Duke of 254 Westminster School 17, 18–19, 23, 43, 204 and Oxbridge 300, 302 Whetstone, Rachel 144, 145 Whitehouse, Mary 212 White’s 143–4, 169 Whittingdale, John 161, 177 Who’s Who 289, 292 Wilkinson, Ellen 87–8, 90 Willetts, David 93–4, 307–8 Wilshaw, Sir Michael 120, 205, 240, 340 Wilson, Harold 25, 95, 99, 180, 287 Winchester College 15–17, 23, 28, 81, 257 and abuse 212–14, 215 and bursaries 229, 230–2 and fees 111–12, 113 and international students 199–200 and Oxford 25, 301 and soldiers 52, 53 Witheridge, Rev.


pages: 462 words: 129,022

People, Power, and Profits: Progressive Capitalism for an Age of Discontent by Joseph E. Stiglitz

"Robert Solow", affirmative action, Affordable Care Act / Obamacare, barriers to entry, basic income, battle of ideas, Berlin Wall, Bernie Madoff, Bernie Sanders, business cycle, Capital in the Twenty-First Century by Thomas Piketty, carried interest, central bank independence, clean water, collective bargaining, corporate governance, corporate social responsibility, creative destruction, Credit Default Swap, crony capitalism, deglobalization, deindustrialization, disintermediation, diversified portfolio, Donald Trump, Edward Snowden, Elon Musk, Erik Brynjolfsson, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, Firefox, Fractional reserve banking, Francis Fukuyama: the end of history, full employment, George Akerlof, gig economy, global supply chain, greed is good, income inequality, information asymmetry, invisible hand, Isaac Newton, Jean Tirole, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John von Neumann, Joseph Schumpeter, labor-force participation, late fees, low skilled workers, Mark Zuckerberg, market fundamentalism, mass incarceration, meta analysis, meta-analysis, minimum wage unemployment, moral hazard, new economy, New Urbanism, obamacare, patent troll, Paul Samuelson, pension reform, Peter Thiel, postindustrial economy, price discrimination, principal–agent problem, profit maximization, purchasing power parity, race to the bottom, Ralph Nader, rent-seeking, Richard Thaler, Robert Bork, Robert Gordon, Robert Mercer, Robert Shiller, Robert Shiller, Ronald Reagan, secular stagnation, self-driving car, shareholder value, Shoshana Zuboff, Silicon Valley, Simon Kuznets, South China Sea, sovereign wealth fund, speech recognition, Steve Jobs, The Chicago School, The Future of Employment, The Great Moderation, the market place, The Rise and Fall of American Growth, the scientific method, The Wealth of Nations by Adam Smith, too big to fail, trade liberalization, transaction costs, trickle-down economics, two-sided market, universal basic income, Unsafe at Any Speed, Upton Sinclair, uranium enrichment, War on Poverty, working-age population

But we have seen a far more sinister side, as, for instance, Russia has repeatedly interfered in democratic elections, seemingly in an attempt to undermine confidence in Western democracy. The new technologies can be used for manipulation, not only to enhance economic profits, but also to foster certain views, and cast doubt on others. Those with more money can do this better—and the family of Robert Mercer and others who funded Cambridge Analytica in their secretive and subversive attempt to manipulate the 2016 election have shown how it can be done. Thus, the new technologies have opened a new avenue through which power and money begets more power and money. A host of reforms have been proposed, none convincingly up to the task. Some put greater onus on the platforms. Germany, perhaps not surprisingly given its history, has taken a strong position on the dissemination of hate speech.

., 176, 180 knowledge and growth, 183–86 and productivity, xxiv as public good, 141 Trump’s disdain for, xvii and wealth of nations, 9 knowledge-based economy, 237 knowledge gap, 96, 98 knowledge institutions, undermining of, 233–34 Koch brothers, 20, 43, 279n40 Krueger, Alan, 42 Kurz, Mordecai, 54 Kuznets, Simon, 258n9 Kuznets’s Law, 258n9 labeling of food, 88 labor contracts, 73 labor force participation, 42, 181–83, 193 labor income, 51, 54 labor markets, atomistic, 64–66 Land O’Lakes, 352n23 learning society, creating, 183–86 Lee Se-dol, 315n1 legal system, bypassing by arbitration panels, 56–57 lending, 110–11; See also credit Levin, Carl, 311n6 liberalization of markets, See market liberalization life, quality of, 209–21; See also standards of living life expectancy, 14, 41 Lighthizer, Robert, xvi living standards, See standards of living loans, See credit lobbyists, 85, 102, 107, 206, 220 local market power, 61 location-based policies, 187–88 long-term investors, 106 long-term savers, 106 long-term unemployment, insurance for, 189 loopholes, in 2017 tax bill, xvii–xix, 85, 194, 206, 237 low-income trap, 44 low-skilled workers automation and, 118, 119, 122 competitive labor markets and, 198 globalization and, 21, 82, 86 job polarization and, 119 social justice and, 198 trade agreements and, 80 Luther, Martin, 10 machines, as workers, 119 MacLean, Nancy, 160 macroeconomic factors, 89, 190 macro economy, 194 Madoff, Bernie, 145, 311n4 majority rights, voting reform and, 161 Malthus, Thomas Robert, 9 Manchester, England, 188 mandatory voting, 172 manufacturing, tariffs and, 91 March on Washington for Jobs and Freedom (1963), 176 market concentration, 55–57, 108 market economy, 30 market failures, 209–10, 214–16 market forces, as impersonal, 51 market fundamentalism, 150, 239 market liberalization, xiii, 4, 21–22 marketplace of ideas, 75–76 market power, 47–78 and AI, 123–35 antitrust laws to curb, 68–76 and Big Data, 128 creating wealth vs. taking wealth, 49–50 diminishing share of labor and capital, 52–54 and division of national income pie, 51–52 of employers over workers, 64–67 implicit rules of economic game, 62 increase in, 54–62 as inimical to growth, 62–64, 183 and innovation, 57–60, 63–64 and intellectual property rights, 74–75 and labor markets, 64–67 in marketplace of ideas, 75–76 and mergers, 72–73, 108 need to constrain excesses of, 70–72 and political divide, 234 and private investment in research, 184 reasons for increases in, 61 and rents, 54 and technology, 73–74, 122 and wage suppression, 65–66 markets as basis for economy, xii–xiii excessive faith in, 154 failure to achieve full employment, 193–94 failure to address work–life balance, 192 failure to create prosperity, xxii–xxiv failure to provide public goods, 140–41 government’s role in managing, 180 limits of, 24 as means rather than ends, 24 need to restructure, 244 markups, 55, 62 Marshall, John, 241 mass incarceration, See incarceration MasterCard, 60 materialism, 30 media and marketplace of ideas, 75–76 and myth of American Dream, 225 and society’s checks and balances, 11 Trump’s attacks on, 15 Medicare, 13, 142, 168, 210 men, in labor force, 38, 42 mercantilism, 8, 240 Mercer, Robert, 132 merchant fees, 60, 70 mergers banks’ profits from, 107–8 and market power, 72–73 in media outlets, 75 preemptive, 60–61, 70, 73 vertical, 325n17 Merkel, Angela, 268n42 “Mickey Mouse” provision, 74 Microsoft, 58, 75, 325n17 middle-class societies, 13 migrant labor, 163 minimum wage, 86, 87, 199, 274n21 MIT (Massachusetts Institute of Technology), 16 moats, 48, 57–58, 62–63; See also barriers to entry/competition mobility, place-based policies and, 188 monetary policy, 83, 121 money in politics, 167–70; See also campaign spending agenda for reducing power of, 171–74 campaign spending, 171–73 as cause of current problems, 239 Citizens United case, 166, 169–70, 172 curbing influence of, 176–78 disclosure laws, 171 revolving doors and, 173–74 technology and, 132, 246 voting reform and, 162–63 money laundering, 168, 169 monopoly defined, 55 and income inequities, 198 and intellectual property rights, 74–75 natural, 61, 134 and net neutrality, 148 perfect competition vs., 56 and rents, 52 tech companies and Big Data, 131 monopsony, 64, 198 moral sentiments, 229 moral turpitude, 7, 30, 103, 240 mortgage risk, 107 mortgage system, 216–18 movements, need for new, 174–76 multilateral trade deficit, 90–91 multinational corporations, tax avoidance by, 85, 99, 108 multinational development banks, 106 Murdoch, Robert, 133, 177 Musk, Elon, 266n33 Muslim travel ban, 165 Myriad, 126–27 myths, failings masked by, 224–26 National Defense Education Act, 210 National Federation of Independent Business v.


Mindf*ck: Cambridge Analytica and the Plot to Break America by Christopher Wylie

4chan, affirmative action, Affordable Care Act / Obamacare, availability heuristic, Berlin Wall, Bernie Sanders, big-box store, Boris Johnson, British Empire, call centre, Chelsea Manning, chief data officer, cognitive bias, cognitive dissonance, colonial rule, computer vision, conceptual framework, cryptocurrency, Daniel Kahneman / Amos Tversky, desegregation, Dominic Cummings, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, Etonian, first-past-the-post, Google Earth, housing crisis, income inequality, indoor plumbing, information asymmetry, Internet of things, Julian Assange, Lyft, Marc Andreessen, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, Network effects, new economy, obamacare, Peter Thiel, Potemkin village, recommendation engine, Renaissance Technologies, Robert Mercer, Ronald Reagan, Rosa Parks, Sand Hill Road, Scientific racism, Shoshana Zuboff, side project, Silicon Valley, Skype, uber lyft, unpaid internship, Valery Gerasimov, web application, WikiLeaks, zero-sum game

After pulling late nights and working through the weekend, we sent the report to Bannon the following Monday, and he immediately understood the possibilities of what we could accomplish. He was fully on board. In fact, he called the SCL office after reading the report and was almost giddy. “This is so great, guys,” he kept saying. Now we just had to persuade Robert Mercer. * * * — A COUPLE OF WEEKS after this, one evening in late November 2013, Nix called me at home. “Pack a bag,” he said. “You’re flying to New York tomorrow.” He, Tadas Jucikas, and I were going to present our findings to Robert Mercer and his daughter Rebekah. Nix flew out first thing in the morning, but for some reason he’d booked Jucikas and me on a later flight. We landed at JFK around four in the afternoon, with our meeting scheduled to start at five. As we stood in line at U.S. customs, my phone rang.

I realized he had a bit of a libertarian streak, but we hadn’t talked that much about politics. Then I remembered I had lost my wallet. I called Nix to tell him how everything had gone—and that I needed a new ticket. “Chris, I’m busy, sort it out yourself.” * * * — BANNON’S INTEREST IN OUR work wasn’t merely academic; he had big ideas for SCL. He told Nix of a major right-wing donor who might be persuaded to make an investment in the firm. Robert Mercer was unusual for a billionaire. He’d gotten a Ph.D. in computer science in the early 1970s, then went on to become a cog in the wheel at IBM for twenty-some years. In 1993, he joined a hedge fund called Renaissance Technologies, where he used data science and algorithms to inform his investments—and made a stupid amount of money doing it. Mercer wasn’t one of these wheeler-dealer types who frenetically bought and sold businesses.

And these profs have just told me that I can get tens of millions of Facebook profiles for…a million dollars, give or take. This was a no-brainer. I asked Stillwell if I could run some tests on their data. I wanted to see if we could replicate our results from Trinidad, where we had access to similar types of Internet browsing data. If the Facebook profiles proved as valuable as I hoped, we would not only be able to fulfill Robert Mercer’s desire to create a powerful tool—what was even cooler was that we could mainstream a whole new field of academia: computational psychology. We were standing at the frontier of a new science of behavioral simulation and I was bursting with excitement at the prospect. * * * — FACEBOOK LAUNCHED IN 2004 as a platform to connect students and peers in college. In a few years, the site grew to become the largest social network in the world—a place where almost everyone, even your parents, shared photos, posted innocuous status updates, and organized parties.


pages: 391 words: 123,597

Targeted: The Cambridge Analytica Whistleblower's Inside Story of How Big Data, Trump, and Facebook Broke Democracy and How It Can Happen Again by Brittany Kaiser

Albert Einstein, Amazon Mechanical Turk, Asian financial crisis, Bernie Sanders, bitcoin, blockchain, Boris Johnson, Burning Man, call centre, centre right, Chelsea Manning, clean water, cognitive dissonance, crony capitalism, Dominic Cummings, Donald Trump, Edward Snowden, Etonian, haute couture, illegal immigration, Julian Assange, Mark Zuckerberg, Menlo Park, Nelson Mandela, off grid, open borders, Renaissance Technologies, Robert Mercer, rolodex, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, Snapchat, statistical model, the High Line, the scientific method, WikiLeaks, young professional

“The Facebook Dilemma,” Frontline, PBS, October 29, 2018. 6.Ibid. 7.Ibid. 8.Ibid. 9: PERSUASION 1.James Swift, “Contagious Interviews Alexander Nix,” Contagious.com, September 28, 2016, https://www.contagious.com/news-and-views/interview-alexander-nix. 10: UNDER THE INFLUENCE 1.Jane Mayer, “The Reclusive Hedge-Fund Tycoon Behind the Trump Presidency,” The New Yorker, March 27, 2017, https://www.newyorker.com/magazine/2017/03/27/the-reclusive-hedge-fund-tycoon-behind-the-trump-presidency. 2.Jim Zarroli, “Robert Mercer Is a Force to Be Reckoned with in Finance and Conservative Politics,” NPR.org, May 26, 2017, https://www.npr.org/2017/05/26/530181660/robert-mercer-is-a-force-to-be-reckoned-with-in-finance-and-conservative-politic?t=1562072425069. 3.Gray, “What Does the Billionaire Family Backing Donald Trump Really Want?” 4.Matt Oczkowski, Molly Schweickert, “DJT Debrief Document. Trump Make America Great Again; Understanding the Voting Electorate,” PowerPoint presentation, Cambridge Analytica office, New York, December 7, 2016. 5.Lauren Etter, Vernon Silver, and Sarah Frier, “How Facebook’s Political Unit Enables the Dark Art of Digital Propaganda,” Bloomberg.com, December 21, 2017, https://www.bloomberg.com/news/features/2017–12–21/inside-the-facebook-team-helping-regimes-that-reach-out-and-crack-down. 6.Nancy Scola, “How Facebook, Google, and Twitter ‘Embeds’ Helped Trump in 2016,” Politico, October 26, 2017, https://www.politico.com/story/2017/10/26/facebook-google-twitter-trump-244191. 11: BREXIT BRITTANY 1.Jeremy Herron and Anna-Louise Jackson, “World Markets Roiled by Brexit as Stocks, Pound Drop; Gold Soars,” Bloomberg.com, June 23, 2016, https://www.bloomberg.com/news/articles/2016–06–23/pound-surge-builds-as-polls-show-u-k-to-remain-in-eu-yen-slips. 2.Aaron Wherry, “Canadian Company Linked to Data Scandal Pushes Back at Whistleblower’s Claims: AggregateIQ Denies Links to Scandal-Plagued Cambridge Analytica,” CBC, April 24, 2018, https://www.cbc.ca/news/politics/aggregate-iq-mps-cambridge-wylie-brexit-1.4633388. 13: POSTMORTEM 1.

What was more, according to the article, Cambridge was using that data as a weapon to affect the outcome of the Republican primaries and make Ted Cruz the GOP nominee.1 The story read like the plot of a spy novel. In it, reporter Harry Davies alleged that Cambridge had covertly acquired the Facebook data set and was now “embedded” in the Cruz campaign and deploying a powerful secret psyops weapon for targeting vulnerable voters. Behind the plot was the owner of Cambridge Analytica, Robert Mercer, who was, according to the Davies piece, a Dr. Evil–like American billionaire whose motivation was to disrupt the U.S. political system and advance a fringe right-wing agenda. The method Cambridge had used to acquire the data put it in direct violation of Facebook’s terms of service. CA had contracted with a man who himself violated Facebook’s service agreement when he used a third-party app, the infamous Friends API, to “hoover up” vast amounts of private information.

As it was, the ICO’s investigation determined there was “no evidence of a working relationship between [Cambridge Analytica] and Leave.EU proceeding beyond this initial phase.”2 A few days later, on a Saturday, an investigative journalist named Carole Cadwalladr published an article in the Guardian that took a long, hard look at what she alleged was a connection between Cambridge Analytica, Leave.EU, and Robert Mercer. Coming hot on the heels of the Das Magazin piece, which had slightly rattled Cambridge’s cage with claims that we had stolen user data and weaponized it to unethical ends, the Cadwalladr article was a hard blow. Cadwalladr’s article focused on campaign spending issues in general, including a potential violation even by Vote Leave, the Brexit campaign that had won the designation over Leave.EU.


pages: 918 words: 257,605

The Age of Surveillance Capitalism by Shoshana Zuboff

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

Their efforts suggest how quickly we lose our bearings as institutionalization first establishes a sense of normalcy and social acceptance and then gradually produces the numbness that accompanies habituation. This process begins with business plans and marketing messages, new products and services, and journalistic representations that appear to accept the new facts as given.73 Among this new cohort of mercenaries was Cambridge Analytica, the UK consulting firm owned by the reclusive billionaire and Donald Trump backer Robert Mercer. The firm’s CEO, Alexander Nix, boasted of its application of personality-based “micro-behavioral targeting” in support of the “Leave” and the Trump campaigns during the ramp-up to the 2016 Brexit vote and the US presidential election.74 Nix claimed to have data resolved “to an individual level where we have somewhere close to four or five thousand data points on every adult in the United States.”75 While scholars and journalists tried to determine the truth of these assertions and the role that these techniques might have played in both 2016 election upsets, the firm’s new chief revenue officer quietly announced the firm’s less glamorous but more lucrative postelection strategy: “After this election, it’ll be full-tilt into the commercial business.”

Biddle, “Facebook Uses Artificial Intelligence to Predict Your Future Actions.” 78. “Introducing FBLearner Flow: Facebook’s AI Backbone,” Facebook Code, April 16, 2018, https://code.facebook.com/posts/1072626246134461/introducing-fblearner-flow-facebook-s-ai-backbone. 79. Andy Kroll, “Cloak and Data: The Real Story Behind Cambridge Analytica’s Rise and Fall,” Mother Jones, March 24, 2018, https://www.motherjones.com/politics/2018/03/cloak-and-data-cambridge-analytica-robert-mercer. 80. Carole Cadwalladr, “‘I Made Steve Bannon’s Psychological Warfare Tool’: Meet the Data War Whistleblower,” Guardian, March 18, 2018, http://www.the guardian.com/news/2018/mar/17/data-war-whistleblower-christopher-wylie-faceook-nix-bannon-trump; Kroll, “Cloak and Data.” 81. Matthew Rosenberg, Nicholas Confessore, and Carole Cadwalladr, “How Trump Consultants Exploited the Facebook Data of Millions,” New York Times, March 17, 2018, https://www.nytimes.com/2018/03/17/us/politics/cambridge-analytica-trump-campaign.html; Emma Graham-Harrison and Carole Cadwalladr, “Revealed: 50 Million Facebook Profiles Harvested for Cambridge Analytica in Major Data Breach,” Guardian, March 17, 2018, http://www.theguardian.com/news/2018/mar/17/cambridge-analytica-facebook-influence-us-election; Julia Carrie Wong and Paul Lewis, “Facebook Gave Data About 57bn Friendships to Academic,” Guardian, March 22, 2018, http://www.theguardian.com/news/2018/mar/22/facebook-gave-data-about-57bn-friendships-to-academic-aleksandr-kogan; Olivia Solon, “Facebook Says Cambridge Analytica May Have Gained 37m More Users’ Data,” Guardian, April 4, 2018, http://www.theguardian.com/technology/2018/apr/04/facebook-cambridge-analytica-user-data-latest-more-than-thought. 82.

See also behavioral modification; economies of action; uncontracts means of production: machine intelligence as, 95–96, 97f; serves means of behavioral modification, 8, 9, 11, 19–20, 67, 339, 351. See also means of behavioral modification Meckling, William, 38 media use, international study of “unplugging” from, 445, 446 medical fields: and emotion analytics, 288; and internet of things, 247–251 mental health: depression, 275, 287, 446, 464–465; and Facebook use, 446, 463–465; monitoring of, 412; predictions of, 275 Mercer, Robert, 278 Mercury News, 116 meta-data, 117–118, 245, 272–273, 275 Meyer, Max, 362–363, 363–364, 364–366, 372, 412, 633n39, 634n42, 634n44, 635n45 Meyer, Michelle, 304 m-health (mobile health apps), 248–251 Michaels, Jon, 119 Microsoft, 24, 400; Bing search engine, 95, 162, 163; collaboration with metal-cutting factory, 407–409; Cortana digital assistant, 163–164, 165, 255, 400; Inktomi search engine, 71; and insurers, 217; patents filed by, 411–412; revenues of, 165–166, 405; surveillance capitalism spreads to, 9, 162–163; and voice recognition, 263; Windows 10 operating system, 164–165.


pages: 533

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

3D printing, additive manufacturing, affirmative action, agricultural Revolution, Airbnb, airport security, Andrew Keen, artificial general intelligence, augmented reality, automated trading system, autonomous vehicles, basic income, Bertrand Russell: In Praise of Idleness, bitcoin, blockchain, brain emulation, British Empire, business process, Capital in the Twenty-First Century by Thomas Piketty, cashless society, Cass Sunstein, cellular automata, cloud computing, computer age, computer vision, continuation of politics by other means, correlation does not imply causation, crowdsourcing, cryptocurrency, digital map, distributed ledger, Donald Trump, easy for humans, difficult for computers, Edward Snowden, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, Filter Bubble, future of work, Google bus, Google X / Alphabet X, Googley, industrial robot, informal economy, intangible asset, Internet of things, invention of the printing press, invention of writing, Isaac Newton, Jaron Lanier, John Markoff, Joseph Schumpeter, Kevin Kelly, knowledge economy, lifelogging, Metcalfe’s law, mittelstand, more computing power than Apollo, move fast and break things, move fast and break things, natural language processing, Network effects, new economy, night-watchman state, Oculus Rift, Panopticon Jeremy Bentham, pattern recognition, payday loans, price discrimination, price mechanism, RAND corporation, ransomware, Ray Kurzweil, Richard Stallman, ride hailing / ride sharing, road to serfdom, Robert Mercer, Satoshi Nakamoto, Second Machine Age, selection bias, self-driving car, sexual politics, sharing economy, Silicon Valley, Silicon Valley startup, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, smart contracts, Snapchat, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, technological singularity, the built environment, The Structural Transformation of the Public Sphere, The Wisdom of Crowds, Thomas L Friedman, universal basic income, urban planning, Watson beat the top human players on Jeopardy!, working-age population

I understand that the account ‘@imposterbusters’ has itself been suspended by Twitter. 17. Peter Martinez, ‘Study Reveals Whopping 48M Twitter Accounts Are Actually Bots’, CBS News, 10 March 2017 <http://www.cbsnews. com/news/48-million-twitter-accounts-bots-university-ofsouthern-california-study/?ftag=CNM-00-10aab7e&linkId= 35386687> (accessed 1 December 2017). 18. Carole Cadwalladr, ‘Robert Mercer:The Big Data Billionaire Waging War on Mainstream Media’, The Guardian, 26 February 2017 <https:// www.theguardian.com/politics/2017/feb/26/robert-mercer-breitbart-war-on-media-steve-bannon-donald-trump-nigel-farage> (accessed 1 December 2017). 19. See Leo Kelion and Shiroma Silva, ‘Pro-Clinton Bots “Fought Back but Outnumbered in Second Debate” ’, BBC News, 19 October 2016<http://www.bbc.com/news/technology-37703565> (accessed 1 December 2017); Amanda Hess, ‘On Twitter, a Battle Among Political Bots’, New York Times, 14 December 2016 <https://mobile. nytimes.com/2016/12/14/arts/on-twitter-a-battle-among-politicalbots.html?

MIT Technology Review, 7 Feb. 2017 <https://www.technologyreview.com/s/603431/as-goldman-embracesautomation-even-the-masters-of-the-universe-are-threatened/ ? s e t = 6 0 3 5 8 5 & u t m _ c o n t e n t = bu f f e rd 5 a 8 f & u t m _ m e d i u m = social&utm_source=twitter.com&utm_campaign=buffer> (accessed 1 Dec. 2017). Cadwalladr, Carole. ‘Robert Mercer: The Big Data Billionaire Waging War on Mainstream Media’. The Guardian, 26 Feb. 2017 <https://www. theguardian.com/politics/2017/feb/26/robert-mercer-breitbart-war-onmedia-steve-bannon-donald-trump-nigel-farage> (accessed 1 Dec. 2017). Calabresi, Guido, and Philip Bobbit. Tragic Choices: The Conflicts Society Confronts in the Allocation of Tragically Scarce Resources. New York: W. W. Norton & Company, 1978. OUP CORRECTED PROOF – FINAL, 28/05/18, SPi РЕЛИЗ ПОДГОТОВИЛА ГРУППА "What's News" VK.COM/WSNWS Bibliography 445 Calvo, Rafael A., Sidney D’Mello, Jonathan Gratch, and Arvid Kappas, eds.

See Sasha Issenberg, ‘How Obama’s Team Used Big Data to Rally Voters’, MIT Techology Review, 19 December 2012 <https://www. technologyreview.com/s/509026/how-obamas-team-usedbig-data-to-rally-voters/> (accessed 1 December 2017). 34. ‘Joseph Schumpeter’, Wikipedia, last edited 23 December 2017 <https://en.wikipedia.org/wiki/Joseph_Schumpeter> (accessed 21 January 2018). 35. Pedro Domingos, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World (London: Allen Lane, 2015), 17. 36. Carole Cadwalladr, ‘Robert Mercer:The Big Data Billionaire Waging War on Mainstream Media’, The Guardian, 26 February 2017 <https:// www.theguardian.com/politics/2017/feb/26/robert-mercerbreitbart-war-on-media-steve-bannon-donald-trump-nigel-farage> (accessed 1 December 2017). 37. Edward L. Bernays, ‘The Engineering of Consent’, ANNALS of the American Academy of Political and Social Science 250, no. 1 (1947), 113–20, cited in Zeynep Tufekci, ‘Engineering the Public: Big Data, Surveillance and Computational Politics’, First Monday 19, no. 7 (7 July 2014). 38.


pages: 170 words: 49,193

The People vs Tech: How the Internet Is Killing Democracy (And How We Save It) by Jamie Bartlett

Ada Lovelace, Airbnb, Amazon Mechanical Turk, Andrew Keen, autonomous vehicles, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, central bank independence, Chelsea Manning, cloud computing, computer vision, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, Dominic Cummings, Donald Trump, Edward Snowden, Elon Musk, Filter Bubble, future of work, gig economy, global village, Google bus, hive mind, Howard Rheingold, information retrieval, Internet of things, Jeff Bezos, job automation, John Maynard Keynes: technological unemployment, Julian Assange, manufacturing employment, Mark Zuckerberg, Marshall McLuhan, Menlo Park, meta analysis, meta-analysis, mittelstand, move fast and break things, move fast and break things, Network effects, Nicholas Carr, off grid, Panopticon Jeremy Bentham, payday loans, Peter Thiel, prediction markets, QR code, ransomware, Ray Kurzweil, recommendation engine, Renaissance Technologies, ride hailing / ride sharing, Robert Mercer, Ross Ulbricht, Sam Altman, Satoshi Nakamoto, Second Machine Age, sharing economy, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, smart cities, smart contracts, smart meter, Snapchat, Stanford prison experiment, Steve Jobs, Steven Levy, strong AI, TaskRabbit, technological singularity, technoutopianism, Ted Kaczynski, the medium is the message, the scientific method, The Spirit Level, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, too big to fail, ultimatum game, universal basic income, WikiLeaks, World Values Survey, Y Combinator

In 2008, for example, analysts working for Barack Obama assigned a pair of scores to every voter in the country that predicted how likely they were to cast a ballot, and whether they supported his campaign.7 Hillary Clinton, too, had an extremely sophisticated system of targeting voters online.8 Every election now is a mini arms race. And this time the Republican Party turned to a company, Cambridge Analytica, in order to get the edge on the opposition. It was not a coincidental choice. One of Cambridge Analytica’s key investors is the billionaire businessman and Trump backer Robert Mercer, a famously reclusive computer programmer who made his fortune as co-chief executive of the New York-based hedge fund, Renaissance Technologies. RenTech, as it is known, uses big data and sophisticated algorithms to predict trends in global markets and place winning bets on them. In this world tiny gains, a fraction of a per cent here or there, can yield huge rewards. In 2013 Cambridge Analytica was set up as an offshoot of a company called ‘Strategic Communications Laboratories’ (SCL), which had extensive experience in branding and influencing public opinion, specialising in military and intelligence psychological operations, or ‘psy-ops’ – tasks like persuading young men not to join Al-Qaeda.

If every voter is a data point who receives, not messages from politicians, but a perfectly targeted machine-generated advert, finely tuned and retuned to suit a particular personality and mood, an algorithm which runs itself and improves iteratively, without making any serious effort to engage with you – then elections will become little more than software wars. But the more politics becomes a question of smart analysis and nudges rather than argument, the further power will shift away from those with good ideas and towards those with good data and lots of money. * * * • • • It turned out that Project Alamo was a small piece of a much bigger puzzle in which influential people battled over the shape of reality. Robert Mercer had also invested in Breitbart News – best described as a right-wing Huffington Post that specialises in stories castigating liberals, bad Muslims, and the ‘mainstream media’ – which became a highly influential source of anti-Clinton and pro-Trump news. According to the academic Jonathan Albright, the US election was dominated by a ‘micro-propaganda machine’, a network of thousands of web pages from the radical right hyper-linking to each other and spreading ‘false, hyper-biased, and politically loaded information’.


pages: 1,294 words: 210,361

The Emperor of All Maladies: A Biography of Cancer by Siddhartha Mukherjee

Barry Marshall: ulcers, conceptual framework, discovery of penicillin, experimental subject, iterative process, Joan Didion, life extension, longitudinal study, Louis Pasteur, medical residency, meta analysis, meta-analysis, mouse model, New Journalism, phenotype, randomized controlled trial, Robert Mercer, scientific mainstream, Silicon Valley, social web, statistical model, stem cell, women in the workforce, Year of Magical Thinking, éminence grise

By the early winter of 1948, more children were at his clinic: a three-year-old boy brought with a sore throat, a two-and-a-half-year-old girl with lumps in her head and neck, all eventually diagnosed with childhood ALL. Deluged with antifolates from Yella and with patients who desperately needed them, Farber recruited additional doctors to help him: a hematologist named Louis Diamond, and a group of assistants, James Wolff, Robert Mercer, and Robert Sylvester. Farber had infuriated the authorities at Children’s Hospital with his first clinical trial. With this, the second, he pushed them over the edge. The hospital staff voted to take all the pediatric interns off the leukemia chemotherapy unit (the atmosphere in the leukemia wards, it was felt, was far too desperate and experimental and thus not conducive to medical education)—in essence, leaving Farber and his assistants to perform all the patient care themselves.

., 26 Marlboro Man, 251 Marmite, 28 Marshall, Barry, 276, 281, 282–84, 456 Martin, Steve, 358 Masi, Phil, 97–98 Massachusetts, 325 Massachusetts General Hospital, 3, 56, 223, 320, 390, 398, 403, 437, 451 mastectomies, 49, 419 of Atossa, 5, 41–42, 463 disfigurement from, 65–66, 294 prophylactic, 457–58, 464 radical, 23, 64–72, 73, 109–10, 173, 193–95, 196, 197, 198–201, 202, 218, 219, 225, 294, 463 simple (local), 67, 197, 201, 464 success rate of, 66–69 Master Settlement Agreement (MSA), 273 Matter, Alex, 432–33 mauve, 81–82 Mayer, Robert, 130–31, 311, 326, 328 Mayfield, Jerry, 441, 442–43 MD Anderson Cancer Center, 147, 366, 438 measurement: of leukemia, 19 of negative claims, 167–68 of radiation, 74 in War on Cancer, 227, 231, 232–33 Medical and Chirurgical Society, 157 Medical Journal of Australia, 283 Medical Research Council (British), 131, 243–44 Medical World News, 349 medicine: synthetic chemistry and, 83–84 as technological art, 462 Mek protein, 387, 454 melanoma, 451 Memorial Sloan-Kettering, 92, 135, 138, 167n, 184, 234, 424 Mendel, Gregor, 343–44, 346, 364, 366, 369 meningiomas, 71 menopausal symptoms, 456 Mercer, Robert, 34 Merck, 21 Meselson, Matthew, 345 meta-analysis, 261 metastasis, metastases, 16, 38, 39, 55, 58, 123, 135, 136, 154, 161, 196–97, 204, 223, 391, 442, 465, 467 of breast cancer, 67, 76, 161, 217, 218, 302–3, 314, 322, 325, 329, 419, 422, 424, 463, 465 of Hodgkin’s lymphoma, 163 as inevitable, 79 of lung cancer, 208, 256, 267, 268, 307, 389–90, 403 methotrexate, 127, 132–33, 137, 138, 140, 162, 164, 219, 220, 338 Mexico, cigarette regulation in, 274 Meyer, Willy, 65, 78–79, 80, 219 mice, transgenic, 382–83, 384 microtubules, 140 Middle Ages, medical knowledge in, 49–50, 51–53 Million Women Study, 456 Milosz, Czeslaw, 116 Milstein, Cesar, 417, 419 Ministry of Health, British, 243 Ministry of Health, Mexican, 274 Minot, George, 27–28, 29 Mississippi, antitobacco lawsuit of, 272–73 mitosis, 451 mitosis, pathological, 348, 351, 355, 359, 387, 391 see also hyperplasia, pathological Mizutani, Satoshi, 353 molecular biology, “central dogma” of, 346, 352, 354, 357 molecular pumps, 442 molecules: decoy, 31, 87 structural view of, 432 as switches, 28 see also receptors Moloney, William, 143 Monod, Jacques, 20, 345, 346 mononucleosis, 175 Montagnier, Luc, 318 Moore, Charles, 64 Moore, Michael, 272–73 MOPP, 164–66, 208 Morbid Anatomy of Some of the Most Important Parts of the Human Body, The (Baillie), 53 Morgan, Thomas Hunt, 344, 346, 347–48, 364 Morison, Robert, 116 morphine, 63, 149, 225 mortality rates, of cancer, xi, 25, 105, 228–30, 293, 401 age-adjusted, 230–31, 232–33, 330 of breast cancer, 296, 297, 300–301, 401–2 dynamic equilibrium in, 330–31 mortality rates, of tuberculosis, 229 Morton, William, 56 motility, of cancer cells, 386, 387, 388 see also metastasis, metastases MRIs, 457, 464 Mukherjee, Leela, 398 Mukherjee, Siddhartha: Berne and, 467–70 and daughter’s birth, 398–99 as oncology fellow, 2–5, 168, 190, 305–6, 307–8, 337, 390, 398–99, 437–38, 467 Orman and, 152–53, 399–400 palliative care suggested by, 223–24 Reed and, 2–3, 7, 17–18, 127, 168–69, 190, 337, 338–39, 400, 448–49 Sorenson and, 153–55 tobacco-cancer link in patients of, 274–75 Muller, Hermann Joseph, 347–48 multidose regimens, see chemotherapy, high-dose multidrug regimens in multiple myeloma, 309, 443–44 mummies, cancer in, 43, 45 Murayama, Hashime, 288 Murphy, Mary Lois, 92 mustard gas, see nitrogen mustard mutagens, mutagenesis, 278, 303, 347, 348, 362, 364, 406, 456 mutation, genetic, 377 in bacteria, 277–78 Cancer Genome Atlas and, 450–54 causes of, see mutagens, mutagenesis driver (active), 453 frequency of, 451–52 in fruit flies, 347 functional vs. structural view of, 455 as governing all aspects of cancer, 387–88, 462 as mechanism of carcinogenesis, 6, 39, 176, 278, 357, 362, 370, 380–83, 384–88, 390–92, 403, 406, 449–50, 462, 464–65 passenger (passive), 452–53 see also oncogenes myc (c-myc) gene, 382–83, 384, 391, 410, 412, 453–54, 458 mycobacteria, 84, 131 myelodysplasia, 306, 309, 312 myeloid cells, 16–17 Myriad Genetics, 381 Nathan, David, 140 National Alliance of Breast Cancer Organizations (NABCO), 327 National Breast Cancer Coalition (NBCC), 426, 429 National Cancer Act (1971), 188, 189 National Cancer Institute (NCI), 15, 114, 130, 158, 159, 166, 177, 188, 228, 231, 318, 325, 330, 339, 374, 393, 443 chemotherapy protocols of, 132–42, 143–50, 164–66, 206–8, 219–20, 232, 310, 317 Clinical Center of, 128–29, 139, 145, 162, 165, 260 creation of, 25–26 Institutional Board of, 137 mammography project (BCDDP) of, 296–98, 302 Pap smear trial of, 289–90 preventative strategies neglected by, 233–34 Special Virus Cancer Program of, 175–76, 280–81, 356, 357 National Cancer Institute Act (1937), 25 National Health Service, British, 294 National Institutes of Health (NIH), 25n, 121, 187, 188, 202–3, 260, 319 National Library of Medicine, 261 National Program for the Conquest of Cancer, 184 National Science Foundation (NSF), 121 National Surgical Adjuvant Breast and Bowel Project (NSABP), 200–201 National Tuberculosis Association, 259 natural selection, 248 Nature, 354, 379 Nature Medicine, 435 nausea, from chemotherapy, 165, 205–6, 209, 226, 305 Nazis, 290 Neely, Matthew, 25, 173 negative statistical claims, 197–98 Nelson, Marti, 424–25, 429 “funeral procession” for, 425–26 neoplasia, 16, 42, 385 neu, 410–11, 412, 413, 420 neuroblastomas, 410, 413 New England Journal of Medicine, 35–36, 161, 229, 330, 385 Newton, Isaac, 370 New York, HIP in, 294–96, 297 New York, N.Y., AIDS in, 316, 318 New York Amsterdam News, 286 New York Times, 24, 26–27, 105, 117, 119–20, 180–81, 183, 319, 327, 455 Neyman, Jerzy, 197–98 nicotine: addictive properties of, 270–71 see also cigarettes; smoking; tobacco; tobacco industry Nisbet, Robert, 193 nitrogen mustard, 207, 220, 257 bone marrow affected by, 88, 90 DNA damaged by, 163, 406 hyperplasia as halted by, 163, 406 as mustard gas, 87–88, 89–90, 162–63 nitrosoguanidine derivatives, 278 Nixon, Richard M., 180–81, 183, 184, 187–88 Nobel Prize, 28, 87, 91, 176, 348, 363 Norris Center, 323 Norton, Larry, 327, 426 Novartis, 436, 439 Nowell, Peter, 365 NSABP-04 trial, 200–201, 203, 220 Nuland, Sherwin, 38 Ochsner, Alton, 256–57 Oedipus the King (Sophocles), 321 Office of Scientific Research and Development (OSRD), 90, 119 Oliver Twist (Dickens), 239 oncogenes, 363, 366, 370–71, 380, 384, 402, 409–11, 412, 415, 431, 439, 443, 450, 453, 454, 462, 466 amplification of, 416 pathological hyperplasia induced by, 357–59, 372, 431 proto-, see proto-oncogenes see also specific genes oncology, oncologists, 304, 433 AIDS and, 316–17 death and, 4, 306–8, 337–38 fellowships in, 2–5, 168 origin of term, 47 overconfidence of, 223, 226, 231–32, 234, 308, 310 palliative care and, 224–26, 307 patients’ relationships with, 199, 202, 209, 306–8, 449 radiation, see radiation therapy OncoMouse, 382–83, 384 onkos, 47 etymology of, 466–67 “On Some Morbid Appearances of the Absorbent Glands and Spleen” (Hodgkin), 157 opiates, 226 Oregon Health and Science University (OHSU), 434 Orman, Ben, 151–53, 155, 399–400 Osler, William, 45 osteosarcomas (bone tumors), 43 ovarian cancer, 59, 162, 346, 381, 450, 451, 457 ovaries, removal of, 214, 215 Pacific yew tree, 206 Pack, George, 70–71 Padhy, Lakshmi Charon, 410–11 Page, Irvine, 187 paleopathology, 42 palliative care, 223–26, 231, 307 drug trials for, 226 pancreas, 154, 414 pancreatic cancer, 154, 158, 450, 451, 465 Panel of Consultants, 184, 188 Panzer, Fred, 270 Papanicolaou, George, 286–90, 291, 384–85, 386, 401 Papanicolaou, Maria, 287 papillomavirus, 174, 349n, 381n Pap smears, 228, 286, 287–90, 296, 303, 331, 381, 385, 401 Paré, Ambroise, 49 Paris, University of, 51 Park, Roswell, 24, 45 Parliament cigarettes, 269 Pasteur, Louis, 57 pathology, pathologists, 11–12, 14 Hodgkin’s approach to, 156–57 Patterson, James, 183 PCP (Pneumocystis carinii), 165, 315–16 Pearson, Egon, 197–98 pectoralis major, 64–65 pectoralis minor, 64 pellagra, 110 penicillin, 21–22, 122, 129, 465–66 Penicillium, 122 Pepper, Claude, 26n peptic ulcers, 281–84 Perkin, William, 81–82, 83 pernicious anemia, 27–28, 31 Peru, 42–43 pesticides, 456–57 Peters, Vera, 159–60 Peters, William, 311–15, 319–20, 321, 325, 326, 329 Peto, Richard, 241, 249, 273–74, 462 pharmaceutical industry, 426 see also specific companies Philadelphia chromosome, 365, 430–31 Philip Morris, 251, 269–71, 273 phlegm, 48 phosphorylation, 358–59, 361, 380, 418, 431–32 Piccolo, Brian, 181 Pim, Isabella, 58 Pinkel, Donald, 123, 167–68, 170, 178 pitchblende, 74 pituitary cells, 414 placebos, in randomized trials, 131–32, 319 placenta, 135, 219 platelets, 18 Plato, 370 Pneumocystis carinii (PCP), 165, 315–16 pneumonectomy, 242 pneumonia, 45 PCP, 165, 315–16 Poet Physicians, 60 polio, 22, 229, 342, 466 national campaign against, 93–94, 175 Popper, Karl, 370 population, U.S., aging of, 230 Postmortem Examination, The (Farber), 19 Pott, Percivall, 173, 237–39, 241, 276, 447 precancer, 286, 306, 455 Auerbach’s research on, 258–59, 284, 289 prednisone, 127, 140, 143, 149 see also VAMP regimen Premarin, 213 preventive medicine, 281 epidemiology and, 290 see also cancer prevention procarbazine, 162, 164 product-liability lawsuits, 269–73, 401 progesterone, 456 “Progress Against Cancer?”


pages: 305 words: 79,303

The Four: How Amazon, Apple, Facebook, and Google Divided and Conquered the World by Scott Galloway

activist fund / activist shareholder / activist investor, additive manufacturing, Affordable Care Act / Obamacare, Airbnb, Amazon Web Services, Apple II, autonomous vehicles, barriers to entry, Ben Horowitz, Bernie Sanders, big-box store, Bob Noyce, Brewster Kahle, business intelligence, California gold rush, cloud computing, commoditize, cuban missile crisis, David Brooks, disintermediation, don't be evil, Donald Trump, Elon Musk, follow your passion, future of journalism, future of work, global supply chain, Google Earth, Google Glasses, Google X / Alphabet X, Internet Archive, invisible hand, Jeff Bezos, Jony Ive, Khan Academy, longitudinal study, Lyft, Mark Zuckerberg, meta analysis, meta-analysis, Network effects, new economy, obamacare, Oculus Rift, offshore financial centre, passive income, Peter Thiel, profit motive, race to the bottom, RAND corporation, ride hailing / ride sharing, risk tolerance, Robert Mercer, Robert Shiller, Robert Shiller, Search for Extraterrestrial Intelligence, self-driving car, sentiment analysis, shareholder value, Silicon Valley, Snapchat, software is eating the world, speech recognition, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Stewart Brand, supercomputer in your pocket, Tesla Model S, Tim Cook: Apple, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, undersea cable, Whole Earth Catalog, winner-take-all economy, working poor, young professional

March 10, 2016. https://www.usatoday.com/story/money/markets/2016/03/10/13-big-companies-keep-growing-like-crazy/81544188/. 18. Grassegger, Hannes, and Mikael Krogerus. “The Data That Turned the World Upside Down.” Motherboard. January 28, 2017. https://motherboard.vice.com/en_us/article/how-our-likes-helped-trump-win. 19. Cadwalladr, Carole. “Robert Mercer: The big data billionaire waging war on mainstream media.” Guardian. February 26, 2017. https://www.theguardian.com/politics/2017/feb/26/robert-mercer-breitbart-war-on-media-steve-bannon-donald-trump-nigel-farage. 20. “As many as 48 million Twitter accounts aren’t people, says study.” CNBC. April 12, 2017. http://www.cnbcafrica.com/news/technology/2017/04/10/many-48-million-twitter-accounts-arent-people-says-study/. 21. L2 Analysis of LinkedIn Data. 22.


pages: 558 words: 168,179

Dark Money: The Hidden History of the Billionaires Behind the Rise of the Radical Right by Jane Mayer

affirmative action, Affordable Care Act / Obamacare, American Legislative Exchange Council, anti-communist, Bakken shale, bank run, battle of ideas, Berlin Wall, Capital in the Twenty-First Century by Thomas Piketty, carried interest, centre right, clean water, Climategate, Climatic Research Unit, collective bargaining, corporate raider, crony capitalism, David Brooks, desegregation, diversified portfolio, Donald Trump, energy security, estate planning, Fall of the Berlin Wall, George Gilder, housing crisis, hydraulic fracturing, income inequality, Intergovernmental Panel on Climate Change (IPCC), invisible hand, job automation, low skilled workers, mandatory minimum, market fundamentalism, mass incarceration, Mont Pelerin Society, More Guns, Less Crime, Nate Silver, New Journalism, obamacare, Occupy movement, offshore financial centre, oil shale / tar sands, oil shock, plutocrats, Plutocrats, Powell Memorandum, Ralph Nader, Renaissance Technologies, road to serfdom, Robert Mercer, Ronald Reagan, school choice, school vouchers, The Bell Curve by Richard Herrnstein and Charles Murray, The Chicago School, the scientific method, University of East Anglia, Unsafe at Any Speed, War on Poverty, working poor

Soon after, Democrats began criticizing the carried-interest tax loophole and other accounting gimmicks that helped financiers amass so much wealth. In the wake of the 2008 market crash, as Obama and the Democrats began talking increasingly about Wall Street reforms, financiers like Schwarzman, Cohen, and Singer who flocked to the Koch seminars had much to lose. The hedge fund run by another of the Kochs’ major investors, Robert Mercer, an eccentric computer scientist who made a fortune using sophisticated mathematical algorithms to trade stocks, also seemed a possible government target. Democrats in Congress were considering imposing a tax on stock trading, which the firm he co-chaired, Renaissance Technologies, did in massive quantities at computer-driven high frequency. Although those familiar with his thinking maintained that his political activism was separate from his pecuniary interests, Mercer had additional business reasons to be antigovernment.

(After The New Yorker published my investigative article on the Kochs, “Covert Operations,” that August, The Daily Caller was the chosen receptacle for the retaliatory opposition research on me, although, after it proved false, the Web site decided not to run it.) Only in 2011 did it surface that in New York, at least, the “Ground Zero mosque” controversy had been stirred up for political gain in part by money from Robert Mercer, the co-CEO of the $15 billion Long Island hedge fund Renaissance Technologies. To aid a conservative candidate in New York, Mercer gave $1 million to help pay for ads attacking supporters of the “Ground Zero mosque.” A former computer programmer who had a reputation as a brilliant mathematician and an eccentric loner, Mercer was a relative newcomer to the Koch summits. But he was immediately impressed by the organization.

The club had developed the use of fratricide as a tactic to keep officeholders in line after becoming frustrated that many candidates it backed became more moderate in office. It discovered that all it had to do was threaten a primary challenge, and “they start wetting their pants,” one founder joked. Its top funders included many in the Koch network, including the billionaire hedge fund managers Robert Mercer and Paul Singer and the private equity tycoon John Childs. The Young Guns portrayed their opposition to compromise as a matter of pure principle, but beneath the surface huge vested interests were at play. The president and Boehner were close to negotiating what they called a “grand bargain” that anticipated closing some tax loopholes. The Young Guns were categorically opposed to reforms that might cut into the profits of hedge funds and private equity firms.


pages: 788 words: 223,004

Merchants of Truth: The Business of News and the Fight for Facts by Jill Abramson

23andMe, 4chan, Affordable Care Act / Obamacare, Alexander Shulgin, Apple's 1984 Super Bowl advert, barriers to entry, Bernie Madoff, Bernie Sanders, Charles Lindbergh, Chelsea Manning, citizen journalism, cloud computing, commoditize, corporate governance, creative destruction, crowdsourcing, death of newspapers, digital twin, diversified portfolio, Donald Trump, East Village, Edward Snowden, Ferguson, Missouri, Filter Bubble, future of journalism, glass ceiling, Google Glasses, haute couture, hive mind, income inequality, information asymmetry, invisible hand, Jeff Bezos, Joseph Schumpeter, Khyber Pass, late capitalism, Marc Andreessen, Mark Zuckerberg, move fast and break things, move fast and break things, Nate Silver, new economy, obamacare, Occupy movement, performance metric, Peter Thiel, phenotype, pre–internet, race to the bottom, recommendation engine, Robert Mercer, Ronald Reagan, Saturday Night Live, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, skunkworks, Snapchat, social intelligence, social web, Steve Jobs, Steven Levy, technoutopianism, telemarketer, the scientific method, The Wisdom of Crowds, Tim Cook: Apple, too big to fail, WikiLeaks

The day after Coppins’s piece was published, Trump’s handler Sam Nunberg, who had granted the reporter access, resigned, calling the story “an incredible pejorative hit piece” and wishfully declaring the BuzzFeed writer’s “professional reputation . . . null and void.” Trump had already blacklisted the site. In mounting his counterstrike, Trump would call upon a new ally, one who was itching to make use of a well-stocked arsenal. He enlisted Bannon, who had positioned himself to play a big role in politics with funding from Robert Mercer. Mercer wanted to expand Breitbart, and Bannon, seeing his opportunity, wrote up a business plan on his friend’s behalf. That summer Mercer ponied up the $10 million and made Bannon a co-owner and director of the company. In the immediate aftermath of the Coppins piece, Bannon’s Breitbart marshalled an impressive multipronged takedown of the BuzzFeed author. In six distinct stories, all given significant play on the site’s homepage, Breitbart rebutted the smear job.

., 402 Knight Ridder newspaper chain, 26, 67, 226 sale of, 235–36 Knoxville, Johnny, 51, 57, 180 Koch brothers, 382 Kosinski, Michal, 278–79 Kurtz, Howard, 92, 93 Kushner, Jared, 224, 416 Laessig, Gavon, 314–15 Landman, Jon, 192 Larry King Live (TV show), 92 Larsen, Kaj, 355, 362 Last Week Tonight (TV show), 389 Lattman, Peter, 343–44 Laurence, Guy, 358 Law of the Few, 112 Leen, Jeff, 233 Lelyveld, Joe, 8, 152, 191, 372, 375, 429 Lennard, Natasha, 361–62 Leonhardt, David, 212, 381 Leopold, Jason, 178, 345 Lerer, Kenneth, 20–23, 24, 25, 31, 103, 123, 328, 344 Levien, Meredith Kopit, 375, 376 Levy, Cliff, 172 Lewandowski, Corey, 409 Lewinsky, Monica, 195, 239, 284 Lewis, Anthony, 421 Lewis, Luke, 311 Lexington Herald-Leader, 235 LGBT rights, 140 Libby, Lewis “Scooter,” 93, 198 Liberman, Megan, 135 Liberty Film Festival, 285 Libya, kidnapping of correspondents in, 208 Lichtblau, Eric, 215, 383–85 Lieberman, Joseph, 51–52 Lifetime, 335 Lily, The, 425 Lipton, Eric, 390 Lipton, Martin, 63 Lockheed Martin, 54–55 London Underground, 2005 terrorist attack in, 55 long-form journalism: as Marilyn Thompson’s specialty, 233–37 Post’s reputation for, 233 speeded-up news cycle as enemy of, 237, 417 Los Angeles Times, 3–4, 25, 236, 383 Baquet as editor of, 199 Tribune company’s purchase of, 226 wall between news and advertising departments crossed by, 71 Love, Reggie, 177 Loxodo (Post metrics tool), 267, 414 Luo, Patrick, 206 Lytvynenko, Jane, 340–41 Ma, Christopher, 95–96 Ma, Olivia, 95 McCain, John, 131, 132 McChrystal, Stanley, 130 McClatchy newspaper chain, 80, 94, 226, 236 McConnell, Mitch, 236 Macedonia, fake news industry in, 297–98 machine learning, 34–35, 109, 330–31 McInnes, Gavin, 42–43, 147, 148 American Conservative column of, 50 in buyback of Vice, 47 in exit from Vice, 58, 181, 369 as Proud Boys founder, 368 racism and misogyny of, 43–44, 46, 48–49, 50, 59 as Vice Media cofounder, 43–44 McIntire, Mike, 379 McKinsey & Company, 68–69, 71, 72, 193, 213 Magazine, Das, 279 Maher, Bill, 44, 178, 347 Mainland, Lexi, 203–4 Manafort, Paul, 382 Mansformation (TV show), 335 Marlow, Alex, 287 Marlow, Cameron, 16, 17, 104 Marra, Greg, 105 Marshall, Josh, 73, 94 Martel, Ned, 250 Martin, Trayvon, 111 Mashable, 344, 367 Massie, Chris, 313, 316 Mastromonaco, Alyssa, 177, 348, 363 Mayer, Jane, 196, 307, 382 Meet the Press (TV show), 339 Meme Magic Secrets Revealed (Gionet), 312 memes, definition of, 17 Menkes, Suzy, 211 Mercer, Robert, 306, 307 Mercer family, 279, 298, 307, 374, 382 Me Too movement, 210, 361, 392, 425, 426 metrics: Chartbeat and, 243–47, 262, 266 Post’s use of, 266–68, 414, 416 Times’s use of, 267, 425 Meyer, Eugene, 83 Miami Herald, 201 Mic, 275 Miller, Judith, 79, 80, 93, 385 Miller, Katherine, 305, 308–9, 316–17, 321 Miller, Zeke, 129, 135, 339 mimetic desire, 273 MIT Media Lab, 16, 18 Mohonk Group, 76 Mojica, Jason: journalistic credibility of, 356 sexual harassment accusations against, 361–63 Veltroni’s affair with, 359–61, 362 as Vice News head, 352, 356–57 Monde, Le, 113 Moore, Roy, 416, 425 Moretti, Eddy, 50, 160 Morgenson, Gretchen, 190 Morris, Errol, 180 Morris, Hamilton, 180–81 Morton, John, 261 Morton, Thomas, 148 appointed Vice website editor, 150 as archetypical Vice reader, 147 Gross Jar and, 149–50 Gullah moonshine video of, 155–56 and HBO weekly Vice show, 355 immersive videos of, 155–58, 171–72 inaccurate Uganda documentary by, 172 as media star, 180–81 on-air persona of, 155 Vice articles by, 150 Vice articles of, 151–52 Vice’s hiring of, 147–48 Mossberg, Walt, 240 Mother Jones, 324 Moynihan, Michael, 351 MSNBC, 377 MTV, 51–52, 57, 152–53, 154 Mueller, Robert, 382, 416 Muir, David, 427 Murdoch, Rupert, 28, 60, 67, 154, 177, 229, 420, 427 Vice investment of, 366 Wall Street Journal acquired by, 183, 229 Murphy, Eileen, 203 MySpace, 154 Narisetti, Raju, 266 digitally-experienced news staff hired by, 247–48, 249 in exit from Post, 251 named Post managing editor, 238 revenue-generating projects pushed by, 250 staff cuts by, 243 website metrics as focus of, 242–43, 245 website traffic increased by, 250–51 National Public Radio, 77 National Security Agency (NSA), Snowden leaks and, 80, 215, 259–60, 268, 382 native advertising, 40–41, 52, 71, 412–13 Abramson’s opposition to, 214, 215 authenticity and, 160 BuzzFeed’s use of, 120–23, 136–37, 337, 343 importance of verisimilitude in, 121, 136 as needing to harmonize with surrounding content, 161 Obama 2012 campaign and, 136–37 Vice Media and, 158–59 virality and, 122–23 Needleman, Deborah, 210 Negroponte, Nicholas, 16 Netflix, 329, 344 network news, declining audience for, 153 Nevins, Sheila, 178 NewFronts, 336 New Museum of Contemporary Art, 19 New Republic, 135, 139 News about the News, The (Downie and Kaiser), 89 news cycle, speeding up of, 5, 32–33, 98, 133–34, 185, 237 accuracy as victim of, 238 as enemy of investigative journalism, 383 internet and, 239–40 news fatigue, 26 NewsFeed (podcast), 342 news media: cuts to foreign desks by, 174 digital, see digital news media emotionally charged stories in, 111 female-centric projects of, 425 imperilled watchdog function of, 89 internet and unbundling of, 52 loss of public trust in, 3, 4, 80, 95, 185, 386–87, 424, 426–27 news media (cont.)


pages: 584 words: 187,436

More Money Than God: Hedge Funds and the Making of a New Elite by Sebastian Mallaby

Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, currency peg, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, John Meriwether, Kenneth Rogoff, Kickstarter, Long Term Capital Management, margin call, market bubble, market clearing, market fundamentalism, merger arbitrage, money market fund, moral hazard, Myron Scholes, natural language processing, Network effects, new economy, Nikolai Kondratiev, pattern recognition, Paul Samuelson, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Mercer, rolodex, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, survivorship bias, technology bubble, The Great Moderation, The Myth of the Rational Market, the new new thing, too big to fail, transaction costs

He had no use for ideas that came from academic finance: For a while the faculty in East Setauket plowed through the academic finance journals and met weekly to discuss the latest articles, but then it abandoned this as fruitless. The Renaissance researchers built systems that were in a class of their own. “I can only look at them and realize that you have the gods of the business and then you have mere mortals like me,” Wadhwani said, echoing the view of the entire industry.20 IN 1993 SIMONS MADE TWO IMPORTANT ADDITIONS TO HIS brain trust: Peter Brown and Robert Mercer. They came from IBM’s research center, and they drove much of the success of Medallion over the next years, eventually taking the reins when Simons opted for retirement. The two men complemented each other well. Brown was a magnesium flare of energy: He slept five hours per night, riffed passionately on every topic of the day, and for a while got around the office on a unicycle. Mercer was the calm half of the duo: He was an icy cold poker player; he never recalled having a nightmare; his IBM boss jokingly called him an automaton.

Shaw grew out of statistical arbitrage in equities, with strong roots in fundamental intuitions about stocks, Renaissance grew out of technical trading in commodities, a tradition that treats price data as paramount.28 Whereas D. E. Shaw hired quants of all varieties, usually recruiting them in their twenties, the crucial early years at Renaissance were largely shaped by established cryptographers and translation programmers—experts who specialized in distinguishing fake ghosts from real ones. Robert Mercer echoes some of Wepsic’s wariness about false correlations: “If somebody came with a theory about how the phases of Venus influence markets, we would want a lot of evidence.” But he adds that “some signals that make no intuitive sense do indeed work.” Indeed, it is the nonintuitive signals that often prove the most lucrative for Renaissance. “The signals that we have been trading without interruption for fifteen years make no sense,” Mercer explains.

Later versions did use some, but they played a far smaller role than in traditional translation programs. 27. Wepsic interview. 28. John Magee, a leading technician of the 1950s, made a point of reading the newspapers two weeks late in order to be sure that knowledge of the economy would not cloud his judgment. 29. Mercer says, “We will contemplate any proposed signal. But if somebody comes with a theory that does not make intuitive sense, we would examine it especially carefully.” (Robert Mercer, interview with the author, July 28, 2008.) The same willingness to trade on statistical evidence was shared by earlier contributors to Medallion’s success. For example, Elwyn Berlekamp recalls, “Mostly we looked at statistics at Medallion. We found that attempts to look at fundamentals did not get us very far.” Elwyn Berlekamp, interview with the author, July 24, 2008. It is also interesting that Brown and Mercer’s coauthors who followed them to Renaissance, Stephen and Vincent Della Pietra, explicitly presented their experience with statistical machine translation as relevant to finding order in other types of data, including financial data.


pages: 374 words: 114,600

The Quants by Scott Patterson

Albert Einstein, asset allocation, automated trading system, beat the dealer, Benoit Mandelbrot, Bernie Madoff, Bernie Sanders, Black Swan, Black-Scholes formula, Blythe Masters, Bonfire of the Vanities, Brownian motion, buttonwood tree, buy and hold, buy low sell high, capital asset pricing model, centralized clearinghouse, Claude Shannon: information theory, cloud computing, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, Doomsday Clock, Edward Thorp, Emanuel Derman, Eugene Fama: efficient market hypothesis, fixed income, Gordon Gekko, greed is good, Haight Ashbury, I will remember that I didn’t make the world, and it doesn’t satisfy my equations, index fund, invention of the telegraph, invisible hand, Isaac Newton, job automation, John Meriwether, John Nash: game theory, Kickstarter, law of one price, Long Term Capital Management, Louis Bachelier, mandelbrot fractal, margin call, merger arbitrage, money market fund, Myron Scholes, NetJets, new economy, offshore financial centre, old-boy network, Paul Lévy, Paul Samuelson, Ponzi scheme, quantitative hedge fund, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, risk-adjusted returns, Robert Mercer, Rod Stewart played at Stephen Schwarzman birthday party, Ronald Reagan, Sergey Aleynikov, short selling, South Sea Bubble, speech recognition, statistical arbitrage, The Chicago School, The Great Moderation, The Predators' Ball, too big to fail, transaction costs, value at risk, volatility smile, yield curve, éminence grise

Renaissance has applied that skill to enormous strings of market numbers, such as tick-by-tick data in oil prices, while looking at other relationships the data have with assets such as the dollar or gold. Another clue can be found in the company’s decision in the early 1990s to hire several individuals with expertise in the obscure, decidedly non–Wall Street field of speech recognition. In November 1993, Renaissance hired Peter Brown and Robert Mercer, founders of a speech recognition group at IBM’s Thomas J. Watson Research Center in Yorktown Heights, New York, in the hills of Westchester County. Brown came to be known as a freakishly hard worker at the fund, often spending the night at Renaissance’s East Setauket headquarters on a Murphy bed with a whiteboard tacked to the bottom of it. Worried about his health, he became an avid squash player because he deduced that it was the most efficient method of exercising.

There were other big changes in Simons’s life, hints that he was preparing to step down from the firm he’d first launched in 1982. In 2008, he’d traveled to China to propose a sale of part of Renaissance to the China Investment Corp., the $200 billion fund owned and run by the Chinese government. No deal was struck, but it was a clear sign that the aging math whiz was ready to step aside. Indeed, later in the year Simons retired as CEO of Renaissance, replaced by the former IBM voice recognition gurus Peter Brown and Robert Mercer. Perhaps most shocking of all, the three-pack-a-day Simons had quit smoking. Meanwhile, other top quants mixed and mingled. Neil Chriss, whose wedding had seen the clash of Taleb and Muller over whether it was possible to beat the market, held session at a table with several friends. Chriss was a fast-rising and brilliant quant, a true mathematician who’d taught for a time at Harvard. He’d recently launched his own hedge fund, Hutchin Hill Capital, which received financial backing from Renaissance and had knocked the cover off the ball in 2008.


pages: 486 words: 150,849

Evil Geniuses: The Unmaking of America: A Recent History by Kurt Andersen

affirmative action, Affordable Care Act / Obamacare, airline deregulation, airport security, always be closing, American ideology, American Legislative Exchange Council, anti-communist, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, basic income, Bernie Sanders, blue-collar work, Bonfire of the Vanities, bonus culture, Burning Man, call centre, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, centre right, computer age, coronavirus, corporate governance, corporate raider, COVID-19, Covid-19, creative destruction, Credit Default Swap, cryptocurrency, deindustrialization, Donald Trump, Elon Musk, ending welfare as we know it, Erik Brynjolfsson, feminist movement, financial deregulation, financial innovation, Francis Fukuyama: the end of history, future of work, game design, George Gilder, Gordon Gekko, greed is good, High speed trading, hive mind, income inequality, industrial robot, interchangeable parts, invisible hand, Isaac Newton, James Watt: steam engine, Jane Jacobs, Jaron Lanier, Jeff Bezos, jitney, Joan Didion, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Joseph Schumpeter, knowledge worker, low skilled workers, Lyft, Mark Zuckerberg, market bubble, mass immigration, mass incarceration, Menlo Park, Naomi Klein, new economy, Norbert Wiener, Norman Mailer, obamacare, Peter Thiel, Picturephone, plutocrats, Plutocrats, post-industrial society, Powell Memorandum, pre–internet, Ralph Nader, Right to Buy, road to serfdom, Robert Bork, Robert Gordon, Robert Mercer, Ronald Reagan, Saturday Night Live, Seaside, Florida, Second Machine Age, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, Stewart Brand, strikebreaker, The Death and Life of Great American Cities, The Future of Employment, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, too big to fail, trickle-down economics, Tyler Cowen: Great Stagnation, Uber and Lyft, uber lyft, union organizing, universal basic income, Unsafe at Any Speed, urban planning, urban renewal, very high income, wage slave, Wall-E, War on Poverty, Whole Earth Catalog, winner-take-all economy, women in the workforce, working poor, young professional, éminence grise

The first Bush administration had suspended the federal antitrust rule forbidding networks from also owning the shows they aired, then New York’s Mayor Rudy Giuliani successfully pressured the local cable operator, owned by Time Warner, to carry Fox News. Soon Murdoch also had conservative media’s high end covered with The Weekly Standard, cofounded by Irving Kristol’s son, Bill. In 2007, Murdoch added The Wall Street Journal to his portfolio and Breitbart News launched, funded by the financial billionaire (and Koch associate) Robert Mercer. The elite would be conscripted (and coopted) at scale on college campuses and in Washington, but now through every medium the rabble would be roused as well, 24/7. * * * — Around 1980, donations by business PACs to candidates for Congress started exceeding those made by unions, but never by more than half until 2000, after which the corporate sums were twice those of organized labor, then more than triple.

The more civil disobedience the better, however you want to do it.” So why, according to polls, were two-thirds of Americans in favor of the national quasi-quarantine? Because, this presidential adviser and would-be Fed governor said, “the American people are sheep.” The two Koch-created enterprises and Moore were joined by a newer organization also devoted to promoting right-wing economics, the Convention of States, funded by Robert Mercer—hedge fund billionaire, early Breitbart News investor, Trump’s biggest 2016 donor—and overseen by a cofounder of the Tea Party Patriots and (such a long game) a strategist for David Koch’s 1980 Libertarian vice-presidential campaign. In Michigan, the protests were organized and promoted by existing Republican groups, one connected to the right-wing billionaire DeVos family, and in Idaho by a group funded by a new Coors, the son of the counter-Establishment founder Joseph.*6 The mission of those demonstrations, as The Washington Post reported, was “making opposition to stay-at-home orders—which had been in place in most states for only a couple of weeks or less—appear more widespread than is suggested by polling.”


pages: 283 words: 87,166

Reaching for Utopia: Making Sense of an Age of Upheaval by Jason Cowley

anti-communist, Berlin Wall, Bernie Sanders, Big bang: deregulation of the City of London, Boris Johnson, centre right, Charles Lindbergh, coherent worldview, Corn Laws, corporate governance, crony capitalism, David Brooks, deindustrialization, deskilling, Donald Trump, Etonian, eurozone crisis, Fall of the Berlin Wall, illegal immigration, liberal world order, Neil Kinnock, Occupy movement, offshore financial centre, old-boy network, open borders, plutocrats, Plutocrats, Right to Buy, Robert Mercer, Ronald Reagan, University of East Anglia

The collective shock of the liberal establishment, they still can’t get to grips with it, and they’re trying to find a reason why this illogical thing, as they see it, happened. In this country, they put it down to lies, and in America, it’s the Russians!’ Ah, the Russians – let’s hope they love their children, too, as Sting once sang. Carole Cadwalladr, an Observer feature writer, has been determinedly investigating the operations of the data mining and analytics firm Cambridge Analytica and its connections to Robert Mercer, an American hedge fund billionaire and libertarian, who is a prominent Trump supporter. Cadwalladr is convinced that Mercer and Farage are at the centre of a network of alt-right white nationalists and libertarian billionaires who are intent not only on destabilising the West but engendering hate and overturning the liberal order. Cadwalladr has been mocked as paranoid and a conspiracy theorist on social media by Farage associates Arron Banks and Andy Wigmore of Leave.EU, which also posted a video that abused her on Twitter.


pages: 302 words: 85,877

Cult of the Dead Cow: How the Original Hacking Supergroup Might Just Save the World by Joseph Menn

4chan, A Declaration of the Independence of Cyberspace, Apple II, autonomous vehicles, Berlin Wall, Bernie Sanders, bitcoin, Chelsea Manning, commoditize, corporate governance, Donald Trump, dumpster diving, Edward Snowden, Firefox, Google Chrome, Haight Ashbury, Internet of things, Jacob Appelbaum, Jason Scott: textfiles.com, John Markoff, Julian Assange, Mark Zuckerberg, Mitch Kapor, Naomi Klein, Peter Thiel, pirate software, pre–internet, Ralph Nader, ransomware, Richard Stallman, Robert Mercer, self-driving car, side project, Silicon Valley, Skype, slashdot, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, Stuxnet, Whole Earth Catalog, WikiLeaks, zero day

See software programs, malicious Mandiant, 134 Mann, Sally, 22–23 Manning, Chelsea (formerly Bradley), 143–144 Marlinspike, Moxie, 152, 162, 178 Masters of Deception (MoD), 25–29, 32, 54 Matasano Security, 125 Mathewson, Nick, 129, 140, 155 Matlock. See Noonan, Timothy Mayer, Marissa, 198 McAfee, 29, 107 McGill University, 166 MCI, 10, 12–13 media, cDc relationship with, 58–62, 67–68, 80. See also Hong Kong Blondes Medium (website), 99 Mentor, the, 44 Mercer, Rebekah, 196 Mercer, Robert, 196 Merry Pranksters, 22–23 Messiah Village, 48–49 Metasploit, 177 #MeToo, 158 Microsoft, 37, 63, 108, 196, 212 BackOffice software, 66, 69 Back Orifice, response to, 67–69, 77, 82–83, 96–97 hackers working for, 38, 50, 111–112, 122–124, 193 security vulnerabilities, 45, 56, 72–73, 82–83, 85, 111–112 See also Back Orifice; Windows military, 74, 78, 117–118, 136, 185, 209 See also United States government Miller, Charlie, 178–179 Miloševic, Slobodan, 102–103 MindSpring, 68 MindVox, 30–32, 63, 145 MIT, 37–38, 40, 45–46, 50, 53, 72–73 Mitnick, Kevin, 35, 44 Mixter.


The Road to Unfreedom: Russia, Europe, America by Timothy Snyder

active measures, affirmative action, Affordable Care Act / Obamacare, American ideology, anti-globalists, Bernie Sanders, centre right, Charles Lindbergh, crony capitalism, Dissolution of the Soviet Union, Donald Trump, hiring and firing, income inequality, John Markoff, means of production, Mikhail Gorbachev, New Journalism, obamacare, offshore financial centre, Robert Mercer, sexual politics, Transnistria, WikiLeaks, women in the workforce, zero-sum game

Heimbach quotations: Michel, “Beyond Trump and Putin”; see also Heather Digby Parton, “Trump, the alt-right and the Kremlin,” Salon, Aug. 17, 2017. Bannon, David Bossie, and Citizens United: Michael Wolff, “Ringside with Steve Bannon at Trump Tower as the President-Elect’s Strategist Plots ‘An Entirely New Political Movement,’ ” Hollywood Reporter, Nov. 18, 2016. Bannon and Mercers: Matthew Kelly, Kate Goldstein, and Nicholas Confessore, “Robert Mercer, Bannon Patron, Is Leaving Helm of $50 Billion Hedge Fund,” NYT, Nov. 2, 2017. Bannon’s extreme-Right ideology Bannon quotation: Owen Matthews, “Alexander Dugin and Steve Bannon’s Ideological Ties to Vladimir Putin’s Russia,” NW, April 17, 2017. Bannon’s ideology and films: Ronald Radosh, “Steve Bannon, Trump’s Top Guy, Told Me He Was ‘A Leninist’ Who Wants to ‘Destroy the State,’ ” DB, Aug. 22, 2016; Jeremy Peters, “Bannon’s Views Can be Traced to a Book That Warns, ‘Winter Is Coming,’ ” NYT, April 8, 2017; Owen Matthews, “Alexander Dugin and Steve Bannon’s Ideological Ties to Vladimir Putin’s Russia,” NW, April 17, 2017; Christopher Dickey and Asawin Suebsaeng, “Steve Bannon’s Dream: A Worldwise Ultra-Right,” DB, Nov. 13, 2016.


pages: 561 words: 120,899

The Theory That Would Not Die: How Bayes' Rule Cracked the Enigma Code, Hunted Down Russian Submarines, and Emerged Triumphant From Two Centuries of Controversy by Sharon Bertsch McGrayne

Bayesian statistics, bioinformatics, British Empire, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, double helix, Edmond Halley, Fellow of the Royal Society, full text search, Henri Poincaré, Isaac Newton, Johannes Kepler, John Markoff, John Nash: game theory, John von Neumann, linear programming, longitudinal study, meta analysis, meta-analysis, Nate Silver, p-value, Pierre-Simon Laplace, placebo effect, prediction markets, RAND corporation, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, Robert Mercer, Ronald Reagan, speech recognition, statistical model, stochastic process, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Turing test, uranium enrichment, Yom Kippur War

., 135 Maclean, Donald, 85, 86 Madansky, Albert, 120–28, 149, 176 Madison, James, 155–58, 159–61 Mahon, Patrick, 68 mammograms, 255–57 Marie, Maximilien, 34 Markov, Andrei Andreyevich, 222 Markov chains, 149, 151, 221–26, 246 Markowitz, Harry, 236 mathematics: astronomy and, 14, 16–18, 19 business and, 142–43, 148–49, 151 Enigma code and, 62, 63 The Federalist papers and, 157–58 Laplace, and, 14 Laplace and, 14, 15, 16, 18–24, 33 military and, 97, 119 nuclear weapons and, 186–92, 193 probability and, 23–24, 33, 72 reason and, 4 religion and, 4, 5–6, 11 science and, 167 search and, 186–92, 193, 201 Second World War and, 63–64 statistics and, 97–98, 101, 103–6, 130, 157–58, 167, 214 submarines and, 201 Mauchly, John, 168 Maxwell, James Clerk, 37 Mayer, Maria Goeppert, 222 Mayerson, Allen L., 95 McCarthy, Joseph, 85, 87 McCullagh, Peter, 169 MCMC, 224–6, 232. See also Markov chains Monte Carlo simulation McNamara, Robert, 194 means, 130–32 medical devices, 228–29 medicine: cancer, x, 108–9, 110–14, 215–16, 227–28, 235, 255–57 diagnosis in, 135, 226–29, 255–57 heart attacks, x, 114–16 strokes, 226–27, 244 treatment in, 116, 235 X-rays, 53 Mercer, Robert L., 237–38, 245–47 Meshenberg, M. P., 101 meta-analysis, 215–16 metric system, 29 Metropolis, Nicholas, 222–23, 224 Michie, Donald, 81, 82 Microsoft, 242–43 military: asteroids and, 209 in Cold War, generally, 164–65, 173–75, 215 equal probabilities and, 38, 73 of France, 29, 38–40 image analysis and, 240, 241 inverse probability and, 38 mathematics and, 97 nuclear weapons and, 119–28, 182–95 robotics and, 240 of Russia, 72–73 satellites and, 209 statistics and, 97 submarines and, 194–203, 206–8 translation and, 247 weapons systems and, 241.


pages: 519 words: 155,332

Tailspin: The People and Forces Behind America's Fifty-Year Fall--And Those Fighting to Reverse It by Steven Brill

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, activist fund / activist shareholder / activist investor, affirmative action, Affordable Care Act / Obamacare, airport security, American Society of Civil Engineers: Report Card, asset allocation, Bernie Madoff, Bernie Sanders, Blythe Masters, Bretton Woods, business process, call centre, Capital in the Twenty-First Century by Thomas Piketty, carried interest, clean water, collapse of Lehman Brothers, collective bargaining, computerized trading, corporate governance, corporate raider, corporate social responsibility, Credit Default Swap, currency manipulation / currency intervention, Donald Trump, ending welfare as we know it, failed state, financial deregulation, financial innovation, future of work, ghettoisation, Gordon Gekko, hiring and firing, Home mortgage interest deduction, immigration reform, income inequality, invention of radio, job automation, knowledge economy, knowledge worker, labor-force participation, laissez-faire capitalism, Mahatma Gandhi, Mark Zuckerberg, mortgage tax deduction, new economy, obamacare, old-boy network, paper trading, performance metric, post-work, Potemkin village, Powell Memorandum, quantitative hedge fund, Ralph Nader, ride hailing / ride sharing, Robert Bork, Robert Gordon, Robert Mercer, Ronald Reagan, shareholder value, Silicon Valley, Social Responsibility of Business Is to Increase Its Profits, telemarketer, too big to fail, trade liberalization, union organizing, Unsafe at Any Speed, War on Poverty, women in the workforce, working poor

“Before Citizens United,” Israel explained, “you at least had a good view of what the opponent would spend and what you would need to spend. Now, you’re flying blind, because in the last two or three weeks some super PAC could come in with $20 million and wipe you out. You always have to keep raising money, just in case.” Super PACs raised $1.8 billion in the 2016 election cycle, as people such as Robert Mercer on the right and Tom Steyer on the left became the most important political power players most Americans had never heard of. That does not count at least $250 million in “dark money” raised by tax-exempt entities that did not call themselves political action committees, but made their own independent expenditures without even having to reveal donors because they instead declared that they were social welfare or educational organizations.


pages: 510 words: 163,449

How the Scots Invented the Modern World: The True Story of How Western Europe's Poorest Nation Created Our World and Everything in It by Arthur Herman

British Empire, California gold rush, creative destruction, do-ocracy, financial independence, global village, invisible hand, Isaac Newton, James Watt: steam engine, Joan Didion, joint-stock company, laissez-faire capitalism, land tenure, mass immigration, means of production, new economy, New Urbanism, North Sea oil, oil shale / tar sands, Republic of Letters, Robert Mercer, spinning jenny, The Wealth of Nations by Adam Smith, transcontinental railway, trickle-down economics, urban planning, urban renewal, working poor

There the old chieftain, who had once boasted of having five hundred warriors at his beck and call, expired, surrounded by the clansmen he had led to defeat and death. The slaughter among the clan leadership was heavy. Grapeshot had shattered both of Lord Lochiel’s ankles, and he had to be carried off the field. The only regimental commanders to escape unwounded were Lord George Murray, Lord Ardshiel, and Lord Nairne—although Nairne’s brother, Robert Mercer of Aldie, was killed, as was Mercer’s son Thomas. Their bodies were never found. Only three of the Mackintosh officers survived. But if the Jacobite chieftains and their tacksmen paid a heavy price for their misplaced loyalties, it was their followers who suffered most from the retributions of Cumberland and his soldiers. We can try to make various excuses for their behavior. We can say war and its aftermath is often very nasty, and that the killing of prisoners and noncombatants is more common than most of us care to admit.