Book Read Free

Everyday Chaos

Page 27

by David Weinberger


  ________. “Don’t Make AI Artificially Stupid in the Name of Transparency.” Wired, Jan. 28, 2018. https://perma.cc/X9N4-8RBT. Longer version published as “Optimization over Explanation: Maximizing the Benefits of Machine Learning without Sacrificing Its Intelligence.” Medium, Jan. 28, 2018. https://perma.cc/H538-3Q2G.

  ________. Everything Is Miscellaneous. New York: Times Books, 2007.

  ________. “[liveblog][PAIR] Antonio Torralba on Machine Vision, Human Vision.” Joho the Blog, Sept. 26, 2017. https://perma.cc/E9HB-DQQH.

  ________. “On the Merits of Obscurity.” Rhapsody, Summer 2016.

  ________. “Our Machines Now Have Knowledge We’ll Never Understand.” (Also known under the title “Alien Knowledge.”) Wired, Apr. 18, 2017. https://perma.cc/SP4T-AKZP.

  ________. “Present Shock: When Everything Happens Now—Douglas Rushkoff, author of Present Shock, in Conversation with David Weinberger.” Harvard Law School, June 18, 2013. https://perma.cc/9N8Y-TD2Y.

  ________. “The Rise, Fall, and Possible Rise of Open News Platforms.” Shorenstein Center on Media, Politics and Public Policy, July 10, 2015. https://perma.cc/34CU-F7KD.

  ________. “Shift Happens.” Chronicle of Higher Education: Chronicle Review, Apr. 22, 2012. https://perma.cc/8XPS-84WN.

  ________. Too Big to Know. New York: Times Books, 2011.

  Weiner, Eric. “What Made Ancient Athens a City of Genius?” Atlantic, Feb. 10, 2016. https://perma.cc/QE9X-TSZV.

  Welchman, Lisa. “Keynote: The Paradox of Open Growth.” Slide deck, DrupalCon, Prague, Sept. 25, 2013. https://perma.cc/7WNS-VAN6.

  Wells, H. G. The Discovery of the Future. New York: B. W. Huebsch, 1913. https://perma.cc/G9EP-3QFK.

  ________. The Time Machine. London: Heinemann, 1895. https://perma.cc/L55F-PE7V.

  “What Does a Chicken Sexer Do?” Sokanu. Accessed Sept. 30, 2018. https://perma.cc/XYP5-FZVN.

  White, Jonathan. Tides: The Science and Spirit of the Ocean. San Antonio, TX: Trinity University Press, 2017.

  White, Lynn, Jr. Medieval Technology and Social Change. Oxford: Oxford University Press, 1962.

  Whitrow, G. J. Time in History. New York: Barnes and Noble, 1988.

  “Why Isn’t the Sky Blue?” RadioLab, May 20, 2012. Podcast, 22:23. https://perma.cc/239Y-L2C6.

  Wilkinson, Angela, and Roland Kupers. “Living in the Futures.” Harvard Business Review, May 2013. https://perma.cc/L96E-5M9H.

  Willis, A. J. “Forum.” Functional Ecology 11 (1997): 268–271.

  Wilson, Mark. “AI Is Inventing Languages Humans Can’t Understand. Should We Stop It?” Fast Company, July 14, 2017. https://perma.cc/LS5L-85Q7.

  Wolfram, Stephen. A New Kind of Science. Champaign, IL: Wolfram Media, 2002.

  Wong, Kate. “Human Ancestors Made Deadly Stone-Tipped Spears 500,000 Years Ago.” Scientific American, Nov. 15, 2012. https://perma.cc/ET3R-3KRJ.

  ________. “Oldest Arrowheads Hint at How Modern Humans Overtook Neandertals.” Scientific American, Nov. 7, 2012. https://perma.cc/7H9E-V8G2.

  Yala, Hesbon Ondiek, Harry Ododa, and Chelang’a James. “The Impact of Total Quality Management (TQM) Policy on Customer Satisfaction at Kenya Power and Lighting Company (KPLC) in Uasin Gishu County, Kenya (2010–2012).” International Journal of Academic Research and Development 3, no. 2 (Mar. 2018): 187–193. https://perma.cc/ZC5L-BG5L.

  Yeen, Jecelyn. “AI Translate: Bias? Sexist? Or This Is the Way It Should Be?” Hackernoon, Oct. 6, 2017. https://perma.cc/2A7N-KKSX.

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  Zittrain, Jonathan. The Future of the Internet, and How to Stop It. New Haven, CT: Yale University Press, 2008.

  INDEX

  A/B testing, 4–7, 9

  accidents, classifying events as, 36–38

  accuracy, machine learning and, 43

  Acquia, 133

  added value to products, open platforms and, 89–90

  Adopt-a-Siren app, 91

  agile development, 14, 83–85, 103, 140

  Allenby, Braden R., 11–12, 158

  AlphaGo, 2, 59–60, 160

  Ancient Egyptians, Greeks, Hebrews, prediction and, 22–23

  Anderson, Chris, 173

  Angst & Panik, 112

  Anscombe, Elizabeth, 188

  anticipate-and-prepare strategy, 77–79

  Apple, 89, 123–124

  application programming interfaces (APIs), 88, 94, 103, 117, 136

  applications (apps), 90, 91, 117

  Aristotle, 149, 188, 190, 191, 192, 210n11

  armillary, 47–48

  Arnold, Henry “Hap,” 128

  arrowheads, production process, 76–77

  artificial intelligence (AI)

  assigning moral decisions to, 184–189

  explanations and, 66–68

  governance of, 72–73

  stages of innovation and, 159–160

  See also Deep learning; Machine learning

  artificial neural networks, 54, 60

  Asimov, Isaac, 184, 185, 214n23

  AT&T, 146, 154

  Austin, J. L., 186–187

  autonomous vehicles (AVs), optimization of, 68–73

  Bacon, Francis, 150

  Bacon, Roger, 153

  Bain & Company, 129

  Barlow, John Perry, 163

  “Battle of the Book, The” (Swift), 150

  Beckman, Johann, 210–211n23

  behavior, nonrational levers for changing human, 174–175

  Bentham, Jeremy, 182

  Berners-Lee, Tim, 117

  best practices, 82–83

  bias, artificial intelligence and, 59, 67

  Bing, 5, 104

  Bitvore, 55, 67–68

  Bjerknes, Vilhelm, 24, 44, 45

  Bolcer, Greg, 55

  Book of Why, The (Pearl), 104

  Box, George E. P., 47

  Bricklin, Dan, 45–46

  Brown, John Seely, 94, 176

  browsing, 97–99

  Butler, Samuel, 160

  Butterfield, Stewart, 81–82

  Buytaert, Dries, 132–134, 135, 137

  Cambridge Analytica, 120

  Carr, Nicholas, 164

  Carson, Rachel, 13

  Casas, Josefina, 10

  cascades, 115

  Castle Wolfenstein/Castle Smurfenstein (games), 89–90, 204n28

  categorization, meaning and, 190, 191

  causality, interoperability as new, 109–119

  causal models for machine learning, 114, 115

  cause and effect, illusion of, 113–115

  certainty, prediction and, 20–25

  Chamberlain, Neville, 65

  change, theory of, 8–11

  Chaos Theory, 12–14, 45

  Chengyu, 142

  chicken sexing, 39–41

  Chopra, Aneesh, 83, 85

  Clairaut, Alexis Claude, 28–29

  Clark, Andy, 165–166

  Clark, David, 95

  Clausewitz, Carl von, 126

  clock metaphor, for workings of universe, 25–27, 116

  Cloud Healthcare Pledge, 104

  Cluetrain Manifesto, The (Levine, et al.), 17

  Code for America, 91

  Cold War strategy, 128, 130–131

  complexity

  beyond prediction, 14–15

  deep learning and, 66

  embrace of, 169–170

  machine learning and, 7, 36

  complex systems surrounding us, 11–12

  computational irreducibility, principle of, 35

  computer

  introduction of, 32–33

  as tool, 153–154

  universe as vast, 34–35

  conceptual models, 40–43

  armillary and, 48

 
; construction of, 59

  Mississippi River basin model and, 51–52

  spreadsheets and, 46–47

  tides and, 50

  consequentialism, 182

  Conway, John, Game of Life, 33–35, 161

  Copernicus, Nicolaus, 47

  copyright constraints, 99

  counterfactual approach, 57

  creativity, obscurity enabling, 143

  credit score companies, model used to calculate, 55–56

  Curie, Jeff, 55

  Darwin, George, 49

  data, transparency of, 72

  Data.gov, 91

  Davison, Lang, 94, 176

  Dean, Howard, 133

  “Declaration of the Independence of Cyberspace” (Barlow), 163

  deep learning, 1–2, 15

  complexity and, 66

  human bias and, 59

  simplification and, 60

  Deep Patient, 1–2, 10, 53–55

  Deming, W. Edwards, 82

  Dennett, Daniel C., 34

  deontology, 182, 188

  differences, interoperability and, 109–111

  digital networks, strategy and, 140

  distance, interoperability and, 112

  Da Vinci, Leonardo, 149

  Doctrine of Double Effect, 183

  Dr. Strangelove, or: How I Learned to Stop Worrying and Love the Bomb (film), 128

  Dropbox, 81

  Drucker, Peter, 125

  Drunkard’s Walk, The (Mlodinow), 31

  Drupal, 132–135, 137–138

  earthquake prediction, 19–20, 24, 196n

  ecosystem of interoperability, 116–119

  effect, proportional to change, 10–11

  18F (federal digital agency), 84

  Einstein, Albert, 27

  emergent effects, of complex systems, 13

  empowerment, obscurity creating, 143

  End of Competitive Advantage, The (McGrath), 129

  “End-to-End Arguments in System Design” (Saltzer, Reed & Clark), 95

  engagement, obscurity enabling, 143–144

  “Equal Opportunity” fairness, 186

  Erewhon (Butler), 160

  events, classifying as accidents, 36–38

  Everything Bad Is Good for You (Johnson), 178

  explanations, 62–68, 170–172

  artificial intelligence and, 66–68

  for crash of JAL 123, 62–65

  levers without, 173–175

  optimization over, 68–73

  as social acts, 64–65

  use of, 67–68

  for war, 65–66

  Facebook, 85–87, 106, 120, 190

  fairness

  “Equal Opportunity,” 186

  machine learning and, 186–187

  Fan Hui, 2

  Ferguson protests, 92

  Fermat, Pierre de, 30

  Ferrentino, Marc, 106

  FICO, 55–56, 67–68

  Figuier, Louis, 211n23

  Fitbit, 90

  Flickr, 112

  flip-chart strategies, 130, 132

  Foo Camp “unconferences,” 93–94

  Foot, Philippa, 183

  Ford, Henry, 76, 82

  forecasts

  predictions vs., 197n4

  statistical, 44–45

  Foucault, Michel, 119–120

  Fourth Paradigm, 173

  Frankston, Bob, 46

  Freedman, Lawrence, 126

  Frischmann, Brett, 69

  future, control of, 192–193

  Future of Ideas, The (Lessig), 154

  Future of the Internet, and How to Stop It, The (Zittrain), 156

  Galaxy Note 7, 8

  Galileo, 47

  Game of Life, 33–35, 161

  Game of Thrones (television series), 180

  Gardner, Martin, 34

  Gasser, Urs, 103, 110

  generative adversarial network, 160

  generative progress, 160–163

  generativity

  internet and, 155–157

  traditional progress vs., 157–159

  Gibson, Stanford, 51

  Gilligan, Carol, 187

  GitHub, 92–93, 154–155, 157

  Go (game), 2

  See also AlphaGo

  Google, 54, 104, 173

  A/B testing and, 5

  AlphaGo, 2, 59–60, 160

  dumbbell recognition in machine learning system, 59, 189, 191–192

  Knowledge Graph, 190

  Schema.org and, 105

  search engine, 97–98

  TensorFlow, 136–137, 138, 154

  Google Photos, 112

  government

  new practices recognizing chaos beneath apparent order, 14–15

  as platform, 91

  Grand Theft Auto V (game), 90

  graphs, 86, 105–106, 190

  gravitation, Newton’s theory of universal, 26, 28, 206n16

  gravity, Occupy movement and, 176–177

  Gray, Jim, 173

  ground truth, 42

  Hagel, John, 94, 176

  Halley, Edmund, 27–28

  Halley’s comet, 28–29, 30

  handwriting recognition, machine learning and, 41–43

  Hannay, Timo, 170

  Hardt, Moritz, 186

  Harvard University Library, 98–99

  Hashtags, Twitter, 113, 117–118, 177

  HealthCare.gov site launch, 83–84

  Hedberg, Mitch, 151

  Hidden Persuaders (Packard), 174

  Hillis, Daniel, 113–115

  History of Inventions (Beckman), 210–211n23

  History of Technology (5 volumes), 152, 210n23, 211n24

  Hitler, Adolf, 65–66

  Hooke, Robert, 27

  Horsey, Richard, 39

  Huawei, 142

  hugeness (scale) of machine learning and the internet, 7

  Hume, David, 114

  Huygens, Christian, 30–31

  hyperlinks, 112

  IBM, 33, 104

  ice water challenge, 10

  id Software, 89–90

  IFTTT (If This Then That), 117

  IMVU, 80–81

  In a Different Voice (Gilligan), 187

  Inevitable’s, The (Kelly), 95

  innovation, artificial intelligence and, 159–160

  In Search of Excellence (Peters), 82

  internet

  architected for unanticipation, 95–96

  collections of information on, 99–100

  experience of chaos and, 118

  generativity and, 155–157

  interoperability and, 7, 104, 116, 155, 156

  machine learning and, 6–7

  unpredictability and, 101

  Internet of Things, 111

  interoperability, 101–121

  defined, 102

  internet and, 104, 116, 155, 156

  as new causality, 109–119

  possibilities and, 139–142

  signs and, 119–121

  standards and, 102–108

  Interop (Palfry & Gasser), 103, 110

  inverted cascades, 115

  IRC (Internet Relay Chat), 81

  I Robot (Asimov), Three Laws of Robotics, 184, 185

  JAL 123, crash of, 62–65

  Jennings, Andrew, 55–56

  Jobs, Steve, 123–124

  Johnson, Steven, 178

  Jomini, Antoine Henri, 206n16

  Kahn, Herman, 128

  Kant, Immanuel, 115

  Kelly, Kevin, 95

  Kelvin (Lord), 49–50

  King, Martin Luther, Jr., 148

  knowledge

  imperfection of human, 3–4, 26

  technological progress and, 151, 161

  truth and, 40

  Knowledge Graph, 190

  Knox, Bernard, 23

  Kubrick, Stanley, 128

  Kuhn, Thomas, 61, 158

  Kurzweil, Raymond, 34

  Lalande, Joseph Jérôme Lefrançois de, 28–29

  Laplace, Pi
erre-Simon, 26–27, 31, 49, 50, 116, 170

  Laplace’s demon, 26, 35, 44–45

  laws, idea that things happen according to, 8–9

  See also Newton, Isaac

  Lean Startup, The (Ries), 80

  Lego Group, 102

  Lepaute, Nicole-Reine Étable de la Brière, 29

  Lessig, Lawrence, 154

  levers, function of, 176–177

  Levy, Steven, 46

  libraries, browsing and, 98–99

  Linnaeus, Carl, 182, 190, 191

  Lorenz, Edward, 12

  machine learning, 2, 3, 15, 117

  accuracy and, 43

  chicken sexing and, 40–41

  conceptual models and, 52

  constantly changing models of, 61–62

  embrace of complexity and, 36

  “explanations” and, 172

  fairness and, 186–187

  handwriting recognition and, 41–43

  internet and, 6–7

  interoperability and, 7, 103–104

  optimization and, 68–73

  prediction and causal models, 114, 115

  signs and, 120–121

  undoing idea that human mind attuned to truth of universe, 167

  working models and, 53–62

  Managing for Results (Drucker), 125

  Martin, George R. R., 180

  mash up data, 15

  McAlister, Matt, 87–88

  McGrath, Rita Gunther, 129, 131, 140

  McLuhan, Marshall, 159

  McNamara, Robert, 188

  McNealy, Scott, 124, 139

  meaning

  explanations, 170–172

  levers without explanations, 173–175

  morality and, 181–189

  notion of, 189–192

  rejection of lever-based of change, 175–177

  storytelling and, 178–181

  Menelaus, 125–126

  metaphors

  clock, 25–27, 116

  shaping our experience through, 116

  microformats, 107–108, 117

  Microsoft, 104, 107

  military strategy, 126, 127–128, 130, 207n7

  minimum viable product (MVP), 14, 80–83

  Mississippi River basic model, 50–53

  Mittelstadt, Brent, 57

  Mlodinow, Leonard, 31

  modding/mods, 15, 89–90, 103, 117, 204n28

  model-free explanations, 173

  models

  explanations and, 63

  purpose of, 52–53

  weather, 44–45

  See also Conceptual models; Working models

  Model T’s design process, 76

  MONIAC (Monetary National Income Analogue Computer), 50–51, 52

  morality, 181–189

  motivational research (MR), 174–175

  Mullen, Edward, 84

  Musk, Elon, 69, 135, 136

 

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