pattern recognition

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pages: 372 words: 101,174

How to Create a Mind: The Secret of Human Thought Revealed by Ray Kurzweil

Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Albert Michelson, anesthesia awareness, anthropic principle, brain emulation, cellular automata, Claude Shannon: information theory, cloud computing, computer age, Dean Kamen, discovery of DNA, double helix, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Isaac Newton, iterative process, Jacquard loom, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Norbert Wiener, optical character recognition, pattern recognition, Peter Thiel, Ralph Waldo Emerson, random walk, Ray Kurzweil, reversible computing, selective serotonin reuptake inhibitor (SSRI), self-driving car, speech recognition, Steven Pinker, strong AI, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Wall-E, Watson beat the top human players on Jeopardy!, X Prize

., 28–29, 206–7, 217 dimming of, 29, 59 hippocampus and, 101–2 as ordered sequences of patterns, 27–29, 54 redundancy of, 59 unexpected recall of, 31–32, 54, 68–69 working, 101 Menabrea, Luigi, 190 metacognition, 200, 201 metaphors, 14–15, 113–17, 176–77 Michelson, Albert, 18, 19, 36, 114 Michelson-Morley experiment, 19, 36, 114 microtubules, 206, 207, 208, 274 Miescher, Friedrich, 16 mind, 11 pattern recognition theory of (PRTM), 5–6, 8, 11, 34–74, 79, 80, 86, 92, 111, 172, 217 thought experiments on, 199–247 mind-body problem, 221 Minsky, Marvin, 62, 133–35, 134, 199, 228 MIT Artificial Intelligence Laboratory, 134 MIT Picower Institute for Learning and Memory, 101 MobilEye, 159 modeling, complexity and, 37–38 Modha, Dharmendra, 128, 195, 271–72 momentum, 20–21 conservation of, 21–22 Money, John William, 118, 119 montane vole, 119 mood, regulation of, 106 Moore, Gordon, 251 Moore’s law, 251, 255, 268 moral intelligence, 201 moral systems, consciousness as basis of, 212–13 Moravec, Hans, 196 Morley, Edward, 18, 19, 36, 114 Moskovitz, Dustin, 156 motor cortex, 36, 99 motor nerves, 99 Mountcastle, Vernon, 36, 37, 94 Mozart, Leopold, 111 Mozart, Wolfgang Amadeus, 111, 112 MRI (magnetic resonance imaging), 129 spatial resolution of, 262–65, 263, 309n MT (V5) visual cortex region, 83, 95 Muckli, Lars, 225 music, as universal to human culture, 62 mutations, simulated, 148 names, recalling, 32 National Institutes of Health, 129 natural selection, 76 geologic process as metaphor for, 14–15, 114, 177 see also evolution Nature, 94 nematode nervous system, simulation of, 124 neocortex, 3, 7, 77, 78 AI reverse-engineering of, see neocortex, digital bidirectional flow of information in, 85–86, 91 evolution of, 35–36 expansion of, through AI, 172, 266–72, 276 expansion of, through collaboration, 116 hierarchical order of, 41–53 learning process of, see learning linear organization of, 250 as metaphor machine, 113 neural leakage in, 150–51 old brain as modulated by, 93–94, 105, 108 one-dimensional representations of multidimensional data in, 53, 66, 91, 141–42 pattern recognition in, see pattern recognition pattern recognizers in, see pattern recognition modules plasticity of, see brain plasticity prediction by, 50–51, 52, 58, 60, 66–67, 250 PRTM as basic algorithm of, 6 pruning of unused connections in, 83, 90, 143, 174 redundancy in, 9, 224 regular grid structure of, 82–83, 84, 85, 129, 262 sensory input in, 58, 60 simultaneous processing of information in, 193 specific types of patterns associated with regions of, 86–87, 89–90, 91, 111, 152 structural simplicity of, 11 structural uniformity of, 36–37 structure of, 35–37, 38, 75–92 as survival mechanism, 79, 250 thalamus as gateway to, 100–101 total capacity of, 40, 280 total number of neurons in, 230 unconscious activity in, 228, 231, 233 unified model of, 24, 34–74 as unique to mammalian brain, 93, 286n universal processing algorithm of, 86, 88, 90–91, 152, 272 see also cerebral cortex neocortex, digital, 6–8, 41, 116–17, 121–78, 195 benefits of, 123–24, 247 bidirectional flow of information in, 173 as capable of being copied, 247 critical thinking module for, 176, 197 as extension of human brain, 172, 276 HHMMs in, 174–75 hierarchical structure of, 173 knowledge bases of, 177 learning in, 127–28, 175–76 metaphor search module in, 176–77 moral education of, 177–78 pattern redundancy in, 175 simultaneous searching in, 177 structure of, 172–78 virtual neural connections in, 173–74 neocortical columns, 36–37, 38, 90, 124–25 nervous systems, 2 neural circuits, unreliability of, 185 neural implants, 243, 245 neural nets, 131–35, 144, 155 algorithm for, 291n–97n feedforward, 134, 135 learning in, 132–33 neural processing: digital emulation of, 195–97 massive parallelism of, 192, 193, 195 speed of, 192, 195 neuromorphic chips, 194–95, 196 neuromuscular junction, 99 neurons, 2, 36, 38, 43, 80, 172 neurotransmitters, 105–7 new brain, see neocortex Newell, Allen, 181 New Kind of Science, A (Wolfram), 236, 239 Newton, Isaac, 94 Nietzsche, Friedrich, 117 nonbiological systems, as capable of being copied, 247 nondestructive imaging techniques, 127, 129, 264, 312n–13n nonmammals, reasoning by, 286n noradrenaline, 107 norepinephrine, 118 Notes from Underground (Dostoevsky), 199 Nuance Speech Technologies, 6–7, 108, 122, 152, 161, 162, 168 nucleus accumbens, 77, 105 Numenta, 156 NuPIC, 156 obsessive-compulsive disorder, 118 occipital lobe, 36 old brain, 63, 71, 90, 93–108 neocortex as modulator of, 93–94, 105, 108 sensory pathway in, 94–98 olfactory system, 100 Oluseun, Oluseyi, 204 OmniPage, 122 One Hundred Years of Solitude (García Márquez), 283n–85n On Intelligence (Hawkins and Blakeslee), 73, 156 On the Origin of Species (Darwin), 15–16 optical character recognition (OCR), 122 optic nerve, 95, 100 channels of, 94–95, 96 organisms, simulated, evolution of, 147–53 overfitting problem, 150 oxytocin, 119 pancreas, 37 panprotopsychism, 203, 213 Papert, Seymour, 134–35, 134 parameters, in pattern recognition: “God,” 147 importance, 42, 48–49, 60, 66, 67 size, 42, 49–50, 60, 61, 66, 67, 73–74, 91–92, 173 size variability, 42, 49–50, 67, 73–74, 91–92 Parker, Sean, 156 Parkinson’s disease, 243, 245 particle physics, see quantum mechanics Pascal, Blaise, 117 patch-clamp robotics, 125–26, 126 pattern recognition, 195 of abstract concepts, 58–59 as based on experience, 50, 90, 273–74 as basic unit of learning, 80–81 bidirectional flow of information in, 52, 58, 68 distortions and, 30 eye movement and, 73 as hierarchical, 33, 90, 138, 142 of images, 48 invariance and, see invariance, in pattern recognition learning as simultaneous with, 63 list combining in, 60–61 in neocortex, see pattern recognition modules redundancy in, 39–40, 57, 60, 64, 185 pattern recognition modules, 35–41, 42, 90, 198 autoassociation in, 60–61 axons of, 42, 43, 66, 67, 113, 173 bidirectional flow of information to and from thalamus, 100–101 dendrites of, 42, 43, 66, 67 digital, 172–73, 175, 195 expectation (excitatory) signals in, 42, 52, 54, 60, 67, 73, 85, 91, 100, 112, 173, 175, 196–97 genetically determined structure of, 80 “God parameter” in, 147 importance parameters in, 42, 48–49, 60, 66, 67 inhibitory signals in, 42, 52–53, 67, 85, 91, 100, 173 input in, 41–42, 42, 53–59 love and, 119–20 neural connections between, 90 as neuronal assemblies, 80–81 one-dimensional representation of multidimensional data in, 53, 66, 91, 141–42 prediction by, 50–51, 52, 58, 60, 66–67 redundancy of, 42, 43, 48, 91 sequential processing of information by, 266 simultaneous firings of, 57–58, 57, 146 size parameters in, 42, 49–50, 60, 61, 66, 67, 73–74, 91–92, 173 size variability parameters in, 42, 67, 73–74, 91–92, 173 of sounds, 48 thresholds of, 48, 52–53, 60, 66, 67, 111–12, 173 total number of, 38, 40, 41, 113, 123, 280 universal algorithm of, 111, 275 pattern recognition theory of mind (PRTM), 5–6, 8, 11, 34–74, 79, 80, 86, 92, 111, 172, 217 patterns: hierarchical ordering of, 41–53 higher-level patterns attached to, 43, 45, 66, 67 input in, 41, 42, 44, 66, 67 learning of, 63–64, 90 name of, 42–43 output of, 42, 44, 66, 67 redundancy and, 64 specific areas of neocortex associated with, 86–87, 89–90, 91, 111, 152 storing of, 64–65 structure of, 41–53 Patterns, Inc., 156 Pavlov, Ivan Petrovich, 216 Penrose, Roger, 207–8, 274 perceptions, as influenced by expectations and interpretations, 31 perceptrons, 131–35 Perceptrons (Minsky and Papert), 134–35, 134 phenylethylamine, 118 Philosophical Investigations (Wittgenstein), 221 phonemes, 61, 135, 137, 146, 152 photons, 20–21 physics, 37 computational capacity and, 281, 316n–19n laws of, 37, 267 standard model of, 2 see also quantum mechanics Pinker, Steven, 76–77, 278 pituitary gland, 77 Plato, 212, 221, 231 pleasure, in old and new brains, 104–8 Poggio, Tomaso, 85, 159 posterior ventromedial nucleus (VMpo), 99–100, 99 prairie vole, 119 predictable outcomes, determined outcomes vs., 26, 239 President’s Council of Advisors on Science and Technology, 269 price/performance, of computation, 4–5, 250–51, 257, 257, 267–68, 301n–3n Principia Mathematica (Russell and Whitehead), 181 probability fields, 218–19, 235–36 professional knowledge, 39–40 proteins, reverse-engineering of, 4–5 qualia, 203–5, 210, 211 quality of life, perception of, 277–78 quantum computing, 207–9, 274 quantum mechanics, 218–19 observation in, 218–19, 235–36 randomness vs. determinism in, 236 Quinlan, Karen Ann, 101 Ramachandran, Vilayanur Subramanian “Rama,” 230 random access memory: growth in, 259, 260, 301n–3n, 306n–7n three-dimensional, 268 randomness, determinism and, 236 rationalization, see confabulation reality, hierarchical nature of, 4, 56, 90, 94, 172 recursion, 3, 7–8, 56, 65, 91, 153, 156, 177, 188 “Red” (Oluseum), 204 redundancy, 9, 39–40, 64, 184, 185, 197, 224 in genome, 271, 314n, 315n of memories, 59 of pattern recognition modules, 42, 43, 48, 91 thinking and, 57 religious ecstacy, 118 “Report to the President and Congress, Designing a Digital Future” (President’s Council of Advisors on Science and Technology), 269 retina, 95 reverse-engineering: of biological systems, 4–5 of human brain, see brain, human, computer emulation of; neocortex, digital Rosenblatt, Frank, 131, 133, 134, 135, 191 Roska, Boton, 94 Rothblatt, Martine, 278 routine tasks, as series of hierarchical steps, 32–33 Rowling, J.

Ultimately our brains, combined with the technologies they have fostered, will permit us to create a synthetic neocortex that will contain well beyond a mere 300 million pattern processors. Why not a billion? Or a trillion? The Structure of a Pattern The pattern recognition theory of mind that I present here is based on the recognition of patterns by pattern recognition modules in the neocortex. These patterns (and the modules) are organized in hierarchies. I discuss below the intellectual roots of this idea, including my own work with hierarchical pattern recognition in the 1980s and 1990s and Jeff Hawkins (born in 1957) and Dileep George’s (born in 1977) model of the neocortex in the early 2000s. Each pattern (which is recognized by one of the estimated 300 million pattern recognizers in the neocortex) is composed of three parts.

., 28–29, 206–7, 217 dimming of, 29, 59 hippocampus and, 101–2 as ordered sequences of patterns, 27–29, 54 redundancy of, 59 unexpected recall of, 31–32, 54, 68–69 working, 101 Menabrea, Luigi, 190 metacognition, 200, 201 metaphors, 14–15, 113–17, 176–77 Michelson, Albert, 18, 19, 36, 114 Michelson-Morley experiment, 19, 36, 114 microtubules, 206, 207, 208, 274 Miescher, Friedrich, 16 mind, 11 pattern recognition theory of (PRTM), 5–6, 8, 11, 34–74, 79, 80, 86, 92, 111, 172, 217 thought experiments on, 199–247 mind-body problem, 221 Minsky, Marvin, 62, 133–35, 134, 199, 228 MIT Artificial Intelligence Laboratory, 134 MIT Picower Institute for Learning and Memory, 101 MobilEye, 159 modeling, complexity and, 37–38 Modha, Dharmendra, 128, 195, 271–72 momentum, 20–21 conservation of, 21–22 Money, John William, 118, 119 montane vole, 119 mood, regulation of, 106 Moore, Gordon, 251 Moore’s law, 251, 255, 268 moral intelligence, 201 moral systems, consciousness as basis of, 212–13 Moravec, Hans, 196 Morley, Edward, 18, 19, 36, 114 Moskovitz, Dustin, 156 motor cortex, 36, 99 motor nerves, 99 Mountcastle, Vernon, 36, 37, 94 Mozart, Leopold, 111 Mozart, Wolfgang Amadeus, 111, 112 MRI (magnetic resonance imaging), 129 spatial resolution of, 262–65, 263, 309n MT (V5) visual cortex region, 83, 95 Muckli, Lars, 225 music, as universal to human culture, 62 mutations, simulated, 148 names, recalling, 32 National Institutes of Health, 129 natural selection, 76 geologic process as metaphor for, 14–15, 114, 177 see also evolution Nature, 94 nematode nervous system, simulation of, 124 neocortex, 3, 7, 77, 78 AI reverse-engineering of, see neocortex, digital bidirectional flow of information in, 85–86, 91 evolution of, 35–36 expansion of, through AI, 172, 266–72, 276 expansion of, through collaboration, 116 hierarchical order of, 41–53 learning process of, see learning linear organization of, 250 as metaphor machine, 113 neural leakage in, 150–51 old brain as modulated by, 93–94, 105, 108 one-dimensional representations of multidimensional data in, 53, 66, 91, 141–42 pattern recognition in, see pattern recognition pattern recognizers in, see pattern recognition modules plasticity of, see brain plasticity prediction by, 50–51, 52, 58, 60, 66–67, 250 PRTM as basic algorithm of, 6 pruning of unused connections in, 83, 90, 143, 174 redundancy in, 9, 224 regular grid structure of, 82–83, 84, 85, 129, 262 sensory input in, 58, 60 simultaneous processing of information in, 193 specific types of patterns associated with regions of, 86–87, 89–90, 91, 111, 152 structural simplicity of, 11 structural uniformity of, 36–37 structure of, 35–37, 38, 75–92 as survival mechanism, 79, 250 thalamus as gateway to, 100–101 total capacity of, 40, 280 total number of neurons in, 230 unconscious activity in, 228, 231, 233 unified model of, 24, 34–74 as unique to mammalian brain, 93, 286n universal processing algorithm of, 86, 88, 90–91, 152, 272 see also cerebral cortex neocortex, digital, 6–8, 41, 116–17, 121–78, 195 benefits of, 123–24, 247 bidirectional flow of information in, 173 as capable of being copied, 247 critical thinking module for, 176, 197 as extension of human brain, 172, 276 HHMMs in, 174–75 hierarchical structure of, 173 knowledge bases of, 177 learning in, 127–28, 175–76 metaphor search module in, 176–77 moral education of, 177–78 pattern redundancy in, 175 simultaneous searching in, 177 structure of, 172–78 virtual neural connections in, 173–74 neocortical columns, 36–37, 38, 90, 124–25 nervous systems, 2 neural circuits, unreliability of, 185 neural implants, 243, 245 neural nets, 131–35, 144, 155 algorithm for, 291n–97n feedforward, 134, 135 learning in, 132–33 neural processing: digital emulation of, 195–97 massive parallelism of, 192, 193, 195 speed of, 192, 195 neuromorphic chips, 194–95, 196 neuromuscular junction, 99 neurons, 2, 36, 38, 43, 80, 172 neurotransmitters, 105–7 new brain, see neocortex Newell, Allen, 181 New Kind of Science, A (Wolfram), 236, 239 Newton, Isaac, 94 Nietzsche, Friedrich, 117 nonbiological systems, as capable of being copied, 247 nondestructive imaging techniques, 127, 129, 264, 312n–13n nonmammals, reasoning by, 286n noradrenaline, 107 norepinephrine, 118 Notes from Underground (Dostoevsky), 199 Nuance Speech Technologies, 6–7, 108, 122, 152, 161, 162, 168 nucleus accumbens, 77, 105 Numenta, 156 NuPIC, 156 obsessive-compulsive disorder, 118 occipital lobe, 36 old brain, 63, 71, 90, 93–108 neocortex as modulator of, 93–94, 105, 108 sensory pathway in, 94–98 olfactory system, 100 Oluseun, Oluseyi, 204 OmniPage, 122 One Hundred Years of Solitude (García Márquez), 283n–85n On Intelligence (Hawkins and Blakeslee), 73, 156 On the Origin of Species (Darwin), 15–16 optical character recognition (OCR), 122 optic nerve, 95, 100 channels of, 94–95, 96 organisms, simulated, evolution of, 147–53 overfitting problem, 150 oxytocin, 119 pancreas, 37 panprotopsychism, 203, 213 Papert, Seymour, 134–35, 134 parameters, in pattern recognition: “God,” 147 importance, 42, 48–49, 60, 66, 67 size, 42, 49–50, 60, 61, 66, 67, 73–74, 91–92, 173 size variability, 42, 49–50, 67, 73–74, 91–92 Parker, Sean, 156 Parkinson’s disease, 243, 245 particle physics, see quantum mechanics Pascal, Blaise, 117 patch-clamp robotics, 125–26, 126 pattern recognition, 195 of abstract concepts, 58–59 as based on experience, 50, 90, 273–74 as basic unit of learning, 80–81 bidirectional flow of information in, 52, 58, 68 distortions and, 30 eye movement and, 73 as hierarchical, 33, 90, 138, 142 of images, 48 invariance and, see invariance, in pattern recognition learning as simultaneous with, 63 list combining in, 60–61 in neocortex, see pattern recognition modules redundancy in, 39–40, 57, 60, 64, 185 pattern recognition modules, 35–41, 42, 90, 198 autoassociation in, 60–61 axons of, 42, 43, 66, 67, 113, 173 bidirectional flow of information to and from thalamus, 100–101 dendrites of, 42, 43, 66, 67 digital, 172–73, 175, 195 expectation (excitatory) signals in, 42, 52, 54, 60, 67, 73, 85, 91, 100, 112, 173, 175, 196–97 genetically determined structure of, 80 “God parameter” in, 147 importance parameters in, 42, 48–49, 60, 66, 67 inhibitory signals in, 42, 52–53, 67, 85, 91, 100, 173 input in, 41–42, 42, 53–59 love and, 119–20 neural connections between, 90 as neuronal assemblies, 80–81 one-dimensional representation of multidimensional data in, 53, 66, 91, 141–42 prediction by, 50–51, 52, 58, 60, 66–67 redundancy of, 42, 43, 48, 91 sequential processing of information by, 266 simultaneous firings of, 57–58, 57, 146 size parameters in, 42, 49–50, 60, 61, 66, 67, 73–74, 91–92, 173 size variability parameters in, 42, 67, 73–74, 91–92, 173 of sounds, 48 thresholds of, 48, 52–53, 60, 66, 67, 111–12, 173 total number of, 38, 40, 41, 113, 123, 280 universal algorithm of, 111, 275 pattern recognition theory of mind (PRTM), 5–6, 8, 11, 34–74, 79, 80, 86, 92, 111, 172, 217 patterns: hierarchical ordering of, 41–53 higher-level patterns attached to, 43, 45, 66, 67 input in, 41, 42, 44, 66, 67 learning of, 63–64, 90 name of, 42–43 output of, 42, 44, 66, 67 redundancy and, 64 specific areas of neocortex associated with, 86–87, 89–90, 91, 111, 152 storing of, 64–65 structure of, 41–53 Patterns, Inc., 156 Pavlov, Ivan Petrovich, 216 Penrose, Roger, 207–8, 274 perceptions, as influenced by expectations and interpretations, 31 perceptrons, 131–35 Perceptrons (Minsky and Papert), 134–35, 134 phenylethylamine, 118 Philosophical Investigations (Wittgenstein), 221 phonemes, 61, 135, 137, 146, 152 photons, 20–21 physics, 37 computational capacity and, 281, 316n–19n laws of, 37, 267 standard model of, 2 see also quantum mechanics Pinker, Steven, 76–77, 278 pituitary gland, 77 Plato, 212, 221, 231 pleasure, in old and new brains, 104–8 Poggio, Tomaso, 85, 159 posterior ventromedial nucleus (VMpo), 99–100, 99 prairie vole, 119 predictable outcomes, determined outcomes vs., 26, 239 President’s Council of Advisors on Science and Technology, 269 price/performance, of computation, 4–5, 250–51, 257, 257, 267–68, 301n–3n Principia Mathematica (Russell and Whitehead), 181 probability fields, 218–19, 235–36 professional knowledge, 39–40 proteins, reverse-engineering of, 4–5 qualia, 203–5, 210, 211 quality of life, perception of, 277–78 quantum computing, 207–9, 274 quantum mechanics, 218–19 observation in, 218–19, 235–36 randomness vs. determinism in, 236 Quinlan, Karen Ann, 101 Ramachandran, Vilayanur Subramanian “Rama,” 230 random access memory: growth in, 259, 260, 301n–3n, 306n–7n three-dimensional, 268 randomness, determinism and, 236 rationalization, see confabulation reality, hierarchical nature of, 4, 56, 90, 94, 172 recursion, 3, 7–8, 56, 65, 91, 153, 156, 177, 188 “Red” (Oluseum), 204 redundancy, 9, 39–40, 64, 184, 185, 197, 224 in genome, 271, 314n, 315n of memories, 59 of pattern recognition modules, 42, 43, 48, 91 thinking and, 57 religious ecstacy, 118 “Report to the President and Congress, Designing a Digital Future” (President’s Council of Advisors on Science and Technology), 269 retina, 95 reverse-engineering: of biological systems, 4–5 of human brain, see brain, human, computer emulation of; neocortex, digital Rosenblatt, Frank, 131, 133, 134, 135, 191 Roska, Boton, 94 Rothblatt, Martine, 278 routine tasks, as series of hierarchical steps, 32–33 Rowling, J.


pages: 187 words: 55,801

The New Division of Labor: How Computers Are Creating the Next Job Market by Frank Levy, Richard J. Murnane

Atul Gawande, business cycle, call centre, computer age, Computer Numeric Control, correlation does not imply causation, David Ricardo: comparative advantage, deskilling, Frank Levy and Richard Murnane: The New Division of Labor, Gunnar Myrdal, hypertext link, index card, information asymmetry, job automation, knowledge economy, knowledge worker, low skilled workers, low-wage service sector, pattern recognition, profit motive, Robert Shiller, Robert Shiller, Ronald Reagan, speech recognition, talking drums, telemarketer, The Wealth of Nations by Adam Smith, working poor

She has never done an ad campaign for spaghetti sauce, but she has done ad campaigns for other quick-to-prepare meals and her knowledge of those cases gives her a useful starting point for thinking about the sauce.14 We do much of our creative work in this style: we use pattern recognition to see similarities between a new problem and relevant past work, call that past work to mind, and appropriately apply the knowledge gained in doing that work. In this case, pattern recognition occurs in seeing the points of similarity between the current problem and past experience. If the focus of this book were cognitive psychology, we would include still other models of human information processing. But for our focus— how computers alter work—a short list of models including rules-basedlogic, pattern recognition, and case-based reasoning (pattern recognition’s first cousin) serves quite well. 24 CHAPTER 2 THE LIMITS OF PATTERN RECOGNITION If we take a step back, we can see that we are well along in answering the questions we have raised: • Computers excel at processing information through the application of rules. • Computers could substitute for the Liffe traders because the information processing required to match buy and sell orders (the main task of the floor traders) could be fully described in step-bystep rules. • Stephen Saltz processed the information in the echocardiogram by recognizing patterns rather than applying step-by-step rules.

His recognition of a particular pattern helped him to form a diagnosis. Pattern recognition is an equally plausible description of how the truck driver and the four-year-old girl processed what they saw and heard. But notice that in most of these cases, people are recognizing something closer to a concept—what psychologists call a schema—than a precise template. We expect the fouryear-old girl to recognize a blue oval bowl even if she so far has seen only WHY PEOPLE STILL MATTER 23 green and red round bowls. Stephen Saltz and the truck driver have the same ability to generalize. Within cognitive science, pattern recognition is a more controversial processing model than rules-based logic. Some researchers argue that if we could peer deeply enough into people’s minds, we would find that what we call pattern recognition rests on a series of deeply embedded rules—not the If-Then-Do rules of rules-based logic but probability rules that use information to guess at a likely concept—“that blue oval glass thing filled with fruit is probably a bowl,” or, “that high wailing sound is probably a fire engine’s siren.”

Some researchers argue that if we could peer deeply enough into people’s minds, we would find that what we call pattern recognition rests on a series of deeply embedded rules—not the If-Then-Do rules of rules-based logic but probability rules that use information to guess at a likely concept—“that blue oval glass thing filled with fruit is probably a bowl,” or, “that high wailing sound is probably a fire engine’s siren.” Because of this possibility, cognitive scientists disagree about whether pattern recognition is a truly distinct processing method or a processing method that relies on rules that we cannot articulate.13 But just as rules-based logic offers a useful description of the mortgage underwriter’s processing, pattern recognition offers a useful description of Saltz’s processing of the echocardiogram—as well as much of the information processing that we all do. Pattern recognition is also important in the process of creating something new through what cognitive psychologists call “case-based reasoning.” For example, an advertising writer is asked to develop a campaign for a new spaghetti sauce.


pages: 250 words: 73,574

Nine Algorithms That Changed the Future: The Ingenious Ideas That Drive Today's Computers by John MacCormick, Chris Bishop

Ada Lovelace, AltaVista, Claude Shannon: information theory, fault tolerance, information retrieval, Menlo Park, PageRank, pattern recognition, Richard Feynman, Silicon Valley, Simon Singh, sorting algorithm, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, traveling salesman, Turing machine, Turing test, Vannevar Bush

It covers pattern recognition algorithms, which sneak into the list of great computer science ideas despite violating the very first criterion: that ordinary computer users must use them every day. Pattern recognition is the class of techniques whereby computers recognize highly variable information, such as handwriting, speech, and faces. In fact, in the first decade of the 21st century, most everyday computing did not use these techniques. But as I write these words in 2011, the importance of pattern recognition is increasing rapidly: mobile devices with small on-screen keyboards need automatic correction, tablet devices must recognize handwritten input, and all these devices (especially smartphones) are becoming increasingly voice-activated. Some websites even use pattern recognition to determine what kind of advertisements to display to their users.

Yet the computers at web search companies are constantly performing these computations. In this chapter, on the other hand, we examine an area in which humans have a natural advantage: the field of pattern recognition. Pattern recognition is a subset of artificial intelligence and includes tasks such as face recognition, object recognition, speech recognition, and handwriting recognition. More specific examples would include the task of determining whether a given photograph is a picture of your sister, or determining the city and state written on a hand-addressed envelope. Thus, pattern recognition can be defined more generally as the task of getting computers to act “intelligently” based on input data that contains a lot of variability. The word “intelligently” is in quotation marks here for good reason: the question of whether computers can ever exhibit true intelligence is highly controversial.

But first, we need a more scientific description of the problem we are trying to solve. WHAT'S THE PROBLEM? The tasks of pattern recognition might seem, at first, to be almost absurdly diverse. Can computers use a single toolbox of pattern recognition techniques to recognize handwriting, faces, speech, and more? One possible answer to this question is staring us (literally) in the face: our own human brains achieve superb speed and accuracy in a wide array of recognition tasks. Could we write a computer program to achieve the same thing? Before we can discuss the techniques that such a program might use, we need to somehow unify the bewildering array of tasks and define a single problem that we are trying to solve. The standard approach here is to view pattern recognition as a classification problem. We assume that the data to be processed is divided up into sensible chunks called samples, and that each sample belongs to one of a fixed set of possible classes.


pages: 330 words: 88,445

The Rise of Superman: Decoding the Science of Ultimate Human Performance by Steven Kotler

Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Clayton Christensen, data acquisition, delayed gratification, deliberate practice, fear of failure, Google Earth, haute couture, impulse control, Isaac Newton, Jeff Bezos, jimmy wales, Kevin Kelly, Lao Tzu, lateral thinking, life extension, lifelogging, low earth orbit, Maui Hawaii, pattern recognition, Ray Kurzweil, risk tolerance, rolodex, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Stanford marshmallow experiment, Steve Jobs, Walter Mischel, X Prize

By the time the serotonin has arrived the state has already happened. It’s a signal things are coming to an end, not just beginning.” These five chemicals are flow’s mighty cocktail. Alone, each packs a punch, together a wallop. Consider the chain of events that takes us from pattern recognition through future prediction. Norepinephrine tightens focus (data acquisition); dopamine jacks pattern recognition (data processing); anandamide accelerates lateral thinking (widens the database searched by the pattern recognition system). The results, as basketball legend Bill Russell explains in his biography Second Wind, really do feel psychic: Every so often a Celtic game would heat up so that it would become more than a physical or even mental game, and would be magical. That feeling is difficult to describe, and I certainly never talked about it when I was playing.

The texture of the knob is wrong. We are making continuous low-level predictions in parallel across all our senses. So important is prediction to survival, that when the brain guesses correctly—i.e., when the brain’s pattern-recognition system identifies a correct pattern—we get a reward, a tiny squirt of the feel-good neurochemical dopamine. Dopamine feels really good. Cocaine, widely considered one of the most addictive substances on earth, does little besides cause the brain to release dopamine and then block its reuptake. This same rush reinforces pattern recognition—it’s why learning happens. But dopamine actually does double duty. Not only does this neurotransmitter help us learn new patterns, it also amps up attention and reduces noise in neural networks, making it easier for us to notice more patterns.

In all fields, to make exceptional discoveries you need risk—you’re just never going to have a breakthrough without it.” When these athletes actually take a risk (putting themselves in a high-consequence situation rather than a high-risk environment), an even bigger neurochemical response is facilitated. Risk taking itself releases another big squirt of dopamine, further enhancing performance and increasing pattern recognition. Once the pattern-recognition system lights onto the proper response (i.e., identifies the chunk that will save the athlete’s hide in this particular situation), even more dopamine is released and the cascade continues. As we know these facts, we also know a bit about hacking the “high consequence” flow trigger. For starters, risk is always relative. While some danger must be courted for flow, confrontations with mortality are not required.


pages: 205 words: 20,452

Data Mining in Time Series Databases by Mark Last, Abraham Kandel, Horst Bunke

call centre, computer vision, discrete time, G4S, information retrieval, iterative process, NP-complete, p-value, pattern recognition, random walk, sensor fusion, speech recognition, web application

., and Casacuberta, F. (2000). Use of Median String for Classification. Proc. of Int. Conf. on Pattern Recognition, pp. 907–910. 27. Martinez-Hinarejos, C.D. Juan, A. and Casacuberta, F. (2001). Improving Classification Using Median String and NN Rules. Proc. of National Symposium on Pattern Recognition and Image Analysis. 28. Marzal, A. and Vidal, E. (1993). Computation of Normalized Edit Distance and Applications. IEEE Trans. on PAMI, 15(9), 926–932. 29. Marzal, A. and Barrachina, S. (2000). Speeding up the Computation of the Edit Distance for Cyclic Strings. Proc. of Int. Conf. on Pattern Recognition, 2, pp. 895–898. 30. Mico, L. and Oncina, J. (2001). An Approximate Median Search Algorithm in Non-Metric Spaces. Pattern Recognition Letters, 22(10), 1145–1151. 31. O’Toole, A.J., Price, T., Vetter, T., Barlett, J.C., and Blanz, V. (1999). 3D Shape and 2D Surface Textures of Human Faces: The Role of “Averages” in Attractiveness and Age.

DATA MINING IN TIME SERIES DATABASES SERIES IN MACHINE PERCEPTION AND ARTIFICIAL INTELLIGENCE* Editors: H. Bunke (Univ. Bern, Switzerland) P. S. P. Wang (Northeastern Univ., USA) Vol. 43: Agent Engineering (Eds. Jiming Liu, Ning Zhong, Yuan Y. Tang and Patrick S. P. Wang) Vol. 44: Multispectral Image Processing and Pattern Recognition (Eds. J. Shen, P. S. P. Wang and T. Zhang) Vol. 45: Hidden Markov Models: Applications in Computer Vision (Eds. H. Bunke and T. Caelli) Vol. 46: Syntactic Pattern Recognition for Seismic Oil Exploration (K. Y. Huang) Vol. 47: Hybrid Methods in Pattern Recognition (Eds. H. Bunke and A. Kandel ) Vol. 48: Multimodal Interface for Human-Machine Communications (Eds. P. C. Yuen, Y. Y. Tang and P. S. P. Wang) Vol. 49: Neural Networks and Systolic Array Design (Eds. D. Zhang and S. K. Pal ) Vol. 50: Empirical Evaluation Methods in Computer Vision (Eds.

Dynamic Computation of Generalized Median Strings, Pattern Analysis and Applications. (Accepted for publication.) 19. Juan and Vidal, E. (1998). Fast Median Search in Metric Spaces, in A. Amin and D. Dori (eds.). Advances in Pattern Recognition, pp. 905–912, SpringerVerlag. 20. Kohonen, T. (1985). Median Strings. Pattern Recognition Letters, 3, 309–313. 21. Kruskal, J.B. (1983). An Overview of Sequence Comparison: Time Warps, String Edits, and Macromolecules. SIAM Reviews, 25(2), 201–237. 22. Kruzslicz, F. (1999) Improved Greedy Algorithm for Computing Approximate Median Strings. Acta Cybernetica, 14, 331–339. 23. Lewis, T., Owens, R., and Baddeley, A. (1999). Averaging Feature Maps. Pattern Recognition, 32(9), 1615–1630. 24. Lopresti, D. and Zhou, J. (1997). Using Consensus Sequence Voting to Correct OCR Errors. Computer Vision and Image Understanding, 67(1), 39–47. 25.


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Gamification by Design: Implementing Game Mechanics in Web and Mobile Apps by Gabe Zichermann, Christopher Cunningham

airport security, future of work, game design, lateral thinking, minimum viable product, pattern recognition, Ruby on Rails, social graph, social web, urban planning, web application

Flirtation and Romance onboarding, Onboarding, The Onboarding Challenge, Make Winners, Make Winners, Guiding Player Experience, Guiding Player Experience, The Onboarding Challenge, The Onboarding Challenge, The Onboarding Challenge, Nike Plus: Making Fitness Fun, Health Month challenges and goals of process, The Onboarding Challenge exercise, designing A versus B quiz, Guiding Player Experience Health Month, Health Month Nike Plus application, Nike Plus: Making Fitness Fun setting up novice player to be winner, Make Winners operant conditioning, Reinforcement order of player’s first minute, The Order of a Player’s First Minute organizing and creating order, 4. Organizing and Creating Order organizing groups of people, 4. Organizing and Creating Order Orkut, Social networking score out-of-scale promotional opportunities, 9. Fame, Getting Attention overjustification/replacement bias, Intrinsic versus Extrinsic Motivation P paginate( ) method, Optimizing Leaderboard Output pattern recognition, 1. Pattern Recognition, 1. Pattern Recognition advanced subway rider (example), 1. Pattern Recognition Pavlov, Ivan, Reinforcement personal motivators, Designing the Games pet games, virtual, 12. Nurturing, Growing photo-sharing websites, Skill points Pink, Daniel H., Intrinsic versus Extrinsic Motivation Plants vs. Zombies iPhone game, Progression of difficulty player detail view, Altered Beast site, The Trophy Case player re-engagement in social engagement loops, Engagement Loop Examples players, Foundations, Player Types, Player Types, Social Games ranking top five social actions taken by, Social Games types of, Player Types, Player Types breakdown of percentages, Player Types pluralization features, Ruby on Rails, Adding a Player’s Level to Topic Posts points, Game Mechanics, Video game score, Social networking score, Point Systems, Point Systems, Experience points, Redeemable points, Karma points, Karma points, How to Use Point Systems, Level Design, How to Use Point Systems, How to Use Point Systems, Virtual economies, Dual economy, Dual economy, Dual economy, Example: Assigned Point Values, Levels, Level Design, Game Mechanics and Dynamics in Greater Depth, 5.

., images on Flickr’s home page 10 Being the Hero “Rescue the maiden” challenges Friends ask for help, you respond with help MacGruber: things are going to blow up in 10…9… 11 Gaining Status Badges, trophies—especially public ones Scarce, limited-edition items that are public Public, obvious scores and leaderboards 12 Nurturing, Growing Tamagotchi style: feed this thing regularly or it will die Points that expire in the absence of activity, growth Pyramid scoring, with cumulative scores for teams and leaders [a] Cover these columns with a sheet of paper first and reveal as needed [b] Cover these columns with a sheet of paper first and reveal as needed [c] Cover these columns with a sheet of paper first and reveal as needed Game Mechanics in Depth Most of the game mechanics in Table 5-1 can be useful and interesting to your gamified system. Understanding when and how to use them can be daunting, so we’ve provided some depth here on each of these interactions. 1. Pattern Recognition Pattern recognition is the dynamic user interaction most associated with unpacking systemic complexity. When players seek to understand the world around them, and discover the “hidden meaning” or ways that complex items interact, they are seeking pattern recognition. Once patterns are detected, players organize the world around those patterns, and they usually feel intrinsically rewarded just for having discovered them. As an example, consider people standing on subway platforms. If you ride a particular subway system regularly, you recognize certain patterns about where to stand to optimize both getting a seat and exiting the subway at your destination (see Figure 5-2).

Essentially, riders have leveled up if they know where and when they can get a seat or exit a crowded station a few minutes faster. Figure 5-2. Pattern recognition: the advanced subway rider knows where to stand to get a seat or to be close to the right exit (image licensed under CC; photo by http://www.flickr.com/photos/piercedavid). Exit Strategy, shown in Figure 5-3, is an application that lets players know where to be to best exit the subway. It takes ad hoc knowledge from users/maps and makes it publicly available. It can’t help you get a seat, though. Figure 5-3. Pattern recognition: the advanced subway rider game, Exit Strategy. For pattern recognition, there are a number of game mechanics we can use to create player engagement. Some of the most common are: Memory-game interactions For example, the card-matching memory game we grew up with, where like objects are revealed to players and then must be remembered.


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The Age of Spiritual Machines: When Computers Exceed Human Intelligence by Ray Kurzweil

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Buckminster Fuller, call centre, cellular automata, combinatorial explosion, complexity theory, computer age, computer vision, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, Everything should be made as simple as possible, first square of the chessboard / second half of the chessboard, fudge factor, George Gilder, Gödel, Escher, Bach, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, John Markoff, John von Neumann, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, Marshall McLuhan, Menlo Park, natural language processing, Norbert Wiener, optical character recognition, ought to be enough for anybody, pattern recognition, phenotype, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Robert Metcalfe, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, social intelligence, speech recognition, Steven Pinker, Stewart Brand, stochastic process, technological singularity, Ted Kaczynski, telepresence, the medium is the message, There's no reason for any individual to have a computer in his home - Ken Olsen, traveling salesman, Turing machine, Turing test, Whole Earth Review, Y2K

Humans are far more skilled at recognizing patterns than in thinking through logical combinations, so we rely on this aptitude for almost all of our mental processes. Indeed, pattern recognition comprises the bulk of our neural circuitry. These faculties make up for the extremely slow speed of human neurons. The reset time on neural firing is about five milliseconds, permitting only about two hundred calculations per second in each neural connection.25 We don’t have time, therefore, to think too many new thoughts when we are pressed to make a decision. The human brain relies on precomputing its analyses and storing them for future reference. We then use our pattern-recognition capability to recognize a situation as comparable to one we have thought about and then draw upon our previously considered conclusions. We are unable to think about matters that we have not thought through many times before.

The answer is, she doesn’t. She uses her neural nets’ pattern-recognition abilities, which provide the foundation for much of skill formation. The neural nets of the ten-year-old have had a lot of practice in comparing the observed flight of the ball to her own actions. Once she has learned the skill, it becomes second nature, meaning that she has no idea how she does it. Her neural nets have gained all the insights needed: Take a step back if the ball has gone above my field of view; take a step forward if the ball is below a certain level in my field of view and no longer rising, and so on. The human ballplayer is not mentally computing equations. Nor is there any such computation going on unconsciously in the player’s brain. What is going on is pattern recognition, the foundation of most human thought. One key to intelligence is knowing what not to compute.

Actually, computerized investment programs are using both evolutionary algorithms and neural nets; but the computerized neural nets are not nearly as flexible as the human variety just yet. THIS NOTION THAT WE DON’T REALLY UNDERSTAND HOW WE RECOGNIZE THINGS BECAUSE MY PATTERN-RECOGNITION STUFF IS DISTRIBUTED ACROSS A REGION OF MY BRAIN ... Yes. WELL, IT DOES SEEM TO EXPLAIN A FEW THINGS. LIKE WHEN I JUST SEEM TO KNOW WHERE MY KEYS ARE EVEN THOUGH I DON’T REMEMBER HAVING PUT THEM THERE. OR THAT ARCHETYPAL OLD WOMAN WHO CAN TELL WHEN A STORM IS COMING, BUT CAN’T REALLY EXPLAIN HOW SHE KNOWS. That’s actually a good example of the strength of human pattern recognition. That old woman has a neural net that is triggered by a certain combination of other perceptions—animal movements, wind patterns, sky color, atmospheric changes, and so on. Her storm-detector neural net fires and she senses a storm, but she could never explain what triggered her feeling of an impending storm.


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

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

Current satellite technology is capable of observing ground-level features about an inch in size and is not affected by bad weather, clouds, or darkness.188 The massive amount of data continually generated would not be manageable without automated image recognition programmed to look for relevant developments. Medicine. If you obtain an electrocardiogram (ECG) your doctor is likely to receive an automated diagnosis using pattern recognition applied to ECG recordings. My own company (Kurzweil Technologies) is working with United Therapeutics to develop a new generation of automated ECG analysis for long-term unobtrusive monitoring (via sensors embedded in clothing and wireless communication using a cell phone) of the early warning signs of heart disease.189 Other pattern-recognition systems are used to diagnose a variety of imaging data. Every major drug developer is using AI programs to do pattern recognition and intelligent data mining in the development of new drug therapies. For example SRI International is building flexible knowledge bases that encode everything we know about a dozen disease agents, including tuberculosis and H. pylori (the bacteria that cause ulcers).190 The goal is to apply intelligent datamining tools (software that can search for new relationships in data) to find new ways to kill or disrupt the metabolisms of these pathogens.

Although these neurons execute calculations at extremely slow speeds (typically two hundred transactions per second), the brain as a whole is massively parallel: most of its neurons work at the same time, resulting in up to one hundred trillion computations being carried out simultaneously. The massive parallelism of the human brain is the key to its pattern-recognition ability, which is one of the pillars of our species' thinking. Mammalian neurons engage in a chaotic dance (that is, with many apparently random interactions), and if the neural network has learned its lessons well, a stable pattern will emerge, reflecting the network's decision. At the present, parallel designs for computers are somewhat limited. But there is no reason why functionally equivalent nonbiological re-creations of biological neural networks cannot be built using these principles. Indeed, dozens of efforts around the world have already succeeded in doing so. My own technical field is pattern recognition, and the projects that I have been involved in for about forty years use this form of trainable and nondeterministic computing.

Other research on the visual system of the macaque monkey includes studies on many specific types of cells, connectivity patterns, and high-level descriptions of information flow.102 Extensive literature supports the use of what I call "hypothesis and test" in more complex pattern-recognition tasks. The cortex makes a guess about what it is seeing and then determines whether the features of what is actually in the field of view match its hypothesis.103 We’re often more focused on the hypothesis than the actual test, which explains why people often see and hear what they expect to perceive rather than what is actually there. "Hypothesis and test" is also a useful strategy in our computer-based pattern-recognition systems. Although we have the illusion of receiving high-resolution images from our eyes, what the optic nerve actually sends to the brain is just outlines and clues about points of interest in our visual field.


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Data Mining: Concepts, Models, Methods, and Algorithms by Mehmed Kantardzić

Albert Einstein, bioinformatics, business cycle, business intelligence, business process, butter production in bangladesh, combinatorial explosion, computer vision, conceptual framework, correlation coefficient, correlation does not imply causation, data acquisition, discrete time, El Camino Real, fault tolerance, finite state, Gini coefficient, information retrieval, Internet Archive, inventory management, iterative process, knowledge worker, linked data, loose coupling, Menlo Park, natural language processing, Netflix Prize, NP-complete, PageRank, pattern recognition, peer-to-peer, phenotype, random walk, RFID, semantic web, speech recognition, statistical model, Telecommunications Act of 1996, telemarketer, text mining, traveling salesman, web application

For both, autoassociation and heteroassociation, there are two main phases in the application of an ANN for pattern-association problems: 1. the storage phase, which refers to the training of the network in accordance with given patterns, and 2. the recall phase, which involves the retrieval of a memorized pattern in response to the presentation of a noisy or distorted version of a key pattern to the network. 7.4.2 Pattern Recognition Pattern recognition is also a task that is performed much better by humans than by the most powerful computers. We receive data from the world around us via our senses and are able to recognize the source of the data. We are often able to do so almost immediately and with practically no effort. Humans perform pattern recognition through a learning process, so it is with ANNs. Pattern recognition is formally defined as the process whereby a received pattern is assigned to one of a prescribed number of classes. An ANN performs pattern recognition by first undergoing a training session, during which the network is repeatedly presented a set of input patterns along with the category to which each particular pattern belongs.

., Data Mining: A Hands-On Approach for Business Professionals, Prentice Hall, Inc., Upper Saddle River, NJ, 1998. Han, J., M. Kamber, Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann, San Francisco, CA, 2006. Jain, A., R. P. W. Duin, J. Mao, Statistical Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 4–37. Kennedy, R. L., et al. Solving Data Mining Problems through Pattern Recognition, Prentice Hall, Upper Saddle River, NJ, 1998. Kil, D. H., F. B. Shin, Pattern Recognition and Prediction with Applications to Signal Characterization, AIP Press, Woodburg, NY, 1996. Liu, H., H. Motoda, eds., Feature Extraction, Construction and Selection: A Data Mining Perspective, Kluwer Academic Publishers, Boston, MA, 1998. Liu, H., H. Motoda, Feature Selection for Knowledge Discovery and Data Mining, Second Printing, Kluwer Academic Publishers, Boston, 2000.

Louis, 2000, pp. 429–436. Gose, E., R. Johnsonbaugh, S. Jost, Pattern Recognition and Image Analysis, Prentice Hall, Inc., Upper Saddle River, NJ, 1996. Han, J., M. Kamber, Data Mining: Concepts and Techniques, 2nd edition, Morgan Kaufmann, San Francisco, CA, 2006. Hand, D., H. Mannila, P. Smyth, Principles of Data Mining, The MIT Press, Cambridge, MA, 2001. Jackson, J., Data Mining: A Conceptual Overview, Communications of the Association for Information Systems, Vol. 8, 2002, pp. 267–296. Jain, A., R. P. W. Duin, J. Mao, Statistical Pattern Recognition, IEEE Transactions on Pattern Analysis and Machine Intelligence, Vol. 22, No. 1, 2000, pp. 4–37. Kennedy, R. L., et al., Solving Data Mining Problems through Pattern Recognition, Prentice Hall, Upper Saddle River, NJ, 1998. McCullagh, P., J.


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AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

The input might be a sound wave (for interpreting a request to a digital home assistant), a sequence of genes (for predicting someone’s susceptibility to a disease), or an English phrase (for translation into Spanish). As the table below indicates, anything you can represent as a set of numbers can be used as an input in one of these pattern-recognition systems. As we’ll discuss later, though, sometimes it’s obvious how to represent an input as a number, and sometimes it isn’t. In this chapter, you’ll learn the two key ideas behind how these pattern-recognition systems work: 1. In AI, a “pattern” is a prediction rule that maps an input to an expected output. 2. “Learning a pattern” means fitting a good prediction rule to a data set. There’s a bit of math involved here, but have no fear: these ideas are actually quite simple and elegant once you get to know them, and we’re going to spend the rest of the chapter helping you do just that.

Food and Drug Administration approved Medtronic’s latest model, the first artificial pancreas designed to level out both high and low blood-sugar levels.45 AI for Medical Imaging Diagnostic imaging offers an even more immediate example of where AI can make a difference. Many common forms of medical image analysis, from looking at a chest X-ray to examining cancer cells under a microscope, involve a classic pattern-recognition problem: the inputs are the features extracted from the image, and the output is the diagnosis. As we learned in chapter 2, computers are absolutely brilliant at learning how to predict outputs from inputs, especially for images—and with more data and newer pattern-recognition algorithms, they’re getting better all the time. For some image-driven diagnoses, you soon may not even need to visit a doctor’s office. Take, for example, the problem of diagnosing a skin lesion. The stakes here are high: melanoma causes more than 10,000 deaths per year in the United States alone.

See Great Andromeda Nebula anomaly detection averaging bias (type of anomaly) coin clipping and Formula 1 racing and fraud and importance of variability law enforcement and Moneyball NBA and overdispersion (type of anomaly) Patriots coin toss record and simulated coin toss record smart cities and square-root rule (de Moivre’s equation) and Trial of the Pyx (Royal Mint fraud protection) Apple data storage iPhone market dominance pattern-recognition system Aristophanes: The Frogs artificial intelligence (AI) algorithms and anxieties regarding assumptions and bias in, bias out contraception and criminal justice system and democratization of diffusion and dissemination of enabling technological trends image classification image recognition model rust models versus reality Moravec paradox policy rage to conclude bias robot cars salaries SLAM problem (simultaneous localization and mapping) speech recognition talent and workforce twenty questions game and See also anomaly detection; Bayes’s rule; health care and medicine; natural language processing (NLP); pattern recognition; personalization; prediction rules assumptions astronomy Alpha Centauri Bayes’s rule and Great Andromeda Nebula Leavitt’s original equation Leavitt’s prediction rule data measuring stars Milky Way nebulae oscillation of a pulsating star parallax pulsating stars statistics and Athey, Alex automation.


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Emergence by Steven Johnson

A Pattern Language, agricultural Revolution, Brewster Kahle, British Empire, Claude Shannon: information theory, complexity theory, Danny Hillis, Douglas Hofstadter, edge city, epigenetics, game design, garden city movement, Gödel, Escher, Bach, hive mind, Howard Rheingold, hypertext link, invisible hand, Jane Jacobs, Kevin Kelly, late capitalism, Marshall McLuhan, mass immigration, Menlo Park, Mitch Kapor, Murano, Venice glass, Naomi Klein, new economy, New Urbanism, Norbert Wiener, pattern recognition, pez dispenser, phenotype, Potemkin village, price mechanism, profit motive, Ray Kurzweil, slashdot, social intelligence, Socratic dialogue, stakhanovite, Steven Pinker, The Death and Life of Great American Cities, The Wealth of Nations by Adam Smith, theory of mind, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, trickle-down economics, Turing machine, Turing test, urban planning, urban renewal, Vannevar Bush

A field of research that had been characterized by a handful of early-stage investigations blossomed overnight into a densely populated and diverse landscape, transforming dozens of existing disciplines and inventing a handful of new ones. In 1969, Marvin Minsky and Seymour Papert published “Perceptrons,” which built on Selfridge’s Pandemonium device for distributed pattern recognition, leading the way for Minsky’s bottom-up Society of Mind theory developed over the following decade. In 1972, a Rockefeller University professor named Gerald Edelman won the Nobel prize for his work decoding the language of antibody molecules, leading the way for an understanding of the immune system as a self-learning pattern-recognition device. Prigogine’s Nobel followed five years later. At the end of the decade, Douglas Hofstadter published Gödel, Escher, Bach, linking artificial intelligence, pattern recognition, ant colonies, and “The Goldberg Variations.” Despite its arcane subject matter and convoluted rhetorical structure, the book became a best-seller and won the Pulitzer prize for nonfiction.

As the futurist Ray Kurzweil writes, “Humans are far more skilled at recognizing patterns than in thinking through logical combinations, so we rely on this aptitude for almost all of our mental processes. Indeed, pattern recognition comprises the bulk of our neural circuitry. These faculties make up for the extremely slow speed of human neurons.” The human mind is poorly equipped to deal with problems that need to be solved serially—one calculation after another—given that neurons require a “reset time” of about five milliseconds, meaning that neurons are capable of only two hundred calculations per second. (A modern PC can do millions of calculations per second, which is why we let them do the heavy lifting for anything that requires math skills.) But unlike most computers, the brain is a massively parallel system, with 100 billion neurons all working away at the same time. That parallelism allows the brain to perform amazing feats of pattern recognition, feats that continue to confound digital computers—such as remembering faces or creating metaphors.

In the simplest terms, they solve problems by drawing on masses of relatively stupid elements, rather than a single, intelligent “executive branch.” They are bottom-up systems, not top-down. They get their smarts from below. In a more technical language, they are complex adaptive systems that display emergent behavior. In these systems, agents residing on one scale start producing behavior that lies one scale above them: ants create colonies; urbanites create neighborhoods; simple pattern-recognition software learns how to recommend new books. The movement from low-level rules to higher-level sophistication is what we call emergence. Imagine a billiard table populated by semi-intelligent, motorized billiard balls that have been programmed to explore the space of the table and alter their movement patterns based on specific interactions with other balls. For the most part, the table is in permanent motion, with balls colliding constantly, switching directions and speed every second.


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Race Against the Machine: How the Digital Revolution Is Accelerating Innovation, Driving Productivity, and Irreversibly Transforming Employment and the Economy by Erik Brynjolfsson

"Robert Solow", Amazon Mechanical Turk, Any sufficiently advanced technology is indistinguishable from magic, autonomous vehicles, business cycle, business process, call centre, combinatorial explosion, corporate governance, creative destruction, crowdsourcing, David Ricardo: comparative advantage, easy for humans, difficult for computers, Erik Brynjolfsson, factory automation, first square of the chessboard, first square of the chessboard / second half of the chessboard, Frank Levy and Richard Murnane: The New Division of Labor, hiring and firing, income inequality, intangible asset, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Khan Academy, Kickstarter, knowledge worker, Loebner Prize, low skilled workers, minimum wage unemployment, patent troll, pattern recognition, Paul Samuelson, Ray Kurzweil, rising living standards, Robert Gordon, self-driving car, shareholder value, Skype, too big to fail, Turing test, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, wealth creators, winner-take-all economy, zero-sum game

Of course, these are only a small sample of the myriad IT-enabled innovations that are transforming manufacturing, distribution, retailing, media, finance, law, medicine, research, management, marketing, and almost every other economic sector and business function. Where People Still Win (at Least for Now) Although computers are encroaching into territory that used to be occupied by people alone, like advanced pattern recognition and complex communication, for now humans still hold the high ground in each of these areas. Experienced doctors, for example, make diagnoses by comparing the body of medical knowledge they’ve accumulated against patients’ lab results and descriptions of symptoms, and also by employing the advanced subconscious pattern recognition abilities we label “intuition.” (Does this patient seem like they’re holding something back? Do they look healthy, or is something off about their skin tone or energy level?’) Similarly, the best therapists, managers, and salespeople excel at interacting and communicating with others, and their strategies for gathering information and influencing behavior can be amazingly complex.

By 2002 (the last year for which consistent data are available), that number had fallen to 1.79, a decline of nearly 14 percent. If, as these examples indicate, both pattern recognition and complex communication are now so amenable to automation, are any human skills immune? Do people have any sustainable comparative advantage as we head ever deeper into the second half of the chessboard? In the physical domain, it seems that we do for the time being. Humanoid robots are still quite primitive, with poor fine motor skills and a habit of falling down stairs. So it doesn’t appear that gardeners and restaurant busboys are in danger of being replaced by machines any time soon. And many physical jobs also require advanced mental abilities; plumbers and nurses engage in a great deal of pattern recognition and problem solving throughout the day, and nurses also do a lot of complex communication with colleagues and patients.

As its title implies, it’s a description of the comparative capabilities of computers and human workers. In the book’s second chapter, “Why People Still Matter,” the authors present a spectrum of information-processing tasks. At one end are straightforward applications of existing rules. These tasks, such as performing arithmetic, can be easily automated. After all, computers are good at following rules. At the other end of the complexity spectrum are pattern-recognition tasks where the rules can’t be inferred. The New Division of Labor gives driving in traffic as an example of this type of task, and asserts that it is not automatable: The … truck driver is processing a constant stream of [visual, aural, and tactile] information from his environment. … To program this behavior we could begin with a video camera and other sensors to capture the sensory input.


Data Mining: Concepts and Techniques: Concepts and Techniques by Jiawei Han, Micheline Kamber, Jian Pei

bioinformatics, business intelligence, business process, Claude Shannon: information theory, cloud computing, computer vision, correlation coefficient, cyber-physical system, database schema, discrete time, distributed generation, finite state, information retrieval, iterative process, knowledge worker, linked data, natural language processing, Netflix Prize, Occam's razor, pattern recognition, performance metric, phenotype, random walk, recommendation engine, RFID, semantic web, sentiment analysis, speech recognition, statistical model, stochastic process, supply-chain management, text mining, thinkpad, Thomas Bayes, web application

Machine learning and pattern recognition research is published in the proceedings of several major machine learning, artificial intelligence, and pattern recognition conferences, including the International Conference on Machine Learning (ML), the ACM Conference on Computational Learning Theory (COLT), the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), the International Conference on Pattern Recognition (ICPR), the International Joint Conference on Artificial Intelligence (IJCAI), and the American Association of Artificial Intelligence Conference (AAAI). Other sources of publication include major machine learning, artificial intelligence, pattern recognition, and knowledge system journals, some of which have been mentioned before. Others include Machine Learning (ML), Pattern Recognition (PR), Artificial Intelligence Journal (AI), IEEE Transactions on Pattern Analysis and Machine Intelligence (PAMI), and Cognitive Science.

Textbooks and reference books on machine learning and pattern recognition include Machine Learning by Mitchell [Mit97]; Pattern Recognition and Machine Learning by Bishop [Bis06]; Pattern Recognition by Theodoridis and Koutroumbas [TK08]; Introduction to Machine Learning by Alpaydin [Alp11]; Probabilistic Graphical Models: Principles and Techniques by Koller and Friedman [KF09]; and Machine Learning: An Algorithmic Perspective by Marsland [Mar09]. For an edited collection of seminal articles on machine learning, see Machine Learning, An Artificial Intelligence Approach, Volumes 1 through 4, edited by Michalski et al. MCM83, MCM86, KM90 and MT94 and Readings in Machine Learning by Shavlik and Dietterich [SD90]. Machine learning and pattern recognition research is published in the proceedings of several major machine learning, artificial intelligence, and pattern recognition conferences, including the International Conference on Machine Learning (ML), the ACM Conference on Computational Learning Theory (COLT), the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), the International Conference on Pattern Recognition (ICPR), the International Joint Conference on Artificial Intelligence (IJCAI), and the American Association of Artificial Intelligence Conference (AAAI).

Exercises 1.1 What is data mining? In your answer, address the following:(a) Is it another hype? (b) Is it a simple transformation or application of technology developed from databases, statistics, machine learning, and pattern recognition? (c) We have presented a view that data mining is the result of the evolution of database technology. Do you think that data mining is also the result of the evolution of machine learning research? Can you present such views based on the historical progress of this discipline? Address the same for the fields of statistics and pattern recognition. (d) Describe the steps involved in data mining when viewed as a process of knowledge discovery. 1.2 How is a data warehouse different from a database? How are they similar? 1.3 Define each of the following data mining functionalities: characterization, discrimination, association and correlation analysis, classification, regression, clustering, and outlier analysis.


pages: 339 words: 88,732

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

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

Algorithms are simplifications; they can’t and don’t take everything into account (like a billionaire uncle who has included the applicant in his will and likes to rock-climb without ropes). Algorithms do, however, include the most common and important things, and they generally work quite well at tasks like predicting payback rates. Computers, therefore, can and should be used for mortgage approval.* . . . But Lousy at Pattern Recognition At the other end of Levy and Murnane’s spectrum, however, lie information processing tasks that cannot be boiled down to rules or algorithms. According to the authors, these are tasks that draw on the human capacity for pattern recognition. Our brains are extraordinarily good at taking in information via our senses and examining it for patterns, but we’re quite bad at describing or figuring out how we’re doing it, especially when a large volume of fast-changing information arrives at a rapid pace. As the philosopher Michael Polanyi famously observed, “We know more than we can tell.”2 When this is the case, according to Levy and Murnane, tasks can’t be computerized and will remain in the domain of human workers.

As futurist Kevin Kelly put it “You’ll be paid in the future based on how well you work with robots.”7 Sensing Our Advantage So computers are extraordinarily good at pattern recognition within their frames, and terrible outside them. This is good news for human workers because thanks to our multiple senses, our frames are inherently broader than those of digital technologies. Computer vision, hearing, and even touch are getting exponentially better all the time, but there are still tasks where our eyes, ears, and skin, to say nothing of our noses and tongues, surpass their digital equivalents. At present and for some time to come, the sensory package and its tight connection to the pattern-recognition engine of the brain gives us a broader frame. The Spanish clothing company Zara exploits this advantage and uses humans instead of computers to decide which clothes to make.

Zara relies on its store managers around the world to order exactly, and only, the merchandise that will sell in that location over the next few days.8 Managers figure this out not by consulting algorithms but instead by walking around the store, observing what shoppers (particularly cool ones) are wearing, talking to them about what they like and what they’re looking for, and generally doing many things at which people excel. Zara store managers do a lot of visual pattern recognition, engage in complex communication with customers, and use all of this information for two purposes: to order existing clothes using a broad frame of inputs, and to engage in ideation by telling headquarters what kinds of new clothes would be popular in their location. Zara has no plans to switch from human-based to machine-based ordering any time soon, and we think they’re making a very smart decision. So ideation, large-frame pattern recognition, and the most complex forms of communication are cognitive areas where people still seem to have the advantage, and also seem likely to hold on to it for some time to come.


pages: 360 words: 100,991

Heart of the Machine: Our Future in a World of Artificial Emotional Intelligence by Richard Yonck

3D printing, AI winter, artificial general intelligence, Asperger Syndrome, augmented reality, Berlin Wall, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, crowdsourcing, Elon Musk, en.wikipedia.org, epigenetics, friendly AI, ghettoisation, industrial robot, Internet of things, invention of writing, Jacques de Vaucanson, job automation, John von Neumann, Kevin Kelly, Law of Accelerating Returns, Loebner Prize, Menlo Park, meta analysis, meta-analysis, Metcalfe’s law, neurotypical, Oculus Rift, old age dependency ratio, pattern recognition, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, software as a service, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, telepresence, telepresence robot, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

To many of these, they applied a range of pattern recognition techniques in order to train systems to detect the meanings and variations of expression, something we humans do naturally and effortlessly. Pattern recognition is a branch of machine learning and artificial intelligence that has been developing in sophistication for decades. Because it’s a very specifically focused form of artificial intelligence, it’s sometimes referred to as a form of narrow or weak AI. Though these programs attempt to replicate the incredible pattern recognition our own brains perform so easily, the techniques used by our neurons can’t be fully emulated using machine logic. Therefore the methods employed by computers to perform these tasks are considerably different. For instance, in machine vision pattern recognition, a number of steps must be taken to assign meaning to an object or scene.

In many respects researchers had already been doing this a century before, studying autonomic responses that eventually led to the development of polygraphs and other forms of lie detectors. (For more on this, see chapter 10.) Reading facial expressions, though, is in many respects a considerably harder problem than mere visual pattern recognition and matching, at least for a machine. The nuances and variations that occur—across cultures, individuals, even a single person’s face—can be so great that it wasn’t very long ago that many people considered this to be an insurmountable problem for a computer. Even with the pattern recognition capabilities made possible by the computers available at that time, the problem still remained of how to classify and distinguish what was actually being detected. For instance, great variation exists between highly expressive people and those who are more reserved.

First, a series of technological conditions are now in place that allow this particular form of affective recognition to be developed: webcams and smartphone cameras of sufficient resolution and speed; available processing power in all of our devices, be they desktops, laptops, and perhaps most importantly our smartphones; and connectivity and transmission speeds that allow devices to connect to servers and services, whether they’re hardwired, Wi-Fi connected, or cellular devices. The second reason is much more interesting. Computer-based pattern recognition and deep learning are technologies that have attained a considerable level of sophistication and ability in recent years. In some respects this can result in pattern recognition that is far more capable than what people do naturally; in others, it is nowhere near as adept. This may be because when there are reasonably structured universal features to something—say, the tines of a fork, the four wheels of a car, or the features of the alphabet—a vision system based on an artificial neural network can be trained to do amazingly well, even under poor conditions.


The Deep Learning Revolution (The MIT Press) by Terrence J. Sejnowski

AI winter, Albert Einstein, algorithmic trading, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, Baxter: Rethink Robotics, bioinformatics, cellular automata, Claude Shannon: information theory, cloud computing, complexity theory, computer vision, conceptual framework, constrained optimization, Conway's Game of Life, correlation does not imply causation, crowdsourcing, Danny Hillis, delayed gratification, discovery of DNA, Donald Trump, Douglas Engelbart, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Google Glasses, Google X / Alphabet X, Guggenheim Bilbao, Gödel, Escher, Bach, haute couture, Henri Poincaré, I think there is a world market for maybe five computers, industrial robot, informal economy, Internet of things, Isaac Newton, John Conway, John Markoff, John von Neumann, Mark Zuckerberg, Minecraft, natural language processing, Netflix Prize, Norbert Wiener, orbital mechanics / astrodynamics, PageRank, pattern recognition, prediction markets, randomized controlled trial, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, theory of mind, Thomas Bayes, Thomas Kuhn: the structure of scientific revolutions, traveling salesman, Turing machine, Von Neumann architecture, Watson beat the top human players on Jeopardy!, X Prize, Yogi Berra

Here I will focus on just one of the pioneers of those early decades, Frank Rosenblatt (figure 3.4), whose perceptron is the direct antecedent of deep learning.6 Learning from Examples Undeterred by our lack of understanding about brain function, neural network AI pioneers plunged ahead with cartoon versions of neurons and how they are connected with one another. Frank Rosenblatt at Cornell University (figure 3.4) was one of the earliest to mimic the architecture of our visual system for automatic pattern recognition.7 He invented a deceptively simple network called a “perceptron,” a learning algorithm that could learn how to classify patterns into categories, such as letters of the alphabet. Algorithms are step-by-step procedures that you follow to achieve particular goals, much as you would a recipe to bake a cake (chapter 13 will explain algorithms in general). If you understand the basic principles for how a perceptron learns to solve a pattern recognition problem, you are halfway to understanding how deep learning works. The goal of a perceptron is to determine whether an input pattern is a member of a category, such as cats, in an image.

If individuals can quickly get an expert assessment, they will see their doctors office at an early stage of a skin disease, when it is easier to treat. All doctors will become better at diagnosing rare skin diseases with the help of deep learning.16 Deep Cancer The detection of metastatic breast cancer in images of lymph node biopsies on slides is done by experts who make mistakes, mistakes that have deadly consequences. This is a pattern recognition problem for which deep learning should excel. And indeed, a deep learning network trained on a large dataset of slides for which ground truth was known reached an accuracy of 0.925, good but not as good as experts who achieved 0.966 on the same test set.17 However, when the predictions of deep learning were combined The Rise of Machine Learning 11 Figure 1.5 Artist’s impression of a deep learning network diagnosing a skin lesion with high accuracy, cover of February 2, 2017, issue of Nature.

The company employs more than 400 engineers and neuroscientists in a culture that is a blend between academia and start-ups. The synergies between neuroscience and AI run deep and are quickening. Learning How to Become More Intelligent Is AlphaGo intelligent? There has been more written about intelligence than any other topic in psychology except consciousness, both of which are difficult to define. Psychologists since the 1930s distinguish between fluid intelligence, which uses reasoning and pattern recognition in new situations to solve new problems, without depending on previous knowledge, and crystallized intelligence, which depends on previous knowledge and is what the standard IQ tests measure. Fluid intelligence follows a developmental trajectory, reaching a peak in early adulthood and decreasing with age, whereas crystallized intelligence increases slowly and asymptotically as you age until fairly late in life.


pages: 586 words: 186,548

Architects of Intelligence by Martin Ford

3D printing, agricultural Revolution, AI winter, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, barriers to entry, basic income, Baxter: Rethink Robotics, Bayesian statistics, bitcoin, business intelligence, business process, call centre, cloud computing, cognitive bias, Colonization of Mars, computer vision, correlation does not imply causation, crowdsourcing, DARPA: Urban Challenge, deskilling, disruptive innovation, Donald Trump, Douglas Hofstadter, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Fellow of the Royal Society, Flash crash, future of work, gig economy, Google X / Alphabet X, Gödel, Escher, Bach, Hans Rosling, ImageNet competition, income inequality, industrial robot, information retrieval, job automation, John von Neumann, Law of Accelerating Returns, life extension, Loebner Prize, Mark Zuckerberg, Mars Rover, means of production, Mitch Kapor, natural language processing, new economy, optical character recognition, pattern recognition, phenotype, Productivity paradox, Ray Kurzweil, recommendation engine, Robert Gordon, Rodney Brooks, Sam Altman, self-driving car, sensor fusion, sentiment analysis, Silicon Valley, smart cities, social intelligence, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, Ted Kaczynski, The Rise and Fall of American Growth, theory of mind, Thomas Bayes, Travis Kalanick, Turing test, universal basic income, Wall-E, Watson beat the top human players on Jeopardy!, women in the workforce, working-age population, zero-sum game, Zipcar

JOSH TENENBAUM: To me, the idea of using neural networks with lots of layers is also just one tool in the toolkit. What that’s good at is problems of pattern recognition, and it has proven to be a practical, scalable route for it. Where that kind of deep learning has really had success is either in problems that are traditionally seen as pattern recognition problems, like speech recognition and object recognition, or problems that can be in some way coerced into or turned into pattern recognition problems. Take Go for example. AI researchers have long believed that playing Go would require some kind of sophisticated pattern recognition, but they didn’t necessarily understand that it could be solved using the same kind of pattern recognition approaches you would use for perception problems in vision and speech. However, now people have shown that you can take neural networks, the same kind that were developed in those more traditional pattern recognition domains, and you can use them as part of a solution to playing Go, as well as chess, or similar board games.

If you look beyond any one task, like playing Go or playing chess, to the broader problems of intelligence, though, the idea that you’re going to turn all of the intelligence into a pattern recognition problem is ridiculous, and I don’t think any serious person can believe that. I mean maybe some people will say that, but that just seems crazy to me. Every serious AI researcher has to think two things simultaneously. One is they have to recognize that deep learning and deep neural networks have contributed a huge amount to what we can do with pattern recognition, and that pattern recognition is going to be a part of probably any intelligent system’s success. At the same time, you also have to recognize that intelligence goes way beyond pattern recognition in all the ways I was talking about. There are all these activities of modeling the world, such as explaining, understanding, imagining, planning, and building out new models, and deep neural networks don’t really address that.

At that point, I was working for a company called Electronic Memories, and the rise of semiconductors left me without a job. That was how I came to academia, where I pursued my old dreams of doing pattern recognition and image encoding. MARTIN FORD: Did you go directly to UCLA from Electronic Memories? JUDEA PEARL: I tried to go to the University of Southern California, but they wouldn’t hire me because I was too sure of myself. I wanted to teach software, even though I’d never programmed before, and the Dean threw me out of his office. I ended up at UCLA because they gave me a chance of doing the things that I wanted to do, and I slowly migrated into AI from pattern recognition, image encoding, and decision theory. The early days of AI were dominated by chess and other game-playing programs, and that enticed me in the beginning, because I saw there a metaphor for capturing human intuition.


pages: 350 words: 98,077

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, Bernie Sanders, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, dark matter, Douglas Hofstadter, Elon Musk, en.wikipedia.org, Gödel, Escher, Bach, I think there is a world market for maybe five computers, ImageNet competition, Jaron Lanier, job automation, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, license plate recognition, Mark Zuckerberg, natural language processing, Norbert Wiener, ought to be enough for anybody, pattern recognition, performance metric, RAND corporation, Ray Kurzweil, recommendation engine, ride hailing / ride sharing, Rodney Brooks, self-driving car, sentiment analysis, Silicon Valley, Singularitarianism, Skype, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

Clune, “Deep Neural Networks Are Easily Fooled: High Confidence Predictions for Unrecognizable Images,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), 427–36. 22.  See, for example, M. Mitchell, An Introduction to Genetic Algorithms (Cambridge, Mass.: MIT Press, 1996). 23.  Nguyen, Yosinski, and Clune, “Deep Neural Networks Are Easily Fooled.” 24.  M. Sharif et al., “Accessorize to a Crime: Real and Stealthy Attacks on State-of-the-Art Face Recognition,” in Proceedings of the 2016 ACM SIGSAC Conference on Computer and Communications Security (2016), 1528–40. 25.  K. Eykholt et al., “Robust Physical-World Attacks on Deep Learning Visual Classification,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018), 1625–34. 26.  S. G. Finlayson et al., “Adversarial Attacks on Medical Machine Learning,” Science 363, no. 6433 (2019): 1287–89. 27.  

Hofstadter, Gödel, Escher, Bach: an Eternal Golden Braid (New York: Basic Books, 1979), 603. 23.  E. Davis and G. Marcus, “Commonsense Reasoning and Commonsense Knowledge in Artificial Intelligence,” Communications of the ACM 58, no. 9 (2015): 92–103. 24.  O. Vinyals et al., “Show and Tell: A Neural Image Caption Generator,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), 3156–64; A. Karpathy and L. Fei-Fei, “Deep Visual-Semantic Alignments for Generating Image Descriptions,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2015), 3128–37. 25.  Figure 39 is a simplified version of the system described in Vinyals et al., “Show and Tell.” 26.  J. Markoff, “Researchers Announce Advance in Image-Recognition Software,” New York Times, Nov. 17, 2014. 27.  J. Walker, “Google’s AI Can Now Caption Images Almost as Well as Humans,” Digital Journal, Sept. 23, 2016, www.digitaljournal.com/tech-and-science/technology/google-s-ai-now-captions-images-with-94-accuracy/article/475547. 28.  

., “Attacking Visual Language Grounding with Adversarial Examples: A Case Study on Neural Image Captioning,” in Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, vol. 1, Long Papers (2018), 2587–97. Reprinted with permission of Hongge Chen and the Association for Computational Linguistics. here Figure 44: Dorothy Alexander / Alamy Stock Photo. here Figure 45: From www.foundalis.com/res/bps/bpidx.htm. Original images are from M. Bongard, Pattern Recognition (New York: Spartan Books, 1970). here Figure 46: From www.foundalis.com/res/bps/bpidx.htm. Original images are from M. Bongard, Pattern Recognition (New York: Spartan Books, 1970). here Figure 47: Author. here Figure 48: Photographs taken by author. here Figure 49: www.nps.gov/dena/planyourvisit/pets.htm (public domain); pxhere.com/en/photo/1394259 (public domain); Peter Titmuss / Alamy Stock Photo; Thang Nguyen, www.flickr.com/photos/70209763@N00/399996115, licensed under Creative Commons Attribution-ShareAlike 2.0 Generic license (creativecommons.org/licenses/by-sa/2.0/).


pages: 301 words: 89,076

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

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

That, however, is exactly what AI does extremely well. If you replace “experience” with “data”—so experience-based pattern recognition becomes data-based pattern recognition—you have a pretty good description of the activities where machine learning has or soon will be better than the average human. This is already happening in medicine. Medical Jobs Healthcare is a very large sector. In the US, about 12 million people work in the industry. Only one out of twenty of these are doctors; nurses make up one in five. The UK’s National Health Service directly employs 1.5 million. Much of healthcare is fairly routine, but almost all of it turns around experience-based pattern recognition. This puts it squarely in the path of advancing AI. White-collar robots are good and getting better at processing images and patient history information.

What Else Can’t Machine Learning Learn? AI is really just data-based pattern recognition, and pattern recognition is not intelligence. AI is thus not intelligence in the broad sense of the word that psychologists use. White-collar robots trained by machine learning do not have a capacity to think; they cannot reason, plan, or solve problems they have not seen before; and they cannot think abstractly or comprehend complex ideas that are more than patterns in data. Computer scientists may eventually find ways to give white-collar robots general intelligence, but that is a long way off—and most definitely not a clear and pressing problem for Europe’s and America’s middle class. One key limitation is data. AI pattern recognition is usually based on structured data—data where the questions and answers are clear.

Psychologists define intelligence as: “A very general mental capability that, among other things, involves the ability to reason, plan, solve problems, think abstractly, comprehend complex ideas, learn quickly and learn from experience.”13 Today’s AI is not intelligent in this sense. Machine learning does only the last two items in the psychologists’ list: learn quickly and learn from experience. Even the revolutionary machine learning applications we see today—like Siri and self-driving cars—are just computer programs that recognize patterns in data and then act, or make suggestions based on the patterns they find. The pattern recognition is astonishing, often superhuman in specific areas. But pattern recognition is not “intelligence” as the word is generally used when speaking about intelligent animals like humans, chimpanzees, or dolphins. AI should really stand for “almost intelligent,” not artificial intelligence. Digital technology is an amazing thing to behold. To some it is fascinating. To others it is frightening. But one thing that should be obvious to all is that it will change our economies, our lives, and our communities.


pages: 153 words: 45,871

Distrust That Particular Flavor by William Gibson

AltaVista, British Empire, cognitive dissonance, cuban missile crisis, edge city, informal economy, Joi Ito, means of production, megastructure, pattern recognition, proxy bid, telepresence, Vannevar Bush, Whole Earth Catalog

Not so much because I shamelessly, if less eloquently, rehash the best part of the Observer piece you may already have read here, but because of a weird internal conflict, at the time, between fiction and non. All of the good stuff I encountered in Tokyo, that time (aside from the Australian girl crossing the street) got siphoned off, exclusively, into Pattern Recognition, the novel I was writing at the time. Cayce’s Tokyo, in Pattern Recognition, is the Tokyo I encountered, at Wired’s considerable expense. None of which I was able to access for Wired. Just not possible. The fiction-writing space was occupied, this time, and my very cursory showing, in this piece, is the result of my having had no place, within myself, to do the work required. Really I should have found a way to spot-weld on some inner sidewalk, but all I managed to do was something that feels to me, in the end, literally phoned in.

And others, like my own Garage Kubrick, will use the same technology to burrow more deeply, more obsessively, more gloriously, into the insoluble mystery of the self, even as the Château Marmont outlasts the media platform and the studio system that gave it birth. I fall asleep imagining someone building a virtual Marmont, and in one of the bungalows, a character is falling asleep…. My novel Pattern Recognition was gestating, as I wrote this, the “Garage Kubrick” morphing from protagonist (or antagonist, or possibly just agonist) to MacGuffin, though I didn’t know it. Pattern Recognition would eventually manage to be published just ahead of the launch of YouTube, a very good thing considering certain of its plot points. In a world with YouTube, it’s probably much more difficult to induce a magazine to put you up in the Marmont to watch digital films, so that was good timing as well.

DISTRUST THAT PARTICULAR FLAVOR TITLES BY WILLIAM GIBSON Neuromancer Count Zero Burning Chrome Mona Lisa Overdrive Virtual Light Idoru All Tomorrow’s Parties Pattern Recognition Spook Country Zero History DISTRUST THAT PARTICULAR FLAVOR WILLIAM GIBSON G. P. Putnam’s Sons New York G. P. PUTNAM’S SONS Publishers Since 1838 Published by the Penguin Group Penguin Group (USA) Inc., 375 Hudson Street, New York, New York 10014, USA • Penguin Group (Canada), 90 Eglinton Avenue East, Suite 700, Toronto, Ontario M4P 2Y3, Canada (a division of Pearson Penguin Canada Inc.) • Penguin Books Ltd, 80 Strand, London WC2R 0RL, England • Penguin Ireland, 25 St Stephen’s Green, Dublin 2, Ireland (a division of Penguin Books Ltd) • Penguin Group (Australia), 250 Camberwell Road, Camberwell, Victoria 3124, Australia (a division of Pearson Australia Group Pty Ltd) • Penguin Books India Pvt Ltd, 11 Community Centre, Panchsheel Park, New Delhi–110 017, India • Penguin Group (NZ), 67 Apollo Drive, Rosedale, North Shore 0632, New Zealand (a division of Pearson New Zealand Ltd) • Penguin Books (South Africa) (Pty) Ltd, 24 Sturdee Avenue, Rosebank, Johannesburg 2196, South Africa Penguin Books Ltd, Registered Offices: 80 Strand, London WC2R 0RL, England Copyright © 2012 by William Gibson Ent.


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Present Shock: When Everything Happens Now by Douglas Rushkoff

algorithmic trading, Andrew Keen, bank run, Benoit Mandelbrot, big-box store, Black Swan, British Empire, Buckminster Fuller, business cycle, cashless society, citizen journalism, clockwork universe, cognitive dissonance, Credit Default Swap, crowdsourcing, Danny Hillis, disintermediation, Donald Trump, double helix, East Village, Elliott wave, European colonialism, Extropian, facts on the ground, Flash crash, game design, global pandemic, global supply chain, global village, Howard Rheingold, hypertext link, Inbox Zero, invention of agriculture, invention of hypertext, invisible hand, iterative process, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, lateral thinking, Law of Accelerating Returns, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, Milgram experiment, mutually assured destruction, negative equity, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, passive investing, pattern recognition, peak oil, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, the medium is the message, The Wisdom of Crowds, theory of mind, Turing test, upwardly mobile, Whole Earth Catalog, WikiLeaks, Y2K, zero-sum game

By the time the story is posted to the Web, stocks are actually lower, and the agencies are hard at work searching for a housing report or consumer index that may explain the new trend, making the news services appear to be chasing their own tails. This doesn’t mean pattern recognition is futile. It only shows how easy it is to draw connections where there are none, or where the linkage is tenuous at best. Even Marshall McLuhan realized that a world characterized by electronic media would be fraught with chaos and best navigated through pattern recognition. This is not limited to the way we watch media but is experienced in the way we watch and make choices in areas such as business, society, and war. Rapid churn on the business landscape has become the new status quo, as giants like Kodak fall and upstarts like Facebook become more valuable than oil companies.

It is a diary, both egocentric and self-consumed. Moreover, once stored, it is locked down. History no longer changes with one’s evolving sensibilities; it describes limits and resists reinterpretation. One’s path narrows. As far as doing pattern recognition in a landscape of present shock, the user must identify entirely with sequences of self. Everything relates, as long as it relates back to himself. Where was I when I saw that? How did I think of that the first time I did it? How does that reflect on me? This is the difference between the networked sensibility and paranoia—between pattern recognition and full-fledged fractalnoia. The fractalnoid is developing the ability to see the connections between things but can only understand them as having something to do with himself. This is the very definition of paranoia.

In particular, what does this do to business and finance, which are relying on increasingly derivative forms of investment? Next we look at what happens when we try to make sense of our world entirely in the present tense. Without a timeline through which to parse causes and effects, we instead attempt to draw connections from one thing to another in the frozen moment, even when such connections are forced or imaginary. It’s a desperate grasp for real-time pattern recognition I’ll call “Fractalnoia.” Finally, we face “Apocalypto”—the way a seemingly infinite present makes us long for endings, by almost any means necessary. We will encounter drone pilots contending with the stress of dropping bombs on a distant war zone by remote control before driving home to the suburbs for supper an hour later. We will see the way the physical real estate of Manhattan is being optimized for the functioning of the ultrafast trading algorithms now running the stock market—as well as what this means for the human traders left in the wake.


pages: 278 words: 70,416

Smartcuts: How Hackers, Innovators, and Icons Accelerate Success by Shane Snow

3D printing, Airbnb, Albert Einstein, attribution theory, augmented reality, barriers to entry, conceptual framework, correlation does not imply causation, David Heinemeier Hansson, deliberate practice, disruptive innovation, Elon Musk, Fellow of the Royal Society, Filter Bubble, Google X / Alphabet X, hive mind, index card, index fund, Isaac Newton, job satisfaction, Khan Academy, Kickstarter, lateral thinking, Law of Accelerating Returns, Lean Startup, Mahatma Gandhi, meta analysis, meta-analysis, pattern recognition, Peter Thiel, popular electronics, Ray Kurzweil, Richard Florida, Ronald Reagan, Ruby on Rails, Saturday Night Live, self-driving car, side project, Silicon Valley, Steve Jobs, superconnector

Richter and the others wanted to try to ride it out. But, rather than fall into the impending trough and get crushed by the wave, 18-year-old Sonny decided to bail. III. There are two ways to catch a wave: exhausting hard work—paddling—and pattern recognition—spotting a wave early and casually drifting to the sweet spot. “There are people who make careers based on the fact that they know how to read the ocean better than others,” says Pat O’Connell, ’90s surfing legend and trainer. “It’s just about knowing the ocean. It’s timing.” Sonny Moore seemed to have that pattern recognition; he spotted the rise of social networks and became a power user, ultimately setting himself up to front an emerging band. From First to Last spotted the fast rise of screamo before most bands—and mainstream audiences—saw it coming.

“High-expertise” individuals were identified by how many Coach and Louis Vuitton handbags they owned. The results were the same. In a given domain—be it surfing or accounting or political fund-raising—the familiarity that leads to pattern recognition seems to come with experience and practice. Fencing masters recognize opportunities in opponents’ moves because of the sheer amount of practice time logged into their heads. Leaders and managers who use their gut to make decisions often do so based on decades of experience, archived and filed away in the folds of their cerebrums. “Intuition is the result of nonconscious pattern recognition,” Dane tells me. However, his research shows that, while logging hours of practice helps us see patterns subconsciously, we can often do just as well by deliberately looking for them. In many fields, such pattern hunting and deliberate analysis can yield results just as in the basketball example—high accuracy on the first try.

In each case, while the pioneers were entrenched in early technology and practices, the tailgaters got ahead. Once you jump on the first wave, it’s costly to back off from the commitment. And by that time it’s usually too late to take advantage of the second wave. Of course, on rare occasions that first wave actually is the best wave in a set. How is a surfer, much less a businessperson, to judge when to make a move? Pattern recognition can help here as well. The way to predict the best waves in a proverbial set is established by researchers Fernando F. Suarez and Gianvito Lanzolla, who in Academy of Management Review explain that when market and technology growth are smooth and steady, the first mover gets the inertia and an advantage. When industry change is choppy, the fast follower—the second mover—gets the benefits of the first mover’s pioneering work and often catches a bigger wave, unencumbered.


pages: 523 words: 148,929

Physics of the Future: How Science Will Shape Human Destiny and Our Daily Lives by the Year 2100 by Michio Kaku

agricultural Revolution, AI winter, Albert Einstein, Asilomar, augmented reality, Bill Joy: nanobots, bioinformatics, blue-collar work, British Empire, Brownian motion, cloud computing, Colonization of Mars, DARPA: Urban Challenge, delayed gratification, double helix, Douglas Hofstadter, en.wikipedia.org, friendly AI, Gödel, Escher, Bach, hydrogen economy, I think there is a world market for maybe five computers, industrial robot, Intergovernmental Panel on Climate Change (IPCC), invention of movable type, invention of the telescope, Isaac Newton, John Markoff, John von Neumann, life extension, Louis Pasteur, Mahatma Gandhi, Mars Rover, mass immigration, megacity, Mitch Kapor, Murray Gell-Mann, new economy, oil shale / tar sands, optical character recognition, pattern recognition, planetary scale, postindustrial economy, Ray Kurzweil, refrigerator car, Richard Feynman, Rodney Brooks, Ronald Reagan, Search for Extraterrestrial Intelligence, Silicon Valley, Simon Singh, social intelligence, speech recognition, stem cell, Stephen Hawking, Steve Jobs, telepresence, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, trade route, Turing machine, uranium enrichment, Vernor Vinge, Wall-E, Walter Mischel, Whole Earth Review, X Prize

Neurons, too, are digital (they can fire or not fire), but they can also be analog, transmitting continuous signals as well as discrete ones. TWO PROBLEMS WITH ROBOTS Given the glaring limitations of computers compared to the human brain, one can appreciate why computers have not been able to accomplish two key tasks that humans perform effortlessly: pattern recognition and common sense. These two problems have defied solution for the past half century. This is the main reason why we do not have robot maids, butlers, and secretaries. The first problem is pattern recognition. Robots can see much better than a human, but they don’t understand what they are seeing. When a robot walks into a room, it converts the image into a jumble of dots. By processing these dots, it can recognize a collection of lines, circles, squares, and rectangles. Then a robot tries to match this jumble, one by one, with objects stored in its memory—an extraordinarily tedious task even for a computer.

So we split up the image into many layers. As soon as the computer processes one layer of the image, it integrates it with the next layer, and so on. In this way, step by step, layer by layer, it mimics the hierarchical way that our brains process images. (Poggio’s program cannot perform all the feats of pattern recognition that we take for granted, such as visualizing objects in 3-D, recognizing thousands of objects from different angles, etc., but it does represent a major milestone in pattern recognition.) Later, I had an opportunity to see both the top-down and bottom-up approaches in action. I first went to the Stanford University’s artificial intelligence center, where I met STAIR (Stanford artificial intelligence robot), which uses the top-down approach. STAIR is about 4 feet tall, with a huge mechanical arm that can swivel and grab objects off a table.

The highest form of this sensing would be the ability to recognize and understand objects in the environment. Humans can immediately size up their environment and act accordingly and hence rate high on this scale. However, this is where robots score badly. Pattern recognition, as we have seen, is one of the principal roadblocks to artificial intelligence. Robots can sense their environments much better than humans, but they do not understand or recognize what they see. On this scale of consciousness, robots score near the bottom, near the insects, due to their lack of pattern recognition. The next-higher level of consciousness involves self-awareness. If you place a mirror next to most male animals, they will immediately react aggressively, even attacking the mirror. The image causes the animal to defend its territory.


pages: 292 words: 94,324

How Doctors Think by Jerome Groopman

affirmative action, Atul Gawande, Daniel Kahneman / Amos Tversky, deliberate practice, fear of failure, framing effect, index card, iterative process, lateral thinking, medical malpractice, medical residency, Menlo Park, pattern recognition, placebo effect, stem cell, theory of mind

It simply happened too fast. Dr. Pat Croskerry, an emergency room doctor in Halifax, Nova Scotia, began his academic career as a developmental psychologist and now studies physician cognition. He explained to me that "flesh-and-blood decision-making" pivots on what is called pattern recognition. The key cues to a patient's problem—whether from the medical history, physical examination, x-ray studies, or laboratory tests—coalesce into a pattern that the physician identifies as a specific disease or condition. Pattern recognition, Croskerry told me, "reflects an immediacy of perception." It occurs within seconds, largely without any conscious analysis; it draws most heavily on the doctor's visual appraisal of the patient. And it does not occur by a linear, step-by-step combining of cues. The mind acts like a magnet, pulling in the cues from all directions.

In response to Stan's cries of pain and pleas for action, several students injected a possibly lethal dose of morphine. "What happened to you, Jerry, in Mr. Morgan's room is what happened to the students with Stan," Dr. Oriol said. "It is as if everything that you learn in school is erased." Simulations with Stan are designed to act as a bridge between analytical learning in classrooms and pattern recognition performed at the peak of the Yerkes-Dodson curve. But, as Oriol and others readily admit, there still will come that first moment when the novice can no longer be a novice, when he is the one who must take responsibility for a living, breathing patient in need. Extreme arousal happens not only during the first encounter with a William Morgan, but throughout internship and residency. During this training, young doctors gradually learn how to move themselves back from the edge of the Yerkes-Dodson curve toward points of effective performance.

But, Delgado continued, that was a single experience corresponding to a single stereotype. "It is impossible to catalog all of the stereotypes that you carry in your mind," she said, "or to consistently recognize that you are fitting the individual before you into a stereotypical mold. But you don't want to have to make a mistake to learn with each stereotype." Rather, Delgado believes, patients and their families should be aware that a doctor relies on pattern recognition in his work and, understandably, draws on stereotypes to make decisions. With that knowledge, they can help him avoid attribution errors. Is this really possible? I asked. "Sure, it's not easy for laypeople to do," Delgado said, "because patients and their families are especially reluctant to question a doctor's thinking when their questioning suggests his thinking is colored by personal prejudice or bias."


pages: 378 words: 110,408

Peak: Secrets From the New Science of Expertise by Anders Ericsson, Robert Pool

Albert Einstein, deliberate practice, iterative process, longitudinal study, meta analysis, meta-analysis, pattern recognition, randomized controlled trial, Richard Feynman, Rubik’s Cube, sensible shoes

See brain adaptability plateaus, 161–65 play, 184–88, 215 Polgár, Judit, 182, 183, 187 Polgár, Klara, 180–83 Polgár, László, 180–83, 189 Polgár, Sofia, 182, 183 Polgár, Susan, 181–82, 183, 184, 185, 188 Poor Richard’s Almanack (Franklin), 156–57 positive feedback, motivation and, 22, 173–76 potential and adaptability, xix–xx, 47–49 practice ability and, 8, 9, 43 in blindfold chess, 51–54 vs. deliberate practice, 9, 132 digit memorization study, 2–5 effective techniques of, 9 fighter pilot school, 116–20 highly developed methods of, 85–89 as key to success, 230 music students, 77–79 solitary practice, 91, 92 vs. talent, in chess, 225–33 usual approach to, 11–14, 48–49, 121 See also purposeful practice; feedback praise, 186, 187, 189, 190, 239, 240 presbyopia study, 36–37 principles, of deliberate practice, 97–100 prodigies, xii–xiii, 211, 212, 214 See also expert performers; talent Professional Golfers’ Association, 146 Professor Moriarty, 226 prostate cancer study, 139–40 Psychology of Music (journal), xiv pull-ups, 34 purposeful practice adaptability, 41–47 brain response to (taxi studies), 30–32 characteristics of, 14–22 comfort zone, 17–22 defined, 98 vs. deliberate practice, 98 in digit study, 13–14 feedback, 16–17 focus, 15–16 goals, 15 highly developed methods of, 85–89 length of, 171 limits of, 22–25 musicians, 79–80 vs. naive practice, 14 recipe for, 22 vs. traditional approach, 11–14, 48–49 as work, 166–67 See also deliberate practice pushups, 33–34 Q quantitative vs. qualitative problems, 252 quarterbacks, pattern recognition, 64–65 quitting, 169–70, 173 R radiologists, 125–27, 141 Radio-Symphonie-Orchester Berlin, 88, 94 rat muscle study, 39 reading, mental representations and, 66–68 red flag, 121 Red Force pilots, 116–18 Renwick, James, 77–79 reproduction, of a master, 160–61, 214 retrieval structure comprehension and, 67 memory and, 24, 61 See also mental representations Richards, Nigel, 202–3, 205–6 ringmaster practice, 158–59 Road to Excellence, The (Ericsson), xxi rock climbing, pattern recognition, 65 Rubik’s Cube, 157–58 running age, 195 engagement in, 152, 153–54 Faloon, Steve and, 4, 15, 22 Hägg, Gunder, 172–73 mental representations, 82 pain of, 171 records in, 6, 107 Rush, Mark Alan, 201–2 Russell, JaMarcus, 236 S Sachs-Ericsson, Natalie, 95 Saikhanbayar, Tsogbadrakh, 84–85 Sakakibara, Ayako, xiv–xv Sanders, Lisa, 68–71 Sanker, David, 7 savants, 219–22 Schelew, Ellen, 243–47 Science (magazine), 42–43, 254 Scrabble, 202–3, 205–6 Scripps National Spelling Bee, 165–66 self-evaluation, of performance deliberate practice and, 99 fighter pilot school, 117 golf practice, 177–78 plateaus, 164–65 radiologists, 127 See also feedback; measurement, of performance self-fulfilling prophecy, of talent, 238–42 self-motivation, 191, 193 Servizio, Charles, 34 Sharma, Vikas, 8 Sherlock Holmes, 226 Shiffrin, Mikaela, 187 Shockley, William, 234 short-term memory digit study, 2–5 limit to, 2 role of, 61 siblings, of experts, 187–88 Simon, Herb, 55–56, 57, 257 simulator practice, 130, 143–44 simultaneous interpreters, 198 Sinatra, Frank, xiv Singh, Fauja, 195 singing, 151, 223–24 skill learning adaptability, see adaptability brain structure, 43–45 deliberate practice, 100 engagement, 150–54 individual instruction, 147–50, 165–66 vs. knowledge, 130–37 mental representations, 76–82 plateaus, 161–65 purposeful practice, see purposeful practice usual approach to, 11–14 skills-based training, 137–44 sleep, 92–93, 154, 170, 171 soccer, patterns in, 63–64 social motivation, 173–76 solitary practice, 91, 92, 109, 147–50, 165–66, 176–77, 230, 232 sommeliers, 104–5 Spectator, The (magazine), 155, 156, 159–60, 255 Spencer, David Richard, 7 sports.

So here is a major part of the answer to the question we asked at the end of the last chapter: What exactly is being changed in the brain with deliberate practice? The main thing that sets experts apart from the rest of us is that their years of practice have changed the neural circuitry in their brains to produce highly specialized mental representations, which in turn make possible the incredible memory, pattern recognition, problem solving, and other sorts of advanced abilities needed to excel in their particular specialties. The best way to understand exactly what these mental representations are and how they work is, fittingly enough, to develop a good mental representation of the concept mental representation. And just as was the case with dog, the best way to develop a mental representation of mental representations is to spend a little time getting to know them, stroking their fur, patting their little heads, and watching as they perform their tricks.

See London cabbie studies Cambridge Handbook of Expertise and Expert Performance, The (Ericsson), xxi cancer detection, 125–27 Capablanca, José Raúl, 54 Carl Wieman Science Education Initiative, 243, 245 Carnegie Mellon University, 1, 3, 4, 131 carotid artery tear puzzle, 68–72 cellular response, homeostasis, 37–41 cerebellum, musical training, 43, 197 Chaffin, Roger, 80–82 Chambliss, Daniel, 153 Charing Cross, 28–29, 30 Chase, Bill chess study, 55–56 “chunks,” 57 digit memorization study, 3, 5, 20, 22, 162–63 Cheney, Elyse, 75 chess ability as predictor, 56 Alekhine, Alexander, 50–54 Franklin, Benjamin, 18–19, 155, 160 hours of practice, 96–97 improvement, 151 IQ, 227–33, 241 memory, 55–58 mental representations, 60–62, 67, 70 Polgár family experiment, 180–83, 184–85, 189 practice vs. talent, 225–33 See also grandmasters childhood of experts, 173–74, 184–88 Lemieux, Mario, 214–15 perfect pitch, xiii–xiv physical skills, 196–97 pianists, 45 “chunks,” 57 clicker questions, 245–47, 252–53 climbers, pattern recognition, 65 Close, Kerry, 165 coaches. See teachers and coaches Colvin, Geoff, 145 comedians, 159 comfort zone deliberate practice, 97–98, 99, 170 fighter pilot training, 119 freshman physics course, 253 homeostasis, 38–40 potential, 47–49 purposeful practice and, 17–22 pushing past, 40–41 commitment, 192–94 Commonwealth Games, 219 comprehension, mental representations, 66–68 concentration and focus deliberate practice and, 99, 169 as hard work, 21 individual instruction, 150–54 length of, 171 physics study, 245–47 purposeful practice and, 15–16 three Fs, 159 Concordia University, 191 confidence, of success, 172 congenital amusia, 224 context, mental representations and, 66–68 continuing medical education, 18, 134–36 Cooke, Ed, 161 copying, 160–61, 214 corpus callosum, 197 Cortot, Alfred, 6–7 cost, financial, 193–94 Coughlin, Natalie, 152–53 creativity, 203–6 Csikszentmihalyi, Mihaly, 257 D “Dan Plan” project, 146, 177–79 Davis, Dave, 134–35 Davis, Miles, xiii Dawes, Robyn, 105 DeLay, Dorothy, 20 deliberate practice average person and, 146 in business, 122–24 challenges in writing about, 75 defined, 98 future of, 247–55, 256–59 goals, 48, 99, 111, 154, 177 knowledge vs. skill, 130–37 length of, 171 as lonely pursuit, 176–77 myths of, 121–22 naming of, xxi, 10 physics education study, 243–47 plateaus, 161–65 purpose of, 75 vs. purposeful practice, 14–22, 98 skills-based training, 137–44 “ten-thousand-hour rule” and, 110 traits of, 99–100 See also practice; purposeful practice deliberate-practice mindset, 120–21 desire, to improve, 171 Deslauriers, Louis, 243–47 diagnosis, process of, 68–72 digit memorization Faloon, Steve, 2–5 hours of practice, 110 mental representations, 59 plateau in progress, 162–63 Wang, Feng, 101–2 world record in, 84–85 See also Faloon, Steve Dilbert (comic), 113 diving, competitive, 6, 44–45 Dix, Walter, 120 dogfights, 115–19 Dolan, Eamon, 76 domain specificity, in mental representations, 60 Donatelli, Dario, 25, 131 Donny (autistic savant), 221 double-somersault dive, 6 E Eagles (team), 248 Eastern Illinois University, 216 education continuing medical education, 18, 134–36 deliberate practice, see deliberate practice feedback from teachers, 108–9, 246–47 freshman physics course, 243–47, 252–54 future of, 247–55, 256–59 Jump Math, 224–25, 251 mental representations, 250–51 skills-based training, 137–44 teachers, see teachers and coaches Top Gun approach, see Top Gun approach effort, 166, 167 Einstein, Albert, 44, 203 electromagnetic wave unit, 243–47 Elio, Renée, 23–24, 84, 85 engagement, 150–54, 169 length of, 171 physics study, 245–47 See also focus English as a second language, 158 Epstein, David, 215, 216 Ericsson, K.


pages: 250 words: 9,029

Everything Bad Is Good for You: How Popular Culture Is Making Us Smarter by Steven Johnson

Columbine, complexity theory, corporate governance, delayed gratification, edge city, Flynn Effect, game design, Marshall McLuhan, pattern recognition, profit motive, race to the bottom, sexual politics, social intelligence, Steve Jobs, the market place

Perhaps if you j ust look a little harder you'll be in luck-it's got to be around here somewhere . " How do these findi ngs connect to games ? Researchers have long suspected that geometric games like Tetris have such a hypnotic hold over us (longtime Tetris players have vivid dreams about the game) because the game's elemen­ tal shapes activate modules in our visual system that execute low-level forms of pattern recognition-sensing parallel and perpendicular lines, for instance. These modules are churn­ ing away in the background all the time, but the simplified graphics of Tetris bring them front and center i n our con­ sciousness. I believe that what Tetris does to our visual cir­ cui try, most video games do to the reward circuitry of the brain. Real life is full of rewards, which is one reason why there 36 ST E V E N J O H N S O N are now s o many forms o f addiction .

What is Simon's motivation ? E V J, R Y T H I N G B A D I S G O O D F O R Y o u 59 Word pro blems of this sort have little to offer in the way of moral lessons or psychological depth; they won't make students more effective communicators or teach them tech­ nical skills. But most of us readily agree that they are good for the mind on some fundamental level : they teach abstract skills in probability, i n pattern recognition, in understand­ ing causal relations that can be applied in countless situa­ tions, both personal and professional . The problems that confront the garners of Zelda can be readily transl ated into this form, and indeed in translating a core property of the experience is revealed: You need to cross a gorge to reach a valuable desti­ nation. At one end of the gorge a large rock stands in front of a river, blocking the {low of water.

Tests that measure g often do away with words and numbers , re­ placing them with questions that rely exclusively o n i mages, testing the subj ect's ability to see patterns and complete se­ quences with elemental shapes and obj ects, as in this ex- I 48 ST E V E N J O H N S O N ample from the Raven Progressive Matrices test, which asks you to fill the blank space with the correct shape from the eight options below : 6 8 The centrality of the g scores to the Flynn Effect is telling. If you look at i ntelligence tests that track skills influenced by the classroom-the Wechsler vocabulary or arithmetic tests, for instance-the intelligence boom fades from view; SAT scores have fluctuated erratically over the past decades. But if you look solely at unschooled problem-solving and E V E R Y T H I N G B A D I S G O O D F O R Yo u 1 49 pattern-recognition skills, the progressive trend j u mps into focus. There's so mething mysterious in these simultaneous trends: if g exists in a cultural vacu um, how can scores be rising at such a clip ? And more puzzling, how can those scores be rising faster than other intelligence measures that do reflect education? The mystery disappears if you assume that these general pro blem-solving skills a re influenced by culture , j ust not the part of culture that we conventi onally associate with making people smarte r.


pages: 424 words: 114,905

Deep Medicine: How Artificial Intelligence Can Make Healthcare Human Again by Eric Topol

23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, augmented reality, autonomous vehicles, bioinformatics, blockchain, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, conceptual framework, creative destruction, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, David Brooks, digital twin, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fault tolerance, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, microbiome, natural language processing, new economy, Nicholas Carr, nudge unit, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, speech recognition, Stephen Hawking, text mining, the scientific method, Tim Cook: Apple, War on Poverty, Watson beat the top human players on Jeopardy!, working-age population

Of more than a thousand cases retrospectively reviewed by the tumor board and Watson, more than 30 percent were augmented by AI information, particularly related to treatment options for specific mutations.60 LIKE RADIOLOGY AND pathology, dermatology involves a great deal of pattern recognition. Skin conditions are among the most frequent reasons for seeing a doctor—they account for 15 percent of all doctor visits! Unlike radiology and pathology, however, about two-thirds of skin conditions are diagnosed by non-dermatologists, who frequently get the diagnosis wrong: some articles cite error rates as high as 50 percent. And, of course, dermatologists don’t just look at and diagnose skin rashes and lesions, they often treat or excise them. But the pattern recognition of skin problems is a big part of medicine and a chance for artificial intelligence to play a significant role. With relatively few practicing dermatologists in the United States, it’s a perfect case for machines to kick in.

Many in the medical community were frankly surprised by what deep learning could accomplish: studies that claim AI’s ability to diagnose some types of skin cancer as well as or perhaps even better than board-certified dermatologists; to identify specific heart-rhythm abnormalities like cardiologists; to interpret medical scans or pathology slides as well as senior, highly qualified radiologists and pathologists, respectively; to diagnose various eye diseases as well as ophthalmologists; and to predict suicide better than mental health professionals. These skills predominantly involve pattern recognition, with machines learning those patterns after training on hundreds of thousands, and soon enough millions, of examples. Such systems have just gotten better and better, with the error rates for learning from text-, speech-, and image-based data dropping well below 5 percent, whizzing past the human threshold (Figure 1.3). Although there must be some limit at which the learning stops, we haven’t reached it yet.

Deep phenotyping is both thick, spanning as many types of data as you can imagine, and long, covering as much of our lives as we can, because many metrics of interest are dynamic, constantly changing over time. A few years ago, I wrote a review in which I said we needed medical data that spanned “from prewomb to tomb.”3 A former mentor told me that I should have called the span “from lust to dust.” But you get the idea of deep and long data. Second is deep learning, which will play a big part of medicine’s future. It will not only involve pattern recognition and machine learning that doctors will use for diagnosis but a wide range of applications, such as virtual medical coaches to guide consumers to better manage their health or medical condition. It will also take on efficiency in the hospital setting, using machine vision to improve patient safety and quality, ultimately reducing the need for hospital rooms by facilitating remote, at-home monitoring.


pages: 337 words: 103,522

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

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

A pattern in the chaos of the jungle is likely to be evidence of the presence of another animal – and you’d better take notice cos that animal might eat you (or maybe you could eat it). The human code is extremely good at reading patterns, interpreting how they might develop, and responding appropriately. It is one of our key assets, and it plays into our appreciation for the patterns in music and art. It turns out that pattern recognition is precisely what I do as a mathematician when I venture into the unexplored reaches of the mathematical jungle. I can’t rely on a simple step-by-step logical analysis of the local environment. That won’t get me very far. It has to be combined with an intuitive feel for what might be out there. That intuition is built up by time spent exploring the known space. But it is often hard to articulate logically why you might believe that there is interesting territory out there to explore.

When people I meet declare (as alas so often happens): ‘I don’t have a brain for maths’, I counter that in fact we all have evolved to have mathematical brains because our brains are good at spotting patterns. Sometimes they are too good, reading patterns into data where none exists, as many viewers did when confronted with Richter’s random coloured squares at the Serpentine Gallery. For me, some of the very first pattern recognition comes along with some of the very first art to be drawn. The cave paintings in Lascaux depict exquisite images of animals racing across the walls. The movement of a stampede of aurochs is amazingly captured in these frozen images. It is intriguing to ask why the artist felt compelled to represent these images underground. What role did they play? Alongside these images are what I believe to be some of the earliest recorded mathematics.

FURTHER READING Machine Learning: The Power and Promise of Computers That Learn by Example. The report by the Royal Society that Margaret Boden, Demis Hassabis and I helped prepare. Issued in April 2017. It can be viewed online at http://­royalsociety­.org/machine­-learning. Books Alpaydin, Ethem, Machine Learning, MIT Press, 2016 Barthes, Roland, S/Z, Farrar, Straus and Giroux, 1991 Berger, John, Ways of Seeing, Penguin Books, 1972 Bishop, Christopher, Pattern Recognition and Machine Learning, Springer, 2007 Boden, Margaret, The Creative Mind: Myths and Mechanisms, Weidenfeld and Nicolson, 1990 , AI: Its Nature and Future, OUP, 2016 Bohm, David, On Creativity, Routledge, 1996 Bostrom, Nick, Superintelligence: Paths, Dangers, Strategies, OUP, 2014 Braidotti, Rosi, The Posthuman, Polity Press, 2013 Brandt, Anthony and David Eagleman, The Runaway Species: How Human Creativity Remakes the World, Canongate, 2017 Brynjolfsson, Erik and Andrew McAfee, The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies, Norton, 2014 Cawelti, John, Adventure, Mystery, and Romance: Formula Stories as Art and Popular Culture, University of Chicago Press, 1977 Cheng, Ian, Emissaries Guide to Worlding, Verlag der Buchhandlung Walther Konig, 2018; Serpentine Galleries/Fondazione Sandretto Re Rebaudengo, 2018 Cope, David, Virtual Music: Computer Synthesis of Musical Style, MIT Press, 2001 , Computer Models of Musical Creativity, MIT Press, 2005 Domingos, Pedro, The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World, Basic Books, 2015 Dormehl, Luke, The Formula: How Algorithms Solve All Our Problems … and Create More, Penguin Books, 2014 , Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future, W.


pages: 764 words: 261,694

The Elements of Statistical Learning (Springer Series in Statistics) by Trevor Hastie, Robert Tibshirani, Jerome Friedman

Bayesian statistics, bioinformatics, computer age, conceptual framework, correlation coefficient, G4S, greed is good, linear programming, p-value, pattern recognition, random walk, selection bias, speech recognition, statistical model, stochastic process, The Wisdom of Crowds

In data mining applications, usually only a small fraction of the large number of predictor variables that have been included in the analysis are actually relevant to prediction. Also, unlike many applications such as pattern recognition, there is seldom reliable domain knowledge to help create especially relevant features and/or filter out the irrelevant ones, the inclusion of which dramatically degrades the performance of many methods. In addition, data mining applications generally require interpretable models. It is not enough to simply produce predictions. It is also desirable to have information providing qualitative understanding of the relationship 352 10. Boosting and Additive Trees between joint values of the input variables and the resulting predicted response value. Thus, black box methods such as neural networks, which can be quite useful in purely predictive settings such as pattern recognition, are far less useful for data mining. These requirements of speed, interpretability and the messy nature of the data sharply limit the usefulness of most learning procedures as offthe-shelf methods for data mining.

Bickel, P. and Levina, E. (2004). Some theory for Fisher’s linear discriminant function,“Naive Bayes”, and some alternatives when there are many more variables than observations, Bernoulli 10: 989–1010. Bickel, P. J., Ritov, Y. and Tsybakov, A. (2008). Simultaneous analysis of lasso and Dantzig selector, Annals of Statistics. to appear. Bishop, C. (1995). Neural Networks for Pattern Recognition, Clarendon Press, Oxford. Bishop, C. (2006). Pattern Recognition and Machine Learning, Springer, New York. Bishop, Y., Fienberg, S. and Holland, P. (1975). Discrete Multivariate Analysis, MIT Press, Cambridge, MA. Boyd, S. and Vandenberghe, L. (2004). Convex Optimization, Cambridge University Press. Breiman, L. (1992). The little bootstrap and other methods for dimensionality selection in regression: X-fixed prediction error, Journal of the American Statistical Association 87: 738–754.

., Bartlett, P. and Frean, M. (2000). Boosting algorithms as gradient descent, 12: 512–518. Massart, D., Plastria, F. and Kaufman, L. (1983). Non-hierarchical clustering with MASLOC, The Journal of the Pattern Recognition Society 16: 507–516. McCullagh, P. and Nelder, J. (1989). Generalized Linear Models, Chapman and Hall, London. McCulloch, W. and Pitts, W. (1943). A logical calculus of the ideas imminent in nervous activity, Bulletin of Mathematical Biophysics 5: 115– 133. Reprinted in Anderson and Rosenfeld (1988), pp 96-104. McLachlan, G. (1992). Discriminant Analysis and Statistical Pattern Recognition, Wiley, New York. Mease, D. and Wyner, A. (2008). Evidence contrary to the statistical view of boosting (with discussion), Journal of Machine Learning Research 9: 131–156. Meinshausen, N. (2007).


pages: 406 words: 109,794

Range: Why Generalists Triumph in a Specialized World by David Epstein

Airbnb, Albert Einstein, Apple's 1984 Super Bowl advert, Atul Gawande, Checklist Manifesto, Claude Shannon: information theory, Clayton Christensen, clockwork universe, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, deliberate practice, Exxon Valdez, Flynn Effect, Freestyle chess, functional fixedness, game design, Isaac Newton, Johannes Kepler, knowledge economy, lateral thinking, longitudinal study, Louis Pasteur, Mark Zuckerberg, medical residency, meta analysis, meta-analysis, Mikhail Gorbachev, Nelson Mandela, Netflix Prize, pattern recognition, Paul Graham, precision agriculture, prediction markets, premature optimization, pre–internet, random walk, randomized controlled trial, retrograde motion, Richard Feynman, Richard Feynman: Challenger O-ring, Silicon Valley, Stanford marshmallow experiment, Steve Jobs, Steve Wozniak, Steven Pinker, Walter Mischel, Watson beat the top human players on Jeopardy!, Y Combinator, young professional

In 2009, Kahneman and Klein took the unusual step of coauthoring a paper in which they laid out their views and sought common ground. And they found it. Whether or not experience inevitably led to expertise, they agreed, depended entirely on the domain in question. Narrow experience made for better chess and poker players and firefighters, but not for better predictors of financial or political trends, or of how employees or patients would perform. The domains Klein studied, in which instinctive pattern recognition worked powerfully, are what psychologist Robin Hogarth termed “kind” learning environments. Patterns repeat over and over, and feedback is extremely accurate and usually very rapid. In golf or chess, a ball or piece is moved according to rules and within defined boundaries, a consequence is quickly apparent, and similar challenges occur repeatedly. Drive a golf ball, and it either goes too far or not far enough; it slices, hooks, or flies straight.

Kasparov concluded that the humans on the winning team were the best at “coaching” multiple computers on what to examine, and then synthesizing that information for an overall strategy. Human/Computer combo teams—known as “centaurs”—were playing the highest level of chess ever seen. If Deep Blue’s victory over Kasparov signaled the transfer of chess power from humans to computers, the victory of centaurs over Hydra symbolized something more interesting still: humans empowered to do what they do best without the prerequisite of years of specialized pattern recognition. In 2014, an Abu Dhabi–based chess site put up $20,000 in prize money for freestyle players to compete in a tournament that also included games in which chess programs played without human intervention. The winning team comprised four people and several computers. The captain and primary decision maker was Anson Williams, a British engineer with no official chess rating. His teammate, Nelson Hernandez, told me, “What people don’t understand is that freestyle involves an integrated set of skills that in some cases have nothing to do with playing chess.”

What seemed like the single best analogy did not do well on its own. Using a full “reference class” of analogies—the pillar of the outside view—was immensely more accurate. Think back to chapter 1, to the types of intuitive experts that Gary Klein studied in kind learning environments, like chess masters and firefighters. Rather than beginning by generating options, they leap to a decision based on pattern recognition of surface features. They may then evaluate it, if they have time, but often stick with it. This time will probably be like the last time, so extensive narrow experience works. Generating new ideas or facing novel problems with high uncertainty is nothing like that. Evaluating an array of options before letting intuition reign is a trick for the wicked world. In another experiment, Lovallo and his collaborator Ferdinand Dubin asked 150 business students to generate strategies to help the fictitious Mickey Company, which was struggling with its computer mouse business in Australia and China.


Driverless: Intelligent Cars and the Road Ahead by Hod Lipson, Melba Kurman

AI winter, Air France Flight 447, Amazon Mechanical Turk, autonomous vehicles, barriers to entry, butterfly effect, carbon footprint, Chris Urmson, cloud computing, computer vision, connected car, creative destruction, crowdsourcing, DARPA: Urban Challenge, digital map, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Google Earth, Google X / Alphabet X, high net worth, hive mind, ImageNet competition, income inequality, industrial robot, intermodal, Internet of things, job automation, Joseph Schumpeter, lone genius, Lyft, megacity, Network effects, New Urbanism, Oculus Rift, pattern recognition, performance metric, precision agriculture, RFID, ride hailing / ride sharing, Second Machine Age, self-driving car, Silicon Valley, smart cities, speech recognition, statistical model, Steve Jobs, technoutopianism, Tesla Model S, Travis Kalanick, Uber and Lyft, uber lyft, Unsafe at Any Speed

Source: Jason Yosinski, Cornell University Figure 11.1 A 3-D map built using a process of simultaneous localization and mapping (SLAM). Source: Jakob Engel, Jorg Stuckler, and Daniel Cremers, “Large-Scale Direct Slam with Stereo Cameras,” in 2015 IEEE International Conference on Intelligent Robots and Systems (IROS), pp. 1935–1942. IEEE, 2015; Andreas Geiger, Philip Lenz, and Raquel Urtasun, “Are We Ready for Autonomous Driving? The KITTI Vision Benchmark Suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3354–3361, IEEE, 2012. Figure 12.1 GM’s Electric Networked-Vehicle (EN-V) Concept pod, an autonomous two-seater codeveloped with Segway for short trips in cities. Source: General Motors Figure 12.2 The most common job in most U.S. states in 2014 was truck driving. Source: National Public Radio Figure 12.3 Passengers relax with electronics in this driverless concept mockup. Source: Rinspeed AG; image © Rinspeed, Inc.

For a machine learning algorithm, this can actually be surprisingly difficult! A database would have to store information separately about the case where a car is in front and where a motorcycle is in front. A machine learning algorithm, on the other hand, would “learn” from the car example and be able to generalize to the motorcycle example automatically. Another way to think about machine learning is that it is “pattern recognition”—the act of teaching a program to react to or recognize patterns.5 While machine-learning techniques sound organic—the software learns to recognize patterns or to solve certain problems—what’s actually happening is that an algorithm parses vast amounts of data to look for statistical patterns. Using the statistical patterns found, the algorithm then builds a mathematical model that ranks the probability of various possible outcomes to make predictions or reach a decision.

Yet his dogged determination to destroy early attempts to build neural networks actually steered artificial-intelligence research down a long, blind alley for decades. In part as a result of Minsky’s influence, the 1970s was the golden age of symbolic AI. The more researchers tried to emulate human intelligence by programming it, however, the more they unwittingly demonstrated how little we understand how the brain really works. The neural-network renaissance Meanwhile, the development of neural networks for pattern recognition continued to lurch forward. In 1975, Harvard PhD student Paul Werbos created a new and improved Perceptron. By now, the term perceptron had become generic, meaning a layer of artificial neurons in a neural network. By this time, analog hardware contraptions like the original Perceptron had been set aside and artificial-intelligence researchers built neural networks in software. Werbos offered two key improvements that would accelerate the development of artificial neural networks.


pages: 477 words: 75,408

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

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

This doubling phenomenon is known as Moore's Law, after one of the founders of the chip manufacturer Intel. We will return to this exponential growth later in this chapter, as understanding it is fundamental to comprehending the scale of the changes that are coming our way. The book argues that AI systems are on the verge of wholesale automation of white collar jobs – jobs involving cognitive skill such as pattern recognition and the acquisition, processing and transmission of information. In fact it argues that the process is already under way, and that the US is experiencing a jobless recovery from the Great Recession of 2008 thanks to this automation process. Ford claims that middle class jobs in the US are being hollowed out, with average incomes going into decline, and inequality increasing. He acknowledges that it is hard to disentangle the impact of automation from that of globalisation and off-shoring, but he remains convinced that AI-led automation is already harming the prospects of the majority of working Americans.

Hanging onto work So the third and final part of the book discusses the interventions which could maximise the bounty while minimising the spread. In particular, Brynjolfsson and McAfee want to answer a question they are often asked: “I have children in school. How should I be helping them prepare for the future?”[xxxvii] They are optimistic, believing that for many years to come, humans will be better than machines at generating new ideas, thinking outside the box (which they call “large-frame pattern recognition”) and complex forms of communication. They believe that humans' superior capabilities in these areas will enable most of us to keep earning a living, although they think the education system needs to be re-vamped to emphasise those skills, and downplay what they see as today's over-emphasis on rote learning. They praise the Montessori School approach of “self-directed learning, hands-on engagement with a wide variety of materials … and a largely unstructured school day.”

Its microphone can record your voice, and that data can help gauge your mood, or diagnose Parkinson's disease or schizophrenia. All this data can be analysed to a certain level within the phone itself, and in many cases that will suffice to provide an effective diagnosis. If symptoms persist, or if the diagnosis is unclear or unconvincing, the data can be uploaded into the cloud, i.e., to server farms run by companies like Amazon and Google. The heart of diagnosis is pattern recognition. When sophisticated algorithms compare and contrast a set of symptoms with data from millions or even billions of other patients, the quality of diagnosis can surpass what any single human doctor could offer. Ross Crawford and Jonathan Roberts are professors of orthopaedic research and robotics respectively at Queensland University of Technology. In an article in January 2016,[ccxxxviii] they argued that doctors need to understand that diagnostic services can be made available more cheaply with the assistance of machine intelligence, and reach all the patients who need them, not just those in rich countries who are already manifesting symptoms.


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Our Final Invention: Artificial Intelligence and the End of the Human Era by James Barrat

AI winter, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, Thomas Bayes, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day

But what happens in between? Nobody understands. The output of “black box” artificial intelligence tools can’t ever be predicted. So they can never be truly and verifiably “safe.” * * * But they’ll likely play a big role in AGI systems. Many researchers today believe pattern recognition—what Rosenblatt’s Perceptron aimed for—is our brain’s chief tool for intelligence. The inventor of the Palm Pilot and Handspring Treo, Jeff Hawkins, pioneered handwriting recognition with ANNs. His company, Numenta, aims to crack AGI with pattern recognition technology. Dileep George, once Numenta’s Chief Technology Officer, now heads up Vicarious Systems, whose corporate ambition is stated in their slogan: We’re Building Software that Thinks and Learns Like a Human. Neuroscientist, cognitive scientist, and biomedical engineer Steven Grossberg has come up with a model based on ANNs that some in the field believe could really lead to AGI, and perhaps the “ultraintelligence” whose potential Good saw in neural networks.

From observing birds, and experimenting, they derived principles of flight. The cognitive sciences are the brain’s “principles of flight.” OpenCog’s organizing theme is that intelligence is based on high-level pattern recognition. Usually, “patterns” in AI are chunks of data (files, pictures, text, objects) that have been classified—organized by category—or will be classified by a system that’s been trained on data. Your e-mail’s “spam” filter is an expert pattern recognizer—it recognizes one or more traits of unwanted e-mail (for example, the words “male enhancement” in the subject line) and segregates it. OpenCog’s notion of pattern recognition is more refined. The pattern it finds in each thing or idea is a small program that contains a kind of description of the thing. This is the machine version of a concept. For example, when you see a dog you instantly grasp a lot about it—you hold a concept of a dog in your memory.

The first talks on which Good based “Speculations Concerning the First Ultraintelligent Machine” came out two years later. The intelligence explosion was born. Good was more right than he knew about ANNs. Today, artificial neural networks are an artificial intelligence heavyweight, involved in applications ranging from speech and handwriting recognition to financial modeling, credit approval, and robot control. ANNs excel at high level, fast pattern recognition, which these jobs require. Most also involve “training” the neural network on massive amounts of data (called training sets) so that the network can “learn” patterns. Later it can recognize similar patterns in new data. Analysts can ask, based on last month’s data, what the stock market will look like next week. Or, how likely is someone to default on a mortgage, given a three year history of income, expenses, and credit data?


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Bounce: Mozart, Federer, Picasso, Beckham, and the Science of Success by Matthew Syed

barriers to entry, battle of ideas, Berlin Wall, combinatorial explosion, deliberate practice, desegregation, Fall of the Berlin Wall, fear of failure, Isaac Newton, Norman Mailer, pattern recognition, placebo effect, zero-sum game

So he tested chess masters under “blitz” conditions, where each player has only five minutes on the clock, with around six seconds per move (in standard conditions there are forty moves in a ninety-minute period, allowing around two minutes, fifteen seconds per move). Klein found that, for chess experts, the move quality hardly changed at all in blitz conditions, even though there was barely enough time to take the piece, move it, release it, and hit the timer. Klein then tested the pattern-recognition theory of decision making directly. He asked chess experts to think aloud as they studied midgame positions. He asked them to tell him everything they were thinking, every move considered, including the poor ones, and especially the very first move considered. He found that the first move considered was not only playable but also in many cases the best possible move from all the alternatives.

But amid the mayhem, Gretzky can discern the game’s underlying pattern and flow, and anticipate what’s going to happen faster and in more detail than anyone else in the building…. Several times during a game you’ll see him making what seem to be aimless circles on the other side of the rink from the traffic, and then, as if answering a signal, he’ll dart ahead to a spot where, an instant later, the puck turns up. This is a perfect example of expert decision making in practice: circumventing combinatorial explosion via advanced pattern recognition. It is precisely the same skill wielded by Kasparov, but on an ice hockey pitch rather than a chessboard. How was Gretzky able to do this? Let’s hear from the man himself: “I wasn’t naturally gifted in terms of size and speed; everything I did in hockey I worked for.” And later: “The highest compliment that you can pay me is to say that I worked hard every day…. That’s how I came to know where the puck was going before it even got there.”

That’s how I came to know where the puck was going before it even got there.” All of which helps to explain a qualification that was made earlier in the chapter: you will remember that the ten-thousand-hour rule was said to apply to any complex task. What is meant by complexity? In effect, it describes those tasks characterized by combinatorial explosion; tasks where success is determined, first and foremost, by superiority in software (pattern recognition and sophisticated motor programs) rather than hardware (simple speed or strength). Most sports are characterized by combinatorial explosion: tennis, table tennis, soccer, hockey, and so on. Just try to imagine, for a moment, designing a robot capable of solving the real-time spatial, motor, and perceptual challenges necessary to defeat Roger Federer on a tennis court. The complexities are almost impossible to define, let alone solve.


pages: 268 words: 81,811

Flash Crash: A Trading Savant, a Global Manhunt, and the Most Mysterious Market Crash in History by Liam Vaughan

algorithmic trading, backtesting, bank run, barriers to entry, Bernie Madoff, Black Swan, Bob Geldof, centre right, collapse of Lehman Brothers, Donald Trump, Elliott wave, eurozone crisis, family office, Flash crash, high net worth, High speed trading, information asymmetry, Jeff Bezos, Kickstarter, margin call, market design, market microstructure, Nick Leeson, offshore financial centre, pattern recognition, Ponzi scheme, Ralph Nelson Elliott, Ronald Reagan, sovereign wealth fund, spectrum auction, Stephen Hawking, the market place, Tobin tax, tulip mania, yield curve, zero-sum game

All they had to do was correctly predict whether the market would go up or down more often than they were wrong, and they would be rich and free. The reality, of course, was that it was very difficult to consistently beat the market after costs, particularly when you were so far from the flow of information. The selection process for Nav and his fellow interviewees was in three parts. First, candidates were given the McQuaig Mental Agility Test, a multiple-choice psychometric exam testing pattern recognition and verbal reasoning. This was followed by a one-on-one session in which they were asked to quickly multiply two- and three-digit numbers in their heads. Those who made it through were invited back a few days later for a two-hour interview, where they were questioned on how they would react to various hypothetical scenarios. IDT was looking for candidates who demonstrated high levels of dominance, analytical ability, sociability, and risk taking, as well as a passion for the markets.

Like the red-jacketed locals in the pits, he is seeking to use his perceived bulk to boss the market around. This perpetual gamesmanship helps explain why nine in every ten orders placed in the futures markets were canceled before they could be filled. In the same way poker players try to deduce their opponents’ hands from their betting patterns or the twitch above their left eye, traders will use pattern recognition and statistical analysis to fill in some of the gaps in their knowledge. Fifty offers keep appearing on the sell side of the market exactly ten minutes apart? Maybe it’s an algorithm that can be exploited. Someone keeps placing bids of 139 lots? Perhaps it’s a complacent day trader who hasn’t bothered to mix up his order size. With hundreds of market participants active at any time, the permutations are endless.

The first element involved statistically analyzing changes in the order book and elsewhere for information that indicated whether prices would rise or fall. Inputs might include the number and type of resting orders at different levels, how fast prices are moving around, and the types of market participants active at any time. “Think of it as a giant data science project,” explains one HFT owner. For years, Nav had used his superior pattern recognition and recall skills to read the ebbs and flows of the order book until it became second nature, but even the most gifted human scalper is no match for a computer at parsing large amounts of data. When it came to speed, the leading HFT firms invested hundreds of millions of dollars in computers, cable, and telecommunications equipment to ensure they could react first in what was often a winner-takes-all game.


pages: 391 words: 71,600

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

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

Although Amazon did not report its AWS revenues in those days, they were the clear leader, building a huge business without any real challenge from Microsoft. In his annual letter to shareholders in April 2011, just as I was beginning my new role, Amazon CEO Jeff Bezos gleefully offered a short course on the computer science and economics underlying their burgeoning cloud enterprise. He wrote about Bayesian estimators, machine learning, pattern recognition, and probabilistic decision making. “The advances in data management developed by Amazon engineers have been the starting point for the architectures underneath the cloud storage and data management services offered by Amazon Web Services (AWS),” he wrote. Amazon was leading a revolution and we had not even mustered our troops. Years earlier I had left Sun Microsystems to help Microsoft capture the lead in the enterprise market, and here we were once again far behind.

The cloud has made tremendous computing power available to everyone, and complex algorithms can now be written to discern insights and intelligence from the mountains of data. But far from Baymax or Brenner, AI today is some ways away from becoming what’s known as artificial general intelligence (AGI), the point at which a computer matches or even surpasses human intellectual capabilities. Like human intelligence, artificial intelligence can be categorized by layer. The bottom layer is simple pattern recognition. The middle layer is perception, sensing more and more complex scenes. It’s estimated that 99 percent of human perception is through speech and vision. Finally, the highest level of intelligence is cognition—deep understanding of human language. These are the building blocks of AI, and for many years Microsoft has invested in advancing each of these tiers—statistical machine learning tools to make sense of data and recognize patterns; computers that can see, hear, and move, and even begin to learn and understand human language.

And so our vision is to build tools that have true artificial intelligence infused across agents, applications, services, and infrastructure: We’re harnessing artificial intelligence to fundamentally change how people interact with agents like Cortana, which will become more and more common in our lives. Applications like Office 365 and Dynamics 365 will have AI baked-in so that they can help us focus on things that matter the most and get more out of every moment. We’ll make the underlying intelligence capabilities of our own services—the pattern recognition, perception, and cognitive capabilities—available to every application developer in the world. And, lastly, we’re building the world’s most powerful AI supercomputer and making that infrastructure available to anyone. A range of industries are using these AI tools. McDonald’s is creating an AI system that can help its workers take your order in the drive-through line, making ordering food simpler, more efficient, and more accurate.


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

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

While significant progress has been made in developing machine-learning techniques, it is still an emerging science and much research needs to be undertaken to improve the effectiveness and robustness of the models produced. Each broad class of analytic is now discussed in turn, though it should be noted that they are often used in combination. For example, data mining and pattern recognition might provide the basis for prediction or optimisation, or statistics might be used in data mining to detect patterns or in calculating a prediction, or visualisation might be used in data mining or to communicate outputs from simulations, and so on. Data mining and pattern recognition Data mining is the process of extracting data and patterns from large datasets (Manyika et al. 2011). It is premised on the notion that all massive datasets hold meaningful information that is non-random, valid, novel, useful and ultimately understandable (Han et al. 2011).

These analytics, as applied in business and science, seek to answer four basic sets of questions (Minelli et al. 2013): Description: what and when did something happen? How often does it happen? Explanation: why did it happen? What is its impact? Prediction: what is likely to happen next? What if we did this or that? Prescription: what is the optimal answer or outcome? How is that achieved? The answers to these questions are derived from four broad classes of analytics: data mining and pattern recognition; data visualisation and visual analytics; statistical analysis; and prediction, simulation, and optimisation. Each of these is discussed in brief, but first the pre-analytics phase and machine learning are introduced as they are central to all four. Pre-analytics All data analytics require the data to be analysed to be pre-prepared; that is readied and checked. H.J. Miller (2010) and Han et al. (2011) set out four such processes with respect to scaled and big data that are usually undertaken in sequence, though they do not have to be executed in any particular order and maybe undertaken iteratively: Data selection: determining a subset of the variables that have most utility, and potentially a sampling frame for those variables.

How Amazon constructed its recommendation system was based on scientific reasoning, underpinned by a guiding model and accompanied by empirical testing designed to improve the performance of the algorithms it uses. Likewise, Google undertakes extensive research and development, it works in partnership with scientists and it buys scientific knowledge, either funding research within universities or by buying the IP of other companies, to refine and extend the utility of how it organises, presents and extracts value from data. Thus, if statistical algorithms find patterns in data it is because pattern recognition science, along with domain-specific knowledge, has been employed. Third: data can speak for themselves free of human bias or framing. Related to the notion that the production of knowledge from big data occurs unmoored from science is the idea that big data analytics enable data to speak for themselves unencumbered by contextualisation or the vagaries of human elucidation. Not only are data supposedly generated free from theory, but their interpretation and meaning can similarly take place in a scientific vacuum.


pages: 407 words: 90,238

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

3D printing, Alexander Shulgin, augmented reality, Berlin Wall, Bernie Sanders, bitcoin, blockchain, Burning Man, Colonization of Mars, crowdsourcing, David Brooks, delayed gratification, disruptive innovation, Electric Kool-Aid Acid Test, Elon Musk, en.wikipedia.org, high batting average, hive mind, Hyperloop, impulse control, informal economy, Jaron Lanier, John Markoff, Kevin Kelly, lateral thinking, Mason jar, Maui Hawaii, McMansion, means of production, Menlo Park, meta analysis, meta-analysis, music of the spheres, pattern recognition, Peter Thiel, PIHKAL and TIHKAL, Ray Kurzweil, ride hailing / ride sharing, risk tolerance, science of happiness, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Silicon Valley startup, Skype, Steve Jobs, Tony Hsieh, urban planning

For a great discussion of dopamine’s role in flow, see Gregory Burns, Satisfaction: The Science of Finding True Fulfillment (New York: Henry Holt, 2005), pp. 146–74. 27. These chemicals amp up the brain’s pattern recognition abilities: P. Krummenacher, C. Mohr, H. Haker, and P. Brugger, “Dopamine, Paranormal Belief, and the Detection of Meaningful Stimuli,” Journal of Cognitive Neuroscience 22, no. 8 (2010): 1670–81; Georg Winterer and Donald Weinberger, “Genes, Dopamine, and Cortical Signal-to-Noise Ration in Schizophrenia,” Trends in Neuroscience 27, no. 11 (November 2004); and S. Kroener, L. J. Chandler, P. Phillips, and Jeremy Seamans, “Dopamine Modulates Persistent Synaptic Activity and Enhances the Signal to Noise Ratio in the Prefrontal Cortex,” PLoS One 4, no. 8 (August): e6507. Also see Michael Sherman’s great talk on how dopamine/pattern recognition lead to strange beliefs: http://www.ted.com/talks/michael_shermer_on_believing_strange_things?

The conscious mind is a potent tool, but it’s slow, and can manage only a small amount of information at once. The subconscious, meanwhile, is far more efficient. It can process more data in much shorter time frames. In ecstasis, the conscious mind takes a break, and the subconscious takes over. As this occurs, a number of performance-enhancing neurochemicals flood the system, including norepinephrine and dopamine. Both of these chemicals amplify focus, muscle reaction times, and pattern recognition. With the subconscious in charge and those neurochemicals in play, SEALs can read micro-expressions across dark rooms at high speeds. So, when a team enters hostile terrain, they can break complex threats into manageable chunks. They quickly segment the battle space into familiar situations they know how to handle, like guards that need disarming or civilians that need corralling, and unfamiliar situations—a murky shape in a far corner—that may or may not be a threat.

Often, an ecstatic experience25 begins when the brain releases norepinephrine and dopamine into our system. These neurochemicals raise heart rates,26 tighten focus, and help us sit up and pay attention. We notice more of what’s going on around us, so information normally tuned out or ignored becomes more readily available. And besides simply increasing focus, these chemicals amp up the brain’s pattern recognition abilities,27 helping us find new links between all this incoming information. As these changes are taking place, our brainwaves slow from agitated beta to calmer alpha,28 shifting us into daydreaming mode: relaxed, alert, and able to flit from idea to idea without as much internal resistance. Then parts of the prefrontal cortex begin shutting down.29 We experience the selflessness, timelessness, and effortlessness of transient hypofrontality.


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Designing Great Data Products by Jeremy Howard, Mike Loukides, Margit Zwemer

AltaVista, Filter Bubble, PageRank, pattern recognition, recommendation engine, self-driving car, sentiment analysis, Silicon Valley, text mining

They started with an objective like, “I want my car to drive me places,” and then designed a covert data product to accomplish that task. Engineers are often quietly on the leading edge of algorithmic applications because they have long been thinking about their own modeling challenges in an objective-based way. Industrial engineers were among the first to begin using neural networks, applying them to problems like the optimal design of assembly lines and quality control. Brian Ripley’s seminal book on pattern recognition gives credit for many ideas and techniques to largely forgotten engineering papers from the 1970s. When designing a product or manufacturing process, a drivetrain-like process followed by model integration, simulation and optimization is a familiar part of the toolkit of systems engineers. In engineering, it is often necessary to link many component models together so that they can be simulated and optimized in tandem.

The levers are the vehicle controls we are all familiar with: steering wheel, accelerator, brakes, etc. Next, we consider what data the car needs to collect; it needs sensors that gather data about the road as well as cameras that can detect road signs, red or green lights, and unexpected obstacles (including pedestrians). We need to define the models we will need, such as physics models to predict the effects of steering, braking and acceleration, and pattern recognition algorithms to interpret data from the road signs. As one engineer on the Google self-driving car project put it in a recent Wired article, “We’re analyzing and predicting the world 20 times a second.” What gets lost in the quote is what happens as a result of that prediction. The vehicle needs to use a simulator to examine the results of the possible actions it could take. If it turns left now, will it hit that pedestrian?


pages: 340 words: 97,723

The Big Nine: How the Tech Titans and Their Thinking Machines Could Warp Humanity by Amy Webb

Ada Lovelace, AI winter, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, Bayesian statistics, Bernie Sanders, bioinformatics, blockchain, Bretton Woods, business intelligence, Cass Sunstein, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Deng Xiaoping, distributed ledger, don't be evil, Donald Trump, Elon Musk, Filter Bubble, Flynn Effect, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, natural language processing, New Urbanism, one-China policy, optical character recognition, packet switching, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Sand Hill Road, Second Machine Age, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, smart cities, South China Sea, sovereign wealth fund, speech recognition, Stephen Hawking, strong AI, superintelligent machines, technological singularity, The Coming Technological Singularity, theory of mind, Tim Cook: Apple, trade route, Turing machine, Turing test, uber lyft, Von Neumann architecture, Watson beat the top human players on Jeopardy!, zero day

Already, China’s economy is 30 times larger than it was just three decades ago. Baidu, Tencent, and Alibaba may be publicly traded giants, but typical of all large Chinese companies, they must bend to the will of Beijing. China’s massive population of 1.4 billion citizens puts it in control of the largest, and possibly most important, natural resource in the era of AI: human data. Voluminous amounts of data are required to refine pattern recognition algorithms—which is why Chinese face recognition systems like Megvii and SenseTime are so attractive to investors. All the data that China’s citizens are generating as they make phone calls, buy things online, and post photos to social networks are helping Baidu, Alibaba, and Tencent to create best-in-class AI systems. One big advantage for China: it doesn’t have the privacy and security restrictions that might hinder progress in the United States.

Yet we’ve both learned what an apple is and the general characteristics of how an apple tastes, what its texture is, and how it smells. During our lifetimes, we’ve learned to recognize what an apple is through reinforcement learning—someone taught us what an apple looked like, its purpose, and what differentiates it from other fruit. Then, over time and without conscious awareness, our autonomous biological pattern recognition systems got really good at determining something was an apple, even if we only had a few of the necessary data points. If you see a black-and-white, two-dimensional outline of an apple, you know what it is—even though you’re missing the taste, smell, crunch, and all the other data that signals to your brain this is an apple. The way you and Alexa both learned about apples is more similar than you might realize.

At MIT, computer scientist Joseph Weizenbaum wrote an early AI system called ELIZA, a chat program named after the ingenue in George Bernard Shaw’s play Pygmalion.23 This development was important for neural networks and AI because it was an early attempt at natural language processing, and the program accessed various prewritten scripts in order to have conversations with real people. The most famous script was called DOCTOR,24 and it mimicked an empathetic psychologist using pattern recognition to respond with strikingly humanistic responses. The Dartmouth workshop had now generated international attention, as did its researchers, who’d unexpectedly found themselves in the limelight. They were nerdy rock stars, giving everyday people a glimpse into a fantastical new vision of the future. Remember Rosenblatt, the psychologist who’d created the first neural net? He told the Chicago Tribune that soon machines wouldn’t just have ELIZA programs capable of a few hundred responses, but that computers would be able to listen in on meetings and type out dictation, “just like a office secretary.”


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Smart Machines: IBM's Watson and the Era of Cognitive Computing (Columbia Business School Publishing) by John E. Kelly Iii

AI winter, call centre, carbon footprint, crowdsourcing, demand response, discovery of DNA, disruptive innovation, Erik Brynjolfsson, future of work, Geoffrey West, Santa Fe Institute, global supply chain, Internet of things, John von Neumann, Mars Rover, natural language processing, optical character recognition, pattern recognition, planetary scale, RAND corporation, RFID, Richard Feynman, smart grid, smart meter, speech recognition, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!

Within a year, the team had designed the chips that arrived in the box from East Fishkill. Rather than processing data sequentially, as in classic von Neumann computing, the chips mimic the brain’s event-driven, distributed, and parallel processing. Using those chips, team members were able to write applications, including teaching the system to play Pong, one of the earliest computer games, which demonstrated capabilities such as navigation, machine vision, and pattern recognition.10 In mid-2013, IBM produced a second generation of chips, with the design name TrueNorth. They possess approximately the same number of neurons as a bumblebee. If you put one hundred of the chips together in a computing system, you’re got something that occupies less than two liters of volume and consumes less energy than a lightbulb. Dharmendra calls it a “brain box.” The coming generations of brain boxes will be able to combine information from different sensors and draw conclusions from it, much as humans combine what they see and hear to understand what’s going on around them.11 Dharmendra’s team has developed a number of scenarios to help people understand how their technologies might be employed, using anywhere from a single chip to dozens of them.

The flower might be designed to give visual cues to the participants in meetings, opening if the conversation is dynamic and useful and closing if it is boring and repetitive.12 The cognitive chips and Watson are complementary technologies. For the sake of simplicity, you can think of them as the right brain and left brain of the era of cognitive systems. Watson, the left brain, focuses on language and analytical thinking. The cognitive chips address senses and pattern recognition. Over the coming years, IBM scientists hope to meld the Watson and TrueNorth capabilities together to create a holistic computing intelligence. SCENARIO: MEDICAL IMAGING ASSISTANT Over the past century, tremendous progress has been made in technologies designed to help spot evidence of disease inside the human body. X rays, ultrasound, computed tomography, nuclear medicine, and magnetic-resonance imaging make it easier to identify and treat everything from heart disease and breast cancer to complex gastrointestinal maladies.

One of the key elements of innovation for the era of cognitive systems will be the willingness to use the real world, in all of its messiness and complexity, as a living laboratory for developing new technologies. Milind grew up in what he calls the “concrete jungle” of Pune, India. He loved the fact that even though it was a large city, he could get almost anywhere he wanted to go on foot. Later, he studied for a Ph.D. in electrical engineering at the University of Illinois at Urbana-Champaign. The focus of his dissertation was on pattern recognition in multimedia content, which, it turns out, has some similarities to mapping the movements of people in cities. After working at IBM Research for several years, he got interested in using data and advanced analytics tools to solve transportation problems. His boss suggested that he find a city that would be willing to let him work with real data. Milind chose Dubuque because IBM was opening a service-delivery center there and because the city’s mayor, Roy D.


pages: 289 words: 87,292

The Strange Order of Things: The Biological Roots of Culture by Antonio Damasio

Albert Einstein, biofilm, business process, Daniel Kahneman / Amos Tversky, double helix, Gordon Gekko, invention of the wheel, invention of writing, invisible hand, job automation, mental accounting, meta analysis, meta-analysis, microbiome, Norbert Wiener, pattern recognition, Peter Singer: altruism, planetary scale, profit motive, Ray Kurzweil, Richard Feynman, self-driving car, Silicon Valley, Steven Pinker, Thomas Malthus

Nervous systems allow for the precise localization of sensory stimulation and for the coordination of complex and diverse physiological processes that integrate all major life-regulatory systems in the homeostatic effort. Giorgio Santoni, Claudio Cardinali, Maria Beatrice Morelli, Matteo Santoni, Massimo Nabissi, and Consuelo Amantini, “Danger- and Pathogen-Associated Molecular Patterns Recognition by Pattern-Recognition Receptors and Ion Channels of the Transient Receptor Potential Family Triggers the Inflammasome Activation in Immune Cells and Sensory Neurons,” Journal of Neuroinflammation 12, no. 1 (2015): 21; McMahon, La Russa, and Bennett, “Crosstalk Between the Nociceptive and Immune Systems in Host Defense and Disease”; Ardem Patapoutian, Simon Tate, and Clifford J. Woolf, “Transient Receptor Potential Channels: Targeting Pain at the Source,” Nature Reviews Drug Discovery 8, no. 1 (2009): 55–68; Takaaki Sokabe and Makoto Tominaga, “A Temperature-Sensitive TRP Ion Channel, Painless, Functions as a Noxious Heat Sensor in Fruit Flies,” Communicative and Integrative Biology 2, no. 2 (2009): 170–73; Farina et al., “Pain-Related Modulation of the Human Motor Cortex.” 4.

Woolf, “Transient Receptor Potential Channels: Targeting Pain at the Source,” Nature Reviews Drug Discovery 8, no. 1 (2009): 55–68; Takaaki Sokabe and Makoto Tominaga, “A Temperature-Sensitive TRP Ion Channel, Painless, Functions as a Noxious Heat Sensor in Fruit Flies,” Communicative and Integrative Biology 2, no. 2 (2009): 170–73; Farina et al., “Pain-Related Modulation of the Human Motor Cortex.” 4. Santoni et al., “Danger- and Pathogen-Associated Molecular Patterns Recognition by Pattern-Recognition Receptors and Ion Channels of the Transient Receptor Potential Family Triggers the Inflammasome Activation in Immune Cells and Sensory Neurons”; Sokabe and Tominaga, “Temperature-Sensitive TRP Ion Channel, Painless, Functions as a Noxious Heat Sensor in Fruit Flies.” 5. Colin Klein and Andrew B. Barron, “Insects Have the Capacity for Subjective Experience,” Animal Sentience 1, no. 9 (2016): 1.

Artificial organs and prosthetic limbs are not new either, nor are, on the shady side of the street, the performance enhancers that get Olympic athletes and Tour de France champions in so much trouble. Gaining access to strategies and devices that can speed up movement or improve one’s intellectual performance is hardly problematic except for competitions. The application of artificial intelligence to medical diagnostics is very promising. Diagnosis of illnesses and interpretation of diagnostic procedures are the bread and butter of medicine and depend on pattern recognition. Machine learning programs are a natural tool in this area and have achieved reliable and trustworthy results.2 By comparison with some of the currently contemplated genetic interventions, the developments in this general area are largely benign and potentially valuable. The most likely and immediate scenario is the achievement of prosthetic enhancement devices that could serve to not only compensate for missing functions but also enhance or augment human perception.


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Advances in Artificial General Intelligence: Concepts, Architectures and Algorithms: Proceedings of the Agi Workshop 2006 by Ben Goertzel, Pei Wang

AI winter, artificial general intelligence, bioinformatics, brain emulation, combinatorial explosion, complexity theory, computer vision, conceptual framework, correlation coefficient, epigenetics, friendly AI, G4S, information retrieval, Isaac Newton, John Conway, Loebner Prize, Menlo Park, natural language processing, Occam's razor, p-value, pattern recognition, performance metric, Ray Kurzweil, Rodney Brooks, semantic web, statistical model, strong AI, theory of mind, traveling salesman, Turing machine, Turing test, Von Neumann architecture, Y2K

Like De Garis et al, Looks is concerned with ways of making evolutionary learning much more efficient with a view toward enabling it to play a leading role in AGI – but the approach is completely different. Rather than innovating on the hardware side, Looks suggests a collection of fundamental algorithmic innovations, which ultimately constitute a proposal to replace evolutionary learning with a probabilistic-pattern-recognition based learning algorithm (MOSES = Meta-Optimizing Semantic Evolutionary Search) that grows a population of candidate problem solutions via repeatedly recognizing probabilistic patterns in good solutions and using these patterns to generate new ones. The key ideas underlying MOSES are motivated by cognitive science ideas, most centrally the notion of “adaptive representation building” – having the learning algorithm figure out the right problem representation as it goes along, as part of the learning process, rather than assuming a well-tuned representation right from the start.

The chapter by Matthew Ikle’ et al, reviews aspects of Probabilistic Logic Networks (PLN) -- an AI problem-solving approach that, like MOSES, has been created with a view toward integration into the Novamente AI framework, as well as toward stand-alone performance. PLN is a probabilistic logic framework that combines probability theory, term logic and predicate logic with various heuristics in order to provide comprehensive forward and backward chaining inference in contexts ranging from mathematical theorem-proving to perceptual pattern-recognition, and speculative inductive and abductive inference. The specific topic of this chapter is the management of “weight of evidence” within PLN. Like NARS mentioned above and Peter Walley’s imprecise probability theory [23], PLN quantifies truth values using a minimum of two numbers (rather than, for instance, a single number representing a probability or fuzzy membership value). One approach within PLN is to use two numbers (s,n), where s represents a probability value, and n represents a “weight of evidence” defining how much evidence underlies that probability value.

Since the routing takes about 40 minutes for a full chip, this is too slow. So by having a generic model routed once only in the chip, we can send it different data for different neural net module evolutions. Sending in data to the already routed chip takes only a few seconds. As a more concrete illustration of this “generic evolution” idea, we provide the following example. Assume we want to evolve several hundred 2D pattern recognition neural net modules. The pattern to be detected is “shone” onto an 8 by 8 pixel grid of photo-cell detectors, each of whose signal strength outputs is strong if strong light falls on the photo-cell, and is weak if the light falling on it is weak. These 64 light intensity output signals are fed into a fully connected 16 neuron neural network. Hence each neuron receives 4 external signals from the pixel grid.


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You Are Not a Gadget by Jaron Lanier

1960s counterculture, accounting loophole / creative accounting, additive manufacturing, Albert Einstein, call centre, cloud computing, commoditize, crowdsourcing, death of newspapers, different worldview, digital Maoism, Douglas Hofstadter, Extropian, follow your passion, hive mind, Internet Archive, Jaron Lanier, jimmy wales, John Conway, John von Neumann, Kevin Kelly, Long Term Capital Management, Network effects, new economy, packet switching, PageRank, pattern recognition, Ponzi scheme, Ray Kurzweil, Richard Stallman, Silicon Valley, Silicon Valley startup, slashdot, social graph, stem cell, Steve Jobs, Stewart Brand, Ted Nelson, telemarketer, telepresence, The Wisdom of Crowds, trickle-down economics, Turing test, Vernor Vinge, Whole Earth Catalog

A computer was watching my face through a digital camera and generating varied opprobrious percussive sounds according to which funny face it recognized in each moment.† (Keeping a rhythm with your face is a strange new trick—we should expect a generation of kids to adopt the practice en masse any year now.) This is the sort of deceptively silly event that should be taken seriously as an indicator of technological change. In the coming years, pattern-recognition tasks like facial tracking will become commonplace. On one level, this means we will have to rethink public policy related to privacy, since hypothetically a network of security cameras could automatically determine where everyone is and what faces they are making, but there are many other extraordinary possibilities. Imagine that your avatar in Second Life (or, better yet, in fully realized, immersive virtual reality) was conveying the subtleties of your facial expressions at every moment.

While there are still a great many qualities in our experience that cannot be represented in software using any known technique, engineers have finally gained the ability to create software that can represent a smile, and write code that captures at least part of what all smiles have in common. This is an unheralded transformation in our abilities that took place around the turn of our new century. I wasn’t sure I would live to see it, though it continues to surprise me that engineers and scientists I run across from time to time don’t realize it has happened. Pattern-recognition technology and neuroscience are growing up together. The software I used at NAMM was a perfect example of this intertwining. Neuroscience can inspire practical technology rather quickly. The original project was undertaken in the 1990s under the auspices of Christoph von der Malsburg, a University of Southern California neuroscientist, and his students, especially Hartmut Neven. (Von der Malsburg might be best known for his crucial observation in the early 1980s that synchronous firing—that is, when multiple neurons go off at the same moment—is important to the way that neural networks function.)

Their spacing was determined by the average speed of the drivers on the road. When your speed matched that average, the ride would feel less bumpy. You couldn’t see the bumps with your eyes except right at sunset, when the horizontal red light rays highlighted every irregularity in the ground. At midday you had to drive carefully to avoid the hidden information in the road. Digital algorithms must approach pattern recognition in a similarly indirect way, and they often have to make use of a common procedure that’s a little like running virtual tires over virtual bumps. It’s called the Fourier transform. A Fourier transform detects how much action there is at particular “speeds” (frequencies) in a block of digital information. Think of the graphic equalizer display found on audio players, which shows the intensity of the music in different frequency bands.


Programming Computer Vision with Python by Jan Erik Solem

augmented reality, computer vision, database schema, en.wikipedia.org, optical character recognition, pattern recognition, text mining, Thomas Bayes, web application

International Journal of Computer Vision, 2001. [30] Daniel Scharstein and Richard Szeliski. High-accuracy stereo depth maps using structured light. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. [31] Toby Segaran. Programming Collective Intelligence. O’Reilly Media, 2007. [32] Jianbo Shi and Jitendra Malik. Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22:888–905, August 2000. [33] Jianbo Shi and Carlo Tomasi. Good features to track. In 1994 IEEE Conference on Computer Vision and Pattern Recognition (CVPR’94), pages 593–600, 1994. [34] Noah Snavely, Steven M. Seitz, and Richard Szeliski. Photo tourism: Exploring photo collections in 3d. In SIGGRAPH Conference Proceedings, pages 835–846. ACM Press, 2006

[3] Gary Bradski and Adrian Kaehler. Learning OpenCV. O’Reilly Media Inc., 2008. [4] Martin Byröd. An optical Sudoku solver. In Swedish Symposium on Image Analysis, SSBA. http://www.maths.lth.se/matematiklth/personal/byrod/papers/sudokuocr.pdf, 2007. [5] Antonin Chambolle. Total variation minimization and a class of binary mrf models. In Energy Minimization Methods in Computer Vision and Pattern Recognition, Lecture Notes in Computer Science, pages 136–152. Springer Berlin / Heidelberg, 2005. [6] T. Chan and L. Vese. Active contours without edges. IEEE Trans. Image Processing, 10(2):266–277, 2001. [7] Chih-Chung Chang and Chih-Jen Lin. LIBSVM: a library for support vector machines, 2001. Software available at http://www.csie.ntu.edu.tw/~cjlin/libsvm. [8] D. Cremers, T. Pock, K. Kolev, and A.

Learning Python. O’Reilly Media Inc., 2009. [21] Will McGugan. Beginning Game Development with Python and Pygame. Apress, 2007. [22] F. Meyer. Color image segmentation. In Proceedings of the 4th Conference on Image Processing and its Applications, pages 302–306, 1992. [23] D. Nistér and H. Stewénius. Scalable recognition with a vocabulary tree. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), volume 2, pages 2161–2168, 2006. [24] Travis E. Oliphant. Guide to NumPy. http://www.tramy.us/numpybook.pdf, 2006. [25] M. Pollefeys, L. Van Gool, M. Vergauwen, F. Verbiest, K. Cornelis, J. Tops, and R. Koch. Visual modeling with a hand-held camera. International Journal of Computer Vision, 59(3):207–232, 2004. [26] Marc Pollefeys. Visual 3d modeling from images—tutorial notes.


Bulletproof Problem Solving by Charles Conn, Robert McLean

active transport: walking or cycling, Airbnb, Amazon Mechanical Turk, asset allocation, availability heuristic, Bayesian statistics, Black Swan, blockchain, business process, call centre, carbon footprint, cloud computing, correlation does not imply causation, Credit Default Swap, crowdsourcing, David Brooks, Donald Trump, Elon Musk, endowment effect, future of work, Hyperloop, Innovator's Dilemma, inventory management, iterative process, loss aversion, meta analysis, meta-analysis, Nate Silver, nudge unit, Occam's razor, pattern recognition, pets.com, prediction markets, principal–agent problem, RAND corporation, randomized controlled trial, risk tolerance, Silicon Valley, smart contracts, stem cell, the rule of 72, the scientific method, The Signal and the Noise by Nate Silver, time value of money, transfer pricing, Vilfredo Pareto, walkable city, WikiLeaks

As this new era of the problem solving organization takes hold, we expect it will trigger even more interest in how teams go about sharpening complex problem solving and critical thinking skills—what is called mental muscle by the authors of The Mathematical Corporation.2  The other side of the equation is the increasing importance of machine learning and artificial intelligence in addressing fast‐changing systems. Problem solving will increasingly utilize advances in machine learning to predict patterns in consumer behavior, disease, credit risk, and other complex phenomena, termed machine muscle. To meet the challenges of the twenty‐first century, mental muscle and machine muscle have to work together. Machine learning frees human problem solvers from computational drudgery and amplifies the pattern recognition required for faster organizational response to external challenges. For this partnership to work, twenty‐first century organizations need staff who are quick on their feet, who learn new skills quickly, and who attack emerging problems with confidence. The World Economic Forum in its Future of Jobs Report3  placed complex problem solving at #1 in its top 10 skills for jobs in 2020. Here is their list of important skills that employers are seeking: It is becoming very clear that job growth is focused in areas where tasks are nonroutine and cognitive, versus routine and manual.

When we encounter genuinely novel problems, we sometimes persist in using frames that are unhelpful or misleading in the new and different context. For centuries, astronomers thought the sun revolved around the earth, and scratched their heads when important navigation problems weren't easy to solve! The next section addresses team processes that can help speed problem solving while avoiding the potholes of false pattern recognition. Team Processes in Problem Disaggregation and Prioritization These problem solving steps benefit substantially from teamwork, and solo practitioners should consider enlisting family or friends to assist them here. Because it is often difficult to see the structure of the problem, team brainstorming is hugely valuable, especially when trying different lenses or cleaving frames. One technique we have both found valuable is to use large yellow Post‐it notes to capture team member ideas on early logic tree elements, as shown in Exhibit 3.18.

Any time we have done something before and had it be successful, it is more likely that our brains will quickly choose this path again, without conscious thought. Just like learning to say a foreign phrase, the first time is labored and painfully self‐conscious. With time it becomes, literally, second nature and disappears into our automatic mental systems. This is great for speaking a new language or riding a bike or tying your shoes … but not so great for systematic problem solving. Our kind of problem solving can and does benefit from pattern recognition, of course. When you correctly see that a particular business problem is best cleaved by a return on capital tree or a supply curve, it makes problem solving faster and more accurate. Philip Tetlock reports that grandmasters at chess have 50,000–100,000 patterns stored in this deep memory system.2 But when we incorrectly see a familiar framing for a new kind of problem, we risk disastrously wrong solutions or endless work getting back on track.


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Data Science for Business: What You Need to Know About Data Mining and Data-Analytic Thinking by Foster Provost, Tom Fawcett

Albert Einstein, Amazon Mechanical Turk, big data - Walmart - Pop Tarts, bioinformatics, business process, call centre, chief data officer, Claude Shannon: information theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, data acquisition, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, information retrieval, intangible asset, iterative process, Johann Wolfgang von Goethe, Louis Pasteur, Menlo Park, Nate Silver, Netflix Prize, new economy, p-value, pattern recognition, placebo effect, price discrimination, recommendation engine, Ronald Coase, selection bias, Silicon Valley, Skype, speech recognition, Steve Jobs, supply-chain management, text mining, The Signal and the Noise by Nate Silver, Thomas Bayes, transaction costs, WikiLeaks

So a reader with some background in regression analysis may encounter new and even seemingly contradictory lessons.[12] Machine Learning and Data Mining The collection of methods for extracting (predictive) models from data, now known as machine learning methods, were developed in several fields contemporaneously, most notably Machine Learning, Applied Statistics, and Pattern Recognition. Machine Learning as a field of study arose as a subfield of Artificial Intelligence, which was concerned with methods for improving the knowledge or performance of an intelligent agent over time, in response to the agent’s experience in the world. Such improvement often involves analyzing data from the environment and making predictions about unknown quantities, and over the years this data analysis aspect of machine learning has come to play a very large role in the field. As machine learning methods were deployed broadly, the scientific disciplines of Machine Learning, Applied Statistics, and Pattern Recognition developed close ties, and the separation between the fields has blurred. The field of Data Mining (or KDD: Knowledge Discovery and Data Mining) started as an offshoot of Machine Learning, and they remain closely linked.

An instance is also sometimes called a feature vector, because it can be represented as a fixed-length ordered collection (vector) of feature values. Unless stated otherwise, we will assume that the values of all the attributes (but not the target) are present in the data. Many Names for the Same Things The principles and techniques of data science historically have been studied in several different fields, including machine learning, pattern recognition, statistics, databases, and others. As a result there often are several different names for the same things. We typically will refer to a dataset, whose form usually is the same as a table of a database or a worksheet of a spreadsheet. A dataset contains a set of examples or instances. An instance also is referred to as a row of a database table or sometimes a case in statistics. The features (table columns) have many different names as well.

Some methods avoiding committing to a k by retrieving a very large number of instances (e.g., all instances, k = n) and depend upon distance weighting to moderate the influences. Sidebar: Many names for nearest-neighbor reasoning As with many things in data mining, different terms exist for nearest-neighbor classifiers, in part because similar ideas were pursued independently. Nearest-neighbor classifiers were established long ago in statistics and pattern recognition (Cover & Hart, 1967). The idea of classifying new instances directly by consulting a database (a “memory”) of instances has been termed instance-based learning (Aha, Kibler, & Albert, 1991) and memory-based learning (Lin & Vitter, 1994). Because no model is built during “training” and most effort is deferred until instances are retrieved, this general idea is known as lazy learning (Aha, 1997).


pages: 144 words: 43,356

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

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

It soon became apparent that there were diminishing returns to investment in these systems, and when the second AI winter thawed in the early 1990s it was thanks to the rise of more statistical approaches, which are often collectively termed machine learning. Machine Learning Machine learning is the process of creating and refining algorithms which can produce conclusions based on data without being explicitly programmed to do so. It overlaps closely with a number of other domains, including pattern recognition: machine learning algorithms are becoming increasingly impressive at recognising images, for instance. Another is computational statistics, which is the development of algorithms to implement statistical methods on computers. A third is the field of data mining, and its offshoot Big Data. Data mining is the process of discovering previously unknown properties in large data sets, whereas machine learning systems usually make predictions based on information which is already known to the experimenter, using training data.

Early hopes for the quick development of thinking machines were dashed, however, and neural nets fell into disuse until the late 1980s, when they experienced a renaissance along with what came to be known as deep learning thanks to pioneers Yann LeCun (now at Facebook), Geoff Hinton (now at Google) and Yoshua Bengio, a professor at the University of Montreal. Yann LeCun describes deep learning as follows. “A pattern recognition system is like a black box with a camera at one end, a green light and a red light on top, and a whole bunch of knobs on the front. The learning algorithm tries to adjust the knobs so that when, say, a dog is in front of the camera, the red light turns on, and when a car is put in front of the camera, the green light turns on. You show a dog to the machine. If the red light is bright, don’t do anything.

It is amazing how quickly we humans become habituated to the marvels we create, and simply take them for granted. We will look at this again later in this chapter. Similarly, some people are dismissive of the progress made by artificial intelligence since the discipline began 60 years ago, complaining that we don’t yet have a machine which knows it is alive. But it is daft to dismiss as failures today’s best pattern recognition systems, self-driving cars, and machines which can beat any human at many games of skill. Informed scepticism about near-term AGI We should take more seriously the arguments of very experienced AI researchers who claim that although the AGI undertaking is possible, it won’t be achieved for a very long time. Rodney Brooks, a veteran AI researcher and robot builder, says “I think it is a mistake to be worrying about us developing [strong] AI any time in the next few hundred years.


pages: 320 words: 87,853

The Black Box Society: The Secret Algorithms That Control Money and Information by Frank Pasquale

Affordable Care Act / Obamacare, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, call centre, Capital in the Twenty-First Century by Thomas Piketty, Chelsea Manning, Chuck Templeton: OpenTable:, cloud computing, collateralized debt obligation, computerized markets, corporate governance, Credit Default Swap, credit default swaps / collateralized debt obligations, crowdsourcing, cryptocurrency, Debian, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Fall of the Berlin Wall, Filter Bubble, financial innovation, financial thriller, fixed income, Flash crash, full employment, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, informal economy, information asymmetry, information retrieval, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, Julian Assange, Kevin Kelly, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, Marc Andreessen, Mark Zuckerberg, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, quantitative easing, race to the bottom, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, search engine result page, shareholder value, Silicon Valley, Snapchat, social intelligence, Spread Networks laid a new fibre optics cable between New York and Chicago, statistical arbitrage, statistical model, Steven Levy, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, WikiLeaks, zero-sum game

51 Although internet giants say their algorithms are scientific and neutral tools, it is very difficult to verify those claims.52 And while they have become critical economic infrastructure, trade secrecy law permits managers to hide their methodolo- INTRODUCTION—THE NEED TO KNOW 15 gies, and business practices, deflecting scrutiny.53 Chapter 3 examines some personal implications of opaque search technology, along with larger issues that it raises in business and law. Like the reputation and search sectors, the finance industry has characterized more and more decisions as computable, programmable procedures. Big data enables complex pattern recognition techniques to analyze massive data sets. Algorithmic methods of reducing judgment to a series of steps were supposed to rationalize finance, replacing self-serving or biased intermediaries with sound decision frameworks. And they did reduce some inefficiencies. But they also ended up firmly building in some dubious old patterns of credit castes and corporate unaccountability.54 The black boxes of finance replaced familiar old problems with a triple whammy of technical complexity, real secrecy, and trade secret laws.

When we click on an ad promising a discount, there’s probably a program behind the scenes calculating how much more it can charge us on the basis of our location,3 or whether we’re using a Mac or PC, or even court records.4 It’s not only the National Security Agency (NSA) that covets total information awareness; that’s the goal of marketers, too. They want that endless array of data points to develop exhaustive profiles. Of us. Pattern recognition is the name of the game— connecting the dots of past behavior to predict the future. Are you a fierce comparison shopper, or the relaxed kind who’s OK spending a few extra dollars for a plane ticket or a movie if it saves some trouble? Firms want to know, and they can find out quite easily. Every business wants a data advantage that will let it target its ideal customers. Sometimes the results are prosaic and predictable: your favorite retailer may pop up as an ad on every other website you visit.

How many of the rest of us are mysteriously “weblined” into categories we know nothing about?56 Even the partial exposure of such data transfers is unusual. In most cases, they stay well hidden. But reporters are beginning to open up the black box of consumer profiling, as Charles Duhigg did in his 2012 report on Target, the second-largest U.S. discount retailer and a company that prides itself on knowing when its customers are pregnant.57 For a retailer of that size, the pattern recognition was easy. First, Target’s statisticians compiled a database of “the known pregnant”—people who had signed up for baby registries. Then they compared the purchases of consumers in that data set to the purchases made by Target shoppers as a whole. (Every Target shopper has a “Guest ID” number, tied to credit card, e-mail address, and other such identifiers.) By analyzing where the pregnant shoppers diverged the most from the general data set, they identified “signals” of pregnancy-related purchases.


pages: 413 words: 119,587

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

"Robert Solow", A Declaration of the Independence of Cyberspace, AI winter, airport security, Apple II, artificial general intelligence, Asilomar, augmented reality, autonomous vehicles, basic income, Baxter: Rethink Robotics, Bill Duvall, bioinformatics, Brewster Kahle, Burning Man, call centre, cellular automata, Chris Urmson, Claude Shannon: information theory, Clayton Christensen, clean water, cloud computing, collective bargaining, computer age, computer vision, crowdsourcing, Danny Hillis, DARPA: Urban Challenge, data acquisition, Dean Kamen, deskilling, don't be evil, Douglas Engelbart, Douglas Engelbart, Douglas Hofstadter, Dynabook, Edward Snowden, Elon Musk, Erik Brynjolfsson, factory automation, From Mathematics to the Technologies of Life and Death, future of work, Galaxy Zoo, Google Glasses, Google X / Alphabet X, Grace Hopper, Gunnar Myrdal, Gödel, Escher, Bach, Hacker Ethic, haute couture, hive mind, hypertext link, indoor plumbing, industrial robot, information retrieval, Internet Archive, Internet of things, invention of the wheel, Jacques de Vaucanson, Jaron Lanier, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, knowledge worker, Kodak vs Instagram, labor-force participation, loose coupling, Marc Andreessen, Mark Zuckerberg, Marshall McLuhan, medical residency, Menlo Park, Mitch Kapor, Mother of all demos, natural language processing, new economy, Norbert Wiener, PageRank, pattern recognition, pre–internet, RAND corporation, Ray Kurzweil, Richard Stallman, Robert Gordon, Rodney Brooks, Sand Hill Road, Second Machine Age, self-driving car, semantic web, shareholder value, side project, Silicon Valley, Silicon Valley startup, Singularitarianism, skunkworks, Skype, social software, speech recognition, stealth mode startup, Stephen Hawking, Steve Ballmer, Steve Jobs, Steve Wozniak, Steven Levy, Stewart Brand, strong AI, superintelligent machines, technological singularity, Ted Nelson, telemarketer, telepresence, telepresence robot, Tenerife airport disaster, The Coming Technological Singularity, the medium is the message, Thorstein Veblen, Turing test, Vannevar Bush, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, William Shockley: the traitorous eight, zero-sum game

Joining Kurzweil are a diverse group of scientists and engineers who believe that once they have discovered the mechanism underlying the biological human neuron, it will be simply a matter of scaling it up to create an AI. Jeff Hawkins, a successful Silicon Valley engineer who had founded Palm Computing with Donna Dubinsky, coauthored On Intelligence in 2004, which argued that the path to human-level intelligence lay in emulating and scaling up neocortex-like circuits capable of pattern recognition. In 2005, Hawkins formed Numenta, one of a growing list of AI companies pursuing pattern recognition technologies. Hawkins’s theory has parallels with the claims that Kurzweil makes in How to Create a Mind, his 2012 effort to lay out a recipe for intelligence. Similar paths have been pursued by Dileep George, a Stanford-educated artificial intelligence researcher who originally worked with Hawkins at Numenta and then left to form his own company, Vicarious, with the goal of developing “the next generation of AI algorithms,” and Henry Markram, the Swiss researcher who has enticed the European Union into supporting his effort to build a detailed replica of the human brain with one billion euros in funding.

During the past half decade that acceleration has led to rapid improvement in technologies that are necessary components for artificial intelligence: computer vision, speech recognition, and robotic touch and manipulation. Machines now also taste and smell, but recently more significant innovations have come from modeling human neurons in electronic circuits, which has begun to yield advances in pattern recognition—mimicking human cognition. The quickening pace of AI innovation has led some, such as Rice University computer scientist Moshe Vardi, to proclaim the imminent end of a very significant fraction of all tasks performed by humans, perhaps as soon as 2045.2 Even more radical voices argue that computers are evolving at such a rapid pace that they will outstrip the intellectual capabilities of humans in one, or at the most two more generations.

When the acquisition was first reported it was rumored that because of the power and implications of the technology Google would set up an “ethics board” to evaluate any unspecified “advances.”51 It has remained unclear whether such oversight will be substantial or whether it was just a publicity stunt to hype the acquisition and justify its price. It is undeniable that AI and machine-learning algorithms have already had world-transforming application in areas as diverse as science, manufacturing, and entertainment. Examples range from machine vision and pattern recognition essential in improving quality in semiconductor design and so-called rational drug discovery algorithms, which systematize the creation of new pharmaceuticals, to government surveillance and social media companies whose business model is invading privacy for profit. The optimists hope that potential abuses will be minimized if the applications remain human-focused rather than algorithm-centric.


pages: 385 words: 99,985

Pattern Recognition by William Gibson

carbon-based life, Frank Gehry, lateral thinking, Mars Rover, Maui Hawaii, offshore financial centre, old-boy network, pattern recognition

WILLIAM GIBSON PATTERN RECOGNITION 1. THE WEBSITE OF DREADFUL NIGHT Five hours' New York jet lag and Cayce Pollard wakes in Camden Town to the dire and ever-circling wolves of disrupted circadian rhythm. It is that flat and spectral non-hour, awash in limbic tides, brainstem stirring fitfully, flashing inappropriate reptilian demands for sex, food, sedation, all of the above, and none really an option now. Not even food, as Damien's new kitchen is as devoid of edible content as its designers' display windows in Camden High Street. Very handsome, the upper cabinets faced in canary-yellow laminate, the lower with lacquered, unstained apple-ply. Very clean and almost entirely empty, save for a carton containing two dry pucks of Weetabix and some loose packets of herbal tea. Nothing at all in the German fridge, so new that its interior smells only of cold and long-chain monomers.

Something completed years ago, and meted out now, for some reason, in these snippets? She hasn't gone to the forum. Spoilers. She wants each new fragment to impact as cleanly as possible. Parkaboy says you should go to new footage as though you've seen no previous footage at all, thereby momentarily escaping the film or films that you've been assembling, consciously or unconsciously, since first exposure. Homo sapiens is about pattern recognition, he says. Both a gift and a trap. She slowly depresses the plunger. Pours coffee into a mug. She's draped her jacket cape-style round the smooth shoulders of one robotic nymph. Balanced on its stainless pubis, the white torso reclines against the gray wall. Neutral regard. Eyeless serenity. Five in the evening and she can barely keep her eyes open. Lifts her cup of black unsweetened coffee.

Fully imagined cultural futures were the luxury of another day, one in which 'now' was of some greater duration. For us, of course, things can change so abruptly, so violently, so profoundly, that futures like our grandparents' have insufficient 'now' to stand on. We have no future because our present is too volatile." He smiles, a version of Tom Cruise with too many teeth, and longer, but still very white. "We have only risk management. The spinning of the given moment's scenarios. Pattern recognition." Cayce blinks. "Do we have a past, then?" Stonestreet asks. "History is a best-guess narrative about what happened and when," Bigend says, his eyes narrowing. "Who did what to whom. With what. Who won. Who lost. Who mutated. Who became extinct." "The future is there," Cayce hears herself say, "looking back at us. Trying to make sense of the fiction we will have become. And from where they are, the past behind us will look nothing at all like the past we imagine behind us now."


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Total Recall: How the E-Memory Revolution Will Change Everything by Gordon Bell, Jim Gemmell

airport security, Albert Einstein, book scanning, cloud computing, conceptual framework, Douglas Engelbart, full text search, information retrieval, invention of writing, inventory management, Isaac Newton, John Markoff, lifelogging, Menlo Park, optical character recognition, pattern recognition, performance metric, RAND corporation, RFID, semantic web, Silicon Valley, Skype, social web, statistical model, Stephen Hawking, Steve Ballmer, Ted Nelson, telepresence, Turing test, Vannevar Bush, web application

International Technology Roadmap for Semiconductors Web site. http://www.itrs.net Disk Storage Roadmaps are available from individual vendors and market intelligence firms like IDC that show nearly annual doubling of disk densities. Arai, Masayuki. 2009. “Optical Disks Used for Long-Term Storage by 2010.” Tech-On! (March 6). Rydning, John, and Jeff Janukowicz. 2009. “Worldwide Hard Disk Drive Component 2008-2012 Forecast Update.” IDC (February 1). There is a large research community advancing work on data mining, pattern recognition, and machine learning. Here are just a few starting points: Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. New York: Springer. Kargupta, H., et al. (eds.). 2009. Next Generation of Data Mining. London: Chapman and Hall. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. International Conference on Data Mining (ICDM). SIAM International Conference on Data Mining. Total Recall predicts the future based on technology trends.

Earlier, I pointed out that it was a fallacy to worry about having enough time to watch your whole life. An individual would never want to watch his whole life, and knows what he may want to look for in his e-memories. But for the historian it truly is a challenge, because a historian doesn’t know what to search for or what can safely be ignored, having not lived the life in question. Thus, historians will become more and more adept at using data mining and pattern recognition, and will come to demand the latest in tools for comparing videos, performing handwriting recognition, converting speech to text, classifying background noise, and much more. They will rely on computing power to help summarize, classify, and identify anomalies, so that they can safely pass over their subject’s typical commute to work but not miss the one where she made an unusual stop. Many hours of the subject’s life may be classified as “reading,” during which time the title of what she reads should usually suffice.

See also files-and-folders organization automatic summarization categorization schemes and clutter and data analysis and DSpace and electronic memory and implementation of Total Recall and indexing and lifelong learning and lifetime periods and scanned documents Ornish, Dean Orwell, George OS X, Otlet, Paul Outlook ownership of data . See also law and legal issues Ozzie, Ray P pacemakers Palm paperless environment and digitizing data and implementation of Total Recall and organization and origin of MyLifeBits and scanners parenting passwords patents pattern recognition PDF files and books and born-digital documents and data portability and file names and health data and optical character recognition and scanning documents pedometers peer-to-peer encryption pen scanners Pensieve Pepys project performance analysis persistence of memories personal computers (PCs) and desktop search and e-textbooks and enjoying e-memories and implementation of Total Recall and information availability and memex and miniaturization and unified communications personal data.


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The Age of the Infovore: Succeeding in the Information Economy by Tyler Cowen

Albert Einstein, Asperger Syndrome, business cycle, Cass Sunstein, cognitive bias, David Brooks, en.wikipedia.org, endowment effect, Flynn Effect, framing effect, Google Earth, impulse control, informal economy, Isaac Newton, loss aversion, Marshall McLuhan, Naomi Klein, neurotypical, new economy, Nicholas Carr, pattern recognition, phenotype, placebo effect, Richard Thaler, selection bias, Silicon Valley, social intelligence, the medium is the message, The Wealth of Nations by Adam Smith, theory of mind

Atonal music tends to have less structure at the larger scales of sonic organization—at least in terms of orders that most people can enjoy—and so to unappreciative listeners it sounds like random noise. You won’t find traditional harmonies or hummable tunes. With additional listening to a piece, repeated motifs often become apparent, but they’re hard to find and they don’t satisfy most people in the way that the progressive elucidation of a Haydn motif does, much less a Buddy Holly song. So understanding or appreciating the structure of atonal music requires some special skills of pattern recognition. The atonal music fans may be better at some kinds of cognitive processing, and better at constructing some kinds of hierarchies from their sensory input, relative to typical listeners. When viewed through this lens of neurology, many of the critical dialogues about contemporary music seem beside the point, as they don’t get at the real source of the difference of opinion. Diana Raffman, a philosopher at the University of Toronto, wrote an essay that surveys the cognitive difficulties behind appreciating atonal music and she concludes that it, as music, must not be an acceptable art form.

The aesthetic lushness of the world will be increasingly distributed into baroque nooks and crannies, in a manner that would honor a Borges short story. It’s not usually put in such terms, but I think of art connoisseurship as a fundamental part of the profile of autistics. Go back to the list of the cognitive strengths of autism in chapter 2. Autistics have, on average, superior visual perception, a better-developed sense of pitch, superior abilities for pattern recognition, and superior abilities for spotting details in visual pictures, compared to non-autistics. Yet, as discussed in chapter 6, autistics may be less skilled at enjoying some kinds of fictional narrative. Dr. Hans Asperger saw the aesthetic side of autistics clearly. He wrote: Another distinctive trait one finds in some autistic children is a rare maturity of taste in art. Normal children have no time for more sophisticated art.

Living in 2009, we often take ideas about a free society for granted but in fact such ideas have been totally absent throughout most of human history and they still have not taken hold in many parts of our world. An understanding of a free society and its benefits does not come naturally to most human beings and that understanding had to be discovered and communicated by people with some highly atypical minds. On average autistics are better with print-based modes of reasoning than with oral discourse. Many autistics have a strong memory for factual details, strong pattern recognition skills, and an ability to interpret principles of equality very literally and in a cosmopolitan manner. These are exactly the sorts of skills that go into legalistic and constitutional reasoning. Furthermore those skills are especially relevant in appreciating social systems based on written laws, rather than systems based on unspoken or implicit personal favors. One thinker who did very much appreciate the abstract workings of the economy—and a good legal system—was the late economics Nobel laureate Friedrich A.


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Tyler Cowen-Discover Your Inner Economist Use Incentives to Fall in Love, Survive Your Next Meeting, and Motivate Your Dentist-Plume (2008) by Unknown

airport security, Andrei Shleifer, big-box store, British Empire, business cycle, cognitive dissonance, cross-subsidies, fundamental attribution error, George Santayana, haute cuisine, market clearing, microcredit, money market fund, pattern recognition, Ralph Nader, Stephen Hawking, The Wealth of Nations by Adam Smith, trade route, transaction costs

Our views of the world, and even (especially!) our views of ourselves, are rife with fallacies that can be seen through the lens of good economics. Your Inner Economist sees patterns that you might not be seeing at first glance. This book is about uncovering these hidden patterns in the world, and in our choices. Pattern recognition is one key to making better decisions. If it isn't helping us see more patterns, it isn't good economics. The psychologist A. deGroot performed some fascinating experiments with pattern recognition in the context of chess. He arranged chess pieces on a board as they would occur in the normal course of a I Want a Banana; I Buy One I 9 game. He allowed both chess masters and chess novices to observe the arrangement. The pieces were then swept away and the masters and novices were both asked to re-create the placement of the pieces.

Yet others build ever more complicated mathematical models of human behavior, sometimes under the heading of "game theory." Economists in think tanks agitate for one public policy over another. At a simpler level, many textbooks teach basic terminology and how to manipulate graphs and symbols. This book will do none of those things. Instead I will focus on using economics to hone our skills of pattern recognition-in other words, how to discover our Inner Economists. In most real-world settings, we don't have the time or inclination to "crunch the numbers," even in the unlikely event that all the data stood before us (do you know your dentist's income-tax return, as needed to calculate his optimal bonus for good work?). We have to make rapid decisions, yet we want to make the best decisions possible.

See also relationships Mason &- Dixon (Pynchon), 65 McGrew, Bob, 38 meetings, 42-45 Me Factor and art, 53-55 and attention, 74 and books, 66 and music, 67, 71-72 and truthfulness, 106 242 men, 79-85, Ill, 179 meritocracies, 18, 123 Metropolitan Museum of Art, 51-52 1\letropolitan Opera, New York City, 191 Mexico and Mexicans kidnappers and kidnapping in, 168 labor market, 149-50, 151 Mexican cuisine, 148, 151, 154, 159 prostitution, 169 and volunteerism, 186 micro-credit, 214-17 micro-expressions, 105 Miller, Henry, 65 modesty, I 14 Mona Lisa, 57, 60 Mondrian, Piet, 54, 57 money, 11-29 applying parables, 22-29 Car Salesman Parable, 16,22,26,45 cash gifts, 212-13 and cooperation, 19-21 and cultural consumption, 48 Dirty Dishes Parable, 13-16, 26 and effort required, 22-24, 26 and helplessness, 29 as incentive, 11-13, 16,22-29 and intrinsic motivation, 24-25, 26 limitations of, 3 micro-credit, 214-17 Parking Tickets Parable, 16-22, 33,45 and scarcity, 47-48 and shopping, 124-25 and social approval, 25-26 See also incentives monkeys, 163-64 monkfish, 140-41 motivation and altruism, 187, 202 for book purchases, 64-65 complexity of, 14 and incentives, 2, 32, 33 intrinsic motivation, 24-25, 26, 32, 33-34,46 and markets, 3 Index for recipes, 160-62 role in economic theory, 5 of writers, 64 movies, 73-74 museums, 55, 56, 59 music, 58-59,66-72, 76 Muslims, 116 narratives, 75-76, 89,136-37,183 national health insurance, 134-36 Netherlands, 147 New Orleans, Louisiana, 89 New York, 152, 153, 159 New Zealand, 149 9/11 terrorist attacks, 198 Nirvana, 68, 71 Norway, 20-21, 150, 186 Nostromo (Conrad), 62 Oaxaca, Mexico, 154 Oberholzer-Gee, Felix, 178-79 online computer games, 173, 177 O'Sullivan, Maureen, 106 Oswald, Andrew, 179-80 Paglia, Camille, 65 PAL (Personal Assistance Link), 43 parenting, 88,122-23,130-31,179 Parker, Randall, 43 Parking Tickets Parable, 16-22,33,45 pattern recognition, 8- 10 penalties. See incentives perceptions, 18-19, 34, 208 performance in capitalist economies, 123 and education, 24-25, 86, 122-23, 125 motivation to perform, 12, 13-16, 33, 38-39 Perkins, H. Wesley, 34 personal ads, 96-99 Ph.D. title, 108, 109-10 Philippines, 167-68 pickup lines, 83-85 politics, 115, 134 Index pornography, 182 possessions, 76-77 Postcard Test, 7 poverty and altruism, 197-98,200,201, 206 and begging, 187-92, 198,201-2 and food quality, 148-50 Pralec, Drazen, 106 Presley, Elvis, 67-68 pride, 116-17, 131-32, 167 prison life, 167 probabilities, 127-28 Prof Scam (Sykes), 6 prostitution, 169 punctuality, 34-37, 38 punishments.


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Machine Learning for Hackers by Drew Conway, John Myles White

call centre, centre right, correlation does not imply causation, Debian, Erdős number, Nate Silver, natural language processing, Netflix Prize, p-value, pattern recognition, Paul Erdős, recommendation engine, social graph, SpamAssassin, statistical model, text mining, the scientific method, traveling salesman

In machine learning, the learning occurs by extracting as much information from the data as possible (or reasonable) through algorithms that parse the basic structure of the data and distinguish the signal from the noise. After they have found the signal, or pattern, the algorithms simply decide that everything else that’s left over is noise. For that reason, machine learning techniques are also referred to as pattern recognition algorithms. We can “train” our machines to learn about how data is generated in a given context, which allows us to use these algorithms to automate many useful tasks. This is where the term training set comes from, referring to the set of data used to build a machine learning process. The notion of observing data, learning from it, and then automating some process of recognition is at the heart of machine learning and forms the primary arc of this book.

The best machine learning practitioners are those with both practical and theoretical experience, so we encourage you to go out and develop both. Along the way, have fun hacking data. You have a lot of powerful tools, so go apply them to questions you’re interested in! Works Cited Books [Adl10] JosephAdler. R in a Nutshell. O’Reilly Media, 2010. [Abb92] EdwinAAbbot Flatland: A Romance of Many Dimensions. Dover Publications, 1992. [Bis06] ChristopherMBishop Pattern Recognition and Machine Learning. Springer; 1st ed. 2006. Corr.; 2nd printing ed. 2007. [GH06] AndrewGelmanJenniferHill. Data Analysis Using Regression and Multilevel/Hierarchical Models. Cambridge University Press, 2006. [HTF09] TrevorHastieRobertTibshiraniJeromeFriedman. The Elements of Statistical Learning. Springer, 2009. [JMR09] OwenJonesRobertMaillardetAndrewRobinson. Introduction to Scientific Programming and Simulation Using R.

gamma distribution, Exploratory Data Visualization, Exploratory Data Visualization Gaussian distribution, Exploratory Data Visualization (see bell curve) geom_density function, Exploratory Data Visualization (see also density plot) geom_histogram function, Aggregating and organizing the data, Exploratory Data Visualization (see also histogram) geom_line function, Analyzing the data geom_point function, Visualizing the Relationships Between Columns (see also scatterplot) geom_smooth function, Visualizing the Relationships Between Columns, Linear Regression in a Nutshell, Predicting Web Traffic, Defining Correlation, Nonlinear Relationships Between Columns: Beyond Straight Lines, Introducing Polynomial Regression geom_text function, Exploring senator MDS clustering by Congress Gephi software, Analyzing Twitter Networks, Visualizing the Clustered Twitter Network with Gephi, Visualizing the Clustered Twitter Network with Gephi get.edgelist function, Building Your Own “Who to Follow” Engine getURL function, Working with the Google SocialGraph API ggplot object, Aggregating and organizing the data ggplot2 package, Loading and Installing R Packages, Loading libraries and the data, Aggregating and organizing the data, Analyzing the data, Analyzing the data, Further Reading on R, Analyzing US Senator Roll Call Data (101st–111th Congresses), Exploring senator MDS clustering by Congress (see also specific functions) MDS results using, Analyzing US Senator Roll Call Data (101st–111th Congresses) plotting themes of, Analyzing the data resources for, Further Reading on R two plots using, Exploring senator MDS clustering by Congress ggsave function, Aggregating and organizing the data, Analyzing the data glm function, SVMs: The Support Vector Machine, SVMs: The Support Vector Machine glmnet function, Preventing Overfitting with Regularization, Preventing Overfitting with Regularization, Text Regression, Logistic Regression to the Rescue, Comparing Algorithms glmnet package, Loading and Installing R Packages, Preventing Overfitting with Regularization global optimum, Introduction to Optimization Goffman, Erving (social scientist), Social Network Analysis regarding nature of human interaction, Social Network Analysis Google, Priority Features of Email, Priority Features of Email, Hacking Twitter Social Graph Data priority inbox by, Priority Features of Email, Priority Features of Email SocialGraph API, Hacking Twitter Social Graph Data (see SGA) gradient, Introduction to Optimization graph.coreness function, Local Community Structure GraphML files, Local Community Structure grepl function, Functions for Extracting the Feature Set, Functions for Extracting the Feature Set, Functions for Extracting the Feature Set, Working with the Google SocialGraph API grid search, Introduction to Optimization, Introduction to Optimization gsub function, Organizing location data, Functions for Extracting the Feature Set H hacker, Machine Learning for Hackers hclust function, Local Community Structure head function, Loading libraries and the data heavy-tailed distribution, Exploratory Data Visualization, Exploratory Data Visualization Heider, Fritz (psychologist), Building Your Own “Who to Follow” Engine social balance theory by, Building Your Own “Who to Follow” Engine help, for R language, Loading libraries and the data help.search function, Loading libraries and the data hierarchical clustering of node distances, Local Community Structure, Visualizing the Clustered Twitter Network with Gephi histogram, Aggregating and organizing the data, Aggregating and organizing the data, Exploratory Data Visualization, Exploratory Data Visualization I IDEs, for R, IDEs and Text Editors ifelse function, Converting date strings and dealing with malformed data igraph library, Analyzing Twitter Networks igraph package, Loading and Installing R Packages, Working with the Google SocialGraph API, Local Community Structure install.packages function, Loading and Installing R Packages inv.logit function, Logistic Regression to the Rescue is.character function, Inferring the Types of Columns in Your Data is.factor function, Inferring the Types of Columns in Your Data is.na function, Dealing with data outside our scope, Aggregating and organizing the data is.numeric function, Inferring the Types of Columns in Your Data J jittering, This or That: Binary Classification K k-core analysis, Local Community Structure, Local Community Structure k-nearest neighbors algorithm, The k-Nearest Neighbors Algorithm (see kNN algorithm) KDE (kernel density estimate), Exploratory Data Visualization (see density plot) kernel trick, SVMs: The Support Vector Machine (see SVM (support vector machine)) kNN (k-nearest neighbors) algorithm, The k-Nearest Neighbors Algorithm, R Package Installation Data, R Package Installation Data, R Package Installation Data, Comparing Algorithms comparing to other algorithms, Comparing Algorithms R package installation case study using, R Package Installation Data, R Package Installation Data knn function, The k-Nearest Neighbors Algorithm Königsberg Bridge problem, Social Network Analysis L L1 norm, Preventing Overfitting with Regularization L2 norm, Preventing Overfitting with Regularization label features, of email, Priority Features of Email labels, compared to factors, Inferring the Types of Columns in Your Data Lambda, for regularization, Preventing Overfitting with Regularization, Preventing Overfitting with Regularization, Text Regression, Ridge Regression lapply function, Organizing location data, Aggregating and organizing the data, Functions for Extracting the Feature Set, Analyzing US Senator Roll Call Data (101st–111th Congresses), Exploring senator MDS clustering by Congress length function, Converting date strings and dealing with malformed data library function, Loading and Installing R Packages line plot, Analyzing the data line, in scatterplot, Linear Regression in a Nutshell linear kernel SVM, SVMs: The Support Vector Machine, Comparing Algorithms linear regression, R for Machine Learning, Introducing Regression, Linear Regression in a Nutshell, The Baseline Model, The Baseline Model, Regression Using Dummy Variables, Regression Using Dummy Variables, Linear Regression in a Nutshell, Linear Regression in a Nutshell, Linear Regression in a Nutshell, Linear Regression in a Nutshell, Predicting Web Traffic, Predicting Web Traffic, Predicting Web Traffic, Defining Correlation, Defining Correlation, Defining Correlation, Nonlinear Relationships Between Columns: Beyond Straight Lines, Nonlinear Relationships Between Columns: Beyond Straight Lines, Introduction to Optimization, Introduction to Optimization adapting for nonlinear relationships, Nonlinear Relationships Between Columns: Beyond Straight Lines, Nonlinear Relationships Between Columns: Beyond Straight Lines assumptions in, Linear Regression in a Nutshell, Linear Regression in a Nutshell baseline model for, The Baseline Model, The Baseline Model correlation as indicator of, Defining Correlation, Defining Correlation dummy variables for, Regression Using Dummy Variables, Regression Using Dummy Variables lm function for, R for Machine Learning, Linear Regression in a Nutshell, Linear Regression in a Nutshell, Predicting Web Traffic, Defining Correlation, Introduction to Optimization optimizing, Introduction to Optimization web traffic predictions case study using, Predicting Web Traffic, Predicting Web Traffic linearity assumption, Linear Regression in a Nutshell, Linear Regression in a Nutshell Linux, installing R language on, Linux list structure, Organizing location data list.files function, Analyzing US Senator Roll Call Data (101st–111th Congresses) lm function, R for Machine Learning, Linear Regression in a Nutshell, Linear Regression in a Nutshell, Predicting Web Traffic, Defining Correlation, Introduction to Optimization load function, Comparing Algorithms loading data, Loading libraries and the data (see data, loading) log base-10 transformation, A log-weighting scheme log function, A log-weighting scheme log-transformations, A log-weighting scheme log-weighting scheme, A log-weighting scheme, A log-weighting scheme log1p function, A log-weighting scheme logarithms, A log-weighting scheme logistic regression, Logistic Regression to the Rescue, Logistic Regression to the Rescue, The k-Nearest Neighbors Algorithm, SVMs: The Support Vector Machine, SVMs: The Support Vector Machine, SVMs: The Support Vector Machine, SVMs: The Support Vector Machine, Comparing Algorithms comparing to other algorithms, Comparing Algorithms glm function for, SVMs: The Support Vector Machine, SVMs: The Support Vector Machine when not to use, The k-Nearest Neighbors Algorithm, SVMs: The Support Vector Machine, SVMs: The Support Vector Machine lubridate package, Unsupervised Learning M Mac OS X, installing R language on, Mac OS X machine learning, Machine Learning for Hackers, How This Book Is Organized, How This Book Is Organized, Using R, Using R, Works Cited, Works Cited compared to statistics, Using R as pattern recognition algorithms, Using R resources for, How This Book Is Organized, Works Cited, Works Cited malformed data, Converting date strings and dealing with malformed data, Converting date strings and dealing with malformed data match function, Dealing with data outside our scope matrices, What Is Data?, Writing Our First Bayesian Spam Classifier, A Brief Introduction to Distance Metrics and Multidirectional Scaling, A Brief Introduction to Distance Metrics and Multidirectional Scaling, A Brief Introduction to Distance Metrics and Multidirectional Scaling conversions to, Writing Our First Bayesian Spam Classifier data as, What Is Data?


pages: 308 words: 84,713

The Glass Cage: Automation and Us by Nicholas Carr

Airbnb, Airbus A320, Andy Kessler, Atul Gawande, autonomous vehicles, Bernard Ziegler, business process, call centre, Captain Sullenberger Hudson, Charles Lindbergh, Checklist Manifesto, cloud computing, computerized trading, David Brooks, deliberate practice, deskilling, digital map, Douglas Engelbart, drone strike, Elon Musk, Erik Brynjolfsson, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, global supply chain, Google Glasses, Google Hangouts, High speed trading, indoor plumbing, industrial robot, Internet of things, Jacquard loom, James Watt: steam engine, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, Kevin Kelly, knowledge worker, Lyft, Marc Andreessen, Mark Zuckerberg, means of production, natural language processing, new economy, Nicholas Carr, Norbert Wiener, Oculus Rift, pattern recognition, Peter Thiel, place-making, plutocrats, Plutocrats, profit motive, Ralph Waldo Emerson, RAND corporation, randomized controlled trial, Ray Kurzweil, recommendation engine, robot derives from the Czech word robota Czech, meaning slave, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, TaskRabbit, technoutopianism, The Wealth of Nations by Adam Smith, turn-by-turn navigation, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche

Up until that fateful October day, it was taken for granted that many important skills lay beyond the reach of automation. Computers could do a lot of things, but they couldn’t do everything. In an influential 2004 book, The New Division of Labor: How Computers Are Creating the Next Job Market, economists Frank Levy and Richard Murnane argued, convincingly, that there were practical limits to the ability of software programmers to replicate human talents, particularly those involving sensory perception, pattern recognition, and conceptual knowledge. They pointed specifically to the example of driving a car on the open road, a talent that requires the instantaneous interpretation of a welter of visual signals and an ability to adapt seamlessly to shifting and often unanticipated situations. We hardly know how we pull off such a feat ourselves, so the idea that programmers could reduce all of driving’s intricacies, intangibilities, and contingencies to a set of instructions, to lines of software code, seemed ludicrous.

Psychomotor skills get rusty, which can hamper the pilot on those rare but critical occasions when he’s required to take back the controls. There’s growing evidence that recent expansions in the scope of automation also put cognitive skills at risk. When more advanced computers begin to take over planning and analysis functions, such as setting and adjusting a flight plan, the pilot becomes less engaged not only physically but mentally. Because the precision and speed of pattern recognition appear to depend on regular practice, the pilot’s mind may become less agile in interpreting and reacting to fast-changing situations. He may suffer what Ebbatson calls “skill fade” in his mental as well as his motor abilities. Pilots are not blind to automation’s toll. They’ve always been wary about ceding responsibility to machinery. Airmen in World War I, justifiably proud of their skill in maneuvering their planes during dogfights, wanted nothing to do with the fancy Sperry autopilots.26 In 1959, the original Mercury astronauts rebelled against NASA’s plan to remove manual flight controls from spacecraft.27 But aviators’ concerns are more acute now.

When doctors make diagnoses, they draw on their knowledge of a large body of specialized information, learned through years of rigorous education and apprenticeship as well as the ongoing study of medical journals and other relevant literature. Until recently, it was difficult, if not impossible, for computers to replicate such deep, specialized, and often tacit knowledge. But inexorable advances in processing speed, precipitous declines in data-storage and networking costs, and breakthroughs in artificial-intelligence methods such as natural language processing and pattern recognition have changed the equation. Computers have become much more adept at reviewing and interpreting vast amounts of text and other information. By spotting correlations in the data—traits or phenomena that tend to be found together or to occur simultaneously or sequentially—computers are often able to make accurate predictions, calculating, say, the probability that a patient displaying a set of symptoms has or will develop a particular disease or the odds that a patient with a certain disease will respond well to a particular drug or other treatment regimen.


pages: 396 words: 117,149

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

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

(Unfortunately, as of this writing Google’s self-driving cars still confuse windblown plastic bags with deer.) Often, the procedures are quite simple, and it’s the knowledge at their core that’s complex. If you can tell which e-mails are spam, you know which ones to delete. If you can tell how good a board position in chess is, you know which move to make (the one that leads to the best position). Machine learning takes many different forms and goes by many different names: pattern recognition, statistical modeling, data mining, knowledge discovery, predictive analytics, data science, adaptive systems, self-organizing systems, and more. Each of these is used by different communities and has different associations. Some have a long half-life, some less so. In this book I use the term machine learning to refer broadly to all of them. Machine learning is sometimes confused with artificial intelligence (or AI for short).

In the electron’s case, it flips up if the weighted sum of the neighbors exceeds a threshold and flips (or stays) down otherwise. Inspired by this, he defined a type of neural network that evolves over time in the same way that a spin glass does and postulated that the network’s minimum energy states are its memories. Each such state has a “basin of attraction” of initial states that converge to it, and in this way the network can do pattern recognition: for example, if one of the memories is the pattern of black-and-white pixels formed by the digit nine and the network sees a distorted nine, it will converge to the “ideal” one and thereby recognize it. Suddenly, a vast body of physical theory was applicable to machine learning, and a flood of statistical physicists poured into the field, helping it break out of the local minimum it had been stuck in.

The economist Milton Friedman even argued in a highly influential essay that the best theories are the most oversimplified, provided their predictions are accurate, because they explain the most with the least. That seems to me like a bridge too far, but it illustrates that, counter to Einstein’s dictum, science often progresses by making things as simple as possible, and then some. No one is sure who invented the Naïve Bayes algorithm. It was mentioned without attribution in a 1973 pattern recognition textbook, but it only took off in the 1990s, when researchers noticed that, surprisingly, it was often more accurate than much more sophisticated learners. I was a graduate student at the time, and when I belatedly decided to include Naïve Bayes in my experiments, I was shocked to find it did better than all the other algorithms I was comparing, save one—luckily, the algorithm I was developing for my thesis, or I might not be here now.


pages: 389 words: 119,487

21 Lessons for the 21st Century by Yuval Noah Harari

1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, Bernie Sanders, bitcoin, blockchain, Boris Johnson, call centre, Capital in the Twenty-First Century by Thomas Piketty, carbon-based life, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, deglobalization, Donald Trump, failed state, Filter Bubble, Francis Fukuyama: the end of history, Freestyle chess, gig economy, glass ceiling, Google Glasses, illegal immigration, Intergovernmental Panel on Climate Change (IPCC), Internet of things, invisible hand, job automation, knowledge economy, liberation theology, Louis Pasteur, low skilled workers, Mahatma Gandhi, Mark Zuckerberg, mass immigration, means of production, Menlo Park, meta analysis, meta-analysis, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-work, purchasing power parity, race to the bottom, RAND corporation, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, transatlantic slave trade, Tyler Cowen: Great Stagnation, universal basic income, uranium enrichment, Watson beat the top human players on Jeopardy!, zero-sum game

., ‘Communication Mediated through Natural Language Generation in Big Data Environments: The Case of Nomao’, Journal of Computer and Communication 5 (2017), 125–48; facial recognition: Florian Schroff, Dmitry Kalenichenko and James Philbin, ‘FaceNet: A Unified Embedding for Face Recognition and Clustering’, IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2015), 815–23; and driving: Cristiano Premebida, ‘A Lidar and Vision-based Approach for Pedestrian and Vehicle Detection and Tracking’, 2007 IEEE Intelligent Transportation Systems Conference (2007). 3 Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus & Giroux, 2011); Dan Ariely, Predictably Irrational (New York: Harper, 2009); Brian D. Ripley, Pattern Recognition and Neural Networks (Cambridge: Cambridge University Press, 2007); Christopher M. Bishop, Pattern Recognition and Machine Learning (New York: Springer, 2007). 4 Seyed Azimi et al., ‘Vehicular Networks for Collision Avoidance at Intersections’, SAE International Journal of Passenger Cars – Mechanical Systems 4 (2011), 406–16; Swarun Kumar et al., ‘CarSpeak: A Content-Centric Network for Autonomous Driving’, SIGCOM Computer Communication Review 42 (2012), 259–70; Mihail L.

In the last few decades research in areas such as neuroscience and behavioural economics allowed scientists to hack humans, and in particular to gain a much better understanding of how humans make decisions. It turned out that our choices of everything from food to mates result not from some mysterious free will, but rather from billions of neurons calculating probabilities within a split second. Vaunted ‘human intuition’ is in reality ‘pattern recognition’.3 Good drivers, bankers and lawyers don’t have magical intuitions about traffic, investment or negotiation – rather, by recognising recurring patterns, they spot and try to avoid careless pedestrians, inept borrowers and dishonest crooks. It also turned out that the biochemical algorithms of the human brain are far from perfect. They rely on heuristics, shortcuts and outdated circuits adapted to the African savannah rather than to the urban jungle.

N. 310, 315–16 Good Samaritan parable 57 Google 31, 36, 39, 40, 41, 48, 53–4, 68, 77, 78, 90, 91, 178, 282; Glass 92; Maps 54, 55; Translate 262 gorilla 94–5, 98 Gove, Michael 46 Great Barrier Reef, Australian 116 Great Depression 251 Great Ukrainian Famine (1932–3) 33, 238 Greece 13, 154–5, 181; ancient 52, 95–6, 177, 181, 182, 291 greenhouse gases 117, 119 groupthink 218–20, 230 Guardian 306 Guevara, Che 9–10, 11, 133 Gulf War, First (1990–91) 172, 174 Haber, Fritz 194, 195 Haiti 150 Hamas 173 HaMevaser 97 Hammurabi 188 Hamodia 97 happiness xiv, 41–2, 201, 202, 211, 245, 251, 252, 273, 309 Harry Potter 234 Hastings, Battle of (1066) 178–9 Hayek, Friedrich 130, 131 healthcare 11, 16, 40, 112; AI and 22–3, 24–5, 28, 48–9, 50, 106–7; basic level of 41; religion and 128, 129 Hebrew Old Testament 184–96 Hillel the Elder, Rabbi 190 Hinduism 105, 108, 127, 129, 131, 133, 134, 181, 186, 191, 200, 203, 208, 235, 269– 70, 278, 283–4, 285, 291 Hirohito, Emperor of Japan 235 Hiroshima, atomic bombing of (1945) 112, 115, 178 Hitler, Adolf 9, 11, 66–7, 96, 108, 178, 211, 231, 237, 295 Holocaust 184, 236, 248, 272, 293 Holocene 116 hominids 110, 122 Homo Sapiens: communities, size of 90, 110, 111; disappearance of 122; emergence of 185; emotions and decision-making 58; as post-truth species 233, 238–9, 242; religion and 185, 188, 198; as storytelling animal 269, 275; superhumans and 41, 75, 246; working together in groups 218 homosexuality 50, 61, 135, 200, 205–6, 300 Hsinbyushin, King of Burma 305 Hugh of Lincoln 235–6 human rights xii, 4, 11, 15, 44, 93, 95, 96, 101, 211–12, 306 human sacrifice 289 humility 180, 181–96; ethics before the Bible and 186–90; Jews/Judaism and 182–96; monotheism and 190–3; religion and 181–96; science and 193–5 Hungary 169 hunter-gatherers 73, 100, 108, 111, 147, 187, 218, 224, 226, 230 Hussein, Saddam 180 Huxley, Aldous: Brave New World 251–5 IBM 23, 29, 30–1 identity, mass: religion and 133–7 ignorance 217–22; individuality and 218, 219–20; knowledge illusion, the 218–19; power and 220–2; rationality and 217–18 illness 48–9, 69, 129, 134 immigration xi, 4, 16, 93–4, 108, 115, 138, 139–55 imperialism 9, 10, 11, 14, 15, 63, 79, 106, 136, 145, 178, 191–2, 212 Inca 289 incest, secular ethics and 205, 206 India 4, 10, 15, 39, 74, 76, 100, 106, 109, 113, 115, 127, 131, 181, 182, 183, 184, 191, 192, 193, 260, 266, 285–6, 310, 315 Indian Pala Empire 139 individuality: AI and 22, 23, 27; myth of 218–20 Indonesia 10, 14, 26, 102–3, 105 Industrial Revolution 16, 19, 33, 34, 74, 186, 266 inequality see equality information technology/infotech xii, 1, 6, 7, 16, 17, 21, 33, 48, 66, 80–1, 83, 120–1, 176 inorganic life, creation of xii, 78, 122, 246 Inoue, Nissho 305 Inside Out (movie) 249–51, 267 Instagram 301 Institution of Mechanical Engineers 118 intelligence, consciousness and ix, 68–70, 122, 245–6 see also artificial intelligence (AI) International Olympic Committee (IOC) 104 Internet 6, 15, 40, 65, 71, 73, 88, 122, 232, 246, 263 intuition 20–1, 47 Iran 90, 94, 106, 107–8, 120, 130–1, 134, 135, 137, 138, 139, 173, 181, 200, 289 Iran-Iraq War (1980) 173 Iraq 5, 13, 94, 106, 159, 165, 172, 173, 174, 175, 177, 178, 210, 219, 288, 295, 296 Islam xi, 13, 15, 17–18, 87, 93–4, 96, 97–8, 101, 106, 107, 126, 127, 133, 137, 161, 168, 169, 177, 181, 183, 184, 186, 191, 196, 199, 248, 289, 295, 296 Islamic fundamentalism 87, 93–4, 97–8, 101, 106, 107, 161, 177, 188, 191, 199, 248, 295, 296 Islamic State 93, 94, 97–8, 101, 106, 107, 177, 188, 191, 199, 248, 295, 296 Isonzo, tenth Battle of the (1917) 160 Israel 15, 42–3, 64–5, 88, 97, 101, 103, 106, 107, 108, 111, 122, 130, 131, 134, 135, 137, 138, 141–2, 160, 163, 173–4, 182, 183–4, 186, 189, 190, 191, 221, 224, 233, 242, 272, 274–5, 277, 290, 294 Israel Defence Forces (IDF) 173 Italy 38, 103, 172, 173, 179, 251, 292, 294, 295 Ivory Coast 103, 188 Japan 13, 54, 107, 119, 120, 135–7, 139, 148, 161, 162, 171, 173, 179–80, 182, 184, 186, 232, 235, 251, 285, 305–6 Jerusalem 15, 57, 165, 183, 239, 274, 279, 298 Jesus 108, 128, 131, 133, 187, 190, 212–13, 237, 283, 284, 289, 291–2, 306 Jewish Enlightenment 194 Jewish Great Revolt (66–70 ad) 239 Jews/Judaism 8, 15, 42–3, 57, 58, 96–7, 131, 132, 134, 137, 138, 142, 182–7, 188, 189–96, 208, 233, 235–6, 239, 272, 273–4, 279, 284, 289–90 jobs: AI and xiii, 8, 18, 19–43, 59–60, 63, 109, 259–68; education and 259–68; immigration and 139, 141, 149, 152 Johnson, Boris 46 Johnson, Lyndon B. 113, 114 Joinville, Jean de 296 Juche 137 Judah 189 judiciary 4, 44 justice xiii, 188–9, 222, 223–30, 270, 277, 288; complex nature of modern world and 223– 8; effort to know and 224; morality of intentions and 225–6; roots of sense of 223 kamikaze 136 Kanad, Acharya 181 Kant, Immanuel 58–9, 60 Karbala, Iraq 288, 289 Kasparov, Garry 29, 31 KGB 48, 299 Khan, Genghis 175, 179 Khomeini, Ayatollah 130, 131, 288 Kidogo (chimpanzee) 187–8 Kim Il-sung 137 Kim Jong-Il 180 Kim Jong-Un 64 Kinsey scale 50 Kiribati, Republic of 119, 120 Kita, Ikki 305 knowledge illusion, the 218–19 Korea 104, 135, 137, 171, 180, 275, 285 Labour Party 45 Laozi 190, 267 Leave campaign 46 Lebanon 173 Leviticus 189 LGBT 135, 200 liberalism/liberal democracy xii, xiv, xv, 3–4, 33, 46, 55, 141, 154–5, 210, 217, 237, 301; AI and 6–9, 17–8, 43, 44–72, 217, 220, 230; alternatives to 5, 11–15, 17; birth of 33; choice and 45–6, 297–300; crisis/loss of faith in x, xi, xiv–xv, 1, 3–18, 44, 45, 46, 55, 141; crises faced by, periodic 9–18; education and 261;elections/referendums and 45–6, 210–11; equality and 74, 76, 80; immigration and 4, 138, 141, 142–3, 144; individual, faith in 44–9, 55, 217, 220, 230, 297–302; liberty and 44–72; meaning of life stories and 297–302; nationalism and 11, 14–15, 112; reinvention of 16–17; secular ethics and 210 Liberation Theology 133 liberty xii, xiii, 3, 4, 10–11, 17, 44–72, 83, 108, 204, 211, 299; AI future development and 68–72; authority shift from humans to AI 43, 44–72, 78, 268; decision-making and 47–61; digital dictatorships and 61–8; free will/feelings, liberal belief in 44–6 Libya 5, 172, 173 life expectancy 41, 107, 109, 260, 264, 265 Life of Brian (film) 186, 220 Lincoln Cathedral 236 Lincoln, Abraham 12 Lion King, The (movie) 270–1, 273, 275–6, 297, 299 Lockerbie bombing, Pan Am flight 103 (1988) 160 Lody (chimpanzee) 187–8 logic bombs 77, 178 Louis IX, King of France 296 Louis XIV, King of France 66, 96 Louis XVI, King of France 207 Lucas, George 298 Luhansk People’s Republic 232 machine learning 8, 19, 25, 30, 31, 33, 64, 65, 67, 245, 267, 268 Mahabharata 181 Mahasatipatthana Sutta 303 Mahavira 190 Maimonides 193 Maji Maji Rebellion (1905–7) 239 Mali 229 Manchester Arena bombing (May, 2017) 160 Manchukuo 232 Manchuria 180 Mansoura, Battle of (1250) 296 Markizova, Gelya 237 martyrs 287–9, 295–6 Marx, Karl 94, 130, 131, 133, 209, 210, 213, 246, 248, 262; The Communist Manifesto 262, 273 Marxism 15, 137, 209–10, 213 Marxism-Leninism 12, 137 Mashhad, Iran 289 Mass, Christian ceremony of 283 Matrix, The (movie) 245, 246–8, 249, 255 Maxim gun 178 May, Theresa 114 Maya 186, 193 McIlvenna, Ted 200 meaning xiii–xiv, 269–308; Buddhism and 302–6; stories and 269–83, 291–8, 301–2, 306–8; individual/liberalism and 297–302; rituals and 283–91; romance and 280–1; successful stories 276–7 meat, clean 118–19 media: government control of 12–13; post-truth and 238; terrorism and 166, 167 Meir, Golda 233 Merkel, Angela 95, 96, 97 Mesha Stele 191 Mesopotamia 189 Methodism 200 #MeToo movement xi, 164 Mexican border wall 8 Mexican-American war (1846–48) 172 Mexico 8, 106, 151, 172, 260, 261, 266 Mickiewicz, Adam 307 Middle East 13, 15, 78, 106, 139, 142, 143, 161, 173, 175, 177, 188, 193, 199, 296 Mill, John Stuart 58, 60 Milwaukee County Zoo 187 mind, meditation and 310–18 Mishra, Pankaj 94 Mitsui 305 Moab 191 Modi, Narendra 114, 179 morality see justice Moses, prophet 186–7, 188–9, 274 movies, AI and 51–2, 69, 245–51, 255, 267, 268 Mubarak, Hosni 63 Muhammad, Prophet 15, 94, 181, 182, 187, 288 Mumbai x, 17 Murph (chimpanzee) 188 music, AI and 25–8 Muslims 13, 55, 62, 63, 93–4, 96, 98, 100, 104, 107, 130–3, 134, 143, 145, 148, 150, 152, 153, 165, 172, 181, 184, 185, 190, 191, 193, 200, 203–4, 208, 230, 233, 235, 271–2, 284, 288, 292, 295, 296, 306 Mussolini, Benito 295 My Lai massacre (1968) 62, 63 Myanmar 306 Nakhangova, Mamlakat 237–8 nanotechnology 76 national liberation movements 10 nationalism xi, 14, 83, 109, 110–26, 132, 160, 176–7, 179, 181, 230, 241, 309; AI and 120–6; benefits of 111–12; ecological crisis and xi, 115–20, 121, 122–3, 124; Europe and 124–5; fascism and 292–5; ideology, lack of unifying 176–7; liberalism, as alternative to 11–15, 17, 112; nostalgia and 14–15; nuclear weapons and 112–15, 121–2, 123, 124; origins of 110–12; post-truth and 231–3; religion and 137–8, 305, 307; rituals/sacrifice and 286–8, 292–5; story and meaning of 272, 273–5, 276, 277–8, 280, 286–7, 292–5, 306–8; suffering and 306–8; technology and 120–2, 123–4 National Rifle Association (NRA) 291–2 Native American Indians 79, 147, 185, 186, 191 NATO 175, 177, 231 natural selection 58, 93, 94, 122 Nazi Germany 10, 66, 96, 134, 136, 212, 213, 226, 237, 251, 279, 295 Nepal 103 Netanyahu, Benjamin 173, 179, 221 Netflix 52, 55 Netherlands 10, 14, 38, 186 neuroscience 20 New York Times 243 New Zealand 76, 105 Ngwale, Kinjikitile 239 Nigeria 90, 101, 103, 127, 159, 165 Nile: Basin 113; River 111; Valley 172, 296 9/11 159, 160, 161, 162–3, 166, 168, 195 Nobel Prize 193, 194, 195 North Korea 4, 64, 106, 107–8, 137, 138, 169, 171, 178 Northern Ireland: Troubles 132 nuclear weapons/war 14, 34, 107–8, 112–15, 116, 119, 121, 122, 123, 124, 137, 138, 154, 165, 167–70, 178, 179, 181 Nuda, Yuzu 54 nurses 24, 107 Obama, Barack 4, 12, 15–16, 98, 151, 168 oligarchy 12–13, 15, 76, 176 Olympics: (1016) (Medieval) 103–5; (1980) 103; (1984) 103; (2016) 101–2; (2020) 105; Christian emperors suppress 192 opportunity costs 168 Orthodox Christianity 13, 15, 137, 138, 183, 237, 282, 308 Orwell, George 63, 64; Nineteen Eighty-Four 52, 252 Oscar (chimpanzee) 188 Ottoman Empire 153 Pakistan 102, 153, 159, 200, 286 Palestinians 64–5, 101, 103, 160, 233, 274, 275, 282 Paris terror attacks (November, 2015) 160, 295–6 Parks, Rosa 207, 299 Passover 284 Pasteur, Louis 299 pattern recognition 20 Pearl Harbor attack (1941) 135, 161, 162 Pegu, King of 305 Pentagon 162 People’s Liberation Army (PLA) 178 Peter the Great 175 Phelps, Michael 102 Philippines 127, 161 phosphorus 116 Picasso, Pablo 299 Pixar 249–50 Plato 181, 182 Pokémon Go 92 Poland 15, 103, 137, 142, 169, 177, 186, 231, 279–80, 307–8 polar regions, ice melt in 117 post-truth xiii, 230, 231–45; action in face of 242–4; branding/advertising and 238; history of 231–3; Homo Sapiens as post-truth species 233–6; knowledge and belief, line between 240–2; nation states and 236–8; scientific literature and 243–4; truth and power, relationship between 241–2; uniting people and 239–40 Pravda 237, 243 Princeton Theological Seminary 57 Protestants 108, 132, 213 Putin, Vladimir 12, 13, 15, 80, 114, 175, 176, 177, 231, 232, 233, 238 Qatar 142 Qin Dynasty 171 Quran 127, 130, 131, 132, 181, 198, 233, 235, 272, 298 racism 60, 137, 141, 142, 146, 147, 150–2, 154, 182, 185, 190, 226 Radhakrishnan, Sarvepalli 286 rationality 45, 47, 180, 217–18, 219, 220, 282 Reagan, Ronald 44 refugees x, 117, 123, 140, 141, 142, 144, 147, 148, 155, 205 regulation: AI and 6, 22, 34–5, 61, 77–81, 123; environmental 118, 130, 133, 219, 225 religion xi–xii, xiii, 14, 17, 46, 57, 83, 106, 108, 126, 127–38, 160, 248, 255, 260; authority and 46–7; community and 89, 91; distortions of ancient traditions and 96–8; economics and 33, 106; future of humanity and 127–8; God/gods and see God and gods; humility and 181–96; identity and 128, 133–7; immigration and 141–3, 144, 153; meaning of life stories and see meaning; meditation and 315–16; monotheism, birth of 190–3; nationalism and 15, 17, 137–8, 309; policy problems (economics) and 128, 130–3; post-truth and 233–7, 239, 241; science and 127–30, 193–5; secularism and see secularism; technical problems (agriculture) and 127–30; unemployment and 42–3 see also under individual religion name renewable energy 118, 120, 127, 128–30 robots 249, 250; inequality and 76–7; jobs/work and 19, 22, 24, 29–30, 34, 36, 37, 39, 42; as soldiers 61–8, 76–7, 168; war between humans and 70, 246 Rokia 229 Roman Empire 177, 184, 191, 192, 235, 239, 282 Romania 103, 169 Russia 5, 9, 12–13, 15, 64, 76, 100, 101, 105, 113, 114, 119–20, 122, 134, 135, 137, 137, 138, 139, 168–9, 171, 174–7, 179, 182, 231–2, 236, 237, 238, 242, 248, 251, 260, 277, 294, 307, 308 see also Soviet Union Sabbath, Jewish 188, 189, 290 sacrifice 60–1, 91, 112, 120, 136, 141, 182, 190, 274, 275, 279, 283–91, 302–3, 305, 307, 308 Sanders, Bernie 292 Sapiens: A Brief History of Humankind (Harari) 183 Saudi Arabia 102, 120, 131, 134, 135, 137, 139, 148 science fiction 51, 61, 68–9, 70, 244, 245–55; Brave New World 251–5; free will, Inside Out and concept of 249–51; intelligence with consciousness, confusion of 68–9, 246; mind’s inability to free itself from manipulation 245–55; ‘self’, definition of and 255; technology used to manipulate/control human beings, outlines dangers of 246–9; The Matrix 245, 246–8, 249, 255; The Truman Show 246–7, 248, 255, 268 scientific literature 243–4 Scientific Revolution 193, 195 Scotland: independence referendum (2014) 124–5 Second World War (1939–45) 3, 10, 11, 100, 123, 124, 179–80, 184, 293 secularism 42, 127, 130, 143, 183, 194, 195, 199, 200, 201, 202, 203–14, 229–30, 290; compassion and 205–6; courage and 207–8; definition of 203; equality and 206–7; freedom and 207; secular ideal/ethics of 204–9; Stalin and 209–10, 212 Serbia 175, 275, 276, 282 sexuality: AI and 50; law and 61; liberalism and 299; religion and 200, 300; secularism and 205–6 Shakespeare, William 25, 55–6, 252; Hamlet 297 Shechtman, Dan 194 Shiite Muslims 131, 134, 137, 138, 288–9 Shinto 135–7, 186 Shulhan Arukh (code of Jewish law) 195 Shwedagon Pagoda, Burma 305 Siam 304–5 Sikhs 186, 284 Silicon Valley 39, 76, 85, 178, 217, 299 skin colour 151, 152 slavery 96, 148, 151, 177, 226 Sloman, Steven 218 smart bomb 136 social media 50 solar energy 119, 120 soldiers, AI and 61–8, 76–7, 168 Somme, Battle of the (1 July, 1916) 160 Song Empire, Chinese 104, 259 South Africa 13, 76 South East Asia 100 South Korea 13, 120 Soviet Union 5, 8, 9, 10, 15, 48, 65, 103, 114, 169, 172, 174–5, 176, 209–10, 237–8, 248, 279, 280, 299 see also Russia Spain 48, 124, 125, 236, 260, 289 Spanish Inquisition 48, 96, 199, 212, 213, 289, 299 speciation (divergence of mankind into biological castes or different species) 76 Spinoza 193 Srebrenica massacre (1995) 62–3 St Paul the Apostle 190 Stalin, Joseph 66, 67, 96, 175, 176, 209–10, 211, 212, 237, 238, 243 Stockfish 8 31 Stone Age 73, 86, 182, 187, 217, 218, 233 stress 32, 57, 264 strongmen 5, 165 Suez Canal 172 suffering: AI and xii, 49; Buddhism and 303–4; fake news and 242; meditation and 309, 313; nation states and 307–8; reality and 242, 286–7, 306–8; sacrifice and 287, 289; secular ethics and 201, 205–9 Sumerian city states 188 Sunni Muslims 104, 131, 134 superhumans 41, 75, 211–12, 246 surveillance systems 63–5 Sweden 101, 105, 112, 141 Syria 13, 29, 93, 94, 106, 114, 139, 141, 147, 148, 159, 171, 173, 175, 176, 223, 228, 261, 295, 296 Taiwan 100, 102, 104, 135 Taliban 30, 101, 153 Talmud 42, 43, 97, 132, 183, 186, 189, 193, 235 Tasmanians, aboriginal 227 tax 6, 37, 40, 90–1, 105, 118, 130, 205, 286, 291 Tea Party 219, 291 technology 87; animal welfare and 118–19; ecological collapse and 118–19, 122–3; education and 266–8; equality and 72–81; human bodies and 88–9; liberal democracy and 6–9, 16, 17–18; liberty and 44–72; nationalism and 120–4; science fiction and 245–55; threat from future xii–xiii, xiv, 1–81, 123, 176, 178–9, 245–55, 259–68; war and 99–100, 123, 176, 178–9; work and 19–43 see also artificial intelligence (AI) and under individual are of technology Tel el-Kebir, Battle of (1882) 172 television, AI and 51–2 Temple of Yahweh, Jerusalem 15 Ten Commandments 186, 187, 199, 291 Tencent (technology company) 40, 41, 77 terrorism: AI and 65, 69; etymology 159; fear of xi, xiii, 93, 155, 159–70, 217, 237, 249; media and 166, 167, 170; nuclear weapons 167–70; numbers killed by 23, 159; state reaction to 163–7; strategy of 159–63; suicide/martyrdom 295–6 Tesla 59, 60–1 Thatcher, Margaret 44–5 Theodosian Decrees (391) 192 Theodosius, Roman Emperor 192 Third World War see war 3-D printers 39 Tibet 232 Tiranga (tricolor) (Indian national flag) 285–6 Tojo, General 180 Torah 190, 194 trolley problems 57, 60 Truman Show, The (movie) 246–7, 248, 255, 268 Trump, Donald xi, 5, 8, 9, 11, 14–15, 40, 114, 150–1, 232, 233, 312 truth 12, 54, 215–55; Google and 54; ignorance and 217–22; justice and 223–30; meditation and see meditation; nationalism and 277–8, 293; post-truth and xiii, 231–45; reality and 306–8; science fiction and 245–55; secular commitment to 204–14; suffering and 308 Tsuyoshi, Inukai 305 Tunisia xi Turkey 5, 15, 127, 141, 169, 181, 260 Twitter 91, 235, 238 Ukraine 33, 101, 114, 169, 174, 176, 177, 219, 231–2, 238, 242 ultra-Orthodox Jews 42–3, 97, 195 Umayyad caliphs 94 unemployment 8, 18, 19, 21, 30, 32–3, 34, 37, 43 see also jobs United Nations 15, 101; Declaration of Human Rights 211 United States 4, 5, 8, 11, 14–15, 24, 29, 33, 39, 40, 62, 63, 64, 65, 67, 76, 79, 94, 96, 99–100, 103–4, 106, 107, 108, 113, 114, 115, 118, 119, 120, 121, 127, 130, 131, 133, 135, 136, 142, 145, 147, 150–1, 152, 159, 161, 162, 165, 168, 169, 172, 173, 175, 177, 178, 182, 185, 191, 194, 200, 219, 227, 230, 231, 236, 242, 275 Universal Basic Income (UBI) 37–43 universe, age of 274 university, deciding what to study at 54–5 University of Oxford 310 US Air Force (USAF) 29, 30 useless class 18, 30, 32, 121 US National Highway Traffic Safety Administration 24 US presidential election: (1964) 113, 114; (2016) 8, 85 Vedas 127, 131, 132, 198, 235, 240, 298 Victoria, Queen 15, 178 Vietnam 14, 100, 104, 176, 285 Vietnam War (1955–75) 62, 63, 100, 172, 173 violence: ethics and 200–2; nationalism and 112; number of deaths attributed to 16, 114; terrorism and see terrorism; war and see war Vipassana meditation 310, 312, 315–16 Vishwamitra 181 Wahhabism 137 Waksman, Selman 194 Walt Disney Pictures 249–51, 267, 270 war xi, xiii, 138, 170, 171–80; AI and 61–8, 123–4; economy and 177–9; possibility of 123–4, 138, 170, 171–80; religion and 138; spreads ideas, technology and people 99–100; stupidity/folly and 179–80; successful wars, decline in number of 171–80; technological disruption increases likelihood of 123–4 Warmland (fictional nation) 148–50, 152–4 War on Terror 168 Watson (IBM computer system) 20–1 weapons 123, 136, 212, 224–5; autonomous weapons systems 63; nuclear 14, 34, 107–8, 112–15, 116, 119, 121, 122, 123, 124, 137, 138, 154, 165, 167–70, 178, 179, 181; terrorism and see terrorism; weapons of mass destruction xiv, 167–70 welfare state xii, 10–11, 76 West Bank 64–5, 224 White Memorial Retreat Center, California 200 Wilhelm II, Kaiser 95, 96 William the Conqueror 178–9 Willow (movie) 298 Wirathu, Ashin 306 work/employment, AI and 18, 19–43 see also jobs World Health Organization (WHO) 22–3 Wright brothers 181, 299 Xi Jinping 12 Yahweh 15, 191, 291 Yemen 173, 195 YouTube 50, 102 Yugoslavia 169 Zakkai, Rabbi Yochanan ben 195 Zen meditation 305–6 Zionism 111, 184, 233, 272, 273–4, 276, 279 Zuckerberg, Mark 80, 81, 85–6, 87, 88, 89–90, 93 @vintagebooks penguin.co.uk/vintage This ebook is copyright material and must not be copied, reproduced, transferred, distributed, leased, licensed or publicly performed or used in any way except as specifically permitted in writing by the publishers, as allowed under the terms and conditions under which it was purchased or as strictly permitted by applicable copyright law.


pages: 499 words: 144,278

Coders: The Making of a New Tribe and the Remaking of the World by Clive Thompson

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Ada Lovelace, AI winter, Airbnb, Amazon Web Services, Asperger Syndrome, augmented reality, Ayatollah Khomeini, barriers to entry, basic income, Bernie Sanders, bitcoin, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, cellular automata, Chelsea Manning, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crowdsourcing, cryptocurrency, Danny Hillis, David Heinemeier Hansson, don't be evil, don't repeat yourself, Donald Trump, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, Firefox, Frederick Winslow Taylor, game design, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, Guido van Rossum, Hacker Ethic, HyperCard, illegal immigration, ImageNet competition, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Menlo Park, microservices, Minecraft, move fast and break things, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Oculus Rift, PageRank, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, pink-collar, planetary scale, profit motive, ransomware, recommendation engine, Richard Stallman, ride hailing / ride sharing, Rubik’s Cube, Ruby on Rails, Sam Altman, Satoshi Nakamoto, Saturday Night Live, self-driving car, side project, Silicon Valley, Silicon Valley ideology, Silicon Valley startup, single-payer health, Skype, smart contracts, Snapchat, social software, software is eating the world, sorting algorithm, South of Market, San Francisco, speech recognition, Steve Wozniak, Steven Levy, TaskRabbit, the High Line, Travis Kalanick, Uber and Lyft, Uber for X, uber lyft, universal basic income, urban planning, Wall-E, Watson beat the top human players on Jeopardy!, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

When they analyzed the results, three things jumped out. One was that programmers were avid problem solvers, “crazy about finding the answers to problems and solving all sorts of puzzles, including all forms of mathematical and mechanical activities.” This probably wasn’t terribly surprising to managers of the time. In fact, since there weren’t yet many universities formally teaching coding, they used logic and pattern-recognition tests to figure out whether any newbies ought to be a coder. As Nathan Ensmenger notes in The Computer Boys Take Over, they were already selecting for puzzle lovers, and were beginning to shift toward hiring men over women. One ad for IBM around that time—entitled “Are YOU the man to command electronic giants?”—asked, “Do you have an orderly mind that enjoys such games as chess, bridge or anagrams . . .?”

But it’s half to a third of the size of what they write.” Why the difference? It’s experience, he says. Inexperienced programmers dive in and start devising their code on the screen; they mistake the length of the program, the act of writing lines, with productivity. But the more experienced programmer ponders the problem they’re trying to solve, the task the code is supposed to do. When they write, it’s guided by years of pattern recognition: They’ve seen this sort of array-sorting problem, and know the most elegant way to address it. And they know the converse is true, too. When a particular function—a subroutine in a program—becomes long and labyrinthine, odds are high it could be made more efficient. Often the act of fixing it, refactoring it, involves bits of fractal meta-efficiency: You find places where your code repeats itself, and you remove the duplication.

You needed to be logic-minded, good at math, and meticulous, they figured. In this respect, gender stereotypes could work in women’s favor. Some executives argued that women’s traditional expertise at fastidious pastimes like knitting and weaving imparted precisely this mind-set. (The 1968 book Your Career in Computers argued that people who liked “cooking from a cookbook” would make good programmers.) Mostly, firms gave potential coders a simple pattern-recognition test, which many women readily passed. Most hires were then trained on the job, which made the field particularly receptive to neophytes of any gender. (“Know Nothing about Computers? Then We’ll Teach You (and pay you while doing so),” as one ad enthused.) Eager to recruit women, IBM even crafted a brochure entitled My Fair Ladies. Another ad by the firm English Electric showed a bob-haired woman chewing a pen, noting that “Some of English Electric Leo’s best computer programmers are as female as anything.”


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Age of Context: Mobile, Sensors, Data and the Future of Privacy by Robert Scoble, Shel Israel

Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Edward Thorp, Elon Musk, factory automation, Filter Bubble, G4S, Google Earth, Google Glasses, Internet of things, job automation, John Markoff, Kickstarter, lifelogging, Marc Andreessen, Mars Rover, Menlo Park, Metcalfe’s law, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Robert Metcalfe, Saturday Night Live, self-driving car, sensor fusion, Silicon Valley, Skype, smart grid, social graph, speech recognition, Steve Jobs, Steve Wozniak, Steven Levy, Tesla Model S, Tim Cook: Apple, ubercab, urban planning, Zipcar

When they examine urban centers, they watch for emerging patterns from which they can glean insights for municipal clients. Pattern recognition enables them to identify problems sooner, and resolve them faster than their competition. IBM has already accumulated some impressive accomplishments in the few short years the practice has been in existence. Although the company is globally focused, we asked them specifically about U.S.-based projects. Of the ones they submitted, here are our favorites: Memphis. Police say that IBM’s predictive analytics have helped them identify criminal hot spots, which allows them to anticipate where—and when—serious crimes are likely to occur. Based on the data, they reallocated patrol cars and other resources, reducing major and violent crime by as much as 30 percent. Pattern recognition also helps police understand trends that previously went unnoticed.

., is making progress on “biologically inspired intelligent agents” that will deliver results by searching for ideas, instead of merely keywords. In short, their technology builds tools that emulate the way the human brain works. Essentially, humans recognize patterns—sometimes highly complex ones. We can detect the fundamental features and meaning of text, time and visual data, key components of context. Pattern recognition, which started just a few years ago, is now reaching the state where database search tools are starting to think like people think. They don’t yet do it as effectively as we humans do it, but they do it faster and far more efficiently. There is a dark side to these growing capabilities. We should watch for the unintended consequences that always seem to accompany significant change. The potential for data abuse and the loss of privacy head the list of concerns.


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Late Bloomers: The Power of Patience in a World Obsessed With Early Achievement by Rich Karlgaard

Airbnb, Albert Einstein, Amazon Web Services, Apple's 1984 Super Bowl advert, Bernie Madoff, Bob Noyce, Brownian motion, Captain Sullenberger Hudson, cloud computing, cognitive dissonance, Daniel Kahneman / Amos Tversky, deliberate practice, Electric Kool-Aid Acid Test, Elon Musk, en.wikipedia.org, experimental economics, fear of failure, financial independence, follow your passion, Frederick Winslow Taylor, hiring and firing, Internet of things, Isaac Newton, Jeff Bezos, job satisfaction, knowledge economy, labor-force participation, longitudinal study, low skilled workers, Mark Zuckerberg, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, move fast and break things, move fast and break things, pattern recognition, Peter Thiel, Sand Hill Road, science of happiness, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, Steve Wozniak, theory of mind, Tim Cook: Apple, Toyota Production System, unpaid internship, upwardly mobile, women in the workforce, working poor

But managing software projects and software businesses shifts the balance of desired skills from Gf to Gc. That is why you saw Diane Greene, in her early sixties, leading one of Google’s most important businesses, Google Cloud. And why billionaire Tom Siebel, in his mid-sixties, is leading his latest software company, C3, in the hotly competitive space of artificial intelligence and the Internet of Things. In a sense, our brains are constantly forming neural networks and pattern-recognition capabilities that we didn’t have in our youth when we had blazing synaptic horsepower. As we get older, we develop new skills and refine others, including social awareness, emotional regulation, empathy, humor, listening, risk-reward calibration, and adaptive intelligence. All these skills enhance our potential to bloom and rebloom. What about our creativity, our ability to land upon the unexpected insight?

Sadly, the typical career today reflects the assembly-line thinking of the early twentieth century. We take a job, move up to greater responsibility and pay levels, then are abruptly forced to retire or are laid off around sixty. Law and accounting firms have a term for this: up-and-out. A kinder clock for human development would set aside up-and-out and picture a person’s career as an arc. While we decline in some ways (synaptic speed, short-term memory), we gain in others (real-life pattern recognition, emotional IQ, wisdom). Our creative and innovative capacities remain strong in different ways as we age. I believe enlightened employers have a grand opportunity to be more creative about career paths. I’ve spent time with thousands of executives in my career in journalism, and they all tell me the same thing: Talent recruitment and retention is a priority. A company that fails at it, that pushes its employees out the door when they reach a certain age, is not tapping their full capacities.

It stands to reason that the more information we have stored in our brains, the more easily we can detect familiar patterns. Contrary to popular ideas about aging and creativity, many older adults discern patterns faster, determining what’s important and what’s trivial, then jumping to the logical solution. Elkhonon Goldberg, the NYU neuroscientist, has said that “cognitive templates” develop in older brains based on pattern recognition and form the basis for wise behavior and better decisions. As he observed in The Wisdom Paradox, Goldberg began to realize that as he aged, he was increasingly adept at a kind of “mental magic.” “Something rather intriguing is happening in my mind that did not happen in the past,” he wrote. “Frequently, when I am faced with what would appear from the outside to be a challenging problem, the grinding mental computation is somehow circumvented, rendered, as if by magic, unnecessary.


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Machine, Platform, Crowd: Harnessing Our Digital Future by Andrew McAfee, Erik Brynjolfsson

"Robert Solow", 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, Amazon Mechanical Turk, Amazon Web Services, artificial general intelligence, augmented reality, autonomous vehicles, backtesting, barriers to entry, bitcoin, blockchain, British Empire, business cycle, business process, carbon footprint, Cass Sunstein, centralized clearinghouse, Chris Urmson, cloud computing, cognitive bias, commoditize, complexity theory, computer age, creative destruction, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, Dean Kamen, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, family office, fiat currency, financial innovation, George Akerlof, global supply chain, Hernando de Soto, hive mind, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, law of one price, longitudinal study, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Mark Zuckerberg, meta analysis, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, Plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Ronald Coase, Satoshi Nakamoto, Second Machine Age, self-driving car, sharing economy, Silicon Valley, Skype, slashdot, smart contracts, Snapchat, speech recognition, statistical model, Steve Ballmer, Steve Jobs, Steven Pinker, supply-chain management, TaskRabbit, Ted Nelson, The Market for Lemons, The Nature of the Firm, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, two-sided market, Uber and Lyft, Uber for X, uber lyft, ubercab, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

The brains of young children are specialized for language learning: they operate on statistical principles to discern the patterns in language§ (for example, When Mom talks about herself as the subject, she uses the word “I” and puts it at the start of the sentence. When she’s the object, she uses “me” and places it later). Because adults’ brains are different, they usually learn the rules explicitly when acquiring a new language. Early on, the AI community split into two similarly differentiated camps. One pursued so-called rule-based, or “symbolic,” artificial intelligence,¶ while the other built statistical pattern recognition systems. The former tried to bring about artificial intelligence the way an adult tries to learn a second language; the latter tried to make it happen in much the same way that children learn their first language. At first, it looked as though the symbolic approach would dominate. At the 1956 Dartmouth conference, for example, Allen Newell, J. C. Shaw, and future Nobel prize winner Herbert Simon demonstrated their “Logic Theorist” program, which used the rules of formal logic to automatically prove mathematical theorems.

CHAPTER 5 WHERE TECHNOLOGY AND INDUSTRY STILL NEED HUMANITY There are three rules for writing a novel. Unfortunately, no one knows what they are. — attributed to Somerset Maugham (1874–1965) “WHICH ABILITIES WILL CONTINUE TO BE UNIQUELY HUMAN as technology races ahead?” That’s the most common question we hear about minds and machines. As the digital toolkit challenges human superiority in routine information processing, pattern recognition, language, intuition, judgment, prediction, physical dexterity, and so many other things, are there any areas where we should not expect to be outstripped? Do Androids Dream Up Creative Leaps? The most common answer we hear to the question posed in the preceding paragraph is “creativity.” Many people we’ve spoken with, if not most, argue that there’s something irreducible or ineffable about the human ability to come up with a new idea.

., 166–67 Angry Birds, 159–61 anonymity, digital currency and, 279–80 Antikythera mechanism, 66 APIs (application programming interfaces), 79 apophenia, 44n apparel, 186–88 Apple; See also iPhone acquiring innovation by acquiring companies, 265 and industrywide smartphone profits, 204 leveraging of platforms by, 331 Postmates and, 173, 185 profitability (2015), 204 revenue from paid apps, 164 “Rip, Mix, Burn” slogan, 144n as stack, 295 application programming interfaces (APIs), 79 AppNexus, 139 apps; See also platforms for banking, 89–90 demand curve and, 157–61 iPhone, 151–53 App Store, 158 Apter, Zach, 183 Aral, Sinan, 33 Archilochus, 60–61 architecture, computer-designed, 118 Aristophanes, 200 Arnaout, Ramy, 253 Arthur, Brian, 47–48 artificial general intelligence (AGI), 71 artificial hands, 272–75 artificial intelligence; See also machine learning current state of, 74–76 defined, 67 early attempts, 67–74 implications for future, 329–30 rule-based, 69–72 statistical pattern recognition and, 72–74 Art of Thinking Clearly, The (Dobelli), 43 arts, digital creativity in, 117–18 Ashenfelter, Orley, 38–39 ASICs (application-specific integrated circuits), 287 assets and incentives, 316 leveraging with O2O platforms, 196–97 replacement by platforms, 6–10 asymmetries of information, 206–10 asymptoting, 96 Atkeson, Andrew, 21 ATMs, 89 AT&T, 96, 130 August (smart door lock), 163 Austin, Texas, 223 Australia, 100 Authorize.Net, 171 Autodesk, 114–16, 119, 120 automated investing, 266–70 automation, effect on employment/wages, 332–33 automobiles, See cars Autor, David, 72, 101 background checks, 208, 209 back-office work, 82–83 BackRub, 233 Baidu, 192 Bakos, Yannis, 147n Bakunin, Mikhail, 278 Ballmer, Steve, 151–52 bandwagon effect, 217 banking, virtualization and, 89–90, 92 Bank of England, 280n bank tellers, 92 Barksdale, Jim, 145–46 barriers to entry, 96, 220 Bass, Carl, 106–7, 119–20 B2B (business-to-business) services, 188–90 Beastmode 2.0 Royale Chukkah, 290 Behance, 261 behavioral economics, 35, 43 Bell, Kristen, 261, 262 Benioff, Mark, 84–85 Benjamin, Robert, 311 Benson, Buster, 43–44 Berlin, Isiah, 60n Berners-Lee, Tim, 33, 34n, 138, 233 Bernstein, Michael, 260 Bertsimas, Dimitris, 39 Bezos, Jeff, 132, 142 bias of Airbnb hosts, 209–10 in algorithmic systems, 51–53 digital design’s freedom from, 116 management’s need to acknowledge, 323–24 and second-machine-age companies, 325 big data and Cambrian Explosion of robotics, 95 and credit scores, 46 and machine learning, 75–76 biology, computational, 116–17 Bird, Andrew, 121 Bitcoin, 279–88 China’s dominance of mining, 306–7 failure mode of, 317 fluctuation of value, 288 ledger for, 280–87 as model for larger economy, 296–97 recent troubles with, 305–7 and solutionism, 297 “Bitcoin: A Peer-to-Peer Electronic Cash System” (Nakamoto), 279 BlaBlaCar, 190–91, 197, 208 BlackBerry, 168, 203 Blitstein, Ryan, 117 blockchain as challenge to stacks, 298 and contracts, 291–95 development and deployment, 283–87 failure of, 317 and solutionism, 297 value as ledger beyond Bitcoin, 288–91 Blockchain Revolution (Tapscott and Tapscott), 298 Bloomberg Markets, 267 BMO Capital Markets, 204n Bobadilla-Suarez, Sebastian, 58n–59n Bock, Laszlo, 56–58 bonds, 131, 134 bonuses, credit card, 216 Bordeaux wines, 38–39 Boudreau, Kevin, 252–54 Bowie, David, 131, 134, 148 Bowie bonds, 131, 134 brand building, 210–11 Brat, Ilan, 12 Bredeche, Jean, 267 Brin, Sergey, 233 Broward County, Florida, 40 Brown, Joshua, 81–82 Brusson, Nicolas, 190 Burr, Donald, 177 Bush, Vannevar, 33 business conference venues, 189 Business Insider, 179 business processes, robotics and, 88–89 business process reengineering, 32–35 business travelers, lodging needs of, 222–23 Busque, Leah, 265 Buterin, Vitalik, 304–5 Byrne, Patrick, 290 Cairncross, Francis, 137 California, 208; See also specific cities Calo, Ryan, 52 Cambrian Explosion, 94–98 Cameron, Oliver, 324 Camp, Garrett, 200 capacity, perishing inventory and, 181 Card, David, 40 Care.com, 261 cars automated race car design, 114–16 autonomous, 17, 81–82 decline in ownership of, 197 cash, Bitcoin as equivalent to, 279 Casio QV-10 digital camera, 131 Caves, Richard, 23 Caviar, 186 CDs (compact discs), 145 cell phones, 129–30, 134–35; See also iPhone; smartphones Census Bureau, US, 42 central bankers, 305 centrally planned economies, 235–37 Chabris, Chris, 3 Chambers, Ephraim, 246 Champy, James, 32, 34–35, 37, 59 Chandler, Alfred, 309n Chase, 162 Chase Paymentech, 171 check-deposit app, 162 children, language learning by, 67–69 China Alibaba in, 7–8 concentration of Bitcoin wealth in, 306–7 and failure mode of Bitcoin, 317 mobile O2O platforms, 191–92 online payment service problems, 172 robotics in restaurants, 93 Shanghai Tower design, 118 Xiaomi, 203 Chipotle, 185 Choudary, Sangeet, 148 Christensen, Clay, 22, 264 Churchill, Winston, 301 Civil Aeronautics Board, US, 181n Civis Analytics, 50–51 Clash of Clans, 218 classified advertising revenue, 130, 132, 139 ClassPass, 205, 210 and economics of perishing inventory, 180–81 future of, 319–20 and problems with Unlimited offerings, 178–80, 184 and revenue management, 181–84 user experience, 211 ClassPass Unlimited, 178–79 Clear Channel, 135 clinical prediction, 41 Clinton, Hillary, 51 clothing, 186–88 cloud computing AI research, 75 APIs and, 79 Cambrian Explosion of robotics, 96–97 platform business, 195–96 coaches, 122–23, 334 Coase, Ronald, 309–13 cognitive biases, 43–46; See also bias Cohen, Steven, 270 Coles, John, 273–74 Collison, John, 171 Collison, Patrick, 171–74 Colton, Simon, 117 Columbia Record Club, 131 commoditization, 220–21 common sense, 54–55, 71, 81 companies continued dominance of, 311–12 continued relevance of, 301–27 DAO as alternative to, 301–5 decreasing life spans of, 330 economics of, 309–12 future of, 319–26 leading past the standard partnership, 323–26 management’s importance in, 320–23 markets vs., 310–11 as response to inherent incompleteness of contracts, 314–17 solutionism’s alternatives to, 297–99 TCE and, 312–15 and technologies of disruption, 307–9 Compass Fund, 267 complements (complementary goods) defined, 156 effect on supply/demand curves, 157–60 free, perfect, instant, 160–63 as key to successful platforms, 169 and open platforms, 164 platforms and, 151–68 and revenue management, 183–84 Stripe and, 173 complexity theory, 237 Composite Fund (D.


pages: 223 words: 60,909

Technically Wrong: Sexist Apps, Biased Algorithms, and Other Threats of Toxic Tech by Sara Wachter-Boettcher

Airbnb, airport security, AltaVista, big data - Walmart - Pop Tarts, Donald Trump, Ferguson, Missouri, Firefox, Grace Hopper, job automation, Kickstarter, lifelogging, Mark Zuckerberg, Menlo Park, move fast and break things, move fast and break things, natural language processing, pattern recognition, Peter Thiel, recommendation engine, ride hailing / ride sharing, self-driving car, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, Tim Cook: Apple, Travis Kalanick, upwardly mobile, women in the workforce, zero-sum game

The idea is that the system would notice that each item tagged as a lowercase “p” had similarities—that they always had a descender immediately connected to a bowl, for example. The wider the range of handwriting used in the training data, the more accurate the system’s pattern recognition will be when you let it loose on new data. And once it’s accurately identifying new forms of the lowercase “p,” those patterns can be added into the network too. This is how a neural network gets smarter over time.12 Like our brains, though, computers struggle to understand complex objects, such as photographs, all at once; there are simply too many details and variations to make sense of. Instead they have to learn patterns, like we learned as children. This kind of pattern recognition happens in layers: small details, like the little point at the top of a cat’s ear, get connected to larger concepts, like the ear itself, which then gets connected to the larger concept of a cat’s head, and so on—until the system builds up enough layers, sometimes twenty or more, to make sense of the full image.13 The connections between each of those layers are what turn the collection of data into a neural network.

Startup success, Reuters writer Sarah McBride concluded, wasn’t actually much different from success in other elite professions. “A prestigious degree, a proven track record and personal connections to power-brokers are at least as important as a great idea,” she wrote. “Scrappy unknowns with a suitcase and a dream are the exceptions, not the rule.” 3 As Sharon Vosmek, CEO of the venture accelerator Astia, put it, “They call it pattern recognition, but in other industries they call it profiling or stereotyping.” 4 Meanwhile, women hardly get funded at all: only 10 percent of the 187 Silicon Valley startups that received Series A funding in 2016 were woman-led, up a meager 2 percent from the year before.5 Yet much of the tech industry still clings to meritocracy like a tattered baby blanket. Sequoia Capital partner Greg McAdoo insisted, “This business is a meritocracy by and large.” 6 David Sacks, an early executive at PayPal, claimed that “if meritocracy exists anywhere on earth, it is in Silicon Valley.” 7 Until 2014, code-hosting platform GitHub even put a rug emblazoned with “United Meritocracy of GitHub” in the center of its Oval Office–replica waiting room.8 In the fall of 2016, the Atlantic sent out its annual “pulse of the technology industry” survey to influential executives, founders, and thinkers—and found that “men were three times as likely as women to say Silicon Valley is a meritocracy.” 9 It’s not just tech that wants desperately to believe in a meritocracy, of course.


pages: 571 words: 105,054

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, G4S, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman

Students of economics and finance would do well enrolling in ML courses, rather than econometrics. Econometrics may be good enough to succeed in financial academia (for now), but succeeding in business requires ML. What do you say to people who dismiss ML algorithms as black boxes? If you are reading this book, chances are ML algorithms are white boxes to you. They are transparent, well-defined, crystal-clear, pattern-recognition functions. Most people do not have your knowledge, and to them ML is like a magician's box: “Where did that rabbit come from? How are you tricking us, witch?” People mistrust what they do not understand. Their prejudices are rooted in ignorance, for which the Socratic remedy is simple: education. Besides, some of us enjoy using our brains, even though neuroscientists still have not figured out exactly how they work (a black box in itself).

Tay (2001): “Financial forecasting using support vector machines.” Neural Computing & Applications, Vol. 10, No. 2, pp. 184–192. Cao, L., F. Tay and F. Hock (2003): “Support vector machine with adaptive parameters in financial time series forecasting.” IEEE Transactions on Neural Networks, Vol. 14, No. 6, pp. 1506–1518. Cervelló-Royo, R., F. Guijarro, and K. Michniuk (2015): “Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data.” Expert Systems with Applications, Vol. 42, No. 14, pp. 5963–5975. Chang, P., C. Fan and J. Lin (2011): “Trend discovery in financial time series data using a case-based fuzzy decision tree.” Expert Systems with Applications, Vol. 38, No. 5, pp. 6070–6080. Kuan, C. and L. Tung (1995): “Forecasting exchange rates using feedforward and recurrent neural networks.”

The validation set is used to evaluate the trained parameters, and the testing is run only on the one configuration chosen in the validation phase. In what case does this procedure still fail? What is the key to avoiding selection bias? Bibliography Bharat Rao, R., G. Fung, and R. Rosales (2008): “On the dangers of cross-validation: An experimental evaluation.” White paper, IKM CKS Siemens Medical Solutions USA. Available at http://people.csail.mit.edu/romer/papers/CrossVal_SDM08.pdf. Bishop, C. (1995): Neural Networks for Pattern Recognition, 1st ed. Oxford University Press. Breiman, L. and P. Spector (1992): “Submodel selection and evaluation in regression: The X-random case.” White paper, Department of Statistics, University of California, Berkeley. Available at http://digitalassets.lib.berkeley.edu/sdtr/ucb/text/197.pdf. Hastie, T., R. Tibshirani, and J. Friedman (2009): The Elements of Statistical Learning, 1st ed. Springer.


pages: 229 words: 68,426

Everyware: The Dawning Age of Ubiquitous Computing by Adam Greenfield

augmented reality, business process, defense in depth, demand response, demographic transition, facts on the ground, game design, Howard Rheingold, Internet of things, James Dyson, knowledge worker, late capitalism, Marshall McLuhan, new economy, Norbert Wiener, packet switching, pattern recognition, profit motive, QR code, recommendation engine, RFID, Steve Jobs, technoutopianism, the built environment, the scientific method

It may well be that a full mug on my desk implies that I am also in the room, but this is not always going to be the case, and any system that correlates the two facts had better do so pretty loosely. Products and services based on such pattern-recognition already exist in the world—I think of Amazon's "collaborative filtering"–driven recommendation engine—but for the most part, their designers are only now beginning to recognize that they have significantly underestimated the difficulty of deriving meaning from those patterns. The better part of my Amazon recommendations turn out to be utterly worthless—and of all commercial pattern-recognition systems, that's among those with the largest pools of data to draw on. Lest we forget: "simple" is hard. In fact, Kindberg and Fox remind us that "[s]ome problems routinely put forward [in ubicomp] are actually AI-hard"—that is, as challenging as the creation of an artificial human-level intelligence.

(We'll see in the next thesis that such a degree of explicitness poses significant challenges socially as well as semantically.) Take exercise, or play, or sexuality, all of which will surely become sites of intense mediation in a fully developed everyware milieu. Something as simple as hiking in the wilderness becomes almost unrecognizable when overlaid with GPS location, sophisticated visual pattern-recognition algorithms, and the content of networked geological, botanical, and zoological databases—you won't get lost, surely, or mistake poisonous mushrooms for the edible varieties, but it could hardly be said that you're "getting away from it all." Even meditation is transformed into something new and different: since we know empirically that the brains of Tibetan monks in deep contemplation show regular alpha-wave patterns, it's easy to imagine environmental interventions, from light to sound to airflow to scent, designed to evoke the state of mindfulness, coupled to a body-monitor setting that helps you recognize when you've entered it.


pages: 245 words: 64,288

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

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

The focus of the job is to analyse and evaluate visual images, the parameters of which are well defined since they are often coming directly from computerised scanning devices. It is a closed system, with a number of variables well known and pretty much already defined, and the process is very repetitive. What this means is a database of information (thirteen years of studies and training) connected to a visual recognition system (the radiologist’s brain) is a process that already exists today and finds many applications. Visual pattern recognition software is already highly sophisticated, one such example is Google Images. You can upload an image to the search engine, Google uses computer vision techniques to match your image to other images in the Google Images index and additional image collections. From those matches, they try to generate an accurate “best guess” text description of your image, as well as find other images that have the same content as your uploaded image

* * * * * * Figure 6.2: I upload my image, named “guess-what-this.is.jpg” * * * * * * Figure 6.3: The software correctly recognises it as the Robot ASIMO by Honda, and offers similar images in return. Notice that the proposed images show ASIMO in different positions and angles, not the same image in different sizes. This algorithm recognises millions of different patterns, as it is a general-purpose application. A task-specific pattern recognition software is less complex to develop, although it must be much more accurate (the stakes are higher). * * * Similarly, many governments have access to software that can help identify terrorists in airports based on visual analysis of security photographs33. CCTV cameras in London and many other cities have advanced systems that track people’s faces and can help the police identify potential criminals34.

If you are thinking decades, you are in for a surprise. 7.6 AI Assistants You might remember the May of 1997, when the legendary chess player Garry Kasparov was defeated by IBM Deep Blue in what has been called “the most spectacular chess event in history”79. At the time the plan of IBM was to rely on the computational superiority of their machine using brute force,80 crunching billions of combinations; against the intuition, memory recall and pattern recognition of the Russian chess grandmaster. Nobody believed it represented an act of intelligence of any sort, since it worked in a very mechanistic way. Boy, we have gone so far since then. The classical “Turing test approach” has been largely abandoned as a realistic research goal, and is now just an intellectual curiosity (the annual Loebner prize for realistic chattiest81), but helped spawn the two dominant themes of modern cognition and artificial intelligence: calculating probabilities and producing complex behaviour from the interaction of many small, simple processes.


pages: 97 words: 31,550

Money: Vintage Minis by Yuval Noah Harari

23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, European colonialism, Flash crash, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, pattern recognition, Ponzi scheme, self-driving car, telemarketer, The Future of Employment, The Wealth of Nations by Adam Smith, trade route, transatlantic slave trade, Watson beat the top human players on Jeopardy!, zero-sum game

Until today high intelligence always went hand in hand with a developed consciousness. Only conscious beings could perform tasks that required a lot of intelligence, such as playing chess, driving cars, diagnosing diseases or identifying terrorists. However, we are now developing new types of non-conscious intelligence that can perform such tasks far better than humans. For all these tasks are based on pattern recognition, and non-conscious algorithms may soon excel human consciousness in recognising patterns. Science-fiction movies generally assume that in order to match and surpass human intelligence, computers will have to develop consciousness. But real science tells a different story. There might be several alternative ways leading to super-intelligence, only some of which pass through the straits of consciousness.

Veterinary assistants – 86 per cent. Security guards – 84 per cent. Sailors – 83 per cent. Bartenders – 77 per cent. Archivists – 76 per cent. Carpenters – 72 per cent. Lifeguards – 67 per cent. And so forth. There are of course some safe jobs. The likelihood that computer algorithms will displace archaeologists by 2033 is only 0.7 per cent, because their job requires highly sophisticated types of pattern recognition, and doesn’t produce huge profits. Hence it is improbable that corporations or government will make the necessary investment to automate archaeology within the next twenty years. Of course, by 2033 many new professions are likely to appear, for example, virtual-world designers. But such professions will probably require much more creativity and flexibility than current run-of-the-mill jobs, and it is unclear whether forty-year-old cashiers or insurance agents will be able to reinvent themselves as virtual-world designers (try to imagine a virtual world created by an insurance agent!).


pages: 211 words: 77

The Little Schemer by Daniel P. Friedman, Matthias Felleisen, Duane Bibby

Gödel, Escher, Bach, pattern recognition, Y Combinator

Although Scheme can be described quite formally, understanding Scheme does not require a particularly mathematical inclination. In fact, The Little Schemer is based on lecture notes from a two-week "quickie" introduction to Scheme for students with no previous programming experience and an admitted dislike for anything mathematical. Many of these students were preparing for careers in public affairs. It is our belief that writing programs recursively in Scheme is essentially simple pattern recognition. Since our only concern is recursive programming, our treatment is limited to the whys and wherefores of just a few Scheme features: car, cdr, cons, eq?, null?, zero?, add!, sub!, number?, and, or, quote, lambda, define, and condo Indeed, our language is an idealized Scheme. The Little Schemer and The Seasoned Schemer will not introduce you to the practical world of programming, but a mastery of the concepts in these books provides a start toward understanding the nature of computation.

Therefore, to avoid a circularity, our basic arithmetic addition and subtraction must be written using different symbols: + and -, respectively. We do not give any formal definitions in this book. We believe that you can form your own definitions and will thus remember them and understand them better than if we had written each one for you. But be sure you know and understand the Laws and Commandments thoroughly before passing them by. The key to learning Scheme is "pattern recognition." The Commandments point out the patterns that you will have already seen. Early in the book, some concepts are narrowed for simplicity; later, they are expanded and qualified. You should also know that, while everything in the book is Scheme, Scheme itself is more general and incorporates more than we could intelligibly cover in an introductory text. After you have mastered this book, you can read and understand more advanced and comprehensive books on Scheme.


pages: 356 words: 105,533

Dark Pools: The Rise of the Machine Traders and the Rigging of the U.S. Stock Market by Scott Patterson

algorithmic trading, automated trading system, banking crisis, bash_history, Bernie Madoff, butterfly effect, buttonwood tree, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, fixed income, Flash crash, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, Joseph Schumpeter, latency arbitrage, Long Term Capital Management, Mark Zuckerberg, market design, market microstructure, pattern recognition, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, Sergey Aleynikov, Small Order Execution System, South China Sea, Spread Networks laid a new fibre optics cable between New York and Chicago, stealth mode startup, stochastic process, transaction costs, Watson beat the top human players on Jeopardy!, zero-sum game

As algorithmic trading grew, large investors were finding it harder to trade large chunks of stock. More and more trades were sliced and diced into small, round-numbered pieces—two hundred, three hundred shares—that algos could more easily juggle. The algos deployed complex methods to hunt out the large whale orders the big firms traded, such as “pinging” dark pools with orders that they canceled seconds later. Some used AI pattern-recognition methods to detect their prey. Relatively small at first, the dark pools would grow larger and larger as electronic trading expanded dramatically in the coming years. The electronic traders and the Plumbers who built the pools they swam in didn’t see anything wrong with the market they’d help create. They celebrated themselves as democratizers, cracking the insider machine that had picked the pockets of mom and pop for decades.

Wissner-Gross and Freer provided a map dotted with optimal hubs all along the earth’s surface. Many of the hubs lay in the oceans, leading to the fanciful notion that particularly ambitious high-frequency trading outfits would plant themselves in the middle of the Atlantic or the Mediterranean or the South China Sea and get the jump on competitors using floating micro-islands populated by small communities of elite pattern-recognition programmers overseeing the hyperfast flow of data through their superservers. Better yet: unmanned pods of densely packed microprocessors overseen by next-generation AI Bots processing billions of orders streaming out of other unmanned AI pods positioned optimally around the world, the silent beams of high-frequency orders shifting trillions across the earth’s oceans at light speeds, all automated, beyond the scope of humans to remotely grasp the nature of the transactions.

There was little question the computer revolution that made the AI dream a reality was irrevocably altering financial markets. Information about companies, currencies, bonds, and every other tradable instrument was digitized, fast as light. So-called machine-readable news was a hot new commodity. Breaking news about corporate events such as earnings reports was coded so that superfast algorithms could pick through it and react. Media outlets such as Reuters and Dow Jones published machine-readable news that pattern-recognition computers scanned and reacted to in the blink of an eye. High-tech trading firms gobbled up the information and gunned orders into the market at a rate faster than the beating wings of a hummingbird. With masses of data streaming through thousands of miles of fiber-optic cables laced around the world, and more people plugged into the Web through social networks, entirely novel techniques for leveraging data for trading were cropping up.


pages: 385 words: 111,113

Augmented: Life in the Smart Lane by Brett King

23andMe, 3D printing, additive manufacturing, Affordable Care Act / Obamacare, agricultural Revolution, Airbnb, Albert Einstein, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, blockchain, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, congestion charging, crowdsourcing, cryptocurrency, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, future of work, gig economy, Google Glasses, Google X / Alphabet X, Hans Lippershey, Hyperloop, income inequality, industrial robot, information asymmetry, Internet of things, invention of movable type, invention of the printing press, invention of the telephone, invention of the wheel, James Dyson, Jeff Bezos, job automation, job-hopping, John Markoff, John von Neumann, Kevin Kelly, Kickstarter, Kodak vs Instagram, Leonard Kleinrock, lifelogging, low earth orbit, low skilled workers, Lyft, M-Pesa, Mark Zuckerberg, Marshall McLuhan, megacity, Metcalfe’s law, Minecraft, mobile money, money market fund, more computing power than Apollo, Network effects, new economy, obamacare, Occupy movement, Oculus Rift, off grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, RFID, ride hailing / ride sharing, Robert Metcalfe, Satoshi Nakamoto, Second Machine Age, selective serotonin reuptake inhibitor (SSRI), self-driving car, sharing economy, Shoshana Zuboff, Silicon Valley, Silicon Valley startup, Skype, smart cities, smart grid, smart transportation, Snapchat, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, TaskRabbit, technological singularity, telemarketer, telepresence, telepresence robot, Tesla Model S, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, Turing complete, Turing test, uber lyft, undersea cable, urban sprawl, V2 rocket, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks

In the digital or information age, there was an initial push towards process efficiency, such as the early mainframes (like ERMA5), and further automation in the factory and production space. In the 1990s, this extended to business processes and operations being automated at an enterprise level with enterprise-wide software solutions like SAP. However, the Internet went further and disrupted distribution mechanics such as we saw in the book and music industries. The Augmented Age will bring about a huge rethink of processes involving dynamic decision-making, pattern recognition and advisory services as machine intelligence optimises those processes and feedback loops. Whereas the Internet was most commonly about disruption of distribution, availability of information and rethinking the value chain, the next age will be about disruption of information, intelligence and advice (the application of information and intelligence) itself. The Augmented Age will bring with it four major disruptions, and the emergence of two longer-term disruptive technologies: Artificial Intelligence that disrupts the nature of advice, that is better at everyday tasks like driving, health care and basic services than humans.

Whether it is advice on how to optimise your day’s activities from your bathroom mirror, an interactive chef that helps you cook your next meal or a travel advisory algorithm that optimises which flights you should book to increase your chance of an upgrade, there will increasingly be advice embedded into the world around us where it matters the most. There will be very few instances where a human who can give you advice at a future time with inferior data will compete with technologically embedded, contextual advice in real time. Machines Will Be Better at Learning about You Machine learning has been limited in the past by pattern recognition, natural speech and other deficiencies, but machines are beginning to catch up quickly. The advantage that machines connected to the Internet of Things and sensors will have is that they will be able to learn about your behaviour much more efficiently than service organisations today. How do service organisations today learn about your preferences? There are really only four ways: • demographic-based assumptions • surveys, marketing databases and user panels • data you’ve previously entered into the system or on a form • preferences you might input into an app, online portal or other configurator All of these are imprecise ways of measuring your preferences and behaviour, and at a very minimum depend on both your diligence and honesty in answering, and the effectiveness of the organisation in collecting and synthesizing that data.

Soon, our so-called “phones”11 will not only take instructions from us but also sense the tone and tenor of our voice, listening for choices, instructions or preferences, and making recommendations of experiences we might enjoy. While the humble camera and speaker are how we most commonly experience visual and audio innovations today, what lies beneath them has far more revolutionary potential. Next-generation retail experiences will evolve into something akin to neural and bio-synchronicity. The use of biometrics (fingerprint, facial, iris and voice identification), pattern recognition (emotional, stimulus response, location-based), behavioural psychology, sensory integration and augmented reality will change the retail experience into something that is a hybrid of technology and experiential design. We’ve talked about smart health care, smart banking, etc., in the other chapters of the book, so it’s natural that retailers and hospitality will seek to merge retail, travel and tech as much as possible.


pages: 717 words: 150,288

Cities Under Siege: The New Military Urbanism by Stephen Graham

addicted to oil, airport security, anti-communist, autonomous vehicles, Berlin Wall, call centre, carbon footprint, clean water, congestion charging, creative destruction, credit crunch, DARPA: Urban Challenge, defense in depth, deindustrialization, digital map, edge city, energy security, European colonialism, failed state, Food sovereignty, Gini coefficient, global supply chain, Google Earth, illegal immigration, income inequality, knowledge economy, late capitalism, loose coupling, market fundamentalism, mass incarceration, McMansion, megacity, moral panic, mutually assured destruction, Naomi Klein, New Urbanism, offshore financial centre, one-state solution, pattern recognition, peak oil, planetary scale, private military company, Project for a New American Century, RAND corporation, RFID, Richard Florida, Scramble for Africa, Silicon Valley, smart transportation, surplus humans, The Bell Curve by Richard Herrnstein and Charles Murray, urban decay, urban planning, urban renewal, urban sprawl, Washington Consensus, white flight, white picket fence

In 2005 it emerged that the US Capitol Police in Washington, DC, had become the first police department in the country to adopt a ‘shoot-to-kill’ policy for dealing with suspected suicide bombers. Associated with this was the doctrine of ‘behavior pattern recognition’, intended as a means to ‘identify and isolate the type of behavior that might precede an attack’.120 Both shoot-to-kill policies and behavior pattern recognition techniques have a long history in Israel, and since 2001, Israeli experts have trained law enforcement and security personnel from across the world on their implementation. The International Association of Chiefs of Police (IACP), a global organization that supports training of and co-operation among police, has been instrumental in this rapid international diffusion of Israeli doctrine regarding shoot-to-kill and behavior pattern recognition. The day after the devastating suicide bombing attacks on London’s tubes and buses, the IACP released its guidelines for dealing with potential suicide bombers, instructing ‘police officers to look for certain behavioral and physical characteristics, similar to those identified in behavior pattern recognition guidelines’.

As part of the broader shift towards robotic vehicles that fuels major competitions such as Urban Challenge (discussed in Chapter 10), the US Army envisages that one third of all US military ground vehicles will be entirely robotic by 2015. In a 2004 article, defence journalist Maryanne Lawlor47 discusses the development of ‘autonomous mechanized combatant’ air and ground vehicles, as well as what she calls ‘tactical autonomous combatants’ under development for the US Air Force. These are being designed, she notes, to use pattern-recognition software for ‘time-critical targeting’. This involves rapidly linking sensors to automated weapons so that targets that are automatically sensed and ‘recognized’ by databases can be quickly, continually, and automatically destroyed. In US military parlance, such doctrine is widely termed ‘compressing the kill chain’ or ‘sensor to shooter warfare’.48 According to Lawlor, the ‘swarming of unmanned systems’ project team at US Joint Forces Command’s Joint Concept Development & Experimentation Directorate, based in Norfolk, Virginia, has made so much progress that ‘autonomous, networked and integrated robots may be the norm rather than the exception by 2025’.49 By that date, she predicts, ‘technologies could be developed … that would allow machines to sense a report of gunfire in an urban environment to within one meter, triangulating the position of the shooter and return fire within a fraction of a second’, and she argues that such robo-war systems will ‘help save lives by taking humans out of harm’s way’.50 Apparently, only US military personnel fall within the category ‘human’.

The day after the devastating suicide bombing attacks on London’s tubes and buses, the IACP released its guidelines for dealing with potential suicide bombers, instructing ‘police officers to look for certain behavioral and physical characteristics, similar to those identified in behavior pattern recognition guidelines’. They also promoted ‘the use of lethal force, encouraging officers to aim for the suspect’s head and shoot-to-kill’. The IACP has held a number of training events in Israel to enable US and UK law enforcement officers to learn these policies.121 The implications of such imitation emerged during the investigations that followed the killing of a young Brazilian man, Jean Charles de Menezes, in London’s Stockwell tube station by anti-terrorist British police on 22 July 2005. In the scandal that followed, the extent to which Israeli shoot-to-kill counterterrorist policy had diffused to other states became starkly evident.


pages: 280 words: 73,420

Crapshoot Investing: How Tech-Savvy Traders and Clueless Regulators Turned the Stock Market Into a Casino by Jim McTague

algorithmic trading, automated trading system, Bernie Madoff, Bernie Sanders, Bretton Woods, buttonwood tree, buy and hold, computerized trading, corporate raider, creative destruction, credit crunch, Credit Default Swap, financial innovation, fixed income, Flash crash, High speed trading, housing crisis, index arbitrage, locking in a profit, Long Term Capital Management, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, pattern recognition, Ponzi scheme, quantitative trading / quantitative finance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, Vanguard fund, Y2K

Someone in the market was using the equivalent of steroids to trade in and out of the market faster than everybody else. As the men began to track down the hombre, they learned just how radically Regulation NMS had changed the market, and it surprised them. The change had engendered an explosion in the number of high-frequency traders plying the markets with super-charged computers and advanced pattern-recognition and statistical software designed to beat the market. These guys always had been around, but now there seemed to be a lot more of them, and their robotic trading machines were much faster than anything ever deployed in the markets. They programmed these overclocked computers to make money buying and selling stocks without direct human oversight. For every dozen firms, there were hundreds of these robotic trading wunderkinds, and their numbers were growing every day because venture capitalists and hedge funds were bankrolling start-ups left and right.

What the new regime did was anger the institutional investors, especially mutual funds. There was a new breed of trader prowling the stock market: the hedge fund. They liked to front-run institutional orders, which is why the institutions primarily had been trading in the old upstairs market, out of sight. Now the institutions would be defenseless prey for the hedge funds, which had an algorithmic advantage. The hedge funds had begun using pattern-recognition software, which could spot heavy buying and selling of specific securities and predict with an accuracy ranging from 60% to 80% how far the specific stock would move up or down on the next several trades. Thus, the hedge funds had the ability to front-run the market. They’d buy stock on an exchange where the targeted stock was priced low and go to Instinet or another ATS and sell the stock at the higher price predicted by the algorithm, knowing that big mutual funds were buying the stock in those venues.

Quants also were experts in statistical probability and would analyze historical data for patterns thought to repeat under certain circumstances, believing that future uncertainty could be reduced by collecting such information. For example, certain buzz words in the minutes of a Federal Reserve Board meeting might affect the stock market in a specific way. High-frequency traders eagerly embraced this kind of pattern-recognition software. The Quants, who predated the HFT industry by 20 years, often founded hedge funds as opposed to trading exclusively on their own dime. This practice invited regulatory scrutiny and the associated expenses whenever a member of the Quant fraternity had an exceptional meltdown. The SEC tried to regulate hedge funds in the wake of the Long Term Capital Management (LTCM) hedge fund debacle in 1998, but the courts threw out its rules.


pages: 238 words: 77,730

Final Jeopardy: Man vs. Machine and the Quest to Know Everything by Stephen Baker

23andMe, AI winter, Albert Einstein, artificial general intelligence, business process, call centre, clean water, commoditize, computer age, Frank Gehry, information retrieval, Iridium satellite, Isaac Newton, job automation, pattern recognition, Ray Kurzweil, Silicon Valley, Silicon Valley startup, statistical model, theory of mind, thinkpad, Turing test, Vernor Vinge, Wall-E, Watson beat the top human players on Jeopardy!

Modern males now display muscles as mating attire, much the way peacocks fan their otherwise useless feathers. It would be all too easy to dismiss human foes of the IBM machine as cognitive versions of circus strongmen: trivia wunderkinds. But from the very beginning, Ferrucci saw that the game required far more than the simple regurgitation of facts. It involved strategy, decision making, pattern recognition, and a knack for nuance in the language of the clues. As the computer grew from a whimsical idea into a Jeopardy behemoth, it underwent an entire education, triumphing in some areas, floundering in others. Its struggles, whether in untangling language or grappling with abstract ideas, highlighted the areas in which humans maintain an edge. It is in the story of Watson’s development that we catch a glimpse of the future of human as well as machine intelligence.

Yet teaching the machines proved extraordinarily difficult. One of the biggest challenges was to anticipate the responses of humans. The machines weren’t up to it. And they had serious trouble with even the most basic forms of perception, such as seeing. For example, researchers struggled to teach machines to perceive the edges of things in the physical world. As it turned out, it required experience and knowledge and advanced powers of pattern recognition just to look through a window and understand that the oak tree in the yard was a separate entity. It was not connected to the shed on the other side of it or a pattern on the glass or the wallpaper surrounding the window. The biggest obstacle, though, was language. In the early days, it looked beguilingly easy. It was just a matter of programming the machine with vocabulary and linking it all together with a few thousand rules—the kind you’d find in a grammar book.

In this tiny negotiation, far beyond the range and capabilities of machines, two people can bridge the gap between the formal definition of a word and what they really want to say. It’s hard to nail down the exact end of AI winter. A certain thaw set in when IBM’s computer Deep Blue bested Garry Kasparov in their epic 1997 showdown. Until that match, human intelligence, with its blend of historical knowledge, pattern recognition, and the ability to understand and anticipate the behavior of the person across the board, ruled the game. Human grandmasters pondered a rich set of knowledge, jewels that had been handed down through the decades—from Bobby Fischer’s use of the Sozin Variation in his 1972 match with Boris Spassky to the history of the Queen’s Gambit Denied. Flipping through scenarios at about three per second—a glacial pace for a computing machine—these grandmasters looked for a flash of inspiration, an insight, the hallmark of human intelligence.


pages: 252 words: 74,167

Thinking Machines: The Inside Story of Artificial Intelligence and Our Race to Build the Future by Luke Dormehl

Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic trading, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, book scanning, borderless world, call centre, cellular automata, Claude Shannon: information theory, cloud computing, computer vision, correlation does not imply causation, crowdsourcing, drone strike, Elon Musk, Flash crash, friendly AI, game design, global village, Google X / Alphabet X, hive mind, industrial robot, information retrieval, Internet of things, iterative process, Jaron Lanier, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kickstarter, Kodak vs Instagram, Law of Accelerating Returns, life extension, Loebner Prize, Marc Andreessen, Mark Zuckerberg, Menlo Park, natural language processing, Norbert Wiener, out of africa, PageRank, pattern recognition, Ray Kurzweil, recommendation engine, remote working, RFID, self-driving car, Silicon Valley, Skype, smart cities, Smart Cities: Big Data, Civic Hackers, and the Quest for a New Utopia, social intelligence, speech recognition, Stephen Hawking, Steve Jobs, Steve Wozniak, Steven Pinker, strong AI, superintelligent machines, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, too big to fail, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!

Another researcher named Arthur Samuel thought the name sounded ‘phony’, while still others – Alan Newell and Herbert Simon – immediately reverted to calling their work ‘complex information programming’. The rapid division of Artificial Intelligence into different specialties didn’t take long. For evidence, look no further than the UK’s ‘Mechanisation of Thought Processes’ conference, organised at the National Physical Laboratory in Teddington, Middlesex in 1958. Just two years after the Dartmouth conference, AI was already split into fields including ‘artificial thinking, character and pattern recognition, learning, mechanical language translation, biology, automatic programming, industrial planning and clerical mechanisation’. The period that followed is often considered to be the glory days of classic AI. The field was fresh, apparent progress was being made, and thinking machines seemed to lurk just over the horizon. It didn’t hurt that funding was plentiful, either – largely thanks to government organisations such as the US Defense Department’s Advanced Research Projects Agency (ARPA).

As is often the case with an exciting field that resonates with the general public, part of the blame must lie with the press. Overenthusiasm meant that impressive, if incremental, advances were often written up as though truly smart machines were already here. For example, one heavily hyped project was a 1960s robot called SHAKEY, described as the world’s first general-purpose robot capable of reasoning about its own actions. In doing so, it set benchmarks in fields like pattern recognition, information representation, problem solving and natural language processing. That alone should have been enough to make SHAKEY exciting, but journalists couldn’t resist a bit of embellishment. As such, when SHAKEY appeared in Life magazine in 1970, he was hailed not as a promising combination of several important research topics, but as the world’s ‘first electronic person’. Tying SHAKEY into the space mania still carrying over from the previous year’s Moon landing, Life’s reporter went so far as to claim SHAKEY could ‘travel about the Moon for months at a time without a single beep of direction from the earth’.

To make it the ‘Worldwide Headquarters’ they thought it should be, they kitted it out with a few tables, three chairs, a turquoise shag rug, a folding ping-pong table and a few other items. The garage door had to be left open for ventilation. It must have seemed innocuous at the time, but over the next two decades, Larry Page and Sergey Brin’s company would make some of the biggest advances in AI history. These spanned fields including machine translation, pattern recognition, computer vision, autonomous robots and far more, which AI researchers had struggled with for half a century. Virtually none of it was achieved using Good Old-Fashioned AI. The company’s name, of course, was Google. CHAPTER 2 Another Way to Build AI IT IS 2014 and, in the Google-owned London offices of an AI company called DeepMind, a computer whiles away the hours by playing an old Atari 2600 video game called Breakout.


pages: 280 words: 74,559

Fully Automated Luxury Communism by Aaron Bastani

"Robert Solow", autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Bretton Woods, capital controls, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, David Ricardo: comparative advantage, decarbonisation, dematerialisation, Donald Trump, double helix, Elon Musk, energy transition, Erik Brynjolfsson, financial independence, Francis Fukuyama: the end of history, future of work, G4S, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, liberal capitalism, low earth orbit, low skilled workers, M-Pesa, market fundamentalism, means of production, mobile money, more computing power than Apollo, new economy, off grid, pattern recognition, Peter H. Diamandis: Planetary Resources, post scarcity, post-work, price mechanism, price stability, private space industry, Productivity paradox, profit motive, race to the bottom, RFID, rising living standards, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, stem cell, Stewart Brand, technoutopianism, the built environment, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, transatlantic slave trade, Travis Kalanick, universal basic income, V2 rocket, Watson beat the top human players on Jeopardy!, Whole Earth Catalog, working-age population

While that was an iconic moment in the unfolding story of humans and machines, it paled in comparison to Watson, also built by IBM, when it later defeated Ken Jennings and Brad Rutter – two of the greatest Jeopardy! players in the history of the TV quiz show. Chess is a uniquely challenging game, but Jeopardy!, which demands real-time pattern recognition and creative thinking, more closely resembles the features associated with distinctively human intelligence. Not long after, Ken Jennings neatly summed up what that defeat might mean for white-collar work – which values pattern recognition and creative thinking – over the coming decades. Just as factory jobs were eliminated in the twentieth century by new assembly-line robots, Brad and I were the first knowledge-industry workers put out of work by the new generation of ‘thinking’ machines. ‘Quiz show contestant’ may be the first job made redundant by Watson, but I’m sure it won’t be the last.

While the robot itself is not automated – it instead grants a human surgeon far higher levels of dexterity and precision – the paths to automating a range of its regular operations resemble the blueprint for a self-driving car: you give a powerful data processor huge amounts of information, machine learning and a scalpel. The first part allows algorithms to model and reproduce outcomes and work their way through highly repetitive tasks, while the second allows for immediate and smart responses to unexpected situations. In medicine, you can see how that would be applied to pretty much anything – from eye examinations to treating prostate cancer or taking blood. In areas more dependent on pattern recognition, such as radiology, machines have even more of an advantage. Radiologists use medical images like X-rays, CT and PET scans, MRIs and ultrasounds to diagnose and treat patients. While the field has greatly improved patient care over the last few decades, it has contributed to escalating costs and is relatively labour intensive. That is, until now. Arterys, a medical imaging system, reads MRIs of the heart and measures blood flow through its ventricles.


The Autistic Brain: Thinking Across the Spectrum by Temple Grandin, Richard Panek

Asperger Syndrome, correlation does not imply causation, dark matter, David Brooks, deliberate practice, double helix, ghettoisation, if you see hoof prints, think horses—not zebras, impulse control, Khan Academy, Mark Zuckerberg, meta analysis, meta-analysis, mouse model, neurotypical, pattern recognition, phenotype, Richard Feynman, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Steve Jobs, theory of mind, twin studies

The way the autistic brain engages with biological motion is reminiscent of Tito’s description of focusing on a door at the expense of seeing the room, or a description by Donna Williams I once read, of her being entranced by individual motes of dust. The interpretation of this tendency as a deficit in social pattern recognition was adopted by R. Peter Hobson in an influential series of studies he spearheaded in the 1980s at the Institute of Psychiatry in London. Did children with autism prefer to sort photographs according to facial expressions exhibited (happy or sad) or the type of hat worn (floppy or woolen)? The hats won. Did children with autism have trouble putting the pieces of a face together into an interpretation of facial emotions? Yes.9 These are important findings. But there can be a flip side to a deficit in social pattern recognition: a strength in pure pattern recognition—being really good at seeing the trees. Studies have repeatedly shown that people with autism perform better than neurotypicals on embedded-figure tests—a variation on the old something’s-hidden-in-the-picture game.


pages: 178 words: 43,631

Spoiled Brats: Short Stories by Simon Rich

dumpster diving, immigration reform, Kickstarter, Occupy movement, pattern recognition

“If he wants to talk,” I said, “then he can climb down.” My mother stared at me angrily for a moment—then galloped off screaming into the night. “I met with the scientists,” I told my parents the next day. “They said I was the smartest chimp they’d ever seen.” “La-di-da,” my father said. My mother was standing behind him, in her usual grooming position. Neither looked up at me. “They tested me on memory, pattern recognition, and object permanence,” I told them. “There were dozens of chimps, but I scored the highest.” “Good for you,” my father grumbled, his voice thick with sarcasm. Nobody said anything for a while. Eventually, my mother broke the silence. “It was a big day at the shit pile,” she said. “Your father found three grubs.” He grunted with pleasure, clearly relieved to be the center of attention again.

“Oh, I don’t know,” he said, waving his paws around. “Please!” “Oh, all right. So one time, I’m in my tree, and Jane—” “He means Jane Goodall—” A whirring noise sounded in the clearing below. I looked down and sighed. Fitzbaum was already searching for my replacement. His truck was loaded with various testing apparatuses. I recognized a large plastic box from the day we’d met. It was a simple pattern-recognition test. You climbed inside and watched as three colored orbs lit up. If you hit the corresponding levers in the correct order, you won a banana. I could still remember how Fitzbaum had beamed when I solved it on my very first try. I heard some rustling in the trees around us. Dozens of chimps were balanced on high branches, watching Fitzbaum skeptically. “Here, chimpy, chimpy,” my old friend said, dragging the plastic box out of his truck.


pages: 159 words: 42,401

Snowden's Box: Trust in the Age of Surveillance by Jessica Bruder, Dale Maharidge

anti-communist, Bay Area Rapid Transit, Berlin Wall, blockchain, Broken windows theory, Burning Man, cashless society, Chelsea Manning, citizen journalism, computer vision, crowdsourcing, Donald Trump, Edward Snowden, Elon Musk, Ferguson, Missouri, Filter Bubble, Firefox, Internet of things, Jeff Bezos, Julian Assange, license plate recognition, Mark Zuckerberg, mass incarceration, medical malpractice, Occupy movement, off grid, pattern recognition, Peter Thiel, Robert Bork, Shoshana Zuboff, Silicon Valley, Skype, social graph, Steven Levy, Tim Cook: Apple, web of trust, WikiLeaks

She kindly offered to let us sit with the archive too. By this point, however, we’d come to feel that the focus of our book was the human relationships that shepherded Snowden’s box — rather than the material inside. So we let it go. So much had happened since the box arrived. Dale and I were driven by a desire for deeper understanding, a sense of what it all meant. As Snowden himself once put it: “Humans are by nature pattern-recognition machines. We search for meaning, whether in the circumstances of our lives or on the surface of toast.” Like Laura, we emerged from the dark tunnel of those tense years. We made peace knowing that there are questions we’ll never be able to answer. We’re only human, after all. We do what we can with the information we have. And we move on. But the experience changed us. Even though the world is full of things we can’t control, we do what we can.

p. 130 Jabber singled out in Assange indictment: Glenn Greenwald and Micah Lee, “The US Government’s Indictment of Julian Assange Poses Grave Threats to Press Freedom,” Intercept, April 11, 2019. p. 130 Micah Lee comments on Assange indictment: twitter. com/micahflee/status/1116340396643082240?lang=en. p. 130 Snowden comments on Assange indictment: twitter. com/snowden/status/1131657973745496066?lang=en. p. 131 Snowden on humans as “pattern-recognition machines”: Poitras, Astro Noise, 121. pp. 132–3 Orwell on being human: George Orwell, “In Front of Your Nose,” Tribune (London), March 22, 1946. Appendix p. 136 Signal’s 400 percent jump in downloads: Jeff John Roberts, “This Messaging App Saw a Surge after Trump’s Election,” Fortune, December 2, 2016. p. 136 Moxie Marlinspike on Trump: Brian X. Chen, “Worried about the Privacy of Your Messages?


pages: 294 words: 86,601

Mind Wide Open: Your Brain and the Neuroscience of Everyday Life by Steven Johnson

Columbine, double helix, epigenetics, experimental subject, Gödel, Escher, Bach, James Watt: steam engine, l'esprit de l'escalier, lateral thinking, pattern recognition, phenotype, social intelligence, Steven Pinker, theory of mind, zero-sum game

Whether that wiring comes courtesy of my genes or my lived experience, or via some combination package, is not necessarily relevant. What matters are the incoming stimuli and the pattern of activity that they spark: your brain taking in a certain configuration of sensory data from the outside world (or from your imagination or your memory banks) and triggering a neurochemical reaction in your head. Pattern recognition instead of code breaking-this may be the simplest way to describe the difference between the twenty-first-century Freud and the original. The two approaches can be readily blurred together. After all, you need pattern recognition tools to break a code. But breaking a code involves a further step-translating the encoded message back into its original form. In reality, those patterns in my own head don’t conceal a secret meaning that analysis can unearth after intense scrutiny; they don’t have symbolic depth.

Broca’s area of frontal lobes of prefrontal lobes of Wernicke’s area of nervous system neuroanatomy neurochemicals neuroendocrinology neurofeedback technology brain wave measurement and graphic display and high-speed processing of history of interactive process of mind expansion and mode switching and popular acceptance of recreational uses of sports and therapeutic uses of training in neurology neuromap fallacy neuroprofiles neurons atrophy of communication of mirror synapses and neuropsychoanalysis neuroscience see also brain science neurotrainers neurotransmitters New Age New England Journal of Medicine New York University (NYU) nicotine Nietzsche, Friedrich nonsense norepinephrine Norretranders, Tor Notes from the Underground (Dostoyevsky) nucleus accumbens numbers, memory of nurturing instinct occipital lobe Ochsner, Kevin Oedipal complex opium poppy optical illusions optic nerve orbicularis oculi organs Othmer, Sigfried Othmer, Susan Othmer Institute overeaters Oxford Dictionary of Quotations oxycontin oxygen oxytocin bonding and stress and Pac-Man pain memories of relief of panic Panksepp, Jaak paralysis parapsychology parietal lobe Parkinson’s disease pattern recognition Pavlov, Ivan Peak Achievement Trainer peptides periaqueductal gray (PAG) personal appearance personality competing brain systems and split true two sides of unified bisocial theory of PET scans PGA tournaments pharmacology phenylethylamine philosophy phobias phonological loop photography phrenology physics physiology Pink Floyd planning play PlayStation pleasure brain circuitry for chemical fixes for drive for shivers and chills of see also drugs, recreational pleasure principle post-traumatic stress disorder prairie voles pregnancy primal scream therapy protein synthesis Proust, Marcel Provine, Robert Prozac psyche competing forces of psychiatry psychic economy psychoanalysis psychology evolutionary pop psychopharmacology psychotherapy public speaking Quake qualia quantum behavior racism radioactive material Raiders of the Lost Ark Rain Man Rand, Ayn rats Ravel, Maurice reading skills “Reading the Mind in the Eyes” test Reagan, Ronald reasoning skills reconsolidation process reductionism reflexes rejection sensitivity relaxation deep repetition compulsion repression of memories reproduction reptiles reptilian brain, see brain stem response: autonomia conditioned knee-jerk startle see also fight-or-flight response Ritalin rivalry Robbins, Jim Rodenbough, John Rosenblum, Hal rumination Sacks, Oliver sadness Sagan, Carl Salpêtrière Hospital SATs schadenfreude schizophrenics Schopenhauer, Arthur science Science Seagal, Steven Seiden, Leslie self-awareness brain science and unified widening and changing of self-doubt self-exploration self-help self-improvement self-knowledge September 11 attacks serotonin sex climactic evolution and hormones and instincts of love and in men vs. women monogamous reproduction and Shakespeare, William Sime, Wes simulation theory sincerity skin sleep deprivation smallpox smiles fake genuine love and muscular underpinnings of Smith, Patti snakes social Darwinism social skills impairment of see also mind reading Solms, Mark sound electric shock combined with fear memories of sound waves spacial skills spandrels speech intonation and listening to public spinal column spinning sports: accidents in neurofeedback and startle response Stern, Daniel stomach, nervous storms stress brain activity and hormones and job-related male vs. female responses to memory and oxytocin and physical damage of relief of triggers of stroke left-hemisphere long-term deficits of Stroop Interference Test sugar superego superstition supervisory attention control support groups surprise sweating Symons, Donald sympathy faking of Symphony in the Brain, A (Robbins) synapses neurons and strengthening of talking cure see also psychoanalysis taste Taylor, Shelley telepathy temporal lobe “Tending Instinct, The” (Taylor) testosterone thalamus auditory cortex link to visual theory of other minds therapies see also specific therapies thermodynamics thought: assessment of other people’s controlling of feeling separated from shutting off 3-D space tickling transendental meditation (TM) transference triune brain “Uncanny, The” (Freud) unconscious consciousness vs.


pages: 480 words: 119,407

Invisible Women by Caroline Criado Perez

Affordable Care Act / Obamacare, augmented reality, Bernie Sanders, collective bargaining, crowdsourcing, Diane Coyle, Donald Trump, falling living standards, first-past-the-post, gender pay gap, gig economy, glass ceiling, Grace Hopper, Hacker Ethic, Indoor air pollution, informal economy, lifelogging, low skilled workers, mental accounting, meta analysis, meta-analysis, Nate Silver, new economy, obamacare, Oculus Rift, offshore financial centre, pattern recognition, phenotype, post-industrial society, randomized controlled trial, remote working, Silicon Valley, Simon Kuznets, speech recognition, stem cell, Stephen Hawking, Steven Levy, the built environment, urban planning, women in the workforce, zero-sum game

Boler agrees. ‘We did talk to some VC investors who didn’t believe [Elvie] was an interesting proposition,’ she tells me. The other problem women face when it comes to getting investment is ‘pattern recognition’.13 A corollary of ‘culture fit’, pattern recognition sounds data-driven, but it’s basically just a fancy term for looks-similar-to-something-that-has-worked-in-the-past – where ‘something’ could be white-male-founder-who-dropped-out-of-Harvard-and-wears-hoodies. Genuinely: I dated a guy who was working on a start-up and he referenced this uniform when he was talking about getting funding. Hoody-based pattern recognition is real. And this emphasis on recognising a typically male pattern may be exacerbated by the common belief that tech is a field where inborn ‘genius’ (which, as we’ve seen, is stereotypically associated with men14) is more important than working hard (hence fetishising Harvard dropouts).

Australia gender pay gap gendered poverty Gillard ministries (2010–13) homelessness leisure time maternity. leave medical research military murders paternity. leave political representation precarious work school textbooks sexual assault/harassment taxation time-use surveys unpaid work Australia Institute Austria autism auto-plastics factories Autoblog autoimmune diseases automotive plastics workplaces Ayrton, Hertha Azerbaijan babies’ cries baby bottles Baker, Colin Baku, Azerbaijan Ball, James Bangladesh Bank of England banknotes Barbican, London Barcelona, Catalonia beauticians de Beauvoir, Simone Beer, Anna Beijing, China Belgium Berkman Center for Internet and Society Besant, Annie BI Norwegian Business School bicarbonate of soda Big Data bile acid composition biomarkers biomass fuels biomechanics Birka warrior Birmingham, West Midlands bisphenol A (BPA) ‘bitch’ bladder ‘Blank Space’ (Swift) blind recruitment blood pressure Bloom, Rachel Bloomberg News Bock, Laszlo body fat body sway Bodyform Boesel, Whitney Erin Boler, Tania Bolivia Boosey, Leslie Boserup, Ester Bosnia Boston Consulting Group Botswana Bouattia, Malia Boulanger, Béatrice Bourdieu, Pierre Bovasso, Dawn Boxing Day tsunami (2004) boyd, danah brain ischaemia Brazil breasts cancer feeding and lifting techniques pumps reduction surgery and seat belts and tactile situation awareness system (TSAS) and uniforms Bretherton, Joanne Brexit Bricks, New Orleans brilliance bias Brin, Sergey British Electoral Survey British Journal of Pharmacology British Medical Journal British Medical Research Council British National Corpus (BNC) Broadly Brophy, Jim and Margaret Buick Bulgaria Burgon, Richard Bush, Stephen Buvinic, Mayra BuzzFeed Cabinet caesarean sections Cairns, Alex California, United States Callanan, Martin Callou, Ada Calma, Justine calorie burning Cambridge Analytica Cameron, David Campbell Soup Canada banknotes chemical exposure childcare crime homelessness medical research professor evaluations 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Czech Republic daddy quotas Daly, Caroline Louisa Data2x Davis-Blake, Alison Davis, Wendy Davison, Peter defibrillators deforestation Delhi, India dementia Democratic Party Democratic Republic of the Congo Democratic United Party dengue fever Denmark dental devices Department for Work and Pensions depression diabetes diarrhoea diet diethylstilbestrol (DES) disabled people disasters Ditum, Sarah diversity-valuing behavior DNA (deoxyribonucleic acid) Do Babies Matter (Goulden, Mason, and Wolfinger) Doctor Who domestic violence Donison, Christopher Doss, Cheryl ‘draw a scientist’ driving dry sex Dyas-Elliott, Roger dysmenorrhea E3 Eagle, Angela early childhood education (ECE) Ebola economics Economist, The Edexcel education Edwards, Katherine Einstein, Albert elderly people Eliot, George Elks lodges Elvie emoji employment gender pay gap occupational health parental leave precarious work sexual assault/harassment and unpaid work ‘End of Theory, The’ (Anderson) endocrine disrupting 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fitness devices flexible working Folbre, Nancy Food and Agriculture Organization (FAO) Food and Drug Administration (FDA) football forced marriage Ford Fordham, Maureen fragile states France Franklin, Rosalind Frauen-Werk-Stadt free weights Freeman, Hadley French language Freud, Sigmund From Poverty to Power (Green) funeral rites FX gaming GapJumpers Gates Foundation Gates, Melinda gathering Geffen, David gender gender data gap academia agriculture algorithms American Civil War (1861–5) brilliance bias common sense crime Data2x female body historical image datasets innovation male universality medical research motion sickness occupational health political representation pregnancy self-report bias sexual assault/harassment smartphones speech-recognition technology stoves taxation transport planning unpaid work warmth vs competence Gender Equality Act (1976) Gender Global Practice gender pay gap gender-fair forms gender-inflected languages gendered poverty genderless languages Gendersite General Accounting Office generic masculine genius geometry Georgetown University German language German Society of Epidemiology Germany academia gender pay gap gender-inflected language Landesamt für Flüchtlingsangelegenheiten (LAF) medical research precarious work refugee camps school textbooks unpaid work Gezi Park protests (2013) Ghana gig economy Gild Gillard, Julia GitHub Glencore Global Alliance for Clean Cookstoves Global Gender Gap Index Global Media Monitoring Project Golden Globes Google artificial intelligence (AI) childcare Images maternity leave News Nexus petabytes pregnancy parking promotions search engine speech-recognition software Translate Gosling, Ryan Gothenburg, Sweden Gove, Michael Government Accounting Office (GAO) Great Depression (1929–39) Greece Green, Duncan Greenberg, Jon groping gross domestic product (GDP) Grown, Caren Guardian Gujarat earthquake (2001) Gulf War (1990–91) gyms H1N1 virus Hackers (Levy) hand size/strength handbags handprints haptic jackets Harman, Harriet Harris, Kamala Harvard University hate crimes/incidents Hawking, Stephen Haynes, Natalie Hayward, Sarah Hazards Health and Safety at Work Act (1974) Health and Safety Executive (HSE) health-monitoring systems healthcare/medicine Hearst heart attacks disease medication rhythm abnormalities surgery Heat St Heinrich Böll Foundation Helldén, Daniel Henderson, David Henry Higgins effect Henry VIII, King of England Hensel, Fanny hepatitis Hern, Alex high-efficiency cookstoves (HECs) Higher Education Statistics Agency Himmelweit, Sue hip belts history Hodgkin’s disease Holdcrofity, Anita Hollaback ‘Hollywood heart attack’ Homeless Period, The homelessness hopper fare Hopper, Grace hormones House of Commons Household Income Labour Dynamics of Australia Survey housekeeping work Howard, Todd human immunodeficiency virus (HIV) Human Rights Act (1998) Human Rights Watch human–computer interaction Hungary hunter-gatherer societies Huntingdon, Agnes Hurricane Andrew (1992) Hurricane Katrina (2005) Hurricane Maria (2017) hyperbolic geometry hysterectomies hysteria I Am Not Your Negro Iceland identity Idomeni camp, Greece Illinois, United States images immune system Imperial College London Inc Income of Nations, The (Studenski) indecent exposure Independent India Boxing Day tsunami (2004) gendered poverty gross domestic product (GDP) Gujarat earthquake (2001) political representation sexual assault/harassment stoves taxation toilets unpaid work Indian Ocean tsunami (2004) Industrial Revolution (c. 1760–1840) influenza Inmujeres innovation Institute for Fiscal Studies Institute for Women’s Policy Research Institute of Medicine Institute of Women’s Policy Research (IWPR) institutionalised rape Insurance Institute for Highway Safety Inter-agency Working Group on Reproductive Health in Crises Inter-Parliamentary Union’s (IPU) Internal Revenue Service (IRS) International Agency Research on Cancer International Conference on Intelligent Data Engineering and 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maternity leave mathematics Mazarra, Glen McCabe, Jesse McCain, John McGill University McKinsey McLean, Charlene Medela medicine/healthcare Medline Memorial University Mendelssohn, Felix Mendes, Eva Mendoza-Denton, Rodolfo menopause menstruation mental health meritocracy Messing, Karen meta gender data gap MeToo movement Metroid mewar angithi (MA) Mexico Miami, Florida mice Microsoft migraines military Milito, Beth Miller, Maria Minassian, Alek Minha Casa, Minha Vida miscarriages Mismeasure of Woman, The (Tavris) misogyny Mitchell, Margaret Mogil, Jeffrey Mongolia Montreal University Morgan, Thomas Hunt morphine motion parallax motion sickness Motorola multiple myeloma Mumbai, India murders Murray, Andrew muscle music My Fair Lady myometrial blood ‘Myth’ (Rukeyser) nail salons Naipaul, Vidiadhar Surajprasad naive realism National Aeronautics and Space Administration (NASA) National Autistic Society National Democratic Institute National Health Service (NHS) National Highway Traffic Safety Administration (NHTSA) National Institute for Health and Case Excellence (NICE) National Institute of Health Revitalization Act (1993) National Institutes of Health (NIH) National Union of Students (NUS) natural gender languages Nature Navarro, Jannette Naya Health Inc Nea Kavala camp, Greece Neitzert, Eva Neolithic era Netflix Netherlands neutrophils New Jersey, United States New Orleans, Louisiana New Statesman New York, United States New York Committee for Occupational Safety & Health (NYCOSH) New York Philharmonic Orchestra New York Times New Yorker New Zealand Newham, London Nigeria Nightingale, Florence Nobel Prize nomunication Norris, Colleen Norway Nottingham, Nottinghamshire nurses Nüsslein-Volhard, Christiane O’Neil, Cathy O’Neill, Rory Obama, Barack Occupational Health and Safety Administration occupational health Oedipus oestradiol oestrogen office temperature Olympic Games Omron On the Generation of Animals (Aristotle) orchestras Organisation for Economic Cooperation and Development (OECD) Organisation for the Study of Sex Differences Orissa, India osteopenia osteoporosis ovarian cancer Oxfam Oxford English Dictionaries Oxford University oxytocin pacemakers pain sensitivity pairing Pakistan Pandey, Avanindra paracetamol parental leave Paris, France Parkinson’s disease parks passive tracking apps paternity leave patronage networks pattern recognition Payne-Gaposchkin, Cecilia peace talks pelvic floor pelvic inflammatory disease pelvic stress fractures pensions performance evaluations periods Persian language personal protective equipment (PPE) Peru petabytes Pew Research Center phantom-limb syndrome phenylpropanolamine Philadelphia, Pennsylvania Philippines phobias phthalates Physiological Society pianos Plato plough hypothesis poetry Poland police polio political representation Politifact Pollitzer, Elizabeth Portland, Oregon Portugal post-natal depression post-traumatic stress disorder (PTSD) poverty Powell, Colin PR2 Prada prams precarious work pregnancy Pregnant Workers Directive (1992) premenstrual syndrome (PMS) primary percutaneous coronary interventions (PPCI) Prinz-Brandenburg, Claudia progesterone projection bias prolapse promotions proportional representation (PR) Prospect Union Prospect Public Monuments and Sculptures Association public sector equality duty (PSED) public transport Puerto Rico purchasing authority ‘quantified self’ community Quebec, Canada QuiVr radiation Rajasthan, India rape RateMyProfessors.com recruitment Red Tape Challenge ‘Redistribution of Sex, The’ Reference Man Reformation refugees Renaissance repetitive strain injury (RSI) Representation of the People Act (1832) Republican Party Resebo, Christian Reykjavik, Iceland Rhode Island, United States Rio de Janeiro, Brazil risk-prediction models road building Road Safety on Five Continents Conference Roberts, David Robertson, Adi robots Rochdale, Manchester Rochon Ford, Anne Rudd, Kevin Rukeyser, Muriel Russian Federation Rwanda Sacks-Jones, Katharine Saenuri Party Safecity SafetyLit Foundation Sánchez de Madariaga, Inés Sandberg, Sheryl Sanders, Bernard Santos, Cristine Schalk, Tom Schenker, Jonathan Schiebinger, Londa School of Oriental and African Studies (SOAS) school textbooks Schumann, Clara science, technology, engineering and maths (STEM) Scientific American scientists Scotland Scythians ‘sea of dudes’ problem Seacole, Mary seat belts Second World War (1939.45) self-report bias September 11 attacks (2001) Serbia Sessions, Jefferson severe acute respiratory syndrome (SARS) sex Sex Discrimination Act (1975) sex robots sex-disaggregated data agriculture chemical exposure conflict employment fall-detection devices fitness devices gendered poverty medical research precarious work smartphones taxation transport urban design virtual reality voice recognition working hours sexual violence/harassment shape-from-shading Sherriff, Paula Shield, The shifting agriculture Sierra Leone sildenafil citrate Silicon Valley Silver, Nate Singh, Jyoti single parents single-member districts (SMD) Siri Skåne County, Sweden skeletons skin Slate Slocum, Sally Slovenia smartphones snow clearing social capital social data Social Democratic Party (SDP) social power socialisation Solna, Sweden Solnit, Rebecca Somalia Sony Ericsson Sounds and Sweet Airs (Beer) South Africa South Korea Soviet Union (1922–91) Spain Spanish language Speak with a Geek speech-recognition technology Sphinx sports Sprout Pharmaceuticals Sri Lanka St Mark’s, Venice St Vincent & the Grenadines stab vests Stack Overflow Stanford University staple crops Star Wars Starbucks Starkey, David statins statues stem cells Stevens, Nettie Stockholm, Sweden Stoffregen, Tom stoves Streisand, Barbra streptococcal toxic shock syndrome stress strokes Strozzi, Barbara Studenski, Paul Sulpicia Supreme Court Sweden Birka warrior car crashes councils crime depression gender pay gap heart attacks murders paternity leave political representation refugee camps snow clearing sports taxation unpaid work youth urban regeneration Swedish National Road and Transport Research Institute Swift, Taylor swine flu Swinson, Joanne Kate ‘Jo’ Switzerland Syria Systran tactile situation awareness system (TSAS) Taimina, Daina Taiwan Tate, Angela Tatman, Rachael Tavris, Carol taxation teaching evaluations Teaching Excellence Framework (TEF) tear gas tech industry television temperature Temperature Temporary Assistance to Needy Families tennis tenure-track system text corpora thalidomide ThinkProgress Thor three-stone fires time poverty time-use surveys TIMIT corpus Tin, Ida toilets Toksvig, Sandi tools Toronto, Ontario Tottenham, London Toyota Trades Union Congress (TUC) tradition transit captives transportation treadmills trip-chaining troponin Trump, Donald tuberculosis (TB) Tudor period (1485–1603) Tufekci, Zeynep Turkey Twitter Uberpool Uganda Ukraine ulcerative colitis Ulrich, Laurel Thatcher Umeå, Sweden Understanding Girls with ADHD (Littman) unemployment unencumbered people Unicode Consortium Unison United Association of Civil Guards United Kingdom academia austerity autism banknotes breast pumps Brexit (2016–) Fire Brigade caesarean sections children’s centres chronic illness/pain coastguards councils employment gap endometriosis Equality Act (2010) flexible working gender pay gap gendered poverty general elections generic masculine gross domestic product (GDP) heart attacks homelessness Human Rights Act (1998) leisure time maternity leave medical research military murders music nail salons occupational health paternity leave pedestrians pensions personal protective equipment (PPE) police political representation precarious work public sector equality duty (PSED) Representation of the People Act (1832) scientists Sex Discrimination Act (1975) sexual assault/harassment single parents statues stress taxation toilets transportation trip-chaining universities unpaid work Yarl’s Wood Detention Centre United Nations Children’s Fund (UNICEF) Commission on the Status of Women Data2x Economic Commission for Africa Food and Agriculture Organization (FAO) homicide survey Human Development Report and peace talks Population Fund Security Council Resolution 1325 (2000) and stoves and Switzerland and toilets and unpaid childcare Women’s Year World Conference on Women United States academia Affordable Care Act (2010) Agency for International Development (USAID) Alzheimer’s disease banknotes bisphenol A (BPA) breast pumps brilliance bias Bureau of Labor Statistics car crashes chief executive officers (CEO) childbirth, death in Civil War (1861–5) construction work councils crime early childhood education (ECE) employment gap endocrine disrupting chemicals (EDCs) endometriosis farming flexible working gender pay gap gendered poverty generic masculine Great Depression (1929–39) gross domestic product (GDP) healthcare heart attacks Hurricane Andrew (1992) Hurricane Katrina (2005) Hurricane Maria (2017) immigration 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agriculture and algorithms and gross domestic product (GDP) and occupational health and stoves and transport in workplace and zoning upper body strength upskirting urinals urinary-tract infections urination uro-gynaecological problems uterine failure uterine tybroids Uttar Pradesh, India Uzbekistan vaccines vagina Valium Valkrie value-added tax (VAT) Van Gulik, Gauri Venice, Italy venture capitalists (VCs) Veríssimo, Antônio Augusto Viagra Victoria, Queen of the United Kingdom video games Vienna, Austria Vietnam Vikings Villacorta, Pilar violence virtual reality (VR) voice recognition Volvo voting rights Vox voyeurism Wade, Virginia Walker, Phillip walking wallet to purse Walmart warfare warmth vs competence Warsaw Pact Washington Post Washington Times Washington, DC, United States WASHplus WaterAid Watson, James We Will Rebuild weak contractions Weapons of Math Destruction (O’Neil) West Bengal, India whiplash Wiberg-Itzel, Eva Wikipedia Wild, Sarah Williams, Gayna Williams, Serena Williams, Venus Williamson, £eresa Willow Garage Wimbledon Windsor, Ontario Winter, Jessica Wired Wolf of Wall Street, The Wolfers, Justin Wolfinger, Nicholas ‘Woman the Gatherer’ (Slocum) Women and Equalities Committee Women Will Rebuild Women’s Budget Group (WBG) Women’s Design Service Women’s Engineering Society Women’s Refugee Commission Women’s Year Woolf, Virginia workplace safety World Bank World Cancer Research Fund World Cup World Economic Forum (WEF) World Health Organization (WHO) World Meteorological Organisation worm infections Woskow, Debbie Wray, Susan Wyden, Robert XY cells Y chromosome Yale University Yarl’s Wood Detention Centre, Bedford Yatskar, Mark Yemen Yentl syndrome Yezidis Youth Vote, The youthquake Zambia zero-hour contracts Zika zipper quotas zombie stats zoning Zou, James Photo by Rachel Louise Brown CAROLINE CRIADO PEREZ is a writer, broadcaster, and feminist activist and was named Liberty Human Rights Campaigner of the Year and OBE by the Queen.


pages: 410 words: 119,823

Radical Technologies: The Design of Everyday Life by Adam Greenfield

3D printing, Airbnb, augmented reality, autonomous vehicles, bank run, barriers to entry, basic income, bitcoin, blockchain, business intelligence, business process, call centre, cellular automata, centralized clearinghouse, centre right, Chuck Templeton: OpenTable:, cloud computing, collective bargaining, combinatorial explosion, Computer Numeric Control, computer vision, Conway's Game of Life, cryptocurrency, David Graeber, dematerialisation, digital map, disruptive innovation, distributed ledger, drone strike, Elon Musk, Ethereum, ethereum blockchain, facts on the ground, fiat currency, global supply chain, global village, Google Glasses, IBM and the Holocaust, industrial robot, informal economy, information retrieval, Internet of things, James Watt: steam engine, Jane Jacobs, Jeff Bezos, job automation, John Conway, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, joint-stock company, Kevin Kelly, Kickstarter, late capitalism, license plate recognition, lifelogging, M-Pesa, Mark Zuckerberg, means of production, megacity, megastructure, minimum viable product, money: store of value / unit of account / medium of exchange, natural language processing, Network effects, New Urbanism, Occupy movement, Oculus Rift, Pareto efficiency, pattern recognition, Pearl River Delta, performance metric, Peter Eisenman, Peter Thiel, planetary scale, Ponzi scheme, post scarcity, post-work, RAND corporation, recommendation engine, RFID, rolodex, Satoshi Nakamoto, self-driving car, sentiment analysis, shareholder value, sharing economy, Silicon Valley, smart cities, smart contracts, social intelligence, sorting algorithm, special economic zone, speech recognition, stakhanovite, statistical model, stem cell, technoutopianism, Tesla Model S, the built environment, The Death and Life of Great American Cities, The Future of Employment, transaction costs, Uber for X, undersea cable, universal basic income, urban planning, urban sprawl, Whole Earth Review, WikiLeaks, women in the workforce

They’re looking for something that might help them master the combinatorial explosion of possibility on a planet where nine billion people are continually knitting their own world-lines; for just a little reassurance, in a world populated by so many conscious actors that it often feels like it’s spinning out of anyone’s control. These are impulses I think most of us can relate to, and intuitively react to with some sympathy. And it’s this class of desires that I think we should keep in mind as we explore the mechanics of machine learning, automated pattern recognition and decision-making. For all the arrogance, the reductionism and the more than occasional wrongheadedness that crop up in this development effort, I believe it is founded in a set of responses to the world that most all of us have experienced at one time or another. If nothing else, to consider automation with any seriousness is to be presented with a long, poignant and richly elaborated index of our deepest longings and fears.

The evidence presented to us by the current generation of algorithmic tools suggests that this is a fool’s errand, that there can and will be no “escape from politics” into the comfort of governance by math. What we will be left with is a picture of ourselves, a diagram of all the ways in which we’ve chosen to allocate power, and an unforgiving map of the consequences. Whether we will ever summon the courage to confront those consequences with integrity is something that no algorithm can decide. What happens when pattern-recognition systems disclose uncomfortable truths to us, or at least uncomfortable facts? We hardly lack familiarity with the conscious introduction of uncomfortable facts into public debate. Self-delighted pop contrarians like Malcolm Gladwell and the Freakonomics team of Steven D. Levitt and Stephen J. Dubner have built careers on observing seemingly counterintuitive correlations that turn out to have a reasonable amount of explanatory force—for example, claims that the observed downturn in violent crime in the United States following 1991 can be traced to the more liberal access to abortion that American women had enjoyed starting twenty years earlier.44 Their arguments tend to take the form “everything you think you know is wrong,” and despite what might appear to be a slap-in-the-face quality, they’re easily assimilated by the mainstream culture.

Of more concern is the notion that this digitized instruction set is a package. It can travel over any network, reside in and activate any processing system set up to parse it. We may joke, uneasily, about the lack of foresight implicit in teaching a global mesh of adaptive machines the highly lethal skills of a master swordsman. But it also points toward a time when just about any human skill can be mined for its implicit rules and redefined as an exercise in pattern recognition and reproduction, even those seemingly most dependent on soulful improvisation. One final thought. We’re already past having to reckon with what happens when machines replicate the signature moves of human mastery, whether the strokes of Rembrandt’s brush or those of Machii’s sword. What we now confront is the possibility of machines transcending our definitions of mastery, pushing outward into an enormously expanded envelope of performance.


ucd-csi-2011-02 by Unknown

bioinformatics, en.wikipedia.org, pattern recognition, The Wisdom of Crowds

Kenis, J. Lerner, and D. van Raaij. Network analysis of collaboration structure in Wikipedia. In Proceedings of the 18th international conference on World wide web, pages 731–740. ACM, 2009. [5] J. Giles. Internet encyclopaedias go head to head. Nature, 438(7070):900–901, 2005. [6] R. Giugno and D. Shasha. GraphGrep: A fast and universal method for querying graphs. In International Conference on Pattern Recognition, volume 16, pages 112–115, 2002. [7] N.T. Korfiatis, M. Poulos, and G. Bokos. Evaluating authoritative sources using social networks: an insight from Wikipedia. Online Information Review, 30(3):252–262, 2006. [8] Andrew Lih. Wikipedia as participatory journalism: reliable sources? metrics for evaluating collaborative media as a news resource. In In Proceedings of the 5th International Symposium on Online Journalism, pages 16–17, 2004. [9] B.D.


pages: 566 words: 153,259

The Panic Virus: The True Story Behind the Vaccine-Autism Controversy by Seth Mnookin

Albert Einstein, British Empire, Cass Sunstein, cognitive dissonance, correlation does not imply causation, Daniel Kahneman / Amos Tversky, en.wikipedia.org, illegal immigration, index card, Isaac Newton, loss aversion, meta analysis, meta-analysis, mouse model, neurotypical, pattern recognition, placebo effect, Richard Thaler, Saturday Night Live, selection bias, Solar eclipse in 1919, Stephen Hawking, Steven Pinker, the scientific method, Thomas Kuhn: the structure of scientific revolutions

When, in 2002, an exhaustive federal study found that breast cancer rates in Long Island were in fact barely distinguishable from those in the rest of the country, the news received a fraction of the attention the initial scare had caused. Even if that hadn’t been the case—even if there had been a higher-than-average rate of breast cancer in the area—the most likely explanation would have been that Long Island found itself in a random eddy of disease rather than the victim of a hidden carcinogen.34 If large-scale pattern recognition is hard to practice in your neighborhood, it’s nearly impossible to conduct over the Internet. Even when you know that an online community selects for a certain type of person—say, politically minded liberals or ardent conspiracy theorists—sustained encounters with a small group of like-minded people almost inevitably lead to the conclusion that everyone thinks the way you do. (This phenomenon is addressed in more depth in Chapter 16.)

Today, there’s an almost universal acceptance that what has traditionally been perceived as “rational” thought is in fact intimately connected with our emotions. This discovery has led to an explosion of interest in the cognitive biases we use to convince ourselves that the truth lies with what we feel rather than with what the evidence supports. The origins of many of these traits can be traced back to the primitive conditions in which they were selected for millennia ago. Take pattern recognition, which evolutionary biologists like to explain through fables about our ancestors: Imagine a primitive hunter-gatherer. Now imagine he sees a flicker of movement on the horizon, or hears a rustle at his feet. Maybe it was nothing—or maybe it was a lion out hunting for dinner or a snake slithering through the grass. In each of those examples, the negative repercussions of not taking an actual threat seriously will likely result in death—and the end of that particular individual’s genetic line.

Unfortunately, evolution is a blunt tool, and a by-product of that protective instinct is a tendency to connect the dots even when there are no underlying shapes to be drawn. When our yearning to feel in control and our ability to recognize randomness are in conflict, the urge to feel in control almost always wins—as was likely the case when Lorraine Pace became convinced that there were an unusually high number of breast cancer cases in her Long Island community. (The technical name for this tendency is the clustering illusion.) Pattern recognition and the clustering illusion are just two of literally dozens of cognitive biases that have been identified over the past several decades. Some of the others have been alluded to earlier in this book: When SafeMinds members set out to write an academic paper about a hypothesis they already believed to be true, they set themselves up for expectation bias, where a researcher’s initial conjecture leads to the manipulation of data or the misinterpretation of results, and selection bias, where the meaning of data is distorted by the way in which it was collected.


pages: 327 words: 91,351

Traders at Work: How the World's Most Successful Traders Make Their Living in the Markets by Tim Bourquin, Nicholas Mango

algorithmic trading, automated trading system, backtesting, buy and hold, commodity trading advisor, Credit Default Swap, Elliott wave, fixed income, Long Term Capital Management, paper trading, pattern recognition, prediction markets, risk tolerance, Small Order Execution System, statistical arbitrage, The Wisdom of Crowds, transaction costs, zero-sum game

Schabacker in the early 1930s and then picked up in the Edwards and Magee book, Technical Analysis of Stock Trends, which was initially published in the 1940s. That book has been referred to as the Bible of classical charting, and it is that for me. I have a worn and torn copy, and I refer to it daily. I’m a classical charter in every sense. Bourquin: Can you describe what “classical charting” means? Is pattern recognition the essence of it? Brandt: Yes. I’m a pattern trader, and that’s become controversial these days, because some of the new quant traders are saying that classical charting doesn’t work anymore. Now, I’m not sure exactly why they are saying that, because classical charting still works. It just works in slightly different ways and for slightly different reasons. I look specifically at weekly charts.

For example, a pullback that reverses right back up and makes a double-top action before pulling back and again making a break to the upside signifies a wave extension. That tells me that the buyers rushed in, and the sellers were overcome. Now sellers are nowhere to be found, and that’s my chance to go long. The very reason I chose a background in statistics was because I’d been very, very good at pattern recognition all my life. That is the one skill that I brought into trading that I feel has really helped me to get over the top. Bourquin: Since settling on Fibonacci as your primary method, are you continuously adapting how you do it, or does your strategy remain constant? Baiynd: I’m always adapting. I aim to identify small market shifts and effectively respond to them. For instance, the 127.2 percent Fibonacci retracement level was once a very hard and fast number, but in recent months, I’ve noticed a shift in favor of the 138.2 percent level.

As my confidence in relationship-driven trading started to build, I began to achieve consistent trading results. I was trading along the crude curve, the Brent curve, and Brent oil. I was also trading heating oil versus ICE gasoil futures. All of these different relationships I was looking at were providing me with multiple trading opportunities. Some of the opportunities were based purely on statistical measure, some were based on technical measures, and some were based on pattern recognition measures. Regardless, each step along the way has equipped me in a different way, and all of those tools are what I’m now using to define my trading in the present and going forward. Bourquin: If somebody wanted to get started in agricultural pairs trading, like you’re doing, where is a good place for them to start? What should they be watching and monitoring? Hemminger: The Chicago Mercantile Exchange [CME] obviously is great.


pages: 309 words: 91,581

The Great Divergence: America's Growing Inequality Crisis and What We Can Do About It by Timothy Noah

assortative mating, autonomous vehicles, blue-collar work, Bonfire of the Vanities, Branko Milanovic, business cycle, call centre, collective bargaining, computer age, corporate governance, Credit Default Swap, David Ricardo: comparative advantage, Deng Xiaoping, easy for humans, difficult for computers, Erik Brynjolfsson, Everybody Ought to Be Rich, feminist movement, Frank Levy and Richard Murnane: The New Division of Labor, Gini coefficient, Gunnar Myrdal, income inequality, industrial robot, invisible hand, job automation, Joseph Schumpeter, longitudinal study, low skilled workers, lump of labour, manufacturing employment, moral hazard, oil shock, pattern recognition, Paul Samuelson, performance metric, positional goods, post-industrial society, postindustrial economy, purchasing power parity, refrigerator car, rent control, Richard Feynman, Ronald Reagan, shareholder value, Silicon Valley, Simon Kuznets, Stephen Hawking, Steve Jobs, The Spirit Level, too big to fail, trickle-down economics, Tyler Cowen: Great Stagnation, union organizing, upwardly mobile, very high income, Vilfredo Pareto, War on Poverty, We are the 99%, women in the workforce, Works Progress Administration, Yom Kippur War

When that’s the case, the technician has to figure out the problem by eliminating possibilities through trial and error. Something else that can’t easily be achieved through rule-based logic is pattern recognition, which depends on an understanding of context as well as an ability to recognize analogies that lie outside the immediate context. Perhaps the technician has never seen a seat in this particular minivan model that wouldn’t move forward or back, but he has seen power windows that won’t go up or down. If so, he might follow a hunch that whatever electronic component interfered with the power windows is here interfering with the power seats. Computers’ difficulty with pattern recognition explains why, when you order concert tickets online, you are required to look at a random assembly of letters bent weirdly out of shape and type them into your keyboard.

Levy and Murnane write that when the London International Financial Futures and Options Exchange substituted computers for trading pits in 1999, the pit traders whose jobs it eliminated could make $450,000 in a good year. In a September 2011 five-part series for Slate, the technology writer Farhad Manjoo describes incursions that robots have made into a number of high-paid professions, including medicine and the law. “If you do a single thing—and especially if there’s a lot of money in that single thing,” Manjoo writes, “you should put a Welcome, Robots! doormat outside your office.” Nor is pattern recognition the exclusive province of people in high-income jobs. A truck driver making a left turn on a busy city street, Levy and Murhane note, has to process visual and aural information about what’s happening on the street; tactile information about the truck’s probable speed once he hits the accelerator; and split-second calculations about probable trajectories for people and other vehicles. All this is well beyond the ability of a computer.8 Or so it seemed when Levy and Murnane wrote their book.


pages: 339 words: 94,769

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

AI winter, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, artificial general intelligence, Asilomar, autonomous vehicles, basic income, Benoit Mandelbrot, Bill Joy: nanobots, Buckminster Fuller, cellular automata, Claude Shannon: information theory, Daniel Kahneman / Amos Tversky, Danny Hillis, David Graeber, easy for humans, difficult for computers, Elon Musk, Eratosthenes, Ernest Rutherford, finite state, friendly AI, future of work, Geoffrey West, Santa Fe Institute, gig economy, income inequality, industrial robot, information retrieval, invention of writing, James Watt: steam engine, Johannes Kepler, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Kevin Kelly, Kickstarter, Laplace demon, Loebner Prize, market fundamentalism, Marshall McLuhan, Menlo Park, Norbert Wiener, optical character recognition, pattern recognition, personalized medicine, Picturephone, profit maximization, profit motive, RAND corporation, random walk, Ray Kurzweil, Richard Feynman, Rodney Brooks, self-driving car, sexual politics, Silicon Valley, Skype, social graph, speech recognition, statistical model, Stephen Hawking, Steven Pinker, Stewart Brand, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, telemarketer, telerobotics, the scientific method, theory of mind, Turing machine, Turing test, universal basic income, Upton Sinclair, Von Neumann architecture, Whole Earth Catalog, Y2K, zero-sum game

Today smartphones and the Internet are bringing the human drive toward augmentation into realms more central to our identity as intelligent beings. They are giving us, in effect, quick access to a vast collective awareness and a vast collective memory. At the same time, autonomous artificial intelligences have become world champions in a wide variety of “cerebral” games, such as chess and Go, and have taken over many sophisticated pattern-recognition tasks, such as reconstructing what happened during complex reactions at the Large Hadron Collider from a blizzard of emerging particle tracks to find new particles; or gathering clues from fuzzy X-ray, fMRI, and other types of images to diagnose medical problems. Where is this drive toward self-enhancement and innovation taking us? While the precise sequence of events and the timescale over which they’ll play out are impossible to predict (or, at least, beyond me), some basic considerations suggest that eventually the most powerful embodiments of mind will be quite different things from human brains as we know them today.

Those two advantages are synergistic, since it is interactive development that sculpts the massively wired but sprawling structure of the infant brain, enabled by exponential growth of neurons and synapses, to get tuned in to the extraordinary instrument it becomes. Computer scientists are beginning to discover the power of the brain’s architecture: Neural nets, whose basic design, as their name suggests, was directly inspired by the brain’s, have scored some spectacular successes in game playing and pattern recognition, as noted. But present-day engineering has nothing comparable—in the (currently) esoteric domain of self-reproducing machines—to the power and versatility of neurons and their synapses. This could become a new, great frontier of research. Here, too, biology might point the way, as we come to understand biological development well enough to imitate its essence. Altogether, the advantages of artificial over natural intelligence appear permanent, while the advantages of natural over artificial intelligence, though substantial at present, appear transient.

VISIBLE/INVISIBLE The artist Paul Klee often talked about art as “making the invisible visible.” In computer technology, most algorithms work invisibly, in the background; they remain inaccessible in the systems we use daily. But lately there has been an interesting comeback of visuality in machine learning. The ways that the deep-learning algorithms of AI are processing data have been made visible through applications like Google’s DeepDream, in which the process of computerized pattern recognition is visualized in real time. The application shows how the algorithm tries to match animal forms with any given input. There are many other AI visualization programs that, in their way, also “make the invisible visible.” The difficulty in the general public perception of such images is, in Steyerl’s view, that these visual patterns are viewed uncritically as realistic and objective representations of the machine process.


pages: 297 words: 91,141

Market Sense and Nonsense by Jack D. Schwager

3Com Palm IPO, asset allocation, Bernie Madoff, Brownian motion, buy and hold, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index arbitrage, index fund, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, transaction costs, two-sided market, value at risk, yield curve

There are many CTAs who use a discretionary rather than systematic approach. Also, many CTAs use strategies that have nothing to do with trend following. A partial sampling of alternative approaches includes: Countertrend approach (or mean reversion). Pattern recognition. Fundamental systematic approach (systems that are based on fundamental inputs rather than price movements). Fundamental discretionary approach. Spread trading (long positions in one futures contract versus short positions in another contract in the same market or a related market). Multisystem (e.g., combination of trend-following, countertrend, and pattern recognition systems). Managed futures are often categorized as a separate asset class rather than as a hedge fund category. One reason for this distinction is that managers who trade futures markets for U.S. clients are subject to mandatory registration and strict regulation, neither of which is true for hedge funds.

Today’s statistical arbitrage models are far more complex, simultaneously trading hundreds or thousands of securities based on their relative price movements and correlations, subject to the constraint of maintaining multidimensional market neutrality (e.g., market, sector, etc.). Although mean reversion is typically at the core of this strategy, statistical arbitrage models may also incorporate other types of uncorrelated or even inversely correlated strategies, such as momentum and pattern recognition. Statistical arbitrage involves highly frequent trading activity, with trades lasting between seconds and days. Fixed income arbitrage. This strategy seeks to profit from perceived mispricings between different interest rate instruments. Positions are balanced to maintain neutrality to changes in the broad interest rate level, but may express directional biases in terms of the yield curve—anticipated changes in the yield relationship between short-term, medium-term, and long-term interest rates.


pages: 330 words: 91,805

Peers Inc: How People and Platforms Are Inventing the Collaborative Economy and Reinventing Capitalism by Robin Chase

Airbnb, Amazon Web Services, Andy Kessler, banking crisis, barriers to entry, basic income, Benevolent Dictator For Life (BDFL), bitcoin, blockchain, Burning Man, business climate, call centre, car-free, cloud computing, collaborative consumption, collaborative economy, collective bargaining, commoditize, congestion charging, creative destruction, crowdsourcing, cryptocurrency, decarbonisation, different worldview, do-ocracy, don't be evil, Elon Musk, en.wikipedia.org, Ethereum, ethereum blockchain, Ferguson, Missouri, Firefox, frictionless, Gini coefficient, hive mind, income inequality, index fund, informal economy, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Lean Startup, Lyft, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, openstreetmap, optical character recognition, pattern recognition, peer-to-peer, peer-to-peer lending, peer-to-peer model, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, smart cities, smart grid, Snapchat, sovereign wealth fund, Steve Crocker, Steve Jobs, Steven Levy, TaskRabbit, The Death and Life of Great American Cities, The Future of Employment, The Nature of the Firm, transaction costs, Turing test, turn-by-turn navigation, Uber and Lyft, uber lyft, Zipcar

In Thinking, Fast and Slow, Daniel Kahneman describes the two modes of thought that we all engage in. “Thinking fast” is what we usually do. He argues that humans are great at fast pattern recognition, recognizing subtle local cues and context, and adjusting immediately to take these into account. Conversely, computers are pretty terrible at these things. While we have to trick ourselves into “thinking slow,” taking the time to make the mathematical and rational calculations, this type of analysis is easy for computers. The optimal Peers Inc platforms allow computers to do what they do best—complex and not-so-complex math—and deliver the results to people, allowing us to engage in what we do best: creativity, pattern recognition, and contextualizing. In an interview on The Colbert Report, Vint Cerf, the Internet pioneer, remarked “[There are] about 3 billion people online right now.

But we know that we can find abundance in excess capacity; that platforms can organize, simplify, and provide peers with resources; that in the process, they radically accelerate learning and propagate new discoveries; and that by engaging with peers as co-creators, we bring passion, ingenuity, and local and customized applications together into resilient and redundant systems. Everything gets better because the parts together form a new whole. The Peers Inc organization can produce previously impossible growth, unprecedented acceleration of learning and innovation, and the powerful joining of human experience, adaptability, and pattern recognition with supercomputing. The three miracles potentially provide us a way forward through climate change, resource scarcity, and explosive population growth. They also produce some rewarding business opportunities along the way. We can, in fact, make megacities livable and address the needs of the more than seven billion people now on the planet (a figure expected to grow to eleven billion by 2050) through more efficient use of our resources.


pages: 237 words: 50,758

Obliquity: Why Our Goals Are Best Achieved Indirectly by John Kay

Andrew Wiles, Asian financial crisis, Berlin Wall, bonus culture, British Empire, business process, Cass Sunstein, computer age, corporate raider, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, discovery of penicillin, diversification, Donald Trump, Fall of the Berlin Wall, financial innovation, Gordon Gekko, greed is good, invention of the telephone, invisible hand, Jane Jacobs, lateral thinking, Long Term Capital Management, Louis Pasteur, market fundamentalism, Myron Scholes, Nash equilibrium, pattern recognition, Paul Samuelson, purchasing power parity, RAND corporation, regulatory arbitrage, shareholder value, Simon Singh, Steve Jobs, Thales of Miletus, The Death and Life of Great American Cities, The Predators' Ball, The Wealth of Nations by Adam Smith, ultimatum game, urban planning, value at risk

When you have learned the direct solution, you begin to learn more oblique approaches. The general public, by contrast, didn’t know or care whether the practitioners they observed were following the rules or not. The qualities the general public tended to value were confidence and decisiveness—and, most of all, results—and these were the qualities they generally saw in the most successful paramedics. When Klein interviewed these practitioners, he concluded that pattern recognition rather than calculation was the key to their success. They used successive limited comparison, they made an assessment and if evidence seemed inconsistent with that assessment, they adopted an alternative. In the same way, novice chess players are taught simple rules—which exchanges of material should be accepted and which rejected. Novice chess players often lose because they make mistakes and don’t apply these basic rules.

moral values and as “muddling through” necessity of objectives achieved by order and pluralism in political pragmatism in randomness in as term ubiquity of wealth and “O Captain, My Captain” (Whitman) oil industry “On First Looking into Chapman’s Homer” (Keats) On the Origin of Species (Darwin) optics optimism pain response Paley, William Panama Canal paramedics parameters paraplegics parents Paris Pascale, Richard Pasteur, Louis pattern recognition Pearl Harbor attack (1941) pensions “perfection of form” Perot, Ross personal computers perspective Pfizer pharmaceuticals industry philanthropy photographs physics Picasso, Pablo Picture of Dorian Gray, The (Wilde) Planck, Max Plan Voisin pluralism Plutarch poetry Polanyi, Michael Pol Pot population growth Porras, Jerry I.


pages: 377 words: 97,144

Singularity Rising: Surviving and Thriving in a Smarter, Richer, and More Dangerous World by James D. Miller

23andMe, affirmative action, Albert Einstein, artificial general intelligence, Asperger Syndrome, barriers to entry, brain emulation, cloud computing, cognitive bias, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Brooks, David Ricardo: comparative advantage, Deng Xiaoping, en.wikipedia.org, feminist movement, Flynn Effect, friendly AI, hive mind, impulse control, indoor plumbing, invention of agriculture, Isaac Newton, John von Neumann, knowledge worker, Long Term Capital Management, low skilled workers, Netflix Prize, neurotypical, Norman Macrae, pattern recognition, Peter Thiel, phenotype, placebo effect, prisoner's dilemma, profit maximization, Ray Kurzweil, recommendation engine, reversible computing, Richard Feynman, Rodney Brooks, Silicon Valley, Singularitarianism, Skype, statistical model, Stephen Hawking, Steve Jobs, supervolcano, technological singularity, The Coming Technological Singularity, the scientific method, Thomas Malthus, transaction costs, Turing test, twin studies, Vernor Vinge, Von Neumann architecture

Of course, the dining room in your new house wouldn’t be as good as it would be if you placed primary importance on the quality of the dining room. The stronger the correlation between bathroom and dining room quality, the better the dining room you will get if you succeed in getting a home with fantastic bathrooms. SELECTING AGAINST CERTAIN TYPES OF INTELLIGENCE Embryo selection against autism would likely reduce the number of geniuses. High-functioning autistics often excel at pattern recognition, a skill vital to success in science and mathematics. Albert Einstein, Isaac Newton, Charles Darwin, and Socrates have all been linked to Asperger syndrome, a disorder (or at least a difference) on the autism spectrum.206 Parents with strong math backgrounds are far more likely to have autistic children, perhaps an indication that having lots of “math genes” makes one susceptible to autism.207 Magnetic resonance imaging has shown that on average, autistic two-year-olds have larger brains than their non-autistic peers do.208 A Korean study found the percentage of autistics who had a superior IQ was greater than that found in the general population.209 Some autistics have an ability called “hyperlexia,” characterized by having average or above-average IQs and word-reading ability well above what would be expected given their ages.

As US military tests have demonstrated, modafinil reduces the cognitive decline that otherwise accompanies sleep deprivation.245 Astronauts on the International Space Station use modafinil “to optimize performance while fatigued.”246 Studies on small groups of non-sleep-deprived, healthy test subjects found that modafinil was useful for monotonous tasks that taxed working memory247 and improved performance on tests of “digit span, visual PRM [pattern recognition memory], spatial planning . . . and SSRT [stop signal reaction time].”248 Beta blockers, a set of drugs primarily used to treat heart conditions, can also enhance performance—mental or musical—in healthy people. Those drugs work by reducing the physical symptoms of anxiety and are used by professional musicians, white-collar workers, and public speakers to protect their performance under stress.249 A study published in 2011 showed the equivalent of a six-point IQ gain from four weeks’ use of the supplement Ceretrophin (the trademark for a combination of huperzine, vinpocetine, acetyl-l-carnitine, Rhodiola rosea, and alpha-lipoic acid).250 Increasing an average person’s IQ _by six points would move him from being smarter than 50 percent of the population to being smarter than 65.5 percent of it.

See also friendly artificial intelligence (AI), 44, 187 intelligence explosion, 22–28 ultra-AI (Devil), 30, 35, 46, 202, 208 University of Kentucky, 103–4 US Civil War, 187 US Department of Defense (DOD), 8 US militaries and Prisoners’ Dilemma, 48–53 US weapons policy, xiii V vaccines, 166 vacuum tubes, 4 van Gogh, Vincent, 92 Van Sickle, Stephen, 215 Vaseline, 111 Vassar, Michael, 44 venture capitalists, 123, 185–86 video games, 106, 113, 129, 155, 167, 183, 209, 212 Vinge, Vernor, ix, xviii, 36 virtual reality, 42, 139, 150, 171, 181, 210 virtual-reality technology, 183 Virtual World Golf, 167 visual pattern recognition, 105 volcanoes, super, 197 von Neumann, John, xii–xiii, xv, 96, 199–200 von Neumann—level AI, 6 W wages of cab drivers, 156 Walmart, 204 Washington Post, 172 weapons of mass destruction, 201 website http://www.lesswrong.com, 37 Weiner, Zach, 151 welfare, 146–47 welfare recipients, 125 What Intelligence Tests Miss (Stanovich), 65–66 white-collar jobs, 194 “Why Women Live Longer” (Scientific American), 179 widget factory, 57 Wikipedia article, 69, 104, 133 Witten, Ed, 96 Wolfram, Stephen, 35 women, fertile, 78 women past optimal egg age, 88 worker productivity, 132, 140 working memory, 105, 113–16, 212 World of Warcraft (video game), 106, 167 wormhole, 165 Wright, John C., 41 Wrong, Less, 37 Y Yale Daily News, 86 year 1066, 187 year 1997, 108 year 2000, 35, 87 year 2001, 210 year 2002, 36 year 2004, 181 year 2005, 103, 194 year 2006, 103 year 2007, x, 108 year 2008, 35, 109, 126 year 2010, x year 2011, 67, 70, 106, 116 year 2012, 11 year 2015, 115 year 2020, 5, 72, 216 year 2021, 108 year 2022, 96 year 2023, 119 year 2025, 8, 36–37, 193 year 2027, 37 year 2029, xvi, xvii, 177 year 2030, xviii, 5, 9, 37, 99 year 2035, 178 year 2045, 9, 37, 175–76, 178, 200, 216 year 2049, xvii, 21 year 2050, 11 year 2080, 37 Yesalis, Charles E., 100 Yew, Lee Kuan, 92 YouTube video, 17–20 Yudkowsky, Eliezer biological humans and emulations, 147 cryonics as “an ambulance ride to the future,” 213 Cryonics Institute, 214 extrapolation theory, 42 friendliness, preferred approach to, 41 game involving super-smart AI, 32–33 Harry Potter and the Methods of Rationality, 37–38 helping people “reprogram” themselves to become more rational, 37 humanity is at a very unstable point in history, 45 quotes of, 21, 34, 55, 83 seed AI will undergo intelligence explosion, 36 Singularity as simple but wrong, 41 Singularity from an intelligence explosion, 203 Singularity Institute for Artificial Intelligence, 35–36, 208 theory of friendly AI, incomplete, 36–37 Yudkowsky/Hanson debate, 203–5 Z Zeus (god), 41–42


pages: 347 words: 97,721

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

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

He also identified a human strength in the range and speed of our adaptability, in contrast to both other animals and machines. More recently, economists Frank Levy and Richard Murnane put a finer point on things, saying (in their persuasive book, The New Division of Labor: How Computers Are Creating the Next Job Market) that the great strengths of humans are expert thinking and complex communication. The brain’s gift for pattern recognition is the key to what they call “expert thinking,” which is what allows humans, but not computers, to imagine new ways of solving problems (ways, in other words, that have not already been discovered and spelled out step by step). By complex communication, they mean communication that involves a broader interpretation of a situation than could be gained by the explicitly transmitted information.

As Levy writes in a 2010 working paper for the Organisation for Economic Co-operation and Development (OECD), it involves not only listening to the patient’s words, but also his body language, tone of voice, eye contact, and incomplete sentences. He notes, “The doctor must be particularly alert for the famous ‘last minute’ of an appointment when the patient, on his way out the door, looks over his shoulder and says ‘By the way, my wife says I should tell you about this pain I have in my stomach.’”6 Levy’s MIT colleagues Erik Brynjolfsson and Andy McAfee agree with pattern recognition and complex communication as uniquely human traits, and they add a third: ideation. “Scientists come up with new hypotheses,” they write. “Chefs add a new dish to the menu. Engineers on a factory floor figure out why a machine is no longer working properly. Steve Jobs and his colleagues at Apple figure out what kind of tablet computer we actually want. Many of these activities are supported and accelerated by computers, but none are driven by them.”

The goal is to combine automation of physical tasks and cognitive tasks. For example, a robot could start combining all the information about how much torque is applied in a screw. Robots are, after all, a big bucket of sensors. A truly intelligent robot could begin to see what works in terms of how much torque in a screw leads to field failures. It could combine its own sensor data with warranty data, pattern recognition, and so forth.” There is plenty of evidence that robots are becoming more autonomous at the DARPA (Defense Advanced Research Projects Agency) Robotics Challenge, staged annually since 2012. The robot contestants are expected to complete eight tasks, from driving a utility vehicle to connecting a fire hose and turning a valve. The robots mostly struggle to accomplish all eight tasks, but three entrants completed them all in the 2015 competition, which was won by a South Korean university team.


pages: 314 words: 101,034

Every Patient Tells a Story by Lisa Sanders

data acquisition, discovery of penicillin, high batting average, index card, medical residency, meta analysis, meta-analysis, natural language processing, pattern recognition, Pepto Bismol, randomized controlled trial, Ronald Reagan

When the “MI express” pulls out of the station, far too often everything that doesn’t fit—like David’s complaint about his loss of strength—is left behind. Pat Croskerry is an emergency room physician and a doctor who has written extensively about diagnostic thinking. The brain, says Croskerry, uses two basic strategies in working to figure things out. One is what Croskerry calls an intuitive approach. This “nonanalytic” approach works by pattern recognition. He describes it as a “process of matching [a] new situation to one of many exemplars in your memory which are retrievable rapidly and effortlessly. As a consequence, it may require no more mental effort for a clinician to recognize that the current patient is having a heart attack than it is for a child to recognize that a four-legged beast is a dog.” This is the instant recognition of the true expert described by Malcolm Gladwell in his book Blink—fast, associative, inductive.

Croskerry believes that the best diagnostic thought incorporates both modes, with the intuitive mode allowing experienced physicians to recognize the pattern of an illness—the illness script—and the analytic mode addressing the essential question in diagnosis—what else could this be?—and providing the tools and structures that lead to other possible answers. For Christine Twining, the doctor who finally diagnosed David Powell with pernicious anemia, there was no Blink-like moment of pattern recognition and epiphany when she first heard him describe his symptoms. One thing seemed clear: he wasn’t having a heart attack. She felt the patient’s fear and frustration. “He was afraid I was going to send him home with reassurances that it wasn’t his heart and without figuring out what it was. But I couldn’t send him home; I didn’t have a clue what he had.” Because there was no instantaneous sense of recognition triggered by Powell’s odd combination of chest pain, weakness, and anemia, Twining was forced to approach the problem systematically, considering the possible diagnoses for each of his very different symptoms and pursuing a slower, more rational approach to the patient that ultimately brought her the answer.

For all their limitations, well-trained human beings are still remarkably good at sizing up a problem, rapidly eliminating irrelevant information, and zeroing in on a “good-enough” decision. This is why human chess players held out for so long against computer opponents whose raw computational and memory abilities were many orders of magnitude better than those of a human brain. Humans devise shortcut strategies for making decisions and drawing conclusions that are simply impossible for computers. Humans are also extraordinarily good at pattern recognition—in chess, skilled players are able to size up the entire board at a glance and develop a feel, an intuition, for potential threats or opportunities. It took decades and millions of dollars to create a computer that was as good as a human at the game of chess. It is a complex game requiring higher order thinking but is two-dimensional and based on clear, fixed rules using pieces that never vary.


pages: 317 words: 100,414

Superforecasting: The Art and Science of Prediction by Philip Tetlock, Dan Gardner

Affordable Care Act / Obamacare, Any sufficiently advanced technology is indistinguishable from magic, availability heuristic, Black Swan, butterfly effect, buy and hold, cloud computing, cuban missile crisis, Daniel Kahneman / Amos Tversky, desegregation, drone strike, Edward Lorenz: Chaos theory, forward guidance, Freestyle chess, fundamental attribution error, germ theory of disease, hindsight bias, index fund, Jane Jacobs, Jeff Bezos, Kenneth Arrow, Laplace demon, longitudinal study, Mikhail Gorbachev, Mohammed Bouazizi, Nash equilibrium, Nate Silver, Nelson Mandela, obamacare, pattern recognition, performance metric, Pierre-Simon Laplace, place-making, placebo effect, prediction markets, quantitative easing, random walk, randomized controlled trial, Richard Feynman, Richard Thaler, Robert Shiller, Robert Shiller, Ronald Reagan, Saturday Night Live, scientific worldview, Silicon Valley, Skype, statistical model, stem cell, Steve Ballmer, Steve Jobs, Steven Pinker, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!

“We agree on most of the issues that matter,” they concluded in a 2009 paper.19 There is nothing mystical about an accurate intuition like the fire commander’s. It’s pattern recognition. With training or experience, people can encode patterns deep in their memories in vast number and intricate detail—such as the estimated fifty thousand to one hundred thousand chess positions that top players have in their repertoire.20 If something doesn’t fit a pattern—like a kitchen fire giving off more heat than a kitchen fire should—a competent expert senses it immediately. But as we see every time someone spots the Virgin Mary in burnt toast or in mold on a church wall, our pattern-recognition ability comes at the cost of susceptibility to false positives. This, plus the many other ways in which the tip-of-your-nose perspective can generate perceptions that are clear, compelling, and wrong, means intuition can fail as spectacularly as it can work.

Thanks to project coleader Barbara Mellers, and the volunteers who endured a grueling battery of psychological tests before they started forecasting, we had the data to do that.2 To gauge fluid intelligence, or raw crunching power, volunteers had to figure out puzzles like the one on page 108, where the goal is to fill in the missing space at the lower right. Solving it requires identifying the rules generating patterns in the row (each row must have a distinctive symbol in the center of its figures) and each column (each column must contain all three shapes). The correct answer is the second figure in the second row.3 High-powered pattern recognition skills won’t get you far, though, if you don’t know where to look for patterns in the real world. So we measured crystallized intelligence—knowledge—using some U.S.-centric questions like “How many Justices sit on the Supreme Court?” and more global questions like “Which nations are permanent members of the UN Security Council?” Before we get to the results, bear in mind that several thousand people volunteered for the GJP in the first year and the 2,800 who were motivated enough to work through all the testing and make forecasts were far from a randomly selected sample.


pages: 178 words: 52,637

Quality Investing: Owning the Best Companies for the Long Term by Torkell T. Eide, Lawrence A. Cunningham, Patrick Hargreaves

air freight, Albert Einstein, backtesting, barriers to entry, buy and hold, cashless society, cloud computing, commoditize, Credit Default Swap, discounted cash flows, discovery of penicillin, endowment effect, global pandemic, haute couture, hindsight bias, low cost airline, mass affluent, Network effects, oil shale / tar sands, pattern recognition, shareholder value, smart grid, sovereign wealth fund, supply-chain management

John Ruskin Preface This book began as a small internal project at AKO Capital, an equity fund based in London that has enjoyed a compound annual growth rate more than double that of the market (9.4% per annum versus the MSCI Europe’s 3.9%)1 and delivered excess returns of approximately 8% per annum on its long book2 since inception a decade ago. The project’s initial scope was to institutionalize lessons learned from refining the fund’s quality-focused investment philosophy over that time. What we have come to understand is that successful investing involves a degree of pattern recognition: while industries and companies are diverse and economic environments endlessly changing, strongly performing investments tend to have commonalities. Making sense of these commonalities can help build a strong investment portfolio. After amassing a substantial body of material to share with new members of the AKO team – and to remind veterans of what they had once learned but might by now have forgotten – it became obvious that the results should be shared with the fund’s investors as well.

In 2007, Experian (as the combined UK and US business was then known) acquired Serasa, Brazil’s market leader in the field, founded in 1968 by a consortium of regional banks. Through such global agglomerations of venerable data compilers, Experian’s databases are the product of a lengthy and intense process of collecting, matching, contrasting, verifying, and analyzing abundant information. Contemporary global systems add incremental bits of information daily, each being trivial but when added to the storehouse able to enhance credit histories and facilitate pattern recognition. So besides costing a fortune to build, data accumulate daily that amplify returns. Individual data contributors expect to benefit from the aggregation of credit information from fellow creditors, creating a powerful network effect. The result is an industry prone to consolidation. In the US, the market is dominated by three credit agencies with approximately even market shares; outside the US, most markets are duopolies, with one dominant and one subservient rival.


pages: 331 words: 104,366

Deep Thinking: Where Machine Intelligence Ends and Human Creativity Begins by Garry Kasparov

3D printing, Ada Lovelace, AI winter, Albert Einstein, AltaVista, barriers to entry, Berlin Wall, business process, call centre, Charles Lindbergh, clean water, computer age, Daniel Kahneman / Amos Tversky, David Brooks, Donald Trump, Douglas Hofstadter, Drosophila, Elon Musk, Erik Brynjolfsson, factory automation, Freestyle chess, Gödel, Escher, Bach, job automation, Leonard Kleinrock, low earth orbit, Mikhail Gorbachev, Nate Silver, Norbert Wiener, packet switching, pattern recognition, Ray Kurzweil, Richard Feynman, rising living standards, rolodex, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, Skype, speech recognition, stem cell, Stephen Hawking, Steven Pinker, technological singularity, The Coming Technological Singularity, The Signal and the Noise by Nate Silver, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero-sum game

We select openings from our personal mental library according to our preferences and preparation for our opponent. Move generation seems to involve more visuospatial brain activity than the sort of calculation that goes into solving math problems. That is, we literally visualize the moves and positions, although not in a pictorial way, as many early researchers assumed. The stronger the player, the more they demonstrate superior pattern recognition and doing the sort of “packaging” of information for recall that experts call “chunking.” Then comes understanding and assessing what we see in our mind’s eye, the evaluation aspect. Different players of equal strength often have very different opinions of a given position and recommend entirely different moves and strategies. There is ample room here for disparate styles, creativity, brilliancy, and, of course, terrible mistakes.

Spurious lists of “highest IQs in history” might find me between Albert Einstein and Stephen Hawking, both of whom have probably taken as many proper IQ tests as I have: zero. In 1987, the German news magazine Der Spiegel sent a small group of experts to a hotel in Baku to administer a battery of tests to measure my brainpower in different ways, some specially designed to test my memory and pattern recognition abilities. I have no idea how closely these approximated a formal IQ test, nor do I much care. The chess tests proved I was very good at chess, the memory tests that I had a very good memory, neither of which was much of a revelation. My weakness, they told me, was “figural thinking,” apparently proven after I blanked out for a while when tasked with filling in some dots with pencil lines.

Binet’s insights into the differences between innate talent and acquired knowledge and experience defined the field. “One becomes a good player,” he wrote. “But one is born an excellent player.” Binet would go on to create the IQ test with Theodore Simon. In 1946, Binet’s work was advanced by the Dutch psychologist Adriaan de Groot, whose extensive testing of chess players revealed the importance of pattern recognition and peeled away at the mysteries of human intuition in decision making. John McCarthy, the American computer scientist who coined the term “artificial intelligence” in 1956, called chess the “Drosophila of AI,” referring to how the humble fruit fly was the ideal subject for countless seminal scientific experiments in biology, especially genetics. By the end of the 1980s, the computer chess community had largely resigned this great experiment.


pages: 430 words: 107,765

The Quantum Magician by Derek Künsken

commoditize, epigenetics, industrial robot, iterative process, microbiome, orbital mechanics / astrodynamics, p-value, pattern recognition, Schrödinger's Cat, Turing test

Two hundred and fifty kilometers from the Limpopo to the Mutapa. Hesitantly, he said, “I need to understand what your ships can do with wormholes: how fast they can induce one, how far they can go, how fast they can transit, and how fast systems come online after emergence.” Belisarius didn’t meet their eyes. In savant, meeting people’s eyes was like looking into a box of puzzle pieces, making the pattern recognition tendencies in his brain hyperactive, facial expressions swirling into cycles of false positives. The colonel’s fingers twitched, and a thrumming resonated through the ship. Gravity lurched on under their feet. Belisarius’s brain, thirsty for logic and abstractions, began chopping up the name Mutapa. Encyclopedic implants fed him information as fast as he could drink it. Mutapa, a medieval kingdom founded by a prince of greater Zimbabwe.

Entering the fugue was to become one among countless things in the indeterminacy of the quantum world. His stomach twisted. He’d stood on the diving board, staring at his reflection. He hadn’t stepped off the diving board for a decade. Few Homo quantus could enter the fugue at all, and even then only with great difficulty. For them, entering the fugue was like climbing a steep hill. Engineered instincts assisted them. Geneticists had strengthened the instinct for pattern-recognition and curiosity, bringing it closer in each generation to the strength of the instinct for self-preservation. They’d overshot their goal in Belisarius. His need to learn and understand was as strong as his sense of self-preservation. He couldn’t rely on his instincts; they might kill him. There was no predicting what his brain would do when his consciousness was extinguished. The fugue was dangerous to him.

Del Casal leaned back and crossed his arms. “The Puppet was right to pose the question. What are you doing here, Arjona? As much as the Puppets, you have been built with passions and desires, none of which are satisfied by money or confidence schemes.” “We’re all more than our instincts.” “But are you? Part of the early design of the Homo quantus project was to attach particular mental states, discovery and pattern recognition, to the pleasure centers of the brain. That is hardwired. Why are you not in your Garret?” “I’ve found out how to move past my instincts, as all rational beings must.” “Hollow words, Arjona. We certainly all have to fulfill our programming, no matter who the programmer. Pressures less than six hundred atmospheres are lethal to Stills. The Puppets, except for mutants like Gates-15, cannot live far from the Numen.


pages: 261 words: 10,785

The Lights in the Tunnel by Martin Ford

"Robert Solow", Albert Einstein, Bill Joy: nanobots, Black-Scholes formula, business cycle, call centre, cloud computing, collateralized debt obligation, commoditize, creative destruction, credit crunch, double helix, en.wikipedia.org, factory automation, full employment, income inequality, index card, industrial robot, inventory management, invisible hand, Isaac Newton, job automation, John Markoff, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, knowledge worker, low skilled workers, mass immigration, Mitch Kapor, moral hazard, pattern recognition, prediction markets, Productivity paradox, Ray Kurzweil, Search for Extraterrestrial Intelligence, Silicon Valley, Stephen Hawking, strong AI, technological singularity, Thomas L Friedman, Turing test, Vernor Vinge, War on Poverty

In spite of the radiologist’s training requirement of at least thirteen additional years beyond high school, it is conceptually quite easy to envision this job being automated. The primary focus of the job is to analyze and evaluate visual images. Furthermore, the parameters of each image are highly defined since they are often coming directly from a computerized scanning device. Visual pattern recognition software is a rapidly developing field that has already produced significant results. The government currently has access to software that can help identify terrorists in airports based on visual analysis of security photographs.22 Real world tasks such as this are probably technically more difficult than analyzing a medical scan because the environment and objects in the image are far more varied.

Although the practical applications of artificial intelligence have so far emphasized brute force solutions, it is by no means true that this is the only approach being taken in the field. A very important area of study revolves around the idea of neural nets, which are a special type of computer that is built upon a model of the human brain. Neural nets are currently being used in areas such as visual pattern recognition. In the future, we can probably expect some important advances in this area, especially as the engineers who design neural nets work more closely with scientists who are uncovering the secrets of how our brains work. One thing that probably jumps out at you as we speak of lawyers and radiologists is that these people make a lot of money. The average radiologist in the United States makes over $300,000.


pages: 209 words: 63,649

The Purpose Economy: How Your Desire for Impact, Personal Growth and Community Is Changing the World by Aaron Hurst

Airbnb, Atul Gawande, barriers to entry, big-box store, business process, call centre, carbon footprint, citizen journalism, commoditize, corporate social responsibility, crowdsourcing, disintermediation, Elon Musk, Firefox, glass ceiling, greed is good, housing crisis, informal economy, Jane Jacobs, jimmy wales, Khan Academy, Kickstarter, Lean Startup, longitudinal study, means of production, Mitch Kapor, new economy, pattern recognition, Peter Singer: altruism, Peter Thiel, QR code, Ray Oldenburg, remote working, Ronald Reagan, selection bias, sharing economy, Silicon Valley, Silicon Valley startup, Steve Jobs, TaskRabbit, Tony Hsieh, too big to fail, underbanked, women in the workforce, young professional, Zipcar

Furthermore, as I will argue in this book, it is likely that in 20 years, the pursuit of purpose will eclipse the third American economy, the Information Economy. Purpose, Purpose—Everywhere I’m not an economist, a sociologist, or a psychologist. I am an entrepreneur. Entrepreneurs constantly look for opportunities, hoping to find emerging trends or spot inspiration for new products or services. This kind of pattern recognition first helped me see the enormous potential for pro bono and has now helped me discern the underlying thread in what appears to be myriad emerging trends of the last decade. It’s helped me comprehend how they are all driven by the pursuit of purpose—together, they create the Purpose Economy. For more than a decade, I focused intensely on achieving the Taproot Foundation’s mission. When I finally came up for air and reflected on our progress, I realized that the pro bono movement was nearing a tipping point, as pro bono service had started to go mainstream.

Balancing them actually increases all three, rather than depleting any one. She learns by returning. She returns by earning. It is a virtuous cycle. It is important to appreciate what Jennifer has done by fully integrating these parts of her work at the same time. The new narrative presents a concept that we shouldn’t wait to revisit at age 60, but rather integrate into our careers from the start and throughout our lives. Pattern Recognition Amy Wrzesniewski teaches a course for MBA students at Yale’s School of Management, with the aim of helping them more thoughtfully navigate their careers. It is a powerful course that requires an enormous amount of self-reflection on the part of her students. She often ends up holding office hours with many of them, becoming their de facto career coach. Yale attracts students that tend to be purpose-oriented, and it is rare that a student enters the course without the intention to make an impact in the world.


pages: 512 words: 162,977

New Market Wizards: Conversations With America's Top Traders by Jack D. Schwager

backtesting, beat the dealer, Benoit Mandelbrot, Berlin Wall, Black-Scholes formula, butterfly effect, buy and hold, commodity trading advisor, computerized trading, Edward Thorp, Elliott wave, fixed income, full employment, implied volatility, interest rate swap, Louis Bachelier, margin call, market clearing, market fundamentalism, money market fund, paper trading, pattern recognition, placebo effect, prediction markets, Ralph Nelson Elliott, random walk, risk tolerance, risk/return, Saturday Night Live, Sharpe ratio, the map is not the territory, transaction costs, War on Poverty

[She laughs wholeheartedly at the recollection.] Why do you believe you have excelled as a trader? I believe my most important skill is an ability to perceive patterns in the market. 1 think this aptitude for pattern recognition is probably related to my heavy involvement with music. Between the ages of five and twentyone, I practiced piano for several hours every single day. In college, I had a dual major of economics and musical composition. Musical scores are just symbols and patterns. Sitting there for hours every day, analyzing scores, probably helped that part of my brain related to pattern recognition. Also, practicing an instrument for several hours every day helps develop discipline and concentration—two skills that are very useful as a trader. Could you elaborate some more on the parallels between music and the markets?

The successes are much more startling than the failures. You remember William Eckhardt / 115 the times when the oracle really hit the nail on the head, and you tend to forget the cases in which the prediction was ambiguous or wrong. Your comments basically seem to imply that chart reading is just laden with pitfalls and unfounded assumptions. Yes, it is. There may be people out there who can do it, but I certainly can’t. Every pattern recognition chart trader I know makes the trades he really likes larger than the trades he doesn’t like as much. In general, that’s not a good idea. You shouldn’t be investing yourself in the individual trades at all. And it’s certainly wrong to invest yourself more in some trades than others. Also, if you think you’re creating the profitable situation by having an eye for charts, it’s very difficult not to feel excessively responsible if the trade doesn’t work.

Overbought/oversold indicator. A technical indicator that attempts to define when prices have risen (declined) too far, too fast, and hence are vulnerable to a reaction in the opposite direction. The concept of overbought/oversold is also often used in association with contrary opinion to describe when a large majority of traders are bullish or bearish. P and L. Shorthand for profit/loss. Pattern recognition. A price-forecasting method that uses historical chart patterns to draw analogies to current situations. Glossary / 491 Pit. The area where a futures contract is traded on the exchange floor. Also sometimes called the Ring. Position limit. See Limit position. Price/earnings (P/E) ratio. The price of a stock divided by the company’s annual earnings. Put/call ratio. The volume of put options divided by the volume of call options.


pages: 463 words: 118,936

Darwin Among the Machines by George Dyson

Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, anti-communist, British Empire, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer age, Danny Hillis, Donald Davies, fault tolerance, Fellow of the Royal Society, finite state, IFF: identification friend or foe, invention of the telescope, invisible hand, Isaac Newton, Jacquard loom, James Watt: steam engine, John Nash: game theory, John von Neumann, low earth orbit, Menlo Park, Nash equilibrium, Norbert Wiener, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, phenotype, RAND corporation, Richard Feynman, spectrum auction, strong AI, the scientific method, The Wealth of Nations by Adam Smith, Turing machine, Von Neumann architecture, zero-sum game

It may be, however, that in this process logic will have to undergo a pseudomorphosis to neurology to a much greater extent than the reverse.”12 Computers, by the 1980s, had evolved perfect memories, but the memory of the computer industry was short. “If your friends in AI persist in ignoring their past, they will be condemned to repeat it, at a high cost that will be borne by the taxpayers,” warned Ulam, who turned out to be right.13 For a neural network to perform useful computation, pattern recognition, associative memory, or other functions a system of value must be established, assigning the raw material of meaning on an equitable basis to the individual units of information—whether conveyed by marbles, pulses of electricity, hydraulic fluid, charged ions, or whatever else is communicated among the components of the net. This process corresponds to defining a utility function in game theory or mathematical economics, a problem to which von Neumann and Morgenstern devoted a large portion of their book.

An influential step in this direction was an elder and much less ambitious cousin of Leviathan named Pandemonium, developed by Oliver Selfridge at the Lincoln Laboratory using an IBM 704. Instead of attempting to comprehend something as diffuse and complex as the SAGE air-defense system, Pandemonium was aimed at comprehending Morse code sent by human operators—a simple but nontrivial problem in pattern recognition that had confounded all machines to date. Selfridge’s program was designed to learn from its mistakes as it went along. Pandemonium—“the uproar of all demons”—sought to embody the Darwinian process whereby information is selectively evolved into perceptions, concepts, and ideas. The prototype operated on four distinct levels, a first approximation to the manifold levels by which a cognitive system makes sense of the data it receives.

Robert (1904–1967), 78–79, 82, 91, 94–95 optical fiber communications, 7, 8–9, 203–204, 207 Opticks (Newton), 227 Order, 62, 170, 222 and disorder, in hydrodynamics, 85 origins of, 29, 112, 170, 177, 188–89 order codes, 90, 93, 105–106, 121, 123 Organisms. see also artificial life; evolution; life; microorganisms; origins of life; parasitism; symbiogenesis; symbiosis collective, 2–3, 13, 27, 150, 170, 175, 191–92 complexity of, 11–13, 112, 117, 126, 150, 181, 190–92 and machines, comparison with, 100–101, 181, 191 multicellular, 13, 115, 123, 160 organizations as, 179, 183 processors as, 215–16 reliable, from unreliable parts, 44, 108, 150 scale of, 7–8, 174–75, 186, 208 Origin of Species (Darwin), 18, 19, 23, 24, 116, 190 origins of life, 9, 12–13, 28–32, 111–13, 177, 202. see also symbiogenesis Origins of Life (Dyson), 29–30, 32 Ortvay, Rudolf, 89 OS/360 (IBM 360 operating system), 121–22 Oslo, University of, 119–20 Ouroboros (Garrett), 226–27 Overlords (of Childhood’s End), 224 overmind, dangers of, 224 Oxford University, 2, 63, 132, 160 oxygen, 121, 202 P packet switching Babbage on, 42, 81 origins and development of, 143, 147–52, 205–207 proliferation of, 12, 122 Paley, William (1743–1805), 188–89 Pandemonium (Selfridge), 72, 184–85, 189 pangenesis, 20 panspermia, 28 Parallel Distributed Processing (Rumelhart and McClelland), 159 parallel processing in biology, 82, 110, 115, 155, 159, 219 and computers, 12, 86–87, 108, 115, 126–27, 155, 197, 205 parasitism, 12, 29, 97, 114–16, 120, 185, 201, 223, 227 and evolution of software, 121–23 and origins of eukaryotic cells, 12, 29, 112, 115 Pascal, Blaise (1623–1666), 36 Passages from the Life of a Philosopher (Babbage), 42 pattern recognition, 10, 62, 158, 184 Patterson, George W., 59 Payne, Diana, 64 Pehrson, Björn, 133, 137, 139 Peirce, Charles Sanders (1839–1914), 58–59 penny post, and Babbage, 42 Pepys, Samuel (1633–1703), 5, 134 perception, 6, 51, 156, 158, 184, 218, 222 Petty, Charles, 161 Petty, William (1623–1687), 160–62, 171 phenotype, and distinction from genotype, 30–31, 117–19 Philco 2000 (transistorized computer), 183 philosophical algebra (Hooke), 135 Philosophical Club (Oxford), 132, 160 photosynthesis, 170 physics, 50, 73, 85, 125, 130, 174, 197, 216 Piaget, Jean, 94 Pickard, G.


pages: 239 words: 70,206

Data-Ism: The Revolution Transforming Decision Making, Consumer Behavior, and Almost Everything Else by Steve Lohr

"Robert Solow", 23andMe, Affordable Care Act / Obamacare, Albert Einstein, big data - Walmart - Pop Tarts, bioinformatics, business cycle, business intelligence, call centre, cloud computing, computer age, conceptual framework, Credit Default Swap, crowdsourcing, Daniel Kahneman / Amos Tversky, Danny Hillis, data is the new oil, David Brooks, East Village, Edward Snowden, Emanuel Derman, Erik Brynjolfsson, everywhere but in the productivity statistics, Frederick Winslow Taylor, Google Glasses, impulse control, income inequality, indoor plumbing, industrial robot, informal economy, Internet of things, invention of writing, Johannes Kepler, John Markoff, John von Neumann, lifelogging, Mark Zuckerberg, market bubble, meta analysis, meta-analysis, money market fund, natural language processing, obamacare, pattern recognition, payday loans, personalized medicine, precision agriculture, pre–internet, Productivity paradox, RAND corporation, rising living standards, Robert Gordon, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley startup, six sigma, skunkworks, speech recognition, statistical model, Steve Jobs, Steven Levy, The Design of Experiments, the scientific method, Thomas Kuhn: the structure of scientific revolutions, unbanked and underbanked, underbanked, Von Neumann architecture, Watson beat the top human players on Jeopardy!

But Minnesota is mainly Haydock’s base camp these days. He flies out most Sunday afternoons to work on projects with clients, returning Thursday night or Friday. After a day spent with Haydock, at his home and over a walleye dinner at a downtown restaurant, listening to him describe his craft, I couldn’t help but think of the contrast with Cayce Pollard, the protagonist in William Gibson’s 2003 novel Pattern Recognition. She is a young marketing savant, a cool hunter. Her typical attire is a shrunken cotton T-shirt, worn with black jeans, boots, and a bomber jacket. She possesses, Gibson writes, “an unusual intuitive sensitivity for branding.” Haydock may not be hip or intuitive like Cayce Pollard. But he brings something else—data science. His PhD is in operations research, which applies math and statistics to complex decisions.

Randall, 40 Mount Sinai Hospital, 8, 13–14, 15 data science and genomic research at, 163–65, 171, 173–81 medical data and human experience, 68–70 Mundie, Craig, 203 Nakashima, George, 65 Naked Society, The (Packard), 184 Narayanan, Arvind, 204 Nest learning thermostat, 143–45 Google and, 152–53 human behavior and, 147–52 Never-Ending Language Learning system (NELL), of Carnegie Mellon University, 110–11 New York State, Medicaid fraud prevention in, 48 Norvig, Peter, 116 Norway, 48 “notice and choice,” in data collection of personal information, 186, 187–88 Noyes, Eliot, 49 “numerical imagination,” of Hammerbacher, 13–14 Oak Ridge National Laboratory, 176 Obama administration, big data and, 203–4 O’Donnell, Tim, 180–81 OfficeMax, 188–89 Olmo, Harold, 126 Olson, Mike, 101 online advertising, 84–85 as “socio-technical construct,” 193–95 open-source code, IBM and, 9 operations research, 154 optimization, at IBM, 46 Packard, Vance, 184 Palmisano, Samuel, 49–51, 53 “Parable of Google Flu: Traps in Big Data Analysis, The” (Science), 108 Pattern Recognition (Gibson), 154 Paul, Sharoda, 135 payday lending market, 104–7 Pennebaker, James, 199 Pentland, Alex, 15, 203–4, 206 Perlich, Claudia, 120 personality traits, values, and needs, 198–99 personally identifying information, privacy concerns and, 187–92 Pieroni, Stephanie, 36 Pitts, Martha, 57 Pitts, Shereline, 57 Pop-Tarts, beer, and hurricane data, 104 precision agriculture, E. & J.


pages: 271 words: 62,538

The Best Interface Is No Interface: The Simple Path to Brilliant Technology (Voices That Matter) by Golden Krishna

Airbnb, computer vision, crossover SUV, en.wikipedia.org, fear of failure, impulse control, Inbox Zero, Internet Archive, Internet of things, Jeff Bezos, Jony Ive, Kickstarter, Mark Zuckerberg, new economy, Oculus Rift, pattern recognition, QR code, RFID, self-driving car, Silicon Valley, Skype, Snapchat, Steve Jobs, technoutopianism, Tim Cook: Apple, Y Combinator, Y2K

“Mountains, rivers . . . figures in quick movement,” he wrote. “With such walls and blends of different stones it comes about as it does with the sound of bells, in whose clanging you may discover every name and word that you can imagine.”10 Leonardo may have been one of the first to record11 a visual and auditory form of the psychological state apophenia—an error in perception—called pareidolia. It’s a form of pattern recognition encoded in us for survival gone haywire, where a false stimulus (like a sound, vibration, or image) is perceived as authentic.12 Leonardo visually experienced it through stains on the wall. Edgar Allan Poe’s character experienced it through sound after a murder.13 It’s how a Rorschach inkblot test works. And it’s what Dr. Rothberg experienced when he heard phantom vibrations from his smartphone.

Leonardo da Vinci described seeing characters in natural markings on stone walls, which he believed could help inspire his artworks.” David Robson, “Neuroscience: Why Do We See Faces in Everyday Objects?,” BBC, July 30, 2014. http://www.bbc.com/future/story/20140730-why-do-we-see-faces-in-objects 12 “Apophenia is an error of perception: The tendency to interpret random patterns as meaningful . . . Pareidolia is visual apophenia . . . For our hominid ancestors, pattern recognition was essential for recognizing both food and predators.” John W. Hoopes, “11-11-11, Apophenia, and the Meaning of Life,” Psychology Today, November 11, 2011. http://www.psychologytoday.com/blog/reality-check/201111/11-11-11-apophenia-and-the-meaning-life 13 “The murder is carefully calculated, and the murderer hides the body by cutting it into pieces and hiding it under the floorboards. Ultimately the narrator’s guilt manifests itself in the hallucination that the man’s heart is still beating under the floorboards.”


pages: 250 words: 64,011

Everydata: The Misinformation Hidden in the Little Data You Consume Every Day by John H. Johnson

Affordable Care Act / Obamacare, Black Swan, business intelligence, Carmen Reinhart, cognitive bias, correlation does not imply causation, Daniel Kahneman / Amos Tversky, Donald Trump, en.wikipedia.org, Kenneth Rogoff, labor-force participation, lake wobegon effect, Long Term Capital Management, Mercator projection, Mercator projection distort size, especially Greenland and Africa, meta analysis, meta-analysis, Nate Silver, obamacare, p-value, PageRank, pattern recognition, publication bias, QR code, randomized controlled trial, risk-adjusted returns, Ronald Reagan, selection bias, statistical model, The Signal and the Noise by Nate Silver, Thomas Bayes, Tim Cook: Apple, wikimedia commons, Yogi Berra

Sometimes, the best way to uncover the truth is by asking questions. If you see the headline “Eating brownies tied to weight gain” and you simply ask, “How is eating brownies tied to weight gain?” the answer should reveal the true relationship between these two variables. HERE COMES THE SUN Perhaps another reason that so many people conflate correlation with causation is because of the way we’re hardwired to interpret data. “The human brain is a pattern-recognition machine,” explained Ron Friedman in an interview. Friedman is a social psychologist who specializes in human motivation, and the author of The Best Place to Work: The Art and Science of Creating an Extraordinary Workplace. “In the past, before the invention of books or the search engine, uncovering links between cause and effect was essential to our survival,” noted Friedman. “Our brains evolved to look for order and find predictability.

., 25 Nike, 53 NPD Group, 21 NWEA Measures of Academic Progress (MAP), 22–23 O Obama, Barack, 23, 27–30 observations, definition of, 13. See also samples/sampling Oliver, John, 95 Olympic judges, 39–40 omitted variables, 48–50, 54–58, 126, 145, 147, 149 “only,” misrepresentation based on, 95–96 Oster, Emily, 6, 54, 78, 79 outliers, 38–42, 119 being a good consumer of, 42–43 in forecasting, 126 overconfidence, 139–140 P Pantene Pro-V Smooth shampoo ads, 7 pattern recognition, 60–61, 110–111 p-hacking, 79 Pinsker, Joe, 144–145 polls, 37–38, 68–69, 73 populations. See also samples/sampling averages and differences in, 34–35 definition of, 13 samples representativeness of, 14 prediction errors, 128–129 prediction intervals, 128–129 predictions, 127–128. See also forecasting pregnancy, alcohol and caffeine consumption during, 54–55, 79 Prescott, J. J., 58, 76, 135 presidential campaigns/elections averages/aggregates and, 27–30, 44 cherry-picking in, 115–116 forecasting, 132, 137 polls and, 37–38, 68–69, 73 sampling and, 20 terms of office and, 41 Princeton Review of schools, 19 printed material vs. online differences in consumption/interpretation of, 7 willingness to question, 93–94 printed vs. online material memory of, 2 probability, 70–71, 81 coincidence and, 138–139 forecasting and, 131 proxies, 49–50 psychology research, 15–16 publication bias, 80 p-values, 71, 72, 79 Q questions/questioning, 7–8 cherry-picking and, 122 correlation vs. causation, 60 of print vs. online information, 93–94 quote mining, 116 R Radio Television Digital News Association, 36 random chance, multiple comparison problem and, 75–76, 80–81 random samples, 65–68 Rate My Professor, 51–52 Reagan, Ronald, 9 recall of printed vs. online material, 2 Reinhart, Carmen, 97–98 relationships, 5–6.


Work in the Future The Automation Revolution-Palgrave MacMillan (2019) by Robert Skidelsky Nan Craig

3D printing, Airbnb, algorithmic trading, Amazon Web Services, anti-work, artificial general intelligence, autonomous vehicles, basic income, business cycle, cloud computing, collective bargaining, correlation does not imply causation, creative destruction, data is the new oil, David Graeber, David Ricardo: comparative advantage, deindustrialization, deskilling, disintermediation, Donald Trump, Erik Brynjolfsson, feminist movement, Frederick Winslow Taylor, future of work, gig economy, global supply chain, income inequality, informal economy, Internet of things, Jarndyce and Jarndyce, Jarndyce and Jarndyce, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, John von Neumann, Joseph Schumpeter, knowledge economy, Loebner Prize, low skilled workers, Lyft, Mark Zuckerberg, means of production, moral panic, Network effects, new economy, off grid, pattern recognition, post-work, Ronald Coase, Second Machine Age, self-driving car, sharing economy, Steve Jobs, strong AI, technoutopianism, The Chicago School, The Future of Employment, the market place, The Nature of the Firm, The Wealth of Nations by Adam Smith, Thorstein Veblen, Turing test, Uber for X, uber lyft, universal basic income, wealth creators, working poor

This is a classic case of a ‘non-routine’ task—a doctor would struggle to articulate exactly how she makes a diagnosis, find it hard to pinpoint the precise rules and thinking processes she goes through in reaching a decision. Yet a system was recently developed at Stanford that can tell whether or not a freckle is cancerous as accurately as leading dermatologists. How does it work? It is not trying to copy the doctor’s reasoning processes. It ‘knows’ or ‘understands’ nothing at all about medicine. Instead it has a database of about 129,450 past cases and is running a sort of pattern recognition algorithm through them, hunting for similarities between those images and the photo of any troublesome lesion under scrutiny. It does not matter that this task is ‘non-routine’, that a human being might not be able to explain exactly how she makes a diagnosis— this system is performing the task in a very different way, an unhuman one, based on the analysis of more possible cases than a doctor could hope to review in her lifetime.

Wants, 3, 30, 88 Neoclassical economics, 4, 55, 60, 62, 73 Netherlands, the/Holland, 6, 68, 151, 163, 177, 181–183 Network effects, 138 Networks, 45, 48, 138, 196 Neumann, John von, 99 New Zealand, 179 Nübler, Irmgard, 6, 194, 196 Index O Obama, Barack, 164, 165, 171 Obligation, 38, 53, 73–79 Occupations, 16, 40, 41, 46, 47, 58, 70, 83, 84, 86, 87, 90, 92, 106, 178, 184, 190–192, 194 OECD, 66–68, 178 O’Neil, Cathy, 6 Ontology of work, 65 Organisations dynamics of, 164 Osborne, Michael, 90 Oswald, A, 60 209 Pre-modern/pre-industrial work, 3, 11, 47, 48 Productivity, 7, 10, 79, 86, 87, 176, 178–180, 183–185, 190–192, 199 Professional work, 1, 39 Profits (different profit models), 14–18, 30, 48, 75, 79, 93, 134, 135, 138, 152, 191 Protestant work ethic, 28 Public services, 94, 167 Puritan (view of work), 28, 75, 166 R P Painting Fool, The, 115, 116, 120 Parenting, 75, 76 Patocka, Jan, 9, 21 Pattern recognition, 129 Peasant labour, 41 Perez, Carlota, 192 Philosophy of work, 30 Physical labour, 3 Piasna, Agnieszka, 181, 183 Piece-work, 30 Platform economy/platform capitalism, 6, 140 Polanyi, Karl, 192, 193 Polanyi, Michael, 127 Policy (argument against), 7, 21, 67, 68, 95, 157–173, 180, 181, 183–185, 189–200 Population, 2, 12, 15–17, 19, 28, 30, 89, 90, 117, 147, 158, 172, 198 Postmates, 136 Post-work society, 59 Poverty, 15, 47, 59, 67, 177 Redistribution, 79, 169, 199 Redundancy, 10, 12, 15–17, 19, 78, 179 Religion/religious ritual, 12, 28, 194 Remittances, 40 Responsibility, 44, 47, 76–79, 106, 107, 115, 118, 136 Retail sector, 87, 137 Retirement, 19, 67, 78 Ricardo, David, 2, 13–17 Robinson, James, 194 Robotisation, 21, 94, 95, 192 Robots carers, 106 Romantic (view of work), 34, 35 Ruskin, John, 34 S Safety nets, 67, 68 Sahlins, Marshall, 26, 158 Salazar-Xirinachs, Jose M., 198 Schumpeter, Joseph, 190, 194 Scientific management, 30 Scott, James C., 28 210 Index Searle, John, 100–103 Self-employment, 69–70, 75 Self-realisation, 57, 165 Sennett, Richard, 3 Services/service sector low frequency vs. high frequency, 134 work, 40, 68, 161, 163 Singularity, 116 Skidelsky, Edward, 60, 176 Skidelsky, Robert, 60, 176 Skills acquisition, 33, 70 skilled vs. unskilled labour/jobs, 67 Slavery, 11, 29, 30, 45 Smartphones, 140 Smiles, Samuel, 28 Smith, Adam, 12, 13, 27, 35, 54, 55, 65 Smith, Rob, 177 Social drawing rights, 70 Social interaction, 53, 88, 91 Social media, 77, 138, 168 Societal knowledge base, 196–197 Sociology (of work), 166 Spencer, David, 4, 54, 59, 61 Spinning mills (cotton industry?)


pages: 50 words: 13,399

The Elements of Data Analytic Style by Jeff Leek

correlation does not imply causation, Netflix Prize, p-value, pattern recognition, Ronald Coase, statistical model

It also makes it easier to see if there are magnitude effects, for example when small values of x and y are more similar than large values of x and y. Figure 5.7 A Bland-Alman Plot with confidence intervals 5.12 Common mistakes 5.12.1 Optimizing style too quickly The goal is to quickly understand a data set, during exploratory data analysis speed is more important than style, so tools that make beautiful graphics but take time should be avoided. 5.12.2 False pattern recognition One of the most common mistakes in exploratory data analysis is to identify and interpret a pattern without trying to break it down. Any strong pattern in a data set should be checked for confounders and alternative explanations. 5.12.3 Failing to explore data and jumping to statistical tests A common failure, particularly when using automated software, is to immediately apply statistical testing procedures and to look for statistical significance without exploring the data first. 5.12.4 Failing to look at patterns of missing values and the impact they might have on conclusions.


pages: 778 words: 239,744

Gnomon by Nick Harkaway

Albert Einstein, back-to-the-land, banking crisis, Burning Man, choice architecture, clean water, cognitive dissonance, fault tolerance, fear of failure, gravity well, high net worth, impulse control, Isaac Newton, Khartoum Gordon, lifelogging, neurotypical, pattern recognition, place-making, post-industrial society, Potemkin village, Richard Feynman, Scramble for Africa, self-driving car, side project, Silicon Valley, skunkworks, the market place, trade route, urban planning, urban sprawl

It appears significant because you have now met it in what appear to be several different environments. You forget that all these environments exist only in Diana Hunter’s consciousness. She chose the word, and it was subsequently selected at random – a single coincidence. With each iteration of it inside the psychodrama, however, it accrues greater weight for you. It is the disadvantage of human pattern recognition, exacerbated by the fact that you designate it an unusual word. However, there are half a million words in the English language and an extensive vocabulary includes perhaps thirty-five thousand of them, meaning that there are more unusual words in English than commonplace ones by a factor of fourteen. In fact, ‘gnomon’ is common in several specialist areas, meaning that its selection by Hunter is within the bounds of ordinary behaviour.

It is a peculiar skill of interface, teasing the machine to unlock a cloud of possible conjunctions, focusing on a given object for just long enough to trigger a deeper evaluation, then skating away along a connection so that it, too, unfolds to reveal its extension in the conceptual space behind the wall. For a moment she holds her breath, watching a single triangle form at the bottom and rove left and up – shark! – then snorts at herself as the lines spiral and twist into a new configuration. Pattern recognition is a liar. No watery god-monster is going to consume her case today. Gods and monsters. Her gaze drifts to the Roman syncretist bubbles in that portion of the wall given over to Hunter’s narratives. Athenais dreamed a room of lies which came true, and a man died. Was sacrificed. Death setting everything in motion. She lingers, and the constant pressure of her eyes on the topic loosens the bonds between items, each becoming its own centre of annotated, projected meaning and possible subtext.

‘Yes, I’m aware that you don’t approve. On the other hand, you’re alive. I will say, you had us all just a little bit worried there. We really don’t hold with people departing this life while in our care. We take it amiss. The nurses, in particular, would have been rather vexed with you. Anyway, hello. I’ve taken over your treatment today.’ Treatment, bollocks. ‘Yes, well. Be that as it may.’ S’a trick. ‘Yes, your pattern recognition is running rather too high. Almost conspiratorially so. How do you feel about the moon landings?’ Funny man not funny. ‘You find humour inappropriate? Perhaps you’re right. Let me just adjust this – there. Now. Do you still think we know one another?’ Oh … No. Silly. ‘Not at all. A biological error. I’m afraid your brain is dysfunctional at the moment. We’re working on it. We’ll have you shipshape in no time – but you need to try to help us, from within.


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

By following economies, he got advance warning of the climate for stocks. If a currency was heading downward, export stocks would be a buy. If interest rates were rising, it was time to short real-estate developers. To his sense of companies and economies Druckenmiller added a third skill: technical analysis. His first boss in Pittsburgh had been a student of charts, and although most stock pickers disdained this pattern recognition as voodoo, Druckenmiller soon found it could be useful. It was one thing to do the fundamental analysis that told you that a stock or bond was overvalued; it was another to know when the market would correct, and the charts hinted at the answers. Technical analysis taught Druckenmiller to be alert to market waves, to combine the trading agility of Paul Tudor Jones with the stock-picking strengths of Julian Robertson.

But once the firm realized that the correlations made intuitive sense—they reflected the technology euphoria that had pushed into all these industries—they seemed more likely to be tradable.27 Moreover, signals based on intuition have a further advantage: If you understand why they work, you probably understand why they might cease to work, so you are less likely to keep trading them beyond their point of usefulness. In short, Wepsic is saying that pure pattern recognition is a small part of what Shaw does, even if the firm does some of it. Again, this presents a contrast with Renaissance. Whereas D. E. 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.

Each time Simons’s picture appeared on the cover of a financial magazine, more eager institutional money flooded into quantitative trading systems. Simons himself capitalized on this phenomenon. In 2005 he launched a new venture, the Renaissance Institutional Equities Fund, which was designed to absorb an eye-popping $100 billion in institutional savings. The only way this huge amount could be manageable was to branch out from short-term trading into more liquid longer-term strategies—and since pure pattern recognition works best for short-term trades, it followed that Simons was offering a fund that would rely on different sorts of signal—ones that might already have been mined by D. E. Shaw and other rivals. By the summer of 2007, the new Simons venture had raked in more than $25 billion, making it one of the largest hedge funds in the world. But then the financial crisis hit. Like almost everybody else, Simons felt the consequences. 14 PREMONITIONS OF A CRISIS By the middle of the 2000s, the scale and persistence of hedge funds’ success was transforming the structure of the industry.


pages: 222 words: 76,854

Art of Learning by Josh Waitzkin

fear of failure, G4S, Mahatma Gandhi, pattern recognition, South China Sea

This was an exciting time. As I internalized Tai Chi’s technical foundation, I began to see my chess understanding manifesting itself in the Push Hands game. I was intimate with competition, so offbeat strategic dynamics were in my blood. I would notice structural flaws in someone’s posture, just as I might pick apart a chess position, or I’d play with combinations in a manner people were not familiar with. Pattern recognition was a strength of mine as well, and I quickly picked up on people’s tells. As the months turned into years, my training became more and more vigorous and I learned how to dissolve away from attacks while staying rooted to the ground. It is a sublime feeling when your root kicks in, as if you are not standing on the ground but anchored many feet deep into the earth. The key is relaxed hip joints and spring-like body mechanics, so you can easily receive force by coiling it down through your structure.

I knew from chess that a superior artist could often get into the head of the opponent, mesmerize him with will or strategic mastery, using what I playfully like to call Jedi Mind Tricks. As far as I understood, the keys to these moments were penetrating insight into what makes the other tick and technical virtuosity that makes the discovery and exploitation invisible to the opponent. On the other hand, Chinese martial arts tend to focus more on energy than pattern recognition. My goal was to find a hybrid—energetic awareness, technical fluidity, and keen psychological perception. Chess meets Tai Chi Chuan. In time, I have come to understand those words, At the opponent’s slightest move, I move first, as pertaining to intention—reading and ultimately controlling intention. The deepest form of adherence or shadowing involves a switching of roles, where the follower becomes the followed in a relationship in which time seems to twist in a tangle of minds—this is how the great Tai Chi or Aikido artist guides the opponent into a black hole, or appears to psychically impel the other to throw himself on the ground.


pages: 345 words: 75,660

Prediction Machines: The Simple Economics of Artificial Intelligence by Ajay Agrawal, Joshua Gans, Avi Goldfarb

"Robert Solow", Ada Lovelace, AI winter, Air France Flight 447, Airbus A320, artificial general intelligence, autonomous vehicles, basic income, Bayesian statistics, Black Swan, blockchain, call centre, Capital in the Twenty-First Century by Thomas Piketty, Captain Sullenberger Hudson, collateralized debt obligation, computer age, creative destruction, Daniel Kahneman / Amos Tversky, data acquisition, data is the new oil, deskilling, disruptive innovation, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, everywhere but in the productivity statistics, Google Glasses, high net worth, ImageNet competition, income inequality, information retrieval, inventory management, invisible hand, job automation, John Markoff, Joseph Schumpeter, Kevin Kelly, Lyft, Minecraft, Mitch Kapor, Moneyball by Michael Lewis explains big data, Nate Silver, new economy, On the Economy of Machinery and Manufactures, pattern recognition, performance metric, profit maximization, QWERTY keyboard, race to the bottom, randomized controlled trial, Ray Kurzweil, ride hailing / ride sharing, Second Machine Age, self-driving car, shareholder value, Silicon Valley, statistical model, Stephen Hawking, Steve Jobs, Steven Levy, strong AI, The Future of Employment, The Signal and the Noise by Nate Silver, Tim Cook: Apple, Turing test, Uber and Lyft, uber lyft, US Airways Flight 1549, Vernor Vinge, Watson beat the top human players on Jeopardy!, William Langewiesche, Y Combinator, zero-sum game

Radiologists have feared that machines might replace them since the early 1960s.3 What makes today’s technology different? Machine-learning techniques are increasingly good at predicting missing information, including identification and recognition of items in images. Given a new set of images, the techniques can efficiently compare millions of past examples with and without disease and predict whether the new image suggests the presence of a disease. This kind of pattern recognition to predict disease is what radiologists do.4 IBM, with its Watson system, and many startups have already commercialized AI tools in radiology. Watson can identify a pulmonary embolism and a wide range of other heart issues. One startup, Enlitic, uses deep learning to detect lung nodules (a fairly routine exercise) but also fractures (more complex). These new tools are at the heart of Hinton’s forecast but are a subject for discussion among radiologists and pathologists.5 What does our approach suggest about the future of radiologists?

See also jobs “Lady Lovelace’s Objection,” 13 Lambrecht, Anja, 196 language translation, 25–27, 107–108 laws of robotics, 115 learning -by-using, 182–183 in the cloud vs. on the ground, 188–189, 202 experience and, 191 in-house and on-the-job, 185 language translation, 26–27 pathways to, 182–184 privacy and data for, 189–190 reinforcement, 13, 145, 183–184 by simulation, 187–188 strategy for, 179–194 supervised, 183 trade-offs in performance and, 181–182 when to deploy and, 184–187 Lederman, Mara, 168–169 Lee, Kai-Fu, 219 Lee Se-dol, 8 legal documents, redacting, 53–54, 68 legal issues, 115–117 Lewis, Michael, 56 Li, Danielle, 58 liability, 117, 195–198 lighting, cost of, 11 London cabbies, 76–78 Lovelace, Ada, 12, 13 Lyft, 88–89 Lytvyn, Max, 96 machine learning, 18 adversarial, 187–188 churn prediction and, 32–36 complexity and, 103–110 from data, 45–47 feedback for, 46–47 flexibility in, 36 judgment and, 83 one-shot, 60 regression compared with, 32–35 statistics and prediction and, 37–40 techniques, 8–9 transformation of prediction by, 37–40 Mailmobile, 103 management AI’s impact on, 3 by exception, 67–68 Mastercard, 25 mathematics, made cheap by computers, 12, 14 Mazda, 124 MBA programs, student recruitment for, 127–129, 133–139 McAfee, Andrew, 91 Mejdal, Sig, 161 Microsoft, 9–10, 176, 180, 202–204, 215, 217 Tay chatbot, 204–205 mining, automation in, 112–114 Misra, Sanjog, 93–94 mobile-first strategy, 179–180 Mobileye, 15 modeling, 99, 100–102 Moneyball (Lewis), 56, 161–162 monitoring of predictions, 66–67 multivariate regression, 33–34 music, digital, 12, 61 Musk, Elon, 209, 210, 221 Mutual Benefit Life, 124–125 Napster, 61 NASA, 14 National Science and Technology Council (NSTC), 222–223 navigation apps, 77–78, 88–90, 106 Netscape, 9–10 neural networks, 13 New Economy, 10 New York City Fire Department, 197 New York Times, 8, 218 Nordhaus, William, 11 Norvig, Peter, 180 Nosko, Chris, 199 Novak, Sharon, 169–170 Numenta, 223 Nymi, 201 Oakland Athletics, 56, 161–162 Obama, Barack, 217–218 objectives, identifying, 139 object recognition, 7, 28–29 Olympics, Rio, 114–115 omitted variables, 62 one-shot learning, 60 On Intelligence (Hawkins), 39 Open AI, 210 optimization, search engine, 64 oracles, 23 organizational structure, 161–162 Osborne, Michael, 149 Otto, 157–158 outcomes in decision making, 74–76, 134–138 job redesign and, 142 outsourcing, 169–170, 171 Page, Larry, 179 Paravisini, Daniel, 66–67 pattern recognition, 145–147 Pavlov, Ivan, 183 payoff calculations, 78–81 in drug discovery, 136 judgment in, 87–88 Pell, Barney, 2 performance, trade-offs between learning and, 181–182, 187 performance reviews, 172–173 photography digital, 14 sports, automation of, 114–115 Pichai, Sundar, 179–180 Piketty, Thomas, 213 Pilbara, Australia, mining in, 112–114 policy, 3, 210 power calculations, 48 prediction, 23–30 about the present, 23–24 behavior affected by, 23 bias in, 34–35 complements to, 15 consequences of cheap, 29 credit card fraud prevention and, 24–25 in decision making, 74–76, 134–138 definition of, 13, 24 by exception, 67–68 human strengths in, 60 human weaknesses in, 54–58 improvements in, 25–29 as intelligence, 2–3, 29, 31–41 in language translation, 25–27 machine weaknesses in, 58–65 made cheap, 13–15 selling, 176–177 techniques, 13 unanticipated correlations and, 36–37 of what a human would do, 95–102 predictive text, 130 preferences, 88–90, 96–97, 98 selling consumer, 176–177 presidential elections, 59 prices effects of reduced AI, 9–11 human judgment in, 100 sales causality and, 63–64 for ZipRecruiter, 93–94 privacy issues, 19, 49, 98 China and, 219–220 country differences in, 219–221 data collection, 189–190 probabilistic programming, 38, 40 processes.


pages: 254 words: 76,064

Whiplash: How to Survive Our Faster Future by Joi Ito, Jeff Howe

3D printing, Albert Michelson, Amazon Web Services, artificial general intelligence, basic income, Bernie Sanders, bitcoin, Black Swan, blockchain, Burning Man, buy low sell high, Claude Shannon: information theory, cloud computing, Computer Numeric Control, conceptual framework, crowdsourcing, cryptocurrency, data acquisition, disruptive innovation, Donald Trump, double helix, Edward Snowden, Elon Musk, Ferguson, Missouri, fiat currency, financial innovation, Flash crash, frictionless, game design, Gerolamo Cardano, informal economy, interchangeable parts, Internet Archive, Internet of things, Isaac Newton, Jeff Bezos, John Harrison: Longitude, Joi Ito, Khan Academy, Kickstarter, Mark Zuckerberg, microbiome, Nate Silver, Network effects, neurotypical, Oculus Rift, pattern recognition, peer-to-peer, pirate software, pre–internet, prisoner's dilemma, Productivity paradox, race to the bottom, RAND corporation, random walk, Ray Kurzweil, Ronald Coase, Ross Ulbricht, Satoshi Nakamoto, self-driving car, SETI@home, side project, Silicon Valley, Silicon Valley startup, Simon Singh, Singularitarianism, Skype, slashdot, smart contracts, Steve Ballmer, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, supply-chain management, technological singularity, technoutopianism, The Nature of the Firm, the scientific method, The Signal and the Noise by Nate Silver, There's no reason for any individual to have a computer in his home - Ken Olsen, Thomas Kuhn: the structure of scientific revolutions, universal basic income, unpaid internship, uranium enrichment, urban planning, WikiLeaks

Whatever the size of the board, the goal is to capture as much territory, and as many of your opponent’s stones, as possible. “Chess,” the German master Richard Teichmann once said, “is 99 percent tactics,” and success requires seeing the long-term consequences of any given move. But no earthly intelligence could compute the possible outcomes from the 361 moves that greet a competitor when facing an empty Go board. Go prodigies tend to possess uncanny pattern-recognition skills and rely on their intuition. In fMRI studies the right hemisphere of the brain—the side that governs visual awareness and holistic awareness—lights up more strongly in Go players than the left.4 In fact, with its nearly infinite possibilities, a Go board has more in common with the painter’s blank canvas than it does with the game of chess. The Chinese, who probably invented the game around the time the Old Testament was being written, must have thought so.

Held in one of the university’s largest lecture halls, the DeepMind event drew a standing-room-only crowd—students were all but hanging off the walls to hear Hassabis describe how their approach to machine learning had allowed their team to prove the experts who had predicted it would take ten years for a computer to beat a virtuoso like Sedol wrong. The key was a clever combination of deep learning—a kind of pattern recognition, similar to how a human brain (or Google) can recognize a cat or a fire truck after seeing many images—and “learning” so that it could guess statistically what something was likely to be, or in the case of Go, what a human player, considering all of the games of the past, was likely to play in a particular situation. This created a very rudimentary model of a Go player that guessed moves based on patterns it learned from historical matches.


pages: 296 words: 78,631

Hello World: Being Human in the Age of Algorithms by Hannah Fry

23andMe, 3D printing, Air France Flight 447, Airbnb, airport security, augmented reality, autonomous vehicles, Brixton riot, chief data officer, computer vision, crowdsourcing, DARPA: Urban Challenge, Douglas Hofstadter, Elon Musk, Firefox, Google Chrome, Gödel, Escher, Bach, Ignaz Semmelweis: hand washing, John Markoff, Mark Zuckerberg, meta analysis, meta-analysis, pattern recognition, Peter Thiel, RAND corporation, ransomware, recommendation engine, ride hailing / ride sharing, selection bias, self-driving car, Shai Danziger, Silicon Valley, Silicon Valley startup, Snapchat, speech recognition, Stanislav Petrov, statistical model, Stephen Hawking, Steven Levy, Tesla Model S, The Wisdom of Crowds, Thomas Bayes, Watson beat the top human players on Jeopardy!, web of trust, William Langewiesche

Is this headache perfectly normal or a sign of something more sinister? Is it worth prescribing a course of antibiotics to make this boil go away? All are questions of pattern recognition, classification and prediction. Skills that algorithms happen to be very, very good at. Of course, there are many aspects of being a doctor that an algorithm will probably never be able to replicate. Empathy, for one thing. Or the ability to support patients through social, psychological, even financial difficulties. But there are some areas of medicine where algorithms can offer a helping hand. Especially in the roles where medical pattern recognition is found in its purest form and classification and prediction are prized almost to the exclusion of all else. Especially in an area like pathology. Pathologists are the doctors a patient rarely meets.


pages: 257 words: 80,100

Time Travel: A History by James Gleick

Ada Lovelace, Albert Einstein, Albert Michelson, Arthur Eddington, augmented reality, butterfly effect, crowdsourcing, Doomsday Book, index card, Isaac Newton, John von Neumann, luminiferous ether, Marshall McLuhan, Norbert Wiener, pattern recognition, Richard Feynman, Schrödinger's Cat, self-driving car, Stephen Hawking, telepresence, wikimedia commons

For ancient astronomers to forecast the movements of heavenly bodies was vindication and triumph; to predict an eclipse was to rob it of its terror; medical science has labored for centuries to eradicate diseases and extend the lifetimes that fatalists call fixed; in the first powerful application of Newton’s laws to earthly mechanics, students of gunnery computed the parabolic trajectories of cannonballs, the better to send them to their targets; twentieth-century physicists not only managed to change the course of warfare but then dreamt of using their new computing machines to forecast and even control the earth’s weather. Because, why not? We are pattern-recognition machines, and the project of science is to formalize our intuitions, do the math, in hopes not just of understanding—a passive, academic pleasure—but of bending nature, to the limited extent possible, to our will. Remember Laplace’s perfect intelligence, vast enough to comprehend all the forces and the positions and to submit them to analysis. “To it nothing would be uncertain, and the future as the past would be present to its eyes.”

The virtual world is build on transtemporality. Gibson, who always felt time travel to be an implausible magic, avoided it through ten novels written across thirty years.*6 Indeed, as his imagined futures kept crowding in on the conveyor belt of the present, he renounced the future altogether. “Fully imagined futures were the luxury of another day, one in which ‘now’ was of some greater duration,” says Hubertus Bigend in the 2003 Pattern Recognition. “We have no future because our present is too volatile.” The future stands upon the present, and the present is quicksand. Back to the future once more, though, in Gibson’s eleventh novel, The Peripheral. A near future interacts with a far future. Cyberspace gave him a way in. New rules of time travel: matter cannot escape its time but information can. The future discovers that it can email the past.


pages: 268 words: 75,850

The Formula: How Algorithms Solve All Our Problems-And Create More by Luke Dormehl

3D printing, algorithmic trading, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, big data - Walmart - Pop Tarts, call centre, Cass Sunstein, Clayton Christensen, commoditize, computer age, death of newspapers, deferred acceptance, disruptive innovation, Edward Lorenz: Chaos theory, Erik Brynjolfsson, Filter Bubble, Flash crash, Florence Nightingale: pie chart, Frank Levy and Richard Murnane: The New Division of Labor, Google Earth, Google Glasses, High speed trading, Internet Archive, Isaac Newton, Jaron Lanier, Jeff Bezos, job automation, John Markoff, Kevin Kelly, Kodak vs Instagram, lifelogging, Marshall McLuhan, means of production, Nate Silver, natural language processing, Netflix Prize, Panopticon Jeremy Bentham, pattern recognition, price discrimination, recommendation engine, Richard Thaler, Rosa Parks, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Slavoj Žižek, social graph, speech recognition, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, the scientific method, The Signal and the Noise by Nate Silver, upwardly mobile, Wall-E, Watson beat the top human players on Jeopardy!, Y Combinator

As Rushkoff writes, these series don’t work their magic through a linear plot, but instead create contrasts through association, by nesting screens within screens, and by giving viewers the tools to make connections between various forms of media . . . The beginning, the middle, and the end have almost no meaning. The gist is experienced in each moment as new connections are made and false stories are exposed or reframed. In short, these sorts of shows teach pattern recognition, and they do it in real time.41 Even today’s most popular films no longer exist as unitary entities, but as nodes in larger franchises—with sequels regularly announced even before the first film is shown. It’s no accident that in this setting many of the most popular blockbusters are based on comic-book properties: a medium in which, unlike a novel, plot points are ongoing with little expectation of an ultimate resolution.

“They instructed their doctors to gather less information on their patients,” Gladwell writes, explaining how doctors were told to instead zero in “on just a few critical pieces of information about patients . . . like blood pressure and the ECG—while ignoring everything else, like the patient’s age and weight and medical history. And what happened? Cook County is now one of the best places in the United States at diagnosing chest pain.”4 Recent medical algorithms have been shown to yield equally impressive results in other areas, such as an algorithm able to diagnose for Parkinson’s disease by listening to a person’s voice over the telephone, and another pattern-recognition algorithm able to, quite literally, “sniff” for diseases like cancer. Algorithmizing the World Can everything be subject to algorithmization? There are two ways to answer this question. The first is to approach it purely on a technical level. At present, no, everything cannot be “solved” by an algorithm. At time of writing, for instance, recognizing objects with anything close to the ability of a human is still a massive challenge.


pages: 230 words: 76,655

Choose Yourself! by James Altucher

Airbnb, Albert Einstein, Bernie Madoff, bitcoin, cashless society, cognitive bias, dark matter, Elon Musk, estate planning, Mark Zuckerberg, money market fund, Network effects, new economy, PageRank, passive income, pattern recognition, payday loans, Peter Thiel, Ponzi scheme, Rodney Brooks, rolodex, Saturday Night Live, sharing economy, short selling, side project, Silicon Valley, Skype, software as a service, Steve Jobs, superconnector, Uber for X, Vanguard fund, Y2K, Zipcar

You have to remember your experiences, study your failures, try to note what you did right and what you did wrong, and remember it all for future experiences. Future experiences will almost never be exactly like the old experiences. But they give you the ability to say, “Hmm, this is like the time four years ago when X, Y, and Z happened.” And then you are engaging in… * * * Using Pattern Recognition Being able to recognize when current circumstances are like an experience you’ve had in the past or an experience someone else you’ve studied had in the past is critical to mastery. Pattern recognition is a combination of all of the above: study + history + experience + talent + a new thing… love. * * * Loving It Andre Agassi has famously said he doesn’t love tennis. I believe this and I don’t believe it. We all know that there are all kinds of love. There’s unconditional love, which is very hard to maintain.


pages: 567 words: 122,311

Lean Analytics: Use Data to Build a Better Startup Faster by Alistair Croll, Benjamin Yoskovitz

Airbnb, Amazon Mechanical Turk, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, barriers to entry, Bay Area Rapid Transit, Ben Horowitz, bounce rate, business intelligence, call centre, cloud computing, cognitive bias, commoditize, constrained optimization, en.wikipedia.org, Firefox, Frederick Winslow Taylor, frictionless, frictionless market, game design, Google X / Alphabet X, Infrastructure as a Service, Internet of things, inventory management, Kickstarter, lateral thinking, Lean Startup, lifelogging, longitudinal study, Marshall McLuhan, minimum viable product, Network effects, pattern recognition, Paul Graham, performance metric, place-making, platform as a service, recommendation engine, ride hailing / ride sharing, rolodex, sentiment analysis, skunkworks, Skype, social graph, social software, software as a service, Steve Jobs, subscription business, telemarketer, transaction costs, two-sided market, Uber for X, web application, Y Combinator

While the data you’re collecting at this stage is qualitative, it has to be material enough so that you can honestly say, “Yes, this problem is painful enough that I should go ahead and build a solution.” One customer doesn’t make a market. You can’t speak with a few people, get generic positive feedback, and decide it’s worth jumping in. Signs You’ve Found a Problem Worth Tackling The key to qualitative data is patterns and pattern recognition. Here are a few positive patterns to look out for when interviewing people: They want to pay you right away. They’re actively trying to (or have tried to) solve the problem in question. They talk a lot and ask a lot of questions demonstrating a passion for the problem. They lean forward and are animated (positive body language). Here are a few negative patterns to look out for: They’re distracted.

exercise for, The Squeeze Toy optimizing, Model + Stage Drives the Metric You Track picking, Solare Focuses on a Few Key Metrics, Measuring the MVP reasons for using, Moz Tracks Fewer KPIs to Increase Focus, Get Executive Buy-in SEOmoz case study, The Discipline of One Metric That Matters Solare Ristorante case study, Four Reasons to Use the One Metric That Matters squeeze toy aspect of, Drawing Lines in the Sand Open Leadership (Li), Engagement Funnel Changes open rate metric, Mailing List Effectiveness optimization about, Data-Driven Versus Data-Informed constrained, Data-Driven Versus Data-Informed diminishing returns for, What to Do When You Don’t Have a Baseline OMTM and, Model + Stage Drives the Metric You Track revenue, Pricing Metrics Orbitz travel agency, Data-Driven Versus Data-Informed, Pricing Metrics organizational culture, instilling, How to Instill a Culture of Data in Your Company ORID approach, How Rally Builds New Features with a Lean Approach Osterwalder, Alex, The Lean Canvas outliers, pitfalls to avoid, Data-Driven Versus Data-Informed OutSight service, How Coradiant Found a Market Ozzie, Ray, Skunk Works for Intrapreneurs O’Donnell, Christopher, Freemium Versus Paid P P&G (Proctor & Gamble), Stars, Dogs, Cows, and Question Marks PaaS (Platform as a Service) model, Model Two: Software as a Service (SaaS) Pacheco, Carlos, Sharing with Others Pacific Crest study, Paid Enrollment paid engine (engines of growth), Virality Engine paid enrollment fremium models versus, Freemium Versus Paid in SaaS model, Paid Enrollment Palihapitiya, Chamath, Attacking the Leading Indicator, Causality Hacks the Future Parmar, Jay, A/B and Multivariate Testing Parse.ly case study, Customer Lifetime Value > Customer Acquisition Cost Patil, DJ, Incumbents patterns and pattern recognition identifying in people’s feedback, Running Lean and How to Conduct a Good Interview ProductPlanner site, Timehop Experiments with Content Sharing to Achieve Virality qualitative data and, Finding a Problem to Fix (or, How to Validate a Problem) paywall model, Wrinkles: Hidden Affiliates, Background Noise, Ad Blockers, and Paywalls Pelletier-Normand, Alexandre, Model Three: Free Mobile App, Mobile Download Size, Bottom Line penny machine example, Metrics for the Revenue Stage percent active mobile users/players metric, Sincerely Learns the Challenges of Mobile Customer Acquisition percent of flagged listings metric, Conversion Rates and Segmentation percentage of active users/players metric, Model Three: Free Mobile App percentage of mobile users who pay metric, Percent Active Mobile Users/Players percentage of users who pay metric, Model Three: Free Mobile App, Average Revenue Per User Perez, Sarah, Mobile Customer Lifetime Value Photoshop application, The Minimum Viable Vision Picatic site, A/B and Multivariate Testing Pinterest site affiliate relationships and, Wrinkles: Hidden Affiliates, Background Noise, Ad Blockers, and Paywalls e-commerce model and, What Mode of E-commerce Are You?

identifying pain points, Cloud9 IDE Interviews Existing Customers leading the witness in, How to Avoid Leading the Witness looking for patterns in, Finding a Problem to Fix (or, How to Validate a Problem) scoring considerations, How to Avoid Leading the Witness understanding customer’s daily life, A “Day in the Life” of Your Customer Problem-Solution Canvas about, The Minimum Viable Vision Current Status box, The Problem-Solution Canvas Lessons Learned box, The Problem-Solution Canvas Top Problems box, The Problem-Solution Canvas Varsity News Network case study, VNN Uses the Problem-Solution Canvas to Solve Business Problems Proctor & Gamble (P&G), Stars, Dogs, Cows, and Question Marks product plans, How Rally Builds New Features with a Lean Approach product returns metric, Leading Versus Lagging Metrics product type (business model), About Those People ProductPlanner site, Timehop Experiments with Content Sharing to Achieve Virality proof-of-concept trials, Revenue: Direct Sales and Support Pruijn, Ivar, Cloud9 IDE Interviews Existing Customers purchases per year metric, A Practical Example Q qidiq tool case study, Iterating the MVP QRR (quarterly recurring revenue), The Penny Machine qualified leads metric, Leading Versus Lagging Metrics qualitative metrics about, What Makes a Good Metric?, Finding a Problem to Fix (or, How to Validate a Problem) bias in, Running Lean and How to Conduct a Good Interview in Empathy stage, Stage One: Empathy MVP process and, Measuring the MVP patterns and pattern recognition in, Finding a Problem to Fix (or, How to Validate a Problem) quantitative versus, What Makes a Good Metric? trends and, Running Lean and How to Conduct a Good Interview quantitative metrics about, What Makes a Good Metric? getting answers at scale, Finding People to Talk To limitations of, Data-Driven Versus Data-Informed measuring effects of features, Seven Questions to Ask Yourself Before Building a Feature qualitative versus, What Makes a Good Metric?


pages: 685 words: 203,949

The Organized Mind: Thinking Straight in the Age of Information Overload by Daniel J. Levitin

airport security, Albert Einstein, Amazon Mechanical Turk, Anton Chekhov, Bayesian statistics, big-box store, business process, call centre, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, conceptual framework, correlation does not imply causation, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, delayed gratification, Donald Trump, en.wikipedia.org, epigenetics, Eratosthenes, Exxon Valdez, framing effect, friendly fire, fundamental attribution error, Golden Gate Park, Google Glasses, haute cuisine, impulse control, index card, indoor plumbing, information retrieval, invention of writing, iterative process, jimmy wales, job satisfaction, Kickstarter, life extension, longitudinal study, meta analysis, meta-analysis, more computing power than Apollo, Network effects, new economy, Nicholas Carr, optical character recognition, Pareto efficiency, pattern recognition, phenotype, placebo effect, pre–internet, profit motive, randomized controlled trial, Rubik’s Cube, shared worldview, Skype, Snapchat, social intelligence, statistical model, Steve Jobs, supply-chain management, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Bayes, Turing test, ultimatum game, zero-sum game

As Scott Grafton, a top neurologist at UC Santa Barbara, says, “Experience and implicit knowledge really matter. I recently did clinical rounds with two emergency room doctors who had fifty years of clinical experience between them. There was zero verbal gymnastics or formal logic of the kind that Kahneman and Tversky tout. They just recognize a problem. They have gained skill through extreme reinforcement learning, they become exceptional pattern recognition systems. This application of pattern recognition is easy to understand in a radiologist looking at X-rays. But it is also true of any great clinician. They can generate extremely accurate Bayesian probabilities based on years of experience, combined with good use of tests, a physical exam, and a patient history.” A good doctor will have been exposed to thousands of cases that form a rich statistical history (Bayesians call this a prior distribution) on which they can construct a belief around a new patient.

problem Buchenroth, T., Garber, F., Gowker, B., & Hartzell, S. (2012, July). Automatic object recognition applied to Where’s Waldo? Aerospace and Electronics Conference (NAECON), 2012 IEEE National, 117–120. and, Garg, R., Seitz, S. M., Ramanan, D., & Snavely, N. (2011, June). Where’s Waldo: Matching people in images of crowds. Proceedings of the 24th IEEE Conference on Computer Vision and Pattern Recognition, 1793–1800. Wikipedia is an example of crowdsourcing Ayers, P., Matthews, C., & Yates, B. (2008). How Wikipedia works: And how you can be a part of it. San Francisco, CA: No Starch Press, p. 514. More than 4.5 million people Kickstarter, Inc. (2014). Seven things to know about Kickstarter. Retrieved from http://www.kickstarter.com the group average comes Surowiecki, J. (2005).

See brain physiology news media, 338–40 Newton, Isaac, 162 New Yorker, 120, 336 New York Times, 6, 339, 365 Nietzsche, Friedrich, 375 Nixon, Richard, 201 NMDA receptor, 167 nonlinear thinking and perception, 38, 215, 217–18, 262, 380 Norman, Don, 35 number needed to treat metric, 236, 240, 247, 264, 264 Obama, Barack, 219, 303 object permanence, 24 Office of Presidential Correspondence, 303 Olds, James, 101 Old Testament, 151 O’Neal, Shaquille, 352–53 One Hundred Names for Love (Ackerman), 364–65 online dating, 130–34, 422n130, 423n132 optical character recognition (OCR), 93, 119, 119 optimal information, 308–10 orders of magnitude, 354–55, 358–59, 361, 363, 400n7 organizational structure, 271–76, 315–18, 470n315, 471n317 Otellini, Paul, 380–81 Overbye, Dennis, 6, 19 Oxford English Dictionary, 114 Oxford Filing Supply Company, 93–94 Page, Jimmy, 174 pair-bonding, 128, 142 paperwork, 293–306 Pareto optimality, 269 parking tickets, 237, 451n237 Parkinson’s disease, 167–68 passwords, xx, 103–5 Patel, Shreena, 258 paternalism, medical, 245, 257 pattern recognition, 28, 249 Patton, George S., 73–74 peak performance, 167, 189, 191–92, 203, 206 Peer Instruction (Mazur), 367 perfectionism, 174, 199–200 periodic table of elements, 372–73, 373, 480n372 Perry, Bruce, 56 Peterson, Jennifer, 368 pharmaceuticals, 256–57, 343, 345–46 Picasso, Pablo, 283 Pierce, John R., 73 Pirsig, Robert, 69–73, 89, 295–97, 300 placebo effect, 253, 255 place memory, 82–83, 106, 293–94 planning, 43, 161, 174–75, 319–26 Plato, 14, 58, 65–66 plausibility, 350, 352, 478n352 Plimpton, George, 200 Plutarch, 340 Poldrack, Russ, 97 Polya, George, 357 Ponzo illusion, 21, 22 positron emission tomography (PET), 40 prediction, 344–45 prefrontal cortex, 161 Area 47, 287 and attention, 16–17, 43, 45–46 and changing behaviors, 176 and children’s television, 368 and creative time, 202, 210 and decision-making, 277, 282 and flow state, 203, 207 and information overload, 8 and literary fiction, 367 and manager/worker distinction, 176 and multitasking, 96, 98, 307 and procrastination, 197, 198, 200–201 and sleep, 187 and task switching, 171–72 and time organization, 161, 165–66, 174, 180 See also brain physiology preselection effect, 331, 343 Presidential Committee on Information Literacy, 365 primacy effect, 55, 408n56 primates, 17–18, 125–26, 135 Prince, 174 Princeton Theological Seminary, 145–46 prior distributions, 249 prioritization, 5–7, 33–35, 379–80 probability.


pages: 302 words: 86,614

The Alpha Masters: Unlocking the Genius of the World's Top Hedge Funds by Maneet Ahuja, Myron Scholes, Mohamed El-Erian

activist fund / activist shareholder / activist investor, Asian financial crisis, asset allocation, asset-backed security, backtesting, Bernie Madoff, Bretton Woods, business process, call centre, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Donald Trump, en.wikipedia.org, family office, fixed income, high net worth, interest rate derivative, Isaac Newton, Long Term Capital Management, Marc Andreessen, Mark Zuckerberg, merger arbitrage, Myron Scholes, NetJets, oil shock, pattern recognition, Ponzi scheme, quantitative easing, quantitative trading / quantitative finance, Renaissance Technologies, risk-adjusted returns, risk/return, rolodex, short selling, Silicon Valley, South Sea Bubble, statistical model, Steve Jobs, systematic trading, zero-sum game

Almost everyone who’s worked at Third Point has at least gone through a two-year training program in an investment bank, plus done a couple years at a private equity firm, doing modeling and valuation work.” For Loeb, having an MBA isn’t as critical as having the training. New hires need about two or three years of experience in a field other than the public investment world, like mergers and acquisitions. “I don’t like the word ‘instinct,’” says Loeb, “because it just sounds like a gut thing. I think what we call instinct is really a type of pattern recognition, which comes from experience looking at the companies and industries and situations that work.” Loeb looks for another quality as well: success at something other than work and school. “We’ve had a lot of excellent musicians and athletes here,” he says. “I don’t want to dismiss the importance of academic credentials, but we want bright people who are really diligent and hardworking, but also have real tenacity and grit who enjoy what they do and have an incredible passion for investing.”

And there may be information flows that stop because the analyst doesn’t want to give any more bad news to the partners who had invested in the idea. So, there’s disproportionate risk and disproportionate reward. Chanos says: “We have a different approach, one in which the partners generate ideas—an investment theme, let’s say—and the analysts help to validate, build upon, or disprove those ideas. Our approach encourages intellectual curiosity, collaboration, and an openness.” Chanos thinks pattern recognition is important and something that simply takes experience, which is one reason why he tasks the firm’s partners with originating the ideas. The partners have extensive experience in seeing patterns in odd-looking financial or press statements, for example. Through their mosaic of experience and wisdom, they are in the strongest position to decide what strategies to explore and not get distracted by the markets’ daily vagaries.


pages: 519 words: 142,646