pattern recognition

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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, Charles Babbage, Claude Shannon: information theory, cloud computing, computer age, Computing Machinery and Intelligence, Dean Kamen, discovery of DNA, double helix, driverless car, en.wikipedia.org, epigenetics, George Gilder, Google Earth, Hans Moravec, Isaac Newton, iterative process, Jacquard loom, Jeff Hawkins, John von Neumann, Law of Accelerating Returns, linear programming, Loebner Prize, mandelbrot fractal, Nick Bostrom, Norbert Wiener, optical character recognition, PalmPilot, 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

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.

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., 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.

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., 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.

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, digital divide, 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, PalmPilot, pattern recognition, profit motive, Robert Shiller, Ronald Reagan, Salesforce, speech recognition, tacit knowledge, 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.

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If the person is speaking about her male cousin, we will probably interpret “BILL” as a person’s first name unless she is speaking about her cousin’s financial problems, in which case we may have to listen more closely. When we try to program pattern recognition, problems of perception and the requirements for contextual knowledge limit what we can recognize. In technical terms, pattern recognition is programmed using neural nets and other “machine learning” software. These methods are based on the conjecture advanced earlier that human pattern recognition itself may rest on probabilistic rules. Recall that in programs using rules-based logic, the explicit rules can be stated before the program is ever written.

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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.

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

Ada Lovelace, AltaVista, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, 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

These codes are everywhere: they are used in all hard disk drives, many network transmissions, on CDs and DVDs, and even in some computer memories—but they do their job so well that we are never even aware of them. Chapter 6 is a little exceptional. 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.

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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. In addition, I have a personal bias toward pattern recognition, which is my own area of research. So chapter 6 describes three of the most interesting and successful pattern recognition techniques: nearest-neighbor classifiers, decision trees, and neural networks. Compression algorithms, discussed in chapter 7, form another set of great ideas that help transform a computer into a genius at our fingertips.

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

Abraham Maslow, adjacent possible, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Clayton Christensen, data acquisition, delayed gratification, deliberate practice, do what you love, escalation ladder, fear of failure, Google Earth, haute couture, impulse control, Isaac Newton, Jeff Bezos, Jeff Hawkins, 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), Sheryl Sandberg, Silicon Valley, SimCity, SpaceShipOne, Stanford marshmallow experiment, Steve Jobs, TED Talk, time dilation, Virgin Galactic, Walter Mischel, X Prize

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.

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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.

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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.

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

backpropagation, 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

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.

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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.

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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).

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

airport security, business logic, future of work, game design, gamification, Ian Bogost, lateral thinking, minimum viable product, pattern recognition, power law, Ruby on Rails, SimCity, social graph, social web, systems thinking, urban planning, web application

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.

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., 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.

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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.

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

Ada Lovelace, Alan Greenspan, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alvin Toffler, Any sufficiently advanced technology is indistinguishable from magic, backpropagation, Buckminster Fuller, call centre, cellular automata, Charles Babbage, classic study, combinatorial explosion, complexity theory, computer age, computer vision, Computing Machinery and Intelligence, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, Everything should be made as simple as possible, financial engineering, first square of the chessboard / second half of the chessboard, flying shuttle, fudge factor, functional programming, George Gilder, Gödel, Escher, Bach, Hans Moravec, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, John Gilmore, 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, punch-card reader, quantum entanglement, 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, Stuart Kauffman, technological singularity, Ted Kaczynski, telepresence, the medium is the message, The Soul of a New Machine, 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, world market for maybe five computers, 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.

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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. A successful person isn’t necessarily better than her less successful peers at solving problems; her pattern-recognition facilities have just learned what problems are worth solving. Building Silicon Nets Most computer-based neural net applications today simulate their neuron models in software.

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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.

The Loop: How Technology Is Creating a World Without Choices and How to Fight Back by Jacob Ward

2021 United States Capitol attack, 4chan, Abraham Wald, AI winter, Albert Einstein, Albert Michelson, Amazon Mechanical Turk, assortative mating, autonomous vehicles, availability heuristic, barriers to entry, Bayesian statistics, Benoit Mandelbrot, Big Tech, bitcoin, Black Lives Matter, Black Swan, blockchain, Broken windows theory, call centre, Cass Sunstein, cloud computing, contact tracing, coronavirus, COVID-19, crowdsourcing, cuban missile crisis, Daniel Kahneman / Amos Tversky, dark matter, data science, deep learning, Donald Trump, drone strike, endowment effect, George Akerlof, George Floyd, hindsight bias, invisible hand, Isaac Newton, Jeffrey Epstein, license plate recognition, lockdown, longitudinal study, Lyft, mandelbrot fractal, Mark Zuckerberg, meta-analysis, natural language processing, non-fungible token, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, QAnon, RAND corporation, Richard Thaler, Robert Shiller, selection bias, self-driving car, seminal paper, shareholder value, smart cities, social contagion, social distancing, Steven Levy, survivorship bias, TikTok, Turing test

When it comes to the central systems of modern life—employment, housing, criminal justice, education—discrimination on the basis of something like gender, race, or sexual orientation is against the law. And when we encounter pattern-recognition systems that turn out to amplify that prejudice, we can start from an agreed understanding that prejudice is bad and illegal. The difficulty, of course, is that pattern-recognition systems are difficult to examine for something like discriminatory choices. Machine learning happens inside a black box of improvisational computational effort—literally a roomful of monkeys assigned to peck away at typewriters in the hopes of eventually and accidentally writing Shakespeare.

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But AI-driven reform like this isn’t likely to happen across our lives the way AI-driven marketing, political messaging, and curation is. The reason is simple: you don’t make easy money on reform. There is no lucrative market for this kind of pattern recognition the way there is for one that helps you find a sales lead or identify a job candidate. But there is a place that capitalist considerations could create an incentive. Perhaps the financial strain of lawsuits in the case of police shootings can help to create a market. It could be that pattern-recognition systems could be used to avoid losing money. The family of Louisville medical worker Breonna Taylor reached a $12 million settlement with the city—the largest payout in Louisville’s history—after its officers shot and killed her in her apartment.

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The second loop is the way that modern forces—consumer technology, capitalism, marketing, politics—have sampled the innermost loop of behavior and reflected those patterns back at us, resulting in everything from our addiction to cigarettes and gambling to systemic racism in real estate and machine learning. The outermost loop is the one for which this book is named. It’s what I worry is coming, a future in which our ancient and modern tendencies have been studied, sampled, fed into automated pattern-recognition systems, and sold back to us in servings we will be doubly conditioned to consume without a second thought. It’s the loop that has already erased our conscious musical tastes and replaced them with weekly suggestions from Spotify, based on what was served last week. It’s the loop that samples our political tendencies and social appetites and puts us together on Facebook with people whose patterns complement our own, such that we become attached to a powerful and all-consuming group identity, and have no patience for people outside of it.

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

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

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.

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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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Of course, even this method of communication has been very beneficial, as it has distinguished us from other animals and has been an enabling factor in the creation of technology. Human skills are able to develop only in ways that have been evolutionarily encouraged. Those skills, which are primarily based on massively parallel pattern recognition, provide proficiency for certain tasks, such as distinguishing faces, identifying objects, and recognizing language sounds. But they're not suited for many others, such as determining patterns in financial data. Once we fully master pattern-recognition paradigms, machine methods can apply these techniques to any type of pattern.158 Machines can pool their resources in ways that humans cannot. Although teams of humans can accomplish both physical and mental feats that individual humans cannot achieve, machines can more easily and readily aggregate their computational, memory, and communications resources.

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

Albert Einstein, algorithmic bias, backpropagation, 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.

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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.

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Chandola, V. Kumar, Similarity Measures for Categorical Data: A Comparative Evaluation, SIAM Conference, 2008, pp. 243–254. Bow, S., Pattern Recognition and Image Preprocessing, Marcel Dekker, New York, 1992. Chen, C. H., L. F. Pau, P. S. P. Wang, Handbook of Pattern Recognition & Computer Vision, World Scientific Publ. Co., Singapore, 1993. Dzeroski, S., N. Lavrac, eds., Relational Data Mining, Springer, Berlin, 2001. 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, Elsevier Inc., San Francisco, CA, 2006.

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

Abraham Wald, Air France Flight 447, Albert Einstein, algorithmic bias, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, Big Tech, Black Lives Matter, Bletchley Park, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, fake news, Flash crash, Grace Hopper, Gödel, Escher, Bach, Hans Moravec, Harvard Computers: women astronomers, Higgs boson, index fund, information security, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, machine translation, 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, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, Salesforce, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, systems thinking, 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.

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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.

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Correctional Offender Management Profiling for Alternative Sanctions (COMPAS) Craven, John credit cards digital assistants and fraud Crimean War criminal justice system cucumbers data, missing data accumulation, pace of data mining data science anomaly detection and assumptions and democracy and feature engineering health care and imputation institutional commitment and legacy of Florence Nightingale lurking variable pattern recognition and personalization and prediction rules and user-based collaborative filtering data sets anomalies in assumptions and bias in, bias out ImageNet Visual Recognition Challenge massive pattern recognition and privacy sharing databases compilers and health care natural language processing Netflix smart cities de Moivre’s equation (square-root rule) decision-making anomaly detection and human voting deep learning corn yields and electricity demands and gender portrayals in film and honeybees and prediction rules and privacy and Descartes Labs Dickens, Charles Christmas Carol, A Martin Chuzzlewit digital assistants Alexa (Amazon) algorithms and Google Home medicine and speech recognition and DiMaggio, Joe Dole, Bob Duke University early-warning systems Earth Echo, Amazon e-commerce Eggo, Rosalind Einstein, Albert energy industry Facebook advertisers anomaly detection “data for gossip” bargain data sets data storage image classification and recognition market dominance pattern-recognition system personalization presidential election of 2016 and targeted marketing Facebook Messenger fake news financial industry Bayes’s rule and investing gambling strategy indexing strategy Fitbit Ford, Henry Formula 1 racing Fowler, Samuel Lemuel Friedman, Milton Friends (television series) Gawande, Atul: The Checklist Manifesto Geena Davis Institute on Gender in Media gender bias in films stereotypes word vectors and Google anomaly detection data sets data storage image classification Inception (neural-network model) market dominance pattern-recognition system personalization search engine self-driving car speech recognition TensorFlow word2vec model Google Google DeepMind Google Doodle Google Flu Trends Google Home Google Ngram Viewer Google Translate Google Voice Gould, Stephen Jay GPS technology Great Andromeda Nebula.

I, Warbot: The Dawn of Artificially Intelligent Conflict by Kenneth Payne

Abraham Maslow, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, anti-communist, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, Asperger Syndrome, augmented reality, Automated Insights, autonomous vehicles, backpropagation, Black Lives Matter, Bletchley Park, Boston Dynamics, classic study, combinatorial explosion, computer age, computer vision, Computing Machinery and Intelligence, coronavirus, COVID-19, CRISPR, cuban missile crisis, data science, deep learning, deepfake, DeepMind, delayed gratification, Demis Hassabis, disinformation, driverless car, drone strike, dual-use technology, Elon Musk, functional programming, Geoffrey Hinton, Google X / Alphabet X, Internet of things, job automation, John Nash: game theory, John von Neumann, Kickstarter, language acquisition, loss aversion, machine translation, military-industrial complex, move 37, mutually assured destruction, Nash equilibrium, natural language processing, Nick Bostrom, Norbert Wiener, nuclear taboo, nuclear winter, OpenAI, paperclip maximiser, pattern recognition, RAND corporation, ransomware, risk tolerance, Ronald Reagan, self-driving car, semantic web, side project, Silicon Valley, South China Sea, speech recognition, Stanislav Petrov, stem cell, Stephen Hawking, Steve Jobs, strong AI, Stuxnet, technological determinism, TED Talk, theory of mind, TikTok, Turing machine, Turing test, uranium enrichment, urban sprawl, V2 rocket, Von Neumann architecture, Wall-E, zero-sum game

They’ll decide how to respond to their adversaries and how to coordinate with their allies using very different techniques from ours. That means different tactics in battle, but also the prospect for larger differences in war. And then there’s creativity. Humans have it in spades. The incumbent world chess champion Magnus Carlsen is a great example of human creativity—a mix of natural genius (for memory, and pattern recognition), but with something extra. Carlsen argues that this is intuition, an instinct for the right move. But what is intuition, and what makes Carlsen’s instincts better than mine? And why are the world’s best chess players vulnerable to number crunching machines that don’t use intuition at all? Might the most creative generals be similarly vulnerable?

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Famously, at one early conference in 1956, participants, now legendary names in the field, judged that in just a few short weeks they would make ‘a significant advance’ in using computers to tackle ‘problems now reserved for humans’.16 Heady stuff. Two approaches to mind Two cognitive tasks with military utility seemed particularly promising—object recognition and natural language translation. Like codebreaking, and indeed chess, both tasks drew on key attributes of the new computers—pattern recognition and logic. In turn, those two attributes played to the strength of two rival approaches to machine intelligence that emerged early on and have shaped the field ever since. As so often in AI, both approaches were inspired by human minds. Understanding more about them will help us understand more about the possibilities for warbot minds and the ways they have developed to this point.

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And they had money to spend on talent too—hoovering up the new AI stars from their university posts: LeCun to Facebook, Hinton to Google, Ng to Google, then Baidu. The new tech giants saw the huge value in using intelligent machines to make sense of the unending torrent of internet data—to find out more about their customers and offer new services. Like the military before them, they shared an interest in pattern recognition, search, and image and language processing. When it came, the revolution would be feline. But that was only the start. It’s not even a decade since Google’s cat breakthrough, and the pace of innovation is still accelerating, another important feature of the modern AI landscape. Now deep nets can recognise individual faces, lip read video imagery and produce a simultaneous transcription.

Emergence by Steven Johnson

A Pattern Language, agricultural Revolution, AOL-Time Warner, 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, Lewis Mumford, Marshall McLuhan, mass immigration, Menlo Park, mirror neurons, Mitch Kapor, Murano, Venice glass, Naomi Klein, new economy, New Urbanism, Norbert Wiener, PalmPilot, pattern recognition, pez dispenser, phenotype, Potemkin village, power law, price mechanism, profit motive, Ray Kurzweil, SimCity, 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

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.”

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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. Because each individual neuron is so slow, Kurzweil explains, “we don’t have time . . . 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 compatible to one we have thought about and then draw upon our previously considered conclusions.”

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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.

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

Abraham Maslow, 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, driverless car, 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, general purpose technology, 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, machine translation, minimum wage unemployment, patent troll, pattern recognition, Paul Samuelson, Ray Kurzweil, rising living standards, Robert Gordon, Robert Solow, self-driving car, shareholder value, Skype, the long tail, too big to fail, Turing test, Tyler Cowen, Tyler Cowen: Great Stagnation, Watson beat the top human players on Jeopardy!, wealth creators, winner-take-all economy, zero-sum game

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?’)

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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.

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After the chat session ended, both customers and employees were asked whether the automatically translated messages were useful—whether they were clear enough to allow the people to take meaningful action. Approximately 90% reported that they were. In this case, automatic translation was good enough for business purposes. The Google driverless car shows how far and how fast digital pattern recognition abilities have advanced recently. Lionbridge’s GeoFluent shows how much progress has been made in computers’ ability to engage in complex communication. Another technology developed at IBM’s Watson labs, this one actually named Watson, shows how powerful it can be to combine these two abilities and how far the computers have advanced recently into territory thought to be uniquely human.

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

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

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).

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Other sources of publication include the Journal of the Royal Statistical Society, The Annals of Statistics, the Journal of American Statistical Association, Technometrics, and Biometrika. 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].

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(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?

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

2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 3D printing, access to a mobile phone, additive manufacturing, Airbnb, Alan Greenspan, 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, Boston Dynamics, British Empire, business cycle, business intelligence, business process, call centre, carbon tax, Charles Lindbergh, Chuck Templeton: OpenTable:, clean water, combinatorial explosion, computer age, computer vision, congestion charging, congestion pricing, corporate governance, cotton gin, creative destruction, crowdsourcing, data science, David Ricardo: comparative advantage, digital map, driverless car, employer provided health coverage, en.wikipedia.org, Erik Brynjolfsson, factory automation, Fairchild Semiconductor, 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, general purpose technology, global village, GPS: selective availability, Hans Moravec, 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, Jevons paradox, 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, Kiva Systems, 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, One Laptop per Child (OLPC), pattern recognition, Paul Samuelson, payday loans, post-work, power law, price stability, Productivity paradox, profit maximization, Ralph Nader, Ray Kurzweil, recommendation engine, Report Card for America’s Infrastructure, Robert Gordon, Robert Solow, Rodney Brooks, Ronald Reagan, search costs, 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 Cathedral and the Bazaar, the long tail, The Signal and the Noise by Nate Silver, The Wealth of Nations by Adam Smith, total factor productivity, transaction costs, Tyler Cowen, Tyler Cowen: Great Stagnation, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, Y2K

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.

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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.

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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.

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

3D printing, AI winter, AlphaGo, Apollo 11, artificial general intelligence, Asperger Syndrome, augmented reality, autism spectrum disorder, backpropagation, Berlin Wall, Bletchley Park, brain emulation, Buckminster Fuller, call centre, cognitive bias, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, crowdsourcing, deep learning, DeepMind, Dunning–Kruger effect, Elon Musk, en.wikipedia.org, epigenetics, Fairchild Semiconductor, friendly AI, Geoffrey Hinton, 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, Metcalfe’s law, mirror neurons, Neil Armstrong, neurotypical, Nick Bostrom, Oculus Rift, old age dependency ratio, pattern recognition, planned obsolescence, pneumatic tube, RAND corporation, Ray Kurzweil, Rodney Brooks, self-driving car, Skype, social intelligence, SoftBank, software as a service, SQL injection, Stephen Hawking, Steven Pinker, superintelligent machines, technological singularity, TED Talk, telepresence, telepresence robot, The future is already here, The Future of Employment, the scientific method, theory of mind, Turing test, twin studies, Two Sigma, undersea cable, Vernor Vinge, Watson beat the top human players on Jeopardy!, Whole Earth Review, working-age population, zero day

Some worked with physiological signals such as electromyography, blood volume pulse, galvanic skin response, and respiration. 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.

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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.

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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 bias, algorithmic trading, AlphaGo, Amazon Web Services, Any sufficiently advanced technology is indistinguishable from magic, augmented reality, autonomous vehicles, backpropagation, Baxter: Rethink Robotics, behavioural economics, 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, data science, deep learning, DeepMind, delayed gratification, Demis Hassabis, Dennis Ritchie, discovery of DNA, Donald Trump, Douglas Engelbart, driverless car, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, Flynn Effect, Frank Gehry, future of work, Geoffrey Hinton, 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, Jim Simons, John Conway, John Markoff, John von Neumann, language acquisition, Large Hadron Collider, machine readable, Mark Zuckerberg, Minecraft, natural language processing, Neil Armstrong, Netflix Prize, Norbert Wiener, OpenAI, orbital mechanics / astrodynamics, PageRank, pattern recognition, pneumatic tube, prediction markets, randomized controlled trial, Recombinant DNA, recommendation engine, Renaissance Technologies, Rodney Brooks, self-driving car, Silicon Valley, Silicon Valley startup, Socratic dialogue, speech recognition, statistical model, Stephen Hawking, Stuart Kauffman, 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!, world market for maybe five computers, X Prize, Yogi Berra

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.

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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.

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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.

Architects of Intelligence by Martin Ford

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

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.

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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.

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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.

Artificial Intelligence: A Guide for Thinking Humans by Melanie Mitchell

Ada Lovelace, AI winter, Alignment Problem, AlphaGo, Amazon Mechanical Turk, Apple's 1984 Super Bowl advert, artificial general intelligence, autonomous vehicles, backpropagation, Bernie Sanders, Big Tech, Boston Dynamics, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, cognitive dissonance, computer age, computer vision, Computing Machinery and Intelligence, dark matter, deep learning, DeepMind, Demis Hassabis, Douglas Hofstadter, driverless car, Elon Musk, en.wikipedia.org, folksonomy, Geoffrey Hinton, 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, machine translation, Mark Zuckerberg, natural language processing, Nick Bostrom, Norbert Wiener, ought to be enough for anybody, paperclip maximiser, 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, tacit knowledge, tail risk, TED Talk, the long tail, theory of mind, There's no reason for any individual to have a computer in his home - Ken Olsen, trolley problem, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, world market for maybe five computers

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.  

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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/).

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To give you the flavor of all this, here are some of Kurzweil’s predictions: By the 2020s molecular assembly will provide tools to effectively combat poverty, clean up our environment, overcome disease, [and] extend human longevity. * * * By the end of the 2030s … brain implants based on massively distributed intelligent nanobots will greatly expand our memories and otherwise vastly improve all our sensory, pattern-recognition, and cognitive abilities. * * * Uploading a human brain means scanning all of its salient details and then reinstantiating those details into a suitably powerful computational substrate.… The end of the 2030s is a conservative projection for successful [brain] uploading.23 * * * A computer will pass the Turing test by 2029.24 * * * As we get to the 2030s, artificial consciousness will be very realistic.

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

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

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.

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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.

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The point of this is that social intelligence is something that will prove valuable in the competition with white-collar robots. Many of us are socially brilliant. Better yet from a social stability perspective, it is not necessarily the most educated among us that have these talents. 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.

Alpha Trader by Brent Donnelly

Abraham Wald, algorithmic trading, Asian financial crisis, Atul Gawande, autonomous vehicles, backtesting, barriers to entry, beat the dealer, behavioural economics, bitcoin, Boeing 747, buy low sell high, Checklist Manifesto, commodity trading advisor, coronavirus, correlation does not imply causation, COVID-19, crowdsourcing, cryptocurrency, currency manipulation / currency intervention, currency risk, deep learning, diversification, Edward Thorp, Elliott wave, Elon Musk, endowment effect, eurozone crisis, fail fast, financial engineering, fixed income, Flash crash, full employment, global macro, global pandemic, Gordon Gekko, hedonic treadmill, helicopter parent, high net worth, hindsight bias, implied volatility, impulse control, Inbox Zero, index fund, inflation targeting, information asymmetry, invisible hand, iterative process, junk bonds, Kaizen: continuous improvement, law of one price, loss aversion, low interest rates, margin call, market bubble, market microstructure, Market Wizards by Jack D. Schwager, McMansion, Monty Hall problem, Network effects, nowcasting, PalmPilot, paper trading, pattern recognition, Peter Thiel, prediction markets, price anchoring, price discovery process, price stability, quantitative easing, quantitative trading / quantitative finance, random walk, Reminiscences of a Stock Operator, reserve currency, risk tolerance, Robert Shiller, secular stagnation, Sharpe ratio, short selling, side project, Stanford marshmallow experiment, Stanford prison experiment, survivorship bias, tail risk, TED Talk, the scientific method, The Wisdom of Crowds, theory of mind, time dilation, too big to fail, transaction costs, value at risk, very high income, yield curve, you are the product, zero-sum game

Using this analogy, macro analysis, and his concerns about derivatives, portfolio insurance, and equity futures without price limits, Tudor predicted the Crash of ‘87 and returned 62% in October 1987!65 In 2019, this type of analogy would be tweeted twenty-two times by day traders and online bloggers and would be revealed in the output of every machine learning, AI and pattern recognition machine at every hedge fund and bank in the world. Computers are significantly better than humans when it comes to unbiased pattern recognition. I do believe, though, there is still an edge in pattern recognition. Experienced human traders can still combine complex pieces of information to form conclusions that would elude the best computers. Leverage, carry and selling risk premium Forever Borrow at 2% and buy an asset that yields 7%.

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When that happens, you owe me a beer. Trader bias #7: Apophenia Chaos is not dangerous until it begins to look orderly. MAX GUNTHER, The Zurich Axioms Great traders are masters of pattern recognition. They will see a setup and recognize it either consciously or unconsciously. I remember many times after great runs of trading that I felt like I knew what was going to happen before it happened. This ability comes from the incredible powers of human pattern recognition. The problem is: Humans are built to see patterns, even when none exist. Apophenia or patternicity are terms psychologists use to describe the human predisposition to see patterns or connections, even where none exist.

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For most good trades, I will have 3 to 5 decent reasons and very few arguments the other way. This trade is a good example of how cross-market analysis gives me greater confidence to take the other side of a flow-driven move that is not supported by fundamentals or by action in other markets. Pattern recognition and indicator scanning The final major source of trade ideas (after narrative, news, and dislocations from fair value) is pattern recognition and indicator scanning. This can mean many things and depends greatly on what products you trade. Good traders recognize patterns in their market and understand which patterns are the result of randomness and which are sending a persistent signal.

Distrust That Particular Flavor by William Gibson

AltaVista, Alvin Toffler, Bletchley Park, British Empire, cognitive dissonance, cuban missile crisis, disinformation, Disneyland with the Death Penalty, edge city, Future Shock, imposter syndrome, informal economy, Joi Ito, means of production, megastructure, military-industrial complex, Neal Stephenson, pattern recognition, proxy bid, restrictive zoning, Snow Crash, space junk, technological determinism, 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.

…

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.

Present Shock: When Everything Happens Now by Douglas Rushkoff

"Hurricane Katrina" Superdome, algorithmic trading, Alvin Toffler, Andrew Keen, bank run, behavioural economics, 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, Future Shock, 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, James Bridle, John Nash: game theory, Kevin Kelly, laissez-faire capitalism, lateral thinking, Law of Accelerating Returns, Lewis Mumford, loss aversion, mandelbrot fractal, Marshall McLuhan, Merlin Mann, messenger bag, Milgram experiment, mirror neurons, mutually assured destruction, negative equity, Network effects, New Urbanism, Nicholas Carr, Norbert Wiener, Occupy movement, off-the-grid, passive investing, pattern recognition, peak oil, Peter Pan Syndrome, price mechanism, prisoner's dilemma, Ralph Nelson Elliott, RAND corporation, Ray Kurzweil, recommendation engine, scientific management, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, SimCity, Skype, social graph, South Sea Bubble, Steve Jobs, Steve Wozniak, Steven Pinker, Stewart Brand, supply-chain management, technological determinism, the medium is the message, The Wisdom of Crowds, theory of mind, Tragedy of the Commons, 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.

…

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.

…

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.

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

3D printing, Airbnb, Albert Einstein, Apollo 11, attribution theory, augmented reality, barriers to entry, conceptual framework, correlation does not imply causation, David Heinemeier Hansson, deliberate practice, disruptive innovation, Elon Musk, fail fast, Fellow of the Royal Society, Filter Bubble, Ford Model T, 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, Neil Armstrong, pattern recognition, Peter Thiel, popular electronics, Ray Kurzweil, Richard Florida, Ronald Reagan, Ruby on Rails, Saturday Night Live, self-driving car, seminal paper, Sheryl Sandberg, side project, Silicon Valley, social bookmarking, Steve Jobs, superconnector, vertical integration

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.

…

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.

…

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.

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, Alvin Toffler, Apollo 11, Asilomar, augmented reality, Bill Joy: nanobots, bioinformatics, blue-collar work, British Empire, Brownian motion, caloric restriction, caloric restriction, cloud computing, Colonization of Mars, DARPA: Urban Challenge, data science, delayed gratification, digital divide, double helix, Douglas Hofstadter, driverless car, en.wikipedia.org, Ford Model T, friendly AI, Gödel, Escher, Bach, Hans Moravec, 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, Large Hadron Collider, life extension, Louis Pasteur, Mahatma Gandhi, Mars Rover, Mars Society, mass immigration, megacity, Mitch Kapor, Murray Gell-Mann, Neil Armstrong, new economy, Nick Bostrom, 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, SpaceShipOne, speech recognition, stem cell, Stephen Hawking, Steve Jobs, synthetic biology, telepresence, The future is already here, The Wealth of Nations by Adam Smith, Thomas L Friedman, Thomas Malthus, trade route, Turing machine, uranium enrichment, Vernor Vinge, Virgin Galactic, Wall-E, Walter Mischel, Whole Earth Review, world market for maybe five computers, X Prize

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.

…

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.

…

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.

Nerdy, Shy, and Socially Inappropriate by Cynthia Kim

Asperger Syndrome, autism spectrum disorder, do what you love, fear of failure, neurotypical, pattern recognition

The rules became more of a burden than a support. Choice receded; rigidity crept in. A few months before learning about Asperger’s, I began to realize that I rely on pattern recognition and rules to navigate social interaction. At the time, I didn’t understand that these were coping strategies. I simply thought I had an unusual approach to socializing. Now I know that many autistic people rely on explicit rules, social scripts, pattern recognition, and lots of practice to make social interaction easier. Seeing my rules for what they were—a tool that I’d instinctively developed to make sense of the world—led me to an important realization: the rules are supposed to serve me, not the other way around.

…

and communication see body language and non-verbal communication; social/communication skills and deficits and competencies 216–17 coping strategies see coping strategies/mechanisms diagnosis see diagnosis of Asperger’s Syndrome/ASD and emotion see emotions and empathy see empathy and executive function deficit see executive function (EF) and face blindness 43 gender differences 20, 3–21, 24 growing older on the spectrum 133–7 and insomnia 128–33 Kanner’s definition of autism 21 labeling see labeling motor development delay 118 and perfectionism 191–3 and perimenopause symptoms 134–5 and sensory sensitivities see sensory sensitivities strengths associated with 210–15 and thinking see thinking traits making autistic individuals vulnerable to bullying 29 and triggers 66–7 Baumeister, Roy 179 bliss 145 blogging 19–18, 230, 231 blunted affect 37–9 body, autistic 117–37 the autistic brain 69, 74, 141, 146, 9–168, 171 see also executive function (EF) and clumsiness 117–18 and growing older on the spectrum 133–7 and insomnia 128–33 and sensation see sensory seeking; sensory sensitivities; stimming and sports/exercise 57, 106, 119–28 body language and non-verbal communication 6–35, 46–37, 51 blunted affect 37–9 eye contact 39, 42–40, 44–5 facial expression see facial expression intentional employment of 39–41 non-verbal cues 39, 6–45, 51, 64, 65, 223 brains 69, 74, 107, 141, 146, 171 brain–body communication 168–9 executive function see executive function (EF) and inhibition 165 bullying 9–25, 223 calmness 214 catastrophizing 193–7 change, resistance to 86, 2–91, 166 checklists 178 chunking 177 clumsiness 117–18 cognitive empathy 81, 96, 154–7 cognitive flexibility 162, 166 communication see body language and non-verbal communication; language; social/communication skills and deficits compassion 231–2 competencies 216–17 compromise 66 confrontation skills deficit 41–140, 148 contentment 144–5 control 182–5 meltdowns and loss of see meltdowns coping strategies/mechanisms 29, 32, 9–86, 135, 189, 214, 218 and control 183–5 with executive function deficit 172–3 pattern recognition see pattern recognition rescue strategies for new parents 76–7 routines as 86, 94–89, 166 rules as 36, 86, 87–9 for sensory sensitivities 109–16 special interests 96–101 strengths as coping mechanisms 210–12 curiosity 211 decision making 158–61 dependability 214–15 depression 127 detachment 80, 198, 214 see also withdrawal/shutdown determination 141, 215 developmental disability 31–4 diagnosis of Asperger’s Syndrome/ASD and acceptance 228–32 and being understood 61–60, 71 and DSM-5 21 gender differences with diagnosis rates 2–21, 23 growing up undiagnosed 18–13, 34–20, 6–53, 59, 86, 147, 182, 192–3 and labeling see labeling late diagnosis 14, 21 resistance to change as a criterion for 91–2 and self-redefinition 208–27 self-understanding growing from 6–35, 61–60, 72, 182, 10–208, 225, 229–32 sharing your diagnosis with your child 82 Diagnostic and Statistical Manual of Mental Disorders (DSM-5) 21 disability, developmental 4–31, 209–10 discipline 214–15 domestic violence 223 driving 77 ego depletion 179–80 elation 144, 145 emotions 138–57 alexithymia 141–4 anger constellation 146–9 catastrophizing as an emotional magnet 196 discriminating the target of others’ expressions of 140–41 emotional detachment 80, 198, 214 and empathy see empathy expressing love and feelings 68, 69, 72, 74, 138 feeling overwhelmed by others’ emotions 141, 156 getting in touch with feelings 144–52 happiness constellation 144–6 identifying 9–138, 140, 141–3 meltdowns see meltdowns modulating the strength of 40–139, 141 sadness constellation 149–52 and sensations 142, 6–145, 149, 170 see also anxiety; panic; shame empathy 152–7 cognitive (perspective taking) 81, 96, 7–154, 210, 231 meaning of a lack of 157 Sally–Anne test 155 vs. sympathy 153–4 executive function (EF) 158–81 acting on a problem 168–73 and the anger constellation 146–7 attention 162, 164–5 conservation of EF, and ego depletion 179–81 decision making 158–61 inhibition 165, 178 initiating actions 165 monitoring actions 165–6 planning 93, 161, 163 problem solving see problem solving procrastination and EF fail 173–8 and stimming 180–81 and time agnosia 178–9 verbal reasoning 164 exercise see sports and exercise face blindness (prosopagnosia) 43 facial expression 36 blunted affect 37–9 eye contact 39, 42–40, 44–5 faking 39 frowning 16, 38 reading 42–4 family responsibilities household see household management parenting see parenting fear 18, 26, 27, 105, 110, 204, 231 of abandonment 151 and catastrophizing 196 irrationality of 221, 223 of not being good enough 147 see also anxiety feeling see emotions; sensory sensitivities fight or flight response 45 food sensitivities 13–112, 115 Frost Multidimensional Perfectionism Scale 192 frowning 16, 38 frustration 146 functioning labels 215–18 see also labeling games 23–4 gender differences with ASD in diagnosis 20, 21–3 in play 24 generalizing 63, 210, 212 giftedness 30–33 grief 150–51 guilt 75 handholding 64 happiness 144–6 headbanging 75, 103, 106, 202, 203, 205 honesty 210–11 household management 61–2 and parenting 73–4 humiliation 147 hypersensitivity see sensory sensitivities hyposensitivity 170–73 indignation 147–8 infodumping 95–6 inhibition 165, 178 insomnia 128–33 intelligence 21, 2–31, 33, 51–50, 71 interests relationships and common interests 56–7 special 77, 96–101 interoceptive feedback 170 irritability 147 joy 145 joys of parenting a toddler 77–9 Kanner, Leo 21 Kim, Jess 2–71, 75, 8–77, 79–85 Kim, Sang Hwan 56, 57, 58, 62–59, 64, 6–65, 67, 70–68, 71, 75, 3–82, 85, 92, 6–135, 41–140, 148, 183, 188, 189, 200, 224 labeling 32–30, 208–27 finding the right labels 209–15 functioning labels in practice 215–18 and self-redefinition 208–27 and the wish for normality 225–6 language body language see body language and non-verbal communication glitches 135–6 literal interpretation 27, 67–8 social use of (pragmatics) 45–52 verbal cues 49, 67 verbal processing 47–51 light sensitivities 110–11 Linden, Fabian 227 literal interpretation 27, 67–8 love compassion 231–2 expressing love 68, 69, 72, 74 and parenting 72, 74 and sex see sex life loyalty 211 marriage 70–59, 75, 188–9 see also parenting; sex life martial arts 57, 106, 121–7 melatonin 132–3 meltdowns 54, 60, 67, 75, 110, 116, 124, 171, 200–207 anatomy of a meltdown 201–5 helping someone through a meltdown 207 nightmares as forms of meltdown 148 triggers 205–6 see also headbanging memory problems 136–7 working memory 164 see also reminders menopause/perimenopause 133, 134–5 metaphors 27 mindfulness 176 mindful physical activity 127 missing word problem 135–6 monologuing 95 motor coordination 18–117, 134 motor development delay 118 music 76 neurotypical people/behaviour acting on a problem 168 Aspie parenting of see parenting emotional interaction 140 expressing love 68, 69, 70 filtering sensory data 170–71 marriage to an Aspie partner 70–59, 75, 188 the neurotypical brain 69 and special interests 99 subtle communication ways 67 nightmares 148 NO reflex 92–4 non-verbal communication see body language and non-verbal communication nonjudgmentalism 210 optimism 211–12 panic 159, 185, 204 and the NO reflex 93, 94 see also meltdowns parenting 54, 71–85 and the child’s venture into the world 79–82 expressing love 72, 74 infancy and motherhood 74–7 joys of parenting a toddler 77–9 rescue strategies for new parents 76–7 and retreat 75, 83 routines 78 sharing your ASD diagnosis with your child 82 support 75, 82–3 teenagers and the approach to adulthood 82–5 unconventional 72–4 pattern recognition 27, 33, 34, 36, 86, 87, 88, 214 peace 145 peer pressure 81 perfectionism 26, 189–93 perimenopause 133, 134–5 perseveration 95–6 perspective taking 81, 96, 7–154, 210, 231 Sally–Anne test 155 see also empathy pets 76 phones, communication problems with 48–9 physical activity see sports and exercise planning 93, 161, 163 aids 178 strategies 176–7 play 4–23, 31 and parenting 78–9 pragmatics 46–52 prioritizing 172, 177 problem solving 4–163, 210, 211 acting on a problem 168–73 and executive function impairment 175–8 procrastinating in 173–5 procrastination 173–5 prosopagnosia (face blindness) 43 rage 148 relationships 53–70 alexithymia and 142–3 apologizing 62 balancing adaptation and acceptance 69–70 and common interests 56–7 and compromise 66 and empathy see empathy expressing love 68, 69, 72, 74 see also sex life intimate 4–63, 114–15 see also sex life making friends 7–56, 79–80 marriage 70–59, 75, 188–9 see also sex life and the need for withdrawal 65–6 not being understood 59–60 parent–child see parenting shrinking–growing cycle 56–9 spontaneous affection 64 unintentional hurtfulness 60–59, 62–3 reminders 74, 167, 177, 180 software/apps 176–7 resentment 148 resistance to change 86, 2–91, 166 and the NO reflex 92–4 rigidity 86–96 rigid thinking 7–146, 166 see also perseveration; resistance to change; routines; rules rocking chairs 76 Rogers, K., Dziobek, I. et al. 156–7 routines appropriateness of 91 as coping strategies 86, 94–89, 166 and the NO reflex 92–4 and parenting 74, 78 and problem solving 176 and resistance to change 86, 2–91, 166 and spontaneity 91, 92 rules as coping strategies 36, 86, 87–9 learning social rules 27, 33–4 and spontaneity 89 sadness 149–52 Sally–Anne test 155 self-care 187–8 self-control 182–5 sensory filtering 170–71 sensory regulation/stimming 4–102, 9–108, 81–180, 230 sensory seeking 9–104, 124, 126 sensory sensitivities 63, 109–16 and the anger constellation 146–7 emotions and sensations 142, 6–145, 149, 170 hyposensitivity 170–73 impact on intimacy 63, 114–15 interoceptive feedback 170 to light 110–11 and parenting 73, 74 sensory overload 63, 74, 110, 116, 127 to smell 112–13 to sound 111–12 to taste 112–13 to touch 63, 64, 113–15 sex life and “bliss” 145 and sensory sensitivities 63, 114–15 sexual abuse 223 shame 75, 147, 182, 183, 185–9 shutdown see withdrawal/shutdown shyness 14, 23, 27 sincerity 211 singing 76 sleep problems 128–33 nightmares 148 smell sensitivities 112–13 social/communication skills and deficits 35–52 and aphasia 135–6 apologizing 62 blunted affect 37–9 body language see body language and non-verbal communication; facial expression of a child with an Aspie parent 84–5 compensating for a partner’s deficits 65, 75, 85 confrontation skills deficit 41–140, 148 coping strategies in social interaction see coping strategies/mechanisms eye contact 39, 42–40, 44–5 generalizing 63, 210, 212 learning social rules 27, 33–4 need for explicit communication 67–8 neurotypical subtlety in communication 67 non-verbal communication see body language and non-verbal communication; facial expression not being understood 59–60 and parenting 73 perseverance with communication 67–8 pragmatic impairments 46–52 and relationships see relationships scaring others 36–41 and social anxiety 220–25 social use of language 45–52 telephone communication problems 48–9 unintentional hurtfulness 60–59, 62–3 verbal cues 49, 67 verbal reasoning 164 see also language social power balance 45 social scripts 86, 88, 167, 180 sound sensitivities 111–12 special interests 77, 96–101 spontaneity and rules/routines 89, 91, 92 spontaneous affection 64 sports and exercise 57, 106, 28–119, 134 mindful physical activity 127 and sensory seeking 124, 126 and sleep 130 stimming 4–102, 9–108, 81–180, 230 stress response fight or flight 45 triggers 66–7 sympathy 153–4 tactile sensitivities/defensiveness 63, 64, 113–15 taste sensitivities 112–13 telephone communication problems 48–9 thinking in absolutes 193–7 catastrophizing 193–7 flexible 162, 166 generalizing 63, 210, 212 perspective taking 81, 96, 7–154, 210, 231 rigid 7–146, 166 verbal reasoning 164 time agnosia 178–9 touch 63, 64 tactile sensitivities/defensiveness 63, 64, 113–15 triggers of meltdown 205–6 of smell and taste sensitivities 112 of stress response 66–7 of withdrawal 199 values 211 verbal processing 47–51 verbal reasoning 164 walking 77 water 76 Willey, Liane Holliday 64 withdrawal/shutdown 60, 67, 110, 116, 197–200 common signs of 198 helping someone through 207 need for withdrawal in a relationship 65–6 parenting and retreat 75, 83 and resurfacing 200 wonder 145 working memory 164 Zen 208 Also available Pretending to be Normal Living with Asperger’s Syndrome (Autism Spectrum Disorder) Liane Holliday Willey Foreword by Tony Attwood ISBN 978 1 84905 755 4 eISBN 978 1 84642 498 4 Compelling and witty, Liane Holliday Willey’s account of growing to adulthood as an undiagnosed ‘Aspie’ has been read by thousands of people on and off the autism spectrum since it was first published in 1999.

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•Even when I pay attention to facial expressions and body language—which I rarely do—they carry little meaning for me. •My ability to process verbal communication and to communicate with speech is impaired, while my ability to communicate in writing is a strength. •I’ve developed unusual ways of navigating social situations, substituting rules, pattern recognition, and scripted language for perspective taking and reading nonverbal communication. These four realizations helped put a lifetime of social awkwardness into perspective—everything from why I struggle with completing simple everyday transactions with strangers to why I scare small children.

How Doctors Think by Jerome Groopman

affirmative action, Atul Gawande, classic study, Daniel Kahneman / Amos Tversky, deliberate practice, fear of failure, framing effect, if you see hoof prints, think horses—not zebras, index card, iterative process, lateral thinking, machine translation, medical malpractice, medical residency, Menlo Park, pattern recognition, placebo effect, seminal paper, stem cell, theory of mind

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.

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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.

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"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."

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

Albert Einstein, autism spectrum disorder, deliberate practice, digital rights, iterative process, longitudinal study, 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.

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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.

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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.

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, Golden age of television, Marshall McLuhan, pattern recognition, profit motive, public intellectual, race to the bottom, sexual politics, SimCity, 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.

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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.

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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?

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

"World Economic Forum" Davos, 23andMe, Affordable Care Act / Obamacare, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Apollo 11, artificial general intelligence, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, Big Tech, bioinformatics, blockchain, Cambridge Analytica, cloud computing, cognitive bias, Colonization of Mars, computer age, computer vision, Computing Machinery and Intelligence, conceptual framework, creative destruction, CRISPR, crowdsourcing, Daniel Kahneman / Amos Tversky, dark matter, data science, David Brooks, deep learning, DeepMind, Demis Hassabis, digital twin, driverless car, Elon Musk, en.wikipedia.org, epigenetics, Erik Brynjolfsson, fake news, fault tolerance, gamification, general purpose technology, Geoffrey Hinton, George Santayana, Google Glasses, ImageNet competition, Jeff Bezos, job automation, job satisfaction, Joi Ito, machine translation, Mark Zuckerberg, medical residency, meta-analysis, microbiome, move 37, natural language processing, new economy, Nicholas Carr, Nick Bostrom, nudge unit, OpenAI, opioid epidemic / opioid crisis, pattern recognition, performance metric, personalized medicine, phenotype, placebo effect, post-truth, randomized controlled trial, recommendation engine, Rubik’s Cube, Sam Altman, self-driving car, Silicon Valley, Skinner box, speech recognition, Stephen Hawking, techlash, TED Talk, text mining, the scientific method, Tim Cook: Apple, traumatic brain injury, trolley problem, 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.

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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).

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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.

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

3D printing, Ada Lovelace, Albert Einstein, algorithmic bias, AlphaGo, Alvin Roth, Andrew Wiles, Automated Insights, Benoit Mandelbrot, Bletchley Park, Cambridge Analytica, Charles Babbage, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, data is the new oil, data science, deep learning, DeepMind, Demis Hassabis, Donald Trump, double helix, Douglas Hofstadter, driverless car, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, Flash crash, Gödel, Escher, Bach, Henri Poincaré, Jacquard loom, John Conway, Kickstarter, Loebner Prize, machine translation, mandelbrot fractal, Minecraft, move 37, music of the spheres, Mustafa Suleyman, 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, stable marriage problem, 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.

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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.

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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.

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

algorithmic bias, backpropagation, Bayesian statistics, bioinformatics, computer age, conceptual framework, correlation coefficient, data science, G4S, Geoffrey Hinton, greed is good, higher-order functions, linear programming, p-value, pattern recognition, random walk, selection bias, sparse data, speech recognition, statistical model, stochastic process, The Wisdom of Crowds

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.

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., 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.

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For each example the goal is to use the inputs to predict the values of the outputs. This exercise is called supervised learning. We have used the more modern language of machine learning. In the statistical literature the inputs are often called the predictors, a term we will use interchangeably with inputs, and more classically the independent variables. In the pattern recognition literature the term features is preferred, which we use as well. The outputs are called the responses, or classically the dependent variables. 2.2 Variable Types and Terminology The outputs vary in nature among the examples. In the glucose prediction example, the output is a quantitative measurement, where some measurements are bigger than others, and measurements close in value are close in nature.

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

Airbnb, Albert Einstein, Apollo 11, 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, deep learning, deliberate practice, Exxon Valdez, fail fast, Flynn Effect, Freestyle chess, functional fixedness, game design, Gene Kranz, Isaac Newton, Johannes Kepler, knowledge economy, language acquisition, lateral thinking, longitudinal study, Louis Pasteur, Mark Zuckerberg, medical residency, messenger bag, meta-analysis, Mikhail Gorbachev, multi-armed bandit, 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, Silicon Valley billionaire, Stanford marshmallow experiment, Steve Jobs, Steve Wozniak, Steven Pinker, sunk-cost fallacy, systems thinking, Walter Mischel, Watson beat the top human players on Jeopardy!, Y Combinator, young professional

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.

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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.

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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.

The Joys of Compounding: The Passionate Pursuit of Lifelong Learning, Revised and Updated by Gautam Baid

Abraham Maslow, activist fund / activist shareholder / activist investor, Airbnb, Alan Greenspan, Albert Einstein, Alvin Toffler, Andrei Shleifer, asset allocation, Atul Gawande, availability heuristic, backtesting, barriers to entry, beat the dealer, Benoit Mandelbrot, Bernie Madoff, bitcoin, Black Swan, book value, business process, buy and hold, Cal Newport, Cass Sunstein, Checklist Manifesto, Clayton Christensen, cognitive dissonance, collapse of Lehman Brothers, commoditize, corporate governance, correlation does not imply causation, creative destruction, cryptocurrency, Daniel Kahneman / Amos Tversky, deep learning, delayed gratification, deliberate practice, discounted cash flows, disintermediation, disruptive innovation, Dissolution of the Soviet Union, diversification, diversified portfolio, dividend-yielding stocks, do what you love, Dunning–Kruger effect, Edward Thorp, Elon Musk, equity risk premium, Everything should be made as simple as possible, fear index, financial independence, financial innovation, fixed income, follow your passion, framing effect, George Santayana, Hans Rosling, hedonic treadmill, Henry Singleton, hindsight bias, Hyman Minsky, index fund, intangible asset, invention of the wheel, invisible hand, Isaac Newton, it is difficult to get a man to understand something, when his salary depends on his not understanding it, Jeff Bezos, John Bogle, Joseph Schumpeter, junk bonds, Kaizen: continuous improvement, Kickstarter, knowledge economy, Lao Tzu, Long Term Capital Management, loss aversion, Louis Pasteur, low interest rates, Mahatma Gandhi, mandelbrot fractal, margin call, Mark Zuckerberg, Market Wizards by Jack D. Schwager, Masayoshi Son, mental accounting, Milgram experiment, moral hazard, Nate Silver, Network effects, Nicholas Carr, offshore financial centre, oil shock, passive income, passive investing, pattern recognition, Peter Thiel, Ponzi scheme, power law, price anchoring, quantitative trading / quantitative finance, Ralph Waldo Emerson, Ray Kurzweil, Reminiscences of a Stock Operator, reserve currency, Richard Feynman, Richard Thaler, risk free rate, risk-adjusted returns, Robert Shiller, Savings and loan crisis, search costs, shareholder value, six sigma, software as a service, software is eating the world, South Sea Bubble, special economic zone, Stanford marshmallow experiment, Steve Jobs, Steven Levy, Steven Pinker, stocks for the long run, subscription business, sunk-cost fallacy, systems thinking, tail risk, Teledyne, the market place, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, time value of money, transaction costs, tulip mania, Upton Sinclair, Walter Mischel, wealth creators, Yogi Berra, zero-sum game

lens can help you avoid making hurried decisions in life based on emotions. Whether you are looking to invest in a business or in a new relationship or career, never forget to ask, “And then what?” It may well turn out to be a lifesaver. CHAPTER 29 PATTERN RECOGNITION Charlie and I have seen—we’re not remotely perfect at this—but we’ve seen patterns. Pattern recognition gets very important in evaluating humans and businesses. And the pattern recognition isn’t 100 percent and none of the patterns exactly repeat themselves, but there are certain things in business and securities markets we’ve seen over and over. —Warren Buffett Untapped Pricing Power Within the growth stock model, there’s a sub-position: There are actually businesses that you will find a few times in a lifetime where any manager could raise the return enormously just by raising prices—and yet they haven’t done it.

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The Market Is Efficient Most, but Not All, of the Time 24. The Dynamic Art of Portfolio Management and Individual Position Sizing 25. To Finish First, You Must First Finish SECTION V—DECISION-MAKING 26. Read More History and Fewer Forecasts 27. Updating Our Beliefs in Light of New Evidence 28. Life Is a Series of Opportunity Costs 29. Pattern Recognition 30. Acknowledging the Role of Luck, Chance, Serendipity, and Randomness 31. The Education of a Value Investor Conclusion: Understanding the True Essence of Compounding Appendix A Appendix B Acknowledgments Notes Bibliography Index FOREWORD A little more than twenty years ago, I discovered Warren Buffett, Charlie Munger, Ben Graham and the art of Intelligent Investing.

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Investors are better off with a few solid long-term choices than flitting from one speculation to another, always chasing the latest hot stock in the market. (Better to have a few good, long-term friends rather than changing your friends every week for short-term advantage.) The gap between price and value ultimately will determine our returns, but picking the right business is possibly the most important step in reducing errors. Improving pattern recognition skills increases the probability of successfully identifying the right businesses to invest in. Without doubt, cigar butts as a category have the potential to outperform quality over a short holding period, but over the long term, companies with increasing intrinsic value over time are the clear winners.

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

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

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.

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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.

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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.

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

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

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.

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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.

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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.

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

AI winter, air gap, AltaVista, Amazon Web Services, artificial general intelligence, Asilomar, Automated Insights, Bayesian statistics, Bernie Madoff, Bill Joy: nanobots, Bletchley Park, brain emulation, California energy crisis, cellular automata, Chuck Templeton: OpenTable:, cloud computing, cognitive bias, commoditize, computer vision, Computing Machinery and Intelligence, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, drone strike, dual-use technology, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Hacker News, Hans Moravec, Isaac Newton, Jaron Lanier, Jeff Hawkins, John Markoff, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, machine translation, mutually assured destruction, natural language processing, Neil Armstrong, Nicholas Carr, Nick Bostrom, optical character recognition, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, precautionary principle, prisoner's dilemma, Ray Kurzweil, Recombinant DNA, Rodney Brooks, rolling blackouts, 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 Jurvetson, Steve Wozniak, strong AI, Stuxnet, subprime mortgage crisis, 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

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.

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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.

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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.

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, seminal paper, sugar pill, 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.

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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….

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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.

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 Monday: stock market crash in 1987, Black Swan, Bob Geldof, centre right, collapse of Lehman Brothers, data science, Donald Trump, Elliott wave, eurozone crisis, family office, financial engineering, Flash crash, Great Grain Robbery, high net worth, High speed trading, information asymmetry, Jeff Bezos, Kickstarter, land bank, margin call, market design, market microstructure, Market Wizards by Jack D. Schwager, Navinder Sarao, Nick Leeson, offshore financial centre, pattern recognition, Ponzi scheme, proprietary trading, Ralph Nelson Elliott, Reminiscences of a Stock Operator, Ronald Reagan, selling pickaxes during a gold rush, sovereign wealth fund, spectrum auction, Stephen Hawking, the market place, Timothy McVeigh, Tobin tax, tulip mania, yield curve, zero-sum game

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.

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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.

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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.

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

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

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.

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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.

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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 Creative Curve: How to Develop the Right Idea, at the Right Time by Allen Gannett

Alfred Russel Wallace, collective bargaining, content marketing, data science, David Brooks, deliberate practice, Desert Island Discs, Elon Musk, en.wikipedia.org, gentrification, glass ceiling, iterative process, lone genius, longitudinal study, Lyft, Mark Zuckerberg, McMansion, pattern recognition, profit motive, randomized controlled trial, recommendation engine, Richard Florida, ride hailing / ride sharing, Salesforce, Saturday Night Live, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, Snapchat, South of Market, San Francisco, Steve Jobs, TED Talk, too big to fail, uber lyft, work culture

Professor Robert Baron, who studies the intersection of entrepreneurship and psychology, wanted to understand how entrepreneurs ferret out opportunities. The answer, he found, is pattern recognition. One of our brain’s fundamental tasks is identifying patterns. This task is critical in helping both to protect us and to discover rewarding opportunities. As we discussed, if something poses a threat to us, we want to avoid it. If something promises a potential reward, we want to explore it. Pattern recognition relies on two mental models, both of which Professor Baron believes are used by entrepreneurs to come up with new ideas. The first is a prototype—but not the kind of prototype that might immediately come up in most people’s minds.

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over $5 billion: “United Therapeutics Corporation,” Google Finance (2017) https://www.fool.com/​quote/nasdaq/​united-therapeutics/uthr. Professor Robert Baron: Robert A. Baron, “Opportunity Recognition as Pattern Recognition: How Entrepreneurs ‘Connect the Dots’ to Identify New Business Opportunities,” Academy of Management Perspectives, February 2006, http://www.iedmsu.ru/​download/​fa4_1.pdf. The answer, he found, is pattern recognition: Ibid. Earlier I mentioned serial entrepreneur Kevin Ryan: Quotes from Ryan drawn from my interviews with him. Ted Sarandos has continued to consume: These details relating to Sarandos’s professional life drawn mostly from my interviews with him.

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Sally has worked with numerous people in her career, good and bad, and these colleagues and friends have become, for her, exemplars. Sally would automatically compare Tom to the best, or most promising, employees she’s worked with in the past, and upon deciding that Tom in some ways is similar to a former star colleague, she quickly offers him a job. One critical facet of pattern recognition is that it allows entrepreneurs to develop an uncanny instinct for opportunity. Research shows that when entrepreneurs have significant prior knowledge, they no longer need to engage in slow, deliberate searches for new ideas. On the contrary, their prior experience gives them a rich library of exemplars they can access automatically.

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

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

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.

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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.

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None of these practical, technical achievements are the kind of result that most people associate with the navel-gazing world of psychedelics. But similar outcomes are happening in Fadiman’s current survey of microdosing among professionals. With more than four hundred responses from people in dozens of fields, the majority, as Fadiman recently explained, report “enhanced pattern recognition [and] can see more of the pieces at once of a problem they are trying to solve.” With these developments, psychedelics have begun moving from recreational diversion to performance-enhancing supplement. “A shift began about four or five years ago,” author and venture capitalist Tim Ferriss45 told us.

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

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

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).

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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.

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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.

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

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

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.

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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.

The Pattern Seekers: How Autism Drives Human Invention by Simon Baron-Cohen

23andMe, agricultural Revolution, airport security, Albert Einstein, Apollo 11, Asperger Syndrome, assortative mating, autism spectrum disorder, bioinformatics, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, David Attenborough, discovery of penicillin, Elon Musk, en.wikipedia.org, Fellow of the Royal Society, Greta Thunberg, intentional community, invention of agriculture, Isaac Newton, James Watt: steam engine, Jim Simons, lateral thinking, longitudinal study, Menlo Park, meta-analysis, neurotypical, out of africa, pattern recognition, phenotype, Rubik’s Cube, Silicon Valley, six sigma, Skype, social intelligence, Stephen Hawking, Steven Levy, Steven Pinker, systems thinking, theory of mind, twin studies, zero-sum game

It’s not that our ancestors before 70,000 to 100,000 years ago were incapable of recognizing all patterns; they could certainly recognize simple patterns such as “A is associated with B.” Even a rat or monkey can do this, using “statistical learning”: keeping track of how likely it is that A will be associated with B.34 And a rat or monkey can also use other pattern recognition processes, such as associative learning, which is particularly powerful when B is a reward or a punishment.35 Simple associative learning of this kind can explain how our hominid ancestors produced simple tools—like using a rock as a hammer to crack open a shell to get the nut inside, or using a stone ax to cut and scrape—and how they learned what was good to eat.

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Neuroscientist Mike Lombardo and I examined brain-scanning (functional magnetic resonance imaging, or fMRI) data from studies in which a person is asked to do tasks related to systemizing. We looked at tasks involving attention to detail, error checking, reasoning, rule learning, numerical reasoning, and pattern recognition. All these aspects of systemizing use the sensory-perceptual areas of the brain. This makes sense because, when you systemize, you analyze the detail coming in through your senses, telling you what the world is actually like. In addition, the fMRI studies show that the Systemizing Mechanism depends strongly on lateral frontoparietal connections within the brain and a brain region called the intraparietal sulcus (IPS).43 The IPS is also active when humans engage in tool-making and is atypical in children with dyscalculia (those who struggle to systemize numbers).

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A third possibility is that assortative mating among hyper-systemizers might be the result of two people being attracted to each other because their minds are similar.16 But how could we test this? We invited mothers and fathers of autistic children to take a systemizing test. We used the Embedded Figures Test, a pattern recognition test that involves finding a target piece hidden within a design. We had previously given this test to autistic adults, who are faster and more accurate at it than are typical adults, probably because, being hyper-systemizers, they break down information into its component parts as rapidly as possible, to then look for if-and-then patterns.

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

"Friedman doctrine" OR "shareholder theory", Ada Lovelace, AI winter, air gap, Airbnb, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic bias, AlphaGo, Andy Rubin, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Bayesian statistics, behavioural economics, Bernie Sanders, Big Tech, bioinformatics, Black Lives Matter, blockchain, Bretton Woods, business intelligence, Cambridge Analytica, Cass Sunstein, Charles Babbage, Claude Shannon: information theory, cloud computing, cognitive bias, complexity theory, computer vision, Computing Machinery and Intelligence, CRISPR, cross-border payments, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, deep learning, DeepMind, Demis Hassabis, Deng Xiaoping, disinformation, distributed ledger, don't be evil, Donald Trump, Elon Musk, fail fast, fake news, Filter Bubble, Flynn Effect, Geoffrey Hinton, gig economy, Google Glasses, Grace Hopper, Gödel, Escher, Bach, Herman Kahn, high-speed rail, Inbox Zero, Internet of things, Jacques de Vaucanson, Jeff Bezos, Joan Didion, job automation, John von Neumann, knowledge worker, Lyft, machine translation, Mark Zuckerberg, Menlo Park, move fast and break things, Mustafa Suleyman, natural language processing, New Urbanism, Nick Bostrom, one-China policy, optical character recognition, packet switching, paperclip maximiser, pattern recognition, personalized medicine, RAND corporation, Ray Kurzweil, Recombinant DNA, ride hailing / ride sharing, Rodney Brooks, Rubik’s Cube, Salesforce, Sand Hill Road, Second Machine Age, self-driving car, seminal paper, 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, surveillance capitalism, technological singularity, The Coming Technological Singularity, the long tail, 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

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.

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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.

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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.

The Myth of Artificial Intelligence: Why Computers Can't Think the Way We Do by Erik J. Larson

AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, autonomous vehicles, Big Tech, Black Swan, Bletchley Park, Boeing 737 MAX, business intelligence, Charles Babbage, Claude Shannon: information theory, Computing Machinery and Intelligence, conceptual framework, correlation does not imply causation, data science, deep learning, DeepMind, driverless car, Elon Musk, Ernest Rutherford, Filter Bubble, Geoffrey Hinton, Georg Cantor, Higgs boson, hive mind, ImageNet competition, information retrieval, invention of the printing press, invention of the wheel, Isaac Newton, Jaron Lanier, Jeff Hawkins, John von Neumann, Kevin Kelly, Large Hadron Collider, Law of Accelerating Returns, Lewis Mumford, Loebner Prize, machine readable, machine translation, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, PageRank, PalmPilot, paperclip maximiser, pattern recognition, Peter Thiel, public intellectual, Ray Kurzweil, retrograde motion, self-driving car, semantic web, Silicon Valley, social intelligence, speech recognition, statistical model, Stephen Hawking, superintelligent machines, tacit knowledge, technological singularity, TED Talk, The Coming Technological Singularity, the long tail, the scientific method, The Signal and the Noise by Nate Silver, The Wisdom of Crowds, theory of mind, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, Yochai Benkler

3 Marcus goes on to point out that such theories are entirely too generic to advance the ball in neuroscience and related efforts in AI and the cognitive sciences: “Almost anything any creature does could at some level be seen as hierarchical-­pattern recognition; that’s why the idea has been around since the late nineteen-­fi fties. But simply asserting that the mind is a hierarchical-­pattern recognizer by itself tells us too l­ ittle: it d­ oesn’t say why h­ uman beings are the sort of creatures that use language (rodents presumably have a capacity for hierarchical-­pattern recognition, too, but d­ on’t talk), and it d­ oesn’t explain why many h­ umans strug­gle constantly with issues of self-­ control, nor why we are the sort of creatures who leave tips in restaurants in towns to which we ­w ill never return.” 4 Such generic theories are, ironically, also inspired by Big Data AI in a roundabout but very real way.

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T H E WOR L D W I DE W EB The emergence of the World Wide Web spurred resurgence in AI for a ­simple reason: data. Suddenly, the availability of massive datasets, and in par­tic­u ­lar of text corpora (web pages) from the combined efforts of millions of new web users, breathed life into old, “shallow” statistical and pattern-­recognition approaches. Suddenly, what used to be shallow became adequate and worked. Supervised learning algorithms such as artificial neural networks (neural nets, for short), decision trees, and Bayesian classifiers had existed in university labs for de­cades. But without large datasets, they h­ adn’t yet shown much promise on in­ter­est­ing prob­lems like face recognition, or text classification, or spam or fraud detection.

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The solution is then made available or passed back to lower layers u­ ntil we have a coherent prediction based on the original input. 264 T he F ­ uture of the M yth K U R Z W E I L’ S H I E R A R C H I C A L -­PA T T E R N R ECOGN IT ION T H EORY The hierarchical nature of the neocortex has also been noted by Ray Kurzweil, who writes in his 2012 How to Create a Mind that “the neocortex is responsible for dealing with our ability to deal with patterns and to do so in a hierarchical fashion. Animals without a neocortex (basically non-­mammals) are largely incapable of understanding hierarchies.”2 Kurzweil credits his own hierarchical theory, which he calls the pattern recognition theory of mind, as working off accepted neuroscience findings on the structure and function of the ­human neocortex, and also off progenitors like Hawkins’s own hierarchical account. The hierarchical structure of the neocortex is indeed well-­founded neuroscience. The columnar organ­ization of the neocortex was first discovered by American neuroscientist Vernon Mountcastle in 1957.

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

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

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.

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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.

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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.

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

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

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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.

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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.

Genius Makers: The Mavericks Who Brought A. I. To Google, Facebook, and the World by Cade Metz

AI winter, air gap, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, AlphaGo, Amazon Robotics, artificial general intelligence, Asilomar, autonomous vehicles, backpropagation, Big Tech, British Empire, Cambridge Analytica, carbon-based life, cloud computing, company town, computer age, computer vision, deep learning, deepfake, DeepMind, Demis Hassabis, digital map, Donald Trump, driverless car, drone strike, Elon Musk, fake news, Fellow of the Royal Society, Frank Gehry, game design, Geoffrey Hinton, Google Earth, Google X / Alphabet X, Googley, Internet Archive, Isaac Newton, Jeff Hawkins, Jeffrey Epstein, job automation, John Markoff, life extension, machine translation, Mark Zuckerberg, means of production, Menlo Park, move 37, move fast and break things, Mustafa Suleyman, new economy, Nick Bostrom, nuclear winter, OpenAI, PageRank, PalmPilot, pattern recognition, Paul Graham, paypal mafia, Peter Thiel, profit motive, Richard Feynman, ride hailing / ride sharing, Ronald Reagan, Rubik’s Cube, Sam Altman, Sand Hill Road, self-driving car, side project, Silicon Valley, Silicon Valley billionaire, Silicon Valley startup, Skype, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Ballmer, Steven Levy, Steven Pinker, tech worker, telemarketer, The Future of Employment, Turing test, warehouse automation, warehouse robotics, Y Combinator

This gave decades of onlookers the impression that scientists were on the verge of re-creating the powers of the brain when, in reality, they were not. In 1966, a few dozen researchers traveled to Puerto Rico, gathering at the Hilton hotel in San Juan. They met to discuss the latest advances in what was then called “pattern recognition”—technology that could identify patterns in images and other data. Whereas Rosenblatt viewed the Perceptron as a model of the brain, others saw it as a means of pattern recognition. In later years, some commentators imagined Rosenblatt and Minsky locking horns at academic conferences like the one in San Juan, openly debating the future of the Perceptron, but their rivalry was implicit.

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Eighty-nine pages in, as an aside, the book mentioned the Perceptron, calling it a device “capable of formulating simple hypotheses from regular exposure to raw data,” and LeCun was hooked, instantly enamored with the idea of a machine that could learn. Learning, he believed, was inextricable from intelligence. “Any animal with a brain can learn,” he often said. At a time when few researchers paid much attention to neural networks and those that did saw it not as artificial intelligence but as just another form of pattern recognition, LeCun homed in on the idea during his years as an undergraduate at the École Supérieure d’Ingénieurs en Électrotechnique et Électronique. Most of the papers he studied were written in English by Japanese researchers, because Japan was one of the few places where this research still went on.

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As Hinton said: “He hasn’t got enough Trump in him.” Sitting at his desk inside Chauffeur, Krizhevsky was at the heart of this AI boom, but he didn’t see his role as all that important, and he didn’t see any of it as artificial intelligence. It was deep learning, and deep learning was just mathematics, pattern recognition, or, as he called it, “nonlinear regression.” These techniques had been around for decades. It was merely that people like him had come along at the right time, when there was enough data and enough processing power to make it all work. The technologies he built were in no way intelligent. They worked only in very particular situations.

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, backpropagation, bioinformatics, brain emulation, classic study, combinatorial explosion, complexity theory, computer vision, Computing Machinery and Intelligence, conceptual framework, correlation coefficient, epigenetics, friendly AI, functional programming, G4S, higher-order functions, information retrieval, Isaac Newton, Jeff Hawkins, John Conway, Loebner Prize, Menlo Park, natural language processing, Nick Bostrom, Occam's razor, p-value, pattern recognition, performance metric, precautionary principle, 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.

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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).

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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.

Survival of the Richest: Escape Fantasies of the Tech Billionaires by Douglas Rushkoff

"World Economic Forum" Davos, 4chan, A Declaration of the Independence of Cyberspace, agricultural Revolution, Airbnb, Alan Greenspan, Amazon Mechanical Turk, Amazon Web Services, Andrew Keen, AOL-Time Warner, artificial general intelligence, augmented reality, autonomous vehicles, basic income, behavioural economics, Big Tech, biodiversity loss, Biosphere 2, bitcoin, blockchain, Boston Dynamics, Burning Man, buy low sell high, Californian Ideology, carbon credits, carbon footprint, circular economy, clean water, cognitive dissonance, Colonization of Mars, coronavirus, COVID-19, creative destruction, Credit Default Swap, CRISPR, data science, David Graeber, DeepMind, degrowth, Demis Hassabis, deplatforming, digital capitalism, digital map, disinformation, Donald Trump, Elon Musk, en.wikipedia.org, energy transition, Ethereum, ethereum blockchain, European colonialism, Evgeny Morozov, Extinction Rebellion, Fairphone, fake news, Filter Bubble, game design, gamification, gig economy, Gini coefficient, global pandemic, Google bus, green new deal, Greta Thunberg, Haight Ashbury, hockey-stick growth, Howard Rheingold, if you build it, they will come, impact investing, income inequality, independent contractor, Jane Jacobs, Jeff Bezos, Jeffrey Epstein, job automation, John Nash: game theory, John Perry Barlow, Joseph Schumpeter, Just-in-time delivery, liberal capitalism, Mark Zuckerberg, Marshall McLuhan, mass immigration, megaproject, meme stock, mental accounting, Michael Milken, microplastics / micro fibres, military-industrial complex, Minecraft, mirror neurons, move fast and break things, Naomi Klein, New Urbanism, Norbert Wiener, Oculus Rift, One Laptop per Child (OLPC), operational security, Patri Friedman, pattern recognition, Peter Thiel, planetary scale, Plato's cave, Ponzi scheme, profit motive, QAnon, RAND corporation, Ray Kurzweil, rent-seeking, Richard Thaler, ride hailing / ride sharing, Robinhood: mobile stock trading app, Sam Altman, Shoshana Zuboff, Silicon Valley, Silicon Valley billionaire, SimCity, Singularitarianism, Skinner box, Snapchat, sovereign wealth fund, Stephen Hawking, Steve Bannon, Steve Jobs, Steven Levy, Steven Pinker, Stewart Brand, surveillance capitalism, tech billionaire, tech bro, technological solutionism, technoutopianism, Ted Nelson, TED Talk, the medium is the message, theory of mind, TikTok, Torches of Freedom, Tragedy of the Commons, universal basic income, urban renewal, warehouse robotics, We are as Gods, WeWork, Whole Earth Catalog, work culture , working poor

Contents Introduction:  Meet The Mindset 1        The Insulation Equation BILLIONAIRE BUNKER STRATEGIES 2        Mergers and Acquisitions ALWAYS HAVE AN EXIT STRATEGY 3        A Womb with a View YOU ARE SAFE IN YOUR TECHNO-BUBBLE 4        The Dumbwaiter Effect OUT OF SIGHT, OUT OF MIND 5        Selfish Genes SCIENTISM OVER MORALITY 6        Pedal to the Metal DEHUMANIZE, DOMINATE, AND EXTRACT 7        Exponential WHEN YOU CAN GO NO FURTHER, GO META 8        Persuasive Tech IF YOU COULD JUST PUSH A BUTTON 9        Visions from Burning Man WE ARE AS GODS 10      The Great Reset TO SAVE THE WORLD, SAVE CAPITALISM 11      The Mindset in the Mirror RESISTANCE IS FUTILE 12      Cybernetic Karma HOISTED BY THEIR OWN PETARD 13      Pattern Recognition EVERYTHING COMES BACK Acknowledgments Notes Survival of the Richest INTRODUCTION Meet The Mindset I got invited to a super-deluxe resort to deliver a speech to what I assumed would be a hundred or so investment bankers. It was by far the largest fee I had ever been offered for a talk—about a third of my annual salary as a professor at a public college—all to deliver some insight on “the future of technology.”

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Maybe the fear of AI—this awareness of something they believe to be greater than themselves—will be enough to make holders of The Mindset less disdainful of the rest of humanity, and help them begin to see themselves as on the same team as everyone else. After all, they’re not escaping from us; they’re escaping from their own creations. Their fate will ultimately depend on whether their artificial intelligences adopt the mindset of their creators. 13 Pattern Recognition EVERYTHING COMES BACK A s I type these words, Jeff Bezos is making his first trip into space on his privately funded rocket ship, Blue Origin. He has reached Zero G a week after fellow billionaire Richard Branson got there in a slightly less explosively priapic fashion, his craft first hoisted up to the sky by some airplanes before setting off for higher altitudes.

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There’s no place to externalize harm, but that becomes a strong incentive to engage in practices that benefit the community instead of poisoning or impoverishing it. This, in turn, inspires innovation and efficiencies rarely achieved when companies are financialized by distant shareholders. McLuhan predicted that in order to orient ourselves properly in the digital age, we would need to develop pattern recognition—the ability to soften our focus from the particular details of any situation in order to perceive the greater patterns. Digital feedback loops are helping us see that our media, technology, culture, economy, and natural world all have at least as much of a cyclical character as a linear one.

The Alignment Problem: Machine Learning and Human Values by Brian Christian

Albert Einstein, algorithmic bias, Alignment Problem, AlphaGo, Amazon Mechanical Turk, artificial general intelligence, augmented reality, autonomous vehicles, backpropagation, butterfly effect, Cambridge Analytica, Cass Sunstein, Claude Shannon: information theory, computer vision, Computing Machinery and Intelligence, data science, deep learning, DeepMind, Donald Knuth, Douglas Hofstadter, effective altruism, Elaine Herzberg, Elon Musk, Frances Oldham Kelsey, game design, gamification, Geoffrey Hinton, Goodhart's law, Google Chrome, Google Glasses, Google X / Alphabet X, Gödel, Escher, Bach, Hans Moravec, hedonic treadmill, ImageNet competition, industrial robot, Internet Archive, John von Neumann, Joi Ito, Kenneth Arrow, language acquisition, longitudinal study, machine translation, mandatory minimum, mass incarceration, multi-armed bandit, natural language processing, Nick Bostrom, Norbert Wiener, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, OpenAI, Panopticon Jeremy Bentham, pattern recognition, Peter Singer: altruism, Peter Thiel, precautionary principle, premature optimization, RAND corporation, recommendation engine, Richard Feynman, Rodney Brooks, Saturday Night Live, selection bias, self-driving car, seminal paper, side project, Silicon Valley, Skinner box, sparse data, speech recognition, Stanislav Petrov, statistical model, Steve Jobs, strong AI, the map is not the territory, theory of mind, Tim Cook: Apple, W. E. B. Du Bois, Wayback Machine, zero-sum game

In Advances in Neural Information Processing Systems, 1471–79, 2016. Bellman, Richard. Dynamic Programming. Princeton, NJ: Princeton University Press, 1957. Bendale, Abhijit, and Terrance Boult. “Towards Open World Recognition.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 1893–1902. CVPR, 2015. Bengio, Yoshua. “Neural Net Language Models.” Scholarpedia 3, no. 1 (2008): 3881. http://www.scholarpedia.org/article/Neural_net_language_models. Bengio, Yoshua, Réjean Ducharme, Pascal Vincent, and Christian Jauvin. “A Neural Probabilistic Language Model.” Journal of Machine Learning Research 3 (2003): 1137–55.

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Springer Science & Business Media, 1985. Demski, Abram, and Scott Garrabrant. “Embedded Agency.” CoRR abs/1902.09469 (2019). Deng, Jia, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei. “ImageNet: A Large-Scale Hierarchical Image Database.” In 2009 IEEE Conference on Computer Vision and Pattern Recognition, 248–55. IEEE, 2009. Denker, John S., and Yann LeCun. “Transforming Neural-Net Output Levels to Probability Distributions.” In Advances in Neural Information Processing Systems 3, 853–59, 1991. Denker, John, Daniel Schwartz, Ben Wittner, Sara Solla, Richard Howard, Lawrence Jackel, and John Hopfield.

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In Proceedings of the 17th International Conference on Autonomous Agents and Multiagent Systems, 122–30. International Foundation for Autonomous Agents and Multiagent Systems, 2018. Fong, Ruth, and Andrea Vedaldi. “Net2Vec: Quantifying and Explaining How Concepts Are Encoded by Filters in Deep Neural Networks.” In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 8730–38. 2018. “Four Out of Ten Violate Parole, Says Legislator.” Chicago Tribune, May 15, 1937. Frederick, Shane, and George Loewenstein. “Hedonic Adaptation.” In Well-Being: The Foundations of Hedonic Psychology, edited by Daniel Kahneman, Edward Diener, and Norbert Schwarz, 302–29. New York: Russell Sage Foundation, 1999.

New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale

affirmative action, Affordable Care Act / Obamacare, Airbnb, algorithmic bias, Amazon Mechanical Turk, Anthropocene, augmented reality, Automated Insights, autonomous vehicles, basic income, battle of ideas, Bernie Sanders, Big Tech, Bill Joy: nanobots, bitcoin, blockchain, Brexit referendum, call centre, Cambridge Analytica, carbon tax, citizen journalism, Clayton Christensen, collective bargaining, commoditize, computer vision, conceptual framework, contact tracing, coronavirus, corporate social responsibility, correlation does not imply causation, COVID-19, critical race theory, cryptocurrency, data is the new oil, data science, decarbonisation, deep learning, deepfake, deskilling, digital divide, digital twin, disinformation, disruptive innovation, don't be evil, Donald Trump, Douglas Engelbart, driverless car, effective altruism, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Evgeny Morozov, fake news, Filter Bubble, finite state, Flash crash, future of work, gamification, general purpose technology, Google Chrome, Google Glasses, Great Leap Forward, green new deal, guns versus butter model, Hans Moravec, high net worth, hiring and firing, holacracy, Ian Bogost, independent contractor, informal economy, information asymmetry, information retrieval, interchangeable parts, invisible hand, James Bridle, Jaron Lanier, job automation, John Markoff, Joi Ito, Khan Academy, knowledge economy, late capitalism, lockdown, machine readable, Marc Andreessen, Mark Zuckerberg, means of production, medical malpractice, megaproject, meta-analysis, military-industrial complex, Modern Monetary Theory, Money creation, move fast and break things, mutually assured destruction, natural language processing, new economy, Nicholas Carr, Nick Bostrom, Norbert Wiener, nuclear winter, obamacare, One Laptop per Child (OLPC), open immigration, OpenAI, opioid epidemic / opioid crisis, paperclip maximiser, paradox of thrift, pattern recognition, payday loans, personalized medicine, Peter Singer: altruism, Philip Mirowski, pink-collar, plutocrats, post-truth, pre–internet, profit motive, public intellectual, QR code, quantitative easing, race to the bottom, RAND corporation, Ray Kurzweil, recommendation engine, regulatory arbitrage, Robert Shiller, Rodney Brooks, Ronald Reagan, self-driving car, sentiment analysis, Shoshana Zuboff, Silicon Valley, Singularitarianism, smart cities, smart contracts, software is eating the world, South China Sea, Steve Bannon, Strategic Defense Initiative, surveillance capitalism, Susan Wojcicki, tacit knowledge, TaskRabbit, technological solutionism, technoutopianism, TED Talk, telepresence, telerobotics, The Future of Employment, The Turner Diaries, Therac-25, Thorstein Veblen, too big to fail, Turing test, universal basic income, unorthodox policies, wage slave, Watson beat the top human players on Jeopardy!, working poor, workplace surveillance , Works Progress Administration, zero day

Each box in the grid can be identified as either skin or not skin, smooth or not smooth, along hundreds or even thousands of binaries, many of which would never be noticeable by the human eye. And even more sensing is available. Medical images may also encode data at the pixel or voxel (3-D pixel) level that map to what our hands, nose, or ear might sense—and far more. Pattern recognition via machine vision found early commercial success with banks, which needed a way to recognize numbers on checks (given the wide variety of human handwriting). With enough examples of written numbers and computational power, this recognition can become nearly perfect. Thus, machine vision is “superhuman” in many respects, in terms of both “ingesting” data and comparing those data to millions of other images.

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Although AI may create more bounty for all, potentially boosting welfare via effects on all of these areas, it can just as readily accelerate unwanted surveillance, social division, and zero-sum competition. These effects can have just as much or more effect on lifespan and well-being as advances in surgical robots or AI-optimized diets. For most healthy people, doctoring looks like a simple task of pattern recognition (diagnosis) resulting in procedure or prescription. The idealized TV doctor judiciously weighs the evidence, forcefully recommends a course of action, and moves on to the next case. Were things so simple, robots could eventually stand in for doctors. But in the real world of medical practice, this image is long outdated.

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The digital / non-digital divide is, however, very useful in another way: it is much easier to imagine a future machine-vision system effortlessly parsing the difference between paint and pixels than convincingly distinguishing between a still from the beginning of a pornographic film and a photograph documenting the lives of sex workers in a tasteful and artistic way. The former is a mere pattern-recognition task, and we can imagine hundreds of thousands of images uncontroversially classified as one or the other, perhaps by workers on Amazon’s Mechanical Turk platform (and perhaps even via unsupervised machine learning).55 The latter is a matter of moral and aesthetic judgment—and that judgment may change over time, or vary by place.

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

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.

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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.

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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

You Are Not a Gadget by Jaron Lanier

1960s counterculture, Abraham Maslow, accounting loophole / creative accounting, additive manufacturing, Albert Einstein, Bear Stearns, call centre, cloud computing, commoditize, crowdsourcing, death of newspapers, different worldview, digital Maoism, Douglas Hofstadter, Extropian, follow your passion, General Magic , hive mind, Internet Archive, Jaron Lanier, jimmy wales, John Conway, John Perry Barlow, John von Neumann, Kevin Kelly, Long Term Capital Management, Neal Stephenson, Network effects, new economy, packet switching, PageRank, pattern recognition, Ponzi scheme, Project Xanadu, Ray Kurzweil, Richard Stallman, Savings and loan crisis, Silicon Valley, Silicon Valley startup, slashdot, social graph, stem cell, Steve Jobs, Stewart Brand, Stuart Kauffman, synthetic biology, technological determinism, Ted Nelson, telemarketer, telepresence, the long tail, 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.

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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.

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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.

Bulletproof Problem Solving by Charles Conn, Robert McLean

active transport: walking or cycling, Airbnb, Amazon Mechanical Turk, asset allocation, availability heuristic, Bayesian statistics, behavioural economics, Big Tech, Black Swan, blockchain, book value, business logic, business process, call centre, carbon footprint, cloud computing, correlation does not imply causation, Credit Default Swap, crowdsourcing, David Brooks, deep learning, Donald Trump, driverless car, drop ship, Elon Musk, endowment effect, fail fast, fake news, future of work, Garrett Hardin, Hyperloop, Innovator's Dilemma, inventory management, iterative process, loss aversion, megaproject, 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, SimCity, smart contracts, stem cell, sunk-cost fallacy, the rule of 72, the scientific method, The Signal and the Noise by Nate Silver, time value of money, Tragedy of the Commons, transfer pricing, Vilfredo Pareto, walkable city, WikiLeaks

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.

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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.

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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.

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, Apollo 13, 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, data science, David Brooks, en.wikipedia.org, Erik Brynjolfsson, Gini coefficient, Helicobacter pylori, independent contractor, 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, SoftBank, speech recognition, Steve Jobs, supply-chain management, systems thinking, Teledyne, text mining, the long tail, 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.

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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. Both fields are concerned with the analysis of data to find useful or informative patterns.

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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.

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

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

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.

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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.

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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.

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

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

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.

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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.

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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.

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

Adam Curtis, Affordable Care Act / Obamacare, Alan Greenspan, algorithmic trading, Amazon Mechanical Turk, American Legislative Exchange Council, asset-backed security, Atul Gawande, bank run, barriers to entry, basic income, Bear Stearns, Berlin Wall, Bernie Madoff, Black Swan, bonus culture, Brian Krebs, business cycle, business logic, 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, data science, Debian, digital rights, don't be evil, drone strike, Edward Snowden, en.wikipedia.org, Evgeny Morozov, Fall of the Berlin Wall, Filter Bubble, financial engineering, financial innovation, financial thriller, fixed income, Flash crash, folksonomy, full employment, Gabriella Coleman, Goldman Sachs: Vampire Squid, Google Earth, Hernando de Soto, High speed trading, hiring and firing, housing crisis, Ian Bogost, informal economy, information asymmetry, information retrieval, information security, interest rate swap, Internet of things, invisible hand, Jaron Lanier, Jeff Bezos, job automation, John Bogle, Julian Assange, Kevin Kelly, Kevin Roose, knowledge worker, Kodak vs Instagram, kremlinology, late fees, London Interbank Offered Rate, London Whale, machine readable, Marc Andreessen, Mark Zuckerberg, Michael Milken, mobile money, moral hazard, new economy, Nicholas Carr, offshore financial centre, PageRank, pattern recognition, Philip Mirowski, precariat, profit maximization, profit motive, public intellectual, quantitative easing, race to the bottom, reality distortion field, recommendation engine, regulatory arbitrage, risk-adjusted returns, Satyajit Das, Savings and loan crisis, 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, technological solutionism, the scientific method, too big to fail, transaction costs, two-sided market, universal basic income, Upton Sinclair, value at risk, vertical integration, WikiLeaks, Yochai Benkler, 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.

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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.

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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.)

Pattern Recognition by William Gibson

carbon-based life, content marketing, Frank Gehry, gentrification, intentional community, lateral thinking, machine translation, Mars Rover, Maui Hawaii, offshore financial centre, old-boy network, pattern recognition, rent stabilization

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.

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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.

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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.

Artificial Whiteness by Yarden Katz

affirmative action, AI winter, algorithmic bias, AlphaGo, Amazon Mechanical Turk, autonomous vehicles, benefit corporation, Black Lives Matter, blue-collar work, Californian Ideology, Cambridge Analytica, cellular automata, Charles Babbage, cloud computing, colonial rule, computer vision, conceptual framework, Danny Hillis, data science, David Graeber, deep learning, DeepMind, desegregation, Donald Trump, Dr. Strangelove, driverless car, Edward Snowden, Elon Musk, Erik Brynjolfsson, European colonialism, fake news, Ferguson, Missouri, general purpose technology, gentrification, Hans Moravec, housing crisis, income inequality, information retrieval, invisible hand, Jeff Bezos, Kevin Kelly, knowledge worker, machine readable, Mark Zuckerberg, mass incarceration, Menlo Park, military-industrial complex, Nate Silver, natural language processing, Nick Bostrom, Norbert Wiener, pattern recognition, phenotype, Philip Mirowski, RAND corporation, recommendation engine, rent control, Rodney Brooks, Ronald Reagan, Salesforce, Seymour Hersh, Shoshana Zuboff, Silicon Valley, Silicon Valley billionaire, Silicon Valley ideology, Skype, speech recognition, statistical model, Stephen Hawking, Stewart Brand, Strategic Defense Initiative, surveillance capitalism, talking drums, telemarketer, The Signal and the Noise by Nate Silver, W. E. B. Du Bois, Whole Earth Catalog, WikiLeaks

Anh Nguyen, Jason Yosinski, and Jeff 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.   34.   Oriol 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.   35.   The incident occurred during a protest in the village of Nabi Saleh in the West Bank in the summer of 2015. See “A Perfect Picture of the Occupation,” editorial, Haaretz, August 31, 2015.   36.   

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In the 1990s probabilistic modeling and inference were becoming AI’s dominant new computational engine and starting to displace logic-based approaches to reasoning within the field. These probabilistic frameworks, which crystalized in the 1980s, did not always develop under the umbrella of “AI” but also under headings such as “statistical pattern recognition,” “data mining,” or “information retrieval.”97 Regardless, these frameworks were being absorbed into AI’s familiar narratives. An article in AI Magazine titled “Is AI Going Mainstream at Last? A Look Inside Microsoft Research” (1993) exemplifies this turn. The piece omits AI’s history of shifting in and out of the mainstream, claiming that “AI” merely had its “15 minutes of fame in the mid-80s,” but that new developments in probabilistic modeling could put it back on the map.

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Press release, November 20, 2017, https://engineering.nyu.edu/news/new-artificial-intelligence-research-institute-launches. Nguyen, Anh, Jason Yosinski, and Jeff 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. Nicas, Jack. “Atlanta Asks Google Whether It Targeted Black Homeless People.” New York Times, October 4, 2019. Nilsson, Nils J. “Artificial Intelligence: Employment, and Income.” In Impacts of Artificial Intelligence, ed. Robert Trappl, 103–23. Amsterdam: North Holland, 1986. ________.

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

Albert Einstein, Asperger Syndrome, autism spectrum disorder, behavioural economics, business cycle, Cass Sunstein, cognitive bias, David Brooks, en.wikipedia.org, endowment effect, Flynn Effect, folksonomy, framing effect, Google Earth, Gregor Mendel, 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, Tyler Cowen

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.

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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.

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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.

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

"World Economic Forum" Davos, airport security, Andrei Shleifer, big-box store, British Empire, business cycle, cognitive dissonance, cross-subsidies, fundamental attribution error, gentrification, George Santayana, haute cuisine, low interest rates, market clearing, microcredit, money market fund, pattern recognition, Ralph Nader, retail therapy, Stephen Hawking, The Wealth of Nations by Adam Smith, trade route, Tragedy of the Commons, transaction costs, Tyler Cowen

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.

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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?).

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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.

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

airport security, Albert Einstein, book scanning, cloud computing, Computing Machinery and Intelligence, conceptual framework, Douglas Engelbart, full text search, information retrieval, invention of writing, inventory management, Isaac Newton, Ivan Sutherland, John Markoff, language acquisition, 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, Steve Bannon, Ted Nelson, telepresence, Turing test, Vannevar Bush, web application

“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).

…

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.

…

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.

Machine Learning for Hackers by Drew Conway, John Myles White

call centre, centre right, correlation does not imply causation, data science, Debian, Erdős number, Nate Silver, natural language processing, Netflix Prize, off-by-one error, 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.

…

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.

…

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?

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, cognitive load, computerized trading, David Brooks, deep learning, deliberate practice, deskilling, digital map, Douglas Engelbart, driverless car, drone strike, Elon Musk, Erik Brynjolfsson, Evgeny Morozov, Flash crash, Frank Gehry, Frank Levy and Richard Murnane: The New Division of Labor, Frederick Winslow Taylor, future of work, gamification, global supply chain, Google Glasses, Google Hangouts, High speed trading, human-factors engineering, indoor plumbing, industrial robot, Internet of things, Ivan Sutherland, 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, low interest rates, Lyft, machine readable, 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, profit motive, Ralph Waldo Emerson, RAND corporation, randomized controlled trial, Ray Kurzweil, recommendation engine, robot derives from the Czech word robota Czech, meaning slave, scientific management, Second Machine Age, self-driving car, Silicon Valley, Silicon Valley ideology, software is eating the world, Stephen Hawking, Steve Jobs, systems thinking, tacit knowledge, TaskRabbit, technological determinism, technological solutionism, technoutopianism, TED Talk, The Wealth of Nations by Adam Smith, turn-by-turn navigation, Tyler Cowen, US Airways Flight 1549, Watson beat the top human players on Jeopardy!, William Langewiesche

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.

…

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.

…

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.

Scary Smart: The Future of Artificial Intelligence and How You Can Save Our World by Mo Gawdat

3D printing, accounting loophole / creative accounting, AI winter, AlphaGo, anthropic principle, artificial general intelligence, autonomous vehicles, basic income, Big Tech, Black Lives Matter, Black Monday: stock market crash in 1987, butterfly effect, call centre, carbon footprint, cloud computing, computer vision, coronavirus, COVID-19, CRISPR, cryptocurrency, deep learning, deepfake, DeepMind, Demis Hassabis, digital divide, digital map, Donald Trump, Elon Musk, fake news, fulfillment center, game design, George Floyd, global pandemic, Google Glasses, Google X / Alphabet X, Law of Accelerating Returns, lockdown, microplastics / micro fibres, Nick Bostrom, off-the-grid, OpenAI, optical character recognition, out of africa, pattern recognition, Ponzi scheme, Ray Kurzweil, recommendation engine, self-driving car, Silicon Valley, smart contracts, Stanislav Petrov, Stephen Hawking, subprime mortgage crisis, superintelligent machines, TED Talk, TikTok, Turing machine, Turing test, universal basic income, Watson beat the top human players on Jeopardy!, Y2K

Human traders will leave the market or start using AI tools themselves, and what will we be left with? Machines trading against other machines. An intelligent stock-trading machine is given only one target – to maximize profit. As we have seen with the other intelligent machines that we’ve invented so far, through pattern recognition machines are likely to devise ingenious solutions that we have never encountered before. For example, they may discover that driving the price of a certain category of stock all the way down to zero may free up some capital that maximizes the profit to be found in trading other stocks. They may decide to communicate, as shareholders, to the AI running Google Search by suggesting changes in the way business is done, or threaten to trade the price down.

…

It’s the pattern of the colour that has changed and with it the sounds that your parent makes also change. Your little brain reasons that the sound that you make is not associated with the white, but rather with the colours scribbled on it, and so it starts to focus more on the patterns and ignore the white. Each brain, from that moment onwards, develops its very own pattern-recognition engine, but a reasonable example, at least for the way my Mo brain thinks, would be something like this: Numbers seem to always be contained within a rectangle that can be split into two squares, a top one and a lower one. Each of these squares can contain endless random scribbles in them but those scribbles can be recognized reasonably accurately if each square is then subdivided into four smaller quadrants.

…

If your parents fought with each other, you are likely to grow up feeling tense even if they told you to remain calm. If they told you to work hard when they didn’t, you probably won’t be pulling all-nighters very often. Children don’t learn from what you say. They learn from what you do. This is the same old pattern recognition that creates intelligence in humans and machines alike – monkey see, monkey do. This is why, in the parenting model I suggested above, the pursuit of happiness and compassion is not directed at the children but ideally demonstrated by the parent, as an example to copy. This is also why programming machines with algorithms and tasks that are good for humanity is not enough.

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, backpropagation, basic income, Bayesian statistics, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Charles Babbage, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, creative destruction, crowdsourcing, Danny Hillis, data is not the new oil, data is the new oil, data science, deep learning, DeepMind, double helix, Douglas Hofstadter, driverless car, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, Geoffrey Hinton, global village, Google Glasses, Gödel, Escher, Bach, Hans Moravec, incognito mode, information retrieval, Jeff Hawkins, job automation, John Markoff, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, large language model, lone genius, machine translation, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, Nick Bostrom, NP-complete, off grid, P = NP, PageRank, pattern recognition, phenotype, planetary scale, power law, 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 long tail, 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, yottabyte, zero-sum game

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.

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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.

21 Lessons for the 21st Century by Yuval Noah Harari

"World Economic Forum" Davos, 1960s counterculture, accounting loophole / creative accounting, affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, algorithmic trading, augmented reality, autonomous vehicles, Ayatollah Khomeini, basic income, behavioural economics, Bernie Sanders, bitcoin, blockchain, Boris Johnson, Brexit referendum, call centre, Cambridge Analytica, Capital in the Twenty-First Century by Thomas Piketty, carbon tax, carbon-based life, Charlie Hebdo massacre, cognitive dissonance, computer age, computer vision, cryptocurrency, cuban missile crisis, decarbonisation, DeepMind, deglobalization, disinformation, Donald Trump, Dr. Strangelove, failed state, fake news, 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, Mohammed Bouazizi, mutually assured destruction, Naomi Klein, obamacare, pattern recognition, post-truth, post-work, purchasing power parity, race to the bottom, RAND corporation, restrictive zoning, Ronald Reagan, Rosa Parks, Scramble for Africa, self-driving car, Silicon Valley, Silicon Valley startup, TED Talk, transatlantic slave trade, trolley problem, Tyler Cowen, 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.

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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.

…

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.

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

"Margaret Hamilton" Apollo, "Susan Fowler" uber, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, 4chan, 8-hour work day, Aaron Swartz, Ada Lovelace, AI winter, air gap, Airbnb, algorithmic bias, AlphaGo, Amazon Web Services, Andy Rubin, Asperger Syndrome, augmented reality, Ayatollah Khomeini, backpropagation, barriers to entry, basic income, behavioural economics, Bernie Sanders, Big Tech, bitcoin, Bletchley Park, blockchain, blue-collar work, Brewster Kahle, Brian Krebs, Broken windows theory, call centre, Cambridge Analytica, cellular automata, Charles Babbage, Chelsea Manning, Citizen Lab, clean water, cloud computing, cognitive dissonance, computer vision, Conway's Game of Life, crisis actor, crowdsourcing, cryptocurrency, Danny Hillis, data science, David Heinemeier Hansson, deep learning, DeepMind, Demis Hassabis, disinformation, don't be evil, don't repeat yourself, Donald Trump, driverless car, dumpster diving, Edward Snowden, Elon Musk, Erik Brynjolfsson, Ernest Rutherford, Ethereum, ethereum blockchain, fake news, false flag, Firefox, Frederick Winslow Taylor, Free Software Foundation, Gabriella Coleman, game design, Geoffrey Hinton, glass ceiling, Golden Gate Park, Google Hangouts, Google X / Alphabet X, Grace Hopper, growth hacking, Guido van Rossum, Hacker Ethic, hockey-stick growth, HyperCard, Ian Bogost, illegal immigration, ImageNet competition, information security, Internet Archive, Internet of things, Jane Jacobs, John Markoff, Jony Ive, Julian Assange, Ken Thompson, Kickstarter, Larry Wall, lone genius, Lyft, Marc Andreessen, Mark Shuttleworth, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microdosing, microservices, Minecraft, move 37, move fast and break things, Nate Silver, Network effects, neurotypical, Nicholas Carr, Nick Bostrom, no silver bullet, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, Oculus Rift, off-the-grid, OpenAI, operational security, opioid epidemic / opioid crisis, PageRank, PalmPilot, paperclip maximiser, 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, scientific management, 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, systems thinking, TaskRabbit, tech worker, techlash, TED Talk, 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!, WeWork, WikiLeaks, women in the workforce, Y Combinator, Zimmermann PGP, éminence grise

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?”

…

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.

…

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.

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, driverless car, Edward Snowden, Edward Thorp, Elon Musk, factory automation, Filter Bubble, G4S, gamification, Google Earth, Google Glasses, Internet of things, job automation, John Markoff, Kickstarter, lifelogging, Marc Andreessen, Marc Benioff, Mars Rover, Menlo Park, Metcalfe’s law, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, 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, TSMC, ubercab, urban planning, Zipcar

., 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.

…

IBM has taken that and is applying it to its Smarter Planet initiative, addressing the complex global issues of health, banking and cities. The Smarter Cities initiative is now a global business for IBM, with projects all over the world. 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.

…

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. For example, they now know that car thefts increase on rainy nights. San Francisco. IBM’s use of data and embedded sensors has reduced pollution emanating from the city’s thousand miles of sewer lines. Public utilities report that IBM’s preventive intelligence has helped reduce improve their preventive to corrective maintenance repair costs ratio by 11 percent.

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, behavioural economics, Bernie Madoff, Bob Noyce, book value, Brownian motion, Captain Sullenberger Hudson, cloud computing, cognitive dissonance, Daniel Kahneman / Amos Tversky, David Sedaris, deliberate practice, Electric Kool-Aid Acid Test, Elon Musk, en.wikipedia.org, experimental economics, Fairchild Semiconductor, fear of failure, financial independence, follow your passion, Ford Model T, Frederick Winslow Taylor, Goodhart's law, hiring and firing, if you see hoof prints, think horses—not zebras, Internet of things, Isaac Newton, Jeff Bezos, job satisfaction, knowledge economy, labor-force participation, Larry Ellison, longitudinal study, low skilled workers, Mark Zuckerberg, meta-analysis, Moneyball by Michael Lewis explains big data, move fast and break things, pattern recognition, Peter Thiel, power law, reality distortion field, Sand Hill Road, science of happiness, scientific management, shareholder value, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, Steve Wozniak, sunk-cost fallacy, tech worker, TED Talk, theory of mind, Tim Cook: Apple, Toyota Production System, unpaid internship, upwardly mobile, women in the workforce, working poor

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.

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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.

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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.

Upgrade by Blake Crouch

bioinformatics, butterfly effect, cognitive dissonance, correlation does not imply causation, COVID-19, CRISPR, dark matter, deepfake, double helix, Douglas Hofstadter, driverless car, drone strike, glass ceiling, Google Earth, Gödel, Escher, Bach, Hyperloop, independent contractor, job automation, low earth orbit, messenger bag, mirror neurons, off grid, pattern recognition, phenotype, ride hailing / ride sharing, supervolcano, time dilation

I shook my head, certain I had never heard of that gene before. “It’s linked to learning stimulus-response associations faster. How about NLGN3?” I said, “Enhanced learning and spatial-learning abilities.” “GluK4.” “Low bipolar risk and higher cognitive function.” Edwin looked up at me. “Your cognition, memory, concentration, pattern recognition—it’s all been targeted for enhancement. Have you experienced gains across these areas?” “Yes.” “Since when?” “Over the last three weeks.” “Do you get how astounding this is?” For a moment, I couldn’t speak. I had suspected that something had been done to me, but to hear it confirmed knocked the wind out of me.

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If it takes ninety days for the jellyfish to cover the entire ocean, how long would it take for the jellyfish to cover half of the ocean?” “You’re wasting my time and yours,” I said. “And yours.” “Please provide an answer. We have to work up to the hard ones.” “Eighty-nine days.” We tackled spatial reasoning, visual/perceptual and classification skills. Logical reasoning. And finally pattern recognition. “Logan, what is the next term in the following sequence: 0, 1, 1, 2, 3, 5, 8, 13, 21, 34?” I pored over the sequence on his laptop screen. “Fifty-five.” “How did you get that?” “Well, that’s the Fibonacci sequence. Each number is the sum of the two that precede it.” “You just happen to know the Fibonacci sequence off the top of your head?”

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These DNA payloads were already beginning to exercise their edits to myriad genes, involving multiple cascades of pathways—each a light touch to the genome, almost a butterfly effect that would have, over time, changed Tiffany’s genome and led to augmentation of her intelligence, longevity, and resilience, finally lifting her to some version of my level of being. After seeing Kara and Andrew, I was beginning to suspect that, while the upgrade altered expressions of intelligence, memory, and physical prowess across the board, it could exponentially ramp up preexisting proclivities—strength, agility, and coordination for people like Andrew and Kara. Pattern recognition and people reading for the more intellectually inclined like me. Tiffany had been on the same journey toward becoming an enhanced form of Homo sapiens that I had taken when Andrew shot her at the top of those stairs. Using the software, I began to isolate the genetic codes of multiple viral packages, which had delivered the upgrade to her.

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

"World Economic Forum" Davos, 3D printing, additive manufacturing, AI winter, Airbnb, airline deregulation, airport security, Albert Einstein, algorithmic bias, AlphaGo, Amazon Mechanical Turk, Amazon Web Services, Andy Rubin, AOL-Time Warner, artificial general intelligence, asset light, augmented reality, autism spectrum disorder, autonomous vehicles, backpropagation, backtesting, barriers to entry, behavioural economics, bitcoin, blockchain, blood diamond, 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, CRISPR, crony capitalism, crowdsourcing, cryptocurrency, Daniel Kahneman / Amos Tversky, data science, Dean Kamen, deep learning, DeepMind, Demis Hassabis, discovery of DNA, disintermediation, disruptive innovation, distributed ledger, double helix, driverless car, Elon Musk, en.wikipedia.org, Erik Brynjolfsson, Ethereum, ethereum blockchain, everywhere but in the productivity statistics, Evgeny Morozov, fake news, family office, fiat currency, financial innovation, general purpose technology, Geoffrey Hinton, George Akerlof, global supply chain, Great Leap Forward, Gregor Mendel, Hernando de Soto, hive mind, independent contractor, information asymmetry, Internet of things, inventory management, iterative process, Jean Tirole, Jeff Bezos, Jim Simons, jimmy wales, John Markoff, joint-stock company, Joseph Schumpeter, Kickstarter, Kiva Systems, law of one price, longitudinal study, low interest rates, Lyft, Machine translation of "The spirit is willing, but the flesh is weak." to Russian and back, Marc Andreessen, Marc Benioff, Mark Zuckerberg, meta-analysis, Mitch Kapor, moral hazard, multi-sided market, Mustafa Suleyman, Myron Scholes, natural language processing, Network effects, new economy, Norbert Wiener, Oculus Rift, PageRank, pattern recognition, peer-to-peer lending, performance metric, plutocrats, precision agriculture, prediction markets, pre–internet, price stability, principal–agent problem, Project Xanadu, radical decentralization, Ray Kurzweil, Renaissance Technologies, Richard Stallman, ride hailing / ride sharing, risk tolerance, Robert Solow, Ronald Coase, Salesforce, 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, synthetic biology, tacit knowledge, TaskRabbit, Ted Nelson, TED Talk, the Cathedral and the Bazaar, The Market for Lemons, The Nature of the Firm, the strength of weak ties, Thomas Davenport, Thomas L Friedman, too big to fail, transaction costs, transportation-network company, traveling salesman, Travis Kalanick, Two Sigma, two-sided market, Tyler Cowen, Uber and Lyft, Uber for X, uber lyft, ubercab, Vitalik Buterin, warehouse robotics, Watson beat the top human players on Jeopardy!, winner-take-all economy, yield management, zero day

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.

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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.”

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., 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.

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

"Susan Fowler" uber, Abraham Maslow, Airbnb, airport security, algorithmic bias, AltaVista, big data - Walmart - Pop Tarts, Big Tech, Black Lives Matter, data science, deep learning, Donald Trump, fake news, false flag, Ferguson, Missouri, Firefox, Grace Hopper, Greyball, Hacker News, hockey-stick growth, independent contractor, job automation, Kickstarter, lifelogging, lolcat, Marc Benioff, Mark Zuckerberg, Max Levchin, Menlo Park, meritocracy, microaggression, move fast and break things, natural language processing, Northpointe / Correctional Offender Management Profiling for Alternative Sanctions, off-the-grid, pattern recognition, Peter Thiel, real-name policy, recommendation engine, ride hailing / ride sharing, Salesforce, self-driving car, Sheryl Sandberg, Silicon Valley, Silicon Valley startup, Snapchat, Steve Jobs, Tactical Technology Collective, TED Talk, Tim Cook: Apple, Travis Kalanick, upwardly mobile, Wayback Machine, women in the workforce, work culture , zero-sum game

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.

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“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.

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, behavioural economics, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, data science, diversification, diversified portfolio, en.wikipedia.org, financial engineering, fixed income, Flash crash, G4S, Higgs boson, 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 free rate, 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

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.

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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.

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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.

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, gamification, 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, Jeff Hawkins, jimmy wales, job automation, John Markoff, Kevin Kelly, Khan Academy, Kickstarter, Kiva Systems, 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, quantum entanglement, race to the bottom, Ray Kurzweil, recommendation engine, RFID, Rodney Brooks, selection bias, self-driving car, seminal paper, slashdot, smart cities, software as a service, software is eating the world, speech recognition, Steven Pinker, strong AI, synthetic biology, technological singularity, TED Talk, Turing test, Vernor Vinge, warehouse automation, warehouse robotics, women in the workforce

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

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* * * * * * 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.

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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.

Everyware: The Dawning Age of Ubiquitous Computing by Adam Greenfield

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

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.

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(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.

Designing the Mind: The Principles of Psychitecture by Designing the Mind, Ryan A Bush

Abraham Maslow, adjacent possible, Albert Einstein, algorithmic bias, augmented reality, butterfly effect, carbon footprint, cognitive bias, cognitive load, correlation does not imply causation, data science, delayed gratification, deliberate practice, drug harm reduction, effective altruism, Elon Musk, en.wikipedia.org, endowment effect, fundamental attribution error, hedonic treadmill, hindsight bias, impulse control, Kevin Kelly, Lao Tzu, lifelogging, longitudinal study, loss aversion, meta-analysis, Own Your Own Home, pattern recognition, price anchoring, randomized controlled trial, Silicon Valley, Stanford marshmallow experiment, Steven Pinker, systems thinking, Walter Mischel

Homo sapiens is a storytelling animal, that thinks in stories rather than in numbers or graphs, and believes that the universe itself works like a story, replete with heroes and villains, conflicts and resolutions, climaxes and happy endings. - Yuval Noah Harari, 21 Lessons for the 21st Century Our minds were built to find patterns. Pattern recognition is highly advantageous from a biological perspective, as we would not be able to identify predators, find food, or recognize our family members without it.8 But our brains have limited bandwidth, so they have to cut corners. We make hasty judgments, oversimplify the factors at play, and quickly construct narratives and explanations for the complex outcomes in our world.

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Of course every story, including the one this book is telling, is false - in the sense that it necessarily oversimplifies reality, but we have a tendency to cling to our stories as if they were perfect representations of reality. In pathological extremes, this delusional over-recognition of patterns manifests in disorders like schizophrenia.9 Some light examples of pattern recognition gone haywire are found in optical illusions and pareidolia, the tendency to see patterns like faces in abstract images or hear voices in noise. We hear our house creak and believe we’ve encountered a ghost, attributing agency where there is none. Some more concerning examples are some of the heuristics and generalizations we regularly employ.

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But the evidence suggests it is surprisingly easy to be delusional about our own self-interest. Though there is less research on them, ancient and modern thinkers have uncovered many errors we all share which limit our acquisition of wisdom. The algorithms which most often lead people astray in life are not simply distortions of memory, prediction, or pattern recognition in the external world, but distortions of introspection. And now that we have laid the groundwork for the mechanisms that bias our beliefs about the outer universe, we will build on it to reveal the biases of the inner universe. We saw in the previous chapter that the illusion of control causes us to believe we have more control over our circumstances than we actually do, and to attribute unrelated outcomes to our own actions.1 But we are also wired to falsely believe we know which outcomes would make us happy.

Money: Vintage Minis by Yuval Noah Harari

23andMe, agricultural Revolution, algorithmic trading, AlphaGo, Anne Wojcicki, autonomous vehicles, British Empire, call centre, credit crunch, DeepMind, European colonialism, Flash crash, Ford Model T, greed is good, job automation, joint-stock company, joint-stock limited liability company, lifelogging, low interest rates, Nick Bostrom, pattern recognition, peak-end rule, Ponzi scheme, self-driving car, Suez canal 1869, telemarketer, The future is already here, 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

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.

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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!).

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

Gödel, Escher, Bach, pattern recognition, Y Combinator

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.

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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.

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, Apollo 11, Apollo Guidance Computer, Apple II, artificial general intelligence, asset allocation, augmented reality, autonomous vehicles, barriers to entry, bitcoin, Bletchley Park, blockchain, Boston Dynamics, business intelligence, business process, call centre, chief data officer, Chris Urmson, Clayton Christensen, clean water, Computing Machinery and Intelligence, congestion charging, CRISPR, crowdsourcing, cryptocurrency, data science, deep learning, DeepMind, deskilling, different worldview, disruptive innovation, distributed generation, distributed ledger, double helix, drone strike, electricity market, Elon Musk, Erik Brynjolfsson, Fellow of the Royal Society, fiat currency, financial exclusion, Flash crash, Flynn Effect, Ford Model T, future of work, gamification, Geoffrey Hinton, gig economy, gigafactory, Google Glasses, Google X / Alphabet X, Hans Lippershey, high-speed rail, 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, Kim Stanley Robinson, Kiva Systems, 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, Neal Stephenson, Neil Armstrong, Network effects, new economy, Nick Bostrom, obamacare, Occupy movement, Oculus Rift, off grid, off-the-grid, packet switching, pattern recognition, peer-to-peer, Ray Kurzweil, retail therapy, RFID, ride hailing / ride sharing, Robert Metcalfe, Salesforce, 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, Snow Crash, social graph, software as a service, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, synthetic biology, systems thinking, TaskRabbit, technological singularity, TED Talk, telemarketer, telepresence, telepresence robot, Tesla Model S, The future is already here, The Future of Employment, Tim Cook: Apple, trade route, Travis Kalanick, TSMC, Turing complete, Turing test, Twitter Arab Spring, uber lyft, undersea cable, urban sprawl, V2 rocket, warehouse automation, warehouse robotics, Watson beat the top human players on Jeopardy!, white picket fence, WikiLeaks, yottabyte

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.

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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?

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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.

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

Alan Greenspan, algorithmic trading, automated trading system, banking crisis, bash_history, Bear Stearns, Bernie Madoff, Black Monday: stock market crash in 1987, butterfly effect, buttonwood tree, buy and hold, Chuck Templeton: OpenTable:, cloud computing, collapse of Lehman Brothers, computerized trading, creative destruction, Donald Trump, financial engineering, fixed income, Flash crash, Ford Model T, Francisco Pizarro, Gordon Gekko, Hibernia Atlantic: Project Express, High speed trading, information security, Jim Simons, Joseph Schumpeter, junk bonds, latency arbitrage, Long Term Capital Management, machine readable, Mark Zuckerberg, market design, market microstructure, Michael Milken, military-industrial complex, pattern recognition, payment for order flow, pets.com, Ponzi scheme, popular electronics, prediction markets, quantitative hedge fund, Ray Kurzweil, Renaissance Technologies, seminal paper, 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, three-martini lunch, Tragedy of the Commons, transaction costs, uptick rule, Watson beat the top human players on Jeopardy!, zero-sum game

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.

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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.

…

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.

Cities Under Siege: The New Military Urbanism by Stephen Graham

"hyperreality Baudrillard"~20 OR "Baudrillard hyperreality", addicted to oil, airport security, Alan Greenspan, Anthropocene, anti-communist, autonomous vehicles, Berlin Wall, call centre, carbon footprint, clean tech, clean water, congestion charging, creative destruction, credit crunch, DARPA: Urban Challenge, defense in depth, deindustrialization, digital map, disinformation, Dr. Strangelove, driverless car, edge city, energy security, European colonialism, export processing zone, failed state, Food sovereignty, gentrification, Gini coefficient, global supply chain, Global Witness, Google Earth, illegal immigration, income inequality, knowledge economy, late capitalism, Lewis Mumford, loose coupling, machine readable, market fundamentalism, mass incarceration, McMansion, megacity, military-industrial complex, moral panic, mutually assured destruction, Naomi Klein, New Urbanism, offshore financial centre, one-state solution, pattern recognition, peak oil, planetary scale, post-Fordism, private military company, Project for a New American Century, RAND corporation, RFID, Richard Florida, Scramble for Africa, Seymour Hersh, Silicon Valley, SimCity, 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.

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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’. 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.

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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’.

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

"World Economic Forum" Davos, Ada Lovelace, agricultural Revolution, AI winter, Albert Einstein, Alexey Pajitnov wrote Tetris, algorithmic management, algorithmic trading, AlphaGo, Amazon Mechanical Turk, Apple II, artificial general intelligence, Automated Insights, autonomous vehicles, backpropagation, Bletchley Park, book scanning, borderless world, call centre, cellular automata, Charles Babbage, Claude Shannon: information theory, cloud computing, computer vision, Computing Machinery and Intelligence, correlation does not imply causation, crowdsourcing, deep learning, DeepMind, driverless car, drone strike, Elon Musk, Flash crash, Ford Model T, friendly AI, game design, Geoffrey Hinton, global village, Google X / Alphabet X, Hans Moravec, 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, machine translation, Marc Andreessen, Mark Zuckerberg, Menlo Park, Mustafa Suleyman, natural language processing, Nick Bostrom, Norbert Wiener, out of africa, PageRank, paperclip maximiser, pattern recognition, radical life extension, Ray Kurzweil, recommendation engine, remote working, RFID, scientific management, 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, tech billionaire, technological singularity, The Coming Technological Singularity, The Future of Employment, Tim Cook: Apple, Tony Fadell, too big to fail, traumatic brain injury, Turing machine, Turing test, Vernor Vinge, warehouse robotics, Watson beat the top human players on Jeopardy!

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.

…

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’.

…

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.

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

Alan Greenspan, algorithmic trading, automated trading system, Bear Stearns, Bernie Madoff, Bernie Sanders, Black Monday: stock market crash in 1987, 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, junk bonds, locking in a profit, Long Term Capital Management, machine readable, margin call, market bubble, market fragmentation, market fundamentalism, Myron Scholes, naked short selling, Nixon triggered the end of the Bretton Woods system, pattern recognition, Ponzi scheme, proprietary trading, quantitative trading / quantitative finance, Renaissance Technologies, Ronald Reagan, Sergey Aleynikov, short selling, Small Order Execution System, statistical arbitrage, technology bubble, transaction costs, uptick rule, 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.

…

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.

Fully Automated Luxury Communism by Aaron Bastani

"Peter Beck" AND "Rocket Lab", Alan Greenspan, Anthropocene, autonomous vehicles, banking crisis, basic income, Berlin Wall, Bernie Sanders, Boston Dynamics, Bretton Woods, Brexit referendum, capital controls, capitalist realism, cashless society, central bank independence, collapse of Lehman Brothers, computer age, computer vision, CRISPR, David Ricardo: comparative advantage, decarbonisation, deep learning, dematerialisation, DIY culture, Donald Trump, double helix, driverless car, electricity market, Elon Musk, energy transition, Erik Brynjolfsson, fake news, financial independence, Francis Fukuyama: the end of history, future of work, Future Shock, G4S, general purpose technology, Geoffrey Hinton, Gregor Mendel, housing crisis, income inequality, industrial robot, Intergovernmental Panel on Climate Change (IPCC), Internet of things, Isaac Newton, James Watt: steam engine, Jeff Bezos, Jeremy Corbyn, Jevons paradox, job automation, John Markoff, John Maynard Keynes: technological unemployment, Joseph Schumpeter, Kevin Kelly, Kuiper Belt, land reform, Leo Hollis, liberal capitalism, low earth orbit, low interest rates, 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 spaceflight, Productivity paradox, profit motive, race to the bottom, rewilding, RFID, rising living standards, Robert Solow, scientific management, Second Machine Age, self-driving car, sensor fusion, shareholder value, Silicon Valley, Simon Kuznets, Slavoj Žižek, SoftBank, stem cell, Stewart Brand, synthetic biology, technological determinism, 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!, We are as Gods, 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.

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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.

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

23andMe, AI winter, Albert Einstein, artificial general intelligence, behavioural economics, business process, call centre, clean water, commoditize, computer age, Demis Hassabis, Frank Gehry, information retrieval, Iridium satellite, Isaac Newton, job automation, machine translation, pattern recognition, Ray Kurzweil, Silicon Valley, Silicon Valley startup, statistical model, The Soul of a New Machine, theory of mind, thinkpad, Turing test, Vernor Vinge, vertical integration, 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.

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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.

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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.

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

Apollo 11, Asperger Syndrome, autism spectrum disorder, correlation does not imply causation, dark matter, David Brooks, deliberate practice, double helix, ghettoisation, Gregor Mendel, if you see hoof prints, think horses—not zebras, impulse control, Khan Academy, Mark Zuckerberg, meta-analysis, mouse model, neurotypical, pattern recognition, phenotype, Richard Feynman, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Steve Jobs, The future is already here, theory of mind, traumatic brain injury, twin studies

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.

Personal Kanban: Mapping Work, Navigating Life by Jim Benson, Tonianne Demaria Barry

Abraham Maslow, Albert Einstein, Asperger Syndrome, Bluma Zeigarnik, corporate governance, Howard Rheingold, intangible asset, job satisfaction, Kaizen: continuous improvement, Kanban, Ken Thompson, pattern recognition, performance metric, The Wisdom of Crowds, urban planning, Yogi Berra

Touching, feeling, physically interacting with our tasks transforms work-as-a-concept into work-as-a-concrete-experience. The physical experience of taking abstract ideas and turning them into tangible objects (sticky notes) impacts our learning by improving our understanding while promoting memory retention and skill synthesis. Kinesthetic Feedback 2: Pattern Recognition Each time we move a sticky note, we receive kinesthetic feedback: the tactile action is both a data point and a reward. A regular succession of these movements creates a cadence, a rhythm of work. Cadence creates an expectation. We begin to notice patterns (types of tasks most often delayed, tasks that require additional help), make distinctions (what work we enjoy, who we enjoy working with), and can adjust our prioritizations to suit.