pattern recognition

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

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

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

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

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


pages: 187 words: 55,801

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

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

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

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

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


pages: 250 words: 73,574

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

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

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

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

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


pages: 330 words: 88,445

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

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Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Clayton Christensen, data acquisition, delayed gratification, deliberate practice, fear of failure, Google Earth, haute couture, impulse control, Isaac Newton, Jeff Bezos, jimmy wales, Kevin Kelly, Lao Tzu, life extension, Maui Hawaii, pattern recognition, Ray Kurzweil, risk tolerance, rolodex, Silicon Valley, Steve Jobs, Walter Mischel, X Prize

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

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

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


pages: 696 words: 143,736

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

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Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Buckminster Fuller, call centre, cellular automata, combinatorial explosion, complexity theory, computer age, computer vision, cosmological constant, cosmological principle, Danny Hillis, double helix, Douglas Hofstadter, first square of the chessboard / second half of the chessboard, fudge factor, George Gilder, Gödel, Escher, Bach, I think there is a world market for maybe five computers, information retrieval, invention of movable type, Isaac Newton, iterative process, Jacquard loom, Jacquard loom, John von Neumann, Lao Tzu, Law of Accelerating Returns, mandelbrot fractal, Marshall McLuhan, Menlo Park, natural language processing, Norbert Wiener, optical character recognition, pattern recognition, phenotype, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, Richard Feynman, Schrödinger's Cat, Search for Extraterrestrial Intelligence, self-driving car, Silicon Valley, speech recognition, Steven Pinker, Stewart Brand, stochastic process, technological singularity, Ted Kaczynski, telepresence, the medium is the message, traveling salesman, Turing machine, Turing test, Whole Earth Review, Y2K

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

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

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


pages: 205 words: 20,452

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

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4chan, call centre, computer vision, discrete time, information retrieval, iterative process, NP-complete, p-value, pattern recognition, random walk, sensor fusion, speech recognition, web application

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

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

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

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

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

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

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

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


pages: 72 words: 21,361

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

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

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

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

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

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

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

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

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

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


pages: 339 words: 88,732

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

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

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

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

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


pages: 278 words: 70,416

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

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

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

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

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


pages: 323 words: 95,939

Present Shock: When Everything Happens Now by Douglas Rushkoff

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

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

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

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


pages: 153 words: 45,871

Distrust That Particular Flavor by William Gibson

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

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

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

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


pages: 523 words: 148,929

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

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

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

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

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


pages: 378 words: 110,408

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

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Albert Einstein, deliberate practice, iterative process, meta analysis, meta-analysis, pattern recognition, randomized controlled trial, Richard Feynman, Richard Feynman, sensible shoes

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

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

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


pages: 250 words: 9,029

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

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

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

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

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


pages: 292 words: 94,324

How Doctors Think by Jerome Groopman

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

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

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

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

The Economic Singularity: Artificial intelligence and the death of capitalism by Calum Chace

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

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

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

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


pages: 23 words: 5,264

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

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

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

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


pages: 294 words: 81,292

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

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3D printing, AI winter, Amazon Web Services, artificial general intelligence, Automated Insights, Bernie Madoff, Bill Joy: nanobots, brain emulation, cellular automata, cloud computing, cognitive bias, computer vision, cuban missile crisis, Daniel Kahneman / Amos Tversky, Danny Hillis, data acquisition, don't be evil, Extropian, finite state, Flash crash, friendly AI, friendly fire, Google Glasses, Google X / Alphabet X, Isaac Newton, Jaron Lanier, John von Neumann, Kevin Kelly, Law of Accelerating Returns, life extension, Loebner Prize, lone genius, mutually assured destruction, natural language processing, Nicholas Carr, optical character recognition, PageRank, pattern recognition, Peter Thiel, prisoner's dilemma, Ray Kurzweil, Rodney Brooks, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Silicon Valley, Singularitarianism, Skype, smart grid, speech recognition, statistical model, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Steve Wozniak, strong AI, Stuxnet, superintelligent machines, technological singularity, The Coming Technological Singularity, traveling salesman, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, zero day

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

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

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

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

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

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

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

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


pages: 118 words: 35,663

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

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

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

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

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


pages: 303 words: 67,891

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

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

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

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

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


pages: 385 words: 99,985

Pattern Recognition by William Gibson

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carbon-based life, Frank Gehry, Mars Rover, Maui Hawaii, offshore financial centre, pattern recognition

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

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

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


pages: 291 words: 77,596

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

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

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

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

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


pages: 224 words: 64,156

You Are Not a Gadget by Jaron Lanier

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

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

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

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


pages: 254 words: 72,929

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

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

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

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

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


pages: 320 words: 87,853

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

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

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

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

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


pages: 413 words: 119,587

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

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

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

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

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


pages: 202 words: 59,883

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

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Albert Einstein, Apple II, augmented reality, call centre, Chelsea Manning, cloud computing, connected car, Edward Snowden, Elon Musk, factory automation, Filter Bubble, Google Earth, Google Glasses, Internet of things, job automation, Kickstarter, Mars Rover, Menlo Park, New Urbanism, PageRank, pattern recognition, RFID, ride hailing / ride sharing, 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, urban planning, Zipcar

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

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


pages: 144 words: 43,356

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

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

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

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

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


pages: 308 words: 84,713

The Glass Cage: Automation and Us by Nicholas Carr

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

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

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

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


pages: 356 words: 105,533

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

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

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

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

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


pages: 211 words: 77

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

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Gödel, Escher, Bach, pattern recognition, Y Combinator

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

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


pages: 717 words: 150,288

Cities Under Siege: The New Military Urbanism by Stephen Graham

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

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

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

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


pages: 396 words: 117,149

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

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3D printing, Albert Einstein, Amazon Mechanical Turk, Arthur Eddington, Benoit Mandelbrot, bioinformatics, Black Swan, Brownian motion, cellular automata, Claude Shannon: information theory, combinatorial explosion, computer vision, constrained optimization, correlation does not imply causation, crowdsourcing, Danny Hillis, data is the new oil, double helix, Douglas Hofstadter, Erik Brynjolfsson, experimental subject, Filter Bubble, future of work, global village, Google Glasses, Gödel, Escher, Bach, information retrieval, job automation, John Snow's cholera map, John von Neumann, Joseph Schumpeter, Kevin Kelly, lone genius, mandelbrot fractal, Mark Zuckerberg, Moneyball by Michael Lewis explains big data, Narrative Science, Nate Silver, natural language processing, Netflix Prize, Network effects, NP-complete, P = NP, PageRank, pattern recognition, phenotype, planetary scale, pre–internet, random walk, Ray Kurzweil, recommendation engine, Richard Feynman, Richard Feynman, Second Machine Age, self-driving car, Silicon Valley, speech recognition, statistical model, Stephen Hawking, Steven Levy, Steven Pinker, superintelligent machines, the scientific method, The Signal and the Noise by Nate Silver, theory of mind, transaction costs, Turing machine, Turing test, Vernor Vinge, Watson beat the top human players on Jeopardy!, white flight

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

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

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


pages: 229 words: 68,426

Everyware: The Dawning Age of Ubiquitous Computing by Adam Greenfield

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

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

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


pages: 280 words: 73,420

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

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

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

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

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


pages: 238 words: 77,730

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

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

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

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

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

ucd-csi-2011-02 by Unknown

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

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


pages: 512 words: 162,977

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

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

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

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

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

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

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

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

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

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


pages: 327 words: 91,351

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

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

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

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

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


pages: 377 words: 97,144

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

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

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

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

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


pages: 309 words: 91,581

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

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

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

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


pages: 294 words: 86,601

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

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Columbine, double helix, epigenetics, experimental subject, Gödel, Escher, Bach, James Watt: steam engine, l'esprit de l'escalier, pattern recognition, phenotype, Steven Pinker, theory of mind

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

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


pages: 317 words: 100,414

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

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

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

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


pages: 314 words: 101,034

Every Patient Tells a Story by Lisa Sanders

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

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

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

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


pages: 347 words: 97,721

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

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

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

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

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


pages: 297 words: 91,141

Market Sense and Nonsense by Jack D. Schwager

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

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

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


pages: 330 words: 91,805

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

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3D printing, Airbnb, Amazon Web Services, Andy Kessler, banking crisis, barriers to entry, bitcoin, blockchain, Burning Man, business climate, call centre, car-free, cloud computing, collaborative consumption, collaborative economy, collective bargaining, congestion charging, crowdsourcing, cryptocurrency, decarbonisation, don't be evil, Elon Musk, en.wikipedia.org, ethereum blockchain, Ferguson, Missouri, Firefox, frictionless, Gini coefficient, hive mind, income inequality, index fund, informal economy, Internet of things, Jane Jacobs, Jeff Bezos, jimmy wales, job satisfaction, Kickstarter, Lean Startup, Lyft, means of production, megacity, Minecraft, minimum viable product, Network effects, new economy, Oculus Rift, openstreetmap, optical character recognition, pattern recognition, peer-to-peer lending, Richard Stallman, ride hailing / ride sharing, Ronald Coase, Ronald Reagan, Satoshi Nakamoto, Search for Extraterrestrial Intelligence, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, smart cities, smart grid, Snapchat, sovereign wealth fund, Steve Crocker, Steve Jobs, Steven Levy, TaskRabbit, The Death and Life of Great American Cities, The Nature of the Firm, transaction costs, Turing test, Uber and Lyft, Zipcar

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

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


pages: 261 words: 10,785

The Lights in the Tunnel by Martin Ford

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

Radiology is one the most popular specialties for newly minted doctors because it offers relatively high pay and regular work hours; radiologists generally don’t need to work weekends or handle emergencies. In spite of the radiologist’s training requirement of at least thirteen additional years beyond high school, it is conceptually quite easy to envision this job being automated. The primary focus of the job is to analyze and evaluate visual images. Furthermore, the parameters of each image are highly defined since they are often coming directly from a computerized scanning device. Visual pattern recognition software is a rapidly developing field that has already produced significant results. The government currently has access to software that can Copyrighted Material – Paperback/Kindle available @ Amazon THE LIGHTS IN THE TUNNEL / 64 help identify terrorists in airports based on visual analysis of security photographs.22 Real world tasks such as this are probably technically more difficult than analyzing a medical scan because the environment and objects in the image are far more varied.

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


pages: 237 words: 50,758

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

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

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

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


pages: 209 words: 63,649

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

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3D printing, Airbnb, Atul Gawande, barriers to entry, big-box store, business process, call centre, carbon footprint, citizen journalism, corporate social responsibility, crowdsourcing, disintermediation, Elon Musk, Firefox, glass ceiling, greed is good, housing crisis, informal economy, Jane Jacobs, jimmy wales, Khan Academy, Kickstarter, Lean Startup, means of production, new economy, pattern recognition, Peter Singer: altruism, Peter Thiel, Ray Oldenburg, remote working, Richard Feynman, Ronald Reagan, sharing economy, Silicon Valley, Silicon Valley startup, Steve Jobs, TaskRabbit, Tony Hsieh, too big to fail, underbanked, women in the workforce, young professional, Zipcar

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

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


pages: 178 words: 43,631

Spoiled Brats: Short Stories by Simon Rich

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

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

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


pages: 239 words: 70,206

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

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

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

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


pages: 268 words: 75,850

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

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

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

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

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

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

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

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


pages: 257 words: 80,100

Time Travel: A History by James Gleick

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

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

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


pages: 103 words: 24,033

The Immigrant Exodus: Why America Is Losing the Global Race to Capture Entrepreneurial Talent by Vivek Wadhwa

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3D printing, card file, corporate governance, crowdsourcing, Elon Musk, immigration reform, labour mobility, open economy, pattern recognition, Ray Kurzweil, Sand Hill Road, Silicon Valley, Silicon Valley startup, software as a service, Y2K

Maptia, built by a team of geographers, economists, and coders who are British, Chinese, and Swiss seeks to revolutionize online mapping technology to allow brands and individuals to capture untapped value inherent in geotagged visual content. Andean Designs, with the team hailing from India, seeks to create designer ceramics based on Andean culture. Biometry Cloud, with a team from Chile, is selling cloud-based pattern-recognition web services, APIs, and libraries for mobile devices. The company Dr. Busca, with a team from Brazil, allows patients to book doctor appointments online from mobile devices. Start-Up Chile has attracted a number of entrepreneurs from the United States, as well. H2020 uses mobile phones to collect and map water data to help communities and industries understand the dynamics of their water resources.


pages: 574 words: 164,509

Superintelligence: Paths, Dangers, Strategies by Nick Bostrom

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agricultural Revolution, AI winter, Albert Einstein, algorithmic trading, anthropic principle, anti-communist, artificial general intelligence, autonomous vehicles, barriers to entry, bioinformatics, brain emulation, cloud computing, combinatorial explosion, computer vision, cosmological constant, dark matter, DARPA: Urban Challenge, data acquisition, delayed gratification, demographic transition, Douglas Hofstadter, Drosophila, Elon Musk, en.wikipedia.org, epigenetics, fear of failure, Flash crash, Flynn Effect, friendly AI, Gödel, Escher, Bach, income inequality, industrial robot, informal economy, information retrieval, interchangeable parts, iterative process, job automation, John von Neumann, knowledge worker, Menlo Park, meta analysis, meta-analysis, mutually assured destruction, Nash equilibrium, Netflix Prize, new economy, Norbert Wiener, NP-complete, nuclear winter, optical character recognition, pattern recognition, performance metric, phenotype, prediction markets, price stability, principal–agent problem, race to the bottom, random walk, Ray Kurzweil, recommendation engine, reversible computing, social graph, speech recognition, Stanislav Petrov, statistical model, stem cell, Stephen Hawking, strong AI, superintelligent machines, supervolcano, technological singularity, technoutopianism, The Coming Technological Singularity, The Nature of the Firm, Thomas Kuhn: the structure of scientific revolutions, transaction costs, Turing machine, Vernor Vinge, Watson beat the top human players on Jeopardy!, World Values Survey

For example, neural networks exhibited the property of “graceful degradation”: a small amount of damage to a neural network typically resulted in a small degradation of its performance, rather than a total crash. Even more importantly, neural networks could learn from experience, finding natural ways of generalizing from examples and finding hidden statistical patterns in their input.23 This made the nets good at pattern recognition and classification problems. For example, by training a neural network on a data set of sonar signals, it could be taught to distinguish the acoustic profiles of submarines, mines, and sea life with better accuracy than human experts—and this could be done without anybody first having to figure out in advance exactly how the categories were to be defined or how different features were to be weighted.

Suppose that the brain’s plasticity were such that it could learn to detect patterns in some new input stream arbitrary projected onto some part of the cortex by means of a brain–computer interface: why not project the same information onto the retina instead, as a visual pattern, or onto the cochlea as sounds? The low-tech alternative avoids a thousand complications, and in either case the brain could deploy its pattern-recognition mechanisms and plasticity to learn to make sense of the information. Networks and organizations Another conceivable path to superintelligence is through the gradual enhancement of networks and organizations that link individual human minds with one another and with various artifacts and bots. The idea here is not that this would enhance the intellectual capacity of individuals enough to make them superintelligent, but rather that some system composed of individuals thus networked and organized might attain a form of superintelligence—what in the next chapter we will elaborate as “collective superintelligence.”77 Humanity has gained enormously in collective intelligence over the course of history and prehistory.

Machine minds designed ab initio could do away with cumbersome legacy systems that helped our ancestors deal with aspects of the natural environment that are unimportant in cyberspace. Digital minds might also be designed to take advantage of fast serial processing unavailable to biological brains, and to make it easy to install new modules with highly optimized functionality (e.g. symbolic processing, pattern recognition, simulators, data mining, and planning). Artificial intelligence might also have significant non-technical advantages, such as being more easily patentable or less entangled in the moral complexities of using human uploads. 24. If p1 and p2 are the probabilities of failure at each step, the total probability of failure is p1 + (1 – p1)p2 since one can fail terminally only once. 25. It is possible, of course, that the frontrunner will not have such a large lead and will not be able to form a singleton.


pages: 548 words: 147,919

How Everything Became War and the Military Became Everything: Tales From the Pentagon by Rosa Brooks

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airport security, Albert Einstein, Berlin Wall, big-box store, clean water, cognitive dissonance, Edward Snowden, facts on the ground, failed state, illegal immigration, Internet Archive, Mark Zuckerberg, pattern recognition, Peace of Westphalia, personalized medicine, RAND corporation, Silicon Valley, South China Sea, Turing test, unemployed young men, Wall-E, War on Poverty, WikiLeaks

This has led to an increase in so-called signature strikes: drone strikes against unidentified people presumed to be targetable enemies because of their communications patterns and travel patterns.69 The post-9/11 USA PATRIOT Act effectively eliminated the pre-9/11 firewall between foreign intelligence gathering and domestic law enforcement.70 Today, law enforcement officials can access a wide range of sensitive information (including Internet records, telephone metadata, library records, and credit and banking information of U.S. citizens) as long as they can show “reasonable grounds to believe that the tangible things sought are relevant to an authorized investigation . . . to obtain foreign intelligence information not concerning a United States person or to protect against international terrorism or clandestine intelligence activities.”71 This has benefits, but it’s also easy to imagine information gained in this manner—say, evidence of extramarital affairs or psychiatric treatment—being repurposed by law enforcement officials to put pressure on potential witnesses or informants in nonterrorism-related cases.72 Similarly, the sophisticated pattern recognition technologies originally developed for military and intelligence purposes can also easily be used by domestic law enforcement officials. Imagine, for instance, the police use of drone-based imaging technologies such as the military’s Gorgon Stare, a platform that permits the visualization and videotaping of whole neighborhoods. Police will soon have the ability to use such technologies to track the movements and communications of thousands of people, searching for those whose travel patterns suggest links to gang activity.73 The trouble is, these powerful new technologies can also introduce powerful new errors, leading to faulty guilt-by-association assumptions. Say pattern recognition technologies determine that the same unidentified man is repeatedly driving to, from, and between the houses of known gang members, or telephone metadata indicate frequent calls between the man and the known gang members.

See Privacy and Civil Liberties Oversight Board, “Report on the Telephone Records Program Conducted Under Section 215 of the USA PATRIOT Act and on the Operations of the Foreign Intelligence Surveillance Court,” January 23, 2014, www.pclob.gov/SiteAssets/Pages/default/PCLOB-Report-on-the-Telephone-Records-Program.pdf. 72. Timothy B. Lee, “Here’s How Phone Metadata Can Reveal Your Affairs, Abortions, and Other Secrets,” Washington Post, August 27, 2013, www.washingtonpost.com/blogs/the-switch/wp/2013/08/27/heres-how-phone-metadata-can-reveal-your-affairs-abortions-and-other-secrets. 73. Imagine, for instance, the use of pattern recognition technologies to identify, investigate, and potentially entrap users of prohibited drugs. See generally Marc Jonathan Blitz, “Video Surveillance and the Constitution of Public Space: Fitting the Fourth Amendment to a World That Tracks Image and Identity,” 82 Texas Law Review 1349, 1351–52 (2004). 74. See Emily Bell et al., “Comment to Review Group on Intelligence and Communications Technologies Regarding the Effects of Mass Surveillance on the Practice of Journalism,” October 4, 2013, http://towcenter.org/wp-content/uploads/2013/10/Letter-Effect-of-mass-surveillance-on-journalism.pdf. 75.


pages: 463 words: 118,936

Darwin Among the Machines by George Dyson

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

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

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

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


pages: 584 words: 187,436

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

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

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

But once the firm realized that the correlations made intuitive sense—they reflected the technology euphoria that had pushed into all these industries—they seemed more likely to be tradable.27 Moreover, signals based on intuition have a further advantage: If you understand why they work, you probably understand why they might cease to work, so you are less likely to keep trading them beyond their point of usefulness. In short, Wepsic is saying that pure pattern recognition is a small part of what Shaw does, even if the firm does some of it. Again, this presents a contrast with Renaissance. Whereas D. E. Shaw grew out of statistical arbitrage in equities, with strong roots in fundamental intuitions about stocks, Renaissance grew out of technical trading in commodities, a tradition that treats price data as paramount.28 Whereas D. E. Shaw hired quants of all varieties, usually recruiting them in their twenties, the crucial early years at Renaissance were largely shaped by established cryptographers and translation programmers—experts who specialized in distinguishing fake ghosts from real ones.

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


pages: 685 words: 203,949

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

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

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

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

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


pages: 903 words: 235,753

The Stack: On Software and Sovereignty by Benjamin H. Bratton

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1960s counterculture, 3D printing, 4chan, Ada Lovelace, additive manufacturing, airport security, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, Amazon Mechanical Turk, Amazon Web Services, augmented reality, autonomous vehicles, Berlin Wall, bioinformatics, bitcoin, blockchain, Buckminster Fuller, Burning Man, call centre, carbon footprint, carbon-based life, Cass Sunstein, Celebration, Florida, charter city, clean water, cloud computing, connected car, corporate governance, crowdsourcing, cryptocurrency, dark matter, David Graeber, deglobalization, dematerialisation, disintermediation, distributed generation, don't be evil, Douglas Engelbart, Edward Snowden, Elon Musk, en.wikipedia.org, Eratosthenes, ethereum blockchain, facts on the ground, Flash crash, Frank Gehry, Frederick Winslow Taylor, future of work, Georg Cantor, gig economy, global supply chain, Google Earth, Google Glasses, Guggenheim Bilbao, High speed trading, Hyperloop, illegal immigration, industrial robot, information retrieval, intermodal, Internet of things, invisible hand, Jacob Appelbaum, Jaron Lanier, Jony Ive, Julian Assange, Khan Academy, linked data, Mark Zuckerberg, market fundamentalism, Marshall McLuhan, Masdar, McMansion, means of production, megacity, megastructure, Menlo Park, Minecraft, Monroe Doctrine, Network effects, new economy, offshore financial centre, oil shale / tar sands, packet switching, PageRank, pattern recognition, peak oil, performance metric, personalized medicine, Peter Thiel, phenotype, place-making, planetary scale, RAND corporation, recommendation engine, reserve currency, RFID, Sand Hill Road, self-driving car, semantic web, sharing economy, Silicon Valley, Silicon Valley ideology, Slavoj Žižek, smart cities, smart grid, smart meter, social graph, software studies, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Startup school, statistical arbitrage, Steve Jobs, Steven Levy, Stewart Brand, Stuxnet, Superbowl ad, supply-chain management, supply-chain management software, TaskRabbit, the built environment, The Chicago School, the scientific method, Torches of Freedom, transaction costs, Turing complete, Turing machine, Turing test, universal basic income, urban planning, Vernor Vinge, Washington Consensus, web application, WikiLeaks, working poor, Y Combinator

This is the essential comfort of information visualization, especially dashboards, because instead of being elusive and rare, data arrive in bewildering excess. This complicates its assignment to clarify things for us, as any query of the world results in so much raw information coming back to the User in response that another question must be asked of the first answer in the form of a reductive visualization, and inside the bounding frame of that diagram, pattern recognition begins to take over for interpretation. This drawing of coherent wholes gathers multiple events and effects into a conceptual whole as if they were a single thing, and in the interfacial landscape, they in fact are one and many at the same time (just as an automobile comprises hundreds of smaller machines, not to mention legal, political, and cultural attributes and determinants, but these are understood in the singular, a car, and the plural at the same time).

Ultimately the provision of an affectively compelling and instrumentally effective image of a composite interface chain becomes a strategic expertise, as information designers carve out a niche to provide convincing images of organization, tempo, and narrative.38 For Users, they compose cognitive maps of multiple layers of exchange drawn at once, gathered into provisional total images (for which pattern recognition and intentional, motivated interpretation can start to merge.)39 Moreover, such image interfaces are not only maps of flows as they exist according to whatever logic of reduction they invoke; they are also tools that reproject and extend their conceptual gathering of relations back out onto the world. Once more, unlike static diagrams, such interfaces can directly affect what they represent; as the chain of signification runs both from the event up through a chain of representation to the image represents it, it also runs back down to the event, and so the User-manipulated image of the thing becomes the medium through which the thing can also be manipulated.

Smarr's broader intellectual project for the systemic establishment of digital medicine envisions the coembodiment of information at the scale of 7 billion humans and zillions of genes, environmentally bound molecules, proteins, and microbes, all contributing to a comprehensive diagnostic simulation and treatment metabiopolitics, a universal biocomputation intersecting with the universal ecocomputation that Griffith's demonstration popularizes. As a research model, it draws an explosion of traditional, individuated patient models into pluralized platforms in which every User's genomic, nutritional, neuronal, microbial, and environmental data would be systematically aggregated into an information commons where new kinds of analysis and pattern recognition could mature. The thickened interrelations of intrinsic and extrinsic force further dissolve the individuation of the singular patient toward alternative, as yet unnamed patterns of biological plasticity. The tracing of pathologies across multiple biological scales, over time and over multiple populations at such comprehensive scope and granular detail, would surely also reform basic concepts of “disease,” from one recognizing swatches of individuated symptoms toward one governing nuanced economies of symbiotic infection, transfer, and immunization across multiple host sites, and smart enough to see some contamination as enabling health, not preventing it.27 In time we see projects designing artificial intelligence systems that could interpret the exabytes of real time and archived data and produce interpretive causal models, images of emergent patterns, that startle diagnosticians and patients alike.


pages: 396 words: 112,748

Chaos by James Gleick

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Benoit Mandelbrot, butterfly effect, cellular automata, Claude Shannon: information theory, discrete time, Edward Lorenz: Chaos theory, experimental subject, Georg Cantor, Henri Poincaré, Isaac Newton, iterative process, John von Neumann, Louis Pasteur, mandelbrot fractal, Murray Gell-Mann, Norbert Wiener, pattern recognition, Richard Feynman, Richard Feynman, Stephen Hawking, stochastic process, trade route

Leon Glass of McGill University in Montreal was trained in physics and chemistry, where he indulged an interest in numbers and in irregularity, too, completing his doctoral thesis on atomic motion in liquids before turning to the problem of irregular heartbeats. Typically, he said, specialists diagnose many different arrhythmias by looking at short strips of electrocardiograms. “It’s treated by physicians as a pattern recognition problem, a matter of identifying patterns they have seen before in practice and in textbooks. They really don’t analyze in detail the dynamics of these rhythms. The dynamics are much richer than anybody would guess from reading the textbooks.” At Harvard Medical School, Ary L. Goldberger, co-director of the arrhythmia laboratory of Beth Israel Hospital in Boston, believed that the heart research represented a threshold for collaboration between physiologists and mathematicians and physicists.

In the study of moving fluids Libchaber builds his giant liquid-helium box, while Pierre Hohenberg and Günter Ahlers study the odd-shaped traveling waves of convection. In astronomy chaos experts use unexpected gravitational instabilities to explain the origin of meteorites—the seemingly inexplicable catapulting of asteroids from far beyond Mars. Scientists use the physics of dynamical systems to study the human immune system, with its billions of components and its capacity for learning, memory, and pattern recognition, and they simultaneously study evolution, hoping to find universal mechanisms of adaptation. Those who make such models quickly see structures that replicate themselves, compete, and evolve by natural selection. “Evolution is chaos with feedback,” Joseph Ford said. The universe is randomness and dissipation, yes. But randomness with direction can produce surprising complexity. And as Lorenz discovered so long ago, dissipation is an agent of order.


pages: 224 words: 12,941

From Gutenberg to Google: electronic representations of literary texts by Peter L. Shillingsburg

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British Empire, computer age, double helix, HyperCard, hypertext link, interchangeable parts, invention of the telephone, means of production, optical character recognition, pattern recognition, Saturday Night Live, Socratic dialogue

Publishers and their assistants – editors, compositors, printers, and so on – copy, regularize, beautify, and multiply what the author has done, creating many new objects consisting of molecules of paper and ink. They can do so because they are familiar with the rules and conventions of composition and transmission of texts.12 Readers then receive these material objects and, seeing recognizable 11 12 Web-crawlers and pattern recognition machines that seem to ‘‘read’’ texts and categorize content according to the ‘‘meaning of the text’’ or to index or otherwise parse texts on behalf of readers might be thought of as an exception – reading for meaning without human agency. But the ‘‘machine’’ has to be taught by a human agent how to react to the text and can only react in programmed ways to patterns already foreseen. Ambiguous, ironic, indirect or mendacious or facetious texts would very likely be misread in ways humans might be expected to avoid.

Jr. 61–2 history 135 accounts 135–6 evidence 135–6; limitations of 136 generative 54 selection principle 136 textual 84 Hockey, Susan 10, 142 Holdeman, David 22 Housman, A.E. 153, 162, 179 Howells, William Dean 179 HyperNietzsche project 91, 108, 142 ignorance 127, 136, 192 information 2 bogus 194 overload 165 quality of 2 infrastructure 112 inspiration 58 Institute for Advanced Technology in the Humanities (IATH) 90–1 intention 53, 55, 65 agent of 55 authorial 13, 18, 25, 45, 53, 55–6, 80, 175–6, 181–2, 187 editorial 17 publisher’s 17 of text 57 intentional fallacy 56, 60 (see also unintentional fallacy) intertextuality 55 intervention, editorial 19 Jackendoff, Ray 43, 46 Jansohn, Christa 151 Johnson, Samuel 189, 192 Jonson, Ben Cambridge Ben Jonson Works 142 Joyce, James 89 The Joyce Estate 89 Kamuf, Peggy 51 Karlsson, Lina 4 Katz, Joseph 84 Keane, Robert 131 Keats, John 68, 74, 126–7 Kegley, Russell 124 Kermode, Frank 42 Kiernan, Kevin 10, 108 knowledge 104–6, 136 historical 130 knowledge sites 2, 5, 69, 88, 92, 95, 97, 100, 137, 148 permission fees 103 quality of 103 Kock, Paul de 30 Landow, George 10, 149 language biological capacity 42 reliability in 41 social development 42 Lavangino, John 10 Law, Graham 129 Lawrence, T.E. 33 Lee, Marshall 80 Lévi-Strauss, Claude 60 lexical codes 16, 17 literary theories 85 Lochard, Eric-Olivier 91, 108 Lougy, Robert E. 158 Lowry, Malcolm 187–8 Luke, Hugh J. 73 MacFarland, Helen 15 MacLean, Rauri 147 Mailloux, Steven 8 Malm, Linda 4, 12 Malory, Sir Thomas 179 Manilius 30 manuscript 12–13, 84 as copytext 168 marginalia 34 markup (tei, sgml, html, jitm, xml, etc.) 89, 94, 98, 106–8, 111, 114–15, 117, 124, 142–3 jitm 118–120 overlapping hierarchies 98, 143 Martens, Gunter 174–5 Matthiessen, F.O. 178 McCarty, Willard 10, 86 McCleod, Randall 110 McGann, Jerome 8, 10–11, 26–8, 38, 72, 76, 142–3, 152–3, 172–3, 185–6, 189 A Critique of Modern Textual Criticism 111 Index ‘‘The Gutenberg Variations’’ 73 Radiant Textuality 74, 143 McKenzie, D.F. 8, 26, 72 McKerrow, Ronald B. 9, 153 McLaverty, James 8 meaning 13, 41, 52–3, 55 adventitious 73, 75 author 8, 12, 52, 61–2, 70, 74, 81 construction of 61 determinate 51, 62, 69 historical 73 intended 68, 73 limits to 61 reader 74, 146 speaker 17 text 55, 65, 140 voluntary 52 media, electronic 4 Melville, Herman Moby-Dick 156 Northwest Newberry edition of Moby Dick 156 White Jacket 178, 186 Meriwether, James B. 40, 180 methodologies 191 Metzdorf Collection, the 129 Middleton, Thomas 164 Milton, John 8 misunderstandings 42, 58, 60, 62, 68 Model Editions Partnership 142 Modern Language Association Center for Editions of American Authors 140, 163 Committee on Scholarly Editions 140–1, 168 Guidelines for Electronic Scholarly Editions 94, 111 Moore, Marianne 40 Murray, John 72 Myers, Gary 64 Nations, Boyd 124 New Testament Project 142 Nichol, John W. 178 Nietzsche, Friedrich see HyperNietzsche Project noise 12, 20 Nowell-Smith, Simon 132 Oliphant, Dave 153 orientations 6 paper 27 Parker, David 142 Parker, Herschel 84, 152–3, 172–3 Parrish Collection, the 129, 146 Pater, Walter 25 Patten, Robert 131 pattern recognition 57 Peacock, Thomas Love 30 Peirce, C.S. 60 Pettit, Alexander 123 Pickwoad, Nicholas 123 Piers Plowman archive 4, 142 Pizer, Donald 84 Plachta, Bodo 26, 170, 173 Plato 58–9 Postal, Paul M. 75 Pound, Ezra 142 presentational elements 16 preservation 2, 23 print culture 88 print literature 34, 75 electronic representation of 3, 76, 85 limitations of 3, 85 re-presentation 20 reproduction of 15–16 print technology 85 constraints of 4, 7 moveable type 36 stereotyping 136 printers 178–9, 186–7 programmers 94 Project Gutenberg 21, 87 proofreading 19, 22 publisher 135, 186–7 German 176 publishing 130 economics of 130 punctuation 35, 181–2, 187 rhetorical 37 syntactical 37 Purdy, R.


pages: 406 words: 88,820

Television disrupted: the transition from network to networked TV by Shelly Palmer

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barriers to entry, call centre, disintermediation, en.wikipedia.org, hypertext link, interchangeable parts, invention of movable type, James Watt: steam engine, linear programming, market design, pattern recognition, recommendation engine, Saturday Night Live, shareholder value, Skype, spectrum auction, Steve Jobs, subscription business, Telecommunications Act of 1996, Vickrey auction, yield management

You can describe a picture of a girl on a motorcycle on a desert road as: girl on motorcycle, girl on motorscooter, woman on bike, woman on motorbike, woman on motorcycle, woman on hog, woman on Harley, girl in desert, girl on moped in desert … too bad if your consumer searches for “motor cycling in the desert.” These are just some minor issues when dealing with the subtleties of language-based metatagging. Now, let’s imagine the difficulties that automated rich media metatagging might encounter. Video cannot be searched by a simple program for keywords or phrases. To automate the process you will need pattern recognition algorithms for the video portion of the file and speech to text or sound pattern recognition algorithms for the audio portion. As you can imagine, the computer and Copyright © 2006, Shelly Palmer. All rights reserved. 3-Television.Chap Three v3.qxd 3/20/06 7:21 AM Page 46 46 C H A P T E R 3 Internet programming power needed to accomplish these goals pushes the practical limits of current technology. That being said, digital asset management, metatagging and rich media search engines are being developed everywhere.


pages: 302 words: 86,614

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

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

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

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


pages: 368 words: 96,825

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

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

Flow’s Creative Trigger If you look under the hood of creativity, what you see is pattern recognition (the brain’s ability to link new ideas together) and risk taking (the courage to bring those new ideas into the world). Both of these experiences produce powerful neurochemical reactions and the brain rides these reactions deeper into flow. This means, for those of us who want more flow in our lives, we have to think different, it’s as simple as that. Instead of tackling problems from familiar angles, go at them backward and sideways and with style. Go out of your way to stretch your imagination. Massively up the amount of novelty in your life; the research shows that new environments and experiences are often the jumping-off point for new ideas (more opportunity for pattern recognition). Most important, make creativity a value and a virtue.


pages: 304 words: 88,773

The Ghost Map: A Street, an Epidemic and the Hidden Power of Urban Networks. by Steven Johnson

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call centre, clean water, correlation does not imply causation, Dean Kamen, double helix, edge city, germ theory of disease, Google Earth, Jane Jacobs, John Nash: game theory, John Snow's cholera map, lone genius, Louis Pasteur, megacity, mutually assured destruction, New Urbanism, nuclear winter, pattern recognition, peak oil, side project, Steven Pinker, Stewart Brand, The Death and Life of Great American Cities, the scientific method, trade route, unbiased observer, working poor

Eating meat or vegetation that has already begun the decomposition process poses a significant health risk, as does eating foods that have been contaminated with fecal matter—precisely because of the microbial life-forms that are doing the decomposing. Putrefying foods release several organic compounds into the air; they have names like putrescine and cadaverine. Bacteria recycling energy stored in fecal matter releases hydrogen sulfide into the air. Disgust at the scent of any of these compounds is as close to a universal human trait as we know. You can think of it as a form of evolutionary pattern recognition: over millions of years of evolution, natural selection hit upon the insight that the presence of hydrogen sulfide molecules in the air was a reasonably good predictor that microbial life-forms that could be dangerous if swallowed were nearby. And so the brain evolved a system for setting off an alarm whenever those molecules were detected. Nausea itself was a survival mechanism: it was better to void the contents of your stomach than run the risk that the smell was coming from the antelope you’d just finished eating.

No one knows when H5N1 will learn to pass directly from human to human, and it remains at least a theoretical possibility that it will never develop that trait. But planning for its emergence makes sense, because if such a strain does appear and starts spreading around the globe, there won’t be the equivalent of a pump handle to remove. This is why we’re vaccinating poultry workers in Thailand, why the news of some errant bird migration in Turkey can cause shudders in Los Angeles. This is why the pattern recognition and local knowledge and disease mapping that helped make Broad Street understandable have never been more essential. This is why a continued commitment to public-health institutions remains one of the most vital roles of states and international bodies. If H5N1 does manage to swap just the right piece of DNA from a type A flu virus, we could well see a runaway epidemic that would burn through some of the world’s largest cities at a staggering rate, thanks both to the extreme densities of our cities and the global connectivity of jet travel.


pages: 353 words: 91,520

Most Likely to Succeed: Preparing Our Kids for the Innovation Era by Tony Wagner, Ted Dintersmith

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affirmative action, Airbnb, Albert Einstein, Bernie Sanders, Clayton Christensen, David Brooks, en.wikipedia.org, Frederick Winslow Taylor, future of work, immigration reform, income inequality, index card, Jeff Bezos, jimmy wales, Khan Academy, Kickstarter, knowledge economy, knowledge worker, low skilled workers, Lyft, Mark Zuckerberg, means of production, new economy, pattern recognition, Paul Graham, Peter Thiel, Ponzi scheme, pre–internet, school choice, Silicon Valley, Skype, Steven Pinker, TaskRabbit, the scientific method, unpaid internship, Y Combinator

They were needed for numerous professions—surveyors, furniture-makers, architects, merchant marine officers, military personnel, scientists, and engineers—that were vital to our nation’s economy and security. And in some situations, lives depended on the ability to perform low-level operations quickly, error free, under immense time pressure. * * * 20th-Century Model: Math Skills Needed to Succeed * * * Memorization of low-level procedures Pattern recognition Ability to perform calculations by hand Speed Accuracy Ability to perform well under time pressure * * * Our present-day math curriculum was established during the heyday of the slide rule. Invented by Reverend William Oughtred four hundred years ago, this device enables a slide rule whiz to perform multiplication, division, exponentials, logs, and trig functions on hairy numbers.

Gauge its originality. Check for logical or computational flaws. Give feedback on their ability to present and defend their work. We just can’t rank their work on a bell curve against that of millions of peers. * * * 21st-Century Model Math Skills Needed to Succeed * * * Deeply understanding the problem Structuring the problem and representing it symbolically Creative problem-solving Pattern recognition to understand which math “tools” are relevant Adept use of available computational resources Critical evaluation of first-pass results Estimation, statistics, and decision-making Taking chances, risking failure, and iterating to refine and perfect Synthesizing results Presenting/communicating complex quantitative information Collaboration Asking questions about complex quantitative information * * * Our math priorities affect the futures of millions.


pages: 347 words: 99,969

Through the Language Glass: Why the World Looks Different in Other Languages by Guy Deutscher

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Alfred Russel Wallace, correlation does not imply causation, offshore financial centre, pattern recognition, Ralph Waldo Emerson, Silicon Valley, Steven Pinker

It is quite enough for a toddler to see a few pictures of a cat in a picture book, and the next time she sees a cat, even if it’s ginger rather than tabby, even if it has longer hair, a shorter tail, only one eye, and a hind leg missing, she will still recognize it as a cat rather than a dog or bird or rose. Children’s instinctive grasp of such concepts shows that human brains are innately equipped with powerful pattern-recognition algorithms, which sort similar objects into groups. So concepts such as “cat” or “bird” must somehow correspond to this inborn aptitude to categorize the world. So far, then, we seem to have arrived at a simple answer to the question of whether language reflects culture or nature. We have drawn a neat map and divided language into two distinct territories: the domain of labels and the land of concepts.

See also verb-noun fusion case endings morphology and plurality and “Nubians” exhibit (Berlin, 1878) “Ode to the Sea” (Neruda) Odyssey (Homer) Old English “On the Color Sense in Primitive Times and Its Evolution” (Geiger) On the Historical Evolution of the Color Sense (Magnus) “orange” wavelength of Origin of Species, The (Darwin) Orwell, George Ovaherero tribe Oxford English Dictionary Paiute Papua New Guinea parametric variations theory passive vocabulary Pasternak, Boris pattern-recognition algorithms Perkins, Revere Philosophy Today photoreceptor cells Pindar “pink” wavelengths of light and Pinker, Steven Pirahã Planck, Max plurality Polish Portuguese primates Primitive Culture (Tylor) “primitive” peoples, See also specific groups and languages changing attitudes of anthropologists to color words in languages of complex grammar and Geiger’s sequence and Torres Straits study on pronouns “purple” race Ray, Verne “red” “black” and as first color named Geiger’s sequence and Homer and Magnus’s evolution of color sense and primitive people and wavelength, energy, and retina and red-green blindness Regier, Terry relativism retina Rivarol, Antoine de Rivers, W.


pages: 357 words: 95,986

Inventing the Future: Postcapitalism and a World Without Work by Nick Srnicek, Alex Williams

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3D printing, additive manufacturing, air freight, algorithmic trading, anti-work, back-to-the-land, banking crisis, battle of ideas, blockchain, Bretton Woods, call centre, capital controls, carbon footprint, Cass Sunstein, centre right, collective bargaining, crowdsourcing, cryptocurrency, David Graeber, decarbonisation, deindustrialization, deskilling, Doha Development Round, Elon Musk, Erik Brynjolfsson, Ferguson, Missouri, financial independence, food miles, Francis Fukuyama: the end of history, full employment, future of work, gender pay gap, housing crisis, income inequality, industrial robot, informal economy, intermodal, Internet Archive, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Maynard Keynes: technological unemployment, late capitalism, low skilled workers, manufacturing employment, market design, Martin Wolf, means of production, minimum wage unemployment, Mont Pelerin Society, neoliberal agenda, New Urbanism, Occupy movement, oil shale / tar sands, oil shock, patent troll, pattern recognition, post scarcity, postnationalism / post nation state, precariat, price stability, profit motive, quantitative easing, reshoring, Richard Florida, rising living standards, road to serfdom, Robert Gordon, Ronald Reagan, Second Machine Age, secular stagnation, self-driving car, Slavoj Žižek, social web, stakhanovite, Steve Jobs, surplus humans, the built environment, The Chicago School, Tyler Cowen: Great Stagnation, universal basic income, wages for housework, We are the 99%, women in the workforce, working poor, working-age population

These are tasks that computers are perfectly suited to accomplish once a programmer has created the appropriate software, leading to a drastic reduction in the numbers of routine manual and cognitive jobs over the past four decades.22 The result has been a polarisation of the labour market, since many middle-wage, mid-skilled jobs are routine, and therefore subject to automation.23 Across both North America and Western Europe, the labour market is now characterised by a predominance of workers in low-skilled, low-wage manual and service jobs (for example, fast-food, retail, transport, hospitality and warehouse workers), along with a smaller number of workers in high-skilled, high-wage, non-routine cognitive jobs.24 The most recent wave of automation is poised to change this distribution of the labour market drastically, as it comes to encompass every aspect of the economy: data collection (radio-frequency identification, big data); new kinds of production (the flexible production of robots,25 additive manufacturing,26 automated fast food); services (AI customer assistance, care for the elderly); decision-making (computational models, software agents); financial allocation (algorithmic trading); and especially distribution (the logistics revolution, self-driving cars,27 drone container ships and automated warehouses).28 In every single function of the economy – from production to distribution to management to retail – we see large-scale tendencies towards automation.29 This latest wave of automation is predicated upon algorithmic enhancements (particularly in machine learning and deep learning), rapid developments in robotics and exponential growth in computing power (the source of big data) that are coalescing into a ‘second machine age’ that is transforming the range of tasks that machines can fulfil.30 It is creating an era that is historically unique in a number of ways. New pattern-recognition technologies are rendering both routine and non-routine tasks subject to automation: complex communication technologies are making computers better than humans at certain skilled-knowledge tasks, and advances in robotics are rapidly making technology better at a wide variety of manual-labour tasks.31 For instance, self-driving cars involve the automation of non-routine manual tasks, and non-routine cognitive tasks such as writing news stories or researching legal precedents are now being accomplished by robots.32 The scope of these developments means that everyone from stock analysts to construction workers to chefs to journalists is vulnerable to being replaced by machines.33 Workers who move symbols on a screen are as at risk as those moving goods around a warehouse.

Likewise, while an automated transport system may not be subject to driver strikes, it may be open to strikes by programmers and IT technicians, as well as being more susceptible to blockades, because of the technical limitations of self-driving cars. These vehicles function by reducing environmental variation, making them ‘more akin to a train running on invisible tracks’.80 The intentional manipulation of the environment is therefore likely to be particularly disruptive. Equally, the use of pattern recognition algorithms in various tasks (e.g., diagnostics, emotion- and face-detection, surveillance) is highly susceptible to disruption.81 A technical understanding of machines like these is essential to understanding how to interrupt them, and any future left must be as technically fluent as it is politically fluent. In the end, what is required is an analysis of the automation trends that are restructuring production and circulation, and a strategic understanding of where new points of leverage might develop.


pages: 322 words: 88,197

Wonderland: How Play Made the Modern World by Steven Johnson

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Ada Lovelace, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Berlin Wall, bitcoin, Book of Ingenious Devices, Buckminster Fuller, Claude Shannon: information theory, Clayton Christensen, colonial exploitation, computer age, conceptual framework, crowdsourcing, cuban missile crisis, Drosophila, Fellow of the Royal Society, game design, global village, Hedy Lamarr / George Antheil, HyperCard, invention of air conditioning, invention of the printing press, invention of the telegraph, Islamic Golden Age, Jacquard loom, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, Jane Jacobs, John von Neumann, joint-stock company, Joseph-Marie Jacquard, Landlord's Game, lone genius, megacity, Minecraft, Murano, Venice glass, music of the spheres, Necker cube, New Urbanism, Oculus Rift, On the Economy of Machinery and Manufactures, pattern recognition, pets.com, placebo effect, probability theory / Blaise Pascal / Pierre de Fermat, profit motive, QWERTY keyboard, Ray Oldenburg, spice trade, spinning jenny, statistical model, Steve Jobs, Steven Pinker, Stewart Brand, supply-chain management, talking drums, the built environment, The Great Good Place, the scientific method, The Structural Transformation of the Public Sphere, trade route, Turing machine, Turing test, Upton Sinclair, urban planning, Victor Gruen, Watson beat the top human players on Jeopardy!, white flight, Whole Earth Catalog, working poor, Wunderkammern

In cognitive science, the litany of insights that derived from the study of chess could almost fill an entire textbook, insights that have helped us understand the human capacity for problem solving, pattern recognition, visual memory, and the crucial skill that scientists call, somewhat awkwardly, chunking, which involves grouping a collection of ideas or facts into a single “chunk” so that they can be processed and remembered as a unit. (A chess player’s ability to recognize and often name a familiar sequence of moves is a classic example of mental chunking.) Some cognitive scientists compared the impact of chess on their field to Drosophila, the fruit fly that played such a central role in early genetics research. But the prominence of chess in the first fifty years of both cognitive and computer science also produced a distorted vision of intelligence itself. It helped cement the brain-as-computer metaphor: a machine driven by logic and pattern recognition, governed by elemental rules that could be decoded with enough scrutiny.


pages: 366 words: 87,916

Fluent Forever: How to Learn Any Language Fast and Never Forget It by Gabriel Wyner

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card file, crowdsourcing, en.wikipedia.org, index card, Kevin Kelly, Kickstarter, meta analysis, meta-analysis, pattern recognition, Ralph Waldo Emerson, Ronald Reagan, Skype, spaced repetition, Steve Jobs, Steven Pinker, Yogi Berra

These tests are as basic as they get—they play a recording (“lock”) and then ask you what word you heard (“rock” or “lock”?)—but what they lack in panache they make up for in results. I used them to learn the (obnoxiously difficult) sounds of Hungarian in twenty minutes a day for ten days. They’re also a lot of fun; you can feel your ears changing with each repetition. The Benefits of Ear Training: Pattern Recognition and Pattern Breaking When you use minimal pair testing at the beginning of your language journey, you’ll learn much faster in the long run. You’ll have an easier time remembering new words, because they no longer sound foreign. You’ll also understand native speakers better, because your ears are in sync with their speech. Instead of wasting your time correcting bad pronunciation habits, you’ll be able to spend your time consuming language at breakneck speed.

.), 1.1, 1.2, 1.3, 6.1, 6.2, 7.1 minimal pairs, 3.1, 3.2, 3.3, bm3.1, bm7.1 mission critical language mistakes, 5.1, 5.2, 6.1, 6.2, 6.3 mnemonic filter mnemonic imagery cards game, 4.1, 5.1, bm7.1 for gender, 4.1, 4.2, bm4.1, bm4.2 of patterns mnemonics See also visual memory Molaison, Henry monolingual dictionary, 1.1, 6.1, 6.2, 6.3, bm6.1, bm7.1, bm7.2 Moonwalking with Einstein (Foer) more is less, 3.1, bm7.1 mouth training, 3.1, 3.2 movies, 6.1, 6.2, 6.3, bm7.1 multiple definitions multiplication multisearches music MyLanguageExchange.com, 6.1, 6.2 Netflix, 6.1, bm7.1 networks neural patterns neurons, 2.1, 2.2, 2.3, 2.4, 3.1, bm2.1, bm7.1, bm7.2 Nolan, Christopher nondeclarative memory nouns, 4.1, 5.1, 5.2, 5.3, 5.4, bm3.1 numbers online dictionary Ortega, Lourdes output, 5.1, bm7.1 overlearning PAO system. See person-action-object (PAO) system Paris (France) Parker, Charlie pattern breaking pattern matching pattern memorization pattern recognition patterns, 3.1, 3.2, 3.3, 4.1, 5.1, 5.2, 5.3 person-action-object (PAO) system, 5.1, bm7.1 personal connection, 2.1, 2.2, 4.1, 4.2, 4.3, bm2.1, bm5.1 phoneme, 3.1, bm7.1, bm7.2 phonetic alphabet, 3.1, 3.2, 3.3, bm2.1 phonetic transcription phrase, bm5.1, bm5.2, bm5.3, bm5.4, bm6.1 phrase book, 1.1, 1.2, 4.1, 4.2, bm7.1 pictures. See Google Images; image(s) plural, 5.1, nts.1 Portuguese language prefix pronoun pronunciation, 1.1, 1.2, 3.1 back chaining, 3.1, bm7.1 of first words guides, 1.1, 3.1, bm7.1 habits, 3.1, 3.2 of new sounds off-road route paths through resources rules, 1.1, 3.1 and spelling standard route trainers, 3.1, 3.2, 3.3, bm3.1, bm7.1 videos, 3.1, bm7.1 r and l sounds, 3.1, app4.1, app4.2, nts.1 reading, 6.1, 6.2, 6.3 Reagan, Ronald recall, 2.1, 2.2, bm7.1, bm7.2 recordings, 3.1, 3.2, 3.3, 4.1, 4.2, bm3.1, bm3.2, bm3.3, bm7.1 relevance repetition Rhinospike.com, 3.1, bm7.1 Roosevelt, Theodore Rosetta Stone (program) Rotokas language Rousseau, Jean-Jacques, 3.1, 3.2 rulebooks Russian language, 1.1, bm5.1, app1.1 Sagan, Carl Schaefer, Charles Schwarzenegger, Arnold, 2.1, 5.1, 5.2 self-directed writing, 5.1, 6.1 sentence(s) breaking down, 5.1, 5.2 of children creating your own example, 5.1, 6.1, 6.2, 6.3, bm6.1 finding first flash cards for from grammar book learning, 5.1, 5.2 order in recordings of sound of vocabulary word in Shaw, George Bernard singing, 1.1, 3.1 625 words, 2.1, 4.1, 4.2, 4.3, 4.4, 4.5, 5.1, 5.2, 6.1, bm4.1, bm4.2, bm4.3, bm4.4, bm4.5, bm5.1, bm7.1, bm7.2, bm7.3, app5.1 Skype, 6.1, 6.2, bm7.1, bm7.2 slang Smith, Alexander Smith, Patti sound, 2.1, 2.2, 3.1, 4.1 in Chinese foreign in IPA, 3.1, app4.1 and language learning language’s system as level of processing for pronunciation trainers rules of sentences and spelling, 3.1, 3.2, 3.3, 4.1, bm3.1 working with See also recordings spaced repetition systems (SRSs) choosing and corrections definition of flash cards in, 2.1, 2.2, 4.1, bm1.1, bm1.2, bm3.1 as key to language learning learning to use and patterns for pronunciation resources for sentences, 5.1, 6.1 as timing procedure, 2.1, 2.2 See also Anki (program); Leitner box Spanish language, 3.1, app1.1, app4.1, app4.2 speech.


pages: 319 words: 90,965

The End of College: Creating the Future of Learning and the University of Everywhere by Kevin Carey

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Albert Einstein, barriers to entry, Berlin Wall, business intelligence, carbon-based life, Claude Shannon: information theory, complexity theory, declining real wages, deliberate practice, discrete time, double helix, Douglas Engelbart, Downton Abbey, Drosophila, Firefox, Frank Gehry, Google X / Alphabet X, informal economy, invention of the printing press, inventory management, Khan Academy, Kickstarter, low skilled workers, Lyft, Mark Zuckerberg, meta analysis, meta-analysis, natural language processing, Network effects, open borders, pattern recognition, Peter Thiel, pez dispenser, ride hailing / ride sharing, Ronald Reagan, Sand Hill Road, self-driving car, Silicon Valley, Silicon Valley startup, social web, South of Market, San Francisco, speech recognition, Steve Jobs, technoutopianism, transcontinental railway, Vannevar Bush

So your brain required only a brief glance at that combination of letters to understand that it meant “encyclopedia” and all the word implies. The process was largely automatic, just as you don’t consciously think “that broad, flat surface located approximately three feet below me is the floor, which will support my weight” when you climb out of bed in the morning. The brain’s capacity for pattern recognition is adaptable, flexible, and strong. If the word in that sentence had been spelled “encylopedia,” you would not have been utterly baffled, even though it is missing the second letter c. You might not have even noticed the mistake at all. If, on the other hand, I had written “iderebagon,” you probably would have been confused, unless you happen to be fluent in the verb forms of High Valyrian, a language spoken by certain characters on the HBO television series Game of Thrones.

Employers were also faced with another version of a familiar problem: how to sort through a lot of information with limited resources and limited time. Brassiere inventory management is a snap compared to figuring out human beings. As information technology destroyed jobs that involved simple and repetitive tasks, like painting car parts or shelving paper files, and globalization moved other low-skill jobs overseas, the American jobs that remained fell into several large categories. Some required creativity, judgment, and pattern recognition. Others involved interacting with other people by providing services of different kinds. It’s hard to tell if someone you don’t know personally will be good at either of those kinds of jobs. Living anonymously in large communities is a peculiar modern condition. As transportation and communications became cheaper and people increasingly moved from one urban and suburban area to the next, companies hiring employees were faced with the difficult task of selecting from among large numbers of complete strangers.


pages: 346 words: 92,984

The Lucky Years: How to Thrive in the Brave New World of Health by David B. Agus

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3D printing, active transport: walking or cycling, Affordable Care Act / Obamacare, Albert Einstein, butterfly effect, clean water, cognitive dissonance, crowdsourcing, Danny Hillis, Drosophila, Edward Lorenz: Chaos theory, en.wikipedia.org, epigenetics, Kickstarter, medical residency, meta analysis, meta-analysis, microbiome, microcredit, mouse model, Murray Gell-Mann, New Journalism, pattern recognition, personalized medicine, phenotype, placebo effect, publish or perish, randomized controlled trial, risk tolerance, statistical model, stem cell, Steve Jobs, Thomas Malthus, wikimedia commons

Which is how art experts can often assess the authenticity of a work of art in an instant, getting an actual physical feeling as they look at a sculpture or painting. Something in their gut tells them this is the real thing or a rip-off. The reason I’m sharing all this detail about spurious art and the experts who argue over what they see is that the subject of objectivity and artistry—and pattern recognition—is relevant in health. We all establish patterns of behavior, or habits, that play into our health and the path that our health takes. And we all have hunch power: the ability to know instinctively what we should be doing to lead healthy, strong lives. These two characteristics—habits and intuition—are ultimately what make us human and will allow us to capitalize on the Lucky Years. As we encounter a wealth of data and technologies to help us understand our individual contexts, we’ll be equipped to take control of our health like never before, shape new habits, gain better intuition, and be well prepared for what the future holds.

., 28, 116 New York Academy of Medicine, 2 New York Cancer Hospital, 28 see also Memorial Sloan Kettering Cancer Center New York University, 204 New Zealand, 45, 46 Nexium (esomeprazole), 86 night blindness, 235 NIH Human Microbiome Project, 120 Nike, 199 Nobel Peace Prize, 232 Nobel Prize, 33, 34, 102 “nocebo” effect, 165 noncommunicable diseases, premature deaths from, 130, 131, 132 Northeastern University, 68 Northwestern University, 41 Norton, Larry, 60–61, 62 Nottingham, University of, 87 Nurses’ Health Study, 142–43, 216–17 nursing college, 235 nutritional studies, 161–69 honesty and, 162 lack of reliable data from, 162–63, 164 Nyhan, Brendan, 157, 158, 160 Obama, Barack, 11, 114, 115, 117 obesity and overweight, 22, 47, 121, 122, 123, 147, 188, 194, 215 breast cancer and, 133 chronic disease and, 141 honesty about, 132–34 obsessive-compulsive disorder (OCD), 122 Obstetrics & Gynecology, 132–33 Olser Library of Medicine (McGill University), 73 omega-3 fatty acids, 182–83 omeprazole (Prilosec), 86 “On Lines and Planes of Closest Fit to Systems of Points in Space” (Pearson), 95 Only the Paranoid Survive (Grove), 7 open-access model, 179 opioids, 145 optimism, health and, 65–69 Oregon, University of, 199 Oregon Health & Science University, 109 Ornish, Dean, 166–68 Osler, William, 15, 37, 71–73, 72, 73, 75, 126, 145, 153, 223 Othello (Shakespeare), 202 Ottawa, University of, 183 overweight, see obesity and overweight Oxford University, 216 oxidative stress, 175 oxytocin, 211 p53 gene, 57–58 pain relievers, risks of, 145–46 Paleo diet, 142, 163 parabiosis, 1–4, 3, 21 parasites, spread of, 103 Parkinson’s disease, 59, 108, 163 pattern recognition, 227 PD-L1, 29–30 Pearson, Karl, 95 Pediatric MATCH, 117 Pediatrics, 133 pelvic bone cancer, 176 Pennington Biomedical Research Center, 192 Pennsylvania, University of, 73, 75 Perelman School of Medicine at, 208 perceptual intuition, 228–29 personalized medicine, see precision medicine Peto, Richard, 57 Peto’s paradox, 57 PET (positron-emission tomography) scan, 230 pharmaceutical industry, 166 drug prices and, 56–57, 115–17 public distrust of, 18, 19, 69, 157 pharmacogenomics, precision medicine and, 115 phenylalanine, 12 phenylketonuria (PKU), 12 Philosophical magazine, 95 physical activity, 140 physicians: house calls by, 80 public distrust of, 17–19, 157 pit latrines, 234 Pittsburgh, University of, 196, 214 placebos, 53 plaques, 183 plasma transfusions, 4–5 plate discipline, 204 Plato, 185 PLOS Medicine, 178 pneumonia, 161 polio virus, in immunotherapy, 30, 31 Pope, Frank, 2 population growth, technology and, 27 portable electronic devices, health care and, 79, 90–91 Post hoc, ergo propter hoc fallacy, 156 precision medicine, 8, 20, 36, 102–25 art vs. science in, 112, 118 cancer treatment and, 115 context and, 114–15, 117 cost of, 56–57 historical roots of, 113 pharmacogenomics and, 115 technology and, 37–70 Precision Medicine Initiative, 114, 117 “Predictability: Does the Flap of a Butterfly’s Wings in Brazil Set Off a Tornado in Texas?”


pages: 138 words: 27,404

OpenCV Computer Vision With Python by Joseph Howse

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augmented reality, computer vision, Debian, optical character recognition, pattern recognition

He had a final project based on this subject and published it on HCI Spanish congress. He participated in Blender source code, an open source and 3D-software project, and worked in his first commercial movie Plumiferos—Aventuras voladoras as a Computer Graphics Software Developer. David now has more than 10 years of experience in IT, with more than seven years experience in computer vision, computer graphics, and pattern recognition working on different projects and startups, applying his knowledge of computer vision, optical character recognition, and augmented reality. He is the author of the DamilesBlog (http://blog.damiles.com), where he publishes research articles and tutorials about OpenCV, computer vision in general, and Optical Character Recognition algorithms. He is the co-author of Mastering OpenCV with Practical Computer Vision Projects , Daniel Lélis Baggio, Shervin Emami, David Millán Escrivá, Khvedchenia Ievgen, Naureen Mahmood, Jasonl Saragih, and Roy Shilkrot, Packt Publishing.


pages: 797 words: 227,399

Robotics Revolution and Conflict in the 21st Century by P. W. Singer

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agricultural Revolution, Albert Einstein, Any sufficiently advanced technology is indistinguishable from magic, Atahualpa, barriers to entry, Berlin Wall, Bill Joy: nanobots, blue-collar work, borderless world, clean water, Craig Reynolds: boids flock, cuban missile crisis, en.wikipedia.org, Ernest Rutherford, failed state, Fall of the Berlin Wall, Firefox, Francisco Pizarro, Frank Gehry, friendly fire, game design, George Gilder, Google Earth, Grace Hopper, I think there is a world market for maybe five computers, if you build it, they will come, illegal immigration, industrial robot, interchangeable parts, invention of gunpowder, invention of movable type, invention of the steam engine, Isaac Newton, Jacques de Vaucanson, job automation, Johann Wolfgang von Goethe, Law of Accelerating Returns, Mars Rover, Menlo Park, New Urbanism, pattern recognition, private military company, RAND corporation, Ray Kurzweil, RFID, robot derives from the Czech word robota Czech, meaning slave, Rodney Brooks, Ronald Reagan, Schrödinger's Cat, Silicon Valley, speech recognition, Stephen Hawking, strong AI, technological singularity, The Coming Technological Singularity, The Wisdom of Crowds, Turing test, Vernor Vinge, Wall-E, Yogi Berra

Ultimately, it could only be sure beyond a reasonable doubt with a DNA sample, which would occupy a massive part of its processing power. By comparison, pretty much any two-year-old human instantly “knows” that an apple is not a tomato, without any calculation. At the same time, that toddler can pick his nose, kick a ball, and realize that it is raining outside. Thus, the toddler may not be able to count to infinity, but they blow the computer out of the water when it comes to pattern recognition and multitasking, which is 95 percent of what we ask our brains to think on. “If you think it’s easy for you to do, most of the time it’s very difficult for robots to do,” says Takeo Kanade, director of Carnegie Mellon University’s Robotics Institute. So it seems unfair then to compare machines to humans in this type of intelligence. What should matter more in defining intelligence is simply whether there is some use made of information in order to achieve the task.

I asked an executive at one defense contractor whether he agreed with the crazy ideas being bandied about on singularities and robots becoming as smart as humans. He replied, “If this war keeps going on a few more years, then yes.” Robert Epstein sees the military’s role as more than simply funding the Singularity. It is the most likely integrator needed to bring it all together. He describes how there are all sorts of research programs and companies around the globe, working on various technologies, from pattern recognition software and robotic sensors to artificial intelligence and subatomic microchips. “When you marry all that up with the strategic planning that the military brings to the table, you will end up with a qualitative advance like no other. At that point prediction of what comes next becomes difficult. . . . That’s when you hit the Singularity, where all the rules change, in part because we are no longer making the rules.”

It is far less controversial in Asia. Indeed, South Korea sent two robot snipers with rifles to Iraq in 2004 with essentially no debate; they were reported in the media to have “nearly 100%” accuracy. Even more notable is the Autonomous Sentry Gun, made by Samsung. The company, more known for making high-definition TVs, has integrated a machine gun with two cameras (infrared and zooming) and pattern recognition software processors. The gun system cannot only identify, classify, and destroy moving targets from over a mile away, but, as Louis Ramirez of Gizmodo relates, “also has a speaker that beckons the fool that walks near it to surrender before being pulverized.” South Korea plans to use the robo-machine guns to stand guard along the 155-mile demilitarized zone (DMZ) that borders North Korea.


pages: 669 words: 210,153

Tools of Titans: The Tactics, Routines, and Habits of Billionaires, Icons, and World-Class Performers by Timothy Ferriss

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Airbnb, artificial general intelligence, asset allocation, Atul Gawande, augmented reality, back-to-the-land, Bernie Madoff, Bertrand Russell: In Praise of Idleness, Black Swan, blue-collar work, Buckminster Fuller, business process, Cal Newport, call centre, Checklist Manifesto, cognitive bias, cognitive dissonance, Colonization of Mars, Columbine, correlation does not imply causation, David Brooks, David Graeber, diversification, diversified portfolio, Donald Trump, effective altruism, Elon Musk, fault tolerance, fear of failure, Firefox, follow your passion, future of work, Google X / Alphabet X, Howard Zinn, Hugh Fearnley-Whittingstall, Jeff Bezos, job satisfaction, Johann Wolfgang von Goethe, Kevin Kelly, Kickstarter, Lao Tzu, life extension, Mahatma Gandhi, Mark Zuckerberg, Mason jar, Menlo Park, Mikhail Gorbachev, Nicholas Carr, optical character recognition, PageRank, passive income, pattern recognition, Paul Graham, Peter H. Diamandis: Planetary Resources, Peter Singer: altruism, Peter Thiel, phenotype, post scarcity, premature optimization, QWERTY keyboard, Ralph Waldo Emerson, Ray Kurzweil, recommendation engine, rent-seeking, Richard Feynman, Richard Feynman, risk tolerance, Ronald Reagan, sharing economy, side project, Silicon Valley, skunkworks, Skype, Snapchat, social graph, software as a service, software is eating the world, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, superintelligent machines, Tesla Model S, The Wisdom of Crowds, Thomas L Friedman, Wall-E, Washington Consensus, Whole Earth Catalog, Y Combinator

A number of Nobel Prize laureates in chemistry, biology, and elsewhere attribute breakthroughs to LSD. Jim once worked on a study involving large companies and research institutes trying to solve incredibly difficult problems like new circuit board designs. Volunteers were given psychedelics, and 44 out of the 48 problems were “solved,” meaning resulted in a patent, product, or publication. Jim attributes this to enhanced focus and pattern recognition. Low enough doses (i.e., 100 mcg of LSD or 200 mg of mescaline) can immensely increase the capacity to solve problems. “We said, ‘You may come to this study, and we’ll give you the most creative day of your life. But you have to have a problem which obsesses you that you have been working on for a couple of months and that you’ve failed [to solve].’ . . . We wanted them to have . . . an emotional ‘money in the game.’

Sometimes you need to stop doing things you love in order to nurture the one thing that matters most.” ✸ The worst advice you hear being given out often? “‘Look for patterns.’ As an entrepreneur and investor, I am surrounded by people who try to categorize and generalize the factors that make a company successful. . . . Most people forget that innovation (and investing in innovation) is a business of exceptions. “It’s easy to understand why most investors rely on pattern recognition. It starts with a successful company that surprises everyone with a new model. Perhaps it is Uber and on-demand networks, Airbnb and the sharing economy, or Warby Parker and vertically integrated e-commerce. What follows is endless analysis and the mass adoption of a playbook that has already been played. . . . Sure, [those companies] may create a successful derivative, but they won’t change the world.

Harris), Tinker, Tailor, Soldier, Spy; Little Drummer’s Girl; The Russia House; The Spy Who Came in from the Cold (John le Carré), The Big Short: Inside the Doomsday Machine (Michael Lewis), The Checklist Manifesto (Atul Gawande), all of Lee Child’s books Godin, Seth: Makers; Little Brother (Cory Doctorow), Understanding Comics (Scott McCloud), Snow Crash; The Diamond Age (Neal Stephenson), Dune (Frank Herbert), Pattern Recognition (William Gibson) AUDIOBOOKS: The Recorded Works (Pema Chödrön), Debt (David Graeber), Just Kids (Patti Smith), The Art of Possibility (Rosamund Stone Zander and Benjamin Zander), Zig Ziglar’s Secrets of Closing the Sale (Zig Ziglar), The War of Art (Steven Pressfield) Goldberg, Evan: Love You Forever (Robert Munsch), Watchmen; V for Vendetta (Alan Moore), Preacher (Garth Ennis), The Hitchhiker’s Guide to the Galaxy (Douglas Adams), The Little Prince (Antoine de Saint-Exupéry) Goodman, Marc: One Police Plaza (William Caunitz), The 4-Hour Workweek (Tim Ferriss), The Singularity Is Near (Ray Kurzweil), Superintelligence: Paths, Dangers, Strategies (Nick Bostrom) Hamilton, Laird: The Bible, Natural Born Heroes (Christopher McDougall), Lord of the Rings (J.R.R.

Evidence-Based Technical Analysis: Applying the Scientific Method and Statistical Inference to Trading Signals by David Aronson

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Albert Einstein, Andrew Wiles, asset allocation, availability heuristic, backtesting, Black Swan, capital asset pricing model, cognitive dissonance, compound rate of return, Daniel Kahneman / Amos Tversky, distributed generation, Elliott wave, en.wikipedia.org, feminist movement, hindsight bias, index fund, invention of the telescope, invisible hand, Long Term Capital Management, mental accounting, meta analysis, meta-analysis, p-value, pattern recognition, Ponzi scheme, price anchoring, price stability, quantitative trading / quantitative finance, Ralph Nelson Elliott, random walk, retrograde motion, revision control, risk tolerance, risk-adjusted returns, riskless arbitrage, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, statistical model, systematic trading, the scientific method, transfer pricing, unbiased observer, yield curve, Yogi Berra

Felsen, Decision Making under Uncertainty: An Artificial Intelligence Approach (New York: CDS Publishing Company, 1976). 514 NOTES 31. Two firms with which I had contact in the late 1970s that were using statistical pattern recognition and adaptive learning networks were Braxton Corporation in Boston, Massachusetts, and AMTEC Inc. in Ogden, Utah. I started Raden Research Group, Inc. in 1982. 32. A.M. Safer, “A Comparison of Two Data Mining Techniques to Predict Abnormal Stock Market Returns, Intelligent Data Analysis 7, no. 1 (2003), 3–14; G. Armano, A. Murru, and F. Roli, “Stock Market Prediction by a Mixture of Genetic-Neural Experts,” International Journal of Pattern Recognition & Artificial Intelligence 16, no. 5 (August 2002), 501–528; G. Armano, M. Marchesi, and A. Murru, “A Hybrid Genetic-Neural Architecture for Stock Indexes Forecasting,” Information Sciences 170, no. 1 (February 2005), 3–33; T.

These results are discussed in Chapter 4, “Short Term Price Drift.” The chapter also contains an excellent list of references of other research relating to this topic. 30. A.M. Safer, “A Comparison of Two Data Mining Techniques to Predict Abnormal Stock Market Returns,” Intelligent Data Analysis 7, no. 1 (2003), 3–14; G. Armano, A. Murru, and F. Roli, “Stock Market Prediction by a Mixture of Genetic-Neural Experts,” International Journal of Pattern Recognition & Artificial Intelligence 16, no. 5 (August 2002), 501–528; G. Armano, M. Marchesi, and A. Murru, “A Hybrid Genetic-Neural Architecture for Stock In- Notes 479 dexes Forecasting,” Information Sciences 170, no. 1 (February 2005), 3–33; T. Chenoweth, Z.O. Sauchi, and S. Lee, “Embedding Technical Analysis into Neural Network Based Trading Systems,” Applied Artificial Intelligence 10, no. 6 (December 1996), 523–542; S.


pages: 855 words: 178,507

The Information: A History, a Theory, a Flood by James Gleick

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Ada Lovelace, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, Albert Einstein, AltaVista, bank run, bioinformatics, Brownian motion, butterfly effect, citation needed, Claude Shannon: information theory, clockwork universe, computer age, conceptual framework, crowdsourcing, death of newspapers, discovery of DNA, double helix, Douglas Hofstadter, en.wikipedia.org, Eratosthenes, Fellow of the Royal Society, Gödel, Escher, Bach, Henri Poincaré, Honoré de Balzac, index card, informal economy, information retrieval, invention of the printing press, invention of writing, Isaac Newton, Jacquard loom, Jacquard loom, Jaron Lanier, jimmy wales, John von Neumann, Joseph-Marie Jacquard, Louis Daguerre, Marshall McLuhan, Menlo Park, microbiome, Milgram experiment, Network effects, New Journalism, Norbert Wiener, On the Economy of Machinery and Manufactures, PageRank, pattern recognition, phenotype, pre–internet, Ralph Waldo Emerson, RAND corporation, reversible computing, Richard Feynman, Richard Feynman, Simon Singh, Socratic dialogue, Stephen Hawking, Steven Pinker, stochastic process, talking drums, the High Line, The Wisdom of Crowds, transcontinental railway, Turing machine, Turing test, women in the workforce

.♦) For their first meeting they invited Warren McCulloch. They talked not just about understanding brains but “designing” them. A psychiatrist, W. Ross Ashby, announced that he was working on the idea that “a brain consisting of randomly connected impressional synapses would assume the required degree of orderliness as a result of experience”♦—in other words, that the mind is a self-organizing dynamical system. Others wanted to talk about pattern recognition, about noise in the nervous system, about robot chess and the possibility of mechanical self-awareness. McCulloch put it this way: “Think of the brain as a telegraphic relay, which, tripped by a signal, emits another signal.” Relays had come a long way since Morse’s time. “Of the molecular events of brains these signals are the atoms. Each goes or does not go.” The fundamental unit is a choice, and it is binary.

.… One could no doubt develop an adequate description of the results in S-R terms … but such a description is clumsy compared to the information theory description.”♦ Broadbent founded an applied psychology division at Cambridge University, and a flood of research followed, there and elsewhere, in the general realm of how people handle information: effects of noise on performance; selective attention and filtering of perception; short-term and long-term memory; pattern recognition; problem solving. And where did logic belong? To psychology or to computer science? Surely not just to philosophy. An influential counterpart of Broadbent’s in the United States was George Miller, who helped found the Center for Cognitive Studies at Harvard in 1960. He was already famous for a paper published in 1956 under the slightly whimsical title “The Magical Number Seven, Plus or Minus Two: Some Limits on Our Capacity for Processing Information.”♦ Seven seemed to be the number of items that most people could hold in working memory at any one time: seven digits (the typical American telephone number of the time), seven words, or seven objects displayed by an experimental psychologist.

consciousness, 2.1, 2.2, 8.1, 8.2, epl.1, epl.2, epl.3 continuous signal systems, 6.1, 7.1 Cooke, William, 5.1, 5.2 Cooper, Pat Coote, Edmund Coy, George W. Crick, Francis, 10.1, 10.2, 10.3, 10.4, 10.5 Crow, James F. n cryptography assessment of system security Babbage’s work in, 5.1, 5.2 early strategies, 5.1, 5.2 Enigma system of, 7.1, 7.2 information theory and mathematics of, 5.1, 5.2, 5.3, 7.1, 7.2 mental skill for pattern recognition in, 7.1, 7.2 perfect cipher system popular interest in, 5.1, 5.2 quantum, 13.1, 13.2, 13.3, 13.4 RSA encryption Shannon’s work on, prl.1, prl.2, 7.1, 7.2, 7.3, 7.4, 7.5, 7.6 Turing’s work on, 7.1, 7.2, 7.3, 7.4 voice encryption, 7.1, 7.2 writing and, 5.1, 5.2 see also code crystals, 9.1, 9.2, 10.1 culture communication technology and, 15.1, 15.2, 15.3 communicative capacity of drums, 1.1, 1.2 function of meme perceptions changed by telegraphy, 5.1, 5.2 replication and transmission of thought processes biased by, 2.1, 2.2 see also oral culture Cummings, E.


pages: 458 words: 135,206

CTOs at Work by Scott Donaldson, Stanley Siegel, Gary Donaldson

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Amazon Web Services, bioinformatics, business intelligence, business process, call centre, centre right, cloud computing, computer vision, connected car, crowdsourcing, data acquisition, distributed generation, domain-specific language, glass ceiling, pattern recognition, Pluto: dwarf planet, Richard Feynman, Richard Feynman, shareholder value, Silicon Valley, Skype, smart grid, smart meter, software patent, thinkpad, web application, zero day

It's something that still fascinates me, but I've never really used physics in an applied approach in my work. G. Donaldson: So you moved away from the financial services world and established the company that you currently run. Can you tell us a little about that transition and what your company does? Bloore: Sure. So what our company does is make images searchable by looking at the patterns within the pixels themselves. We are entirely focused on using computer vision and pattern recognition algorithms to make large image sets searchable. It's very different from using keywords to search images. G. Donaldson: That's a big shift from developing risk management software to pixel imagery and software. Where did that shift come from? What was your interest in going in that direction? Bloore: I would say that it's entirely personal. I have a very visual memory, and that's what ultimately drives what direction the company took.

Bloore: Well, as the team grows, at a certain point there will be less direct contact day-to-day with all the development staff. I'll see that as an unfortunate day, but it's probably a necessary step. G. Donaldson: You have a lead engineer for your technology staff? Is that how the chain of command works? Bloore: Right now I probably play the role of the lead engineer, and then I have engineers who have specific domain knowledge. For example, the actual coding of the pattern recognition algorithms resides with a lead engineer. G. Donaldson: Well, let's shift and talk a little bit about your technologies. You've being doing a very good job of outlining what you've encountered. If you had to summarize, what are the most important technologies to your company? Bloore: Certainly, the technology behind TinEye is the most important one we have, and that's the ability to find an exact or modified copy of an image.


pages: 404 words: 131,034

Cosmos by Carl Sagan

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Albert Einstein, Alfred Russel Wallace, Arthur Eddington, clockwork universe, dematerialisation, double helix, Drosophila, Edmond Halley, Eratosthenes, Ernest Rutherford, germ theory of disease, invention of movable type, invention of the telescope, Isaac Newton, Lao Tzu, Louis Pasteur, Magellanic Cloud, Mars Rover, Menlo Park, music of the spheres, pattern recognition, planetary scale, Search for Extraterrestrial Intelligence, spice trade, Tunguska event

For the Air through which we look upon the Stars, is in perpetual tremor.… The only remedy is the most serene and quiet Air, such as may perhaps be found on the tops of the highest mountains above the grosser Clouds.” *There was a brief flurry when the uppercase letter B, a putative Martian graffito, seemed to be visible on a small boulder in Chryse. But later analysis showed it to be a trick of light and shadow and the human talent for pattern recognition. It also seems remarkable that the Martians should have tumbled independently to the Latin alphabet. But there was just a moment when resounding in my head was the distant echo of a word from my boyhood—Barsoom. *The largest are 3 kilometers across at the base, and 1 kilometer high—much larger than the pyramids of Sumer, Egypt or Mexico on Earth. They seem eroded and ancient, and are, perhaps, only small mountains, sandblasted for ages.

The neurochemistry of the brain is astonishingly busy, the circuitry of a machine more wonderful than any devised by humans. But there is no evidence that its functioning is due to anything more than the 1014 neural connections that build an elegant architecture of consciousness. The world of thought is divided roughly into two hemispheres. The right hemisphere of the cerebral cortex is mainly responsible for pattern recognition, intuition, sensitivity, creative insights. The left hemisphere presides over rational, analytical and critical thinking. These are the dual strengths, the essential opposites, that characterize human thinking. Together, they provide the means both for generating ideas and for testing their validity. A continuous dialogue is going on between the two hemispheres, channeled through an immense bundle of nerves, the corpus callosum, the bridge between creativity and analysis, both of which are necessary to understand the world.


pages: 398 words: 31,161

Gnuplot in Action: Understanding Data With Graphs by Philipp Janert

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bioinformatics, business intelligence, centre right, Debian, general-purpose programming language, iterative process, mandelbrot fractal, pattern recognition, random walk, Richard Stallman, six sigma

The idea behind Chernoff-faces, for example, is to encode each quantity as a facial feature in a stylized human face: size of the mouth or distance between eyes, and so on. The observer then tries to find the faces that are “most similar” or “least similar” to one another.) For much larger data sets, one may resort to computationally intensive methods, which go under the name of data mining or more specifically pattern recognition and machine learning.9 The latter set of methods is a highly active area of research. I must say that I experience a certain degree of discomfort with the “random search” character of some multivariate methods. The purpose of data analysis is to gain insight into the problem domain that the data came from, but any brute-force method that isn’t guided by intuition about the problem domain runs the risk of being about the numbers only, not about the actual system that the data came from originally.

Multivariate classification methods, such as the parallel coordinates technique introduced here, can be a useful starting point when faced with large and unsystematic data sets, or any time we don’t have good intuition about the actual problem 8 9 Two short and accessible introductory texts are Multivariate Statistical Methods: A Primer by Bryan F.J. Manly (Chapman & Hall, 3rd ed., 2004) and Introduction to Multivariate Analysis by Chris Chatfield and A. Collins (Chapman & Hall, 1981). Three introductory texts, in approximate order of increasing sophistication, are: Pattern Recognition and Machine Learning by Christopher M. Bishop (Springer, 2007); Pattern Classification by Richard O. Duda, Peter E. Hart, David G. Stork (Wiley-Interscience, 2nd ed., 2000); and The Elements of Statistical Learning by T. Hastie, R. Tibshirani, J. H. Friedman (Springer, 2003). 270 CHAPTER 13 Fundamental graphical methods domain. We can use these methods to develop strategies for more detailed analysis, but we must make sure to tie the results back to the original problem.


pages: 394 words: 118,929

Dreaming in Code: Two Dozen Programmers, Three Years, 4,732 Bugs, and One Quest for Transcendent Software by Scott Rosenberg

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A Pattern Language, Berlin Wall, c2.com, call centre, collaborative editing, conceptual framework, continuous integration, Douglas Engelbart, Douglas Hofstadter, Dynabook, en.wikipedia.org, Firefox, Ford paid five dollars a day, Francis Fukuyama: the end of history, Grace Hopper, Gödel, Escher, Bach, Howard Rheingold, index card, Internet Archive, inventory management, Jaron Lanier, John von Neumann, knowledge worker, life extension, Loma Prieta earthquake, Menlo Park, Merlin Mann, new economy, Nicholas Carr, Norbert Wiener, pattern recognition, Paul Graham, Potemkin village, RAND corporation, Ray Kurzweil, Richard Stallman, Ronald Reagan, semantic web, side project, Silicon Valley, Singularitarianism, slashdot, software studies, South of Market, San Francisco, speech recognition, stealth mode startup, stem cell, Stephen Hawking, Steve Jobs, Stewart Brand, Ted Nelson, Therac-25, thinkpad, Turing test, VA Linux, Vannevar Bush, Vernor Vinge, web application, Whole Earth Catalog, Y2K

Why not build software around the same principle of pattern recognition that human beings use to interface with reality? Base it on probability rather than certainty? Have it “try to be an ever better guesser rather than a perfect decoder”? These ideas have helped the field of robotics make progress in recent times after long years of frustrating failure with the more traditional approach of trying to download perfect models of the world, bit by painful bit, into our machines. “When you de-emphasize protocols and pay attention to patterns on surfaces, you enter into a world of approximation rather than perfection,” Lanier wrote. “With protocols you tend to be drawn into all-or-nothing high-wire acts of perfect adherence in at least some aspects of your design. Pattern recognition, in contrast, assumes the constant minor presence of errors and doesn’t mind them.”


pages: 481 words: 125,946

What to Think About Machines That Think: Today's Leading Thinkers on the Age of Machine Intelligence by John Brockman

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3D printing, agricultural Revolution, AI winter, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, algorithmic trading, artificial general intelligence, augmented reality, autonomous vehicles, bitcoin, blockchain, clean water, cognitive dissonance, Colonization of Mars, complexity theory, computer age, computer vision, constrained optimization, corporate personhood, cosmological principle, cryptocurrency, cuban missile crisis, Danny Hillis, dark matter, discrete time, Elon Musk, Emanuel Derman, endowment effect, epigenetics, Ernest Rutherford, experimental economics, Flash crash, friendly AI, Google Glasses, hive mind, income inequality, information trail, Internet of things, invention of writing, iterative process, Jaron Lanier, job automation, John von Neumann, Kevin Kelly, knowledge worker, loose coupling, microbiome, Moneyball by Michael Lewis explains big data, natural language processing, Network effects, Norbert Wiener, pattern recognition, Peter Singer: altruism, phenotype, planetary scale, Ray Kurzweil, recommendation engine, Republic of Letters, RFID, Richard Thaler, Rory Sutherland, Search for Extraterrestrial Intelligence, self-driving car, sharing economy, Silicon Valley, Skype, smart contracts, speech recognition, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, superintelligent machines, supervolcano, the scientific method, The Wisdom of Crowds, theory of mind, Thorstein Veblen, too big to fail, Turing machine, Turing test, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

The patterns involved can easily exceed what the human mind can grasp. That will increase as computers improve. The real advance has been in the number-crunching power of digital computers. That has come from the steady Moore’s Law doubling of circuit density every two years or so, not from any fundamentally new algorithms. That exponential rise in crunch power lets ordinary-looking computers tackle tougher problems of Big Data and pattern recognition. Consider the most popular algorithms in Big Data and machine learning. One algorithm is unsupervised (requires no teacher to label data). The other is supervised (requires a teacher). They account for a great deal of applied AI. The unsupervised algorithm is called k-means clustering, arguably the most popular algorithm for working with Big Data. It clusters like with like and underlies Google News.

It doesn’t always get to the top of the highest hill of probability; it does almost always get to the top of the nearest hill. That may be the best any learning algorithm can do in general. Carefully injected noise and other tweaks can speed the climb. But all paths still end at the top of the hill in a maximum-likelihood equilibrium. They all end in a type of machine-learning nirvana of locally optimal pattern recognition or function approximation. Those hilltop equilibria will look ever more impressive and intelligent as computers get faster. But they involve no more thinking than calculating some sums and then picking the biggest. Thus much of machine thinking is just machine hill-climbing. Marvin Minsky’s 1961 review paper “Steps Toward Artificial Intelligence” makes for a humbling read in this context, because so little has changed algorithmically since he wrote it.


pages: 436 words: 141,321

Reinventing Organizations: A Guide to Creating Organizations Inspired by the Next Stage of Human Consciousness by Frederic Laloux, Ken Wilber

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Albert Einstein, augmented reality, blue-collar work, Buckminster Fuller, call centre, carbon footprint, conceptual framework, corporate social responsibility, crowdsourcing, failed state, future of work, hiring and firing, index card, interchangeable parts, invisible hand, job satisfaction, Johann Wolfgang von Goethe, Kenneth Rogoff, meta analysis, meta-analysis, pattern recognition, post-industrial society, quantitative trading / quantitative finance, randomized controlled trial, shareholder value, Silicon Valley, the market place, the scientific method, Tony Hsieh

A tipping point seems to have been reached at which advanced robotics and artificial intelligence (including machine-learning, language-translation, and speech- and pattern-recognition software) are beginning to render even many middle-income jobs obsolete. Travel agents have already largely been replaced by automated websites, and bank clerks by ATMs. Lawyers start to feel the heat now that smart algorithms can search case law, evaluate the issues at hand, and summarize the results. Software has already shown it can perform legal discovery far more cheaply and more thoroughly than lawyers and paralegals in many cases. Radiologists, who can earn over $300,000 a year in the United States after 13 years of college education and internship, are in a similar boat. Automated pattern-recognition software can do much of the work of scanning tumor slides and X-ray images at a fraction of the cost.


pages: 420 words: 143,881

The Blind Watchmaker; Why the Evidence of Evolution Reveals a Universe Without Design by Richard Dawkins

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epigenetics, Eratosthenes, Fellow of the Royal Society, Menlo Park, pattern recognition, phenotype, random walk, silicon-based life, Steven Pinker

But natural selection doesn’t choose genes directly, it chooses the effects that genes have on bodies, technically called phenotypic effects. The human eye is good at choosing phenotypic effects, as is shown by the numerous breeds of dogs, cattle and pigeons, and also, if I may say so, as is shown by Figure 5. To make the computer choose phenotypic effects directly, we should have to write a very sophisticated pattern-recognition program. Pattern-recognizing programs exist. They are used to read print and even handwriting. But they are difficult, ‘state of the art’ programs, needing very large and fast computers. Even if such a pattern-recognition program were not beyond my programming capabilities, and beyond the capacity of my little 64-kilobyte computer, I wouldn’t bother with it. This is a task that is better done by the human eye, together with – and this is more to the point – the 10-giganeurone computer inside the skull.


pages: 566 words: 163,322

The Rise and Fall of Nations: Forces of Change in the Post-Crisis World by Ruchir Sharma

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3D printing, Asian financial crisis, backtesting, bank run, banking crisis, Berlin Wall, Bernie Sanders, BRICs, business climate, business process, call centre, capital controls, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, central bank independence, centre right, colonial rule, Commodity Super-Cycle, corporate governance, crony capitalism, currency peg, dark matter, debt deflation, deglobalization, deindustrialization, demographic dividend, demographic transition, Deng Xiaoping, Doha Development Round, Donald Trump, Edward Glaeser, Elon Musk, eurozone crisis, failed state, Fall of the Berlin Wall, falling living standards, Francis Fukuyama: the end of history, Freestyle chess, Gini coefficient, hiring and firing, income inequality, indoor plumbing, industrial robot, inflation targeting, Internet of things, Jeff Bezos, job automation, Joseph Schumpeter, Kenneth Rogoff, knowledge economy, labor-force participation, Malacca Straits, Mark Zuckerberg, market bubble, megacity, Mexican peso crisis / tequila crisis, mittelstand, moral hazard, New Economic Geography, North Sea oil, oil rush, oil shale / tar sands, oil shock, pattern recognition, Peter Thiel, pets.com, Plutocrats, plutocrats, Ponzi scheme, price stability, Productivity paradox, purchasing power parity, quantitative easing, Ralph Waldo Emerson, random walk, rent-seeking, reserve currency, Ronald Coase, Ronald Reagan, savings glut, secular stagnation, Shenzhen was a fishing village, Silicon Valley, Silicon Valley startup, Simon Kuznets, smart cities, Snapchat, South China Sea, sovereign wealth fund, special economic zone, spectrum auction, Steve Jobs, The Wisdom of Crowds, Thomas Malthus, total factor productivity, trade liberalization, trade route, tulip mania, Tyler Cowen: Great Stagnation, unorthodox policies, Washington Consensus, WikiLeaks, women in the workforce, working-age population

To help navigate the normal condition of the world—an environment prone to booms, busts, and protests—this book outlines ten rules for spotting whether a country is on the rise, on the decline, or just muddling through. Together the rules work as a system for spotting change. They are most applicable to emerging nations, in part because those nations’ economic and political institutions are less well established, making them more vulnerable to political and financial upheaval. However, as I will show along the way, many of the rules find useful applications in the developed world. Pattern Recognition: The Principles Behind the Rules A few basic principles underlie all the rules. The first is impermanence. At the height of the 2000s boom, a variety of global forces—easy money pouring out of Western banks, spiking prices of commodities, and soaring global trade—doubled the growth rate of emerging economies. The scale of the boom was unprecedented—by 2007, the number of nations expanding faster than 5 percent reached one hundred, or five times the postwar norm—but forecasters assumed this freak event was a turning point.

., 79 Kiss of Debt (Rule 9), 297–328 Zielinski play, 297–98, 299, 323 see also debt Kohler, Hans-Peter, 35 Koizumi, Junichiro, 335, 351 Kudrin, Alexei, 60, 67, 74 Kuznets, Simon, 101, 299 Lagarde, Christine, 58 Laos, 180 Latin America, 364–70 economic cycles in, 93, 291, 310, 342 infrastructure in, 186 and location, 177 regional alliances, 179, 182–83, 366 wealth gap in, 95–96, 97, 125–26 see also specific nations Lavagna, Roberto, 74 leadership: aging, 3, 59, 71–72, 75, 114 arrogance of, 59, 60, 61, 62, 71, 72 autocrats vs. democrats, 21, 63, 82, 85–91, 132, 333, 365 charismatic, 61, 68, 69 in circle of life, 60–62, 364–65 corruption of, 3, 12, 60, 61, 67, 91, 97, 105–6, 114, 127–29, 226, 320, 379 and elections, 364–65 fresh, 63, 64–70, 75, 76–77, 86, 94, 388 populist, 59, 77–80, 96–99, 363–64 and regional alliances, 179 stale, 21, 59, 61, 63, 70–77, 86, 92, 94, 249, 376, 392–93 technocrats, 63, 80–85, 237 tenure of, 73–75, 90 Li, Robin, 113 Libya, 4, 74, 92, 167, 168, 185 life expectancy, 10, 19, 25, 26, 28, 44 and retirement, 37, 38–40 Li Keqiang, 84, 150, 312 location, 166–200 and economic growth, 175–78, 199–200 and global trade, 171–75, 176–89, 199–200 population centers, 189–97 regional alliances, 173–75, 178–84, 199 service cities, 197–200 and shipping routes, 185–86, 402–3 long term, myth of, 399–401 López Michelsen, Alfonso, 188 Lucas, Robert, 280 Lucas paradox, 280 Lula da Silva, Luiz Inácio, 66, 69, 71, 74, 79, 284 Ma, Jack, 113, 114 Macri, Mauricio, 83, 365–66 Maduro, Nicolás, 158 Ma Huateng, 113 Maktoum, Sheikh Mohammed bin Rashid Al, ruling family of, 166–67 Malaysia, 190, 342 banking crisis in (1990s), 316 debt in, 299, 300, 306, 315, 325 economic cycle in, 29, 327, 378–79 and global trade, 174, 178, 180 hype about, 330, 331, 345, 348 immigration to, 48, 51 investment in, 206, 231 leadership in, 60, 76, 379 manufacturing in, 205 state banks in, 151, 323–24 wealth in, 107, 118 Malta, 342, 348 Malthus, Thomas, 27, 343 Manuel, Trevor, 341 manufacturing: and economic growth, 15–16, 216, 227, 341 for export, 207–8 and GDP, 204–6, 214–15 and innovation, 15, 204 international, 213–15, 216 investment in, 203–6, 232 and productivity, 15, 204–5, 207 stabilizing effect of, 215–17 virtuous cycle of, 206–10, 215, 221, 228 Mao Zedong, 84 Marcos, Ferdinand, 7, 79, 334 Marino, Roger, 49 markets: cycles of, viii–x, 74–75 and leadership, 79–81 predictive power of, 13–14, 75 Marx, Karl, 92 Mauro, Paolo, 336 Mboweni, Tito, 245 Meirelles, Henrique, 69, 245 Menem, Carlos, 82 Mercosur trade bloc, 182–83, 366 Merkel, Angela, 45, 387, 388 Mexico, 368–70 breakdown in government functions, 201–2 corruption in, 141 currency crisis (1994) in, 5, 65, 148, 273, 281, 285, 298, 324 debt of, 291, 298, 324–25 economic cycle in, 98, 348, 400 government spending in, 140, 141, 148 and immigration, 53–54 inflation in, 242, 246, 370 infrastructure in, 232, 369 investment in, 203, 205, 220, 232, 368, 369 leadership in, 81, 94, 98, 368–69 location of, 168, 169, 177, 193, 199–200, 370 manufacturing in, 369–70 and oil, 130, 368, 369 population centers in, 193–94, 199 population growth rates in, 26, 30, 369 and regional alliances, 183, 199, 370 social unrest in, 70, 77, 98 technology in, 219–20 wealth gap in, 115, 120, 364 workforce in, 40, 43 middle class: anger of, 3, 5, 72–73, 76–77 growing, 204 jobs for, 213 and wealth inequality, 102 Middle East: Arab Spring, 4, 31, 76, 91–92, 167, 242 energy subsidies in, 156, 157 investment in, 169–70 political unrest in, 167, 168, 169, 170, 396 restrictions on women in, 42 see also specific nations middle-income trap (hype), 344–45 Mikitani, Hiroshi, 110 Modi, Narenda, 79, 94, 210, 350–51, 370, 373–75 Mohamad, Mahathir, 60, 231, 280, 330 money flows, 2, 5, 268–70, 275, 277, 279–90, 292, 295–96 Monti, Mario, 81 Morales, Evo, 76 Morocco, 168, 185, 190, 199 Moynihan, Daniel Patrick, 234–35 Mozambique, 225, 354 Mubarak, Gamal, 133 Mubarak, Hosni, 76, 92 Mugabe, Robert, 86, 88–89, 96–97, 373 Musk, Elon, 123–24 Myanmar, 187, 333, 334 Nakasone, Yasuhiro, 46 Nanda, Ramana, 220 natural resources, 223–29 Nehru, Vikram, 81, 82 Netherlands, 41–42, 50, 176, 224–25, 255, 256, 299 news media, see hype New Zealand, 244 Nguyen Tan Dung, 90–91 Nicaragua, 98 Niger, 66, 339 Nigeria: commodities economy of, 4, 174, 223, 225–27, 228, 293, 394, 398 corruption in, 226–27, 398 economic cycle in, 88, 348, 398 GDP in, 12, 87, 227 government spending in, 141–42 hype about, 348, 398 inflation in, 242 leadership in, 352–53, 365, 398 and regional alliance, 182 slipping backward, 202, 205, 232, 398 workforce in, 19, 29, 31, 185 North America Free Trade Agreement (NAFTA), 183 North Korea, 74, 86, 96, 136, 174 Norway, 90, 225, 300, 346, 348 Nyerere, Julius, 96 Obasanjo, Olusegun, 398 OECD (Organization for Economic Cooperation and Development), 40, 44, 48–49 oil: and bad billionaires, 100, 111 and corruption, 226 curse of, 12, 169, 224, 227 and “Dutch disease,” 224–25 exporters of, 155, 174, 341, 394 extraction of, 124–25 investment in, 223, 224, 225, 228 offshore, 130 price of, 4, 29, 60, 62, 64, 65, 89, 111, 114, 154–55, 227–29, 257, 268, 282, 293, 333, 342, 348, 362, 393, 394, 398 shale, 228–29 Okonjo-Iweala, Ngozi, 227 Oman, 342 Omidyar, Pierre, 49 OPEC, 64, 65, 240, 333 Ortega, Daniel, 98 Osborne, Michael, 54 Osnos, Evan, 145 Ostry, Jonathan, 125–26 Ozden, Caglar, 51 Pacific Alliance, 183–84, 199 Pakistan, 142, 180, 370–73 infrastructure in, 187, 372 leadership in, 77, 94, 96, 372 service jobs in, 212 war with India, 97, 375 workforce in, 31, 42, 370–71, 373 Palaiologos, Yannis, 164 Paldam, Martin, 241 Palestine, 175 Papademos, Lucas, 80 Paraguay, 182, 183, 242 Park Chung-hee, 85 Park Geun-hye, 47, 383 pattern recognition, 7–11 Peña Nieto, Enrique, 94, 115, 368–69 People Matter (Rule 1), 23–57; see also workforce per capita income, 10, 339–40, 348 Perils of the State (Rule 4), 132–65 and bad billionaires, 127–29 devaluing the currency, 290–96, 381 energy subsidies, 156–58 and leaders, see leadership meddling in private companies, 158–62, 246, 250–51 and population growth, 33, 35–36 privatization, 161–62, 177 sensible role for state, 162–65 state banks, 134, 151–54, 243–44 state companies as political tools, 154–56, 165 Perón, Juan, 333 Persson, Stefan, 174, 179 Peru, 52, 188, 190, 291 commodities economy of, 174, 228, 368 investment in, 228 leadership in, 98 and Pacific Alliance, 183–84 workforce in, 43 pessimism, prevailing fashion of, 9, 275, 360, 399 Philippines, 180, 193, 194, 198 economic recovery in, 327, 350, 375–76, 378 economic slowdown in, 346 hype about, 333, 334, 348 investment in, 232, 375–76 leadership in, 79, 97, 376 population growth in, 31 service jobs in, 212–13, 221 social unrest in, 70, 77 wealth redistribution in, 97 workforce in, 19, 31 Piketty, Thomas, 104 Piñera, Sebastián, 34–35, 95–96, 98, 102, 126 Pinochet, Augusto, 82, 98, 99 Pires, Pedro, 353 Poland, 139, 190, 339 billionaires in, 109, 120, 391 currency of, 288–90 debt in, 391 investment in, 205, 215, 391 leadership in, 74–75, 365 location of, 168, 176, 177, 198–200 political shift in, 391 population growth rate in, 30, 39 private economy in, 159, 160, 161 workforce in, 39, 391, 392 population growth: baby bonuses, 33–36 decline in, 19–20, 24–32, 44, 56, 392, 394, 399 and economic growth, 29–32, 57 forecasts of, 25, 27, 28, 32 measurement of, 21 and replacement fertility rate, 26, 33, 37, 47 and workforce growth, 18–19, 39, 57 populism, 59, 60, 77–80, 81–82, 96–99, 100, 247, 363–64, 389 Portugal, 29, 30, 39, 288 Price of Onions (Rule 7), 234–61; see also inflation; prices prices, 234–61 asset, 257–61 of commodities, 111, 114, 174, 228, 263, 278, 341–42, 354, 365, 386; see also oil consumer, 235, 256, 257–61 and currency, 294 and deflation, 256 falling, 4, 5, 253–54 of food, 234, 235, 236, 241–42, 365 to foreign buyers, 258 “Mapping the World’s Prices,” 265 of money, 304 public anger about, 237, 241–42 stabilizing, 261 of stocks, 313 validity of data about, 13 wage-price spiral, 240 Pritchett, Lant, 336, 347 production: chain of, 27 as growth factor, 15–16 productivity: elusive x factor in, 20 and government spending, 148–49 and ICOR, 149 and immigration, 51 in manufacturing, 15, 204–5, 207 measurement of, 17, 18–21 and population trends, 39, 57 and technology, 220–21 Putin, Vladimir, 18, 42, 392–94 and billionaires, 114, 115, 350 and economic reforms, 58–59, 60, 61, 67–69, 78, 284 hype about, 342, 350, 393, 394 and populism, 59 rise to power, 61, 66, 67 and social unrest, 73–74 and stale leadership, 60, 71–72, 76, 114, 159–60, 392 and state intervention, 159–60, 350 Qaddafi, Muammar el-, 74 Rahman, Sheikh Mujibur, 96 Rajan, Raghuram, 251 Rajapaksa, Mahinda, 180, 373 Rao, Jaithirth, 209–10 Razak, Najib, 379 Reagan, Ronald, 64–65, 66, 70 Real Effective Exchange Rate (REER), 265, 267, 272 real estate: bad billionaires in, 100, 105, 107, 108, 110–11, 364, 369 and debt, 310–13 investment binges in, 222–23, 228, 229–31, 382, 387 prices of, 108, 257–61, 309, 312, 382, 389 and state banks, 154 recessions: 1930s (Great Depression), 172, 173, 254, 258 1970s, 2, 64, 65 1980s, 2, 64, 305 1990s, 2, 6, 13, 64, 66, 68, 83, 138, 148, 161 2007–2009, see global financial crisis in Asia (1997-1998), see Asia China as possible source of, 2–3 cycles of, 2, 14, 64–66, 87–88 debt as cause of, 260, 298–99, 301, 303–4, 308–10, 317–19 dot-com bust, see dot-com bubble forecasting of, 14, 337–38, 400 official documentation of, 14 U.S. origins of, 2, 132, 303–4, 305–6, 308–9, 327–28, 362 Regional Comprehensive Economic Partnership, 173 rent-seeking industries, 110–11, 122, 123, 124 Renzi, Matteo, 81, 94 Rhodes-Kropf, Matthew, 220 Rickards, James, 168 Robinson, James, 176 robots, 27, 36, 54–57, 214 Rockefeller, John D., 124 Rodrik, Dani, 207 Romania, 87, 162, 238, 348, 391–92 Rothschild, Baron de, 349 Rousseff, Dilma, 80, 152–53, 155, 366, 368 Roy, Nilanjana, 236 Rubin, Robert, 341 Russia, 193, 326, 344 aging population in, 72 authoritarianism in, 3, 60 author’s speech in, 58–59 banking crisis (1990s), 61 billionaires in, 103, 107, 114–15, 116, 119, 120, 364, 393 birth rate in, 26 brain drain from, 52 capital flight from, 52, 281, 282, 367 commodities economy in, 4, 205, 225, 341, 367, 376, 393, 397 currency of, 263, 265, 268, 281, 282, 289, 291, 393, 394 debt in, 59, 291, 327, 394 economic growth in, 17, 60, 63, 66, 69, 159, 340, 358 economic slowdown in, 346, 392 education in, 17 financial deepening in, 327 GDP of, 4, 175, 394 government spending in, 139, 149, 394 hype about, 4, 338, 347, 348, 350 inflation in, 241, 242, 246 international business in, 18, 394 international sanctions against, 282 investment in, 205, 208, 232, 394–95, 397 leadership in, 349, 392–94; see also Putin, Vladimir oil in, 29, 60, 62, 72, 114, 155, 159, 265, 282, 341, 342, 393, 394 oligarchs in, 107, 114–15, 160, 268 per capita income in, 61, 68 political cronyism in, 4, 159, 160 reforms in, 58, 59, 60, 61, 66, 67, 68, 78 social unrest in, 4, 73–74 state banks in, 151–52, 159, 282 stock exchange in, 161 tech companies in, 17, 159–60 workforce in, 30, 42, 155, 393 Rwanda, 181, 182, 199 Rybolovlev, Dmitry, 107 Sakurauchi, Yoshio, 329–30 Samuelson, Paul, 13 Santos, Manuel, 188, 189 Sassen, Saskia, 197 Saudi Arabia, 29, 42, 170, 190 currency of, 396 energy subsidies in, 156, 157 GDP in, 87 government spending in, 139, 396 leadership in, 87, 396 and oil prices, 227–28, 279, 333, 394 roller-coaster economy of, 227–28 savings, 16, 277–79 Scandinavia, 35, 42 Schlumpeter, Joseph, 360 Schularick, Moritz, 259 service businesses, 204, 210–13 service cities, 197–200 Sharif, Nawaz, 94, 372 Sharma, Rahul, 170–71 Shinawatra, Thaksin, 79, 97 Shinawatra, Yingluck, 78–79, 217 Sierra Leone, 225 Silk Road, 8, 187–88, 399 Singapore, 32, 33, 175, 182, 238, 346, 348 Singh, Manmohan, 62, 73, 74–75, 133, 187, 234–37, 250 Sirisena, Maithripala, 181 Sisi, Abdel Fattah el-, 76, 157 social upheavals: Arab Spring, 4, 31, 76, 91–92, 167, 242 “middle-class rage,” 72–73 as revolts against stale leadership, 21, 61, 70, 72, 73–74, 77, 92 spread of, 3, 91–92 South Africa: commodities economy in, 223, 225, 263, 376, 397 corruption in, 164 currency in, 397 debt of, 291 decline in development of, 3, 6, 205, 346, 397 economic growth in, 10, 38, 90, 340 HDI ranking, 10 immigration to, 48 investment in, 232, 397 leadership in, 76, 90, 352–53, 397 life expectancy in, 10 social unrest in, 4, 73, 74 South Asia, 370–75, 400 commodities economies in, 371 political instability in, 180, 373 regional alliances, 179, 180 restrictions on women in, 42 Southeast Asia, 375–80 economic cycle in, 310, 324, 326, 327, 349, 375–76, 400 and global trade, 176, 178, 179–80 and hype, 330, 331 South Korea, 190, 197–98 and China, 382 debt in, 216, 321 economic growth in, 10, 66–67, 87, 175, 216, 238, 308 government spending in, 140, 146 HDI ranking, 10 homogeneity in, 46–47 hype about, 333, 334, 339 inflation in, 237, 246 and international business, 179, 382–83 investment in, 205, 206, 218, 221, 225, 238, 253 leadership in, 66–67, 82, 85, 93, 349 literacy in, 17 manufacturing in, 205, 212–13, 214–15, 216, 225, 382–83 per capita income in, 215, 316 productivity in, 20 robots in, 56 technology in, 218, 221, 295, 382 wealth in, 107–8, 109, 116, 117–18, 120, 121–22, 383 workforce in, 40, 43, 44, 47, 56, 140, 383 Soviet Union: after 1991, see Russia central plan of, 81, 85 fall of, 29, 67, 107, 109, 151, 198, 208, 242 Spain, 29, 32, 192 current account in, 288 debt in, 327–28, 389–90 internal devaluation in, 287 manufacturing in, 387 Spence, Michael, 341 Spence Commission, 341–42 Sri Lanka, 180–81, 187, 212, 365, 370–71, 373 stagflation, 64, 65, 240, 395 stagnation, 6, 83, 88, 91, 105, 172, 192 state banks, 134, 151–54 state capitalism, 133–35, 155 stock markets: best time to buy in, 349 and crisis of 2008, 146–47 mania/crash, 258–61, 313–14, 318 signals from, 13, 74–75, 133, 134, 258, 313 state-run companies in, 135 “structural reform,” 62–63, 163 Studwell, Joe, 17, 143–44 Sudan, 142, 185 Suharto, 59–60, 82, 93, 293, 320–21, 330 Summers, Lawrence, 104, 336, 347 supply and demand, 256–57 supply networks, 235, 238, 239–40, 243, 253, 292, 365 supply side, 24 Surowiecki, James, 13 Sweden, 42, 50, 136, 138 billionaires in, 108, 116, 121, 123 debt in, 300 economic cycle in, 17, 90, 256 financial crisis (1990s), 317 and inflation, 245 Switzerland, 41, 50, 121, 138–39, 198, 294 Syria: and Arab Spring, 92, 167 civil war in, 4, 92, 168, 224 economic cycle in, 87, 88 leadership in, 89 refugees from, 2, 44, 48 Taiwan, 144, 151, 190 banking crises in (1995, 1997), 316, 317 and China, 382–83 debt in, 291, 307, 317–18 economic growth in, 87, 175, 238, 308, 348 government spending in, 140, 146 hype about, 333, 334, 345, 346, 348 and international business, 382–83 investment in, 205, 218 leadership in, 82, 86, 93 literacy in, 17 per capita income in, 316 and regional alliances, 179, 383 and technology, 221, 295 wealth in, 107–8, 118, 120, 122 working-age population in, 383 Tanzania, 96, 181–82 taxes: corporate, 63, 138 cutting, 67 evading, 128, 137, 142, 164 failure to collect, 141–43 and government spending, 136–37 import tariffs, 172 inheritance, 124 and public services, 140 Taylor, Alan M., 259, 304, 310 technology: automation, 214 cycle of, 8, 124, 218–21 driverless cars, 54 and immigration, 51–52 investment in, 218–21, 229, 233, 255 and jobs, 101, 211, 212 and leisure time, 199 and productivity, 20, 51, 119, 220–21 robot workers, 27, 36, 54–57, 214 and service businesses, 210, 211–13 3-D printing, 8, 214 Tetlock, Philip, 400 Thailand, 47–48, 189–90 capital flight from, 272, 292 commodities economy in, 342, 379 credit binge in, 199, 297, 298–302, 306, 315, 328, 380 currency of, 217, 267, 271–73, 273, 285–86, 292 economic contraction in, 286, 349, 379 economic growth in, 79, 217, 256, 348, 380 economic recovery of, 288, 302, 325, 327 and hype, 330, 349 infrastructure in, 207–8, 230 and international trade, 174, 178, 179–80, 216 investment in, 206, 217, 225, 230–31 leadership in, 78–79, 97, 379–80 manufacturing in, 216–17, 225, 227, 379 military coup in (2014), 379–80 population growth rates in, 30, 47 social unrest in, 78–79, 189, 190, 217 state banks in, 151, 321, 323–24 Thatcher, Margaret, 64–65, 68, 94 Thiel, Peter, 104, 119, 125 thrift, 16, 277–79 Time, 331, 334–35, 347, 349, 350, 352 tourism, 2, 37, 199, 211, 288, 384–85 trade balance, 269 Transatlantic Trade and Investment Partnership, 173, 179 Trans-Pacific Partnership, 173, 178, 361, 377, 378, 383, 384, 386 Trudeau, Justin, 386 Trump, Donald, 53, 364 Tsai Ing-wen, 383 Tunisia, 91–92, 224 Turkey, 190, 326 currency of, 273–74, 280, 283, 291, 292, 293, 396 debt of, 291, 306, 327, 328 economic growth in, 66, 69, 72 financial deepening in, 327 government spending in, 139, 247–48 hype about, 345, 348 immigration to, 48 inflation in, 241, 242, 246, 247–50, 326 leadership in, 60, 66, 71–72, 74, 349, 395 and location, 395–96 per capita income in, 68, 331, 348 population growth in, 31 populist nationalism in, 60, 72, 247 reforms in, 67–68, 72, 248, 249, 331 social unrest in, 4, 61, 72, 73, 74 wealth in, 114, 116, 120 Tusk, Donald, 74–75 Uganda, 87, 181, 354–55 United Arab Emirates, 167, 170 United Kingdom, see Britain United Nations (UN), 10, 47 on population growth, 19, 25, 27, 33, 44–45 United States, 194–95, 360–64 billionaires in, 107, 108, 114, 116, 118–19, 121, 123–25, 364 birth rate in, 26 checks and balances in, 364 credit markets in, 13, 298, 303–4, 305–6, 316 currency of, 266, 271, 272, 362–63 current account deficit in, 278, 362–63 debt in, 363 economic growth in, 3, 288, 337–38, 340 economic recovery in, 24, 64–65, 102, 360 economic strength of, 266, 400 financial speculation in, 102 and geopolitics, 172–73 and global trade, 184, 185, 402–3 government spending in, 138, 139 and hype, 361–62 and immigration, 45, 49–50, 52, 53, 360 industrialization in, 144, 215 inflation in, 240–41, 258 infrastructure in, 207, 208 life expectancy in, 39, 40 and location, 176–77, 200 long boom of, 255–56 manufacturing in, 204, 213, 214, 215, 361 oil and gas in, 228–29, 362 per capita income in, 32, 66, 339, 346 polarization in, 62–63, 132, 363–64 productivity in, 20, 51–52, 220–21, 257, 303 recessions originating in, 2, 132, 303–4, 305–6, 308–9, 327–28, 362 and regional alliances, 173–75, 178, 183, 188, 199, 361, 383, 384, 386 “second term curse” in, 70–71 technology in, 20, 218, 221, 294, 303, 361–62 Treasury bonds, 280 Washington Consensus, 132–33 and wealth gap, 101, 102, 364 workforce in, 19, 32, 37, 41–42, 43–44, 360 Uribe, Álvaro, 77, 183, 350 Uruguay, 300 Velasco, Juan, 98 Venezuela, 4, 158 economic cycle in, 87, 346, 365 leadership in, 64, 69, 76, 77, 98, 365 oil in, 333, 334 and regional alliances, 182, 366 Vietnam, 42, 202 billionaires in, 118 Communist Party in, 377–78 currency in, 295 fiscal deficit in, 377 and global trade, 174, 176–78, 180, 295 hype about, 345 inflation in, 378 investment in, 378 leadership in, 90–91 location of, 168, 177–78, 185, 378 manufacturing in, 213, 378 per capita income in, 178, 378 population centers in, 190, 191, 199 Viravaidya, Mechai, 47 Volcker, Paul, 241, 245, 335 wage-price spiral, 240 Walton family, 119 Wang Jianlin, 114 wealth: balance in, 103 billionaire lists, 100, 103, 104, 116, 117, 120–21 and capital flight, 52–53, 107, 279–81, 292 creation of, 99, 103, 115 crony capitalism, 105–6, 112, 130, 332 of entrepreneurs, 118–19, 122 in family empires/inherited, 104, 116–21 measures of, 101 redistribution of, 95, 96–98, 99, 101, 126 of robber barons, 124 scale of, 107–10 and state meddling, 127–29 wealth gap, 95–96, 99–102, 364 and corruption, 127–29 and easy money, 101–2, 108 and economic declines, 125–27 rise of, 129–31 welfare states, 64, 65, 93, 97, 126, 138, 140–41 Wen Jiabao, 307, 308, 311–12 Widodo, Joko, 143, 157, 163, 376–77 Wiesel, Elie, 331–32 wildebeest, survival of, viii, ix, xi women: and birth rates, 18, 25–26, 28, 33–36, 43, 44, 47, 392 economic restrictions on, 42 education of, 26, 41 working, 28, 34, 35, 36, 40–44, 47 workforce: aging, 392 and available jobs, 32, 37, 55 and baby bonuses, 33–36 and economic growth, 24, 26, 52 global, 55–56 growth rate in, 28–32 highly skilled, 48–54 hours worked by, 18 and immigration, 28, 44–54 manual labor, 213 new people in, 28, 36, 57 participation rate in, 36–37 and pension funds, 279 and population declines, 24–32, 35, 38, 43, 44, 56 and productivity data, 18–19, 39 replaced by machines, 16, 24 and retirement, 36–40 robots in, 54–57 skilled, 48–54 wages, 101, 184, 185, 204, 214, 243, 257 women in, 28, 34, 35, 36, 40–44, 47 World Bank: on convergence, 339, 341 data set of, 407 on economic growth factors, 12, 18, 342, 346 forecasting record of, 336, 338 on inflation, 242 on infrastructure, 186, 187 on middle-income trap, 345 on new business, 48 on service sector, 210–11 Spence Commission, 341–42 on wealth gap, 100 on workforce, 42, 51 world economy, 358–401 absence of optimism in, 359 combined scores of, 358–59 crisis (2008), see global financial crisis disruptions of, 358–59 potential growth rate of, 359 world maps, 356–57, 402–3 see also specific nations World Trade Organization, 177 Wu Jinglian, 314 Xiao Gang, 311 Xi Jinping, 120, 156, 187, 208 Yellen, Janet, 101 Yeltsin, Boris, 67, 242 Yemen, 92 Yudhoyono, Susilo Bambang (SBY), 93, 157 Zambia, 96, 354 Zambrano, Lorenzo, 219–20 Zeihan, Peer, 184 Zielinski, Robert, The Kiss of Debt, 297–98, 299, 323 Zimbabwe, 86, 88–89, 96–97, 373 Zoellick, Robert, 242 “zombie companies,” 318–19 Zong Qinghou, 113 Zuckerberg, Mark, 104, 119, 124 Zuma, Jacob, 352, 397 ALSO BY RUCHIR SHARMA Breakout Nations: In Pursuit of the Next Economic Miracles ABOUT THE AUTHOR Ruchir Sharma is head of emerging markets and chief global strategist at Morgan Stanley Investment Management, with more than $20 billion of assets under management.


pages: 479 words: 144,453

Homo Deus: A Brief History of Tomorrow by Yuval Noah Harari

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23andMe, agricultural Revolution, algorithmic trading, Anne Wojcicki, anti-communist, Anton Chekhov, autonomous vehicles, Berlin Wall, call centre, Chris Urmson, cognitive dissonance, Columbian Exchange, computer age, Deng Xiaoping, don't be evil, European colonialism, experimental subject, falling living standards, Flash crash, Frank Levy and Richard Murnane: The New Division of Labor, glass ceiling, global village, invention of writing, invisible hand, Isaac Newton, job automation, Kevin Kelly, means of production, Mikhail Gorbachev, Minecraft, Moneyball by Michael Lewis explains big data, mutually assured destruction, new economy, pattern recognition, Peter Thiel, placebo effect, Ray Kurzweil, self-driving car, Silicon Valley, Silicon Valley ideology, stem cell, Steven Pinker, telemarketer, too big to fail, trade route, Turing machine, Turing test, ultimatum game, Watson beat the top human players on Jeopardy!

Until today, high intelligence always went hand in hand with a developed consciousness. Only conscious beings could perform tasks that required a lot of intelligence, such as playing chess, driving cars, diagnosing diseases or identifying terrorists. However, we are now developing new types of non-conscious intelligence that can perform such tasks far better than humans. For all these tasks are based on pattern recognition, and non-conscious algorithms may soon excel human consciousness in recognising patterns. This raises a novel question: which of the two is really important, intelligence or consciousness? As long as they went hand in hand, debating their relative value was just a pastime for philosophers. But in the twenty-first century, this is becoming an urgent political and economic issue. And it is sobering to realise that, at least for armies and corporations, the answer is straightforward: intelligence is mandatory but consciousness is optional.

Veterinary assistants – 86 per cent. Security guards – 84 per cent. Sailors – 83 per cent. Bartenders – 77 per cent. Archivists – 76 per cent. Carpenters – 72 per cent. Lifeguards – 67 per cent. And so forth. There are of course some safe jobs. The likelihood that computer algorithms will displace archaeologists by 2033 is only 0.7 per cent, because their job requires highly sophisticated types of pattern recognition, and doesn’t produce huge profits. Hence it is improbable that corporations or government will make the necessary investment to automate archaeology within the next twenty years.19 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 your run-of-the-mill job, and it is unclear whether forty-year-old cashiers or insurance agents will be able to reinvent themselves as virtual-world designers (just try to imagine a virtual world created by an insurance agent!).


pages: 405 words: 117,219

In Our Own Image: Savior or Destroyer? The History and Future of Artificial Intelligence by George Zarkadakis

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3D printing, Ada Lovelace, agricultural Revolution, Airbnb, Alan Turing: On Computable Numbers, with an Application to the Entscheidungsproblem, anthropic principle, Asperger Syndrome, autonomous vehicles, barriers to entry, battle of ideas, Berlin Wall, bioinformatics, British Empire, business process, carbon-based life, cellular automata, Claude Shannon: information theory, combinatorial explosion, complexity theory, continuous integration, Conway's Game of Life, cosmological principle, dark matter, dematerialisation, double helix, Douglas Hofstadter, Edward Snowden, epigenetics, Flash crash, Google Glasses, Gödel, Escher, Bach, income inequality, index card, industrial robot, Internet of things, invention of agriculture, invention of the steam engine, invisible hand, Isaac Newton, Jacquard loom, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, job automation, John von Neumann, Joseph-Marie Jacquard, millennium bug, natural language processing, Norbert Wiener, On the Economy of Machinery and Manufactures, packet switching, pattern recognition, Paul Erdős, post-industrial society, prediction markets, Ray Kurzweil, Rodney Brooks, Second Machine Age, self-driving car, Silicon Valley, speech recognition, stem cell, Stephen Hawking, Steven Pinker, strong AI, technological singularity, The Coming Technological Singularity, the scientific method, theory of mind, Turing complete, Turing machine, Turing test, Tyler Cowen: Great Stagnation, Vernor Vinge, Von Neumann architecture, Watson beat the top human players on Jeopardy!, Y2K

Several repetitions later, the artificial neural network has ‘learned’ how to perform a good match between input and output – in other words it has learned how to correctly recognise the input. Artificial neural networks have since been used to perform pattern recognition in visual systems, machine learning, as well as other applications in which it has been difficult to code in a conventional way. But the approach was more or less abandoned by the late 1990s as new and more sophisticated statistical and signal-processing techniques arrived on the scene, which could perform most pattern recognition tasks satisfactorily using conventional computer architectures. However, connectionism has enjoyed a spectacular comeback in recent years. An important innovation in electronics called ‘memristor’ has played a key role in the revival of neural computing.


pages: 163 words: 42,402

Machine Learning for Email by Drew Conway, John Myles White

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call centre, correlation does not imply causation, Debian, natural language processing, Netflix Prize, pattern recognition, recommendation engine, SpamAssassin, text mining

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.


pages: 189 words: 57,632

Content: Selected Essays on Technology, Creativity, Copyright, and the Future of the Future by Cory Doctorow

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book scanning, Brewster Kahle, Burning Man, en.wikipedia.org, informal economy, information retrieval, Internet Archive, invention of movable type, Jeff Bezos, Law of Accelerating Returns, Metcalfe's law, mutually assured destruction, new economy, optical character recognition, patent troll, pattern recognition, Ponzi scheme, post scarcity, QWERTY keyboard, Ray Kurzweil, RFID, Sand Hill Road, Skype, slashdot, social software, speech recognition, Steve Jobs, Turing test, Vernor Vinge

The brain is not one big neural net, the brain is several hundred different regions, and we can understand each region, we can model the regions with mathematics, most of which have some nexus with chaos and self-organizing systems. This has already been done for a couple dozen regions out of the several hundred. "We have a good model of a dozen or so regions of the auditory and visual cortex, how we strip images down to very low-resolution movies based on pattern recognition. Interestingly, we don't actually see things, we essentially hallucinate them in detail from what we see from these low resolution cues. Past the early phases of the visual cortex, detail doesn't reach the brain. "We are getting exponentially more knowledge. We can get detailed scans of neurons working in vivo, and are beginning to understand the chaotic algorithms underlying human intelligence.


pages: 352 words: 64,282

MongoDB: The Definitive Guide by Kristina Chodorow, Michael Dirolf

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create, read, update, delete, Debian, pattern recognition, web application

If we know some of the characteristics of our data, for instance, that there are only 43 chemicals with a freezing point of -20°, we can rearrange the $all to do that query first: all.put("$elemMatch", fp); all.put("$elemMatch", mw); all.put("$elemMatch", bp); criteria.put("index", new BasicDBObject("$all", all)); Now the database can quickly find those 43 elements and, for the subsequent clauses, has to scan only 43 elements (instead of 1 million). Figuring out a good ordering for arbitrary searches is the real trick of course, of course. This could be done with pattern recognition and data aggregation algorithms that are beyond the scope of this book. News Aggregator: PHP We will be creating a basic news aggregation application: users submit links to interesting sites, and other users can comment and vote on the quality of the links (and other News Aggregator: PHP | 159 comments). This will involve creating a tree of comments and implementing a voting system. Installing the PHP Driver The MongoDB PHP driver is a PHP extension.


pages: 227 words: 62,177

Numbers Rule Your World: The Hidden Influence of Probability and Statistics on Everything You Do by Kaiser Fung

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American Society of Civil Engineers: Report Card, Andrew Wiles, Bernie Madoff, Black Swan, call centre, correlation does not imply causation, cross-subsidies, Daniel Kahneman / Amos Tversky, edge city, Emanuel Derman, facts on the ground, Gary Taubes, John Snow's cholera map, moral hazard, p-value, pattern recognition, profit motive, Report Card for America’s Infrastructure, statistical model, the scientific method, traveling salesman

is much more worthy of exploration. Forgetting what the textbooks say, most practitioners believe the answer is quite often yes. In the case of credit scoring, correlation-based statistical models have been wildly successful even though they do not yield simple explanations for why one customer is a worse credit risk than another. The parallel development of this type of model by researchers in numerous fields, such as pattern recognition, machine learning, knowledge discovery, and data mining, also confirms its practical value. In explaining how credit scoring works, statisticians emphasize the similarity between traditional and modern methods; much of the criticism leveled at credit-scoring technology applies equally to credit officers who make underwriting decisions by handcrafted rules. Credit scores and rules of thumb both rely on information from credit reports, such as outstanding account balances and past payment behavior, and such materials contain inaccurate data independently of the method of analysis.


pages: 199 words: 47,154

Gnuplot Cookbook by Lee Phillips

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bioinformatics, computer vision, general-purpose programming language, pattern recognition, statistical model, web application

He has been the reviewer for numerous scientific articles, research proposals, and books, and has been a judge in the German Federal Competition in Computer Science on several occasions. His main interests are functional programming and machine-learning algorithms. David Millán Escrivá was 8 years old when he wrote his first program on 8086 PC with Basic language. He has more than 10 years of experience in IT. He has worked on computer vision, computer graphics, and pattern recognition. Currently he is working on different projects about computer vision and AR. I would like to thank Izanskun and my daughter Eider. www.PacktPub.com Support files, eBooks, discount offers, and more You might want to visit www.PacktPub.com for support files and downloads related to your book. Did you know that Packt offers eBook versions of every book published, with PDF and ePub files available?


pages: 179 words: 42,006

Startup Weekend: How to Take a Company From Concept to Creation in 54 Hours by Marc Nager, Clint Nelsen, Franck Nouyrigat

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Amazon Web Services, barriers to entry, business climate, invention of the steam engine, James Watt: steam engine, Mark Zuckerberg, minimum viable product, pattern recognition, Silicon Valley, transaction costs, web application, Y Combinator

Having witnessed his own and other startup problems for years, Blank says, “The same issues arose time and again: big company management styles versus entrepreneurs wanting to shoot from the hip; founders versus professional managers; engineering versus marketing; marketing versus sales; missed schedule issues; sales missing the plan, running out of money, [or having to] raise new money.” He says he “began to gain an appreciation of how world-class venture capitalists develop pattern recognition for these common types of problems. ‘Oh yes, company X, they're having problem 343. Here are the six likely ways that it will resolve, with these probabilities.’” Well, maybe it's not that exact. Blank talked to a few of his friends in the venture capital business who acknowledged that they had noticed these problems over the years. They had developed a pretty good sense of which firms were going to succeed and which would fail based on these patterns.


pages: 133 words: 42,254

Big Data Analytics: Turning Big Data Into Big Money by Frank J. Ohlhorst

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algorithmic trading, bioinformatics, business intelligence, business process, call centre, cloud computing, create, read, update, delete, data acquisition, DevOps, fault tolerance, linked data, natural language processing, Network effects, pattern recognition, performance metric, personalized medicine, RFID, sentiment analysis, six sigma, smart meter, statistical model, supply-chain management, Watson beat the top human players on Jeopardy!, web application

Those data, combined with other public data such as census, meteorological, and even social networking data, create a unique capability that services the customer and Amazon as well. Much the same can be said for Facebook, where Big Data comes into play for critical features such as friend suggestions, targeted ads, and other member-focused offerings. Facebook is able to accumulate information by using analytics that leverage pattern recognition, data mash-ups, and several other data sources, such as a user’s preferences, history, and current activity. Those data are mined, along with the data from all of the other users, to create focused recommendations, which are reported to be quite accurate for the majority of users. BIG DATA REACHES DEEP Google leverages the Big Data model as well, and it is one of the originators of the software elements that make Big Data possible.


pages: 219 words: 63,495

50 Future Ideas You Really Need to Know by Richard Watson

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23andMe, 3D printing, access to a mobile phone, Albert Einstein, artificial general intelligence, augmented reality, autonomous vehicles, BRICs, Buckminster Fuller, call centre, clean water, cloud computing, collaborative consumption, computer age, computer vision, crowdsourcing, dark matter, dematerialisation, digital Maoism, Elon Musk, energy security, failed state, future of work, Geoffrey West, Santa Fe Institute, germ theory of disease, happiness index / gross national happiness, hive mind, hydrogen economy, Internet of things, Jaron Lanier, life extension, Marshall McLuhan, megacity, natural language processing, Network effects, new economy, oil shale / tar sands, pattern recognition, peak oil, personalized medicine, phenotype, precision agriculture, profit maximization, RAND corporation, Ray Kurzweil, RFID, Richard Florida, Search for Extraterrestrial Intelligence, self-driving car, semantic web, Skype, smart cities, smart meter, smart transportation, statistical model, stem cell, Stephen Hawking, Steve Jobs, Steven Pinker, Stewart Brand, strong AI, Stuxnet, supervolcano, telepresence, The Wisdom of Crowds, Thomas Malthus, Turing test, urban decay, Vernor Vinge, Watson beat the top human players on Jeopardy!, web application, women in the workforce, working-age population, young professional

This means that instead of working on one computation after another (albeit at very fast speeds) a quantum computer can work on different computations at the same time. Hey presto, a computer with processing speeds a million or more times faster than anything that’s currently available, but more importantly, a computer able to solve problems that conventional computers cannot—for example, pattern recognition or code breaking. Quantum computing also has another major advantage. With conventional, silicon-based computers, overheating and energy use is a major problem. With quantum computers it’s not. Do such computers currently exist? At the moment the answer is no, certainly in the sense of being commercially scalable, usable or practical. But it’s a reasonably safe bet to say that they will.

The Art of Profitability by Adrian Slywotzky

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business process, Indoor air pollution, Isaac Newton, pattern recognition, rolodex, shareholder value

But it’s probably the hardest lesson of all.” He paused. “Take a look at this as well.” Zhao handed Steve a copy of Sources of Power by Gary Klein. “You don’t have to read the whole thing. Just these pages.” He gave Steve a slip of paper with the numbers 31-33, 39-40, 42, 148-151, 154-156, 169, 260, 289-290. Steve arched his eyebrows. Zhao laughed. “No, I didn’t go through it picking pages. I just looked in the index under ‘Pattern recognition.’” Steve stuck the book in his backpack. “Next week, then?” “Two weeks.” Neither man said what they were both thinking. One more session to go. 148 THE ART OF PROFITABILITY 23 Digital Profit May 17. Steve’s work with Zhao was almost over. Steve stopped before entering the lobby of 44 Wall Street. He looked southward to the Statue of Liberty and the gorgeous spring morning that spread out from Wall Street toward the open ocean beyond.

Power Systems: Conversations on Global Democratic Uprisings and the New Challenges to U.S. Empire by Noam Chomsky, David Barsamian

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affirmative action, Affordable Care Act / Obamacare, Albert Einstein, Chelsea Manning, collective bargaining, colonial rule, corporate personhood, David Brooks, discovery of DNA, double helix, failed state, Howard Zinn, hydraulic fracturing, income inequality, inflation targeting, Julian Assange, land reform, Martin Wolf, Mohammed Bouazizi, Naomi Klein, new economy, obamacare, Occupy movement, oil shale / tar sands, pattern recognition, quantitative easing, Ralph Nader, Ralph Waldo Emerson, single-payer health, sovereign wealth fund, The Wealth of Nations by Adam Smith, theory of mind, Tobin tax, union organizing, Upton Sinclair, uranium enrichment, WikiLeaks

By, say, two years, there’s pretty good evidence that the children have mastered the rudiments of the language. They may just produce one-word or two-word sentences, but there’s now experimental and other evidence that a lot more is in there. By three or four, a normal child will have extensive language capacity. Either this is a miracle or it’s biologically driven. There are just no other choices. There are attempts to claim that language acquisition is a matter of pattern recognition or memorization, but even a superficial look at those proposals shows that they collapse very quickly. It doesn’t mean that they’re not being pursued. In fact, those lines of inquiry are very popular. In my view, though, they’re just an utter waste of time. There are some very strange ideas out there. For instance, a lot of quite fashionable work claims that children acquire language because humans have the capacity to understand the perspective of another person, according to what’s called theory of mind.

The Handbook of Personal Wealth Management by Reuvid, Jonathan.

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asset allocation, banking crisis, BRICs, collapse of Lehman Brothers, correlation coefficient, credit crunch, cross-subsidies, diversification, diversified portfolio, estate planning, financial deregulation, fixed income, high net worth, income per capita, index fund, interest rate swap, laissez-faire capitalism, land tenure, market bubble, merger arbitrage, new economy, Northern Rock, pattern recognition, Ponzi scheme, prediction markets, risk tolerance, risk-adjusted returns, risk/return, short selling, side project, sovereign wealth fund, statistical arbitrage, systematic trading, transaction costs, yield curve

Systematic trading (CTAs) These funds attempt to profit from patterns in market moves at different time horizons. Typically short-term CTAs are equipped to benefit from sharp intra-day moves, with longer-term CTAs seeking to generate profits from more established trends. Short-term CTAs have developed sophisticated platforms where the average holding period can range from minutes to just several trading days. Typically, mathematical approaches are used focusing on momentum and pattern recognition; additionally a large percentage of their resource is spent on developing the technological infrastructure, as often the portfolio needs to be monitored and adjusted on a realtime basis. Fundamental to the strategy is a stop-loss system that limits the downside should a trade go wrong. Keeping an eye on trading costs is also key as turnover is very high. Short-term CTAs must specialize in liquid markets, and often volatility and short-term trend reversals can aid performance; however rapid intrasession whipsawing may not be beneficial.


pages: 301 words: 74,571

Idoru by William Gibson

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experimental subject, Kowloon Walled City, means of production, pattern recognition, place-making, telepresence

I 0 2 147 L "Okay,' she said."What are the nodal points?" I Laney looked at the bubbles on the surface of his beer. "It's like seeing things in clouds," Laney said. "Except the things you see are really there." She put her sake down. "Yamazaki promised me you weren't crazy." "It's not crazy It's something to do with how I process low-level, broad-spectrum input. Something to do with pattern-recognition." "And Slitscan hired you on the basis of that?" "They hired me when I demonstrated that it works, But I can't do that with the kind of data you showed me today" "Why not?" Laney raised his beer. "Because it's like trying to have a drink with a bank. It's not a person. It doesn't drink. There's no place for it to sit." He drank. "Rez doesn't generate patterns I can read, because everything he does is at one remove.


pages: 257 words: 13,443

Statistical Arbitrage: Algorithmic Trading Insights and Techniques by Andrew Pole

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algorithmic trading, Benoit Mandelbrot, Chance favours the prepared mind, constrained optimization, Dava Sobel, Long Term Capital Management, Louis Pasteur, mandelbrot fractal, market clearing, market fundamentalism, merger arbitrage, pattern recognition, price discrimination, profit maximization, quantitative trading / quantitative finance, risk tolerance, Sharpe ratio, statistical arbitrage, statistical model, stochastic volatility, systematic trading, transaction costs

Late entry will lower return on a bet, but only marginally. Modeling and trading catastrophe moves must embody a higher state of alertness. 11.3 TREND CHANGE IDENTIFICATION There is a rich statistical literature on change point identification with many interesting models and approaches providing plenty of fascination. Our purpose here is mundane by comparison, though challenging nonetheless. (If any kind of pattern recognition in financial data were not so challenging, we would hardly be writing and reading about it.) An extremely useful approach from statistical process control relies on Cuscore statistics (Box and Luceno 1987). Consider first a catastrophe superimposed on an underlying rising series. Figure 11.6 shows a base trend with slope coefficient 1.0 with a catastrophe move having slope coefficient 1.3 beginning at time 10.


pages: 298 words: 81,200

Where Good Ideas Come from: The Natural History of Innovation by Steven Johnson

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Ada Lovelace, Albert Einstein, Alfred Russel Wallace, carbon-based life, Cass Sunstein, cleantech, complexity theory, conceptual framework, cosmic microwave background, crowdsourcing, data acquisition, digital Maoism, discovery of DNA, Dmitri Mendeleev, double entry bookkeeping, double helix, Douglas Engelbart, Drosophila, Edmond Halley, Edward Lloyd's coffeehouse, Ernest Rutherford, Geoffrey West, Santa Fe Institute, greed is good, Hans Lippershey, Henri Poincaré, hive mind, Howard Rheingold, hypertext link, invention of air conditioning, invention of movable type, invention of the printing press, invention of the telephone, Isaac Newton, Islamic Golden Age, Jacquard loom, James Hargreaves, James Watt: steam engine, Jane Jacobs, Jaron Lanier, John Snow's cholera map, Joseph Schumpeter, Joseph-Marie Jacquard, Kevin Kelly, lone genius, Louis Daguerre, Louis Pasteur, Mason jar, Mercator projection, On the Revolutions of the Heavenly Spheres, online collectivism, packet switching, PageRank, patent troll, pattern recognition, price mechanism, profit motive, Ray Oldenburg, Richard Florida, Richard Thaler, Ronald Reagan, side project, Silicon Valley, silicon-based life, six sigma, Solar eclipse in 1919, spinning jenny, Steve Jobs, Steve Wozniak, Stewart Brand, The Death and Life of Great American Cities, The Great Good Place, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, transaction costs, urban planning

Had the information network automatically suggested that the Radical Fundamentalist Unit officials read the Phoenix memo after the Minnesota office began its investigation into Moussaoui, the last few weeks of summer might have played out very differently. But however smart the network itself could have been, it still required a comparable connection to take place in the minds of the participants. If David Frasca had read the memo that Ken Williams had addressed to him, he might well have been able to connect the two hunches, using the advanced pattern recognition technology of the human brain. The failure of those two networks to connect the Phoenix and Minnesota hunches was partly attributable to the practically medieval information technology employed by the FBI. But even if the Bureau had miraculously upgraded its network in the summer of 2001, the two hunches would likely have remained apart, because the lack of connections in the Automated Case Support system was a design principle, not merely the result of old-fashioned technology.


pages: 279 words: 75,527

Collider by Paul Halpern

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Albert Einstein, Albert Michelson, anthropic principle, cosmic microwave background, cosmological constant, dark matter, Ernest Rutherford, Gary Taubes, gravity well, horn antenna, index card, Isaac Newton, Magellanic Cloud, pattern recognition, Richard Feynman, Richard Feynman, Ronald Reagan, Solar eclipse in 1919, statistical model, Stephen Hawking

Only a subset of the elementary particles could even be found in atoms; most had nothing to do with them save their reactions to the fundamental forces. It would be like walking into a barn and finding the placid cows and sheep being serenaded by wild rhinoceri, hyenas, platypi, mammoths, and a host of unidentified alien creatures. Given the ridiculously diverse menagerie nature had revealed, finding any semblance of unity would require extraordinary pattern-recognition skills, a keen imagination, and a hearty sense of humor. 6 A Tale of Two Rings the Tevatron and Super Proton Synchrotron Its true that there were a few flaws in my logic. The rivers of ground water that flowed through their experiments, the walls of piling rusting away, the impossible access, and all without benefit of toilet facilities. But someof the users had their finest moment down in these pitys–the discovery of beauty, the bottom quark, where else?!


pages: 239 words: 56,531

The Secret War Between Downloading and Uploading: Tales of the Computer as Culture Machine by Peter Lunenfeld

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Albert Einstein, Andrew Keen, Apple II, Berlin Wall, British Empire, Brownian motion, Buckminster Fuller, Burning Man, butterfly effect, computer age, crowdsourcing, cuban missile crisis, Dissolution of the Soviet Union, don't be evil, Douglas Engelbart, Dynabook, East Village, Edward Lorenz: Chaos theory, Fall of the Berlin Wall, Francis Fukuyama: the end of history, Frank Gehry, Grace Hopper, gravity well, Guggenheim Bilbao, Honoré de Balzac, Howard Rheingold, invention of movable type, Isaac Newton, Jacquard loom, Jacquard loom, Jane Jacobs, Jeff Bezos, John von Neumann, Mark Zuckerberg, Marshall McLuhan, Mercator projection, Mother of all demos, mutually assured destruction, Network effects, new economy, Norbert Wiener, PageRank, pattern recognition, planetary scale, Plutocrats, plutocrats, Post-materialism, post-materialism, Potemkin village, RFID, Richard Feynman, Richard Feynman, Richard Stallman, Robert X Cringely, Schrödinger's Cat, Search for Extraterrestrial Intelligence, SETI@home, Silicon Valley, Skype, social software, spaced repetition, Steve Ballmer, Steve Jobs, Steve Wozniak, Ted Nelson, the built environment, The Death and Life of Great American Cities, the medium is the message, Thomas L Friedman, Turing machine, Turing test, urban planning, urban renewal, Vannevar Bush, walkable city, Watson beat the top human players on Jeopardy!, William Shockley: the traitorous eight

The point of MaSAI is to take the maxim of open-source software developers—“Given enough eyeballs, all bugs are shallow”—and apply this to other realms of social life and the built environment. 121 CHAPTER 5 One project that points the way is called Stardust@home, which has assembled a huge group of people to use the network to search for interstellar dust collected by a recent space mission.33 In 2004, the Stardust interstellar dust collector passed through the coma of a comet named Wild2 and captured potentially thousands of dust grains in its aerogel collectors. In 2006, the craft returned to Earth and the search for these grains began in earnest. To find these grains—the first contemporary space dust to ever be identified—within the aerogel is no easy task, though, because they are randomly spaced and tiny. It is also difficult to run pattern-recognition software because the traces that the grains leave are similar to other deformations in the gel. Human perception is well suited to this kind of detailed discrimination, however. Twenty-five years ago, the University of California at Berkeley team would have trained a group of laboratory assistants, and set them to work for the next four or five years. But the Stardust team had another model to draw on.


pages: 225 words: 11,355

Financial Market Meltdown: Everything You Need to Know to Understand and Survive the Global Credit Crisis by Kevin Mellyn

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asset-backed security, bank run, banking crisis, Bernie Madoff, bonus culture, Bretton Woods, collateralized debt obligation, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, cuban missile crisis, disintermediation, diversification, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Francis Fukuyama: the end of history, global reserve currency, Home mortgage interest deduction, Isaac Newton, joint-stock company, liquidity trap, London Interbank Offered Rate, margin call, market clearing, moral hazard, mortgage tax deduction, Northern Rock, offshore financial centre, paradox of thrift, pattern recognition, pension reform, pets.com, Plutocrats, plutocrats, Ponzi scheme, profit maximization, pushing on a string, reserve currency, risk tolerance, risk-adjusted returns, road to serfdom, Ronald Reagan, shareholder value, Silicon Valley, South Sea Bubble, statistical model, The Great Moderation, the payments system, too big to fail, value at risk, very high income, War on Poverty, Y2K, yield curve

Above all, we have seen this movie before, in the abject failure of socialism and the activist governments of the twentieth century to deliver economic growth and sustained prosperity for their populations. THE NEW NORMAL This all matters for you and your money because a political environment actively hostile to capitalism and finance is emerging, what the CEO of the giant bond fund PIMCO, Mohamed El-Erian, calls the ‘‘New Normal.’’ As humans, we all depend on pattern recognition. We automatically assume that what has happened in our lifetimes is normal, and any diversion from the straight projection of the past into the future is temporary. We live in the ‘‘normal’’ we have experienced. Unless you are a very old American and remember the economic drift and hopelessness of the 1970s, unless you were out of college and working well before the Thatcher and Reagan revolutions of 1979 and Conclusion 1980s, you will take the long bull market of the last quarter century for granted.


pages: 259 words: 73,193

The End of Absence: Reclaiming What We've Lost in a World of Constant Connection by Michael Harris

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4chan, Albert Einstein, AltaVista, Andrew Keen, augmented reality, Burning Man, cognitive dissonance, crowdsourcing, dematerialisation, en.wikipedia.org, Filter Bubble, Firefox, Google Glasses, informal economy, information retrieval, invention of movable type, invention of the printing press, invisible hand, James Watt: steam engine, Jaron Lanier, jimmy wales, Kevin Kelly, Loebner Prize, Marshall McLuhan, McMansion, Nicholas Carr, pattern recognition, pre–internet, Republic of Letters, Silicon Valley, Skype, Snapchat, social web, Steve Jobs, the medium is the message, The Wisdom of Crowds, Turing test

• • • • • Besides the progress of chatbots, we now have software that can map twenty-four points on your face, allowing it to identify a range of emotions and issue appropriate responses. We also have Q sensors—bands worn on the wrist that measure your “emotional arousal” by monitoring body heat and the skin’s electrical conductance. But the root problem remains unchanged. Whether we’re talking about “affective computers” or “computational empathy,” at a basic level we’re still discussing pattern recognition technology and the ever more sophisticated terrain of data mining. Always, the goal is to “humanize” an interface by the enormous task of filtering masses of lived experience through a finer and finer mesh of software. Many of the minds operating at the frontier of this effort come together at MIT’s Media Lab, where researchers are busy (in their own words) “inventing a better future.”


pages: 291 words: 81,703

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

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

Machines can double-check human diagnoses, catch the mistakes of very tired doctors, and keep up with and store new developments in the medical literature. And by the way, that literature doubles in size every few years, much more rapidly than any human can follow. For all of our scientific and medical progress, misdiagnoses remain common. Of course, it can be irresponsible to rely completely on the computer’s pattern recognition skills, since the human eye will pick up image errors or flubbed data inputs in a way that the machine may not. But a man–machine team is less vulnerable to machine oversights; human supervision is often stipulated by the companies that market medical software. One medical innovation would run a patient’s reported symptoms through a Watson-like software program and see what might be wrong, drawing upon extensive databases.


pages: 237 words: 64,411

Humans Need Not Apply: A Guide to Wealth and Work in the Age of Artificial Intelligence by Jerry Kaplan

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Affordable Care Act / Obamacare, Amazon Web Services, asset allocation, autonomous vehicles, bank run, bitcoin, Brian Krebs, buy low sell high, Capital in the Twenty-First Century by Thomas Piketty, combinatorial explosion, computer vision, corporate governance, crowdsourcing, en.wikipedia.org, Erik Brynjolfsson, estate planning, Flash crash, Gini coefficient, Goldman Sachs: Vampire Squid, haute couture, hiring and firing, income inequality, index card, industrial robot, invention of agriculture, Jaron Lanier, Jeff Bezos, job automation, John Maynard Keynes: Economic Possibilities for our Grandchildren, Loebner Prize, Mark Zuckerberg, mortgage debt, natural language processing, Own Your Own Home, pattern recognition, Satoshi Nakamoto, school choice, Schrödinger's Cat, Second Machine Age, self-driving car, sentiment analysis, Silicon Valley, Silicon Valley startup, Skype, software as a service, The Chicago School, Turing test, Watson beat the top human players on Jeopardy!, winner-take-all economy, women in the workforce, working poor, Works Progress Administration

See machine learning neurons, 23, 27, 29 programmable, 24 New York Times, 170 Occupy Wall Street, 170 oil spill (2010). See BP oil spill online sales, 7, 90, 136, 177–78, 181–82 advertising and, 64–76, 132 employment effects of, 139–40, 142 product reviews and, 55, 143. See also Amazon Onsale.com, 96 Oracle, 114–15 OSHA (Occupational Safety and Health) rules, 37 Own Your Own Home program, 168 Oxford University, 152 Pandora, 16 paralegals, 148 part-time work, 185 pattern recognition, 25, 55 payroll taxes, 14, 154 PBI. See public benefit index Pearson, Harry, 193 pension funds, 12, 14 Perlman, Steve, 127 personal public transit. See autonomous vehicles personhood, 90, 215n9 corporations vs. synthetic intellects, 199–200 persuasion, 70, 136 pharaoh (ancient Egypt), 115–16 philanthropy, 58, 113, 118–19 phonograph, invention of, 192 pictures: AI recognition of, 39 website links to, 65 pixels, 65, 66 pollution reduction, 168, 195 poverty, 3, 12, 15 preceptrons (programmatic neurons), 24 prepaid cell phone cards, 55 prices: advertising space bids, 69, 70, 71 Amazon practices, 97–105 automation effects on, 73, 132 comparison of, 14, 54, 100–101 public-interest systems and, 56 stock fluctuations (see stock markets) Princeton University, 113 productivity, 12, 132, 136 professions, 11, 145–46, 157 professors, 11, 151 profits, 55, 72, 103 progressives, 163–65 prostitutes, 40, 144–45 protein folding research, 58 psychopaths, 79–80 public benefit index (PBI), 14, 15, 180–81, 182 public interest, 56, 163–65, 169, 178 public service, 58, 114, 184, 185 public transit.


pages: 262 words: 80,257

The Eureka Factor by John Kounios

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Albert Einstein, call centre, Captain Sullenberger Hudson, deliberate practice, en.wikipedia.org, Flynn Effect, Google Hangouts, impulse control, invention of the telephone, invention of the telescope, Isaac Newton, Louis Pasteur, meta analysis, meta-analysis, Necker cube, pattern recognition, Silicon Valley, Skype, Steve Jobs, theory of mind, Wall-E, William of Occam

Chessmasters invest an enormous amount of time studying classic games to minimize the amount of raw calculation that they have to do during a tournament. To nullify the benefits of his opponents’ preparation, Larsen often used strange openings and made unexpected moves. This forced his opponents to “throw out the book,” or, rather, “throw out the box,” and rely on mental computation instead of preparation and pattern recognition. Neutralizing the benefits of preparation frequently enabled him to triumph because his ability to calculate his way through novel positions was often superior, especially when his adversaries were rattled by his unusual moves. This strategy was extremely effective in tournaments in which he played no more than one or two games against any single individual, but it was less effective when he played a match of many games against a single player.


pages: 300 words: 77,787

Investing Demystified: How to Invest Without Speculation and Sleepless Nights by Lars Kroijer

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Andrei Shleifer, asset allocation, asset-backed security, Bernie Madoff, bitcoin, Black Swan, BRICs, Carmen Reinhart, cleantech, compound rate of return, credit crunch, diversification, diversified portfolio, equity premium, estate planning, fixed income, high net worth, implied volatility, index fund, invisible hand, Kenneth Rogoff, market bubble, passive investing, pattern recognition, prediction markets, risk tolerance, risk/return, Robert Shiller, Robert Shiller, sovereign wealth fund, too big to fail, transaction costs, Vanguard fund, yield curve, zero-coupon bond

Microsoft also arranged for Ability personnel to visit its senior management at offices around the world, both in sales and development, and Susan also talks to some of Microsoft’s leading clients. Like the research analysts from the banks, Ability has an army of expert PhDs who study sales trends and spot new potential challenges (they were among the first to spot Facebook and Google). Further, Ability has economists who study the US and global financial system in detail as the world economy affects Microsoft’s performance. Ability also has mathematicians with trading pattern recognition technology to help with the analysis. Susan loves reading books about technology and every finance/investing book she can get her hands on, including all the Buffett and value investor books. Susan and her team know everything there is to know about the stocks she follows (including a few things she probably shouldn’t know, but she keeps that close to her chest), some of which are much smaller and less well researched than Microsoft.


pages: 240 words: 73,209

The Education of a Value Investor: My Transformative Quest for Wealth, Wisdom, and Enlightenment by Guy Spier

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Albert Einstein, Atul Gawande, Benoit Mandelbrot, big-box store, Black Swan, Checklist Manifesto, Clayton Christensen, Daniel Kahneman / Amos Tversky, Exxon Valdez, Gordon Gekko, housing crisis, Isaac Newton, Long Term Capital Management, Mahatma Gandhi, mandelbrot fractal, NetJets, pattern recognition, pre–internet, random walk, Ronald Reagan, South Sea Bubble, Steve Jobs, winner-take-all economy, young professional

The long-term survivors possess a more sophisticated grasp of risk, including the ability to see when the situation is much less risky than the stock price might suggest. With JPMorgan Chase and the other money-center banks there was a lot of uncertainty, but very little risk. Bridge wasn’t the only game that captured my imagination or refined my mental habits. I also rediscovered the joys of chess, a wonderful game of analysis and pattern recognition. I first fell in love with chess at Harvard, thanks to my classmate Mark Pincus, who later founded Zynga, a social gaming company that made him a billionaire. Back in our student days, Mark noticed an unused chess set in my dorm and asked me to play. He thrashed me. I bought a stack of chess books and we continued to play. I gradually got better and started to win some games. After graduating, I became a member of the Manhattan Chess Club and played pickup games in the park to escape from the horrors of my job at D.

The Supermen: The Story of Seymour Cray and the Technical Wizards Behind the Supercomputer by Charles J. Murray

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Albert Einstein, Berlin Wall, fear of failure, John von Neumann, pattern recognition, Ralph Waldo Emerson, Silicon Valley

Roughly a thousand codebreakers worked at the navy's Ne- braska Avenue facility-a tidy, red brick, former girl's school 10- cated in a quiet section of the city. These codebreakers were about as diverse and unconventional as any task force ever mar- shalled by the V.S. military. They had backgrounds in mathemat- ics, physics, engineering, astronomy, and just about any discipline requiring intuitive pattern recognition. Some were chess experts, bridge champions, and musicians. Many had Ph.D's; a few were world-renowned scientists. When the war began, they had decrypted messages like those of U-66 without the benefit of machines. To do it, they had qui- THE CODEBREAKERS / 9 etly labored over volumes of coded messages, searching for pat- terns that departed from pure randomness. When they had stum- bled upon a pattern, they had conferred, discussed, debated, bickered, and fought among themselves until groups of them had finally arrived at a consensus.


pages: 272 words: 76,089

Billions & Billions: Thoughts on Life and Death at the Brink of the Millennium by Carl Sagan

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Albert Einstein, anti-communist, clean water, cosmic abundance, dark matter, demographic transition, Exxon Valdez, F. W. de Klerk, germ theory of disease, invention of agriculture, invention of radio, invention of the telegraph, invention of the telephone, Isaac Newton, Mikhail Gorbachev, pattern recognition, planetary scale, prisoner's dilemma, profit motive, Ralph Waldo Emerson, Ronald Reagan, stem cell, the scientific method, Thomas Malthus

It is a scandalous waste, and we should not countenance it. It is time to learn from those who fell here. And it is time to act. In part the American Civil War was about freedom; about extending the benefits of the American Revolution to all Americans, to make valid for everyone that tragically unfulfilled promise of "liberty and justice for all." I'm concerned about a lack of historical pattern recognition. Today the fighters for freedom do not wear three-cornered hats and play the fife and drum. They come in other costumes. They may speak other languages. They may adhere to other religions. The color of their skin may be different. But the creed of liberty means nothing if it is only our own liberty that excites us. People elsewhere are crying, "No taxation without representation," and in Western and Eastern Africa, or the West Bank of the Jordan River, or Eastern Europe, or Central America they are shouting in increasing numbers, "Give us liberty or give us death."


pages: 263 words: 75,610

Delete: The Virtue of Forgetting in the Digital Age by Viktor Mayer-Schönberger

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en.wikipedia.org, Erik Brynjolfsson, Firefox, full text search, George Akerlof, information retrieval, information trail, Internet Archive, invention of movable type, invention of the printing press, moveable type in China, Network effects, packet switching, pattern recognition, RFID, slashdot, Steve Jobs, Steven Levy, The Market for Lemons, The Structural Transformation of the Public Sphere, Vannevar Bush

For example, from millions of individual impulses our brain constructs a picture, perhaps of something black and moving, and a split second later we “realize” there is a little black dog running towards us. That more abstract information—a black dog swiftly approaching us—is more usable than millions of individual bits of information contained in a series of snapshots of something black getting closer and closer. More importantly, it is also much more compact. As our nerve cells process the incoming information, from simple stimuli to pattern recognition, a tremendous amount of information is deliberately lost. It is the first layer of unconscious biological forgetting—and one we rarely realize. Once an external stimulus makes it through that initial layer or after we have formed a thought, it is then usually stored in what’s called short-term memory. In this state, we can easily retrieve it, but information in short-term memory fades very quickly—in a matter of seconds, eliminating most of the information.


pages: 1,318 words: 403,894

Reamde: A Novel by Neal Stephenson

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air freight, airport security, crowdsourcing, Google Earth, industrial robot, informal economy, large denomination, megacity, new economy, pattern recognition, Ponzi scheme, pre–internet, ransomware, side project, Skype, slashdot, South China Sea, the built environment, the scientific method, young professional

Corvallis and most of the other techies hated this idea because of its sheer bogosity, which was screamingly obvious to any person of technical acumen who thought about it for more than a few seconds. If their pattern-recognition software could identify the moving travelers and vectorize their body positions well enough to translate their movements into T’Rain, then it could just as easily notice, automatically, with no human intervention, when one of those figures was walking the wrong way and sound the alarm. There was no need at all to have human players in the loop. They should just spin out the pattern-recognition part of it as a separate business. Richard understood and acknowledged all of this—and did not care. “Did you, or did you not, tell me that this was all marketing? What part of your own statement did you not understand?”

He had done a bit of research on it later and learned that the more sophisticated airports had hired psychologists to tackle the problem and devised some clever tricks. For example, they would digitally insert fake images of guns into the video feed from an x-ray machine, frequently enough that the screeners would see false-color silhouettes of revolvers and semiautomatics and IEDs glide across their visual fields several times a day, instead of once every ten years. That, according to the research, was enough to prevent their pattern-recognition neurons from being reclaimed and repurposed by brain processes that were more fruitful, or at least more entertaining. The brain, as far as Richard could determine from haphazard skimming of whatever came up on Google, was sort of like the electrical system of Mogadishu. A whole lot was going on in Mogadishu that required copper wire for conveyance of power and information, but there was only so much copper to go around, and so what wasn’t being actively used tended to get pulled down by militias and taken crosstown to beef up some power-hungry warlord’s private, improvised power network.

They spun that up into a demo, which they showed to several regional airports that were too small and poorly funded to afford fancy, expensive, alarm-equipped one-way doors, and thus had to rely on the bored-employee-sitting-in-a-chair-by-the-door technology. They parlayed those meetings into a deal that gave them access to live 24/7 security camera footage from a couple of those airports. The footage, of course, just showed people walking through the exit. They patched that footage into pattern recognition software that identified the shapes of the individual humans and translated them into vector data in 3D space. This made it possible to import all the data into the T’Rain game engine. The same positions and movements were conferred on avatars from the T’Rain world. The stream of human passengers walking down the corridor in their blazers, their high heels, their Chicago Bears sweatpants, became a stream of K’Shetriae, Dwinn, trolls, and other fantasy characters, dressed in chain mail, plate armor, and wizards’ robes, moving down a stone-lined passageway at the exit of the mighty Citadel of Garzantum.


pages: 626 words: 181,434

I Am a Strange Loop by Douglas R. Hofstadter

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Albert Einstein, Andrew Wiles, Benoit Mandelbrot, Brownian motion, double helix, Douglas Hofstadter, Georg Cantor, Gödel, Escher, Bach, Isaac Newton, James Watt: steam engine, John Conway, John von Neumann, mandelbrot fractal, pattern recognition, Paul Erdős, place-making, probability theory / Blaise Pascal / Pierre de Fermat, publish or perish, random walk, Ronald Reagan, self-driving car, Silicon Valley, telepresence, Turing machine

My list is a random walk through an everyday kind of mental space, drawn up in order to give a feel for the phenomena in which we place the most stock and in which we most profoundly believe (sour grapes and wild goose chases being quite real to most of us), as opposed to the forbidding and inaccessible level of quarks and gluons, or the only slightly more accessible level of genes and ribosomes and transfer RNA — levels of “reality” to which we may pay lip service but which very few of us ever think about or talk about. And yet, for all its supposed reality, my list is pervaded by vague, blurry, unbelievably elusive abstractions. Can you imagine trying to define any of its items precisely? What on earth is the quality known as “tackiness”? Can you teach it to your kids? And please give me a pattern-recognition algorithm that will infallibly detect sleazeballs! Reflected Communist Bachelors with Spin 1/2 are All Wet As a simple illustration of how profoundly wedded our thinking is to the blurry, hazy categories of the macroworld, consider the curious fact that logicians — people who by profession try to write down ironclad, razor-sharp rules of logical inference that apply with impeccable precision to linguistic expressions — seldom if ever resort to the level of particles and fields for their canonical examples of fundamental, eternal truths.

Or could it be that only a certain part of the brain is Conscious? What are the exact boundaries of a Conscious physical entity? What organizational or chemical property of a physical structure is it that graces it with the right to be invaded by a dollop of Consciousness? What mechanism in nature makes the elusive elixir of Consciousness glom onto some physical entities and spurn others? What wondrous pattern-recognition algorithm does Consciousness possess so as to infallibly recognize just the proper kinds of physical objects that deserve it, so it can then bestow itself onto them? How does Consciousness know to do this? Does it somehow go around the physical world in search of candidate objects to glom onto? Or does it shine a metaphorical flashlight metaphorically down at the world and examine it piece by piece, occasionally saying to itself, “Aha!

Analysis of Financial Time Series by Ruey S. Tsay

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Asian financial crisis, asset allocation, Black-Scholes formula, Brownian motion, capital asset pricing model, compound rate of return, correlation coefficient, data acquisition, discrete time, frictionless, frictionless market, implied volatility, index arbitrage, Long Term Capital Management, market microstructure, martingale, p-value, pattern recognition, random walk, risk tolerance, short selling, statistical model, stochastic process, stochastic volatility, telemarketer, transaction costs, value at risk, volatility smile, Wiener process, yield curve

G. (1982), Random Coefficient Autoregressive Models: An Introduction, Lecture Notes in Statistics, 11. Springer-Verlag: New York. Ray, B. K., and Tsay, R. S. (2000), “Long-range dependence in daily stock volatilities,” Journal of Business & Economic Statistics, 18, 254–262. Taylor, S. J. (1994), “Modeling stochastic volatility,” Mathematical Finance, 4, 183–204. Tong, H. (1978), “On a threshold model,” in Pattern Recognition and Signal Processing, ed. C.H. Chen, Sijhoff & Noordhoff: Amsterdam. Tong, H. (1990), Non-Linear Time Series: A Dynamical System Approach, Oxford University Press: Oxford. Tsay, R. S. (1987), “Conditional heteroscedastic time series models,” Journal of the American Statistical Association, 82, 590–604. Analysis of Financial Time Series. Ruey S. Tsay Copyright  2002 John Wiley & Sons, Inc.

M. (1984), An Introduction to Bispectral Analysis and Bilinear Time Series Models, Lecture Notes in Statistics, 24. Springer-Verlag: New York. Teräsvirta, T. (1994), “Specification, estimation, and evaluation of smooth transition autoregressive models,” Journal of the American Statistical Association, 89, 208–218. Tiao, G. C., and Tsay, R. S. (1994), “Some Advances in Nonlinear and Adaptive Modeling in Time Series,” Journal of Forecasting, 13, 109–131. Tong, H. (1978), “On a threshold model,” in Pattern Recognition and Signal Processing, ed. C.H. Chen, Sijhoff & Noordhoff: Amsterdam. Tong, H. (1983), Threshold Models in Nonlinear Time Series Analysis, Lecture Notes in Statistics, Springer-Verlag: New York. Tong, H. (1990), Non-Linear Time Series: A Dynamical System Approach, Oxford University Press: Oxford. Tsay, R. S. (1986), “Nonlinearity tests for time series,” Biometrika, 73, 461–466. Tsay, R. S. (1989),“Testing and modeling threshold autoregressive processes,” Journal of the American Statistical Association, 84, 231–240.


pages: 272 words: 19,172

Hedge Fund Market Wizards by Jack D. Schwager

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asset-backed security, backtesting, banking crisis, barriers to entry, Bernie Madoff, Black-Scholes formula, British Empire, Claude Shannon: information theory, cloud computing, collateralized debt obligation, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, James Dyson, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative finance, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve

It made me pay attention to every market, every day. I did that for over 10 years. Even when we got CQG screens, which had graphics, I still continued to update my printed charts manually for a long time. It was a daily routine of looking at each chart and thinking about what the patterns were telling you. I would even turn the charts upside down to see whether the pattern looked different to me. Over time, it helped me develop a sense of pattern recognition. Were you trading your own account when you were a broker? I had been trading my own account all along. How did you do? I did fine. I made money virtually every year, but I didn’t make a lot of money. One thing I did wrong was that I thought only technical factors were important and that fundamentals didn’t matter. The other thing I did wrong was that every time I made money, I would pull it out.

As the name implies, these types of systems will seek to take positions opposite to an ongoing trend when system algorithms signal that the trend is overextended. There is a third category of systematic approaches whose signals do not seek to profit from either continuations or reversals of trend. These types of systems are designed to identify patterns that suggest a greater probability for either higher or lower prices over the near term. Woodriff is among the small minority of CTAs who employ such pattern-recognition approaches, and he does so using his own unique methodology. He is one of the most successful practitioners of systematic trading of any kind. Woodriff grew up on a working farm near Charlottesville, Virginia. Woodriff’s perceptions of work were colored by his childhood experiences. When he was in high school, Woodriff thought it was sad that most people loved Fridays and hated Mondays. “I was going to make sure that wasn’t me,” he says.


pages: 798 words: 240,182

The Transhumanist Reader by Max More, Natasha Vita-More

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23andMe, Any sufficiently advanced technology is indistinguishable from magic, artificial general intelligence, augmented reality, Bill Joy: nanobots, bioinformatics, brain emulation, Buckminster Fuller, cellular automata, clean water, cloud computing, cognitive bias, cognitive dissonance, combinatorial explosion, conceptual framework, Conway's Game of Life, cosmological principle, data acquisition, discovery of DNA, Drosophila, en.wikipedia.org, experimental subject, Extropian, fault tolerance, Flynn Effect, Francis Fukuyama: the end of history, Frank Gehry, friendly AI, game design, germ theory of disease, hypertext link, impulse control, index fund, John von Neumann, joint-stock company, Kevin Kelly, Law of Accelerating Returns, life extension, Louis Pasteur, Menlo Park, meta analysis, meta-analysis, moral hazard, Network effects, Norbert Wiener, P = NP, pattern recognition, phenotype, positional goods, prediction markets, presumed consent, Ray Kurzweil, reversible computing, RFID, Richard Feynman, Ronald Reagan, silicon-based life, Singularitarianism, stem cell, stochastic process, superintelligent machines, supply-chain management, supply-chain management software, technological singularity, Ted Nelson, telepresence, telepresence robot, telerobotics, the built environment, The Coming Technological Singularity, the scientific method, The Wisdom of Crowds, transaction costs, Turing machine, Turing test, Upton Sinclair, Vernor Vinge, Von Neumann architecture, Whole Earth Review, women in the workforce

And it would be okay if some fraction were not identifiable. It’s the third requirement that concerns me; the neurotransmitter concentrations, which are contained in structures that are finer yet than the interneuronal connections. These are, in my view, also critical aspects of the brain’s learning process. We see the analogue of the ­neurotransmitter concentrations in the simplified neural net models that I use routinely in my pattern recognition work, The learning of the net is reflected in the connection weights as well as the connection topology (some neural net methods allow for self-organization of the ­topology, some do not, but all provide for self-organization of the weights). Without the weights, the net has no competence. If the very-fine-resolution neurotransmitter concentrations are not identifiable, the ­downside is not equivalent to merely an amnesia patient who has lost his memory of his name, profession, family members, etc.

Development of expert systems in medicine indicate that humans can master about 100,000 concepts in a domain. If we estimate that this ‘professional’ knowledge represents as little as 1 percent of the overall pattern and knowledge store of a human, we arrive at an estimate of 107 chunks. Based on my own experience in designing systems that can store similar chunks of knowledge in either rule-based expert systems or self-organizing pattern-recognition systems, a reasonable estimate is about 106 bits per chunk (pattern or item of knowledge), for a total capacity of 1013 (10 trillion) bits for a human’s functional memory. … [W]e will be able to purchase 1013 bits of memory for one thousand dollars by around 2018. Keep in mind that this memory will be millions of times faster than the electrochemical memory process used in the human brain and thus will be far more effective.


pages: 348 words: 39,850

Data Scientists at Work by Sebastian Gutierrez

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Albert Einstein, algorithmic trading, bioinformatics, bitcoin, business intelligence, chief data officer, clean water, cloud computing, computer vision, continuous integration, correlation does not imply causation, crowdsourcing, data is the new oil, DevOps, domain-specific language, follow your passion, full text search, informal economy, information retrieval, Infrastructure as a Service, inventory management, iterative process, linked data, Mark Zuckerberg, microbiome, Moneyball by Michael Lewis explains big data, move fast and break things, natural language processing, Network effects, nuclear winter, optical character recognition, pattern recognition, Paul Graham, personalized medicine, Peter Thiel, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, recommendation engine, Renaissance Technologies, Richard Feynman, Richard Feynman, self-driving car, side project, Silicon Valley, Skype, software as a service, speech recognition, statistical model, Steve Jobs, stochastic process, technology bubble, text mining, the scientific method, web application

I approached the problem by searching the literature for machines that could learn and realized that, at least in the early 1980s, nobody was working on these types of problems. The only literature I could find was from the 1960s www.it-ebooks.info 47 48 Chapter 3 | Yann LeCun, Facebook and some of it from the 1970s, but mostly from the 1960s. It was the old work on the sort of neural net version 1.0, from the 1950s. Things like the perceptron and other techniques like this and then the statistical pattern recognition literature that followed in the early 1970s. But by the time I started to take an interest in this research area, the field had been pretty much been abandoned by the research community. This time period is sometimes referred to as the “neural net winter.” I graduated—though my specialty was not actually machine learning, as there was no such thing as machine learning back then. In fact, in France at that time, there wasn’t even such thing as computer science.

Kohavi, Ronny (speaker), 34 LinkedIn, 27, 34 low level process, 22, 29 marketing organization, 22 Max, 40 metadata, 26 motivated people, 22 non–data scientist, 23 non-numerical data, 40 on-demand Internet media, 19 one-on-one talks, 28 operations research, 23 personalization algorithms and recommender systems, 21 personalization model, 24, 27 Poisson distributions, 41 predictive model, 23, 29–30, 37, 43 probability distributions, 41 product org, 20 qualitative research, 26 regression model, 38 research path, 31 riveting and exciting experience, 20 search autocomplete, 21 self-selection, 38 source data, 27 streaming score, 30 studying models, 27 tackling implications, 31 technology selection, 34 Teradata, 33 text analytics, 40 thought process, 25 time, projects, and priorities, 31 tool-set knowledge, 36 viewing data, 24 VP of Science and Algorithms, Netflix, 19 Smith, Anna analytics engineer, 199 big transition, 202 Bitly, 204, 213 conferences, 214 data science interviews, 213 data scientist, 199 disciplines, 215 domain experience, 215 high bullshit radar, 215 humility, 215 outside of work, 216 Reddit data, 215 Twitter, 214 work and learning, 214 blog, 201 business development person and myself, 202 communication, 203 computer science–related work, 201 data science, 204 eBay and Amazon, 201 galaxy project, 200 Hadoop cluster, 202 informatics and data science–type, 200 information, 204 JSON format, 204 machine learning algorithms, 201 MapReduce program, 204 mathematics, 213 normalization technique, 203 Personality-wise, 205 physics, 204 pretty and understandable, 203 problem approach, 213 problem solving and thinking, 205 project-based metrics, 212 quantum computers, 200 recommendations system, 212 Rent the Runway, 204 assumption, 210 body measurements and fit, 207 champion data, 206 cohesive community, 218 collaborative effort, 206 collaborative supportive community, 211 comfortable and expose personal details, 211 consulting-type team role, 206 CTO, 206 customary data engineering, 207 customer support piece, 207 D3.js tools, 209 data guarding, 217 data scientist, 210 display behavior, 210 distance metric, 208 dress size, 208 ego-driven field, 218 fabrics, 208 www.it-ebooks.info Index fashion industry, 206 feedback and opinions, 217 fun projects, 216 Google analytics, 209 in New York, 218, 219 knowledge and insights, 216 latent variable, 210 marketing and the financial reports, 205 mobile devices, 209 non-data pieces, 205 operations side, 205 personal feedback mechanism, 211 pixel logs, 208 positivity and patience, 218 Python, 209 real-world physical attributes, 211 reviews, 211 right dress, 211 strategy, 217 systems and frameworks, 205 Tableau reports, 205 Team Geek, 217 variations, 208 warehouse operations piece, 206 warehouse-to-consumer-and-back piece, 207 web-site operations piece, 207 Runway product, 200 slow transition, 201 social commitment, 199 statistical confidence, 203 trial and error, 203 websites, 202 Statistical pattern recognition, 48 Sunlight Foundation, 319 T Techmeme and Prismatic, 95 Teradata, 33, 319 Tunkelang, Daniel AT&T Bell Labs, 83 Bill Gates, 103 challenging problems, 85 Chief Scientist, Endeca, 83 cocktail party, 94 communication, 97 compare and contrast, 85 conservative assumptions, 99 crazy and novel ideas, 98 creative problem solvers, 100 crowdsourcing, 97 customers’ data, 93 data science, 92, 102, 104 data science trade, 97 data types, 95 decision tree model, 90 digital library, 93 economic graph, 88 economic opportunity, 88 entropy calculations, 93 Galene, 87 Google, 89, 91 head of Search Quality, LinkedIn, 83–84 health and well-being, 104 hiring and outreach, 95 hiring and training people, 102 hiring process, 101 human–computer interaction, 86 hypothesis generation, 96 IBM Thomas J.


pages: 648 words: 170,770

Leviathan Wakes by James S. A. Corey

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gravity well, Kuiper Belt, pattern recognition

I’ve dug through raw stuff, looking for anything hidden inside, but for the life of me, I can’t find a thing. I’ve even had the Roci digging through the data for the last couple hours, looking for patterns. She has really good software for that sort of thing. But so far, nothing.” She tapped on the screen again and the raw data began spooling past faster than Holden could follow. In a small window inside the larger screen, the Rocinante’s pattern-recognition software worked to find meaning. Holden watched it for a minute, but his eyes quickly unfocused. “Lieutenant Kelly died for this data,” he said. “He left the ship while his mates were still fighting. Marines don’t do that unless it matters.” Naomi shrugged and pointed at the screen with resignation. “That’s what was on his cube,” she said. “Maybe there’s something steganographic, but I don’t have another dataset to compare it to.”

Perhaps it was enough just knowing that someone on the other side of the political and military divide had seen the same evidence they had seen and drawn the same conclusions. Maybe it left room for hope. He switched his hand terminal back to the Eros feed. A strong throbbing sound danced below a cascade of noise. Voices rose and fell and rose again. Data streams spewed into one another, and the pattern-recognition servers burned every spare cycle making something from the resultant mess. Julie took his hand, the dream so convincing he could almost pretend he felt it. You belong with me, she said. As soon as it’s over, he thought. It was true he kept pushing back the end point of the case. First find Julie, then avenge her, and now destroy the project that had claimed her life. But after that was accomplished, he could let go.


pages: 319 words: 89,477

The Power of Pull: How Small Moves, Smartly Made, Can Set Big Things in Motion by John Hagel Iii, John Seely Brown

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Albert Einstein, Andrew Keen, barriers to entry, Black Swan, business process, call centre, Clayton Christensen, cleantech, cloud computing, corporate governance, Elon Musk, en.wikipedia.org, future of work, game design, George Gilder, Isaac Newton, job satisfaction, knowledge economy, knowledge worker, loose coupling, Louis Pasteur, Malcom McLean invented shipping containers, Maui Hawaii, medical residency, Network effects, packet switching, pattern recognition, pre–internet, profit motive, recommendation engine, Ronald Coase, shareholder value, Silicon Valley, Skype, smart transportation, software as a service, supply-chain management, The Nature of the Firm, too big to fail, trade liberalization, transaction costs

Interweaving herself among them, Ellen helps them take a piece of this, and mix it with a bit of that, to see if something new emerges. What would happen if she introduced the broadcast executive to the woman running the charity? How about connecting the Stanford professor with the lobbyist? And so on. Ellen Levy is practicing pull, using the approaches we described in Chapter 3: deep listening, beacon sending, pattern recognition, surface exposure, and, above all, a passion for new ideas and gifted people. What makes all of this work, and makes it very different from traditional forms of networking, is Ellen’s focus. In the old days, networking was all about meeting people who could help you advance in your own career. Ellen takes a very different approach. From the outset, she has used deep listening to develop a deep understanding of the other person’s needs and interests.


pages: 327 words: 103,336

Everything Is Obvious: *Once You Know the Answer by Duncan J. Watts

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affirmative action, Albert Einstein, Amazon Mechanical Turk, Black Swan, butterfly effect, Carmen Reinhart, Cass Sunstein, clockwork universe, cognitive dissonance, collapse of Lehman Brothers, complexity theory, correlation does not imply causation, crowdsourcing, death of newspapers, discovery of DNA, East Village, easy for humans, difficult for computers, edge city, en.wikipedia.org, Erik Brynjolfsson, framing effect, Geoffrey West, Santa Fe Institute, happiness index / gross national happiness, high batting average, hindsight bias, illegal immigration, interest rate swap, invention of the printing press, invention of the telescope, invisible hand, Isaac Newton, Jane Jacobs, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, lake wobegon effect, Long Term Capital Management, loss aversion, medical malpractice, meta analysis, meta-analysis, Milgram experiment, natural language processing, Netflix Prize, Network effects, oil shock, packet switching, pattern recognition, performance metric, phenotype, planetary scale, prediction markets, pre–internet, RAND corporation, random walk, RFID, school choice, Silicon Valley, statistical model, Steve Ballmer, Steve Jobs, Steve Wozniak, supply-chain management, The Death and Life of Great American Cities, the scientific method, The Wisdom of Crowds, too big to fail, Toyota Production System, ultimatum game, urban planning, Vincenzo Peruggia: Mona Lisa, Watson beat the top human players on Jeopardy!, X Prize

Journal of Consumer Research 25 (3):187–217. Bielby, William T., and Denise D. Bielby. 1994. “ ‘All Hits Are Flukes’: Institutionalized Decision Making and the Rhetoric of Network Prime-Time Program Development.” American Journal of Sociology 99 (5):1287–313. Bishop, Bill. 2008. The Big Sort: Why the Clustering of Like-Minded America Is Tearing Us Apart. New York: Houghton Mifflin. Bishop, Christopher M. 2006. Pattern Recognition and Machine Learning. New York: Springer. Black, Donald. 1979. “Common Sense in the Sociology of Law.” American Sociological Review 44 (1):18–27. Blass, Thomas. 2009. The Man Who Shocked the World: The Life and Legacy of Stanley Milgram. New York: PublicAffairs Books. Bollen, Johan, Alberto Pepe, and Huina Mao. 2009. “Modeling Public Mood and Emotion: Twitter Sentiment and Socio-economic Phenomena.”


pages: 317 words: 84,400

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

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

Cope says Annie’s penchant for tasteful originality could push her past most human composers, who simply build on the work of the past, which, in turn, was built on older works. Cope offers this without emotion, but he still harbors bruises from the music elite’s reaction to Emmy. He won’t admit it, but he seems ready for a fight. The arguments will be honest on both sides. The specter that the greatest music ever created could be broken down into pattern recognition, rule making, and controlled rule breaking is just too much for some musical types to take. Artists tend to be people who define themselves by their work. They perceive their creativity as what separates them from the person in the cubicle, the salesman on the road, the MBA doting on a spreadsheet—the banal masses. Cope suggests that this artisan shield of superiority can be reduced to a long, efficient equation rather than some mystically endowed and carefully cultured gift.


pages: 312 words: 35,664

The Mathematics of Banking and Finance by Dennis W. Cox, Michael A. A. Cox

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barriers to entry, Brownian motion, call centre, correlation coefficient, inventory management, iterative process, linear programming, meta analysis, meta-analysis, P = NP, pattern recognition, random walk, traveling salesman, value at risk

The chance of this type of calculation producing anything meaningful is at best remote. That regulators, for capital calculation, could use such calculations is also fraught with concerns. However, later amendments to regulatory rules will almost certainly deal with such matters. 31 An Introduction to Neural Networks 31.1 INTRODUCTION In business you do occasionally encounter what are referred to as neural networks. These are typically used for pattern recognition and are based on a series of historic assumptions, assessments and/or observations. Normally a set of observations is used as the input into the network, which is then trained to assign each of these observations to one class or more. The observations consist of a number of features and an associated class assignment, which may be continuous (so a full range of solutions are possible) or discrete (so only certain solutions are possible).


pages: 392 words: 104,760

Babel No More: The Search for the World's Most Extraordinary Language Learners by Michael Erard

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Asperger Syndrome, business process, business process outsourcing, call centre, complexity theory, European colonialism, pattern recognition, Skype, Steven Pinker, theory of mind

His verbal memory was very good: like Christopher, he had a sponge-like memory for prose and lists of words. Anecdotally, musical ability and foreign-language ability are often tied together: languages and music both are formal systems involving sequences of discrete units, and an individual must be disciplined to perform well. It’s true that speech sounds and music share areas of the brain, and that there’s also a basic similarity in visual and auditory pattern recognition. But when C.J. took the Seashore Tests of Musical Ability (developed by Carl Seashore in 1919), his scores on a memory test for melody and for sequences of rhythm and pitch were average. In his case, at least, the anecdotal connection didn’t hold. Possible explanations for talented language learning fall into two general areas. One view says: What matters is a person’s sense of mission and dedication to language learning.


pages: 266 words: 86,324

The Drunkard's Walk: How Randomness Rules Our Lives by Leonard Mlodinow

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Albert Einstein, Alfred Russel Wallace, Antoine Gombaud: Chevalier de Méré, Atul Gawande, Brownian motion, butterfly effect, correlation coefficient, Daniel Kahneman / Amos Tversky, Donald Trump, feminist movement, forensic accounting, Gerolamo Cardano, Henri Poincaré, index fund, Isaac Newton, law of one price, pattern recognition, Paul Erdős, probability theory / Blaise Pascal / Pierre de Fermat, RAND corporation, random walk, Richard Feynman, Richard Feynman, Ronald Reagan, Stephen Hawking, Steve Jobs, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, V2 rocket, Watson beat the top human players on Jeopardy!

When a teacher initially believes that one student is smarter than another, he selectively focuses on evidence that tends to confirm the hypothesis.48 When an employer interviews a prospective candidate, the employer typically forms a quick first impression and spends the rest of the interview seeking information that supports it.49 When counselors in clinical settings are advised ahead of time that an interviewee is combative, they tend to conclude that he is even if the interviewee is no more combative than the average person.50 And when people interpret the behavior of someone who is a member of a minority, they interpret it in the context of preconceived stereotypes.51 The human brain has evolved to be very efficient at pattern recognition, but as the confirmation bias shows, we are focused on finding and confirming patterns rather than minimizing our false conclusions. Yet we needn’t be pessimists, for it is possible to overcome our prejudices. It is a start simply to realize that chance events, too, produce patterns. It is another great step if we learn to question our perceptions and our theories. Finally, we should learn to spend as much time looking for evidence that we are wrong as we spend searching for reasons we are correct.


pages: 502 words: 107,510

Natural Language Annotation for Machine Learning by James Pustejovsky, Amber Stubbs

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Amazon Mechanical Turk, bioinformatics, cloud computing, computer vision, crowdsourcing, easy for humans, difficult for computers, finite state, game design, information retrieval, iterative process, natural language processing, pattern recognition, performance metric, sentiment analysis, social web, speech recognition, statistical model, text mining

In Proceedings of the 8th International Conference on Language Resources and Evaluation (LREC’12), Istanbul, Turkey. Snow, Rion, Brendan O’Connor, Daniel Jurafsky, and Andrew Y. Ng. 2008. “Cheap and Fast—But Is It Good? Evaluating Non-Expert Annotations for Natural Language Tasks.” In Proceedings of EMNLP-08. Sorokin, Alexander, and David Forsyth. 2008. “Utility data annotation with Amazon Mechanical Turk.” In Proceedings of the Computer Vision and Pattern Recognition Workshops. Index A note on the digital index A link in an index entry is displayed as the section title in which that entry appears. Because some sections have multiple index markers, it is not unusual for an entry to have several links to the same section. Clicking on any link will take you directly to the place in the text in which the marker appears. Symbols (κ) Kappa scores, Annotate with the Specification, Cohen’s Kappa (κ)–Cohen’s Kappa (κ), Fleiss’s Kappa (κ)–Fleiss’s Kappa (κ) Cohen’s Kappa (κ), Cohen’s Kappa (κ)–Cohen’s Kappa (κ) Fleiss’s Kappa (κ), Fleiss’s Kappa (κ)–Fleiss’s Kappa (κ) Χ-squared (chi-squared) test, Other evaluation metrics A A Standard Corpus of Present-Day American English (Kucera and Francis), A Brief History of Corpus Linguistics (see Brown Corpus) active learning algorithms, Active Learning adjudication, Creating the Gold Standard (Adjudication), MAI User Guide–Saving Files MAI as tool for, MAI User Guide–Saving Files Allen, James, Using Models Without Specifications, Related Research Amazon Elastic Compute Cloud, Distributed Computing Amazon’s Mechanical Turk (MTurk), The Infrastructure of an Annotation Project American Medical Informatics Association (AMIA), Organizations and Conferences American National Corpus (ANC), A Brief History of Corpus Linguistics Analysis of variance (ANOVA) test, Other evaluation metrics Analyzing Linguistic Data: A Practical Introduction to Statistics using R (Baayen), Corpus Analytics annotated corpus, The Importance of Language Annotation annotation environments, Choosing an Annotation Environment, Choosing an Annotation Environment, Choosing an Annotation Environment, Tools, MAE User Guide–Frequently Asked Questions annotation units, support for, Choosing an Annotation Environment chosing, Choosing an Annotation Environment MAE (Multipurpose Annotation Environment), MAE User Guide–Frequently Asked Questions process enforcement in, Choosing an Annotation Environment revising, Tools annotation guideline(s), Model the Phenomenon, Specification Versus Guidelines, Be Prepared to Revise, Writing the Annotation Guidelines, Writing the Annotation Guidelines, Example 1: Single Labels—Movie Reviews, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 2: Multiple Labels—Film Genres, Example 3: Extent Annotations—Named Entities, Example 4: Link Tags—Semantic Roles, Example 4: Link Tags—Semantic Roles, Guidelines, Specifications, Guidelines, and Other Resources–Specifications, Guidelines, and Other Resources categories, using in, Example 2: Multiple Labels—Film Genres classifications, defining and clarifying, Example 1: Single Labels—Movie Reviews labels, importance of clear definitions for, Example 2: Multiple Labels—Film Genres limits on number of labels, effects of, Example 2: Multiple Labels—Film Genres link tags, Example 4: Link Tags—Semantic Roles list of available, Specifications, Guidelines, and Other Resources–Specifications, Guidelines, and Other Resources multiple lables, use of and considerations needed for, Example 2: Multiple Labels—Film Genres named entities, defining, Example 3: Extent Annotations—Named Entities and outside information, Example 2: Multiple Labels—Film Genres reproducibility, Example 2: Multiple Labels—Film Genres revising, Guidelines revising, need for, Be Prepared to Revise semantic roles, Example 4: Link Tags—Semantic Roles specifications vs., Specification Versus Guidelines writing, Writing the Annotation Guidelines annotation standards, Different Kinds of Standards–Other Standards Affecting Annotation, ISO Standards–Annotation specification standards, Community-Driven Standards, Other Standards Affecting Annotation, Other Standards Affecting Annotation, Other Standards Affecting Annotation, Applying and Adopting Annotation Standards–ISO Standards and You, Unique Labels: Movie Reviews, Multiple Labels: Film Genres, Linked Extent Annotation: Semantic Roles, Linked Extent Annotation: Semantic Roles, ISO Standards and You community-driven, Community-Driven Standards data storage format and, Other Standards Affecting Annotation date format and, Other Standards Affecting Annotation ISO standards, ISO Standards–Annotation specification standards LAF (Linguistic