statistical, 37, 228, 297, 300, 307
statistical relational, 309
supervised, 57-202, 209, 214, 220, 222, 226
unsupervised, 203-233
Learning algorithms (learners)
Alchemy, 250-259
children’s learning and, 203-204
control of, 45
curse of dimensionality and, 188
empirical evaluation of, 76
evaluation and, 283
interaction with, 264-267
machine learning and, 6-10
optimization and, 283
prediction and, 39
representation and, 283
speed and, 139-142
as superpredators, 8-9
variety of tasks undertaken by, 23-25
See also individual algorithms
LeCun, Yann, 113, 195
Lehman Brothers, 106
Leibniz, Gottfried, 58, 64, 175, 198
Lenat, Doug, 35
Lewis, Michael, 39
Linear regression, 15, 50, 113, 182, 214, 306
LinkedIn, 269, 271
Lipson, Hod, 121
Local minimum, 102, 103, 110-111
Local optima, 111, 128
Locally weighted regression, 182, 306
Locke, John, 58, 93, 178
Logic, 50, 33, 49, 80-81
Bayesians and, 173
computers and, 2
Master Algorithm and, 240, 244, 245-246
probability and, 173-175, 245-246, 306, 309
unified with graphical models, 245-250
Logical inference, Alchemy and, 256
Logic gates, 96
Logistic curve. See S curves
Long-tail phenomenon, 12, 299
Long-term potentiation, 27
Loopy belief propagation, 163-164, 231
Lorenz, Konrad, 138
Low-pass filter, 133
Machine learners, knowledge engineers vs., 34-38
Machine learning, 6-10
analogy and, 178-179
bias and variance and, 78-79
big data and, 15-16
business and, 10-13
chunking, 223-227
clustering, 205-210
dimensionality reduction, 211-217
effect on employment, 276-279
exponential function and, 73-74
fitness function and, 123
further readings, 297-298
future of, 21-22
impact on daily life, 298
effect on employment, 276-279
meta-learning, 237-239
nature vs. nurture debate and, 29, 137-139
Newton’s principle and, 65-66
planetary-scale, 256-259
politics and, 16-19
principal-component analysis, 211-217
problem of unpredictability and, 38-40
reinforcement learning, 218-223, 226-227
relational learning, 227-233
relationship to artificial intelligence, 8
science and, 13-16, 235-236
significance tests and, 76-77
as technology, 236-237
Turing point and, 286, 288
war and, 19-21, 279-282
See also Algorithms
Machine-learning problem, 61-62, 109-110
Machine-translation systems, 154
MacKay, David, 170
Madrigal, Alexis, 273-274
Malthus, Thomas, 178, 235
Manchester Institute of Biotechnology, 16
Mandelbrot set, 30, 300
Margins, 192-194, 196, 241, 242, 243, 307
Markov, Andrei, 153
Markov chain Monte Carlo (MCMC), 164-165, 167, 170, 231, 241, 242, 253, 256
Markov chains, 153-155, 159, 304-305
Markov logic. See Markov logic networks (MLNs)
Markov logic networks (MLNs), 246-259, 309-310
classes and, 257
complexity and, 258-259
parts and, 256-257
with hierarchical structure, 256-257
See also Alchemy
Markov networks, 171-172, 229, 240, 245, 253, 306
Marr, David, 89
Marr’s three levels, 89
Master Algorithm, 239-246
Alchemy and, 250-259
Bayes’ theorem and, 148
brain as, 26-28
CanceRx, 259-261
candidates that fail as, 48-50
chunking and, 226
complexity of, 40-41
as composite picture of current and future learners, 263-264
computer science and, 32-34
equation, 50
evolution and, 28-29
five tribes and, 51-55
future and, 292
goal of, 39
Google and, 282
invention of, 25-26
Markov logic networks and, 236-250
meta-learning and, 237-239
physics and, 29-31
practical applications of, 41-45
statistics and, 31-32
symbolism and, 90-91
theory of everything and, 46-48
Turing point and, 286, 288
as unifier of machine learning, 237
unity of knowledge and, 31
Match.com, 12, 265
Matrix factorization for recommendation systems, 215
Maximum likelihood principle, 166-167, 168
Maxwell, James Clerk, 235
McCulloch, Warren, 96
McKinsey Global Institute, 9
MCMC. See Markov chain Monte Carlo (MCMC)
Means-ends analysis, 225
Mechanical Turk, 14
Medical data, sharing of, 272-273
Medical diagnosis, 23, 147, 149-150, 160, 169, 228-229, 248-249
Memorization, 48
Memory, time as principal component of, 217
Mencken, H. L., 230
Mendeleev, Dmitri, 235
Meta-learning, 237-239, 255, 309
Methane/methanol, 197-198
Michalski, Ryszard, 69, 70, 90
Michelangelo, 2
Microprocessor, 48-49, 236
Microsoft, 9, 22
Kinect, 88, 237, 238
Windows, 12, 133, 224
Xbox Live, 160-161
Microsoft Research, 152
Military robots, 21, 279-282, 299, 310
Mill, John Stuart, 93
Miller, George, 224
Minsky, Marvin, 35, 38, 100-101, 102, 110, 112, 113
Mitchell, Tom, 64, 69, 90
Mixability, 135
MLNs. See Markov logic networks (MLNs)
Moby Dick (Melville), 72
Molecular biology, data and, 14
Moneyball (Lewis), 39
Mooney, Ray, 76
Moore’s law, 287
Moravec, Hans, 288
Muggleton, Steve, 80
Multilayer perceptron, 108-111
autoencoder, 116-118
Bayesian, 170
driving a car and, 113
Master Algorithm and, 244
NETtalk system, 112
reinforcement learning and, 222
support vector machines and, 195
Music composition, case-based reasoning and, 199
Music Genome Project, 171
Mutation, 124, 134-135, 241, 252
Naïve Bayes classifier, 151-153, 171, 304
Bayesian networks and, 158-159
clustering and, 209
Master Algorithm and, 245
medical diagnosis and, 23
relational learning and, 228-229
spam filters and, 23-24
text classification and, 195-196
Narrative Science, 276
National Security Agency (NSA), 19-20, 232
Natural selection, 28-29, 30, 52
as algorithm, 123-128
Nature
Bayesians and, 141
evolutionaries and, 137-142
symbolists and, 141
Nature (journal), 26
Nature vs. nurture debate, machine learning and, 29, 137-139
Neal, Radford, 170
Nearest-neighbor algorithms, 24, 178-186, 202, 306-307
dimensionality and, 186-190
Negative examples, 67
Netflix, 12-13, 183-184, 237, 266
Netflix Prize, 238, 292
Netscape, 9
NETtalk system, 112
Network effect, 12, 299
Neumann, John von, 72, 123
Neural learning, fitness and, 138-139
Neural networks, 99, 100, 112-114, 122, 204
convolutional, 117-118, 302-303
Master Algorithm and, 240, 244, 245
reinforcement learning and, 222
spin glasses and, 102-103
Neural network structure, Baldwin effect and, 139
Neurons
action potentials and, 95-96, 104-105
Hebb’s rule and, 93-94
McCulloch-Pitts model of, 96-97
processing in brain and, 94-95
See also Perceptron
Neuroscience, Master Algorithm and, 26-28
Newell, Allen, 224-226, 302
Newhouse, Neil, 17
Newman, Mark, 160
Newton, Isaac, 293
attribute selection, 189
laws of, 4, 14, 15, 46, 235
rules of induction, 65-66, 81, 82