Newtonian determinism, Laplace and, 145
Newton phase of science, 39-400
New York Times (newspaper), 115, 117
Ng, Andrew, 117, 297
Nietzche, Friedrich, 178
NIPS. See Conference on Neural Information Processing Systems ((NIPS)
“No free lunch” theorem, 59, 62-65, 70-71
“No Hands Across America,” 113
Noise, 73, 91, 155
Nonlinear dimensionality reduction, 215-217
Nonlinear models, 15, 112-114
Nonuniformity, 189-190
NOR gate, 49
Normal distributions, 187-188, 210
Normative theories, descriptive theories vs., 141-142, 304
Norvig, Peter, 152
NOT gate, 96
NOT operation, 2
Nowlan, Steven, 139
NP-completeness, 32-34, 102
NSA. See National Security Agency (NSA)
Nurture, nature vs., 29, 137-139
Obama, Barack, 17
Objective reality, Bayesians and, 167
Occam’s razor, 77-78, 196, 300-301
OkCupid, 265, 269, 310
O’Keefe, Kevin, 206
On Intelligence (Hawkins), 28, 118
Online analytical processing, 8
Online dating, 265-266, 269, 310
Open-source movement, 45, 279, 292
Optimization, 30-31, 33, 109, 135, 239, 241, 283
constrained, 193-195
O’Reilly, Tim, 9
The Organization of Behavior (Hebb), 93
OR gate, 96
The Origin of Species (Darwin), 28, 123
OR operation, 2
Overfitting, 59, 70-75, 126, 169, 301
avoiding, 76-77
hypotheses and, 73-75
Master Algorithm and, 243
nearest-neighbor algorithm and, 183
noise and, 73
singularity and, 287
support vector machines and, 196
P = NP question, 33-34
PAC learning, 74-75
Page, Larry, 55, 154, 227
PageRank algorithm, 154, 305
PAL (Personalized Assistant that Learns) project, 255
Pandora, 171
Papadimitriou, Christos, 135
Papert, Seymour, 100-101, 102, 110, 112, 113
Parallax effect, 287
Parallel processing, 257-258
Parasites, 135
Pascal, Blaise, 63
Pattern recognition, 8. See also Machine learning
Patterns in data, 70-75
PCA. See Principal-component analysis (PCA)
Pearl, Judea, 156-157, 163, 305
Pensées (Pascal), 63
Pentagon, 19, 37
Perceptron, 96-101, 108-111, 152, 265. See also Multilayer perceptron
Perceptrons (Minsky & Papert), 100-101, 113
Personal data
ethical responsibility to share some types of, 272-273
as model, 267-270
professional management of, 273-276
sharing or not, 270-276
types of, 271-273
value of, 274
Phase transitions, 105-107, 288
Physical symbol system hypothesis, 89
Physics, 29-31, 46-47, 50
Pitts, Walter, 96
Planetary-scale machine learning, 256-259
Planets, computing duration of year of, 131-133
Plato, 58
Point mutation, 124
Poisson’s equation, 30
Policing, predictive, 20
Politics, machine learning and, 16-19, 299
Positive examples, 67, 69
Posterior probability, 146-147, 241, 242, 243, 249
Poverty of the stimulus argument, 36-37
Power law of practice, 224-225
The Power of Habit (Duhigg), 223
Practice
learning and, 223
power law of, 224-225
Predictive analytics, 8. See also Machine learning
Predictive policing, 20
Presidential election, machine learning and 2012, 16-19
Principal-component analysis (PCA), 211-217, 255, 308
Principia (Newton), 65
Principal components of the data, 214
Principle of association, 93
Principle of indifference, 145
Principle of insufficient reason, 145
Principles of Psychology (James), 93
Prior probability, 146-147
Privacy, personal data and, 275
Probabilistic inference, 52, 53, 161-166, 242, 256, 305
Probability
applied to poetry, 153-154
Bayesian networks and, 156-158
Bayesians and meaning of, 149, 169-170
Bayes’ theorem and, 145-149
estimating, 148-149
frequentist interpretation of, 149
logic and, 173-175, 245-246, 306, 309
Master Algorithm and, 245-246
posterior, 146-147
prior, 146-147
Probability theory, Laplace and, 145
Probably Approximately Correct (Valiant), 75
Problem solving
learning as, 226
theory of, 225
Procedures, learners and, 8
Programming by example, 298
Programming, machine learning vs., 7-8
Programs, 4
computers writing own, 6
survival of the fittest, 131-134
Program trees, 131-133
Prolog programming language, 252-253
Punctuated equilibria, 127, 303
Pushkin, Alexander, 153
Python, 4
Quinlan, J. Ross, 88, 90
Random forest, 238
Rationalists, 57-58
Reasoning, 57-58
analogical, 179, 197
case-based, 197-200, 307
transistors and, 2
Recommendation systems, 12-13, 42, 183-185, 268, 286
Redistribution of income, 278-279
Red Queen hypothesis, 135
Reinforcement learning, 218-223, 226-227, 254, 308
Relational databases, 236
Relational learning, 227-233, 254
Representation
learning algorithms and, 283
Markov logic networks and, 249
Master Algorithm and, 239-240, 241, 243
Retailers, sets of rules and stocking, 69-70
Rewards of states, 218-222
Richardson, Matt, 231, 246
Ridiculograms, 160
Ridley, Matt, 135
RISE algorithm, 201-202, 308
Robotic Park, 121
Robot rights, 285
Robots
empathy-eliciting, 285
evolution of, 121-22, 137, 303
genetic programming and, 133
housebots, 42-43, 218, 255
military, 19-21, 279-282, 299, 310
probabilistic inference and, 166
Romney, Mitt, 17
Rosenberg, Charles, 112
Rosenblatt, Frank, 97, 99, 100, 113
Rosenbloom, Paul, 224-226
Rove, Karl, 17
Rubin, Donald, 209
Rule-based learning, 69-70, 201-202
Rule mining, 301
Rule of succession, 145-146
Rules
filtering spam, 125-127
induction of, 81-82
instances and, 201
Master Algorithm and, 244
sets of, 68-71, 90, 91
See also If… then… rules
Rumelhart, David, 112
Russell, Bertrand, 61
Rutherford, Ernest, 236
Safeway, 272
Saffo, Paul, 106
Sahami, Mehran, 151-152
Saint Paul, 144
Sampling principle, 258
Samuel, Arthur, 219
Sander, Emmanuel, 200
Satisfiability of a logical formula, 33-34, 106
Schapire, Rob, 238
Schemas, 129
Science
analogy and, 178
effect of machine learning on jobs in, 278
frequentism and, 167
machine learning and, 13-16, 235-236, 299
phases of, 39-40
The Sciences of the Artificial (Simon), 41
S curves, 104-107, 111, 249, 252, 287
Search engines, 9, 152, 227-228
Sejnowski, Terry, 103, 112
Selective breeding, genetic algorithms and, 123-124
Self-driving cars. See Driverless cars
Self-organizing systems, 8. See also Machine learning
Semantic network, 255, 309
Sets of classes, 86-87
Sets of concepts, 86-87
Sets of rules, 68-70, 90, 91
power of, 70-71
Sex, 124-126, 134-137
Shannon, Claude, 1-2
Shavlik, Jude, 76
Sigmoid curve. See S curves
Significance tests, 87