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

Clustering, 205-210, 254, 257

hierarchical, 210

Cluster prototypes, 207-208

Clusters, 205-210

“Cocktail party” problem, 215

Cognition, theory of, 226

Coin toss, 63, 130, 167-168

Collaborative filtering systems, 183-184, 306-307

Columbus test, 113

Combinatorial explosion, 73-74

Commoner, Barry, 158

Commonsense reasoning, 35, 118-119, 145, 276-277, 300

Complexity monster, 5-6, 7, 43, 246

Compositionality, 119

Computational biologists, use of hidden

Markov models, 155

Computers

decision making and, 282-286

evolution of, 286-289

human interaction with, 264-267

as learners, 45

logic and, 2

S curves and, 105

as sign of Master Algorithm, 34

simulating brain using, 95

as unifier, 236

writing own programs, 6

Computer science, Master Algorithm and, 32-34

Computer vision, Markov networks and, 172

Concepts, 67

conjunctive, 66-68

set of rules and, 68-69

sets of, 86-87

Conceptual model, 44, 152

Conditional independence, 157-158

Conditional probabilities, 245

Conditional random fields, 172, 306

Conference on Neural Information Processing Systems (NIPS), 170, 172

Conjunctive concepts, 65-68, 74

Connectionists/connectionism, 51, 52, 54, 93-119

Alchemy and, 252

autoencoder and, 116-118

backpropagation and, 52, 107-111

Boltzmann machine and, 103-104

cell model, 114-115

connectomics, 118-119

deep learning and, 115

further reading, 302-303

Master Algorithm and, 240-241

nature and, 137-142

neural networks and, 112-114

perceptron, 96-101, 107-108

S curves and, 104-107

spin glasses and, 102-103

symbolist learning vs., 91, 94-95

Connectomics, 118-119

Consciousness, 96

Consilience (Wilson), 31

Constrained optimization, 193-195, 241, 242

Constraints, support vector machines and, 193-195

Convolutional neural networks, 117-119, 303

Cope, David, 199, 307

Cornell University, Creative Machines Lab, 121-122

Cortex, 118, 138

unity of, 26-28, 299-300

Counterexamples, 67

Cover, Tom, 185

Crawlers, 8-9

Creative Machines Lab, 121-122

Credit-assignment problem, 102, 104, 107, 127

Crick, Francis, 122, 236

Crossover, 124-125, 134-136, 241, 243

Curse of dimensionality, 186-190, 196, 201, 307

Cyber Command, 19

Cyberwar, 19-21, 279-282, 299, 310

Cyc project, 35, 300

DARPA, 21, 37, 113, 121, 255

Darwin, Charles, 28, 30, 131, 235

algorithm, 122-128

analogy and, 178

Hume and, 58

on lack of mathematical ability, 127

on selective breeding, 123-124

variation and, 124

Data

accuracy of held-out, 75-76

Bayes’ theorem and, 31-32

control of, 45

first principal component of the, 214

human intuition and, 39

learning from finite, 24-25

Master Algorithm and, 25-26

patterns in, 70-75

sciences and complex, 14

as strategic asset for business, 13

theory and, 46

See also Big data; Overfitting; Personal data

Database engine, 49-50

Databases, 8, 9

Data mining, 8, 73, 232-233, 298, 306. See also Machine learning

Data science, 8. See also Machine learning

Data scientist, 9

Data sharing, 270-276

Data unions, 274-275

Dawkins, Richard, 284

Decision making, artificial intelligence and, 282-286

Decision theory, 165

Decision tree induction, 85-89

Decision tree learners, 24, 301

Decision trees, 24, 85-90, 181-182, 188, 237-238

Deduction

induction as inverse of, 80-83, 301

Turing machine and, 34

Deductive reasoning, 80-81

Deep learning, 104, 115-118, 172, 195, 241, 302

DeepMind, 222

Democracy, machine learning and, 18-19

Dempster, Arthur, 209

Dendrites, 95

Descartes, René, 58, 64

Descriptive theories, normative theories vs., 141-142, 304

Determinism, Laplace and, 145

Developmental psychology, 203-204, 308

DiCaprio, Leonardo, 177

Diderot, Denis, 63

Diffusion equation, 30

Dimensionality, curse of, 186-190, 307

Dimensionality reduction, 189-190, 211-215, 255

nonlinear, 215-217

Dirty Harry (film), 65

Disney animators, S curves and, 106

“Divide and conquer” algorithm, 77-78, 80, 81, 87

DNA sequencers, 84

Downweighting attributes, 189

Driverless cars, 8, 113, 166, 172, 306

Drones, 21, 281

Drugs, 15, 41-42, 83. See also Cancer drugs

Duhigg, Charles, 223

Dynamic programming, 220

Eastwood, Clint, 65

Echolocation, 26, 299

Eddington, Arthur, 75

Effect, law of, 218

eHarmony, 265

Eigenfaces, 215

80/20 rule, 43

Einstein, Albert, 75, 200

Eldredge, Niles, 127

Electronic circuits, genetic programming and, 133-134

Eliza (help desk), 198

EM (expectation maximization) algorithm, 209-210

Emotions, learning and, 218

Empathy-eliciting robots, 285

Empiricists, 57-58

Employment, effect of machine learning on, 276-279

Enlightenment, rationalism vs. empiricism, 58

Entropy, 87

Epinions, 231

Equations, 4, 50

Essay on Population (Malthus), 178, 235

Ethics, robot armies and, 280-281

Eugene Onegin (Pushkin), 153-154

“Explaining away” phenomenon, 163

Evaluation

learning algorithms and, 283

Markov logic networks and, 249

Master Algorithm and, 239, 241, 243

Evolution, 28-29, 121-142

Baldwinian, 139

Darwin’s algorithm, 122-128

human-directed, 286-289, 311

Master Algorithm and, 28-29

of robots, 121-122, 137, 303

role of sex in, 134-137

technological, 136-137

See also Genetic algorithms

Evolutionaries, 51, 52, 54

Alchemy and, 252-253

exploration-exploitation dilemma, 128-130, 221

further reading, 303-304

genetic programming and, 52

Holland and, 127

Master Algorithm and, 240-241

nature and, 137-139

Evolutionary computation, 121-142

Evolutionary robotics, 121-122, 303

Exclusive-OR function (XOR), 100-101, 112, 195

Exploration-exploitation dilemma, 128-130, 221

Exponential function, machine learning and, 73-74

The Extended Phenotype (Dawkins), 284

Facebook, 44, 291

data and, 14, 274

facial recognition technology, 179-180

machine learning and, 11

relational learning and, 230

sharing via, 271-272

Facial identification, 179-180, 182

False discovery rate, 77, 301

Farming, as analogy for machine learning, 6-7

Feature selection, 188-189

Feature template, 248

Feature weighting, 189

Ferret brain rewiring, 26, 299

Feynman, Richard, 4

Filter bubble, 270

Filtering spam, rule for, 125-127

First principal component of the data, 214

Fisher, Ronald, 122

Fitness

Fisher on, 122

in genetic programming, 132

Master Algorithm and, 243

neural learning and, 138-139

sex and, 135

Fitness function, 123-124

Fitness maximum, genetic algorithms and, 127-128, 129

Fix, Evelyn, 178-179, 186

Fodor, Jerry, 38

Forecasting, S curves and, 106

Foundation Medicine, 41, 261

Foundation (Asimov), 232

Fractal geometry, 30, 300

Freakonomics (Dubner & Levitt), 275

Frequentist interpretation of probability, 149

Freund, Yoav, 238

Friedman, Milton, 151

Frontiers, 185, 187, 191, 196

“Funes the Memorious” (Borges), 71

Futility of bias-free learning, 64