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

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 alsoIf… 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

Silver, Nate, 17, 238

Similarity, 178, 179

Similarity measures, 192, 197-200, 207

Simon, Herbert, 41, 225-226, 302

Simultaneous localization and mapping (SLAM), 166

Singularity, 28, 186, 286-289, 311

The Singularity Is Near (Kurzweil), 286

Siri, 37, 155, 161-162, 165, 172, 255

SKICAT (sky image cataloging and analysis tool), 15, 299

Skills, learners and, 8, 217-227

Skynet, 282-286

Sloan Digital Sky Survey, 15

Smith, Adam, 58

Snow, John, 183

Soar, chunking in, 226

Social networks, information propagation in, 231

The Society of Mind (Minsky), 35

Space complexity, 5

Spam filters, 23-24, 151-152, 168-169, 171

Sparse autoencoder, 117

Speech recognition, 155, 170-172, 276, 306

Speed, learning algorithms and, 139-142

Spin glasses, brain and, 102-103

Spinoza, Baruch, 58

Squared error, 241, 243

Stacked autoencoder, 117

Stacking, 238, 255, 309

States, value of, 219-221