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

FuturICT project, 258

Galileo, 14, 72

Galois, Évariste, 200

Game theory, machine learning and, 20

Gaming, reinforcement learning and, 222

Gates, Bill, 22, 55, 152

GECCO (Genetic and Evolutionary Computing Conference), 136

Gene expression microarrays, 84-85

Generalizations, choosing, 60, 61

Generative model, Bayesian network as, 159

Gene regulation, Bayesian networks and, 159

Genetic algorithms, 122-128

Alchemy and, 252

backpropagation vs., 128

building blocks and, 128-129, 134

schemas, 129

survival of the fittest programs, 131-134

The Genetical Theory of Natural Selection (Fisher), 122

Genetic programming, 52, 131-133, 240, 244, 245, 252, 303-304

sex and, 134-137

Genetic Programming (Koza), 136

Genetic search, 241, 243, 249

Genome, poverty of, 27

Gentner, Dedre, 199

Ghani, Rayid, 17

The Ghost Map (Johnson), 182-183

Gibson, William, 289

Gift economy, 279

Gleevec, 84

Global Alliance for Genomics and Health, 261

Gödel, Escher, Bach (Hofstadter), 200

Good, I. J., 286

Google, 9, 44, 291

A/B testing and, 227

AdSense system, 160

communication with learner, 266-267

data gathering, 272

DeepMind and, 222

knowledge graph, 255

Master Algorithm and, 282

Naïve Bayes and, 152

PageRank and, 154, 305

problem of induction and, 61

relational learning and, 227-228

search results, 13

value of data, 274

value of learning algorithms, 10, 12

Google Brain network, 117

Google Translate, 154, 304

Gould, Stephen Jay, 127

GPS, 212-214, 216, 277

Gradient descent, 109-110, 171, 189, 193, 241, 243, 249, 252, 257-258

Grammars, formal, 36-37

Grandmother cell, perceptron and, 99-100

Graphical models, 240, 245-250

Graphical user interfaces, 236

The Guns of August (Tuchman), 178

Handwritten digit recognition, 189, 195

Hart, Peter, 185

Hawking, Stephen, 47, 283

Hawkins, Jeff, 28, 118

Hebb, Donald, 93, 94

Hebb’s rule, 93, 94, 95

Heckerman, David, 151-152, 159-160

Held-out data, accuracy of, 75-76

Help desks, 198

Hemingway, Ernest, 106

Heraclitus, 48

Hidden Markov model (HMM), 154-155, 159, 210, 305

Hierarchical structure, Markov logic network with, 256-257

Hill climbing, 135, 136, 169, 189, 252

Hillis, Danny, 135

Hinton, Geoff, 103, 104, 112, 115, 137, 139

The Hitchhiker’s Guide to the Galaxy (Adams), 130

HIV testing, Bayes’ theorem and, 147-148

HMM. See Hidden Markov model (HMM)

Ho, Yu-Chi, 113

Hodges, Joe, 178-179, 186

Hofstadter, Douglas, 200

Holland, John, 122-128, 129, 130, 131, 134

Homo technicus, 288-289

Hopfield, John, 102-103, 170

Hopfield networks, 103, 116, 302

Horning, J. J., 36-37

Howbert, Jeff, 292

How to Create a Mind (Kurzweil), 28

H &R Block, 277

Hubble, Edwin, 14-15

Human complexity

as complexity monster, 5

machine learning and, 258-259

Human control of artificial intelligence, 282-286

Human-directed evolution, 286-289, 311

Human intuition, data and, 39

Humanities, machine learning and, 278

Human Rights Watch, 281

Hume, David, 58-59, 62, 63, 93, 178, 300-301

Hume’s problem of induction, 58-59, 145, 169, 197, 251

Humie Awards, 134

Hunt, Earl, 88

Hyperplanes, 98, 100, 195, 196

Hyperspace, 107-111, 187

Hypotheses

Bayesians and, 144, 167-168

machine learning and, 13-15

overfitting and, 73-75

preference for simpler, 77-78

Red Queen, 135

testing, 13-15, 49

IBM, 13, 37, 219

ICML. See International Conference on Machine Learning (ICML)

If … then … rules, 68-71, 84-85, 125-127, 132, 152, 155-156, 201-202, 244-245, 254

Ill-posed problem, 64

Immortality, genetic algorithms and, 126

Incognito mode, 266

Income, basic guaranteed, 279

Independent-component analysis, 215

Indexers, 8, 9

Indifference, principle of, 145

Induction

decision tree, 85-89

further readings, 300-302

as inverse of deduction, 80-83, 301

Master Algorithm and, 34

Newton’s rules of, 65-66

problem of, 59-62

Inductive logic programming. See Inverse deduction

Inductivist turkey, 61

Inference

Alchemy and, 256-257

Bayesian networks and, 161-166

Information, cyberwar and, 19-20

Information gain, 87, 188

Information processing systems, study of, 89

Information Revolution, 9

Instance-based learning, 201-202, 250

Institute of Control Sciences, 190

Intelligence

computers and, 35, 286, 287

symbolists and, 52, 89, 302

International Conference on Machine Learning (ICML), 136

Internet, 231, 236

Intuition, evidence and, 39

Inverse deduction, 52, 80-83, 90-91, 301

Alchemy and, 252

cell model and, 115

computational intensiveness of, 85

cure for cancer and, 83-85

Master Algorithm and, 90, 241, 242-243

Newton’s principle and, 82-83

shortcomings of, 91

IPsoft, 198

Irrelevant attributes, nearest neighbor algorithm and, 186-187, 188-189

Isomap, 217, 255, 308

Iterative search, 28

Jackel, Larry, 195

James, William, 93, 178, 205

Java, 4

Jelinek, Fred, 37

Jesus, 144

Jevons, William Stanley, 80

Johnson, Steven, 182-183

Jordan, Michael, 164, 170

Junction trees, 163

Kaggle.com, 292

Kahneman, Daniel, 141

Kalman filter, 155, 305

Kant, Immanuel, 178

Kekulè, August, 178

Kennedy, John F., 36, 177-178, 182

Kepler, Johannes, 65, 131

laws of, 40, 65, 131

Kepler phase of science, 39-40

Kernels, 192, 196, 243, 307

Keyword matching, 20

Kinect, 88, 237, 238

Kipling, Rudyard, 68

k -means algorithm, 208, 210, 308

k -nearest-neighbor algorithm, 183

Knowledge, 8, 52, 64

unity of, 31

Knowledge acquisition bottleneck, 89-90

Knowledge-based system, 89-90

Knowledge discovery, 8. See also Machine learning

Knowledge engineering

machine learning and, 102

symbolist learning and, 90

Knowledge engineers, 251

machiner learners vs., 34-38

Knowledge graph, 255

Koza, John, 131, 132, 134, 136

Krugman, Paul, 232

Kurzweil, Ray, 28, 83, 186, 286-289

Laird, John, 226

Laird, Nan, 209

Landmine example, support vector machines and, 192-193

Lang, Kevin, 136

Language learning, 36-37

Laplace, Pierre-Simon de, 144-145

Latent semantic analysis, 215, 308

Law of effect, 218

Law of similarity, 178

Lazy learning, 180-182

Learning

across problem domains, 199

by association, 93-94

Bayesian, 144, 166-170, 174-175

children’s, 203-204, 308

deep, 104, 115-118, 172, 195, 241

instance-based, 201-202

knowledge and, 64

lazy, 180-182

as problem solving, 226

reinforcement, 218-223, 308

rule-based, 69-70, 201-202

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