Artificial Intelligence: Humans at the Heart of Algorithms
Autor Alan Dixen Limba Engleză Paperback – 18 feb 2025
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Specificații
ISBN-13: 9780367515980
ISBN-10: 0367515989
Pagini: 704
Ilustrații: 552
Dimensiuni: 210 x 280 mm
Ediția:2
Editura: CRC Press
Colecția Chapman and Hall/CRC
Locul publicării:Boca Raton, United States
ISBN-10: 0367515989
Pagini: 704
Ilustrații: 552
Dimensiuni: 210 x 280 mm
Ediția:2
Editura: CRC Press
Colecția Chapman and Hall/CRC
Locul publicării:Boca Raton, United States
Public țintă
Adult education, General, and PostgraduateCuprins
List of Figures xxv
Preface xxxv
Author Bio xxxvii
Chapter 1 ■ Introduction 1
1.1 WHAT IS ARTIFICIAL INTELLIGENCE? 1
1.1.1 How much like a human: strong vs. weak AI 1
1.1.2 Top-down or bottom-up: symbolic vs. sub-symbolic 2
1.1.3 A working definition 3
1.1.4 Human intelligence 3
1.1.5 Bottom up and top down 4
1.2 HUMANS AT THE HEART 4
1.3 A SHORT HISTORY OF ARTIFICIAL INTELLIGENCE 5
1.3.1 The development of AI 6
1.3.2 The physical symbol system hypothesis 8
1.3.3 Sub-symbolic spring 9
1.3.4 AI Renaissance 10
1.3.5 Moving onwards 11
1.4 STRUCTURE OF THIS BOOK – A LANDSCAPE OF AI 11
Section I Knowledge-Rich AI
Chapter 2 ■ Knowledge in AI 15
2.1 OVERVIEW 15
2.2 INTRODUCTION 15
2.3 REPRESENTING KNOWLEDGE 16
2.4 METRICS FOR ASSESSING KNOWLEDGE REPRESENTATION SCHEMES
19
2.5 LOGIC REPRESENTATIONS 20
2.6 PROCEDURAL REPRESENTATION 23
vii
viii ■ Contents
2.6.1 The database 23
2.6.2 The production rules 23
2.6.3 The interpreter 24
2.6.4 An example production system: making a loan 24
2.7 NETWORK REPRESENTATIONS 26
2.8 STRUCTURED REPRESENTATIONS 28
2.8.1 Frames 29
2.8.2 Scripts 29
2.9 GENERAL KNOWLEDGE 31
2.10 THE FRAME PROBLEM 32
2.11 KNOWLEDGE ELICITATION 33
2.12 SUMMARY 33
Chapter 3 ■ Reasoning 37
3.1 OVERVIEW 37
3.2 WHAT IS REASONING? 37
3.3 FORWARD AND BACKWARD REASONING 39
3.4 REASONING WITH UNCERTAINTY 40
3.4.1 Non-monotonic reasoning 40
3.4.2 Probabilistic reasoning 41
3.4.3 Certainty factors 43
3.4.4 Fuzzy reasoning 45
3.4.5 Reasoning by analogy 46
3.4.6 Case-based reasoning 46
3.5 REASONING OVER NETWORKS 48
3.6 CHANGING REPRESENTATIONS 51
3.7 SUMMARY 51
Chapter 4 ■ Search 53
4.1 INTRODUCTION 53
4.1.1 Types of problem 53
4.1.2 Structuring the search space 57
4.2 EXHAUSTIVE SEARCH AND SIMPLE PRUNING 63
4.2.1 Depth and breadth first search 63
4.2.2 Comparing depth and breadth first searches 65
4.2.3 Programming and space costs 67
4.2.4 Iterative deepening and broadening 68
Contents ■ ix
4.2.5 Finding the best solution – branch and bound 69
4.2.6 Graph search 70
4.3 HEURISTIC SEARCH 70
4.3.1 Hill climbing andbest first – goal-directed search 72
4.3.2 Finding the best solution – the A∗ algorithm 72
4.3.3 Inexact search 75
4.4 KNOWLEDGE-RICH SEARCH 77
4.4.1 Constraint satisfaction 78
4.5 SUMMARY 80
Section II Data and Learning
Chapter 5 ■ Machine learning 85
5.1 OVERVIEW 85
5.2 WHY DO WE WANT MACHINE LEARNING? 85
5.3 HOW MACHINES LEARN 87
5.3.1 Phases of machine learning 87
5.3.2 Rote learning and the importance of generalization 89
5.3.3 Inputs to training 90
5.3.4 Outputs of training 91
5.3.5 The training process 92
5.4 DEDUCTIVE LEARNING 93
5.5 INDUCTIVE LEARNING 94
5.5.1 Version spaces 95
5.5.2 Decision trees 99
5.5.2.1 Building a binary tree 99
5.5.2.2 More complex trees 102
5.5.3 Rule induction and credit assignment 103
5.6 EXPLANATION-BASED LEARNING 104
5.7 EXAMPLE: QUERY-BY-BROWSING 105
5.7.1 What the user sees 105
5.7.2 How it works 105
5.7.3 Problems 107
5.8 SUMMARY 107
Chapter 6 ■ Neural Networks 109
6.1 OVERVIEW 109
x ■ Contents
6.2 WHY USE NEURAL NETWORKS? 109
6.3 THE PERCEPTRON 110
6.3.1 The XOR problem 112
6.4 THE MULTI-LAYER PERCEPTRON 113
6.5 BACKPROPAGATION 114
6.5.1 Basic principle 115
6.5.2 Backprop for a single layer network 116
6.5.3 Backprop for hidden layers 117
6.6 ASSOCIATIVE MEMORIES 117
6.6.1 Boltzmann Machines 119
6.6.2 Kohonen self-organizing networks 121
6.7 LOWER-LEVEL MODELS 122
6.7.1 Cortical layers 122
6.7.2 Inhibition 123
6.7.3 Spiking neural networks 123
6.8 HYBRID ARCHITECTURES 124
6.8.1 Hybrid layers 124
6.8.2 Neurosymbolic AI 125
6.9 SUMMARY 126
Chapter 7 ■ Statistical and Numerical Techniques 129
7.1 OVERVIEW 129
7.2 LINEAR REGRESSION 129
7.3 VECTORS AND MATRICES 132
7.4 EIGENVALUES AND PRINCIPAL COMPONENTS 134
7.5 CLUSTERING AND K-MEANS 136
7.6 RANDOMNESS 138
7.6.1 Simple statistics 138
7.6.2 Distributions and long-tail data 140
7.6.3 Least squares 142
7.6.4 Monte Carlo techniques 142
7.7 NON-LINEAR FUNCTIONS FOR MACHINE LEARNING 144
7.7.1 Support Vector Machines 144
7.7.2 Reservoir Computing 145
7.7.3 Kolmogorov-Arnold Networks 146
7.8 SUMMARY 147
Contents ■ xi
Chapter 8 ■ Going Large: deep learning and big data 151
8.1 OVERVIEW 151
8.2 DEEP LEARNING 152
8.2.1 Why are many layers so difficult? 153
8.2.2 Architecture of the layers 153
8.3 GROWING THE DATA 156
8.3.1 Modifying real data 157
8.3.2 Virtual worlds 157
8.3.3 Self learning 157
8.4 DATA REDUCTION 158
8.4.1 Dimension reduction 159
8.4.1.1 Vector space techniques 159
8.4.1.2 Non-numeric features 160
8.4.2 Reduce total number of data items 161
8.4.2.1 Sampling 161
8.4.2.2 Aggregation 161
8.4.3 Segmentation 162
8.4.3.1 Class segmentation 162
8.4.3.2 Result recombination 162
8.4.3.3 Weakly-communicating partial analysis 163
8.5 PROCESSING BIG DATA 164
8.5.1 Why it is hard – distributed storage and computation 164
8.5.2 Principles behind MapReduce 165
8.5.3 MapReduce for the cloud 166
8.5.4 If it can go wrong – resilience for big processing 167
8.6 DATA AND ALGORITHMS AT SCALE 169
8.6.1 Big graphs 169
8.6.2 Time series and event streams 170
8.6.2.1 Multi-scale with mega-windows 170
8.6.2.2 Untangling streams 171
8.6.2.3 Real-time processing 171
8.7 SUMMARY 171
Chapter 9 ■ Making Sense of Machine Learning 175
9.1 OVERVIEW 175
9.2 THE MACHINE LEARNING PROCESS 175
xii ■ Contents
9.2.1 Training phase 176
9.2.2 Application phase 177
9.2.3 Validation phase 177
9.3 EVALUATION 178
9.3.1 Measures of effectiveness 178
9.3.2 Precision–recall trade-off 180
9.3.3 Data for evaluation 182
9.3.4 Multi-stage evaluation 182
9.4 THE FITNESS LANDSCAPE 183
9.4.1 Hill-climbing and gradient descent / ascent 183
9.4.2 Local maxima and minima 184
9.4.3 Plateau and ridge effects 185
9.4.4 Local structure 186
9.4.5 Approximating the landscape 186
9.4.6 Forms of fitness function 187
9.5 DEALING WITH COMPLEXITY 188
9.5.1 Degrees of freedom and dimension reduction 188
9.5.2 Constraints and dependent features 189
9.5.3 Continuity and learning 191
9.5.4 Multi-objective optimisation 193
9.5.5 Partially labelled data 194
9.6 SUMMARY 196
Chapter 10 ■Data Preparation 199
10.1 OVERVIEW 199
10.2 STAGES OF DATA PREPARATION 199
10.3 CREATING A DATASET 200
10.3.1 Extraction and gathering of data 200
10.3.2 Entity reconciliation and linking 201
10.3.3 Exception sets 202
10.4 MANIPULATION AND TRANSFORMATION OF DATA 202
10.4.1 Types of data value 203
10.4.2 Transforming to the right kind of data 204
10.5 NUMERICAL TRANSFORMATIONS 205
10.5.1 Information 205
10.5.2 Normalising data 207
Contents ■ xiii
10.5.3 Missing values – filling the gaps 207
10.5.4 Outliers – dealing with extremes 209
10.6 NON-NUMERIC TRANSFORMATIONS 211
10.6.1 Media data 211
10.6.2 Text 212
10.6.3 Structure transformation 214
10.7 AUTOMATION AND DOCUMENTATION 214
10.8 SUMMARY 216
Section III Specialised Areas
Chapter 11 ■Game playing 221
11.1 OVERVIEW 221
11.2 INTRODUCTION 221
11.3 CHARACTERISTICS OF GAME PLAYING 223
11.4 STANDARD GAMES 225
11.4.1 A simple game tree 225
11.4.2 Heuristics and minimax search 225
11.4.3 Horizon problems 227
11.4.4 Alpha–beta pruning 228
11.4.5 The imperfect opponent 229
11.5 NON-ZERO-SUM GAMES AND SIMULTANEOUS PLAY 229
11.5.1 The prisoner’s dilemma 230
11.5.2 Searching the game tree 230
11.5.3 No alpha–beta pruning 232
11.5.4 Pareto-optimality 232
11.5.5 Multi-party competition and co-operation 233
11.6 THE ADVERSARY IS LIFE! 233
11.7 PROBABILITY 235
11.8 NEURAL NETWORKS FOR GAMES 236
11.8.1 Where to use a neural network 236
11.8.2 Training data and self play 238
11.9 SUMMARY 238
Chapter 12 ■Computer vision 243
12.1 OVERVIEW 243
12.2 INTRODUCTION 243
xiv ■ Contents
12.2.1 Why computer vision is difficult 243
12.2.2 Phases of computer vision 244
12.3 DIGITIZATION AND SIGNAL PROCESSING 245
12.3.1 Digitizing images 245
12.3.2 Thresholding 246
12.3.3 Digital filters 248
12.3.3.1 Linear filters 249
12.3.3.2 Smoothing 249
12.3.3.3 Gaussian filters 251
12.3.3.4 Practical considerations 252
12.4 EDGE DETECTION 252
12.4.1 Identifying edge pixels 253
12.4.1.1 Gradient operators 253
12.4.1.2 Robert’s operator 253
12.4.1.3 Sobel’s operator 256
12.4.1.4 Laplacian operator 257
12.4.1.5 Successive refinement and Marr’s primal sketch 258
12.4.2 Edge following 259
12.5 REGION DETECTION 260
12.5.1 Region growing 261
12.5.2 The problem of texture 261
12.5.3 Representing regions – quadtrees 262
12.5.4 Computational problems 263
12.6 RECONSTRUCTING OBJECTS 263
12.6.1 Inferring three-dimensional features 263
12.6.1.1 Problems with labelling 266
12.6.2 Using properties of regions 267
12.7 IDENTIFYING OBJECTS 269
12.7.1 Using bitmaps 269
12.7.2 Using summary statistics 270
12.7.3 Using outlines 271
12.7.4 Using paths 272
12.8 FACIAL AND BODY RECOGNITION 273
12.9 NEURAL NETWORKS FOR IMAGES 276
12.9.1 Convolutional neural networks 276
12.9.2 Autoencoders 277
Contents ■ xv
12.10 GENERATIVE ADVERSARIAL NETWORKS 279
12.10.1 Generated data 279
12.10.2 Diffusion models 280
12.10.3 Bottom-up and top-down processing 281
12.11 MULTIPLE IMAGES 281
12.11.1 Stereo vision 282
12.11.2 Moving pictures 284
12.12 SUMMARY 285
Chapter 13 ■Natural language understanding 289
13.1 OVERVIEW 289
13.2 WHAT IS NATURAL LANGUAGE UNDERSTANDING? 289
13.3 WHY DO WE NEED NATURAL LANGUAGE UNDERSTANDING? 290
13.4 WHY IS NATURAL LANGUAGE UNDERSTANDING DIFFICULT? 290
13.5 AN EARLY ATTEMPT AT NATURAL LANGUAGE UNDERSTANDING:
SHRDLU 292
13.6 HOW DOES NATURAL LANGUAGE UNDERSTANDING WORK? 293
13.7 SYNTACTIC ANALYSIS 295
13.7.1 Grammars 296
13.7.2 An example: generating a grammar fragment 297
13.7.3 Transition networks 299
13.7.4 Context-sensitive grammars 302
13.7.5 Feature sets 303
13.7.6 Augmented transition networks 304
13.7.7 Taggers 304
13.8 SEMANTIC ANALYSIS 305
13.8.1 Semantic grammars 306
13.8.1.1 An example: a database query interpreter revisited 306
13.8.2 Case grammars 307
13.9 PRAGMATIC ANALYSIS 310
13.9.1 Speech acts 311
13.10 GRAMMAR-FREE APPROACHES 311
13.10.1 Template matching 311
13.10.2 Keyword matching 312
13.10.3 Predictive methods 312
13.10.4 Statistical methods 313
13.11 SUMMARY 314
xvi ■ Contents
13.12 SOLUTION TO SHRDLU PROBLEM 315
Chapter 14 ■Time Series and Sequential Data 317
14.1 OVERVIEW 317
14.2 GENERAL PROPERTIES 317
14.2.1 Kinds of temporal and sequential data 317
14.2.2 Looking through time 318
14.2.3 Processing temporal data 320
14.2.3.1 Windowing 320
14.2.3.2 Hidden state 321
14.2.3.3 Non-time domain transformations 321
14.3 PROBABILITY MODELS 322
14.3.1 Markov Model 323
14.3.2 Higher-order Markov Model 324
14.3.3 Hidden Markov Model 326
14.4 GRAMMAR AND PATTERN-BASED APPROACHES 327
14.4.1 Regular expressions 327
14.4.2 More complex grammars 328
14.5 NEURAL NETWORKS 329
14.5.1 Window-based methods 329
14.5.2 Recurrent Neural Networks 331
14.5.3 Long-term short-term memory networks 332
14.5.4 Transformer models 332
14.6 STATISTICAL AND NUMERICAL TECHNIQUES 332
14.6.1 Simple data cleaning techniques 333
14.6.2 Logarithmic transformations and exponential growth 334
14.6.3 ARMA models 335
14.6.4 Mixed statistics/ML models 336
14.7 MULTI-STAGE/SCALE 337
14.8 SUMMARY 339
Chapter 15 ■Planning and robotics 343
15.1 OVERVIEW 343
15.2 INTRODUCTION 343
15.2.1 Friend or foe? 343
15.2.2 Different kinds of robots 344
15.3 GLOBAL PLANNING 345
Contents ■ xvii
15.3.1 Planning actions – means–ends analysis 345
15.3.2 Planning routes – configuration spaces 348
15.4 LOCAL PLANNING 350
15.4.1 Local planning and obstacle avoidance 350
15.4.2 Finding out about the world 353
15.5 LIMBS, LEGS AND EYES 356
15.5.1 Limb control 356
15.5.2 Walking – on one, two or more legs 359
15.5.3 Active vision 361
15.6 PRACTICAL ROBOTICS 363
15.6.1 Controlling the environment 363
15.6.2 Safety and hierarchical control 364
15.7 SUMMARY 365
Chapter 16 ■Agents 369
16.1 OVERVIEW 369
16.2 SOFTWARE AGENTS 369
16.2.1 The rise of the agent 370
16.2.2 Triggering actions 371
16.2.3 Watching and learning 372
16.2.4 Searching for information 374
16.3 REINFORCEMENT LEARNING 376
16.3.1 Single step learning 376
16.3.2 Choices during learning 378
16.3.3 Intermittent rewards and credit assignment 379
16.4 COOPERATING AGENTS AND DISTRIBUTED AI 379
16.4.1 Blackboard architectures 380
16.4.2 Distributed control 382
16.5 LARGER COLLECTIVES 383
16.5.1 Emergent behaviour 383
16.5.2 Cellular automata 384
16.5.3 Artificial life 384
16.5.4 Swarm computing 385
16.5.5 Ensemble methods 386
16.6 SUMMARY 388
Chapter 17 ■Web scale reasoning 391
xviii ■ Contents
17.1 OVERVIEW 391
17.2 THE SEMANTIC WEB 391
17.2.1 Representing knowledge – RDF and triples 392
17.2.2 Ontologies 394
17.2.3 Asking questions – SPARQL 395
17.2.4 Talking about RDF – reification, named graphs and provenance
396
17.2.5 Linked data – connecting the Semantic Web 398
17.3 MINING THE WEB: SEARCH AND SEMANTICS 402
17.3.1 Search words and links 402
17.3.2 Explicit markup 403
17.3.3 External semantics 405
17.4 USING WEB DATA 408
17.4.1 Knowledge-rich applications 408
17.4.2 The surprising power of big data 409
17.5 THE HUMAN WEB 412
17.5.1 Recommender systems 412
17.5.2 Crowdsourcing and human computation 414
17.5.3 Social media as data 416
17.6 SUMMARY 417
Section IV Humans at the Heart
Chapter 18 ■Expert and decision support systems 421
18.1 OVERVIEW 421
18.2 INTRODUCTION – EXPERTS IN THE LOOP 421
18.3 EXPERT SYSTEMS 422
18.3.1 Uses of expert systems 423
18.3.2 Architecture of an expert system 425
18.3.3 Explanation facility 425
18.3.4 Dialogue and UI component 427
18.3.5 Examples of four expert systems 428
18.3.5.1 Example 1: MYCIN 428
18.3.5.2 Example 2: PROSPECTOR 429
18.3.5.3 Example 3: DENDRAL 429
18.3.5.4 Example 4: XCON 430
18.3.6 Building an expert system 430
Contents ■ xix
18.3.7 Limitations of expert systems 431
18.4 KNOWLEDGE ACQUISITION 431
18.4.1 Knowledge elicitation 432
18.4.1.1 Unstructured interviews. 432
18.4.1.2 Structured interviews. 433
18.4.1.3 Focused discussions. 433
18.4.1.4 Role reversal. 433
18.4.1.5 Think-aloud. 433
18.4.2 Knowledge Representation 434
18.4.2.1 Expert system shells 434
18.4.2.2 High-level programming languages 434
18.4.2.3 Ontologies 434
18.4.2.4 Selecting a tool 435
18.5 EXPERTS AND MACHINE LEARNING 436
18.5.1 Knowledge elicitation for ML 438
18.5.1.1 Acquiring tacit knowledge 438
18.5.1.2 Feature selection 438
18.5.1.3 Expert labelling 438
18.5.1.4 Iteration and interaction 439
18.5.2 Algorithmic choice, validation and explanation 439
18.6 DECISION SUPPORT SYSTEMS. 441
18.6.1 Visualisation 442
18.6.2 Data management and analysis 443
18.6.3 Visual Analytics 444
18.6.3.1 Visualisation in VA 445
18.6.3.2 Data management and analysis for VA 446
18.7 STEPPING BACK 447
18.7.1 Who is it about? 447
18.7.2 Why are we doing it? 447
18.7.3 Wider context 449
18.7.4 Cost–benefit balance 450
18.8 SUMMARY 451
Chapter 19 ■AI working with and for humans 455
19.1 OVERVIEW 455
19.2 INTRODUCTION 455
xx ■ Contents
19.3 LEVELS AND TYPES OF HUMAN CONTACT 457
19.3.1 Social scale 457
19.3.2 Visibility and embodiment 458
19.3.3 Intentionality 458
19.3.4 Who is in control 459
19.3.5 Levels of automation 460
19.4 ON A DEVICE – INTELLIGENT USER INTERFACES 462
19.4.1 Low-level input 462
19.4.2 Conversational user interfaces 462
19.4.3 Predicting what next 464
19.4.4 Finding and managing information 464
19.4.5 Helping with tasks 466
19.4.6 Adaptation and personalisation 467
19.4.7 Going small 468
19.5 IN THE WORLD – SMART ENVIRONMENTS 469
19.5.1 Configuration 470
19.5.2 Sensor fusion 470
19.5.3 Context and activity 472
19.5.4 Designing for uncertainty in sensor-rich smart environments 473
19.5.5 Dealing with hiddenness – a central heating controller 474
19.6 DESIGNING FOR AI–HUMAN INTERACTION 476
19.6.1 Appropriate intelligence – soft failure 476
19.6.2 Feedback – error detection and repair 477
19.6.3 Decisions and suggestions 478
19.6.4 Case study: OnCue – appropriate intelligence by design 480
19.7 TOWARDS HUMAN–MACHINE SYNERGY 481
19.7.1 Tuning AI algorithms for interaction 481
19.7.2 Tuning interaction for AI 482
19.8 SUMMARY 483
Chapter 20 ■When things go wrong 487
20.1 OVERVIEW 487
20.2 INTRODUCTION 487
20.3 WRONG ON PURPOSE? 488
20.3.1 Intentional bad use 488
20.3.2 Unintentional problems 489
Contents ■ xxi
20.4 GENERAL STRATEGIES 490
20.4.1 Transparency and trust 490
20.4.2 Algorithmic accountability 491
20.4.3 Levels of opacity 492
20.5 SOURCES OF ALGORITHMIC BIAS 493
20.5.1 What is bias? 493
20.5.2 Stages in machine learning 494
20.5.3 Bias in the training data 494
20.5.4 Bias in the objective function 497
20.5.5 Bias in the accurate result 498
20.5.6 Proxy measures 499
20.5.7 Input feature choice 500
20.5.8 Bias and human reasoning 500
20.5.9 Avoiding bias 501
20.6 PRIVACY 502
20.6.1 Anonymisation 502
20.6.2 Obfuscation 503
20.6.3 Aggregation 503
20.6.4 Adversarial privacy 504
20.6.5 Federated learning 504
20.7 COMMUNICATION, INFORMATION AND MISINFORMATION 505
20.7.1 Social media 505
20.7.2 Deliberate misinformation 506
20.7.3 Filter bubbles 507
20.7.4 Poor information 507
20.8 SUMMARY 508
Chapter 21 ■Explainable AI 513
21.1 OVERVIEW 513
21.2 INTRODUCTION 513
21.2.1 Why we need explainable AI 514
21.2.2 Is explainable AI possible? 515
21.3 AN EXAMPLE – QUERY-BY-BROWSING 515
21.3.1 The problem 516
21.3.2 A solution 516
21.3.3 How it works 517
xxii ■ Contents
21.4 HUMAN EXPLANATION – SUFFICIENT REASON 518
21.5 LOCAL AND GLOBAL EXPLANATIONS 519
21.5.1 Decision trees – easier explanations 519
21.5.2 Black-box – sensitivity and perturbations 520
21.6 HEURISTICS FOR EXPLANATION 522
21.6.1 White-box techniques 523
21.6.2 Black-box techniques 524
21.6.3 Grey-box techniques 526
21.7 SUMMARY 529
Chapter 22 ■Models of the mind – Human-Like Computing 533
22.1 OVERVIEW 533
22.2 INTRODUCTION 533
22.3 WHAT IS THE HUMAN MIND? 534
22.4 RATIONALITY 535
22.4.1 ACTR 536
22.4.2 SOAR 537
22.5 SUBCONSCIOUS AND INTUITION 538
22.5.1 Heuristics and imagination 539
22.5.2 Attention, salience and boredom 539
22.5.3 Rapid serial switching 540
22.5.4 Disambiguation 541
22.5.5 Boredom 542
22.5.6 Dreaming 542
22.6 EMOTION 543
22.6.1 Empathy and theory of mind 544
22.6.2 Regret 546
22.6.3 Feeling 548
22.7 SUMMARY 549
Chapter 23 ■Philosophical, ethical and social issues 553
23.1 OVERVIEW 553
23.2 THE LIMITS OF AI 553
23.2.1 Intelligent machines or engineering tools? 554
23.2.2 What is intelligence? 554
23.2.3 Computational argument vs. Searle’s Chinese Room 555
23.3 CREATIVITY 556
Contents ■ xxiii
23.3.1 The creative process 557
23.3.2 Generate and filter 557
23.3.3 The critical edge 558
23.3.4 Impact on creative professionals 558
23.4 CONSCIOUSNESS 559
23.4.1 Defining consciousness 559
23.4.2 Dualism and materialism 560
23.4.3 The hard problem of consciousness 561
23.5 MORALITY OF THE ARTIFICIAL 561
23.5.1 Morally neutral 561
23.5.2 Who is responsible? 563
23.5.3 Life or death decisions 563
23.5.4 The special ethics of AI 565
23.6 SOCIETY AND WORK 565
23.6.1 Humanising AI or dehumanising people 566
23.6.2 Top-down: algorithms grading students 566
23.6.3 Bottom-up: when AI ruled France 568
23.6.4 AI and work 569
23.7 MONEY AND POWER 570
23.7.1 Finance and markets 571
23.7.2 Advertising and runaway AI 572
23.7.3 Big AI: the environment and social impact 573
23.8 SUMMARY 575
Section V Looking Forward
Chapter 24 ■Epilogue: what next? 581
24.1 OVERVIEW 581
24.2 CRYSTAL BALL 581
24.3 WHAT NEXT: AI TECHNOLOGY 582
24.3.1 Bigger and Better 582
24.3.2 Smaller and Smarter 582
24.3.3 Mix and Match 584
24.3.4 Partners with People 584
24.4 WHAT NEXT: AI IN THE WORLD 585
24.4.1 Friend or Foe? 585
24.4.2 Boom then Bust 586
xxiv ■ Contents
24.4.3 Everywhere and nowhere 586
24.5 SUMMARY – FROM HYPE TO HOPE 586
Bibliography 589
Index
Preface xxxv
Author Bio xxxvii
Chapter 1 ■ Introduction 1
1.1 WHAT IS ARTIFICIAL INTELLIGENCE? 1
1.1.1 How much like a human: strong vs. weak AI 1
1.1.2 Top-down or bottom-up: symbolic vs. sub-symbolic 2
1.1.3 A working definition 3
1.1.4 Human intelligence 3
1.1.5 Bottom up and top down 4
1.2 HUMANS AT THE HEART 4
1.3 A SHORT HISTORY OF ARTIFICIAL INTELLIGENCE 5
1.3.1 The development of AI 6
1.3.2 The physical symbol system hypothesis 8
1.3.3 Sub-symbolic spring 9
1.3.4 AI Renaissance 10
1.3.5 Moving onwards 11
1.4 STRUCTURE OF THIS BOOK – A LANDSCAPE OF AI 11
Section I Knowledge-Rich AI
Chapter 2 ■ Knowledge in AI 15
2.1 OVERVIEW 15
2.2 INTRODUCTION 15
2.3 REPRESENTING KNOWLEDGE 16
2.4 METRICS FOR ASSESSING KNOWLEDGE REPRESENTATION SCHEMES
19
2.5 LOGIC REPRESENTATIONS 20
2.6 PROCEDURAL REPRESENTATION 23
vii
viii ■ Contents
2.6.1 The database 23
2.6.2 The production rules 23
2.6.3 The interpreter 24
2.6.4 An example production system: making a loan 24
2.7 NETWORK REPRESENTATIONS 26
2.8 STRUCTURED REPRESENTATIONS 28
2.8.1 Frames 29
2.8.2 Scripts 29
2.9 GENERAL KNOWLEDGE 31
2.10 THE FRAME PROBLEM 32
2.11 KNOWLEDGE ELICITATION 33
2.12 SUMMARY 33
Chapter 3 ■ Reasoning 37
3.1 OVERVIEW 37
3.2 WHAT IS REASONING? 37
3.3 FORWARD AND BACKWARD REASONING 39
3.4 REASONING WITH UNCERTAINTY 40
3.4.1 Non-monotonic reasoning 40
3.4.2 Probabilistic reasoning 41
3.4.3 Certainty factors 43
3.4.4 Fuzzy reasoning 45
3.4.5 Reasoning by analogy 46
3.4.6 Case-based reasoning 46
3.5 REASONING OVER NETWORKS 48
3.6 CHANGING REPRESENTATIONS 51
3.7 SUMMARY 51
Chapter 4 ■ Search 53
4.1 INTRODUCTION 53
4.1.1 Types of problem 53
4.1.2 Structuring the search space 57
4.2 EXHAUSTIVE SEARCH AND SIMPLE PRUNING 63
4.2.1 Depth and breadth first search 63
4.2.2 Comparing depth and breadth first searches 65
4.2.3 Programming and space costs 67
4.2.4 Iterative deepening and broadening 68
Contents ■ ix
4.2.5 Finding the best solution – branch and bound 69
4.2.6 Graph search 70
4.3 HEURISTIC SEARCH 70
4.3.1 Hill climbing andbest first – goal-directed search 72
4.3.2 Finding the best solution – the A∗ algorithm 72
4.3.3 Inexact search 75
4.4 KNOWLEDGE-RICH SEARCH 77
4.4.1 Constraint satisfaction 78
4.5 SUMMARY 80
Section II Data and Learning
Chapter 5 ■ Machine learning 85
5.1 OVERVIEW 85
5.2 WHY DO WE WANT MACHINE LEARNING? 85
5.3 HOW MACHINES LEARN 87
5.3.1 Phases of machine learning 87
5.3.2 Rote learning and the importance of generalization 89
5.3.3 Inputs to training 90
5.3.4 Outputs of training 91
5.3.5 The training process 92
5.4 DEDUCTIVE LEARNING 93
5.5 INDUCTIVE LEARNING 94
5.5.1 Version spaces 95
5.5.2 Decision trees 99
5.5.2.1 Building a binary tree 99
5.5.2.2 More complex trees 102
5.5.3 Rule induction and credit assignment 103
5.6 EXPLANATION-BASED LEARNING 104
5.7 EXAMPLE: QUERY-BY-BROWSING 105
5.7.1 What the user sees 105
5.7.2 How it works 105
5.7.3 Problems 107
5.8 SUMMARY 107
Chapter 6 ■ Neural Networks 109
6.1 OVERVIEW 109
x ■ Contents
6.2 WHY USE NEURAL NETWORKS? 109
6.3 THE PERCEPTRON 110
6.3.1 The XOR problem 112
6.4 THE MULTI-LAYER PERCEPTRON 113
6.5 BACKPROPAGATION 114
6.5.1 Basic principle 115
6.5.2 Backprop for a single layer network 116
6.5.3 Backprop for hidden layers 117
6.6 ASSOCIATIVE MEMORIES 117
6.6.1 Boltzmann Machines 119
6.6.2 Kohonen self-organizing networks 121
6.7 LOWER-LEVEL MODELS 122
6.7.1 Cortical layers 122
6.7.2 Inhibition 123
6.7.3 Spiking neural networks 123
6.8 HYBRID ARCHITECTURES 124
6.8.1 Hybrid layers 124
6.8.2 Neurosymbolic AI 125
6.9 SUMMARY 126
Chapter 7 ■ Statistical and Numerical Techniques 129
7.1 OVERVIEW 129
7.2 LINEAR REGRESSION 129
7.3 VECTORS AND MATRICES 132
7.4 EIGENVALUES AND PRINCIPAL COMPONENTS 134
7.5 CLUSTERING AND K-MEANS 136
7.6 RANDOMNESS 138
7.6.1 Simple statistics 138
7.6.2 Distributions and long-tail data 140
7.6.3 Least squares 142
7.6.4 Monte Carlo techniques 142
7.7 NON-LINEAR FUNCTIONS FOR MACHINE LEARNING 144
7.7.1 Support Vector Machines 144
7.7.2 Reservoir Computing 145
7.7.3 Kolmogorov-Arnold Networks 146
7.8 SUMMARY 147
Contents ■ xi
Chapter 8 ■ Going Large: deep learning and big data 151
8.1 OVERVIEW 151
8.2 DEEP LEARNING 152
8.2.1 Why are many layers so difficult? 153
8.2.2 Architecture of the layers 153
8.3 GROWING THE DATA 156
8.3.1 Modifying real data 157
8.3.2 Virtual worlds 157
8.3.3 Self learning 157
8.4 DATA REDUCTION 158
8.4.1 Dimension reduction 159
8.4.1.1 Vector space techniques 159
8.4.1.2 Non-numeric features 160
8.4.2 Reduce total number of data items 161
8.4.2.1 Sampling 161
8.4.2.2 Aggregation 161
8.4.3 Segmentation 162
8.4.3.1 Class segmentation 162
8.4.3.2 Result recombination 162
8.4.3.3 Weakly-communicating partial analysis 163
8.5 PROCESSING BIG DATA 164
8.5.1 Why it is hard – distributed storage and computation 164
8.5.2 Principles behind MapReduce 165
8.5.3 MapReduce for the cloud 166
8.5.4 If it can go wrong – resilience for big processing 167
8.6 DATA AND ALGORITHMS AT SCALE 169
8.6.1 Big graphs 169
8.6.2 Time series and event streams 170
8.6.2.1 Multi-scale with mega-windows 170
8.6.2.2 Untangling streams 171
8.6.2.3 Real-time processing 171
8.7 SUMMARY 171
Chapter 9 ■ Making Sense of Machine Learning 175
9.1 OVERVIEW 175
9.2 THE MACHINE LEARNING PROCESS 175
xii ■ Contents
9.2.1 Training phase 176
9.2.2 Application phase 177
9.2.3 Validation phase 177
9.3 EVALUATION 178
9.3.1 Measures of effectiveness 178
9.3.2 Precision–recall trade-off 180
9.3.3 Data for evaluation 182
9.3.4 Multi-stage evaluation 182
9.4 THE FITNESS LANDSCAPE 183
9.4.1 Hill-climbing and gradient descent / ascent 183
9.4.2 Local maxima and minima 184
9.4.3 Plateau and ridge effects 185
9.4.4 Local structure 186
9.4.5 Approximating the landscape 186
9.4.6 Forms of fitness function 187
9.5 DEALING WITH COMPLEXITY 188
9.5.1 Degrees of freedom and dimension reduction 188
9.5.2 Constraints and dependent features 189
9.5.3 Continuity and learning 191
9.5.4 Multi-objective optimisation 193
9.5.5 Partially labelled data 194
9.6 SUMMARY 196
Chapter 10 ■Data Preparation 199
10.1 OVERVIEW 199
10.2 STAGES OF DATA PREPARATION 199
10.3 CREATING A DATASET 200
10.3.1 Extraction and gathering of data 200
10.3.2 Entity reconciliation and linking 201
10.3.3 Exception sets 202
10.4 MANIPULATION AND TRANSFORMATION OF DATA 202
10.4.1 Types of data value 203
10.4.2 Transforming to the right kind of data 204
10.5 NUMERICAL TRANSFORMATIONS 205
10.5.1 Information 205
10.5.2 Normalising data 207
Contents ■ xiii
10.5.3 Missing values – filling the gaps 207
10.5.4 Outliers – dealing with extremes 209
10.6 NON-NUMERIC TRANSFORMATIONS 211
10.6.1 Media data 211
10.6.2 Text 212
10.6.3 Structure transformation 214
10.7 AUTOMATION AND DOCUMENTATION 214
10.8 SUMMARY 216
Section III Specialised Areas
Chapter 11 ■Game playing 221
11.1 OVERVIEW 221
11.2 INTRODUCTION 221
11.3 CHARACTERISTICS OF GAME PLAYING 223
11.4 STANDARD GAMES 225
11.4.1 A simple game tree 225
11.4.2 Heuristics and minimax search 225
11.4.3 Horizon problems 227
11.4.4 Alpha–beta pruning 228
11.4.5 The imperfect opponent 229
11.5 NON-ZERO-SUM GAMES AND SIMULTANEOUS PLAY 229
11.5.1 The prisoner’s dilemma 230
11.5.2 Searching the game tree 230
11.5.3 No alpha–beta pruning 232
11.5.4 Pareto-optimality 232
11.5.5 Multi-party competition and co-operation 233
11.6 THE ADVERSARY IS LIFE! 233
11.7 PROBABILITY 235
11.8 NEURAL NETWORKS FOR GAMES 236
11.8.1 Where to use a neural network 236
11.8.2 Training data and self play 238
11.9 SUMMARY 238
Chapter 12 ■Computer vision 243
12.1 OVERVIEW 243
12.2 INTRODUCTION 243
xiv ■ Contents
12.2.1 Why computer vision is difficult 243
12.2.2 Phases of computer vision 244
12.3 DIGITIZATION AND SIGNAL PROCESSING 245
12.3.1 Digitizing images 245
12.3.2 Thresholding 246
12.3.3 Digital filters 248
12.3.3.1 Linear filters 249
12.3.3.2 Smoothing 249
12.3.3.3 Gaussian filters 251
12.3.3.4 Practical considerations 252
12.4 EDGE DETECTION 252
12.4.1 Identifying edge pixels 253
12.4.1.1 Gradient operators 253
12.4.1.2 Robert’s operator 253
12.4.1.3 Sobel’s operator 256
12.4.1.4 Laplacian operator 257
12.4.1.5 Successive refinement and Marr’s primal sketch 258
12.4.2 Edge following 259
12.5 REGION DETECTION 260
12.5.1 Region growing 261
12.5.2 The problem of texture 261
12.5.3 Representing regions – quadtrees 262
12.5.4 Computational problems 263
12.6 RECONSTRUCTING OBJECTS 263
12.6.1 Inferring three-dimensional features 263
12.6.1.1 Problems with labelling 266
12.6.2 Using properties of regions 267
12.7 IDENTIFYING OBJECTS 269
12.7.1 Using bitmaps 269
12.7.2 Using summary statistics 270
12.7.3 Using outlines 271
12.7.4 Using paths 272
12.8 FACIAL AND BODY RECOGNITION 273
12.9 NEURAL NETWORKS FOR IMAGES 276
12.9.1 Convolutional neural networks 276
12.9.2 Autoencoders 277
Contents ■ xv
12.10 GENERATIVE ADVERSARIAL NETWORKS 279
12.10.1 Generated data 279
12.10.2 Diffusion models 280
12.10.3 Bottom-up and top-down processing 281
12.11 MULTIPLE IMAGES 281
12.11.1 Stereo vision 282
12.11.2 Moving pictures 284
12.12 SUMMARY 285
Chapter 13 ■Natural language understanding 289
13.1 OVERVIEW 289
13.2 WHAT IS NATURAL LANGUAGE UNDERSTANDING? 289
13.3 WHY DO WE NEED NATURAL LANGUAGE UNDERSTANDING? 290
13.4 WHY IS NATURAL LANGUAGE UNDERSTANDING DIFFICULT? 290
13.5 AN EARLY ATTEMPT AT NATURAL LANGUAGE UNDERSTANDING:
SHRDLU 292
13.6 HOW DOES NATURAL LANGUAGE UNDERSTANDING WORK? 293
13.7 SYNTACTIC ANALYSIS 295
13.7.1 Grammars 296
13.7.2 An example: generating a grammar fragment 297
13.7.3 Transition networks 299
13.7.4 Context-sensitive grammars 302
13.7.5 Feature sets 303
13.7.6 Augmented transition networks 304
13.7.7 Taggers 304
13.8 SEMANTIC ANALYSIS 305
13.8.1 Semantic grammars 306
13.8.1.1 An example: a database query interpreter revisited 306
13.8.2 Case grammars 307
13.9 PRAGMATIC ANALYSIS 310
13.9.1 Speech acts 311
13.10 GRAMMAR-FREE APPROACHES 311
13.10.1 Template matching 311
13.10.2 Keyword matching 312
13.10.3 Predictive methods 312
13.10.4 Statistical methods 313
13.11 SUMMARY 314
xvi ■ Contents
13.12 SOLUTION TO SHRDLU PROBLEM 315
Chapter 14 ■Time Series and Sequential Data 317
14.1 OVERVIEW 317
14.2 GENERAL PROPERTIES 317
14.2.1 Kinds of temporal and sequential data 317
14.2.2 Looking through time 318
14.2.3 Processing temporal data 320
14.2.3.1 Windowing 320
14.2.3.2 Hidden state 321
14.2.3.3 Non-time domain transformations 321
14.3 PROBABILITY MODELS 322
14.3.1 Markov Model 323
14.3.2 Higher-order Markov Model 324
14.3.3 Hidden Markov Model 326
14.4 GRAMMAR AND PATTERN-BASED APPROACHES 327
14.4.1 Regular expressions 327
14.4.2 More complex grammars 328
14.5 NEURAL NETWORKS 329
14.5.1 Window-based methods 329
14.5.2 Recurrent Neural Networks 331
14.5.3 Long-term short-term memory networks 332
14.5.4 Transformer models 332
14.6 STATISTICAL AND NUMERICAL TECHNIQUES 332
14.6.1 Simple data cleaning techniques 333
14.6.2 Logarithmic transformations and exponential growth 334
14.6.3 ARMA models 335
14.6.4 Mixed statistics/ML models 336
14.7 MULTI-STAGE/SCALE 337
14.8 SUMMARY 339
Chapter 15 ■Planning and robotics 343
15.1 OVERVIEW 343
15.2 INTRODUCTION 343
15.2.1 Friend or foe? 343
15.2.2 Different kinds of robots 344
15.3 GLOBAL PLANNING 345
Contents ■ xvii
15.3.1 Planning actions – means–ends analysis 345
15.3.2 Planning routes – configuration spaces 348
15.4 LOCAL PLANNING 350
15.4.1 Local planning and obstacle avoidance 350
15.4.2 Finding out about the world 353
15.5 LIMBS, LEGS AND EYES 356
15.5.1 Limb control 356
15.5.2 Walking – on one, two or more legs 359
15.5.3 Active vision 361
15.6 PRACTICAL ROBOTICS 363
15.6.1 Controlling the environment 363
15.6.2 Safety and hierarchical control 364
15.7 SUMMARY 365
Chapter 16 ■Agents 369
16.1 OVERVIEW 369
16.2 SOFTWARE AGENTS 369
16.2.1 The rise of the agent 370
16.2.2 Triggering actions 371
16.2.3 Watching and learning 372
16.2.4 Searching for information 374
16.3 REINFORCEMENT LEARNING 376
16.3.1 Single step learning 376
16.3.2 Choices during learning 378
16.3.3 Intermittent rewards and credit assignment 379
16.4 COOPERATING AGENTS AND DISTRIBUTED AI 379
16.4.1 Blackboard architectures 380
16.4.2 Distributed control 382
16.5 LARGER COLLECTIVES 383
16.5.1 Emergent behaviour 383
16.5.2 Cellular automata 384
16.5.3 Artificial life 384
16.5.4 Swarm computing 385
16.5.5 Ensemble methods 386
16.6 SUMMARY 388
Chapter 17 ■Web scale reasoning 391
xviii ■ Contents
17.1 OVERVIEW 391
17.2 THE SEMANTIC WEB 391
17.2.1 Representing knowledge – RDF and triples 392
17.2.2 Ontologies 394
17.2.3 Asking questions – SPARQL 395
17.2.4 Talking about RDF – reification, named graphs and provenance
396
17.2.5 Linked data – connecting the Semantic Web 398
17.3 MINING THE WEB: SEARCH AND SEMANTICS 402
17.3.1 Search words and links 402
17.3.2 Explicit markup 403
17.3.3 External semantics 405
17.4 USING WEB DATA 408
17.4.1 Knowledge-rich applications 408
17.4.2 The surprising power of big data 409
17.5 THE HUMAN WEB 412
17.5.1 Recommender systems 412
17.5.2 Crowdsourcing and human computation 414
17.5.3 Social media as data 416
17.6 SUMMARY 417
Section IV Humans at the Heart
Chapter 18 ■Expert and decision support systems 421
18.1 OVERVIEW 421
18.2 INTRODUCTION – EXPERTS IN THE LOOP 421
18.3 EXPERT SYSTEMS 422
18.3.1 Uses of expert systems 423
18.3.2 Architecture of an expert system 425
18.3.3 Explanation facility 425
18.3.4 Dialogue and UI component 427
18.3.5 Examples of four expert systems 428
18.3.5.1 Example 1: MYCIN 428
18.3.5.2 Example 2: PROSPECTOR 429
18.3.5.3 Example 3: DENDRAL 429
18.3.5.4 Example 4: XCON 430
18.3.6 Building an expert system 430
Contents ■ xix
18.3.7 Limitations of expert systems 431
18.4 KNOWLEDGE ACQUISITION 431
18.4.1 Knowledge elicitation 432
18.4.1.1 Unstructured interviews. 432
18.4.1.2 Structured interviews. 433
18.4.1.3 Focused discussions. 433
18.4.1.4 Role reversal. 433
18.4.1.5 Think-aloud. 433
18.4.2 Knowledge Representation 434
18.4.2.1 Expert system shells 434
18.4.2.2 High-level programming languages 434
18.4.2.3 Ontologies 434
18.4.2.4 Selecting a tool 435
18.5 EXPERTS AND MACHINE LEARNING 436
18.5.1 Knowledge elicitation for ML 438
18.5.1.1 Acquiring tacit knowledge 438
18.5.1.2 Feature selection 438
18.5.1.3 Expert labelling 438
18.5.1.4 Iteration and interaction 439
18.5.2 Algorithmic choice, validation and explanation 439
18.6 DECISION SUPPORT SYSTEMS. 441
18.6.1 Visualisation 442
18.6.2 Data management and analysis 443
18.6.3 Visual Analytics 444
18.6.3.1 Visualisation in VA 445
18.6.3.2 Data management and analysis for VA 446
18.7 STEPPING BACK 447
18.7.1 Who is it about? 447
18.7.2 Why are we doing it? 447
18.7.3 Wider context 449
18.7.4 Cost–benefit balance 450
18.8 SUMMARY 451
Chapter 19 ■AI working with and for humans 455
19.1 OVERVIEW 455
19.2 INTRODUCTION 455
xx ■ Contents
19.3 LEVELS AND TYPES OF HUMAN CONTACT 457
19.3.1 Social scale 457
19.3.2 Visibility and embodiment 458
19.3.3 Intentionality 458
19.3.4 Who is in control 459
19.3.5 Levels of automation 460
19.4 ON A DEVICE – INTELLIGENT USER INTERFACES 462
19.4.1 Low-level input 462
19.4.2 Conversational user interfaces 462
19.4.3 Predicting what next 464
19.4.4 Finding and managing information 464
19.4.5 Helping with tasks 466
19.4.6 Adaptation and personalisation 467
19.4.7 Going small 468
19.5 IN THE WORLD – SMART ENVIRONMENTS 469
19.5.1 Configuration 470
19.5.2 Sensor fusion 470
19.5.3 Context and activity 472
19.5.4 Designing for uncertainty in sensor-rich smart environments 473
19.5.5 Dealing with hiddenness – a central heating controller 474
19.6 DESIGNING FOR AI–HUMAN INTERACTION 476
19.6.1 Appropriate intelligence – soft failure 476
19.6.2 Feedback – error detection and repair 477
19.6.3 Decisions and suggestions 478
19.6.4 Case study: OnCue – appropriate intelligence by design 480
19.7 TOWARDS HUMAN–MACHINE SYNERGY 481
19.7.1 Tuning AI algorithms for interaction 481
19.7.2 Tuning interaction for AI 482
19.8 SUMMARY 483
Chapter 20 ■When things go wrong 487
20.1 OVERVIEW 487
20.2 INTRODUCTION 487
20.3 WRONG ON PURPOSE? 488
20.3.1 Intentional bad use 488
20.3.2 Unintentional problems 489
Contents ■ xxi
20.4 GENERAL STRATEGIES 490
20.4.1 Transparency and trust 490
20.4.2 Algorithmic accountability 491
20.4.3 Levels of opacity 492
20.5 SOURCES OF ALGORITHMIC BIAS 493
20.5.1 What is bias? 493
20.5.2 Stages in machine learning 494
20.5.3 Bias in the training data 494
20.5.4 Bias in the objective function 497
20.5.5 Bias in the accurate result 498
20.5.6 Proxy measures 499
20.5.7 Input feature choice 500
20.5.8 Bias and human reasoning 500
20.5.9 Avoiding bias 501
20.6 PRIVACY 502
20.6.1 Anonymisation 502
20.6.2 Obfuscation 503
20.6.3 Aggregation 503
20.6.4 Adversarial privacy 504
20.6.5 Federated learning 504
20.7 COMMUNICATION, INFORMATION AND MISINFORMATION 505
20.7.1 Social media 505
20.7.2 Deliberate misinformation 506
20.7.3 Filter bubbles 507
20.7.4 Poor information 507
20.8 SUMMARY 508
Chapter 21 ■Explainable AI 513
21.1 OVERVIEW 513
21.2 INTRODUCTION 513
21.2.1 Why we need explainable AI 514
21.2.2 Is explainable AI possible? 515
21.3 AN EXAMPLE – QUERY-BY-BROWSING 515
21.3.1 The problem 516
21.3.2 A solution 516
21.3.3 How it works 517
xxii ■ Contents
21.4 HUMAN EXPLANATION – SUFFICIENT REASON 518
21.5 LOCAL AND GLOBAL EXPLANATIONS 519
21.5.1 Decision trees – easier explanations 519
21.5.2 Black-box – sensitivity and perturbations 520
21.6 HEURISTICS FOR EXPLANATION 522
21.6.1 White-box techniques 523
21.6.2 Black-box techniques 524
21.6.3 Grey-box techniques 526
21.7 SUMMARY 529
Chapter 22 ■Models of the mind – Human-Like Computing 533
22.1 OVERVIEW 533
22.2 INTRODUCTION 533
22.3 WHAT IS THE HUMAN MIND? 534
22.4 RATIONALITY 535
22.4.1 ACTR 536
22.4.2 SOAR 537
22.5 SUBCONSCIOUS AND INTUITION 538
22.5.1 Heuristics and imagination 539
22.5.2 Attention, salience and boredom 539
22.5.3 Rapid serial switching 540
22.5.4 Disambiguation 541
22.5.5 Boredom 542
22.5.6 Dreaming 542
22.6 EMOTION 543
22.6.1 Empathy and theory of mind 544
22.6.2 Regret 546
22.6.3 Feeling 548
22.7 SUMMARY 549
Chapter 23 ■Philosophical, ethical and social issues 553
23.1 OVERVIEW 553
23.2 THE LIMITS OF AI 553
23.2.1 Intelligent machines or engineering tools? 554
23.2.2 What is intelligence? 554
23.2.3 Computational argument vs. Searle’s Chinese Room 555
23.3 CREATIVITY 556
Contents ■ xxiii
23.3.1 The creative process 557
23.3.2 Generate and filter 557
23.3.3 The critical edge 558
23.3.4 Impact on creative professionals 558
23.4 CONSCIOUSNESS 559
23.4.1 Defining consciousness 559
23.4.2 Dualism and materialism 560
23.4.3 The hard problem of consciousness 561
23.5 MORALITY OF THE ARTIFICIAL 561
23.5.1 Morally neutral 561
23.5.2 Who is responsible? 563
23.5.3 Life or death decisions 563
23.5.4 The special ethics of AI 565
23.6 SOCIETY AND WORK 565
23.6.1 Humanising AI or dehumanising people 566
23.6.2 Top-down: algorithms grading students 566
23.6.3 Bottom-up: when AI ruled France 568
23.6.4 AI and work 569
23.7 MONEY AND POWER 570
23.7.1 Finance and markets 571
23.7.2 Advertising and runaway AI 572
23.7.3 Big AI: the environment and social impact 573
23.8 SUMMARY 575
Section V Looking Forward
Chapter 24 ■Epilogue: what next? 581
24.1 OVERVIEW 581
24.2 CRYSTAL BALL 581
24.3 WHAT NEXT: AI TECHNOLOGY 582
24.3.1 Bigger and Better 582
24.3.2 Smaller and Smarter 582
24.3.3 Mix and Match 584
24.3.4 Partners with People 584
24.4 WHAT NEXT: AI IN THE WORLD 585
24.4.1 Friend or Foe? 585
24.4.2 Boom then Bust 586
xxiv ■ Contents
24.4.3 Everywhere and nowhere 586
24.5 SUMMARY – FROM HYPE TO HOPE 586
Bibliography 589
Index
Notă biografică
Alan Dix is Director of the Computational Foundry at Swansea University, a 30 million pound initiative to boost computational research in Wales with a strong focus on creating social and economic benefit. Previously Alan has worked in a mix of academic, commercial and government roles. Alan is principally known for his work in human-computer interaction, and is the author of one of the major international textbooks on HCI as well as of over 450 research publications from formal methods to intelligent interfaces and design creativity. Technically, he works equally happily with AI and machine learning alongside traditional mathematical and statistical techniques. He has a broad understanding of mathematical, computational and human issues, and he authored some of the earliest papers on gender and ethnic bias in black box-algorithms.
Descriere
This new edition expands and revises the book throughout, with new material added to existing chapters, including short case studies, as well as adding new chapters on explainable AI, and big data.