Buch, Englisch, 256 Seiten
A Guide for Non-Technical Readers
Buch, Englisch, 256 Seiten
ISBN: 978-1-394-43114-4
Verlag: Wiley
Understand how artificial intelligence actually works, from first principles to generative models
AI Explained: A Guide for Non-Technical Readers builds understanding of artificial intelligence from first principles rather than diving in at the top. Written by experienced policy and technical experts, the book walks through rules and logic-based approaches, statistical methods, neural networks, machine learning, and generative models in accessible, structured terms.
Each major section concludes with a dedicated use-cases chapter grounding abstract concepts in practical scenarios drawn from healthcare, law, and business. Rather than teaching readers how to build or deploy AI, the book answers a more fundamental question: how do these systems achieve the outcomes they produce? Coverage of AI policy, ethics, and societal impact rounds out the treatment, informed directly by the authors' advisory roles with governments and international bodies.
Readers will also find: - A structured progression from foundational principles through symbolic, statistical, connective, learning, and generative approaches to AI
- Real-world use cases connecting each branch of AI to familiar scenarios in professional and everyday contexts
- Balanced coverage of AI ethics, regulation, and societal impact drawn from direct policy advisory experience
- Clear explanations of how neural networks, machine learning algorithms, and large language models produce their results
- An approachable foundation suited equally to cross-disciplinary university courses and independent professional development
Written for lawyers, business leaders, policymakers, healthcare professionals, educators, journalists, and other non-technical professionals who need to understand AI rather than build it, this book replaces confusion with structured knowledge of how artificial intelligence systems operate across the full scope of the field.
So if you are curious but not confident, informed but not technical, or simply trying to make sense of the rapid changes around you – if you’ve ever felt like AI is something happening to you rather than something you can actively engage with – then this book is for you.
Autoren/Hrsg.
Weitere Infos & Material
Contents
About the Author xv
Introduction 1
Behind the Curtain 2
Who Is This Book For? 2
Demystifying AI 3
The Road Ahead 5
Chapter 1: The Evolution of AI 7
The Birth of Electronic Computation 7
Alan Turing and Computability 8
Claude Shannon and Information Theory 9
John von Neumann and Stored Programmes 10
A New Science 11
The Dartmouth Conference 11
A Seminal Conference 12
The Infancy of Computing 12
AI Visionaries 12
The Gathering 13
Legacy 13
The Timeline of AI 14
Early Foundations (1940–1956) 14
The Birth of AI (1956–1969) 15
The First AI Winter (1970–1980) 16
AI Research Resurgence (1980–1987) 16
The Second AI Winter (1987–1997) 17
Rise of Machine Learning (1998–2015) 18
Renaissance and Commercialisation (2015–Present) 19
Generative AI and Machine Creativity (2022–Present) 20
Summary 20
Chapter 2: Symbolic AI 23
Overview 23
Enabling Machine Understanding 24
Core Concepts and Mechanisms 26
In-depth 28
Computable Information 28
Encoding 29
Symbolic Representation 30
Knowledge Representation 32
Information Classification 33
Classes, Instances, and Schemas 34
Rules 35
Imperative vs. Declarative Rules 36
Conditional Rules 36
First-Order Logic 37
Reasoning 38
Inference 39
Wider Reasoning 40
Common Sense Reasoning 40
Knowledge Growth 41
Triples 42
Ontologies 43
Common Tools and Techniques 45
Decision Trees 45
Rules-Based Engines 46
Expert Systems 48
Knowledge Graphs 49
Natural Language Processing 51
Use Cases 53
Automated Medical Diagnosis 53
Expert Systems and Decision Trees 54
Personalised Medical Assessments 54
Outcome 54
Legal Case Analysis 55
Rules-Based Engines 55
Enhanced Legal Strategy and Decision-Making 55
Outcome 56
Financial Fraud Detection 56
Decision Tree 56
Enhancing Security and Trust 57
Outcome 57
Drug Discovery and Development 57
Ontologies and Knowledge Graphs 57
Accelerating Drug Development 58
Outcome 58
Conclusion 58
Chapter 3: Statistical AI 61
Overview 62
Data-Driven Approaches 62
Statistics 63
Core Concepts and Mechanisms 65
In-depth 67
Data, Data, Data. 67
Data, Information, Knowledge, Wisdom 68
Structured vs. Unstructured Data 69
Space and Time 70
Wider Data Classification Schemes 72
Sampling 73
Types of Sampling 74
Considerations and Challenges 74
Probability Theory 75
Independent, Dependent, and Correlated Events 76
Trends, Patterns, and Predictions 77
Distributions 79
Hypothesis Testing 80
Clusters and Classifications 82
Clustering vs. Classifying 83
Discovering Object Clusters 83
Features, Underfitting, and Overfitting 84
Regression Analysis 85
Data Assumptions for Regression 86
Simple vs. Multiple Linear Regression 86
Challenges and Considerations 87
Bayesian Methods 88
Handling Uncertainties 89
Bayesian Networks 90
Challenges and Considerations 90
Use Cases 91
Movie Recommendations 91
Clustering and Classifying 92
Personalised Recommendations 92
Outcome 92
Health Risk Assessment 92
Sampling and Regression Analysis 93
Personalised Risk Profiles 93
Outcome 93
Financial Market Forecasting 93
Probability Theory and Bayesian Methods 94
Comparative Analysis, Uncertainty, and Volatility 94
Outcome 94
Credit Scoring 95
Regression Analysis and Bayesian Methods 95
Comparative Analysis and Market Adaptation 95
Outcome 95
Conclusion 96
Chapter 4: Connected AI 99
Overview 99
Connections 100
The Human Brain 100
Simulating Biological Neurons 101
The Perceptron 101
Problems and Limitations 102
Resurgence and Later Developments 102
Core Concepts and Mechanisms 103
In-depth 105
Graph Theory 105
Nodes, Links, Graphs, and Networks 107
Relationships, Rules, and Signals 109
Neural Nets 109
Contrasts with Biological Brains 112
Modern Neural Networks in Practice 113
Convolutional Neural Networks 114
Recurrent Neural Networks 116
Advanced Connected Techniques 118
Use Cases 119
Inventory Management Systems 119
Basic Neural Nets and Knowledge Graphs 120
Optimised Stock Levels and Movements 120
Outcome 120
Financial Fraud Detection 121
Advanced Neural Networks 121
Safer Financial Ecosystems 121
Outcome 122
Healthcare Image Diagnostics 122
Convolutional Image Analysis 122
Early Diagnosis of Critical Conditions 122
Outcome 123
Autonomous Driving 123
Recurrent Sequential Data Processing 123
Safer Roads and Safer Journeys 123
Outcome 124
Conclusion 124
Chapter 5: Learning AI 127
Overview 128
Machine Learning 128
Contrasts with Human Learning 129
The Learning Process 130
Core Concepts and Mechanisms 131
In-depth 134
Learning from Data 134
Learning Strategies 134
Applying Digital Learning 135
Supervised Learning 136
Gathering Labelled Data 138
Neural Nets and Feature Selection 140
Weights and Biases in Neural Networks 140
Backpropagation—Learning from Mistakes 142
Quality and Accuracy 143
Unsupervised Learning 144
Uncovering Patterns in Data 145
Working in Multidimensions 147
Clustering 149
Dimensionality Reduction 151
Relationships and Associations 152
Anomaly Detection 153
Quality and Accuracy 155
Challenges and Considerations 156
Reinforcement Learning 156
Contrasting Machine Learning Approaches 159
Advanced Learning Techniques 160
Deep Learning 160
Transfer Learning 161
Ensemble Learning 162
Meta-Learning 162
Federated Learning 162
Use Cases 163
Email Spam Detection 163
Supervised Learning 164
Improved Email Experience and Security 164
Outcome 164
Customer Segmentation in Marketing 165
Unsupervised Learning 165
Personalised Marketing Strategies 165
Outcome 166
Game-Playing AI 166
Reinforcement Learning 166
Achieving Strategic Mastery 166
Outcome 167
Voice Recognition 167
Deep Learning 167
Enhanced User Interaction 168
Outcome 168
Conclusion 168
Chapter 6: Generative AI 171
Overview 171
Machine Creativity 172
Emergence of Non-Human Methodologies 173
Creativity Algorithms 174
Core Concepts and Mechanisms 175
In-depth 178
Mathematical Representations 178
Autoencoders 179
Variational Autoencoders 182
Generative Adversarial Networks 183
Transformers and Large Language Models 185
Chunking 188
Embedding 190
Text Generation 192
Challenges and Limitations 194
Diffusion Models and Image Generation 196
The Forward Process: Adding Noise 197
The Reverse Process: Learning to Denoise with Conditions 197
Generating Images from User Prompts 198
Orchestration and Agentic AI 199
AI Orchestration 199
Agentic AI 202
Multimodal Generative AI 203
Use Cases 204
Medical Image Synthesis 204
Variational Autoencoders 204
Enhanced Patient Outcomes 205
Outcome 205
AI Assistants 205
Transformers 205
Assistance via Natural Language 206
Outcome 206
Images from Text 206
Diffusion Models 207
Enabling Visual Communication 207
Outcome 207
Digital Actor Resurrection and De-aging 208
Generative Adversarial Networks (GANs) 208
Epic Sagas with Consistent Actors 208
Outcome 208
Conclusion 209
Afterword 211
The Winding Road of AI 212
Charting the Road Ahead 214
Scaling and Applying AI 215
AGI and Superintelligence 216
Emerging Directions 218
International Perspectives 220
The AI Experience 221
Opportunities and Challenges 222
Indispensable Intelligence 223
Transformational Impact 224
Education 224
Healthcare 225
Defence 226
The Price of Progress 227
Choosing the Future We Want 229
Index 231
