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Artificial Intuition : The Improbable Deep Learning Revolution

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Artificial Intuition : The Improbable Deep Learning Revolution

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I challenge you to find a study that is as fascinating and exciting as Deep Learning. Deep Learning is a new kind of Artificial Intelligence technology that emerged at around 2012. Daniel Kahneman, the Nobel prize-winning psychologist, and economist introduced the idea that the human brain has two kinds of cognitive or thinking machinery. He dubbed this "Thinking Fast and Slow". The fast cognitive mechanism is commonly understood as intuition. The slower kind is of the deliberative kind and is understood as rational thought. This book frames the emergent technology of Deep Learning as a kind of artificial intuition. This idea of Deep Learning as intuition is an extremely compelling narrative and can form a very solid mental model to think about its ramifications. We are all going to be profoundly impacted by this new kind of Artificial Intelligence and it is critical we all develop at least a good intuition of how and why it will transform our technology and ultimately our civilization.

The images on the cover of the book are generated by Deep Learning automation. 

Preface 12

1 Introduction 14

A New Kind of Artificial Intelligence 16

What is Deep Learning? 20

Explaining to a Five Year Old 23

Tribes of Artificial Intelligence 24

The Deep Learning Industrial Complex 31

Heart Throbbing Hardware 36

The Sputnik Moment in Asia 40

2 Deep Learning is Eating the World 44

Self Driving Machines 44

Artistic Machines 47

The Uncanny Valley 49

Reading Human Minds 51

Machines that Teach 54

Summary 55

3 Our Cognitive Stack 59

Variety of Human Intelligences 60

Learning using Intuition 64

Natural Stupidity 71

Hacking Intuition 74

Human Legacy Bias 77

4 Artificial Intuition 82

Limits of Rationality 83

Artificial Intuition 86

Characteristics of Artificial Intuition 93

Growing Innate Intuition 97

Intuition Demolishes Logic 101

5 Uncertain Reality 105

Unknowable Knowns 106

Probability and Causality 108

Cargo Cult Science and Alchemy 116

Exploitation, Exploration and Representation 124

A Roadmap for Deep Learning 129

Meta Model 134

Stochastic Gradient Descent 138

A Reality Checklist 145

Deep Learning Limitations 147

Artificial General Intelligence 153

6 The Learning Universe 156

Alien Intelligences in Our Midst 157

Biological Brains are Digital 160

The Holographic Principle 163

Imaginary Numbers 169

Non-Equilibrium Information Dynamics 175

Chaos and Entanglement 183

Deconstructing Complexity 188

Sand Piles 194

The Origins of Life and Learning 197

7 Capitalism in the Age of Intelligence 203

Disruption with Learning Platforms 204

Architectures of Participation 209

Levels of Automation 212

The Responsive Corporation 215

8 Knowledge Creation 219

Accelerated Productivity 220

The Agile Manifesto 223

Pattern Languages 227

Alchemy and Black Magic 232

Data is the new Vineyard 234

Humans in the Loop 235

9 Contextual Adaptation 240

Biologically Inspired Architecture 241

Generalization 243

Deep Teaching 247

Three Dimensions of Cognition 250

The Spectrum of Embodied Learning 256

10 Conversational Cognition 262

Coordinating Rationality and Intuition 265

Learning to Communicate 270

The Many Body Problem 274

Explainability 277

AlphaGo Zero’s Self-Learning 279

Architectures of Collaboration 287

Machine Self-Awareness 293

The Strange Loop 298

11 Human Compatible AI 304

Jobs that are Safe 305

Temporal Impedance Mismatch 311

Decisions and Responsibility 312

AI Regulation 314

Seven Deadly Sins 317

Provably Beneficial AI 321

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