THLAI Book: Table of Contents

Contents

Preface. 8

Acknowledgements. 11

PART ZERO: AI level setting. 12

Introduction. 12

Chapter 1 AI and machine learning. 13

Chapter 2 Elements of neural networks history. 15

2.1 Introduction. 15

2.2 From birth of neural networks to an AI winter. 15

2.3 Backpropagation. 16

2.4 Deep learning. 17

PART ONE: Neuroscience implications for HLAI 19

Introduction. 19

Chapter 3 Brain properties. 19

3.1 Introduction. 19

3.2 The neuron. 20

3.3 Action potential 23

3.4 Dendrites. 25

3.5 Glial cells. 26

3.6 Grid neurons. 27

3.7 Mirror neurons. 28

3.8 Electrical communication in the brain: axons, dendrites, nucleus, and synapses. 28

3.9 Molecular communication in the brain: neuromodulators and neurotransmitters. 31

3.10 Human memory. 33

3.11 Brain lateralization. 36

3.12 Brain folds and neocortex columnar structure. 38

3.13 Early brain development 39

3.14 Brain activity, sparsity, and normalization. 41

3.15 Neuron mixed selectivity. 42

3.16 Neural oscillations. 43

Chapter 4 Cognitive processes. 46

4.1 Introduction. 46

4.2 Cognition. 46

4.3 Human consciousness. 47

4.4 Animal consciousness. 49

4.5 Sensory thinking. 50

4.6 Vision and the rules of perception: visual intelligence. 51

Chapter 5 Time and space in the brain. 53

PART TWO: Theories, models, and algorithms. 55

Introduction. 55

Chapter 6 Theories of consciousness. 56

Chapter 7 Neurorobotics: Embodied AI 58

Chapter 8 Engineered brain architectures. 60

8.1 Introduction. 60

8.2 Cognitive architectures. 60

8.2.1 Soar. 61

8.2.2 ACT-R. 63

8.2.3 Semantic pointer architecture. 65

8.3 Adaptive resonant theory model of the brain. 66

8.4 Harmonic oscillator recurrent neural networks. 69

8.5 Numenta AI models. 71

8.5.1 Introduction. 71

8.5.2 HTM and thousand brain theory. 71

8.6 Deep learning neural networks. 75

8.6.1 The birth of generative AI 75

8.6.2 LLM emergent properties. 77

8.7 Biologically plausible models. 83

8.7.1 Backpropagation in the brain. 83

8.7.2 Reinforcement learning. 85

8.7.3 Natural selection algorithms. 86

8.7.4 Biologically plausible neural networks. 87

8.7.5 Causal inference. 88

8.7.6 Spike-based computation. 90

8.8 Hyperdimensional computing. 91

Chapter 9 AI hardware. 93

9.1 Introduction. 93

9.2 Neuromorphic processors. 93

9.2.1 Intel’s neuromorphic computing research. 95

9.2.2 Rain Neuromorphics. 96

9.3 NeuRRAM analog chip. 97

9.4 Nvidia AI GPUs. 97

9.5 In vitro neurons learn to play Pong. 97

PART THREE: Speculations towards human-level AI 100

Introduction. 100

Chapter 10 The possibility of creating HLAI 101

10.1 Three types of HLAI 101

10.2 Drawing ideas from neuroscience. 102

10.3 Consciousness and HLAI 103

10.4 Visual thinking and consciousness. 105

10.5 Brain lateralization, visual processing, and intelligence. 108

10.6 Memory in HLAI systems. 109

10.7 The executive seat in the brain versus diffuse decision making. 109

Chapter 11 Methods to build HLAI 111

11.1 Introduction. 111

11.2 The intelligent robot as scientist 113

11.3 Evolving intelligent systems. 116

11.4 The key attributes and tests of an A/HLAI system.. 116

Chapter 12 Beyond HLAI 121

Epilogue. 123

Appendix. 124

Notes on the scientific method. 124

Glossary. 126

References. 127

Index. 147

Author: Eitan Michael Azoff 152

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