Toward Human-Level Artificial Intelligence: How Neuroscience Can Inform the Pursuit of Artificial General Intelligence or General AI

✍ Scribed by Eitan Michael Azoff

• Publisher: CRC Press
• Year: 2024
• Tongue: English
• Leaves: 193
• Edition: 1
• Category: Library

✦ Synopsis

Is a computer simulation of a brain sufficient to make it intelligent? Do you need consciousness to have intelligence? Do you need to be alive to have consciousness? This book has a dual purpose. First, it provides a multi-disciplinary research survey across all branches of neuroscience and AI research that relate to this book’s mission of bringing AI research closer to building a human-level AI (HLAI) system. It provides an encapsulation of key ideas and concepts, and provides all the references for the reader to delve deeper; much of the survey coverage is of recent pioneering research. Second, the final part of this book brings together key concepts from the survey and makes suggestions for building HLAI. This book provides accessible explanations of numerous key concepts from neuroscience and artificial intelligence research, including:

• The focus on visual processing and thinking and the possible role of brain lateralization toward visual thinking and intelligence.
• Diffuse decision making by ensembles of neurons.
• The inside-out model to give HLAI an inner "life" and the possible role for cognitive architecture implementing the scientific method through the plan-do-check-act cycle within that model (learning to learn).
• A neuromodulation feature such as a machine equivalent of dopamine that reinforces learning.
• The embodied HLAI machine, a neurorobot, that interacts with the physical world as it learns.

This book concludes by explaining the hypothesis that computer simulation is sufficient to take AI research further toward HLAI and that the scientific method is our means to enable that progress. This book will be of great interest to a broad audience, particularly neuroscientists and AI researchers, investors in AI projects, and lay readers looking for an accessible introduction to the intersection of neuroscience and artificial intelligence.

✦ Table of Contents

Cover
Half Title
Title Page
Copyright Page
Dedication
Table of Contents
Preface
Acknowledgments
Part Zero Al Level Setting
Introduction
1 AI and Machine Learning
2 Elements of Neural Networks History
2.1 Introduction
2.2 From Birth of Neural Networks to an AI Winter
2.3 Backpropagation
2.4 Deep Learning
Part One Neuroscience Implications for HLAI
Introduction
3 Brain Properties
3.1 Introduction
3.2 The Neuron
3.3 Action Potential
3.4 Dendrites
3.5 Glial Cells
3.6 Grid Neurons
3.7 Mirror Neurons
3.8 Electrical Communication in the brain: Axons, Dendrites, Nucleus, and Synapses
3.9 Molecular Communication in the Brain: Neuromodulators and Neurotransmitters
3.10 Human Memory
3.11 Brain Lateralization
3.12 Brain Folds and Neocortex Columnar Structure
3.13 Early Brain Development
3.14 Brain Activity, Sparsity, and Normalization
3.15 Neuron Mixed Selectivity
3.16 Neural Oscillations
4 Cognitive Processes
4.1 Introduction
4.2 Cognition
4.3 Human Consciousness
4.4 Animal Consciousness
4.5 Sensory Thinking
4.6 Vision and The Rules of Perception: Visual Intelligence
5 Time and Space in the Brain
Part Two Theories, Models, and Algorithms
Introduction
6 Theories of Consciousness
7 Neurorobotics: Embodied AI
8 Engineered Brain Architectures
8.1 Introduction
8.2 Cognitive Architectures
8.3 Adaptive Resonance Theory Model of The Brain
8.4 Harmonic Oscillator Recurrent Neural Networks
8.5 Numenta AI Models
8.6 Deep Learning Neural Networks
8.7 Biologically Plausible Models
8.8 Hyperdimensional Computing
9 AI Hardware
9.1 Introduction
9.2 Neuromorphic Processors
9.3 NeuRRAM Analog Chip
9.4 Nvidia AI GPUs
9.5 In Vitro Neurons Learn to Play Pong
Part Three Speculations Toward Human-level AI
Introduction
10 The Possibility of Creating HLAI
10.1 Three Types of HLAI
10.2 Neuroscience Inspiration
10.3 Consciousness and HLAI
10.4 Visual Thinking and Consciousness
10.5 Brain Lateralization, Visual Processing, and Intelligence
10.6 Memory in HLAI systems
10.7 The Executive Seat in the Brain Versus Diffuse Decision Making
11 Methods to build HLAI
11.1 Introduction
11.2 The Intelligent Robot as Scientist
11.3 Evolving intelligent Systems
11.4 LLM Intelligence and Potential for HLAI
11.5 The Key Attributes and Tests of an A/HLAI System
12 Beyond HLAI
Epilogue
Appendix: Notes on the Scientific Method
Glossary
References
Author: Eitan Michael Azoff
Index