Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives.
The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters.
1. Introduction - p. xix 2. 1. Advances in AI, neural networks, and brain computing: An introduction - p. 1 3. Part 1: Fundamentals of neural networks and brain computing - p. 8 4. 2. Nature's learning rule: The Hebbian-LMS algorithm - p. 11 5. 3. A half century of progress toward a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders - p. 41 6. 4. Meaning versus information, prediction versus memory, and question versus answer - p. 61 7. 5. The brain-mind-computer trichotomy: Hermeneutic approach - p. 77 8. Part 2: Brain-inspired AI systems - p. 89 9. 6. The new AI: Basic concepts, and urgent risks and opportunities in the internet of things - p. 93 10. 7. Computers versus brains: Challenges of sustainable artificial and biological intelligence - p. 129 11. 8. Brain-inspired evolving and spiking connectionist systems - p. 145 12. 9. Pitfalls and opportunities in the development and evaluation of artificial intelligence systems - p. 173 13. 10. Theory of the brain and mind: Visions and history - p. 193 14. 11. From synapses to ephapsis: Embodied cognition and wearable personal assistants - p. 205 15. Part 3: Cutting-edge developments in deep learning and intelligent systems - p. 222 16. Explainable deep learning to information extraction in diagnostics and electrophysiological multivariate time series - p. 225 17. 13. Computational intelligence in cyber-physical systems and the Internet of Things - p. 251 18. 14. Evolving deep neural networks - p. 269 19. 15. Evolving GAN formulations for higher-quality image synthesis - p. 289 20. 16. Multiview learning in biomedical applications - p. 307 21. 17. Emergence of tool construction and tool use through hierarchical reinforcement learning - p. 325 22. 18. A Lagrangian framework for learning in graph neural networks - p. 343 23. Index - p. 367
Artificial Intelligence in the Age of Neural Networks and Brain Computing, Second Edition demonstrates that present disruptive implications and applications of AI is a development of the unique attributes of neural networks, mainly machine learning, distributed architectures, massive parallel processing, black-box inference, intrinsic nonlinearity, and smart autonomous search engines. The book covers the major basic ideas of "brain-like computing" behind AI, provides a framework to deep learning, and launches novel and intriguing paradigms as possible future alternatives.
The present success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel, and Amazon, can be interpreted using the perspective presented in this book by viewing the co-existence of a successful synergism among what is referred to as computational intelligence, natural intelligence, brain computing, and neural engineering. The new edition has been updated to include major new advances in the field, including many new chapters.
1. Introduction - p. xix 2. 1. Advances in AI, neural networks, and brain computing: An introduction - p. 1 3. Part 1: Fundamentals of neural networks and brain computing - p. 8 4. 2. Nature's learning rule: The Hebbian-LMS algorithm - p. 11 5. 3. A half century of progress toward a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders - p. 41 6. 4. Meaning versus information, prediction versus memory, and question versus answer - p. 61 7. 5. The brain-mind-computer trichotomy: Hermeneutic approach - p. 77 8. Part 2: Brain-inspired AI systems - p. 89 9. 6. The new AI: Basic concepts, and urgent risks and opportunities in the internet of things - p. 93 10. 7. Computers versus brains: Challenges of sustainable artificial and biological intelligence - p. 129 11. 8. Brain-inspired evolving and spiking connectionist systems - p. 145 12. 9. Pitfalls and opportunities in the development and evaluation of artificial intelligence systems - p. 173 13. 10. Theory of the brain and mind: Visions and history - p. 193 14. 11. From synapses to ephapsis: Embodied cognition and wearable personal assistants - p. 205 15. Part 3: Cutting-edge developments in deep learning and intelligent systems - p. 222 16. Explainable deep learning to information extraction in diagnostics and electrophysiological multivariate time series - p. 225 17. 13. Computational intelligence in cyber-physical systems and the Internet of Things - p. 251 18. 14. Evolving deep neural networks - p. 269 19. 15. Evolving GAN formulations for higher-quality image synthesis - p. 289 20. 16. Multiview learning in biomedical applications - p. 307 21. 17. Emergence of tool construction and tool use through hierarchical reinforcement learning - p. 325 22. 18. A Lagrangian framework for learning in graph neural networks - p. 343 23. Index - p. 367
