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.
- Developed from the 30th anniversary of the International Neural Network Society (INNS) and the 2017 International Joint Conference on Neural Networks (IJCNN
- Authored by top experts, global field pioneers, and researchers working on cutting-edge applications in signal processing, speech recognition, games, adaptive control and decision-making
- Edited by high-level academics and researchers in intelligent systems and neural networks
- Includes all new chapters, including topics such as Frontiers in Recurrent Neural Network Research; Big Science, Team Science, Open Science for Neuroscience; A Model-Based Approach for Bridging Scales of Cortical Activity; A Cognitive Architecture for Object Recognition in Video; How Brain Architecture Leads to Abstract Thought; Deep Learning-Based Speech Separation and Advances in AI, Neural Networks
Auflage
Sprache
Verlagsort
Verlagsgruppe
Elsevier Science & Techn.
Dateigröße
ISBN-13
978-0-323-95816-5 (9780323958165)
Schweitzer Klassifikation
1. Advances in AI, neural networks, and brain computing: An introduction PART 1 Fundamentals of neural networks and brain computing 2. Nature's learning rule: The Hebbian-LMS algorithm 3. A half century of progress toward a unified neural theory of mind and brain with applications to autonomous adaptive agents and mental disorders 4. Meaning versus information, prediction versus memory, and question versus answer 5. The brain-mind-computer trichotomy: Hermeneutic approach PART 2 Brain-inspired AI systems 6. The new AI: Basic concepts, and urgent risks and opportunities in the internet of things 7. Computers versus brains: Challenges of sustainable artificial and biological intelligence 8. Brain-inspired evolving and spiking connectionist systems for life-long and developmental learning 9. Pitfalls and opportunities in the development and evaluation of artificial intelligence systems 10. Theory of the brain and mind visions and history 11. From synapses to ephapsis: Embodied cognition and wearable personal assistants PART 3 Cutting-edge developments in deep learning and intelligent systems 12. Explainable deep learning to information extraction in diagnostics and electrophysiological multivariate time series 13. Computational intelligence in the time of cyber-physical systems and the Internet of Things 14. Evolving deep neural networks 15. Evolving GAN formulations for higher-quality image synthesis 16. Multiview learning in biomedical applications 17. Emergence of tool construction and tool use through hierarchical reinforcement learning 18. A Lagrangian framework for learning in graph neural networks
