Artificial Intelligence in the Age of Neural Networks and Brain Computing

In recent years, neural networks have re-emerged in relation to artificial intelligence (AI), representing a well-grounded paradigm rooted in disciplines from physics and psychology to information science and engineering. 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. This 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 Second Edition has been updated to include major new advances in the field, including seven all 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 The Second Edition includes seven 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; Advances in AI, Neural Networks INDICE: 1. Nature's Learning Rule: The Hebbian-LMS Algorithm 2. A Half Century of Progress Toward a Unified Neural Theory of Mind and Brain with Applications to Autonomous Adaptive Agents and Mental Disorders 3. Third Gen AI as Human Experience Based Expert Systems 4. The Brain-Mind-Computer Trichotomy: Hermeneutic Approach 5. From Synapses to Ephapsis: Embodied Cognition and Wearable Personal Assistants 6. Evolving and Spiking Connectionist Systems for Brain-Inspired Artificial Intelligence 7. Pitfalls and Opportunities in the Development and Evaluation of Artificial Intelligence Systems 8. The New AI: Basic Concepts, and Urgent Risks and Opportunities in the Internet of Things 9. Theory of the Brain and Mind: Visions and History 10. Computers Versus Brains: Game Is Over or More to Come 11. Deep Learning Approaches to Electrophysiological Multivariate Time-Series Analysis 12. Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things 13. Multiview Learning in Biomedical Applications 14. Meaning Versus Information, Prediction Versus Memory, and Question Versus Answer, 15. Evolving Deep Neural Networks 16. Frontiers in recurrent neural network research 17. Big Science, Team Science, Open Science for Neuroscience 18. A Model-Based Approach for Bridging Scales of Cortical Activity 19. A Cognitive Architecture for Object Recognition in Video 20. How brain architecture leads to abstract thought 21. Deep learning-based speech separation 22. Advances in AI, neural networks, deep learning, and brain computing

• ISBN: 978-0-323-96104-2
• Editorial: Academic Press
• Encuadernacion: Rústica
• Páginas: 400
• Fecha Publicación: 01/11/2022
• Nº Volúmenes: 1
• Idioma: Inglés