Artificial Intelligence in the Age of Neural Networks and Brain Computing

The comprehensive guide to neural network advances in artificial intelligence (AI)

Robert Kozma (Edited by), Cesare Alippi (Edited by), Yoonsuck Choe (Edited by), Francesco Carlo Morabito (Edited by)

9780128154809

Paperback / softback, published 2 November 2018

352 pages
23.4 x 19 x 2.3 cm, 0.72 kg

Artificial Intelligence in the Age of Neural Networks and Brain Computing demonstrates that existing 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 future alternatives. The success of AI-based commercial products proposed by top industry leaders, such as Google, IBM, Microsoft, Intel and Amazon can be interpreted using this book.

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 Computational Intelligence in the Time of Cyber-Physical Systems and the Internet of Things 12. Multiview Learning in Biomedical Applications 13. Meaning Versus Information, Prediction 14. Versus Memory, and Question Versus Answer 15. Evolving Deep Neural Networks

Subject Areas: Biomedical engineering [MQW]