Main description:

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.

Contents:

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