Description
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 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.
