Artificial Intelligence in the Age of Neural Networks and Brain Computing

2. Auflage 2023
ISBN: 978-0-323-96104-2
Verlag: William Andrew Publishing

Buch, Englisch, 400 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 820 g

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.

Zielgruppe

Researchers, engineers, and post-doc students in computational intelligence, neural engineering, and advanced AI practitioners

Autoren/Hrsg.

Kozma, Robert

Alippi, Cesare

Choe, Yoonsuck

Fachgebiete

• Technische Wissenschaften Sonstige Technologien | Angewandte Technik Medizintechnik, Biomedizintechnik
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz
• Medizin | Veterinärmedizin Medizin | Public Health | Pharmazie | Zahnmedizin Medizin, Gesundheitswesen Medizintechnik, Biomedizintechnik, Medizinische Werkstoffe

Inhaltsverzeichnis

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

Über Autor(innen)

Choe, Yoonsuck
Dr. Yoonsuck Choe Ph.D. received his B.S. degree in computer science from Yonsei University, Seoul, Korea, and his M.S. and Ph.D. degrees in computer sciences from the University of Texas at Austin, Austin, Texas, USA. He is Professor and Director of the Brain Networks Laboratory in the Department of Computer Science and Engineering at Texas A&M University. During 2017 to 2019, he led the machine learning lab and the AI core team at Samsung Research as a corporate vice president. His research interest is broadly in imaging, modeling, and understanding brain function, from the local circuit level up to the whole brain scale, with a focus on the temporal and sensorimotor aspects of brain operation. He has published extensively in the above areas with over 140 publications that include three best paper awards and one best paper award nomination. He served as the program chair for the International Joint Conference on Neural Networks in 2015, and as the general chair in 2017. He is currently on the editorial board of IEEE Transactions on Cognitive and Developmental Systems.

Kozma, Robert
Dr. Robert Kozma Ph.D (Fellow of IEEE, Fellow of INNS) is Professor of Mathematical Sciences, the University of Memphis, and Professor of Computer Science at University of Massachusetts Amherst. He holds a PhD in Physics and 2 MSc degrees in Mathematics and Power Engineering. His research is focused on computational neurodynamics, large-scale brain networks, and applying biologically motivated and cognitive principles for the development of intelligent systems. Previous affiliations include visiting positions at NASA/JPL, Sarnoff Co., Princeton, NJ; Lawrence Berkeley Laboratory (LBL); AFRL, Dayton, OH; joint EECS/Neurobiology appointment at UC Berkeley; Associate Professor at Tohoku University, Sendai, Japan; Lecturer at Otago University, Dunedin, New Zealand. His research career started over 35 years ago as a research fellow at the Hungarian Academy of Sciences, Budapest, Hungary. He has published 8 books, 350+ papers, has 3 patent submissions. His research has been supported by NSF, NASA, JPL, AFRL, DARPA, FedEx, and by other agencies. He is President of INNS (2017-2018), serves on the Board of IEEE SMC Society (2016-2018); has served on the AdCom of the IEEE Computational Intelligence Society (2009-2012) and the Board of Governors of the International Neural Network Society (2007-2012). He has been General Chair of IJCNN2009, Atlanta, USA. He is Associate Editor of Neural Networks, Neurocomputing, Cognitive Systems Research, and Cognitive Neurodynamics. Dr. Kozma is the recipient of the INNS Gabor Award.

Alippi, Cesare
Dr. Cesare Alippi Ph.D received his degree in electronic engineering cum laude and his PhD from Politecnico di Milano, Italy. Currently, he is a Full Professor at the Politecnico di Milano, Milano, Italy and Università della Svizzera italiana, Lugano, Switzerland. He has been a visiting researcher at UCL (UK), MIT (USA), ESPCI (F), CASIA (RC), A*STAR (SIN), and UKobe (JP). Dr. Alippi is an IEEE Fellow, Board of Governors member of the International Neural Network Society, Board of Directors member of the European Neural Network Society, Past Vice-President education of the IEEE Computational Intelligence Society, past Associate editor of the IEEE Computational Intelligence Magazine, the IEEE-Transactions on Instrumentation and Measurements, the IEEE-Transactions on Neural Networks. In 2016 he received the Gabor award from the International Neural Networks Society and the IEEE Computational Intelligence Society Outstanding Transactions on Neural Networks and Learning Systems Paper Award; in 2013 the IBM Faculty award; in 2004 the IEEE Instrumentation and Measurement Society Young Engineer Award. Current research activity addresses adaptation and learning in non-stationary environments and Intelligence for embedded and cyber-physical systems. He holds 5 patents, has published one monograph book, 6 edited books and more than 200 papers in international journals and conference proceedings.