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.
- 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
Researchers, engineers, post-doc students in computational intelligence, neural engineering and advanced AI practitioners
1. Autonomy of robots: Should we be afraid of robot intelligence and what can we do about it?
2. Computational intelligence in the time of Cyber-physical systems and the Internet-of-Things
3. The brain-mind-computer trichotomy: hermeneutic approach
4. Hebbian-LMS, An Unsupervised Biologically-Based Training Algorithm For Neural Network
5. Conceptional Design of the Trustworthiness of Computational Artificial Intelligence
6. Revolutionary new brain-mind approaches
7. From synapses to ephapsis
8. Deep Learning of Streaming data in Spiking Neural networks and Spatio-Temporal Data Machines
9. Pitfalls and Opportunities in the Development and Evaluation of AI systems
10. Robust and Explainable Neural Networks for Adversarial Environment - a survey
11. Neural Networks in Computational Cognitive Neuroscience
12. Neural networks in the context of goal-directed robot manipulation
13. A Deep Learning Approach to Electrophysiological Multivariate Time Series Analysis
14. Multi-view learning in biomedical applications
15. Meaning vs. Information, Prediction vs. Memory, and Question vs. Answer
16. Evolution of Deep Learning Networks
- No. of pages:
- © Academic Press 2018
- 1st June 2018
- Academic Press
- Paperback ISBN:
ROBERT KOZMA (Fellow of IEEE, Fellow of INNS) is Professor of Mathematical Sciences, the University of Memphis, TN, and Professor of Computer Science at University of Massachusetts Amherst, MA, USA. 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 research fellow at 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.”
Professor of Mathematical Sciences, University of Memphis, TN; Professor of Computer Science at University of Massachusetts Amherst, MA, USA
CESARE ALIPPI received the degree in electronic engineering cum laude in 1990 and the PhD in 1995 from Politecnico di Milano, Italy. Currently, he is a Full Professor with 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), UKobe (JP). 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 about 200 papers in international journals and conference proceedings.
Professor, Politecnico di Milano, Milano, Italy and Universita della Svizzera italiana, Lugano, Switzerland
YOONSUCK CHOE received his B.S. degree in computer science from Yonsei University Yonsei, Korea, in 1993, and his M.S. and Ph.D. degrees in computer sciences from the University of Texas at Austin, Austin, TX, USA, in 1995 and 2001. He is an Professor and Director of the Brain Networks Laboratory in the Department of Computer Science and Engineering at Texas A&M University. 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 100 publications that include two 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 Neural Networks, IEEE Transactions on Neural Networks and Learning Systems, and served as topic editor on computational neuroanatomy for the Encyclopedia of Computational Neuroscience (Springer).
Professor and Director, Brain Networks Laboratory, Department of Computer Science and Engineering, Texas A&M University, USA
Professor, Electrical Engineering, University of Reggio Calabria, Italy