Artificial Intelligence in the Age of Neural Networks and Brain Computing
Coordinators: Kozma Robert, Alippi Cesare, Choe Yoonsuck, Morabito Francesco CarloLanguage: Anglais
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420 p. · 19.1x23.5 cm · Paperback
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
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
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 Neura