Artificial Intelligence in the Age of Neural Networks and Brain Computing
Coordonnateurs : Kozma Robert, Alippi Cesare, Choe Yoonsuck, Morabito Francesco Carlo
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
Date de parution : 11-2018
Ouvrage de 420 p.
Disponible chez l'éditeur (délai d'approvisionnement : 14 jours).
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