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Cognitive Intelligence: Deep Learning, Thinking, and Reasoning by Brain-Inspired Systems
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Author(s): Yingxu Wang (University of Calgary, Canada), Bernard Carlos Widrow (Stanford University, USA), Lotfi A. Zadeh (UC Berkeley, USA), Newton Howard (University of Oxford, UK), Sally Wood (Santa Clara University, USA), Virendrakumar C. Bhavsar (University of New Brunswick, Canada), Gerhard Budin (Vienna University, Austria), Christine W. Chan (University of Regina, Canada), Rodolfo A. Fiorini (Politecnico Di Milano University, Italy), Marina L. Gavrilova (University of Calgary, Canada)and Duane F. Shell (University of Nebraska-Lincoln, USA)
Copyright: 2020
Pages: 24
Source title:
Deep Learning and Neural Networks: Concepts, Methodologies, Tools, and Applications
Source Author(s)/Editor(s): Information Resources Management Association (USA)
DOI: 10.4018/978-1-7998-0414-7.ch084
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Abstract
The theme of IEEE ICCI*CC'16 on Cognitive Informatics (CI) and Cognitive Computing (CC) was on cognitive computers, big data cognition, and machine learning. CI and CC are a contemporary field not only for basic studies on the brain, computational intelligence theories, and denotational mathematics, but also for engineering applications in cognitive systems towards deep learning, deep thinking, and deep reasoning. This paper reports a set of position statements presented in the plenary panel (Part I) in IEEE ICCI*CC'16 at Stanford University. The summary is contributed by invited panelists who are part of the world's renowned scholars in the transdisciplinary field of CI and CC.
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