Cognitive Computing: Methodologies for Neural Computing and Semantic Computing in Brain-Inspired Systems

Cognitive Computing: Methodologies for Neural Computing and Semantic Computing in Brain-Inspired Systems

Yingxu Wang (International Institute of Cognitive Informatics and Cognitive Computing (ICIC), University of Calgary, Calgary, Canada), Victor Raskin (LING/CERIAS/CS/CIT, Purdue University, West Lafayette, IN, USA), Julia Rayz (Purdue University, West Lafayette, IN, USA), George Baciu (GAMA Lab, Department of Computing, Hong Kong Polytechnic University, Hung Hom, Hong Kong), Aladdin Ayesh (De Montfort University, Leicester, UK), Fumio Mizoguchi (Tokyo University of Science, Shinjuku, Japan), Shusaku Tsumoto (University of Shimane, Matsue, Japan), Dilip Patel (Faculty of Business, London South Bank University, London, UK) and Newton Howard (University of Oxford, Oxford, UK)
DOI: 10.4018/IJSSCI.2018010101
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Cognitive Computing (CC) is a contemporary field of studies on intelligent computing methodologies and brain-inspired mechanisms of cognitive systems, cognitive machine learning and cognitive robotics. The IEEE conference ICCI*CC'17 on Cognitive Informatics and Cognitive Computing was focused on the theme of neurocomputation, cognitive machine learning and brain-inspired systems. This article reports the plenary panel (Part II) in IEEE ICCI*CC'17 at Oxford University. The summary is contributed by distinguished panelists who are part of the world's renowned scholars in the transdisciplinary field of cognitive computing.
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1. Introduction

Cognitive Computing (CC) is a novel paradigm of intelligent computing platforms and methodologies for developing cognitive and autonomous systems mimicking the mechanisms of the brain (Wang, 2002, 2003, 2007a, 2009a-c, 2011b, 2012e, 2013c, 2015a, 2016a, 2017a; Wang et al., 2009, 2010, 2016; Howard et al., 2017). CC emerged from transdisciplinary studies in both natural intelligence in cognitive/brain sciences (Anderson, 1983; Sternberg, 1998; Reisberg, 2001; Wilson & Keil, 2001; Wang, 2002, 2007a; Wang et al., 2002, 2007a, 2009a, 2009b, 2016a, 2017a) and artificial intelligence in computer science (Bender, 1996; Poole et al., 1997; Zadeh, 1999, 2016; Widrow, 2015; Widrow et al., 2015; Wang, 2010a, 2016c). Formal models are sought for revealing the principles and mechanisms of the brain. This leads to the theory of abstract intelligence (αI) (Wang, 2009a, 2012c) that investigates into the brain via not only inductive syntheses of theories and principles of intelligence science through mathematical engineering, but also deductive analyses of architectural and behavioral instances of natural and artificial intelligent systems through cognitive engineering. The key methodology suitable for dealing with the nature of αI is mathematical engineering, which is an emerging discipline of contemporary engineering that studies the formal structural models and functions of complex abstract and mental objects as well as their systematic and rigorous manipulations (Wang, 2015a; Wang et al., 2017a).

Fundamental theories of CC cover the Layered Reference Model of the Brain (LRMB) (Wang et al., 2006), the Object-Attribute-Relation (OAR) model of internal information and knowledge representation (Wang, 2007c), the Cognitive Functional Model of the Brain (CFMB) (Wang & Wang, 2006), Abstract Intelligence (αI), Neuroinformatics (Wang, 2013a; Wang & Fariello, 2012), Denotational Mathematics (Wang, 2008, 2009d, 2012a, 2012b), Cognitive Linguistics (Wang and Berwick, 2012, 2013), the Spike Frequency Modulation (SFM) Theory of neural signaling (Wang, 2016h), the Neural Circuit Theories (Wang and Fariello, 2012), and cognitive systems (Wang et al., 2017). Recent studies on LRMB reveal an entire set of cognitive functions of the brain and their cognitive processes, which explain the cognitive mechanisms and processes of the natural intelligence with 52 cognitive processes at seven layers known as the sensation, action, memory, perception, cognitive, inference, and intelligence layers (Wang et al., 2006).

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