Professor, University of Calgary
Yingxu Wang is professor of cognitive systems, brain science, software science, and denotational mathematics. He is the Founding President of International Institute of Cognitive Informatics and Cognitive Computing (ICIC). He is Fellow of BCS, ICIC and WIF, P.Eng, and Senior Members of IEEE and ACM. He has held visiting professor positions at Univ. of Oxford (1995, 2018-20), Stanford Univ. (2008, 16), UC Berkeley (2008), and MIT (2012). He received a PhD in Computer Science from the Nottingham Trent University, UK, in 1998 and has been a full professor since 1994. He is the founder and steering committee chair of IEEE Int’l Conference Series on Cognitive Informatics and Cognitive Computing (ICCI*CC) since 2002. He is founding Editor-in-Chiefs of Int’l Journal of Cognitive Informatics & Natural Intelligence, of Software Science & Computational Intelligence, and of Mathematical & Computational Methods. He is Associate Editor of IEEE Trans. on Systems, Man, and Cybernetics-Systems (TSMC-Systems), Cognitive and Development Systems (TCDS), and SMCM, and the IEEE Computer Society Representative to the steering committee of TCDS. He is Chair of IEEE SMCS TC-BCS on Brain-inspired Cognitive Systems, and Co-Chair of IEEE CS TC-CLS on Computational Life Science. He is an IEEE FDC Steering Board Member on Symbiotic Autonomous Systems Initiative, and members of the IEEE Brain and SPS Autonomous Systems Initiatives. Dr. Wang is recognized by Google search as the initiator of a few cutting-edge research fields including cognitive informatics, cognitive computers, denotational mathematics (concept algebra, process algebra, system algebra, semantic algebra, big data algebra, and visual semantic algebra), abstract intelligence (aI), the 3rd generation of information theory in the knowledge space, the spike frequency modulation (SFM) theory of neurology, mathematical modeling of the brain, the 6th category of machine knowledge learning, the discovery of the basic unit of knowledge as a binary relation (bir), and the cognitive knowledge base theory. His basic studies have been across contemporary disciplines of sciences including systems, cybernetics, intelligence, robotics, knowledge, computer, information, brain, cognition, software, data, neurology, and linguistics sciences.
Keynote Title: How Will Autonomous Systems and Cognitive Robots Augment Human Intelligence?
Abstract: Intelligence has been a gifted power and privilege of humans naturally generated by the brain. However, machine intelligence is emerging pervasively triggered by human curiosity and the maturing understanding of the brain. The untrivial development will lead to a contemporary artifact of hybrid intelligence. This keynote lecture analyzes the cognitive advantages and disadvantaged of human and machine intelligence, where the former contributes inductive power, creative problem-solving ability, and the adaptivity for dealing with uncertainty; while the latter provides a fast iterative engine, accurate interaction to sensors/servos, and explicit access to addressable memory. The talk will elaborate how hybrid intelligence may be generated for augmenting human’s and machine’s intelligent power to an unprecedented level by hybrid intelligence. Three paradigms of hybrid intelligent systems will be presented: a) The sixth form of machine learning, i.e., Machine Knowledge Learning (MKL), as an ultimate goal of human learning where the basic unit of knowledge is discovered as a binary relation (bir) [Wang, 2018]; b) Cognitive Robots (CR); and c) Autonomous Systems (AS). One of the ground-breaking advances in hybrid intelligence systems is their ability to learn human knowledge in order to comprehend the semantics for rational inferences rather than symbolic ones underpinned by concept algebra and semantic algebra. Another key development is on autonomous decision making by AS driven by inference algebra for deriving complex decisions in real-time and mission-critical systems.