University of Calgary
Dr. Yingxu Wang is professor of cognitive systems, brain science, software science, and intelligent mathematics. He is the founding President of International Institute of Cognitive Informatics and Cognitive Computing (I2CICC). He is FIEEE, FBCS, FI2CICC, FAAIA, FWIF, and P.Eng. He has held visiting professor positions at Univ. of Oxford (1995, 2018-2022), Stanford Univ. (2008, 2016), UC Berkeley (2008), MIT (2012), and a distinguished visiting professor at Tsinghua Univ. (2019-2022). 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 and Associate Editors of 10+ Int’l Journals and IEEE Transactions. 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. His basic research has spanned across contemporary scientific disciplines of intelligence, mathematics, knowledge, robotics, computer, information, brain, cognition, software, data, systems, cybernetics, neurology, and linguistics. He has published 600+ peer reviewed papers and 38 books/proceedings. He has presented 65 invited keynote speeches in international conferences. He has served as honorary, general, and program chairs for 40 international conferences. He has led 10+ international, European, and Canadian research projects as PI. He is recognized by Google Scholar as world top 1 in Software Science, top 1 in Cognitive Robots, top 8 in Autonomous Systems, top 2 in Cognitive Computing, and top 1 in Knowledge Science with an h-index 62. He is recognized by ResearchGate as among the world’s top 1% scholars in general and in several contemporary fields encompassing artificial intelligence, autonomous systems, theoretical computer science, engineering mathematics, software engineering, cognitive science, information science, and computational linguistics, etc.
Keynote Title: From Data-Aggregative Learning to Cognitive Knowledge Learning Enabled by Autonomous AI Theories and Intelligent Mathematics
Abstract: This keynote lecture presents a fundamental AI theory and the HMML framework for the design and implementation of Autonomous Machine Learning (AML). It is discovered that the basic unit of knowledge for AML is a binary relation (bir) [28,46], which is no longer a bit as that of data at low-level learning. It is recognized that no classic AI machine could achieve the level of AML by traditional data-aggregation neural network technologies, because high-level intelligence, encompassing inductive knowledge acquisition, causal reasoning, and robust decision-making, is cognitively independent from data and their magnitude. Therefore, the emerging technology of advanced AML for knowledge acquisition will trigger unprecedented AAI technologies beyond current level of data-driven AI systems according to the HMML theory