Keynote Speaker
Prof. Chun-Hsien CHEN
Nanyang Technological University,Sinapore
Chun-Hsien CHEN is Associate Professor (tenured), Director of the Design Stream, and Professor-in-Charge of the Design & Human Factors Lab in the School of Mechanical & Aerospace Engineering, Nanyang Technological University, Singapore. He received his BS degree in Industrial Design from National Cheng Kung University, Taiwan, MS and Ph.D. degrees in Industrial Engineering from the University of Missouri-Columbia, USA. He has several years of product design & development experience in the industry. His teaching and research interests are design science in product design and development; engineering/design informatics for managing/supporting digital design & manufacturing; and human factors and management of human performance. He has more than 270 publications in these areas. Prof. Chen has served as a Technical Reviewer for National Science and Technology Awards (Singapore), National Research Foundation of Korea, The Knowledge Foundation (KK) HÖG 16 Project, Sweden, and a Judge for Pin Up Design Awards (South Korea), an Advisory Board member for ISTE (International Society of Transdisciplinary Engineering), an Advisory Committee member for the various international conferences held in USA, Europe, Brazil, China, Korea, Malaysia, Hong Kong and Taiwan. Prof. Chen has been appointed as Editor-in-Chief of Advanced Engineering Informatics (ADVEI), a SCI journal published by Elsevier (UK), since January 2013. Besides ADVEI, he is/was an editorial board member of Recent Patents on Engineering, Journal of Kansei, Heliyon, etc. He is/was a Shanghai Eastern Scholar (2011 – 2014), a Guest Professor of Tianjin University (since 2013), a Visiting Professor of National Cheng Kung University (2011), a Guest Professor of Shanghai Maritime University (since 2006), and Chaoyang University of Technology (2008 – 2010). He was appointed by China Institute of FTZ (free trade zone) Supply Chain, Shanghai Maritime University, as a Guest Chair Professor (2016 – 2020).
Speech Title: Human-Centric Smart Product-Service Systems in Industry 4.X.
Abstract: Technology, consumer sophistication and business globalization have led to a highly competitive business environment which demands faster new product (tangible or intangible) introduction and more complex and value-added, customized products. Since the primary role of product design is to bridge users and technological systems in contexts of product use, it is increasingly important to focus on human-centric concerns, such as understanding the users’ behaviour, needs and requirements of different social and cultural segments. As these human-centric factors become more important in product design and development along with increasing complexity from technological advances such as networking and embedded technologies, multi-disciplinary information management becomes critical for achieving high product integrity. Yet, because of the complexity, uncertainty and cross-disciplinary nature of human and societal factors, formal mechanisms for incorporating these factors consistently into the product design and development process have not been well established. In this regard, Smart Product-Service System (Smart PSS) is emerging as an IT-driven value cocreation business strategy by integrating smart, connected products and its generated digitalized and e-services into a single solution to meet individual customer needs in a sustainable manner, especially important in the era of Industry 4.0 and beyond.
Keywords: Human-centric design, smart product-service systems, design informatics, digital twin.
Prof. Dr. Yingxu Wang
FIEEE, FBCS, FI2CICC, FAAIA, FWIF, P.Eng
President, International Institute of Cognitive Informatics and Cognitive Computing (I2CICC)
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, and FWIF. 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 1.0% 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.
Speech Title: What can’t AI Do? The Emergence of Autonomous AI (AAI) beyond Data Convolution and Pretrained Learning.
Keywords：Intelligence science, abstract intelligence, intelligent mathematics, autonomous AI (AAI), hyperstructures, machine knowledge learning, cognitive robots, theoretical foundations, constraints, challenges, cognitive robots.
Abstract：
Intelligence is the most wondrous capability of humans (conscious) or AI equivalents (unconscious) that transfers information into rational behaviors or cumulative knowledge. Advances in Intelligence Science (IS) [1], [2], [3], [4] have revealed that the basic unit of intelligence is a binary process (bip) [1]. It leads to a fundamental theory known as abstract intelligence (aI) [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15] underpinned by Intelligent Mathematics (IM) [16], [17], [18], [19], [20], [21], [22], [23], [24], which explains how autonomous intelligence is generated based on static procedural knowledge formally manipulated by Real-Time Process Algebra (RTPA) [25], [26], Inference Algebra (IA) [27], [28], [29], and Visual Semantic Algebra (VSA) [30].
According to the Layered Model of Abstract Intelligence (LMAI) of IS [1], [31], [32], the maturity levels of both human and machine intelligence are aggregated across the levels of reflexive, imperative, adaptive, autonomous, and cognitive intelligence from the bottom up [1], inline with the Layered Reference Model of the Brain (LRMB) [33], [34] in brain science. LMAI indicates that current AI technologies are still immature towards human-like autonomous and conscious intelligence constrained by the inherent challenges to current AI technologies characterized as pretrained, reflexive, unexplainable, and unconscious machine intelligence.
This keynote lecture presents a theoretical framework of IS. It elaborates why the maturity of current AI technologies is still bounded at the reflexive and semi-adaptive levels against the LMAI of IS. It introduces contemporary theories sand methodologies for AAI and training-free machine learning particularly in real-time applications. A set of fundamental challenges to classic AI will be rigorously analyzed encompassing causal reasoning, knowledge learning, semantic understanding, indiscrete hyperstructure processing, divergent system behavior manipulation, emotion and affection sensing, creativity enabling, training-free learning, real-time cumulative learning, and autonomous intelligence generation based on the AAI theory of IS [35], [36], [37], [38], [39], [40], [41], [42], [43], [44], [45], [46], [47], [48].
References：
[1] Y. Wang, et al., (2021), Perspectives on the Philosophical, Cognitive and Mathematical Foundations of Symbiotic Autonomous Systems, Philosophical Transactions of Royal Society (A), Oxford, UK, 379(2207):1-20.
[2] Y. Wang (2019), On Intelligence Science and Autonomous Systems, Keynote, 4th Int’l Conf. Intelligence and Interactive Systems & Applications (IISA’19), Bangkok, June, pp. 3.
[3] Y. Wang, et al. (2021). Towards a Theoretical Framework of Autonomous Systems underpinned by Intelligence and Systems Sciences, IEEE/CAS Journal of Automatica Sinica, 8(1):52-63.
[4] Y. Wang (2021). On the Emergence of Autonomous Systems towards Deep Thinking Machines and General AI, Keynote, IEEE 20th Int’l Conf. on Cognitive Informatics & Cognitive Computing (ICCI*CC’21), Oct., Banff, Canada, Online, p.5.
[5] Y. Wang (2009). On Abstract Intelligence: Toward a Unified Theory of Natural, Artificial, Machinable, and Computational Intelligence. Int’l Journal Software Science & Comp. Intelligence, 1(1):1-17.
[6] Y. Wang (2007). Cognitive Informatics Foundations of Nature and Machine Intelligence, Keynote, 6th IEEE Int’l Con. Cognitive Informatics (ICCI'07), Lake Tahoe, USA, July, pp. 2-12.
[7] Y. Wang and E. Tunstel (2019). Emergence of Abstract Sciences & Transdisciplinary Advances in Systems, Man, and Cybernetics. IEEE Sys., Man & Cybernetics Magazine, 5(2):12-19.
[8] Y. Wang (2016). On Cognitive Foundations and Mathematical Theories of Knowledge Science, Int’l Journal of Cognitive Informatics and Natural Intelligence, 10(2):1-24.
[9] Y. Wang (2003). On Cognitive Informatics, Brain and Mind, 4(2):151-167.
[10] Y. Wang (2009). On Cognitive Computing, International Journal of Software Science and Computational Intelligence (IJSSCI), 1(3):1-15. http://doi.org/10.4018/jssci.2009070101.
[11] Y. Wang (2007). The Theoretical Framework of Cognitive Informatics, Int’l Journal of Cognitive Informatics and Natural Intelligence, 1(1):1-27.
[12] Y. Wang, et al. (2011). Perspectives on the Field of Cognitive Informatics and Its Future Development, Int’l Journal of Cognitive Informatics and Natural Intelligence, 5(1):1-17.
[13] Y. Wang (2009). On Cognitive Foundations of Creativity and the Cognitive Process of Creation, Int’l Journal of Cognitive Informatics and Natural Intelligence, 3(4), 1-18.
[14] Y. Wang, S. Kwong, et al. (2020). Brain-Inspired Systems: A Transdisciplinary Exploration on Cognitive Cybernetics, Humanity, and Systems Science towards AI, IEEE System, Man and Cybernetics Magazine, 6(1): 6-13.
[15] Y. Wang, D. Zhang, and W. Kinsner (2010). Advances in Cognitive Informatics and Cognitive Computing, Springer Verlag.
[16] Y. Wang (2020). Intelligent Mathematics (IM): Indispensable Mathematical Means for General AI, Autonomous Systems, Deep Knowledge Learning, Cognitive Robots, & Intelligence Science, IEEE 19th Int’l Conf. Cognitive Informatics & Cognitive Computing (ICCI*CC’20), Tsinghua Univ., China, Sept., p.5.
[17] Y. Wang (2012). Contemporary Mathematics as a Meta-methodology of Science, Engineering, Society, and Humanity, Journal of Advanced Mathematics and Applications, 1(2):1-3.
[18] Y. Wang (2012). On Denotational Mathematics Foundations for the Next Generation of Computers: Cognitive Computers for Knowledge Processing, Journal of Advanced Mathematics and Applications 1(1):121-133.
[19] Y. Wang (2006), On Concept Algebra and Knowledge Representation, 5th IEEE Int’l Conference on Cognitive Informatics (ICCI’06), pp.320-331.
[20] Y. Wang (2010), On Concept Algebra for Computing with Words (CWW), Int’l J. of Semantic Computing, 4(3):331-356.
[21] Y. Wang (2013), On Semantic Algebra: A Denotational Mathematics for Cognitive Linguistics, Machine Learning, and Cognitive Computing, Journal of Advanced Mathematics and Applications, 2(2):145-161.
[22] Y. Wang (2010), On Formal and Cognitive Semantics for Semantic Computing, Int’l Journal of Semantic Computing, 4(2):203-237.
[23] Y Wang and G. Fariello (2012). On Neuroinformatics: Mathematical Models of Neuroscience and Neurocomputing, J. Advanced Mathematics and Applications, 1(2):206-217.
[24] Y. Wang (2015). Formal Cognitive Models of Data, Information, Knowledge, and Intelligence, WSEAS Trans. Computers, 14:770-781.
[25] Y. Wang (2002). The Real-Time Process Algebra (RTPA), Annals of Software Engineering, 14(1):235-274
[26] Wang, Y. (2008), Deductive Semantics of RTPA, International Journal of Cognitive Informatics and Natural Intelligence, 2(2):95-121.
[27] Y. Wang (2012), Inference Algebra (IA): A Denotational Mathematics for Cognitive Computing & Machine Reasoning (II), Int’l Journal of Cognitive Informatics & Natural Intelligence, 6(1):21-47.
[28] Y. Wang (2014), Fuzzy Causal Inferences based on Fuzzy Semantics of Fuzzy Concepts in Cognitive Computing, WSEAS Transactions on Computers, 13:430-441.
[29] Y. Wang (2011). On Cognitive Models of Causal Inferences and Causation Networks, Int’l Journal of Software Science and Computational Intelligence, 3(1):50-60.
[30] Y. Wang (2012). On Visual Semantic Algebra (VSA): A Denotational Mathematical Structure for Modeling and Manipulating Visual Objects and Patterns. in Software & Intelligent Sciences: New Transdisciplinary Findings, pp. 68-81.
[31] Y. Wang, B. Widrow, L.A. Zadeh, et al., (2016). Cognitive Intelligence: Deep Learning, Thinking, and Reasoning with Brain-Inspired Systems, Int/l Journal of Cognitive Informatics and Natural Intelligence, 10(4):1-21.
[32] Y. Wang (2003). On Cognitive Informatics, Brain and Mind, 4(2):151-167.
[33] Y. Wang, Y Wang, S Patel, D Patel (2006). A Layered Reference Model of the Brain (LRMB), IEEE Transactions on Systems, Man, and Cybernetics, Part C, 36(2):124-133.
[34] Y. Wang (2002). Cognitive Models of the Brain, First IEEE Int’l Conf. Cognitive Informatics (ICCI’02), Aug., pp. 259-269.
[35] M.L. Gavrilova, Y. Wang, et al. (2018). KINECT Sensor Gesture and Activity Recognition, IEEE Consumer Electronics Magazine, 7(1):88-94.
[36] Y. Wang (2011). On Cognitive Models of Causal Inferences and Causation Networks, Int’l Journal of Software Science and Computational Intelligence, 3(1):50-60.
[37] Y. Wang (2007). The Theoretical Framework and Cognitive Process of Learning, Proc. 6th IEEE Int’l Conference on Cognitive Informatics (ICCI’07), IEEE CS Press, Lake Tahoe, USA, Aug., pp. 470-479.
[38] Y. Wang (2015). Cognitive Learning Methodologies for Brain-Inspired Cognitive Robotics, Int’l Journal of Cognitive Informatics & Natural Intelligence, 9(2):37-54.
[39] Wang, Y., Y. Zhang, P. Sheu et al. (2010). The Formal Design Models of an Automatic Teller Machine (ATM), Int’l Journal of Software Science and Computational Intelligence, 2(1):102-131.
[40] Y. Wang (2007). On Cognitive Informatics Foundations of Knowledge & Formal Knowledge Systems, 6th IEEE Int’l Conf. Cognitive Informatics (ICCI’07), Lake Tahoe, Aug., pp. 263-272.
[41] Y. Wang (2004). On Cognitive Informatics Foundations of Software Engineering, 3rd IEEE Int’l Conf. on Cognitive Informatics (ICCI'04), IEEE CS Press, Canada, Aug., pp. 22-31.
[42] Y. Wang (2007). Software Engineering Foundations: A Software Science Perspective, Auerbach, CRC, NY, USA, 1,488pp.
[43] Y. Wang (2014). Software Science: On General Mathematical Models and Formal Properties of Software, Journal of Advanced Mathematics and Applications, 3(2):130-147.
[44] Y. Wang, R.C. Berwick, et al. (2011). Cognitive Informatics and Cognitive Computing in Year 10 and Beyond, Int’l Journal of Cognitive Informatics and Natural Intelligence, 5(4): 1-2.
[45] Y. Wang (2015): Cognitive Robotics and Mathematical Engineering, 14th IEEE Int’l Conf. Cognitive Informatics & Cognitive Computing (ICCI*CC’15), Tsinghua Univ., July, p.2.
[46] Y. Tian, Y. Wang, et al. (2011). A Formal Knowledge Representation System (FKRS) for the Intelligent Knowledge Base of a Cognitive Learning Engine, Int’l Journal of Software Science and Computational Intelligence, 3(4):1-17.
[47] Y. Wang (2012). Towards the Next Generation of Cognitive Computers: Knowledge vs. Data Computers, Keynote, 12th Int’l Conf. Computational Science & Applications (ICCSA'12), Brazil, Springer, June, pp.3.
[48] Y. Wang (2015). Towards the Abstract System Theory of System Science for Cognitive and Intelligent Systems, Springer Journal of Complex and Intelligent Systems, 1(3):1-22.
