AI for Automation of Intelligence: A Paradigm Shift from Newton’s “Big Laws Small Data” to Merton’s “Big Data Small Laws”
Abstract:
Over the history of automation, Descriptive Control that use models as tools mainly for systems analysis and synthesis have dominated the field in both theories and applications. Today, as Internet of Things, Big Data, Cloud Computing, and Blockchain become pervasive and new infrastructures for smart automation, effective and efficient management beyond traditional control of complex systems such as networked and social systems overwhelms existing methods and demands a new way of thinking and action: From Industrial Automation to Knowledge Automation, that is, Automaton of Intelligence for complex information processing and decision-making under uncertainty and diversity. Obviously, Artificial Intelligence will play a vital role in this new and even disruptive endeavor, as historically and intrinsically prescribed in Norbert Wiener’s Cybernetics in 1948 and John McCarthy’s workshop proposal for Dartmouth AI Conference in 1955.
We present a brief summary for our effort over the last two decades on research, development, and application of Monadotic Control or Parallel Control that use models primary for the purpose of knowledge representation and data generation. Specifically, ACP-based Parallel Control utilizes Artificial Systems for modeling and description, Computational Experiments for analysis and predication, and parallel execution of actual-artificial decisions for process control, behavioral management and goal prescription of complex systems, especially Cyber-Physical-Social Systems. The ACP approach provides a framework to support the three steps process of “Small Data to Big Data to Small Intelligence”, i.e., generating big data from small data and then reducing big data to small intelligence or precision knowledge for specific tasks via various AI methods. This enables the seamless insertion and integration of AI techniques in automation for Parallel Control that combines Descriptive Control, Predicative Control, and Prescriptive Control, a paradigm shift from Issac Newton’s “Big Laws, Small Data” to Robert Merton’s “Big Data, Small Laws”. Monads, i.e., software-defined knowledge robots, are key actors in parallel control and play the essential role in the process of knowledge automation. Case studies from Intelligent Transportation, Autonomous Driving, Smart Grids, Chemical Plants, and Systems in Agriculture, Energy, Ecology, Education, Art, Medicine, and Manufacturing will be reported and discussed in this keynote speech.
Biography:
Fei-Yue Wang received his Ph.D. in Computer and Systems Engineering from Rensselaer Polytechnic Institute, Troy, New York in 1990. He joined the University of Arizona in 1990 and became a Professor and Director of the Robotics and Automation Lab (RAL) and Program in Advanced Research for Complex Systems (PARCS). In 1999, he founded the Intelligent Control and Systems Engineering Center at the Institute of Automation, Chinese Academy of Sciences (CAS), Beijing, China, under the support of the Outstanding Overseas Chinese Talents Program from the State Planning Council and ”100Talent Program” from CAS, and in 2002, was appointed as the Director of the Key Lab of Complex Systems and Intelligence Science, CAS. From 2006 to 2010, he was Vice President for Research, Education, and Academic Exchanges at the Institute of Automation, CAS. In 2011, he became the State Specially Appointed Expert and the Director of the State Key Laboratory for Management and Control of Complex Systems.
Dr. Wang’s current research focuses on methods and applications for parallel systems, social computing, parallel intelligence and knowledge automation. He was the Founding Editor-in-Chief of the International Journal of Intelligent Control and Systems (1995-2000), Founding EiC of IEEE ITS Magazine (2006-2007), EiC of IEEE Intelligent Systems (2009-2012), and EiC of IEEE Transactions on ITS (2009-2016). Currently he is EiC of IEEE Transactions on Computational Social Systems, Founding EiC of IEEE/CAA Journal of Automatica Sinica, and Chinese Journal of Command and Control. Since 1997, he has served as General or Program Chair of more than 20 IEEE, INFORMS, ACM, and ASME conferences. He was the President of IEEE ITS Society (2005-2007), Chinese Association for Science and Technology (CAST, USA) in 2005, the American Zhu Kezhen Education Foundation (2007-2008), and the Vice President of the ACM China Council (2010-2011). Since 2008, he has been the Vice President and Secretary General of Chinese Association of Automation. Dr. Wang has been elected as Fellow of IEEE, INCOSE, IFAC, ASME, and AAAS. In 2007, he received the National Prize in Natural Sciences of China and was awarded the Outstanding Scientist by ACM for his research contributions in intelligent control and social computing. He received IEEE ITS Outstanding Application and Research Awards in 2009, 2011 and 2015, and IEEE SMC Norbert Wiener Award in 2014.