Keynote Speaker
Prof.Clarence W. de Silva
Clarence W. de Silva has served as a Full Professor of Mechanical Engineering at the University of British Columbia (UBC), Vancouver, Canada, since 1988. Prior to that, he served as Assistant and Associate Professor of Mechanical Engineering at Carnegie Mellon University (1978-87), Pittsburgh, USA. He graduated from the University of Ceylon (now Sri Lanka) obtaining a B.Sc. Engineering degree with First Class Honors and also receiving the Dr. C. H. Hewavitarana Prize, in the entire class of engineering. After working in the Sri Lankan industry for several years, he went overseas, obtained a MASc degree from the University of Toronto, PhD degrees from MIT and the University of Cambridge, and in 2020, the ScD degree, the so-called “Higher Doctorate,” from the University of Cambridge. He is a Fellow of: IEEE, ASME, the Canadian Academy of Engineering, and the Royal Society of Canada. Also, he has been a Senior Canada Research Chair Professor in Mechatronics and Industrial Automation, NSERC-BC Packers Chair Professor in Industrial Automation, Mobil Endowed Chair Professor, Lilly Fellow (USA), Senior Fulbright Fellow (UK), Killam Fellow (UBC), Erskine Fellow (New Zealand), Professorial Fellow (Australia), Faculty Fellow (UBC), Distinguished Visiting Fellow of the Royal Academy of Engineering, UK, and a Peter Wall Scholar (UBC). He has authored 26 books and over 600 technical papers, with more than half of which are in archival journals.
Keynote Abstract
Myths and Realities of AI Technologies for the Developing World
This talk will start by indicating the importance of “Intelligence” in autonomous practical systems. Next, it will outline some important practical applications of Intelligent Systems, including those developed by groups worldwide and in the Industrial Automation Laboratory at the University of British Columbia, headed by the author. Some myths, misconceptions and shortcomings related to Intelligent Systems will be pointed out. The main shortcomings concern mechanical requirements, the nature of intelligence, and the achievable level of precision.
Artificial Intelligence (AI) is a broad term. The meaning of intelligence, in that context, is various. Its application methodology is various as well. The older concepts of AI primarily involved rule-based and soft computing methodologies. The new methodologies of AI and machine learning primarily involve data-based concepts, which are used in deep learning, deep neural networks, compositional neural networks, and so on. In short, in the context of “ artificial learning,” what is commonplace today is the use of data. However, a more appropriate mindset is evolving, where the primary focus is to use the “Physics” of the system, in the process of learning. Still, AI is not a panacea, and is presently more appropriate in applications that do not require high precision (e.g., music, medical diagnosis, advisory systems). Nevertheless, future variations should become conducive to high-precision applications. Then, one has to be aware of myths, false expectations, and present realities of AI.
The talk will conclude by mentioning future trends and key opportunities of Artificial Intelligence, particularly for developing countries.
