Deep learning has given rise to a major revolution in the field of artificial intelligence (AI), with large enterprises and institutions investing tremendous financial and intellectual resources to harness this powerful paradigm. However, a major challenge with the democratization and proliferation of deep learning as commodity AI for all is the sheer complexity of current deep neural networks, making them ill-suited for operational use in a large number of scenarios. Taking inspiration from biological evolution, we explore the idea of "Can deep neural networks evolve naturally over successive generations into highly efficient deep neural networks?" by introducing the notion of synthesizing new highly efficient, yet powerful deep neural networks over successive generations via an evolutionary process from ancestor deep neural networks. This talk presents recent findings that support such an evolutionary synthesis paradigm for achieving operational deep intelligence across a wide variety of scenarios.
Dr. Alexander Wong is the Canada Research Chair in Medical Imaging Systems, co-director of the Vision and Image Processing Research Group, and an associate professor in the Department of Systems Design Engineering at the University of Waterloo. He has published over 300 refereed journal and conference papers, as well as patents, in various fields such as computational imaging, artificial intelligence, computer vision, and multimedia systems. In the area of artificial intelligence, his focus is on operational deep intelligence (a pioneer in evolutionary deep intelligence, discovery radiomics, and random deep intelligence via deep-structured fully-connected graphical models).
Please join us for coffee, refreshments, and an opportunity to meet Dr. Wong and other attendees from 2:00 – 2.30 p.m.