It Only Took 60 Years to Solve Artificial Intelligence - That Wasn't so Hard, Was it?
Wednesday, October 31st, 11:00 - 12:00
Short Abstract: It has been 62 years since the founding Dartmouth AI workshop. In the 1960's, researchers at MIT believed that developing a computer program to simulate a significant part of human vision could be accomplished by employing several undergraduate students over a summer. They quickly found out they were mistaken. Fast forward to the present day. AI is everywhere, solving all manner of hard problems and triggering debates about its ethics and societal impact. In contrast, scientists studying human (and animal) vision and intelligence have made no corresponding claims for breakthroughs in their understanding of human intelligence. Although a great deal has been learned in the past 60 years to be sure, each major discovery seems to emphasize how much remains unknown. Understanding the brain and how intelligent behaviour is produced remains a major challenge. Noting that the current AI successes are often claimed to be due to mimicking brain processes, is there a disconnect here? This presentation will suggest that there has been some tacit, gradual moving of the goalposts taking place and that there is indeed a disconnect. An understanding of the brain and behavioural sciences reveals many directions for future AI research, and importantly, how far there is still to go. Dramatically, this will require the abandonment of some of the longest held computational standards. Examples from my lab will illustrate.
Biography: John Tsotsos is Distinguished Research Professor of Vision Science in the Dept. of Electrical Engineering and Computer Science and Director of the Centre for Innovation in Computing at Lassonde, at York University. He has Adjunct Professorships in Computer Science and in Ophthalmology and Vision Sciences at the University of Toronto. He received his doctorate in Computer Science from the University of Toronto developing the first computer system to interpret visual motion depicted in digital image sequences, with application to heart motion analysis. After a postdoctoral fellowship in Cardiology at Toronto General Hospital, he joined the University of Toronto on faculty in both Computer Science and in Medicine. In 1980 he founded the highly respected Computer Vision Group at the University of Toronto, which he led for 20 years. He moved to York University in 2000 as Director of the Centre for Vision Research. Under his directorship, the centre was ranked in the top six interdisciplinary vision research organizations in the world. He has been a Canadian Heart Foundation Research Scholar (1981-83), a Fellow of the Canadian Institute for Advanced Research (1985-95) and currently holds the Canada Research Chair in Computational Vision (2003-2024). He has received many awards and honours including several best paper awards, among them a 1987 inaugural Marr Prize citation, the 1997 CITO Innovation Award for Leadership in Product Development, the 2006 Canadian Image Processing and Pattern Recognition Society Award for Research Excellence and Service, the 1st President's Research Excellence Award by York University on the occasion of the University's 50th anniversary in 2009, the 2011 Geoffrey J. Burton Memorial Lectureship from the United Kingdom's Applied Vision Association for significant contribution to vision science, has been an ACM Distinguished Speaker, and is an IEEE Fellow. He was elected as Fellow of the Royal Society of Canada in 2010, and was awarded its 2015 Sir John William Dawson Medal for sustained excellence in multidisciplinary research, the first computer scientist to be so honoured. He has co-founded or been a principal of several companies, and holds a number of patents and technology transfer licences. Visiting positions were held at: University of Hamburg, Germany; Polytechnical University of Crete, Greece; Center for Advanced Studies at IBM Canada; INRIA Sophia-Antipolis, France; and, the Massachusetts Institute of Technology, USA. His current research focuses on a comprehensive theory of visual attention in humans. A practical outlet for this theory forms a second focus, embodying elements of the theory into the vision systems of mobile robots.