Explain Yourself - A Semantic Stack for Artificial Intelligence

Abstract:
Artificial Intelligence is the pursuit of the science of intelligence. The journey includes everything from formal reasoning, high-performance game playing, natural language understanding, and computer vision. Each AI experimental domain is littered along a spectrum of scientific explainability, all the way from high-performance but opaque predictive models, to multi-scale causal models. While the current AI pandemic is preoccupied with human intelligence and primitive unexplainable learning methods, the science of AI requires what all other science requires: accurate explainable causal models. The presentation introduces a sketch of a semantic stack model, which attempts to provide a framework for both scientific understanding and implementation of intelligent systems. A key idea is that intelligence should include an ability to model, predict, and explain application domains, which, for example, would transform purely performance-oriented systems into instructors as well.

Biography:
Randy Goebel is currently professor of Computing Science in the Department of Computing Science at the University of Alberta, Associate Vice President (Research) and Associate Vice President (Academic), and Fellow and co-founder of the Alberta Machine Intelligence Institute (AMII). He received the B.Sc. (Computer Science), M.Sc. (Computing Science), and Ph.D. (Computer Science) from the Universities of Regina, Alberta, and British Columbia, respectively. Professor Goebel's theoretical work on abduction, hypothetical reasoning and belief revision is internationally well know, and his recent research is focused on the formalization of visualization and explainable artificial intelligence (XAI). He has been a professor or visiting professor at the University of Waterloo, University of Regina, University of Tokyo, Hokkaido University, Multi-media University (Malaysia), National Institute of Informatics, and a visiting researcher at NICTA (now Data 61) in Australia, and DFKI and VW Data:Lab in Germany. He has worked on optimization, algorithm complexity, systems biology, and natural language processing, including applications in legal reasoning and medical informatics.