Thursday, 20 April 2017

Four Way Interview - Hector Levesque

Hector Levesque is Professor Emeritus in the Department of Computer Science at the University of Toronto. He worked in the area of knowledge representation and reasoning in artificial intelligence. He is the co-author of a graduate textbook and co-founder of a conference in this area. He received the Computers and Thought Award in 1985 near the start of his career, and the Research Excellence Award in 2013 near the end, both from IJCAI (the International Joint Conferences on Artificial Intelligence). His latest title is Common Sense, The Turing Test, and the Quest for Real AI.

Why computer science?

Computer science is not really the science of computers, but the science of computation, a certain kind of information processing, with only a marginal connection to electronics. (I prefer the term used in French and other languages, informatics, but it never really caught on in North America.) Information is somewhat like gravity: once you are made aware of it, you realize that it is everywhere. You certainly cannot have a Theory of Everything without a clear understanding of the role of information. 

Why this book?

AI is the part of computer science concerned with the use of information in the sort of intelligent behaviour exhibited by people. While there is an incredible amount of buzz (and money) surrounding AI technology these days, it is mostly concerned with what can be learned by training on massive amounts of data. My book makes the case that this is an overly narrow view of intelligence, that what people are able to do, and what early AI researchers first proposed to study, goes well beyond this.

What's next?

I have a technical monograph with Gerhard Lakemeyer published in 2000 by MIT Press on the logic of knowledge bases, that is, on the relationship between large-scale symbolic representations and abstract states of knowledge. We are working on a new edition that would incorporate some of what we have learned about knowledge and knowledge bases since then. 

What's exciting you at the moment?

For me, the most exciting work in AI these days, at least in the theoretical part of AI, concerns the general mathematical and computational integration of logical and probabilistic reasoning seen, for example, in the work of Vaishak Belle. It's pretty clear to all but diehards that both types of knowledge will be needed, but previous solutions have been somewhat ad hoc and required giving up something out of one or the other.

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