7 September 2012
12noon - 1pm
Venue: Room 303S.561, City Campus
Department of Computer Science seminar by Dr Michael Witbrock, The University of Auckland.
We probably don't yet have the computers capable of supporting human-level AI, but we're getting quite close. And we're developing powerful algorithms for Machine Learning, Language Understanding, and various kinds of inference (probablistic, classification-based, inductive, abductive, analogical, decductive) as we do so. So where is "Good Old Fashioned AI" in all this? And, now that computers ae really pretty fast, what can we do with techniques, like first-order representations and deduction, that characterised the early days of AI. The answer to the latter question is, quite a lot, including making some interesting steps towards meaningful Human Computer Collaboration.
In this talk, I will focus on elements of this progress at Cycorp, where a very broad set of pre-existing inferentially productive representations, extensive use of deductive inference, and a partial ability to map between logical and textual representations, sometimes interactively, is beginning to significantly enhance our ability to build broad-coverage, reasoning-based applications.
Michael has a PhD in Computer Science from Carnegie Mellon University and a BSc Hons in Psychology from Otago University. He is very intersted in discussing both AI research in his home country, NZ, and the opportunities for NZ-based, AI-based start ups. He currently is Vice President for Research at Cycorp. Before joining Cycorp, in 2001, to direct its knowledge formation and dialogue processing efforts, he had been Principal Scientist at Terra Lycos, working on integrating statistical and knowledge based approaches to understanding web user behaviour, a research scientist at Just Systems Pittsburgh Research Center, working on statistical summarisation, and a systems scientist at Carnegie Mellon on the Informedia visual and spoken document information retrieval project. His current research focused on automated reading to inferentially-productive representations, and knowledge capture and use, more broadly. He is author of numerous publications in areas ranging across computational linguistics, speech modelling and recognition, neural networks, automated inference, automated reading and multimedia information retrieval, and has dabbled in web browser design, genetic design and parallel computer architecture.
After the talk, there will be bagels and refreshments served in the new shared Common Room on the 4th floor. All are welcome.