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Technical Keynote Lecture

Professor Chris Bishop (Microsoft Research)

Third Generation Machine Intelligence

Abstract

The first successful applications of machine intelligence were based on expert systems constructed using rules elicited from human experts. Limitations in the applicability of this approach helped drive the second generation of machine intelligence methods, as typified by neural networks and support vector machines, which can be characterised as black-box statistical models fitted to large data sets. In this talk I will describe a new paradigm for machine intelligence, based on probabilistic graphical models, which has emerged over the last five years and which allows strong prior knowledge from domain experts to be combined with machine learning techniques to enable a new generation of large-scale applications. The talk will be illustrated with tutorial examples as well as real-world case studies.

Chris Bishop is Chief Research Scientist at Microsoft Research Cambridge. He is also Professor of Computer Science at the University of Edinburgh, and a Fellow of Darwin College Cambridge. He is a Fellow of the Royal Society of Edinburgh and a Fellow of the Royal Academy of Engineering. Chris recently presented the prestigious Royal Institution Christmas Lectures (initiated by Michael Faraday in 1825) which were broadcast on five successive evenings at prime time on UK national television. He authored the widely-adopted text book on neural computing: “Neural Networks for Pattern Recognition” (Oxford University Press, 1995, 13,000 citations) and is the author of the new leading textbook in the machine learning field: Pattern Recognition and Machine Learning (Springer, 2006).