Dr. Peter Norvig has been at Google, Inc. since 2001 as the Director of Machine Learning, Search Quality, and Research. He is a Fellow of the American Association for Artificial Intelligence and co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field. Previously he was the senior computer scientist at NASA and head of the 200-person Computational Sciences Division at Ames Research Center. Before that he was Chief Scientist at Junglee, Chief designer at Harlequin Inc, and Senior Scientist at Sun Microsystems Laboratories.

Dr. Norvig received a B.S. in Applied Mathematics from Brown University and a Ph.D. in Computer Science from the University of California at Berkeley. He has been a Professor at the University of Southern California and a Research Faculty Member at Berkeley. He has over fifty publications in various areas of Computer Science, concentrating on Artificial Intelligence, Natural Language Processing and Software Engineering, including the books Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX.

Google opened its first full-cycle European Engineering centre in Zurich, Switzerland in 2004, followed by other centres in Trondheim, Norway and London, UK. Since 2004 we have been drawing talented software engineers, programmers, and computer scientists from various European countries to join our entrepreneurial and dynamic team.

We have exciting career opportunities for Computer Science graduates at all of these locations. If you like working on challenging and intriguing problems in an entrepreneurial environment that fosters creative approaches and solutions, then a role with Google could be right for you.

If you're interested to find out more, you can talk informally after the presentation with a representative from Google's campus team, or you can visit http://www.google.com/jobs/students.html

So far we know of exactly one system in which trillions of facts are transmitted to billions of learners: the system of publishing the written word. No other system comes within a factor of a million of this performance benchmark. This is despite the fact that the written word is notoriously imprecise and ambiguous.

In the early days of AI, most work was on creating a new system of transmission -- a new representation language, and/or a new axiomization of a domain. Well-structured data was manipulated by sound means. One near future for AI is "in the middle" between author and reader. It will remain expensive to create knowledge in any formal language but AI can leverage the work of millions of authors by understanding, classifying, prioritizing, translating, summarizing and presenting the written word in an intelligent just-in-time basis to billions of potential readers.

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