The Unreasonable Effectiveness of Data
Speaker: Peter Norvig, Google, Inc.
Location: Warren Weaver Hall 109
Date: September 17, 2010, 2 p.m.
Host: Ken Perlin & G4LI
In decades past, models of human language were wrought from the sweat and pencils of linguists. In the modern day, it is more common to think of language modeling as an exercise in probabilistic inference from data: we observe how words and combinations of words are used, and from that build computer models of what the phrases mean. This approach is hopeless with a small amount of data, but somewhere in the range of millions or billions of examples, we pass a threshold, and the hopeless suddenly becomes effective, and computer models sometimes meet or exceed human performance. This talk gives examples of the data available in large repositories of text, images, and videos, and shows some tasks that can be accomplished with the resulting models.
This event is organized by NYU's Courant Institute of Mathematical Sciences and the Games for Learning Institute (G4LI), a joint research endeavor of Microsoft Research and a consortium of universities. G4LI studies the educational use of digital video games and investigates their socio-cultural, cognitive, and emotional impact.
Peter Norvig is Director of Research at Google Inc. He is a Fellow of the AAAI and the ACM and co-author of Artificial Intelligence: A Modern Approach, the leading textbook in the field. Previously he was head of Computational Sciences at NASA and a faculty member at USC and Berkeley.
In-person attendance only available to those with active NYU ID cards.