Practice Makes Perfect: How Billions of Examples Lead to Better Models

General
Location: Marina Ballroom D Level: Novice

The Internet gives us access to billions of pages of information, along with billions of pictures and hundreds of millions of videos. Of course, a person could never look at all of them, but computers are faster than humans—what can a computer learn from all this information?

In this talk we will see that a computer might not learn in the same way that a person does, but it can use massive amounts of data to perform selected tasks very well. We will see that a computer can correct spelling mistakes, translate from Arabic to English, and recognize celebrity faces about as well as an average human—and can do it all by learning from examples rather than by relying on programming.

Photo of Peter Norvig

Peter Norvig

Google

Peter Norvig is a director of research at Google. In his prior role, as director of search quality, he directed Google’s core Search Algorithms Group, which means he was the manager of record responsible for answering more queries than anyone else in the history of the world. Previously, he was the head of the Computational Sciences Division at the NASA Ames Research Center (NASA’s senior computer scientist) and received the NASA Exceptional Achievement Award in 2001. He was also an assistant professor at the University of Southern California and a research faculty member in the Computer Science Department at the University of California, Berkeley. He has over 50 publications in computer science, concentrating on artificial intelligence, natural language processing, and software engineering. He is the author of Paradigms of AI Programming: Case Studies in Common Lisp, Verbmobil: A Translation System for Face-to-Face Dialog, and Intelligent Help Systems for UNIX and coauthor of Artificial Intelligence: A Modern Approach, the leading textbook in the field. He is also the author of the Gettysburg Powerpoint Presentation and has written the world’s longest palindromic sentence. Peter is a fellow of the American Association for Artificial Intelligence and the Association for Computing Machinery. He holds a PhD from UC Berkeley, where he was recognized with a distinguished alumni award in 2006.

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