ACM Awards Judea Pearl the Turing Award for Work on Artificial Intelligence
PC Magazine (03/15/12) Michael J. Miller
ACM announced that University of California, Los Angeles professor Judea Pearl is this year's winner of the A.M. Turing Award for his work on artificial intelligence. The award, considered the highest honor in computer science, recognizes Pearl for devising a framework for reasoning with imperfect data that has changed the strategy for real-world problem solving. ACM executive director John White says Pearl was singled out for work that "was instrumental in moving machine-based reasoning from the rules-bound expert systems of the 1980s to a calculus that incorporates uncertainty and probabilistic models." Pearl worked out techniques for attempting to reach the best conclusion, even when there is a level of uncertainty. Internet pioneer Vinton Cerf says Pearl's research "is applicable to an extremely wide range of applications in which only partial information is available to draw upon to reach conclusions." He also says the successful business models of companies that search the Internet owe a debt to Pearl's work. Pearl generated the framework for Bayesian networks, which provides a compact method for representing probability distributions. This framework has played a substantial role in reshaping approaches to machine learning, which currently has a heavy reliance on probabilistic and statistical inference, and which underlies most recognition, fault diagnosis, and machine-translation systems.
PC Magazine (03/15/12) Michael J. Miller
ACM announced that University of California, Los Angeles professor Judea Pearl is this year's winner of the A.M. Turing Award for his work on artificial intelligence. The award, considered the highest honor in computer science, recognizes Pearl for devising a framework for reasoning with imperfect data that has changed the strategy for real-world problem solving. ACM executive director John White says Pearl was singled out for work that "was instrumental in moving machine-based reasoning from the rules-bound expert systems of the 1980s to a calculus that incorporates uncertainty and probabilistic models." Pearl worked out techniques for attempting to reach the best conclusion, even when there is a level of uncertainty. Internet pioneer Vinton Cerf says Pearl's research "is applicable to an extremely wide range of applications in which only partial information is available to draw upon to reach conclusions." He also says the successful business models of companies that search the Internet owe a debt to Pearl's work. Pearl generated the framework for Bayesian networks, which provides a compact method for representing probability distributions. This framework has played a substantial role in reshaping approaches to machine learning, which currently has a heavy reliance on probabilistic and statistical inference, and which underlies most recognition, fault diagnosis, and machine-translation systems.
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