George Rebane
The Association for Computing Machinery announced today that it has named Dr Judea Pearl of the University of California, Los Angeles as the recipient of the A.M. Turing Award for 2011. The announcement itself begins –
NEW YORK, March 15, 2012—ACM, the Association for Computing Machinery today named Judea Pearl of the University of California, Los Angeles the winner of the 2011 ACM A.M. Turing Award for innovations that enabled remarkable advances in the partnership between humans and machines that is the foundation of Artificial Intelligence (AI). Pearl pioneered developments in probabilistic and causal reasoning and their application to a broad range of problems and challenges. He created a computational foundation for processing information under uncertainty, a core problem faced by intelligent systems. He also developed graphical methods and symbolic calculus that enable machines to reason about actions and observations, and to assess cause-effect relationships from empirical findings. His work serves as the standard method for handling uncertainty in computer systems, with applications ranging from medical diagnosis, homeland security and genetic counseling to natural language understanding and mapping gene expression data. His influence extends beyond artificial intelligence and even computer science, to human reasoning and the philosophy of science.
The Turing Award, widely considered the "Nobel Prize in Computing," carries a $250,000 prize, with financial support provided by Intel Corporation and Google Inc. It is named for the British mathematician Alan M. Turing, whose 100th anniversary will be celebrated in June at the ACM 2012 Turing Centenary Celebration that includes 34 past Turing Award winners along with Pearl.
"Like Alan Turing himself, Pearl turned his thinking to constructing procedures that might be harnessed to perform tasks traditionally associated with human intelligence," said Vint Cerf, chair of the ACM 2012 Turing Centenary Celebration, and a former ACM Turing Award recipient. "His accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and led to extraordinary achievements in machine learning, and they have redefined the term 'thinking machine.'" Cerf pointed to Pearl’s innovation as a quantum leap from Turing’s "test" dating to the 1950s, when Turing set out to discover if machines could think. "Pearl's work on reasoning with uncertainty as well as his game˗changing contributions to machine reasoning about causality have had a pervasive influence not only on machine learning but on natural language processing, computer vision, robotics, computational biology, econometrics, cognitive science, and statistics," Cerf said.
The rest of the announcement may be read here.
Judea Pearl and I first became acquainted as colleagues on various defense research projects during the 1970s when he consulted for the small ‘black studies’ company that I headed during that period. Subsequently our families became friends and over the years we spent many evenings in exciting discussions and song (Judea on guitar and me on the keyboard - Judea is an excellent musician in voice, choral, and several instruments.). It was Judea who convinced me to complete my doctorate and join his small troupe of students at UCLA working on the various research tasks that served as the halo around the magnificent tour de force that Judea Pearl presented to the world when he showed how Bayesian networks could be constructed and correctly computed.
Once his 1988 Probabilistic Reasoning in Intelligent Systems: Networks of Plausible Inference was published, Bayes nets became and have remained the state-of-the-art in machine intelligence. Bayesian inference now is the standard in all applications that required correct probabilistic reasoning, decisions under uncertainty, and management of risk - in short in all human endeavors. Every field that has not embraced what Pearl started with Bayes is now scrambling to catch up.
I have long had the honor of calling Judea Pearl my teacher, mentor, colleague, and friend. Those of us who had the good fortune to recognize early his genius and accomplishments were amply rewarded by what he taught us, and how he guided our individual contributions (my own humble efforts were in machine learning and causality). It is most appropriate that Judea now joins the pantheon of the recognized greats in computer science.
The Value of Stereotyping
George Rebane
But if you understand the underpinnings of stereotyping, it can be a valuable tool in making all kinds of decisions, and also serve as a mirror for better understanding yourself. I was motivated to write this piece when recently reading Daniel Kahneman’s monumental Thinking, Fast and Slow which dances around the subject without getting into the nitty-gritty of it because of the little math involved. Kahneman, recent Nobelist and co-father of behavioral economics with the late Amos Tversky, is also a giant in the field of psychology. In that field stereotyping comes up under the forbidding label of representativeness (q.v. – which is short for quod vide or ‘which see’, and I’ve concluded that its modern version is simply ‘google it’.)
Stereotyping is the use of a template of characteristics that are thought to belong to members of a particular class more frequently than members of the general population of which the class is a subset. The template of characteristics is also known as the stereotypical characteristics like, say, a plastic pocket protector full of writing instruments more often seen in the shirt pockets of male engineers than in the pockets of other men.
Stereotyping has gotten a bad rap in our society, and its use is roundly criticized in the public forum. However, stereotyping, so as to assign or exclude someone from membership in a given class, is built in to almost every critter that lives on this planet. Why? Simply because it is a low cost survival technique that has evolved in all species to simplify quick decisions about the famous ‘Three F’ functions – feed, fight, or mount - important to everyone.
Now consider seeing a good looking woman, finely coiffed, beautifully dressed, and dripping with expensive jewelry, getting out of a chauffeured limousine in front of a fancy restaurant. You wonder if she’s a member of the currently notorious ‘1%’. More particularly, what are the chances (friendly word for ‘probability’) that she is a member of that exclusive class, given she has some of the stereotypical characteristics of that class that you just observed? If you had been facing the other way and your friend told you that a woman just got out a car behind you; then without seeing her, what would you answer if he mused whether the woman belonged to the '1%'?
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