Google search, I.B.M.’s Watson Jeopardy-winning computer, credit-card fraud detection and automated speech recognition.
There seems not much in common on that list. But it is a representative sampling of the kinds of modern computing chores that use the ideas and technology developed by Judea Pearl, the winner of this year’s Turing Award.
Dr. Pearl, 75, a professor at the University of California, Los Angeles, is being honored for his contributions to the development of artificial intelligence.
In the 1970s and 1980s, the dominant approach to artificial intelligence was to try to capture the process of human judgment in rules a computer could use. They were called rules-based expert systems.
Dr. Pearl championed a different approach of letting computers calculate probable outcomes and answers. It helped shift the pursuit of artificial intelligence onto more favorable terrain for computing.
Dr. Pearl’s work on Bayesian networks — named for the 18th-century English mathematician Thomas Bayes — provided “a basic calculus for reasoning with uncertain information, which is everywhere in the real world,” said Stuart Russell, a professor of computer science at the University of California, Berkeley. “That was a very big step for artificial intelligence.”
Of course, I believe Bayesian networks are highly relevant to quantum computing and quantum Shannon Information theory. Quantum Mechanics is, after all, just another probability theory.
I first heard of Bayesian Networks from Bill Gates, founder of Microsoft.
Okay, Okay, I don’t really know the guy. I don’t even know his chauffeur. Nevertheless, there is some kernel of truth, this time at least, to my statements.
Since that day, forever etched in my memory, when I overheard Bill Gates’s boffo remarks about Bayesian Networks, I have been a devoted fan of them, especially of their quantum version. My very first paper in ArXix is entitled, appropriately enough, “Quantum Bayesian Nets”. My understanding of q-b-nets has increased quite a lot since that paper. Many of my subsequent papers have dealt with q-b-nets (better than cabinets). Sometime ago, I wrote a Mac application called “Quantum Fog” that does quantum Bayesian networks. (It uses algorithms of exponential complexity so it is only intended for pedagogical purposes. ) And of course, this blog is named after qbnets, and many of its post are about the very subject.
Addendum: Above, I only mentioned Judea Pearl’s work on Bayesian networks. More recently, Pearl has also written some papers and a book on his own theory of causality (which is an extra structure built on top of the foundation of Bayesian networks). However, to date, his theory of causality (“causality calculus”) has been used by others MUCH less frequently than his previous work on Bayesian networks, especially in industrial computer applications.