Top boffin Leslie Valiant, of Harvard University, has been named the winner of the 2010 Turing Award for his efforts to develop computational learning theory.
Valiant won the “Nobel Prize for computing” and $250,000 for a lifetime of work bringing together machine learning and computational complexity.
His work has lead to breakthroughs in artificial intelligence as well as computing practices such as natural language processing, handwriting recognition, and computer vision.
Valiant’s work created several new subfields of theoretical computer science, and developed models for parallel computing.
More famously he was behind many of the mathematical foundations of computer learning, an area of study that has led to breakthroughs such as IBM Corp.’s Watson, the machine built to play “Jeopardy!”
In 1984 he published a paper called the “Theory of the Learnable,” in Communications of the ACM. It turned out to be a best seller as it put machine learning on a sound mathematical footing.
It also created a new area for research area known as Computational Learning Theory. In the article Valiant worked out a general framework and a few concrete computational models.
His method of “Probably Approximately Correct” (PAC) learning has become a standard model for studying the learning process and has been adopted by the sub-editors of TechEye.
Building on Valient’s work, other boffins have developed algorithms that adapt their behaviour in response to feedback from the environment.