A Harvard University professor has been awarded a top technology prize for research that has paved the way for computers that more closely mimic how humans think, including the one that won a “Jeopardy!” tournament.
Leslie Valiant, who teaches computer science and applied mathematics at Harvard’s School of Engineering and Applied Sciences, was awarded the A.M. Turing Award for 2010, the Association for Computing Machinery said Wednesday. The $250,000 award is considered the Nobel Prize of computing and is named after the famous British mathematician Alan M. Turing.
Some of Valiant’s biggest contributions concern 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 matches aired last month, the computer breezed past two of the game show’s top winners in a display of how far computer scientists have come in programming computers to understand the subtleties of human language and make decisions based on the mountains of data the machines are able to store.
The association cited contributions that have led to advances in artificial intelligence and areas such as natural language processing, handwriting recognition and computer vision. It also cited his influential models for “parallel computing,” or processing many different kinds of data at once rather than the one-at-a-time approach of traditional computing.
Intel Corp., the world’s biggest computer chip maker, and Google Inc., the Internet search leader, provide funding for the prize.
ACM President Alain Chesnais said Valiant’s accomplishments over the past 30 years have led to “extraordinary achievements” in machine learning.
“His work has produced modeling that offers computationally inspired answers on fundamental questions like how the brain ‘computes,'” Chesnais said. “His profound vision in computer science, mathematics, and cognitive theory have been combined with other techniques to build modern forms of machine learning and communication, like IBM’s ‘Watson’ computing system, that have enabled computing systems to rival a human’s ability to answer questions.”
The organization cited Valiant’s “Theory of the Learnable,” published in 1984 in Communications of the Association for Computing Machinery, as one of the “seminal contributions to machine learning.” His 1982 paper, “A Scheme for Fast Parallel Communication,” offered a simple solution to data congestion when computers communicate over networks with limited capacity.
Highlights of Valiant’s work, as noted by ACM:
- Computational Learning Theory
Valiant’s “Theory of the Learnable,” published in 1984 in Communications of the ACM, is considered one of the seminal contributions to machine learning. It put machine learning on a sound mathematical footing and laid the foundations for a new research area known as Computational Learning Theory. He provided a general framework as well as concrete computational models, and his approach of “Probably Approximately Correct” (PAC) learning has become a standard model for studying the learning process. His work has led to the development of algorithms that adapt their behavior in response to feedback from the environment. Mainstream research in AI has embraced his viewpoint as a critical tool for designing intelligent systems.
- Algebraic Computation Theory
One of Valiant’s key contributions to computational complexity was his work on the complexity of enumeration problems. Its impact was to show the inherent difficulty in counting the number of solutions not just to computationally hard problems, but also to those whose decision complexity is relatively “easy.” This work led to the theory of algebraic computation, which established a framework for understanding which algebraic formulas can be evaluated efficiently. Valiant also introduced the class #P and proved the permanent to be complete for this class. His paper “Completeness Classes in Algebra,” published in 1979, brought algebraic techniques into the toolbox of theoretical computer science and his work on #P set the stage for the development of interactive proofs for problems beyond NP.
- Development of Models for Parallel Computing
Valiant’s insight into the theory of parallel and distributed computing offers another broad area of important contributions. His 1982 paper “A Scheme for Fast Parallel Communication” described a simple parallel routing scheme that offered a solution to congestion problems, which occur when multiple computers try to communicate over networks with limited capacity. He also introduced the “bulk synchronous parallel” (BSP) computing model, which describes different types of multiprocessor computers based on how efficiently they synchronize and communicate internally. The BSP model explains why the performance of an algorithm may vary between different parallel computers.
- Recent Research Focus
More recently, Valiant has contributed to computational neuroscience, offering a concrete mathematical model of the brain and relating its architecture to complex cognitive functions. In his 1994 book Circuits of the Mind,he details a promising new computational approach to studying the intricate workings of the human brain. It focuses on the brain’s ability to quickly access a massive store of accumulated information during reasoning processes despite the extreme constraints imposed by the brain’s finite number of neurons, their limited speed of communication, and their restricted interconnectivity. The book offers a new approach to brain science for students and researchers in computer science, neurobiology, neurosciences, artificial intelligence, and cognitive science.
The award will be presented June 4 at a ceremony in San Jose, Calif.
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