A British professor at Harvard University has won the $250,000 Turing Award for his work in machine learning and artificial intelligence.
Leslie Valiant was awarded the prize – which has been referred to as the Nobel Prize for computing – for work which has led to advances in artificial intelligence as well as natural language processing, handwriting recognition and computer vision.
He’s also launched several subfields of theoretical computer science, says the prize committee, and developed models for parallel computing. His ideas underlie the success of Watson, the IBM supercomputer which recently beat a human champion at Jeopardy.
“Leslie Valiant’s accomplishments over the last 30 years have provided the theoretical basis for progress in artificial intelligence and 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’,” says Alain Chesnais, president of the Association for Computing Machinery.
“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.”
Valiant’s ‘Theory of the Learnable‘, published in 1984, gave a sound mathematical basis to machine learning, and laid the foundations for Computational Learning Theory. His approach of ‘Probably Approximately Correct’ (PAC) learning has become a standard model for studying the learning process.
Valiant is currently the T Jefferson Coolidge Professor of Computer Science and Applied Mathematics at Harvard University’s School of Engineering and Applied Sciences.
His recent work has concerned computational neuroscience, offering a concrete mathematical model of the brain and relating its architecture to complex cognitive functions.