Turing Award Goes to A.I. Pioneers Andrew Barto and Richard Sutton

In 1977, Andrew Barto, as a researcher at the University of Massachusetts, Amherst, began exploring a new theory that neurons behaved like hedonists. The basic idea was that the human brain was driven by billions of nerve cells that were each trying to maximize pleasure and minimize pain.

A year later, he was joined by another young researcher, Richard Sutton. Together, they worked to explain human intelligence using this simple concept and applied it to artificial intelligence. The result was “reinforcement learning,” a way for A.I. systems to learn from the digital equivalent of pleasure and pain.

On Wednesday, the Association for Computing Machinery, the world’s largest society of computing professionals, announced that Dr. Barto and Dr. Sutton had won this year’s Turing Award for their work on reinforcement learning. The Turing Award, which was introduced in 1966, is often called the Nobel Prize of computing. The two scientists will share the $1 million prize that comes with the award.

Over the past decade, reinforcement learning has played a vital role in the rise of artificial intelligence, including breakthrough technologies such as Google’s AlphaGo and OpenAI’s ChatGPT. The techniques that powered these systems were rooted in the work of Dr. Barto and Dr. Sutton.

“They are the undisputed pioneers of reinforcement learning,” said Oren Etzioni, a professor emeritus of computer science at the University of Washington and founding chief executive of the Allen Institute for Artificial Intelligence. “They generated the key ideas — and they wrote the book on the subject.”

Their book, “Reinforcement Learning: An Introduction,” which was published in 1998, remains the definitive exploration of an idea that many experts say is only beginning to realize its potential.