Two pioneers in the field of reinforcement learning, Andrew Barto and Richard Sutton, are the winners of this year's A.M. Turing Award, the tech world's equivalent of the Nobel Prize.
Barto, 76, and Sutton, 67, began their research in the late 1970s, laying the foundation for many of the significant AI advancements in the past decade. Central to their work was the development of so-called hedonistic machines that could continuously adapt their behavior in response to positive signals.
Reinforcement learning, a key aspect of their research, was behind a Google computer program's victory over the world라이브 바카라 top human players in the ancient Chinese board game Go in 2016 and 2017. It has also played a crucial role in enhancing popular AI tools like ChatGPT, optimizing financial trading, and enabling a robotic hand to solve a Rubik라이브 바카라 Cube.
Barto said the field was not fashionable when he and his doctoral student, Sutton, began crafting their theories and algorithms at the University of Massachusetts, Amherst.
“We were kind of in the wilderness,” Barto said in an interview with The Associated Press. “Which is why it라이브 바카라 so gratifying to receive this award, to see this becoming more recognized as something relevant and interesting. In the early days, it was not.”
Google sponsors the annual $1 million prize, which was announced Wednesday by the Association for Computing Machinery.
Barto, now retired from the University of Massachusetts, and Sutton, a longtime professor at Canada's University of Alberta, aren't the first AI pioneers to win the award named after British mathematician, codebreaker and early AI thinker Alan Turing. But their research has directly sought to answer Turing's 1947 call for a machine that “can learn from experience” — which Sutton describes as “arguably the essential idea of reinforcement learning.”
In particular, they borrowed from ideas in psychology and neuroscience about the way that pleasure-seeking neurons respond to rewards or punishment. In one landmark paper published in the early 1980s, Barto and Sutton set their new approach on a specific task in a simulated world: balance a pole on a moving cart to keep it from falling. The two computer scientists later co-authored a widely used textbook on reinforcement learning.
“The tools they developed remain a central pillar of the AI boom and have rendered major advances, attracted legions of young researchers, and driven billions of dollars in investments,” said Google라이브 바카라 chief scientist Jeff Dean in a written statement.
