Teaching machines in the way that animal trainers mold the behavior of dogs or horses has been an important method for developing artificial intelligence and one that was recognized Wednesday with the top computer science award.
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.
Research that Barto, 76, and Sutton, 67, began in the late 1970s paved the way for some of the past decade's AI breakthroughs. At the heart of their work was channeling so-called 鈥渉edonistic鈥� machines that could continuously adapt their behavior in response to positive signals.
Reinforcement learning is what led a Google computer program to beat the of the ancient Chinese board game Go in 2016 . It's also been a key technique in improving popular AI tools like ChatGPT, optimizing financial trading and helping a robotic hand solve a .
But Barto said the field was "not fashionable鈥� when he and his doctoral student, Sutton, began crafting their theories and algorithms at the University of Massachusetts, Amherst.
鈥淲e were kind of in the wilderness,鈥� Barto said in an interview with The Associated Press. 鈥淲hich is why it鈥檚 so gratifying to receive this award, to see this becoming more recognized as something relevant and interesting. In the early days, it was not.鈥�
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 the award named after British mathematician, codebreaker and early . But their research has directly sought to answer Turing's 1947 call for a machine that 鈥渃an learn from experience鈥� 鈥� which Sutton describes as 鈥渁rguably the essential idea of reinforcement learning.鈥�
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.
鈥淭he 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,鈥� said Google鈥檚 chief scientist Jeff Dean in a written statement.
In a joint interview with the AP, Barto and Sutton didn't always agree on how to evaluate the risks of AI agents that are constantly seeking to improve themselves. They also distinguished their work from the branch of generative AI technology that is currently in fashion 鈥� the large language models behind chatbots made by OpenAI, Google and other tech giants that mimic human writing and other media.
鈥淭he big choice is, do you try to learn from people鈥檚 data, or do you try to learn from an (AI) agent鈥檚 own life and its own experience?鈥� Sutton said.
Sutton has dismissed what he describes as overblown concerns about AI's threat to humanity, while Barto disagreed and said 鈥淵ou have to be cognizant of potential unexpected consequences.鈥�
Barto, retired for 14 years, describes himself as a Luddite, while Sutton is embracing a future he expects to have beings of greater intelligence than current humans 鈥� an idea sometimes known as posthumanism.
鈥淧eople are machines. They鈥檙e amazing, wonderful machines,鈥� but they are also not the 鈥渆nd product鈥� and could work better, Sutton said.
鈥淚t鈥檚 intrinsically a part of the AI enterprise,鈥� Sutton said. 鈥淲e鈥檙e trying to understand ourselves and, of course, to make things that can work even better. Maybe to become such things.鈥�
Matt O'brien, The Associated Press
