Latest news with #Barto
Axios
05-03-2025
- Science
- Axios
Pioneers of reinforcement learning named Turing award winners
This year's Turing Award — often called the Nobel Prize of computer science — is going to Andrew Barto and Richard Sutton, the pioneers of a key approach that underlies much of today's artificial intelligence. Why it matters: Reinforcement learning, as the technique is known, posits that computers can learn from their own experiences, using a system of rewards similar to how researchers have trained animals. In a joint interview, Barto and Sutton said the award is extremely rewarding, especially given that for much of their career, the technology they pursued was out of vogue. "When we started, it was extremely unfashionable to do what we were doing," Barto told Axios. "It had been dismissed, actually, by many people." "There were periods of time when I could not get funding because I was not doing the current fashionable topic, and I wasn't going to change to what was fashionable," he said. Sutton added that it was "particularly gratifying" to be given this award since it was Alan Turing who proposed the notion of computers learning from their own experiences in a 1950s paper, though it would take decades for there to be enough computing power to test out the notion. Catch up quick: Sutton, now a computer science professor at Canada's University of Alberta, was Barto's student at the University of Massachusetts in the late 1970s. Throughout the 1980s, the pair wrote a series of influential papers, culminating in their seminal 1998 textbook: "Reinforcement Learning: An Introduction," which has been cited in more than 70,000 academic papers. The approach finally gained prominence in the last decade as DeepMind's AlphaGo began to defeat human players. Reinforcement learning from human feedback is a key method for the training of large language models, while the approach has also proven useful in everything from programming robots to automating chip design. What they're saying: Google's Jeff Dean said reinforcement learning has been central to the advancement of modern AI. "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." Google funds the $1 million prize given each year to the Turing Award winners. What's next: Both Sutton and Barto believe that current fears about AI are overblown, though they acknowledge that highly intelligent systems could cause significant upheaval as society adjusts. Sutton said he sees AGI as the chance to introduce new "minds" into the world without having them develop biologically, through evolution. "I think it's a pivotal moment for our planet," Sutton said. Barto echoed that cautious optimism: "I think there's a lot of opportunity for these systems to improve many aspects of our life and society, assuming sufficient caution is taken."
Yahoo
05-03-2025
- Science
- Yahoo
AI pioneers who channeled 'hedonistic' machines win computer science's top prize
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 'hedonistic' machines that could continuously adapt their behavior in response to positive signals. See for yourself — The Yodel is the go-to source for daily news, entertainment and feel-good stories. By signing up, you agree to our Terms and Privacy Policy. Reinforcement learning is what led a Google computer program to beat the world's best human players of the ancient Chinese board game Go in 2016 and 2017. It's also been a key technique in improving popular AI tools like ChatGPT, optimizing financial trading and helping a robotic hand solve a Rubik's Cube. But 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 it's 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 Google's 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 — the large language models behind chatbots made by OpenAI, Google and other tech giants that mimic human writing and other media. 'The big choice is, do you try to learn from people's data, or do you try to learn from an (AI) agent's own life and its own experience?' Sutton said. Sutton has dismissed what he describes as overblown concerns about AI's threat to humanity, while Barto disagreed and said 'You have to be cognizant of potential unexpected consequences.' 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 — an idea sometimes known as posthumanism. 'People are machines. They're amazing, wonderful machines,' but they are also not the 'end product' and could work better, Sutton said. 'It's intrinsically a part of the AI enterprise,' Sutton said. 'We're trying to understand ourselves and, of course, to make things that can work even better. Maybe to become such things.'
The Hill
05-03-2025
- Science
- The Hill
AI pioneers who channeled ‘hedonistic' machines win computer science's top prize
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 'hedonistic' machines that could continuously adapt their behavior in response to positive signals. Reinforcement learning is what led a Google computer program to beat the world's best human players of the ancient Chinese board game Go in 2016 and 2017. It's also been a key technique in improving popular AI tools like ChatGPT, optimizing financial trading and helping a robotic hand solve a Rubik's Cube. But 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 it's 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 Google's 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 — the large language models behind chatbots made by OpenAI, Google and other tech giants that mimic human writing and other media. 'The big choice is, do you try to learn from people's data, or do you try to learn from an (AI) agent's own life and its own experience?' Sutton said. Sutton has dismissed what he describes as overblown concerns about AI's threat to humanity, while Barto disagreed and said 'You have to be cognizant of potential unexpected consequences.' 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 — an idea sometimes known as posthumanism. 'People are machines. They're amazing, wonderful machines,' but they are also not the 'end product' and could work better, Sutton said. 'It's intrinsically a part of the AI enterprise,' Sutton said. 'We're trying to understand ourselves and, of course, to make things that can work even better. Maybe to become such things.'
Associated Press
05-03-2025
- Science
- Associated Press
AI pioneers who channeled ‘hedonistic' machines win computer science's top prize
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 'hedonistic' machines that could continuously adapt their behavior in response to positive signals. Reinforcement learning is what led a Google computer program to beat the world's best human players of the ancient Chinese board game Go in 2016 and 2017. It's also been a key technique in improving popular AI tools like ChatGPT, optimizing financial trading and helping a robotic hand solve a Rubik's Cube. But 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 it's 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 Google's 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 — the large language models behind chatbots made by OpenAI, Google and other tech giants that mimic human writing and other media. 'The big choice is, do you try to learn from people's data, or do you try to learn from an (AI) agent's own life and its own experience?' Sutton said. Sutton has dismissed what he describes as overblown concerns about AI's threat to humanity, while Barto disagreed and said 'You have to be cognizant of potential unexpected consequences.' 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 — an idea sometimes known as posthumanism. 'People are machines. They're amazing, wonderful machines,' but they are also not the 'end product' and could work better, Sutton said. 'It's intrinsically a part of the AI enterprise,' Sutton said. 'We're trying to understand ourselves and, of course, to make things that can work even better. Maybe to become such things.'
The Independent
05-03-2025
- Science
- The Independent
AI pioneers who channeled 'hedonistic' machines win computer science's top prize
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 'hedonistic' machines that could continuously adapt their behavior in response to positive signals. Reinforcement learning is what led a Google computer program to beat the world's best human players of the ancient Chinese board game Go in 2016 and 2017. It's also been a key technique in improving popular AI tools like ChatGPT, optimizing financial trading and helping a robotic hand solve a Rubik's Cube. But 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 it's 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 Google's 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 — the large language models behind chatbots made by OpenAI, Google and other tech giants that mimic human writing and other media. 'The big choice is, do you try to learn from people's data, or do you try to learn from an (AI) agent's own life and its own experience?' Sutton said. Sutton has dismissed what he describes as overblown concerns about AI's threat to humanity, while Barto disagreed and said 'You have to be cognizant of potential unexpected consequences.' 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 — an idea sometimes known as posthumanism. ' People are machines. They're amazing, wonderful machines,' but they are also not the 'end product' and could work better, Sutton said. 'It's intrinsically a part of the AI enterprise,' Sutton said. 'We're trying to understand ourselves and, of course, to make things that can work even better. Maybe to become such things.'
