A Canadian pioneer in advancing artificial intelligence has earned prestigious accolades.
The Association for Computing Machinery this week named Andrew Barto and Richard Sutton as the recipients of the 2024 ACM A.M. Turing Award for developing the conceptual and algorithmic foundations of reinforcement learning.
In a series of papers beginning in the 1980s, Barto and Sutton constructed the mathematical foundations and developed important algorithms for “reinforcement learning,” a key approach to creating artificially intelligent systems.
Formerly a Distinguished Research Scientist at Deep Mind, Sutton is a Professor in Computing Science at the University of Albert, a Research Scientist at Keen Technologies in Texas, and Chief Scientific Advisor of the Alberta Machine Intelligence Institute.
Barto is Professor Emeritus of Information and Computer Sciences at the University of Massachusetts.
“Research areas ranging from cognitive science and psychology to neuroscience inspired the development of reinforcement learning, which has laid the foundations for some of the most important advances in AI and has given us greater insight into how the brain works,” stated ACM President Yannis Ioannidis.
The ACM award is named for Alan M. Turing, the British mathematician who articulated the mathematical foundations of computing, who built on the millennia-old idea of learning through rewards.
What we knew true of humans and animals, Turing then wondered about regarding computation. His iconic 1950 paper “Computing Machinery and Intelligence” boldly asked the question: “Can machines think?”
Inspired by this development, Barto and Sutton, alongside others, developed many algorithmic approaches for reinforcement learning. This included temporal difference learning, a major advance toward solving reward prediction problems.
“Reinforcement learning, as pioneered by Barto and Sutton, directly answers Turing’s challenge,” explains Jeff Dean, Chief Scientist of Google, which supports the so-called “Nobel Prize in Computing” with a $1 million prize., “Their work has been a lynchpin of progress in AI over the last several decades. 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.”
Indeed, the pair ultimately wrote the book on the topic: “Reinforcement Learning: An Introduction.” Published back in the 90’s, the book remains the standard reference in the field today and has since been cited over 75,000 times.
“Barto and Sutton’s work demonstrates the immense potential of applying a multidisciplinary approach to longstanding challenges in our field,” said Ioannidis. “Reinforcement learning continues to grow and offers great potential for further advances in computing and many other disciplines. It is fitting that we are honouring them with the most prestigious award in our field.”
Interestingly, however, Sutton himself is not currently convinced that modern artificial intelligence is “on the path to full intelligence.” A recent interview with The Logic reveals the expert believes AI mimics human behaviour, but does not truly recognize their actions and learning from mistakes.
“AI systems that you see in practice nowadays are of the sort that they absolutely do not learn when you’re interacting with them,” Sutton informed the paper. “ChatGPT doesn’t change any of its weights based on its experience … It’s not surprised by anything that happens, because it doesn’t have an expectation about what will happen.”
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