The 2024 Turing Award has been awarded to Andrew G. Barto, professor emeritus at the University of Massachusetts Amherst, and Richard S. Sutton, professor at the University of Alberta, for their pioneering contributions to reinforcement learning. Their foundational research has transformed artificial intelligence by enabling machines to learn through trial and error, adapting dynamically to their environments.
Beginning in the 1980s, Barto and Sutton developed key reinforcement learning algorithms, including temporal difference learning, a technique that has become integral to AI advancements. Their seminal textbook, Reinforcement Learning: An Introduction, remains a definitive resource for AI researchers and practitioners. Their work laid the groundwork for major AI breakthroughs, such as Google DeepMind’s AlphaGo, which leveraged reinforcement learning to surpass human champions, and DeepSeek’s R1 reasoning model, which uses similar techniques to improve cost-effective AI foundation models.
The Turing Award, administered by the Association for Computing Machinery (ACM), is often regarded as the “Nobel Prize of Computing.” ACM President Yannis Ioannidis highlighted that reinforcement learning continues to drive AI innovation and deepen understanding of cognitive processes. He emphasized that Barto and Sutton’s contributions are not merely historical but remain at the forefront of AI research, offering vast potential for future advancements across multiple disciplines.
Barto and Sutton join a prestigious list of past recipients, including Yann LeCun, Geoff Hinton, and Yoshua Bengio, who won in 2018 for their work on deep learning. The $1 million award, sponsored by Google, recognizes their lasting impact on AI and computational intelligence.
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