ΑΙhub.org
 

Andrew Barto and Richard Sutton win 2024 Turing Award


by
06 March 2025



share this:

Andrew Barto and Richard Sutton. Image credit: Association for Computing Machinery.

The Association for Computing Machinery, has named Andrew Barto and Richard Sutton as the recipients of the 2024 ACM A.M. Turing Award. The pair have received the honour for “developing the conceptual and algorithmic foundations of reinforcement learning”. In a series of papers beginning in the 1980s, Barto and Sutton introduced the main ideas, constructed the mathematical foundations, and developed important algorithms for reinforcement learning.

The Turing Award comes with a $1 million prize, to be split between the recipients. Since its inception in 1966, the award has honoured computer scientists and engineers on a yearly basis. The prize was last given for AI research in 2018, when Yoshua Bengio, Yann LeCun and Geoffrey Hinton were recognised for their contribution to the field of deep neural networks.

Andrew Barto is Professor Emeritus, Department of Information and Computer Sciences, University of Massachusetts, Amherst. He began his career at UMass Amherst as a postdoctoral Research Associate in 1977, and has subsequently held various positions including Associate Professor, Professor, and Department Chair. Barto received a BS degree in Mathematics (with distinction) from the University of Michigan, where he also earned his MS and PhD degrees in Computer and Communication Sciences.

Richard Sutton is a Professor in Computing Science at the University of Alberta, a Research Scientist at Keen Technologies (an artificial general intelligence company based in Dallas, Texas) and Chief Scientific Advisor of the Alberta Machine Intelligence Institute (Amii). Sutton was a Distinguished Research Scientist at Deep Mind from 2017 to 2023. Prior to joining the University of Alberta, he served as a Principal Technical Staff Member in the Artificial Intelligence Department at the AT&T Shannon Laboratory in Florham Park, New Jersey, from 1998 to 2002. Sutton received his BA in Psychology from Stanford University and earned his MS and PhD degrees in Computer and Information Science from the University of Massachusetts at Amherst.

The two researchers began collaborating in 1978, at the University of Massachusetts at Amherst, where Barto was Sutton’s PhD and postdoctoral advisor.

Find out more



tags:


AIhub is dedicated to free high-quality information about AI.
AIhub is dedicated to free high-quality information about AI.

            AIhub is supported by:



Subscribe to AIhub newsletter on substack



Related posts :

Causal models for decision systems: an interview with Matteo Ceriscioli

  21 Apr 2026
How can we integrate causal knowledge into agents or decision systems to make them more reliable?

A model for defect identification in materials

  20 Apr 2026
A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.

‘Probably’ doesn’t mean the same thing to your AI as it does to you

  17 Apr 2026
Are you sure you and the AI chatbot you’re using are on the same page about probabilities?

Interview with Xinwei Song: strategic interactions in networked multi-agent systems

  16 Apr 2026
Xinwei Song tells us about her research using algorithmic game theory and multi-agent reinforcement learning.

2026 AI Index Report released

  15 Apr 2026
Find out what the ninth edition of the report, which was published on 13 April, says about trends in AI.

Formal verification for safety evaluation of autonomous vehicles: an interview with Abdelrahman Sayed Sayed

  14 Apr 2026
Find out more about work at the intersection of continuous AI models, formal methods, and autonomous systems.

Water flow in prairie watersheds is increasingly unpredictable — but AI could help

  13 Apr 2026
In recent years, the Prairies have seen bigger swings in climate conditions — very wet years followed by very dry ones.

Identifying interactions at scale for LLMs

  10 Apr 2026
Model behavior is rarely the result of isolated components; rather, it emerges from complex dependencies and patterns.



AIhub is supported by:







Subscribe to AIhub newsletter on substack




 















©2026.02 - Association for the Understanding of Artificial Intelligence