ACM A.M. Turing Award
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The ACM A.M. Turing Award is an annual prize given by the Association for Computing Machinery (ACM) for, in the words of the official citation, "contributions of lasting and major technical importance to the computer field."[4] Widely described as the "Nobel Prize of Computing," it is generally considered the highest distinction in computer science and has carried a US$1 million prize, funded entirely by Google, since 2014.[4][5] First given in 1966, it is named after British mathematician Alan Turing, a founder of theoretical computer science and of the conceptual study of artificial intelligence.[19] Two of its awards stand out for AI: the 2018 "deep learning Turing Award" to Yoshua Bengio, Geoffrey Hinton, and Yann LeCun, and the 2024 award announced on 5 March 2025 to Andrew Barto and Richard Sutton "for developing the conceptual and algorithmic foundations of reinforcement learning."[2][3]
Roughly a dozen of its laureates have been recognised primarily for work on artificial intelligence, including Marvin Minsky, John McCarthy, Allen Newell and Herbert A. Simon, Edward Feigenbaum and Raj Reddy, Leslie Valiant, Judea Pearl, Yoshua Bengio, Geoffrey Hinton, Yann LeCun, and most recently Andrew Barto and Richard Sutton, who shared the 2024 award for foundational work on reinforcement learning.[3]
The ACM established the award in 1966 to recognise outstanding technical contributions to the computing community.[5] The first recipient was Alan J. Perlis, cited "for his influence in the area of advanced computer programming techniques and compiler construction."[5] The award is named in honour of Alan Mathison Turing (1912 to 1954), the British mathematician whose 1936 paper "On Computable Numbers, with an Application to the Entscheidungsproblem" introduced the abstract Turing machine, the canonical model of general-purpose computation.[19] Turing also led the codebreaking effort against the German Enigma cipher at Bletchley Park during the Second World War, and his 1950 essay "Computing Machinery and Intelligence" proposed what became known as the Turing test, an early framing of how one might decide whether a machine "thinks."[19] Naming the highest prize in computing after Turing acknowledges all three of these strands: foundations, practical engineering, and the long-running question of machine intelligence.
For much of its history the cash component of the Turing Award was modest, and the recognition itself was the main reward. Intel began funding the award in 2002, and in 2007 Google joined and the prize was raised to US$250,000.[4][5] On 13 November 2014 the ACM announced that the prize would increase to US$1 million, four times its previous level, with the entire amount funded by Google.[4] That figure has remained the standard prize ever since.
| Period | Prize amount | Sponsor(s) |
|---|---|---|
| 1966 to 2006 | Modest cash component, varied over the years | ACM |
| 2007 to 2013 | US$250,000 | Intel and Google |
| 2014 to present | US$1,000,000 |
Nominations for the Turing Award are submitted through the official site at amturing.acm.org, and the ACM Awards Committee evaluates the nominations and selects a recipient.[4] The award is generally given each year to one individual or to a small group whose work is closely related, with at most four recipients sharing any single year.[4] In practice, sharing has usually been by two or three people, as in the 1975 Newell and Simon award, the 1994 Feigenbaum and Reddy award, the 2018 Bengio, Hinton, and LeCun award, and the 2024 Barto and Sutton award.[5]
Nominations remain open year-round, and the recipient is normally announced in early spring of the following calendar year. The 2024 award, for example, was announced on 5 March 2025.[3]
A significant fraction of all Turing Awards have gone to researchers whose primary contributions are in artificial intelligence. The table below lists the AI-focused laureates, the year of their award, the official ACM citation, and a brief note on the work being recognised. For shared awards the citation is shown once and the contribution column describes both recipients.
| Year | Laureate(s) | Affiliation at time of award | Official citation (abridged) | Notes on the contribution |
|---|---|---|---|---|
| 1969 | Marvin Minsky | MIT | "For his central role in creating, shaping, promoting, and advancing the field of artificial intelligence." | Co-founder of the MIT AI Laboratory; built the SNARC neural learning machine in 1951; co-author of the 1969 book Perceptrons; later developed the theory of frames and the Society of Mind model of cognition.[6] |
| 1971 | John McCarthy | Stanford University | Award and lecture, "The Present State of Research on Artificial Intelligence," recognising his contributions to AI. | Coined the term "artificial intelligence" in the proposal for the 1956 Dartmouth Workshop; designed the Lisp programming language; introduced ideas such as time-sharing, garbage collection, and formal common-sense reasoning.[7] |
| 1975 | Allen Newell and Herbert A. Simon | Carnegie Mellon University | "For basic contributions to artificial intelligence, the psychology of human cognition, and list processing." | With Cliff Shaw, designed the Logic Theorist (1956) and the General Problem Solver (1957), and the Information Processing Language (IPL). Simon also received the 1978 Nobel Memorial Prize in Economics for the theory of bounded rationality.[8][9] |
| 1994 | Edward Feigenbaum and Raj Reddy | Stanford University and Carnegie Mellon University | "For pioneering the design and construction of large scale artificial intelligence systems, demonstrating the practical importance and potential commercial impact of artificial intelligence technology." | Feigenbaum led DENDRAL (chemical structure inference) and was the central figure behind expert systems such as MYCIN. Reddy founded the CMU Robotics Institute and led major speech recognition projects including Hearsay-II and Sphinx. Reddy was the first person of Asian heritage to receive the Turing Award.[10][11] |
| 2010 | Leslie Valiant | Harvard University | "For transformative contributions to the theory of computation, including the theory of probably approximately correct (PAC) learning, the complexity of enumeration and of algebraic computation, and the theory of parallel and distributed computing." | Valiant's 1984 paper introduced PAC learning, the foundational framework of computational learning theory and one of the conceptual pillars on which all modern machine learning theory rests.[12] |
| 2011 | Judea Pearl | UCLA | "For fundamental contributions to artificial intelligence through the development of a calculus for probabilistic and causal reasoning." | Developed Bayesian networks in the 1980s; later created the do-calculus and the structural causal model framework that turned causal inference into a formal mathematical discipline.[13] |
| 2018 | Yoshua Bengio, Geoffrey Hinton, and Yann LeCun | Université de Montréal / Mila, University of Toronto and Google, NYU and Facebook AI Research | "For conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing." | Often called the "deep learning Turing Award." The three are commonly referred to as the "Godfathers of AI." Hinton co-authored the 1986 backpropagation paper with Rumelhart and Williams, championed deep belief networks and dropout regularisation, and supervised the 2012 AlexNet system that triggered the deep learning boom. LeCun developed the convolutional neural network architecture and the LeNet system for handwritten digit recognition at Bell Labs in the 1990s. Bengio's group developed neural language models, sequence-to-sequence learning with attention, and was a critical participant in the development of generative adversarial networks (GANs) with student Ian Goodfellow.[2] |
| 2024 | Andrew Barto and Richard Sutton | UMass Amherst (emeritus) and University of Alberta | "For developing the conceptual and algorithmic foundations of reinforcement learning." | Beginning in the 1980s the two introduced temporal-difference learning, actor-critic methods, and the modern formalisation of reinforcement learning as Markov decision processes solved by sample-based learning. Their textbook Reinforcement Learning: An Introduction (1st edition 1998, 2nd edition 2018) is the standard reference and has been cited more than 75,000 times.[1] |
One striking pattern in the table is the gap between 1975 and 1994. After the 1975 award to Newell and Simon there was a 19-year stretch with no AI-focused laureate, which roughly coincides with the second AI winter of the late 1970s and 1980s. The Feigenbaum and Reddy award in 1994 marked the field's return to ACM recognition, but no further AI-focused awards were given until Valiant in 2010. From 2010 onward the cadence has been very different: Valiant 2010, Pearl 2011, the Hinton, LeCun, and Bengio award in 2018, and the Barto and Sutton award in 2024.[5] Together these four awards reflect the maturation of statistical learning theory, probabilistic reasoning, deep learning, and reinforcement learning as central pieces of mainstream computer science.
Several Turing Award winners outside the explicit AI list above have produced work that the AI community uses every day. The list below covers the most relevant of these.
| Year | Laureate | Citation focus | Why AI cares |
|---|---|---|---|
| 1972 | Edsger W. Dijkstra | Programming as an intellectual discipline; structured programming. | Dijkstra's shortest-path algorithm is a basic building block of search and planning, and his style of program reasoning shaped how AI systems are written. |
| 1974 | Donald Knuth | Analysis of algorithms and design of programming languages. | The Art of Computer Programming and the analysis of asymptotic complexity underlie almost every algorithmic statement made in machine learning. |
| 1982 | Stephen Cook | Founding the theory of NP-completeness. | NP-hardness arguments are the standard tool for explaining why exact inference, exact training, and exact decoding in many AI models are intractable, and why we resort to approximations. |
| 1986 | John Hopcroft and Robert Tarjan | Design and analysis of algorithms and data structures. | Their work on graph algorithms is the basis for many large-scale ML pipelines, including connected components, dominators, and various dataflow analyses used in compilers for ML frameworks. |
| 1989 | William Kahan | Numerical analysis and floating-point arithmetic, including the IEEE 754 standard. | Stable, well-defined floating-point arithmetic is what makes large-scale neural network training reproducible at all. The current era of mixed-precision GPU training rests directly on his work. |
| 1995 | Manuel Blum | Foundations of computational complexity theory and applications to cryptography and program checking. | The notion of an interactive proof and program checking influenced verification of learned models and the modern theory of zero-knowledge systems used in privacy-preserving machine learning. |
These are not AI laureates in the strict sense, but they belong in any honest map of how the Turing Award has shaped the science underneath modern AI.
The 2018 Turing Award is sometimes simply called the "deep learning Turing Award." Its citation, "for conceptual and engineering breakthroughs that have made deep neural networks a critical component of computing," reads as a deliberate attempt to capture both the science and the engineering of the wave of progress that followed the 2012 AlexNet result on ImageNet.[2] In its announcement the ACM stated that "working independently and together, Hinton, LeCun and Bengio developed conceptual foundations for the field, identified surprising phenomena through experiments, and contributed engineering advances that demonstrated the practical advantages of deep neural networks."[2]
Geoffrey Hinton, born in London in 1947 and based at the University of Toronto since 1987, was one of three authors of the 1986 paper "Learning representations by back-propagating errors," which made backpropagation the standard training method for multi-layer neural networks.[14] In the 2000s, when most of the field had moved on, Hinton kept working on neural networks, introducing deep belief networks pre-trained with restricted Boltzmann machines and, later, dropout regularisation.[14] He supervised the AlexNet team of Alex Krizhevsky and Ilya Sutskever whose ImageNet entry in 2012 won the contest by a wide margin and is generally taken as the moment that started the modern deep learning era.[14]
Yann LeCun, born in France in 1960, developed the convolutional neural network architecture in the late 1980s while at Bell Labs and applied it to handwritten digit recognition in systems known collectively as LeNet.[15] By the 1990s LeNet was reading a substantial share of cheques in the United States.[15] He joined NYU in 2003 and Facebook AI Research in 2013, where he served as Chief AI Scientist before stepping into the role of Executive Chair at his own startup focused on world models in 2025.[15]
Yoshua Bengio, born in Paris in 1964 and on faculty at the Université de Montréal since 1993, founded what is now the Mila institute.[16] His group introduced the neural probabilistic language model in the early 2000s, did much of the early work on sequence-to-sequence learning with attention, and was the laboratory in which Ian Goodfellow developed generative adversarial networks (GANs) in 2014.[16] Bengio became, in November 2025, the first AI researcher to surpass one million Google Scholar citations.[16]
The three are sometimes referred to collectively as the "Godfathers of AI," though it is worth noting that Jürgen Schmidhuber and others have publicly criticised the 2018 selection as understating the contributions of European labs and earlier work on long short-term memory networks and similar architectures.[2] The criticism is documented but did not change the citation.
The 2024 Turing Award, announced on 5 March 2025, went to Andrew Barto and Richard Sutton, "For developing the conceptual and algorithmic foundations of reinforcement learning."[3] The ACM described their work as introducing "the main ideas, constructing the mathematical foundations, and developing important algorithms" for reinforcement learning in a series of papers beginning in the 1980s, describing it as "one of the most important approaches for creating intelligent systems."[1]
Andrew Barto, born in 1948, joined the University of Massachusetts Amherst in 1977 as a postdoctoral researcher and was based there for the rest of his career, retiring as professor emeritus.[17] He was Sutton's PhD advisor.[17]
Richard Sutton, born around 1957 in Toledo, Ohio, took his BA in psychology from Stanford in 1978 and his MS and PhD in computer science from UMass Amherst in 1980 and 1984, both under Barto.[18] He held positions at GTE Laboratories and AT&T Labs before moving to the University of Alberta in 2003, where he was central to the founding of the Alberta Machine Intelligence Institute (Amii).[18] He spent 2017 to 2023 as a distinguished researcher at Google DeepMind, and since 2023 has been a research scientist at John Carmack's startup Keen Technologies, where he and Carmack are pursuing artificial general intelligence, alongside his Alberta professorship.[1][18]
The specific contributions cited by ACM include temporal-difference (TD) learning, the actor-critic architecture, policy-gradient methods, and the use of neural networks as function approximators within reinforcement learning.[1] Sutton also developed the options framework for temporal abstraction and contributed to the early formalisation of policy gradients.[18] Their textbook Reinforcement Learning: An Introduction (MIT Press, 1998; 2nd edition 2018) is the standard reference for the field, with more than 75,000 citations.[1]
The 2024 citation explicitly notes that the modern wave of practical RL applications, including DeepMind's AlphaGo victory over Lee Sedol in 2016 and over Ke Jie in 2017, came from combining the Barto and Sutton framework with the deep learning techniques recognised in the 2018 award.[20] In that sense, the two awards are deliberately complementary.
The AI-focused Turing Awards have been clustered in a small number of universities and research labs. The table below sorts the AI laureates by their primary affiliation.
| Institution | AI-focused Turing laureates |
|---|---|
| MIT | Marvin Minsky (1969) |
| Stanford University | John McCarthy (1971), Edward Feigenbaum (1994) |
| Carnegie Mellon University | Allen Newell (1975), Herbert A. Simon (1975), Raj Reddy (1994) |
| University of California, Los Angeles (UCLA) | Judea Pearl (2011) |
| Harvard University | Leslie Valiant (2010) |
| University of Toronto | Geoffrey Hinton (2018) |
| New York University | Yann LeCun (2018) |
| Université de Montréal / Mila | Yoshua Bengio (2018) |
| University of Massachusetts Amherst | Andrew Barto (2024) |
| University of Alberta | Richard Sutton (2024) |
The pattern is clear. Until 2018 the AI laureates were concentrated at a small number of US universities, with MIT, Stanford, and Carnegie Mellon dominating. The 2018 award broadened the geography to include the Université de Montréal and the University of Toronto, and the 2024 award added the University of Alberta. So far no recipient of an AI-focused Turing Award has been primarily affiliated with a research institution in Asia, the Middle East, Africa, Oceania, or South America at the time of the award.
The Turing Award is not the only major recognition that AI researchers receive. The table below compares it with several other prizes that AI laureates often hold.
| Prize | Awarding body | Focus | Cash component | Frequency |
|---|---|---|---|---|
| ACM A.M. Turing Award | Association for Computing Machinery | Lifetime contribution to computing, broadly defined | US$1,000,000 (sponsored by Google since 2014) | Annual |
| IJCAI Award for Research Excellence | International Joint Conferences on Artificial Intelligence | Sustained, high-quality AI research throughout a career | Honorary, no fixed cash prize | Roughly biennial |
| AAAI Classic Paper Award | Association for the Advancement of Artificial Intelligence | A specific paper that has had lasting impact, drawn from a single AAAI conference at least 12 years earlier | Honorary | Annual |
| IEEE Frank Rosenblatt Award | IEEE Computational Intelligence Society | Outstanding contributions to biologically and linguistically motivated computational paradigms | Honorarium plus medal | Annual |
| ACM Fellow | Association for Computing Machinery | Outstanding accomplishments in computing and information technology | Honorary | Annual class |
| Royal Society Bakerian Medal and Lecture | Royal Society (United Kingdom) | A single physical-sciences lecture, occasionally given by computing or AI researchers | Honorarium | Annual |
| Nobel Prize in Physics or Chemistry | Royal Swedish Academy of Sciences | Discoveries in physics or chemistry, including AI-enabled science | Roughly US$1.1 million per laureate share, denominated in Swedish krona | Annual |
The Nobel Prize is included for context because, in 2024, two AI-related Nobel Prizes were awarded in the same week. The Nobel Prize in Physics went to John Hopfield and Geoffrey Hinton "for foundational discoveries and inventions that enable machine learning with artificial neural networks," and the Nobel Prize in Chemistry went one half to David Baker "for computational protein design" and one half jointly to Demis Hassabis and John M. Jumper "for protein structure prediction" with AlphaFold2.[22] The Nobel awards are not Turing Awards, but they amount to a striking outside endorsement of the same line of work that the 2018 and 2024 Turing Awards recognised, and they have become part of the broader context for any modern discussion of the Turing Award. See also the article on the AI Nobel Prize discussion.
Over its full history, the Turing Award has gone overwhelmingly to male researchers based in the United States, Canada, and Europe. The first woman to receive it was Frances Allen in 2006 for work on optimising compilers, more than four decades after the prize was created.[5] Barbara Liskov in 2008 and Shafi Goldwasser in 2012 are the other two women among the laureates as of the 2024 award.[5] None of the three were recognised primarily for AI work, although Goldwasser's foundational results on cryptographic proofs underpin modern privacy-preserving machine learning.
The geographic concentration noted earlier extends to the broader award. In the 2010s and 2020s the list expanded to include researchers based in France, the United Kingdom, Israel, Canada, and Hungary, but no laureate has yet been based at an institution in mainland China, India, or Africa at the time of the award.
Within AI, several major subfields do not yet have a dedicated Turing laureate. Natural language processing in particular has produced no Turing Award recipient whose work is described primarily in NLP terms, even though the modern transformer-based language models that dominate the field are an obvious candidate for future recognition. Robotics is another area where the prize has been sparser; Raj Reddy's 1994 award is partly for robotics, but no Turing Award has gone specifically to autonomous robotics or to control theory in robotic systems.
The US$1 million prize was a significant raise in 2014 and remains a substantial sum, but it is now small relative to the valuations that the AI industry attaches to comparable work.[4] Compensation packages for senior research scientists at frontier AI labs and the equity outcomes from AI startup acquisitions have moved well past the Turing prize amount. The prize remains the most prestigious individual recognition in computing, but its monetary component is no longer the differentiator it briefly was.
Every Turing Award laureate is expected to deliver a Turing Lecture, which is published by ACM and is one of the most useful records of how the laureate sees the trajectory of their field.[5] McCarthy's 1971 lecture "The Present State of Research on Artificial Intelligence" and Pearl's 2011 lecture on "The Mechanization of Causal Inference: A Mini-Tutorial" are two examples that are still widely cited as primary sources on the state of AI at the time of the award.
The 2024 calendar year stands out for AI honours. In March 2025 ACM announced the Turing Award for Andrew Barto and Richard Sutton, recognising the foundations of reinforcement learning.[3] In October 2024 the Royal Swedish Academy of Sciences awarded the Nobel Prize in Physics jointly to John Hopfield and Geoffrey Hinton for foundational work that enables machine learning with artificial neural networks, and the Nobel Prize in Chemistry to David Baker, Demis Hassabis, and John Jumper for computational protein design and protein structure prediction.[22] With Hinton holding both the 2018 Turing Award and the 2024 Nobel Prize in Physics, AI now has a single individual who holds both of the most prestigious awards in computing and physics.
Whether this concentration of recognition signals a temporary spike or the beginning of a sustained run of AI-related top-tier prizes is, honestly, unclear. The pattern of Turing Awards from 2010 onward suggests that the ACM has decided AI is one of the central stories of contemporary computing and intends to recognise its leading figures with some regularity. The Nobel committees have shown they are willing to do the same when the science is closely tied to physics or chemistry.
For reference, the table below lists every recipient of the ACM A.M. Turing Award from 1966 to 2025. AI-focused awards are marked with a dot (so a quick eye scan can pick them out).
| Year | Recipient(s) | AI-focused? |
|---|---|---|
| 1966 | Alan J. Perlis | |
| 1967 | Maurice V. Wilkes | |
| 1968 | Richard W. Hamming | |
| 1969 | Marvin Minsky | yes |
| 1970 | James H. Wilkinson | |
| 1971 | John McCarthy | yes |
| 1972 | Edsger W. Dijkstra | |
| 1973 | Charles W. Bachman | |
| 1974 | Donald E. Knuth | |
| 1975 | Allen Newell, Herbert A. Simon | yes |
| 1976 | Michael O. Rabin, Dana S. Scott | |
| 1977 | John W. Backus | |
| 1978 | Robert W. Floyd | |
| 1979 | Kenneth E. Iverson | |
| 1980 | C. Antony R. Hoare | |
| 1981 | Edgar F. Codd | |
| 1982 | Stephen A. Cook | |
| 1983 | Ken Thompson, Dennis M. Ritchie | |
| 1984 | Niklaus Wirth | |
| 1985 | Richard M. Karp | |
| 1986 | John Hopcroft, Robert E. Tarjan | |
| 1987 | John Cocke | |
| 1988 | Ivan Sutherland | |
| 1989 | William Kahan | |
| 1990 | Fernando J. Corbató | |
| 1991 | Robin Milner | |
| 1992 | Butler W. Lampson | |
| 1993 | Juris Hartmanis, Richard E. Stearns | |
| 1994 | Edward Feigenbaum, Raj Reddy | yes |
| 1995 | Manuel Blum | |
| 1996 | Amir Pnueli | |
| 1997 | Douglas Engelbart | |
| 1998 | Jim Gray | |
| 1999 | Frederick P. Brooks Jr. | |
| 2000 | Andrew Chi-Chih Yao | |
| 2001 | Ole-Johan Dahl, Kristen Nygaard | |
| 2002 | Ronald L. Rivest, Adi Shamir, Leonard M. Adleman | |
| 2003 | Alan Kay | |
| 2004 | Vinton G. Cerf, Robert E. Kahn | |
| 2005 | Peter Naur | |
| 2006 | Frances E. Allen | |
| 2007 | Edmund M. Clarke, E. Allen Emerson, Joseph Sifakis | |
| 2008 | Barbara Liskov | |
| 2009 | Charles P. Thacker | |
| 2010 | Leslie Valiant | yes |
| 2011 | Judea Pearl | yes |
| 2012 | Shafi Goldwasser, Silvio Micali | |
| 2013 | Leslie Lamport | |
| 2014 | Michael Stonebraker | |
| 2015 | Whitfield Diffie, Martin E. Hellman | |
| 2016 | Tim Berners-Lee | |
| 2017 | John L. Hennessy, David A. Patterson | |
| 2018 | Yoshua Bengio, Geoffrey Hinton, Yann LeCun | yes |
| 2019 | Edwin E. Catmull, Patrick M. Hanrahan | |
| 2020 | Alfred V. Aho, Jeffrey D. Ullman | |
| 2021 | Jack J. Dongarra | |
| 2022 | Robert M. Metcalfe | |
| 2023 | Avi Wigderson | |
| 2024 | Andrew G. Barto, Richard S. Sutton | yes |
| 2025 | Charles H. Bennett, Gilles Brassard |