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Awards & Recognition

[Andrew Barto and Richard Sutton Receive 2024 ACM A.M. Turing Award](https://awards.acm.org/about/2024-turing)

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[Andrew G. Barto](https://awards.acm.org/award-recipients/barto_9471663) and [Richard S. Sutton](https://awards.acm.org/award-recipients/sutton_0160594) received the 2024 ACM A.M. Turing Award for deve loping 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—one of the most important approaches for creating intelligent systems. Barto is Professor Emeritus of Information and Computer Sciences at the University of Massachusetts, Amherst. Sutton is a Professor of Computer Science at the University of Alberta, a Research Scientist at Keen Technologies, and a Fellow at Amii (Alberta Machine Intelligence Institute). 

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ACM is aware that many in our community are concerned about potential negative effects of recent Executive Orders by the new US Administration on our work in both the US and globally. This includes concerns about p ossible consequences for ACM publishing, conferences, education, and practitioner efforts, as well as concerns about possible harms to our work on inclusion for a strong and diverse technology workforce.

ACM reaffirms its commitment to our [mission and core values](/about-acm/mission-vision-values-goals) in all our scientific and educational activities. This includes global scientific and educational efforts dedicated to advancing the art, science, engineering, and application of computing. We will continue to serve both professional and public interests by fostering the open exchange of information and by promoting the highest professional and ethical standards.

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[CACM Relaunched as Open Access, Web-First Publication](cacm.acm.org/)

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ACM has relaunched _Communications of the ACM_ (CACM) as a web-first publication, accessible to all without charge—including the entire backlog of CACM articles. First published in 1958, CACM is one of the most respected information technology magazines. The web-first model will allow ACM to publish articles more rapidly than before so that readers can keep abreast of the lightning-fast changes in the computing field. At the same time, researchers will be able to reference and cite valuable information and research from CACM articles more quickly. This marks another important milestone in ACM's ongoing transition to a fully open access publisher.

[Prevalence and Prevention of Large Language Model Use in Crowd Work](https://cacm.acm.org/research/prevalence-and-prevention-of-large-language-model-use-in-crowd-work/)

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Crowd work platforms such as Prolific and Amazon play an important part in academia and industry, empowering the creation, annotation, and summarization of data, as well as surveys and experiments. At the same time, large language models (LLMs), such as ChatGPT promise similar capabilities. Yet, if crowd workers were to start using LLMs, this could threaten the validity of data generated using crowd work platforms. In this article from the March issue of _Communications of the ACM_, Veniamin Veselovsky et al. ask: How can we—and should we—prevent LLM use in crowd work?

For Practitioners

[Program Merge: What's Deep Learning Got to Do with It?](https://queue.acm.org/detail.cfm?id=3688155)

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If you regularly work with open-source code or produce software for a large organization, you're already familiar with many of the challenges posed by collaborative programming at scale. And the scale of the problem has gotten much worse. This is what led a group of researchers at MSR (Microsoft Research) to take on the task of complicated merges as a grand program-repair challenge—one they believed might be addressed at least in part by machine learning. To understand the thinking that led to this effort and then follow where that led, Erik Meijer and Terry Coatta spoke with three of the leading figures in the MSR research effort, called DeepMerge

[](https://queue.acm.org/detail.cfm?id=3688155)

[](https://queue.acm.org/detail.cfm?id=3688088)

For Practitioners

[Deterministic Record-and-Replay](https://queue.acm.org/detail.cfm?id=3688088)

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ACM Queue’s "Research for Practice" serves up expert-curated guides to the best of computing research, and relates these breakthroughs to the challenges that software engineers face every day. In this installment, Research for Practice covers the topic of deterministic record-and-replay. Deterministic record-and-replay technologies enable a faithful re-execution of a program that ran in the past. But accomplishing this requires that any nondeterministic inputs to the program be logged during execution. The selection of techniques presented here is curated by Andrew Quinn, Assistant Professor of Computer Science and Engineering at UC Santa Cruz.

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[ACM Code of Ethics](/code-of-ethics)

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The [ACM Code of Ethics and Professional Conduct](/code-of-ethics) was updated in 2018 to address the significant advances in computing technology since the 1992 version, as well as the growing pervasiveness of computing in all aspects of society.

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