Communications of the ACM.

Includes bibliographical references.

To the Members of ACM -- On QR Codes and Safety -- Neighborhood Watch -- What Is Data Science? -- Post-Quantum Cryptography -- Computational Linguistics Finds Its Voice -- Can AI Demonstrate Creativity? -- Four Ways to Add Active Learning to Computing Courses -- The Elephant in the Room -- Ethical AI Is Not about AI -- Software Engineering of Machine Learning Systems -- Building Machine Learning Models like Open Source Software -- The Premature Obituary of Programming -- An Analysis of Black Faculty in CS Research Departments -- From Zero to 100 -- The Arrival of Zero Trust: What Does It Mean? -- Extracting the Essential Simplicity of the Internet -- (Re)Use of Research Results (Is Rampant) -- HPC Forecast: Cloudy and Uncertain -- The Lean Data Scientist: Recent Advances toward Overcoming the Data Bottleneck -- Beautiful Symbolic Abstractions for Safe and Secure Machine Learning -- Proving Data-Poisoning Robustness in Decision Trees --Aftermath Impact.

[Article Title: To the Members of ACM/ Yannis Ioannidis, p. 5]

https://doi.org/10.1145/357864

[Article Title: On QR Codes and Safety/ Vinton G. Cerf, p. 7]

https://doi.org/10.1145/3578891

[Article Title: Neighborhood Watch/ CACM Staff, p. 8-11]

[Article Title: What is Data Science?/ Koby Mike, and Orit Hazzan, p. 12-13]

Abstract: The Communications website, https://cacm.acm.org, features more than a dozen bloggers in the BLOG@CACM community. In each issue of Communications, we'll publish selected posts or excerpts.

https://doi.org/10.1145/3575663

[Article Title: Post-Quantum Cryptography/Don Monroe, p. 15-17]

Abstract: Cryptographers seek algorithms quantum computers cannot break.

https://doi.org/10.1145/3575664

[Article Title: Computational Linguistics Finds its Voice/Samuel Greengard, p. 18-20]

Abstract: Advances in artificial intelligence permit computers to converse with humans in seemingly realistic ways.

https://doi.org/10.1145/3575666

[Article Title: Can AI Demonstrate Creativity?/ Keith Kirkpatrick, p. 21-23]

Abstract: When fed a sufficient amount of training data, artificial intelligence techniques can be used to generate new ideas in several different ways. Is that creativity?

https://doi.org/10.1145/3575665

[Article Title: Four Ways to Add Active Learning to Computing Courses/ Barbara Ericson, p. 26-29]

https://doi.org/10.1145/3576930

[Article Title: The Elephant in the Room/ George V. Neville-Neil, p. 30-31]

https://doi.org/10.1145/3576931

[Article Title: Ethical AI is Not about AI/ Deborah G. Johnson, and Mario Verdicchio, p. 32-34]

https://doi.org/10.1145/3576932

[Article Title: Software Engineering of Machine Learning Systems/ Charles Isbell, Michael L. Littman, and Peter Norvig, p. 35-37]

https://doi.org/10.1145/3539783

[Article Title: Building Machine Learning Models Like Open Source Software/ Colin Raffel, p. 38-40]

https://doi.org/10.1145/3545111

[Article Title: The Premature Obituary of Programming/ Daniel M. Yellin, p. 41-44]

https://doi.org/10.1145/3555367

[Article Title: An Analysis of Black Faculty in CS Research Departments/ Juan E. Gilbert, Jeremy A. Magruder Waisome, and Simone Smarr, p. 45-47]

https://doi.org/10.1145/3571279

[Article Title: From Zero to 100/ Matthew Bush, and Atefeh Mashatan, p. 48-55]

https://doi.org/10.1145/3573127

[Article Title: The Arrival of Zero Trust: What Does it Mean?/ Michael Loftus, Andrew Vezina, Rick Doten, and Atefeh Mashatan, p. 56-62]

https://doi.org/10.1145/3573129

[Article Title: Extracting the Essential Simplicity of the Internet/ James Mccauley, Scott Shenker, and George Varghese, p. 64-74]

https://doi.org/10.1145/3547137

[Article Title: (Re)Use of Research Results (Is Rampant)t/ Maria Teresa Baldassarre, Neil Ernst, Ben Hermann, Tim Menzies, abd Rahul Yedida, p. 75-81]

https://doi.org/10.1145/3554976

[Article Title: HPC Forecast: Cloudy and Uncertain/ Daniel Reed, Dennis Gannon, and Jack Dongarra, p. 82-90]

https://doi.org/10.1145/3552309

[Article Title: The Lean Data Scientist: Recent Advances Toward Overcoming the Data Bottleneck/ Chen Shani, Jonathan Zarecki, and Dafna Shahaf, p. 92-102]

https://doi.org/10.1145/3551635

[Article Title: Technical Perspective: Beautiful Symbolic Abstractions for Safe and Secure Machine Learning/ Martin Vechev, p. 104]

https://doi.org/10.1145/3576893

[Article Title: Proving Data-Poisoning Robustness in Decision Trees/ Samuel Drews, Aws Albarghouthi, and Loris D'Antoni, p. 105-113]

Abstract: Machine learning models are brittle, and small changes in the training data can result in different predictions. We study the problem of proving that a prediction is robust to data poisoning, where an attacker can inject a number of malicious elements into the training set to influence the learned model. We target decision tree models, a popular and simple class of machine learning models that underlies many complex learning techniques. We present a sound verification technique based on abstract interpretation and implement it in a tool called Antidote. Antidote abstractly trains decision trees for an intractably large space of possible poisoned datasets. Due to the soundness of our abstraction, Antidote can produce proofs that, for a given input, the corresponding prediction would not have changed had the training set been tampered with or not. We demonstrate the effectiveness of Antidote on a number of popular datasets.

https://doi.org/10.1145/3576894

[Article Title: Aftermath Impact/ William Sims Bainbridge, p. 116]

Abstract: From the intersection of computational science and technological speculation, with boundaries limited only by our ability to imagine what could be.
An ancient roman dispatched to find the greatest technological advances of the time may lose something of far greater importance.

https://doi.org/10.1145/3578622