Search results for “site:cacm.acm.org”

Fantasy, the Restrained AI Cousin of Hallucination – Communications of the ACM

The huge genre of fiction spans everything from fantasy to romance novels, to science fiction, to historical fiction. Somehow, fiction is much of what people want to read and write. Are users provoking AI to make things up, too? It seems that even though people complain about computers...

Technical Marvels, Part 7: Musical Clocks and Organs – Communications of the ACM

Many clocks contain musical mechanisms: table clocks, standing clocks, longcase clocks, flute-playing clocks, and pocket watches. Automaton figures, furniture (e.g. desks), jewelry, as well as picture clocks sometimes also have built-in musical mechanisms. For the musical program, historical...

Disrupting Networks: Decentralization and the Fediverse – Communications of the ACM

Users of social media have become increasingly sensitive to the fallibilities of centralized networks, such as X/Twitter, Facebook, and Instagram, that are managed by a single company. Frustrations can arise as new recommendation algorithms and advertising strategies are rolled out, company...

Claude 4’s Agency in Practice: Beyond Code Generation – Communications of the ACM

As computing professionals, we’ve grown accustomed to evaluating AI coding tools through familiar metrics: syntactic correctness, benchmark performance and code quality scores. While these measures provide useful baselines, they fail to capture a more transformative capability: the ability to...

Long-Term Mentoring for Computer Science Researchers – Communications of the ACM

Early in the COVID-19 pandemic, we—leaders in the research areas of programming languages (PL) and computer architecture (CA)—realized we had a problem: the only way to form new lasting connections in the community was to already have lasting connections in the community. Our academic communities...

Program Merge: What’s Deep Learning Got to Do with It? – Communications of the ACM

If you regularly work with open-source code or produce software for a large organization, you are already familiar with many of the challenges posed by collaborative programming at scale. Some of the most vexing of these tend to surface as a consequence of the many independent alterations...

Using Data Science to Predict How Rituals Will Evolve – Communications of the ACM

Think about your most personal ritualistic event—walking your dog, grocery shopping, or a weekly meet-up with your friends. Most likely, it takes place in the same place, close to home, with the same people, and at a certain frequency. Rituals occur also on the social level; for example, Sunday...

In short: Learn how data science predicts the evolution of rituals using the Israeli social protest as a case study. Discover the continuity and emotional engagement d... (AI summary)

The Evolution of Computer Science at the University Level – Communications of the ACM

Ben Fishbein graduated in 2016 with a bachelor’s degree in computer science from the University of Illinois at Urbana-Champaign. If he was starting college today, Fishbein said unequivocally that he would still major in computer science....

Free and Open Source Software–and Other Market Failures – Communications of the ACM

Economists and others focused on money—like my bank—have had a lot of trouble figuring out the free and open source software (FOSS) phenomenon, and eventually they seem to have reached the conclusion that it just makes no sense. So, they go with the flow. Recently, very serious people in the FOSS...

Closing the Gap Between AI Output and Human Expectations – Communications of the ACM

You ask an AI to write a quick summary, and it rambles. You want a sharp subject line, and it gives you something straight out of 2010. It’s fast, sure. But it’s not quite right. That “not quite right” feeling? That’s the expectation gap....

In short: Explore why AI output often falls short of human expectations and learn strategies to close the gap. Key areas include context understanding, imperfect training data, and vague prompts. (AI summary)