Computer Science

Cynthia Rudin wants machine learning models, responsible for increasingly important decisions, to show their work.

The “packing coloring” problem asks how many numbers are needed to fill an infinite grid so that identical numbers never get too close to one another. A new computer-assisted proof finds a surprisingly straightforward answer.

By imbuing enormous vectors with semantic meaning, we can get machines to reason more abstractly — and efficiently — than before.

Unpredictability can help computer scientists solve otherwise intractable problems.

The American researcher was recognized for his central role in inventing, standardizing and commercializing the ubiquitous networking technology.

For decades, mathematicians have been inching forward on a problem about which sets contain evenly spaced patterns of three numbers. Last month, two computer scientists blew past all of those results.

Large language models like ChatGPT are now big enough that they’ve started to display startling, unpredictable behaviors.

Arvind Narayanan uses quantitative methods to expose and correct the misuse of quantitative methods.

Cryptographers have shown how perfect security can undermine machine learning models.