This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.

Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.

Trained with a joint understanding of protein and cell behavior, the model could help with diagnosing disease and developing new drugs.

Words like “no” and “not” can cause this popular class of AI models to fail unexpectedly in high-stakes settings, such as medical diagnosis.

A detailed MIT analysis identifies some promising options but also raises unexpected concerns.

The CausVid generative AI tool uses a diffusion model to teach an autoregressive (frame-by-frame) system to rapidly produce stable, high-resolution videos.

“IntersectionZoo,” a benchmarking tool, uses a real-world traffic problem to test progress in deep reinforcement learning algorithms.

A new book coauthored by MIT’s Dimitris Bertsimas explores how analytics is driving decisions and outcomes in health care.

New type of “state-space model” leverages principles of harmonic oscillators.

A new method helps convey uncertainty more precisely, which could give researchers and medical clinicians better information to make decisions.