The system automatically learns to adapt to unknown disturbances such as gusting winds.

SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.

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.

AI that anticipates where crimes are likely to occur and who might commit them has a troubling track record. Democratic accountability could shine a light on the technology and how it’s used.

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