The 16 finalists — representing every school at MIT — will explore generative AI’s impact on privacy, art, drug discovery, aging, and more.

Screen-reader users can upload a dataset and create customized data representations that combine visualization, textual description, and sonification.

Researchers demonstrate a technique that can be used to probe a model to see what it knows about new subjects.

Single-cell gene expression patterns in the brain, and evidence from follow-up experiments, reveal many shared cellular and molecular similarities that could be targeted for potential treatment.

Novel method makes tools like Stable Diffusion and DALL-E-3 faster by simplifying the image-generating process to a single step while maintaining or enhancing image quality.

FeatUp, developed by MIT CSAIL researchers, boosts the resolution of any deep network or visual foundation for computer vision systems.

A new algorithm reduces travel time by identifying shortcuts a robot could take on the way to its destination.

By enabling models to see the world more like humans do, the work could help improve driver safety and shed light on human behavior.

Faster and more accurate than some alternatives, this approach could be useful for robots that interact with humans or work in tight spaces.

MIT spinout DataCebo helps companies bolster their datasets by creating synthetic data that mimic the real thing.