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.

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

Professor Ernest Fraenkel has decoded fundamental aspects of Huntington’s disease and glioblastoma, and is now using computation to better understand amyotrophic lateral sclerosis.

By breaking an intractable problem into smaller chunks, a deep-learning technique identifies the optimal areas for thinning out traffic in a warehouse.

Innovative AI system from MIT CSAIL melds simulations and physical testing to forge materials with newfound durability and flexibility for diverse engineering uses.

Researchers developed a simple yet effective solution for a puzzling problem that can worsen the performance of large language models such as ChatGPT.

Exploiting the symmetry within datasets, MIT researchers show, can decrease the amount of data needed for training neural networks.

The ambient light sensors responsible for smart devices’ brightness adjustments can capture images of touch interactions like swiping and tapping for hackers.