Making robots faster by helping them think ahead
By planning actions based on the future position of a robot, the VLASH technique streamlines motions and accelerates performance.
New method could increase LLM training efficiency
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
Helping data storage keep up with the AI revolution
Storage systems from Cloudian, co-founded by an MIT alumnus, are helping businesses feed data-hungry AI models and agents at scale.
Researchers developed an algorithm that lets a robot “think ahead” and consider thousands of potential motion plans simultaneously.
Researchers fuse the best of two popular methods to create an image generator that uses less energy and can run locally on a laptop or smartphone.
Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.