Complimentary approaches — “HighLight” and “Tailors and Swiftiles” — could boost the performance of demanding machine-learning tasks.

MIT computer scientists developed a way to calculate polygenic scores that makes them more accurate for people across diverse ancestries.

StructCode, developed by MIT CSAIL researchers, encodes machine-readable data in laser-cut objects by modifying their fabrication features.

Researchers coaxed a family of generative AI models to work together to solve multistep robot manipulation problems.

MIT engineers develop a long, curved touch sensor that could enable a robot to grasp and manipulate objects in multiple ways.

Designed to ensure safer skies, “Air-Guardian” blends human intuition with machine precision, creating a more symbiotic relationship between pilot and aircraft.

By analyzing epigenomic and gene expression changes that occur in Alzheimer’s disease, researchers identify cellular pathways that could become new drug targets.

Inspired by physics, a new generative model PFGM++ outperforms diffusion models in image generation.

With Style2Fab, makers can rapidly customize models of 3D-printable objects, such as assistive devices, without hampering their functionality.

Although computer scientists may initially treat data bias and error as a nuisance, researchers argue it’s a hidden treasure trove for reflecting societal values.