By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.

MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.

MIT researchers who share their data recognized at second annual awards celebration.

With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.

Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.

The SecureLoop search tool efficiently identifies secure designs for hardware that can boost the performance of complex AI tasks, while requiring less energy.

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.

AI models that prioritize similarity falter when asked to design something completely new.

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