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.

MIT CSAIL researchers develop advanced machine-learning models that outperform current methods in detecting pancreatic ductal adenocarcinoma.

PhD students interning with the MIT-IBM Watson AI Lab look to improve natural language usage.

MIT researchers introduce a method that uses artificial intelligence to automate the explanation of complex neural networks.

This new method draws on 200-year-old geometric foundations to give artists control over the appearance of animated characters.

“Minimum viewing time” benchmark gauges image recognition complexity for AI systems by measuring the time needed for accurate human identification.

Speranza system brings hope to users that the package they download is functional software, not malware.

MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.

MIT CSAIL researchers established new connections between combinatorial and continuous optimization, which can find global solutions for complex motion-planning puzzles.