By enabling models to see the world more like humans do, the work could help improve driver safety and shed light on human behavior.

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

Adaptive smart glove from MIT CSAIL researchers can send tactile feedback to teach users new skills, guide robots with more precise manipulation, and help train surgeons and pilots.

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

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.