Researchers devise an efficient protocol to keep a user’s private information secure when algorithms use it to recommend products, songs, or shows.

Researchers create a mathematical framework to evaluate explanations of machine-learning models and quantify how well people understand them.

A machine-learning model can identify the action in a video clip and label it, without the help of humans.

Natural language processing models capture rich knowledge of words’ meanings through statistics.

A new neural network approach captures the characteristics of a physical system’s dynamic motion from video, regardless of rendering configuration or image differences.

MIT researchers can now estimate how much information data are likely to contain, in a more accurate and scalable way than previous methods.

Researchers have developed a technique that enables a robot to learn a new pick-and-place task with only a handful of human demonstrations.

MIT CSAIL scientists created an algorithm to solve one of the hardest tasks in computer vision: assigning a label to every pixel in the world, without human supervision.

A new machine-learning system may someday help driverless cars predict the next moves of nearby drivers, cyclists, and pedestrians in real-time.

MIT engineers Edward Adelson and Sandra Liu duo develop a robotic gripper with rich sensory capabilities.