A new machine-learning system helps robots understand and perform certain social interactions.

Reducing the complexity of a powerful machine-learning model may help level the playing field for automatic speech-recognition around the world.

A new method forces a machine learning model to focus on more data when learning a task, which leads to more reliable predictions.

Self-driving Roboats, developed at MIT, set sea in Amsterdam canals. If you don’t get seasick, an autonomous boat might be the right mode of transportation for you. 

Neuroscientists find the internal workings of next-word prediction models resemble those of language-processing centers in the brain.

A new machine-learning system costs less, generates less waste, and can be more innovative than manual discovery methods.

Everyone knows the shortest distance between two points is a straight line. However, when you’re walking along city streets, a straight line may not be possible. How do you decide which way to go?

A new control system, demonstrated using MIT’s robotic mini cheetah, enables four-legged robots to jump across uneven terrain in real-time.

Neural Networks can learn to solve all sorts of problems, from identifying cats in photographs to steering a self-driving car. But whether these powerful, pattern-recognizing algorithms actually understand the tasks they are performing remains an open question.

MIT professor is designing the next generation of smart wireless devices that will sit in the background, gathering and interpreting data, rather than being worn on the body.