When should someone trust an AI assistant’s predictions?
Researchers have created a method to help workers collaborate with artificial intelligence systems.
Researchers have created a method to help workers collaborate with artificial intelligence systems.
MIT researchers lay out a strategy for how universities can help the US regain its place as a semiconductor superpower.
Researchers develop a way to test whether popular methods for understanding machine-learning models are working correctly.
MIT EECS student and Mitchell Scholar hopes to play music in Dublin while working on his MS in intelligent systems.
MIT scientists discuss the future of AI with applications across many sectors, as a tool that can be both beneficial and harmful.
In 2.C01, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions.
David Gamarnik has developed a new tool, the overlap gap property, for understanding computational problems that appear intractable.
A new course teaches students how to use computational techniques to solve real-world problems, from landing a spacecraft to placing cell phone towers.
MIT community members made headlines around the world for their innovative approaches to addressing problems local and global.
Top Institute stories dealt with the return to campus and continued response to Covid-19, MIT’s commitments to climate action, its support of a diverse community, and more.
The year’s popular research stories include a promising new approach to cancer immunotherapy, the confirmation of a 50-year-old theorem, and a major fusion breakthrough.
Assistant professor of civil engineering describes her career in robotics as well as challenges and promises of human-robot interactions.
A new fabrication technique produces low-voltage, power-dense artificial muscles that improve the performance of flying microrobots.
SENSE.nano symposium highlights the importance of sensing technologies in medical studies.
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.