Machine-learning models can make mistakes and be difficult to use, so scientists have developed explanation methods to help users understand when and how they should

Daniela Rus, director of MIT’s Computer Science and Artificial Intelligence Laboratory and MIT professor of electrical engineering and computer science, was recently named a co-recipient

Chatbots can wear a lot of proverbial hats: dictionary, therapist, poet, all-knowing friend. The artificial intelligence models that power these systems appear exceptionally skilled and

For all the talk about artificial intelligence upending the world, its economic effects remain uncertain. There is massive investment in AI but little clarity about

People struggling with their mental health are more likely to browse negative content online, and in turn, that negative content makes their symptoms worse, according

Car design is an iterative and proprietary process. Carmakers can spend several years on the design phase for a car, tweaking 3D forms in simulations

For the first time, MIT sent an organized engagement to the global Conference of the Parties for the Convention on Biological Diversity, which this year

Creating realistic 3D models for applications like virtual reality, filmmaking, and engineering design can be a cumbersome process requiring lots of manual trial and error.

The deep neural network models that power today’s most demanding machine-learning applications have grown so large and complex that they are pushing the limits of

It is fairly common in public discourse for someone to announce, “I brought data to this discussion,” thus casting their own conclusions as empirical and