A new report by MIT researchers highlights the potential of generative AI to help workers with certain writing assignments.

Selecting the right method gives users a more accurate picture of how their model is behaving, so they are better equipped to correctly interpret its predictions.

A new study finds human supervisors have the potential to reduce barriers to deploying autonomous vehicles.

Models trained using common data-collection techniques judge rule violations more harshly than humans would, researchers report.

The method enables a model to determine its confidence in a prediction, while using no additional data and far fewer computing resources than other methods.

This year's fellows will work across research areas including telemonitoring, human-computer interactions, operations research,  AI-mediated socialization, and chemical transformations.

New data suggest most of the growth in the wage gap since 1980 comes from automation displacing less-educated workers.

Researchers make headway in solving a longstanding problem of balancing curious “exploration” versus “exploitation” of known pathways in reinforcement learning.

Mary Ellen Zurko pioneered user-centered security in the 1990s. Now she’s using those insights to help the nation thwart influence operations.

“Interpretability methods” seek to shed light on how machine-learning models make predictions, but researchers say to proceed with caution.