A new AI-based approach for controlling autonomous robots satisfies the often-conflicting goals of safety and stability.

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

Through the Multidisciplinary University Research Initiative, the US Department of Defense supports research projects in areas of critical importance to national defense.

A new machine-learning model makes more accurate predictions about ocean currents, which could help with tracking plastic pollution and oil spills, and aid in search and rescue.

The system they developed eliminates a source of bias in simulations, leading to improved algorithms that can boost the performance of applications.

Associate Professor Tamara Broderick and colleagues build a “taxonomy of trust” to identify where in data analysis processes confidence in its results might break down.

By keeping data fresh, the system could help robots inspect buildings or search disaster zones.

A new measure can help scientists decide which estimation method to use when modeling a particular data problem.

Study shows that if autonomous vehicles are widely adopted, hardware efficiency will need to advance rapidly to keep computing-related emissions in check.

New system can teach a group of cooperative or competitive AI agents to find an optimal long-term solution.