UMD research earns five honors at IROS workshops for advances in navigating around glass, interpreting touch and learning from human video.
UMD researchers identify where language models could help volunteer moderators—and where human judgment remains essential.
Postdoc Laura Fichtner emphasizes the need for participatory approaches that incorporate diverse viewpoints when assessing AI systems.
Philip Resnik tells CBS News how machine learning helped researchers analyze thousands of Reddit posts about reasons for living.
UMD researchers will analyze everyday conversations for signs of trust—and distrust—in AI.
Katie Shilton studies how tech-worker parents are preparing their children for an AI-transformed workplace.
Two Ph.D. graduates of UMD’s CLIP Lab built a machine-readable dataset spanning thousands of U.S. cities and counties.
UMD’s Connor Baumler finds that editing AI-generated text may not erase its stylistic fingerprints.
An interdisciplinary UMD team will investigate why AI falls into human-like reasoning traps—and how to make it more reliable.
The fourth round of seed funding supports multidisciplinary teams tackling one of AI's biggest challenges: earning public trust.
