Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.
Paul Liang
Assistant Professor [AI], shared appointment with Media Arts and Sciences (MAS) and EECS
AI agents could soon become indistinguishable from humans online. Could “personhood credentials” protect people against digital imposters?
The department is pleased to announce the four inaugural recipients of the Transformative Research Fund, an exciting new funding opportunity designed to facilitate bold and pivotal research, especially that which applies recent breakthrough technologies (such as generative AI) to important problems with broad societal impact.
A new algorithm helps robots practice skills like sweeping and placing objects, potentially helping them improve at important tasks in houses, hospitals, and factories.
In controlled experiments, MIT CSAIL researchers discover simulations of reality developing deep within LLMs, indicating an understanding of language beyond simple mimicry.
The approach can detect anomalies in data recorded over time, without the need for any training.
CSAIL researchers introduce a novel approach allowing robots to be trained in simulations of scanned home environments, paving the way for customized household automation accessible to anyone.
A member of the MIT EECS faculty since 2004, Madden succeeds longtime faculty member Arvind in the role.