Study finds non-experts deferred to LLM-based diagnostic assistance, even when it was wrong, while clinicians caught AI errors.
Following the questions where they lead
Assistant Professor Bailey Flanigan has arrived at complex computational methods for helping democracy thrive.
Dimitri Bertsekas, Prolific Author in Optimization, Dynamic Programming and Reinforcement Learning, Dies at 83.
Bertsekas’s research spanned, and had a major influence upon, several fields, including optimization, control, large-scale computation, reinforcement learning, and artificial intelligence.
Helping AI models to meet the real world
Through research and entrepreneurship, Professor Devavrat Shah is helping to design methods that can handle constant decision-making using limited computational resources.
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.
Researchers show that for certain kinds of games, an overlooked class of algorithms performs much better than expected.
When it comes to predicting people’s preferences, it pays to consider “the power of three”
MIT researchers provide a major upgrade to the nearly century-old idea of random utility models.
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.
A new debiasing technique called WRING avoids creating or amplifying biases that can occur with existing debiasing approaches.
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.