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.
Laboratory for Information and Decision Systems (LIDS)
New method aims to keep kids safe from illegal AI-generated content
Researchers developed an auditing technique to test generative AI models for malicious capabilities, without prompting them for illegal outputs.
In game theory, generalists sometimes win out over specialists
Researchers show that for certain kinds of games, an overlooked class of algorithms performs much better than expected.
Could AI tell you where you left your keys?
A new spatial memory system for robots efficiently captures details about the objects they see while exploring their environment.
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.
Games people — and machines — play: Untangling strategic reasoning to advance AI
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.
Solving the “Whac-a-mole dilemma”: A smarter way to debias AI vision models
A new debiasing technique called WRING avoids creating or amplifying biases that can occur with existing debiasing approaches.
Evaluating the ethics of autonomous systems
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
AI system learns to keep warehouse robot traffic running smoothly
This new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput.
A better method for identifying overconfident large language models
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.