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.
Assistant Professor Gabriele Farina mines the foundations of decision-making in complex multi-agent scenarios.
Building on a long-standing MIT–IBM collaboration, the new lab will chart the convergence of AI, algorithms, and quantum computing.
Read MoreThe “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
Read MoreNew dataset of 30,000-plus competition math problems from 47 countries gives AI researchers a harder test — and students worldwide a better training ground.
Read MoreA new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.
Read MoreResearchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
Read MoreResearchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
Read MoreResearchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
Read MoreMIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.
Read MoreThis new approach adapts to decide which robots should get the right of way at every moment, avoiding congestion and increasing throughput.
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