Improving the speed and energy-efficiency of AI agents
A new system, known as Murakkab, optimizes the design and deployment of multistep workflows that power AI applications.
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Algorithms
Exploring the societal impacts of AI
During the AI and Society Forum, leading MIT researchers examined critical questions about AI’s influence on employment and democracy.
Read MoreNew chip could help tiny robots traverse complex environments
Researchers combined an efficient algorithm with dedicated hardware to rapidly generate 3D maps for navigation using minimal memory and power.
Read MoreGames 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.
Read MoreThe MIT-IBM Computing Research Lab launches to shape the future of AI and quantum computing
Building on a long-standing MIT–IBM collaboration, the new lab will chart the convergence of AI, algorithms, and quantum computing.
Read MoreA faster way to estimate AI power consumption
The “EnergAIzer” method generates reliable results in seconds, enabling data center operators to efficiently allocate resources and reduce wasted energy.
Read MoreMIT scientists build the world’s largest collection of Olympiad-level math problems, and open it to everyone
New 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 MoreTeaching AI models to say “I’m not sure”
A new training method improves the reliability of AI confidence estimates without sacrificing performance, addressing a root cause of hallucination in reasoning models.
Read MoreHuman-machine teaming dives underwater
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
Read MoreNew technique makes AI models leaner and faster while they’re still learning
Researchers use control theory to shed unnecessary complexity from AI models during training, cutting compute costs without sacrificing performance.
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