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.
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Jesse Thaler named director of the Laboratory for Nuclear Science
The professor of physics and inaugural director of the NSF AI Institute for Artificial Intelligence and Fundamental Interactions will lead LNS and continue his research in particle physics.
Read MoreImproving 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.
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 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.
Read MoreHelping data centers deliver higher performance with less hardware
Researchers developed a system that intelligently balances workloads to improve the efficiency of flash storage hardware in a data center.
Read MoreAugmenting citizen science with computer vision for fish monitoring
MIT Sea Grant works with the Woodwell Climate Research Center and other collaborators to demonstrate a deep learning-based system for fish monitoring.
Read MoreHow to create “humble” AI
An MIT-led team is designing artificial intelligence systems for medical diagnosis that are more collaborative and forthcoming about uncertainty.
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