From MIT to IBM, expediting AI and quantum deployment
MIT affiliates engage with the MIT-IBM Computing Research Lab to bring rigorous theory to production systems.
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data
When AI art has no author: Study finds generated images often can’t be traced to training data
A new method for surgically removing training examples from a model reveals that as datasets grow, the link between what a model learns and what it produces dissolves.
Read MoreHow a medical database developed at MIT evolved into a global standard of data-sharing
The visionary PhysioNet platform launched 25 years ago, based on a system developed at MIT in the 1970s. It has become one of the most comprehensive biomedical and clinical data repositories in existence.
Read More3 Questions: Neural transparency and the future of AI design
Assistant Professor Pat Pataranutaporn describes a new interface that lets everyday users glimpse inside an AI’s neural network before their chatbot ever says a word.
Read MoreHelping 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.
Read MoreJesse 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.
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