National Science Foundation (NSF)
National Science Foundation (NSF)

NSF renews support for MIT-led AI and physics institute, expanding a new model for discovery

IAIFI enters its second phase with increased funding, broader ambitions, and a growing community at the frontier of AI and fundamental physics.

Teaching AI agents to ask better questions by playing “Battleship”

MIT researchers use the classic game as a test bed for AI agents, finding a small AI model can outperform the biggest ones at 1 percent of the cost.

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.

MIT 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.

Helping 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.

MIT researchers use AI to uncover atomic defects in materials

A new model measures defects that can be leveraged to improve materials’ mechanical strength, heat transfer, and energy-conversion efficiency.

Augmenting 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.

Generative AI improves a wireless vision system that sees through obstructions

With this new technique, a robot could more accurately detect hidden objects or understand an indoor scene using reflected Wi-Fi signals.

3 Questions: On the future of AI and the mathematical and physical sciences

Professor Jesse Thaler describes a vision for a two-way bridge between artificial intelligence and the mathematical and physical sciences — one that promises to advance both.

New method could increase LLM training efficiency

By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.