The 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.
Electrical engineering and computer science (EECS)
Enabling privacy-preserving AI training on everyday devices
A new method could bring more accurate and efficient AI models to high-stakes applications like health care and finance, even in under-resourced settings.
A 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.
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.
Teaching 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.
Jacob Andreas and Brett McGuire named Edgerton Award winners
The associate professors of EECS and chemistry, respectively, are honored for exceptional contributions to teaching, research, and service at MIT.
Human-machine teaming dives underwater
Researchers are developing hardware and algorithms to improve collaboration between divers and autonomous underwater vehicles engaged in maritime missions.
New 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.
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.
Evaluating the ethics of autonomous systems
MIT researchers developed a testing framework that pinpoints situations where AI decision-support systems are not treating people and communities fairly.