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.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
When it comes to predicting people’s preferences, it pays to consider “the power of three”
MIT researchers provide a major upgrade to the nearly century-old idea of random utility models.
MIT affiliates win 2026 Hertz Foundation Fellowships
The fellowships in applied sciences, engineering, and mathematics recognize doctoral students who are pursuing solutions to the most pressing challenges in science and technology.
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.
MIT researchers teach AI models to interpret charts
The new ChartNet training dataset could improve the accuracy of vision-language models that help analyze business trends or interpret scientific figures.
Justin Solomon appointed associate dean of engineering education
MIT faculty member in electrical engineering and computer science to focus on innovation in engineering education and new pedagogical approaches.
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.
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.
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.