Bigger datasets aren’t always better
MIT researchers developed a way to identify the smallest dataset that guarantees optimal solutions to complex problems.
Charting the future of AI, from safer answers to faster thinking
MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.
A faster problem-solving tool that guarantees feasibility
The FSNet system, developed at MIT, could help power grid operators rapidly find feasible solutions for optimizing the flow of electricity.
MIT Schwarzman College of Computing welcomes 11 new faculty for 2025
The faculty members occupy core computing and shared positions, bringing varied backgrounds and expertise to the MIT community.
Fighting for the health of the planet with AI
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
Can large language models figure out the real world?
New test could help determine if AI systems that make accurate predictions in one area can understand it well enough to apply that ability to a different area.
This new approach could lead to enhanced AI models for drug and materials discovery.
A new way to edit or generate images
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.