MIT Schwarzman College of Computing
MIT Schwarzman College of Computing

MIT researchers develop AI tool to improve flu vaccine strain selection

VaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.

Simpler models can outperform deep learning at climate prediction

New research shows the natural variability in climate data can cause AI models to struggle at predicting local temperature and rainfall.

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.

A new way to test how well AI systems classify text

As large language models increasingly dominate our everyday lives, new systems for checking their reliability are more important than ever.

Eco-driving measures could significantly reduce vehicle emissions

New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.

MIT tool visualizes and edits “physically impossible” objects

By visualizing Escher-like optical illusions in 2.5 dimensions, the “Meschers” tool could help scientists understand physics-defying shapes and spark new designs.

New algorithms enable efficient machine learning with symmetric data

This new approach could lead to enhanced AI models for drug and materials discovery.

Robot, know thyself: New vision-based system teaches machines to understand their bodies

Neural Jacobian Fields, developed by MIT CSAIL researchers, can learn to control any robot from a single camera, without any other sensors.

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.

The unique, mathematical shortcuts language models use to predict dynamic scenarios

Language models follow changing situations using clever arithmetic, instead of sequential tracking. By controlling when these approaches are used, engineers could improve the systems’ capabilities.