Electrical engineering and computer science (EECS)
Electrical engineering and computer science (EECS)

A new generative AI approach to predicting chemical reactions

System developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.

3 Questions: The pros and cons of synthetic data in AI

Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.

3 Questions: On biology and medicine’s “data revolution”

Professor Caroline Uhler discusses her work at the Schmidt Center, thorny problems in math, and the ongoing quest to understand some of the most complex interactions in biology.

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.

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.

Researchers glimpse the inner workings of protein language models

A new approach can reveal the features AI models use to predict proteins that might make good drug or vaccine targets.

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.

Helping data storage keep up with the AI revolution

Storage systems from Cloudian, co-founded by an MIT alumnus, are helping businesses feed data-hungry AI models and agents at scale.

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.