Fighting for the health of the planet with AI
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
Assistant Professor Priya Donti’s research applies machine learning to optimize renewable energy.
The approach combines physics and machine learning to avoid damaging disruptions when powering down tokamak fusion machines.
The approach could enable autonomous vehicles, commercial aircraft, or transportation networks that are more reliable in the face of real-world unpredictability.
Artificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.
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.
As large language models increasingly dominate our everyday lives, new systems for checking their reliability are more important than ever.
New research shows automatically controlling vehicle speeds to mitigate traffic at intersections can cut carbon emissions between 11 and 22 percent.
AeroAstro professor and outgoing co-director of the Center for Computational Science and Engineering will play a vital role in fostering community for bilingual computing faculty.
This new approach could lead to enhanced AI models for drug and materials discovery.
MIT researchers found that special kinds of neural networks, called encoders or “tokenizers,” can do much more than previously realized.
The CodeSteer system could boost large language models’ accuracy when solving complex problems, such as scheduling shipments in a supply chain.
A new approach for testing multiple treatment combinations at once could help scientists develop drugs for cancer or genetic disorders.
The MIT Energy Initiative’s annual research symposium explores artificial intelligence as both a problem and a solution for the clean energy transition.
Researchers find nonclinical information in patient messages — like typos, extra white space, and colorful language — reduces the accuracy of an AI model.
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.