Responding to the climate impact of generative AI
Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.
Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.
Department of Mathematics researchers David Roe and Andrew Sutherland seek to advance automated theorem proving; four additional MIT alumni also awarded.
Read MoreMIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models will perform based on smaller models in the same family.
Read MoreMIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.
Read MoreSystem developed at MIT could provide realistic predictions for a wide variety of reactions, while maintaining real-world physical constraints.
Read MoreArtificially created data offer benefits from cost savings to privacy preservation, but their limitations require careful planning and evaluation, Kalyan Veeramachaneni says.
Read MoreVaxSeer uses machine learning to predict virus evolution and antigenicity, aiming to make vaccine selection more accurate and less reliant on guesswork.
Read MoreNew research shows the natural variability in climate data can cause AI models to struggle at predicting local temperature and rainfall.
Read MoreNew 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.
Read MoreAs large language models increasingly dominate our everyday lives, new systems for checking their reliability are more important than ever.
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