data
Creating AI that matters
How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.
Read MoreMethod teaches generative AI models to locate personalized objects
After being trained with this technique, vision-language models can better identify a unique item in a new scene.
Read MoreOptimizing food subsidies: Applying digital platforms to maximize nutrition
An algorithm can change the face of food assistance policy in the Global South, says MIT assistant professor and J-WAFS researcher Ali Aouad.
Read MoreUsing generative AI to diversify virtual training grounds for robots
New tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.
Read MoreMIT affiliates win AI for Math grants to accelerate mathematical discovery
Department of Mathematics researchers David Roe and Andrew Sutherland seek to advance automated theorem proving; four additional MIT alumni also awarded.
Read MoreHow to build AI scaling laws for efficient LLM training and budget maximization
MIT-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 More3 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.
Read More3 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.
Read MoreMIT 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.
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