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3 Questions: How AI is helping us monitor and support vulnerable ecosystems

MIT PhD student and CSAIL researcher Justin Kay describes his work combining AI and computer vision systems to monitor the ecosystems that support our planet.

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Creating AI that matters

How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.

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Method 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.

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Optimizing 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.

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Using 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.

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MIT 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.

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How 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.

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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.

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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.

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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.