Research
Research

Machine learning unlocks secrets to advanced alloys

An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.

Creating and verifying stable AI-controlled systems in a rigorous and flexible way

Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.

AI method radically speeds predictions of materials’ thermal properties

The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.

How to assess a general-purpose AI model’s reliability before it’s deployed

A new technique enables users to compare several large models and choose the one that works best for their task.

Reasoning skills of large language models are often overestimated

New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.

When to trust an AI model

More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.

MIT researchers introduce generative AI for databases

This new tool offers an easier way for people to analyze complex tabular data.

Researchers leverage shadows to model 3D scenes, including objects blocked from view

This technique could lead to safer autonomous vehicles, more efficient AR/VR headsets, or faster warehouse robots.

Understanding the visual knowledge of language models

LLMs trained primarily on text can generate complex visual concepts through code with self-correction. Researchers used these illustrations to train an image-free computer vision system to recognize real photos.

A smarter way to streamline drug discovery

The SPARROW algorithm automatically identifies the best molecules to test as potential new medicines, given the vast number of factors affecting each choice.