MIT-IBM Watson AI Lab
MIT-IBM Watson AI Lab

MIT engineers design proteins by their motion, not just their shape

An AI model generates novel proteins based on how they vibrate and move, opening new possibilities for dynamic biomaterials and adaptive therapeutics.

A better method for identifying overconfident large language models

This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.

MIT-IBM Watson AI Lab seed to signal: Amplifying early-career faculty impact

Academia-industry relationship is an early-stage accelerator, supporting professional progress and research.

A better method for planning complex visual tasks

A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.

New method could increase LLM training efficiency

By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.

Mixing generative AI with physics to create personal items that work in the real world

To help generative AI models create durable, real-world accessories and decor, the PhysiOpt system runs physics simulations and makes subtle tweaks to its 3D blueprints.

Study: Platforms that rank the latest LLMs can be unreliable

Removing just a tiny fraction of the crowdsourced data that informs online ranking platforms can significantly change the results.

Antonio Torralba, three MIT alumni named 2025 ACM fellows

Torralba’s research focuses on computer vision, machine learning, and human visual perception.

Guided learning lets “untrainable” neural networks realize their potential

CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.

A new way to increase the capabilities of large language models

MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.