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.
An AI model generates novel proteins based on how they vibrate and move, opening new possibilities for dynamic biomaterials and adaptive therapeutics.
This new metric for measuring uncertainty could flag hallucinations and help users know whether to trust an AI model.
Academia-industry relationship is an early-stage accelerator, supporting professional progress and research.
A new hybrid system could help robots navigate in changing environments or increase the efficiency of multirobot assembly teams.
By leveraging idle computing time, researchers can double the speed of model training while preserving accuracy.
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.
Removing just a tiny fraction of the crowdsourced data that informs online ranking platforms can significantly change the results.
Torralba’s research focuses on computer vision, machine learning, and human visual perception.
CSAIL researchers find even “untrainable” neural nets can learn effectively when guided by another network’s built-in biases using their guidance method.
MIT-IBM Watson AI Lab researchers developed an expressive architecture that provides better state tracking and sequential reasoning in LLMs over long texts.