Electrical engineering and computer science (EECS)
Electrical engineering and computer science (EECS)

A smarter way for large language models to think about hard problems

This new technique enables LLMs to dynamically adjust the amount of computation they use for reasoning, based on the difficulty of the question.

MIT engineers design an aerial microrobot that can fly as fast as a bumblebee

With insect-like speed and agility, the tiny robot could someday aid in search-and-rescue missions.

New control system teaches soft robots the art of staying safe

MIT CSAIL and LIDS researchers developed a mathematically grounded system that lets soft robots deform, adapt, and interact with people and objects, without violating safety limits.

Researchers discover a shortcoming that makes LLMs less reliable

Large language models can learn to mistakenly link certain sentence patterns with specific topics — and may then repeat these patterns instead of reasoning.

MIT scientists debut a generative AI model that could create molecules addressing hard-to-treat diseases

BoltzGen generates protein binders for any biological target from scratch, expanding AI’s reach from understanding biology toward engineering it.

Understanding the nuances of human-like intelligence

Associate Professor Phillip Isola studies the ways in which intelligent machines “think,” in an effort to safely integrate AI into human society.

Charting the future of AI, from safer answers to faster thinking

MIT PhD students who interned with the MIT-IBM Watson AI Lab Summer Program are pushing AI tools to be more flexible, efficient, and grounded in truth.

MIT researchers propose a new model for legible, modular software

The coding framework uses modular concepts and simple synchronization rules to make software clearer, safer, and easier for LLMs to generate.

Teaching robots to map large environments

A new approach developed at MIT could help a search-and-rescue robot navigate an unpredictable environment by rapidly generating an accurate map of its surroundings.

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.