Computer Science and Artificial Intelligence Laboratory (CSAIL)
Computer Science and Artificial Intelligence Laboratory (CSAIL)

Drones navigate unseen environments with liquid neural networks

MIT researchers exhibit a new advancement in autonomous drone navigation, using brain-inspired liquid neural networks that excel in out-of-distribution scenarios.

MIT CSAIL researchers discuss frontiers of generative AI

Experts convene to peek under the hood of AI-generated code, language, and images as well as its capabilities, limitations, and future impact.

A four-legged robotic system for playing soccer on various terrains

“DribbleBot” can maneuver a soccer ball on landscapes such as sand, gravel, mud, and snow, using reinforcement learning to adapt to varying ball dynamics.

Speeding up drug discovery with diffusion generative models

MIT researchers built DiffDock, a model that may one day be able to find new drugs faster than traditional methods and reduce the potential for adverse side effects.

Learning to grow machine-learning models

New LiGO technique accelerates training of large machine-learning models, reducing the monetary and environmental cost of developing AI applications.

New method accelerates data retrieval in huge databases

Researchers used machine learning to build faster and more efficient hash functions, which are a key component of databases.

Creating a versatile vaccine to take on Covid-19 in its many guises

Aided by machine learning, scientists are working to develop a vaccine that would be effective against all SARS-CoV-2 strains.

Large language models are biased. Can logic help save them?

MIT researchers trained logic-aware language models to reduce harmful stereotypes like gender and racial biases.

MIT-Takeda Program heads into fourth year with crop of 10 new projects

The program leverages MIT’s research expertise and Takeda’s industrial know-how for research in artificial intelligence and medicine.

Helping companies deploy AI models more responsibly

MIT spinout Verta offers tools to help companies introduce, monitor, and manage machine-learning models safely and at scale.