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.
Algorithms
Understanding our place in the universe
Martin Luther King Jr. Scholar Brian Nord trains machines to explore the cosmos and fights for equity in research.
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.
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.
Integrating humans with AI in structural design
A process that seeks feedback from human specialists proves more effective at optimization than automated systems working alone.
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.
Efficient technique improves machine-learning models’ reliability
The method enables a model to determine its confidence in a prediction, while using no additional data and far fewer computing resources than other methods.