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.
MIT Schwarzman College of Computing
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.
A method for designing neural networks optimally suited for certain tasks
With the right building blocks, machine-learning models can more accurately perform tasks like fraud detection or spam filtering.
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.
MIT professor to Congress: “We are at an inflection point” with AI
Aleksander Mądry urges lawmakers to ask rigorous questions about how AI tools are being used by corporations.
Matthew Kearney: Bringing AI and philosophy into dialogue
The computer science and philosophy double-major aims to advance the field of AI ethics.
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.