The new Tayebati Postdoctoral Fellowship Program will support leading postdocs to bring cutting-edge AI to bear on research in scientific discovery or music.
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.
A new method can train a neural network to sort corrupted data while anticipating next steps. It can make flexible plans for robots, generate high-quality video, and help AI agents navigate digital environments.
Associate Professor Julian Shun develops high-performance algorithms and frameworks for large-scale graph processing.
MIT CSAIL researchers created an AI-powered method for low-discrepancy sampling, which uniformly distributes data points to boost simulation accuracy.
David Singer, head of the MIT Department of Political Science, discusses the Strengthening Democracy Initiative, focused on the rigorous study of elections, public opinion, and political participation.
David Singer, head of the MIT Department of Political Science, discusses the Strengthening Democracy Initiative, focused on the rigorous study of elections, public opinion, and political participation.
New dataset of “illusory” faces reveals differences between human and algorithmic face detection, links to animal face recognition, and a formula predicting where people most often perceive faces.
A new method called Clio enables robots to quickly map a scene and identify the items they need to complete a given set of tasks.
