A faster, better way to train general-purpose robots
Inspired by large language models, researchers develop a training technique that pools diverse data to teach robots new skills.
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.
Collaborative multi-university team will pursue new AI-enhanced design tools and high-throughput testing methods for next-generation turbomachinery.
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.
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.
The program will invite students to investigate new vistas at the intersection of music, computing, and technology.
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.