Empowering systemic racism research at MIT and beyond
Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.
Researchers in the MIT Initiative on Combatting Systemic Racism are building an open data repository to advance research on racial inequity in domains like policing, housing, and health care.
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.
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.
The technique leverages quantum properties of light to guarantee security while preserving the accuracy of a deep-learning model.
Researchers find large language models make inconsistent decisions about whether to call the police when analyzing surveillance videos.
“ScribblePrompt” is an interactive AI framework that can efficiently highlight anatomical structures across different medical scans, assisting medical workers to delineate regions of interest and abnormalities.
Researchers developed an easy-to-use tool that enables an AI practitioner to find data that suits the purpose of their model, which could improve accuracy and reduce bias.
The software tool NeuroTrALE is designed to quickly and efficiently process large amounts of brain imaging data semi-automatically.