Daniela Rus named to French National Academy of Medicine
As the Director of MIT’s Computer Science and Artificial Intelligence Laboratory (CSAIL), Rus leads over 1,700 researchers in pioneering innovations to advance computing and improve global well-being.
Researchers at MIT, NYU, and UCLA develop an approach to help evaluate whether large language models like GPT-4 are equitable enough to be clinically viable for mental health support.
MIT researchers introduce Boltz-1, a fully open-source model for predicting biomolecular structures
With models like AlphaFold3 limited to academic research, the team built an equivalent alternative, to encourage innovation more broadly.
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Advancing urban tree monitoring with AI-powered digital twins
The Tree-D Fusion system integrates generative AI and genus-conditioned algorithms to create precise simulation-ready models of 600,000 existing urban trees across North America.
Improving health, one machine learning system at a time
Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.
A causal theory for studying the cause-and-effect relationships of genes
By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
3 questions: Leveraging insights to enable clinical outcomes
Thomas Heldt, associate director of IMES, describes how he collaborates closely with MIT colleagues and others at Boston-area hospitals.
Despite its impressive output, generative AI doesn’t have a coherent understanding of the world
Researchers show that even the best-performing large language models don’t form a true model of the world and its rules, and can thus fail unexpectedly on similar tasks.
Researchers argue that in health care settings, “responsible use” labels could ensure AI systems are deployed appropriately.