Teaching a robot its limits, to complete open-ended tasks safely
The “PRoC3S” method helps an LLM create a viable action plan by testing each step in a simulation. This strategy could eventually aid in-home robots to complete more ambiguous chore…
Computer Science and Artificial Intelligence Laboratory (CSAIL)
AI in health should be regulated, but don’t forget about the algorithms, researchers say
In a recent commentary, a team from MIT, Equality AI, and Boston University highlights the gaps in regulation for AI models and non-AI algorithms in health care.
Researchers reduce bias in AI models while preserving or improving accuracy
A new technique identifies and removes the training examples that contribute most to a machine-learning model’s failures.
Daniela Rus wins John Scott Award
MIT CSAIL director and EECS professor named a co-recipient of the honor for her robotics research, which has expanded our understanding of what a robot can be.
Citation tool offers a new approach to trustworthy AI-generated content
Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.
MIT delegation mainstreams biodiversity conservation at the UN Biodiversity Convention, COP16
First organized MIT delegation highlights the Institute's growing commitment to addressing climate change by showcasing research on biodiversity conservation, AI, and the role of local communities.
A new way to create realistic 3D shapes using generative AI
Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.
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.
Can robots learn from machine dreams?
MIT CSAIL researchers used AI-generated images to train a robot dog in parkour, without real-world data. Their LucidSim system demonstrates generative AI's potential for creating robotics training data.
Four from MIT named 2025 Rhodes Scholars
Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.