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.
Computer Science and Artificial Intelligence Laboratory (CSAIL)
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.
A portable light system that can digitize everyday objects
A new design tool uses UV and RGB lights to change the color and textures of everyday objects. The system could enable surfaces to display dynamic patterns, such as health…
MIT Schwarzman College of Computing launches postdoctoral program to advance AI across disciplines
The new Tayebati Postdoctoral Fellowship Program will support leading postdocs to bring cutting-edge AI to bear on research in scientific discovery or music.
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.
Making it easier to verify an AI model’s responses
By allowing users to clearly see data referenced by a large language model, this tool speeds manual validation to help users spot AI errors.