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.
Researchers propose a simple fix to an existing technique that could help artists, designers, and engineers create better 3D models.
This new device uses light to perform the key operations of a deep neural network on a chip, opening the door to high-speed processors that can learn in real-time.
Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.
The technique could make AI systems better at complex tasks that involve variability.
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.
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.
Yiming Chen ’24, Wilhem Hector, Anushka Nair, and David Oluigbo will start postgraduate studies at Oxford next fall.
MIT and IBM researchers are creating linkage mechanisms to innovate human-AI kinematic engineering.
By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.
