MIT Schwarzman College of Computing
MIT Schwarzman College of Computing

Enabling AI to explain its predictions in plain language

Using LLMs to convert machine-learning explanations into readable narratives could help users make better decisions about when to trust a model.

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.

Photonic processor could enable ultrafast AI computations with extreme energy efficiency

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.

Improving health, one machine learning system at a time

Marzyeh Ghassemi works to ensure health-care models are trained to be robust and fair.

MIT researchers develop an efficient way to train more reliable AI agents

The technique could make AI systems better at complex tasks that involve variability.

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.