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

Researchers develop “ContextCite,” an innovative method to track AI’s source attribution and detect potential misinformation.

MIT engineers developed the largest open-source dataset of car designs, including their aerodynamics, that could speed design of eco-friendly cars and electric vehicles.

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.

The method could help communities visualize and prepare for approaching storms.

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

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.

An AI method developed by Professor Markus Buehler finds hidden links between science and art to suggest novel materials.

By sidestepping the need for costly interventions, a new method could potentially reveal gene regulatory programs, paving the way for targeted treatments.