Computer Science and Artificial Intelligence Laboratory (CSAIL)
Computer Science and Artificial Intelligence Laboratory (CSAIL)

Creating AI that matters

How the MIT-IBM Watson AI Lab is shaping AI-sociotechnical systems for the future.

New software designs eco-friendly clothing that can reassemble into new items

To reduce waste, the Refashion program helps users create outlines for adaptable clothing, such as pants that can be reconfigured into a dress. Each component of these pieces can be replaced, rearranged, or restyled.

Method teaches generative AI models to locate personalized objects

After being trained with this technique, vision-language models can better identify a unique item in a new scene.

Using generative AI to diversify virtual training grounds for robots

New tool from MIT CSAIL creates realistic virtual kitchens and living rooms where simulated robots can interact with models of real-world objects, scaling up training data for robot foundation models.

AI maps how a new antibiotic targets gut bacteria

MIT CSAIL and McMaster researchers used a generative AI model to reveal how a narrow-spectrum antibiotic attacks disease-causing bacteria, speeding up a process that normally takes years.

Responding to the climate impact of generative AI

Explosive growth of AI data centers is expected to increase greenhouse gas emissions. Researchers are now seeking solutions to reduce these environmental harms.

New AI system could accelerate clinical research

By enabling rapid annotation of areas of interest in medical images, the tool can help scientists study new treatments or map disease progression.

What does the future hold for generative AI?

At the inaugural MIT Generative AI Impact Consortium Symposium, researchers and business leaders discussed potential advancements centered on this powerful technology.

How to build AI scaling laws for efficient LLM training and budget maximization

MIT-IBM Watson AI Lab researchers have developed a universal guide for estimating how large language models will perform based on smaller models in the same family.

Machine-learning tool gives doctors a more detailed 3D picture of fetal health

MIT CSAIL researchers developed a tool that can model the shape and movements of fetuses in 3D, potentially assisting doctors in finding abnormalities and making diagnoses.