MIT researchers develop a customized onboarding process that helps a human learn when a model’s advice is trustworthy.

Using machine learning, the computational method can provide details of how materials work as catalysts, semiconductors, or battery components.

A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.

CSAIL's GenSim uses the code within LLMs to automatically generate new robotic behaviors and outline each. 

To teach an AI agent a new task, like how to open a kitchen cabinet, researchers often use reinforcement learning — a trial-and-error process where the agent is rewarded for taking actions that get it closer to the goal.

Generative AI represents a seismic shift in the way we approach creative tasks. A comprehensive understanding of these technologies enables organizations to leverage the power of the technology to increase productivity, improve customer service, enhance user experiences, develop content, create synthetic data, enable new discoveries, and so much more! Become an essential part of your organization’s generative AI journey. Explore the generative AI landscape and the future of productivity with this 6-week course from MIT xPRO coming spring 2024.

MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.

For more than 60 years, MIT has been an undisputed pioneer in developing computing technologies that have transformed the world. The Institute’s largest research lab, the Computer Science and Artificial Intelligence Laboratory (CSAIL), has had a hand in everything from time-sharing and computer graphics to data encryption and early versions of the Internet. 

With 3D inkjet printing systems, engineers can fabricate hybrid structures that have soft and rigid components, like robotic grippers that are strong enough to grasp heavy objects but soft enough to interact safely with humans.