While some of your photos could be understood in an instant, why was this cat photo much more difficult?

The programming included a flagship full-day symposium as well as four subject-specific symposia, all aimed at fostering a dialogue about the opportunities and potential applications of generative artificial intelligence technologies across a diverse range of disciplines.

The series aims to help policymakers create better oversight of AI in society.

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.