Using generative AI, MIT chemists created a model that can predict the structures formed when a chemical reaction reaches its point of no return.

Study shows computational models trained to perform auditory tasks display an internal organization similar to that of the human auditory cortex.

A new method enables optical devices that more closely match their design specifications, boosting accuracy and efficiency.

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.

Human Guided Exploration (HuGE) enables AI agents to learn quickly with some help from humans, even if the humans make mistakes.

By analyzing bacterial data, researchers have discovered thousands of rare new CRISPR systems that have a range of functions and could enable gene editing, diagnostics, and more.

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

With the PockEngine training method, machine-learning models can efficiently and continuously learn from user data on edge devices like smartphones.