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.

Computer vision enables contact-free 3D printing, letting engineers print with high-performance materials they couldn’t use before.

MIT CSAIL researchers combine AI and electron microscopy to expedite detailed brain network mapping, aiming to enhance connectomics research and clinical pathology.

Thirteen new graduate student fellows will pursue exciting new paths of knowledge and discovery.