Three MIT students selected as inaugural MIT-Pillar AI Collective Fellows
The graduate students will aim to commercialize innovations in AI, machine learning, and data science.
The graduate students will aim to commercialize innovations in AI, machine learning, and data science.
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.
Justin Solomon applies modern geometric techniques to solve problems in computer vision, machine learning, statistics, and beyond.
During the last week of November, MIT hosted symposia and events aimed at examining the implications and possibilities of generative AI.
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.
During 18 years of leadership, Evans established new R&D mission areas, strengthened ties to the MIT community, and increased inclusion and education efforts.
A new, data-driven approach could lead to better solutions for tricky optimization problems like global package routing or power grid operation.
Rodney Brooks, co-founder of iRobot, kicks off an MIT symposium on the promise and potential pitfalls of increasingly powerful AI tools like ChatGPT.
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.
Twelve teams of students and postdocs across the MIT community presented innovative startup ideas with potential for real-world impact.
MIT CSAIL researchers innovate with synthetic imagery to train AI, paving the way for more efficient and bias-reduced machine learning.