Demystifying machine-learning systems
A new method automatically describes, in natural language, what the individual components of a neural network do.
Deploying machine learning to improve mental health
MIT scientist Rosalind Picard collaborates with clinicians to develop tools for mental health care delivery.
Cynthia Breazeal named dean for digital learning at MIT
Overseeing business and research units across MIT Open Learning, Breazeal will focus on the future of digital technologies and their applications in education.
A new language for quantum computing
Twist is an MIT-developed programming language that can describe and verify which pieces of data are entangled to prevent bugs in a quantum program.
Seeing into the future: Personalized cancer screening with artificial intelligence
Scientists demonstrate that AI-risk models, paired with AI-designed screening policies, can offer significant and equitable improvements to cancer screening.
Brave Behind Bars: Prison education program focuses on computing skills for women
MIT computer scientists and mathematicians offer an introductory computing and career-readiness program for incarcerated women in New England.
When should someone trust an AI assistant’s predictions?
Researchers have created a method to help workers collaborate with artificial intelligence systems.
Reasserting US leadership in microelectronics
MIT researchers lay out a strategy for how universities can help the US regain its place as a semiconductor superpower.
How well do explanation methods for machine-learning models work?
Researchers develop a way to test whether popular methods for understanding machine-learning models are working correctly.
Q&A: Dolapo Adedokun on computer technology, Ireland, and all that jazz
MIT EECS student and Mitchell Scholar hopes to play music in Dublin while working on his MS in intelligent systems.
The promise and pitfalls of artificial intelligence explored at TEDxMIT event
MIT scientists discuss the future of AI with applications across many sectors, as a tool that can be both beneficial and harmful.
Physics and the machine-learning “black box”
In 2.C01, George Barbastathis demonstrates how mechanical engineers can use their knowledge of physical systems to keep algorithms in check and develop more accurate predictions.
Tackling hard computational problems
David Gamarnik has developed a new tool, the overlap gap property, for understanding computational problems that appear intractable.
Making computation come alive
A new course teaches students how to use computational techniques to solve real-world problems, from landing a spacecraft to placing cell phone towers.
MIT in the media: 2021 in review
MIT community members made headlines around the world for their innovative approaches to addressing problems local and global.