Artificial intelligence system rapidly predicts how two proteins will attach
The machine-learning model could help scientists speed the development of new medicines.
Fast-tracking the search for energy-efficient materials
Doctoral candidate Nina Andrejević combines spectroscopy and machine learning techniques to identify novel and valuable properties in matter.
Where did that sound come from?
MIT neuroscientists have developed a computer model that can answer that question as well as the human brain.
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.
3 Questions: Anuradha Annaswamy on building smart infrastructures
Senior research scientist and her team are designing intelligent systems that could someday transform the way we travel and consume energy.
Computing for ocean environments
MIT ocean and mechanical engineers are using advances in scientific computing to address the ocean’s many challenges, and seize its opportunities.
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.
Scientists make first detection of exotic “X” particles in quark-gluon plasma
The findings could redefine the kinds of particles that were abundant in the early universe.
When should someone trust an AI assistant’s predictions?
Researchers have created a method to help workers collaborate with artificial intelligence systems.
A new way to perform "general inverse design" with high accuracy
SMART breakthrough could help develop technologies that can identify materials according to desired properties for specific applications.
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.
“Hey, Alexa! Are you trustworthy?”
The more social behaviors a voice-user interface exhibits, the more likely people are to trust it, engage with it, and consider it to be competent.
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.