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.
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.
Seeing the plasma edge of fusion experiments in new ways with artificial intelligence
MIT researchers are testing a simplified turbulence theory’s ability to model complex plasma phenomena using a novel machine-learning technique.
Meet the 2021-22 Accenture Fellows
The 2021-22 Accenture Fellows are bolstering research and igniting ideas to help transform global business.
Perfecting pitch perception
Computational modeling shows that both our ears and our environment influence how we hear.
Q&A: Cathy Wu on developing algorithms to safely integrate robots into our world
Assistant professor of civil engineering describes her career in robotics as well as challenges and promises of human-robot interactions.
Nonsense can make sense to machine-learning models
Deep-learning methods confidently recognize images that are nonsense, a potential problem for medical and autonomous-driving decisions.
Machine learning speeds up vehicle routing
Strategy accelerates the best algorithmic solvers for large sets of cities.
Machine-learning system flags remedies that might do more harm than good
The system could help physicians select the least risky treatments in urgent situations, such as treating sepsis.
A tool to speed development of new solar cells
A new computational simulator can help predict whether changes to materials or design will improve performance in new photovoltaic cells.