Unpacking the bias of large language models
In a new study, researchers discover the root cause of a type of bias in LLMs, paving the way for more accurate and reliable AI systems.
Data
Celebrating an academic-industry collaboration to advance vehicle technology
MIT Advanced Vehicle Technology Consortium marks a decade of developing data that improve understanding of how drivers use and respond to increasingly sophisticated automotive features.
Bringing meaning into technology deployment
The MIT Ethics of Computing Research Symposium showcases projects at the intersection of technology, ethics, and social responsibility.
Melding data, systems, and society
A new book from Professor Munther Dahleh details the creation of a unique kind of transdisciplinary center, uniting many specialties through a common need for data science.
Teaching AI models what they don’t know
A team of MIT researchers founded Themis AI to quantify AI model uncertainty and address knowledge gaps.
3 Questions: How to help students recognize potential bias in their AI datasets
Courses on developing AI models for health care need to focus more on identifying and addressing bias, says Leo Anthony Celi.
An anomaly detection framework anyone can use
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
Building networks of data science talent
Through collaborations with organizations like BREIT in Peru, the MIT Institute for Data, Systems, and Society is upskilling hundreds of learners around the world in data science and machine learning.
AI learns how vision and sound are connected, without human intervention
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
Learning how to predict rare kinds of failures
Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.