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.
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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.
The MIT Ethics of Computing Research Symposium showcases projects at the intersection of technology, ethics, and social responsibility.
A new framework from the MIT-IBM Watson AI Lab supercharges language models, so they can reason over, interactively develop, and verify valid, complex travel agendas.
The system automatically learns to adapt to unknown disturbances such as gusting winds.
A team of MIT researchers founded Themis AI to quantify AI model uncertainty and address knowledge gaps.
SketchAgent, a drawing system developed by MIT CSAIL researchers, sketches up concepts stroke-by-stroke, teaching language models to visually express concepts on their own and collaborate with humans.
PhD student Sarah Alnegheimish wants to make machine learning systems accessible.
This new machine-learning model can match corresponding audio and visual data, which could someday help robots interact in the real world.
Researchers are developing algorithms to predict failures when automation meets the real world in areas like air traffic scheduling or autonomous vehicles.
Sendhil Mullainathan brings a lifetime of unique perspectives to research in behavioral economics and machine learning.