MIT researchers advance automated interpretability in AI models
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
An MIT team uses computer models to measure atomic patterns in metals, essential for designing custom materials for use in aerospace, biomedicine, electronics, and more.
Neural network controllers provide complex robots with stability guarantees, paving the way for the safer deployment of autonomous vehicles and industrial machines.
The approach could help engineers design more efficient energy-conversion systems and faster microelectronic devices, reducing waste heat.
Members of the MIT community, supporters, and guests commemorate the opening of the new college headquarters.
New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.
More accurate uncertainty estimates could help users decide about how and when to use machine-learning models in the real world.
This new tool offers an easier way for people to analyze complex tabular data.
MosaicML, co-founded by an MIT alumnus and a professor, made deep-learning models faster and more efficient. Its acquisition by Databricks broadened that mission.