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.
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MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.
A new study shows someone’s beliefs about an LLM play a significant role in the model’s performance and are important for how it is deployed.
The model could help clinicians assess breast cancer stage and ultimately help in reducing overtreatment.
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.
A new technique enables users to compare several large models and choose the one that works best for their task.
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.
Developed by MIT RAISE, the Day of AI curriculum empowers K-12 students to collaborate on local and global challenges using AI.