Introducing structured randomization into decisions based on machine-learning model predictions can address inherent uncertainties while maintaining efficiency.

MAIA is a multimodal agent that can iteratively design experiments to better understand various components of AI systems.

Analysis and materials identified by MIT engineers could lead to more energy-efficient fuel cells, electrolyzers, batteries, or computing devices.

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.

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.

A new technique enables users to compare several large models and choose the one that works best for their task.

New CSAIL research highlights how LLMs excel in familiar scenarios but struggle in novel ones, questioning their true reasoning abilities versus reliance on memorization.