Argonne is developing AI architectures that continue to learn long after deployment, an ability that is critical to autonomous vehicles, robotics, smart sensors, and more.
An Argonne team has developed a machine learning approach for calibrating the center of rotation in x-ray light source tomography data that provides better accuracy than conventional imaging processing-based methods.
From studying a sea slug, researchers have demonstrated a fundamental type of learning in an inorganic system that may serve as a building block for neuromorphic computing and AI applications.
A new machine learning technique that uses data from high-energy X-ray diffraction experiments will significantly reduce model development time and human effort.