Synchrotron sources produce intense X-rays that are indispensable for many fields of modern science. The essential information, which is often buried in an ocean of experimental data, can be extracted using machine learning. But such models still lack important insights about the structure of the systems being studied. By allowing experimentalists to provide such insights to adaptive neural networks, we were able to instantly obtain more accurate results.

Online learning platforms are popular tools for acquiring new knowledge on our own. However, these platforms have significant shortcomings. We present a new algorithm allowing us to trace the knowledge of learners more accurately, creating opportunities for empowerment by adapting the learning process to their personalized needs.