Artificial intelligence represents an important research and teaching field of our Department. For example, deep learning neural networks can significantly improve previous (often only linear) approaches in very many areas. Therefore, they are naturally an integral part of our teaching and also play an important role in many areas of research - such as medical technology - and can serve our medical partners well in diagnoses and analyses.

In addition to classical supervised learning (which is probably the most frequently used case), unsupervised learning (e.g. for data compression or for the detection of unusual signal or data areas) and also so-called "reinforcement learning" are playing an increasingly important role. The latter can be used especially for control and regulation purposes, since successful strategies of control and regulation can be learned in the long run.

In addition to these more application-related sub-aspects, however, the Department is also looking at the energy balance of systems with artificial intelligence. Due to the increasing use of artificial intelligence, this aspect will play an important role in the future. Therefore, the Collaborative Research Center 1461 "Neuroelectronics: Biologically Inspired Information Processing" is increasingly looking at and researching analog variants of artificial intelligence.