Research on the traceability and efficiency of AI technologies

There is hardly any area that is not being significantly transformed by AI-based technologies: from goods production and logistics to medical technology. Artificial intelligence is already part of our everyday lives. In the coming years, AI researchers will lay the foundations for countless further innovations.  

Research at CEE-AI covers the entire spectrum from hardware support and device communication to AI approaches and practical applications. A particular focus here is on the traceability and efficiency of AI technologies. Another question is how to deal with the vast amounts of data generated as AI becomes increasingly widespread. Today, this data is often stored on huge cloud servers abroad. However, AI technologies for autonomous driving or smart home appliances need local computing power to enable safe and efficient collaboration between humans and machines. "Small data" is therefore another important topic that scientists at CEE AI want to address.

• Expansion of a strategically important research area

The Center for Explainable and Efficient AI Technologies CEE AI is jointly supported by Dresden University of Technology (TU Dresden) and the Fraunhofer Society. On the TU side, it comprises the Center for Tactile Internet CETI cluster of excellence and other chairs. On the Fraunhofer Society side, it includes the Fraunhofer Institute for Intelligent Analysis and Information Systems IAIS with its institute location in Dresden in cooperation with Fraunhofer IWU, the Fraunhofer Center for CPS, the Development of Adaptive Systems EAS department of the Fraunhofer Institute for Integrated Circuits IIS, and the Fraunhofer Institute for Transportation and Infrastructure Systems IVI. 

The joint center bundles activities related to artificial intelligence in the region, thereby strengthening, among other things, the mechanical engineering and microelectronics industries, which are concentrated in Dresden. The thematic focus of CEE AI is on "explainable AI," in particular knowledge modeling, and "efficient AI" with a focus on high-performance computing and big data, as well as the efficient integration of AI algorithms into company-owned hardware systems.