CC-KING is the competence center for AI Systems Engineering of the Karlsruhe research institutes Fraunhofer Institute of Optronics, Systems Engineering and Image Exploitation IOSB, FZI Research Center for Information Technology and Karlsruhe Institute of Technology (KIT). It connects top-level AI research and established engineering disciplines and thus aims to facilitate the practical application of methods of artificial intelligence (AI) and machine learning (ML).

Many companies have a true treasury of data that could be used to create value with the help of AI.  However, AI competence is often lacking. The gap is difficult to close because AI experts are rare and, in many cases, unfamiliar with the typical application domains. Therefore, CC-KING offers companies practical support.

The competence center conducts research in close contact with companies on fundamental questions, practical methodologies and concrete application problems from the contexts of mobility and industrial production. Existing research initiatives such as the Karlsruhe Research Factory and the Test Area Autonomous Driving Baden-Württemberg serve as real-life laboratories for the key aspects.

In addition to workshops and conferences on AI Systems Engineering, the direct transfer of results from CC-KING to companies takes place via QuickChecks and TransferChecks. QuickChecks are individual consulting services for end users, who test and validate the use of AI and ML methods on a project-specific basis. Reusable tools are developed for frequently occurring applications in the context of TransferChecks. In addition, an AI Systems Engineering learning lab is being established for the training of employees.

CC-KING is funded by the Ministry of Economics, Labour and Tourism of Baden-Württemberg.





Project structure

In its initial term 2020/2021 the competence center is divided into the following work packages:

  • WP1: Methodological fundamentals (Lead: KIT)
    • WP1.1: Security of AI/ML-based systems
    • WP1.2: Plausibility, traceability and explainability of decisions
    • WP1.3: Integration of previous knowledge, expert knowledge and simulators
    • WP1.4: Model monitoring and model adjustment
  • WP2: Tools and components (Lead: FZI)
    • WP2.1: Procedure model for AI engineering
    • WP2.2: Assistance functions for knowledge acquisition and optimization of AI components
    • WP2.3: AI and ML procedures with limited resources
    • WP2.4: Description and cataloguing of AI/ML methods
  • WP3: Use cases (Lead: Fraunhofer IOSB)
    • WP3.1: CC-KING Production
    • WP3.2: CC-KING Mobility
  • WP4: Project management and transfer (Lead: Fraunhofer IOSB)
    • WP4.1: Project management team
    • WP4.2: Scientific directorate
    • WP4.3: Innovation Advisory Board
    • WP4.4: Technical and scientific management
    • WP4.5: Consulting center for the use of AI and ML components in SMEs
    • WP4.6: AI Engineering Learning Lab for the training of employees from SMEs
    • WP4.7: Public Relations