Dr. Thomas Schmid is a computer scientist who has been developing AI methods and applications for more than fifteen years. He researches and teaches as W1 professor for digital research methods in medicine at Martin Luther University Halle-Wittenberg and as lecturer at Lancaster University in Leipzig. After studying bioinformatics in Tübingen, Gaborone and Berlin, he moved to Leipzig to develop a novel biomedical application based on neural networks. Following a research stay in the United States and pursuing a PhD in computer science from Leipzig University, Thomas established the machine learning research group there in 2018. His current research interests include hybrid AI, deep learning, and biomedical applications of machine learning. Thomas serves frequently as reviewer for international research journals and conferences, referee for funding bodies, and expert in national and international standardization projects. He is a founder of the annual Leipzig Symposium on Intelligent Systems (LEISYS) and has been serving as its chair since 2021. Thomas’ academic expertise of artificial intelligence concepts and his in-depth knowledge of state-of-the-art AI technologies were instrumental in shaping the scientific concept of the AI=MC² taxonomy.

Dr. Wolfgang Hildesheim is a high-energy physicist by background and spent several years conducting research at the CERN and DESY research centers. In 2007, he took over the management of the ‘Automotive, Aerospace and High Tech Practice’ at IBM. From 2009, he led IBM's ‘Big Data Industry Solution Business’ in Europe and supported companies in the development of data-driven business models using advanced analytics. Since 2012 he is jointly responsible for the foundation as well as the development of IBM's business unit ‘Watson, Data Science & Artificial Intelligence’ in Europe in various technical and sales roles. Wolfgang Hildesheim is a regular speaker at conferences and an editor of publications on the topic of Artificial Intelligence. Furthermore, he is actively involved in AI standardization, among others as a member of the coordinating group of the German Standardization Roadmap ‘Artificial Intelligence’. Wolfgang Hildesheim is on the board of BitKom's Artificial Intelligence Working Group as well as the corresponding committee ‘Artificial Intelligence’ of the German Institute for Standardization. In collaboration with industry, research and politics, he is involved as a delegate to the European committee CEN-CENELEC JTC 21 ‘Artificial Intelligence’ in establishing a classification scheme for the homogeneous description of Artificial Intelligence throughout all European Union member states.

Taras Holoyad is an electrical engineer and works at the Federal Network Agency in Mainz in the area of AI standardization. After studying electrical engineering and graduating from the Technical University of Braunschweig, he initially designed electrical machines for road vehicles in the automotive industry before subsequently moving to the main regulatory authority in Germany. In his day-to-day work, Taras has shaped the standardization of AI worldwide as vice chair of the ETSI TC "Methods for testing and specification" committee and project leader for the ISO/IEC 42102 "Taxonomy of AI system methods and capabilities" standard.

About the Authors.- Acknowledgments.- About the Guest Authors.- Foreword.- Preface.- How to read this book?- Part I. Basics.- 1. Artificial Intelligence – yesterday, today, tomorrow.- 2. Models and modeling – the essence of AI development.- 3. Non-stop models – data-driven AI at industrial scale.- 4. Foundation Models – A Disruptive AI Paradigm.- 5. Success with AI business models – 5 factors for data-driven AI.- Part II. 6. The three-dimensional taxonomy for AI systems “AI=MC²”.- 7. Taxonomy of AI methods.- 8. Taxonomy of AI capabilities.- 9. AI characteristics of regulatory relevance.- 10. Analyzing and comparing with the AI=MC² taxonomy.- 11. Designing Modular AI Systems with the AI=MC² Taxonomy.- Part III. AI in practice.- 12. Combining different AI's for intelligent crew planning in shipping.- 13. AI-based customer service – the HUK24 virtual assistant.- 14. AI-based object recognition in remote sensing.- 15. Luminous – a European large language model.- 16. AI-based mobile data capture.- 17. Generative Artificial Intelligence as a Tool for IP Experts.- Part IV. Trustworthiness, standardization and regulation.- 18. Trustworthiness of Artificial Intelligence.- 19. Standardization of Artificial Intelligence.- 20. The AI Act - Europe's law on AI.- 21. AI-based medical devices: Example of conformity assessment.- 22. Quicker to market – The benefits from DIN standards.