KEYNOTE TALK SERIES
Madjid Fathi
Professor & Director (University of Siegen, Germany)
Bio : Madjid Fathi is a professor at the Department of Electrical Engineering and Computer Science, University of Siegen. He is the Chair and Director of the Institute of Knowledge-Based Systems and Knowledge Management. Before he got to Siegen he was visiting Professor at UNM, Florida State and Georgia Tech. in USA. He established the Research Center KMIS (Knowledge Management & Intelligent System) in 2007. He did his sabbatical in U.C. Berkeley from 2012.- 2013 at BISC (Berkeley Initiative of Soft Computing) by Professor Zadeh, the Father of Fuzzy Logic.
His research interests are focused on AI, Knowledge Based system & knowledge management ap-plications in medicine and engineering, computational intelligence and knowledge discovery from text (KDT). Dr. Fathi has published 4 text and 8 edited books, with his students he published more than 250 papers receiving 5 best paper awards. He got the European Award prise ‘Qute’ 2015.He has published the book “Computer Aided Writing” with Dr. Klahold by springer published in 12/2019.
He is also the editor of the book series “Integrated Systems: Innovations and Applications” pub-lished by Springer. Recently Dr. Fathi with Dr. Reza Alam U.C. Berkeley published the Book Inte-grated Systems: Data Driven Engineering, August/24.
Title for Talk: Explainable AI as an Intellectual Paradigm – Data-driven intelligent de-cision support through integration of knowledge graphs
Abstract: Explainable AI (XAI) has become an essential aspect of AI, emphasizing the need for transparency and understanding in artificial intelligence models. XAI isn’t just a model; it embodies the intelligence required to describe and uncover how solutions to complex tasks are achieved. By utilizing cognitive technologies and deep learning, XAI can offer solutions through informed decision-making processes. Crucial to the effective application of AI is the ability to search for the right knowledge and develop strategies for col-lecting and using facts.
The success of explainable AI relies heavily on the extraction and representation of human knowledge, which is then used to train intelligent agents. In this talk, I will discuss the pivotal role of explainable AI models in analyzing and supporting intelligent algorithms, particularly in the context of creating AI sys-tems based on knowledge graphs. Knowledge graphs are advanced knowledge modeling methods ca-pable of organizing and representing information in a general context. They interlink networks of infor-mation, reflecting the contextual relationships between various parts of the extracted knowledge.
When combined with well-designed AI algorithms, knowledge graphs have shown great potential in en-hancing data-driven intelligent decision-making processes. This synergy is particularly evident in fields such as medical applications, smart Industry 4.0, smart cities, and other problem-solving and optimiza-tion domains. By leveraging the strengths of XAI and knowledge graphs, we can achieve more transpar-ent, accurate, and effective AI systems that drive innovation and efficiency across various industries.
Dr. Savas Tumis
(CEO, Whole Brain Strategy Co. Ltd.)
Bio: Prof. Dr. Savas Tumis has been working since 1981 in industrial companies and especially family-owned companies the same as “Hidden Champions” as a Manager, as a CEO, as an Entrepreneur with his own companies, and as a Top Management Consultant in Europe and since 1989 also in Asia.
Prof. Tumis made his PhD Degree in Engineering at the Technical University of Berlin between 1984 and 1987.He is offering his more than 35 years of experience, especially in Market Leader – Hidden Champion Companies, his managerial, entrepreneurial, and advisory experiences to your company to support your success to be a market leader company.
Prof. Dr. Savas Tumis was since 1987 the CEO of several Hidden Champions – Market Leader Companies and established also as an entrepreneur this kind of seven companies and also advised several companies in Asia since 2005. Since 1994 Prof. Dr. Savas Tumis has been applying one of the best methods in the world MLS™ to develop Market Leadership Strategies for companies for a focused niche on the know-how of a certain company, working together with the management team of the current company in internal and confidential workshops.
Oussama Khatib
(Professor, Stanford University)
Bio: Oussama Khatib received his PhD from Sup’Aero, Toulouse, France, in 1980. He is Professor of Computer Science and Director of the Robotics Laboratory at Stanford University. His research in robotics focuses on novel control architectures, algorithms, sensing, and human-friendly designs for advanced capabilities in complex environments. With an emphasis on enabling robots to interact cooperatively and safely with humans and the physical world, these studies bring understanding of human movement for therapy, athletic training, and performance enhancement. This work on understanding human cognitive task representation and physical skills is enabling transfer for increased robot autonomy. With these core capabilities, we are exploring applications in healthcare and wellness, industry and service, farms and smart cities, and dangerous and unreachable settings – deep in oceans, mines, and space. He is President of the International Foundation of Robotics Research (IFRR) and a Fellow of IEEE. He is Editor of the Springer Tracts in Advanced Robotics (STAR) series, and the Springer Handbook of Robotics, awarded the American Publishers Award for Excellence in Physical Sciences and Mathematics. He is recipient of the IEEE Robotics and Automation (IEEE/RAS) Pioneering Award (for his fundamental contributions in robotics research, visionary leadership, and life-long commitment to the field), the IEEE/RAS George Saridis Leadership Award, the Distinguished Service Award, the Japan Robot Association (JARA) Award, the Rudolf Kalman Award, the IEEE Technical Field Award, and the Engelberger Award. Professor Khatib is a member of the National Academy of Engineering.
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