Presentation abstract
Machine learning has become the workhorse of intelligent systems providing means for acquiring new capabilities and drawing inferences to empower intelligent decision making and performing of actions. All machine learning, however, builds on data and assumptions on these data. The underlying data may be expensive and scarce. Even when it is abundant, it may underrepresent important concepts, and generalizations from data may fail to behave as expected. In this talk, we present several approaches of how the integration of knowledge representation and machine learning may make systems more intelligent.
Presenter
Steffen Staab is Professor of Analytic Computing at the University of Stuttgart and Professor of Web and Computer Science at the University of Southampton. He is a spokesperson of the Cluster of Excellence "Data-integrated Simulation Science (SimTech)", a co-chair of the Stuttgart Interchange Forum for Reflecting on Intelligent Systems (IRIS) and founding director of the Institute for Artificial Intelligence at the University of Stuttgart.
Staab received his M.S.E. from University of Pennsylvania (1994), his Dr. rer. nat. from University of Freiburg (1998) and his habilitation at Universität Karlsruhe (2002). From 2004-2020, he was Professor of Database and Information Systems at University of Koblenz, where he founded the Institute for Web Science and Technologies (WeST). Together with former members of his department, he founded Semanux GmbH to facilitate interaction with computers for people with motor impairments using artificial intelligence methods. Staab is a fellow of the European Association for Artificial Intelligence and of the Asia-Pacific AI Association. He received the academy award of Rhineland-Palatinate in 2018.