TY - JOUR A1 - Humm, Bernhard G. A1 - Bense, Hermann A1 - Fuchs, Michael A1 - Gernhardt, Benjamin A1 - Hemmje, Matthias A1 - Hoppe, Thomas A1 - Kaupp, Lukas A1 - Lothary, Sebastian A1 - Schäfer, Kai-Uwe A1 - Thull, Bernhard A1 - Vogel, Tobias A1 - Wenning, Rigo T1 - Machine intelligence today: applications, methodology, and technology JF - Informatik Spektrum N2 - Machine intelligence, a.k.a. artificial intelligence (AI) is one of the most prominent and relevant technologies today. It is in everyday use in the form of AI applications and has a strong impact on society. This article presents selected results of the 2020 Dagstuhl workshop on applied machine intelligence. Selected AI applications in various domains, namely culture, education, and industrial manufacturing are presented. Current trends, best practices, and recommendations regarding AI methodology and technology are explained. The focus is on ontologies (knowledge-based AI) and machine learning. KW - Maschinelles Lernen KW - Computer Science, general KW - Computer Hardware KW - Computer Systems Organization and Communication Networks KW - Software Engineering/Programming and Operating Systems KW - Data Structures and Information Theory KW - Künstliche Intelligenz KW - Ontologie Y1 - 2021 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:523-16759 SN - 0170-6012 SN - 1432-122X VL - 44 IS - 2 SP - 104 EP - 114 PB - Springer Berlin Heidelberg ER - TY - JOUR A1 - Humm, Bernhard G. A1 - Archer, Phil A1 - Bense, Hermann A1 - Bernier, Carolynn A1 - Goetz, Christian A1 - Hoppe, Thomas A1 - Schumann, Fabienne A1 - Siegel, Melanie A1 - Wenning, Rigo A1 - Zender, Alexander T1 - New directions for applied knowledge-based AI and machine learning BT - Selected results of the 2022 Dagstuhl Workshop on Applied Machine Intelligence JF - Informatik Spektrum N2 - In this article, selected new directions in knowledge-based artificial intelligence (AI) and machine learning (ML) are presented: ontology development methodologies and tools, automated engineering of WordNets, innovations in semantic search, and automated machine learning (AutoML). Knowledge-based AI and ML complement each other ideally, as their strengths compensate for the weaknesses of the other discipline. This is demonstrated via selected corporate use cases: anomaly detection, efficient modeling of supply networks, circular economy, and semantic enrichment of technical information. KW - Information Sciences KW - Information and Computing Sciences Y1 - 2023 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:523-19119 SN - 0170-6012 SN - 1432-122X VL - 46 IS - 2 SP - 65 EP - 78 PB - Springer Nature ER -