Rigo Wenning

The article presents selected insights from the tenth Dagstuhl Workshop on Applied Machine Intelligence, highlighting recent advances in generative, knowledge‑based, and hybrid AI. It discusses how generative AI is reshaping creative and editorial workflows. The workshop also examined the limitations of LLMs in explanation tasks and quality assurance, emphasising the need for trustworthy, domain‑sensitive and hybrid approaches that combine neural and symbolic methods. Further contributions explore agentic AI as an emerging paradigm for business information systems and revisit abductive reasoning to better integrate formal inference with human‑centred modelling. Together, these perspectives illustrate how hybrid AI can address current shortcomings and support reliable, transparent and domain‑aligned applications across sectors.

This article presents new trends and applications in generative artificial intelligence (AI) and knowledge-based AI. Foundations and applications of large language models (LLMs) are presented, such as classifying hate speech, easy-to-read language, and retrieval-augmented generation (RAG). The important aspect of user experience (UX) for AI systems is elaborated. Finally, AI solutions in different industry sectors are presented, in particular life sciences, the energy sector, and the manufacturing industry.

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.

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.