Application of multiobjective genetic programming to the design of robot failure recognition systems

We present an evolutionary approach using multiobjective genetic programming (MOGP) to derive optimal feature extraction preprocessing stages for robot failure detection. This data-driven machine learning method is compared both with conventional (nonevolutionary) classifiers and a set of domain-dependent feature extraction methods. We conclude MOGP is an effective and practical design method for failure recognition systems with enhanced recognition accuracy over conventional classifiers, independent of domain knowledge

Extracting tag hierarchies

Tagging items with descriptive annotations or keywords is a very natural way to compress and highlight information about the properties of the given entity. Over the years several methods have been proposed for extracting a hierarchy between the tags for systems with a "flat", egalitarian organization of the tags, which is very common when the tags correspond to free words given by numerous independent people. Here we present a complete framework for automated tag hierarchy extraction based on tag occurrence statistics. Along with proposing new algorithms, we are also introducing different quality measures enabling the detailed comparison of competing approaches from different aspects. Furthermore, we set up a synthetic, computer generated benchmark providing a versatile tool for testing, with a couple of tunable parameters capable of generating a wide range of test beds. Beside the computer generated input we also use real data in our studies, including a biological example with a pre-defined hierarchy between the tags. The encouraging similarity between the pre-defined and reconstructed hierarchy, as well as the seemingly meaningful hierarchies obtained for other real systems indicate that tag hierarchy extraction is a very promising direction for further research with a great potential for practical applications.Comment: 25 pages with 21 pages of supporting information, 25 figure

Articulating tomorrow: Large language models in the service of professional training. A contribution by the Digitalbegleitung (technological monitoring and research) within the framework of the German funding program "Innovationswettbewerb INVITE"

The present paper offers a comprehensive introduction to large language models and their transformative impact on professional training. Language models, especially GPT models, are on the verge of revolutionizing teaching methods and the culture of learning itself. The paper aims to explore the diverse applications, opportunities, and challenges of language models in professional education and training. It presents how language models work and real-world use cases in professional education. The use cases range from filtering and capturing metadata from course descriptions for better findability and interoperability, to improving training in production, supporting role-play-based learning units, and virtual coaching for future leaders. Each case study highlights the specific use of language models, the benefits they bring to educational content, and the insights gained from integrating these technologies into learning systems. This publication is part of an innovation competition focused on connecting and advancing educational and training platforms with modern methods like AI. It underscores the necessity for ongoing research, development, and collaboration to responsibly harness the full potential of large language models in education. (DIPF/Orig.)Das vorliegende Papier bietet eine umfassende Einführung in große Sprachmodelle und ihre transformative Wirkung auf die berufsbezogene Weiterbildung. Sprachmodelle, insbesondere GPT-Modelle, stehen an der Schwelle, Lehrmethoden und die Lernkultur selbst zu revolutionieren. Das Papier zielt darauf ab, die vielfältigen Einsatzmöglichkeiten, Chancen und Herausforderungen von Sprachmodellen in der beruflichen Bildung und Weiterbildung zu erkunden. Es stellt die Funktionsweise von Sprachmodellen und reale Anwendungsfälle in der beruflichen Bildung vor. Die Anwendungsfälle reichen vom Herausfiltern und Erfassen von Metadaten aus Kursbeschreibungen für eine bessere Auffindbarkeit und Interoperabilität, über die Verbesserung der Ausbildung in der Produktion, die Unterstützung von rollenspielbasierten Lerneinheiten, bis hin zum virtuellen Coaching für zukünftige Führungskräfte. Jede Fallstudie reflektiert die spezifische Nutzung von Sprachmodellen, die Vorteile, die sie für den Bildungsinhalt bringen, und die aus der Integration dieser Technologien in Lernsysteme gewonnenen Erkenntnisse. Diese Publikation ist Teil des vom BMBF geförderten Innovationswettbewerbs INVITE, der auf die Vernetzung und Weiterentwicklung von Bildungs- und Weiterbildungsplattformen mit modernen Methoden wie KI fokussiert. Das Papier betont die Notwendigkeit kontinuierlicher Forschung, Entwicklung und Zusammenarbeit, um das volle Potenzial von Sprachmodellen in der Bildung verantwortungsvoll zu nutzen. (DIPF/Orig.

MLI: An API for Distributed Machine Learning

MLI is an Application Programming Interface designed to address the challenges of building Machine Learn- ing algorithms in a distributed setting based on data-centric computing. Its primary goal is to simplify the development of high-performance, scalable, distributed algorithms. Our initial results show that, relative to existing systems, this interface can be used to build distributed implementations of a wide variety of common Machine Learning algorithms with minimal complexity and highly competitive performance and scalability

Opportunities and Challenges of Applying Large Language Models in Building Energy Efficiency and Decarbonization Studies: An Exploratory Overview

In recent years, the rapid advancement and impressive capabilities of Large Language Models (LLMs) have been evident across various domains. This paper explores the application, implications, and potential of LLMs in building energy efficiency and decarbonization studies. The wide-ranging capabilities of LLMs are examined in the context of the building energy field, including intelligent control systems, code generation, data infrastructure, knowledge extraction, and education. Despite the promising potential of LLMs, challenges including complex and expensive computation, data privacy, security and copyright, complexity in fine-tuned LLMs, and self-consistency are discussed. The paper concludes with a call for future research focused on the enhancement of LLMs for domain-specific tasks, multi-modal LLMs, and collaborative research between AI and energy experts