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PublicationArtificial Intelligence. What Is Behind the Technology of the Future?(Springer Nature, 2024-05-16)Artificial Intelligence (AI) is already present in our daily routines, and in the future, we will encounter it in almost every aspect of life - from analyzing X-rays for medical diagnosis, driving autonomous cars, maintaining complex machinery, to drafting essays on environmental problems and drawing imaginative pictures. The potentials of AI are enormous, while at the same time many myths, uncertainties and challenges circulate that need to be tackled. The English translation of the book "Künstliche Intelligenz - Was steckt hinter der Technologie der Zukunft?" originally published in German (Springer Vieweg, 2020), this book is addressed to the general public, from interested citizens to corporate executives who want to develop a better and deeper understanding of AI technologies and assess their consequences. Mathematical basics, terminology, and methods are explained in understandable language. Adaptations to different media such as images, text, and speech and the corresponding generative models are introduced. A concluding discussion of opportunities and challenges helps readers evaluate new developments, demystify them, and assess their relevance for the future.
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PublicationA global scale comparison of risk aggregation in AI assessment frameworks( 2024-05-06)AI applications bear inherent risks in various risk dimensions, such as insufficient reliability, robustness, fairness or data protection. It is well-known that trade-offs between these dimensions can arise, for example, a highly accurate AI application may reflect unfairness and bias of the real-world data, or may provide hard-to-explain outcomes because of its internal complexity. AI risk assessment frameworks aim to provide systematic approaches to risk assessment in various dimensions. The overall trustworthiness assessment is then generated by some form of risk aggregation among the risk dimensions. This paper provides a systematic overview on risk aggregation schemes used in existing AI risk assessment frameworks, focusing on the question how potential trade-offs among the risk dimensions are incorporated. To this end, we examine how the general risk notion, the application context, the extent of risk quantification, and specific instructions for evaluation may influence overall risk aggregation. We discuss our findings in the current frameworks in terms of whether they provide meaningful and practicable guidance. Lastly, we derive recommendations for the further operationalization of risk aggregation both from horizontal and vertical perspectives.
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PublicationDeveloping trustworthy AI applications with foundation models( 2024-04)
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PublicationMachine Learning Operations (MLOps): Grundlagen, Chancen und Herausforderungen beim MLOps-Einsatz in Unternehmen( 2024-04)
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PublicationKreativität der generativen KI( 2024-03-28)In diesem Beitrag wird die Frage diskutiert, ob auch Systeme der generativen KI kreative Inhalte erzeugen können. Es wird zunächst beschrieben, wie solche Systeme intern funktionieren und wie sie potenziell neue Inhalte generieren können. Anschließend wird der kreative Prozess diskutiert und es wird überprüft, ob KI-Systeme kreative Leistungen für die unterschiedlichen Medien Text, Bild und Musik erbringen können. In standardisierten Tests konnte gezeigt werden, dass das Sprachmodell GPT-4 inzwischen kreativere Antworten produziert als Menschen. Ähnliche Tests haben ergeben, dass Bilder, die mit einer älteren Version von DALL-E erstellt wurden, nur schwer von Künstlerbildern zu unterscheiden sind. Aufgrund der stark verbesserten Detailgenauigkeit neuerer Systeme ist davon auszugehen, dass diese heute eine verbesserte Kreativität besitzen. Systeme zur Generierung von Musik können derzeit dagegen noch nicht mit der Kreativität menschlicher Komponist*innen mithalten.
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PublicationIncorporating Query Recommendation for Improving In-Car Conversational Search( 2024-03-23)Retrieval-augmented generation has become an effective mechanism for conversational systems in domain-specific settings. Retrieval of a wrong document due to the lack of context from the user utterance may lead to wrong answer generation. Such an issue may reduce the user engagement and thereby the system reliability. In this paper, we propose a context-guided follow-up question recommendation to internally improve the document retrieval in an iterative approach for developing an in-car conversational system. Specifically, a user utterance is first reformulated, given the context of the conversation to facilitate improved understanding to the retriever. In the cases, where the documents retrieved by the retriever are not relevant enough for answering the user utterance, we employ a large language model (LLM) to generate question recommendation which is then utilized to perform a refined retrieval. An empirical evaluation confirms the effectiveness of our proposed approaches in in-car conversations, achieving 48% and 22% improvement in the retrieval and system generated responses, respectively, against baseline approaches.
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PublicationControlled Randomness Improves the Performance of Transformer Models( 2024-03-19)
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PublicationDeep Dynamic Language Models( 2024-03-15)This thesis investigates the domain of deep dynamic language models, focusing on the integration of temporal dynamics to enhance language modeling and its application in various tasks, such as text generation, recommendation systems, and predicting post popularity. Temporal content change, i.e., trends and themes that change with time featured in document collections such as academic journals, news articles and social media, make the traditional static language models (LMs) not an optimal solution. In order to address this limitation, several approaches to develop dynamic LMs are proposed and explored in this thesis. Initially, the impact of incorporating temporal information is explored, specifically in the context of modeling online communities. For the analysis of temporal content change in Yelp - a crowd-sourced review platform - an instantaneous language model is proposed. This model combines a temporal point process (TPP) for modeling review creation times and a LM to capture textual aspects. Empirical evaluations demonstrate that this model significantly improves the performance of LMs in terms of both language modeling and prediction of review creation time. Building upon the success of the instantaneous LM, the research in this thesis is extended to more application oriented task, such as recommender systems. Recognizing that user preferences and item reviews change over time, the proposed model here leverages users’ reviews to enhance rating predictions. By developing time-interval aware representations, the proposed model outperforms several state-of-the-art recommender systems models in real-world datasets. Additionally, the integration of dynamic topic models into LMs is explored. First, the problem of skewed topic distributions in topic modeling is addressed, which can cause models to learn more general topics present in the majority of documents, rather than rare topics present in only a few documents. A neural dynamic focused topic model is proposed as a solution, which decouples topic activities from topic proportions in documents using sequences of Bernoulli random variables. Experimental evaluations show that this model outperforms state-of-the-art topic models in generalization tasks, while employing a comparable number of parameters and converging two times faster. Furthermore, the performance of large pre-trained language models (LPLMs) in dynamic environments is explored. The empirical analysis on Reddit datasets reveals significant performance drops when predicting the popularity of future posts due to temporal distribution shifts in data. To mitigate this issue, a model is proposed that combines neural variational dynamic topic models and attention mechanisms to infer temporal LM representations. The proposed model exhibit improved performance while utilizing only a fraction of the parameters of LPLMs, and providing interpretable representations that offer insights into real-world events. In summary, this thesis emphasizes the significance of incorporating temporal dynamics into LMs and explores their application in various tasks.
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PublicationWie Agenten und Foundation-Modelle bei der Versorgung Schwerverletzter helfen( 2024-03)
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PublicationSuperkraft Sprachmodell?( 2024-03)Die deutsche Verwaltungslandschaft steht angesichts der notwendigen Digitalisierung und Automatisierung von bisher manuellen Prozessen vor einer grundlegenden Transformation. Der Anstieg an Anträgen für Wohngeld, BAföG oder Einbürgerungsverfahren setzt Behörden zusätzlich unter Druck. Der entstehende Rückstau trägt zu einem sinkenden Vertrauen in die Leistungsfähigkeit der öffentlichen Verwaltung bei. Gleichzeitig muss sie die rückläufigen Mitarbeitendenzahlen infolge des demografischen Wandels kompensieren. Generative Künstliche Intelligenz (GenAI) und insbesondere große Sprachmodelle (Large Language Models, LLMs) spielen hier eine wichtige Rolle, um die Mitarbeitenden zukünftig in ihren Aufgaben zu unterstützen, zu entlasten und hierdurch Freiräume zu schaffen, um sich verstärkt der direkten Interaktion mit Bürgerinnen und Bürgern zu widmen. In diesem Briefing präsentieren Fraunhofer IAIS und Deloitte drei Anwendungsbeispiele großer Sprachmodelle, von welchen die öffentliche Verwaltung schon heute profitieren kann. Bei der Betrachtung zu etablierender Rahmenbedingungen muss zwischen den behördeninternen Voraussetzungen und der staatlichen Infrastruktur unterschieden werden. Diese Publikation betrachtet die Voraussetzungen auf individueller Ebene der Behörden.
