Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS

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  • Publication
    Kreativität der generativen KI
    ( 2024-03-28)
    Paass, Gerhard
    ;
    Hecker, Dirk
    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.
  • Publication
    Data Ecosystems: A New Dimension of Value Creation Using AI and Machine Learning
    ( 2022-07-22)
    Hecker, Dirk
    ;
    Voß, Angelika
    ;
    Wrobel, Stefan
    Machine learning and artificial intelligence have become crucial factors for the competitiveness of individual companies and entire economies. Yet their successful deployment requires access to a large volume of training data often not even available to the largest corporations. The rise of trustworthy federated digital ecosystems will significantly improve data availability for all participants and thus will allow a quantum leap for the widespread adoption of artificial intelligence at all scales of companies and in all sectors of the economy. In this chapter, we will explain how AI systems are built with data science and machine learning principles and describe how this leads to AI platforms. We will detail the principles of distributed learning which represents a perfect match with the principles of distributed data ecosystems and discuss how trust, as a central value proposition of modern ecosystems, carries over to creating trustworthy AI systems.
  • Publication
    Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety
    ( 2022-06-18)
    Houben, Sebastian
    ;
    Albrecht, Stefanie
    ;
    Akila, Maram
    ;
    Bär, Andreas
    ;
    Brockherde, Felix
    ;
    Feifel, Patrick
    ;
    Fingscheidt, Tim
    ;
    Gannamaneni, Sujan Sai
    ;
    Ghobadi, Seyed Eghbal
    ;
    Hammam, Ahmed
    ;
    Haselhoff, Anselm
    ;
    Hauser, Felix
    ;
    Heinzemann, Christian
    ;
    Hoffmann, Marco
    ;
    Kapoor, Nikhil
    ;
    Kappel, Falk
    ;
    Klingner, Marvin
    ;
    Kronenberger, Jan
    ;
    Küppers, Fabian
    ;
    Löhdefink, Jonas
    ;
    Mlynarski, Michael
    ;
    Mock, Michael
    ;
    Mualla, Firas
    ;
    Pavlitskaya, Svetlana
    ;
    Poretschkin, Maximilian
    ;
    Pohl, Alexander
    ;
    Ravi-Kumar, Varun
    ;
    Rosenzweig, Julia
    ;
    Rottmann, Matthias
    ;
    Rüping, Stefan
    ;
    Sämann, Timo
    ;
    Schneider, Jan David
    ;
    Schulz, Elena
    ;
    Schwalbe, Gesina
    ;
    Sicking, Joachim
    ;
    Srivastava, Toshika
    ;
    Varghese, Serin
    ;
    Weber, Michael
    ;
    Wirkert, Sebastian
    ;
    Wirtz, Tim
    ;
    Woehrle, Matthias
    Deployment of modern data-driven machine learning methods, most often realized by deep neural networks (DNNs), in safety-critical applications such as health care, industrial plant control, or autonomous driving is highly challenging due to numerous model-inherent shortcomings. These shortcomings are diverse and range from a lack of generalization over insufficient interpretability and implausible predictions to directed attacks by means of malicious inputs. Cyber-physical systems employing DNNs are therefore likely to suffer from so-called safety concerns, properties that preclude their deployment as no argument or experimental setup can help to assess the remaining risk. In recent years, an abundance of state-of-the-art techniques aiming to address these safety concerns has emerged. This chapter provides a structured and broad overview of them. We first identify categories of insufficiencies to then describe research activities aiming at their detection, quantification, or mitigation. Our work addresses machine learning experts and safety engineers alike: The former ones might profit from the broad range of machine learning topics covered and discussions on limitations of recent methods. The latter ones might gain insights into the specifics of modern machine learning methods. We hope that this contribution fuels discussions on desiderata for machine learning systems and strategies on how to help to advance existing approaches accordingly.
  • Publication
    Safety Assurance of Machine Learning for Perception Functions
    ( 2022-06)
    Burton, Simon
    ;
    Hellert, Christian
    ;
    Hüger, Fabian
    ;
    Mock, Michael
    ;
    Rohatschek, Andreas
    The latest generation of safety standards applicable to automated driving systems require both qualitative and quantitative safety acceptance criteria to be defined and argued. At the same time, the use of machine learning (ML) functions is increasingly seen as a prerequisite to achieving the necessary levels of perception performance in the complex operating environments of these functions. This inevitably leads to the question of which supporting evidence must be presented to demonstrate the safety of ML-based automated driving systems. This chapter discusses the challenge of deriving suitable acceptance criteria for the ML function and describes how such evidence can be structured in order to support a convincing safety assurance case for the system. In particular, we show how a combination of methods can be used to estimate the overall machine learning performance, as well as to evaluate and reduce the impact of ML-specific insufficiencies, both during design and operation.
  • Publication
    Using Interactive Visualization and Machine Learning for Seismic Interpretation
    ( 2022)
    Bogen, Manfred
    ;
    Ewert, Christian
    ;
    Landenberg, André von
    ;
    Rilling, Stefan
    ;
    Wulff, Benjamin
    This book chapter describes the use of interactive visualization and artificial intelligence (AI) for seismic interpretation purposes. After an introduction with some basics about finding oil and gas through seismic interpretation in Sect. 1, we describe two interactive visualization methods called user-driven seismic volume classification in Sect. 2 and semi-automatic detection of anomalies in seismic data based on local histogram analysis in Sect. 3. In Sects. 4 and 5, we describe our approach to use convolutional neural networks (CNNs), a class of deep neural networks, for the detection of geobodies such as fault, channels, and salt domes. In seismic interpretation, confidence in the results and risk minimization is always very important. Which decisions can be made on the results of an artificial intelligence? To address this common concern, we describe in Sect. 6 a method how to understand the operation of the CNNs better and how to thereby increase trust in the findings based on artificial intelligence. As Fraunhofer is about applied research, we had to implement a prototype or solution for our AI-based methods. We called it DeepGeo. We describe DeepGeo in Sect. 7 of this book in detail, before we give a short overview on the status quo on the next things to come from us in the seismic interpretation field with artificial intelligence and deep neural networks.
  • Publication
    Visual Analytics for Characterizing Mobility Aspects of Urban Context
    ( 2021)
    Andrienko, Gennady
    ;
    Andrienko, Natalia
    ;
    Patterson, Fabian
    ;
    Chen, Siming
    ;
    Weibel, Robert
    ;
    Huang, H.
    ;
    Doulkeridis, C.
    ;
    Georgiou, H.
    ;
    Pelekis, N.
    ;
    Theodoridis, Y.
    ;
    Nanni, M.
    ;
    Longhi, L.
    ;
    Koumparos, A.
    ;
    Yasar, A.
    ;
    Kureshi, I.
    Visual analytics science develops principles and methods for efficient humanâcomputer collaboration in solving complex problems. Visual and interactive techniques are used to create conditions in which human analysts can effectively utilize their unique capabilities: the power of seeing, interpreting, linking, and reasoning. Visual analytics research deals with various types of data and analysis tasks from numerous application domains. A prominent research topic is analysis of spatiotemporal data, which may describe events occurring at different spatial locations, changes of attribute values associated with places or spatial objects, or movements of people, vehicles, or other objects. Such kinds of data are abundant in urban applications. Movement data are a quintessential type of spatiotemporal data because they can be considered from multiple perspectives as trajectories, as spatial events, and as changes of space-related attribute values. By example of movement dat a, we demonstrate the utilization of visual analytics techniques and approaches in data exploration and analysis.
  • Publication
    Grundlagen des Maschinellen Lernens
    ( 2021)
    Bauckhage, Christian
    ;
    Hübner, Wolfgang
    ;
    Hug, Ronny
    ;
    Paaß, Gerhard
    ;
    Rüping, Stefan
    Zu definieren, was die menschliche Intelligenz sowie intelligentes Handeln – und da­mit auch die Künstliche Intelligenz – ausmacht, ist außerordentlich schwer und be­schäftigt Philosophen und Psychologen seit Jahrtausenden. Allgemein anerkannt istaber, dass die Fähigkeit zu lernen ein zentrales Merkmal vonIntelligenzist. So ist auchdas Forschungsgebiet desMaschinellen Lernens(engl.machine learning, ML) ein zen­traler Teil der Künstlichen Intelligenz, das hinter vielen aktuellen Erfolgen von KI-Sys­temen steckt.
  • Publication
    Smart Services in the Physical World: Digital Twins
    ( 2020)
    Stojanovic, Ljiljana
    ;
    Bader, Sebastian R.
    Comprehensive, independently operating digital representations of physical assets, provisioned and manipulated through standardized interaction patterns, dissolve between the tangible and virtual world. Real-world developments are reflected in digital models and vice versa. The concept of digital twins combines these facets to integrated entities, specifying the description, appearance, and behavior of real-world entities in virtual models. This chapter explains how smart services enact as digital twins but also how they interact in flexible, loosely coupled networks.