Dr. rer. nat.

Hecker, Dirk

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Big Data 2.0 - mit synthetischen Daten KI-Systeme stärken

2022-12-12 , Hecker, Dirk , Voß, Angelika , Paass, Gerhard , Wirtz, Tim

Bei der Anwendung von Künstlicher Intelligenz (KI) sind fehlende Daten immer noch eine Kernherausforderung und die Kosten zur Beschaffung ein kritischer Faktor für die Wirtschaftlichkeit vieler Geschäftsmodelle. Synthetische, also künstlich generierte Daten bilden einen Ausweg. Ein vielversprechender Lösungsansatz besteht darin, für die Datensynthese selbst ein KI-Modell einzusetzen.

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Sprachversteher

2022-04-09 , Hecker, Dirk , Paass, Gerhard

Tiefe neuronale Sprachmodelle wie GPT-3 schreiben ansprechende Texte, garnieren sie aber oft mit erfundenen Fakten. Jüngste Modelle überprüfen ihre Inhalte selbst und könnten so schon bald Hausaufgaben oder News generieren. Ein Einblick in die Entwicklung.

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Moderne Sprachtechnologien

2021-08 , Temath, Christian , Hecker, Dirk , Jovy-Klein, Florian , Lange, Mirco , Paass, Gerhard , Voß, Angelika , Walter, Oliver , Giesselbach, Sven , Köhler, Joachim , Lehmann, Jens

Sprachschnittstellen sind auf dem Vormarsch und verdrängen zunehmend die Tastatur: im Smartphone, im Auto, zu Hause und in den Betrieben zur Steuerung von technischen Geräten. Immer neue Sprachtechnologien werden vorgestellt, die Geschwindigkeit der Entwicklung ist dabei atemberaubend. Selbst das Schreiben eigener Texte durch KI ist schon möglich und die Forschung arbeitet an der Erzeugung von Bildern aus Texten. Mit dieser Studie möchten wir Ihnen einen Einblick in das spannende Feld der Sprachtechnologien geben, ihre zentralen Funktionsweisen erklären und Einsatzchancen insbesondere durch konkrete Anwendungsfälle und Lösungen aufzeigen.

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tanh Neurons are Bayesian Decision Makers

2021 , Bauckhage, Christian , Sifa, Rafet , Hecker, Dirk

The hyperbolic tangent (tanh) is a traditional choice for the activation function of the neurons of an artificial neural network. Here, we go through a simple calculation that shows that this modeling choice is linked to Bayesian decision theory. Our brief, tutorial-like discussion is intended as a reference to an observation rarely mentioned in standard textbooks.

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The why and how of trustworthy AI

2022-09-03 , Schmitz, Anna , Akila, Maram , Hecker, Dirk , Poretschkin, Maximilian , Wrobel, Stefan

Artificial intelligence is increasingly penetrating industrial applications as well as areas that affect our daily lives. As a consequence, there is a need for criteria to validate whether the quality of AI applications is sufficient for their intended use. Both in the academic community and societal debate, an agreement has emerged under the term “trustworthiness” as the set of essential quality requirements that should be placed on an AI application. At the same time, the question of how these quality requirements can be operationalized is to a large extent still open. In this paper, we consider trustworthy AI from two perspectives: the product and organizational perspective. For the former, we present an AI-specific risk analysis and outline how verifiable arguments for the trustworthiness of an AI application can be developed. For the second perspective, we explore how an AI management system can be employed to assure the trustworthiness of an organization with respect to its handling of AI. Finally, we argue that in order to achieve AI trustworthiness, coordinated measures from both product and organizational perspectives are required.

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Supporting Visual Exploration of Iterative Job Scheduling

2022-03-30 , Andrienko, Gennady , Andrienko, Natalia , Garcia, Jose Manuel Cordero , Hecker, Dirk , Vouros, George A.

We consider the general problem known as job shop scheduling, in which multiple jobs consist of sequential operations that need to be executed or served by appropriate machines having limited capacities. For example, train journeys (jobs) consist of moves and stops (operations) to be served by rail tracks and stations (machines). A schedule is an assignment of the job operations to machines and times where and when they will be executed. The developers of computational methods for job scheduling need tools enabling them to explore how their methods work. At a high level of generality, we define the system of pertinent exploration tasks and a combination of visualizations capable of supporting the tasks. We provide general descriptions of the purposes, contents, visual encoding, properties, and interactive facilities of the visualizations and illustrate them with images from an example implementation in air traffic management. We justify the design of the visualizations based on the tasks, principles of creating visualizations for pattern discovery, and scalability requirements. The outcomes of our research are sufficiently general to be of use in a variety of applications.

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Leitfaden zur Gestaltung vertrauenswürdiger Künstlicher Intelligenz (KI-Prüfkatalog)

2021 , Poretschkin, Maximilian , Schmitz, Anna , Akila, Maram , Adilova, Linara , Becker, Daniel , Cremers, Armin B. , Hecker, Dirk , Houben, Sebastian , Mock, Michael , Rosenzweig, Julia , Sicking, Joachim , Schulz, Elena , Voß, Angelika , Wrobel, Stefan

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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.

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Vom Textgenerator zum digitalen Experten

2021-09-08 , Paaß, Gerhard , Hecker, Dirk

Neue Sprachprogramme wie GPT-3 geben Maschinen nicht nur ein menschenähnliches Sprachgefühl, sondern sollen sie zugleich zu Fachleuten machen können. Was steckt dahinter? Und kann das gelingen?

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Constructing Spaces and Times for Tactical Analysis in Football

2021 , Andrienko, Gennady , Andrienko, Natalia , Anzer, Gabriel , Bauer, Pascal , Budziak, Guido , Fuchs, Georg , Hecker, Dirk , Weber, Hendrik , Wrobel, Stefan

A possible objective in analyzing trajectories of multiple simultaneously moving objects, such as football players during a game, is to extract and understand the general patterns of coordinated movement in different classes of situations as they develop. For achieving this objective, we propose an approach that includes a combination of query techniques for flexible selection of episodes of situation development, a method for dynamic aggregation of data from selected groups of episodes, and a data structure for representing the aggregates that enables their exploration and use in further analysis. The aggregation, which is meant to abstract general movement patterns, involves construction of new time-homomorphic reference systems owing to iterative application of aggregation operators to a sequence of data selections. As similar patterns may occur at different spatial locations, we also propose constructing new spatial reference systems for aligning and matching movements irrespective of their absolute locations. The approach was tested in application to tracking data from two Bundesliga games of the 2018/2019 season. It enabled detection of interesting and meaningful general patterns of team behaviors in three classes of situations defined by football experts. The experts found the approach and the underlying concepts worth implementing in tools for football analysts.

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