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Auflistung nach Schlagwort "artificial intelligence"

1 - 10 von 30
  • Konferenzbeitrag
    A Learning Companion for Ben: Concept for a Digital Learning Environment
    (Mensch und Computer 2019 - Usability Professionals, 2019) Kosztelnik, Zorica
    Primary school education is one of the most important investments of a nation. Could advanced technologies increase its success? Ben is a 6-year-old child who has just finished kindergarten. He is really excited about starting school, choosing his first school bag and meeting his digital learning companion. On what adventures will they embark together? How many new friends will Ben make at school? The project aims to investigate how the potential of digital environments could be used in primary school education. Digital companions encourage children to collaborate and to support each other whilst motivating them by making daily exercises and learning more fun. The author raises the question if we could look at digitality with excitement and curiosity instead of concern. Can we make clear decisions about when digital technologies may actually broaden our horizon and when they have to fade into the background to give space to human-human interactions?
  • Konferenzbeitrag
    Adaptive real-time crop row detection through enhancing a traditional computer vision approach
    (44. GIL - Jahrestagung, Biodiversität fördern durch digitale Landwirtschaft, 2024) Hussaini, Mortesa; Voigt, Max; Stein, Anthony
    Crop row detection is important to enable precise management of fields and optimize the use of resources such as fertilizers and water. Autonomous machines need an effective but also robust real-time row detection system to be able to adapt to different field conditions. In this paper, we present an enhanced crop row detection approach which integrates traditional computer vision methods with further techniques such as k-means clustering or probabilistic Hough transformation. The resulting hybrid method allows for efficient and robust detection of straight and curved crop rows in image and video material. We validate our approach empirically on the crop row benchmark dataset (CRBD) and compare it with other state-of-the-art approaches. Furthermore, we demonstrate that our approach is designed to be adaptive and thus becomes straightforwardly transferable to other experimental setups. To corroborate that, we report on results when our approach is validated on representative corner cases which have been collected in the scope of a research project. Observations and current limitations of our approach are discussed along with possible solutions to overcome them in future work.
  • Workshop
    AI and Health: Using Digital Twins to Foster Healthy Behavior
    (Mensch und Computer 2024 - Workshopband, 2024) Keppel, Jonas; Ivezić, Dijana; Gruenefeld, Uwe; Lukowicz, Paul; Amft, Oliver; Schneegass, Stefan
    This workshop brings researchers together to discuss and explore how artificial intelligence (AI) can be used to improve general health. During our workshop at the MuC conference, we will focus on three main areas: developing ethical AI health recommendations, exploring how smart technologies in our homes can influence our health habits, and understanding how different types of feedback can change our health behaviors. The workshop aims to be a space where various research areas meet, encouraging a shared understanding and creating new ways to use AI to encourage healthy living. By focusing on real-world applications of AI and digital twins, we seek to guide our discussions toward strategies that have a direct and positive impact on individual and societal health.
  • Konferenzbeitrag
    AI-supported selection procedure for spectral sensors based on technical and economic characteristics
    (INFORMATIK 2024, 2024) Menz, Patrick; Klein, Lauritz; Herzog, Andreas
    This study presents an AI-supported spectral sensor selection process that combines technical and economic criteria to recommend the optimal sensor for specific applications, such as quality control of roasted coffee beans. Using a comprehensive database of spectral sensor characteristics, the SMART algorithm guides decisions that focus on both performance and cost-effectiveness. Our methodology involves simulating spectral responses and using an AI model to evaluate sensor effectiveness in classifying coffee bean types. Initial results highlight the method's ability to optimise sensor selection, effectively balancing performance with budget considerations, and underscore its potential to improve user decision making in technology applications and enhance their digital sovereignty.
  • Konferenzbeitrag
    aicracy: Everyday Objects from a Future Society Governed by Artifical Intelligence
    (Mensch und Computer 2019 - Tagungsband, 2019) Hemmert, Fabian; Becker, Piet; Görts, Alexander; Hrlic, David; von Netzer, David; Weld, Christopher J.
    In this paper, we present aicracy, a critical design project that portrays a society ruled by an artificial intelligence. Five hypothetical objects from this society are presented: a bracelet that gives citizens feedback about their deeds, a patch that releases dopamine into its wearer's blood, an office chair that collapses when its user is unproductive, a shopping basket that displays different prices for different users, depending on how much they contribute to society, and a marble-based voting machine.
  • Konferenzbeitrag
    An Anthropomorphic Approach to establish an Additional Layer of Trustworthiness of an AI Pilot
    (Software Engineering 2022 Workshops, 2022) Regli, Christoph; Annighoefer, Björn
    AI algorithms promise solutions for situations where conventional, rule-based algorithms reach their limits. They perform in complex problems yet unknown at design time, and highly efficient functions can be implemented without having to develop a precise algorithm for the problem at hand. Well-tried applications show the AI’s ability to learn from new data, extrapolate on unseen data, and adapt to a changing environment — a situation encountered in fl ight operations. In aviation, however, certifi cation regulations impede the implementation of non-deterministic or probabilistic algorithms that adapt their behaviour with increasing experience. Regulatory initiatives aim at defining new development standards in a bottom-up approach, where the suitability and the integrity of the training data shall be addressed during the development process, increasing trustworthiness in eff ect. Methods to establish explainability and traceability of decisions made by AI algorithms are still under development, intending to reach the required level of trustworthiness. This paper outlines an approach to an independent, anthropomorphic software assurance for AI/ML systems as an additional layer of trustworthiness, encompassing top-down black-box testing while relying on a well-established regulatory framework.
  • Zeitschriftenartikel
    Arbeitswelt 4.0 und Smart Machines: Augmentation als Herausforderung für die Personalentwicklung
    (HMD Praxis der Wirtschaftsinformatik: Vol. 56, No. 4, 2019) Meier, Christoph; Seufert, Sabine; Guggemos, Josef
    Mit der zweiten Welle der Digitalisierung werden Personalentwickler neu herausgefordert. Smart Machines können bereits heute viele anspruchsvolle Verrichtungen ausführen und sie werden kontinuierlich besser. Die damit verbundenen Veränderungen werden zu oft unter dem Aspekt der Substitution von Arbeitskräften diskutiert und zu wenig unter dem Aspekt der Augmentation, d. h., im Hinblick auf das Zusammenwirken von Menschen und „intelligenten“ Maschinen „Hand in Hand“. Eine Diskussion zu den Folgen dieser Veränderungen findet häufig nicht statt, weil eine Verunsicherung der Belegschaft befürchtet wird. Das Konzept der Augmentationsstrategien bietet hier Orientierung und erleichtert die Diskussion – weil es aufzeigt, dass man diesen Veränderungen nicht hilflos ausgeliefert ist, sondern dass verschiedene Strategien für die Weiterentwicklung möglich sind. Augmentation und Augmentationsstrategien sind ein geeigneter Orientierungsrahmen, um die Aufgaben für Personalentwickler zu strukturieren. Auf Augmentation ausgerichtete Personalentwicklung erfordert einen Gesamtprozess, der verschiedene Arbeitsstränge integriert: die Analyse von Veränderung bei Prozessen, bei Aufgabenzuschnitten und bei Kompetenzerfordernissen; die Gestaltung von Entwicklungsangeboten; begleitendes Veränderungsmanagement und Entwicklungsbegleitung; und schliesslich die Erfolgsbestimmung und Wirkungsüberprüfung. Bei all diesen Strängen ist eine enge Zusammenarbeit mit den jeweiligen Fachabteilungen erforderlich. Die Umsetzung dieses Gesamtprozesses erfordert geeignete Arbeitsinstrumente. Beispielsweise zur Standortbestimmung von Beschäftigtengruppen, zur Analyse sich verändernder Aufgaben-Anforderungssysteme, oder zur Augmentations-orientierten Entwicklungsplanung. Es braucht aber auch Personalentwickler, die sich (1) mit fortgeschrittener Digitalisierung auskennen und die (2) Programme für die verschiedenen Augmentationsstrategien entwickeln und glaubwürdig umsetzen können – nicht zuletzt auch dadurch, dass sie diese Strategien in ihrem eigenen Arbeitsfeld selbst leben. Digital transformation poses new challenges for people development. Today, smart machines can already perform tasks that before could be performed only by humans – and they are becoming more powerful continually. These developments are usually discussed with an eye on the substitution of (parts of) the workforce. We propose to focus on augmentation instead, i.e. on the collaboration of humans and smart machines. Often, the changes that are related to digital transformation are not sufficiently discussed in businesses and organizations for fear of creating unease, uncertainty and frustration. The concept of augmentation strategies provides orientation and facilitates discussion. The concept helps see that humans are not subject to developments they cannot influence but rather that there are different options and strategies for development. Augmentation and augmentation strategies are a useful framework for structuring the tasks of specialists engaged in people development. People development oriented by augmentation and augmentation strategies requires a coherent process that integrates different strands of activity: the analysis of changes in processes, in work tasks and roles as well as in competency requirements; the design of development programs; change management activities and support in development activities; and, finally, measurement of success and impact. All these strands of activity require close cooperation of people development specialists with neighboring units and the business. The realization of this coherent process requires a specific set of tools. Tools that help determine the status quo with a particular job family, that support the analysis of changing processes and work tasks, or tools that help draw up augmentation-oriented development plans. In addition to tools, however, people development specialists are required that are knowledgeable about digital transformation and that are able to draft and implement augmentation-oriented development – not the least because they are making use of augmentation strategies in their own field of work.
  • Konferenzbeitrag
    Challenges in Data Preservation for AI and ML Systems
    (INFORMATIK 2024, 2024) Tonkin, Emma L.; Tourte, Gregory J. L.
    The management and preservation of machine learning (ML) and artificial intelligence (AI) data is increasingly a concern for research institutions, as well as for institutions and industry organisations making use of this type of data and method. This paper summarises key issues in this area, presenting the case that there are significant benefits to the industry in developing best practices and joint standards in this area, and identifying the benefits of this approach, as well as highlighting risks and a current paucity of best practice in the area.
  • Workshopbeitrag
    Design Decision Framework for AI Explanations
    (Mensch und Computer 2021 - Workshopband, 2021) Anuyah, Oghenemaro; Fine, William; Metoyer, Ronald
    Explanations can help users of Artificial Intelligent (AI) systems gain a better understanding of the reasoning behind the model’s decision, facilitate their trust in AI, and assist them in making informed decisions. Due to its numerous benefits in improving how users interact and collaborate with AI, this has stirred the AI/ML community towards developing understandable or interpretable models to a larger degree, while design researchers continue to study and research ways to present explanations of these models’ decisions in a coherent form. However, there is still the lack of intentional design effort from the HCI community around these explanation system designs. In this paper, we contribute a framework to support the design and validation of explainable AI systems; one that requires carefully thinking through design decisions at several important decision points. This framework captures key aspects of explanations ranging from target users, to the data, to the AI models in use. We also discuss how we applied our framework to design an explanation interface for trace link prediction of software artifacts.
  • Konferenzbeitrag
    Developing an AI-enabled Industry 4.0 platform - Performance experiences on deploying AI onto an industrial edge device
    (Softwaretechnik-Trends Band 43, Heft 1, 2023) Eichelberger, Holger; Palmer, Gregory; Niederée, Claudia
    Maximizing the benefits of AI for Industry 4.0 is about more than just developing effective new AI methods. Of equal importance is the successful integration of AI into production environments. One open challenge is the dynamic deployment of AI on industrial edge devices within close proximity to manufacturing machines. Our IIP-Ecosphere1 platform was designed to overcome limitations of existing Industry 4.0 platforms. It supports flexible AI deployment through employing a highly configurable low-code based approach, where code for tailored platform components and applications is generated. In this paper, we measure the performance of our platform on an industrial demonstrator and discuss the impact of deploying AI from a central server to the edge. As result, AI inference automatically deployed on an industrial edge is possible, but in our case three times slower than on a desktop computer, requiring still more optimizations.