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    Automatisierte Wissenskommunikation

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    Author(s)
    Holste, Alexander
    Language
    German
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    Abstract
    The Model of Automated Knowledge Communication outlined in this postdoctoral thesis aims to broaden the scope of current conceptualizations of specialized communication to account for self-adapting, AI-/LLM-based machines since these are presently changing the practice of multilingual specialized communication in fundamental ways – hence affording research to adapt. The model facilitates the development of research on specialized communication specifically by integrating, firstly, new approaches toward concepts of expert knowledge and, secondly, the involvement of sophisticated machines able to act increasingly autonomously. The monograph sets out with an overview on the objects of research the model can be applied to as it incorporates approaches of cybernetics, technical sociology, and media sociology. The description of the model is followed by its validation on basis of seven case studies from which some suggestions for further research are derived.
    URI
    https://library.oapen.org/handle/20.500.12657/85661
    Keywords
    Algorithm;Specialized communication;Specialized communication research;Specialist language;Special knowledge;Information design;Information science;AI;Artificial intelligence;Cybernetics;Machine translation;Media psychology;Multilingual communication;Human-machine interaction;Neural machine translation;Rule-based machine translation;Recurrent neural networks;Statistical machine translation ;Sociology of Technology;Text-to-speech;DeepL
    DOI
    10.26530/20.500.12657/85661
    ISBN
    9783732910014, 9783732989355, 9783732989126
    Publisher
    Frank & Timme
    Publisher website
    https://www.frank-timme.de/
    Publication date and place
    Berlin, 2024
    Series
    Wissenskommunikation / Knowledge Communication AMP, 1
    Pages
    390
    Rights
    https://creativecommons.org/licenses/by-nc-nd/4.0/
    • Imported or submitted locally

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    License

    • If not noted otherwise all contents are available under Attribution 4.0 International (CC BY 4.0)

    Credits

    • logo EU
    • This project received funding from the European Union's Horizon 2020 research and innovation programme under grant agreement No 683680, 810640, 871069 and 964352.

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