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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://doab-dev.siscern.org/handle/20.500.12854/193339
            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
            Publisher
            Frank & Timme
            Publication date and place
            Berlin, 2024
            Series
            Wissenskommunikation / Knowledge Communication AMP,
            Pages
            390
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              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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