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oapen-20.500.12657-857232023-12-02T03:40:04Z Beyond Quantity Sudmann, Andreas Echterhölter, Anna Ramsauer, Markus Retkowski, Fabian Schröter, Jens Waibel, Alexander AI Machine Learning Artificial Neural Networks Subsymbolic AI Research on Research Digitalization Technology Digital Media Sociology of Media Sociology of Science Computer Sciences Media Studies bic Book Industry Communication::J Society & social sciences::JF Society & culture: general::JFD Media studies bic Book Industry Communication::P Mathematics & science::PD Science: general issues::PDR Impact of science & technology on society How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately? 2023-12-01T08:52:40Z 2023-12-01T08:52:40Z 2023 book ONIX_20231201_9783839467664_21 9783839467664 9783837667660 9783732867660 https://library.oapen.org/handle/20.500.12657/85723 eng KI-Kritik / AI Critique application/pdf Attribution 4.0 International 9783839467664.pdf https://www.transcript-verlag.de/ transcript Verlag 10.14361/9783839467664 10.14361/9783839467664 b30a6210-768f-42e6-bb84-0e6306590b5c 6567a1f5-e703-4a90-9105-68647648d048 2e58ffb8-60b1-400a-a1fd-eaa53c0005d8 9783839467664 9783837667660 9783732867660 6 360 Bielefeld [...] [...] open access
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How do artificial neural networks and other forms of artificial intelligence interfere with methods and practices in the sciences? Which interdisciplinary epistemological challenges arise when we think about the use of AI beyond its dependency on big data? Not only the natural sciences, but also the social sciences and the humanities seem to be increasingly affected by current approaches of subsymbolic AI, which master problems of quality (fuzziness, uncertainty) in a hitherto unknown way. But what are the conditions, implications, and effects of these (potential) epistemic transformations and how must research on AI be configured to address them adequately?
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