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            AI in Museums

            Reflections, Perspectives and Applications

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            Contributor(s)
            Thiel, Sonja (editor)
            Bernhardt, Johannes C. (editor)
            Language
            English
            Show full item record
            Abstract
            Artificial intelligence is becoming an increasingly important topic in the cultural sector. While museums have long focused on building digital object databases, the existing data can now become a field of application for machine learning, deep learning and foundation model approaches. This goes hand in hand with new artistic practices, curation tools, visitor analytics, chatbots, automatic translations and tailor-made text generation. With a decidedly interdisciplinary approach, the volume brings together a wide range of critical reflections, practical perspectives and concrete applications of artificial intelligence in museums, and provides an overview of the current state of the debate.
            URI
            https://doab-dev.siscern.org/handle/20.500.12854/183654
            Keywords
            Museum; Culture; Digitality; Artificial Intelligence; Digitalization; Science; Museology; Digital Media; Technology; thema EDItEUR::G Reference, Information and Interdisciplinary subjects::GL Library and information sciences / Museology::GLZ Museology and heritage studies; thema EDItEUR::J Society and Social Sciences::JB Society and culture: general::JBC Cultural and media studies::JBCT Media studies
            DOI
            10.14361/9783839467107
            ISBN
            9783839467107, 9783837667103
            Publisher
            transcript Verlag
            Publisher website
            http://www.transcript-verlag.de
            Publication date and place
            Bielefeld, 2023
            Series
            Edition Museum,
            Pages
            320
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            • logo Investir l'avenirInvestir l'avenir
            • logo MESRIMESRI
            • logo EUEuropean Union
              This project received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 871069.

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