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        AI Art

        Machine Visions and Warped Dreams

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        Author(s)
        Zylinska, Joanna
        Language
        English
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        Abstract
        Can computers be creative? Is algorithmic art just a form of Candy Crush? Cutting through the smoke and mirrors surrounding computation, robotics and artificial intelligence, Joanna Zylinska argues that, to understand the promise of AI for the creative fields, we must not confine ourselves solely to the realm of aesthetics. Instead, we need to address the role and position of the human in the current technical setup – including the associated issues of labour, robotisation and, last but not least, extinction. Offering a critique of the socio-political underpinnings of AI, AI Art: Machine Visions and Warped Dreams raises poignant questions about the conditions of art making and creativity today. The book critically examines artworks that use AI, be it in the form of visual style transfer, algorithmic experiment or critical commentary. It also engages with their predecessors, including robotic art and net art. AI Art includes a project from Zylinska’s own art practice titled ‘View from the Window’, which explores human and nonhuman forms of intelligence, perception and action. The book closes with speculation on future art – and on art’s future.
        URI
        https://library.oapen.org/handle/20.500.12657/40042
        Keywords
        computers; algorithmic art; AI
        ISBN
        9781785420863
        Publisher
        Open Humanities Press
        Publication date and place
        London, 2020
        Classification
        Information technology: general topics
        Pages
        181
        Public remark
        3-8-2020 - No DOI registered in CrossRef for ISBN 9781785420856
        Rights
        https://creativecommons.org/licenses/by-sa/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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