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      Artificial Intelligence and Problems in Generative Art Theory

      Proceedings of EVA London 2019 (EVA 2019)

      Electronic Visualisation and the Arts

      8 - 11 July 2019

      Art theory, Generative art, Neural networks, Inceptionism, Deep learning, Artificial intelligence, Complexity theory

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          Abstract

          In previous writing I’ve described what has arguably become the most widely cited theory of generative art. Based on notions from complexity science, and in particular Murray Gell-Mann and Seth Lloyd’s notion of “effective complexity,” I argue that generative art is not a subset of computer art. Rather, generative art turns on the use of autonomous systems and the artist ceding control to those systems. As part of this theory for generative art, I’ve introduced a series of problems. These are not problems in the sense that they require single correct solutions. Rather they are questions that the artist will consider when making a piece; that critics and historians will typically address in their analysis; and that insightful audience members will ponder. They are problems that typically offer multiple opportunities and possibilities. It is notable that, for the most part, these problems equally apply to both digital and non-digital generative art; to generative art past, present, and (it is believed) future; and to ordered, disordered, and complex generative art. In addition, these same problems or questions are generally trivial, irrelevant, or nonsensical when asked in the context of non-generative art. In a sense the applicability of these questions can cleanly divide art into generative art and non-generative art. More importantly, the exploration of these questions can illuminate the analysis and critique of generative art. More recently a new form of neural-network-based artificial intelligence called “deep learning” has appeared on the scene. Deep learning has been applied to digital art creation. In this paper I explore whether the problems in generative art noted above hold up well in this new artificial intelligence context for generative art. The conclusion reached is that our current complexity-based theory of generative art can easily assimilate the use of deep learning.

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          Most cited references 8

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          What is complexity?

           M Gell-mann,  M Gell (1995)
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            What is Generative Art? Complexity theory as a context for art theory

             P. Galanter,  C. Soddu (2003)
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              Generative Art Theory

               P. Galanter,  C. PAUL (2016)
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                Author and article information

                Contributors
                Conference
                July 2019
                July 2019
                : 112-118
                Affiliations
                Texas A&M University

                College Station, TX, USA
                Article
                10.14236/ewic/EVA2019.22
                © Galanter. Published by BCS Learning and Development Ltd. Proceedings of EVA London 2019, UK

                This work is licensed under a Creative Commons Attribution 4.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

                Proceedings of EVA London 2019
                EVA 2019
                London, UK
                8 - 11 July 2019
                Electronic Workshops in Computing (eWiC)
                Electronic Visualisation and the Arts
                Product
                Product Information: 1477-9358BCS Learning & Development
                Self URI (journal page): https://ewic.bcs.org/
                Categories
                Electronic Workshops in Computing

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