Artificial Intelligence and Problems in Generative Art Theory

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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Philip Galanter

Conference

Publication date: July 2019

Publication date (Print): July 2019

Pages: 112-118

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[0001]Texas A&M University

College Station, TX, USA

Article

DOI: 10.14236/ewic/EVA2019.22

SO-VID: e14c8834-1858-468b-934d-1fa646c4711e

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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/

Conference name: Proceedings of EVA London 2019

Conference acronym: EVA 2019

Conference number:

Conference location: London, UK

Conference date: 8 - 11 July 2019

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: Electronic Visualisation and the Arts

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2019.22

Self URI (journal page): https://ewic.bcs.org/

REFERENCES

M. Balter 2002 From a Modern Human’s Brow – or Doodling? Science 295 47 248
J. Baudrillard 1994 Simulacra and simulation Ann Arbor University of Michigan Press
M. A. Boden 2004 The creative mind: myths and mechanisms London Routledge
N. Carroll 1999 Philosophy of art: a contemporary introduction London Routledge
P. Galanter 2003 What is Generative Art? Complexity theory as a context for art theory International Conference on Generative Art Milan, Italy 2003 Generative Design Lab, Milan Polytechnic Art
P. Galanter 2009a Thoughts on Computational Creativity Computational Creativity: An Interdisciplinary Approach Dagstuhl Germany 2009 Schloss Dagstuhl – Leibniz-Zentrum fuer Informatik Germany
P. Galanter 2009b Truth to Process – Evolutionary Art and the Aesthetics of Dynamism International Conference on Generative Art Milan, Italy 2009 Generative Design Lab, Milan Polytechnic Art
P. Galanter 2016a Generative Art Theory C. Paul A Companion to Digital Art John Wiley & Sons Hoboken
P. Galanter 2016b An introduction to complexism Technoetic Arts: A Journal of Speculative Research 14 1-2 9 31
M. Gell-Mann 1995 What is complexity? Complexity – John Whiley and Sons 1 1 1619
A. MordvintsevC. OlahM. Tyka 2015 Inceptionism: Going Deeper into Neural Networks http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html retreived November 11 2015 )

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[1] M. Balter 2002 From a Modern Human’s Brow – or Doodling? Science 295 47 248

[2] J. Baudrillard 1994 Simulacra and simulation Ann Arbor University of Michigan Press

[3] M. A. Boden 2004 The creative mind: myths and mechanisms London Routledge

[4] N. Carroll 1999 Philosophy of art: a contemporary introduction London Routledge

[5] P. Galanter 2003 What is Generative Art? Complexity theory as a context for art theory International Conference on Generative Art Milan, Italy 2003 Generative Design Lab, Milan Polytechnic Art

[6] P. Galanter 2009a Thoughts on Computational Creativity Computational Creativity: An Interdisciplinary Approach Dagstuhl Germany 2009 Schloss Dagstuhl – Leibniz-Zentrum fuer Informatik Germany

[7] P. Galanter 2009b Truth to Process – Evolutionary Art and the Aesthetics of Dynamism International Conference on Generative Art Milan, Italy 2009 Generative Design Lab, Milan Polytechnic Art

[8] P. Galanter 2016a Generative Art Theory C. Paul A Companion to Digital Art John Wiley & Sons Hoboken

[9] P. Galanter 2016b An introduction to complexism Technoetic Arts: A Journal of Speculative Research 14 1-2 9 31

[10] M. Gell-Mann 1995 What is complexity? Complexity – John Whiley and Sons 1 1 1619

[11] A. MordvintsevC. OlahM. Tyka 2015 Inceptionism: Going Deeper into Neural Networks http://googleresearch.blogspot.com/2015/06/inceptionism-going-deeper-into-neural.html retreived November 11 2015 )

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