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Do Machines Produce Art? No. (A Systems-Theoretic Answer.)



Machines do not produce art, social systems do.
Do Machines Produce Art? No. (A Systems-Theoretic Answer.)
Michael Straeubig
University of Plymouth
Machines do not produce art, social systems do.
Machines and Art
From early experiments with computer-
generated visual art in the mid 1960s, the idea of
“art machines”, entities that are not merely tools
or assistants for human artists but capable of
autonomous art production, has gained
significant traction. [1]
Our modern understanding of (capital-A) Art
and the related concept of Fine Arts emerged
during the 18th century. [2] However, any
established notion of art has always been under
negotiation. [3] Proponents of algorithmic art
seek to re-define aesthetic concepts in
information processing terms. [4][5][6]
Contributions like Michael Matejas’ Expressive
AI, Leonel Moura’s stigmergic robots and
Marius Klingemann’s uncanny neural imagery
push the aesthetic boundaries of generative
machines and computational procedures.
But do these machines and algorithms produce
art? I give an answer based on Niklas
Luhmann’s systems-theoretic thinking, and this
answer is: no. [10] Likewise, humans do not
produce art either. Art is not created by any
biological or nonbiological entity but within
social systems, constructed through recursive
networks of communication. [11]
The answer does not change if we recast
generative art as variants of the Turing Test.
[12][13] It does not even change if we
conceptualize machines and humans as
ensembles or take into consideration the fluidity
of their difference. [14][15]
To understand the ramifications of the shift from
the artist as an individual to art as a social
system, it is useful to observe and explore forms
of art that make this approach explicit. “The new
artist” by Alex Straschnoy et al presents a robot
that is performing for a robotic audience. [16]
Techne is an algorithmic community that
produces as well as mutually critiques digital art.
[17] In both projects, the relationship between
audience and artist is re-negotiated and humans
become second-order observers of the art
production. [18]
Machines do not produce art, social systems do.
We may begin to ignore the difference between
human and machine; it does not make a
difference. What we need to do is to restructure
our expectations and to invite more machines
into our art system.
To achieve this, it may be well worthwhile to
revisit systems art as a bridge between
cybernetic tradition and currently emerging
generative techniques. [19][20]
1. Grant D. Taylor. When the Machine Made
Art: The Troubled History of Computer Art,
2. Paul Oskar Kristeller. “The Modern System
of the Arts: A Study in the History of Aesthetics
Part I.” Journal of the History of Ideas 12, no. 4
(October 1951).
3. Louise Norton. “The Richard Mutt Case.” The
Blind Man, May 1917.
4. Frieder Nake. Ästhetik als
Informationsverarbeitung: Grundlagen und
Anwendungen der Informatik im Bereich
ästhetischer Produktion und Kritik. Wien:
Springer, 1974.
5. Jürgen Schmidhuber. “Developmental
Robotics, Optimal Artificial Curiosity,
Creativity, Music, and the Fine Arts.”
Connection Science 18, no. 2 (2006): 173–187.
6. Leon A. Gatys, Alexander S. Ecker, and
Matthias Bethge. “A Neural Algorithm of
Artistic Style.” ArXiv Preprint
ArXiv:1508.06576, 2015.
7. Michael Mateas. “Expressive AI - A Hybrid
Art and Science Practice.” Leonardo: Journal of
the International Society for Arts, Sciences, and
Technology 34, no. 2 (2001): 147–53.
8. Leonel Moura. “Machines That Make Art.” In
Robots and Art, edited by Damith Herath,
Christian Kroos, and Stelarc, 255–69. New
York, NY: Springer Berlin Heidelberg, 2016.
9. Mario Klingemann. “Quasimondo | Mario
Klingemann, Artist,” 2018.
10. Niklas Luhmann. Social Systems. Writing
Science. Stanford, Calif: Stanford University
Press, 1996.
11. Niklas Luhmann. “Das Kunstwerk Und Die
Selbstreproduktion Der Kunst.” In Delfin, 3:51–
69, 1984.
12. Ahmed Elgammal, Bingchen Liu, Mohamed
Elhoseiny, and Marian Mazzone. “CAN:
Creative Adversarial Networks Generating ‘Art’
by Learning About Styles and Deviating from
Style Norms,” 2017.
13. Jörg Räwel. “Können Maschinen denken?”
Telepolis, August 4, 2018.
14. Bruno Latour. “A Collective of Humans and
Nonhumans: Following Daedalus’s Labyrinth.”
In Pandora’s Hope: Essays on the Reality of
Science Studies, 174–215. Cambridge, Mass:
Harvard University Press, 1999.
15. Victor Marques, and Carlos Brito. “The Rise
and Fall of the Machine Metaphor:
Organizational Similarities and Differences
Between Machines and Living Beings.”
Verifiche XLIII, no. 1–4 (2014): 77–111.
16. Axel Straschnoy, Ben Brown, Garth Zeglin,
Geoff Gordon, Iheanyi Umez-Eronini, Marek
Michalowski, Paul Scerri, and Sue Ann Hong.
The New Artist. 2008. http://www.the-new-
17. Johnathan Pagnutti, Kate Compton, and Jim
Whitehead. “Do You Like This Art I Made You:
Introducing Techne, A Creative Artbot
Commune.” In Proceedings of 1st International
Joint Conference of DiGRA and FDG, 2016.
18. Niklas Luhmann. “Observation of the First
and of the Second Order.” In Art as a Social
System, 54–101. Meridian, Crossing Aesthetics.
Stanford, Calif: Stanford University Press,
19. Jack Burnham, 1968. Systems Esthetics.
20. Edward A Shanken, 2009. Reprogramming
Systems Aesthetics: A Strategic Historiography,
in: Proceedings of the Digital Arts and Culture
2009. UC Irvine.
Michael Straeubig (@crcdng) is the Award
Leader for Game Arts and Design and a Marie
Curie Fellow at Plymouth University. He is
researching and exploring the relationships
between systems, play and games in various
media with a focus on mixed reality and
posthuman play.
... Emerging artists such as Mario Klingemann, Anna Ridler, Memo Akten, Sougwen Chung, and Helena Sarin are now experimenting with fresh creative possibilities. The art system tacitly embraces this direction, too, as evident from initial scandals and misunderstandings (Straeubig 2019). ...
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In recent years, we have observed impressive advancements at the intersection of games and artificial intelligence. Often these developments are described in terms of technological progress, while public discourses on their cultural, social and political impact are largely decoupled. I present an alternative rhetoric by speculating about the emergence of AI within social systems. In a radical departure from the dominant discourse, I describe seven roles - Mechanic, Alter/Ego, Observer, Protector, Player, Creator and God - that an AI may assume in the environment of videogames. I reflect on the ramifications of these roles for the idea of an artificial general intelligence (AGI), mainly hoping to irritate the prevailing discussion.
Conference Paper
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We propose a new system for generating art. The system generates art by looking at art and learning about style; and becomes creative by increasing the arousal potential of the generated art by deviating from the learned styles. We build over Generative Adversarial Networks (GAN), which have shown the ability to learn to generate novel images simulating a given distribution. We argue that such networks are limited in their ability to generate creative products in their original design. We propose modifications to its objective to make it capable of generating creative art by maximizing deviation from established styles and minimizing deviation from art distribution. We conducted experiments to compare the response of human subjects to the generated art with their response to art created by artists. The results show that human subjects could not distinguish art generated by the proposed system from art generated by contemporary artists and shown in top art fairs. Human subjects even rated the generated images higher on various scales.
Full-text available
Robots can make art. Based on simple rules and stigmergy it is possible to produce unique artworks that are at least partially independent from the human that triggers the process. I have coined it a “New kind of Art”.
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In fine art, especially painting, humans have mastered the skill to create unique visual experiences through composing a complex interplay between the content and style of an image. Thus far the algorithmic basis of this process is unknown and there exists no artificial system with similar capabilities. However, in other key areas of visual perception such as object and face recognition near-human performance was recently demonstrated by a class of biologically inspired vision models called Deep Neural Networks. Here we introduce an artificial system based on a Deep Neural Network that creates artistic images of high perceptual quality. The system uses neural representations to separate and recombine content and style of arbitrary images, providing a neural algorithm for the creation of artistic images. Moreover, in light of the striking similarities between performance-optimised artificial neural networks and biological vision, our work offers a path forward to an algorithmic understanding of how humans create and perceive artistic imagery.
Our goal in the paper is to offer both an eulogy and a critique of the machine metaphor as a theoretical resource for understanding organic systems. We begin by presenting an abbreviated history of the machine metaphor, pointing out how it was instrumental in the development of modem biologx, as it provided a conceptual basis for an analytical program in the sciences of life. Then we deal with what exactly makes the machine metaphor such a successful resource, pointing to what organisms and machines in fact share in common - based on the relational approaches advanced by Varela and Rosen, we suggest that both are 'constrained systems '. In the third part, we present an alternative way of conceptualising living systems, bringing now the disanalogies with machines to the foreground. Reviewing the independent work of different authors, we show that there is distinct organicist theoretical camp, where the organism is generally understood as an autonomous system. Finally, we observe that many authors from that camp are now reclaiming Kant's treatment of organisms in the Critique of Judgment, in particular the concept of «natural purpose» - but those authors do that with a markedly anti-Kantian goal: to naturalise teleology. Our conclusion is that the view of organism as an autonomous system gives us the key to a naturalistic understanding that can finally overcome the mechanical view of nature so characteristic of modem thought. The machine metaphor, despite all its undeniable contributions to the advancement of biological research, shows itself ultimately insufficient for a complex view of the phenomena of life - and discarding it doesn't need to mean any concession to vitalism: on the contrary, it may be exactly what we need to invigorate a robustly materialist project.
Even in the absence of external reward, babies and scientists and others explore their world. Using some sort of adaptive predictive world model, they improve their ability to answer questions such as what happens if I do this or that? They lose interest in both the predictable things and those predicted to remain unpredictable despite some effort. One can design curious robots that do the same. The author’s basic idea (1990, 1991) for doing so is a reinforcement learning (RL) controller is rewarded for action sequences that improve the predictor. Here, this idea is revisited in the context of recent results on optimal predictors and optimal RL machines. Several new variants of the basic principle are proposed. Finally, it is pointed out how the fine arts can be formally understood as a consequence of the principle: given some subjective observer, great works of art and music yield observation histories exhibiting more novel, previously unknown compressibility/regularity/predictability (with respect to the observer’s particular learning algorithm) than lesser works, thus deepening the observer’s understanding of the world and what is possible in it.
Expressive AI is a new interdiscipline of AI-based cultural production combining art practice and AI research practice. This paper explores the notion of expressive AI by comparing it with other AI discourses, describing how it borrows notions of interpretation and authorship from both art and AI research practice, and by providing preliminary desiderata for the practice.
When the Machine Made Art: The Troubled History of Computer Art
  • Grant D Taylor
Grant D. Taylor. When the Machine Made Art: The Troubled History of Computer Art, 2014.
The Richard Mutt Case
  • Louise Norton
Louise Norton. "The Richard Mutt Case." The Blind Man, May 1917.