Next Article in Journal
Exploring Natural Stone and Building a National Identity: The Geological Exploration of Natural Stone Deposits in the Nordic Countries and the Development of a National-Romantic Architecture
Next Article in Special Issue
Art in the Age of Machine Intelligence
Previous Article in Journal
Reaching for Success: Picasso’s Rise in the Market (The First Two Decades)
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Essay

The Machine as Artist: An Introduction

1
Space Machines Corporation, 3443 Esplanade Ave., Suite 438, New Orleans, LA 70119, USA
2
Department of Computing, Goldsmiths College, University of London, London, UK
*
Author to whom correspondence should be addressed.
Submission received: 2 March 2017 / Accepted: 28 March 2017 / Published: 10 April 2017
(This article belongs to the Special Issue The Machine as Artist (for the 21st Century))

Abstract

:
With the understanding that art and technology are continuing to experience an historic and rapidly intensifying rapprochement—but with the understanding as well that accounts thereof have tended to be constrained by scientific/engineering rigor on the one hand, or have tended to swing to the opposite extreme—it is the goal of this special issue of Arts to provide an opportunity for artists, humanists, scientists, and engineers to consider this development from the broader perspective which it deserves, while at the same time retaining a focus on what must surely be the emerging core of our subject: the state of the art in mechatronics and computation is such that we can now begin to speak comfortably of the machine as artist—and we can begin to hope, as well, that an aesthetic sensitivity on the part of the machine might help lead to a friendlier and more sensitive machine intelligence in general.

The Machine as Artist: An Introduction

If we can accept the 1967 founding of the journal Leonardo [1] and the 1968 publication of Jack Burnham’s Beyond Modern Sculpture [2] as milestones—and the latter of which had an extensive chapter on “Robot and Cyborg Art”—it must come as a shock to realize that the study of electronic techno-art has been established as a formal discipline for half a century, and which study since placed in brackets with the appearance of at least two comprehensive surveys [3,4]. It continues to be the case, however, that there has also been constant and now breath-taking progress, and to the extent that we can at present begin to think of the machine, not as the artist’s subject matter or medium, but as creator or co-creator. Indeed, it is this subject to which the current special issue of Arts is dedicated; and we begin by noting that the literature bears ample witness to this emergence, and with the contributions documented therein falling into several major sub-fields:
  • The kinetic or robotic art works whose movement and/or behavior has become so sophisticated that we are entitled to regard them as performance artists in their own right [5,6,7,8].
  • The algorithmic studio assistants set loose to embellish computer-mediated graphic or sculptural works of art, and which work is then output via large-format ink-jet printer or additive manufacturing system, or as video [9,10,11,12,13,14].
  • The autonomous and cleverly-designed painting robots which, drawing upon the emergent properties of minimally-intelligent systems, are nonetheless able to create striking abstract works [15,16].
  • The far more computationally-intensive anthropomorphic robots (Figure 1) able to create sensitive and imaginative portraits of their human subjects, or engage in other forms of graphic virtuosity [17,18,19,20,21,22,23].
  • The purely computational/AI systems which qualify themselves as aesthetically competent entities, if not actual artists, by their ability to predict the style period and/or author of existing works of graphic art [24,25,26,27,28].
  • The purely computational/AI systems capable of isolating and capturing the style of a given work of graphic art and applying it in an aesthetically-pleasing manner to an arbitrary image [29,30,31,32,33,34,35,36,37].
  • The purely computational/AI systems capable of generating striking imagery based on otherwise mundane or even random visual input fields [38,39,40,41,42].
It is of particular interest and significance, moreover, that these sub-fields tend to overlap within the genre of the traditional graphic arts—the physical robotic systems producing sophisticated portraits, and the purely computational systems generating sophisticated analyses and transformations of historic and well-known paintings—for we have here a coming-together of a number of critical threads.
This overlap is due, in the first place, to the fact that graphic art can of course be represented by two-dimensional arrays of pixels, and is thus ideally suited for computational analysis. Indeed, virtually all of the important results reported under categories 5, 6, and 7 above have been achieved with that same family of computational techniques—the “deep neural network”, or DNN—that has also been responsible for the recent and unprecedented victories of computer over human in master-level Go and Poker tournaments. In other words, the graphic arts have emerged as a vital research arena for the artificial intelligence community, and to some extent as a replacement for the board game—and along with this circumstance comes the opportunity for our own contributors to address the larger questions associated with AI.
And the ultimate question at this point is no longer whether or not artificial intelligence will be capable of achieving some real degree of autonomy [43]; the question, rather, is the degree to which such an autonomous or semi-autonomous intelligence can be designed to operate in a consistently humane and responsible manner [44], and with “responsible”, in this day and age, understood to include an environmental dimension.
But of course it is not merely the status of the graphic arts as a computer-friendly medium that should encourage its various practitioners to take on the question of a humane AI: the far larger point is that the graphic arts represent a creative and non-competitive and distinctly human activity—an activity, in fact, intimately associated with the emergence of humankind from a preoccupation with mere survival [45,46]—and an activity as well in which the entire focus is on sensitivity of observation and execution.
In short—and if we can thereby conclude with Herbert Marcuse that “the aesthetic values are the non-aggressive values par excellence” [47]—then the addition of aesthetic capabilities to the machine intelligence armamentarium would perhaps bring us an important step closer to the addition, as well, of a sense of empathy and responsibility—and it is this possibility that we would like to propose as the focus of our special edition on “The Machine as Artist”.
But let us emphasize here—and as strongly as possible—that it is not only those who have been involved with the computational graphic arts who are making, or who are in a position to make, an important contribution to the genesis of a “friendly AI”. In particular, the artists and scientists and engineers who have worked to bring the robot out of the factory and into public gallery and exhibition spaces are playing a critical role in introducing machine intelligence as a physical as well as mental presence, and we are eager to hear more of their work; and to the extent that our basic thesis is correct, most such contributions will tend to have at least some bearing on the question, “Can there be a humane intelligence apart from the sense of balance and harmony and attention to detail that we normally associate with aesthetics?”
Given, however, the speculative and cross-disciplinary nature of this question, it is anticipated that many of the submissions to this special edition will take the form of scholarly essays or even communications (albeit still subject to peer review); i.e.,—and at the risk of repeating ourselves—we hope to provide here an opportunity for specialists in the fields of computer science, neuroscience, anthropology, and art history to share their thoughts on a more open-ended basis.
In this context—and we rush here to our conclusion, and by way of returning to our central theme—the status of the graphic arts is given a powerful boost by the fact that so distinct is the emergence, and so invariant over time the performance and reception of certain of its styles, that we are entitled to regard it as a phenomenon—a phenomenon as yet imperfectly understood, but no less worthy of study, and potentially no less rewarding, than the phenomenon of a certain mineral ore able to fog unexposed photographic plates. Or in other words, we have here a near-ideal venue for interaction between the humanities and the sciences in respect to the question of a humane machine intelligence; and in support of this claim we exhibit following a group of drawings from the Chauvet Cave created some 32,000 years ago (Figure 2)—and the freshness and clarity and sensitivity of which must instill in us a deep wonder:
And given, finally, that no modern intellectual enterprise can be complete without a reference to the very real environmental threat facing our planet, we note that here also the graphic arts have a critical role to play, and as likewise deeply embedded in our culture and history—and there is perhaps no better example than Audubon’s depiction of the Swallow-tailed Kite (Figure 3).
A computational analysis of the exquisite lines thereof (refined, as we must note, by the master engraver Havell) would almost certainly reveal, from a human factors standpoint, some noteworthy, if not indeed uncanny, qualities; but what should strike us as most uncanny is the fact that the collected set of such images—the graphic art created by Audubon under humble circumstances as he trekked through the wilds of North America—has been responsible for an outpouring of public commitment to environmental preservation to which no modern public relations campaign can bear comparison; i.e., we have here an example of the fact that art has a very real and unique power, and a greater appreciation and understanding of which has now become a vital matter.

Acknowledgments

The authors would like to thank the hard-working artists, scientists, and engineers who have made this special issue a possibility.

Conflicts of Interest

The authors declare no conflicts of interest.

References

  1. Leonardo: Journal of the International Society for the Arts, Sciences and Technology. Cambridge: The MIT Press.
  2. Jack Burnham. Beyond Modern Sculpture: The Effects of Science and Technology on the Sculpture of this Century. New York: George Braziller, 1968. [Google Scholar]
  3. Edward A. Shanken. Art and Electronic Media. New York: Phaidon Press, 2009. [Google Scholar]
  4. Andreas Broeckmann. Machine Art in the Twentieth Century. Cambridge: MIT Press, 2016. [Google Scholar]
  5. Fiammetta Ghedini, and Massimo Bergamasco. “Robotic Creatures: Anthropomorphism and Interaction in Contemporary Art.” In Paper presented at the 19th International Symposium in Robot and Human Interactive Communication, Viareggio, Italy, 13–15 September 2010; pp. 731–36. Available online: https://www.researchgate.net/publication/301899556_Robotic_creatures_Anthropomorphism_and_interaction_in_contemporary_art (accessed on 13 February 2017). [Google Scholar]
  6. Simon Penny. “Robotics and Art, Computationalism and Embodiment.” In Robots and Art: Exploring an Unlikely Symbiosis. Edited by Damith Herath, Christian Kroos and Stelarc. Singapore: Springer, 2016, pp. 47–65. [Google Scholar]
  7. Ken Rinaldo. “Trans-Species Interfaces: A Manifesto for Symbiogenisis.” In Robots and Art: Exploring an Unlikely Symbiosis. Edited by Damith Herath, Christian Kroos and Stelarc. Singapore: Springer, 2016, pp. 113–47. [Google Scholar]
  8. Mari Velonaki, and David Rye. “Designing Robots Creatively.” In Robots and Art: Exploring an Unlikely Symbiosis. Edited by Damith Herath, Christian Kroos and Stelarc. Singapore: Springer, 2016, pp. 379–401. [Google Scholar]
  9. Michael Rees. “Rapid Prototyping: Realizing Convoluted Form and Nesting in Sculpture.” Prototipazione e Produzione Rapida, 1997. [Google Scholar]
  10. Songhua Xu, Francis C. M. Lau, and Yunhe Pan. A Computational Approach to Digital Chinese Painting and Calligraphy. New York: Springer, 2009. [Google Scholar]
  11. Stéphane Sikora. “Balancing Art and Complexity: Joseph Nechvatal’s Computer Virus Project.” THE THING, 2012. Available online: https://post.thing.net/node/3569 (accessed on 13 February 2017). [Google Scholar]
  12. Kevin Holmes. “Quayola and Memo Akten Translate Athletic Movements into Abstract Animations.” Creators. 6 March 2012. Available online: https://creators.vice.com/en_us/article/quayola-and-memo-akten-translate-athletic-movements-into-abstract-animations (accessed on 13 February 2017).
  13. Nicholas Lambert, William Latham, and Frederic Fol Leymarie. “The Emergence and Growth of Evolutionary Art: 1980–1993.” Leonardo 46 (2013): 367–75. [Google Scholar] [CrossRef]
  14. Taney Roniger. “Review of “VORTEX: Recent Animations and Works on Paper by Carter Hodgkin”.” Caldaria. 13 February 2013. Available online: http://www.caldaria.org/2013/06/exhibition-review-carter-hodgkin-by.html (accessed on 13 February 2017).
  15. Leonel Moura. “A New Kind of Art: The Robotic Action Painter.” In Paper presented at the X Generative Art International Conference, Politecnico di Milano University, Milano, Italy, 2007; Available online: http://www.generativeart.com/on/cic/papersGA2007/16.pdf (accessed on 13 February 2017). [Google Scholar]
  16. Stefan Doepner, and Urška Jurman. “Robot Partner—Are Friends Electric? ” In Robots and Art: Exploring an Unlikely Symbiosis. Edited by Damith Herath, Christian Kroos and Stelarc. Singapore: Springer, 2016, pp. 403–23. [Google Scholar]
  17. Sylvain Calinon, Julien Epiney, and Aude Billard. “A Humanoid Robot Drawing Human Portraits.” In Paper presented at the 5th IEEE-RAS International Conference on Humanoid Robots, Tukuba, Japan, 5 December 2005; pp. 161–66. Available online: http://lasa.epfl.ch/publications/uploadedFiles/calinon-humanoids-161.pdf (accessed on 13 February 2017). [Google Scholar]
  18. Steve DiPaola. “Exploring a Parameterised Portrait Painting Space.” International Journal of Arts and Technology 2 (2009): 82–93. [Google Scholar] [CrossRef]
  19. Patrick Tresset, and Frederic Fol Leymarie. “Portrait Drawing by Paul the Robot.” Computers & Graphics 37 (2013): 348–63. [Google Scholar]
  20. Patrick Tresset, and Oliver Deussen. “Artistically Skilled Embodied Agents.” In Paper presented at AISB, Goldsmiths, University of London, UK, 1–4 April 2014; Available online: https://kops.uni-konstanz.de/handle/123456789/27046 (accessed on 13 February 2017). [Google Scholar]
  21. Thomas Lindemeier, Jens Metzner, Lena Pollak, and Oliver Deussen. “Hardware-Based Non-Photorealistic Rendering Using a Painting Robot.” Computer Graphics Forum 34 (2015): 311–23. [Google Scholar] [CrossRef]
  22. Daniel Berio, Sylvain Calinon, and Frederic Fol Leymarie. “Learning Dynamic Graffiti Strokes with a Compliant Robot.” In Paper presented at 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Daejeon, Korea, 9–14 October 2016; pp. 3981–86. Available online: http://publications.idiap.ch/downloads/papers/2016/Berio_IROS_2016.pdf (accessed on 13 February 2017). [Google Scholar]
  23. Ryszard Kluszczyński, ed. Patrick Tresset, Human Traits and the Art of Creative Machines. Gdańsk: Centre for Contemporary Art LAZNIA, 2016.
  24. C. Richard Johnson, Ella Hendriks, Igor J. Berezhnoy, Eugene Brevdo, Shannon M. Hughes, Ingrid Daubechies, Jia Li, Eric Postma, and James Z. Wang. “Image Processing for Artist Identification.” IEEE Signal Processing Magazine 25 (2008). [Google Scholar] [CrossRef]
  25. Lior Shamir, Tomasz Macura, Nikita Orlov, D. Mark Eckley, and Ilya G. Goldberg. “Impressionism, Expressionism, Surrealism: Automated Recognition of Painters and Schools of Art.” ACM Transactions on Applied Perception (TAP) 7 (2010): 8. [Google Scholar] [CrossRef]
  26. Sergey Karayev, Matthew Trentacoste, Helen Han, Aseem Agarwala, Trevor Darrell, Aaron Hertzmann, and Holger Winnemoeller. “Recognizing Image Style.” arXiv, 2013, arXiv:1311.3715. [Google Scholar]
  27. Nanne Van Noord, Ella Hendriks, and Eric Postma. “Toward Discovery of the Artist’s Style: Learning to Recognize Artists by Their Artworks.” IEEE Signal Processing Magazine 32 (2015): 46–54. [Google Scholar] [CrossRef]
  28. Wei Ren Tan, Chee Seng Chan, Hernán E. Aguirre, and Kiyoshi Tanaka. “Ceci n’est pas une pipe: A Deep Convolutional Network for Fine-art Paintings Classification.” In Paper presented at 2016 23rd IEEE International Conference on Image Processing (ICIP), Phoenix, AZ, USA, 25–28 September 2016; pp. 3703–7. Available online: https://www.researchgate.net/publication/305402238_Ceci_n'est_pas_une_pipe_A_Deep_Convolutional_Network_for_Fine-art_Paintings_Classification (accessed on 13 February 2017). [Google Scholar]
  29. Leon A. Gatys, Alexander S. Ecker, and Matthias Bethge. “A Neural Algorithm of Artistic Style.” arXiv, 2015, arXiv:1508.06576. [Google Scholar]
  30. Leon A. Gatys, Alexander S. Ecker, Matthias Bethge, Aaron Hertzmann, and Eli Shechtman. “Controlling Perceptual Factors in Neural Style Transfer.” arXiv, 2016, arXiv:1611.07865. [Google Scholar]
  31. Chuan Li, and Michael Wand. “Combining Markov Random Fields and Convolutional Neural Networks for Image Synthesis.” In Paper presented at the IEEE Conference on Computer Vision and Pattern Recognition, Seattle, WA, USA, 27–30 June 2016; pp. 2479–86. Available online: https://arxiv.org/abs/1601.04589 (accessed on 13 February 2017). [Google Scholar]
  32. Dmitry Ulyanov, Vadim Lebedev, Andrea Vedaldi, and Victor Lempitsky. “Texture Networks: Feed-forward Synthesis of Textures and Stylized Images.” In Paper presented at the International Conference on Machine Learning (ICML), New York, NY, USA, 19–24 June 2016; pp. 1349–1357. Available online: http://jmlr.csail.mit.edu/proceedings/papers/v48/ulyanov16.pdf (accessed on 13 February 2017). [Google Scholar]
  33. Alex J. Champandard. “Semantic Style Transfer and Turning Two-bit Doodles into Fine Artworks.” arXiv, 2016, arXiv:1603.01768. [Google Scholar]
  34. Joachim Denzler, Erik Rodner, and Marcel Simon. “Convolutional Neural Networks as a Computational Model for the Underlying Processes of Aesthetics Perception.” In Computer Vision – ECCV 2016 Workshops, Part I. Lecture Notes in Computer Science, vol. 9913; Edited by Gang Hua and Hervé Jégou. Switzerland: Springer, 2016, pp. 871–87. [Google Scholar]
  35. Yağmur Güçlütürk, Umut Güçlü, Rob van Lier, and Marcel A. J. van Gerven. “Convolutional Sketch Inversion.” In Computer Vision – ECCV 2016 Workshops, Part I. Lecture Notes in Computer Science, vol. 9913; Edited by Gang Hua and Hervé Jégou. Switzerland: Springer, 2016, pp. 810–24. [Google Scholar]
  36. Justin Johnson, Alexandre Alahi, and Li Fei-Fei. “Perceptual Losses for Real-time Style Transfer and Super-resolution.” In Computer Vision – ECCV 2016, Part II. Lecture Notes in Computer Science, vol. 9906; Edited by Bastian Leibe, Jiri Matas, Nicu Sebe and Max Welling. Switzerland: Springer, 2016, pp. 694–711. [Google Scholar]
  37. Chuan Li, and Michael Wand. “Precomputed Real-time Texture Synthesis with Markovian Generative Adversarial Networks.” In Computer Vision – ECCV 2016, Part III. Lecture Notes in Computer Science, vol. 9913; Edited by Bastian Leibe, Jiri Matas, Nicu Sebe and Max Welling. Switzerland: Springer, 2016, pp. 702–16. [Google Scholar]
  38. Alexander Mordvintsev, Christopher Olah, and Mike Tyka. “Inceptionism: Going deeper into Neural Networks.” Google Research Blog. 17 June 2015. Available online: https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html (accessed on 13 February 2017).
  39. Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. “Understanding Neural Networks through Deep Visualization.” arXiv, 2015, arXiv:1506.06579. [Google Scholar]
  40. Leonid Berov, and Kühnberger Kai-Uwe. “Visual Hallucination for Computational Creation.” In Paper presented at the Seventh International Conference on Computational Creativity, Paris, France, 27 June–1 July 2016; Available online: https://www.researchgate.net/publication/304932129_Visual_Hallucination_For_Computational_Creation (accessed on 13 February 2017). [Google Scholar]
  41. Anh Nguyen, Jason Yosinski, Yoshua Bengio, Alexey Dosovitskiy, and Jeff Clune. “Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space.” arXiv, 2016, arXiv:1612.00005. [Google Scholar]
  42. Wei Ren Tan, Chee Seng Chan, Hernan Aguirre, and Kiyoshi Tanaka. “ArtGAN: Artwork Synthesis with Conditional Categorial GANs.” arXiv, 2017arXiv:1702.03410.
  43. Dario Amodei, Chris Olah, Jacob Steinhardt, Paul Christiano, John Schulman, and Dan Mané. “Concrete Problems in AI Safety.” arXiv, 2016, arXiv:1606.06565. [Google Scholar]
  44. Stuart Russell, Daniel Dewey, and Max Tegmark. “Research Priorities for Robust and Beneficial Artificial Intelligence.” Ai Magazine 36 (2015): 105–114. [Google Scholar]
  45. Paul Mellars. “The Impossible Coincidence: A Single-Species Model for the Origins of Modern Human Behavior in Europe.” Evolutionary Anthropology Issues News and Reviews 14 (2005): 12–27. [Google Scholar] [CrossRef]
  46. Ernst Gombrich. “The Miracle at Chauvet.” The New York Review of Books, 14 November 1996, 43 (1996). [Google Scholar]
  47. Herbert Marcuse. Art and Liberation: Collected Papers of Herbert Marcuse. New York: Routledge, 2007, vol. 4, p. 118. [Google Scholar]
Figure 1. Baxter Signing His Name in Graffiti Style [22].
Figure 1. Baxter Signing His Name in Graffiti Style [22].
Arts 06 00005 g001
Figure 2. Group of Chauvet Cave Drawings. By Nachosan - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=32316562.
Figure 2. Group of Chauvet Cave Drawings. By Nachosan - Own work, CC BY-SA 3.0, https://commons.wikimedia.org/w/index.php?curid=32316562.
Arts 06 00005 g002
Figure 3. Swallow-tailed Kite by John James Audubon.
Figure 3. Swallow-tailed Kite by John James Audubon.
Arts 06 00005 g003

Share and Cite

MDPI and ACS Style

Smith, G.W.; Leymarie, F.F. The Machine as Artist: An Introduction. Arts 2017, 6, 5. https://doi.org/10.3390/arts6020005

AMA Style

Smith GW, Leymarie FF. The Machine as Artist: An Introduction. Arts. 2017; 6(2):5. https://doi.org/10.3390/arts6020005

Chicago/Turabian Style

Smith, Glenn W., and Frederic Fol Leymarie. 2017. "The Machine as Artist: An Introduction" Arts 6, no. 2: 5. https://doi.org/10.3390/arts6020005

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop