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Big Data and Creativity

Published online by Cambridge University Press:  15 July 2019

Palle Dahlstedt
Palle Dahlstedt*
Affiliation:
Division of Interaction Design, Department of Computer Science and Engineering, Chalmers University of Technology & University of Gothenburg, SE-412 96 Göteborg, Sweden. Email: palle.dahlstedt@gu.se. Also Department of Communication & Psychology, Aalborg University, Rendsburggade 14, DK-9000 Aalborg, Denmark
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Abstract

Big data and machine learning techniques are increasingly applied to creative tasks, often with strong reactions of both awe and concern. But we have to be careful about where to attribute the creative agency. Is it really the machine that paints like van Gogh, or is it a human that uses a high-level tool to impart one pattern upon another, based on her aesthetic preferences? In this paper, the author analyses the problem of machine creativity, focusing on four central themes: the inherent convergence of machine learning and big data techniques, their dependence on assumptions and incomplete data, the possibility of explorative search as a new creative paradigm, and the related problem of the opacity of results from such methods. The Google Deep Dream project is brought in as an example to illustrate the discussion. Information and complexity are brought into the discussion as central concepts for both creative processes and the resulting artefacts, concluding that the complexity of the interaction between the creative agent and the environment during the creative process is a crucial parameter for meaningful creative output. Based on the exposed limitations in current technologies, the author concludes that the principal creative agency still lies in the developers and users of the tools, not in the data processing itself. Human effort and input still matters. But we can take a constructive approach, regarding big data techniques as tools one order of magnitude more complex than what was available before, allowing artists to work with abstractions previously unfeasible for computational work.

Type
Articles
Information
European Review , Volume 27 , Issue 3 , July 2019 , pp. 411 - 439
Copyright
© Academia Europaea 2019 

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References

References and Notes

From Big Data to Big Understanding Seminar organized by the Royal Swedish Academy of Engineering Sciences, Tällberg Foundation, and SAS Institute. 21 November 2013, Stockholm, Sweden.Google Scholar
Gartner IT Glossary, Big Data, http://www.gartner.com/it-glossary/big-data/ (accessed 15 December 2016).Google Scholar
SAS Institute, Big Data– What it is and why it matters, white paper, http://www.sas.com/en_us/insights/big-data/what-is-big-data.html (accessed 15 December 2016).CrossRefGoogle Scholar
Jing, Y. and Baluja, S. (2008) Pagerank for product image search. In: Proceedings of the 17th international conference on World Wide Web, Beijing, China, pp. 307–316.CrossRefGoogle Scholar
Bobadilla, J., Ortega, F., Hernando, A. and Gutiérrez, A. (2013) Recommender systems survey. Knowledge-Based Systems, 46, pp. 109–132.CrossRefGoogle Scholar
Pachet, F. (2003) The continuator: musical interaction with style. Journal of New Music Research, 32(3), pp. 333–341.CrossRefGoogle Scholar
Notable examples include the artworks of Mark Wattenberg, Nathalie Miebach, Wesley Goatley and Tobias Revell, and the artistic collaborations of Mikael Lundberg and Mats Nordahl, e.g. Traces of an Ongoing Memory. For an overview of the field, see Viégas, F.B. and Wattenberg, M. (2007) Artistic data visualization: beyond visual analytics. In: Proceedings of the International Conference on Online Communities and Social Computing, Beijing, China, Lecture Notes in Computer Science, 456, pp. 182–191.Google Scholar
Tversky, B. (1993) Cognitive maps, cognitive collages, and spatial mental models. In: Proceedings of European Conference on Spatial Information Theory, pp. 14–24.CrossRefGoogle Scholar
Sudol, L.A., Stehlik, M. and Carver, S. (2010) Mental models of data. In: Proceedings of Koli Calling: International Conference on Computing Education Research, Koli National Park, Finland, pp. 55–58.Google Scholar
Chinn, C.A. and Brewer, W.F. (1996) Mental models in data interpretation. Philosophy of Science, 63, pp. 211–219.CrossRefGoogle Scholar
Gentner, D. and Stevens, A.L. (1983). Mental models (Hillsdale, NJ: Erlbaum).Google Scholar
This concept and the processed version of the Bible were originally conceived as part of a collaborative work by the author and the visual artist Mikael Lundberg in 2005, and programed by the author. However, the work was never finished nor exhibited. The other sound examples were prepared for the keynote presentation that this article is based on.Google Scholar
Cope, D. (2005) Computer Models of Musical Creativity (Cambridge, MA: MIT Press).Google Scholar
Cope’s more recent work is more interesting from a novelty point of view, where he personally interacts over time with his probabilistic composition model (which he calls Emily Howell), training it in more complex ways.Google Scholar
Dahlstedt, P. (2001) A MutaSynth in parameter space: interactive composition through evolution. Organised Sound, 6(2), pp. 121–124.CrossRefGoogle Scholar
Bach, J.S. (1899) Thematic Catalogue of the Bach-Gesellschaft Ausgabe, Band 46 (Leipzig: Breitkopf und Härtel).Google Scholar
Griffiths, T. and Yuille, A. (2008) A primer on probabilistic inference. In: Chater, N., and Oaksfors, M. (Eds), The Probabilistic Mind: Prospects for Bayesian Cognitive Science (Oxford: Oxford University Press), pp. 33–57.CrossRefGoogle Scholar
See Mattheson, J. (1739) Der vollkommene Capellmeister (Hamburg: Christian Herold) and J. Mattheson (1713) Das neu-eröffnete Orchestre, vol. 1 (Hamburg: B. Schiller).Google Scholar
Mattheson, J. and Lenneberg, H. (1958) Johann Mattheson on affect and rhetoric in music (II). Journal of Music Theory, 2(2), pp. 193–236.CrossRefGoogle Scholar
Lowrance, R.A. (2013) Instruction, devotion, and affection: three roles of Bach’s well-tempered clavier. Musical Offerings, 4(1), pp. 15–30.CrossRefGoogle Scholar
Kohonen, T. (1982) Self-organized formation of topologically correct feature maps. Biological Cybernetics, 43(1), pp. 59–69.CrossRefGoogle Scholar
Dahlstedt, P. (2011) Autonomous evolution of piano pieces and performances with Ossia II. In: Miranda, E.R. (Ed.), A-Life for Music: Music and Computer Models of Living Systems (Middleton, WI: A-R Editions).Google Scholar
McCormack, J. (2005) Open problems in evolutionary music and art. In: Proceedings of EvoWorkshops 2005 (Heidelberg: Ed. Springer-Verlag), pp. 428–436.Google Scholar
Boden, M. (2004) The Creative Mind: Myths and Mechanisms, 2nd edn (London: Routledge).CrossRefGoogle Scholar
Hofstadter, D. (1985) Variations on a theme as the crux of creativity. In: Metamagical Themas (New York: Basic Books).Google Scholar
Dahlstedt, P. (2012) Between material and ideas: a process-based spatial model of artistic creativity. In: McCormack, J., and D’Inverno, M. (Eds), Computers and Creativity (Berlin, Heidelberg: Springer).Google Scholar
For a historical overview and a more in-depth discussion of generative and restrictive properties of constriants, see chapter 3 of Dahlstedt, P. (2004) Sounds Unheard of: Evolutionary algorithms as creative tools for the contemporary composer. PhD thesis (Göteborg: Chalmers University of Technology).Google Scholar
Finke, R.A., Ward, T.B. and Smith, S.M. (1992) Creative Cognition: Theory, Research, and Applications (Cambridge, MA: MIT Press).Google Scholar
Valkare, G. (1997) Det audiografiska fältet: om musikens förhållande till skriften och den unge Bo Nilssons strategier. PhD thesis (Göteborg: University of Gothenburg).Google Scholar
This paradigm is not completely new, but distinctly different from the more common way of creating things, which may – greatly simplified – be described as musical artefacts being constructed from scratch and evaluated in an iterated tweaking process.Google Scholar
Dahlstedt, P. (2009) Thoughts on creative evolution: a meta-generative approach to composition. Contemporary Music Review, 28(1), pp. 43–55.CrossRefGoogle Scholar
Dahlstedt, P. (2001) Creating and exploring huge parameter spaces: interactive evolution as a tool for sound generation. In: Proceedings of the International Computer Music Conference 2001, Havana, Cuba, pp. 235–242.Google Scholar
Dawkins, R. (1986) The Blind Watchmaker (Harlow: Longman Scientific and Technical).Google Scholar
Sims, K. (1991) Artificial evolution for computer graphics. Computer Graphics, 25(4), pp. 319–328.CrossRefGoogle Scholar
Krueger, M.W. (1977) Responsive environments. In: Proceedings of the June 13-16, 1977, National Computer Conference (ACM), pp. 423–433.CrossRefGoogle Scholar
Gibney, E. (2016) Google AI algorithm masters ancient game of Go. Nature News, 529(7587), p. 445.CrossRefGoogle ScholarPubMed
I am simplifying a bit here, since we know a great deal about genetic low-level mechanisms. But there are still so many things we don’t know about how these detailed changes are expressed in the high-level organism, which is the main point.Google Scholar
Gregory, R.L. (1981) Mind in Science (London: Weidenfeld and Nicolson).Google Scholar
Vygotsky, L.S. (1980) Mind in Society: The Development of Higher Psychological Processes (Cambridge, MA: Harvard University Press).CrossRefGoogle Scholar
Arthur, W.B. (2009) The Nature of Technology: What It Is and How It Evolves (New York: Simon and Schuster).Google Scholar
McCormack, J. and Dorin, A. (2001) Art, emergence and the computational sublime. In: Proceedings of Second Iteration: A Conference on Generative Systems in the Electronic Arts. (Melbourne: CEMA), pp. 67–81.Google Scholar
Inceptionism: going deeper into neural networks. Google Research Blog (17 June 2015). https://research.googleblog.com/2015/06/inceptionism-going-deeper-into-neural.html Google Scholar
https://deepdreamgenerator.com (accessed 17 December 2016). The website has since been updated with more advanced versions of the algorithm.Google Scholar
Chayka, K. (2015) Why Google’s Deep Dream is future kitsch. Pacific Standard (10 July 2015).Google Scholar
For an extended discussion of this problem, see Dahlstedt, P. (2005) Defining spaces of potential art: the significance of representation in computer-aided creativity. In: Description & Creativity Conference, online proceedings, King’s College, Cambridge, UK, 3–5 July 2005.Google Scholar

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