Creative Artificial Intelligence within the Artificial Life Installation “Infranet”

The authors explore how current mainstream data-driven AI approaches can be questioned critically from a perspective of computational creativity and ecosystemic art. This centres on a critique of the future as being over-determined by the past; both from the data used, and in the questions or objectives assumed by training. The main contributions of this paper are to apply alternative creative approaches to nature-inspired artificial intelligence, and to detail some of these through their embodiment in the authors’ artwork “Infranet”. Infranet is a neuro-evolutionary art installation that exhibited at three international locations over 2018-2019. It uses geospatial data of the host city not as a training material but as a habitat for artificial life. In contrast to training-based AI systems, in Infranet there is no objective or fitness function and very little evolutionary pressure or competition. Moreover, it eschews the trend of a large and pre-specified neural network structure in favour of a population of thousands of small interacting neural networks, each with distinct structure, in a "liquid" process of continuous reorganization; resonating with some contemporary theories and models of non-conscious cognition in biological and ecological systems.

Content

Author and article information

Contributors

Graham Wakefield

Haru Hyunkyung Ji

Conference

Publication date: July 2021

Publication date (Print): July 2021

Pages: 199-206

Affiliations

[0001]York University

Toronto, Canada

[0002]OCAD University

Toronto, Canada

Article

DOI: 10.14236/ewic/EVA2021.34

SO-VID: 6c659a95-e6f1-43e2-b540-0647988a6513

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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 2021

Conference acronym: EVA 2021

Conference number:

Conference location: London

Conference date: 5th July – 9th July 2021

Conference sponsor: Electronic Workshops in Computing (eWiC)

Conference theme: AI and the Arts: Artificial Imagination

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1477-9358 BCS Learning & Development

Self URI (article page): https://www.scienceopen.com/hosted-document?doi=10.14236/ewic/EVA2021.34

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

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Comments

Comment on this article

[1] S.P. Adam, S.A.N. Alexandropoulos, P.M. Pardalos and M.N. Vrahatis (2019) No free lunch theorem: A review. Approximation and optimization, pp.57-82.

[2] R.F. Antunes, F.F. Leymarie and W. Latham (2014) Two decades of evolutionary art using computational ecosystems and its potential for virtual worlds. Journal of Virtual Worlds Research, 7(3).

[3] L. Arriagada (2020) CG-Art: demystifying the anthropocentric bias of artistic creativity. Connection Science, 32(4), pp.398-405.

[4] Y.M. Bar-On, R. Phillips and R. Milo (2018) The biomass distribution on Earth. Proceedings of the National Academy of Sciences, 115(25), pp.6506-6511.

[5] H. Bergson (1998) Creative evolution. 1911. Trans. Arthur Mitchell. New York: Dover.

[6] R. Brooks (2017) What is it like to be a robot? [Website]. https://rodneybrooks.com/what-is-it-like-to-be-a-robot (retrieved March 2021)

[7] A. Burkov (2019) The hundred-page machine learning book (Vol. 1). Vancouver: Andriy Burkov.

[8] A. Cardoso, T. Veale and G.A. Wiggins (2009) Converging on the divergent: The history (and future) of the international joint workshops in computational creativity. AI magazine, 30(3), pp.15-15.

[9] G. Cuccu and F. Gomez (2011) April. When novelty is not enough. In European Conference on the Applications of Evolutionary Computation, pp. 234-243. Springer, Berlin, Heidelberg.

[10] G. Deleuze (1990) Bergsonism. Trans. H. Tomlinson & B Habberjam. Zone Books.

[11] M. Equihua, M.E. Aldama, C. Gershenson, O. López-Corona, M. Munguía, O. Pérez-Maqueo and E. Ramírez-Carrillo (2020) Ecosystem antifragility: beyond integrity and resilience. PeerJ, 8, p.e8533.

[12] W. Fontana, 2006. The topology of the possible. In Understanding Change, pp. 67-84. Palgrave Macmillan, London.

[13] N.K. Hayles (2020) Unthought. University of Chicago Press.

[14] A. Hertzmann (2020) Computers do not make art, people do. Communications of the ACM, 63(5), pp.45-48.

[15] A. Jordanous (2012) A standardised procedure for evaluating creative systems: Computational creativity evaluation based on what it is to be creative. Cognitive Computation, 4(3), pp.246-279.

[16] S. Kauffman (2002) Investigations. Oxford University Press, USA.

[17] A. Krizhevsky, I. Sutskever and G.E. Hinton (2012) Imagenet classification with deep convolutional neural networks. Advances in neural information processing systems, 25, pp.1097-1105.

[18] J. Lehman and K.O. Stanley (2011) Abandoning objectives: Evolution through the search for novelty alone. Evolutionary computation, 19(2), pp.189-223.

[19] J. Lehman and K.O. Stanley (2013) Evolvability is inevitable: Increasing evolvability without the pressure to adapt. PloS one, 8(4), p.e62186.

[20] R. Miikkulainen (2020) Creative ai through evolutionary computation. In Evolution in Action: Past, Present and Future, pp. 265-269. Springer, Cham.

[21] H. Miyazaki (2001) Sen to Chihiro no Kamikakushi (Spirited Away) , Directred by Hayao Miyazaki [Film], Japan, Studio Ghibli.

[22] J.B. Mouret (2011) Novelty-based multiobjectivization. In New horizons in evolutionary robotics, pp. 139-154. Springer, Berlin, Heidelberg.

[23] A. Nguyen, J. Yosinski and J. Clune (2015) Deep neural networks are easily fooled: High confidence predictions for unrecognizable images. In Proceedings of the IEEE conference on computer vision and pattern recognition, pp. 427-436.

[24] A.M. Nguyen, J. Yosinski and J. Clune (2015) Innovation engines: Automated creativity and improved stochastic optimization via deep learning. In Proceedings of the 2015 Annual Conference on Genetic and Evolutionary Computation, pp. 959-966.

[25] J. Pinero and R. Solé (2019) Statistical physics of liquid brains. Philosophical Transactions of the Royal Society B, 374(1774), p.20180376.

[26] M. Ragot, N. Martin and S. Cojean (2020) AI-generated vs. Human Artworks. A Perception Bias Towards Artificial Intelligence? In Extended abstracts of the 2020 CHI conference on human factors in computing systems, pp. 1-10.

[27] D. Roselli, J. Matthews and N. Talagala (2019) Managing bias in AI. In Companion Proceedings of The 2019 World Wide Web Conference, pp. 539-544.

[28] K.O. Stanley and R. Miikkulainen (2002) Evolving neural networks through augmenting topologies. Evolutionary computation,10(2), pp.99-127.

[29] C. Szegedy, W. Zaremba, I. Sutskever, J. Bruna, D. Erhan, I. Goodfellow and R. Fergus (2013) Intriguing properties of neural networks. arXiv preprint arXiv:1312.6199.

[30] T Wagenaar (2017). Neataptic. https://github.com/wagenaartje/neataptic (retrieved March 2021)

[31] D.H. Wolpert and W.G. Macready (1995) No free lunch theorems for search. Technical Report SFITR-95-02-010, Santa Fe Institute.

[32] B.G. Woolley and K.O. Stanley (2011) On the deleterious effects of a priori objectives on evolution and representation. In Proceedings of the 13th annual conference on Genetic and evolutionary computation, pp. 957-964.

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