Abstract
Recent advances in artificial intelligence have resulted in developments in various creative arts, specifically in terms of the creator’s instrumental expression and perception of use in the visual domain. Based on actual examples, we confirm the expansion of expression and stimulation of the artist’s creative will. We examine the process of selection, judgment, and interpretation of the outcome by the creator and AI. A creator uses the AI algorithm program as a “tool” initially. However, based on numerous attempts and data sets, the creator himself makes various interpretations of the results and receives inspiration. Such judgment and interpretation of the creator lead to new possibilities and expansion of work through creative will and inspiration. Expert interviews were conducted on the evaluation of works using AI and interpretation of art-related creativity. Results reveal various opinions on the interpretation criteria for instrumental recognition of artists and the evaluation of AI artworks by experts in related fields to date. Based on this, we hope to spark interdisciplinary discussions about responsibilities of AI’s current instrumental use; further, we explore new partner potential based on AI and collaboration in the creative process.
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03 July 2021
In the originally published version of chapter 16, the corresponding author was not correctly marked. This has now been corrected.
References
Is artificial intelligence set to become art’s next medium?|Christie’s. https://www.christies.com/features/A-collaboration-between-two-artists-one-human-one-a-machine-9332-1.aspx. Accessed 08 Feb 2021
Is the art market ready to embrace work made by artificial intelligence? Christie’s will test the waters this fall. https://news.artnet.com/market/artificial-intelligence-christies-1335170. Accessed 08 Feb 2021
Hertzmann, A.: Can computers create art? Arts 7, 18 (2018). https://doi.org/10.3390/arts7020018
Nguyen, A., Yosinski, J., Clune, J.: Understanding innovation engines: automated creativity and improved stochastic optimization via deep learning. Evol. Comput. 24, 545–572 (2016)
Mumford, M.D.: Where have we been, where are we going? Taking stock in creativity research. Creat. Res. J. 15, 107–120 (2003). http://www.springer.com/lncs. Accessed 21 Nov 2016
Veron, P.E.: The nature-nurture problem in creativity. In: Glover, J.A., Ronning, R.R., Reynolds, C.R. (eds.) Handbook of Creativity, pp. 93–110. Plenum Press, New York (1989)
Guilford, J.P.: Creativity: yesterday, today and tomorrow. J. Creat. Behav. 1, 3–14 (1967)
Olwell, R.: Bohm-Biederman Correspondence. In: Bohm, D., Biederman, C., Pylkkanen, P., (eds.) Volume 1: Creativity in Art and Science. Isis, vol. 91, pp. 194–195 (2000)
McFadzean, E.: Creativity in MS/OR: choosing the appropriate technique. interfaces (1999)
Hennessey, B.A., Amabile, T.M.: Creativity. Annu. Rev. Psychol. 61, 569–598 (2010)
Adobe sensei. https://www.adobe.com/sensei/ai-innovations.html/. Accessed 05 Dec 2020
OCULA. https://ocula.com/magazine/art-news/interest-in-google-arts-culture-skyrockets-as/. Accessed 17 Dec 2020
Manovich, L.: The language of new media. https://mitpress.mit.edu/books/language-new-media. Accessed 17 Dec 2021
Ridler, A.: Missing datasets. http://annaridler.com/missing-data. Accessed 08 Feb 2021
Ridler, A.: Fall of the House of Usher II (2017). http://annaridler.com/fall-of-the-house-of-usher-ii. Accessed 01 Feb 2020
Parolo, L.: Ars Electronica 2017 : Festival für Kunst, Technologie und Gesellschaft = Festival for Art, Technology, and Society. Critique d’art. Actualité internationale de la littérature critique sur l’art contemporain (2019)
Colton, S., Halskov, J., Ventura, D., Gouldstone, I., Cook, M., Pérez-Ferrer, B.: The painting fool sees! new projects with the automated painter. Presented at the July 1 (2015)
Shinseungback Kimyonghun. http://ssbkyh.com/works/cloud_face/. Accessed 18 Feb 2021
Saunders, R.S.: Issues of authenticity in autonomously creative systems. In: Proceedings of the Ninth International Conference on Computational Creativity, Salamanca, Spain, 25–29 June 272–279 (2018)
Ren, X.: Rethinking the relationship between humans and computers. Computer 49, 104–108 (2016)
AI Illustrator. https://enpulse9.imweb.me/Paintly. Accessed 20 Feb 2021
Trending Dreams|Deep Dream Generator. https://deepdreamgenerator.com/. Accessed 20 Feb 2021
MGatys, L.A., Ecker, A.S., Bethge, M.: A neural algorithm of artistic style. arXiv:1508.06576 [cs, q-bio]. (2015)
Louise Barriball, K., While, A.: Collecting data using a semi-structured interview: a discussion paper. J. Adv. Nurs. 19, 328–335 (1994)
Palmer, T.: UXtools.co. https://uxtools.co. Accessed 20 Feb 2021
Art, T.A.: TingaTinga African Art|African Art & Paintings Online. https://www.tingatingaart.com/. Accessed 13 Feb 2021
Davis, N., Hsiao, C.-P., Singh, K.Y., Magerko, B.: Co-creative drawing agent with object recognition, p. 7 (2016)
Agüera y Arcas, B.: Art in the age of machine intelligence. Arts 6, 18 (2017)
Regulations for the Implementation of the Copyright Law of the People’s Republic of China, Arts. 2, 3: “Tencent Dreamwriter.” IIC. 51, 652–659 (2020)
Ricketson, S., Ginsburg, J.C.: International Copyright and Neighbouring Rights: The Berne Convention and Beyond. Oxford University Press, Oxford, New York (2006)
Shoyama, R.: Intelligent Agents: Authors, Makers, and Owners of Computer-Generated Works in Canadian Copyright Law. Social Science Research Network, Rochester (2015)
Rub, G.A.: Contracting around copyright: the uneasy case for unbundling of rights in creative works. Univ. Chicago Law Rev. 78, 257–279 (2011)
Netanel, N.: Copyright alienability restrictions and the enhancement of author autonomy: a normative evaluation. Rutgers L.J. 24, 347 (1992)
Elgammal, A., Liu, B., Elhoseiny, M., Mazzone, M.: CAN: Creative adversarial networks, generating “Art” by learning about styles and deviating from style norms (2017)
Srinivasan, R., Uchino, K.: Biases in generative Art—a causal look from the lens of art history (2020)
Acknowledgments
Thank you to the curator, creator, engineer, and IP experts who participated in expert interviews. They do not mention specific real names according to their wishes. We do not mention specific real names according to their wishes.
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Kim, K., Heo, J., Jeong, S. (2021). Tool or Partner: The Designer’s Perception of an AI-Style Generating Service. In: Degen, H., Ntoa, S. (eds) Artificial Intelligence in HCI. HCII 2021. Lecture Notes in Computer Science(), vol 12797. Springer, Cham. https://doi.org/10.1007/978-3-030-77772-2_16
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