Impact of Artificial Intelligence in Visual Art Performance
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Abstract
This study examines AI’s huge impact on visual art performance. This study examines how AI’s history, tools, and uses in visual art have transformed processes and immersed audiences. Machine learning, computer vision, and GANs let artists create, curate, and alter visual art with unprecedented creativity. AI creates visual art performance concepts, styles, and aesthetics. AI helps artists analyze massive data sets, find trends, and try new art forms. Case studies demonstrate how AI has been used in many artistic disciplines to create groundbreaking performances, interactive installations, and fascinating experiences. AI-powered inventions have changed art genres and captivated viewers. Collaborative art has pushed creativity and innovativeness. Audiences can participate in the creative process and co-create meaning and narrative in personalized, immersive, and interactive AI-powered art experiences. AI in visual art performance affects biases, ownership, copyright, transparency, and accountability. The paper advocates for ethical, legal, transparent, and interpretable AI systems in the arts to promote equality and fairness. Examine future AI visual art performance adjustments, enhancements, and challenges. It enhances AI algorithms for artistic expression, emotions, and aesthetic. AI researchers, visual artists, composers, dancers, and performers will create more complex, interactive cross-disciplinary works. Interpretability, bias reduction, and ethics research advance the field.
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References
Liu, X. (2020, October). Artistic reflection on artificial intelligence digital painting. In Journal of Physics: Conference Series (Vol. 1648, No. 3, p. 032125). IOP Publishing.
Caramiaux, B., & Fdili Alaoui, S. (2022). Explorers of Unknown Planets: Practices and Politics of Artificial Intelligence in Visual Arts. Proceedings of the ACM on Human-Computer Interaction, 6(CSCW2), 1-24.
Raschka, S., Patterson, J., & Nolet, C. (2020). Machine learning in python: Main developments and technology trends in data science, machine learning, and artificial intelligence. Information, 11(4), 193.
Aigrain, P. (2019). Artificial intelligence as a tool for artistic creativity: A legal perspective. IIC-International Review of Intellectual Property and Competition Law, 50(4), 430-462.
Akten, M. (2018). Machines Like Us: Aesthetic Perspectives on Artificial Intelligence and Robotics. Leonardo, 51(1), 36-42.
Anadol, R. (2018). Memories of Passersby I. Retrieved from https://refikanadol.com/works/memories-of-passersby-i/
Anadol, R. (2019). Machine Hallucination. Retrieved from https://refikanadol.com/works/machine-hallucination/
Borgesius, F. J., Möller, J., Kruikemeier, S., Möller, J., Oomen, J., & et al. (2019). Robot authors: The implications of AI-generated content for copyright. International Journal of Law and Information Technology, 27(4), 369-399.
Boden, M. A. (2018). Creativity and artificial intelligence. In Handbook of computational creativity (pp. 403-413). Springer.
Brown, E. (2020). Artificial intelligence in the arts: The aesthetics of art and technology. Routledge.
Buolamwini, J., & Gebru, T. (2018). Gender shades: Intersectional accuracy disparities in commercial gender classification. In Proceedings of the 1st Conference on Fairness, Accountability and Transparency (pp. 77-91).
Chen, C. (2021). Artificial Intelligence in Art: Opportunities and Challenges. Arts, 10(2), 44.
Crawford, K. (2017). Can an Algorithm be Agonistic? Ten Scenes from Life in Calculated Publics. Science, Technology & Human Values, 42(1), 77-92.
Elgammal, A. (2017). AICAN: A Computational Creativity Framework for Art. In Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence (IJCAI-17), 3(26), 3699-3705.
Elgammal, A. (2020). AI in the arts: A survey. arXiv preprint arXiv:2009.03626.
Ensmenger, N. (2019). The Ethics of Software: Eight Ethical Principles for Assessing Software and Its Developers. Science and Engineering Ethics, 25(4), 1031-1054.
Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... Luetge, C. (2018). AI4People—An Ethical Framework for a Good AI Society: Opportunities, Risks, Principles, and Recommendations. Minds and Machines, 28(4), 689-707.
Holzinger, A., Langs, G., Denk, H., & Zatloukal, K. (2019). Machine Learning and Deep Learning in Medical Imaging: General Remarks and Future Perspectives. Medical Image Analysis, 52, 24-31.
Johnson, R. (in press). AI in Visual Art Performance: Interactive and Immersive Experiences. ACM SIGGRAPH.
Kumar, A. (2019). The Curious Case of Copyright for Artificial Intelligence Generated Work. Jindal Global Law Review, 10(2), 182-199.
Lieberman, Z. (2017). Hello World! Retrieved from https://vimeo.com/233568265
Machado, I. (2020). Aesthetic Experience and AI. In AI Narratives (pp. 83-102). Springer.
McCormack, J. (2020). Understanding the Creative Role of AI in Visual Arts Practice. Computer Graphics Forum, 39(2), 137-158.
Pindar, C. (2022). Artificial Intelligence and Interactive Art: Reshaping the Boundaries of Creativity. Journal of Interactive Arts, 1(1), 32-48.
Raggett, S. (2021). Dancing with the Machine: An Analysis of Olafur Eliasson and Wayne McGregor's Collaboration in "Tree of Codes". Dance Chronicle, 44(1), 43-67.
Smith, J. (2019). The Impact of Artificial Intelligence on the Arts. Leonardo, 52(4), 345-347.
Vitra Design Museum. (2019). Hello, Robot! Design between Human and Machine. Retrieved from https://www.design-museum.de/en/exhibitions/detailseiten/hello-robot.html
Wang, K. (2021). Art and Artificial Intelligence: Implications, Challenges, and Opportunities. International Journal of Art & Design Education, 40(2), 497-512.
Wang, M. (2021). Artificial Intelligence and Visual Arts: A Critical Survey. Visual Arts Research, 47(2), 118-136.
Yuste, R., Goering, S., Bi, G., Carmena, J. M., Fins, J. J., Friesen, P., ... Huggins, J. E. (2017). Four ethical priorities for neurotechnologies and AI. Nature, 551(7679), 159-163.