Skip to main content

Revolutionizing the Creative Process: Exploring the Benefits and Challenges of AI-Driven Art

  • Conference paper
  • First Online:
Intelligent Computing and Optimization (ICO 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 854))

Included in the following conference series:

  • 303 Accesses

Abstract

This paper examines how Artificial intelligence (AI) has revolutionized the creative process by enabling machines to create art and explore new mediums, technologies, and techniques in artistic expression. The research methodology employed includes a literature review, interviews with leading art professionals, and case studies. The results suggest that AI can be used to expand human creativity by allowing the artist to explore new techniques and technologies as well as to create unique works of art. However, ethical and practical considerations must be considered when working with this type of technology, such as the cost of creating AI-driven art and the imperfect output that can be generated by algorithms. Additionally, AI-driven art may not be accepted into major institutions as its medium, meaning that consideration must be taken to ensure that AI art is presented professionally and respectfully. This paper pursues to provide insight into the impact of AI technology on the creative process and explore the potential benefits and challenges associated with using AI to create artwork.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. de Leon, J.A., et al.: Deep learning approach to 2d capacitive resistivity imaging inversion. In: Intelligent Computing and Optimization. ICO 2022. Lecture Notes in Networks and Systems, vol. 569, pp. 459–468. Springer, Cham (2023)

    Google Scholar 

  2. Historillo, J.F., Rosales, M., Merlin, M., Mandia, E.B.: Color and image analysis approach in determination of soluble copper in water using tannic reaction analysis. In: Intelligent Computing and Optimization. ICO 2022. Lecture Notes in Networks and Systems, vol. 569, pp. 958–971. Springer, Cham (2023)

    Google Scholar 

  3. Hossain, M.A., Hasan, M.A.F.M.R.: Activity identification from natural images using deep CNN. In: Intelligent Computing and Optimization. ICO 2020. Advances in Intelligent Systems and Computing, vol. 1324, pp. 693–707. Springer, Cham (2021)

    Google Scholar 

  4. Wei, X., Wei, R.: Research on interactive art design system based on artificial intelligence technology. Smart Innov. Syst. Technol. 298, 159–166 (2022)

    Article  Google Scholar 

  5. Li, L.: The impact of artificial intelligence painting on contemporary art from disco diffusion’s painting creation experiment. In: 2022 International Conference on Frontiers of Artificial Intelligence and Machine Learning (FAIML), pp. 52-56. IEEE (2022)

    Google Scholar 

  6. Thomas, J.J., Pillai, N.: A deep learning framework on generation of image descriptions with bidirectional recurrent neural networks. In: Intelligent Computing and Optimization. ICO 2018. Advances in Intelligent Systems and Computing, vol. 866, pp. 219–230. Springer, Cham (2019)

    Google Scholar 

  7. Hitsuwari, J., Ueda, Y., Yun, W., Nomura, M.: Does human–AI collaboration lead to more creative art? Aesthetic evaluation of human-made and AI-generated haiku poetry. Comput. Human Behav. 139, 107502 (2023)

    Google Scholar 

  8. Hertzmann, A.: Visual indeterminacy in GAN art. Leonardo 53(4), 424–428 (2020)

    Article  Google Scholar 

  9. Leiser, A., Schlippe, T.: AI in art: simulating the human painting process. In: ArtsIT, Interactivity and Game Creation. ArtsIT 2021. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol. 422, pp. 295–308. Springer, Cham (2022)

    Google Scholar 

  10. Hung, M.C., Nakatsu, R., Tosa, N., Kusumi, T.: Learning of art style using AI and its evaluation based on psychological experiments. Int. J. Arts Technol. 14(3), 171–191 (2022)

    Article  Google Scholar 

  11. Guljajeva, V., Sola, M.C.: Dream painter: an interactive art installation bridging audience interaction, robotics, and creative AI. In: MM’22: Proceedings of the 30th ACM International Conference on Multimedia, pp. 7235–7236 (2022)

    Google Scholar 

  12. Jing, Y., Yang, Y., Feng, Z., Ye, J., Yu, Y., Song, M.: Neural style transfer: a review. IEEE Trans. Vis. Comput. Graph. 26(11), 3365–3385 (2020)

    Article  Google Scholar 

  13. Goenaga, M.A.: A critique of contemporary artificial intelligence art: who is Edmond de Belamy?. AusArt J. Res. Art. 8(1), 49–64 (2020)

    Google Scholar 

  14. Chen, W., Shidujaman, M., Xuelin, T.: AiArt: towards artificial intelligence art. In: MMEDIA 2020: The Twelfth International Conference on Advances in Multimedia, pp. 47–52 (2020)

    Google Scholar 

  15. Hedes, B.-C.: Happening, a controversial hybrid way of cultural expression. Stud. Univ. Babes-Bolyai Dramatica 67(2), 123–140 (2022)

    Google Scholar 

  16. Blomfield, I., Lenette, C.: Artistic representations of refugees: what is the role of the artist? J. Intercult. Stud. 39(3), 322–338 (2018)

    Article  Google Scholar 

  17. Liu, Z.: Research and implementation of digital art media system based on big data aesthetics. Smart Innov. Syst. Technol. 298, 195–203 (2022)

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Dhaneshwar Shah .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Rani, S., Jining, D., Shah, D., Xaba, S., Singh, P.R. (2023). Revolutionizing the Creative Process: Exploring the Benefits and Challenges of AI-Driven Art. In: Vasant, P., et al. Intelligent Computing and Optimization. ICO 2023. Lecture Notes in Networks and Systems, vol 854. Springer, Cham. https://doi.org/10.1007/978-3-031-50151-7_23

Download citation

Publish with us

Policies and ethics