Separate and Reassemble: Generative AI through the lens of art and media histories

Authors

DOI:

https://doi.org/10.11606/issn.1982-8160.v18i2p7-18

Keywords:

AI image generation, digital media, neural networks, computer graphics, generative AI

Abstract

AI image generation represents a logical evolution from early digital media algorithms, starting with basic paint programs in the 1970s and advancing to sophisticated 3D graphics and media creation software by the 1990s. Early algorithms struggled to simulate materials and effects, but advances in the 1970s and 1980s led to realistic simulations of natural phenomena and artistic techniques. Generative AI continues this trend, using neural networks to combine and interpolate visual patterns from extensive datasets. This method of digital media creation underscores the modular and discrete nature of computer-generated imagery, distinguishing it from traditional optical media.

Downloads

Download data is not yet available.

Author Biography

  • Lev Manovich, City University of New York

    Presidential Professor of Computer Science at the City University of New York’s Graduate Center and the Director of the Cultural Analytics Lab. He authored and edited 15 books, including Artificial Aesthetics, Cultural Analytics, Instagram and Contemporary Image, Software Takes Command, and The Language of New Media.

References

ACM SIGGRAPH. (2022). SIGGRAPH ‘22: ACM SIGGRAPH 2022 Conference Proceedings.

Barla, N. (2024, May 14). How to visualize deep learning models. Neptune.ai. https://neptune.ai/blog/deep-learning-visualization.

Bokov, A. (2014). VKhUTEMAS training. Pavilion of the Russian Federation at the 14th International Architecture Exhibition.

Bokov, A. (2021). Avant-garde as method: Vkhutemas and the pedagogy of space, 1920-1930. Park Books.

Corel Painter. (2024, July 5). In Wikipedia. https://en.wikipedia.org/wiki/Corel_Painter.

Manovich, L. (1992). Assembling reality: Myths of computer graphics. Afterimage, 20(2), 12-14.

Manovich, L. (2002). The language of new media. MIT press.

Manovich, L. (2013). Software takes command. Bloomsbury Academic.

Manovich, L. (2018). AI aesthetics. Strelka Press.

Mitchell, W. J. (1996). City of bits: Space, place, and the Infobahn. MIT press.

Olah, C., Mordvintsev, A., & Schubert, L. (2017, November 7). Feature visualization: How neural networks build up their understanding of images. Distill. https://doi.org/10.23915/distill.00007.

Podell, D., English, Z., Lacey, K., Blattmann, A., Dockhorn, T., Müller, J., Penna, J., & Rombach, R. (2023). SDXL: Improving latent diffusion models for high-resolution image synthesis. arXiv. https://arxiv.org/abs/2307.01952.

Smith, A. R. (2001). Digital paint systems: An anecdotal and historical overview. IEEE Annals of the History of Computing, 23(2), 4-30. https://doi.org/10.1109/85.929908.

Smith, A. R. (2021). A biography of the pixel. MIT Press.

Vkhutemas. (2020, June 25). Main course. https://www.vkhutemas.ru/en/structure-eng/faculties-eng/main-course/

Published

2024-08-30

Issue

Section

Dossier

How to Cite

Manovich, L. (2024). Separate and Reassemble: Generative AI through the lens of art and media histories. MATRIZes, 18(2), 7-18. https://doi.org/10.11606/issn.1982-8160.v18i2p7-18