2023 | 37

Browsing 2023 | 37 by Subject "AI"

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  • Article
    AI Image Media through the Lens of Art and Media History
    Manovich, Lev (2023) , S. 34-41
    I’ve been using computer tools for art and design since 1984 and have already seen a few major visual media revolutions, including the development of desktop media software and photorealistic 3D computer graphics and ani- mation, the rise of the web after, and later social media sites and advances in computational photography. The new AI ‘generative media’ revolution appears to be as significant as any of them. Indeed, it is possible that it is as significant as the invention of photography in the nineteenth century or the adoption of linear perspective in western art in the sixteenth. In what follows, I will discuss four aspects of AI image media that I believe are particularly significant or novel. To better understand these aspects, I situate this media within the context of visual media and human visual arts history, ranging from cave paintings to 3D computer graphics.
  • Article
    How to Read an AI Image: Toward a Media Studies Methodology for the Analysis of Synthetic Images
    Salvaggio, Eryk (2023) , S. 83-99
    Image-generating approaches in machine learning, such as GANs and Diffusion, are actually not generative but predictive. AI images are data patterns inscribed into pictures, and they reveal aspects of these image-text datasets and the human decisions behind them. Examining AI-generated images as ‘info- graphics’ informs a methodology, as described in this paper, for the analysis of these images within a media studies framework of discourse analysis. This paper proposes a methodological framework for analyzing the content of these images, applying tools from media theory to machine learning. Using two case studies, the paper applies an analytical methodology to determine how information patterns manifest through visual representations. This methodology consists of generating a series of images of interest, following Roland Barthes’ advice that “what is noted is by definition notable” (BArThEs 1977: 89). It then examines this sample of images as a non-linear sequence. The paper offers examples of certain patterns, gaps, absences, strengths, and weaknesses and what they might suggest about the underlying dataset. The methodology considers two frames of intervention for explaining these gaps and distortions: Either the model imposes a restriction (content policies), or else the training data has included or excluded certain images, through conscious or unconscious bias. The hypothesis is then extended to a more randomized sample of images. The method is illustrated by two examples. First, it is applied to images of faces produced by the StyleGAN2 model. Second, it is applied to images of humans kissing created with DALL·E 2. This allows us to compare GAN and Diffusion models, and to test whether the method might be generalizable. The paper draws some conclusions to the hypotheses generated by the method and presents a final comparison to an actu- al training dataset for StyleGAN2, finding that the hypotheses were accurate.