Sound Research WIKINDX
Sound Research WIKINDX |
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Displaying 1 - 2 of 2 Parameters |
| Zhou, E., & Lee, D. (2023). Generative AI, human creativity, and art. SSRN Electronic Journal. |
 
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| Added by: alexb44 Last edited by: alexb44 05/03/2025, 10:48 | |
| Zhou, E., & Lee, D. (2024). Generative artificial intelligence, human creativity, and art. PNAS Nexus, 3(3). |
 
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| Added by: alexb44 Last edited by: alexb44 24/02/2025, 14:06 | |
| "While generative AI has demonstrated the capability to automatically create new digital artifacts, there remains a significant knowledge gap regarding its impact on productivity in artistic endeavors which lack welldefined objectives, and the long-run implications on human creativity more broadly. In particular, if humans increasingly rely on generative AI for content creation, creative fields may become saturated with generic content, potentially stifling exploration of new creative frontiers." | |
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"A simplified view of human creative novelty with respect to art can be summarized via two main channels through which humans can inject creativity into an artifact: Contents and Visuals. These concepts are rooted in the classical philosophy of symbolism in art which suggests that the contents of an artwork is related to the meaning or subject matter, whereas visuals are simply the physical elements used to convey the content (7). In our setting, Contents concern the focal object(s) and relations depicted in an artifact, whereas Visuals consider the pixel-level stylistic elements of an artifact. Thus, Content and Visual Novelty are measured as the pairwise cosine distance between artifacts in the feature space (see Materials and methods for details on feature extraction and how novelty is measured)."
7: Huang S, Grady P, GPT-3. 2022. Generative AI: a creative new world. Sequoia Capital US/Europe. https://www.sequoiacap.com/article/generative-ai-a-creative-new-world/ |
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| "We define creative productivity as the log of the number of artifacts that a user posts in a month. Figure 1a reveals that upon adoption, artists experience a 50% increase in productivity on average, which then doubles in the subsequent month. For the average user, this translates to approximately 7 additional artifacts published in the adoption month and 15 artifacts in the following month. Beyond the adoption month, user productivity gradually stabilizes to a level that still exceeds preadoption volume. By automating the execution stage of the creative process, adopters can experience prolonged productivity gains compared to their nonadopter counterparts." | |
| "Figure 1b reveals an initial nonsignificant upward trend in the Value of artworks produced by AI adopters. But after 3 months, AI adopters consistently produce artworks judged significantly more valuable than those of nonadopters. This translates to a 50% increase in artwork favorability by the sixth month, jumping from the preadoption average of 2% to a steady 3% rate of earning a favorite per view" | |
| "The result shown in Fig. 1e highlights that average Visual Novelty is decreasing over time among adopters when compared to nonadopters. The same result holds for the maximum Visual Novelty seen in Fig. 1f. This suggests that adopters may be gravitating toward a preferred visual style, with relatively minor deviations from it. This tendency could be influenced by the nature of text-to-image workflows, where prompt engineering tends to follow a formulaic approach to generate consistent, high-quality images with a specific style. As is the case with contents, publicly available fine-tuned checkpoints and adapters for these models may be designed to capture specific visual elements from which users can sample from to maintain a particular and consistent visual style. In effect, AI may be pushing artists toward visual homogeneity." |