Generative artificial intelligence, human creativity, and art

Abstract Recent artificial intelligence (AI) tools have demonstrated the ability to produce outputs traditionally considered creative. One such system is text-to-image generative AI (e.g. Midjourney, Stable Diffusion, DALL-E), which automates humans’ artistic execution to generate digital artworks....

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Vydáno v:PNAS nexus Ročník 3; číslo 3; s. pgae052
Hlavní autoři: Zhou, Eric, Lee, Dokyun
Médium: Journal Article
Jazyk:angličtina
Vydáno: US Oxford University Press 01.03.2024
Témata:
Algorithms
Analysis
Art works
Artificial intelligence
Artists
Automation
Creative ability
Creative process
Creativity
Generative artificial intelligence
Image processing
Novelty
Productivity
Social and Political Sciences
Synesthesia
United States
creative workflow
art
generative AI
human–AI collaboration
impact of AI
ISSN:2752-6542, 2752-6542
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Shrnutí:Abstract Recent artificial intelligence (AI) tools have demonstrated the ability to produce outputs traditionally considered creative. One such system is text-to-image generative AI (e.g. Midjourney, Stable Diffusion, DALL-E), which automates humans’ artistic execution to generate digital artworks. Utilizing a dataset of over 4 million artworks from more than 50,000 unique users, our research shows that over time, text-to-image AI significantly enhances human creative productivity by 25% and increases the value as measured by the likelihood of receiving a favorite per view by 50%. While peak artwork Content Novelty, defined as focal subject matter and relations, increases over time, average Content Novelty declines, suggesting an expanding but inefficient idea space. Additionally, there is a consistent reduction in both peak and average Visual Novelty, captured by pixel-level stylistic elements. Importantly, AI-assisted artists who can successfully explore more novel ideas, regardless of their prior originality, may produce artworks that their peers evaluate more favorably. Lastly, AI adoption decreased value capture (favorites earned) concentration among adopters. The results suggest that ideation and filtering are likely necessary skills in the text-to-image process, thus giving rise to “generative synesthesia”—the harmonious blending of human exploration and AI exploitation to discover new creative workflows.
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Competing Interest: The authors declare no competing interest.
ISSN:2752-6542
2752-6542
DOI:10.1093/pnasnexus/pgae052