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A Technical Note on Generative Art
Ellis, Lillien; Boatright, Benjamin; Fletcher, Eric Technical Note QA-0971 / Published March 19, 2024 / 10 pages.
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In October 2018, Christie’s auction house sold an artwork called Portrait of Edmond Belamy produced by an artificial intelligence (AI) model built to generate new works after being trained on upward of thousands of paintings. Three years later, Jason Allen submitted an art piece to the “digital art/digitally manipulated photography” division of an art competition associated with the 2022 Colorado State Fair. The submission relied heavily on a generative AI program called Midjourney. Instead of collecting works of art or fine-tuning an AI model, Midjourney and other modern text-to-image generators simply accepted a text description (or prompt) of an image and produced an image reflective of that description. These examples point toward the capabilities of AI image generators but also raise several questions: How do these image generators even work? And how did the system used to create Portrait of Edmond Belamy evolve into text-to-image AI art generators like Midjourney? Given the rise of these tools, what are some of the artistic, societal, and legal implications of AI art generators? Regardless of one’s perspective on the previous questions, it is hard to argue that art-generating AI models will not find their way into our lives to some degree. By understanding more about the technology of image generating tools and the different perspectives on them, managers can be better prepared to navigate these tools’ uses and effects—including risks. This preparation will prove helpful as innovative ideas and technological advancements are introduced to more industries, sparking more conversations between decision-makers with a range of perspectives. While AI art and the questions it raises are interesting, the money is in the tools. That means instead of programs like AI art generators designed to process certain inputs and produce certain outputs, business managers across industries will look toward tools for which generative AI art has laid a foundation, and they’ll perhaps seek niche use cases—how value can be found for specifically their industries.




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  • Overview

    In October 2018, Christie’s auction house sold an artwork called Portrait of Edmond Belamy produced by an artificial intelligence (AI) model built to generate new works after being trained on upward of thousands of paintings. Three years later, Jason Allen submitted an art piece to the “digital art/digitally manipulated photography” division of an art competition associated with the 2022 Colorado State Fair. The submission relied heavily on a generative AI program called Midjourney. Instead of collecting works of art or fine-tuning an AI model, Midjourney and other modern text-to-image generators simply accepted a text description (or prompt) of an image and produced an image reflective of that description. These examples point toward the capabilities of AI image generators but also raise several questions: How do these image generators even work? And how did the system used to create Portrait of Edmond Belamy evolve into text-to-image AI art generators like Midjourney? Given the rise of these tools, what are some of the artistic, societal, and legal implications of AI art generators? Regardless of one’s perspective on the previous questions, it is hard to argue that art-generating AI models will not find their way into our lives to some degree. By understanding more about the technology of image generating tools and the different perspectives on them, managers can be better prepared to navigate these tools’ uses and effects—including risks. This preparation will prove helpful as innovative ideas and technological advancements are introduced to more industries, sparking more conversations between decision-makers with a range of perspectives. While AI art and the questions it raises are interesting, the money is in the tools. That means instead of programs like AI art generators designed to process certain inputs and produce certain outputs, business managers across industries will look toward tools for which generative AI art has laid a foundation, and they’ll perhaps seek niche use cases—how value can be found for specifically their industries.

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