Can AI Be as Creative as Humans?
Haonan Wang, James Zou, Michael Mozer, Anirudh Goyal, Alex Lamb,, Linjun Zhang, Weijie J Su, Zhun Deng, Michael Qizhe Xie, Hannah Brown, Kenji, Kawaguchi

TL;DR
This paper theoretically demonstrates that AI can match human creativity if it can adequately fit the data generated by humans, shifting the debate to data fitting capabilities and providing a framework for evaluating AI's creative potential.
Contribution
It introduces the concepts of Relative Creativity and Statistical Creativity, offering a new theoretical framework to assess AI's creative abilities compared to humans.
Findings
AI can be as creative as humans when fitting extensive data.
A new theoretical framework for evaluating AI creativity is proposed.
Practical guidelines for training creative AI models are discussed.
Abstract
Creativity serves as a cornerstone for societal progress and innovation. With the rise of advanced generative AI models capable of tasks once reserved for human creativity, the study of AI's creative potential becomes imperative for its responsible development and application. In this paper, we prove in theory that AI can be as creative as humans under the condition that it can properly fit the data generated by human creators. Therefore, the debate on AI's creativity is reduced into the question of its ability to fit a sufficient amount of data. To arrive at this conclusion, this paper first addresses the complexities in defining creativity by introducing a new concept called Relative Creativity. Rather than attempting to define creativity universally, we shift the focus to whether AI can match the creative abilities of a hypothetical human. The methodological shift leads to a…
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Taxonomy
TopicsMachine Learning in Materials Science · Topic Modeling · Language and cultural evolution
MethodsFocus
