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 provide a theoretical answer to the question of whether AI can be creative. We prove in theory that AI can be as creative as humans under the condition that AI can fit the existing data generated by human creators. Therefore, the debate on AI's creativity is reduced into the question of its ability of fitting a massive 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. Instead of trying to define creativity universally, we shift the focus to whether AI can match the creative abilities of a hypothetical human. This perspective draws inspiration from the Turing Test, expanding upon it to address the challenges and subjectivities inherent in assessing creativity. This methodological shift leads to a statistically quantifiable assessment of AI's creativity, which we term Statistical Creativity. This concept allows for comparisons of AI's creative abilities with those of specific human groups, and facilitates the theoretical findings of AI's creative potential. Building on this foundation, we discuss the application of statistical creativity in prompt-conditioned autoregressive models, providing a practical means for evaluating creative abilities of contemporary AI models, such as Large Language Models (LLMs). In addition to defining and analyzing creativity, we introduce an actionable training guideline, effectively bridging the gap between theoretical quantification of creativity and practical model training.

翻译：创造力是社会进步与创新的基石。随着能够执行曾专属人类创造力任务的高级生成式AI模型兴起，研究AI的创造潜力对其负责任开发与应用至关重要。本文从理论层面回答了AI是否具有创造力的问题。我们证明，在AI能够拟合人类创作者生成的现有数据的条件下，其创造力可与人类媲美。因此，关于AI创造力的争论被简化为其拟合海量数据的能力问题。为得出这一结论，本文首先通过引入"相对创造力"这一新概念来应对定义创造力的复杂性。我们不再试图统一界定创造力，而是将焦点转向AI能否与假设的人类创造力水平相匹配。这一视角受图灵测试启发，并加以扩展以应对评估创造力过程中固有的挑战与主观性。此方法论转变促成了对AI创造力的统计可量化评估，我们称之为"统计创造力"。该概念允许将AI的创造能力与特定人类群体进行比较，并推动了对AI创造潜力的理论发现。基于此，我们讨论了统计创造力在提示条件自回归模型中的应用，为评估当代AI模型（如大型语言模型）的创造能力提供了实践途径。除定义与分析创造力外，我们还提出了一项可操作的训练指南，有效弥合了创造力理论量化与实际模型训练之间的鸿沟。