Can AI Be as Creative as Humans? This paper explores the creative potential of artificial intelligence (AI), arguing that AI can be as creative as humans if it can properly fit the data generated by human creators. The study introduces two key concepts: Relative Creativity and Statistical Creativity. Relative Creativity assesses whether an AI can create works indistinguishable from those of a hypothetical human creator, while Statistical Creativity evaluates whether AI can replicate the creative outputs of specific human groups. The paper demonstrates that AI can achieve creativity by effectively assimilating extensive conditional data, including artworks and their creation conditions and processes. This data enables AI to act as a hypothetical new creator, matching the creative abilities of the human creators it was trained on. The study also provides practical guidelines for evaluating and fostering AI creativity, emphasizing the importance of gathering data on generative conditions and integrating them into the training process. Theoretical and practical contributions include the development of a framework for understanding, evaluating, and enhancing AI creativity. The paper highlights the need to move beyond the accumulation of large datasets and instead focus on the quality and conditions of the data. By doing so, the study aims to establish a theoretical foundation for creativity in AI, bridging the gap between theoretical analysis and practical application. The research also addresses the challenges of defining creativity and the subjectivity involved in assessing it, drawing inspiration from the Turing Test. The findings suggest that AI can achieve creativity by fitting sufficient conditional data without marginalizing the generative conditions. This approach shifts the focus from absolute definitions of creativity to relative comparisons, enabling a more objective assessment of AI's creative potential. The paper concludes that AI can be as creative as humans if it can effectively fit the necessary data, providing a theoretical and practical framework for understanding and enhancing AI creativity.Can AI Be as Creative as Humans? This paper explores the creative potential of artificial intelligence (AI), arguing that AI can be as creative as humans if it can properly fit the data generated by human creators. The study introduces two key concepts: Relative Creativity and Statistical Creativity. Relative Creativity assesses whether an AI can create works indistinguishable from those of a hypothetical human creator, while Statistical Creativity evaluates whether AI can replicate the creative outputs of specific human groups. The paper demonstrates that AI can achieve creativity by effectively assimilating extensive conditional data, including artworks and their creation conditions and processes. This data enables AI to act as a hypothetical new creator, matching the creative abilities of the human creators it was trained on. The study also provides practical guidelines for evaluating and fostering AI creativity, emphasizing the importance of gathering data on generative conditions and integrating them into the training process. Theoretical and practical contributions include the development of a framework for understanding, evaluating, and enhancing AI creativity. The paper highlights the need to move beyond the accumulation of large datasets and instead focus on the quality and conditions of the data. By doing so, the study aims to establish a theoretical foundation for creativity in AI, bridging the gap between theoretical analysis and practical application. The research also addresses the challenges of defining creativity and the subjectivity involved in assessing it, drawing inspiration from the Turing Test. The findings suggest that AI can achieve creativity by fitting sufficient conditional data without marginalizing the generative conditions. This approach shifts the focus from absolute definitions of creativity to relative comparisons, enabling a more objective assessment of AI's creative potential. The paper concludes that AI can be as creative as humans if it can effectively fit the necessary data, providing a theoretical and practical framework for understanding and enhancing AI creativity.