物理学家靠生物揭开AI创造力来源：起因竟是“技术缺陷”

Core Viewpoint - The creativity exhibited by AI, particularly in diffusion models, is hypothesized to be a result of the model architecture itself, rather than a flaw or limitation [1][3][19]. Group 1: Background and Hypothesis - AI systems, especially diffusion models like DALL·E and Stable Diffusion, are designed to replicate training data but often produce novel images instead [3][4]. - Researchers have been puzzled by the apparent creativity of these models, questioning how they generate new samples rather than merely memorizing data [8][6]. - The hypothesis presented by physicists Mason Kamb and Surya Ganguli suggests that the noise reduction process in diffusion models may lead to information loss, akin to a puzzle missing its instructions [8][9]. Group 2: Mechanisms of Creativity - The study draws parallels between the self-assembly processes in biological systems and the functioning of diffusion models, particularly focusing on local interactions and symmetry [11][14]. - The concepts of locality and equivariance in diffusion models are seen as both limitations and sources of creativity, as they force the model to focus on smaller pixel groups without a complete picture [15][19]. - The researchers developed a system called the Equivariant Local Score Machine (ELS) to validate their hypothesis, which demonstrated a 90% accuracy in matching outputs of trained diffusion models [18][19]. Group 3: Implications and Further Questions - The findings suggest that the creativity of diffusion models may be an emergent property of their operational dynamics, rather than a separate, higher-level phenomenon [19][21]. - There remain questions regarding the creativity of other AI systems, such as large language models, which do not rely on the same mechanisms of locality and equivariance [21][22]. - The research indicates that both human and AI creativity may stem from an incomplete understanding of the world, leading to novel and valuable outputs [21][22].