论人类创造力与人工创造力的随机性
On the stochastics of human and artificial creativity
AI总结：
本研究构建融合多学科洞见的人类创造力统计表征以凸显其随机特性，并用其评估强化学习、扩散模型等当代AI系统的创造力水平，指出当前AI尚不具备人类级别的自主创造能力。
摘要：
何为人类创造力的构成要素，计算机是否有可能展现出真正的创造力？我们认为，要在计算机中实现人类水平的智能，即所谓的通用人工智能（Artificial General Intelligence），也必须达到人类水平的创造力。我们通过构建人类创造力的统计表征，结合随机理论、心理学、哲学、神经科学和混沌理论的既有洞见，为这一讨论做出贡献。这一表征凸显了人类创造过程的随机性：该过程既包含受偏置引导的随机生成步骤，也包含依赖灵活可变形偏置结构的评估步骤。随后，我们将所得的人类创造力表征用于评估各类当代AI系统的创造力水平。我们的分析涵盖了强化学习、扩散模型、大语言模型等现代AI算法，探讨了它们在多大程度上能达到人类水平的创造力。我们得出结论：这些技术目前尚不具备人类水平的自主创造行动能力。
英文摘要：
What constitutes human creativity, and is it possible for computers to exhibit genuine creativity? We argue that achieving human-level intelligence in computers, or so-called Artificial General Intelligence, necessitates attaining also human-level creativity. We contribute to this discussion by developing a statistical representation of human creativity, incorporating prior insights from stochastic theory, psychology, philosophy, neuroscience, and chaos theory. This highlights the stochastic nature of the human creative process, which includes both a bias guided, random proposal step, and an evaluation step depending on a flexible or transformable bias structure. The acquired representation of human creativity is subsequently used to assess the creativity levels of various contemporary AI systems. Our analysis includes modern AI algorithms such as reinforcement learning, diffusion models, and large language models, addressing to what extent they measure up to human level creativity. We conclude that these technologies currently lack the capability for autonomous creative action at a human level.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
