arXiv:2411.11494cs.AIcs.CY2024-11

用AI发掘人类无法想到的艺术概念组合,突破认知局限。

Alien Recombination: Exploring Concept Blends Beyond Human Cognitive Availability in Visual Art

  • 通过微调大语言模型识别并生成超出现有认知的创意组合。
  • 发现并生成了人类艺术家从未尝试过的全新概念组合。
  • 适合对艺术创新、跨领域联想感兴趣的创作者与研究者。

尽管AI在游戏策略等受限领域表现出色,其在开放性艺术创作中的真正创造力仍存争议。本文提出,视觉艺术中存在大量未被探索的概念组合,其限制并非源于本质不相容,而是受制于艺术家所处的文化、时间、地理与社会背景带来的认知局限。为此,我们提出Alien Recombination方法,利用微调的大语言模型识别并生成超出人类认知可及性的概念组合。该系统主动建模并对抗‘认知可及性偏见’——即依赖即时可得范例的倾向,从而发现前所未有的艺术组合。这些组合不仅在我们的数据集中未曾出现,且对所有现有艺术家而言均属认知不可及。进一步将组合转化为视觉图像,以探索新颖性的主观感知。结果表明,认知不可达性是优化艺术新颖性的有效指标,优于仅靠温度调节的方法。该方法揭示了将AI驱动的创造力视为组合问题的潜力。

原文摘要 · Abstract (English)

While AI models have demonstrated remarkable capabilities in constrained domains like game strategy, their potential for genuine creativity in open-ended domains like art remains debated. We explore this question by examining how AI can transcend human cognitive limitations in visual art creation. Our research hypothesizes that visual art contains a vast unexplored space of conceptual combinations, constrained not by inherent incompatibility, but by cognitive limitations imposed by artists' cultural, temporal, geographical and social contexts. To test this hypothesis, we present the Alien Recombination method, a novel approach utilizing fine-tuned large language models to identify and generate concept combinations that lie beyond human cognitive availability. The system models and deliberately counteracts human availability bias, the tendency to rely on immediately accessible examples, to discover novel artistic combinations. This system not only produces combinations that have never been attempted before within our dataset but also identifies and generates combinations that are cognitively unavailable to all artists in the domain. Furthermore, we translate these combinations into visual representations, enabling the exploration of subjective perceptions of novelty. Our findings suggest that cognitive unavailability is a promising metric for optimizing artistic novelty, outperforming merely temperature scaling without additional evaluation criteria. This approach uses generative models to connect previously unconnected ideas, providing new insight into the potential of framing AI-driven creativity as a combinatorial problem.

艺术生成创意组合认知局限

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