一种用于客观表征想法的新型数学框架
A Novel Mathematical Framework for Objective Characterization of Ideas
AI总结：
本文提出一种自动化数学框架，将想法表示为高维向量并结合 UMAP、DBSCAN 和 PCA 量化多样性，以客观筛选 CAI 或人类生成的创意。
摘要：
产品设计中对创新的需求使得多产的构思阶段成为必要。使用大型语言模型（LLM，如 GPT（Generative Pre-trained Transformer，生成式预训练 Transformer））的对话式 AI（CAI）系统已被证明在增强人类创造力方面富有成效，能够提供大量新颖且多样的想法。尽管在构思数量方面取得了成功，但对这些想法进行定性评估仍然具有挑战性，并且传统上依赖于人类专家评价。这种方法存在局限，例如人类判断错误、偏见和疏漏。为弥补这一差距，本研究提出了一个用于自动化分析的综合数学框架，以客观评估由 CAI 系统和/或人类生成的大量想法。该框架对于缺乏选择有前景想法经验的新手设计师尤其有利。通过将想法转换为高维向量，并使用 UMAP、DBSCAN 和 PCA 等工具定量测量它们之间的多样性，所提出的方法提供了一种可靠且客观的方式来选择最有前景的想法，从而提高构思阶段的效率。
英文摘要：
The demand for innovation in product design necessitates a prolific ideation phase. Conversational AI (CAI) systems that use Large Language Models (LLMs) such as GPT (Generative Pre-trained Transformer) have been shown to be fruitful in augmenting human creativity, providing numerous novel and diverse ideas. Despite the success in ideation quantity, the qualitative assessment of these ideas remains challenging and traditionally reliant on expert human evaluation. This method suffers from limitations such as human judgment errors, bias, and oversight. Addressing this gap, our study introduces a comprehensive mathematical framework for automated analysis to objectively evaluate the plethora of ideas generated by CAI systems and/or humans. This framework is particularly advantageous for novice designers who lack experience in selecting promising ideas. By converting the ideas into higher dimensional vectors and quantitatively measuring the diversity between them using tools such as UMAP, DBSCAN and PCA, the proposed method provides a reliable and objective way of selecting the most promising ideas, thereby enhancing the efficiency of the ideation phase.
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。
