This paper analyses a visual archive of drawings produced by an interactive robotic art installation where audience members narrated their dreams into a system powered by CLIPdraw deep learning (DL) model that interpreted and transformed their dreams into images. The resulting archive of prompt-image pairs were examined and clustered based on concept representation accuracy. As a result of the analysis, the paper proposes four groupings for describing and explaining CLIP-generated results: clear concept, text-to-text as image, indeterminacy and confusion, and lost in translation. This article offers a glimpse into a collection of dreams interpreted, mediated and given form by Artificial Intelligence (AI), showcasing oftentimes unexpected, visually compelling or, indeed, the dream-like output of the system, with the emphasis on processes and results of translations between languages, sign-systems and various modules of the installation. In the end, the paper argues that proposed clusters support better understanding of the neural model.

翻译：本文分析了一个互动机器人艺术装置生成的视觉绘画档案库，在该装置中，观众将自己的梦境叙述给由CLIPdraw深度学习模型驱动的系统，该系统将他们的梦境解读并转化为图像。由此产生的提示-图像配对档案根据概念表征的准确性进行了分析和聚类。基于分析结果，本文提出了描述和解释CLIP生成结果的四个分组：清晰概念、文到文即图像、不确定性与混淆、以及翻译中的迷失。本文展示了由人工智能解读、中介并赋予形态的梦境集锦，呈现了系统输出中往往出人意料、视觉上引人入胜甚至如同梦境般的效果，重点聚焦于多语言、符号系统及装置各模块间的翻译过程与结果。最后，本文认为所提出的分组有助于更好地理解该神经模型。