Here we present a series of artificial models - a total of four related models - based on machine learning techniques that attempt to learn from existing exhibitions which have been curated by human experts, in order to be able to do similar curatorship work. Out of our four artificial intelligence models, three achieve a reasonable ability at imitating these various curators responsible for all those exhibitions, with various degrees of precision and curatorial coherence. In particular, we can conclude two key insights: first, that there is sufficient information in these exhibitions to construct an artificial intelligence model that replicates past exhibitions with an accuracy well above random choices; and second, that using feature engineering and carefully designing the architecture of modest size models can make them almost as good as those using the so-called large language models such as GPT in a brute force approach.

翻译：本文提出了一系列基于机器学习技术的人工智能模型——共计四个相关模型，这些模型尝试从人类专家策划的现有展览中学习，以期能够执行类似的策展工作。在我们构建的四个模型中，有三个模型在模仿负责各类展览的策展人方面表现出合理的能力，其精度与策展连贯性各有差异。特别值得指出的是，我们可得出两个关键结论：首先，现有展览中包含足够的信息来构建人工智能模型，使其能以远高于随机选择的准确度复现历史展览；其次，通过特征工程与精心设计中等规模模型架构，可使这些模型在效果上几乎媲美采用GPT等大型语言模型的暴力计算方法。