Curating art exhibitions using machine learning

TL;DR

This paper introduces four machine learning models designed to emulate human art curators by learning from existing exhibitions, demonstrating that AI can effectively replicate curatorial decisions with modest models.

Contribution

The study presents a set of four related AI models that learn from curated exhibitions, showing that smaller, carefully designed models can match larger language models in curatorial tasks.

Findings

AI models can replicate past exhibitions with accuracy above random chance.

Feature engineering and architecture design enable modest models to perform comparably to large language models.

Abstract

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.