Speaker
Description
Strong AI seems to solve every question and task in a chat interface which is easy to use by scholars of the humanities. Multimodal approaches in AI lately obscured the sharp distinctions in machine learning with its research field computer vision and various methods like object recognition, pose estimation and scene understanding. Art historians who test this promise of strong AI are either disappointed by false or superficial results, or so stimulated by the recognizable potential that they await the commercial development of LLMs.
In order to analyze to what extent current AI is a game changer for art history, I will describe the ante quem situation and the developments of the past 15 years, to compare the difficulties of earlier approaches with the new problems and biases.
In a certain sense, our work on gesture and pose detection, object recognition, and iconography seems like a set of trial runs. However, it was our data, our annotations, and our models that—despite black-box effects—gave us more transparent results. Formerly, researchers in the field of explainable AI used conventional computer vision methods (e.g., SWIFT) to evaluate convolutional neural networks (CNNs). Must we now use self-trained models of this generation to try to better understand commercial LLMs? To what extent do our art-historical problems remain divergent from the learned conceptions of an LLM?