A Multimodal Turn: Navigating AI Developments in Digital Humanities

Melvin Wevers, University of Amsterdam (Netherlands)

In the ever-evolving landscape of Digital Humanities (DH), research methodologies predominantly centered on textual data. However, the advent of deep learning revolutionized this scope, enabling the automated analysis and labeling of visual materials. Despite their capabilities, these early methods demanded extensive training datasets. The landscape saw another transformation with the rise of multimodal deep learning architectures, such as the Contrastive Language Image Pre-training (CLIP). Such innovations brought about a convergence of GPT-inspired interfaces for visual analysis, broadening the ambit of multimodal research. These technological leaps have now positioned humanists on the cusp of computational visual analysis.

This keynote aims to spotlight these advancements and probe deeper into their alignment with multimodal theory. By doing so, it strives to understand their ramifications on the humanistic engagement with visual mediums. As we journey through this alignment, we find ourselves at a crossroads, grappling with pressing dilemmas of practicality, adaptability, and choice. Can the humanistic community keep pace with these swift technological evolutions? And, more fundamentally, is there an imperative to stay abreast, or should we gravitate towards more established techniques, offering greater control and explainability?