Machine Visions: Mapping Depictions of Machine Vision through Critical Image Synthesis

Abstract

This paper conducts a speculative examination of how AI image synthesisers, which generate novel imagery in response to inputted textual prompts — such as DALL-E, Midjourney, and Stable Diffusion — can be employed reflexively to investigate cultural representations of machine vision technologies. Such work can be framed methodologically as a form of ‘critical image synthesis’: the prompting of imagery that variously interrogates and makes visible the structural biases and cultural imperatives encoded within their originating architectures. In framing AI image synthesisers as an inverted form of machine vision — as generating, rather than classifying imagery through text — an opportunity is afforded to consider how they reflexively characterise themselves within their own latent spaces of representational possibility. Specifically, what kinds of imagery do these systems yield in response to prompts centring on keywords associated with machine vision technologies? And what does this reveal concerning how machine vision is represented and characterised across wider culture? This paper will empirically analyse a selection of prompted outputs from Stable Diffusion V2, treating them as a speculative mapping of contemporary visual themes and imaginaries surrounding machine vision technologies. This paper will then conclude by placing these outputs into dialogue with the author’s own creative practices involving machine vision, generating new image-text combinations that aim to provoke speculative analyses along alternative critical vectors.

Keywords

• AI
• Machine Vision
• Art
• Practice-led Research
• Stable Diffusion
• LAION