Mean images are far from random hallucinations. ![]() It bakes moral, statistical, financial and aesthetic values as well as common and lower-class positions into one dimly compressed setting. The term itself is a composite, which blurs and superimposes seemingly incompatible layers of signification. It is connected to meaning as signifying, to ideas of the common, but also to financial or instrumental means. ‘Mean’ may refer to minor or shabby origins, to the norm, to the stingy or to nastiness. The English word ‘mean’ has several meanings, all of which apply here. All it takes is to remove the noise of reality from my photos and extract the social signal instead the result is a ‘mean image’, a rendition of correlated averages-or: different shades of mean. This is an approximation of how society, through a filter of average internet garbage, sees me. Instead, we might call it a white box algorithm, or a social filter. It is not a ‘black box’ algorithm that is to blame, as Stable Diffusion’s actual code is known. The question is, what mean? Whose mean? Which one? Stable Diffusion renders this portrait of me in a state of frozen age range, produced by internal, unknown processes, spuriously related to the training data. It looks rather mean, or even demeaning but this is precisely the point. So, how did Stable Diffusion get from A to B? It is not the most flattering ‘before and after’ juxtaposition, for sure I would not recommend the treatment. What does Stable Diffusion make of them? Ask the model to render ‘an image of hito steyerl’, and this (Figure 2) is the result. These pictures of mine (Figure 1) show up inside this training data. They may be ‘poor images’ in terms of resolution, but in style and substance they are: mean images.Īn example of how a set of more traditional photographs is converted into a statistical render: the search engine, ‘Have I been trained?’-a very helpful tool developed by the artists Mat Dryhurst and Holly Herndon-allows the user to browse the massive laion-5b dataset used to train Stable Diffusion, one of the most popular deep-learning text-to-image generators. They replace likenesses with likelinesses. They represent the norm by signalling the mean. They converge around the average, the median hallucinated mediocrity. They are not dependent on the actual impact of photons on a sensor, or on emulsion. As data visualizations, they do not require any indexical reference to their object. These renderings represent averaged versions of mass online booty, hijacked by dragnets, in the style of Francis Galton’s blurred eugenicist composites, 8k, Unreal engine. ![]() ![]() The shock of sudden photographic illumination is replaced by the drag of Bell curves, loss functions and long tails, spun up by a relentless bureaucracy. They no longer refer to facticity, let alone truth, but to probability. They shift the focus from photographic indexicality to stochastic discrimination. Visuals created by ml tools are statistical renderings, rather than images of actually existing objects. footnote 1 But the blurry output generated by machine-learning networks has an additional historical dimension: statistics. A while ago, science-fiction writer Ted Chiang described Chat gpt’s text output as a ‘blurry jpeg of all the text in the web’-or: as a semantic ‘poor image’.
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