“Everything we see hides another thing, we always want to see what is hidden by what we see. There is an interest in that which is hidden and which the visible does not show us. This interest can take the form of a quite intense feeling, a sort of conflict, one might say, between the visible that is hidden and the visible that is present.”

René Magritte, interview response to his self-portrait painting Son of Man (1964)

Centre for the Study of the Networked Image, G. Cox, A. Dekker, A. Dewdney, and K. Sluis. “Affordances of the Networked Image”. The Nordic Journal of Aesthetics 2021. Link.

The processes of computation and automation that produce digitized images have displaced the concept of an image once conceived through optical devices such as a photographic plate or a camera mirror that were invented to accommodate the human eye. Computational images exist as information within networks mediated by coded machines. They are increasingly less about what art history understands as representation or photography considers indexing and more an operational product of data processing determined by numerical information. Within this new reality, artificial intelligence (AI) applications are rapidly burgeoning as dominant sources of image production. What becomes of a visual world mediated first by data points from a specific training set expressed through tokens, pixels, and text?

Concerning a specific theory of pictures, I follow W.J.T Mitchell in distinguishing between picture and image. Where pictures are concrete objects, images are virtual and phenomenal appearances presented to a beholder through objects. “To picture” is a deliberate act of visual representation, whereas “to image or imagine” is more elusory, general, and spontaneous.[1] Pictures–and photographs–can be taken, while images are made. I think about what to make of images that are made today, co-constructed by sets of computational logic that test the limits of human representation.

Representation is insufficient as a concept to explain instances when images are made and distributed between machines with little to no human intervention. Here, I identify the capacity of art to transfigure (transmogrify, transduce) the illegibility of computation and AI into new pathways for experiencing the world by material investigations that gesture towards the possibilities of difference. The open indeterminacy of computation allows for an opportunity to decenter normative ideas of what is defined and counted as human. 

Firmly in an epoch of network culture, where computational agency, intelligence, and creativity are legitimate ideas to ponder, I also want to think about its history and its politico-epistemic effects on images. If we can fight to make this new world fairer and more available, wrestled away from its racialized techno-capital-military influences, it is a fight worth having for a future whose cosmology we can start creating today. The relinquishing of the primacy of the human eye and the acknowledgment of the failures of human exceptionalism allow us to experience the world in new and deeper ways. Like symbiotes or cyborgs, we have adopted new epistemic instruments that produce entirely new worlds in collaboration with computing intelligence.

I argue that at its most insidious, the computational automation of imaging and sight (the statistical gaze) leads to second order social consequences that intensify sensory-overload (chaos or arbitrariness or unknowability) and enable abuses of power. By second order I mean consequences that are not the direct goal of technological development but nevertheless part of its outcomes. Two specific consequences I identify are: 1) the ways in which human correspondence with social reality is obfuscated and homogenized by narrow applications of computation, and 2) how the ubiquity of surveillance as an outgrowth of computation is changing the form of power dynamics. 

My unit of analysis follows the development of computation and is thus not reducible to one society or individual, although I focus mostly on its Western origin story and implications. When I consider social relations, I mean the interactions among organisms that live and commune together. Human social relations are increasingly complexified by myriad variables that affect how we communicate, think, exchange, and live. I consider the effects of computation within this broader framework of the social, which for me also encompasses culture.

“Firmly in an epoch of network culture, where computational agency, intelligence, and creativity are legitimate ideas to ponder, I also want to think about its history and its politico-epistemic effects on images.”

My argument implies that computation, as a new form of mediating the world, enables a deluge of opaque image production that challenges how we can know or make sense of things. The human optical system becomes one part of a larger loop of information processing. For example, an analysis estimated that from 2022 to 2023 alone, Artificial Intelligence (AI) was used to produce 15 billion images, a figure that took photography 150 years to reach (circa 1826 until 1975).[2] The argument also implies that beyond simply facilitating this torrent, computation is responsible for the development of visual surveillance tools that enable new ways of monitoring, measuring, predicting, commodifying, and controlling individuals and large groups of people.     

Jacques Rancière’s concept of the distribution of the sensible defines politics in a way that includes what Michel Foucault called the order of things, or what is symbolically representable at any given place and time. When combining the two concepts, the struggle over the methods of sensing and the process of perception establishes a relation I call political. The distribution of the sensible contains the ethical, intellectual, and political as aesthetic experiences. The aesthetic here is not about the judgment of beauty but rather the relationship between sense perception, embodiment, meaning, and social relations.

Rather than a critique, my method of analysis is more akin to what Eyal Weizman and Mathew Fuller define as investigative aesthetics. The practice of investigative aesthetics creates observable composites using various signals that include forensic, technical, material, cultural, political, and ethical evidence. Various online and offline methodologies are combined together in transdisciplinary or antidisciplinary work to render the causes of an event or the existence of an object visible.

Practitioners deploy computational methods within legal, forensic, artistic, and critical frameworks. Investigative aesthetics takes seriously the material conditions through which events occur and attempts to create a public alternative to facts presented by power-holding actors. In so doing, it also points to longer historical processes that shape events and outcomes in the present, what I think of as the archaeology of an event. Weizman and Fuller leverage the technical in the spirit of producing a commons for knowledge that resembles a multisensory, navigable architectural model. To experience and make sense of the world and to feel spurred on to imagine it differently are aesthetic experiences and require investigative practices.

“In so doing, it also points to longer historical processes that shape events and outcomes in the present, what I think of as the archaeology of an event.”

This method expands the aesthetic and logical limitations of a single human subject, and thus expands a collective’s ability to sense and reason. I loosely follow their concept of investigative aesthetics throughout in attempting to agnostically make sense of computational logics and practices while retaining an ethical commitment to harm reduction and the principle of freedom. My methodology includes producing an archive, constructing a theoretical framework, empirically testing through technical and artistic practices, and relying on transdisciplinary research and pedagogy. The goal of my project is to create an ongoing framework for understanding and creating computational images that is open to new information, new case studies, and new practices (evidence).

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[1] Mitchell, W., Representation, in F Lentricchia & T McLaughlin (eds), Critical Terms for Literary Study, 2nd edn, University of Chicago Press, Chicago, 1995, 4.

[2] Every Pixel Journal

“I think the style would be a bit whimsical and abstract and weird, and it tends to blend things in ways you might not ask, in ways that are surprising and beautiful. It tends to use a lot of blues and oranges. It has some favorite colors and some favorite faces. If you give it a really vague instruction, it has to go to its favorites. So, we don’t know why it happens, but there’s a particular woman’s face it likes to draw — we don’t know where it comes from, from one of our 12 training datasets — but people just call it “Miss Journey.” And there’s one dude’s face, which is kind of square and imposing, and he also shows up some time, but he doesn’t have a name yet. But it’s like an artist who has their own faces and colors.” 

David Holz, Midjourney founder, interview with The Verge (2022)

For us humans, the computation involved in generative AI is catalyzing a significant change in the processual truth of what an image is. The web of computational operations in this new production process forces us to re-think what the art history and photography canons call representation. Once conceived through optical concepts and materials such as a vanishing point, a photographic plate, or a camera mirror that were invented to accommodate the human eye, computation requires images to be processed as digitized data, or numerical information.

In this visual investigation, I wanted to see what would happen when a generative AI model is set off on a recursive loop in which its own outputs are iteratively fed back to it as inputs. My hunch, or hypothesis, suggested that the statistical operations of Midjourney would prompt the model to converge to the most probable averages of its dataset when left unattended by human intervention. In this experiment, I wanted to make experienceable in an exaggerated way what could become of image production if it is increasingly automated to produce what is most probable on the Internet.

The increasing presence of AI-generated images is a phenomenon that extends to mobile photography, the metaverse, and scientific observation. Content on the Internet will soon become an AI-majority artifact, which means future datasets used to train newer AI models will rely on synthetic data, creating a closed feedback system that can intensify initial conditions and biases. Researchers have already observed this process in AI-generated natural language experiments. In one experiment, they call this effect model collapse.[1] In the published paper, they include statistical evidence suggesting that recursion with AI-generated data creates a homogenization in outputs that increasingly forgets the tails of its distribution curve. In other words, outliers in the training data become lost as the model reinforces what was originally overrepresented, leading to increasing convergence and more errors. This recursive effect is a slippery slope that poses one of the more troubling aspects of automation I wanted to explore.
           
I designed a test for this by manually setting up a recursive process on Midjourney. I fed the model’s visual outputs back in as its inputs over a series of iterations to gauge how the initial image might change and converge formally when left reproducing without my textual prompting. Midjourney offers the ability to prompt the model with a pair of images rather than words, or a combination of images and words. In the case of the former, the company states on its website and Discord channel that the model “looks at the concepts and aesthetics of each image and merges them into a novel new image.” Just how concepts or aesthetics are defined by the model can’t really be known, although learning how to steer it towards desired outcomes has created a market for what the industry calls prompt engineering.

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[1] https://arxiv.org/abs/2305.17493

meta-diffusion 1, (2023). Initial reference image: Heydar Aliyev Centre by Zaha Hadid. Initial text prompt: “an architectural structure in the shape of a tesseract in the middle of a contemporary Middle Eastern city.” All images produced with equal weights, default settings, v 5.1, medium stylized.

meta-diffusion 2, 2023. No initial reference image. Initial input prompt: “an architectural structure in the shape of a tesseract in the middle of a contemporary Middle Eastern city.” All images produced with equal weights, default settings, v 5.1, medium stylized.

meta-diffusion 3, 2023.  Initial input prompt: “a beautiful woman in a headscarf posing for a photograph.” Did not use grids; selected face I believed to be “darker” out of the first mostly white outputs. All images produced with equal weights, default settings, v 5.1, medium stylized.