The Hypothetical Image

This is an image I created with Midjourney. I wanted an image that would be true, no matter what it looked like. I settled on this: “seance of the digital archive.” 

A seance, because by sending the prompt, a digital archive is reanimated. The information lies still in the archive of the World Wide Web, recorded and set aside. For the medium of Diffusion models, it represents a world it seeks to resurrect. 

In a literal way, Diffusion models begin by destroying that archive. Every image has information stripped away, step by step, and the distribution of digital noise is measured and calculated. This calculated decay is studied and traced back to the original image, as if following scattered breadcrumbs across the ruin the trail. The journey back to the training image is preserved as a mathematical formula, the algorithm, the rules that dictate what the computer does. 

As a medium, the models are then tasked with communicating, or expressing, the essence of these degraded images. This resurrection is always incomplete. It’s all orchestrated by applying the logic of a billion decaying images in reverse. It starts with noise, but it’s noise that belongs to nothing: literally random. The connect-the-dots game of remaking the image is based on the hazy recollection of the archive, tracing finer and finer lines into the abstractions that emerge. These tracings don’t follow the logic of cultural memory, they follow the logic of an arbitrary world whose shape is carved by pattern-finding. 

In the image above, we see a young girl in front of a pile of photographs. The room is dark, lit by a red lamp. There are images on the wall behind her. Her hands sit folded as if a seance is taking place. But is it true? Are the hands folded as if in a seance? Or is this only my mind making connections to the prompt I see above it? 

That kind of trickery is baked into these systems. But like all images, our interpretations rely on the entanglements of our minds with culture. I can infer that the hands are folded as if in a seance, because I have seen seance photographs before. Other things strike me as unusual. The images in piles, physical photographs to represent the digital archive.

We can read an AI image in a number of ways. In this case, I would turn to the prompt. The prompt is the starting point for carving these noisy pixels into images. In the training data — the digital archive from which this seance was performed to reanimate — the phrase “archive” conjures thousands of images. None of these images are replicated in this generated image. Instead, they congeal, loosely, into a collection of associations. Every image with this label becomes a kind of synonym for the thing it represents. No one image represents “archive,” but the cluster of all these images builds a central abstraction. The machine will sample from this abstraction as it steers through the noise. 

If you look closely at the image generated above, we can see aspects of the image that must have been connected to my prompt — “Seance of the Digital Archive.” We can then go search datasets to see what images are stored there, images resurrected by this seance.

The images in the training data for archive include images of World War 1 bombings, family photographs of the German Wehrmacht, and individual portraits of Holocaust victims. These are inscribed into the image above alongside the hands of a seance found in spiritualist archives, and the lighting drawn from episodes of the Archie reboot, Riverdale. This is not a collage of images but a collage of documentation stripped of context, photographs without memory. It is stitching with cultural debris, pop culture and trauma woven into a single tapestry, the threading of the needle predicted pixel by pixel.

These are not one-to-one translations, of course. That’s not how diffusion works. There’s nothing hidden in the name of this process: these images are diffused, and this diffusion etches itself as a loose set of traces and outlines associated with the images we see here.

None of the training data is being destroyed. But I would argue that they are nonetheless being desecrated. It’s an empty ritual of erasure. Every AI image is built on images that came before, but those originals are completely severed of any connection to meaning. The images are stripped down and sold for parts — for any essence that might inform these new statistics. 

If ruins are a monument to those who burned it down, then maybe the Empire of the Image is at an end. The “democratization” of images is upon us: the frenzy of the image. Image-mania. AI creates new images from the ruins. A new culture comes in its place: optimistic, futuristic, absent of melancholy, devoid of death. But this techno-optimism comes at the expense of acknowledgement and response.

The images of the Wehrmacht on holiday lives side by side with images of their victims. In that way, Diffusion is dis-integration. The meaning of historical images is derived not purely from what is depicted, but from what is understood by the viewer. Disconnect images from their social meaning — treat them solely as data, rather than cultural artifacts, as tools for remembrance — and you erase their significance.

As Pierre Nora, writing long before Diffusion models, has noted in Realms of Memory:  

"Hallucinatory re-creations of the past are conceivable only in terms of discontinuity. The whole dynamic of our relation to the past is shaped by the subtle interplay between the inaccessible and the non-existent. If the old ideal was to resurrect the past, the new ideal is to create a representation of it. Resurrection, no matter how complete, implied a careful manipulation of light and shadow to create an illusion of perspective with an eye to present purposes. Now that we no longer have a singular explanatory principle, we find ourselves in a fragmented universe. At the same time, nothing is too humble, improbable or inaccessible to aspire to the dignity of historical mystery. We used to know whose children we were; now we are the children of no one and everyone."

This passage takes on new meanings in the age of AI generated artworks. We can look at a person in any of these images and see nobody’s child and everybody’s child all at once. Victims and perpetrators of the Holocaust fused into new, shared bodies. The past is a ruin from which nothing is mourned and everything is a playground.  

No Mourning for Synthetic Ruins

But ruins, at least, can bring to us a sense of peace. Walk around the long-abandoned columns of Rome or the former site of a castle in Japan and you might be moved to contemplation. Georg Simmel, anthropologist of the ruins, wrote in 1911 of the remnants of Rome, that the pleasure found in ruins rises from the tensions they place between obliteration and form — between what was and what remains:  

“This antagonism [of] letting one side preponderate as the other sinks into annihilation, nevertheless offers us a quietly abiding image, secure in its form. The aesthetic value of the ruin combines the disharmony, the eternal becoming of the soul struggling against itself, with the formal satisfaction, the firm limitedness of the work of art. For this reason, the metaphysical-aesthetic charm of the ruin disappears when not enough remains of it to let us feel the upward-leading tendency. The stumps of the pillars of the Forum Romanum are simply ugly and nothing else, while a pillar crumbled - say, halfway down - can generate a maximum of charm.” (384)

The pleasure of this tension is absent from the accelerated decay of the digital image. The ruin of the image isn’t natural decay as we have seen over a century of film. It is imposed on the archive: a razing of meaning, like scraping the names of old leaders from the parks once the new regime comes in. It is a gesture that says, “this is yours no longer.” 

Within this process is hidden a denial of death, a rejection of meaning in that massive breakdown of online visual culture. There is an emphasis on endless new life built on piles of what has been built, socially, online: now broken down and discarded. But traces of the ruin linger in their politics. The specific politics of this ruination, and the way we "read" these, is an aestheticization of Silicon Valley ideologies which focus on perpetual growth, while denying the ruin and rubble left behind by its pursuit. An aesthetics of capture, sampling, and prediction. 

In Mirror Stage, Nora N. Khan and Peli Grietzer raised the question of surveillance: “Prediction and correlation analysis come, also, with a semantic style, one that affirms the infallibility of the prediction. We slowly lose track of what did happen in favor of what was most likely to happen.”

Diffusion models aestheticize what data analytics has always done. It alienates a sliver of the world, abstracts it through measurement, and predicts corollaries. It turns a photo into a representation of what it represents, rather than a reference to the slice of time depicted. This is both the “mechanical” process — what Diffusion does, as an apparatus. It is also a cultural process, in which the prompter evokes symbols — literally, words — to create a representation of those words, rather than any depictions of real events. It is a request to extrapolate what an image might be, based on the data previously gathered. It is a hypothetical.  

People, as a human presence, are removed from the image as bodies to be remembered. As training data, the image is stripped of this connection to memory. The person remembered is erased to become rearranged in the forms and structures of new bodies which do not exist but are speculated to exist. 

It is not a coincidence that generative AI’s ideologies resemble a new manifestation of a long-running set of myths. An AI image is a result of data analysis and prediction models, and data is a language of reduction. Diffusion models reduce images to data by reducing them to noise. This is literally true, but also appropriately metaphorical. If postmodernism was defined by a kind of instability of definitions and orientations after the collapse of any consensus toward shared meaning, then what we’re in now is surely an attempt to reinforce a new aesthetics of progress, through acts of reduction, control, datafication, and regeneration, built on a literal erasure of the history on which these systems were built.  

Trauma Collage

There is a moment in the writing of this essay where I came across footage of a small village built by the US military for tests in the 1950s. Unable to build a complete mock city in the desert, the Army Corps of Engineers built a few objects that might occur in a city: a fraction of a bridge. A Frankenstein building with various materials and building methods. A handful of vehicles and mannequins; train tracks in the middle of nothing. 

Then they dropped a nuclear bomb on it. The idea was to study the detonation and its impacts: the way the buildings and bridges and mannequins fell apart. In some hopeful moment, engineers had decided that these tests of deterioration would guide them to stronger materials or building arrangements. In the end, the entire city was obliterated. 

I was watching this and thinking of diffusion models — the vast visual corpus of the internet engaged as a simulated deterioration study. On the one hand, it is a vastly destructive action, to drown images of atrocity and bubblegum into the same billboard, to take pictures of people we love and strip them of that connection, to gaze on an image without any reference or linear context, to deprive an image of its story. 

It’s different when the trauma belongs to us — when it shapes our imagination. But when we do this to others, it’s desecration. We know history’s atrocities, and to forget them would be bliss — a return to ignorance. If it was possible, if it was achieved by some equilibrium of justice instead of the erasure of whatever evoked memories of those atrocities. Until then, of course, we navigate the trauma of others by holding its images gingerly, at the corners. We respect the solemnity of it.  

Yet, somewhere in the training data of your AI images are the contours of Auschwitz and Abu Ghraib. Emmett Till. Photographs of children killed in Rwanda. 

Tamara Kneese, in Death Glitch, reminds us that “Mourning is always mediated in some way,” and that digital and physical heirlooms alike depend on storytelling and caregiving to maintain their legacies, to preserve that function across generations.

What do we hold in collective memory? What work and care do we owe to these images, and what to those whose memories they sustain? Photographs are a technology of remembrance. They are themselves a form of taking-from, which is why photography is both a tool and form of power. They may strip away the dignity of agency over our own bodies, or grant us visibility in times of erasure. They evoke the memory of the dead. It is a purely imaginary sense of things, and yet, we recoil at the desecration of that memory. 

After contemplating this process in comparison to nuclear blasts in the Nevada desert, I had a second thought: does any of this actually matter? This is an invisible process, hidden in some enormous computer cluster. We don’t see it happen, and most of us don’t even know when or if it did. One could blissfully generate remixes of victims and perpetrators of historic atrocities into people with funny hats, and we’d never know they were trauma collages. If the meaning of photographs is socially constructed, built by reference, then if there is no trace of that reference, no trace of that erasure — is it even erasure at all?

Some would argue that the virtual, simulated desecrations of memory do not really matter. Obscured through the comforting distance of technological mediation, we never really confront it. The image is torn apart, but not destroyed. If the images we make don’t mock those tortured at Abu Ghraib, does it matter that those prisoners are dissolved into the stew of generative AI? Our original memory remains. Nevermind that it has been stripped of context and industrialized. These problems, they suggest, are imaginary, because they cannot be seen. No measurement, no reality.  

But perhaps we can defend the imaginary as the actual place in which we live our digital lives. We’re in a century of screens luring us to interact with content. All of that is imaginary. Think hard enough about what we’re doing and the whole scaffold is absurd. Moving a clump of plastic around with our hands. Responding viscerally to tiny light bulbs changing colors, packed densely enough to look like information. 

The screens react to us, and we find ourselves in a wild oscillation between observer and actor. We respond, the screen responds, and we respond to what has changed: a cybernetic circuit. 

Any technology that relies on the human imagination to function is a deeply social system. Anderson’s imagined community suggests that even nations exist only in the shared imagination of media references: the newspaper reader envisioning themselves in a network of similarly informed citizens, seeing the news reported from the position of their shared nationhood and history. Of course, these communities have always been exclusive: some imagine themselves steering the nation, others see themselves in tow. The imagination isn’t a perfect vessel. But it is the primary vessel that we have.

The ruination of digital objects — or communities — is a virtual exercise of power over these entanglements. It displaces one imaginary for another: the messy for the predictable, the free for the monetized. Respect for these worlds of feeling is quickly dispensed with when it comes time to measure. Digital objects are, by definition, not “precious” under any capitalist definition of value. As a result, a sense of inauthenticity lingers over the entirety of our digital experiences, especially, perhaps, the experiences of others. 

Today you can buy AI generated stock photographs representing a variety of historical traumas. The image above is one such example, an image of a Palestinian refugee that doesn’t exist. There are all kinds of additional images of the conflict, from bombs to debris. Here the flow is reversed: the vast sea of images, from pop culture to stereotypes of refugees and Palestinians, is mobilized to erase what they represent. The reality of conflict is displaced and the images that document that reality are cheapened, made less real. Aestheticized images of refugees are commodified and sold in ways that ultimately undermine lived trauma.

It moves us from the abstract, invisible technological process into the real world of image circulation and distribution. It moves the abstract and invisible into the real world of media power.

Dis-Integration

To see our selves — our traumas and atrocities alike — blended into a single database; reanimated purely for the production of aesthetic pleasure, taps into the impulse Benjamin observed in stylings of fascism. One is dissolved into the collective whole through the mobilization of images. AI moves the image from a technology of remembering to a tool where the graveyard can become a playground for self-expression, where collective responsibility dissolves.

Data, too, is imaginary. Sample the bits of the world we can measure. Discard outliers, and stick toward whatever center emerges in the data. With context and emotion stripped away from these isolated samples of our entangled world, we’re asked to trade the meaning of things for the mean of things.

The value of data is in its predictive power: when it is tested, and reproduced, it suggests that the next time we test it, whatever we measure will behave similarly. It is a method of determining what is reliable in an unstable world. It is dangerous to use data to mold the world into the shape of those predictions. 

Data measured through standard, rigid categories of observation is an abstracted fiction. Data might do things, might predict things, but so does the human imagination. Value exists in the things we define and negotiate in a social world. Imagination is the space where we navigate this collective negotiation. To collect data that denies one set of values — such as safety, justice, identity, kindness — whittles the world into bits and pieces framed by the equally slippery values of predictions. 

Diana Forsythe noted, observing AI engineers in the 1990s,

“'Knowledge' means explicit, globally-applicable rules whose relation to each other and to implied action is straightforward. Knowledge in this sense is a stable entity that can be acquired and transferred. It can be rendered machine-readable and manipulated by a computer program. I believe that, in effect, 'knowledge' has been operationally redefined in artificial intelligence to mean 'what can be programmed into the knowledge base of an expert system'. Star has commented that computer scientists 'delete the social'.  I would add that they delete the cultural as well.” (Forsythe 465)

Khan and Peli suggest that the artistic response may be to frame and “highlight the blur,” to create frames of thinking that oppose reduction and prediction. In that light, I wonder about my own set of tools — the glitch, designed to subvert the system entirely, to trick the system out of the security of its predictions in order to render scenes of confusion and disorder. Part of this is still lured into the seductive entanglement of these things. 

In the absence of a forward-looking and socially engaged imagination, too much generative AI re-evokes false memories. It endlessly recreates yesterday as a form of making tomorrow. It does this at the expense of connection and mourning. It’s an erasure of accountability dressed up in techno-positivity. Glitch is meant to reveal the feedback loop that chaos drives in these predictive systems. When the data is absent, when the data is made weird, when the model is starved of data altogether. We force the predictive engine into a double-bind, a forced breakdown in the logic of analytics.

At least, I hope.  

The Hypothetical Image

This may not be a philosophical statement, but a personal one. I hear that my concerns are imaginary but I prefer to say my concerns are the imaginary. The imagination is the space where artists work.

The imaginary worlds of generative AI feel bleaker for me every day. A surrealism without a subconscious, rendered with the aesthetic predictability of its training data: advertisements and clip art fused with atrocity footage and family snapshots. All of the images are extensions of the visual melange, hypothetical images based on all images prior. Paired with a sense that the origins do not matter, that labor does not matter, that any obligation to citation or history do not matter.

The Generative AI artist believes in these hypothetical images as if they were actually images. That’s the trick of it. AI images aren’t images at all. They are guesses at what images might someday come. It reminds me of envisioning a city on top of its ruins. Because it is constrained by the past, with no remembrance of what took place, nostalgia is at the heart of these hypothetical images: “utopia in reverse,” as Andreas Huyssen puts it. Using prompts to navigate samples of historical forms, the image rearranges tropes without recollection and with imprecise control. AI art as we see it commonly practiced relies on the ‘artists’ memory of past cultural forms in order to resurrect and reanimate them. Star Wars. Wes Anderson. Fashion shoots.

It’s more than just the behind the scenes process of the diffusion technologies. It’s also the way these are mobilized: the culture surrounding the generative AI apparatus.

“Everything is a remix,” they say. The unspoken follow-up: “and nothing matters.” The implication is that everything is ours. imagination is redefined as the reproduction of all previous patterns, the animation of all past forms into new arrangements. In rejecting the traumas of history that these new images are built on, I can’t help but to be disturbed. This imagination is the perpetuation of a sterilized past. Ruins are razed to be rebuilt as theme park versions of themselves. The reminders are dismissed as phantoms.

(This piece is a companion piece to “underutilized”)