Going forward, each Annual will be spearheaded by a different Editorial Lead working alongside a Research Partner of their choice. Whereas the former will act as the chief tastemaker who finesses every page, the latter—an expert, a collective, an institution—will infuse niche knowledge and forge new connections along the way. Together, they set the Annual’s agenda, select contributors, and invent formats, all while sharing production updates here. We—Alex, Filip, and Greg—will remain hands-on, of course, and support production throughout the process.

Who will lead this year’s Annual? Over the holidays, we reached out to a few choice candidates, all trailblazers whose work we admire and who’d shine in this role. Their responses did not disappoint: after reviewing a stack of excellent proposals, we are thrilled to announce that an extraordinary talent has joined our team—and that we’ve entered production mode. We’ll name names in the coming days and are excited to hand over this production diary to our new collaborator soon.

However, it’s her idiosyncratic collaborations with artists in the HOLO-verse that really captured our attention: she framed Casey Reas’ machine hallucinations in Making Pictures with Generative Adversarial Networks, worked alongside Sondra Perry, Caitlin Cherry, and American Artist on the installation A Wild Ass Beyond: ApocalypseRN, and meditated on Ian Cheng’s mythical simulations in Emissaries Guide to Worlding. And then there are weird and wonderful collusions that are a little harder to categorize: a virtual influencer that chronicled Unsound festival (with Team Rolfes), a libretto script for an opera about fiber-optic technology (with Bill Kouligas and Spiros Hadjidjanos), and fictions for an mixed reality installation about microbes (with Tuomas Laitinen). This transdisciplinary savviness in experimenting with narrative and format matches great with our ambitions for The Annual.

Nora joined our team in February, after outlining a bold vision in response to a private call we circulated amongst several of our favourite writers and editors. “How do we speak about technology as reflection of anxieties?” she asked in her proposal and argued: “Serious journalistic research ‘reveals’ the works of a system; artworks investigate the logic of machine learning; critics narrate the reveals.” It’s been non-stop brainstorming ever since. “I’ve drawn on my long, strange and winding travels through art editorial rooms, in and out of collaboration in art–technology spaces, to expand and hopefully deepen its scope,” she wrote in one of her emails. Similarly, under her leadership, The Annual will create (more) space for vital questions and expand the conversation.

We wholeheartedly welcome Nora to the HOLO team! In just a few short weeks her voice has enriched our thinking, offering fresh perspective, generous reflection, and incisive curiosity. We’re excited to support Nora realize her vision, knowing the HOLO Annual couldn’t be in better hands.

The process of thinking through should take place in public, and is made more rich by being in public. Over the next few months, I hope to share the thinking and conversations around the development of the Annual as a print publication and archive, along with emerging experiments, works, and framings. I will talk through these tensions, between legibility and obfuscation, right here on the blog. We’ll talk about the debates and questions driving the issue, and how they’re being explored on the editorial and research side. I want you to be witness to the process of developing the frame and core themes for this year’s Annual, and to that end, I hope to be in conversation with you, and hear your thoughts. Please feel free to e-mail me here at nora@holo.mg about anything that sparks your interest in these posts.

Next up on the blog: an announcement of our Research Partner. The Research Partner is a vital part of the development of our theme: they are a sounding board and challenge. I will share reflections on my conversations with them, and further down the line, the invited artists, scholars, thinkers who will appear in the issue. Whether we will be talking through predictive methods and histories of algorithms, or broader cultural myths of the role of technology and creative practice, this blog will be a form of representing process in public, of making the collaborative process of producing a magazine—so often hidden in the back alleys, in shadows—fully legible. I’m delighted to share the process with you. Let’s begin.

Like most begrudging digital serfs, each day I also consumed way more content than I could ever handle. I followed the algorithm and was led by its offerings, trying to make sense of what is offered. As a critic tends to do, I also found myself tracking patterns. Patterns in arguments, patterns in semantic structures, patterns in phrases traded easily, without friction, between headlines and captions.

I found myself frustrated with the loops of rhetoric within the technological critiques I’ve made myself for a while now. In 2020, into 2021, there are ever more frantic discussions of black-boxed AI, “light” sonic warfare through LRADs used at protests, and iterative waves of surveillance flowing in the wake of contact tracing. Debates repeat: about human bias driving machine bias, about the imaginary of the clean dataset. Each day has a reveal of a new fresh horror of algorithmic sabotage. Artistic interventions, revealing the violence and inner workings of opaque infrastructure—seemed to end where they began.

It felt more important, this year, to connect our critiques of technology to capitalism, and to understand certain technologies as a direct extension—and expression—of the carceral state. I cracked open Wendy H.K. Chun’s Updating to Remain the Same (2016) and Simone Browne’s Dark Matters (2015) and Jackie Wang’s Carceral Capitalism (2018) again, and kept them open on my desk most of the year. When I read of both police violence and hiring choices alike offloaded onto predictive algorithms, I open Safiya Noble’s Algorithms of Oppression or Merve Emre’s The Personality Brokers. There were gifts—like Neta Bomani’s film Dark matter objects: Technologies of capture and things that can’t be held, with the citations we needed. I learned the most out of centering critical theory/pop sensation Mandy Harris Williams (@idealblackfemale on Instagram) on the feed.

I looked, in short, for the wry minds and voices in criticism, in media studies, in computational theory, in activism, and who also flow easily between these spaces and more, who have been thinking about these issues for a long time. A few had quieted down. Others had never really stopped tracking the patterns or decoding mystification in language. For years in the pages of HOLO, writers and artists have been investigating issues that have only built in intensity and spread: surveillance creep, digital mediation, networked infrastructure, and the necessary, ongoing debunking of technological neutrality.

Most folks have noticed the distinctly generous qualities of Peli’s work, and have an experience reading “A Theory of Vibe,” too. The experience might begin with its opening salvos:

• An autoencoder is a neural network process tasked with learning from scratch, through a kind of trial and error, how to make facsimiles of worldly things. Let us call a hypothetical, exemplary autoencoder ‘Hal.’ We call the set of all the inputs we give Hal for reconstruction—let us say many, many image files of human faces, or many, many audio files of jungle sounds, or many, many scans of city maps—Hal’s ‘training set.’ Whenever Hal receives an input media file x, Hal’s feature function outputs a short list of short numbers, and Hal’s decoder function tries to recreate media file x based on the feature function’s ‘summary’ of x. Of course, since the variety of possible media files is much wider than the variety of possible short lists of short numbers, something must necessarily get lost in the translation from media file to feature values and back: many possible media files translate into the same short list of short numbers, and yet each short list of short numbers can only translate back into one media file. Trying to minimize the damage, though, induces Hal to learn—through trial and error—an effective schema or ‘mental vocabulary’ for its training set, exploiting rich holistic patterns in the data in its summary-and-reconstruction process. Hal’s ‘summaries’ become, in effect, cognitive mapping of its training set, a kind of gestalt fluency that ambiently models it like a niche or a lifeworld.

Through this playful use of Hal, readers are asked to consider and hold the “summaries” Hal makes, the lifeworld it models, to understand how an algorithm learns:

• What an autoencoder algorithm learns, instead of making perfect reconstructions, is a system of features that can generate approximate reconstruction of the objects of the training set. In fact, the difference between an object in the training set and its reconstruction—mathematically, the trained autoencoder’s reconstruction error on the object—demonstrates what we might think of, rather literally, as the excess of material reality over the gestalt-systemic logic of autoencoding. We will call the set of all possible inputs for which a given trained autoencoder S has zero reconstruction error, in this spirit, S’s ‘canon.’ The canon, then, is the set of all the objects that a given trained autoencoder—its imaginative powers bounded as they are to the span of just a handful of ‘respects of variation,’ the dimensions of the features vector—can imagine or conceive of whole, without approximation or simplification. Furthermore, if the autoencoder’s training was successful, the objects in the canon collectively exemplify an idealization or simplification of the objects of some worldly domain. Finally, and most strikingly, a trained autoencoder and its canon are effectively mathematically equivalent: not only are they roughly logically equivalent, it is also fast and easy to compute one from the other. In fact, merely autoencoding a small sample from the canon of a trained autoencoder S is enough to accurately replicate or model S.

From here, we climb the summit to Peli’s core claim:

• […] It is a fundamental property of any trained autoencoder’s canon that all the objects in the canon align with a limited generative vocabulary. The objects that make up the trained autoencoder’s actual worldly domain, by implication, roughly align or approximately align with that same limited generative vocabulary. These structural relations of alignment, I propose, are closely tied to certain concepts of aesthetic unity that commonly imply a unity of generative logic, as in both the intuitive and literary theoretic concepts of a ‘style’ or ‘vibe.’ […] One reason the mathematical-cognitive trope of autoencoding matters, I would argue, is that it describes the bare, first act of treating a collection of objects or phenomena as a set of states of a system rather than a bare collection of objects or phenomena—the minimal, ambient systematization that raises stuff to the level of things, raises things to the level of world, raises one-thing-after-another to the level of experience. […] What an autoencoding gives is something like the system’s basic system-hood, its primordial having-a-way-about-it. How it vibes.

It’s a heady journey, taking many re-reads to sink in. While I wouldn’t dare to try to capture Peli’s dissertation, “Ambient Meaning: Mood, Vibe, System,” I link it here instead for all to dive into, along with an illuminating interview with Brian Ng in which the two scholars debate core concepts in the work. Peli walks through the concept of autoencoders, and the ways optimizers train algorithms for projection and compression, and does so in an inviting, clear manner. This way, when we get to the real challenges of modeling and model training, we’re prepared.

Peli notes to Ng that they understand vibe as “a logically interdependent triplet comprising a worldview, a method of mimesis, and canon of privileged objects, corresponding to the encoder function, projection function, and input-space submanifold of a trained autoencoder.” They labor to create understanding of “a viewpoint where the ‘radically aesthetic’ — art as pure immanent form and artifice and so on — is also very, very epistemic,” noting, further, the ways folks like Aimé Césaire created “home-brew epistemologies […] where the radically aesthetic grounds a crucial form of worldly knowledge.” We eventually get to an exciting set of claims about the cognitive mapping involved in attending to, as Peli writes, the “loose ‘vibe’ of a real-life, worldly domain via its idealization as the ‘style’ or ‘vibe’ of an ambient literary work,” and that, further:

• Learning to sense a system, and learning to sense in relation to a system—learning to see a style, and learning to see in relation to a style—are, autoencoders or no autoencoders, more or less one and the same thing. If the above is right, and an ‘aesthetic unity’ of the kind associated with a ‘style’ or ‘vibe’ is immediately a sensible representation of a logic of difference or change, we can deduce the following rule of cognition: functional access to the data-analysis capacities of a trained autoencoder’s feature function follows, in the very long run, even from appropriate ‘style perception’ or ‘vibe perception’ alone.

I admire the ease with which Peli moves through genres and periods, weaving between literary theory and computational scholarship, helping us, in turn, move from the knottiest aspects of machine learning to the ways literary works might learn from analogies with computation. His scholarship is as generous as it is challenging. Throughout, we’re asked to consider common metaphors and analogies used in machine learning studies. The more traditionally literary-minded are challenged to consider proof of concept in a mathematical analogy, and what artificial neural networks—not particularly the most interesting models of thought—might promise literary theory and critical studies. As we move into realms where folks are frequently moving between cognitive theoretic models, discussions of artificial neural networks, and theory, and criticism, this is a powerful guide. We’re also asked to seriously consider how literary works move towards a ‘good autoencoding’ and in what traditions of aesthetic practice we might understand aesthetics as a kind of autoencoding. Peli creates a language through which we can move back and forth from cherished humanist concepts to the impulses of experimental literature, and to appreciate computational modeling as it helps us to map language out to its edges.

Back in April of 2020, I joined Peli for the third Feature Extraction assembly on Machine Learning, supported by UCLA, described as an exploration of the “politics and aesthetics of algorithms.” We had a fun couple of hours talking about the lifeworlds and strange logics of ML and predictive algorithms, with a group of artists and organizers at Navel in Los Angeles. I was struck, then, re-reading A Theory of Vibe three years later, how vital and alive its arguments, claims, its simultaneous experimentation and coherence, felt. In my many readings of this essay, I admired how Peli probed for unstated and unconsidered perspectives, pointed out blind spots, and how their questions were rooted in a set of exploratory hypotheses about the nature of art, and what computation allows us to see about the nature of art. They seemed a perfect interlocutor for this Annual.

As the Annual’s Research Partner, Peli has been remarkable and generous and good-humored. He is adept at the cold read, helps me make incisive cuts, always offers zoomed-out criticality. He’s helped us raise the stakes of the editorial frame, exposing us to writers and thinkers we’d not have met easily or fluidly otherwise, from computational linguists to researchers studying the computational mind to philosophers who speak through Socratic argument over our Zooms. It’s been thrilling to invite and get to know thinkers from his intense and rare circles.

Our main task, in working together, has been discussion of each prompt around prediction and fantasies of explainability and opacity, and the roles of language in mystification and mythologizing of technology as remote. As a result, the frame is more challenging, more provocative, pushing our respondents to think along broader timescale and social scales, and challenge themselves.

Over the next weeks in this Dossier, we’ll have a number of representations of our research conversations—a close-reading of Kate Crawford’s Atlas of AI, along with snippets from our conversations about the Annual—together. They’ll be gestural excerpts of the deeper conversations underway. We’re sharing ideas and discussing them in tender forms with Peli. His openness, care for thought, and a genuine enthusiasm about unexplored concepts or lesser-theorized angles has only strengthened the possibilities of this issue. Thank you, Peli!

On a final note, a massive editorial project like this Annual requires intensive research and consideration of the wider intellectual and artistic field in which the artists, writers, and thinkers invited are working. I encourage editors to try and find someone who challenges their ideas and positions, and even questions the desire to go with the safer frame. Be in conversation with someone who pushes you intellectually, who will engage enthusiastically with the deeper philosophical and critical possibilities of writing and publishing. The Annual is better for this exchange and relationship—or, forgive me—this very vibe that Peli brings.

This year’s HOLO Annual has emerged in part through conversation between Nora Khan and Peli Grietzer, the Annual’s Research Partner. They discussed Nora’s first drafts of the “unframe” for the Annual (in resisting a frame, one still creates a frame) around prompts of Explainability, Myths of Prediction, Mapping Outside Language, and Ways of Partial Knowing, over months. Once drafts started rolling in, they discussed the ideas of contributors, the different angles each was taking to unpack the prompts, and the directions for suggested edits. They came together about four times to work on unknotting research and editorial knots. A deeper research conversation thread weaves in and out, in which Peli and Nora deconstruct the recent blockbuster Atlas of AI, by Kate Crawford.

The research conversation bubbles underneath the Annual, informing reading and finalizing of essays and commissions, and its effects finding a home in the unframe, all the edits, and the final works. The following excerpt is taken from the earlier stages of the research conversation, which began back in February and March of this year. Nora and Peli discussed the emerging frame of prompts before they were sent out to contributors. They walked through each to problematize the framing. A fuller representation of the research conversation will be published in the HOLO Annual.

Nora: Let’s talk about explainability. Explainable AI is one impetus for this prompt, and the issues involved in it. On one side, you hear this argument, frequently, in speaker circuits and keynotes, that once the black box of AI is open, all the naive, feeble-minded users of technology will somehow have a bit more agency, a bit more understanding, and a bit more confidence in relation to AI that is barely understood by its engineers. In my weaker moments, I’ve made the same argument. Another line of argument about explainability as a solution is grounded in the notion that once we understand an AI’s purpose, its reasoning, we’ll be blessed with an idealized model of reality, and receive, over time, a cleaned-up model of the world that’s been eradicated of bias.

Peli: Even before one gets to different humanistic or social ideals of explainability, there are already ambiguities or polysemies in terms of the more scientific conversation on explainability itself. One aspect of explainability is just: we don’t understand very well why neural network methods work as well as they do. There are a number of competing high level scientific narratives, but all are fairly blurry. None of them are emerging in a particularly solid way. Often you think, well, there’s many important papers on this key deep-learning technique … but find the actual argument given in the paper for why the method works ‘is now widely considered’ inaccurate or post hoc. So there’s an important sense of lack of explanation in AI that’s already at play even before we get to asking ‘can we explain the decisions of an AI like we explain the decisions of a person’—we don’t even know why our design and training methods are successful.

I would say stuff like, as an engineering practice, Deep Learning is poorly scientifically understood. It’s currently one of the least systematic engineering practices, and one of the ones that involve the most empirical trial and error, and guesswork. It’s closer to gardening, than to baking. Baking is probably more scientifically rigorous than deep learning. Deep learning is about the same as gardening, where there are a lot of principles that are useful but you can’t really very well predict what’s going to work, and what’s not going to work when you make a change. I probably don’t actually know enough about gardening to say this.

Anyway, that’s one sense in which there’s no explainability. One could argue that this sense of explainability pertains more to the methods that we use to produce the model, the training methods, the architectures, and less to the resulting AI itself.. But I think these things are connected in the sense in which, if you want to know why a trained neural network model tends to make certain kinds of decisions or predictions, that would often have something to do with the choice of architecture or training procedure. And so we’d often justify the AIs decisions or predictions by saying that they’re a result of a training procedure that’s empirically known to work really well. Then the question is, okay, but why is this the one that works really well? And then the answer is often, “Well, we don’t really know. We tried a bunch of different things for a bunch of years and the architecture and training procedures end up working really well. We have a bunch of theories about it from physics or from dynamical systems or from information theory, but it’s still a bit speculative and blurry.”

So there’s this general lack of scientific explanation of AI techniques. And then there are the senses that are more closely related to predictive models, specifically, and how one describes a predictive model or decision model in terms of all kinds of relevant counterfactuals that we associate with explanation or with understanding.

Nora: It seems this prompt can be tweaked a bit further to ask contributors, what are the stakes of this explainability argument, and what are some of its pitfalls? And further, what kinds of explainability, and explanation, do we even find valuable as human beings? What other models and methods of explainability do we have at play and should consider, and how do we sort through competing models for explainability? I figure this could help better understand the place of artists and writers now who are so often tasked, culturally, with the “storytelling” that is meant to translate and contextualize and communicate what it is that an AI produces, does, how it reasons, in ways that are legible to us.

In one of your earliest e-mails about the Annual, you mentioned how you are much more interested in finding paths to thinking about alternatives to the current order, rather than, say, investing in more critique of AI or demands for explainable AI to support the current (capitalistic, brutal, limiting, and extractive) economic order. It really pushed me to reconsider these prompts, and the ways cycles of discourse rarely step back to ask, but to what end are we doing all of this thinking? In service of who and what and why? I really want to offer contributors the option to critique this concept of explainability in technology, through this specific push in AI: what some have called the flat AI ethics space. When we ask, “What if we can just explain what’s inside of the black box?” we might also ask, to what end?

Making the “decision-making process” of a predator drone more “legible” to the general public seems a fatuous achievement. Even more so if it is an explanation in service of a capitalist state or state capital, and we know how that works.

Peli: Let’s take another example. Say, my loan application got rejected. I want to understand why I got rejected. I might want to know, well, what are the ways in which, if my application was different or if I was different, the result would be different. Or, I can want to ask in what ways, if the model was slightly different, would the decision be different. Or you can ask, take me, and take the actual or the possible successful candidate who is most similar to me, and describe to me the differences between us. It turns out the result is, there’s a variety of ways in which one could, I guess, formulate the kind of counterfactuals one wants to know about in order to feel or rightly feel it has a sense of why a particular decision took place.

Nora: If you were to ask the model of corporate hiring to explain itself, you would hope for a discourse or a dialogue. I say, “Okay, so you show me your blurry model of sorting people, and then I can talk to you about all of the embedded assumptions in that model, and ask, why were these assumptions made? Tell me all the questions and answers that you set up to roughly approximate the ‘summation’ or truth of a person, in trying to type them. And then I can respond to you, model, with all of the different dimensional ways we exist in the world, the ways of being that throw the model into question. What are the misreadings and assumptions here? What cultural and social ideas of human action and risk are they rooted in?” And once we talk, these should be worked back into the system. I really love this fantasy of this explainable model of AI as having a conversation partner who will take in your input, and say, “Oh, yes, my model is blurry. I need to actually iterate and refine, and think about what you’ve said.” It’s very funny.

Peli: Exactly. I think that’s the thing that one, possibly, ultimately hopes for. There might be a real point of irreconcilability between massive-scale information-processing and the kind of discursive reasoning process that’s really precious to humans. I feel like these are conflicts we already see even within philosophy. I feel like there’s a moment within analytic philosophy, where the more you try to incorporate probability and probabilistic decision making into rationality, the more rationality becomes really alien and different from Kantian or Aristotelian rationality that we intuitively—I’m not sure if that’s the right word—that we initially think about with reasoning. Sometimes I worry that there’s a conflict between ideals of discursive rationality and the kind of reasoning that’s involved in massively probabilistic thinking. It seems the things that we are intending to use AIs for, are essentially often massively probabilistic thinking. I do wonder about that: whether the conflict isn’t just between AI or sort of engineering, and this discursive rationality, but also a conflict between massively probabilistic, and predictive, thinking and discursive rationality. I don’t know. I think these are profoundly hard questions.

The gathering of feminist investigations drew on surveillance studies and critical race theory to theorize responses to the violence of racializing surveillance. Between the theorists in this issue and the impact of 3x3x6, it seemed to me that surveillance-prediction regimes of the present moment must be understood as a repetition of every regime that has come before.

In a way, it turned out that the prompts of ‘partial knowing’ and ‘myths of prediction’ are more linked than opposed: Our ways of understanding others are already quite speculative and blurry; how is this blur coded and embedded, and what prediction methods that aim to clarify the blur, or make the blur more precise, are possible?

Even as we struggle to find ways to disentangle ourselves from predictive regimes and algorithmic nudging, we also need to tackle what prediction means, and has meant, for control, for statistics, for computation. This second prompt includes hazy, fuzzy, and over-determinant methods of prediction and discernment. The future, here, is one entirely shaped by algorithmic notions of how we’ll act, move, and react, based on what we do, say, and choose, now—a mediation of the future based on consumption, feeling, that is subject to change, that is passing.

Four powerhouse thinkers—Leigh Alexander, Mimi Ọnụọha, Suzanne Treister, and Jackie Wang—join the Annual to respond to this prompt, Myths of Prediction Over Time. They were asked to think about the rise in magical thinking around prediction and the capacity of predictive technologies to become more intense as technological systems of prediction become more ruthless, stupid, flattening, and their logic, quite known. They look at the history of predictive ‘technologies’ (scrying and tarot and future-casting) as magic, as enchantment, as mystic logic, as it shapes the narratives we have around computational prediction in the present moment.

Together, they are invited to consider the algorithmic sorting of peoples based on deep historical and social bias; at surveillance and capture of fugitive communities; at prediction of a person’s capacity based on limited and contextless data as an ever political undertaking, or at prediction as they interpret it. They might reflect on the various methods for typing personalities, discerning character, and the creation of systems of control based on these partial predictions. They are further invited to look both at predictive systems embedded in justice systems, or in pseudoscientific tests like Myers-Briggs, for example, embedded in corporate personality tests.

Wang, Ọnụọha, Alexander, and Treister are particularly equipped to think on these systems, having consistently established entire spaces of speculation through their arguments.

Stay tuned for more notes on the next two Annual prompts—on mapping outside language, and explainability—and the brilliant contributors on board. Also take note of the first in a series of research transcripts featuring conversation excerpts with our research partner Peli Grietzer about incoming drafts, the frame overall, and, well, all those atlases of AI.

This year’s HOLO Annual has emerged in part through conversation between Nora Khan and Peli Grietzer, the Annual’s Research Partner. They discussed Nora’s first drafts of the “unframe” for the Annual (in resisting a frame, one still creates a frame) around prompts of Explainability, Myths of Prediction, Mapping Outside Language, and Ways of Partial Knowing, over months. Once drafts started rolling in, they discussed the ideas of contributors, the different angles each was taking to unpack the prompts, and the directions for suggested edits. They came together about four times to work on unknotting research and editorial knots. A deeper research conversation thread weaves in and out, in which Peli and Nora deconstruct the recent and influential Atlas of AI, by Kate Crawford.

The research conversation bubbles underneath the whole Annual, informing reading and finalizing of essays and commissions, and its effects finding a home in the unframe, all the edits, and the final works. The following excerpt is taken from the middle stages of the research conversation, which took place in July and August of this year. Nora and Peli discuss the drafts of essays which form the bulk of responses to the Annual Prompt “Ways of Partial Knowing,” and the ideas, debates, and dramas that the authors move through. A fuller representation of the research conversation will be published in the HOLO Annual.

Peli: There is a famous debate between Noam Chomsky and Peter Norvig, an AI guy from the generation between old fashioned AI and deep learning. The debate was about computational linguistics and sophistic linguistics. In an article from the early 2000s on the traits of Chomskyian linguistics, Peter Norvig quotes Chomsky in a derogatory way. He says something like, “Because Chomsky wants some kind of deep, profound understanding that goes beyond what statistics can provide for us, that’s because he’s some kind of mystic.”

You can find many papers, especially from two years ago, saying that deep learning isn’t science; it’s alchemy. The actual scientists tell each other all kinds of stories about, for example, why this method called layer normalization drastically improves results; there are a bunch of different theories about it. They’re either all kind of anecdotally phrased and not mathematically rigorous, or nobody really knows how they work, but there are all these different stories about how they do. One might describe this moment as a pre-scientific, proto-scientific, alchemical stage where we may have not particularly scientifically rigorous explanations, but instead, have complicated, intuitive stories about how the science works.

Nora: I love that. Describing the alchemic stage. I think these essays really capture those intuitive stories we use to understand or make sense of new technologies, or, methods and strategies humans create to approach the unknown. So far, the authors in this section get at partial ways of knowing very obliquely. They also talk about technology obliquely. At a slant and from the side. I love that for a magazine that is about science, art, technology, in explicit terms. I’m appreciative of how each lets the readers do a lot of the connective work, in the spaces between claims and ideas from each author. One author writes about dark speech on the rise, and delineates the different ways mystics and alchemists work with the limitations of language. The reader might ask, are alchemists on the rise in technological spaces? Is the difference between the mystic and alchemist, in relation to power and language, something that we can see in the present moment?

I’m really intrigued by this other piece on mapping GANs on top of tarot, and the idea of using one system to discern patterns in another predictive system. Could we go even deeper and ask, what do we learn about the way that tarot predicts or helps us figure out a place in the world? How does seeing a GAN’s interpretations of tarot images help us rethink and renew our understanding of what tarot does?

Peli: I think you’re super, super onto something here. And in fact, generative adversarial networks themselves are not predictive systems. They’re compression systems.

Nora: Right

Peli:Once you debunk the notion that tarot is “predicting the future,” well, what is it supposed to be? The cards represent different aspects of the human experience. The cards are models of a system, in which human experience is a system of 21 types of events, or 21 types of phenomena. Bring in the GAN, ask it, “Now, model this huge potential event database, resulting from the interaction between …” Actually, how big is the latent space of these GANs nowadays? I think the latent space uses something like 1,000 units. Both of them are archetype systems, because they’re summarizing a certain universal phenomena into archetypes.

Nora: So you have compression on top of compression. I think to your point, even though tarot doesn’t predict the future, what’s interesting is why we talk about them as though they do, even if we know that it’s pattern recognition and compression.

Peli: Yeah. I mean, I think we probably don’t have to be super literal about prediction as being prediction, about the future. I mean, we are actually talking about knowledge systems, right? I feel like prediction here is a bit of a synecdoche for knowledge and inferential systems in general.

Nora: Right, the notion that, based on a certain arrangement of cards on a certain day, there’s something about your character, described at that moment, on that day, before those cards, that is going to suggest what you’re going to be like next week, or a couple months or a year from now. You at least get a bit of steadiness about how to prepare: Here’s what you can expect.” I don’t know if that’s precisely prediction, in the way this essay partly about predictive and carceral policing is talking about, the carceral prediction our societies are embracing, or prediction within a carceral state. But instead, prediction here means a general, hazy, semi-confident narrative about what might happen, in the same way that you glean in an astrological reading.

Peli: Yeah. So, in machine learning: usually, when you turn a predictor, especially in modern machine learning, the predictor does implicit representation learning. GANs are just pure representation learning systems. You can then actually hook them up to a predictor, but usually hooking up predictors to representation learning systems of a different kind than GANs is more effective, for reasons we don’t fully understand.

Many people think that this is also a temporary thing, and one day, these kinds of generated models would be like representation learners for the purposes of then hooking up a predictor. But I think we don’t have to get super, super mired in stuff like defining things as being like prediction, or like other kinds of modeling.

Nora: Agreed! I think this way, prediction becomes a portal in this section, to think about all the hazy ways we have tried to predict, augur, and try to discern what’s coming—and to what end, and what we do with that belief.

Ping me!

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This past summer, I watched Ciro Guerra’s Embrace of the Serpent with friends in San Miguel de Allende. I wept, in no small part because of the brutal depiction of the reality of the rubber holocaust in the Colombian Amazon, but also because of the film’s peripheral hints at the scale of loss of knowledge, lost because it was unreadable, or not in the language of the colonizer. The scale is shattering to try to wrap one’s mind around. The German and American ethnologist and ethnobotanist depicted in the film journey in pursuit of a hallucinogenic yakruna plant. Along the way, they gather Ocaina symbols, depict and mark down ‘the sense’ of rituals. Any Ocaina knowledge that can‘t be ‘put in the box’ or marked in the book in language, is not.

What goes in the travel journals and what is left out, never written down? In 2017, there were an estimated 40 people left who could speak Ocaina, one of many endangered Amazon Indigenous languages.

In Eduardo Kohn’s How Forests Think: Towards an Anthropology Beyond the Human (in which Kohn investigates how “a forest is alive and thinking, as is a dog, a jaguar, a peccary, and a plant,”) we find a central anecdote about jaguars early on. Kohn’s research was in Peru, where he is asked by the Runa (a Quechua speaker’s term for him or herself, referring to “people,”) to sleep with his face up so jaguars passing could recognize him, look at him looking back, and not attack. This is taken up as a moment of clear nonhuman communication, pre-linguistic or beyond language:

“Settling down to sleep under our hunting camp’s thatch lean-to in the foothills of Sumaco Volcano, Juanicu warned me, ‘Sleep faceup! If a jaguar comes he’ll see you can look back at him and he won’t bother you. If you sleep facedown he’ll think you’re aicha [prey; lit., ‘meat’ in Quichua] and he’ll attack.’ If, Juanicu was saying, a jaguar sees you as a being capable of looking back—a self like himself, a you—he’ll leave you alone. But if he should come to see you as prey—an it—you may well become dead meat.”

This is a complex anecdote. When the Runa asked Kohn to sleep face up, the notion implied is that the jaguars needed to see people, but as what? As other conscious beings? As beings worthy of living? Or beings that aren’t worth the risk of being attacked? We place our linguistic and value frameworks on the jaguar. Further, Kohn needed to go to sleep trusting this knowledge, that the jaguar sees the sign of human eyes looking back and then has a way of understanding, or— intuiting?—or perceiving, the sign of his open eyes. I wish to better understand, here, how the jaguar really understands, perceives and sees a world of implied signs, and how nonhuman life forms process signs. In other words, I want words for what there are not yet, quite, words for.

The third prompt of the HOLO Annual is “Mapping and Drawing Outside Language,” and gathered in response are Francis Tseng, Nick Larson, and K Allado-McDowell. In a conversation with research partner Peli Grietzer, we hoped for invited contributors to respond to a prompt around mapping and ways of understanding that are not necessarily based in language.

In shaping this prompt, Peli had introduced us to a piece of writing by Alex Graves of DeepMind, titled “Magic Numbers.” In it, Graves asks, how might we locate “the locus of creative magic within the billions of numbers processed by artificial neural networks?” How can “simple mathematics … cast the same spells as the grey stuff in our skulls?” What forms of mapping and drawing outside language do you deploy? They go on to write:

• If we want to understand how the magic of intelligence can be encoded in numbers, we should give up asking where. To attempt to locate consciousness in a network of interlinked signals, whether simulated or real, is to fall into the homunculus fallacy, the search for a mind within a mind. Instead we should embrace the quality of quantity, the necessity for life of patterns too intricate to put into words, and patterns of patterns, and so on up. The reductive drift of Western thought has tended to leave the celebration of complexity to artists and poets: Hopkins’ “pied beauty” or Whitman’s “I contain multitudes.”

While we were initially thinking of computational drawing and mapping practices that capture the complexity of the mind, Tseng, McDowell, and Larson all came to mind. They’ve worked through this notion through their respective practices, based in social justice and equity-driven modeling, AI aesthetics and research, and subtle investigations of the relationship between interfaces, image compression and degradation, and ideology, respectively. Get to know this heady group more formally below:

The HOLO Annual has emerged, in part, through conversation between Nora Khan and Peli Grietzer, the Annual’s Research Partner. They discussed Nora’s first drafts of the “unframe” for the Annual (in resisting a frame, one still creates a frame) around prompts of Explainability, Myths of Prediction, Mapping Outside Language, and Ways of Partial Knowing, over months. Once drafts started rolling in, they discussed the ideas of contributors, the different angles each was taking to unpack the prompts, and the directions for suggested edits. They came together about four times to work on unknotting research and editorial knots. A deeper research conversation thread weaves in and out, in which Peli and Nora deconstruct the recent and influential Atlas of AI, by Kate Crawford.

In this final excerpt, right before Nora and Peli and contributor Sera Schwarz met up in Berlin after almost year of Annual-related discussions, they talk about ideas in contributors’ essays made in response to the prompts Mapping Outside Language and Explainability, again. Themes included: Alvin Lucier’s I Am Sitting in a Room, artistic use of language that is “in between” meditative practice and that of scientific experimentation, and finding one’s space in between fields, and outside of the explainable, and the mapped.

A fuller representation of Khan and Grietzer’s exchange will be published in the HOLO Annual.

Nora: There’s much more self-reflection and embrace of doubt in this issue than I’m used to seeing in the art, science, and technology discourse space. Meaning, the contributors gathered all take a lot of breaks to reflect on their position, and on what systems they are unwillingly contributing to, regardless of their criticality.

Peli: Oh, interesting. I can sort of see it. One piece was weirdly almost Heideggerian in its ideology—or left Heideggerian. But again, it was interesting to see a piece from someone who is a practicing technologist.

Nora: I think the prompt is coaxing a lot of things out from people. Especially practitioners.

Peli: There’s a certain kind of heavy technology problem and heavy technology solution, and the idea of trying to step outside it. I like that they thought in terms of being very plainspoken, based on their experiences of live, practical technologies. There’s an aspect of a practitioner’s diary or practitioner’s reflection. It’s one of the interesting things in the issue. A bunch of the pieces are by practitioners in technology or art, reflecting on their relationship to these systems and their broader concerns. When a theorist or an academic says these things … well, it can feel much more interesting when you know that the insights arise out of someone’s day to day engagement when acting in a certain field.

Nora: Workers on the inside having a moment of rest outside of the monolith. The pieces are granting you a way in. There’s a lot of, “And what is this all for?” Or, “Am I the bad guy?” There’s a lot of hilarious moments. I don’t know if the prompt elicited that, or if it’s just the time we’re in, but people are asking, across the board, “Maybe I’m the bad guy.” Maybe we all are. No matter how critical or incisive, or ruthlessly interrogative of the spaces they are in, it’s an important question to ask. Especially in many tech- spaces where being good, or making progress, is so built into the ethos and language. To take up a position outside the paradigms of explanation (of their work, to themselves), and outside known language, to assess their work and re-evaluate the stakes. But it’s also important not to romanticize a space outside and ’before’ technology, too.

Peli: That’s a point I’d impress on. Sometimes one might romanticize how agriculture is so integral to life, so it’s somehow especially bad that machines run agriculture. One would maybe want to press on the romanticism of that.

Nora: The idea of old and ancient knowledge that’s being tapped into. I guess one sees this a lot in memoirs of leaving technology. Being so overwhelmed by the horror of it all that they head to the woods.

Peli: There’s something a bit sort of hermetic in it. Is this just an aesthetic preference? Is it a poetic preference? Is this a moral edict, or is there sort of a practical and pragmatic dimension to it? It always makes a bit more sense to me when people romanticize hunter gatherer life than when people romanticize agriculture. I feel like we have so much immediate evidence that people absolutely fucking hated it, from the minute it began existing. You know, the Jewish Bible and Christian testament. The great punishment for getting out of the Garden of Eden is, oh, you will have to till the land in order to eat. That’s the punishment. Humans hated agriculture quite intensely in real time.

Nora: So to romanticize that is quite funny. I think there is a kind of softness in this aesthetic in the more, ah, rhizomatically-oriented art-technology spaces, if we can call them that, that often revolves around The Carrier Bag Theory of Fiction. The longing for mending of clothes, sharpening of knives, tending to compost, fixing things with one’s hand. With the crucial context of being in a major metropolis, connected to the internet.

To move from one high technology system to a ‘lower’ or earlier technology system—well what are the advantages of moving and what are the costs one can’t predict or even see from our position? What would only be visible once outside? As we ask ourselves, potentially, “How have I aligned my life with the more reactionary elements of using these technologies, while critiquing their driving forces,” we might also ask, what would we do on the outside of them? Outside their language and beyond their gates?