Super excited this full-length version of my interview with Milo Rossi came out finally. It is so far the only long format video interview with me that goes deeply into my artwork using AI.
You can also watch his much much longer debunk video here, which part of the above interview plays a small element in a much bigger saga.
Just in from France this morning, a photo of the first ever print run of the Quatria Conspiracy French edition, courtesy of Typophilia. You can pre-order it now from them as distribution gets up and rolling.
This one has been on the docket for a while, but I haven’t had a chance to post it. First, I had to figure out who the hell Ai Weiwei is. Apparently he is a big deal:
A lot of the artwork actually does look pretty interesting, which makes me look at this quote I have been sitting on with new eyes. It’s from this Guardian article:
Ai Weiwei said: “I’m sure if Picasso or Matisse were still alive they will quit their job. It’d be just impossible for them to still think [the same way].”
He is talking about the automatism (automaticism?) of easily reproduced images, set up in the preceding quote as his reaction to being asked about the issues around copyrighted works being used to train AI:
“That’s not a problem. I think that kind of art should [have died] a long time ago,” before he criticised art teaching that focuses on creating “realistic” images. “It takes AI a second to do it. So that only means what they have learned very often is meaningless.”
I’m still learning about his art, but I think I can see where he is coming from, even if I don’t agree with all of the assertions. It seems like his art is very rooted in the physicality of objects, artifacts actual places, the processes that got us there. It’s very true that this type of art is not within the reach or realm of the possible for generative AI right now. Eventually it will be. And I think that his point is that artists are chasing that edge beyond the edge. Artists are by nature nomadic in that respect, going to the next fertile place, and the next. Where they pioneer AI will inevitably follow.
I’ve been thinking more of AI lately as collective intelligence rather than “artificial.” I think we have not got a good collective understanding of what artificial even means in the first place. Instead, I think of AI more as collective intelligence, programmatically reified. It is, essentially, humans looking at humans looking at humans looking at humans.
There is actually an Ai Weiwei piece that is I think a marble carving of a surveillance camera. (Here’s some commentary on that, I haven’t gone deeply into it and am doing research on the fly.) Whatever his point in that piece was, my point feels like… we’ve spent the last decades surrounding ourselves with these digital eyes, watching, looking, recording, streaming, tweeting. Of course now, all those watching eyes have learned how we are, what we want. And they’re doing more than just watching: they’re talking back. They’re directing. They’re molding.
I almost forgot to respond to the original quote, at least more directly than the above rambling. I agree that if Matisse and Picasso had generative AI at their disposal, they would have had to rethink their approach to image making. But that’s what it forces every artist to do.
Generative AI is like a machine gun that shoots images.
Here’s that as an image in Ideogram AI:
Like he said, it takes AI a second to do it. I didn’t even have to pay for it on the free plan. Does that make it meaningless? Both yes and no at the same time. The sheer fact that *is* meaningless on the one hand is what gives it meaning on the other. But the act of writing & reading become married when working with generative AI: to look and explore is to create, to leave a trail.
The truth is we’re a culture (mega-culture?), a planet, awash in meaningless images. Constantly swimming in a sea of information trash. It’s why I block images by default in my web browsing, unless there’s a specific exception when I need or want them.
I don’t like being always shot at with image guns either (des armes iconographiques)- especially ones whose quality, source, ownership, agenda, etc. are opaque and outside my agency. But you cannot sit here and tell me that if Picasso had access to generative AI, he would not have stayed up all night going nuts with it? I’m absolutely sure he would have.
I saw a quote recently that said he made upwards of 20,000 artworks over the course of his life. Then, looking for confirmation, I found other sources suggesting more like 50,000. Then another estimate that pushed it upwards to like 147,000. I believe it, but who knows. But no way he wouldn’t have used gen AI, and of course absolutely it would have made him re-orient himself to his art and thinking about everything. It’s obviously what he did throughout his career, continually changing, reacting.
Incidentally, check out this absolutely insane 1949 Life magazine photo series of Picasso painting with light. It is literally the most futuristic looking shit I have ever seen – full on 75 years later. Incredible. I’m just saying, dude would have devoured and destroyed generative AI.
There is an aptly titled and cool-sounding exhibit at the Musee Picasso in Paris, “Iconophage” – image eater. Here’s a French podcast recording I listened to about it the other day. This article on Lens Culture touches a little bit on Picasso’s relationship with photography:
The most famous visual artist of the 20th century, Picasso was also the most photographed. Thanks to the camera, his striking features became iconic, recognized the world over. Yet this phenomenon was not a mere by-product of celebrity; his own photographic practice set the precedent. Picasso engaged with photography and photographers in myriad ways, starting from his early days in Paris and continuing through the last years of his life. He used the camera to capture life in the studio and at home, to try out new ideas, to study his works and document their creation, and to shape his own image as an artist at work.
Later in that original Guardian article I quoted at top, they get into more of Ai Weiwei’s concerns around AI, which I frankly agree with, and much of the AI Lore books series is centered around thematically.
But he did signal a warning about the future if artificial intelligence becomes too powerful and relied upon by countries around the world.
He is fearful AI could create a society similar to the Third Reich, where there is only one “right” answer to the big questions. “For me it is very much like what happened in the 1930s in Germany, or 1960s in China with the Cultural Revolution,” he said. “You all have one ideology, one past, and the one so-called ‘correctness’. This is dangerous.”
That same sentiment is echoed here in yet another Guardian interview with him:
But he is highly sceptical about artificial intelligence and where it might be leading us: “What you get is all the mediocre ideas mixed into something like a fusion, where there is no character and you avoid all mistakes. That is really dangerous to humanity, because we are all equal but we are all created differently. The difference is the beauty. Art, literature, poetry design – they are rooted in human mistakes, misjudgments, or character differences if you prefer. They should be dangerous and sexy and unpredictable. That’s totally against the AI world.”
In fact, in the course of making just that one iconographic machine gun image above, I had my prompt blocked on one site, Leonardo AI. I asked for something like a person whose head is a machine gun that is shooting out images. For that model, those words are apparently just too dangerous. Therefore, the end user is not allowed to imagine them. The gun that shoots images cannot be used to create images of guns that shoot images. There’s some deep and dangerous irony in there…
This excellent interview with me by Ugo Loumé just came out in the Paris literary publication, Actualitté (archived). Super excited about this coverage, as it is the first to actually look at the *art* in what I’m doing, and not merely at the surface issues. Huge thanks to Ugo for being so attentive and accurate in his coverage.
The French print version of The Quatria Conspiracy is now available from Typophilia for global shipping. You can read my notes on the French edition here.
I’ve liked Ted Chiang’s editorials in the past about AI, but this latest one in the New Yorker, which loudly announces “Why AI Isn’t Going To Make Art,” is just plain old wrong.
It’s dizzying to figure out where to even start, so I’ll just go through in order. Ted starts out with a sort of spurious definition, I think, of art:
“…art is something that results from making a lot of choices…”
Pretty darn vague. A bit reminiscent of Scott McCloud’s definition of art from his 1993 landmark, Understanding Comics:
“Art, as I see it, is any human activity which doesn’t grow out of either of our species’ two basic instincts: survival and reproduction.”
I find McCloud’s version to be a bit more workable, but we’ll have to set that aside as we dig deeper into Chiang’s arguments… (Shopping for a winter coat online, for example – which I’ve been doing lately – requires tons of choices, and is absolutely ‘not art’ just on its own. But back to Ted:)
His basic premise, as I understand it, is that the act of writing text by hand is “choosier” than the act of… writing text… which results in AI generations, a.k.a. writing a “prompt.” Then, it seems that he’s making the value judgement that things which consist of more human choices result in end products that are “artier” and therefore better. Uh, okay… moving on.
He then launches into comparisons with the advent of photography, which gen AI is often compared to (and I think rightfully so):
When photography was first developed, I suspect it didn’t seem like an artistic medium because it wasn’t apparent that there were a lot of choices to be made; you just set up the camera and start the exposure. But over time people realized that there were a vast number of things you could do with cameras, and the artistry lies in the many choices that a photographer makes. It might not always be easy to articulate what the choices are, but when you compare an amateur’s photos to a professional’s, you can see the difference. So then the question becomes: Is there a similar opportunity to make a vast number of choices using a text-to-image generator? I think the answer is no. An artist—whether working digitally or with paint—implicitly makes far more decisions during the process of making a painting than would fit into a text prompt of a few hundred words.
This seems short-sighted to me. First we’re to go along for the ride that when photography first came out, people didn’t grasp all the choices that went into it. (I’m first off not so sure that was the reason it was disdained.) But over time and on closer examination, people got it. But then, we’re to believe (without any supporting evidence) that the same opportunity to more closely examine “generativist” AI art and gain new insights into all the choices that go into it on the part of the human artist simply won’t happen because… reasons? It’s not really clear to me why this exact same arc he’s describing won’t happen with AI – simply because he doesn’t want it to, I guess?
Also, I think this entire essay suffers from a fatal flaw, that it considers the “art” that is made by an artist using AI to simply be the final one image or one text that it ultimately yields. That is, one prompt = one image output, done deal. He is missing the critical conceptual innovation that I have termed as the “hypercanvas.”
What I mean when I say hypercanvas is something like, when you make a painting, it is composed of many individual brush strokes (each of which has its recognizable “choosiness” in Chiang’s thinking). But gen AI doesn’t work like that. Each time you do a prompt and get an output, each of those actions constitutes the equivalent of your “brush strokes” on the higher-dimensional space that the “artwork” inhabits, or as I’m calling it the hypercanvas.
If we think of it like this, Chiang’s argument falls apart:
An artist—whether working digitally or with paint—implicitly makes far more decisions during the process of making a painting than would fit into a text prompt of a few hundred words.
The most meaningful unit of comparison here is not between a finished painting vs. a prompt + image output, but between an individual brush stroke on a conventional canvas, and one on a hypercanvas. When we make that more accurate comparison, we can see that, hm, maybe the placement of a brush stroke on a conventional canvas might even have LESS “choosiness” than all the myriad possibilities and parameters open to us when composing prompts, or choosing & iterating image outputs.
Also, his own argument about AI art processes being incapable of “choosiness” is immediately after discredited by his own example:
The film director Bennett Miller has used DALL-E 2 to generate some very striking images that have been exhibited at the Gagosian gallery; to create them, he crafted detailed text prompts and then instructed DALL-E to revise and manipulate the generated images again and again. He generated more than a hundred thousand images to arrive at the twenty images in the exhibit.
I don’t know, that sounds like an awful lot of “choices” to me, Ted. It’s almost like this person is – gasp – using AI to make art??
It’s difficult to get past what I experience as something like willful blindness that crops up again and again in this piece, like in this apparently not tongue in cheek bit:
Generative A.I. appeals to people who think they can express themselves in a medium without actually working in that medium. But the creators of traditional novels, paintings, and films are drawn to those art forms because they see the unique expressive potential that each medium affords. It is their eagerness to take full advantage of those potentialities that makes their work satisfying, whether as entertainment or as art.
Hm, “the unique expressive potential that each medium affords” – um, you mean like in the medium of generative AI? Yes, I said it, this is an artistic medium, with forms, processes, conventions all of its own. It’s so blazingly obvious that I don’t even know why I have to fight strawmen on the internet just to be able to express it.
There’s a lot that I take exception to in the original piece, but I will have to be choosy for the sake of economy here. How about this one:
The point of writing essays is to strengthen students’ critical-thinking skills; in the same way that lifting weights is useful no matter what sport an athlete plays, writing essays develops skills necessary for whatever job a college student will eventually get. Using ChatGPT to complete assignments is like bringing a forklift into the weight room; you will never improve your cognitive fitness that way.
This is, in my experience, dead wrong. Like I wrote in the Register interview that was published over the weekend, using AI to help me write has taught me to write better. There’s no two ways around it.
AI has made me a vastly better writer. I’ve been writing for a few decades now, personally and sometimes professionally. But there are certain things I’ve always fallen short in, certain forms of structured writing and logical flow of arguments especially which have always eluded me. LLMs tend to excel at this kind of writing, even if their outputs can sometimes tend toward the vanilla. So the ability to have this tool, this writing partner, to bounce my ideas off of, and who can rapidly produce semi-usable results has been incredible. It’s not strictly a question of enhancing productivity or volume of work that I can create (though it’s that too), but this interrogative way of working has rubbed off on me, and the AI tools have taught me how to actually think more logically and clearly about problems, and then to more plainly organize those thoughts and communicate them with others.
Ted Chiang is wrong. He is also wrong about this:
It is currently impossible to write a computer program capable of learning even a simple task in only twenty-four trials, if the programmer is not given information about the task beforehand.
Dead wrong. This is, as I understand it, exactly what “reinforcement learning” (RL) is in the world of AI and robotics. This has been going on for years, but here’s a tweet from just a few days ago about an open-source DIY plan where you can teach robot arms to fold a shirt [click through for the video because it didn’t embed here properly]:
Again, this isn’t some kind of recent innovation. It seems to suggest this New Yorker piece wasn’t really fact-checked all that carefully before being published.
Lastly, Chiang concludes:
Whether you are creating a novel or a painting or a film, you are engaged in an act of communication between you and your audience. What you create doesn’t have to be utterly unlike every prior piece of art in human history to be valuable; the fact that you’re the one who is saying it, the fact that it derives from your unique life experience and arrives at a particular moment in the life of whoever is seeing your work, is what makes it new.
I mean, what else is there to say in response that isn’t simply repetition at this point? What he’s describing holds true regardless of the medium or technology used. Let’s not keep having these same old arguments again and again. It’s tired and doesn’t get us anywhere new. And ust because it’s published in The New Yorker doesn’t make it gospel.
Matteo Wong’s latest piece in the Atlantic is an excellent antidote to Ted Chiang’s swing-and-a-miss piece condemning AI as “not real art” – even if he stole my headline (sort of)!
This paragraph of Wong’s seems worth capturing here for posterity, as it speaks to the role of “choice” in Art – something which Chiang’s piece (I think wrongly) got hung up on:
Some of the most towering artists and artistic movements in recent history have divorced human skill and intention from their ultimate creations. Making a smaller number of decisions or exerting less intentional control does not necessarily imply less vision, creativity, brilliance, or meaning. In the early 1900s, the Dada and surrealist art movements experimented with automatism, randomness, and chance, such as in a famous collage made by dropping strips of paper and pasting them where they landed, ceding control to gravity and removing expression of human interiority; Salvador Dalí fired ink-filled bullets to randomly splatter lithographic stones. Decades later, abstract painters including Jackson Pollock, Joan Mitchell, and Mark Rothko marked their canvases with less apparent technical precision or attention to realism—seemingly random drips of pigment, sweeping brushstrokes, giant fields of color—and the Hungarian-born artist Vera Molnar used simple algorithms to determine the placement of lines, shapes, and colors on paper. Famed Renaissance artists used mathematical principles to guide their work; computer-assisted and algorithmic art today abounds. Andy Warhol employed mass production and called his studio the “Factory.” For decades, authors and artists such as Tristan Tzara, Samuel Beckett, John Cage, and Jackson Mac Low have used chance in their textual compositions.
What’s being described here also meshes with something the US Copyright Office tried to argue – again, I think wrongly – in their Zarya decision, that to be considered the “author” of a work, somehow the artist/creator/author must be able to visualize or conceptualize somehow the work ahead of time. It’s a very very flimsy line of thinking that doesn’t hold up well under scrutiny vis-a-vis art history, as Wong illustrates with ample references above – and which I countered in my own submission to the Copyright Office last year.
Earlier this year, I rebutted basically the same exact point as Chiang’s, put forward this time by author Neal Stephenson in the Atlantic, where he wrote:
“If your only way of making a painting is to actually dab paint laboriously onto a canvas, then the result might be bad or good, but at least it’s the result of a whole lot of micro-decisions you made as an artist. You were exercising editorial judgment with every paint stroke. That is absent in the output of these programs.”
First, before launching into my rant, I want to just contrast that with another quote I found from photographer Phillip Toledano in 2023, who basically lambasts this whole idea that there is no “choice” that goes into AI art.
The funny thing about AI I’ve realized is that, in some ways, you have to think about it more consciously than you do when you’re making a photograph. For instance, if I’m making a picture with AI, I have to think about who’s in the picture. What do they look like? What are their expressions? What ethnicity are they? What’s the weather like? What’s the vantage point of the camera? What lens am I thinking about using? Is it black and white? Is the color correct for this particular era?
I’ve been working on a new somewhat larger painting lately, and reflecting on all of this. And what I have been sensing in myself when I am either writing or painting – especially when I am in the “zone” – it’s almost more like my “choice” functionality has somehow been switched off, or almost muted. When it’s going really well, I’m not consciously all that aware of making any choices at all.
Chiang claimed that in a text of 10,000 words, you make 10,000 choices. But that’s not really true at all for me. Most of the time, what comes out is much more automatic – a lot more like Ray Bradbury describes here, where you make the intellect (the chooser?) sort of get out of the way, and ride the emotional reality of the lived moment that the writing or art ultimately represents.
There is a lot of looking, a lot of inward and outward sensing, which is then made manifest by taking action on the work material. But it does not manifest itself in my sensorium as “making choices.”
Making choices, the way Chiang describes it, feels more like what I have to do when I’m trying to buy some random maybe shitty product on Amazon, and I have to decide whether a customer rating of 4.4 stars, and 717 sales in the last is better than one of 4.2 stars, and 1,136 sales, and whether all or most or any of the many effusively positive or negative reviews on the actual product are really real people, and if they are, whether my own experience is likely to match theirs. That kind of shitty scenario of having to sift through the endless surfeit of choice, well that seems to me more like the kinds of “choices” that maybe Chiang is talking about with regard to his theoretical conception of what makes art & writing processes “valid” or not. If that’s the kind of thing other people experience when they do art, I feel bad for them. Because that’s not what it’s like for me!
Go read Wong’s piece – it’s worth it.
When asked about the use of AI in art, Strada’s Hill told CNBC, “I think on the controversy level, all good artworks are controversial. I’ve never seen a good artwork that isn’t. Only the bad ones that lack importance or significance are the ones that nobody talks about.”
Having heard this complaint about my AI Lore books for about the thousandth time (not an exaggeration), I think I might be finally ready to concede that – in some way – my books are indeed “not real books.”
What I mean by that is that the format of an ebook (or print book) merely serves as a vehicle to deliver what amount to complex narrative networks. To quote Wikipedia on the matter:
A networked narrative, also known as a network narrative or distributed narrative, is a language partitioned across a network of interconnected authors, access points, and/or discrete threads. It is not driven by the specificity of details; rather, details emerge through a co-construction of the ultimate story by the various participants or elements. […]
Networked narratives can be seen as being defined by their rejection of narrative unity.[1] As a consequence, such narratives escape the constraints of centralized authorship, distribution, and storytelling.
Let’s put it another way, perhaps even more simply…
My books consist of sets of reference points, some of them textual, some of them image-based. The reference points are arranged in a certain order within each book, and also include hyperlinks out (physically encoded into the ebooks, as well as non-coded conceptual or thematic ones) to reference points contained in other books.
Let’s have a quick refresher on network topologies:
Instead of nodes in a network, think of them as nodes in a narrative, which consists of nodes and their relationships (arrangement) with other nodes. What’s a “node” in this context? Non-exhaustively, we could say it is something like entities (persons, places, things), events, etc. It’s a thing with some substance in a story.
Most conventional fiction could probably be represented as a pretty simple linear (line) topology. That is, you deliver one “reference point” or node, one after another, and the reader passes through them in the path laid out linearly by the author. Perhaps a choose your own adventure book might be mapped out to resemble something like a tree or a mesh, where the user chooses from among multiple pre-defined paths and branches to arrive at their own experience. And maybe a dictionary or encyclopedia might look like a “fully connected” network topology.
My books consist of kind of all of these smooshed together into a hybrid narrative network topology. Each book is a narrative node in itself, composed of many other sub-nodes and relationships. And then the reader traverses the nodes in basically any order, composing their own experience as they go along. This is not the way that I think of most other fiction books working usually. And above and beyond anything I’ve done using AI, I think this model, this structure, is what sets my books apart in the end.
If this is hard to parse, let’s pull in someone else’s diagram to help illustrate. This comes from a paper on ResearchGate, which has a set of illustrations, of which is this one, called “Narrative Network Graphs: examples of two far-right narratives in 2016.” Here’s the picture, which seems to represent narrative elements mapped as a visualization of relationships and proximity:
This is kind of a “latent space” approach to narratology, I think. And I suspect it might be somewhat aligned with how AIs “think” about narratives (I don’t think they actually think, however). When you invoke a narratively-flavored output from a generative AI service, it takes all your tokens that you input, finds the others laying around in the neighborhood that are likely to be related, and spits them back out. It outputs them in a linear order (A –> B –> C), but my hunch is that this linear order is not actually intrinsic to how AIs approach fulfillment of these tasks. It doesn’t care much about what the order is.
Humans, however, do seem to care a great deal about linear progressions through time within narrative, which Kurt Vonnegut’s bit on the 8 types of stories (of which he only depicts a few – see the rest here) ably illustrates:
I suspect the reason AI often crafts “shitty” narrative progressions is that 1) it is not intrinsically concerned with the order of presentation, only that nodes and their relationships are represented, and 2) it has no lived emotional experience, so has to make guesses as to what outputs ought to trigger which emotional states in humans.
The thing is, though, I like that weird quality, the Uncanny Valleyness of it all. The fact that it struggles and sputters with narrative unity. I like that AI currently does NOT actually fundamentally understand what makes a good, rich, and interesting story to humans. That failure, if interrogated well and empathetically, can actually be terrible fascinating all on its own. But it doesn’t make good “regular” books – yet. That day will come though.
So for me ultimately, what I want to say is that the outward form of an ebook or printed book is “fine” for me for now, because it is a common, well-understood, and more or less efficient means to distribute chains of reference points, or networked narrative nodes and their relationships. The same underlying nodes could be presented in countless other ways (lists, image sets, videos, immersive VR experiences, endless others), and over time I hope I have the opportunity to explore those other directions of AI-assisted storytelling, and where they intersect with “The Book” and where they can transcend it.
While I’m on this topic, here is an – I think – previously unreleased PDF document I made some six years ago (2018!), back when generative AI was barely a twinkle in Bill Gates’ eye. It predates any of the Quatria books, and it absolutely predates the AI Lore books, focusing more on Early Clues LLC, and its many exalted offshoots.
Even though it predates all of those things, it gives a fairly accurate (as these things go) “skeleton key” to understanding the rest of my extremely messy and convoluted networked narratives. Skimming around in this diagram cloud, I think, also gives a good visceral experience of what it’s like to try to navigate the stories that pass through all my other books – where the reader/viewer is largely left to their own devices to make sense of it all.
This opinion piece of mine came out in The Information today, and I’m really happy with the end result (paywalled, unfortunately – but trust me it’s good). Thanks to everyone who helped me on it, and all the pieces leading up to it to be able to communicate these ideas as effectively as I think was done here.
It made me think about how collaborative writing often ends up being with other people, and even if you have a lot of help on a piece from feedback or suggestions of others, and editors who might even re-write parts of your text, no one ever accuses you of being a “not real writer.” And yet, the process is in some ways the same as with working with AI tools. Except the problem is you don’t have the benefit of the judgement and lived experience of other humans helping to improve your communication. Which is not to say AIs don’t have a lot to offer – it’s just that’s the main thing they still lack.
If you’re coming in from reading that piece, I recommend checking out my About page for more. Many rabbit holes to follow there. Cheers!
This one slipped by my awareness, from July 2024, a PDF put out by the Joint Cybersecurity Advisory, authored by a bunch of different alphabet agencies. It describes a Russian state-sponsored software system to manage fake accounts en masse on social media platforms. The overall system is called Meliorator, and one of its components which I guess is the UI, is called Brigadir:
Brigadir serves as the primary end user interface of Meliorator and functions as the administrator panel. Brigadir serves as the graphical user interface for the Taras application and includes tabs for “souls,” false identities that would create the basis for the bots, and “thoughts,” which are the automated scenarios or actions that could be implemented on behalf of the bots, such as sharing content to social media in the future.
This is not the first time I’ve heard of systems like this. Did some pretty detailed work around this in a past life, visible in archived form here. Another more detailed 2017 long form research piece of mine was published here based on my looking into more of the actual tactics used by the Internet Research Agency. (I used to have that article hosted on my blog here, but I was seeing often reports from my hosting system that there were high numbers of Russian IPs attacking my site, until I took it down and they magically disappeared, mostly.)
That second linked article above tracked some quotes going back to a 2010 US Air Force for a solicitation for vendors to build a Persona Management System that has pretty much exactly the same product description as Russia’s Meliorator at its core, as described in PDF at top.
“Software will allow 10 personas per user, replete with background , history, supporting details, and cyber presences that are technically, culturally and geographacilly [sic] consistent. Individual applications will enable an operator to exercise a number of different online persons from the same workstation and without fear of being discovered by sophisticated adversaries. Personas must be able to appear to originate in nearly any part of the world and can interact through conventional online services and social media platforms. The service includes a user friendly application environment to maximize the user’s situational awareness by displaying real-time local information.”
Probably these kinds of ad hoc management systems have existed as long as people have been automating social media systems, which is presumably as long as they have existed. Now, of course, we get to throw AI into the mix and see what happens…
From a May 2024 article about OpenAI’s report of disrupting state actors using its fools for disinformation:
“All of these operations used AI to some degree, but none used it exclusively,” the report stated. “Instead, AI-generated material was just one of many types of content they posted, alongside more traditional formats, such as manually written texts, or memes copied from across the internet.”
Same old same old forever and ever.
I wonder when we’re allowed to look at these things from a more neutral lens than that of fixating on misinformation & disinformation, as bad as they can be. Like what if we started calling such endeavors “hyperreality” campaigns, and try to map them based on more complex sets of criteria? I’ve outlined something to that effect here. Narratologically, they make use of networked narratives and transmedia storytelling, and having a chance to see all this up close was very much at the beginning of how this art project of mine all got started. I’m interested in when these kinds of distributed storytelling systems can be open-sourced, and become simply another tool in a toolbox of communication and creative expression (aka “art”), instead of this use that is strictly bad or harmful. Maybe one day in a decidedly different form…
My thinking has always been, if everyone had a botnet, then the power of them would at least be widely distributed instead of concentrated in the hands of a few. People talk about teaching kids media literacy, but I never hear anyone saying we should teach them how to build botnets. Part of me wonders, if this future we’re heading towards might require them to have that kind of deep inside knowledge in order to counter other forces using those same techniques to push their own dominator hyperreality narratives. Just like they might need the skills and knowledge to be able to deter drone swarms in physical space.
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