https://www.piratewires.com/p/demonic?utm_source=email
A.I. summer. I was in Northern California for a work event a couple weeks ago, walking through a quiet redwood grove, when I stumbled into a bit of service, decided to scratch my Twitter itch with a quick check into the chaos engine, and found myself face-to-face with an A.I.-generated demonic entity named [REDACTED]. In a lengthy thread, a user named Supercomposite narrated her journey with one of the recently-popularized machine learning models native to the phenomena of “generative art.” For people less familiar with the subject, this is a technology capable of reading a written description provided by a human, and generating an original image based on that description. Long story short, the summer of 2022 has been a whole, wild season of robots dreaming — and then painting — electric sheep. But in the case of [REDACTED], Supercomposite purportedly ran a kind of opposite search on a descriptive piece of language with the hope of finding an image the A.I. considered least like her chosen description. The result was strange, but benign. She then ran a similar search on that initially strange result, and a deathly, haunted looking woman with sunken, soulless eyes appeared. Supercomposite, a self-defined “hellmaxxer,” happily tugged the yarn with further searches, and the haunted figure of [REDACTED]’s haunted world, populated by a host of haunted friends, gradually expanded. The discovery was finally shared on Twitter, and the images went viral. Catholics and astrology chicks had questions.
Why did Supercomposite give the cursed thing a name? Why did she pick a name associated with demonology? Most importantly, had she — and we, by extension, as we shared the thread — created an egregore, inadvertently (or purposely) unleashing a malevolent thoughtform on an unsuspecting memespace?
Okay. My purpose here is not to imply A.I. has opened a literal gateway to Hell. But just a few months into semi-popular use, generative machine learning models have acquired a mythology, including a mythology of horrors, and the implication here is meaningful. People are both excited and afraid, emotions that tend to rise when confronted with something new. In the case of deep learning, I believe we are dealing with something fundamentally new, standing on the precipice of paradigmatic change in magnitude not seen since the invention of the internet. There’s incredible promise here. There are also valid concerns. For the purpose of this wire, I’ll focus on the narrow space of creative work.
How is deep learning going to assist rather than replace the average creative worker? If replacement is the goal — valid, by the way, if a net positive for humanity — are we paying the people responsible for work the models have been trained on? Is it possible for a writer, artist, or designer to opt out of assimilation by the creative hive mind? And finally, presuming the amorphous question of culture is worthy of consideration for the architects of an explicitly creative tool, how does a machine designed to mimic, rather than imagine, drive a culture forward, rather than freeze it in time?
A couple years ago, at the top of that spectacular summer 2020 (plague, legalized rioting, social media civil war), the broader public first beheld the awesome success of GPT-3, a language model trained on human writing to (kind of) communicate like a person. By spring of this year, machine learning tools were live with the stunning capability of associating words and images (a decent primer here), which is to say there were now models capable of creating artwork based on a series of human prompts. For example, I open Dall-E, my friendly neighborhood generative model, and ask to see “a cat riding a rocket ship in the style of Monet.” After a few seconds, Dall-E delivers a piece of art that has never before existed.
Behold:
This dumb ass cat? Historic breakthrough, actually. The potential applications of generative models are as obvious as they are endless. Let’s start with something personal.
I’m currently looking to expand the Pirate Wires media empire. This requires the employment of additional, talented writers amenable to my overall ‘vibe,’ and the greater their output the better. Then again, what about robots?
Once we iron out the kinks, it should be possible to train a language model on everything I’ve ever written, feed the model outlines, and produce rough drafts of work, in roughly my own voice, as quickly as I can write the prompts. Then, I can polish the drafts, and produce in an hour what previously took me ten or twelve. By increasing my output so dramatically, I’d be capable of running a media company out of my bedroom staffed by, in a sense, a couple dozen clones. There are analogs to this for artists and designers in pretty much every creative field, presenting the obvious, first, positive utility: this technology, like most information technology, will be very good at amplifying the ability of our very best creative minds (me (you’re welcome (subscribe already, damn))).
Generative models will also compliment creative workers with skills they’ve never had. As a kid, I wrote comic books and television scripts, but I didn’t know any artists, and I had no access to a studio. As image, video, and voice models improve, that won’t matter. Comic writers will be able to ‘collaborate’ with models trained on the greatest artists who ever lived. It’s easy to imagine the technology improving to the point animators can rapidly generate entire television sagas from a script alone, or maybe even the rough outline of a script, with the work of any voice actor who has ever lived. The cost to produce every form of content will plummet. Then, in terms of the sci-fi sort of applications we’re maybe not thinking of?
Imagine an unlimited supply of your favorite TV show. If a sufficiently advanced model trained on scripts and video is also trained on your viewing history, we may see, in our lifetime, a personalized 6th, 7th, 27th season of Stranger Things — tailored specifically to viewer tastes — 30 years after the show’s final episode. In a world of A.I., the future is personalized. Diamond Age status. The Lady’s Illustrated Primer is coming.
Beyond consumer content, from animation to live-action television, movies, and video games, all work that has ever been achieved in the fields of graphic design, fashion design, interior design, architecture, and even software engineering can conceivably be captured, and roughly “democratized,” which is to say many of the people who used to get paid for this kind of work no longer will, but the work itself will be ubiquitous. I want a new, original wardrobe. Done. Plans to remodel and furnish my entire home in accordance with my preferred tastes. Next question. Social media posts, in my voice, meme-ing in the current trends, dotting posts with ads for — back to that wardrobe — my new fall line. I don’t think this is crazy.
Now let’s push it all a little further. Imagine a model trained on the emails, texts, voicemails, and video of a beloved, deceased relative. In my opinion, this doesn’t constitute immortality. But I do think in a world of autonomous generation you’ll probably be talking to something that feels like your mother for the rest of your life, and your great great grandson will wander the digital halls of your family Meta necropolis asking his ancestors for advice on his love life. It’s me, glowing in tasteful archangelic notes, probably holding a sword, telling my progeny to simply stop being a hoe.
Anyway, this is obviously dope as hell. Why are the art people mad?
One thing I’ve been wondering is what might endless “art” on demand mean for the creative fields conceptually? Because it seems the law of supply and demand would dictate the value of most creative output, in a world of unlimited creative output, must fall close to zero. A.I. is a huge topic, and I’m still not sure how to think about it all, which is why I’ve avoided firm opinions. I did however make the mistake of attempting a mild joke on Twitter, thus evoking the frustration of Taylor Lorenz’s favorite reply guy (be kind, he means well).
roon @tszzlif solana is supposed to be a tech propagandist he’s failing at it find the positive spin instead of whatever this is Mike Solana @micsolanatech marketing 2012: what if we liberated you from every physical constraint, effortless mobility, free information and communication, next gen finance, fuck a middle man, here are the keys to the kingdom tech marketing 2022: what if we destroyed art at the conceptual level
Whenever I’m hit with a dunk for an inoffensive, basically common point, my impulse is to look a little closer. Setting aside the interesting question of whether I should be lying more about my feelings pertaining to a nascent technology with potentially world-altering consequence, I’ll simply admit that sure, you’re right, I’m not interested in “finding the positive spin” of generative models. I’m interested in their actual impact. As I’ve now illustrated more potentially positive utility in this technology than most people actually working in the field, I’ll go ahead and flesh out my concerns.