AO3'S content scraped for AI ~ AKA what is generative AI, where did your fanfictions go, and how an AI model uses them to answer prompts
Generative artificial intelligence is a cutting-edge technology whose purpose is to (surprise surprise) generate. Answers to questions, usually. And content. Articles, reviews, poems, fanfictions, and more, quickly and with originality.
It's quite interesting to use generative artificial intelligence, but it can also become quite dangerous and very unethical to use it in certain ways, especially if you don't know how it works.
With this post, I'd really like to give you a quick understanding of how these models work and what it means to “train” them.
From now on, whenever I write model, think of ChatGPT, Gemini, Bloom... or your favorite model. That is, the place where you go to generate content.
For simplicity, in this post I will talk about written content. But the same process is used to generate any type of content.
Every time you send a prompt, which is a request sent in natural language (i.e., human language), the model does not understand it.
Whether you type it in the chat or say it out loud, it needs to be translated into something understandable for the model first.
The first process that takes place is therefore tokenization: breaking the prompt down into small tokens. These tokens are small units of text, and they don't necessarily correspond to a full word.
For example, a tokenization might look like this:
Each different color corresponds to a token, and these tokens have absolutely no meaning for the model.
The model does not understand them. It does not understand WR, it does not understand ITE, and it certainly does not understand the meaning of the word WRITE.
In fact, these tokens are immediately associated with numerical values, and each of these colored tokens actually corresponds to a series of numbers.
Write a story
12-3446-2638494-4749
Once your prompt has been tokenized in its entirety, that tokenization is used as a conceptual map to navigate within a vector database.
NOW PAY ATTENTION: A vector database is like a cube. A cubic box.
Inside this cube, the various tokens exist as floating pieces, as if gravity did not exist. The distance between one token and another within this database is measured by arrows called, indeed, vectors.
The distance between one token and another -that is, the length of this arrow- determines how likely (or unlikely) it is that those two tokens will occur consecutively in a piece of natural language discourse.
For example, suppose your prompt is this:
It happens once in a blue
Within this well-constructed vector database, let's assume that the token corresponding to ONCE (let's pretend it is associated with the number 467) is located here:
The token corresponding to IN is located here:
...more or less, because it is very likely that these two tokens in a natural language such as human speech in English will occur consecutively.
So it is very likely that somewhere in the vector database cube —in this yellow corner— are tokens corresponding to IT, HAPPENS, ONCE, IN, A, BLUE... and right next to them, there will be MOON.
Elsewhere, in a much more distant part of the vector database,
is the token for CAR. Because it is very unlikely that someone would say It happens once in a blue car.
To generate the response to your prompt, the model makes a probabilistic calculation, seeing how close the tokens are and which token would be most likely to come next in human language (in this specific case, English.)
When probability is involved, there is always an element of randomness, of course, which means that the answers will not always be the same.
The response is thus generated token by token, following this path of probability arrows, optimizing the distance within the vector database.
There is no intent, only a more or less probable path.
The more times you generate a response, the more paths you encounter. If you could do this an infinite number of times, at least once the model would respond: "It happens once in a blue car!"
So it all depends on what's inside the cube, how it was built, and how much distance was put between one token and another.
Modern artificial intelligence draws from vast databases, which are normally filled with all the knowledge that humans have poured into the internet.
Not only that: the larger the vector database, the lower the chance of error. If I used only a single book as a database, the idiom "It happens once in a blue moon" might not appear, and therefore not be recognized.
But if the cube contained all the books ever written by humanity, everything would change, because the idiom would appear many more times, and it would be very likely for those tokens to occur close together.
Huggingface has done this.
It took a relatively empty cube (let's say filled with common language, and likely many idioms, dictionaries, poetry...) and poured all of the AO3 fanfictions it could reach into it.
Now imagine someone asking a model based on Huggingface’s cube to write a story.
To simplify: if they ask for humor, we’ll end up in the area where funny jokes or humor tags are most likely. If they ask for romance, we’ll end up where the word kiss is most frequent.
And if we’re super lucky, the model might follow a path that brings it to some amazing line a particular author wrote, and it will echo it back word for word.
(Remember the infinite monkeys typing? One of them eventually writes all of Shakespeare, purely by chance!)
Once you know this, you’ll understand why AI can never truly generate content on the level of a human who chooses their words.
You’ll understand why it rarely uses specific words, why it stays vague, and why it leans on the most common metaphors and scenes. And you'll understand why the more content you generate, the more it seems to "learn."
It doesn't learn. It moves around tokens based on what you ask, how you ask it, and how it tokenizes your prompt.
Know that I despise generative AI when it's used for creativity. I despise that they stole something from a fandom, something that works just like a gift culture, to make money off of it.
But there is only one way we can fight back: by not using it to generate creative stuff.
You can resist by refusing the model's casual output, by using only and exclusively your intent, your personal choice of words, knowing that you and only you decided them.
Let me leave you with one last thought.
Imagine a person coming for advice, who has no idea that behind a language model there is just a huge cube of floating tokens predicting the next likely word.
Imagine someone fragile (emotionally, spiritually...) who begins to believe that the model is sentient. Who has a growing feeling that this model understands, comprehends, when in reality it approaches and reorganizes its way around tokens in a cube based on what it is told.
A fragile person begins to empathize, to feel connected to the model.
They ask important questions. They base their relationships, their life, everything, on conversations generated by a model that merely rearranges tokens based on probability.
And for people who don't know how it works, and because natural language usually does have feeling, the illusion that the model feels is very strong.
There’s an even greater danger: with enough random generations (and oh, the humanity whole generates much), the model takes an unlikely path once in a while. It ends up at the other end of the cube, it hallucinates.
Errors and inaccuracies caused by language models are called hallucinations precisely because they are presented as if they were facts, with the same conviction.
People who have become so emotionally attached to these conversations, seeing the language model as a guru, a deity, a psychologist, will do what the language model tells them to do or follow its advice.
Someone might follow a hallucinated piece of advice.
Obviously, models are developed with safeguards; fences the model can't jump over. They won't tell you certain things, they won't tell you to do terrible things.
Yet, there are people basing major life decisions on conversations generated purely by probability.
Generated by putting tokens together, on a probabilistic basis.
