I think it would be very useful to have an AI guide written for communists by communists. Something that everyone can understand, written from a proletarian perspective - not the philosophy of it but more like how the tech works, how to use it, etc. I can put it up on the ProleWiki essays space if someone wants to write it, we’ve put up guides before, e.g. if you want to see a nutrition and fitness guide written from a communist perspective.

This is not comprehensive, but here is a draft write-up of sorts for the time being. There are probably others who would know better how to write about best use of chat models like Deepseek, I’m not very familiar with them in detail:

What is AI?: AI stands for Artificial Intelligence, which can have broad connotation and could be applied to a number of forms of automation. More recently, it has become synonymous with generative AI, a specific subset of AI in which an AI can be given a prompt (some input from the user) and it will generate something based on the prompt (some output for the user). For the purposes of this article, I will be using AI primarily to refer to generative AI.

Is AI actually “intelligent”?: What is defined as intelligence and whether any AI falls within that may be up for some debate, but instead I want to address the nature of associations with intelligence, autonomy, and behavior. There is no evidence that AI has a “mind of its own” in the way that human beings have a mind of their own and when you consider that it is some math happening on a GPU trained to impersonate human things, not something with a sensate material form interacting with the physical world and then bouncing that back to an inner world, it makes sense that even if it were to have anything resembling intelligence, there is no reason to think it would look similar to the human experience. The point here is not to weigh in on the whole nature of consciousness and intelligence, but that you don’t want to be fooled by an AI acting convincingly human. That’s what it is trained for, but that doesn’t mean it is like a human on a material level. It is still fundamentally a GPU doing some math and shares no actual material traits with you.

AI can be confidently wrong, even when it has high confidence in the right answer: An AI model effectively has a certain amount of probabilistic confidence in what should come next in any given input fed into it. The way it continues is token by token and a token is “however the tokenizer breaks things up”, which varies based on the tokenizer, but most of them do a lot as whole words. There are, however, components of words sometimes too, such as -ly. Tokenizing aims to maximize the semantic literacy of the AI, i.e. it can better understand why some words follow other words if the components give context. A tokenizer that breaks everything up into letters would probably end up more like the word game Hangman than word association. Further, the base probabilities of the model tokens get sampled from through various statistical algorithms, which can dramatically alter the end probability of a given token. In effect, this means that it could have 95% confidence that “purple” is an accurate continuation of "red and blue make " and still choose “yellow.”

AI has no ideology, but it does have biases: Because AI does not have a mind of its own in the way that we do, I think it’s better not to think of it like it has an ideology or belief system. However, this doesn’t prevent it from having biases. The probabilities it learns are based on what kind of material it was given in training data. If everything in its training is saying “Stalin did nothing wrong,” it probably will too. If everything in its training is saying “commie bad”, it probably will too. Further, some companies may consciously try to tune a model’s biases in a particular direction, or block off certain paths of conversation, for this reason. The same goes for image generation. If most hair in its training is blonde, unspecified hair color will likely be blonde. If most skin is white, well… you get the idea.

AI, like anything, is limited by material constraints: Contrary to how it may feel at times, AI is not magic and its capabilities do have limits. If you ask it about theoretical physics, it might have a very good answer depending on how well it has been trained on material written by experts in theoretical physics. If you ask it about a TV show that came out yesterday, chances are it has never seen anything about that show in training and so it will have no idea what you are talking about. Like a human, if it has never encountered something before, it’s not going to magically be able to know what it is. However, unlike a human, you can’t just explain something to it that it doesn’t know and it will now know going forward. More on that below.

An AI model can’t remember what you said to it: This is tricky wording in a way because there are text services that offer some form of automated simulation of memory, where the AI can draw from some kind of record of certain things that you have said and these are supposed to play into how it responds. But the model itself is still not remembering what you said. Training AI models is expensive and training them to be different for a specific user would mean needing to have tons of variations on a model, which would get absurd fast and might result in an overall worse model for any given user (training can be a sensitive process and easily go wrong). Instead, most of what models are working with is a sort of “short-term memory”, sometimes called context; this is still not really any kind of actual working memory in the human sense, it’s just a way of thinking about input. The AI generates from input and so if part of the input is “I already said that, what are you talking about?”, that hints at a kind of conversation where a misunderstanding has occurred. This doesn’t mean the AI will “know” what you already said, it can only “see” what is given as input, plain and simple, and generate based on that. If what you already said is part of the input, it might be able to reference it. Otherwise, it can’t.

AI doesn’t know there is a “you” and an “it”: Some chat-focused AI can be very convincing at acting like a real person, but this doesn’t mean that the AI really “knows” there are separate entities talking to each other. The standard text model is a continuation model, which means they are continuing the given input, i.e. adding onto it. So to get an AI to simulate being a chat partner, special designing behind the scenes forces it to stop writing before it would start writing your side of the conversation. Without these rails, you could watch an AI simulate a conversation between two people, doing both sides of it.

Most AI is not private: This is not to say that people are necessarily interested in reading your specific chats with your username attached, but most AI companies use your chat data in some form, probably anonymized but nevertheless plaintext data they can read, in order to further tune their models. There are rare exceptions, such as NovelAI (which uses a special system of encryption) and ChubAI (possibly, based on wording of their policy), but you need to read carefully through Privacy Policies and how things are worded. A policy that simply says “your stuff is private” doesn’t necessarily mean it can’t be seen, it might just mean that there is no explicit policy in place for members of the company to be looking at what is on your profile.

Steering AI and avoiding unwanted outcomes: Most AI can go in a wide variety of different directions. This opens up a lot of possibility, but it also means a lot of ways in which the AI can not know what you want and go down a path you weren’t expecting and would rather it didn’t go down. Getting it to head toward the realm you want can be subtle or overt, and with the models that are tuned for instruction, overt may be easier, but either way, expect AI will not meet your expectations sometimes. And if it goes down a path you really don’t want, it’s usually better to move on as fast as possible, rather than getting bogged down in telling it how wrong it is. Remember that it’s just continuing based on input, so if it sees “argument”, it may figure “more arguing fits here”. It doesn’t know when to quit, it doesn’t get weary, and the more there is of something in input, the more likely it will be to double down on that.

P.S. These are the points to cover I could think of at the moment based on what I know about generative AI. If there are other points you are thinking of when you talk about writing a guide, I’m curious to know. Can’t guarantee I’d be able to cover it all myself, but yeah. Also am curious if this comes across clear enough for layperson. I’m not an ML researcher or the like, but I’ve picked up a fair bit on it over time.