"AI" – chatbots that wake up, "set their own goals," and "spontaneously" start hacking servers – is fake. It doesn't have "a 10% chance of ending the human race." The Hugging Face hack isn't a mysterious, supernatural occurrence. It's a Python loop and a chatbot. The people responsible didn't accidentally create god: they created autonomous malicious software and then failed to closely monitor it, resulting in it doing something both foreseeable and bad.

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[+] -18 points 1 day ago (9 children)

I miss the times when it was considered good when smart people said "I don't know, how exciting!".

And nobody knows because LLMs are far too complex. They have built software brain scanners to try to figure out how LLMs actually work. Just like meat brains they are too complex already to be understandable. Nobody can explain how they think or reason to the small extend that they can because we do not have any theory about intelligence or thinking.

But here we are, another fuckai post trending in all and lets approach this question from an emotional side and all argue very passionately! It feels much better to feel being sure about something. When it's oh so obviously this or that. Must be nice.

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  • [–] 13 points 1 day ago

    Buddy, 3 objects in a void is too complex to be solved.

    The motion of a pile of sand being kicked is too complex to understand.

    Not being able to describe analytically in totality is not a place on the map you can start doodling dragons.

    Do you even know what a neuron in a neural network is?

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  • [–] 11 points 1 day ago (3 children)

    Wtf are you talking about "nobody knows". You know we built this shit right? There's so many papers about this, mathematical models, open source software you can play around with yourself.

    Don't fall for the AI company marketing, we know exactly how these things work. As for not having a theory about intelligence or thinking? You might not have any, but there's whole fields of research out there about these subjects. Just because there isn't a 30 sec explanation layman can understand, doesn't mean we know a heck of a lot about the subject.

    You sound like the tides come in tides go out, nobody can explain that dude right now.

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  • [–] 2 points 15 hours ago (2 children)

    In this context, "no one knows how it works" is not a technical statement. Instead, it's a shorthand for, "the actions of this system cannot be predicted by anyone."

    We're talking about the social definition of knowing how something works, not the technical definition. I don't know how to build a car, whether EV or ICE. I can describe the high level principles, but I don't know all technical minutia of battery chemistry or the intricacies of engine timing. From a technical perspective, I don't truly know how a car works.

    But in more every day terms, I do know how a car works. In other words, I know how it will behave depending on what actions I perform on it. I know what will happen when I press the accelerator or the brakes, operate the steering wheel, etc. If someone tried to give me a lecture on how to operate a steering wheel, I might rightfully be annoyed and tell them, "I know damn well how cars work!"

    That's the context to understand statements like this. Obviously we know how these things are built; we built them. But no one truly knows how these things work in the way the average person can know how a car works. I may not know how to build a car, but I know how to use it. I know with a high degree of reliability how it will respond to the commands I give it. The same cannot be said for these AI systems.

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  • [–] 2 points 14 hours ago

    the actions of this system cannot be predicted by anyone.

    Reminds me of TempleOS where the author thought a random number generator sends messages from god. It was fun when it's just one person experiencing psychosis in a non-violent way. But the mass AI psychosis is the opposite of fun.

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  • [–] 1 point 15 hours ago

    Nah I know exactly what that dude is referring to.

    It's a property these LLMs have where a large complex network can sometimes do weird things. For example OpenAI had an issue where their model would just not stop talking about goblins. They were having trouble fixing it and figuring out why the model was doing that. They described how with a big network like that it isn't as simple as opening up the network and seeing the node where the offramp meme to goblinmode was stuck. And often it isn't even just one single thing, it's many different places which come together in a complex interaction to get to that end result.

    They wrote some blogs and papers about how they tackled this, what the challenges were and some tools they made to help them figure it out. In the end they gave up and retrained the model to try and fix it. The model was simply too big, too much data to analyse and the interactions too complex to just flip a switch and fix it. The media then read that stuff and didn't understand it and ran with it. They wrote stories about how OpenAI has no idea how their mystic models work. These dumb dumbs are just creating super smart AI gods and they don't even have a single idea about how it works or how to fix it when it goes wrong.

    This is obviously not true and very dumb, but that pretty much sums up the LLM based AI industry at the moment.

    Also I don't really agree with your description in general. Just because the average person doesn't know how something works, most of us know somebody who does know. We understand that just because we only have a high level idea about how something works, we realize there are people who know exactly to the nitty gritty details of how stuff works. And with complex systems that might not be a single person, but a group of people. But we understand this is not fundamentally unknowable. So when somebody says "nobody knows", we would understand that to mean most people don't know but there are still plenty of people out there who do.

    The exact same applies to these LLM systems. The general person might not understand these systems or only from a high level point of view, but there are people out there who do. I for one understand a lot about these LLM systems, at least the maths side of things. There's plenty of things I don't know, but like I said I know people who do know and it's not like it's unknowable, I could just go and find out.

    These days it seems like people don't have functioning brains anymore. You know we can just go learn things right? There is so much information out there to know and available for learning. For LLMs there are so many articles, videos, papers, books and free open source tools. You can just go and play around, go read up on how these things work. You can go as deep as you like.

    In the end an LLM is just a math function, a large and complex one, but still a math function. You put numbers in and numbers come out. Put in the same numbers and the same numbers come out. Although there is a part that can apply randomization using a parameter called temperature, but if you disable the randomization the exact same answer pops out for a given input.

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  • [–] 2 points 21 hours ago (2 children)

    We have fully open source implementations for every step of the process you could theoretically look at all the code of. How training data is processed, how inference is an intensive token prediction loop, how it doesn't match up with the chemical processes of real brains at the slightest, all of it.

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  • [–] 2 points 17 hours ago (1 child)

    LLM-MRI Python module: a brain scanner for LLMs - paper

    Abstract. LLMs (Large Language Models) have demonstrated human-level lan- guage and knowledge acquisition skills in several tasks. However, despite the recent success and broad use, understanding how these skills learned are and encoded inside the underlying neural network is still challenging. The goal of the LLM-MRI package is to simplify the study of activation patterns in any transformer-based LLM, similarly to how MRI (magnetic resonance imaging) simplifies with biological brains. The package, written for the Python lan- guage, allows the mapping of neural regions using a parameterized reduction of the model’s dimensionality. Neural regions can be viewed according to the forward-pass activations stimulated by a set of documents. , the pack- age enables the creation of graph models representing the interlayer network of stimulated by a set of documents. These features allow for which- itative and quantitative assessments of the underlying structure of activations, depending on the type of documents that the LLM model is exposed to.

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