7 ms·

taps the "don't anthropomorphize the LLM" sign They don't have time preference because they don't have intent or reasoning. They can't be "reincarnated" becaus

• coldtea 5mo ago
  That is not that strong an argument as it seems, because we too might very well be "a series of weights for probable next tokens". The main difference is the training part and that it's always-on.
  • nothinkjustai 5mo ago
    We very obviously are not just a series of weights for probable next tokens. Like seriously, you can even ask an LLM and it will tell you our brains work differently to it, and that’s not even including the possibility that we have a soul or any other spiritual substrait.
    • fc417fc802 5mo ago
      Our brains work differently, yes. What evidence do you have that our brains are not functionally equivalent to a series of weights being used to predict the next token? I'm not claiming that to be the case, merely pointing out that you don't appear to have a reasonable claim to the contrary. > not even including the possibility that we have a soul or any other spiritual substrait. If we're going to veer off into mysticism then the LLM discussion is also going to get a lot weirder. Perhaps we ought to stick to a materialist scientific approach?
      • CPLX 5mo ago
        What evidence do you have that a sausage is not functionally equivalent to a cucumber?
        • fc417fc802 5mo ago
          I don't follow. If you provide criteria I can most likely provide evidence, unless your criteria is "vaguely cylindrical and vaguely squishy" in which case I obviously won't be able to. The person I replied to made a definite claim (that we are "very obviously not ...") for which no evidence has been presented and which I posit humanity is currently unable to definitively answer in one direction or the other.
          • CPLX 5mo ago
            When two things are obviously radically different (a squishy mass of trillions of interconnected carbon based blobs fed by some sort of continuous oxygen based chemical reaction, and a series of distributed transitors on silicon wafers) then the burden of proof shifts to the other guy to provide the clear and convincing evidence that they should be considered functionally the same thing.
            • fc417fc802 5mo ago
              But I made no such claim. I was explicit that my position is "humanity is currently unable to definitively answer in one direction or the other". Two things being physically different does not exclude their also having functional similarities. The argument presented amounts to A and B have large physical differences, A does X, therefore B does not do X. That doesn't follow.
        • trinsic2 5mo ago
          [flagged]
        • coldtea 5mo ago
          From certain aspects they're equivalent. Both have mass, have carbon based, both contain DNA/RNA, both are suprinsingly over 50% water, both are food, and both can be tasty when served right. From other aspects they are not. In many cases, one or the other would do. In other cases, you want something more special (e.g. more protein, or less fat).
      • deleted 5mo ago
        [deleted]
      • nothinkjustai 5mo ago
        You are setting the bar in a way that makes “functional equivalence” unfalsifiable. If by “functionally equivalent” you mean “can produce similar linguistic outputs in some domains,” then sure we’re already there in some narrow cases. But that’s a very thin slice of what brains do, and thus not functionally equivalent at all. There are a few non-mystical, testable differences that matter: - Online learning vs. frozen inference: brains update continuously from tiny amounts of data, LLMs do not - Grounding: human cognition is tied to perception, action, and feedback from the world. LLMs operate over symbol sequences divorced from direct experience. - Memory: humans have persistent, multi-scale memory (episodic, procedural, etc.) that integrates over a lifetime. LLM “memory” is either weights (static) or context (ephemeral). - Agency: brains are part of systems that generate their own goals and act on the world. LLMs optimize a fixed objective (next-token prediction) and don’t have endogenous drives.
        • fc417fc802 5mo ago
          I did not claim the ability of current LLMs to be on par with that of humans (equivalently human brains). I objected that you have not presented evidence refuting the claim that the core functionality of human brains can be accomplished by predicting the next token (or something substantially similar to that). None of the things you listed support a claim on the matter in either direction.
    • skeledrew 5mo ago
      Its really just a matter of degrees. There are 1 million, 1 million, 1 trillion parameter LLMs... and you keep scaling those parameters and you eventually get to humans. But it's still probable next tokens (decisions) based on previous tokens (experience).
      • simonh 5mo ago
        They’re both neural networks, but the architectures built using those neural connections, and the way they are trained and operate are completely different. There are many different artificial neural network architectures. They’re not all LLMs. AlphaZero isn’t a LLM. There are Feed Forward networks, recurrent networks, convolutional networks, transformer networks, generative adversarial networks. Brains have many different regions each with different architectures. None of them work like LLMs. Not even our language centres are structured or trained anything like LLMs.
        • coldtea 5mo ago
          >AlphaZero isn’t a LLM. There are Feed Forward networks, recurrent networks, convolutional networks, transformer networks, generative adversarial networks. That's irrelevant though, since all the above are still prediction machines based on weights. If you're ok with the brain being that, then you just changed the architecture (from LLM-like), not the concept.
          • simonh 5mo ago
            That's a different statement, yes brains and LLMs are both neural networks. An LLM is a specific neural architectural structure and training process. Brains are also neural networks, but they are otherwise nothing at all like LLMs and don't function the ways LLMs do architecturally other than being neural networks.
        • skeledrew 5mo ago
          I'd argue that regardless of the architecture, the more sophisticated brain is still a (massive) language model. If you really think about it, language is the construct that allows brains to go beyond raw instinct and actually create concepts that're useful for "intelligently" planning for the future. The real difference is that brains are trained with raw sensory data (nerve impulses) while today's LLMs are trained with human-generated data (text, images, etc).
    • coldtea 5mo ago
      >We very obviously are not just a series of weights for probable next tokens. How exactly? Except via handwaving? I refer to the "brain as prediction machine theory" which is the dominant one atm. >you can even ask an LLM and it will tell you our brains work differently to it It will just tell me platitudes based on weights of the millions of books and articles and such on its training. Kind of like what a human would tell me. >and that’s not even including the possibility that we have a soul or any other spiritual substrait. That's good, because I wasn't including it either.
      • stonogo 5mo ago
        "brain as prediction machine theory" is dominant among whom, exactly? Is it for the same reason that the "watchmaker analogy" was 'dominant' when clockwork was the most advanced technology commonly available?
  • bigstrat2003 5mo ago
    That is a silly point. We very clearly are not "a series of weights for probable next tokens", as we can reason based on prior data points. LLMs cannot.
    • coldtea 5mo ago
      Unless you're using some mystical conception of "reason", nothing about being able to "reason based on prior data points" translates to "we very clearly are not a series of weights for probable next tokens". And in fact LLMs can very well "reason based on prior data points". That's what a chat session is. It's just that this is transient for cost reasons.
  • naikrovek 5mo ago
    We are much more than weights which output probable next tokens. You are a fool if you think otherwise. Are we conscious beings? Who knows, but we’re more than a neural network outputting tokens. Firstly, and most obviously, we aren’t LLMs, for Pete’s sake. There are parts of our brains which are understood (kinda) and there are parts which aren’t. Some parts are neural networks, yes. Are all? I don’t know, but the training humans get is coupled with the pain and embarrassment of mistakes, the ability to learn while training (since we never stop training, really), and our own desires to reach our own goals for our own reasons. I’m not spiritual in any way, and I view all living beings as biological machines, so don’t assume that I am coming from some “higher purpose” point of view.
    • Kim_Bruning 5mo ago
      They're not artificial intelligence neural networks. They're biological neural networks. Brains are made of neurons (which Do The Thing... mysteriously, somehow. Papers are inconclusive!) , Glia Cells (which support the neurons), and also several other tissues for (obvious?) things like blood vessels, which you need to power the whole thing, and other such management hardware. Bioneurons are a bit more powerful than what artificial intelligence folks call 'neurons' these days. They have built in computation and learning capabilities. For some of them, you need hundreds of AI neurons to simulate their function even partially. And there's still bits people don't quite get about them. But weights and prediction? That's the next emergence level up, we're not talking about hardware there. That said, the biological mechanisms aren't fully elucidated, so I bet there's still some surprises there.
    • coldtea 5mo ago
      >We are much more than weights which output probable next tokens. You are a fool if you think otherwise. Are we conscious beings? Who knows, but we’re more than a neural network outputting tokens. That's just stating a claim though. Why is that so? Mine is reffering to the "brain as prediction machine" establised theory. Plus on all we know for the brain's operation (neurons, connections, firings, etc). >There are parts of our brains which are understood (kinda) and there are parts which aren’t. Some parts are neural networks, yes. Are all? What parts aren't? Can those parts still be algorithmically described and modelled as some information exchange/processing? >but the training humans get is coupled with the pain and embarrassment of mistakes Those are versions of negative feedback. We can do similar things to neural networks (including human preference feedback, penalties, and low scores). >the ability to learn while training (since we never stop training, really) I already covered that: "The main difference is the training part and that it's always-on." We do have NNs that are continuously training and updating weights (even in production). For big LLMs it's impractical because of the cost, otherwise totally doable. In fact, a chat session kind of does that too, but it's transient.
  • jsiepkes 5mo ago
    If you claim something might "very well" be something you state you need some better proof. Otherwise we might also "very well" be living in the matrix.
  • dinkumthinkum 5mo ago
    People always say this kind of thing. Human minds are not Turing machines or able to be simulated by Turing machines. When you go about your day doing your tasks, do you require terajoules of energy? I believe it is pretty clear human thinking is not at all like a computer as we know them.
    • coldtea 5mo ago
      >People always say this kind of thing. Human minds are not Turing machines or able to be simulated by Turing machines That's just a claim. Why so? Who said that's the case? >When you go about your day doing your tasks, do you require terajoules of energy? That's the definition of irrelevant. ENIAC needed 150 kW to do about 5,000 additions per second. A modern high-end GPU uses about 450 W to do around 80 trillion floating-point operations per second. That’s roughly 16 billion times the operation rate at about 1/333 the power, or around 5 trillion times better energy efficiency per operation. Given such increase being possible, one can expect a future computer being able to run our mental tasks level of calculation, with similar or better efficiency than us. Furthermore, "turing machine" is an abstraction. Modern CPUs/GPUs aren't turing machines either, in a pragmatic sense, they have a totally different architecture. And our brains have yet another architecture (more efficient at the kind of calculations they need). What's important is computational expressiveness, and nothing you wrote proves that the brains architecture can't me modelled algorithmically and run in an equally efficient machine. Even equally efficient is a red herring. If it's 1/10000 less efficient would it matter for whether the brain can be modelled or not? No, it would just speak to the effectiveness of our architecture.
• Kim_Bruning 5mo ago
  Can we maybe make it "don't anthropoCENTRIZE the LLMs" . The inverse of anthropomorphism isn't any more sane, you see. By analogy: just because a drone is not an airplane, doesn't mean it can't fly! Instead, just look at what the thing is doing. LLMs absolutely have some form of intent (their current task) and some form of reasoning (what else is step-by-step doing?) . Call it simulated intent and simulated reasoning if you must. Meanwhile they also have the property where if they have the ability to destroy all your data, they absolutely will find a way. (Or: "the probability of catastrophic action approaches certainty if the capability exists" but people can get tired of talking like that).
  • Terr_ 5mo ago
    > LLMs absolutely have intent (their current task) That's like saying a 2000cc 4-Cylinder Engine "has the intent to move backward". Even with a very generous definition of "intent", the component is not the system, and we're operating in context where the distinction matters. The LLM's intent is to supply "good" appended text. If it had that kind of intent, we wouldn't be able to make it jump the rails so easily with prompt injection. > and reasoning (what else is step-by-step doing?) . Oh, that's easy: "Reasoning" models are just tweaking the document style so that characters engage in film noir-style internal monologues, latent text that is not usually acted-out towards the real human user. Each iteration leaves more co-generated clues for the next iteration to pick up, reducing weird jumps and bolstering the illusion that the ephemeral character has a consistent "mind."
    • Kim_Bruning 5mo ago
      > That's like saying a 2000cc 4-Cylinder Engine "has the intent to move backward". Even with a very generous definition of "intent", the component is not the system, and we're operating in context where the distinction matters. The LLM's intent is to supply "good" appended text. Fair, but typically you use a 2000cc engine in a car. Without the gearbox, drive train, wheels, chassis, etc attached, the engine sits there and makes noise. When used in practice, it does in fact make the car go forward and backward. Strictly the model itself doesn't have intent, ofc. But in practice you add a context, memory system, some form of prompting requiring "make a plan", and especially <Skills> . In practice there's definitely -well- a very strong directionality to the whole thing. > and bolstering the illusion that the ephemeral character has a consistent "mind." And here I thought it allowed a next token predictor to cycle back to the beginning of the process, so that now you can use tokens that were previously "in the future". Compare eg. multi pass assemblers which use the same trick.
• Aerroon 5mo ago
  No. They don't have time preference like us, because (wall clock) time doesn't exist for them. An LLM only "exists" when it is actively processing a prompt or generating tokens. After it is done, it stops existing as an "entity". A real world second doesn't mean anything to the LLM from its own perspective. A second is only relevant to them as it pertains to us. Time for LLMs is measured in tokens. That's what ticks their clock forward. I suppose you could make time relevant for an LLM by making the LLM run in a loop that constantly polls for information. Or maybe you can keep feeding it input so much that it's constantly running and has to start filtering some of it out to function.
  • roenxi 5mo ago
    You could put timestamps in the prompt.
    • Aerroon 5mo ago
      That would still be time as it pertains to us. Even if I put time stamps into the chat all the LLM knows that it's some amount of time later - it can't actually do anything in the time between two prompts.
• astrange 5mo ago
  An agent has more components than just an LLM, the same way a human brain has more components than just Broca's area.