11 ms·

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 t

by coldtea 6mo 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 6mo 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 6mo 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 6mo ago
    What evidence do you have that a sausage is not functionally equivalent to a cucumber?
    - fc417fc802 6mo 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 6mo 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 6mo 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 6mo ago
      [flagged]
    - coldtea 6mo 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 6mo ago
    [deleted]
  - nothinkjustai 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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.

by coldtea 6mo 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 6mo 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 6mo 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 6mo ago
  What evidence do you have that a sausage is not functionally equivalent to a cucumber?
  - fc417fc802 6mo 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 6mo 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 6mo 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 6mo ago
    [flagged]
  - coldtea 6mo 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 6mo ago
  [deleted]
- nothinkjustai 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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 6mo 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.