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> LLMs get over-analyzed. They’re predictive text models trained to match patterns in their data, statistical algorithms, not brains, not systems with “psycholo

> LLMs get over-analyzed. They’re predictive text models trained to match patterns in their data, statistical algorithms, not brains, not systems with “psychology” in any human sense. Per the predictive processing theory of mind, human brains are similarly predictive machines. "Psychology" is an emergent property. I think it's overly dismissive to point to the fundamentals being simple, i.e. that it's a token prediction algorithm, when it's clear to everyone that it's the unexpected emergent properties of LLMs that everyone is interested in.

xoac 10mo ago
The fact that a theory exists does not mean that it is not garbage
- estearum 10mo ago
  So surely you can demonstrate how the brain is doing much different than this, and go ahead to collect your Nobel?
  - sfn42 10mo ago
    It is not our job to disprove your claim. It is your job to prove it. And then you can go collect your Nobel.
    - estearum 10mo ago
      Yeah sorry but if you call a hypothesis "garbage," you should have a few bullets to back it up. And no, there's no such thing as positive proof.
- ubersketch 10mo ago
  Predictive processing is absolutely not garbage. The dish of neurons that was trained to play Pong was trained using a method that was directly based on the principles of predictive processing. Also I don't think there's really any competitor for the niche predictive processing is filling, and for closing the gap between neuroscience and psychology.
imiric 10mo ago
The difference is that we know how LLMs work. We know exactly what they process, how they process it, and for what purpose. Our inability to explain and predict their behavior is due to the mind-boggling amount of data and processing complexity that no human can comprehend. In contrast, we know very little about human brains. We know how they work at a fundamental level, and we have vague understanding of brain regions and their functions, but we have little knowledge of how the complex behavior we observe actually works. The complexity is also orders of magnitude greater than what we can model with current technology, but it's very much an open question whether our current deep learning architectures are even the right approach to model this complexity. So, sure, emergent behavior is neat and interesting, but just because we can't intuitively understand a system, doesn't mean that we're on the right track to model human intelligence. After all, we find the patterns of the Game of Life interesting, yet the rules for such a system are very simple. LLMs are similar, only far more complex. We find the patterns they generate interesting, and potentially very useful, but anthropomorphizing this technology, or thinking that we have invented "intelligence", is wishful thinking and hubris. Especially since we struggle with defining that word to begin with.
- adleyjulian 10mo ago
  At no point did I say LLMs have human intelligence nor that they model human intelligence. I also didn't say that they are the correct path towards it, though the truth is we don't know. The point is that one could similarly be dismissive of human brains, saying they're prediction machines built on basic blocks of neuro chemistry and such a view would be asinine.
- stevenhuang 10mo ago
  > The difference is that we know how LLMs work. We know exactly what they process, how they process it, and for what purpose All of this is false.
- intull 10mo ago
  I think what comment-OP above means to point at is - given what we know (or, lack thereof) about awareness, consciousness, intelligence, and the likes, let alone the human experience of it all, today, we do not have a way to scientifically rule out the possibility that LLMs aren't potentially self-aware/conscious entities of their own; even before we start arguing about their "intelligence", whatever that may be understood of as. What we do know and have so far, across and cross disciplines, and also from the fact that neural nets are modeled after what we've learned about the human brain, is, it isn't an impossibility to propose that LLMs _could_ be more than just "token prediction machines". There can be 10000 ways of arguing how they are indeed simply that, but there also are a few of ways of arguing that they could be more than what they seem. We can talk about probabilities, but not make a definitive case one way or the other yet, scientifically speaking. That's worth not ignoring or dismissing the few.
dingnuts 10mo ago
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xoac 10mo ago

The fact that a theory exists does not mean that it is not garbage

estearum 10mo ago
So surely you can demonstrate how the brain is doing much different than this, and go ahead to collect your Nobel?
- sfn42 10mo ago
  It is not our job to disprove your claim. It is your job to prove it. And then you can go collect your Nobel.
  - estearum 10mo ago
    Yeah sorry but if you call a hypothesis "garbage," you should have a few bullets to back it up. And no, there's no such thing as positive proof.
ubersketch 10mo ago
Predictive processing is absolutely not garbage. The dish of neurons that was trained to play Pong was trained using a method that was directly based on the principles of predictive processing. Also I don't think there's really any competitor for the niche predictive processing is filling, and for closing the gap between neuroscience and psychology.

imiric 10mo ago

The difference is that we know how LLMs work. We know exactly what they process, how they process it, and for what purpose. Our inability to explain and predict their behavior is due to the mind-boggling amount of data and processing complexity that no human can comprehend. In contrast, we know very little about human brains. We know how they work at a fundamental level, and we have vague understanding of brain regions and their functions, but we have little knowledge of how the complex behavior we observe actually works. The complexity is also orders of magnitude greater than what we can model with current technology, but it's very much an open question whether our current deep learning architectures are even the right approach to model this complexity. So, sure, emergent behavior is neat and interesting, but just because we can't intuitively understand a system, doesn't mean that we're on the right track to model human intelligence. After all, we find the patterns of the Game of Life interesting, yet the rules for such a system are very simple. LLMs are similar, only far more complex. We find the patterns they generate interesting, and potentially very useful, but anthropomorphizing this technology, or thinking that we have invented "intelligence", is wishful thinking and hubris. Especially since we struggle with defining that word to begin with.

adleyjulian 10mo ago
At no point did I say LLMs have human intelligence nor that they model human intelligence. I also didn't say that they are the correct path towards it, though the truth is we don't know. The point is that one could similarly be dismissive of human brains, saying they're prediction machines built on basic blocks of neuro chemistry and such a view would be asinine.
stevenhuang 10mo ago
> The difference is that we know how LLMs work. We know exactly what they process, how they process it, and for what purpose All of this is false.
intull 10mo ago
I think what comment-OP above means to point at is - given what we know (or, lack thereof) about awareness, consciousness, intelligence, and the likes, let alone the human experience of it all, today, we do not have a way to scientifically rule out the possibility that LLMs aren't potentially self-aware/conscious entities of their own; even before we start arguing about their "intelligence", whatever that may be understood of as. What we do know and have so far, across and cross disciplines, and also from the fact that neural nets are modeled after what we've learned about the human brain, is, it isn't an impossibility to propose that LLMs _could_ be more than just "token prediction machines". There can be 10000 ways of arguing how they are indeed simply that, but there also are a few of ways of arguing that they could be more than what they seem. We can talk about probabilities, but not make a definitive case one way or the other yet, scientifically speaking. That's worth not ignoring or dismissing the few.

dingnuts 10mo ago

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