3 ms·

Why is the brain probabilistic instead of deterministic?

estearum 3mo ago
Probabilistic and deterministic are not opposites. The brain is deterministic at the level of specific interactions, which process inputs in a highly chaotic (but still deterministic) manner into probabilistic outcomes. The opposite of deterministic is random, i.e. in the quantum sense of truly no relationship between input and output. There are probably some quantum effects in the brain here and there, but the vast majority of it is just traditional deterministic interactions networked together in such a complex system that the resulting behavior is much, much easier to predict in probabilistic terms than otherwise.
- beepbooptheory 3mo ago
  Ok right, but then I don't see the original point. Or like, you don't seem to be appealing to a hard science/soft science distinction here if the question revolves around what conceptual model is most "useful" to us. Useful for what? For predicting future actions? That makes sense, but does actually that speak to op's (possibly still fraught) metaphysical/semantic point here? It just feels like you are speaking over it, right? They could just as easily counter that a probabilistic model of the physical brain is not a very useful description of human intelligence, so why use it? You can't appeal to materialism here because you are already practicing some implied pragmatism in your commitments. You pretty much have as much ground to stand on as they do!
  - estearum 3mo ago
    I am saying that there is no categorical distinction to be made on functional grounds between artificial intelligence and brains. You cannot say an AI model cannot be intelligent because it's a probability machine, when all available evidence points toward natural intelligence also being generated by probability machines (much more complex ones, called brains).
    - Jensson 3mo ago
      But brains do much more than just predict tokens based on previously seen tokens. I think all the other things brains do are probably important for our intelligence. So, LLM are just next token predictors, brains are next token predictors + many other things in addition, and that makes people still feel LLM are dumb even when they solve a lot of problems using tokens.
      - estearum 3mo ago
        > But brains do much more than just predict tokens based on previously seen tokens. I think all the other things brains do are probably important for our intelligence. Like what? What specific biological structure in the brain could be doing anything other than producing output as a function of 1) current electrical/chemical/thermal inputs and 2) previous electrical/chemical/thermal inputs?
        Jensson 3mo ago
        LLM doesn't just produce output as a function, they are much more specific: they predict text based on text they have been trained on.
        estearum 3mo ago
        Yes that is the item (2) parameter described above.
        Jensson 3mo ago
        No, brains doesn't just try to mimic pasts signals, LLMs do that but brains doesn't. The way they make LLM solve problems is by adding a lot of logical jumps into its data, or break down different problems etc, and then as it predicts the text it predicts these logical jumps and then solves the problem. That is very different from how humans learn to solve problems, you don't feed them a billion different state transitions they have to encode to be able to navigate math, they learn to become proficient at math from a few hundred to a few thousand examples, that is fundamentally different from how LLM can learn. That LLM are so slow learners that requires massive amount of data is a big reason its hard to make them smarter, and its caused by them being next token predictors. And the reason humans can learn with so little data is because we are not just next token predictors.

Probabilistic and deterministic are not opposites. The brain is deterministic at the level of specific interactions, which process inputs in a highly chaotic (but still deterministic) manner into probabilistic outcomes. The opposite of deterministic is random, i.e. in the quantum sense of truly no relationship between input and output. There are probably some quantum effects in the brain here and there, but the vast majority of it is just traditional deterministic interactions networked together in such a complex system that the resulting behavior is much, much easier to predict in probabilistic terms than otherwise.

beepbooptheory 3mo ago
Ok right, but then I don't see the original point. Or like, you don't seem to be appealing to a hard science/soft science distinction here if the question revolves around what conceptual model is most "useful" to us. Useful for what? For predicting future actions? That makes sense, but does actually that speak to op's (possibly still fraught) metaphysical/semantic point here? It just feels like you are speaking over it, right? They could just as easily counter that a probabilistic model of the physical brain is not a very useful description of human intelligence, so why use it? You can't appeal to materialism here because you are already practicing some implied pragmatism in your commitments. You pretty much have as much ground to stand on as they do!
- estearum 3mo ago
  I am saying that there is no categorical distinction to be made on functional grounds between artificial intelligence and brains. You cannot say an AI model cannot be intelligent because it's a probability machine, when all available evidence points toward natural intelligence also being generated by probability machines (much more complex ones, called brains).
  - Jensson 3mo ago
    But brains do much more than just predict tokens based on previously seen tokens. I think all the other things brains do are probably important for our intelligence. So, LLM are just next token predictors, brains are next token predictors + many other things in addition, and that makes people still feel LLM are dumb even when they solve a lot of problems using tokens.
    - estearum 3mo ago
      > But brains do much more than just predict tokens based on previously seen tokens. I think all the other things brains do are probably important for our intelligence. Like what? What specific biological structure in the brain could be doing anything other than producing output as a function of 1) current electrical/chemical/thermal inputs and 2) previous electrical/chemical/thermal inputs?
      - Jensson 3mo ago
        LLM doesn't just produce output as a function, they are much more specific: they predict text based on text they have been trained on.
        estearum 3mo ago
        Yes that is the item (2) parameter described above.
        Jensson 3mo ago
        No, brains doesn't just try to mimic pasts signals, LLMs do that but brains doesn't. The way they make LLM solve problems is by adding a lot of logical jumps into its data, or break down different problems etc, and then as it predicts the text it predicts these logical jumps and then solves the problem. That is very different from how humans learn to solve problems, you don't feed them a billion different state transitions they have to encode to be able to navigate math, they learn to become proficient at math from a few hundred to a few thousand examples, that is fundamentally different from how LLM can learn. That LLM are so slow learners that requires massive amount of data is a big reason its hard to make them smarter, and its caused by them being next token predictors. And the reason humans can learn with so little data is because we are not just next token predictors.