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I think we made a big mistake by using biological terms for computer processes and now we have people like you coming to the """natural""" conclusion of going f

I think we made a big mistake by using biological terms for computer processes and now we have people like you coming to the """natural""" conclusion of going full circle and applying computer terms back to biological processes. There is virtually no parrelel between both worlds besides the naming and no one has any idea about what's going on for real.

black_knight 2mo ago
I keep hearing that. At the same time people makes comments like the one you just answered to, using that analogy heavily. Could someone who understands both say where the analogy breaks down in this case?
- parineum 2mo ago
  The brain is... ~a machine~ ~a steam engine~ ~a computer~ an LLM Analogies help understand the brain but OP is taking the analogy and then going back and applying principles from the analog to the brain. The brain is the brain, it isn't any of those other things.
biorach 2mo ago
I don't see a need to be dismissive. There is a lot of evidence that there is a relationship betwwen sleep and learning. The hypothesis advanced by the OP is worth evaluating on its merits rather than being dismissed as a fale equivalence.
amazingman 2mo ago
This smells a bit like a statement of faith. Digital neurons and neural networks are not the same as their biological counterparts, sure. But it is not an unreasonable proposition that their functions are isomorphic.
- bulbar 2mo ago
  It is unreasonable, because a neuron is categorical different from a so called "neuron" used by AI. Same is true when you compare an actual brain and an AI model.
- toasty228 2mo ago
  > not an unreasonable Yet we have absolutely 0 clue nor proof besides "it feels about right", that's not how the scientific method works.
  - rcxdude 2mo ago
    It's not zero clue. Neurologists have got a lot of observations on how the brain works and there are multiple plausible proposed mechanisms for how the brain might do something like backpropagation, it's just that the details are really messy so there's not a single clear picture that has emerged. (Similarly, there is decent evidence that the brain uses higher-dimensional vector representations of concepts in a similar way to neural nets, at least in some parts of it, but again the details are messy and there's not a clear idea of how these representations emerge or how to decode them reliably)

black_knight 2mo ago

I keep hearing that. At the same time people makes comments like the one you just answered to, using that analogy heavily. Could someone who understands both say where the analogy breaks down in this case?

parineum 2mo ago
The brain is... ~a machine~ ~a steam engine~ ~a computer~ an LLM Analogies help understand the brain but OP is taking the analogy and then going back and applying principles from the analog to the brain. The brain is the brain, it isn't any of those other things.

biorach 2mo ago

I don't see a need to be dismissive. There is a lot of evidence that there is a relationship betwwen sleep and learning. The hypothesis advanced by the OP is worth evaluating on its merits rather than being dismissed as a fale equivalence.

amazingman 2mo ago

This smells a bit like a statement of faith. Digital neurons and neural networks are not the same as their biological counterparts, sure. But it is not an unreasonable proposition that their functions are isomorphic.

bulbar 2mo ago
It is unreasonable, because a neuron is categorical different from a so called "neuron" used by AI. Same is true when you compare an actual brain and an AI model.
toasty228 2mo ago
> not an unreasonable Yet we have absolutely 0 clue nor proof besides "it feels about right", that's not how the scientific method works.
- rcxdude 2mo ago
  It's not zero clue. Neurologists have got a lot of observations on how the brain works and there are multiple plausible proposed mechanisms for how the brain might do something like backpropagation, it's just that the details are really messy so there's not a single clear picture that has emerged. (Similarly, there is decent evidence that the brain uses higher-dimensional vector representations of concepts in a similar way to neural nets, at least in some parts of it, but again the details are messy and there's not a clear idea of how these representations emerge or how to decode them reliably)