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> Aren't animals trained to do all of those things through evolution? Similarly how GPT is trained. No. Animals evolved and are able to do those things. Evolut

> Aren't animals trained to do all of those things through evolution? Similarly how GPT is trained. No. Animals evolved and are able to do those things. Evolution is not training, and evolution has approximately zero to do with how transformers work.

mewpmewp2 3y ago
What's the difference here? Both seem to have adaptive neural networks where those networks change as time goes on due to a reward - for animals, mutated genes being more likely to be given forward if the change was good. Over millions of generations it's statistically likely that more good genes that caused the neural networks to be in a state that is better able to solve problems within the environment get passed on, eventually resulting in an emerging intelligence. For training you similarly change the state of the neural network depending on whether the answer is good or bad.
- inimino 3y ago
  Evolution operates on genes, which do not encode synaptic connections for one thing. The analogy you're making here is so stretched it's hard to begin to say what's wrong with it. Backpropagation and natural selection are about as different as two things can be. About the only thing you can say they have in common is that both can be modeled as optimization processes. What's the difference between a star and a bonfire? Both use fuel and produce heat and light.
  - mewpmewp2 3y ago
    I mean the point was about LLMs not truly being problem solvers because they were trained to do so as opposed to having been evolved through evolution. I'm looking for what the difference is specifically within that dimension. Biological pigeons had their own process of evolution how they reached to have the type of neural networks and systems in themselves that gave them the ability to count - but not in all contexts for sure. So yes, my point is that both have an optimisation process that through time lend them those emerging capabilities.
    - inimino 3y ago
      No, the point was that LLMs are not good at problem solving because they are not good at problem solving, not because they were not evolved. We don't understand how we or animals solve problems, which is why we haven't yet succeeded in replicating that in AI. You're the one bringing in these incredibly strained analogies, because you want GPT 4 to be more than it is or something, I'm not sure.
- nicklecompte 3y ago
  The bigest difference is that training does not change the size or architecture of an artificial neural network, but biological evolution dramatically changes the size and architecture of animals' brains. Your comparison is sincerely vacuous. It vaguely makes sense if you're talking about GPT-3 to GPT-4 (though I don't think it's helpful). It makes no sense if you're talking about training a single neural network.
  - mewpmewp2 3y ago
    I mean that exposure to a lot of training material yielded in the final set of capabilities. Pigeons and their ancestors were exposed to certain situations throughout evolution that yielded in the formation of neural network and its ability to "count". Which I believe is not actual "one, two, three", but just the amount of signals being activated resulting in a certain output from pigeon. There's a difference in how a human counts, except for small numbers which you can intuitively immediately come up with a number. There was training material which were situations to which organisms had to produce output for and if the output was good their genetics survived, eventually forming the neural network that was able to handle this training material well, but similarly producing emergent behaviour like being able to "count". But GPT-Vision can easily do as well what a Pigeon can. What's the exact thing that implies Pigeon is doing it somehow more intelligently? If you ask them on a picture the quantity of something, I'm pretty sure both respond to the amount of this type of signal received either though light waves or pixels encoded for GPT.
    - RaftPeople 3y ago
      > Aren't animals trained to do all of those things through evolution? Similarly how GPT is trained. If you interpret this question at the most abstract level of "aren't both solutions arrived at through training/trial+error method?" - then the answer is probably yes, they are both arrived at in some conceptually similar manner. But they are two very different underlying systems and we don't really understand the biological systems well enough to even be able to truly compare. Beyond that, it seems that humans (switching to humans from pigeons) have some sort of representation/understanding of the world around us such that even if we produce the same result as ChatGPT to a counting question, the information stored within our systems is not equivalent.

mewpmewp2 3y ago

What's the difference here? Both seem to have adaptive neural networks where those networks change as time goes on due to a reward - for animals, mutated genes being more likely to be given forward if the change was good. Over millions of generations it's statistically likely that more good genes that caused the neural networks to be in a state that is better able to solve problems within the environment get passed on, eventually resulting in an emerging intelligence. For training you similarly change the state of the neural network depending on whether the answer is good or bad.

inimino 3y ago
Evolution operates on genes, which do not encode synaptic connections for one thing. The analogy you're making here is so stretched it's hard to begin to say what's wrong with it. Backpropagation and natural selection are about as different as two things can be. About the only thing you can say they have in common is that both can be modeled as optimization processes. What's the difference between a star and a bonfire? Both use fuel and produce heat and light.
- mewpmewp2 3y ago
  I mean the point was about LLMs not truly being problem solvers because they were trained to do so as opposed to having been evolved through evolution. I'm looking for what the difference is specifically within that dimension. Biological pigeons had their own process of evolution how they reached to have the type of neural networks and systems in themselves that gave them the ability to count - but not in all contexts for sure. So yes, my point is that both have an optimisation process that through time lend them those emerging capabilities.
  - inimino 3y ago
    No, the point was that LLMs are not good at problem solving because they are not good at problem solving, not because they were not evolved. We don't understand how we or animals solve problems, which is why we haven't yet succeeded in replicating that in AI. You're the one bringing in these incredibly strained analogies, because you want GPT 4 to be more than it is or something, I'm not sure.
nicklecompte 3y ago
The bigest difference is that training does not change the size or architecture of an artificial neural network, but biological evolution dramatically changes the size and architecture of animals' brains. Your comparison is sincerely vacuous. It vaguely makes sense if you're talking about GPT-3 to GPT-4 (though I don't think it's helpful). It makes no sense if you're talking about training a single neural network.
- mewpmewp2 3y ago
  I mean that exposure to a lot of training material yielded in the final set of capabilities. Pigeons and their ancestors were exposed to certain situations throughout evolution that yielded in the formation of neural network and its ability to "count". Which I believe is not actual "one, two, three", but just the amount of signals being activated resulting in a certain output from pigeon. There's a difference in how a human counts, except for small numbers which you can intuitively immediately come up with a number. There was training material which were situations to which organisms had to produce output for and if the output was good their genetics survived, eventually forming the neural network that was able to handle this training material well, but similarly producing emergent behaviour like being able to "count". But GPT-Vision can easily do as well what a Pigeon can. What's the exact thing that implies Pigeon is doing it somehow more intelligently? If you ask them on a picture the quantity of something, I'm pretty sure both respond to the amount of this type of signal received either though light waves or pixels encoded for GPT.
  - RaftPeople 3y ago
    > Aren't animals trained to do all of those things through evolution? Similarly how GPT is trained. If you interpret this question at the most abstract level of "aren't both solutions arrived at through training/trial+error method?" - then the answer is probably yes, they are both arrived at in some conceptually similar manner. But they are two very different underlying systems and we don't really understand the biological systems well enough to even be able to truly compare. Beyond that, it seems that humans (switching to humans from pigeons) have some sort of representation/understanding of the world around us such that even if we produce the same result as ChatGPT to a counting question, the information stored within our systems is not equivalent.