6 ms·

Not really. The only real example given in the article is when you hook someone up to an fMRI machine, collect data about how the brain looks when it sees a cer

• HarHarVeryFunny 3y ago
  I think the Economist article is exactly right - that despite the massive differences between ANNs and the brain, ANNs are indeed highly suggestive of how some aspects of the brain appear to work. People can criticize the shortcomings of GPT-4, but it's hard to argue that it's at least capable of some level of reasoning (or functionally equivalent if you object to that word!). It's not yet clear exactly how a Transformer works other than at mechanical level of the model architecture (vs the LLM "running on" the architecture), but we are at least starting to glean some knowledge of how the trained model is operating... It seems that pairs of attention heads in consecutive layers are acting in coordination as "induction heads" that in one case are performing a kind of analogical(?) A'B' => AB match-and-copy type of operation. The induction head causes a context token A to be matched (via "attention" key query) with an earlier token A' whose following token B' then causes related token B to be copied to the residual stream at position following A. This seems a very basic type of operation, and no doubt there's a lot more interpretability research to be done, but given the resulting reasoning/cognitive power (even in absense of any working memory or looping!), it seems we don't need to go looking for overly complex exotic mechanisms to begin to understand how the cortex may be operating. It's easy to imagine how this same type of embedded key matching might work in the cortex, perhaps with cortical columns acting as complex pattern matchers. Perhaps the brain's well known ~7 item working memory corresponds to a "context" of sorts that is updated in same way as induction heads update the residual stream. Anything I've written here about correspondence between transformer and cortex is of course massive speculation, but the point is that the ANN's operation does indeed start to suggest how the brain, operating on similar sparse/embedded representations, may be working.
  • amelius 3y ago
    But (how) does the human brain perform backpropagation?
    • HarHarVeryFunny 3y ago
      It probably doesn't use backpropagation of gradients. Instead, the cortex appears to be a prediction engine that uses error feedback (perceptual reality vs prediction) to minimize prediction errors in a conceptually similar type of way. If every "layer" (cortical patch) is doing it's own prediction and receiving feedback, then you don't need any error propagation from one layer to the next.
      • marcosdumay 3y ago
        You just can't go changing the layers of a neural network independently of each other if you are doing guided optimization. For a start, it's not even a given if increasing some weight will increase or decrease the result. The neurons are all tightly coupled. You can do it on unguided optimization, what is one of the reasons I strongly suspect our brains use something similar to simulated annealing.
        • HarHarVeryFunny 3y ago
          Sure, we don't know the exact details (Geoff Hinton spent much of his career trying to answer this question), but at the big picture level it does seem clear that the cortex is a prediction engine that minimizes prediction errors by feedback, and most likely does so in a localized way. Exactly how these prediction updates work is unknown. Could you expand a bit on how you think simulated annealing could work?
          • deleted 3y ago
            [deleted]
          • marcosdumay 3y ago
            Hum... I have no model for what actually happens. It's just that simulated annealing appears quite often on the biologic world, we never found any coordination communication channel that could create something more "intelligent" (AFAIK, there are just a few "neighborhood to global" analog signals available), and there isn't a multiplicity of attempts interacting with each other (again, there is no channel for that) that would lead to a parallel evolutionary strategy. Also, when we learn a mechanical task, our errors tend to cool down around our successes, instead of following a direction. But again, I have no idea of any mechanism that is there. It's more because all the others seem to have been eliminated.
• moffkalast 3y ago
  > We learn precisely nothing from this That's literally never true about anything.
  • cosmojg 3y ago
    >> We learn precisely nothing from this > That's literally never true about anything. But if this were true, data compression would be impossible.
    • moffkalast 3y ago
      OK you got me there I guess, but even learning that we've learned nothing is learning in itself. Otherwise the act of compressing wouldn't give any more information.
      • User23 3y ago
        Read up on Kolmogorov complexity.
        • moffkalast 3y ago
          Knowing that, say, some file contains a sequence of zeros you can run through RLE is information. That part is usually not part of the information content calculation, but without it you can't do compression because the file could just be 100% entropy. Ergo it's useful information. That was sort of my original point anyway, knowing you don't know something makes it a known unknown instead of an unknown unknown. Or in this case a known known.
          • User23 3y ago
            Yes. The encoding/encoder tuple is part of the definition of K complexity. Sipser gives a lucid explanation in his textbook and I highly recommend it.
  • foobarqux 3y ago
    What have we learned specifically in this case?
• raincom 3y ago
  It is the new age phrenology!!