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>Brains can also generalize from a single example, unlike neural networks which require thousands if not millions of examples. There is not a single brain on e

>Brains can also generalize from a single example, unlike neural networks which require thousands if not millions of examples. There is not a single brain on earth that is the blank slate a typical ANN is. "Brains generalize from one example" is pretty dubious. Millions of years of evolution matter. >As I mentioned for example, brains can repurpose neurons for other tasks. Isn't this just a matter of the practical distinction between training and inference and not some fundamental structural limitation ? >Brains also generally do not suffer from catastrophic forgetting in the same way that our simulations tend to. This suggests CF may well be a simple matter of scale - https://palm-e.github.io/ https://palm-e.github.io/ Since individual anns are much closer to synapses, we don't have anything near the scale of the brain yet.

janalsncm 3y ago
> There is not a single brain on earth that is the blank slate a typical ANN is. Of course structure matters, but biological neurons have far more degrees of freedom than those in ANNs. The fact that we even need to keep differentiating between the two is an indication that classifying both as “neurons” is not accurate. > Isn't this just a matter of the practical distinction between training and inference and not some fundamental structural limitation? It’s a difference in capabilities of the things themselves. A biological neuron organically seeks out new connections. Sure we could program that into an ANN somehow but the fact that nodes in an ANN don’t have this capability out of the box is a fundamental difference. > CF may well be a simple matter of scale For a moment, a big enough network might be able to mirror an entire brain with the lottery ticket hypothesis. But if it takes two or ten or a thousand ANN neurons to simulate the degrees of freedom of a biological neuron, are they really the same?
freejazz 3y ago
>There is not a single brain on earth that is the blank slate a typical ANN is. You are the one saying that biological neurons and ANN are similar...

janalsncm 3y ago

> There is not a single brain on earth that is the blank slate a typical ANN is. Of course structure matters, but biological neurons have far more degrees of freedom than those in ANNs. The fact that we even need to keep differentiating between the two is an indication that classifying both as “neurons” is not accurate. > Isn't this just a matter of the practical distinction between training and inference and not some fundamental structural limitation? It’s a difference in capabilities of the things themselves. A biological neuron organically seeks out new connections. Sure we could program that into an ANN somehow but the fact that nodes in an ANN don’t have this capability out of the box is a fundamental difference. > CF may well be a simple matter of scale For a moment, a big enough network might be able to mirror an entire brain with the lottery ticket hypothesis. But if it takes two or ten or a thousand ANN neurons to simulate the degrees of freedom of a biological neuron, are they really the same?

freejazz 3y ago

>There is not a single brain on earth that is the blank slate a typical ANN is. You are the one saying that biological neurons and ANN are similar...