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People really overstate the relationship between ANNs and the brain, they have very different mechanisms and only have a similarity if you squint at 100000 feet. ANNs don't have neurotransmitters or even action potentials.
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But neurotransmitters and action potentials don't help in modeling symbolic structure.

That is, yes, ANNs are not brains. There are countless differences. But are there differences at the computational level? ANNs are meant to model brain computation, not brain biology.

(There is still a lot to debate there, I'm not saying "ANNs are perfect computational models for the brain")

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>But neurotransmitters and action potentials don't help in modeling symbolic structure.

This is exactly why Fodor argues that psychology should be explained on its on level with symbols rather than appealing to neurology. But if you're interested in modeling symbols, there's much better options than ANNs (see nearly any programming language ever).

>ANNs are meant to model brain computation

But we don't really know how that works! So if you know if you're not modelling the low-level behaviour right, you can't assume that there's a correspondence of the higher level computation when you don't really know what that higher level computations are.

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We don't know all the details about how the brain computes, you are right.

But we do have a hypothesis: that it is done by a large number of simple units with very high connectivity and in deep layers. This is what neural networks model.

Personally I was skeptical of this model of the brain, but they have achieved remarkable success in practice, as well as Nobel prizes. The neural networks people may have been onto something all along (I say that grudgingly).

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But the models learn in a very different way than humans, they're certainly vastly more sample inefficient. I'm not saying it isn't impressive or that it isn't necessarily a kind of intelligence, I'm just sceptical as to how much it really tells us about the brain.
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Those are fair points.
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While I agree with this statement, one could argue that what's important is not much the support but the emergent properties - in the same way that a wave is still a wave whether it is in the water or in the air.

Thus putting things similar to neurons in a network and making them able to learn could create behaviors similar to the brain. The fact that that biology used chemistry + electrical signal and computers use ReLU-like activation could be merely choosing the most efficient way to enable training.

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Correct. Worst of all, even if you do build a spiking neural network, the update rule is kind of a mystery. To have a good update rule, a biological neuron needs to be kind of like a tiny computer in its own right. You might be able to model synapses as weights between neurons, but the neuron carries further internal states within in itself and how the "update rule" uses those internal states is not known at all.
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I hate that whole intro - the first four sentences - so much. It’s nothing but unsupported assumptions. Basically, a strawman that they can do battle with in the paper. Not an auspicious start.
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These aren't really strawmen, they're more or less than mainstream opinion in the cognitive sciences from the 80s to maybe 2015-2020 or so.
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Paul Smolensky is a cognitive science titan from that era. He worked with Hinton, Rumelhart, and McClelland on parallel distributed processing, and literally wrote the book on tensor product representations in cognition, with Geraldine Legendre: https://mitpress.mit.edu/9780262516198/the-harmonic-mind-vol...

He's the axis of this particular group of researchers, being the most senior at the place where they all met, Johns Hopkins.

So this is less a straw man and more a quick reminder to his peers: "Right, so, remember this particular thread we've spent the last 40 years hashing out, here we've got another contribution to that particular conversation."

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If it was supposed to be sarcasm, that does help.
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I’m aware, but it was a bad mainstream opinion which was contradicted by every bit of evidence ever since early LISP efforts in the late 1960s (still waiting for the release of SHRDLU 2.0!)

65 years is a long time to be beating your head against an obvious dead end.

The article intro presents the claims as self-evident, when they’re not at all.

But, someone rise pointed out this may have been a dig at those attitudes, which makes more sense.

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got to top 2 HN tho lol
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-1 karma jesus guess i should never make meta commentary lol
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yep
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