[Florin_Andrei]

> Pretty much every time DL is covered by media, there has to be some contrived comparison to human brains

Well, what we've done so far is emulate maybe 1 mm^3 of brain matter - some isolated, very specialized functional blocks in the greater architecture of the brain. They behave as expected - are experts on very narrow topics, but of course fail to integrate their functioning with a larger body of knowledge, because that body just isn't there (yet).

The strength of the human mind is that is has this profusion of little subject matter experts all over the place, covering an enormous array of topics - and then it has an intricate superstructure that integrates the outputs of these narrow expert machines, tweaks their functioning, even subtly alters their inputs, providing coherence to the global output according to the capabilities of the whole system.

We're still far from that complex high level architecture.

[civilitty]

> Well, what we've done so far is emulate maybe 1 mm^3 of brain matter - some isolated, very specialized functional blocks in the greater architecture of the brain. They behave as expected - are experts on very narrow topics, but of course fail to integrate their functioning with a larger body of knowledge, because that body just isn't there (yet).

I think you're falling into the same anthropomorphism trap that the GP is talking about. We haven't even breached the most important topic: neural plasticity - a brain's ability to rewire itself based on a complex feedback loop driven by environmental inputs (which are, at this point in human development, an almost infinitely more complex system of culture built up over tens of thousands of years). From my work in neuroscience, it seems that the computational complexity of the state of the art DL algorithms barely register when compared to a network of a few hundred biological neurons like the nervous system of Caenorhabditis elegans, which is itself far less capable of self reorganization than even the simplest mammalian brain. Hell, even the most basic potentiation that you'd find in decades old research on addiction is far outside the scope of modern machine learning research and we don't yet have any clean mathematical theories that can emulate plasticity like back propagation or gradient descent can with simple learning.

The current hype around neural networks is the equivalent of saying that we've analytically solved the n-body problem when all we've done is solve a system of equations with two linear variables. The domains are connected but only in the trivial sense that both have variables named "x" and "y."

[Florin_Andrei]

I think you're far too eager to look for and criticize anthropomorphism - hence you see it where it's not.

[civilitty]

You said "what we've done so far is emulate maybe 1 mm^3 of brain matter," comparing computational neural networks to us, a biological system - that's literally anthropomorphising.

[skgoa]

You seem to be under the assumption that a typical feedforward DNN is anywhere close to operating like the brain, just on a smaller scale. But that assumption is not correct.

Both the brain and artificial neural networks are connectivist, but that's about where the similarities end. The brain uses completely unknown algorithms and mechanisms that are almost certainly very different from our (current) ANNs. So it's not just a matter of increasing the scale.

[Florin_Andrei]

That is nowhere near what I am saying.