> 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."
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.
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."