I agree. But my interest is in engineering something that works, not necessarily in creating an exact replica of the human brain. That's why my interest falls into the domain of symbolic / sub-symbolic integration - because it strikes me as a faster path to more usable computer intelligence.
I have no problem believing that a sufficiently large ANN, with the right training and inference algorithms, could achieve AGI. My problem is that A. right now achieving that seems very out of reach to me (but I could be wrong) and B. it seems unnecessary to me to remain wedded to the idea of 100% (or even 90% or 80% etc.) fidelity with our biological brains. After all, if we want something just like a human brain, we just need a man, a woman, and 9 months of time.
Anyway, I think it's OK to think of engineering in "short cuts" by using things we know computers are good at, and things we already know how to do, and trying to combine them with ANN's in such a way as to make something useful. Will it ever yield AGI? I have no way of knowing. And even if it does, would that approach actually be faster than a pure ANN approach? Again, I don't know. But for now, I spend my time on symbolic/sub-symbolic integration nonetheless.
I think the problem is that reasoning about our own minds is incredible tough.
Yes, definitely.
For what it's worth, this is my view as well. And I don't think it's particularly naive. Plenty of people have researched and/or are researching aspects of how to do this. But how to combine something like a neural network, with it's distributed (and very opaque) representations, with an inference engine that "wants" to work with discrete symbols is non-obvious. Or at least it appears to be, since nobody apparently has figured out how to do it yet - at least not to the level of yielding AGI.
but I've never heard a compelling argument for why pure ML would get us there.
The simplistic argument would be that ML models are, in some sense, trying to replicate "what the brain does" and it stands to reason that if your current toy ANN's (and let's be honest - the largest ANN's built to date are toys compared to the brain) are something like the brain, then in principle if you scale them up to "brain level" (in terms of numbers of neurons and synapses), you should get more intelligence. Now on the other hand, anybody working with ANN's today will tell you that they are at best "biologically inspired" and aren't even close to actually replicating what biological neural networks do. Soo... while people like Geoffrey Hinton have gone on record as saying that "ANN's are all you need" (I'm paraphrasing, and I don't have a citation handy, sorry) I tend to think that in the short term a valid approach is exactly what you suggested. Combine ML and use it for what it's good at (pattern recognition, largely) and use "old fashioned" symbolic AI for the things that it is good at (reasoning / inference / etc.)
Now, to figure out how to actually do that. :-)