[mindcrime]

I've always imagined AGI (perhaps naively) as being achieved by clever usage of ML, plus some utilization of classical/symbolic AI from pre-AI winter days, plus probably some unknown elements.

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. :-)

[space_fountain]

It seems quite clear to me that human brains are not actually doing much symbolic logic. What symbolic logic we do do has been bolted on using other faculties. I think the problem is that reasoning about our own minds is incredible tough. We want there to be some sort of magic sauce to what makes us, us and so we reject things like ANN's that seem somehow too simple. I think it probably is right that we won't just be able to scale up the number of parameters and get human like performance. There are hints that returns start to level off, but I'm also unsure why people are so sure we can't.

[mindcrime]

It seems quite clear to me that human brains are not actually doing much symbolic logic. What symbolic logic we do do has been bolted on using other faculties.

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.

[space_fountain]

Very fair takes. I could certainly imagine elements being pulled in. For example things like alpha zero are to my understanding already coupling things like tree searches to neural nets. I sort of expect that any general solution would include some of that, but symbolic approaches seem to consistently do worse despite lots of people thinking they won't and plenty of money to be made. I think part of the problem is that what we want with AI is to interface with humans, and humans are using something fuzzy to understand the world so trying to model that rigidly will be hard

[edgyquant]

Even if they did replicate how the brain works our brains aren’t one of these networks trained for specific things it is millions, maybe billions, of them combined.

[mindcrime]

Indeed. The learning / training we do today for ANN's clearly isn't what humans do. So yeah, even if we had billion "neuron" ANN's that were more biologically plausible, we'd probably still have to figure out more about how human learning works, in order to come up with the right way to train the AI.