[red75prime]

Yep, I'm fairly certain that general learning algorithms acting upon an ANN (which is fairly general too, see the universal approximation theorem) can reach and surpass performance of the human brain. As we have approximately zero evidence that the human brain contains "magic," that is something that can't be modeled by an ANN of a practically feasible size. (I know about chaotic processes that can't be modeled precisely. The "magic" here would be the brain using such a process to make useful decisions. "Useful" not in a sense of a mixed strategiy. You can roll dice to do that.)

But, no, it's not "any day now." The required size and structure of the ANN is to be determined.

[nozzlegear]

I think it's a giant leap of faith to go from "there's no evidence that the human brain contains magic" to "ANNs will achieve AGI and we just need to monkey with the parameters to get there." You've ruled out the supernatural, but don't account for mundane limitations like energy, training data, memory or the architecture. That theorem you referenced tells us a sufficiently wide network can (quoting from Wikipedia) "approximate any continuous function to any desired degree of accuracy" – but it doesn't give us any bound on the sample complexity or the energy required. That is to say, just because the theorem says it's technically possible doesn't mean it's literally possible.

[red75prime]

From my POV it's "we haven't found physical processes that allow to do information processing, which is relevant to learning, exponentially faster than digital computers and we aren't likely to find them in the brain."

Or, at least, I don't tend to postulate existence of such mechanisms in the brain after hearing about every empirical ANN deficiency.

BTW, I'm not talking about supernatural. The "magic" could be unknown physical processes, unknown quantum algorithms, unknown physics.