While we don’t know an enormous amount about the brain, we do know a pretty good bit about individual neurons, and I think it’s a good guess, given current science, to say that a solidly accurate simulation of a large number of neurons would lead to a kind of intelligence loosely analogous to that found in animals. I’d completely understand if you disagree, but I consider it a good guess.

If that’s the case, then the gulf between current techniques and what’s needed seems knowable. A means of approximating continuous time between neuron firing, time-series recognition in inputs, learning behavior on inputs prior to actual neuron firing (akin to behavior of dendrites), etc. are all missing functionalities in current techniques. Some or all of these missing parts of biological neuron behavior might be needed to approximate animal intelligence, but I think it’s a good guess that these are the parts that are missing.

AI currently has enormous amounts of money being dumped into it on techniques that are lacking for what we want to achieve with it. As they falter more and more, there will be an enormous financial interest in creating new, more effective techniques, and the most obvious place to look for inspiration will be biology. That’s why I think it’s likely to happen in the next few decades; the hardware should be there in terms of raw compute, there’s an obvious place to look for new ideas, and there’s a ton of financial interest in it.