Any pointers to further reading on why deep learning might be a dead end?
Consider a teratoma or lab-grown organ. Is grafting or engineering another attached sensory mechanism going to bring it closer to being an ordinary organism?
I think whether capabilities are super- or sub- human is a red herring.
Even a really primitive organism is still taking all of its computational capabilities and outputting, implicitly, decisions in one context that is its perceived reality.
A collection of computation and perception modules does not do this, without something else.
I don't think developing "something else" is obviously impossible or would require magic. But I'm not sure anyone sane would want to create it when it inherently creates unlimited risk of running amok. This is what the LessWrong people are afraid of, aren't they?
The former would imply that there is no point using deep learning and other similar techniques at all, and is the common implication when people say something is a "dead end".
The latter is what I believe the current generation of machine learning to be: I do not believe it will lead to AGI, and I am skeptical that it can do a great deal more than what it has already done (it can continue to refine the types of things it already does, and I expect it to do so, but I don't think it will open up new categories of things it can do many more times). But despite that, it does do some very cool things now, and as they are refined, I think they can be commercially successful and generally beneficial tools.
There are distinct architectures and our brains have additional architectural features that lizards' lack.
https://en.wikipedia.org/wiki/Basal_ganglia