I do agree that current methods like deep learning's neural nets will end up being a part of some future general AI -- e.g. the convolutional deep nets used for

I do agree that current methods like deep learning's neural nets will end up being a part of some future general AI -- e.g. the convolutional deep nets used for image recognition are already reputedly similar to some aspects of human/animal biological vision systems. Yet even if all we need is this sort of "seed AI", we still need new architectural insights to be able to create it in the first place. Otherwise someone would have surely demonstrated it by now? If nothing else, such a system would need to evaluate effects of its outputs on its inputs over time (e.g. if I shoot a basketball, it takes seconds before it either goes in or doesn't; if I plant a seed in the ground, months will pass before it sprouts -- or not, depending on conditions). Research into recurrent networks, one possible avenue for doing this, is still pretty primitive. And I'm not sure I'm convinced that this core "seed AI" is sufficient to emulate human cognition. Such a system might effectively integrate audio and visual senses, for example (in order to combine both for prediction tasks), but could such a system ever emulate the sort of continuous verbal inner monologue we all have which narrates our experience? That we have this inner monologue which seems to run alongside our other senses but yet makes use of them (along with stored memories) suggests, at least to me, that some more complicated pathways are involved which link together these various "component systems" (senses, stored memories, emotional states, linguistic synthesis, etc.) beyond just the simple prediction/reward circuitry which I presume the "seed AI" would encapsulate.