For what it's worth, there's a recent PLOS paper that I found to be a real eye-opener. If you haven't already seen it I'd give it a read: http://journals.plos.org/plosone/article?id=10.1371/journal....

It's quite a low-key affair (no GPU clusters and so on) but what I think is remarkable is that it described a fairly complex cognitive (neural) architecture that was able to mimic certain kinds of child-level cognition with TINY amounts of training data. Instead, human supervisors guided the evolution of its cognitive strategy in a white-box kind of fashion to encourage it to answer questions as children did.

In many respects (like training data volume, layer depth, and so on) it couldn't be further from the current deep learning trends, and yet it seemed much more along the lines of what I imagine an actual AGI would be doing, especially one we hope to control.