For what it&#x27;s worth, there&#x27;s a recent PLOS paper that I found to be a real eye-opener. If you haven&#x27;t already seen it I&#x27;d give it a read: <a href="http:&#x2F;&#x2F;journals.plos.org&#x2F;plosone&#x2F;article?id=10.1371&#x2F;journal.pone.0140866" rel="nofollow">http:&#x2F;&#x2F;journals.plos.org&#x2F;plosone&#x2F;article?id=10.1371&#x2F;journal....</a><p>It&#x27;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.<p>In many respects (like training data volume, layer depth, and so on) it couldn&#x27;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.