I think one of the major differences is that humans use multiple independent algorithms when it comes to deciding if there's an ostrich in the picture. And most

I think one of the major differences is that humans use multiple independent algorithms when it comes to deciding if there's an ostrich in the picture. And most of these are far more sophisticated than the NNs. So in practice the way we solve classification problems is fundamentally different, even though it may seem/feel somewhat similar. Most prominently there's object recognition (telling things apart from the background, recognizing parts of objects, etc.), size/distance determination (which uses over a dozen separate algorithms: https://en.wikipedia.org/wiki/Depth_perception https://en.wikipedia.org/wiki/Depth_perception). We can also reconstruct a 3d model from a 2d image and compare that to our idea of what ostriches look like in 3 dimensions. So even if it is done unconsciously, we recognize an ostrich because (among other things) it's the size of an ostrich. The NN has no idea what size is, let alone the size of an ostrich relative to other things. I think the next big breakthrough in machine learning is in ensembles containing NNs trained for completely different things, but acting in a complementary fashion.