I have an affinity for classical, rational AI in that you can correct it and it will take that correction and instantly apply it to is knowledge base. It can also explain why it came to a conclusion. (though obviously this style has its very real limitations)
NNs and other current statistical/connectionist approaches don't really have this capability, which I see as a necessary part of human-level intelligence. They are trained to "get a feel for" particular inputs to indicate particular outputs. If you were to personify the NN analyzing the dog/"ostrich" picture, and ask it why it thinks it's an ostrich, it could only reply with "I dunno, it feels like it's an ostrich". The only way to correct it is to retrain it with careful checks so that it behaviorally "gains a sense" of what looks like a dog vs ostrich more reliably.
Many language/word based features operate similarly. Watson, Siri, Google search, all sort of map strengths of relations with your input words & patterns to some associated results that it just sort of statistically was reinforced with. These can yield information that is of real use for a human to further evaluate and act on, but I wouldn't trust such a system to directly act on those associations; they're wrong too often.
But there is no possibility of actual conceptual discourse with NNs as we know them, to correct them, to inform them, or to ask them to explain their results. This is a fundamental barrier to achieving human-style intelligence.
This is not to say that there aren't NN-based possibilities that might work, like tacking together multiple interacting NNs, each which have the possibility to specialize on concepts and influence other NNs. But too much has focused on single NNs and direct input-to-output "monkey-see, monkey-do" training. It's a manifestation of the Chinese room problem.