So I am a little confused. Where are we on the learning part of AI? As I understand it, the current consensus is to throw as much data as you can at your model

So I am a little confused. Where are we on the learning part of AI? As I understand it, the current consensus is to throw as much data as you can at your model (millions of cat pictures in this article's example) to make it pick up patterns and yet still claim that we are closing in on how the brain works? As far as I can tell no human brain would need that many pictures to see a pattern. In fact, and this is probably more apparent in language, we humans tend to work with degenerate data and still end up with perfect models.