And I think this is the most interesting part.
One of the most depressing things about all of the "this image recognition algorithm performs better than humans on this task" is the idea that we've pretty much solved the problem, and it's just a matter of some more optimization and tweaking to handle a few edge cases.
This kind of problem, where the dominant solution simply gets it so wrong, and the problem cases are uncommon enough that any statistical solution is generally going to treat them as noise, reveals that in fact that there is likely plenty of room for entirely new, novel ways of approaching the problem to handle these kinds of cases better.
It's actually more exciting that there's so much more to be done, than to say "well, it's basically a solved problem, we just need to do some tweaking and optimization."