Just to be clear- "AI" ≠ classification and even more so, AI ≠ machine learning ≠ deep neural nets. The article above runs fast and loose with the terminology, but machine vision and in particular object detection (or classification of objects in images) is one area and one sub-task of AI in general. It happens to be one of the two or three areas that have seen strong empirical results in recent years, but it's by no means the only active area of research (although, thanks to the funding from large technology companies, it is probably the fastest growing one).
It's also very strange to see "brittleness" as a criticism of deep neural networks in particular (the attacks described only work on convolutional neural nets as far as I know). In the past, the type of AI system criticised as "brittle" was the hand-crafted rule-based expert system type of AI. And the reason why that kind of AI was criticised as brittle is because it did not deal very well with the noise in real-world domains, such as in photography or speech etc. Deep neural nets in particular are extremely robust to noise, which is why they work so well in speech and image processing.
I think what you really mean by "brittle" is the tendency of deep nets to be, well, a little too good in dealing with noise. Specifically, they have a tendency to overfit to the noise, because they produce models with very high variance. Indeed, the whole adversarial examples thing is probably best understood as a result of overfitting.
As to "lacking fundamental features of human understanding" what you mean, I think, is that image classifiers only do classification and nothing more- which is true, but then that's what they 're designed to do. Nobody expects an image classifier to have any understanding of the images it's classifying.