One of the most interesting things about ML to me is that it can exploit large amount of data (even not entirely correct) to reach super-human level.

Suppose you trained a classification model on a large amount of images. Some of these images will be probably labelled wrong by humans (e.g. because are blurred, the object is not entirely visible, undecision between two classes, etc.). Other similar images will be labelled right, perhaps by another human. But since the model saw many many images it can average its understanding of the images. Keep in mind that this is not anymore true if you have biased training data, i.e. the error is always done in the same way.