Lost me at the first sentence.
> Deep neural network (DNN) is an indispensable machine learning tool for achieving human-level performance on many learning tasks.
Not to be pedantic, but words matter. Is anyone actually claiming that deep learning achieves true “human-level performance” on any real world open-ended learning task?
Even the most state of the art computer vision/object classification algorithms still don’t generalize to weird input, like familiar objects presented at odd angles.
I get that the author is trying to write something motivating and inspirational, but it feels like claiming “near” or “quasi”-human performance, with disclaimers, would be a more intellectually honest way to introduce the subject.
No, but the text you quoted doesn't say that.
Human level performance in this context means humans perform no better than some algorithm on some specific dataset.
Incidentally, that's also how you get to claim superhuman performance on classification tasks. Just include some classes that aren't commonly known in your dataset, e.g. dog breeds, plant species, or something like that. ;)
Uh, it says DNNs are indispensable for achieving human level performance. That clearly implies that this level of performance is achievable, despite all evidence to the contrary.