[mindgam3]

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.

[svara]

> Is anyone actually claiming that deep learning achieves true “human-level performance” on any real world open-ended learning task?

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. ;)

[mindgam3]

> No, but the text you quoted doesn't say that [deep learning achieves human-level performance

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.

[jdminhbg]

This is a weird interpretation of that sentence. There are lots of fields where human-level performance has been achieved. See Go, for example.

[asah]

Maybe you need an RNN to help parse that sentence!! :-)

Buffalo buffalo Buffalo buffalo buffalo buffalo Buffalo buffalo

etc

[spongepoc]

If you've been following the field at all (i.e. who the paper is aimed at), the sentence is obvious and non-controversial. There have been many tasks where deep learning has even exceeded human performance (a stronger claim than that sentence).

>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.

"some x are not y" does not invalidate "many x are y"

[kahnjw]

Words matter, concretely define "open ended"? Did you just add that phrase to preemptively nullify evidence to the contrary?

Deep learning has surpassed human level performance on many tasks [1][2]... (could add more you get the point).

[1] https://www.sciencedirect.com/science/article/pii/S2215017X1... [2] https://arxiv.org/pdf/1502.01852v1.pdf

[doublekill]

Agreed. Also, nobody is, or should be, using deep neural networks, for legislation and law enforcement. Explainability should be a core design decision when making an algorithm, and not slapped on top of an inherently black box algorithm. Black boxes and even their explanations are used to launder bias and unfairness. And most of these tricks are not even explanations that can be trusted. "Oh look, the cat's head is highlighted, so that's why this picture was classified as a cat!" no insight, no justification, just hoping the network learned some higher level features like humans do, but oh no, when we flip the picture it is suddenly a dog, and when we photoshop the background to be snow, now it is suddenly a polar cat or a pinguin.

Let deep learning do what it is good at, without explaining their performance and errors to anyone: invading your privacy on social networks, helping hedge funds make more money by analyzing Elon Musks tweets, and building military surveillance.

Leave the justifications and explanations to inherently white box models (they are nearly as good in performance as black box now, at least for structured data), and hold off on firing radiologists for a few decades, even though your train set performance is overfitted to be on par with "human-level".

Somehow, somewhere, the deep learning revolution started to drink its own kool-aid and became alergic to critique or solid verifiable computer science. Explainable deep learning does not exist, since half of the time the engineer that build the system can't even explain why it works in the first place. "Strong inspectable feature engineering is hard and time-consuming, so here we shook a box of legos a million times, burned six holes in the ozon layer, and out comes a deep net optimized with gradient descent". End-to-end learning is supposed to be really end-to-end, including the explanation.

[rwilson4]

“Many learning tasks” is a wiggle term. Sure, edge cases exist, but the methods do work impressively well in many cases.