We're doing humans wrong. Maybe not all wrong, and of course, humans are extremely useful things, but think about it: sometimes it almost looks like we're already there. There always going to be an anomaly; lots of them, actually, considering all the things shaded in different patterns. Something have to change.
I agree that we aren't there, but we'll never be there, every system can be fooled, its just a question of 95%, 99% or 99.99%
I don't follow. If you asked a human what the linked image looked like, they'd likely say a face, but if you then asked them what it actually was, they're all going to change their answer to a rock, even specifically a rock on Mars (if given a colour version of this image).
It's true that humans see patterns that aren't there, but does that detract from our ability to recognise objects?
This analogy doesn't hold, because the whole point of these classifiers is to classify things the same way humans would.
The fact that all classifiers -- including human beings -- fail in some cases is a separate issue. The goal is to create a computer classifier that succeeds and fails in the same cases humans do.
https://upload.wikimedia.org/wikipedia/commons/7/77/Martian_...
We're doing humans wrong. Maybe not all wrong, and of course, humans are extremely useful things, but think about it: sometimes it almost looks like we're already there. There always going to be an anomaly; lots of them, actually, considering all the things shaded in different patterns. Something have to change.
I agree that we aren't there, but we'll never be there, every system can be fooled, its just a question of 95%, 99% or 99.99%