In the real world, you don't want AI to instantly flip from 90% confidence in one direction to 90% confidence in the other direction, because it would cause erratic behavior. What would be preferable is a large zone where it gives both labels .45 score. Then you can apply higher-level reasoning based on the possibility that the object could be either of those two labels (i.e. act on the possibility of the most dangerous or most beneficial scenario of the two).

But it doesn't instantly flip the decision in a way that can oscillate, it recognizes the similarity in specific positions where humans would also recognize it, and doesn't recognize anything in between (again the same as humans would). Isn't that the goal, to mimic how us humans do it? Imagine recognizing numbers 6 and 9, you want AI to recognize it with high certainty depending on what part of the digit is up, you don't want 45% certainty that it might be 9 or 6. Or am I missing something?

That doesn't make intuitive sense to me - I don't think humans have such a zone for (all) binary choices. An example that comes to my mind is the rotating Spinning Dancer Illusion[1] - I am 100% confident that she's spinning to the left, or 100% confident she's spinning to the right - there is no middle-ground where my brain tells me "it could be either direction".

Multistable/Bistable perception is not unique to Google Cloud vision - it afflicts humans too.

1. https://en.wikipedia.org/wiki/Spinning_Dancer

This. Maybe the AI needs to know somehow that it's the same image it saw a few seconds ago, but now presented at a different angle or the AI itself should look at an image at different angles, just to be sure, that is how humans sometimes look at pictures if they are confused. Something to contextualize every image wrt to what it recently saw and make the current decision a little less overconfident.

CV usually does look at images with different rotations, scaling, stretch. The point is that GCV doesn't get stuck on duck when the duck is upside down, it behaves the same way as a human classifier.

I'm looking forward to ReCAPTCHA asking "Click the images of things that are upside down."

You'd think, but the entire reason that softmax is so common in ml is because the artificial certainly is the preferred behavior.

In "real world", you expect AI to behave like human. Isn't that the definition?

In this case, a rabbit and duck are approximately the same size and danger level. So few cases where there is harm possible.

What if it was AI looking at bacteria? Or scanning a roadside for IEDs? Or when a guy on a bike when turned and rotated the correct way appears to be a crosswalk paint mark of a guy on a bike?

If our current AI is making different “DEFINITE” determinations based only on image rotation - there is a problem.

Not sure why you're being down-voted, this is exactly the issue.

The image is both a rabbit and a duck regardless of orientation, capturing the object it depicts as a single class with a confidence measure is a mistake.

The magnitude of this mistake becomes apparent when you connect it to real-world decision making, and it becomes highly unsafe.

As is most of ML because it only uses statistical (rather than causal) modelling of the world -- so really, it is only offering us generalised statistical associations. It cannot cope with statistical discontinuities.