I quite surprised at the comments on HN so far as nobody seems to see the significance of this.
Yes, the image is ambiguous.
The point is that Google Cloud Vision gives an unambiguous answer of that image based on the rotation.
Transformations of an image are regularly used to improve the results of image recognition. That process fails quit dramatically if in the course of a transformation the answer given is presented with higher confidence than should be.
Creator of the animation here. Most of the relevant information/context behind the animation (including a link to the repo) is in this Reddit comment: <a href="https://reddit.com/r/dataisbeautiful/comments/aydqig/_/ehzyozr/?context=1" rel="nofollow">https://reddit.com/r/dataisbeautiful/comments/aydqig/_/ehzyo...</a>
When the output switches to rabbit the picture actually resembles a rabbit. I am unsure if this experiment was supposed to be a “haha look how stupid AI is” type thing or not, but it seems like the cloud vision api is performing as intended.
That image is a visual illusion. I find it hard myself to detect that it's a rabbit when it's ears are horizontal like a mouth.<p>Not sure what is the purpose of it, is it to show that even computers vision algorithms can get confused by visual illusions?
Is it concerning that there are short, sudden drops in prediction in the middle of a block otherwise solidly classified as rabbit/duck? I don't know much ML, does anyone know why it'd be so discontinuous?
While the title is clickbaity (as in adversarial examples for fooling neural networks e.g. by adding a baseball ball to a whale to make it shark), I think it shows a nice phenomenon.
I.e. a given illusion works similarily for humans and AI alike.<p>Vide "dirty mind" pictures like posting
<a href="https://images.baklol.com/13_jpegbd9cb76b39e925881bdb2956fd32ac91.jpeg" rel="nofollow">https://images.baklol.com/13_jpegbd9cb76b39e925881bdb2956fd3...</a>
to Clarifai <a href="https://clarifai.com/models/nsfw-image-recognition-model-e9576d86d2004ed1a38ba0cf39ecb4b1" rel="nofollow">https://clarifai.com/models/nsfw-image-recognition-model-e95...</a> gives 88% for NSFW.
It would be cool to visualize this as a kind of pie chart, based on where the ears/beak is pointing. Blue for directions where it sees duck, red for rabbit, and empty for neither.
This seems like a serious concern. What's a possible solution to this problem? Should all orientations be considered valid types? so in this case the image should be both a duck and a rabbit as the response?
On still (not animated, not rotated) preview I saw rabbit first, then in a second I found it can be a duck also, and now it takes efforts to see rabbit again (but I can do it).
I was ONLY seeing clockwise in all images until the counter-clockwise one went about 8 rotations and all of a sudden I saw it counter-clockwise. Now I can’t unsee it.
Does Google Cloud like Duck or Rabbit? That’s where the answer lies.<p>In addition, if Cloud could taste one, it would really help itself with the answer.
There is a children's book about this pairing: <a href="https://www.amazon.com/Duck-Rabbit-Amy-Krouse-Rosenthal/dp/0811868656" rel="nofollow">https://www.amazon.com/Duck-Rabbit-Amy-Krouse-Rosenthal/dp/0...</a>
It’s a drawing of a creature that looks a bit like a rabbit or a duck from different angles but is very clearly neither, at best a bad drawing. That’s the failure here - it’s classifying into one of its categories when it shouldn’t be classifying at all.
This is the infamous Duck-Rabit illusion, right? The classifier seems to be doing a good job.<p><a href="https://en.wikipedia.org/wiki/Rabbit%E2%80%93duck_illusion" rel="nofollow">https://en.wikipedia.org/wiki/Rabbit%E2%80%93duck_illusion</a>