
Can optical illusions fool AI too? - DyslexicAtheist
http://en.ritsumei.ac.jp/news/detail/?id=278
======
maaaats
I recently had fun implementing [0] some of the concepts/math for ambiguous
cylinders by Kokichi Sugihara. Some of his first illusions [1] came from
researching computer vision, where the computer should understand how a 2d
picture supposedly looked in 3d. When feeding it some optical illusions, he
realised it would be possible to make these objects in real life!

[0]: [https://github.com/Matsemann/impossible-
objects](https://github.com/Matsemann/impossible-objects) [1]:
[http://www.isc.meiji.ac.jp/~kokichis/anomalousobjects/anomal...](http://www.isc.meiji.ac.jp/~kokichis/anomalousobjects/anomalousobjectse.html)

~~~
andrewla
The direct links to the stl files in github let you rotate it around and get
some idea of how they actually work [1][2]. This is absolutely mind-blowing!

[1] [https://github.com/Matsemann/impossible-
objects/blob/master/...](https://github.com/Matsemann/impossible-
objects/blob/master/3dfiles/arrow/filledarrow_whole.stl)

[2] [https://github.com/Matsemann/impossible-
objects/blob/master/...](https://github.com/Matsemann/impossible-
objects/blob/master/3dfiles/circlesquare/circleinnersquare_2x2.stl)

~~~
singularity2001
I don't get the first one but squaring the circle is cool

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rollulus
As far as I know, do humans perceive the rotating snakes illusion because our
eyes quickly jump around (saccade). Stare at one point and it stops rotating.
I skimmed the publication, but the word saccade is not in it.

I feel with this AI hype, we are desperate to find human like traits in these
systems.

~~~
xxs
I wonder how far we are from the next AI winter.

AIs have touted with any task to upmost hype Nvidia GPUs come to mind - help
enhance MRI or invent pixels upscaling games.

Trying to sell neural networks as the next magic incarnation seems to be the
default route of many marketing departments.

~~~
antpls
I'm rather young (25-30), so I didn't get to know the first "AI winter", but
to me there is still potential for improvement of neural networks. Both
hardware (new DSP cores) and theories related to NN (transfer learning, layers
architectures) can be improved, and also new business applications who are not
yet developed because of the impact on people's jobs. We didn't reach the full
potential of neural networks, IMHO. Wait before we start connecting 100's of
today's neural networks together and see what happen.

On a bigger scale of time, we could say the "AI winter" actually never
existed, as all those last decades were dedicated to developing the hardwares
and concepts we needed for today's algorithms. It was a long iteration of the
loop 'hardware <-> algorithms <-> applications <-> new hardware needs'

"AI", as I imagine it, is not a product someone can sell. It's not tangible,
it's hardly measurable, it's not only one technology, it's more a
philosophical concept too me.

Edit : As for example, there is right now a link titled "Generating custom
photo-realistic faces using AI" on the front page. It's misnamed, as an actual
"AI" would probably not let itself be "used". The title would be better named
"Generating custom photo-realistic faces using Neural Networks" (or "Deep
Learning", or "Machine Learning")

~~~
gambler
_> We didn't reach the full potential of neural networks_

We didn't reach the full potential of random forests or rule-based systems
either. That's the problem with AI "seasons" and hype. Research being driven
by things that have little to do with its subject.

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darepublic
Quite skeptical about this, someone saying 'I used an NN and got this result
(which I wanted to get)' is pretty easy to manipulate/fake imo.

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edoo
The AI would have to be processing at a high enough level of abstraction to be
fooled the same way. Basic image recognition is... basic.

Check out this adversarial attack on an image classifier. They turn a panda
into a gibbon with a specially designed map that looks like noise at first
without noticeably modifying the image for humans.

[https://blog.openai.com/adversarial-example-
research/](https://blog.openai.com/adversarial-example-research/)

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plopilop
From my layman point of view, this can lead to two different conclusions:

* The DNN is actually getting close to the way human brain works, as it falls into the same mistakes as the human eye;

* There is some kind of universality in these illusions, that every (or most of) vision systems will fall for.

But honestly I think that the second conclusion is a rewriting of the first
one (what is a vision system? What is this universal property?).

Again I'm absolutely not an expert of the field so please correct me if I'm
wrong.

~~~
candiodari
Generally people say that the vision system is a "CNN", convolutional neural
net. Now technically speaking a DNN is usually a 5 CNNs in series + another 50
or so "hidden layers" (or 500 if you're Google, or 10000 if you're the human
brain), so it's somewhat ambiguous. But that's a "usual" thing. Deep neural
nets used to be (20 years ago) anything with more than 1 hidden layer. These
days, people say it's anything 50+ hidden layers, because nobody really uses 1
hidden layer for anything but education.

These networks types differ in what they compare to what. CNNs compare signals
with their neighbourhood (e.g. the pixels in a 9x9 grid), and ignores the
rest. DNNs compare every signal with every other signal. CNNs are much
cheaper, especially now with dedicated hardware. Large DNNs cannot be
accelerated much, and you cannot really build hardware to do that.

Also what distinguishes the optical subsystem of animal brains is the first
stages are CNNs (well, it's a biological system, so the functions are spread
around: the optical sensors in the eye themselves form a CNN, before even
sending what they're seeing on, then there's some 5 layers of CNNs in the
retina itself before it even gets to the optical nerve, which itself is also a
CNN, and delivers the output of that into yet another set of CNNs in your
brain).

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randcraw
I suspect most optical illusions are based on perceptual cues / heuristics
learned by human brains in early childhood, like object size shrinking with
distance, or occlusion showing which object is nearer, perspective, or a
vanishing point giving structure to a scene composed of straight lines. So I
doubt any of these cues will be learned by a deep learning net -- because
they're not essential to learning the target objective efficiently.

So no, I suspect AI is unlikely to be fooled by anything other than tricks
based on the most obvious visual cues (like perceiving that two humans of
greatly different size must be different distances away).

[OK, now I've read the article.]

The article doesn't say what the training objective was for the net. If it was
the ability to predict the perceived direction of rotation for a propeller,
then it should be trivial to train the net to predict rotation in a specific
direction. (Only one of two binary outcomes is needed to declare victory.)

More specifics are needed on the training process (esp the objective(s) and
control images) than the OP article provides.

