How many animals can one find in a random image?
community.wolfram.com
community.wolfram.com
They touch on the cool idea of using an image identification neural network to single out desired shapes from an arbitrarily large corpus of semi-random generated shapes. So the way you program your random generator could determine the style, while the identifier network would determine what will be represented.
This might not be the best title possible, but it's not clickbait-ey, it's just a little vague.
For some inexplicable reason, I find this earth-shatteringly fascinating. If such a bewildering array of patterns can be found in random noise, maybe all patterns -- our attempts to ascribe order to the universe -- are illusive.
But be careful about drawing metaphysical conclusions from this. This is only an example of selection bias. Take random noise, filter it and select examples that probably look animal-like to humans. If the classification algorithm is worth its salt, the selected images are bound to look interesting.
A comparison could be made to the Rorschach test; if the patient finds clear images in the ink blot, that is supposed to say something about the patient, not the image, because the image is by construction random.
It's certainly a justification for using statistics to back up what "feels" like a "true pattern" when really it's just noise.
with the hot water enveloping me in a multi-sensory white noise, i would stand there for time=n finding faces, animals and aliens in the splotches - very zen
http://thumbs.picclick.com/00/s/MTIwMFgxNjAw/z/9W8AAOSwmLlX5...
http://www.ebay.com/itm/Z-986-1Pc-Vintage-Ceramic-Wall-Flori...
The question of pattern recognition in random noise is interesting and a legitimate question. Animals (given our propensity to see then) and an interesting subject matter.
Starting from a random image it's possible to manipulate and filter it to find literally anything. It doesn't mean there are "animal pictures" in the random data; it means the image has been manipulated to look like it. It's a tautology.
The question is about how often image recognition (either human or algorithmic) picks up a signal in random noise -- that is, how often we see things that aren't there (or if you prefer, are there, but by chance). Of course you can find anything, the question is with what frequency.
If you've ever spent much time looking at random patterns, you'd know that we're prone to seeing things that aren't there in noise. Modeling that phenomenon is interesting (at least to me).
As the "animals" are smoothed groups of random data, (and differ every time) , I'd argue it's still (basically) random.