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https://www.starwarsmemes.com/at-at-walker-cloud-formation-pareidolia/
Animals in Random Images
One thing I’ve tried to stress here is combining things in out-of-the-box ways. This is a perfect example: Michael Trott used ImageIdentify to detect animal-like shapes in random noise.
I like the Petroglyph-like results. I also like the concept behind it: take something that is ordinarily meaningless and find meaning by imposing order on it.
Recognition algorithms like this give the computer the ability to look for shapes in clouds, and I think that’s something that has been under-explored. Rather than trying to construct an elaborate set of rules for how to build something, we can now tell the computer what the result should be like, and let it look for almost-right things on its own.
Machine pareidolia can be used creatively.
http://community.wolfram.com/groups/-/m/t/995095
What specific features should visual neurons encode, given the infinity of real-world images and the limited number of neurons available to represent them? We investigated neuronal selectivity in monkey inferotemporal cortex via the vast hypothesis space of a generative deep neural network, avoiding assumptions about features or semantic categories. A genetic algorithm searched this space for stimuli that maximized neuronal firing. This led to the evolution of rich synthetic images of objects with complex combinations of shapes, colors, and textures, sometimes resembling animals or familiar people, other times revealing novel patterns that did not map to any clear semantic category. These results expand our conception of the dictionary of features encoded in the cortex, and the approach can potentially reveal the internal representations of any system whose input can be captured by a generative model.
(via https://www.cell.com/cell/fulltext/S0092-8674(19)30391-5 )
| beauty in a on coming storm |
HE