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from the the-default-SN-color-is-pretty-close-to-Grass-Bat dept.
So if you've ever picked out paint, you know that every infinitesimally different shade of blue, beige, and gray has its own descriptive, attractive name. Tuscan sunrise, blushing pear, Tradewind, etc... There are in fact people who invent these names for a living. But given that the human eye can see millions of distinct colors, sooner or later we're going to run out of good names. Can AI help?
For this experiment, I gave the neural network a list of about 7,700 Sherwin-Williams paint colors along with their RGB values. (RGB = red, green, and blue color values) Could the neural network learn to invent new paint colors and give them attractive names?
The answer, not surprisingly, is no. But some of them are hilarious. My own personal favorites are Gray Pubic (close to aqua blue), Clardic Fug (brownish green), and Stanky Bean (inexplicably a rather nice dusty rose).
http://lewisandquark.tumblr.com/post/160776374467/new-paint-colors-invented-by-neural-network
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(Score: 2) by JoeMerchant on Monday May 22 2017, @02:19PM
The answer is: this particular attempt failed.
A favorite quote of mine: "Try, fail. Try again, fail again. Fail better next time."
The neural network didn't have sufficient cultural contextual reference to do the job given to it. And, this, being a purely subjective topic, is nothing that can ever be 100% "correct." The best I would hope from a neural network (including one implemented in "wetware") is that it might spit out 5 possible names for a color, to be judged by a panel of 5 persons from varied demographics, and that one of those 5 possible names might be rated as at least "acceptable" by all 5 judges and as "good" by more than one.
This would require the kind of backing data store that Big Blue used to win Jepopardy, and a very well refined filter.
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