Should we go with the Sindis Poop or the Burple Simp?

A programmer fed a neural network more than 7,000 Sherwin Williams paint color names, testing the algorithm to see what paint names it colors it would produce. The results are hilarious:

So it turns out you can train a neural network to generate paint colors if you give it a list of 7,700 Sherwin-Williams paint colors as input. How a neural network basically works is it looks at a set of data – in this case, a long list of Sherwin-Williams paint color names and RGB (red, green, blue) numbers that represent the color – and it tries to form its own rules about how to generate more data like it.

Last time I reported results that were, well… mixed. The neural network produced colors, all right, but it hadn’t gotten the hang of producing appealing names to go with them – instead producing names like Rose Hork, Stanky Bean, and Turdly. It also had trouble matching names to colors, and would often produce an “Ice Gray” that was a mustard yellow, for example, or a “Ferry Purple” that was decidedly brown.

These were not great names.

The entire post is worth the read. I think it’s safe to say we don’t have to worry about an uprising of robot overlords anytime soon.

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