Halloween is almost upon us, and if you’re looking for costume inspiration, you might want to check out these AI-generated getups. Janelle Shane, a research scientist based in Colorado, trained a neural network to procedurally generate costume ideas, based on a dataset of 4,500 existing costumes, and ended up coming up with some outrageously funny ideas.
These ranged from the utterly-fucking incomprehensible, to the actually rather reasonable. On the more left-field side of things, there were “Aldonald the Goddess of the Chicken,” “The Game of Nightmare Lightbare” (which actually sounds pretty fun), and the delicious “Statue of pizza.”
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However, my favorite WTF-ism was “The Twin Spider Mermaid“, which our Editor-in-Chief Alejandro offered (nay, insisted) to illustrate in MS Paint for this article.
Some of the costumes invented were actually pretty reasonable. I can imagine someone rocking up to a party, cloaked like “Gandalf the Good Witch,” or dressed as “Starfleet Shark.” (The United Federation of Planets doesn’t discriminate against sharks. It’s the 24th century, dammit.)
And, of course, a huge number of these costumes were of the ‘ooo er’ variety, including “sexy the Spock”, “Sexy barber,” and for fans of Nicki Minaj too cheap to buy a licensed costume, “Sexy the Super bass.” I won’t tell her record label if you don’t.
And I must admit, I’m extremely curious about “Sexy conchpaper.” Mostly because I don’t actually know what a conchpaper is.
Others were generic to the point of absurdity, like “Sports costume” and (wait for it) “Sexy scare costume.”
The neural network produced some absolutely head-scratchingly weird names, too. That’s to be expected — it’s not a person, but rather a machine that’s looking at some data, and trying to extrapolate patterns. This explains howlers like “Grankenstein,” “Sparrow Plapper,” and “A masked scorby-babbersy.”
If you ultimately want to try and create your own AI-made costume names, Janelle Shane has very kindly uploaded her dataset to Github. The tools she used, Torch and char-rnn, are both relatively straightforward. A few months ago, I used it to try and generate CIA malware codenames, which I’m a little bit obsessed with. While my experiment wasn’t as successful as Shane’s (the result of a vastly smaller dataset), it was still good fun.
And if anyone goes to a halloween party dressed as “Pumpkin picard,” I demand pictures.