I'd Like The Pie Heated

A while ago I made a bunch of new pies. Well, I didn’t *make* them because they were neural network invented titles and although it tried to imitate the list of pies I gave it, the neural net’s imitations are imperfect.

The neural network, after all, is a computer program with about as many neurons as an earthworm. It doesn’t understand what the ingredients are, or why some combinations don’t work. Some of its titles were intriguing, though. They sounded mysterious. Potentially delicious and/or magical?

Or maybe it just helps that they’re vague. I decided I wanted more like these. To help it along, I spiced up the pie dataset with the names of cookies and apple varieties from the 1905 edition of Apples of New York. I filtered the names for those that had possessives: Mcaffee’s Nonesuch, Cornell’s Savewell, Wile Ox’s Winter (all apples), combined with Goldy’s Dungeon Bars, Esther’s Bracelets, and Fido’s Rewards (all cookies). Then, to give it added old-school flavor, I added all the Dungeons and Dragons spells that had possessives as well (for example, Ivy’s Irresistible Scent, Freedom’s Toast, and Leomund’s Tiny Hut).

I arranged the training data so the pies would be last (so they would be freshest in the neural net’s virtual mind). Then I gave it one single look at the data.

It turns out that I didn’t manage to prevent the neural net from coming up with bad ideas. Perhaps what I should have done instead was remove all the meat pies from the training data.

But some of the pies were exactly what I’d hoped for.

And some even went a little past “ancient” and into “legendary”