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Robby pulled a list of over 10,000 Broadway plays and musicals (names, starting dates, closing dates, and total number of performances from the Internet Broadway Database, and I gave them to the open-source char-rnn framework written by Andrej Karpathy. What would a neural network make of Broadway musicals? Thanks to lyricist Robby Sandler, we have a chance to find out. Granted, it’s an earthworm that has put its entire tiny brain to the sole task of learning the dataset: I’ve taught neural networks to invent cookbook recipes, name cats and guinea pigs, and even write Harry Potter fan fiction, all with some degree of success. The neural networks I use, however, have the approximate processing power of an earthworm. Powerful neural networks can do impressive things, like translate languages, recognize faces, and even describe scenes in words.
By looking at a dataset and tuning the connections between their own virtual neurons, neural networks can learn to imitate the original dataset. Neural networks are a kind of machine learning program modeled very loosely after the human brain. (Marquee graphic generated with ’s sign generator) Note: please do not actually attempt to create these articles on Wikipedia.īonus material: sign up here to get some more article titles, including a few that were a bit too rude to post here. Wich chemical appearaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa Woo woo woo woo woo woo woo woo woo woo woo woo woo wah ooooooooooooooooooooooooooooooooooo ooooooo ooo on other intortational characters with removable travel Repeated text is easier to learn, and so the neural network tends to latch onto it easily and, especially when I give it a short memory, takes repetition to even wilder excess (see: The Cow With No Lips).īeneral Pissednessessessinessismasticlesismsomic comotute Whoever it is who likes to enter long strings of repeated characters as pranks (I’m looking at you, Sand Person), the neural network shares your obsession. List of fictional characters with the ball It turns out text-generating neural networks are great at mashups and non sequiturs. Those that weren’t, however, fit right in. Even so, most of the generated names were either incomprehensible or memorized from the original dataset. I trained a character-level recurrent neural network (that is, it uses individual letters as building blocks) with a very small memory to prevent it from memorizing the small dataset so quickly. It makes a terrible dataset for a neural network - only 1112 unique entries, some of which are quite long, and big variation in style and subject matter. List of all Wikipedia lists that do not contain themselves List of differences between apples and orangesĬategory:Political posters using an octopus
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People Who delete My Articles have no sense of Humorĭo scented candles burn faster than unscented candlesĪn article that contains nothing but a full stop List of people who died with tortoises on their heads
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How to trick people into thinking you’re a wizard Reader Emily Davis sent me a list of them - here are a few real deleted articles that humans wrote.
These are mostly pages that were submitted as pranks, although a few of them are clever enough that you can’t quite tell. Wikipedia has a page where they list, for entertainment purposes, the titles of a bunch of pages that didn’t meet the cut. The Story of the Star Trek: The Secret of the Story of the Star Wars The Secret of the Story of the Stranger (1996 film) Horse Man Academy 5-R: Cowboy Sheeper WydexĪnd finally, a list of the most quintessential story titles, obtained by setting the creativity to near zero on a highly-trained network: Restricted section (there were quite a few more of these)
Hamburger (Star Trek: The Next Generation) Legend of the Experience of Scarlet Freedom Damageboo Sure enough, when I trained this open-source neural network framework on just the titles alone, it consistently came up with titles that were both varied and (usually) plausible.īelow are some of my favorites, arranged roughly by apparent genre:Īn Enemy of Bob (Homicide: Life on the Street) With a dataset this large, a neural network can achieve impressive results. That’s a lot of plot summaries: 112,936, to be exact.
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Mark Reidl of Georgia Tech is the best kind of geek, and used some cool scripting to extract all the things on Wikipedia with plot summaries: movies, books, tv episodes, video games, etc. 63 wishlist games found in available bundles.So Prof.
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