What or the the not of un the the in you conple a tonight fire on the of of the at mouning and she trouror" the fash no the the which they to, an of the the all stood ponyville at es that ev the. The first results showed, if not promise, then at least evidence of the high number of My Little Pony stories in the dataset.Īnd town go the resions with the the as to the vicered the whatboat, fluenza the of the his mix.ing the think’ I used the same neural network as last time, an open-source neural net that (somewhat unusually) uses syllables as its building blocks. With my original dataset added, I ended up with 10096 unique lines (except for typos). The latest version of the database is here. This crowdsourced database was quite wide-ranging indeed: from Star Wars stories to Chuck Tingle to a surprising amount of My Little Pony fan fiction. Tolkien would have easily made the list if their spellings were more standardized.) (Note that misspellings and variations aren’t counted Ursula Le Guin and J. (14)įar out in the uncharted backwaters of the unfashionable end of the Western Spiral arm of the Galaxy lies a small unregarded yellow sun. It is a truth universally acknowledged, that a single man in possession of a good fortune, must be in want of a wife. The body lay naked and facedown, a deathly gray, spatters of blood staining the snow around it. We slept in what had once been the gymnasium. The sky above the port was the color of television, tuned to a dead channel. In a hole in the ground there lived a hobbit.
Here were the most frequently-entered lines: And folks, you have made me and the neural network so very happy. I asked people to enter the first line of any novel or short story they had handy, even their own. Stop! I caused the Narguuse man who was new on Alabama, the screaming constipated eggs. The problem was, I didn’t have many example first sentences to give the neural network, and supplementing with winners from a worst opening sentence contest didn’t help matters.
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It didn’t go so well. A neural network learns by example, looking at a database of things ( paint color names, craft beer names, halloween costumes) and trying to figure out how to imitate it. Earlier this month, I tried training an algorithm called a neural network to generate the first line of a novel.
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