Author: Georgina Ustik / Source: The Next Web
“Leathery sheets of rain lashed at Harry’s ghost as he walked across the grounds toward the castle. Ron was standing there and doing a kind of frenzied tap dance. He saw Harry and immediately began to eat Hermione’s family.”
“Ron’s Ron shirt was just as bad as Ron himself”
No, this isn’t from a demented new Harry Potter sequel — it was created by Botnik Studios, a creative collective of about 30 writers, artists, and developers from all over the world who use machine tools to remix and augment language.
Coachella band line ups, Harry Potter texts, lyrics to songs… these are just a few of the things Botnik Studios has messed with, turning them into weird, viral projects.
Ahead of Botnik’s TNW Answers session, we spoke to their co-founder, Jamie Brew, on how the collective creates such weird and wonderful work.
How Botnik works
The creative collective is made up of about 30 writers, artists, and developers who, Brew tells us, are “distributed across the country and the planet and coordinated on Slack and email.”
The collective began when Google DeepMind wanted to make a bot for The New Yorker‘s caption contest, which the magazine’s cartoon editor from 1997 to 2017, Bob Mankoff, created. Noticing an uptick in similar projects trying to “solve” creative tasks with automation, Mankoff and Brew created Botnik, which sought to augment instead of automate.
“We use machines to enable new kinds of human creativity. Botnik Studios… grew from the user testers for our first web app, a predictive text keyboard called Voicebox that offers word suggestions based on any source text you feed it.”
Since then, Botnik has recreated a Boy Meets World episode, tourist reviews of Parisian landmarks, ASMR video scripts, and Tinder bios, among others.
The projects typically begin with an observation — one writer will notice “a genre or trope that has a particular way with words they want to play with.
” This idea is then pitched to the rest of the team via Slack. Afterwards, several questions are asked, such as how the text should be divided in order to use the language most effectively:“Do we use a neural net, which is best suited to generating fragments of text that are a couple or a few words long? Do we train a predictive keyboard on all the text at once? Do we create multiple predictive keyboards each trained on a subset of the text, like…
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