Precursors to a Digital Muse

Can machine learning inspire a writer’s process?

Writer Khalid Warsame at the Emerging Writers’ Festival

“Machine learning models embody the closest thing to ‘creativity’ we have ever been able to build with code.”

To kickstart our process, we sat down with generative language expert Ross Goodwin to survey the tech landscape for this project. Very quickly it became clear that we’d want to use a transformer model, which has quickly become the standard for language-based machine learning tasks. In particular, the transformer architecture is great at remembering long-term structure and keeping long outputs (say, an article) coherent.

  1. Between The Lines — a plot building tool where machine learning fills in the gaps between plot points. Start with just the very first and last line of a storyline, and the tool is trained to interpolate between them, generating what would happen in the middle. You can keep doing this until you have an interesting plot to use as a starting point, or inspiration for, a story.
  2. Once Upon A Lifetime — a character life story generator. Writers can input keywords that describe a life they want to generate, perhaps the biography of a character in a story, and get a complete life story that draws from those keywords.
  3. Banter Bot — a character chatbot, where you supply some information about what your character is like, and then can converse with it through text. As you talk more, the character evolves, taking the conversation that’s taking place and learning from it.
racecar ^ driver ^ dog ^ veterinarian ^ accident ` Jane Herman was a racecar driver and dog vet, known for having a huge driving accident during the…\n
gymnast ^ sweden ^ author ^ gold ^ award `
Yan Svenssen was a Swedish gymnast, who, after winning his gold medal, became an author...

“ML tools is a good middle ground. There is what you look to for influence and inspiration… and there’s you and your subconscious as a writer… and I think it sits nicely in between that.” — Jamie Lau

For all the writers, the most surprising aspect of working directly with machine learning was how it was able to get them out of their own heads. For Khalid, interaction with our machine learning models helped him to escape feeling trapped by his thoughts around a story. “That proximity they have to randomness allows me to tease out elements that are interesting much more readily,” he said. Similarly, Tegan enjoyed the element of play that arose from being able to interact and push back against something that had the capability to respond to anything she input:

“You think about writing as this very serious thing… having an element of play in the construction of the writing was eye opening for me.” — Tegan Webb

Machine learning also proved effective at adding believable detail to stories. For example, Jamie found the specificity of Once Upon A Lifetime to be useful when writing realistic scenes. Trained on a corpus of real Wikipedia data, the model could generate events, places, book titles, and numbers easily. In one of Jamie’s sessions, the Once Upon A Lifetime dreamt up a character in a now-defunct band called “The Kraggs”, whose debut album “Down Where the Rivers Don’t Flow” sold “15,000 copies”. That creative specificity encouraged her to take notice of names and numbers like these to enrich the world she was building, and make it feel real.

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The Team at AMI

We bring together artists and engineers to realize projects with machine intelligence at Google.