Deep Learning in Storytelling & Content Creation: A Primer
With the recent news of the closing of Regal & Cineworld cinemas, it appears as if the 100 year old cultural tradition and associated business model of feature length storytelling in the cinema is reaching the inevitable conclusion that analysts have been forecasting for a decade now. Audiences will always go to the cinema, but the days of the feature film being the best method creatively and fiscally for creators to craft narratives are over.
Conversation around future of entertainment is generally centered around user generated content, SVOD platforms, cross platform, free to play models and the intrigue of the Metaverse.
In what ways is arguably the most transient technology of our time, deep learning, going to make an impact on the way we play and experience?
The most broad answer to this question is personalization. Deep learning based technologies will help to personalize the stories that are told to us and the ways we experience them.
After learning about and experimenting with these technologies over the past couple of years as part of the Founding Team of Transforms.ai , I want to outline what I have learned from a technical, creative and business opportunity standpoint.
In this expository essay, I discuss the potential implications for storytelling, practicalities and impracticalities, as well as a very early and idealistic framework for how I see some of these technologies functionally monetizing and fitting into the entertainment economy. I have included some semi-technical descriptions in italics, so feel free to read past these if you are not interested.
While AI has been present in videogames since Nim was released in 1952, the most common use of AI in storytelling has been non-player characters, or NPC’s in video games. NPC’s rose to prominence in the 1990’s with a model called Finite State Machines, or FSM’s. FSM’s script NPC reactions to the player based on the state of the player, and react accordingly. FSM’s have evolved to include dialogue, and innovated on the looping structure of classic FSM’s to have larger but less flexible behaviour trees.