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Multiverse: Exploring Human Machine Learning Interaction Through Cybertextual Generative Literature

We present a prototype of a system for machine learning (ML) powered interactive generative literature called Multiverse. The system employs a set of neural networks models to dynamically generate a literary space from an initial writing prompt provided by its user-reader. The user-reader is able to choose the model used to generate the text as a kind of interactive machine learning (IML). The research explores how interaction design and HCI researchers can engage directly with ML by leveraging the powerful, yet accessible, models afforded by new developments in the field. User-readers testing the prototype found the imperfect aesthetics of the ML-generated texts to be entertaining and engaging but struggled to conceptualize the generated work as a navigable interactive literary space.