Paranoid Transformer: Reading Narrative of Madness as Computational Approach to Creativity
This work addresses computational creativity for researchers in AI and digital humanities, but it appears incremental as it builds on existing theories and methods without claiming major breakthroughs.
The paper tackles the problem of computational creativity by introducing a Paranoid Transformer, a fully autonomous text generation engine that produces raw output interpretable as a mad digital persona's narrative without human post-filtering, and discusses its implications for receptive theory and fringe mental states.
This papers revisits the receptive theory in context of computational creativity. It presents a case study of a Paranoid Transformer - a fully autonomous text generation engine with raw output that could be read as the narrative of a mad digital persona without any additional human post-filtering. We describe technical details of the generative system, provide examples of output and discuss the impact of receptive theory, chance discovery and simulation of fringe mental state on the understanding of computational creativity.