Abstract
This paper revisits the receptive theory in the 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.
Highlights
The studies of computational creativity in the field of text generation commonly aim to represent a machine as a creative writer
Text generation is broadly associated with a creative process, it is based on linguistic rationality and the common sense of the general semantics
While both creativity and aesthetics depend on judgemental evaluation and individual taste that depends on many aspects [4,5], the concept of perception has to be taken into account, when talking about computational creativity
Summary
The studies of computational creativity in the field of text generation commonly aim to represent a machine as a creative writer. The psychological perspective on human creativity tends to apply statistics and generalizing metrics to understand its object [2,3], so creativity becomes introduced through particular measures, which is epistemologically suicidal for aesthetics. While both creativity and aesthetics depend on judgemental evaluation and individual taste that depends on many aspects [4,5], the concept of perception has to be taken into account, when talking about computational creativity. The computational principles are crucial for text generation, the importance of a reading approach to generated narratives is to be revised.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
