Between Predictability and Randomness: Seeking Artistic Inspiration from AI Generative Models
This addresses the challenge of enhancing artistic inspiration for creators by leveraging AI, though it is incremental in exploring specific generative models.
The paper tackled the problem of using AI-generated poetic lines as stimuli for creativity, demonstrating that LSTM-VAE lines achieve evocative impact through resonant imagery and indeterminacy, while LLMs produce technically accomplished but conventional poetry.
Artistic inspiration often emerges from language that is open to interpretation. This paper explores the use of AI-generated poetic lines as stimuli for creativity. Through analysis of two generative AI approaches--lines generated by Long Short-Term Memory Variational Autoencoders (LSTM-VAE) and complete poems by Large Language Models (LLMs)--I demonstrate that LSTM-VAE lines achieve their evocative impact through a combination of resonant imagery and productive indeterminacy. While LLMs produce technically accomplished poetry with conventional patterns, LSTM-VAE lines can engage the artist through semantic openness, unconventional combinations, and fragments that resist closure. Through the composition of an original poem, where narrative emerged organically through engagement with LSTM-VAE generated lines rather than following a predetermined structure, I demonstrate how these characteristics can serve as evocative starting points for authentic artistic expression.