Automating Creativity
<|reference_start|>Automating Creativity: Generative AI (GenAI) has spurred the expectation of being creative, due to its ability to generate content, yet so far, its creativity has somewhat disappointed, because it is trained using existing data following human intentions to generate outputs. The purpose of this paper is to explore what is required to evolve AI from generative to creative. Based on a reinforcement learning approach and building upon various research streams of computational creativity, we develop a triple prompt-response-reward engineering framework to develop the creative capability of GenAI. This framework consists of three components: 1) a prompt model for expected creativity by developing discriminative prompts that are objectively, individually, or socially novel, 2) a response model for observed creativity by generating surprising outputs that are incrementally, disruptively, or radically innovative, and 3) a reward model for improving creativity over time by incorporating feedback from the AI, the creator/manager, and/or the customers. This framework enables the application of GenAI for various levels of creativity strategically.<|reference_end|>
@article{huang2024automating,
    title={Automating Creativity},
    author={Ming-Hui Huang, Roland T. Rust},
    journal={arXiv preprint arXiv:2405.06915},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.06915},
    primaryClass={cs.AI}
}