A Big Data Approach to Computational Creativity
This work addresses the challenge of computational creativity for AI systems, particularly in domains like culinary arts, though it appears incremental as it builds on existing big data techniques.
The paper tackled the problem of achieving a valid selective step in computational creativity by integrating data from creative domains and hedonic psychophysics with big data analytics, resulting in a system that produces novel and high-quality creative artifacts, demonstrated through a culinary recipe and menu system.
Computational creativity is an emerging branch of artificial intelligence that places computers in the center of the creative process. Broadly, creativity involves a generative step to produce many ideas and a selective step to determine the ones that are the best. Many previous attempts at computational creativity, however, have not been able to achieve a valid selective step. This work shows how bringing data sources from the creative domain and from hedonic psychophysics together with big data analytics techniques can overcome this shortcoming to yield a system that can produce novel and high-quality creative artifacts. Our data-driven approach is demonstrated through a computational creativity system for culinary recipes and menus we developed and deployed, which can operate either autonomously or semi-autonomously with human interaction. We also comment on the volume, velocity, variety, and veracity of data in computational creativity.