In Search of the Ingredients of Open-Endedness: Replicating Picbreeder with Large Vision-Language Models

In the context of automating scientific, technological, and creative processes through artificial intelligence-driven assistants, researchers seek to replicate human capacity for generating novel forms and solutions in an open-ended and limitless manner. To investigate whether artificial agents can achieve this, the Picbreeder system was replicated, originally allowing users to create a diverse library of images through the interactive evolution of small neural networks. In this version, human users have been replaced by advanced vision and language models. The results show qualitative differences between the system’s output and the historical human baseline, characterized through metrics of evolutionary innovation and visual and semantic novelty. Factors such as exploratory noise, behavioral diversity among agents, and narrative drive have been studied to understand these differences. This research is significant because it may help understand how artificial intelligence systems can be designed to generate innovative and novel solutions, which could have a substantial impact on how scientific and technological challenges are addressed. Furthermore, this line of inquiry may have implications for the development of commerce and marketplace systems, such as open-garage, which aim to leverage artificial intelligence to enhance user experience.

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