From Text to Life: On the Reciprocal Relationship between Artificial Life and Large Language Models
This work addresses the integration of LLMs and ALife for researchers in both fields, proposing innovative crossover approaches that could redefine perceptions of lifelike intelligence in AI.
The paper explores potential synergies between Large Language Models (LLMs) and Artificial Life (ALife), investigating how LLMs can serve as tools for ALife research and how ALife principles can enhance LLM development to create more adaptive models.
Large Language Models (LLMs) have taken the field of AI by storm, but their adoption in the field of Artificial Life (ALife) has been, so far, relatively reserved. In this work we investigate the potential synergies between LLMs and ALife, drawing on a large body of research in the two fields. We explore the potential of LLMs as tools for ALife research, for example, as operators for evolutionary computation or the generation of open-ended environments. Reciprocally, principles of ALife, such as self-organization, collective intelligence and evolvability can provide an opportunity for shaping the development and functionalities of LLMs, leading to more adaptive and responsive models. By investigating this dynamic interplay, the paper aims to inspire innovative crossover approaches for both ALife and LLM research. Along the way, we examine the extent to which LLMs appear to increasingly exhibit properties such as emergence or collective intelligence, expanding beyond their original goal of generating text, and potentially redefining our perception of lifelike intelligence in artificial systems.