Computational Natural Philosophy: A Thread from Presocratics through Turing to ChatGPT
This is an incremental historical and conceptual analysis of computational approaches to philosophy and AI, relevant to researchers in AI theory and philosophy of science.
The paper traces the development of computational natural philosophy from ancient times to modern AI systems like ChatGPT, highlighting how this perspective has shaped our understanding of cognition and intelligence through interdisciplinary research. It notes that current efforts focus on integrating neural networks with symbolic computing to create hybrid models.
Modern computational natural philosophy conceptualizes the universe in terms of information and computation, establishing a framework for the study of cognition and intelligence. Despite some critiques, this computational perspective has significantly influenced our understanding of the natural world, leading to the development of AI systems like ChatGPT based on deep neural networks. Advancements in this domain have been facilitated by interdisciplinary research, integrating knowledge from multiple fields to simulate complex systems. Large Language Models (LLMs), such as ChatGPT, represent this approach's capabilities, utilizing reinforcement learning with human feedback (RLHF). Current research initiatives aim to integrate neural networks with symbolic computing, introducing a new generation of hybrid computational models.