Computational Natural Philosophy: A Thread from Presocratics through Turing to ChatGPT

TL;DR

This paper traces the evolution of computational natural philosophy from ancient Presocratics to modern AI, highlighting how information and computation frameworks have shaped our understanding of cognition, culminating in advanced models like ChatGPT.

Contribution

It provides a historical and conceptual overview of the development of computational natural philosophy and its influence on contemporary AI systems like ChatGPT.

Findings

AI systems like ChatGPT are based on deep neural networks and reinforcement learning.

Interdisciplinary research has advanced the integration of neural and symbolic computing.

Hybrid models combining neural and symbolic approaches are emerging in current research.

Abstract

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.