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.

Finding regularity in nature is the bread and butter of science. We know that reptiles lay eggs, while mammals bear live young; the Earth revolves around the sun every 365.25 days; electrons glom onto protons like bears onto honey. But what if some oddity seems to defy the laws of nature, like the platypus, an egg-laying mammal? Or a newborn baby who seems to be neither boy nor girl, but something in between? These questions fascinated the founding fathers of science, and their attempts to explain such rarities and marvels helped shape modern science. In fact, nearly all the great philosophers and scientists of 17th century Europe--Descartes, Newton, and Bacon notably among them--were obsessed with anomalies. If they couldn't explain the unlikely--a solar eclipse, a comet hurtling toward Earth, a narwhal tusk (was it a unicorn?)--all bets were off about an underlying explanation of nature. Lorraine Daston, executive director of the Max Planck Institute for the History of Science in Berlin, has spent decades studying the emergence of modern science. One formative experience, she says, was a graduate-school seminar where she and fellow student Katharine Park noticed something strange.