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In this paper, we propose a novel approach to establish a connection betweenlinguistic objects and classes in Large Language Model Machines (LLMMs) such asGPT3.5 and GPT4, and their counterparts in high level programming languageslike Python. Our goal is to promote the development of Digital Nature: aworldview where digital and physical realities are seamlessly intertwined andcan be easily manipulated by computational means. To achieve this, we exploitthe inherent abstraction capabilities of LLMMs to build a bridge between humanperception of the real world and the computational processes that mimic it.This approach enables ambiguous class definitions and interactions betweenobjects to be realized in programming and ubiquitous computing scenarios. Bydoing so, we aim to facilitate seamless interaction between Turing Machineobjects and Linguistic Objects, paving the way for universally accessibleobject oriented descriptions. We demonstrate a method for automaticallytransforming real world objects and their corresponding simulations intolanguage simulable worlds using LLMMs, thus advancing the digital twin concept.This process can then be extended to high level programming languages, makingthe implementation of these simulations more accessible and practical. Insummary, our research introduces a groundbreaking approach to connectlinguistic objects in LLMMs with high level programming languages, allowing forthe efficient implementation of real world simulations. This ultimatelycontributes to the realization of Digital Nature, where digital and physicalworlds are interconnected, and objects and simulations can be effortlesslymanipulated through computational means.

Towards Digital Nature: Bridging the Gap between Turing Machine Objects  and Linguistic Objects in LLMMs for Universal Interaction of Object-Oriented  Descriptions