THE ARCHITECTONICS OF THE "CHINESE ROOM": RECONSTRUCTION AND EVALUATION OF THE THOUGHT EXPERIMENT VIA ARGUMENTATION THEORY
PHILOSOPHY OF MIND
Keywords:
Chinese Room, John Searle, argumentation theory, philosophy of mind, informal logic, rhetoric, thought experiment, analogical reasoning, computational functionalism, meta-argumentation, pragma-dialectics, Strong AI, inferential semantics, LLMAbstract
The article analyzes John Searle's famous "Chinese Room" thought experiment, which denies the ability of artificial intelligence to achieve genuine understanding. The study aims to deconstruct this argumentation step by step to clarify exactly how it is structured, what makes it persuasive, and where its weaknesses lie. For this purpose, a model is applied that examines the text at four levels. The first level clarifies the historical background: specifically, whose ideas and computer programs the author opposed. The second level demonstrates how Searle constructs the experiment's scenario to subtly impose discussion rules that favor his position on the audience. In the third stage, the relevance of comparing a computer's operation to the actions of a person mechanically sorting incomprehensible characters is examined. The analysis reveals that the persuasiveness of the argumentation relies heavily on the assumption that only a biological brain is capable of generating consciousness. At the fourth level, the experiment is tested for robustness using the most well-known objections from critics. The conducted analysis shows that Searle successfully proves a machine's inability to understand meaning solely through the mechanical manipulation of symbols. However, his thought experiment proves vulnerable to the assumption that consciousness can emerge as an entirely new property of highly complex systems. Using modern Large Language Models (LLMs) as an example, the study concludes that the "Chinese Room" argument remains relevant, as it proves that machines do not grasp the physical connection between words and the real world. At the same time, this does not rule out the possibility that artificial intelligence is capable of successfully operating with syntactic connections between words within the language itself.
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