Chinese Room re-visited: How LLM's have real but different understanding of word (www.lesswrong.com)

A recent analysis revisits the longstanding Chinese Room argument by John Searle, which contends that computer programs, including large language models (LLMs), lack true linguistic understanding. The post argues that while LLMs manipulate symbols without inherent meaning, they do exhibit a limited form of understanding tied to the philosophical concept of teleosemantics, where meaning arises through optimization rather than mere syntax. This is significant for the AI/ML community as it challenges the notion of what constitutes “understanding” in artificial intelligence, provoking deeper discussions about the semantic capabilities of LLMs. The argument highlights the need to discern between different levels of understanding—Searle inside the room versus the room's outputs—and introduces the idea that LLMs, through reinforcement learning, can develop functional representations that allow for some degree of semantic grounding. This perspective suggests that as LLMs are exposed to a variety of inputs and optimized for specific tasks, they might start to form an understanding of concepts akin to that seen in biological systems. Therefore, by aligning their symbolic manipulation with reward-driven learning experiences, LLMs could be argued to possess a form of intentionality lacking in Searle's original thought experiment, raising critical questions about the future of AI and its relationship to human cognition.