Chinese Room re-visited: How LLM's have real but different understanding of word
The Chinese Room argument suggests that computer programs, including large language models (LLMs), do not truly understand language. It posits that while LLMs can manipulate symbols and produce fluent text, they lack genuine semantic understanding. The ongoing debate highlights the distinction between syntax and semantics, emphasizing that mere symbol manipulation does not equate to comprehension.
- ▪The Chinese Room argument, introduced by John Searle, challenges the Strong AI hypothesis.
- ▪Searle's thought experiment illustrates that a program can produce fluent language without understanding it.
- ▪The distinction between the room and Searle himself raises questions about the nature of understanding in systems.
Opening excerpt (first ~120 words) tap to expand
TL;DRThe Chinese Room argument implies that computer programs can never really understand language. In this post, I’ll argue that LLM’s have a limited form of language understanding through teleosemantics i.e. the idea that meaning is acquired[1] through optimisation.The Chinese RoomThe Chinese Room argument, introduced by John Searle argues against what he calls the Strong AI hypothesis i.e. The appropriately programmed computer with the right inputs and outputs would thereby have a mind in exactly the same sense human beings have minds. The thought experiment poses a challenge to computational functionalism i.e. that mental phenomena can be described purely by their functional relation to each other.
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