The author concludes that while LLMs have challenged these established theories, it doesn't necessarily mean they will transform AI as some predict. They believe we are still in the early stages of understanding and utilizing these models, and much work remains to be done. The existence of LLMs has changed the landscape of AI and linguistics, but the extent and direction of this change are still uncertain.
Key takeaways:
- The existence of Language Learning Models (LLMs) like ChatGPT-3.5 and ChatGPT-4 has challenged traditional theories in artificial intelligence, including the Turing Test, Searle’s Chinese Room, and Chomsky’s Universal Grammar.
- The Turing Test, which measures a machine's ability to exhibit intelligent behavior equivalent to or indistinguishable from that of a human, has been questioned due to the capabilities of LLMs. The author suggests that LLMs like ChatGPT-3.5 have shown language expertise that people imagined some system as intelligent as a person would have, but it is not the crystalline indication of intelligence that Turing was trying to elucidate.
- Searle’s Chinese Room argument, which suggests that a program can process input to produce output without understanding the language, is exemplified by ChatGPT-3.5. However, the author argues that without grounding in physical reality, a machine cannot understand in the same way humans do.
- Chomsky’s Universal Grammar, the theory that there is a universal set of grammatical structures that underlies all human languages, is also challenged by LLMs. ChatGPT-3.5 learns language without any sort of universal grammar knowledge that Chomsky argues both constrains and enables what humans can learn as they acquire language.