Does ChatGPT know what is a question? (cjauvin.github.io)

A recent exploration into ChatGPT’s operations reveals that the AI functions largely as a predictive model, generating responses not from genuine understanding but by completing sequences of words based on its extensive training. When asked straightforward questions, it resembles a person equipped with a card of phrases in an unfamiliar language—able to produce coherent answers without comprehending their meaning. This comparison invokes John Searle's Chinese Room argument, questioning the authenticity of machine-generated responses compared to human understanding. The significance of this insight for the AI/ML community is profound, as it challenges traditional notions of language comprehension and question-answering. The long-held assumption that an intermediate representation of meaning is essential for understanding is called into question. Instead, the effectiveness of large language models like ChatGPT suggests that valuable linguistic responses can emerge purely from the ability to predict sequences without engaging in deeper cognitive processing. This paradigm shift implies that our definitions of understanding and language processing may need reevaluation, potentially paving the way for new AI architectures that prioritize predictive capabilities over the conventional models of meaning-making.