Editing Openai/6910f719-b034-8006-930d-0d5190a7c617 (section)

===== - Architecture differences: Modern neural nets/LLMs are not the kind of “rulebook + human” symbol‑manipulator that Searle originally posited. Artificial Intelligence Stack Exchange<ref>{{cite web|title=Artificial Intelligence Stack Exchange|url=https://ai.stackexchange.com/questions/39293/is-the-chinese-room-an-explanation-of-how-chatgpt-works|publisher=Artificial Intelligence Stack Exchange|access-date=2025-11-10}}</ref> =====
* Meaning & semantics: A big question is whether LLMs generate responses from pattern correlation only (syntax) or whether that abstracts into semantics (meaning/understanding). Searle’s punch was: syntax cannot yield semantics. Recent work asks whether semantic‑emergence is possible in LLMs. PhilPapers<ref>{{cite web|title=PhilPapers|url=https://philpapers.org/rec/BORLTT-2|publisher=philpapers.org|access-date=2025-11-10}}</ref>
* Novel inputs/adaptation etc.: Some argue that because LLMs can generalize/learn from vast corpora (even if not in the sense of human “experience”), they escape at least some of the static‑system critique of CRA. Others say they still don’t genuinely “understand”. Analytics Vidhya<ref>{{cite web|title=Analytics Vidhya|url=https://www.analyticsvidhya.com/blog/2023/12/exploring-the-intricacies-of-the-chinese-room-experiment-in-ai/|publisher=Analytics Vidhya|access-date=2025-11-10}}</ref>
* Embodiment, world‐interaction, experience: Many of the updates to CRA‐debate emphasise that computers or LLMs don’t have bodies, sensory grounding, real world causal interaction in the way humans do. So the question isn’t only “symbol manipulation” but also “grounding”. Philosophical Investigations<ref>{{cite web|title=Philosophical Investigations|url=https://www.philosophical-investigations.org/2023/04/the-chinese-room-experiment-and-todays.html|publisher=Philosophical Investigations|access-date=2025-11-10}}</ref>