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

=== Assistant: Cool — I found several recent papers/articles 2023‑25 that relate the John Searle “Chinese Room Argument” CRA to modern… ===
Cool — I found several recent papers/articles (2023‑25) that relate the John Searle “Chinese Room Argument” (CRA) to modern large‑language‑model (LLM)‑type AI systems. Here are 6 solid ones with brief summaries of how each treats the CRA / AI understanding debate:

|     |     |     |
| --- | --- | --- |
|     |     |     |
|     |     |     |
|     |     |     |
|     |     |     |
|     |     |     |
|     |     |     |

===== - The CRA remains a live concern: Many authors argue that even modern systems haven’t definitively addressed Searle’s core point (that syntax ≠ semantics). (See #1, #2, #4) =====
* Modern LLMs complicate things: The architecture is not the simple “rulebook + human” that Searle envisaged, so some argue CRA must be reinterpreted rather than discarded. (#1, #2)
* Some authors use the CRA metaphor now as a tool: e.g., “Chinese room with a word transition dictionary” (#3) — showing how thought experiments get adapted for LLMs.
* Others hold that CRA still works as a critique: even if the system is more complex, if it lacks the right kind of semantic grounding, then the CRA’s point holds. (#4)
* There’s recognition that the challenge of meaning, intentionality, understanding remains unresolved: We have better systems, but philosophical gap persists. (#5)
* Policy/tech oriented views integrate CRA style concerns into real‑world risks: e.g., LLMs being used in critical domains but maybe lacking genuine understanding or alignment (#6)

===== From reading these, I conclude: the CRA hasn’t become irrelevant — rather, it’s evolving. The target of the argument shifts: from early symbolic AI to modern LLMs. The metaphor (room + rulebooks) is perhaps outdated in specifics, but the philosophical challenge (what counts as “understanding” for a machine) remains very much active. =====

If you like, I can dig out 10 more papers (including full PDFs) and pull out direct quotes and key arguments from each.