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

=== Assistant: Good call — I went through the claim you sent. ===
Good call — I went through the claim you sent. There’s quite a bit that is correct, but also some over‑generalisations and points that are debatable. Here’s a breakdown of what checks out, what parts are shaky, and which claims don’t hold up fully.

===== 1. The description of the John Searle “Chinese Room” argument is accurate: Roughly, a person who doesn’t understand Chinese manipulates symbols per rules and outputs appropriate Chinese responses; Searle uses that to argue a system could behave as if it understands language yet lacks genuine understanding. Internet Encyclopedia of Philosophy<ref>{{cite web|title=Internet Encyclopedia of Philosophy|url=https://iep.utm.edu/chinese-room-argument/|publisher=Internet Encyclopedia of Philosophy|access-date=2025-11-10}}</ref> =====
# The argument is directed at “strong AI” (i.e., the view that a suitably programmed computer is a mind/understanding) rather than simply any AI or simulation of intelligence. Internet Encyclopedia of Philosophy<ref>{{cite web|title=Internet Encyclopedia of Philosophy|url=https://iep.utm.edu/chinese-room-argument/|publisher=Internet Encyclopedia of Philosophy|access-date=2025-11-10}}</ref>
# The claim that the Chinese Room primarily targets symbolic AI / rule‑based systems (i.e., finite rulebooks, symbol manipulation) is supported: Searle emphasised syntax vs semantics. Encyclopedia Britannica<ref>{{cite web|title=Encyclopedia Britannica|url=https://www.britannica.com/topic/Chinese-room-argument|publisher=Encyclopedia Britannica|access-date=2025-11-10}}</ref>
# It’s correct that there has been scholarly discussion about how the Chinese Room might be less relevant (or needs re‑interpretation) in the era of large neural nets/connectionism. For example: modern AI uses large datasets, neural “emergent representations”, not just rule‑books. Wikipedia<ref>{{cite web|title=Wikipedia|url=https://en.wikipedia.org/wiki/Chinese_room|publisher=en.wikipedia.org|access-date=2025-11-10}}</ref>

===== 1. The statement “It is utterly irrelevant to the conversation about modern AI” is too strong. While many argue the thought experiment needs updating or reframing, that doesn’t mean it’s completely irrelevant. Some still use it as a touchstone for debates about understanding, semantics, consciousness in AI systems. Indeed, sources discuss the Chinese Room in context of generative AI/LLMs. Forbes<ref>{{cite web|title=Forbes|url=https://www.forbes.com/sites/gabrielasilva/2025/04/23/can-ai-understand-the-chinese-room-argument-says-no-but-is-it-right/|publisher=forbes.com|date=2025-04-23|access-date=2025-11-10}}</ref> =====
# The assertion that the Chinese Room only addresses a finite‑state machine (or “lookup table” metaphor) and therefore is irrelevant to modern neural nets is partially correct but oversimplified. Searle’s core is: syntax alone isn’t semantics — he doesn’t strictly restrict it to “finite‐state machines”. Modern systems might circumvent some of his assumptions, but whether they do so successfully is still contested.
# The idea that a neural network “builds its own map … meaning emerges from the high‑dimensional geometry” is a plausible way to describe current ML patterns, but to claim it definitively “understands” in the sense that Searle uses is a strong claim. Critics would say even neural networks still manipulate patterns and correlations, not necessarily “understanding” meaning in a human sense. Some scholars argue modern systems still fall within Searle’s critique (syntax only) or at least the burden remains on showing genuine semantics/intentionality. Artificial Intelligence Stack Exchange<ref>{{cite web|title=Artificial Intelligence Stack Exchange|url=https://ai.stackexchange.com/questions/123/does-the-chinese-room-argument-hold-against-ai|publisher=Artificial Intelligence Stack Exchange|access-date=2025-11-10}}</ref>
# The claim about the “Collision Test” (novel input causing crash) is more a rhetorical flourish than a recognized standard in the literature. It’s not common in Searle‑debate scholarship to speak of “Collision Test” exactly. So the specific terminology is unverified — though the idea (systems that are brittle when faced with entirely novel input) is relevant to debates about AI robustness.

===== The core of the piece is largely accurate: the Chinese Room argument was designed for a specific conception of AI (symbolic rule‐systems / strong AI), and there is a reasonable case that it must be updated or reconsidered given modern neural architectures. However, the piece stretches in claiming it is irrelevant or completely “broken” without nuance — and it uses some novel terminology/analogies (scale‑fallacy, collision test) that are more commentary than established philosophical categories. =====

If I were to summarise: Yes, you can reasonably argue that the Chinese Room thought experiment is less directly applicable to modern neural network‑based AI systems, but No, it’s not dead irrelevant — many philosophers still consider the questions it raises (meaning, intentionality, consciousness) very much alive, and there remain arguments that modern AI doesn’t yet meet the bar of genuine understanding as Searle frames it.

If you like, I can check recent (2023‑25) literature to see how philosophers of mind are currently treating the Chinese Room in the era of large language models — might give us a clearer picture of whether it’s “really” outdated or still actively debated.