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

===== 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.