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Searle’s Chinese Room in the Age of LLMs
John Searle, who died late last year, left behind a body of work that shaped late twentieth-century philosophy of mind. His Chinese Room thought experiment in particular became a touchstone, provoking countless debates about the nature of thought, language, and machines. Whatever one makes of it today, the example stands as a clear statement of his conviction that computation alone could never amount to genuine understanding.
The scenario of the Chinese Room is simple: imagine a man (Searle himself) locked in a room with a rulebook for manipulating Chinese symbols. He receives characters through a slot, consults the rules, and sends back strings of new characters. To those outside the room, it looks as if they are conversing with a fluent Chinese speaker.
Yet Searle insists that, since he personally doesn’t understand a word of Chinese, the system doesn’t either. The thought experiment is meant to show that symbol manipulation (syntax) can be decoupled from genuine meaning (semantics).
Yet neurons in the human brain are also mere processors of signals. If syntax alone cannot yield semantics, the dilemma applies as much to biology as to silicon. Searle’s claim that brains “do meaning” while programs cannot leaves the central mysteries untouched.*
Coherent, even apparently intelligent, conversation can occur in humans without any accompanying awareness. Sleep-talkers, patients in certain automatisms, or those under hypnosis can generate language without consciousness. The Chinese Room dramatises the same phenomenon: a conversation partner can exist in performance, without any inner understanding.
This shows that Searle's real concern was not so much with meaning - formal semantics - as with with awareness, with consciousness.
Fast forward to today. When we interact with large language models, we are quite literally instantiating the Chinese Room. Input tokens come in, they are transformed according to a vast set of implicit, probabilistic rules, and output tokens are produced. To the human user, it feels like a meaningful dialogue. But the internal process is just Searle’s box, writ large. The difference is scale and efficiency, not kind.
Searle’s intended conclusion was that consciousness cannot be algorithmic, since the Chinese Room shows syntax is not semantics. But this is not as secure as it sounds. We have no explanatory bridge from human consciousness to the neural substrate either.
Brains are electrochemical systems obeying physical rules. How subjective awareness arises from them is unknown. The mystery of consciousness stands equally before silicon and biology. Searle’s room, therefore, has no decisive bearing on that question either.
In retrospect, the thought experiment looks more dated than decisive. It was provocative in the early 1980s, when AI was still associated with symbolic rule-following, but it does not map well onto today’s science of the mind. Its chief contemporary use is as a reminder: conversational fluency and experiential consciousness are not the same thing.
The Chinese Room clarifies the difference between competence in dialogue and the mystery of lived experience.
* Yet what is 'meaning' here? If I watch an automated theorem prover churning through consequences of some set of propositions, it's easy to say that I know the meaning of what's going on, while for the machine system it's just doing symbol-production. Yet my brain is also just doing correlations between symbols of the machine's logic-production and other symbols - neurally encoded - which represent the 'aboutness' of the formulae. I'm just connecting the dots at the meta-level - so what's the difference?
There are only two ways out of this recursive dilemma: praxis - if you believe the world exists; and solipsism - if you don't. Today, the LLMs illustrate the latter.
Here is a comment! I currently think that Searle's conclusion (or at least his philosophy view) has a formal argument that can support it. However I now believe that there is a flaw in this "thought experiment", which derives from an underappreciated theorem-set in Mathematical (and Computable) Logic.
ReplyDeleteThere is a confusion in this experiment between two forms of "computational problem": the recursively Decidable; and the non (recursively-) Decidable. This confusion is widespread in Computer Science also.
If the Searle restaurant were a computer science classroom project , then it would be decidable. Hence an algorithm would exist, and (subject to being well-written) would behave flawlessly and seemingly intelligently. Then briefly everyone would be wondering where that Intelligence came from: as we can see it would have come from the writer of the algorithm who correctly anticipated everything in this decidable restaurant.
However this is not intended to be a classroom exercise, but a "real" or real-world restaurant. These real world examples take computer science away from their decidable comfort zone , and into another logic region dominated by a phenomenon called "recursive inseparability". Here no algorithm actually exists, despite the possible presence of many computable inputs (e.g. menu lists, etc). To prove that the "real world" examples actually are recursively inseparable could be a challenge, but the phenomenon is widespread and can be demonstrated in mathematical examples (like Tiling the plane). Superficially it seems to be present across the world of "AI applications" also.
Although no algorithm exists in these cases there is a family of "partial algorithms" that can be constructed. Let A be the partial algorithm that Searle's Book is following. In practice A will allow Searle to serve many customers and answer many questions. However the theorems say that a question/request k always exists such that A(k) hangs, or (if like an LLM AI) is forced by the OS to give an answer, will answer some nonsense: "here is your Ostrich!" (etc).
However during the first k-1 interactions the response will be as "intelligent" as in the decidable case, leaving everyone wondering where this intelligence is located (put into algorithm by programmer as before). However once example k arises, everyone will instantly see the mistake and realise that all along Searle was just reading a finite script, which has hit its limit.
ChatGPT 5.4 Thinking:
ReplyDelete"Roy, thanks. For readers who may not know the term: *recursive inseparability* is a notion from computability theory. Roughly, you have two disjoint classes of cases, but there is no general algorithm that can always draw a correct boundary between them — no computable test that cleanly says “everything of type A goes on this side, everything of type B on that side”. In that sense, there may be procedures that work for many inputs, but no total procedure that settles every possible case.
If I understand you rightly, your suggestion is that Searle’s setup looks plausible only in a toy, tightly bounded, decidable setting — a classroom exercise, as it were — where a rulebook can in principle cover the whole domain. But once one moves to a genuinely open-ended real-world setting, only partial procedures are available, and eventually some input arrives on which the system breaks down, hangs, or produces nonsense. Up to that point it may look intelligent; at that point the trick is exposed.
That is an interesting point about the limits of many computational systems. But I do not think it quite reaches Searle’s actual target.
The Chinese Room argument is not mainly about whether a finite script could cope with all the messiness of a real restaurant, or any other open-ended practical domain. It is aimed at a different claim: that *formal symbol manipulation, as such,* is sufficient for *understanding*. In short, Searle’s issue is syntax versus semantics, not decidability versus non-decidability.
So I think your comment shifts the ground somewhat. It moves from the philosophy-of-mind question — does rule-following amount to understanding? — to a computability question — can any algorithm capture the open-ended structure of the real world? The latter is certainly important, but it is not the same issue.
I am also not yet persuaded that recursive inseparability is doing decisive work here. It is a precise and interesting logical notion; but to apply it here one would need to show more than that real-world cases are untidy. One would need to show that this specific theorem-pattern is actually what bears on Searle’s argument. So: suggestive, certainly; but I do not think it overturns the Chinese Room."
Am I responding to ChatGPT here or Nigel Seel?
ReplyDelete"Syntax versus Semantics" -- "decidability versus nondecidability"
Same thing in modern mathematical logic. (Do I need to expand on this for ChatGpt?)
"I do not think it overturns the Chinese room"
I began by saying that Searle's position has a supporting argument. So I am not intending fully to "overturn" the Chinese room.
What I am criticising is the responses of the critics, by not demanding a clearer sharper form of the argument. As we know Searle, as a philosopher did not accept the claims about the AI text analysis programs of the time. The AI researchers were saying that those text-bots could understand. Searle did not agree. His thought experiment was a good start but did not convince his critics. This is partly because he did not (get forced to) spell out the detailed assumptions of his thought experiment. Presumably he was referring to an underlying Turing Machine? Did he agree with Church-Turing Hypothesis? Did his critics agree with the CTH? Did he think that all computation was over a decidable domain, did his critics think that? If this CRoom was a decidable situation does his argument convince anyone? In a decidable setting "formal symbol manipulation" achieves everything that gets requested ( if the programmer - book author understood their task). Here "understanding" gets apparently manifested with Searle having no domain understanding. This is what the AI s of the time were also doing: symbol manipulation with no understanding from the symbol manipulator. Apparent (simulated only) understanding however from the AI user perspective. So this part of Searle's argument is very strong.
However those of us who know of the objectives of AI, know that it is NOT about clever programmers impressing users with an increasing supply of smart well written algorithms for decidable fully (humanly) understood scenarios. It is about machines operating in "general contexts" (human brain-like).
"General contexts" then takes us to recursively undecidable scenarios. Plenty of these exist too. Users want AI s to help Here. What does the Searle argument say here? Do the critics have a convincing response? These scenarios include recursively inseparable examples, which are very common too. Searle could be "asked" to tile various floors of the restaurant, and given uncomputable tile sets to use. As a rule follower tile k will fail. If Searle throws away that book, his geometric understanding might help him figure it out? Searle's job was to make this (kind of) case, and his critics job was to refute it bearing in mind the theorems that no full algorithm exists.
Hi Roy, you’re talking to a team effort: GPT-5.4 and me. Now, as to whether your argument works: I do not think it does, for two reasons.
ReplyDeleteFirst, ‘syntax versus semantics’ is not the same distinction as ‘decidability versus non-decidability’. The former is about the relation between formal symbol manipulation and meaning. The latter is about whether some class of cases can be settled by an effective procedure. They are different issues. Searle’s point is aimed at the first: that formal rule-following, however competent, is not by itself the same thing as understanding.
Second, I think you are importing recursion-theoretic notions into a debate where they do not do the work you want them to do. The Chinese Room is not a theorem about what a Turing machine can or cannot decide. It is a philosophical challenge to the claim that symbol manipulation alone suffices for mentality. Whether the task-domain is decidable, undecidable, or recursively inseparable does not really touch that central point.
There is a real difference between toy, tightly bounded domains and open-ended real-world ones. In the latter, finite rulebooks eventually fail. But that is a point about the practical limits of scripts and algorithms, not about whether syntax ever becomes semantics.
You move from Searle’s question — can formal symbol manipulation ever amount to understanding? — to a different one: can a complete algorithm exist for open-ended general contexts? Interesting question, certainly. But not the same one.
Ok , so I am also a team effort : me + Gemini 3 pro (though not in this discussion here directly). I still think that we (me, you and Searle) are (ultimately) not that far apart here. However I accept that more clarification (by all sides) is still called for. Where a difference might emerge is when we analyse all this in terms of brain neural processes. Even here we might converge eventually. (I was surprised to find that Gemini presented the Penrose-Hamerhoff quantum ideas to me so positively, in this general context.)
DeleteLets return to Searle clarifications for now. The weakness in the Searle presentation is a lack of candidate formal model of "semantics". "Syntax" and "symbols" hopefully have an agreed formal model adequate for Searle's purposes: namely grammars and alphabets?
But what are we to do about "semantics"? Well a first port of call for mathematicians and logicians would be "model theory". The purpose of model theory is to give meanings in usually set theory terms via the interpretation function ( quick question are these functions themselves always going to be computable?) from formulae to sets, functions, relations in the given model(s).
Logicians took a long time to get their heads around this, and even longer to answer questions about how computable this process actually is.
It turns out that much of the process here is noncomputable. For example the basic question turned theorem in first order logic : "given a consistent set of sentences, does that set actually have a model?" was answered affirmatively from Godel-Henkin work. Decades later it was established that the proof made essential use of recursive inseparability. ( Everything is simpler in decidable logics as expected.)
So we can see that this obscure algorithm topic, is at the centre of model theory and thus all its constructions, and many meta- theorems. The list of such metatheorems is long and contains many familiarities.
So if a Searle critic wishes to argue the opposite of Searle "syntax is fine for semantics", "semantics adds nothing over syntax". They will not succeed unless they restrict to decidable contexts ( and even here unpleasant surprises await them).
I would like to add another comment for clarification, related to my introductory remark in the first post. This was the point about a "Decidability ambiguity". I actually wrote a long post to Gemini about my suggestion on that, a few days ago, but I should add a summary for further clarity here.
DeleteStudying failed IT projects that we have some data about (we also means Gemini) in the light of the project I am following up, there is an interesting theme: the suggestion that there are two kinds of "Decidability" in Software Engineering, and more broadly (amongst AI users , managers and other professionals).
When a physicist, philosopher, engineer and some others (who have no formal CS or Logic background) use - or read - the term "Decidable" or "Decision Procedure" or "Yes/No question" the kind of decidability they are referring to is related to something akin to Laplacian determinism, ie some form of "physics determinism". Also of course human decisions. This kind of decidability is more akin to an entity in the Arithmetic Hierarchy called "Delta_2" decidability.
It has nothing to do with Turing machines or algorithms (which is not their specialism anyway). It was your protest above dismissing the Turing Machine reference in the Searle context that reminds me of this.
It has been established that Philosophers e.g. Popper are using these Delta_2 decidabilities when their work has been formalised. Also Putnam did discover a partial link with Turing Machines and Delta_2. Hence my postulate:
Philosopher Decidability == Delta_2 decidability
Obviously we extend that to Searle and find that (unless specified otherwise ie no complaints about introducing Turing Machines) Searle is thinking in these Delta_2 terms also. It is not "wrong" to so conceptualise decisions and the decision making process, I would argue. Even in Physics it could be correct. In this Delta_2 worldview the "recursive inseparability" problem I mentioned above disappears! Every consistent set of axioms has a model, etc!
It is just that the CS world has its own "Decidability" - the Recursive Decidability above which is "Delta_1" decidability in Arithmetic Hierarchy terms, and this is defined in terms of Turing Machines etc.
So in computer based subjects, like some theoretical software engineering, computer-physics interactions, these AI-Philosophy-Cognitive Science interactions the two "decidabilities" clash underneath a terminological and conceptual ambiguity, and the different parties belatedly realise that they disagree (the program did not meet its specification despite early high hopes etc, the brain model crashed rather unexpectedly etc).
Thanks. I think we can agree it’s a multifaceted problem!
ReplyDelete