The most famous challenge to computational cognitive science and artificial intelligence is the philosopher John Searle's "Chinese Room" argument.

Charles Babbage began the quest to build an intelligent machine in the nineteenth century. Despite finishing neither the Difference nor the Analytical engine, he was aware that the use of mental language for describing the functioning of such machines was figurative. In order to reverse this cautious stance, Alan Turing postulated two decisive ideas that contributed to give birth to Artificial Intelligence: the Turing machine and the Turing test. Nevertheless, a philosophical problem arises from regarding intelligence simulation and make-believe (...) as sufficient to establish that programmed computers are intelligent and have mental states, especially given the nature of mind and its characteristic first-person viewpoint. The origin of Artificial Intelligence is undoubtedly linked to the accounts that inspired John Searle to coin the term strong AI ―or the view that simply equates computers and minds. Especially emphasising the divergence between algorithmic processes and intentional mental states, the Chinese Room thought experiment shows that, since the mind is embodied and able to realise when linguistic understanding takes place, mental states require material implementation, a point that directly conflicts with the accounts that reduce the mind to the functioning of a programmed computer. The experience of linguistic understanding with its typical quale leads to other important philosophical issues. Searle’s theory of intentionality holds that intentional mental states have conditions of satisfaction and appear in semantic networks; thus people know when they understand and what terms are about. In contrast, a number of philosophers maintain that consciousness is only an illusion and that it plays no substantial biological role. However, consciousness is a built-in feature of the system. Moreover, neurological evidence suggests that conscious mental states, qualia and emotions enhance survival chances and are an important part of the phenomenal side of mental life and its causal underpinnings. This renders an important gap between simulating a mind and replicating the properties that allow having mental states and consciousness. On this score, the Turing test and the evidence it offers clearly overestimate simulation and verisimilar make-believe, since such evidence is insufficient to establish that programmed computers have mental life. In summary, this dissertation criticises views which hold that programmed computers are minds and minds are nothing but computers. Despite the arguments in favour of such an equation, they all fail to properly reduce the mind and its first-person viewpoint. Accordingly, the burden of proof still lies with the advocates of strong AI and with those who are willing to deny fundamental parts of the mind to make room for machine intelligence. (shrink)

A criticism of Searle's Chinese Room thought experiment.

Research in artificial intelligence (AI) has led to revise the challenges of the AI initial programme as well as to keep us alert to peculiarities and limitations of human cognition. Both are linked, as a careful further reading of the Turing’s test makes it clear from Searle’s Chinese room apologue and from Dreyfus’ suggestions, and in both cases, ideal had to be turned into operating mode. In order to rise these more pragmatic challenges AI does not hesitate (...) to link together operations of various levels and functionalities, more specific or more general. The challenges are not met by an operating formal system which should have from the outset all the learning skills, but -for instance in simulation- by the dynamics of a succession of solutions open to adjustments as well as to reflexive repeats. (shrink)

Discussions of artificial intelligence have been shaped by two brilliant thought-experiments: Turing’s Imitation Test for thinking systems and Searle’s Chinese Room Argument. In many ways, debates about large language models (LLMs) struggle to move beyond these original, opposing thought-experiments. So, in this paper, I ask whether we can move debate forward by exploring the features Sceptics about LLM abilities take to ground meaning. Section 1 sketches the options, while Sections 2 and 3 explore the common requirement for (...) a robust relation between linguistic signs and external objects. Section 3 argues that concerns about worldly connections can be met and thus that outputs of LLMs should be viewed as genuinely meaningful. Section 4 then turns to the argument that the kind of derived meaning explored in Section 3 is insufficient for LLMs to count as meaningful systems per se, looking at the claim that they must possess original intentionality before we admit them to the space of meaning. I argue that this demand is not a prerequisite for meaning per se, but rather for some kinds of agency or conscious understanding. I suggest that the demands for original intentionality are not currently met by LLMs and that we should be cautious about whether we want them to be. (shrink)

John R. Searle's problem of the Chinese Room poses an important philosophical challenge to the foundations of strong artificial intelligence, and functionalist, cognitivist, and computationalist theories of mind. Searle has recently responded to three categories of criticisms of the Chinese Room and the consequences he attempts to conclude from it, redescribing the essential features of the problem, and offering new arguments about the syntax-semantics gap it is intended to demonstrate. Despite Searle's defense, the (...) class='Hi'>Chinese Room remains ineffective as a counterexample, and poses no real threat to artificial intelligence or mechanist philosophy of mind. The thesis that intentionality is a primitive irreducible relation exemplified by biological phenomena is preferred in opposition to Searle's contrary claim that intentionality is a biological phenomenon exhibiting abstract properties. (shrink)

John Searle's Chinese room argument is perhaps the most influential andwidely cited argument against artificial intelligence (AI). Understood astargeting AI proper – claims that computers can think or do think– Searle's argument, despite its rhetorical flash, is logically andscientifically a dud. Advertised as effective against AI proper, theargument, in its main outlines, is an ignoratio elenchi. It musterspersuasive force fallaciously by indirection fostered by equivocaldeployment of the phrase "strong AI" and reinforced by equivocation on thephrase "causal (...) powers" (at least) equal to those of brains." On a morecarefully crafted understanding – understood just to targetmetaphysical identification of thought with computation ("Functionalism"or "Computationalism") and not AI proper the argument is still unsound,though more interestingly so. It's unsound in ways difficult for high church– "someday my prince of an AI program will come" – believersin AI to acknowledge without undermining their high church beliefs. The adhominem bite of Searle's argument against the high church persuasions of somany cognitive scientists, I suggest, largely explains the undeserved reputethis really quite disreputable argument enjoys among them. (shrink)

Considerations of personal identity bear on John Searle's Chinese Room argument, and on the opposed position that a computer itself could really understand a natural language. In this paper I develop the notion of a virtual person, modelled on the concept of virtual machines familiar in computer science. I show how Searle's argument, and J. Maloney's attempt to defend it, fail. I conclude that Searle is correct in holding that no digital machine could understand language, but (...) wrong in holding that artificial minds are impossible: minds and persons are not the same as the machines, biological or electronic, that realize them. (shrink)

. A comparison is made between two unlikely debates over intelligence. One debate took place in 1550 at Valladolid, Spain, between Bartolomé de las Casas and Juan Gines de Sepúlveda over the intelligence of the Amerindian. The other debate is contemporary, between John Searle and various representatives of the “strong” artificial intelligence community over the adequacy of the Turing test for intelligence. Although the contemporary debate has yet to die down, the Valladolid debate has been over for four (...) hundred years. The question asked here is whether the contemporary debate can profit from the previous one. The common bond providing the basis for contrast is the issue of the “other” which is present in both debates. From this contrast, the observation is made that the question of meaning is intimately tied to the question of intelligence. (shrink)

Viewed in the light of the remarkable performance of ‘Watson’ - IBMs proprietary artificial intelligence computer system capable of answering questions posed in natural language - on the US general knowledge quiz show ‘Jeopardy’, we review two experiments on formal systems - one in the domain of quantum physics, the other involving a pictographic languaging game - whereby behaviour seemingly characteristic of domain understanding is generated by the mere mechanical application of simple rules. By re-examining both experiments in the (...) context of Searle’s Chinese Room Argument, we suggest their results merely endorse Searle’s core intuition: that ‘syntactical manipulation of symbols is not sufficient for semantics’. Although, pace Watson, some artificial intelligence practitioners have suggested that more complex, higher-level operations on formal symbols are required to instantiate understanding in computational systems, we show that even high-level calls to Google translate would not enable a computer qua ‘formal symbol processor’ to understand the language it processes. We thus conclude that even the most recent developments in ‘quantum linguistics’ will not enable computational systems to genuinely understand natural language. (shrink)

It was in 1980 that John Searle first opened the door of his Chinese Room, purporting to show that the conscious mind cannot, in principle, work like a digital computer. Searle, who speaks no Chinese, stipulated that locked in this fictitious space he had a supply of different Chinese symbols, together with instructions for using them . When Chinese characters were passed in to him, he would consult the instructions and pass out more symbols. (...) Neither input nor output would mean anything to him, but it would look to the outsider as though he were answering in Chinese the questions in Chinese that were being passed into him. That, claimed Searle, is exactly the situation with the notorious Turing test for computer intelligence . Only from the outside does the computer appear understand the questions and answers. Inside, all is a formal shuffling of meaningless symbols. John Preston and Mark Bishop, ed., Views into the Chinese Room: New Essays on Searle and Artificial Intelligence, Oxford University Press, 2002, 410pp, ?50 ISBN 0198250576. (shrink)

Viewed in the light of the remarkable performance of ‘Watson’ - IBMs proprietary artificial intelligence computer system capable of answering questions posed in natural language - on the US general knowledge quiz show ‘Jeopardy’, we review two experiments on formal systems - one in the domain of quantum physics, the other involving a pictographic languaging game - whereby behaviour seemingly characteristic of domain understanding is generated by the mere mechanical application of simple rules. By re-examining both experiments in the (...) context of Searle’s Chinese Room Argument, we suggest their results merely endorse Searle’s core intuition: that ‘syntactical manipulation of symbols is not sufficient for semantics’. Although, pace Watson, some artificial intelligence practitioners have suggested that more complex, higher-level operations on formal symbols are required to instantiate understanding in computational systems, we show that even high-level calls to Google translate would not enable a computer qua ‘formal symbol processor’ to understand the language it processes. We thus conclude that even the most recent developments in ‘quantum linguistics’ will not enable computational systems to genuinely understand natural language. (shrink)

I argue that John Searle's (1980) influential Chinese room argument (CRA) against computationalism and strong AI survives existing objections, including Block's (1998) internalized systems reply, Fodor's (1991b) deviant causal chain reply, and Hauser's (1997) unconscious content reply. However, a new ``essentialist'' reply I construct shows that the CRA as presented by Searle is an unsound argument that relies on a question-begging appeal to intuition. My diagnosis of the CRA relies on an interpretation of computationalism as a scientific (...) theory about the essential nature of intentional content; such theories often yield non-intuitive results in non-standard cases, and so cannot be judged by such intuitions. However, I further argue that the CRA can be transformed into a potentially valid argument against computationalism simply by reinterpreting it as an indeterminacy argument that shows that computationalism cannot explain the ordinary distinction between semantic content and sheer syntactic manipulation, and thus cannot be an adequate account of content. This conclusion admittedly rests on the arguable but plausible assumption that thought content is interestingly determinate. I conclude that the viability of computationalism and strong AI depends on their addressing the indeterminacy objection, but that it is currently unclear how this objection can be successfully addressed. (shrink)

The Chinese room argument is a thought experiment of John Searle (1980a) and associated (1984) derivation. It is one of the best known and widely credited counters to claims of artificial intelligence (AI)—that is, to claims that computers do or at least can (someday might) think. According to Searle’s original presentation, the argument is based on two key claims: brains cause minds and syntax doesn’t suffice for semantics. Its target is what Searle dubs “strong AI.” (...) According to strong AI, Searle says, “the computer is not merely a tool in the study of the mind, rather the appropriately programmed computer really is a mind in the sense that computers given the right programs can be literally said to understand and have other cognitive states” (1980a, p. 417). Searle contrasts strong AI with “weak AI.” According to weak AI, computers just simulate thought, their seeming understanding isn’t real understanding (just as-if), their seeming calculation is only as-if calculation, etc. Nevertheless, computer simulation is useful for studying the mind (as for studying the weather and other things). (shrink)

Searle’s Chinese room argument (CRA) was recently charged as being unsound because it makes a logical error. It is shown here that this charge is based on a misinterpretation of the modal scope of a major premise of the CRA and that the CRA does not commit the logical error with which it is charged.

Searle's Chinese Room argument is a general argument that proves that machines do not have mental states in virtue of their programming. I claim that the argument expresses powerful but mistaken intuitions about understanding and the first person point of view. A distinction is drawn between a competence sense and a performance sense of ‘understanding texts’. It is argued that the Chinese Room intuition looks for a special experience (performance) of comprehension, whereas artificial intelligence is attempting (...) to explain the knowledge (competence) required to understand texts. Moreover, a dilemma is sketched for the argument: either Searle hasn't identified the appropriate subject of understanding or he may understand after all. Finally, I question the underlying assumption that the general definition of mental states requires a projectable‐by‐us first person point of view. (shrink)

Searle’s Chinese Room Argument (CRA) has been the object of great interest in the philosophy of mind, artificial intelligence and cognitive science since its initial presentation in ‘Minds, Brains and Programs’ in 1980. It is by no means an overstatement to assert that it has been a main focus of attention for philosophers and computer scientists of many stripes. It is then especially interesting to note that relatively little has been said about the detailed logic of the (...) argument, whatever significance Searle intended CRA to have. The problem with the CRA is that it involves a very strong modal claim, the truth of which is both unproved and highly questionable. So it will be argued here that the CRA does not prove what it was intended to prove. (shrink)

John Searle’s Chinese room argument is a celebrated thought experiment designed to refute the hypothesis, popular among artificial intelligence scientists and philosophers of mind, that “the appropriately programmed computer really is a mind”. Since its publication in 1980, the CRA has evoked an enormous amount of debate about its implications for machine intelligence, the functionalist philosophy of mind, theories of consciousness, etc. Although the general consensus among commentators is that the CRA is flawed, and not withstanding the (...) popularity of the systems reply in some quarters, there is remarkably little agreement on exactly how and why it is flawed. A newcomer to the controversy could be forgiven for thinking that the bewildering collection of diverse replies to Searle betrays a tendency to unprincipled, ad hoc argumentation and, thereby, a weakness in the opposition’s case. In this paper, treating the CRA as a prototypical example of a ‘destructive’ thought experiment, I attempt to set it in a logical framework, which allows us to systematise and classify the various objections. Since thought experiments are always posed in narrative form, formal logic by itself cannot fully capture the controversy. On the contrary, much also hinges on how one translates between the informal everyday language in which the CRA was initially framed and formal logic and, in particular, on the specific conception of possibility that one reads into the logical formalism. (shrink)

Imagine advanced computers that could, by virtue merely of being programmed in the right ways, act, react, communicate, and otherwise behave like humans. Might such computers be capable of understanding, thinking, believing, and the like? The framework developed in this paper for tackling challenging questions of concept application (in any realm of discourse) answers in the affirmative, contrary to Searle’s famous ‘Chinese Room’ thought experiment, which purports to prove that ascribing such mental processes to computers like these would (...) be necessarily incorrect. The paper begins by arguing that the core issue concerns language, specifically the discourse-community-guided mapping of phenomena onto linguistic categories. It then offers a model of how people adapt language to deal with novel states of affairs and thereby lend generality to their words, employing processes of assimilation, lexemic creation, and accommodation (in intersense and intrasense varieties). Attributions of understanding to some computers lie in the middle range on a spectrum of acceptability and are thus reasonable. Possible objections deriving from Searle’s writings require supplementing the model with distinctions between present and future acceptability, and between contemplated and uncontemplated word uses, as well as a literal-figurative distinction that is more sensitive than Searle’s to actual linguistic practice and the multiplicity of subsenses possible within a single literal sense. The paper then critiques two misleading rhetorical features of Searle’s Chinese Room presentation, and addresses a contemporary defense of Searle that seems to confront the sociolinguistic issue, but fails to allow for intrasense accommodation. It concludes with a brief consideration of the proper course for productive future discussion. (shrink)

The Chinese room thought experiment of John Searle militates against strong artificial intelligence, illustrating his claim that syntactical knowledge by itself is neither constitutive nor sufficient for semantic understanding as found in human minds. This thought experiment was put to a behavioural test, concerning the syntax of a finite algebraic field. Input, rules and output were presented with letters instead of numbers. The set of rules was first presented as a table but finally internalized by the participants. (...) Quite in line with Searle's argument, uninformed participants mastered the syntax but did not explicitly report semantic knowledge. In order to test the virtual mind reply to the Chinese room argument, the reaction time pattern of the participants was compared to that of an informed control group. The correlation was quite high but could be traced back to memory load and response priming, i.e. to syntactical factors. No trace of tacit semantic knowledge of the task could be found in the experimental group. (shrink)

More than a decade ago, philosopher John Searle started a long-running controversy with his paper “Minds, Brains, and Programs” (Searle, 1980a), an attack on the ambitious claims of artificial intelligence (AI). With his now famous _Chinese Room_ argument, Searle claimed to show that despite the best efforts of AI researchers, a computer could never recreate such vital properties of human mentality as intentionality, subjectivity, and understanding. The AI research program is based on the underlying assumption that (...) all important aspects of human cognition may in principle be captured in a computational model. This assumption stems from the belief that beyond a certain level, implementational details are irrelevant to cognition. According to this belief, neurons, and biological wetware in general, have no preferred status as the substrate for a mind. As it happens, the best examples of minds we have at present have arisen from a carbon-based substrate, but this is due to constraints of evolution and possibly historical accidents, rather than to an absolute metaphysical necessity. As a result of this belief, many cognitive scientists have chosen to focus not on the biological substrate of the mind, but instead on the abstract causal structure_ _that the mind embodies (at an appropriate level of abstraction). The view that it is abstract causal structure that is essential to mentality has been an implicit assumption of the AI research program since Turing (1950), but was first articulated explicitly, in various forms, by Putnam (1960), Armstrong (1970) and Lewis (1970), and has become known as _functionalism_. From here, it is a very short step to _computationalism_, the view that computational structure is what is important in capturing the essence of mentality. This step follows from a belief that any abstract causal structure can be captured computationally: a belief made plausible by the Church–Turing Thesis, which articulates the power. (shrink)

When in 1979 Zenon Pylyshyn, associate editor of Behavioral and Brain Sciences (BBS, a peer commentary journal which I edit) informed me that he had secured a paper by John Searle with the unprepossessing title of [XXXX], I cannot say that I was especially impressed; nor did a quick reading of the brief manuscript -- which seemed to be yet another tedious "Granny Objection"[1] about why/how we are not computers -- do anything to upgrade that impression.

"in an academic generation a little overaddicted to "politesse," it may be worth saying that violent destruction is not necessarily worthless and futile. Even though it leaves doubt about the right road for London, it helps if someone rips up, however violently, a.

The argument presented in this paper is not a direct attack or defence of the Chinese Room Argument (CRA), but relates to the premise at its heart, that syntax is not sufficient for semantics, via the closely associated propositions that semantics is not intrinsic to syntax and that syntax is not intrinsic to physics. However, in contrast to the CRA’s critique of the link between syntax and semantics, this paper will explore the associated link between syntax and physics. The (...) main argument presented here is not significantly original – it is a simple reflection upon that originally given by Hilary Putnam (Putnam 1988) and criticised by David Chalmers and others: instead of seeking to justify Putnam’s claim that, “every open system implements every Finite State Automaton (FSA)”, and hence that psychological states of the brain cannot be functional states of a computer, I will seek to establish the weaker result that, over a finite time window every open system implements the trace of a particular FSA Q, as it executes program (p) on input (x). That this result leads to panpsychism is clear as, equating Q (p, x) to a specific Strong AI program that is claimed to instantiate phenomenal states as it executes, and following Putnam’s procedure, identical computational (and ex hypothesi phenomenal) states (ubiquitous little ‘pixies’) can be found in every open physical system. (shrink)

The purpose of this paper is to explore a possible resolution to one of the main objections to machine thought as propounded by Alan Turing in the imitation game that bears his name. That machines will, at some point, be able to think is the central idea of this text, a claim supported by a schema posited by Andy Clark and David Chalmers in their paper, “The Extended Mind” (1998). Their notion of active externalism is used to support, strengthen and (...) further what John Searle calls “the systems reply” to his objection to machine thought or strong Artificial Intelligence in his Chinese Room thought experiment. Relevant objections and replies to these objections are considered, then some conclusions about machine thought and the Turing test are examined. (shrink)

Whether it was John Searle’s Chinese Room argument (Searle, 1980) or Roger Penrose’s argument of the non-computable nature of a mathematician’s insight – an argument that was based on Gödel’s Incompleteness theorem (Penrose, 1989), we have always had skeptics that questioned the possibility of realizing strong Artificial Intelligence (AI), or what has become known by Artificial General Intelligence (AGI). But this new book by Landgrebe and Smith (henceforth, L&S) is perhaps the strongest argument ever made (...) against strong AI. It is a very extensive review of what building a mind essentially amounts to drawing on insights and results from biology, physics, linguistics, computability, philosophy, and mathematics. (shrink)

This paper is a complement to the recent wealth of literature suggesting a strong philosophical relationship between artificial life (A-Life) and artificial intelligence (AI). I seek to point out where this analogy seems to break down, or where it would lead us to draw incorrect conclusions about the philosophical situation of A-Life. First, I sketch a thought experiment (based on the work of Tom Ray) that suggests how a certain subset of A-Life experiments should be evaluated. In doing (...) so, I suggest that treating A-Life experiments as if they were just AI experiments applied to a new domain may lead us to see problems (like Searle’s “Chinese room”) which do not exist. In the second half of the paper, I examine the reasons for suggesting that there is a philosophical relationship between the two fields. I characterize the strong thesis for a translation of AI concepts, metaphors, and arguments into A-Life as the “global replacement strategy.” Such a strategy is only fruitful inasmuch as there is a strong analogy between AI and A-Life. I conclude the paper with a discussion of two areas where such a strong analogy seems to break down. These areas relate to eliminative materialism and the lack of a “subjective” element in biology. I conclude that the burden of proof lies with the person who wishes to import a concept from another discipline into A-Life, even if that other discipline is AI. (shrink)

Searle's celebrated Chinese Room Argument has shaken the foundations of Artificial Intelligence. Many refutations have been attempted, but none seem convincing. This paper is an attempt to sort out explicitly the assumptions and the logical, methodological and empirical points of disagreement. Searle is shown to have underestimated some features of computer modeling, but the heart of the issue turns out to be an empirical question about the scope and limits of the purely symbolic model of the (...) mind. Nonsymbolic modeling turns out to be immune to the Chinese Room Argument. The issues discussed include the Total Turing Test, modularity, neural modeling, robotics, causality and the symbol-grounding problem. (shrink)

The logical problem of artificial intelligence—the question of whether the notion sometimes referred to as ‘strong’ AI is self-contradictory—is, essentially, the question of whether an artificial form of life is possible. This question has an immediately paradoxical character, which can be made explicit if we recast it (in terms that would ordinarily seem to be implied by it) as the question of whether an unnatural form of nature is possible. The present paper seeks to explain this paradoxical kind (...) of possibility by arguing that machines can share the human form of life and thus acquire human mindedness, which is to say they can be intelligent, conscious, sentient, etc. in precisely the way that a human being typically is. (shrink)

The Chinese room argument has presented a persistent headache in the search for Artificial Intelligence. Since it first appeared in the literature, various interpretations have been made, attempting to understand the problems posed by this thought experiment. Throughout all this time, some researchers in the Artificial Intelligence community have seen Symbol Grounding as proposed by Harnad as a solution to the Chinese room argument. The main thesis in this paper is that although related, these two issues (...) present different problems in the framework presented by Harnad himself. The work presented here attempts to shed some light on the relationship between John Searle’s intentionality notion and Harnad’s Symbol Grounding Problem. (shrink)

Whether human thinking can be formalized and whether machines can think in a human sense are questions that have been addressed by both Peirce and Searle. Peirce came to roughly the same conclusion as Searle, that the digital computer would not be able to perform human thinking or possess human understanding. However, his rationale and Searle's differ on several important points. Searle approaches the problem from the standpoint of traditional analytic philosophy, where the strict separation of (...) syntax and semantics renders understanding impossible for a purely syntactical device. Peirce disagreed with that analysis, but argued that the computer would only be able to achieve algorithmic thinking, which he considered the simplest type. Although their approaches were radically dissimilar, their conclusions were not. I will compare and analyze the arguments of both Peirce and Searle on this issue, and outline some implications of their conclusions for the field of Artificial Intelligence. (shrink)