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)

In this paper I submit that the “Chinese room” argument rests on the assumption that understanding a sentence necessarily implies being conscious of its content. However, this assumption can be challenged by showing that two notions of consciousness come into play, one to be found in AI, the other in Searle’s argument, and that the former is an essential condition for the notion used by Searle. If Searle discards the first, he not only (...) has trouble explaining how we can learn a language but finds the validity of his own argument in jeopardy. (shrink)

The apparently intelligent doings of computers occasion philosophical debate about artificial intelligence . Evidence of AI is not bad; arguments against AI are: such is the case for. One argument against AI--currently, perhaps, the most influential--is considered in detail: John Searle's Chinese room argument . This argument and its attendant thought experiment are shown to be unavailing against claims that computers can and even do think. CRA is formally invalid and informally fallacious. CRE's putative (...) experimental result is not robust and fails to generalize from understanding to other mental attributes as claimed. Further, CRE depends for its credibility, in the first place, on a dubious tender of the epistemic privilege of overriding all "external" behavioral evidence to first person disavowals of mental properties like understanding. ;Advertised as effective against AI, Searle's argument is an ignoratio elenchi, feigning to refute AI by disputing a similar claim of "strong AI" or Turing machine functionalism metaphysically identifying minds with programs. AI, however, is warranted independently of FUN: even if CRA disproved FUN this would still fail to refute or seriously disconfirm claims of AI. Searle's contention that everyday predications of mental terms of computers are discountable as equivocal "as-if" predications--impugning independent seeming-evidence of AI if tenable--is unwarranted. Lacking intuitive basis, such accusations of ambiguity require theoretical support. The would-be theoretical differentiation of intrinsic intentionality from as-if intentionality Searle propounds to buttress allegations of ambiguity against mental attributions to computers, however, depends either on dubious doctrines of objective intrinsicality according to which meanings are physically in the head or on even more dubious notions of subjective intrinsicality according to which meanings are phenomenologically "in" consciousness. Neither would such would-be differentiae as these unproblematically rule out seeming instances of AI if granted. The dubiousness of as if dualistic identification of thought with consciousness also undermines the epistemic privileging of the "first person point of view" crucial to Searle's thought experiment. (shrink)

_The Chinese room argument_ - John Searle's (1980a) thought experiment and associated (1984) derivation - is one of the best known and widely credited counters to claims of artificial intelligence (AI), i.e., to claims that computers _do_ or at least _can_ (someday might) think. According to Searle's original presentation, the argument is based on two truths: _brains cause minds_ , and _syntax doesn't_ _suffice for semantics_ . Its target, Searle dubs "strong AI": "according to (...) strong AI," according to Searle, "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" to "weak AI". According to weak AI, according to Searle, computers just. (shrink)

Searle's Chinese room argument is analyzed from a cognitive point of view. The analysis is based on a newly developed model of conceptual integration, the many space model proposed by Fauconnier and Turner. The main point of the analysis is that the central inference constructed in the Chinese room scenario is a result of a dynamic, cognitive activity of conceptual blending, with metaphor defining the basic features of the blending. Two important consequences follow: (1) (...) Searle's recent contention that syntax is not intrinsic to physics turns out to be a slightly modified version of the old Chinese room argument; and (2) the argument itself is still open to debate. It is persuasive but not conclusive, and at bottom it is a topological mismatch in the metaphoric conceptual integration that is responsible for the non-conclusive character of the Chinese room argument. (shrink)

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

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)

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)

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.

Detractors of Searle’s Chinese Room Argument have arrived at a virtual consensus that the mental properties of the Man performing the computations stipulated by the argument are irrelevant to whether computational cognitive science is true. This paper challenges this virtual consensus to argue for the first of the two main theses of the persons reply, namely, that the mental properties of the Man are what matter. It does this by challenging many of the arguments and (...) conceptions put forth by the systems and logical replies to the Chinese Room, either reducing them to absurdity or showing how they lead, on the contrary, to conclusions the persons reply endorses. The paper bases its position on the Chinese Room Argument on additional philosophical considerations, the foundations of the theory of computation, and theoretical and experimental psychology. The paper purports to show how all these dimensions tend to support the proposed thesis of the persons reply. (shrink)

John Searle begins his ``Consciousness, Explanatory Inversion and Cognitive Science'' with " ``Ten years ago in this journal I published an article criticising what I call Strong AI, the view that for a system to have mental states it is sufficient for the system to implement the right sort of program with right inputs and outputs. Strong AI is rather easy to refute and the basic argument can be summarized in one sentence: {it a system, me for example, (...) could implement a program for understanding Chinese, for example, without understanding any Chinese at all.} This idea, when developed, became known as the Chinese Room Argument.'' " The Chinese Room Argument can be refuted in one sentence. (shrink)

John Searle has argued that one can imagine embodying a machine running any computer program without understanding the symbols, and hence that purely computational processes do not yield understanding. The disagreement this argument has generated stems, I hold, from ambiguity in talk of 'understanding'. The concept is analysed as a relation between subjects and symbols having two components: a formal and an intentional. The central question, then becomes whether a machine could possess the intentional component with or without (...) the formal component. I argue that the intentional state of a symbol's being meaningful to a subject is a functionally definable relation between the symbol and certain past and present states of the subject, and that a machine could bear this relation to a symbol. I sketch a machine which could be said to possess, in primitive form, the intentional component of understanding. Even if the machine, in lacking consciousness, lacks full understanding, it contributes to a theory of understanding and constitutes a counterexample to the Chinese Room argument. (shrink)

John Searle has argued that one can imagine embodying a machine running any computer program without understanding the symbols, and hence that purely computational processes do not yield understanding. The disagreement this argument has generated stems, I hold, from ambiguity in talk of 'understanding'. The concept is analysed as a relation between subjects and symbols having two components: a formal and an intentional. The central question, then becomes whether a machine could possess the intentional component with or without (...) the formal component. I argue that the intentional state of a symbol's being meaningful to a subject is a functionally definable relation between the symbol and certain past and present states of the subject, and that a machine could bear this relation to a symbol. I sketch a machine which could be said to possess, in primitive form, the intentional component of understanding. Even if the machine, in lacking consciousness, lacks full understanding, it contributes to a theory of understanding and constitutes a counterexample to the Chinese Room argument. (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)

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)

In this paper I submit that the “Chinese room” argument rests on the assumption that understanding a sentence necessarily implies being conscious of its content. However, this assumption can be challenged by showing that two notions of consciousness come into play, one to be found in AI, the other in Searle’s argument, and that the former is an essential condition for the notion used by Searle. If Searle discards the first, he not only (...) has trouble explaining how we can learn a language but finds the validity of his own argument in jeopardy. (shrink)

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)

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)

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)

John Searle's 1980a) thought experiment and associated 1984a) argument is one of the best known and widely credited counters to claims of artificial intelligence (AI), i.e., to claims that computers _do_ or at least _can_ (roughly, someday will) think. According to Searle's original presentation, the argument is based on two truths: _brains cause minds_ , and _syntax doesn't suffice_ _for semantics_ . Its target, Searle dubs "strong AI": "according to strong AI," according to Searle, (...) "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" to "weak AI". According to weak AI, according to Searle, computers just. (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)

Computationalism. According to computationalism, to explain how the mind works, cognitive science needs to find out what the right computations are -- the same ones that the brain performs in order to generate the mind and its capacities. Once we know that, then every system that performs those computations will have those mental states: Every computer that runs the mind's program will have a mind, because computation is hardware independent : Any hardware that is running the right program has the (...) right computational states. (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.

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)

The paper is concerned with John Searle’s famous Chinese room argument. Despite being objected to by some, Searle’s Chinese room argument appears very appealing. This is because Searle’s argument is based on an intuition about the mind that ‘we’ all seem to share. Ironically, however, Chinese philosophers don’t seem to share this same intuition. The paper begins by first analysing Searle’s Chinee room argument. It then introduces (...) what can be seen as the (implicit) Chinese view of the mind. Lastly, it demonstrates a conceptual difference between Chinese and Western philosophy with respect to the notion of mind. Thus, it is shown that one must carefully attend to the presuppositions underlying Chinese philosophising in interpreting Chinese philosophers. (shrink)

"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.

Searle's Chinese Room was supposed to prove that computers can't understand: the man in the room, following, like a computer, syntactical rules alone, though indistinguishable from a genuine Chinese speaker, doesn't understand a word. But such a room is impossible: the man won't be able to respond correctly to questions like What is the time?, even though such an ability is indispensable for a genuine Chinese speaker. Several ways to provide the room (...) with the required ability are considered, and it is concluded that for each of these the room will have understanding. Hence, Searle's argument is invalid. (shrink)

The discussion between Searle and the Churchlands over whether or not symbolmanipulating computers generate semantics will be confronted both with the rulesceptical considerations of Kripke/wittgenstein and with Wittgenstein's privatelanguage argument in order to show that the discussion focuses on the wrong place: meaning does not emerge in the brain. That a symbol means something should rather be conceived as a social fact, depending on a mutual imputation of linguistic competence of the participants of a linguistic practice to one (...) another. The alternative picture will finally be applied to small children, animals, and computers as well. (shrink)

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 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)

Two critical questions spun the web of the Turing test debate. First, can an appropriately programmed machine pass the Turing test? Second, is passing the test by such a machine, ipso facto, considered proof that it is intelligent and hence “minded”? While the first question is technological, the second is purely philosophical. Focusing on the second question, this article interrogates the implication of John Searle’s Chinese room denial of machine intelligence. The thrust of Searle’s argument (...) is that a machine lacks intentionality, so it can only simulate intelligence, not duplicate it. In his thinking, whatever a machine inputs to generate an intelligent output has no bearing on humanlike intelligence. Incidentally, Searle did not classify such a machine’s output as simulated and non-intelligent, nor did he explain how this output is actualised with mere simulation. The connection between “unintelligent machine’s input” and “intelligent machine’s output” is at this point shrouded in mystery. Consequently, the more Searle attempts a denial of machine intelligence in the Chinese room, the more he mystifies it. Using the method of critical analysis, this article advances three fundamental arguments to prove a machines’ obscurity in the Chinese room thought experiment. On the ground of Searle’s conviction, the first argument queries the absurdity in bypassing intentionality to produce intelligence; the second points out the obfuscation in generating intelligence with mere computation, and the third draws attention to the dilemma of classifying a machine’s output either as real-life intelligent behaviour or simulated intelligent behaviour. (shrink)

Searle (1980) constructed the Chinese Room (CR) to argue against what he called \Strong AI": the claim that a computer can understand by virtue of running a program of the right sort. Margaret Boden (1990), in giving the English Reply to the Chinese Room argument, has pointed out that there isunderstanding in the Chinese Room: the understanding required to recognize the symbols, the understanding of English required to read the rulebook, etc. I (...) elaborate on and defend this response to Searle. In particular, I use the insight of the English Reply to contend that Searle's Chinese Room cannot argue against what I call the claim of \Weak Strong AI": there are some cases of understanding that a computer can achieve solely by virtue of that computer running a program. I refute several objections to my defense of the Weak Strong AI thesis. (shrink)

William Rapaport, in “How Helen Keller used syntactic semantics to escape from a Chinese Room,” (Rapaport 2006), argues that Helen Keller was in a sort of Chinese Room, and that her subsequent development of natural language fluency illustrates the flaws in Searle’s famous Chinese Room Argument and provides a method for developing computers that have genuine semantics (and intentionality). I contend that his argument fails. In setting the problem, Rapaport uses his (...) own preferred definitions of semantics and syntax, but he does not translate Searle’s Chinese Room argument into that idiom before attacking it. Once the Chinese Room is translated into Rapaport’s idiom (in a manner that preserves the distinction between meaningful representations and uninterpreted symbols), I demonstrate how Rapaport’s argument fails to defeat the CRA. This failure brings a crucial element of the Chinese Room Argument to the fore: the person in the Chinese Room is prevented from connecting the Chinese symbols to his/her own meaningful experiences and memories. This issue must be addressed before any victory over the CRA is announced. (shrink)

This article is an accompaniment to Anthony Freeman’s review of Views into the Chinese Room, reflecting on some pertinent outstanding questions about the Chinese room argument. Although there is general agreement in the artificial intelligence community that the CRA is somehow wrong, debate continues on exactly why and how it is wrong. Is there a killer counter-argument and, if so, what is it? One remarkable fact is that the CRA is prototypically a thought experiment, (...) yet it has been very little discussed from the perspective of thought experiments in general. Here, I argue that the CRA fails as a thought experiment because it commits the fallacy of undersupposing, i.e., it leaves too many details to be filled in by the audience. Since different commentators will often fill in details differently, leading to different opinions of what constitutes a decisive counter, the result is 21-plus years of inconclusive debate. (shrink)

John Searle’s Chinese Room Argument purports to demonstrate that syntax is not sufficient for semantics, and, hence, because computation cannot yield understanding, the computational theory of mind, which equates the mind to an information processing system based on formal computations, fails. In this paper, we use the CRA, and the debate that emerged from it, to develop a philosophical critique of recent advances in robotics and neuroscience. We describe results from a body of work that contributes (...) to blurring the divide between biological and artificial systems; so-called animats, autonomous robots that are controlled by biological neural tissue and what may be described as remote-controlled rodents, living animals endowed with augmented abilities provided by external controllers. We argue that, even though at first sight, these chimeric systems may seem to escape the CRA, on closer analysis, they do not. We conclude by discussing the role of the body–brain dynamics in the processes that give rise to genuine understanding of the world, in line with recent proposals from enactive cognitive science. (shrink)

I argue in this article that there is a mistake in Searle's Chinese room argument that has not received sufficient attention. The mistake stems from Searle's use of the Church-Turing thesis. Searle assumes that the Church-Turing thesis licences the assumption that the Chinese room can run any program. I argue that it does not, and that this assumption is false. A number of possible objections are considered and rejected. My conclusion is that (...) it is consistent with Searle's argument to hold onto the claim that understanding consists in the running of a program. (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 (...) 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)

Ford’s Helen Keller Was Never in a Chinese Room claims that my argument in How Helen Keller Used Syntactic Semantics to Escape from a Chinese Room fails because Searle and I use the terms ‘syntax’ and ‘semantics’ differently, hence are at cross purposes. Ford has misunderstood me; this reply clarifies my theory.

I make three points about Searle’s philosophical work on consciousness and intentionality. First, I comment on Searle’s presentation and paper “The Problems of Consciousness.” I show that one of Searle’s philosophical claims about the relation between consciousness and intentionality appears to conflict with a demand he makes on acceptable empirical theories of the brain. Second, I argue that closer attention to the difference between conceptual connections and empirical connections corrects and improves Searle’s response to the so-called (...) “Logical Connections” argument, the argument that claims that mental states cannot be causes, since they are conceptually connected with actions. Third, I give a formulation of his Chinese Room argument that avoids some tempting responses. (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)

Harnad and I agree that the Chinese Room Argument deals a knockout blow to Strong AI, but beyond that point we do not agree on much at all. So let's begin by pondering the implications of the Chinese Room. The Chinese Room shows that a system, me for example, could pass the Turing Test for understanding Chinese, for example, and could implement any program you like and still not understand a word of (...) Chinese. Now, why? What does the genuine Chinese speaker have that I in the Chinese Room do not have? The answer is obvious. I, in the Chinese room, am manipulating a bunch of formal symbols; but the Chinese speaker has more than symbols, he knows what they mean. That is, in addition to the syntax of Chinese, the genuine Chinese speaker has a semantics in the form of meaning, understanding, and mental contents generally. (shrink)

The Chinese Room Argument purports to show that‘ syntax is not sufficient for semantics’; an argument which led John Searle to conclude that ‘programs are not minds’ and hence that no computational device can ever exhibit true understanding. Yet, although this controversial argument has received a series of criticisms, it has withstood all attempts at decisive rebuttal so far. One of the classical responses to CRA has been based on equipping a purely computational device (...) with a physical robot body. This response, although partially addressed in one of Searle’s original contra arguments - the ‘robot reply’ - more recently gained friction with the development of embodiment and enactivism1, two novel approaches to cognitive science that have been exciting roboticists and philosophers alike. Furthermore, recent technological advances - blending biological beings with computational systems - have started to be developed which superficially suggest that mind may be instantiated in computing devices after all. This paper will argue that (a) embodiment alone does not provide any leverage for cognitive robotics wrt the CRA, when based on a weak form of embodiment and that (b) unless they take the body into account seriously, hybrid bio-computer devices will also share the fate of their disembodied or robotic predecessors in failing to escape from Searle’s Chinese room. (shrink)

This paper is a follow-up of the first part of the persons reply to the Chinese Room Argument. The first part claims that the mental properties of the person appearing in that argument are what matter to whether computational cognitive science is true. This paper tries to discern what those mental properties are by applying a series of hypothetical psychological and strengthened Turing tests to the person, and argues that the results support the thesis that the (...) Man performing the computations characteristic of understanding Chinese actually understands Chinese. The supposition that the Man does not understand Chinese has gone virtually unquestioned in this foundational debate. The persons reply acknowledges the intuitive power behind that supposition, but knows that brute intuitions are not epistemically sacrosanct. Like many intuitions humans have had, and later deposed, this intuition does not withstand experimental scrutiny. The second part of the persons reply consequently holds that computational cognitive science is confirmed by the Chinese Room thought experiment. (shrink)

John Searle's forthcoming book ‘Rationality in Action’ presents a sophisticated and innovative account of the rationality of action. In the book Searle argues against what he calls the classical model of rationality. In the debate that follows Barry Smith challenges some implications of Searle's account. In particular, Smith suggests that Searle's distinction between observer-relative and observer–independent facts of the world is ill suited to accommodate moral concepts. Leo Zaibert takes on Searle's notion of the gap. (...) The gap exists between the reasons that we have for acting and our actions. According to Searle, whenever there is no gap, our actions exhibit irrationality. Zaibert points out a certain obscurity in Searle's treatment of the gap, particularly in connection with Searle's notion of ‘recognitional rationality’. Finally, Josef Moural examines the interactions between Searle's theory of institutions and his theory of rationality, with emphasis on the connections between intentionality and Searle's notion of the ‘background’. (shrink)

In contrast to many areas of contemporary philosophy, something like a carnival atmosphere surrounds Searle’s Chinese room argument. Not many recent philosophical arguments have exerted such a pull on the popular imagination, or have produced such strong reactions. People from a wide range of fields have expressed their views on the argument. The argument has appeared in Scientific American, television shows, newspapers, and popular science books. Preston and Bishop’s recent volume of essays reflects this (...) interdisciplinary atmosphere. The volume includes essays from computer science, neuroscience, artificial intelligence, cognitive science, sociology, science studies, physics, mathematics, and philosophy. There are two sides to this interdisciplinary mix. On the one hand, it makes for interesting and fun reading for anyone interested in the Chinese room argument, but on the other, it raises the threat that the Chinese room argument might be left in some kind of interdisciplinary no man’s land. The Chinese room argument (CRA) is an argument against the possibility of Strong artificial intelligence (Strong AI). The thesis of Strong AI is that running a program is sufficient for, or constitutive of, understanding: it is merely in virtue of running a 1 particular program that a system understands. Searle appreciates that understanding is a complex notion, and so he has a particular form of understanding in mind: the understanding of simple stories. It seems intuitively obvious that when I read a simple story in English, I understand that story. One could say that somewhere in my head there is understanding going on. However, if I read a simple story written in Chinese (a language I do not speak), then there is no understanding going on. What makes the difference between these two cases? The advocate of Strong AI says that the difference.. (shrink)