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Searle's Chinese Room basically argues that a program cannot make a computer 'intelligent'.

Searle summarises the argument as

Imagine a native English speaker who knows no Chinese locked in a room full of boxes of Chinese symbols (a data base) together with a book of instructions for manipulating the symbols (the program). Imagine that people outside the room send in other Chinese symbols which, unknown to the person in the room, are questions in Chinese (the input). And imagine that by following the instructions in the program the man in the room is able to pass out Chinese symbols which are correct answers to the questions (the output). The program enables the person in the room to pass the Turing Test for understanding Chinese but he does not understand a word of Chinese.

What are the counter-arguments? Including the standard and not so standard. This notion of 'intelligence' intrigues me and I wish to see what other people have written said. Point me to your favourite counter-argument.

I have read Levesque's "Is It Enough to Get the Behaviour Right?"(http://ijcai.org/papers09/Abstracts/241.html) (full paper here) response. As Levesque is an AI researcher I would like to broaden my appreciation of counter-arguments from other fields.

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Searle's Chinese Room experiment doesn't make any sense, but some context is required to bring to light the contradiction inherent in his argument. At the time of its writing, he was attempting to fight an extreme position among AI theorists who supposed that all of the properties of understanding, whatever we agree that entails, could be reduced to or captured by a sufficiently powerful computer acting on symbols by completely formal methods i.e. mathematics.

In a nutshell, the CR experiment supposes it possible for a Chinese-speaker to pass meaningful messages in their native language to a man in the room who doesn't speak it and have them respond intelligently or meaningfully by following a purely algorithmic or formal process, whatever that may entail i.e. using a lookup table, following preset rules, etc.

This is a strange way to argue for intuition, or rather for intuition having the property of non-reducibility. Because if the man in the room can communicate with the man on the outside despite his lack of understanding, and the semantic gap is bridged by a completely formal process, then how is this anything but semantics being reduced to syntax? Exactly what Searle wants to argue against. Once again, the man on the outside understands what goes in and what comes out of the room, and the only means the man has to communicate is to follow a mechanical recipe for writing.

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    It doesn't make sense to you because you have misread Searle's argument that syntax is insufficient for semantics. He is not commenting upon intuition. Note the the person inside the box - the computer, i.e. the person doing the computation - does not understand Chinese. The "semantic gap" remains unbridged. – Mr. Kennedy Oct 28 '16 at 17:09
  • @Mr.Kennedy I do not agree with this answer, but, being an argumentative sod, I do not agree with your comment either. In insisting that the CR could only work if its operator understood Chinese, Searle is tacitly assuming that the instructions themselves cannot give rise to semantics. If the CR argument is not to be immediately dismissed on the grounds of circularity, the 'syntax cannot beget semantics' argument must be made elsewhere. – sdenham Apr 23 '18 at 13:30
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A trivial web search will bring up an enormous amount of material on this. For instance, the Stanford Encyclopedia of Philosophy's entry on Searle's "Chinese Room" thought experiment identifies three major categories of objections and goes into some detail on each (and provides generous citations.) Here is their high-level summary of the categories of possible responses to the argument:

(1) Some critics concede that the man in the room doesn't understand Chinese, but hold that at the same time there is some other thing that does understand. These critics object to the inference from the claim that the man in the room does not understand Chinese to the conclusion that no understanding has been created. There might be understanding by a larger, or different, entity. This is the strategy of The Systems Reply and the Virtual Mind Reply. These replies hold that there could be understanding in the original Chinese Room scenario.

(2) Other critics concede Searle's claim that just running a natural language processing program as described in the CR scenario does not create any understanding, whether by a human or a computer system. But these critics hold that a variation on the computer system could understand. The variant might be a computer embedded in a robotic body, having interaction with the physical world via sensors and motors (“The Robot Reply”), or it might be a system that simulated the detailed operation of an entire brain, neuron by neuron (“the Brain Simulator Reply”).

(3) Finally, some critics do not concede even the narrow point against AI. These critics hold that the man in the original Chinese Room scenario might understand Chinese, despite Searle's denials, or that the scenario is impossible. For example, critics have argued that our intuitions in such cases are unreliable. Other critics have held that it all depends on what one means by “understand”—points discussed in the section on the Intuition Reply. Others (e.g. Sprevak 2007) object to the assumption that any system (e.g. Searle in the room) can run any computer program. And finally some have argued that if it is not reasonable to attribute understanding on the basis of the behavior exhibited by the Chinese Room, then it would not be reasonable to attribute understanding to humans on the basis of similar behavioral evidence (Searle calls this last the “Other Minds Reply”).

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I quite like the chapter in Jack Copeland's Philosophy of AI book on the Chinese room. Even though it's an introductory book, it goes into quite some breadths and depth. The chapter has a summary of the arguments against the Chinese room, and the retorts from Searl and friends, plus counter retorts. Rest of the book also sets the context of the discussions very well and definitely goes into the various ways people understand and use the term 'intelligence' in the AI debates (you might find some you like)

A not too standard problem I have with the argument is its general argument form (non-standard problem because it's not specific to the Chinese room argument.)

The Chinese room is a modal argument which is deployed in a lot in philosophy of mind, -- examples of other modal arguments include things like the zombie argument from David Chalmers, the inverted spectrum argument, even brain in vats and Descarte's evil demon argument etc. They are generally of the form: it is possible that despite the appearance of X we cannot know *that X* because of an equally plausible alternative understanding of the situation which exclude X). In this case, It is possible that even if the instructions lets the box cogently parlay with a Chinese Turing tester, it is more than equally plausible that no real understanding is occurring (sneakily established by the fact that we'd all had prior agreement that the person inside has no understanding of Chinese, and he's the only one doing anything)

I am not sure how seriously to take modal arguments in general, because I feel they are invariably sneaky, and often leads to unfruitfulness due to the way things are set up. A successful modal argument will give you no greater reason to believe either of the options, and the rhetoric is usually set up such that you forget one of the options consists of a natural and familiar scenarios (sitting by the fire, seeing a person, receiving red qualia) and the another scenario which is speculative (deceived by evil demons, seeing a zombie, receiving green qualia instead of read). So each camp can't really engage the other side to have a fruitful discussion because each has a reason to believe that the other person's argument is flawed -- with no real criterion to evaluate either side of the claim. Sneaky IMHO.

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Searle's Chinese Room arguments depends initially on the assumption that, if there is an intelligent system, some component of the system must be intelligent. Carrying this a long way to a conclusion, each lepton and quark in the Universe must be intelligent to some extent. The alternatives to that conclusion are either (a) there is nothing intelligent in the Universe, or (b) intelligence is an emergent phenomenon. I'm going with the latter.

Searle then attempts to answer all objections (such as emergent intelligence) by indulging in more or less relevant discussion before claiming it reduces to the original Chinese Room, which he showed was not intelligent. This is an excellent example of begging the question, in the classic definition (assuming the proposition to be proven).

The most relevant other discussion is the claim that a computer could not be intelligent in the human sense without having human-type senses to sense things like a human. How is a computer to know what a hamburger is without eating one? This seems like a good line of argument, but Searle does not pursue it.

Searle claims, without support, that intelligence must be biological, and states that we know intentionality is biological in nature. I haven't seen this claim anywhere else, so I don't think we do know that.

  • Your options a) and b) don't include the option in which intelligence is held by an immaterial mind rather than an emergent mind. I don't quite recall how that option is framed in the formal literature, but I know it's framed in the literature. – elliot svensson Dec 20 '18 at 19:19
  • @elliotsvensson Good point. The question it raises is what it attaches to. – David Thornley Dec 22 '18 at 21:22
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As for your stated interest consider Searle's remarks here re: the ambiguity of "intelligence"

“Intelligence” is also ambiguous because it is ambiguous between real, honest to John observer-independent intelligence – as, for example, when a human being is thinking about something – and the observer-relative, derivative, metaphorical sense of intelligence – for instance, when we speak of my pocket calculator or computer as displaying intelligence.

Straw people, however, are easy to knock over:

Searle's Chinese Room basically argues that a program cannot make a computer 'intelligent'.

Searle is not arguing "that a program cannot make a computer 'intelligent'" (and this in whichever sense you mean program, intelligent or computer). Searle's Chinese Room demonstrates that semantics are not intrinsic to syntax. Big difference.

The Chinese Room Argument is a refutation of the computational theory of mind, not a refutation of "artificial intelligence". Searle is very clear that he is only arguing against what he identifies as "strong AI" and not "weak AI". Furthermore, he points out that we are biological machines in a sense of the words. As we know computational machines today (i.e. Turing machines), they are insufficient for anything more that syntactical manipulations and do not (read: can not) achieve semantic content. To understand Searle's argument, you will need to familiarize yourself with the distinctions between observer-relative and observer-independent, the epistemic and ontological senses of first- and third-person objectivity and subjectivity, as well the ambiguity of concepts such as "artificial", "intelligent", "information" and such.

To broaden your understanding of counter-arguments, first start with the actual argument you seek counter to:

Searle, John. R. (1980)
"Minds, Brains, and Programs"

Abstract.
This article can be viewed as an attempt to explore the consequences of two propositions. (1) Intentionality in human beings (and animals) is a product of causal features of the brain. I assume this is an empirical fact about the actual causal relations between mental processes and brains. It says simply that certain brain processes are sufficient for intentionality. (2) Instantiating a computer program is never by itself a sufficient condition of intentionality. The main argument of this paper is directed at establishing this claim. The form of the argument is to show how a human agent could instantiate the program and still not have the relevant intentionality. These two propositions have the following consequences (3) The explanation of how the brain produces intentionality cannot be that it does it by instantiating a computer program. This is a strict logical consequence of 1 and 2. (4) Any mechanism capable of producing intentionality must have causal powers equal to those of the brain. This is meant to be a trivial consequence of 1. (5) Any attempt literally to create intentionality artificially (strong AI) could not succeed just by designing programs but would have to duplicate the causal powers of the human brain. This follows from 2 and 4.

"Could a machine think?" On the argument advanced here only a machine could think, and only very special kinds of machines, namely brains and machines with internal causal powers equivalent to those of brains. And that is why strong AI has little to tell us about thinking, since it is not about machines but about programs, and no program by itself is sufficient for thinking.

Furthermore, see this article for a continuation of Searle's argument which demonstrates that syntax is not intrinsic to physics.

You might also enjoy this exchange in the NYR between Searle and Motzkin (note in particular the technical end note re: the Turing test & Turing machines)

The test is very much part of the behaviorism of the era in which Turing wrote his article; and like all such forms of behaviorism it makes a fundamental confusion between the way we would verify the presence of a mental phenomenon from the third person point of view with the actual first person existence of the phenomenon. As interpreted by Motzkin, the Turing test confuses epistemology with ontology.

Lastly, the Turing test is a high bar to get under and it really says nothing at all about the test subject. It says much more about the tester. The Turing test is by no means a litmus test for consciousness. Passing it has been described as little more than "a parlor trick" - the focus upon which has been derided as "obnoxious and stupid" by no less than Marvin Minsky. For exciting work being done in the field of machine learning, check out Jürgen Schmidhuber.

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