What psychological and philosophical significance should we attach to recent efforts at computer simulations of human cognitive capacities? In answering this question, I find it useful to distinguish what I will call “strong” AI from “weak” or “cautious” AI (artificial intelligence). According to weak AI, the principal value of the computer in the study of the mind is that it gives us a very powerful tool. For example, it enables us to formulate and test hypotheses in a more rigorous and precise fashion. But according to strong AI, 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. In strong AI, because the programmed computer has cognitive states, the programs are not mere tools that enable us to test psychological explanations; rather, the programs are themselves the explanations.
I have no objection to the claims of weak AI, at least as far as this article is concerned. My discussion here will be directed at the claims I have defined as those of strong AI, specifically the claim that the appropriately programmed computer literally has cognitive states and that the programs thereby explain human cognition. When I hereafter refer, I have in mind the strong version, as expressed by these two claims.
I will consider the work of Roger Schank and his colleagues at Yale (Schank and Abelson 1977), because I am more familiar with it than with any other similar claims, and because it provides a very clear example of the sort of work I wish to examine. But nothing that follows depends upon the details of Schank’s programs. The same arguments would apply to Winograd’s SHRDLU (Winograd 1973), Weizenbaum’s ELIZA (Weizenbaum 1965), and indeed any Turing machine simulation of human mental phenomena. [See “Further Reading” for Searle’s references.]
Very briefly, and leaving out the various details, one can describe Schank’s program as follows: The aim of the program is to simulate human ability to understand stories. It is characteristic of human being story-understanding capacity that they can answer questions about story even though the information that they give was never explicitly stated in the story. Thus, for example, suppose you are given the following story: “A man went into a restaurant and ordered a hamburger. When the hamburger arrived it was burned to a crisp, and the man stormed of the restaurant angrily, without paying for the hamburger or leaving a tip.” Now, if you are asked “Did the man eat the hamburger?” you presumably answer, “No, he did not.” Similarly, if you are given following story: “A man went into a restaurant and ordered a hamburger when the hamburger came he was very pleased with it; and as he left restaurant he gave the waitress a large tip before paying his bill,” and are asked the question, “Did the man eat the hamburger?” you presumably answer, “Yes, he ate the hamburger.” Now Schank’s machines can similarly answer questions about restaurants in this fashion. To do this, they have a “representation” of the sort of information that human beings have about restaurants, which enables them to answer such questions as those above, given these sorts of stories. When the machine is given the story and then asked the question, the machine will print out answers of the sort that we would expect human beings to give if told similar stories. Partisans of strong AI claim that in this question and answer sequence the machine is not only simulating a human ability but also (1) that the machine can literally be said to understand the story and provide the answers to questions, and (2) that what the machine and its program do explains the human ability to understand the story and answer questions about it.
Both claims seem to me to be totally unsupported by Schank’s work as I will attempt to show in what follows. (I am not, of course, saying Schank himself is committed to these claims.)
One way to test any theory of the mind is to ask oneself what it would be like if my mind actually worked on the principles that the theory says all minds work on. Let us apply this test to the Schank program with the following Gedankenexperiment. Suppose that I’m locked in a room and given a large batch of Chinese writing. Suppose furthermore (as is indeed the case) that I know no Chinese, either written or spoken, and that I’m not even confident that I could recognize Chinese writing as Chinese writing distinct from, say, Japanese writing or meaningless squiggles. To me, Chinese writing is just so many meaningless squiggles. Now suppose further that after this first batch of Chinese writing I am given a second batch of Chinese script together with a set of rules for correlating the second batch with the first batch. The rules are in English, and I understand these rules as well as any other native speaker of English. They enable me to correlate one set of formal symbols with another set of formal symbols, and all that “formal” means here is that I can identify the symbols entirely by their shapes. Now suppose also that I am given a third batch of Chinese symbols together with some instructions, again in English, that enable me to correlate elements of this third batch with the first two batches, and these rules instruct me how to give back certain Chinese symbols with certain sorts of shapes in response to certain sorts of shapes given me in the third batch. Unknown to me, the people who are giving me all of these symbols call the first batch a “script,” they call the second batch a “story,” and they call the third batch “questions.” Furthermore, they call the symbols I give them back in response to the third batch “answers to the questions,” and the set of rules in English that they gave me, they call the “program.” Now just to complicate the story a little, imagine that these people also give me stories in English, which I understand, and they then ask me questions in English about these stories, and I give them back answers in English. Suppose also that after a while I get so good at following the instructions for manipulating the Chinese symbols and the programmers get so good at writing the programs that from the external point of view—that is, from the point of view of somebody outside the room in which I am locked—my answers to the questions are absolutely indistinguishable from those of native Chinese speakers. Nobody just looking at my answers can tell that I don’t speak a word of Chinese. Let us also suppose that my answers to the English questions are, as they no doubt would be, indistinguishable from those of other native English speakers, for the simple reason that I am a native English speaker. From the external point of view—from the point of view of someone reading my “answers”—the answers to the Chinese questions and the English questions are equally good. But in the Chinese case, unlike the English case, I produce the answers by manipulating uninterpreted formal symbols. As far as the Chinese is concerned, I simply behave like a computer: I perform computational operations on formally specified elements. For the purposes of the Chinese, I am simply an instantiation of the computer program.
Now the claims made by strong AI are that the programmed computer understands the stories and that the program in some sense explains human understanding. But we are now in a position to examine these claims in light of our thought experiment.
1. As regards the first claim, it seems to me quite obvious in the example that I do not understand a word of the Chinese stories. I have inputs and outputs that are indistinguishable from those of the native Chinese speaker, and I can have any formal program you like, but I still understand nothing. For the same reasons, Schank’s computer understands nothing of any stories, whether in Chinese, English, or whatever, since in the Chinese case the computer is me, and in cases where the computer is not me, the computer has nothing more than I have in the case where I understand nothing.
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“Minds, Brains, and Programs,” by John R. Searle, from