ABSTRACT 1. THE TURING TEST

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1 THE TURING TEST, SEARLE S CHINESE ROOM, AND E.T. S CHINESE ROOM Jinchang Wang School of Business, Richard Stockton College of New Jersey, Pomona, NJ 08240, USA Jinchang.wang@stockton.edu, ABSTRACT The Turing Test is an empirical approach and a conceptual idea for testing whether a machine s intelligence level is on a par with humans based on the machine s behaviors and performances. Searle s Chinese Room argument challenges the Turing Test by showing that a machine following syntactical programs does not know semantics of the programs and does not understand the meaning of what it is doing. He claimed that such a dumb machine might pass the Turing Test but it was not intelligent. We develop an argument of E.T. s Chinese Room to refute Searle s points. E.T. s Chinese Room shows that using the behaviors due to intelligence, instead of the internal process of generating intelligence, to judge intelligent level of a machine is consistent with the purpose of AI researches, which is to develop intelligent machines with no restrictions on the approaches of generating intelligence. Key Words: artificial intelligence, Turing test, computer intelligence The Turing Test was proposed by Alan Turing in 1950 to test machine intelligence through reviewing the machine s behaviors. The idea of Turing Test remains relevant after sixty years and it has been a subject of intense debate among artificial intelligence (AI) scientists and philosophers. John Searle used his Chinese Room argument to show that a machine would not be intelligent if it did not understand what it was doing albeit it looked intelligent. We in this article challenge Searle s Chinese Room argument with an E.T. s Chinese Room argument, showing that the top priority of AI researchers is to develop machines that act and perform as intelligently as humans do, rather than machines with same internal mechanism as human brains. The idea of the Turing Test serves that purpose well. People may disagree on whether a machine that is able to pass the Turing Test is really intelligent, but they would not have disagreement that a machine that is able to pass the Turing Test would behave intelligently and do intelligent jobs as humans do. In the first section, we review briefly the Turing Test, its features, its role in AI, and debate on it. In Section 2, we discuss Searle s Chinese Room argument and Searle s proof of the dumbness of computers. In Section 3, we present our E.T. s Chinese Room argument that refutes Searle s Chinese Room argument, and elaborate our position on the issue. 1. THE TURING TEST At a conference at Dartmouth College in 1956, artificial intelligence (AI) as a subject of computer sciences was formally established. Six years before that, Alan Turing, a British

2 mathematician and a founder of computer sciences, foreseeing the potential of computer intelligence, put forward a method of testing machine intelligence. The method and the idea behind it are still timely at present, albeit it has been a subject of intense debate in the field of AI and philosophy. In his paper Computing Machinery and Intelligence (1950), Turing suggested that, instead of asking whether machines can think, we should ask whether machines can pass a behavioral intelligence test, which has come to be called the Turing Test: - A machine and a human counterpart are placed in two rooms. An interrogator in the third room, who cannot see the machine and human counterpart, talks to the machine/human through the keyboard and screen. If the interrogator is unable to distinguish the machine from the human, then the machine is assumed to be as intelligent as a human. The Turing Test is both a conceptual idea and an empirical method of testing machine intelligence. It is not supposed to test Can machines think, How well machines think, Whether machines think in the way same as humans. Instead, it is supposed to test How smart machines are. It has three important features, as summarized by Luger & Stubblefield (1989): (1) It gives us an objective notion of intelligence. That is, the behavior in response to a particular set of questions. This provides a standard for determining intelligence which avoids the inevitable debates over the true nature of intelligence. (2) It prevents us from being sidetracked by currently unanswerable questions and equivocal conceptions such as internal process of intelligence, whether or not machine understands what it is doing, whether machine is conscious. (3) It eliminates any bias in favor of living organisms over machine intelligence by focusing on solely the content of the answers to questions. The Turing Test represents an answer to a fundamental issue in AI: - How to test the quality of the product of AI. If we say that the ultimate task of AI researchers is to make machines with intelligence on a par with human, then developing the method to check the quality of the machine is an imperative task. The Turing Test provides an implementable approach and a conception for that issue. It has been cited and discussed widely in AI literatures. It was in significant early papers of AI collected by Webber and Nilsson (1981) and by Luger (1995), in The Encyclopedia of AI (Shapiro, 1992) which contains survey articles on almost every topic in AI; and in Wikipedia. It was in Haugeland book (1985) which gives an account of the philosophical and practical problems of AI, and in Nillson s book (2009) which provides an insightful and comprehensive history of AI. Alan Turing conjectured in 1950 that, by the year 2000 a computer could be programmed well enough to pass the test. He was a little too optimistic about the development of machine intelligence programs have yet to fool a sophisticated human judge. On the other hand, many people are now being fooled when they don t know they might be chatting with a computer. The ELAZA program and Internet chatbots such as MGONZ (Humphrys, 2008) and NATACHATA have fooled their correspondents repeatedly, and the chtbot CYBERLOVER has attracted the attention of law enforcement because of its penchant for tricking fellow chatters into divulging enough personal information that their identity can be stolen. The Loebner Prize competition, held annually since 1991, is the longest-running Turing Test-like contest. (Russell and Norvig 2010)

3 There have been equivocalities in the field of AI, such as what is thinking, what is a machine, what is intelligence, and what is mind. Instead of joining in the debate of those ambiguities, Turing proposed the empirical standard, behavior, for machine intelligence, which is more clearly defined and easily to be implemented. He bypassed the equivocalities, but did not resolve them, which left rooms for a number of criticisms. One is aimed at its bias toward purely symbolic problem-solving tasks. It does not test abilities that require perceptual skill or manual dexterity, even though these are important components of human intelligence. Conversely, the Turing Test needlessly constrains machine intelligence to fit a human mold. Do we really wish the machine to do mathematics as slowly and inaccurately as the human does it? Shouldn t an intelligent machine capitalize on its own assets, such as an infallible memory, rather than trying to emulate human cognition? (Luger and Stubblefield 1989). Art intelligence and sport intelligence are not tested in Turing Test. Shieber (1994) severely criticized the usefulness of the instantiation of Turing Test in the Loebner Prize competition. Ford and Hayes (1995) argued that the test itself was not helpful for AI. Bringsjord (2008) gave an advice for a Turing Test judge. Shieber (2004) and Epstein et.al. (2008) collected a number of debating essays on the Turing Test. Searle s Chinese Room argument (1997) challenged that a machine that passed the Turing Test might not be intelligent because it did not have semantics, which would be discussed in Section 2 and rest of this article. The idea of Turing Test has been extended to testing full range of intelligence. The so-called Total Turing Test includes a video signal so that the interrogator can test the subject s perceptual abilities. To pass the total Turing Test, the computer will need capability of vision to perceive objects, and robotics to manipulate objects and move about. (Russell and Norvig 2010) Two philosophical concepts related to Turing Test are weak AI and strong AI. As Russell and Norvig defined (2010), the weak AI hypothesis asserts that machines could act as if they were intelligent. The strong AI hypothesis asserts that machines that look intelligent are actually thinking (not just simulating thinking). According to Searle (1997), weak AI advocates that computers are useful tools in doing simulations of the mind; while strong AI claims that implementing the right program in any hardware at all is constitutive of mental states. That is, strong AI claims that the implemented program, by itself, is constitutive of having a mind, and the implemented program, by itself, guarantees mental life. Many AI researchers take the weak AI hypothesis for granted, and don t care about the strong AI hypothesis as long as their program works, they don t care whether you call it a simulation of intelligence or real intelligence. 2. SEARLE S CHINESE ROOM ARGUMENT The argument for the Turing Test is that a machine is intelligent if it looks intelligent enough. That is, the standard of machine intelligence is its behavior or performance, rather than the internal mechanism and process that produce intelligence. Many have disagreed at this point. They argued that intelligence is a much more sophisticated and superior matter than it looks, and that a machine looks intelligent might be cheating or be following unintelligent mechanical rules. They acknowledge the facts that computers are inexorably and increasingly becoming smarter and smarter, but they sensed something delicate in human spirit and mind that

4 was missed in the idea of the Turing Test. John Searle, a philosopher at University of California, Berkley, challenged the idea of the Turing Test with his Chinese Room argument (Searle 1997). They (computers) are immensely useful devices for simulating brain process. But the simulation of mental states is no more a mental state than the simulation of an explosion is itself not an explosion. Searle s Chinese Room works in this way: - A person, like Searle himself as he put, who does not know Chinese at all, is locked in a room and is given a rule book. He receives questions in Chinese, looks up the rule book, does operations according to the rules, and give back bunches of symbols of Chinese as the answers. From the views of the people outside the room, he understands Chinese perfectly. But he does not actually understand a single word of Chinese! Searle argues by using his Chinese Room that computers looks capable of doing intelligent work such as answering questions in Chinese, but they do not understand what they are doing. A machine having passed the Turing Test looks as smart as a human, but it is actually not intelligent since it simply following the rules to do mechanical operations without knowing what it is doing for. John Searle rejects Strong AI s claim that the mind is just a computer program. He once tried to prove computer programs can t be mind in three steps: (1) programs are entirely syntactical; (2) minds have semantics; (3) syntax is not same as, or sufficient for, semantics. So, computer programs are not minds. (Searle 1997) The proof has serious flaws. His central argument is, programs can t be mind because syntax is not semantics. But he did not define semantics and he did not prove that syntax will never lead to semantic in any case. He was assuming what he was proving: - He assumes that programs are entirely syntactical and proved that computer programs are entirely syntactical therefore not mind. Searle seemed not serious on his proof. He receded and gave up the intention of proving it in 2002, Someone is bound to ask, can you prove that the computer is not conscious? The answer to this questions is: Of course not. I cannot prove that the computer is not conscious, any more than I can prove that the chair I am sitting on is not conscious. (Searle 2002). With his Chinese Room argument, Searle tried to show that a computer may look intelligent, understanding, touching, and emotional, but it achieved them by following symbol processing rules, which are the senseless simulations of intelligence, understanding, touching, and emotions. Even though such a machine successfully passed Turing Test, it would not be assumed to be intelligent since it would have no understanding or no mind. 3. OUR E.T. S CHINESE ROOM ARGUMENT Searle argues by using his Chinese Room that the person in the room has no semantics about Chinese, therefore not intelligent, by simply following the syntactical rule book, even though his answers make perfect sense. Our questions to Searle s Chinese Room argument are: If the person in the room answered every question in Chinese perfectly, then why don t we think that he understand Chinese? Do we really care how a person does it in his brain when he shows that he knows Chinese?

5 We have developed an E.T. s Chinese Room scenario to address the above questions: E.T., an extraterrestrial stranded on Earth (a figure in movie E.T., directed by Steven Spielberg in 1982), instead of Searle, is locked in the Chinese Room. It answers the questions in Chinese passed to it in some way, following the rule book in Chinese Room or using some functions inside its brain. It answers the questions perfectly in the eyes of the people outside. No one knows how E.T. does it. Now what would people say about E.T. s capability on Chinese? We don t think people would say that we don t know whether E.T. understands Chinese or not, because we do not know how it does that. We don t think people would insist looking into E.T. s brain to investigate how it does it in order to judge whether it was really doing it or was simulating. We don t think people would have problems admitting the fact that E.T. understands Chinese even they do not know how it does it. People would say, Look, E.T. knows Chinese! E.T. is smart and learns fast! There are geniuses or savants in this world who can quickly tell what day any given date is quickly. No one knows how they do it. But it does not prevent us to call their works intelligent. Consider how to test the intelligence of E.T. It seems there is no method better than the Turing Test, as far as we do not want to kill and dissect it (it s likely that even we dissect E.T., we cannot understand what happens inside to generate E.T. s intelligent behaviors, even though we may realize that E.T. s intelligence processing mechanism is different from our biological mechanism). Why is the Turing Test good for testing a robot E.T. from somewhere deep in the universe but no good for a robot made by ourselves? The stumbling block in our minds is: we tend to take something mysterious and unknown as intelligent ; once the unknown becomes known it is no longer intelligent. Thirty years ago, people thought that a machine that could do spelling checking and grammar check would be intelligent. Now, millions of machines can do them, and people do not take them as intelligent functions, because people know that the spelling checking in the computer is nothing but searching on a huge database. We tend to use double standards. For mysterious E.T., we would like to say it is intelligent without insisting to know how it reaches that level of intelligence. But for a robot we make, we would demand its internal process must also be intelligent in addition to its behaviors. With the above arguments on E.T. s Chinese Room, we have to admit that the idea of the Turing Test is objective and reasonable, and it is an empirical and feasible method to test machine s intelligence, although the details of the test can arguably changed to adapt to different scenarios. Among the issues concerning about the intelligence of a machine, such as whether it acts smartly, whether the way it thinks is smart, and whether it thinks in the way same as us, whether it acts smartly is most important and interested by the people who are making intelligent machines. Airplanes made human dreams come to true to fly in the air like birds. Submarines made human dreams come to true to stay and swim deep in the ocean like fish. We have created calculators that calculate at a rate and accuracy no one ever dreamed of. In developing these machines, we did not demand the way machines did to be the same as birds, fish, and human brain. Our concerns were on the machines behaviors : moving in the air which is called flying, moving in the water which is called swimming, or finding results of a mathematical problem which is called solving. As far as the machine can fly, swim, or compute, the people who made it would not be tied down to certain mechanism to realize the

6 designated behavior. If people had restricted themselves on developing machines that worked same way as birds, fish, and human brains, we would not have had airplanes, submarines, and calculators. AI people are working on creating machines whose level of intelligence and consciousness is on a par with humans. The top priority is to make a machine acting as intelligently as a human. How the machine does it is not the top concern. No one has proved that the way our biological brains work is the only way to have intelligence. We thus should not take a particular intelligence process, such as our brain s process, as the standard for intelligence. There are currently alternative ways for automated reasoning with uncertainties, such as rule-based system, frames, neural network, Bayesian network, none of them are the way that exactly occurred in our brains when we are doing reasoning. In fact, we do not know yet what exactly occurs in our brains when we are thinking. For those who insist that machines are not intelligent unless they are thinking exactly in the way our biological brains do, they themselves do not know what they are talking about because no one is aware yet of what is the way of our biological brains. Put it in another way, just because people are unaware of what exactly happens in our brain to come up with intelligence, they insist that only the unknown mysterious process is intelligent. People imitated birds to fly because they knew little about flying and thought the way of birds is the only way of flying. They eventually gave up the way of mimicking birds to fly after they knew more about the flying and aerodynamics: - There turned out to be alternative ways of flying! The benchmark of intelligence should be set up on intelligence itself, rather than the way of generating intelligence. Some may dispute that intelligence is both a process and the outcome of the process. We do not have disagreement on it. But between the outcome and process, our first priority is on the outcome. That is, at this stage, we are more concerned to have machines whose intelligent outcomes are comparable to human intelligent outcomes. The Wright brothers might have been thinking in the same way when they were making their first plane hundred years ago: - Let s make a machine that could fly as the outcome, regardless the way of flying and the definition of flying (some insisted that flying is a process in which two wing flap up and down). Using machine s internal process to judge its intelligent level would empirically cause problems since we do not yet know much about human internal process of generating intelligence. It would be less ambiguous if judging by its external behaviors and actions. 4. CONCLUSION For AI people, the top interest is in making a machine with its intelligence and consciousness level on a par with humans, with whatever approach to generate the intelligence, albeit the research on how our brain works is a topic for scientists in cognitive science, computer sciences including AI, biology, physics, and philosophy. For the top concern of AI, the Turing Test is a proper method by using the external behaviors, rather than internal process, as the measurement to assess the achievements of AI. After people have better understandings of semantics, intelligence, consciousness, mind, and spirit, as well as the process of generating them, an approach of testing machine s mentality better than the Turing Test might emerge.

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