Searle's Argument Against The Turing Test

Through this, Searle argues that if a human and machine receive the same input and then respond by the same output, how are they any different from one another? When given the same purpose, humans and machines have the same response, therefore machines may have a mind. Gilbert Ryle created The computational theory of mind that claims “Computers behave in seemingly rational ways; their inner program causes them to behave in this way and therefore mental states are just like computational states”. He continues on by saying that “If logic can be used to command, and these commands can be coded into logic, then these commands can be coded in terms of 1s and 0s, therefore giving modern computers logic. Through this, how is one to tell if robots don’t have minds if they use logic just like humans do. When the purpose of humans and machines are the same, they may process differently in order to complete that purpose, although they may have the same output. Because humans and machines receive the same input and return the same output, they both have minds in addition to functions and processes in order to do that.

Searles argues with his main concept of the Chinese Room, an experiment Prove his main claim that Strong AI must be actually a real (human) brain to work. I will argue against this to show StrongAi can be possible as long it starts to create rules and not responses.

One of the hottest topics that modern science has been focusing on for a long time is the field of artificial intelligence, the study of intelligence in machines or, according to Minsky, “the science of making machines do things that would require intelligence if done by men”.(qtd in Copeland 1). Artificial Intelligence has a lot of applications and is used in many areas. “We often don’t notice it but AI is all around us. It is present in computer games, in the cruise control in our cars and the servers that route our email.” (BBC 1). Different goals have been set for the science of Artificial Intelligence, but according to Whitby the most mentioned idea about the goal of AI is provided by the Turing Test. This test is also called the

With every passing year, it seems as though humans are becoming more attached to their electronic devices as technology advances at an astounding rate. As a result of this, many people believe that the trade off for having such advanced technology is a decrease in overall brain function, a point that is essentially what Nicholas Carr is arguing is true in his essay Is Google Making Us Stupid?. However, a majority of Carr’s argument lies in the fact that people do not read as much because of the Internet, only want information directly given to them instead of having to read through pages of words, and are becoming dangerously reliant on artificial intelligence. However, based on the recent surge of technological

Even with the correct programming a computer cannot freely think for itself, with its own conscious thought. John Searle is a philosopher of mind and language at UC Berkeley. Searle’s Chinese Room Argument is against the premise of Strong AI. He argues that even though a computer may have the ability to compute the use of syntax (Weak AI), a computer could not be able to understand the meaning behind the words it is communicating. Semantics convey both intentional and un-intentional content in communication. Though a computer could be programmed to recognize which words would convey the correct meaning of a symbol. This,

With the fast-paced development of technology, more and more people get used to reading through computers rather than printed books. While acknowledging the convenience provided by the internet, Nicholas Carr expresses his concerns that “as we come to rely on computers to mediate our understanding of the world, it is our own intelligence that flattens into artificial intelligence” (328) in his article Is Google Making Us Stupid? Published in the 2008 issue of the Atlantic. However, there existed some weaknesses in the argument due to his overstated pathos, inappropriate examples, and faulty logics, which to some extent hurt his persuasiveness.

The thinking process of a human does not have the capabilities of figuring out problems by research in seconds compared to computer intelligence. Thompson proves this by describing how a computer plays chess compared to a human; a computer was able to calculate moves and different solution to win the chess match. A humans capabilities do not match a computers, but placing them together can be amazing. Since humans and computers have different ways of thinking, one can benefit the other. Thompson states that although computers remain superior, one should never put down their capabilities, as humans can be just as intelligent. The differences between article one and two is; in "Is Google Making Us Stupid" by Nicholas Carr, Carr puts technology more as a disadvantage in our brains compared to "Smarter Than You Think" by Clive

The purpose of this paper is to present John Searle’s Chinese room argument in which it challenges the notions of the computational paradigm, specifically the ability of intentionality. Then I will outline two of the commentaries following, the first by Bruce Bridgeman, which is in opposition to Searle and uses the super robot to exemplify his point. Then I will discuss John Eccles’ response, which entails a general agreement with Searle with a few objections to definitions and comparisons. My own argument will take a minimalist computational approach delineating understanding and its importance to the concepts of the computational paradigm.

John Searle 1980(in Cooney, 2000), provides a thought experiment, commonly referred to as the Chinese room argument (CRA), to show that computers, programmed to simulate human cognition, are incapable of understanding language. The CRA requires us to consider a scenario where Searle, who is illiterate in Chinese, finds himself locked in a room with a book containing Chinese characters. Additionally, he has another book which has a set of instructions written in English (which he understands), that allows him to match and manipulate the Chinese characters so that he can provide appropriate written responses (in Chinese) to incoming questions, which are also written in Chinese. Moreover, Searle has a pile of blank paper with which he uses to jot down his answers. Subsequently, Searle becomes so proficient in providing responses that the quality of his answers matches that of a native Chinese speaker. Thus, Searle in the CR functions as a computer would, where he is the system while the books are the program and the blank paper acts as storage.

The assumption is that the person is capable of understanding Chinese, simply because he can manage to assemble a set of answers to questions that would be indistiquishable from a person who speaks Chinese. The problem is that the person in the room does not understand any of the answers, but is simply following instructions. Searle utilizes a system’s ability to pass the Turing test as a parameter in the study, though the person would still indeed not understand Chinese. Searle proceeds to refute the claims of strong AI one at a time, by positioning himself as the one who manipulates the Chinese symbols. The first claim is that a system, which can pass the Turing test, understands the input and output. Searle replies that as the "computer" in the Chinese room, he gains no understanding of Chinese by simply manipulating the symbols according to the formal program, in this case being the complex rules. (Searle, 1980) It was not necessary for the operator to have any understanding of what the interviewer is asking, or the replies that produced. He may not even know that there is a question and answer session going on outside the room.

Through this piece of evidence it is established that even though the machines argue logically they cannot argue properly without warming up their brain plates. Their brain plates are not perfect though they call themselves superior to man. Though they are intelligent they will always be dependent on human intelligence because the humans are the ones that created them.

In summary, the article is split into two pieces. The first is Nicholas Carr’s longing for his brain to be one with the internet, a man made machine. The second part of the article is Google’s standpoint on how our brains should be replaced by artificial intelligence. Carr points out that he is not alone with this concept because ‘Bruce Friedman, blogs regularly about the use of computers in medicine, also has described how the Internet has altered his mental habits […] But in a recently published study of online research habits, we may well be in the midst of a sea of change in the way we read and think’ (228). So the internet is shaping the way we take in information by altering the way we read. Our brains don’t want to even try to

In the article “The Nature of Mental States” Hillary Putnam presents a compelling argument that dealt a “considerable blow” to the Mind-Brain Identity Theory—a theory developed by J.C.C Smart—in which he [Putnam] stated “mental states are multiply realizable.” His argument is driven by functionalism. His functionalist account of mental states like pain differs from what is explained by the Mind-Brain Identity Theory. In turn, he also avoids the issue of multiple realizability, an issue that was proven to be problematic for the Identity Theory. This leads into John Searle 's argument against Strong Artificial Intelligence with his Chinese Room thought experiment in “Minds, Brains, and Programs”. Searle believed that mere functional competence was not sufficient for having a mind, placing functionalism into question.

The authors say that the growth and development of artificial intelligence need to teach machines to learn from themselves. The authors support the idea by introducing two distinct groups and their ideas about machine learning. For instance, one of these groups believes that making manual corrections to algorithms is necessary. This means when a machine does not make a right decision, making manual corrections helps it to learn what the right decision is. This article was recently published in a popular American quarterly magazine. It is somewhat relevant to the topic, and its objectivity is neutral, but there is nothing about the qualifications of the authors on the web. It seems they may not be educated in computer

In attempting to answer the question of whether machines are able to think, Turing redesigns the question around the notion of machines’ effectiveness at mimicking human cognition. Turing proposes to gauge such effectiveness by a variation of an ‘imitation game,’ where a man and a woman are concealed from an interrogator who makes