Searle

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.

Cognitive revolution provided information-processing approach as a new way of mind study. Introduction of digital computers, devices of processing information, inspired many psychologists who began to view mind in the basis of information processing. The ability of computers to process information in stages captured the attention of many cognitive psychologists. By understanding that information in a computer as it is first received by input processor, then storage in memory unit follows, and lastly processing in arithmetic unit, many cognitive psychologists experimented and applied this layout. For instance, Goldstein (2007) shows that Cherry, a psychologist, experimented on attention where he presented two different messages at a go to people and later told them to repeat the messages. He realized that people pay attention to one message among many. He also deduced that the amount of information dealt with have limits. In addition, Broadbent, also a cognitive psychologist, came up with a flow diagram that represented occurrence of events in one’s mind as attention is directed to a single environmental stimulus.

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.

The principle of a strong AI is that an appropriately programmed computer is actually thinking, as opposed to simulating thought. © Principle is equivalent(c?) to accepting the Turing Test as the definition of thought. Which Searle would not be surprised to see that a machine still hasn’t passed. Searle’s argument is straightforward. Searle’s premise on the “Chinese Room” is that someone who understands English sitting in a room with a set of rules that tells this person how to respond to questions written in Chinese in such a way as to pass the Turing test. To Searle this person in the room doesn’t “understand” (e) Chinese. Understanding, being that real comprehension or thought isn’t existent. So to Searle, any machine that can be passed by systems, which behave in purely formal ways (eP), can be explained in purely formal ways ©, just like the Chinese Room, so it is not an adequate test. Main idea to Searle is that person answering Chinese questions is not engaged in “intentional” behavior with theses questions, and is not “thinking about”(eP? Merely reacting to a mix of emotions/past experience) the answers. Thus, the test is

“Logical-Mathematical Intelligence is quantifying things and making hypotheses and proving them.” It connects to “from Blue Nines and Red Words” By: Daniel Tammet. Daniel Tammet eats exactly 45 grams of porridge for breakfast. He weighs it to see

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,

In what follows, I will argue why I disagree with Allen’s reasoning. I will also give my account on why I believe, it is not possible to create such a system that can adequately understand on its own. From my understanding of the Chinese Room Argument, John Searle uses an example of individuals and the Chinese language to undermine the Turing Test. The Turing Test states that two systems have the same intermediary causal program, if their input

     Theorists claim that our cognitive processes are like that of a computer. They have used this as a model to break down the process of the

The second claim of strong AI, which Searle objects to, is the claim that the system explains human understanding. Searle asserts that since the system is functioning, in this case passing the Turing Test, (Brigeman, 1980) there

If the man can internalize the entire system, it can be assumed that he has the ability to hear/see Chinese characters, submit them to his ‘knowledge’ of the rules, and say/write Chinese characters in return. In this way, he (the system) can mimic the understanding of the Chinese characters as well as any native Chinese speaker. What then is the difference? The system’s understanding is in its knowledge of how to respond. The man in the box functions as a brain in his process of internalizing the prompt, searching for the stored information and responding while the input and output functions provide connections with the outside world. Almost all creatures that we acknowledge have understanding possess the ability to recognize an input, respond to it and act accordingly. Thus, the system mimics the behavioral instincts of a living organism that understands. In behaviorist thought, this must mean that the system possesses some form of understanding and awareness. There is no evidence to support that a system that is indistinguishable from us in this way has no intentionality, even if that behavior is coded. Obviously, no such programmer has accomplished the seemingly impossible feat to mimic a human flawlessly, but in there is no evidence to support its impossibility. This then lends itself to the conclusion that although it is not

The Representational Theory of Mind proposes that we, as both physiological and mental beings, are systems which operate based on symbols and interpretations of the meanings of such symbols rather than beings which operate just on physiological processes (chemical reactions and biological processes). It offers that humans and their Minds are computing machines, mental software (the Mind) which runs on physical hardware (the body). It suggests, too, that we are computing machines functioning as something other than a computing machine, just as every other machine does.

* Developments in computer science would lead to parallels being drawn between human thought and the computational functionality of computers, opening entirely new areas of psychological thought. Allen Newell and Herbert Simon spent years developing the concept of artificial intelligence (AI) and later worked with cognitive psychologists regarding the implications of AI. The effective result was more of a framework conceptualization of mental functions with

In his paper “Computing Machinery and Intelligence,” Alan Turing sets out to answer the question of whether machines can think in the same humans can by conceptualizing the question in concrete terms. In simple terms, Turing redefines the question by posing whether a machine can replicate the cognition of a human being. Yet, some may object to the notion that Turing’s new question effectively captures the nature of machines’ capacity for thought or consciousness, such as John Searle. In his Chinese room thought experiment, Searle outlines a scenario that implies machines’ apparent replication of human cognition does not yield conscious understanding. While Searle’s Chinese thought experiment demonstrates how a Turing test is not sufficient to establish that a machine can possess consciousness or thought, this argument does not prove that machines are absolutely incapable of consciousness or thought. Rather, given the ongoing uncertainty of the debate regarding the intelligence of machines, there can be no means to confirm or disconfirm the conscious experience of machines as well as the consciousness of humans by extension of that principle.

And it does, after all, do computation. That's what we define the machine to do. And I had to review a couple of books recently. One book said that we live in a new age, the age of information. And in a wonderful outburst, the author said everything is information. Now that ought to worry us if everything is information. And I read another book. This was an optimistic book. I reviewed-- this for "The New York Review of Books"-- a less optimistic book by a guy who said computers are now so smart they're almost as smart as we are. And pretty soon, they'll be just as smart as we are.

The architectural structure of Artificial intelligence is the notion to present how the mind functions at its level of abstraction. In effect, this leads to the concept of GOFAI to demonstrate how the mind takes in information, and outputs a response. However, GOFAI has become an unsatisfactory portrayal of human cognition, because it demonstrates itself to be nothing more than a system that manipulates symbols (Dreyfus, ppXXI). For GOFAI turns behaviours and knowledge into a formal script, but lacks the essential aspect of epistemology. For GOFAI doesn 't create opinions, nor does it desire to justify its database of information. Rather, it abides by the rules that are written in its programming, in the presumption it’s data is true. For the epistemological notion of creating, obtaining, and justifying knowledge is a natural aspect of the cognition, and is an essential property of the human intellect. Moreover, the concept of Connectionism, demonstrates itself to be a better archetype of the mind, in explaining how the brain functions, and learns. For the theory of Connectionism isn’t limited to being a symbol manipulator, rather it simulates the functionality of the brain, by replicating its neural structure of how it processes information. Arguably, Connectionism is a better representation of the mind, because it doesn 't limit the cognition as a symbol manipulator, in how it demonstrates the notion of learning and retention of knowledge by simulating the neural structure