The Chinese Room Thought Experiment Analysis

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.

Yet, CR experiment blatantly assumes that a person who speaks Chinese can pass meaningful messages to a person who does not speak the same language in a room and, and by following a purely algorithmic or formalised process, are meaningfully and humanely responding, even if this requires using look-up tables, following preset rules, etc. (Cole n.p.; Anon). However, this irreducible intuition argument does not hold for Searle. The strongest objection to the CR theory from this standpoint is that if the person in the room can communicate with the outside person and be thought of as indistinguishable from intelligence, even though the receiving person does not truly understand that the semantic gap is bridged by a completely formal process, then hasn’t Searle completely reduced semantics to syntax? And if so, it seems that Searle argues for exactly the point he is attempting to refute, namely that computers are – at least theoretically – capable of being programmed to such an extent that they have reached a functional level of complexity such that it can be deemed as having a “human mind” (Turing 2004; Anon).

The argument called "Chinese room" was proposed by John Searle and later reproduced in his other works. This argument is directed against the position of the philosophy of mind called functionalism. Elaborating, it may be noted that the argument was directed against the machine functionalism, or, as Searle points out, against the strong version of artificial intelligence.

     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

The system has proven its ability to identify different symbols, what they mean, what to respond with and ultimately responding. In this way, it is possible for the man in the box to have endless conversations with endless native Chinese speakers without their knowledge that he does not understand Chinese. Although the man in the box does not understand Chinese, the system has demonstrated the ability to understand it. In this, the man in the box does not understand Chinese, nor does any other singular part of the room. However, when all parts of the system work together, the entire system can mimic the understanding of Chinese. Chinese speakers (all speakers for that matter) manipulate symbols in a formal system to communicate, just as the system demonstrates the ability to do. The formal manipulation of symbols constitutes an understanding of the symbols, the syntax and the rules that follow. This directly shows that the computer could have understanding as it has the ‘knowledge’ and the ability to manipulate symbols in speaking

It is only a matter of time till a supercomputer capable of more calculation than our brain is produced. Then the next challenge would be to simulate the human intelligence. The human brain executes processes in a method quite different from today's computers.

In his book, How To Create A Mind, Ray Kurzweil expresses his belief that the way the human mind works is not as different from today’s advanced computers as others, including some in artificial intelligence, may think. Kurzweil attempts to break down thinking, the physical brain, and other elements of the mind. He compares these to technologies that are currently in existence, and explains how the merging of the two is right around the corner thanks to one of the main ideas of his book, the Pattern Recognition Theory of Mind, and the Law of Accelerating Returns. The following paragraphs allow for more understanding of the main themes and ideas Kurzweil wrote about, my opinion on social impacts the book may have, as well what I liked and disliked about the book.

In On Intelligence, Jeff Hawkins hypothesizes a system of human intelligence based on memory, predictions, and pattern matching in what he calls a “memory-prediction framework.” Because he studied and worked in computer science, rather than neuroscience, Hawkins has a unique perspective and writes in terms that are accessible to someone from a non-neurological background or standpoint and often analogous to computer architecture, making the subject matter relatable to the interested layman. However, though he offers a plausible structure of the brain that mostly aligns with current thinking, he does not take into account important recent research, omitting neurologically developments that have been accepted by the scientific community. The main difference that he offers, between traditional approaches to artificial intelligence and his thinking, is the importance of drawing on memory (and associated learned patterns and processes) versus computation. He discusses at length a hierarchical system of the brain, but leaves out two very important components to that system: the thalamus and hypothalamus. He also draws broad conclusions without discussing specific mechanisms for how they are achieved, so his theory is not entirely sound, though it could prove to be. Also, possibly because of his background, and not being immersed in the world of biological science, he is able to critique areas of neuroscience and artificial intelligence with interesting points about each. The book

* 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.

Argument 1. “Syntax and semantics”. To begin, John Searle takes into account that humans understand semantics and syntax. That concludes that an intelligent being can demonstrate intention and consciousness through the use of meaning with semantic symbols or behaviours. Searle 's first argument is that “programs are purely formal (syntactic)” (Cole, 1). This argument ignores the science behind how computers and human brains work. The neural

John Searle's thought experiment concerning the "Chinese Room" attempts to disprove that so-called "strong-AI" (artificial intelligence that demonstrates "true" thinking and "understanding") could ever possibly exist. The argument is relatively straightforward: Searle imagines a computer running a program that allows it to communicate in written Chinese the program is capable of recognizing Chinese characters that are entered into it and of formulating a response in written Chinese that makes sense and appears conversant. This program is so perfect that it can convince a native Chinese speaker that the responses, generated solely through a series of complex rules or algorithms that the computer/program uses to react to the Chinese characters it receives as input, are coming from an actual human Chinese speaker. Next, Searle imagines that if he were placed in a room with this computer and if written Chinese communications were slipped under the door, he would be able (through use of the computer) to deliver appropriate responses to these communications without ever understanding a word of Chinese. As the human respondent (in this case Searle) is delivering Chinese responses without any understanding, he concludes that the computer program is working similarly, generating automatic if complex responses without actually understanding the language.

Ticks: That’s all interesting, but have you heard of the Searle’s Chinese room thought experiment? It’s a thought experiment that basically implies that genuine intelligence in AI is impossible to achieve. It’s the thought that, since computer programs are purely symbolic, aren’t capable of genuinely understanding the semantics of objects.

In the future, we may be able to build a computer that is comparable to the human brain, but not until we truly understand one thing. Lewis Thomas talks about this in his essay, "Computers." He says, "It is in our collective behavior that we are most mysterious. We won't be able to construct machines like ourselves until we've understood this, and we're not even close" (Thomas 473). Thomas wrote this essay in 1974, and although we have made many technological advances