Artificial Intelligence

Artificial Intelligence is a branch of science which deals with helping machines find solution to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithms in a computer friendly way. A more or less flexible or efficient approach can be taken depending on the requirements established, which influences how artificial the intelligent behavior appears.

AI is generally associated with computer science, but it has many important links with other fields such as math, psychology, cognition, biology and philosophy, among many others. Our ability to combine knowledge from all these fields will ultimately benefit our progress in the quest of creating an intelligent artificial being.

Computers are fundamentally well suited to performing mechanical computations, using fixed programmed rules. This allows artificial machines to perform simple monotonous tasks efficiently and reliably, which humans are ill-suited to. For more complex problems, things get more difficult. Unlike humans, computers have trouble understanding specific situations, and adapting to new situations. Artificial Intelligence aims to improve machine behavior in tackling such complex tasks.

Together with this, much of AI research is allowing us to understand our intelligent behavior. Humans have an interesting approach to problem-solving, based on abstract thought, high-level deliberative reasoning and pattern recognition. Artificial Intelligence can help us understand this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities.

To date, all the traits of human intelligence have not been captured and applied together to spawn an intelligent artificial creature. Currently, Artificial Intelligence rather seems to focus on lucrative domain specific applications, which do not necessarily require the full extent of AI capabilities. This limit of machine intelligence is known to researchers as narrow intelligence.

There is little doubt among the community that artificial machines will be capable of intelligent thought in the near future. It's just a question of what and when. The machines may be pure silicon, quantum computers or hybrid combinations of manufactured components and neural tissue. As for the date, expect great things to happen within this century!

There are many different approaches to Artificial Intelligence, none of which are either completely right or wrong. Some are obviously more suited than others in some cases, but any working alternative can be defended. Over the years, trends have emerged based on the state of mind of influential researchers, funding opportunities as well as available computer hardware.

Over the past five decades, AI research has mostly been focusing on solving specific problems. Numerous solutions have been devised and improved to do so efficiently and reliably. This explains why the field of Artificial Intelligence is split into many branches, ranging from pattern recognition to artificial life, including evolutionary computation and planning.

The potential applications of Artificial Intelligence are abundant. They stretch from the military for autonomous control and target identification, to the entertainment industry for computer games and robotic pets. Lets also not forget big establishments dealing with huge amounts of information such as hospitals, banks and insurances, who can use AI to predict customer behavior and detect trends.

As you may expect, the business of Artificial Intelligence is becoming one of the major driving forces for research. With an ever growing market to satisfy, there's plenty of room for more personnel. So if you know what you're doing, there's plenty of money to be made from interested big companies!

As a theory in the philosophy of mind, artificial intelligence is the view that human cognitive mental states can be duplicated in computing machinery. Accordingly, an intelligent system is nothing but an information processing system. Discussions of AI commonly draw a distinction between weak and strong AI. Weak AI holds that suitably programmed machines can simulate human cognition. Strong AI, by contrast, maintains that suitably programmed machines are capable of cognitive mental states. The weak claim is unproblematic, since a machine which merely simulates human cognition need not have conscious mental states. It is the strong claim, though, that has generated the most discussion, since this does entail that a computer can have cognitive mental states. In addition to the weak/strong distinction, it is also helpful to distinguish between other related notions. First, cognitive simulation is when a device such as a computer simply has the same input and output as a human. Second, cognitive replication occurs when the same internal causal relations are involved in a computational device as compared with a human brain. Third, cognitive emulation occurs when a computational device has the same causal relations and is made of the same stuff as a human brain. This condition clearly precludes silicon-based computing machines from emulating human cognition. Proponents of weak AI commit themselves only to the first condition, namely cognitive simulation. Proponents of strong AI, by contrast, commit themselves to the second condition, namely cognitive replication, but not the third condition.

Proponents of strong AI are split between two camps: classical computationalists, and connectionists. According to classical computationalism, computer intelligence involves central processing units operating on symbolic representations. That is, information in the form of symbols is processed serially (one datum after another) through a central processing unit. Daniel Dennett, a key proponent of classical computationalism, holds to a top-down progressive decomposition of mental activity. That is, more complex systems break down into more simple ones, which end in binary on-off switches. There is no homunculus, or tiny person inside a cognitive system which does the thinking. Several criticisms have been launched against the classical computationalist position. First, Dennett's theory, in particular, shows only that digital computers do not have homunculi. It is less clear that human cognition can be broken down into such subsystems. Second, there is no evidence for saying that cognition is computational in its structure, rather than saying that it is like computation. Since we do not find computational systems in the natural world, it is safer to presume that human thinking is only like computational processes. Third, human cognition seems to involve a global understanding of one's environment, and this is not so of computational processes. Given these problems, critics contend that human thinking seems to be functionally different than digital or serial programming.

The other school of strong Al is connectionism which contends that cognition is distributed across a number of neural nets, or interconnected nodes. On this view, there is no central processing unit, symbols are not as important, and information is diverse and redundant. Perhaps most importantly, it is consistent with what we know about neurological arrangement. Unlike computational devices, devices made in the neural net fashion can execute commonsense tasks, recognize patterns efficiently, and learn. For example, by presenting a device with a series of male and female pictures, the device picks up on patterns and can correctly identify new pictures as male or female. In spite of these advantages, several criticisms have been launched against connectionism. First, in teaching the device to recognize patterns, it takes too many training sessions, sometimes numbering in the thousands. Human children, by contrast, learn to recognize some patterns after a single exposure. Second, critics point out that neural net devices are not good at rule-based processing higher level reasoning, such as learning language. These tasks are better accomplished by symbolic computation in serial computers. A third criticism is offered by Fodor who maintains that connectionism is presented with a dilemma concerning mental representation:

• Mental representation is cognitive.

• If it is cognitive, then it is systematic (e. g., picking out one color or shape over another).

• If it is systematic, then it is syntactic, like language, and consequently, it is algorithmic.

• However, if it is syntactic, then it is just the same old computationalism.

• If it is not syntactic, then it is not true cognition.

But connectionists may defend themselves against Fodor's attack in at least two ways. First, they may object to premise (2) and claim that cognitive representation is not systematic, but, instead, is pictorial or holistic. Second, connectionists can point out that the same dilemma applies to human cognition. Since, presumably, we would want to deny (4) and (5) as pertains to humans, then we must reject the reasoning that leads to it.

The most well known attack on strong AI, whether classical or connectionist, is John Searle's Chinese Room thought experiment. Searle's target is a computer program which allegedly interprets stories the way humans can by reading between the lines and drawing inferences about events in the story which we draw from our life experience. Proponents of strong AI say that the program in question (1) understands stories, and (2) explains human ability to understand stories (i.e., provides the sufficient conditions for "understanding"). In response, Searle offers the following thought experiment. Suppose that a non-Chinese speaking person is put in a room and given three sets of Chinese characters (a script, a story, and questions about the story). He also receives a set of rules in English which allow him to correlate the three sets of characters with each other (i.e., a program). Although the man does not know the meaning of the Chinese symbols, he gets so good at manipulating symbols that from the outside no one can tell if he is Chinese or not Chinese. For Searle, this goes against both of the above two claims of strong AI. Critics of Searle contend that the Chinese Room thought experiment does not offer a systematic exposition of the problems with strong AI, but instead is more like an expression of a religious conviction which the believer immediately "sees" and the disbeliever does not see.

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