A newborn baby is not what we would call intelligent. Not in the sense of being able to seek and acquire knowledge, make sense of, and make decisions based on it. A baby is conscious and sentient for sure and has a certain level of intelligence hardwired into its chromosomes. But what is most important is that the baby comes into the world ready to learn - to acquire knowledge through experience. Some machines are being built this way using the techniques drawn from Mother Nature.

The area of computer science focusing on creating machines that can engage in human behaviors of intelligence is called "Artificial Intelligence" or AI

The intellectual roots of AI, and the concept of intelligent machines, may be found in Greek mythology. Greek myths of Hephaestus and Pygmalion incorporate the idea of intelligent robots. Many other myths in antiquity involve human-like artifacts. Many mechanical toys and models were actually constructed, e.g., by Hero, Daedalus and other real persons.

In the 13th century talking heads were said to have been created, Roger Bacon and Albert the Great reputedly among the owners. Then in the 16th century after the invention of machines for discovering nonmathematical truths through combinatories, Rabbi Loew of Prague supposedly invented the Golem, a clay man brought to life.

The term "Artificial Intelligence" was coined by John McCarthy in 1956 during the first conference devoted to this subject. He also laid the foundation of the AI industry at the same time earning the title "Father of Artificial Intelligence". The first AI program called "The Logic Theorist" was written by Allen Newell, J.C. Shaw and Herbert Simon in 1956.
 

Intelligent machines have always been a hot favorite of Science fiction authors. Famous examples of intelligent machines in fiction are HAL from "2001, A Space Odyssey", Terminator from "Terminator", "The Virus" by Bill Buchanan. The media has always shown the intelligent machines as evil who turn against their creators and distroy them. In "Terminator" robots were created to serve humans but when the computer became self aware the machines turned against humans.

Another show which follows almost the same theme is "Cleopetra 2525". In this show earth is ruled by machines and all humans have been driven underground. 

Not that robots are always depicted as evil. In the famous movie "Bicentennial Man" a house keeping robot reaches awareness and develops human emotions like love, joy etc. But in majority of the cases the 'Intelligent computers' are portrayed as evil and dengerous.
 

Artificial Intelligence has a lot of advantages for the human society which are ignored most of the time by media and AI opposers. Using AI, machines will be able to do jobs that require detailed instructions, mental alertness and decision making capabilities. Another helpful usage of AI is the area of robotics. Humans will be able to use robots for heavy construction, exploration into unknown territories and outer space, military benefits, or even for personal assistance at private homes. The more use people get out of the machines the less work is required by us. In turn, there will be less injuries and stress to human beings.

Computers can now understand human speech by using speech recognition programs. This allows users to work on computers by talking to it. This facility enables computers to be used by the disabled. Computers can now see using computer vision programs. The vision programs have made it possible to create robots which can see therefore can be used for exploration etc. 

AI also makes games more fun by making the computer controled characters more realistic and human like. AI is also used in teaching programs which give a human touch to impersonal software by adapting to its users.
 

Although the fear of the machines is here, their capabilities are infinite. Whatever humans teach AI, they will suggest in the future if a positive outcome arrives from it. AI is like babies and children, they need to be taught to be kind, well mannered, and intelligent. If they are to make important decisions, they will be wise and a great benefit. Mankind also needs to make sure AI programmers and researchers are keeping things on the level. 

A good example of the creator's views corrupting the 'innocent' AI are portrayed in the book "T2: Infiltrator by S.M Stirling". In the book the programmer primarily responsible for programming SkyNet is a white supremacist. He uses books like main kampf to read to SkyNet to improve its voice recognition sub-routines. Since SkyNet was tought that some human beings are inferiour than others, it started thinking that it was superiour than all humans and revolted against humans.

This is not to say that if something so mechanical and "dead" can possess intelligence, then human beings are no longer special. All machines are subservient to us, we are far from the point of creating machines that will surpass the status of human beings.

The main problem with AI is the fear that if given too much power the AI may turn against their creators. Another problem is that AI can never be flawless. One of the most famous examples is in the movie "Wargames". In this movie humans develop a computer network (Joshua) to command the deployment of America's nuclear arsenal in defense of itself and its NATO allies. The system even allows for simulated war games so that various nuclear war scenarios can be played out and analyzed. When a teenage boy hacks into the system and accidently starts the wargames for real it is a tense moment before the 'Game' is aborted. In this movie the computer had no malicious intentions, it was just playing a game. 

With its learning capabilities AI can accomplish many tasks, but only if the world's conservatives are ready to change and allow this to be a possibility. The people need to be prepared for the worst of AI. Because AI is learning based, there is a fearful thought in mind, will machines learn that being rich and successful is an advantage, then rage war against economic powers and then control the world? This is not unreasonable but as every major religion and philosophy tells us, you cannot appreciate happiness without experiencing sadness. If we are going to create, or help create, a truly intelligent being, then we have to accept that it will learn to hate, as well as to love. It will know bad, as well as Good, Wrong, as well as Right. Although the fear of the machines is here, their capabilities are infinite. AI is like babies and children; they need to be taught to be kind, well mannered, and intelligent. If they are to make important decisions, they will be wise and a great benefit.
 

AI is implemented in many ways. The common implimentations use the fuzzy logic, expert systems and Neural networks.

Fuzzy Logic:

Fuzzy logic is a superset of conventional (Boolean) logic that has been extended to handle the concept of partial truth -- truth values between "completely true" and "completely false". Boolean logic says that something is either on or off, true or false. You are either sleeping or awake. But what about in-between these times e.g. the time in- between sleep and a full state of consciousness?

It was introduced by Dr. Lotfi Zadeh of UC/Berkeley in the 1960's as a means to model the uncertainty of natural language.Its main use is in speech recognition algorithms. Fuzzy logic emerged into the mainstream of information technology in the late 1980's and early 1990's.

Fuzzy set theory implements classes or groupings of data with boundaries that are not sharply defined (i.e., fuzzy). Any methodology or theory implementing "crisp" definitions such as classical set theory, arithmetic, and programming, may be "fuzzified" by generalizing the concept of a crisp set to a fuzzy set with blurred boundaries.

The benefit of extending crisp theory and analysis methods to fuzzy techniques is the strength in solving real-world problems, which inevitably entail some degree of imprecision and noise in the variables and parameters measured and processed for the application. Accordingly, linguistic variables are a critical aspect of some fuzzy logic applications, where general terms such a "large," "medium," and "small" are each used to capture a range of numerical values. While similar to conventional quantization, fuzzy logic allows these stratified sets to overlap (e.g., a 85 kilogram man may be classified in both the "large" and "medium" categories, with varying degrees of belonging or membership to each group). Fuzzy set theory encompasses fuzzy logic, fuzzy arithmetic, fuzzy mathematical programming, fuzzy topology, fuzzy graph theory, and fuzzy data analysis, though the term fuzzy logic is often used to describe all of these. 

Expert systems are computers meant to solve real problems which normally would require a specialised human expert (such as a doctor or a minerologist). Building an expert system therefore first involves extracting the relevant knowledge from the human expert. Such knowledge is often heuristic in nature, based on useful ``rules of thumb'' rather than absolute certainties. Extracting it from the expert in a way that can be used by a computer is generally a difficult task, requiring its own expertise. A knowledge engineer has the job of extracting this knowledge and building the expert system knowledge base.

Expert based systems are currently in use in business in projects like credit rating people to see if they're worth giving credit to or in the prediction of rise and fall in shares in the stock market. An expert system is based on English so is easier to program and maintain than other languages. Expert systems are however only experts in their particular field but have the advantage of unlike humans not grow old or make mistakes and can process information faster.

Neural networks are models based on the working of the human brain, utilizing a distributed processing approach to computation. Neural nets are capable of solving a wide range of problems by "learning" a mathematical model for the problem: the model can then be used to map input data to output data. Anything that can be represented as a number can be fed into a neural network.

Neural networks can be applied to many general problem areas, including classification , filtering, pattern association, optimization, conceptualization and prediction. Main use is in Robort brain manufacture. The first step in creating an artificial neural network application involves identifying the category the problem in question belongs -- not necessarily as easy as it may seem, because many distinct neural network systems are more appropriate than others for a given application. 
 

Below are links to external sites containing more information about AI. If you know some site which should be included here, please let me know.