1.INTRODUCTION:
Artificial Intelligence is the science and engineering of making intelligent machines, especially intelligent computer programs. It includes people,procedure,hardware,software,data and knowledge needed to develop computer systems and machines that demonstrated characteristics of intelligence.It is related to the similar task of using computers to understand human intelligence, but AI does not have to confine itself to methods that are biologically observable. Intelligence is basically the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.There is no solid definition of intelligence relating it to human intelligence. The problem is that it cannot yet be characterize in general what kinds of computational procedures can be called intelligent. We understand some of the mechanisms of intelligence and not others. Sometimes Artificial Intelligence is about simulating human intelligence but not always.On the one hand, we can learn something about how to make machines solve problems by observing other people or just by observing our own methods. On the other hand, most work in AI involves studying the problems the world presents to intelligence rather than studying people or animals. AI textbooks define the field as "the study and design of intelligent agents where an intelligent agent is a system that perceives its environment and takes actions that maximize its chances of success. John McCarthy who coined the term in 1956,defines it as "the science and engineering of making intelligent machines."

2.HISTORY
Although the computer provided the technology necessary for AI, it was not until the early 1950's that the link between human intelligence and machines was really observed. In late 1955, Newell and Simon developed The Logic Theorist, considered by many to be the first AI program. The program, representing each problem as a tree model, would attempt to solve it by selecting the branch that would most likely result in the correct conclusion. The impact that the logic theorist made on both the public and the field of AI has made it a crucial stepping stone in developing the AI field.
In 1956 John McCarthy regarded as the father of AI, organized a conference to draw the talent and expertise of others interested in machine intelligence for a month of brainstorming. He invited them to Vermont for "The Dartmouth summer research project on artificial intelligence." From that point on, because of McCarthy, the field would be known as Artificial intelligence. Although not a huge success,the Dartmouth conference did bring together the founders in AI, and served to lay the groundwork for the future of AI research.
In the early 1980s, AI research was revived by the commercial success of expert systems, a form of AI program that simulated the knowledge and analytical skills of one or more human experts.By 1985 the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation computer project inspired the U.S and British governments to restore funding for academic research in the field. However, beginning with the collapse of the Lisp Machine market in 1987, AI once again fell into disrepute, and a second, longer lasting AI winter began.In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the scenes. Artificial intelligence is used for logistics, data mining, medical diagnosis and many other areas throughout the technology industry. The success was due to several factors: the incredible power of computers today,a greater emphasis on solving specific subproblems, the creation of new ties between AI and other fields working on similar problems,and above all a new commitment by researchers to solid mathematical methods and rigorous scientific standards.

3. How Artificial Intelligence works

3.1 Building intelligence
Artificial intelligence- the ability of machines to emulate human thought or behaviour is created using two distinct elements: knowledge and reasoning.Considerable computing space and speed is needed to impart knowledge to intelligent system.You need to program your computer to have the reasoning abilities to make use of the library of knowledge you give it and to make guesses about new situations,based on that past knowledge.

3.2 Language of A.I.
To perform human-level tasks and understand the world around them, computers must be programmed with mathematical algorithms - sets of rules used for problem solving - and advanced logic that allows machines to think outside the binary world of 'yes' or 'no.'These rules allow intelligent machines to make logical decisions, deal with new situations and perceive the world around them. In modern computer science, there are basically two ways to provide a computer with the ingredients necessary for A.I.

3.2.1.Artificial brains

figure:neural network

One way to achieve A.I. is called the bottom-up approach. This involves building intelligence and then harnessing that intelligence to behave appropriately for the task or environment at hand.An example of one way to do this is to make an electronic replica of the brain's complex network of neurons. These artificial "minds" - called neural nets - are like small sections of brain that are able to relay information between computers in the network. Early computer scientists first got this idea after observing how electrical signals passed between neurons in the human brain. Once you've got the brains - in theory - you've just got to load them up with information and the ability to use it. This teaching process is the same as what happens to humans as they grow up. But for a machine, that process is easier said than done. The main categories of networks are acyclic or feedforward neural networks, where the signal passes in only one direction and recurrent neural networks,which allow feedback. Among the most popular feedforward networks are perceptrons, multi-layer perceptrons and radial basis networks. Among recurrent networks, the most famous is the Hopfield net, a form of attractor network.Neural networks can be applied to the problem of intelligent control(for robotics) or learning, using such techniques as Hebbian learning and competitive learning

3.2.2.One piece at a time
The top-down approach to building A.I. involves attempting to mimic the brain's behavior with computer programs - essentially setting your computer up with an A.I. apprenticeship. This sort of A.I. is most-used today in "expert systems" - programs that try to simulate one element of human thought in greater detail than is possible with more general A.I.
Expert system can be seen in task-specific systems such as work in medicine, industry and communications. They use existing A.I. capabilities to take over very narrow areas of tasks usually performed by humans.
Building an expert system wouldn't let recreate all the thought processes needed to have an intelligent conversation, but it could help kick some serious posterior in a game of chess.Many experts agree that true artificial intelligence will come from combining top-down and bottom-up approaches, like using neural nets as expert systems.

4.Fields in Artificial Intelligence

4.1.Logical AI
What a program knows about the world in general the facts of the specific situation in which it must act, and its goals are all represented by sentences of some mathematical logical language. The program decides what to do by inferring that certain actions are appropriate for achieving its goals.

4.2.Search
AI programs often examine large numbers of possibilities, e.g. moves in a chess game,moves in tic-tac-toe game. Discoveries are continually made about how to do this more efficiently in various domains.
4.3.Pattern recognition
When a program makes observations of some kind, it is often programmed to compare what it sees with a pattern. For example, a vision program may try to match a pattern of eyes and a nose in a scene in order to find a face. More complex patterns, e.g. in a natural language text, in a chess position, or in the history of some event are also studied. These more complex patterns require quite different methods than do the simple patterns that have been studied the most. 4.4.Representation
Facts about the world have to be represented in some way. Usually languages of mathematical logic are used.

4.5.Inference
From some facts, others can be inferred. Mathematical logical deduction is adequate for some purposes, but new methods of non-monotonic inference have been added to logic since the 1970s. The simplest kind of non-monotonic reasoning is default reasoning in which a conclusion is to be inferred by default,but the conclusion can be withdrawn if there is evidence to the contrary. For example, when we hear of a bird, we may infer that it can fly, but this conclusion can be reversed when we hear that it is a penguin. It is the possibility that a conclusion may have to be withdrawn that constitutes the non-monotonic character of the reasoning. Ordinary logical reasoning is monotonic in that the set of conclusions that can the drawn from a set of premises is a monotonic increasing function of the premises. Circumscription is another form of non- monotonic reasoning.

4.6.Common sense knowledge and reasoning
This is the area in which AI is farthest from human-level, in spite of the fact that it has been an active research area since the 1950s. While there has been considerable progress, e.g. in developing systems of non-monotonic reasoning and theories of action, yet more new ideas are needed. The Cyc system contains a large but spotty collection of common sense facts.
4.7.Learning from experience
Programs do learn from experience. The approaches to AI based on connectionism and neural nets specialize in that. There is also learning of laws expressed in logic.Programs can only learn what facts or behaviors their formalisms can represent, and unfortunately learning systems are almost all based on very limited abilities to represent information.
4.8.Planning
Planning programs start with general facts about the world (especially facts about the effects of actions), facts about the particular situation and a statement of a goal. From these, they generate a strategy for achieving the goal. In the most common cases, the strategy is just a sequence of actions.
4.9.Epistemology
This is a study of the kinds of knowledge that are required for solving problems in the world.

4.10.Ontology
Ontology is the study of the kinds of things that exist. In AI, the programs and sentences deal with various kinds of objects, and we study what these kinds are and what their basic properties are.

4.11.Heuristics
A heuristic is a way of trying to discover something or an idea imbedded in a program. The term is used variously in AI. Heuristic functions are used in some approaches to search to measure how far a node in a search tree seems to be from a goal. Heuristic predicates that compare two nodes in a search tree to see if one is better than the other, i.e. constitutes an advance toward the goal, may be more useful.

4.12.Genetic programming
Genetic programming is a technique for getting programs to solve a task by mating random Lisp programs and selecting fittest in millions of generations. It is being developed by John Koza's group.

5.APPLICATIONS-
Artificial intelligence has been used in a wide range of fields including medical diagnosis, stock trading, robot control, law, scientific discovery and toys..Many thousands of AI applications are deeply embedded in the infrastructure of every industry.In the late 90s and early 21st century, AI technology became widely used as elements of larger systems, but the field is rarely credited for these successes.
5.1.Computer Science
AI researchers have created many tools to solve the most difficult problems in computer science.
5.2.Finance
Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition.Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation.
5.3.Medicine
A medical clinic can use artificial intelligence systems to organize bed schedules, make a staff rotation, and provide medical information.Artificial neural networks are used for medical diagnosis, functioning as machine differential diagnosis.
5.4.Transportation
Fuzzy logic is a version of first-order logic which allows the truth of a statement to be represented as a value between 0 and 1, rather than simply True (1) or False (0).
Fuzzy logic controllers have been developed for automatic gearboxes in automobiles (the 2006 Audi TT, VW Toureg and VW Caravell feature the DSP transmission which utilizes Fuzzy logic, a number of Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic based controller).
5.5 Music
The evolution of music has always been affected by technology. With AI, scientists are trying to make the computer emulate the activities of the skillful musician. Composition, performance, music theory, sound processing are some of the major areas on which research in Music and Artificial Intelligence are focusing.

5.6.Aviation
The Air Operations Division, AOD, uses AI for the rule based expert systems. The AOD has use for artificial intelligence for surrogate operators for combat and training simulators, mission management aids, support systems for tactical decision making, and post processing of the simulator data into symbolic summaries.
5.7.Others

Various tools of artificial intelligence are also being widely deployed in homeland security, speech and text recognition, data mining, and e-mail spam filtering. Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition, global voice recognition (from a variety of people in a noisy room), facial expression recognition for interpretation of emotion and non verbal queues. Other applications are robot navigation, obstacle avoidance, object recognition.

CONCLUSION
AI is a common topic in both science fiction and projections about the future of technology and society. The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues, and the potential power of the technology inspires both hopes and fears.Artificial intelligence has been the subject of optimism, but has also suffered setbacks and, today, has become an essential part of the technology industry, providing the heavy lifting for many of the most difficult problems in computer science. The impact of AI on society is a serious area of study for futurists. Academic sources have considered such consequences as a decreased demand for human labor, the enhancement of human ability or experience, and a need for redefinition of human identity and basic values.

REFERENCES

1.Kurzweil 2005
2.Russell, Stuart J.; Norvig, Peter (2003), Artificial Intelligence: A Modern Approach (2nd ed.), Upper Saddle River, New Jersey: Prentice Hall, ISBN 0-13- 790395-2, http://aima.cs.berkeley.edu/
3. http://www.cs.ubc.ca/spider/poole/ci.html
4.Artificial Intelligence-nirali publications