ABSTRACT:-
Artificial Intelligence (AI) is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think". In order to classify machines as "thinking", it is necessary to define intelligence, and may be consciousness if human model is referenced.
“It is not our aim to surprise or shock you--but the simplest way we can summarize is to say that there are now in the world machines that can think, that can learn and that can create. Moreover, their ability to do these things is going to increase rapidly until--in a visible future--the range of problems they can handle will be coextensive with the range to which the human mind has been applied.”
In the following text, an effort is made to drop some light on the vision of AI and a bit of explanation on the fundamental concepts backing the idea and proving its very existence despite of all hypothetical contradicting obsessions of intelligence and consciousness. We will start our quest from the history right from the beginning of AI, through various milestones of phenomenon and methodologies, will discuss on the question of feasibility, approaches and applications, and end with some very obvious questions that surges in our mind (an absolute intelligent behavior) and of course their answers.
INTRODUCTION:-
Artificial Intelligence (AI) is the area of computer science focusing on creating machines that can engage on behaviors that humans consider intelligent. The ability to create intelligent machines has intrigued humans since ancient times and today with the advent of the computer and years of research into AI programming techniques, the dream of smart machines is becoming a reality. Researchers are creating systems which can mimic human thought, understand speech, beat the best human chess player, and countless other feats never before possible.
Artificial Intelligence, or AI for short, is a combination of computer science, physiology, and philosophy. AI is a broad topic, consisting of different fields, from machine vision to expert systems. The element that the fields of AI have in common is the creation of machines that can "think". Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginnings of AI reach back before electronics; AI really began to intrigue researchers with the invention of the computer in 1943. The technology was finally available, or so it seemed, to simulate intelligent behaviour. Over the next four decades, despite many stumbling blocks, AI has grown from a dozen researchers, to thousands of engineers and specialists; and from programs capable of playing checkers, to systems designed to diagnose disease.
HISTORY:-
The term artificial intelligence was first coined in 1956, at the Dartmouth conference, and since then Artificial Intelligence has come a long way from its early roots, driven by dedicated researchers. The beginnings of AI reach back before electronics to philosophers and mathematicians such as George Boole and others theorizing on principles that were used as the foundation of AI Logic. AI really began to intrigue researchers with the invention of the computer in 1943. The technology was finally available, or so it seemed, to simulate intelligent behavior. 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.
Feedback Mechanism Theory:-Norbert Wiener was one of the first Americans to make observations on the principle of feedback theory. The most familiar example of feedback theory is the thermostat: It controls the temperature of an environment by gathering the actual temperature of the house, comparing it to the desired temperature, and responding by turning the heat up or down. What was so important about his research into feedback loops was that Wiener theorized that all intelligent behavior was the result of feedback mechanisms, Mechanisms that could possibly be simulated by machines. This discovery influenced much of early development of AI.
The Logic Theorist:-
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.
APPLICATIONS OF ARTIFICIAL INTELLIGENCE:-
Game Playing:-
You can buy machines that can play master level chess for a few hundred dollars. There is some AI in them, but they play well against people mainly through brute force computation--looking at hundreds of thousands of positions. To beat a world champion by brute force and known reliable heuristics requires being able to look at 200 million positions per second.
Chess--
AI-based game playing programs combine intelligence with entertainment. On game with strong AI ties is chess. World-champion chess playing programs can see ahead twenty plus moves in advance for each move they make. In addition, the programs have an ability to get progress ably better over time because of the ability to learn. Chess programs do not play chess as humans do. In three minutes, Deep Thought (a master program) considers 126 million moves, while human chess master on average considers less than 2 moves. Herbert Simon suggested that human chess masters are familiar with favorable board positions, and the relationship with thousands of pieces in small areas. Computers on the other hand, do not take hunches into account. The next move comes from exhaustive searches into all moves, and the consequences of the moves based on prior learning. Chess programs, running on Cray super computers have attained a rating of 2600 (senior master), in the range of Gary Kasparov, the Russian world champion. Speech Recognition:-
In the 1990s, computer speech recognition reached a practical level for limited purposes. Thus United Airlines has replaced its keyboard tree for flight information by a system using speech recognition of flight numbers and city names. It is quite convenient. On the other hand, while it is possible to instruct some computers using speech, most users have gone back to the keyboard and the mouse as still more convenient.
Expert Systems:-
An expert is a computer program that represents & reasons with knowledge some specialist object with a view to solving problems or giving advice. Expert system is branch of applied ARTIFITIAL INTELEGENCE. An expert system employee’s human knowledge captured in a computer to solve problems that ordinarily requires human expertise. Expert system may completely fulfill function that normally requires human expertise or it may pay the roll of an assistant to human decision maker. The system is based on flexible, human like process, rather than rigid procedures expressed in flowcharts or decision trees.
Computer Vision:-
The world is composed of three-dimensional objects, but the inputs to the human eye and computers' TV cameras are two dimensional. Some useful programs can work solely in two dimensions, but full computer vision requires partial three-dimensional information that is not just a set of two-dimensional views. At present there are only limited ways of representing three-dimensional information directly, and they are not as good as what humans evidently use.
Spoken Language Processing:-
The challenge of spoken language processing is to establish a link between the continuous speech signal and the discrete symbolic representation of written language. The speech signal carries a wide variety of information about speaker, speaking style, intonation, recording acoustics, etc. which is not present in its written counterpart. In other words, we can say the same thing in amazingly different ways, while not posing any problem to a human listener. Dealing with all this variability is one of the great challenges of speech recognition; adding the extra richness to the signal is the complementary challenge for text-to-speech.
Biorobotics:-
Animal-like robots are playing an increasingly important role as a link between the worlds of biology and engineering. The new, multidisciplinary field of biorobotics provides tools for biologists studying animal behavior and test beds for the study and evaluation of biological algorithms for potential engineering applications.
Image Processing & Micro Robots:-
For developing image processing for micro robots AI ,neural network(NN),fuzzy technologies are applied.
THE FUTURE:-
Only experiments with real Creatures in real worlds can answer the natural doubts about our approach. Time will tell.
CONCLUSION:-
We are forced to conclude that an AI technique is a method that exploits knowledge that should be represented in such a way that: The knowledge captures generalizations. In other words, it is not necessary to represents separately each individual situation. Instead, situations that shared important properties are grouped together. If knowledge does not have this property, inordinate amounts of memory and updating will be required. So, we usually call something without this property “data” rather than knowledge.It can easily be modified to correct errors and to reflect changes in the World and in our World view.
Although it is a good topic, but has some drawbacks which possibly Affect our lives and most important our jobs. Time would tell, what happens if a Thinking Machine will develop
(please if you are reading this do comment)
0 comments:
Post a Comment
Thanks for your Valuable comment