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What is AI and the applications of AI and what it has accomplished.

Artificial intelligence:

It is the science and engineering of making intelligent machines, especially intelligent computer

programs. 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 the ability to think, to imagine, to create, memorize, understand, recognize patterns,

make choices, adapt to change and learn from experience. Artificial intelligence is a human endeavor to

create a non-organic machine-based entity, that has all the above abilities of natural organic

intelligence. Hence it is called as 'Artificial Intelligence' (AI).

It is the ultimate challenge for an intelligence, to create an equal, another intelligent being. It is the

ultimate form of art, where the artist's creation, not only inherits the impressions of his thoughts, but

also his ability to think!

AI is divided into two classes:

Strong AI and Weak AI

Strong AI makes the bold claim that computers can be made to think on a level (at least) equal to

humans and Weak AI simply states that some "thinking-like" features can be added to computers to

make them more useful tools... and this has already started to happen (witness expert systems, drive-

by-wire cars and speech recognition software).

Applications of AI

Game Playing:

The machines that can play master level chess for a few hundred dollars. There is some artificial

intelligence in them, but they play well against people mainly through brute force computation i.e.,

looking at hundreds of thousands of positions.

Speech Recognition:

In the 1990s, computer speech recognition reached a practical level for l imited purposes. Thus united

airlines have replaced its keyboard tree for flight information by a system using speech recognition of

flight numbers and city names. 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.

Understanding natural language

Just getting a sequence of words into a computer is not enough. Parsing sentences is not enough either.

The computer has to be provided with an understanding of the domain the text is about, and this is

presently possible only for very limited domains.

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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.

Expert systems

A ``knowledge engineer'' interviews experts in a certain domain and tries to embody their knowledge in

a computer program for carrying out some task. How well this works depends on whether the

intellectual mechanisms required for the task are within the present state of AI. When this turned out

not to be so, there were many disappointing results. One of the first expert systems was MYCIN in 1974,

which diagnosed bacterial infections of the blood and suggested treatments. It did better than medical

students or practicing doctors, provided its limitations were observed. Namely, its ontology included

bacteria, symptoms, and treatments and did not include patients, doctors, hospitals, death, recovery,

and events occurring in time. Its interactions depended on a single patient being considered. Since theexperts consulted by the knowledge engineers knew about patients, doctors, death, recovery, etc., it is

clear that the knowledge engineers forced what the experts told them into a predetermined framework.

In the present state of AI, this has to be true. The usefulness of current expert systems depends on their

users having common sense.

Heuristic classification

One of the most feasible kinds of expert system given the present knowledge of AI is to put some

information in one of a fixed set of categories using several sources of information. An example is

advising whether to accept a proposed credit card purchase. Information is available about the owner of

the credit card, his record of payment and also about the item he is buying and about the establishmentfrom which he is buying it (e.g., about whether there have been previous credit card frauds at this

establishment).

Approaches to AI

Initially, researchers thought that creating an AI would be simply writing programs for each and every

function an intelligence performs! As they went on with this task, they realized that this approach was

too shallow. Even simple functions like face recognition, spacial sense, pattern recognition and language

comprehension were beyond their programming skills!

They understood that to create an AI, they must delve deeper into natural intelligence first. They tried tounderstand how cognition, comprehension, decision-making happen in the human mind. They had to

understand what understanding really means! Some went into the study of the brain and tried to

understand how the network of neurons creates the mind.

Thus, researchers branched into different approaches, but they had the same goal of creating intelligent

machines. Let us introduce ourselves to some of the main approaches to artificial intelligence. They are

divided into two main lines of thought, the bottom up and the top down approach:

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Neural Networks: This is the bottom up approach. It basically aims at mimicking the structure and

functioning of the human brain, to create intelligent behavior. Researchers are attempting to build a

silicon-based electronic network that is modeled on the working and form of the human brain! Our

brain is a network of billions of neurons, each connected with the other.

At an individual level, a neuron has very little intelligence, in the sense that it operates by a simple set of

rules, conducting electric signals through its network. However, the combined network of all these

neurons creates intelligent behavior that is unrivaled and unsurpassed. So these researchers created

network of electronic analogues of a neuron, based on Boolean logic. Memory was recognized to be an

electronic signal pattern in a closed neural network.

How the human brain works is, it learns to realize patterns and remembers them. Similarly, the neural

networks developed have the ability to learn patterns and remember. This approach has its limitations

due to the scale and complexity of developing an exact replica of a human brain, as the neurons number

in billions! Currently, through simulation techniques, people create virtual neural networks. This

approach has not been able to achieve the ultimate goal but there is a very positive progress in the field.

The progress in the development of parallel computing will aid it in the future.

Expert Systems: This is the top down approach. Instead of starting at the base level of neurons, by taking

advantage of the phenomenal computational power of the modern computers, followers of the expert

systems approach are designing intelligent machines that solve problems by deductive logic. It is like the

dialectic approach in philosophy.

This is an intensive approach as opposed to the extensive approach in neural networks. As the name

expert systems suggest, these are machines devoted to solving problems in very specific niche areas.

They have total expertise in a specific domain of human thought. Their tools are like those of a detective

or sleuth. They are programmed to use statistical analysis and data mining to solve problems. They

arrive at a decision through a logical flow developed by answering yes-no questions.

Chess computers like Fritz and its successors that beat chess grandmaster Kasparov are examples of

expert systems. Chess is known as the drosophila or experimental specimen of artificial intelligence.

Applications of AI

Artificial Intelligence in the form of expert systems and neural networks have applications in every field

of human endeavor. They combine precision and computational power with pure logic, to solve

problems and reduce error in operation. Already, robot expert systems are taking over many jobs in

industries that are dangerous for or beyond human ability. Some of the applications divided by domains

are as follows:

Heavy Industries and Space: Robotics and cybernetics have taken a leap combined with artificially

intelligent expert systems. An entire manufacturing process is now totally automated, controlled and

maintained by a computer system in car manufacture, machine tool production, computer chip

production and almost every high-tech process. They carry out dangerous tasks like handling hazardous

radioactive materials. Robotic pilots carry out complex maneuvering techniques of unmanned

spacecrafts sent in space. Japan is the leading country in the world in terms of robotics research and

use.

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Finance: Banks use intelligent software applications to screen and analyze financial data. Softwares that

can predict trends in the stock market have been created which have been known to beat humans in

predictive power.

Computer Science: Researchers in quest of artificial intelligence have created spin offs like dynamic

programming, object oriented programming, symbolic programming, intelligent storage management

systems and many more such tools. The primary goal of creating an artificial intelligence still remains a

distant dream but people are getting an idea of the ultimate path which could lead to it.

Aviation: Air lines use expert systems in planes to monitor atmospheric conditions and system status.

The plane can be put on auto pilot once a course is set for the destination.

Weather Forecast: Neural networks are used for predicting weather conditions. Previous data is fed to a

neural network which learns the pattern and uses that knowledge to predict weather patterns.

Swarm Intelligence: This is an approach to, as well as application of artificial intelligence similar to a

neural network. Here, programmers study how intelligence emerges in natural systems like swarms of

bees even though on an individual level, a bee just follows simple rules. They study relationships innature like the prey-predator relationships that give an insight into how intelligence emerges in a swarm

or collection from simple rules at an individual level. They develop intelligent systems by creating agent

programs that mimic the behavior of these natural systems!

Is artificial Intelligence really possible? Can an intelligence like a human mind surpass itself and create its

own image? The depth and the powers of the human mind are just being tapped. Who knows, it might

be possible, only time can tell! Even if such an intelligence is created, will it share our sense of morals

and justice, will it share our idiosyncrasies? This will be the next step in the evolution of intelligence.

Hope I have succeeded in conveying to you the excitement and possibilities this subject holds!

_______________________________________________________________________

HISTORY:

The field of AI research was founded at a conference on the campus of DartmouthCollege in the

summer of 1956.[27] The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert

Simon, became the leaders of AI research for many decades.[28] They and their students wrote

programs that were, to most people, simply astonishing:[29]computers were solving word problems in

algebra, proving logical theorems and speaking English.[30] By the middle of the 1960s, research in the

U.S. was heavily funded by theDepartment of Defense[31] and laboratories had been established

around the world.[32] AI's founders were profoundly optimistic about the future of the new

field: Herbert Simon predicted that "machines will be capable, within twenty years, of doing any work a

man can do"[33] and Marvin Minsky agreed, writing that "within a generation ... the problem of creating

'artificial intelligence' will substantially be solved".[34]

They had failed to recognize the difficulty of some of the problems they faced.[35] In 1974, in response

to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more

productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI.

The next few years, when funding for projects was hard to find, would later be called an "AI winter".[36]

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In the early 1980s, AI research was revived by the commercial success of expert systems,[37] 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.[38] 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.[39]

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 diagnosisand many other areas

throughout the technology industry.[11] The success was due to several factors: the increasing

computational power of computers (see Moore's law), a greater emphasis on solving specific

subproblems, the creation of new ties between AI and other fields working on similar problems, and a

new commitment by researchers to solid mathematical methods and rigorous scientific standards.[40]

Problems

The general problem of simulating (or creating) intelligence has been broken down into a number of

specific sub-problems. These consist of particular traits or capabilities that researchers would like anintelligent system to display.

Deduction, reasoning, problem solving

Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans were

often assumed to use when they solve puzzles, play board games or make logical deductions. By the late

1980s and '90s, AI research had also developed highly successful methods for dealing with uncertain or

incomplete information, employing concepts from probability and economics.

For difficult problems, most of these algorithms can require enormous computational resources most

experience a "combinatorial explosion": the amount of memory or computer time required becomesastronomical when the problem goes beyond a certain size. The search for more efficient problem

solving algorithms is a high priority for AI research.[43]

Human beings solve most of their problems using fast, intuitive judgments rather than the conscious,

step-by-step deduction that early AI research was able to model.[44] AI has made some progress at

imitating this kind of "sub-symbolic" problem solving: embodied agent approaches emphasize the

importance of sensorimotor skills to higher reasoning; neural netresearch attempts to simulate the

structures inside human and animal brains that give rise to this skill.

Knowledge representation

Main articles: Knowledge representation andCommonsense knowledge

Knowledge representation and knowledge engineering are central to AI research. Many of the problems

machines are expected to solve will require extensive knowledge about the world. Among the things

that AI needs to represent are: objects, properties, categories and relations between

objects;[47] situations, events, states and time;[48] causes and effects;[49]knowledge about knowledge

(what we know about what other people know);[50] and many other, less well researched domains. A

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complete representation of "what exists" is anontology[51] (borrowing a word from

traditional philosophy), of which the most general are called upper ontologies.

Among the most difficult problems in knowledge representation are:

Default reasoning and the qualification problem

Many of the things people know take the form of "working assumptions." For example, if a bird comes

up in conversation, people typically picture an animal that is fist sized, sings, and flies. None of these

things are true about all birds. John McCarthy identified this problem in 1969[52] as the qualification

problem: for any commonsense rule that AI researchers care to represent, there tend to be a huge

number of exceptions. Almost nothing is simply true or false in the way that abstract logic requires. AI

research has explored a number of solutions to this problem.[53]

The breadth of commonsense knowledge

The number of atomic facts that the average person knows is astronomical. Research projects that

attempt to build a complete knowledge base of commonsense knowledge (e.g.,Cyc) require enormous

amounts of laborious ontological engineering they must be built, by hand, one complicated concept

at a time.[54] A major goal is to have the computer understand enough concepts to be able to learn by

reading from sources like the internet, and thus be able to add to its own ontology.[citation needed]

The subsymbolic form of some commonsense knowledge

Much of what people know is not represented as "facts" or "statements" that they could express

verbally. For example, a chess master will avoid a particular chess position because it "feels too

exposed"[55] or an art critic can take one look at a statue and instantly realize that it is a fake.[56] These

are intuitions or tendencies that are represented in the brain non-consciously and sub-

symbolically.[57] Knowledge like this informs, supports and provides a context for symbolic, consciousknowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated

AI or computational intelligence will provide ways to represent this kind of knowledge.[57]

Planning

Main article: Automated planning and scheduling

Intelligent agents must be able to set goals and achieve them.[58] They need a way to visualize the

future (they must have a representation of the state of the world and be able to make predictions about

how their actions will change it) and be able to make choices that maximize the utility (or "value") of the

available choices.[59]

In classical planning problems, the agent can assume that it is the only thing acting on the world and it

can be certain what the consequences of its actions may be.[60] However, if this is not true, it must

periodically check if the world matches its predictions and it must change its plan as this becomes

necessary, requiring the agent to reason under uncertainty.[61]

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Multi-agent planning uses the cooperation and competition of many agents to achieve a given

goal. Emergent behavior such as this is used by evolutionary algorithms and swarm intelligence.[62]

Learning

Main article: Machine learning

Machine learning[63] has been central to AI research from the beginning.[64] Unsupervised learning is

the ability to find patterns in a stream of input. Supervised learning includes bothclassification and

numerical regression. Classification is used to determine what category something belongs in, after

seeing a number of examples of things from several categories. Regression takes a set of numerical

input/output examples and attempts to discover a continuous function that would generate the outputs

from the inputs. In reinforcement learning[65]the agent is rewarded for good responses and punished

for bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The

mathematical analysis of machine learning algorithms and their performance is a branch of theoretical

computer science known as computational learning theory.

Natural language processing

Main article: Natural language processing

Natural language processing gives machines the ability to read and understand the languages that

humans speak. Many researchers hope that a sufficiently powerful natural language processing system

would be able to acquire knowledge on its own, by reading the existing text available over the internet.

Some straightforward applications of natural language processing include information retrieval (or text

mining) and machine translation.

Motion and manipulation

Main article: Robotics

The field of robotics[68] is closely related to AI. Intelligence is required for robots to be able to handle

such tasks as object manipulation[69] andnavigation, with sub-problems of localization (knowing where

you are), mapping (learning what is around you) and motion planning (figuring out how to get

there).[70]

Perception

Main articles: Machine perception, Computer vision, and Speech recognition

Machine perception is the ability to use input from sensors (such as cameras, microphones, sonar and

others more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual

input. A few selected sub problems are speech recognition, facial recognition and object recognition.

Social intelligence

Main article: Affective computing

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- Deep Blue, the first computer to beat the human chess Grandmaster

- Fuzzy controllers in dishwashers, etc.

One persistent 'problem' is that as soon as an AI technique truly succeeds, in the minds of many it

ceases to be AI, becoming something else entirely. For example, when Deep Blue defeated Kasparov,

there were many who said Deep Blue wasn't AI, since after all it was just a brute force parallel minimax

search (!)