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Contents

Introduction

History

Cybernetics and brain simulation

The multitude of programs

Evaluating artificial intelligence

Specialized languages

Transition from lab to life

Applications

Scope of expert systems

Motion and manipulation

Social intelligence

Creativity

Conclusion

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Introduction

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". One of the most challenging approaches facing experts is

building systems that mimic the behavior of the human brain, made up of billions of

neurons, and arguably the most complex matter in the universe.

The field was founded on the claim that a central property of human beings, intelligence

—the sapience of Homo sapiens—can be so precisely described that it can be simulated

by a machine. This raises philosophical issues about the nature of the mind and limits of

scientific hubris, issues which have been addressed by myth, fiction and philosophy since

antiquity. Artificial intelligence has been the subject of breathtaking optimism, has

suffered stunning 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.

AI research is highly technical and specialized, so much so that some critics decry the

"fragmentation" of the field. Subfields of AI are organized around particular problems,

the application of particular tools and around longstanding theoretical differences of

opinion. The central problems of AI include such traits as reasoning, knowledge,

planning, learning, communication, perception and the ability to move and manipulate

objects. General intelligence (or "strong AI") is still a long-term goal of (some) research,

while many researchers no longer believe that this is possible.

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 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. Over the next four decades, despite many stumbling blocks, AI has

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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

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In the middle of the 20th century, a small group of scientists began a new approach to

building intelligent machines, based on recent discoveries in neurology, a new

mathematical theory of information, an understanding of control and stability called

cybernetics, and above all, by the invention of the digital computer, a machine based on

the abstraction of mathematical reasoning.

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

Dartmouth College in the summer of 1956. Computers were solving word problems in

algebra, proving logical theorems and speaking English. By the middle 60s their research

was heavily funded by the U.S. Department of Defense, and those involved made these

predictions:

1965, H. A. Simon: "Machines will be capable, within twenty years, of doing any work a

man can do"

1967, Marvin Minsky: "Within a generation ... the problem of creating 'artificial

intelligence' will substantially be solved."

In the early 80s, 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, and

governments started funding again. A few years later, 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 90s 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.

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Cybernetics and brain simulation

The human brain provides inspiration for artificial intelligence researchers, however there

is no consensus on how closely it should be simulated.

A number of researchers explored the connection between neurology, information theory,

and cybernetics. Some of them built machines that used electronic networks to exhibit

rudimentary intelligence. Many of these researchers gathered for meetings of the

Teleological Society in England. By 1960, this approach was largely abandoned,

although elements of it would be revived in the 1980s.

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The Multitude of programs

The next few years showed a multitude of programs, one notably was SHRDLU.

SHRDLU was part of the microworlds project, which consisted of research and

programming in small worlds (such as with a limited number of geometric shapes). Other

programs who appeared later were STUDENT, which could solve algebra story

problems, and SIR which could understand simple English sentences. The result of these

programs was a refinement in language comprehension and logic.

Companies such as Digital Electronics were using XCON, an expert system designed to

program the large VAX computers. DuPont, General Motors, and Boeing relied heavily

on expert systems Indeed to keep up with the demand for the computer experts,

companies such as Teknowledge and Intellicorp specializing in creating software to aid in

producing expert systems formed. Other expert systems were designed to find and correct

flaws in existing expert systems.

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Evaluating artificial intelligence

Alan Turing proposed a general procedure to test the intelligence of an agent now known

as the Turing test. This procedure allows almost all the major problems of artificial

intelligence to be tested. However, it is a very difficult challenge and at present all agents

fail.

Artificial intelligence can also be evaluated on specific problems such as small problems

in chemistry, hand-writing recognition and game-playing. Such tests have been termed

subject matter expert Turing tests. Smaller problems provide more achievable goals and

there are an ever-increasing number of positive results.

The broad classes of outcome for an AI test are:

optimal: it is not possible to perform better

strong super-human: performs better than all humans

super-human: performs better than most humans

sub-human: performs worse than most humans

For example, performance at checkers (draughts) is optimal, performance at chess is

super-human and nearing strong super-human, and performance at many everyday tasks

performed by humans is sub-human.

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Specialized languages

AI researchers have developed several specialized languages for AI research:

IPL was the first language developed for artificial intelligence. It includes features

intended to support programs that could perform general problem solving, including lists,

associations, schemas (frames), dynamic memory allocation, data types, recursion,

associative retrieval, functions as arguments, generators (streams), and cooperative

multitasking.

Prolog is a declarative language where programs are expressed in terms of relations, and

execution occurs by running queries over these relations. Prolog is particularly useful for

symbolic reasoning, database and language parsing applications. Prolog is widely used in

AI today.

STRIPS is a language for expressing automated planning problem instances. It expresses

an initial state, the goal states, and a set of actions. For each action preconditions (what

must be established before the action is performed) and postconditions (what is

established after the action is performed) are specified.

AI applications are also often written in standard languages like C++ and languages

designed for mathematics, such as MATLAB and Lush.

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Transition from Lab to Life

The impact of the computer technology, AI included was felt. No longer was the

computer technology just part of a select few researchers in laboratories. The personal

computer made its debut along with many technological magazines. Such foundations as

the American Association for Artificial Intelligence also started.

Other fields of AI also made there way into the marketplace during the 1980's. One in

particular was the machine vision field. The work by Minsky and Marr were now the

foundation for the cameras and computers on assembly lines, performing quality control.

Although crude, these systems could distinguish differences shapes in objects using black

and white differences

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Applications of AI

The Artificial Intelligence Applications Institute has many project that they are working

on to make their computers learn how to operate themselves with less human input. To

have more functionality with less input is an operation for AI technology. Two of these

projects are AUSDA and EGRESS.

AUSDA is a program which will exam software to see if it is capable of handling the

tasks you need performed. If it isn't able or isn't reliable AUSDA will instruct you on

finding alternative software which would better suit your needs. According to

AIAI(Artificial Intelligence Applications Institute), the software will try to provide

solutions to problems like "identifying the root causes of incidents in which the use of

computer software is involved, studying different software development approaches, and

identifying aspects of these which are relevant to those root causes producing guidelines

for using and improving the development approaches studied, and providing support in

the integration of these approaches, so that they can be better used for the development

and maintenance of safety critical software."

EGRESS is a program which is studying human reactions to accidents. It is trying to

make a model of how peoples reactions in panic moments save lives. Although it seems

like in tough situations humans would fall apart and have no idea what to do, it is in fact

the opposite. Quick Decisions are usually made and are effective but not flawless. These

computer models will help rescuers make smart decisions in time of need. AI can't be

positive all the time but can suggest actions which we can act out and therefore lead to

safe rescues.Artificial intelligence has successfully been used in a wide range of fields

including medical diagnosis, stock trading, robot control, law, scientific discovery, video

games, toys, and Web search engines. Frequently, when a technique reaches mainstream

use it is no longer considered artificial intelligence, sometimes described as the AI effect.

It may also become integrated into artificial life.

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Scope of Expert Systems

An expert system is able to do the work of a professional. Moreover, a computer system

can be trained quickly, has virtually no operating cost, never forgets what it learns, never

calls in sick, retires, or goes on vacation. Beyond those, intelligent computers can

consider a large amount of information that may not be considered by humans.

Some people once considered an intelligent computer as a possible substitute for human

control over nuclear weapons, citing that a computer could respond more quickly to a

threat. And many AI developers were afraid of the possibility of certain programs like

and the bond that humans were making with the computer. We cannot, however, over

look the benefits of having a computer expert. Forecasting the weather, for example,

relies on many variables, and a computer expert can more accurately pool all of its

knowledge. Still a computer cannot rely on the hunches of a human expert, which are

sometimes necessary in predicting an outcome.

In some fields such as forecasting weather or finding bugs in computer software, expert

systems are sometimes more accurate than humans. But for other fields, such as

medicine, computers aiding doctors will be beneficial, but the human doctor should not

be replaced. Expert systems have the power and range to aid to benefit, and in some cases

replace humans, and computer experts, if used with discretion, will benefit human kind.

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Motion and manipulation

ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs.

Social intelligence

KISMET, a robot with rudimentary social skills.

Emotion and social skills play two roles for an intelligent agent.

It must be able to predict the actions of others, by understanding their motives and

emotional states. (This involves elements of game theory, decision theory, as well

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as the ability to model human emotions and the perceptual skills to detect

emotions.)

For good human-computer interaction, an intelligent machine also needs to

display emotions — at the very least it must appear polite and sensitive to the

humans it interacts with. At best, it should have normal emotions itself.

Creativity

TOPIO, a robot that can play ping-pong, developed by TOSY.

A sub-field of AI addresses creativity both theoretically (from a philosophical and

psychological perspective) and practically (via specific implementations of systems that

generate outputs that can be considered creative).

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Conclusion

First, we should be prepared for a change. Our conservative ways stand in the way of

progress. AI is a new step that is very helpful to the society. Machines can do jobs that

require detailed instructions followed and mental alertness. AI with its learning

capabilities can accomplish those tasks but only if the worlds conservatives are ready to

change and allow this to be a possibility. It makes us think about how early man finally

accepted the wheel as a good invention, not something taking away from its heritage or

tradition.

Secondly, we must be prepared to learn about the capabilities of AI. The more use we get

out of the machines the less work is required by us. In turn less injuries and stress to

human beings. Human beings are a species that learn by trying, and we must be prepared

to give AI a chance seeing AI as a blessing, not an inhibition.

Finally, we need to be prepared for the worst of AI. Something as revolutionary as AI is

sure to have many kinks to work out. There are so many things that can go wrong with a

new system so we must be as prepared as we can be for this new technology.

However, even though the fear of the machines are there, their capabilities are infinite

Whatever we teach AI, they will suggest in the future if a positive outcome arrives from

it. AI are like children that need to be taught to be kind, well mannered, and intelligent. If

they are to make important decisions, they should be wise. We as citizens need to make

sure AI programmers are keeping things on the level. We should be sure they are doing

the job correctly, so that no future accidents occur.

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