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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.
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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.)
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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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