Artificial Intelligence-Present and Future
ABSTRACT
Artificial Intelligence (AI) has grown from small-scale laboratory
science into a technological an industrial success. We know posses an arsenal
of techniques for creating computer programs that control manufacturing
processes, diagnose computer faults and human diseases, design computers, play
grand-master level chess and so on. Basic research in AI has expanded
enormously during this period. Extracting theoretical and practical knowledge
is though a daunting task, in this paper the present and future technological
aspects related to the development, functioning, and applications of AI have
been discussed.
INTRODUCTION
Artificial Intelligence (AI) is the area of computer science
focusing on creating machines that can engage on behaviors that human consider
intelligent. The ability to create intelligent machines has intrigued humans
since ancient times and today with the advent of the computer and 50 years of
research into AI programming techniques, the dream of smart machines is
becoming a reality. Researchers are creating systems which can mimic human
thoughts, understand speech, beat the best human chessplayer and countless
other feats never before possible .The military is applying AI logic to its
hitech systems, and in the near future Artificial Intelligence may impact our
lives. AI is generally associated with computer science, but it has many
important links with other fields such as maths, psychology, and cognition,
Biology and Philosophy, among many others. Our ability to combine knowledge
from all these fields will ultimately benefit our progress in the quest of
creating an intelligent artificial being.
artificial intelligence:
definition and history
Definition
There are many definitions given by different researchers in
different years but a relevant definition to define Artificial Intelligence is
“The science and Engineering of making intelligent machines which can perform
tasks that require intelligence when performed by humans”. Artificial
Intelligent systems are those which can think and act like humans. They are the
systems that can think and act rationally.
HISTORY
After WWII, a
number of people independently started to work on intelligent machines. The
English mathematician Alan Turing may have been the first. He gave a lecture on
it in 1947. He also may have been the first to decide that AI was best
researched by programming computers rather than by building machines. By the
late 1950s, there were many researchers on AI, and most of them were basing
their work on programming computers. Turning’s test was the first test to
implement AI.
TURNING’S TEST
In late 1950, Alan Turning discussed conditions for considering a machine to be intelligent. He
argued that if the machine could successfully pretend to be human to a
knowledgeable observer then you certainly should consider it intelligent. The
Turing test is a one-sided test. A machine that passes the test should
certainly be considered intelligent, but a machine could still be considered
intelligent without knowing enough about humans to imitate a human.
Important features of the test
1. It gives us an objective
notion of intelligence.
2. It prevents us from being sidetracked by such confusing and currently
unanswerable questions.
3.
It eliminates any bias in favor of living organisms.
Artificial Intelligence:
BRANCHES
Here's a list of
some branches .Some of these may be regarded as concepts or topics rather than
full branches.
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.
SEARCH
AI programs often
examine large numbers of possibilities, e.g. moves in a chess game or
inferences by a theorem proving program. Discoveries are continually made about
how to do this more efficiently in various domains.
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.
REPRESENTATION
Facts about the
world have to be represented in some way. Usually languages of mathematical
logic are used.
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.
learning from
experience
Programs do that.
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.
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.
epistemology
This is a study of
the kinds of knowledge that are required for solving problems in the world.
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.
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.
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.
Artificial Intelligence:
APPLICATIONS
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.
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 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.
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 the experts 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 establishment from which he is buying it.
ARtificial intelligence:
ADvantages and disadvantages
Advantages
·
Intelligent
Systems can help experts to solve difficult analysis problems and also to
design new complex devices and circuits.
·
Intelligent
systems can learn from the experiences or the mistakes it has done by
considering them as the examples.
·
Intelligent
system can provide answers to questions using both structured data and free
text.
disadvantages
·
The
process involved in solving the problem is very complicated.
·
The
language is not understandable to common people,only highly skilled person can
understan the natural lanuage of AI.
·
It is
very expensive method,unless required should not be used.
·
It is
time consuming,for preparing the machine for performing the task.
ARTIFICIAL INTELLIGENCE:
CONCLUSION
We have attempted to define artificial intelligence through
discussion of its major areas of research and application. This survey reveals
a young and promising field of study whose primary concern is finding an
effective way to understand and apply intelligent problem solving planning, and
communication skills to a wide range of practical problems. Inspite of the
variety of problems addressed in artificial intelligence research, a number of
important features emerge that seems common to all divisions of fields.
REFERENCES
1.
”ARTIFICIAL INTELLIGENCE”-Second Edition, By
Kevin Knight and Elaine Rich, TMH.
2.
2.”ARTIFICIAL INTELLIGENCE”-Structures and
strategies for complex problem solving, By George.F.Luger, PEA
3.
”ARTIFICIAL INTELLIGENCE”-Third Edition, By
Patrick.H.Winston, PEA.
4.
http://www-formal.stanford.edu/jmc/
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