ARTIFICIAL INTELLIGENCE

ARTIFICIAL INTELLIGENCE
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ABSTRACT

This paper is the introduction to Artificial intelligence (AI). Artificial intelligence

is exhibited by artificial entity, a system is generally assumed to be a computer. AI
systems are now in routine use in economics, medicine, engineering and the military, as
well as being built into many common home computer software applications, traditional
strategy games like computer chess and other video games.

We tried to explain the brief ideas of AI and its application to various fields. It
cleared the concept of computational and conventional categories. It includes various
advanced systems such as Neural Network, Fuzzy Systems and Evolutionary
computation. AI is used in typical problems such as Pattern recognition, Natural language
processing and more. This system is working throughout the world as an artificial brain.

Intelligence involves mechanisms, and AI research has discovered how to make

computers carry out some of them and not others. If doing a task requires only
mechanisms that are well understood today, computer programs can give very impressive
performances on these tasks. Such programs should be considered ``somewhat
intelligent''. It is related to the similar task of using computers to understand human
intelligence.

We can learn something about how to make machines solve problems by observing
other people or just by observing our own methods. On the other hand, most work in AI
involves studying the problems the world presents to intelligence rather than studying
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people or animals. AI researchers are free to use methods that are not observed in people
or that involve much more computing than people can do. We discussed conditions for
considering a machine to be intelligent. We argued that if the machine could successfully
pretend to be human to a knowledgeable observer then you certainly should consider it
intelligent.
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ACKNOWLEDGEMENT

The task of completion of the seminar requires co-operation and contribution of several
individuals. We students of 6th semester (Computer) are grateful to Mr.
………………….. for her kind help and guidance in the completion of the seminar. We
are highly obliged to all of them.

We thank COLLEGE OF ENGINEERING for providing wonderful library without

which the project wouldn’t have materialized. We are also obliged to all my friends and
family for their support throughout.

Thanking you,

Yours sincerely,

SOURABH SHARMA
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TABLE OF CONTENTS

1. ABSTRACT
2. ACKNOWLEDGEMENT
3. INTRODUCTION
4. HISTORY OF AI
5. GOALS
6. CATEGORIES OF AI
7. FIELDS OF AI
8. APPLICATIONS
9. FUTURE SCOPE
10. CONCLUSION
11.BIBLOGRAPHY
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INTRODUCTION
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ARTIFICIAL
The simple definition of artificial is that objects that are made or produced by human
beings rather than occurring naturally.

INTELLIGENCE
The simple definition of intelligence is a process of entail a set of skills of problem
solving, enabling to resolve genuine problems or difficulties that encounters and to create
an effective product and must also entail the potential for finding or creating problems
and thereby laying the groundwork for the acquisition of new knowledge.

ARTIFICIAL INTELLIGENCE
Artificial intelligence is a branch of science which deals with helping machines
find solution to complex problems in a more human like fashion. This generally involves
borrowing characteristics from human intelligence, and applying them as algorithms in a
computer friendly way. A more or less or flexible or efficient approach can be taken
depending on the requirements established, which influences how artificial intelligent
behavior appears.

Artificial intelligence is generally associated with computer science, but it has

many important links with other fields such as maths, psychology, cognition , biology
and philosophy , among many others . Our ability to combine knowledge from all these
fields will ultimately benefits our progress in the quest of creating an intelligent artificial
being.
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A.I is mainly concerned with the popular mind with the robotics development, but
also the main field of practical application has been as an embedded component in the
areas of software development which require computational understandings and
modeling such as such as finance and economics, data mining and physical science.

A.I in the fields of robotics is the make a computational models of human thought
processes. It is not enough to make a program that seems to behave the way human do.
You want to make a program that does it the way humans do it.

In computer science they also the problems bcoz we have to make a computer that
are satisfy for understanding the high-level languages and that was taken to be A.I.
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HISTORY
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HISTORY OF A.I

The intellectual roots of AI, and the concept of intelligent machines, may be found
in Greek mythology. Intelligent artifacts appear in literature since then, with real
mechanical devices actually demonstrating behaviour with some degree of intelligence.
After modern computers became available following World War-II, it has become
possible to create programs that perform difficult intellectual tasks.

1950s: The Beginnings of Artificial Intelligence (AI) Research

With the development of the electronic computer in 1941 and the stored program
computer in 1949 the condition for research in artificial intelligence is given, still the
observation of a link between human intelligence and machines was not widely observed
until the late in 1950

The first working AI programs were written in 1951 to run on the Ferranti Mark I
machine of the University of Manchester (UK): a draughts-playing program written by
Christopher Strachey and a chess-playing program written by Dietrich Prinz.
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The person who finally coined the term artificial intelligence and is regarded as
the father of the of AL is John McCarthy. In 1956 he organized a conference “the
Darthmouth summer research project on artificial intelligence" to draw the talent and
expertise of others interested in machine intelligence of a month of brainstorming. In the
following years AI research centers began forming at the Carnegie Mellon University as
well as the Massachusetts Institute of Technology (MIT) and new challenges were
faced:

1) The creation of systems that could efficiently solve problems by limiting the search.

2) The construction of systems that could learn by themselves.

1960:-

By the middle of the 1960s, research in the U.S. was heavily funded by the
Department of Defense and laboratories had been established around the world. 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" and Marvin Minsky agreed, writing that "within a generation .

By the 1960’s, America and its federal government starting pushing more for
the development of AI. The Department of Defense started backing several programs in
order to stay ahead of Soviet technology. The U.S. also started to commercially market
the sale of robotics to various manufacturers. The rise of expert systems also became
popular due to the creation of Edward Feigenbaum and Robert K. Lindsay’s DENDRAL.
DENDRAL had the ability to map the complex structures of organic chemicals, but like
many AI inventions, it began to tangle its results once the program had too many factors
built into it... the problem of creating 'artificial intelligence' will substantially be solved".
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The same predicament fell upon the program SHRDLU which would use robotics
through a computer so the user could ask questions and give commands in English.

1980:-

In the early 1980s, 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. 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. 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 diagnosis and
many other areas throughout the technology industry

1990 :-
From 1990s until the turn of the century, AI has reached some incredible
landmarks with the creation of intelligent agents. Intelligent agents basically use their
surrounding environment to solve problems in the most efficient and effective manner. In
1997, the first computer (named Deep Blue) beat a world chess champion. In 1995, the
VaMP car drove an entire 158 km racing track without any help from human intelligence.
In 1999, humanoid robots began to gain popularity as well as the ability to walk around
freely. Since then, AI has been playing a big role in certain commercial markets and
throughout the World Wide Web. The more advanced AI projects, like fully adapting
commonsense knowledge, have taken a back-burner to more lucrative industries.
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GOALS
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GOALS OF A.I
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 an intelligent system to display. The traits described below have
received the most attention.

1. Deduction, reasoning, problem solving:-

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 becomes astronomical when the problem
goes beyond a certain size. The search for more efficient problem-solving
algorithms is a high priority for AI research.

Human beings solve most of their problems using fast, intuitive

judgements rather than the conscious, step-by-step deduction that early AI
research was able to model. 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 net research attempts
to simulate the structures inside the brain that give rise to this skill; statistical
approaches to AI mimic the probabilistic nature of the human ability to guess.

2. Knowledge representation:-

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
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are: objects, properties, categories and relations between objects; situations, events,
states and time; causes and effects; knowledge about knowledge (what we know
about what other people know) and many other, less well researched domains. A
representation of "what exists" is an ontology: the set of objects, relations, concepts
and so on that the machine knows about. The most general are called upper
ontologies, which attempt provides a foundation for all other knowledge.

3. Planning:-
Intelligent agents must be able to set goals and achieve them. They need
a way to visualize the future and be able to make choices that maximize the utility
(or "value") of the available choices.
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. However, if the agent is not the only actor, it must periodically ascertain
whether the world matches its predictions and it must change its plan as this
becomes necessary, requiring the agent to reason under uncertainty.

4. Natural language processing:-

Natural language processing gives machines the ability to read and

understand the languages that humans speak. A sufficiently powerful natural
language processing system would enable natural language user interfaces and the
acquisition of knowledge directly from human-written sources, such as Internet
texts. Some straightforward applications of natural language processing include
information retrieval (or text mining) and machine translation.
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A common method of processing and extracting meaning from natural

language is through semantic indexing. Increases in processing speeds and the
drop in the cost of data storage makes indexing large volumes of abstractions of
the users input much more efficient.

5. Motion and manipulation:-

The field of robotics is closely related to AI. Intelligence is required for

robots to be able to handle such tasks as object manipulation and navigation, with
sub-problems of localization (knowing where you are, or finding out where other
things are), mapping (learning what is around you, building a map of the
environment), and motion planning (figuring out how to get there) or path
planning (going from one point in space to another point, which may involve
compliant motion - where the robot moves while maintaining physical contact

with an object).

6. Perception:-

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.

7. Social intelligence:-

Affective computing is the study and development of systems and devices

that can recognize, interpret, process, and simulate human affects. It is an
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interdisciplinary field spanning computer sciences, psychology, and cognitive

science while the origins of the field may be traced as far back as to early
philosophical inquiries into emotion. A motivation for the research is the ability to
simulate empathy. The machine should interpret the emotional state of humans
and adapt its behaviour to them, giving an appropriate response for those
emotions.

Emotion and social skills play two roles for an intelligent agent. First, 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
as the ability to model human emotions and the perceptual skills to detect
emotions.) Also, in an effort to facilitate human-computer interaction, an
intelligent machine might want to be able to display emotions—even if it does not
actually experience them itself—in order to appear sensitive to the emotional
dynamics of human interaction.

8. General intelligence:-

Most researchers think that their work will eventually be incorporated into
a machine with general intelligence (known as strong AI), combining all the skills
above and exceeding human abilities at most or all of them. A few believe that
anthropomorphic features like artificial consciousness or an artificial brain may be
required for such a project.

Many of the problems above may require general intelligence to be

considered solved. For example, even a straightforward, specific task like machine
translation requires that the machine read and write in both languages (NLP),
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follow the author's argument (reason), know what is being talked about
(knowledge), and faithfully reproduce the author's intention (social intelligence). A
problem like machine translation is considered "AI-complete". In order to solve
this particular problem, you must solve all the problems.
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CATEGORIES OF A.I

AI divides roughly into two schools of thought:

1. Conventional AI.
2. Computational Intelligence (CI).

1. Conventional AI :-

Conventional AI mostly involves methods now classified as machine learning,

characterized by formalism and statistical analysis. This is also known as symbolic
AI, logical AI, neat AI and Good Old Fashioned Artificial Intelligence (GOFAI).

Methods include:

 Expert systems: apply reasoning capabilities to reach a conclusion. An expert

system can process large amounts of known information and provide
conclusions based on them.
 Case based reasoning
 Bayesian networks
 Behavior based AI: a modular method of building AI systems by hand.

2. Computational Intelligence (CI) :-

Computational Intelligence involves iterative development or learning (e.g.

parameter tuning e.g. in connectionist systems). Learning is based on empirical data and
is associated with non-symbolic AI, scruffy AI and soft computing.
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Methods include:

 Neural networks: systems with very strong pattern recognition capabilities.

 Fuzzy systems: techniques for reasoning under uncertainty, has been widely
used in modern industrial and consumer product control systems.
 Evolutionary computation: applies biologically inspired concepts such as
populations, mutation and survival of the fittest to generate increasingly better
solutions to the problem. These methods most notably divide into evolutionary
algorithms (e.g. genetic algorithms) and swarm intelligence (e.g. ant
algorithms).

Typical problems to which AI methods are applied:-

 Pattern recognition
o Optical character recognition
o Handwriting recognition
o Speech recognition
o Face recognition
 Natural language processing, Translation and Chatter bots
 Non-linear control and Robotics
 Computer vision, Virtual reality and Image processing
 Game theory and Strategic planning
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Other fields in which AI methods are implemented:-

 Automation:-
 Automation is the use of machines, control systems and information
technologies to optimize productivity in the production of goods and
delivery of services. The correct incentive for applying automation is to
increase productivity, and/or quality beyond that possible with current
human labor levels so as to realize economies of scale, and/or realize
predictable quality levels. Automation greatly decreases the need for
human sensory and mental requirements while increasing load capacity,
speed, and repeatability.

 Cybernetics:-
 Cybernetics in some ways is like the science of organisation, with special
emphasis on the dynamic nature of the system being organised. The
human brain is just such a complex organisation which qualifies for
cybernetic study. It has all the characteristics of feedback, storage, etc.
and is also typical of many large businesses or Government departments.
 Cybernetics is that of artificial intelligence, where the aim is to show how
artificially manufactured systems can demonstrate intelligent behaviour.

 Hybrid intelligent system :-

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 Hybridization of different intelligent systems is an innovative approach

to construct computationally intelligent systems consisting of artificial
neural network, fuzzy inference systems, rough set, approximate
reasoning and derivative free optimization methods such as evolutionary
computation, swarm intelligence, bacterial foraging and so on. The
integration of different learning and adaptation techniques, to overcome
individual limitations and achieve synergetic effects through
hybridization or fusion of these techniques, has in recent years
contributed to a emergence of large number of new superior class of
intelligence known as Hybrid Intelligence.

 Intelligent agent:-
 In artificial intelligence, an intelligent agent (IA) is an autonomous
entity which observes through sensors and acts upon an environment
using actuators (i.e. it is an agent) and directs its activity towards
achieving goals.

 Intelligent control:-
 Intelligent Control or self-organising/learning control is a new emerging
discipline that is designed to deal with problems. Rather than being
model based, it is experiential based. Intelligent Control is the amalgam
of the disciplines of Artificial Intelligence, Systems Theory and
Operations Research. It uses most recent experiences or evidence to
improve its performance through a variety of learning schemas, that for
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practical implementation must demonstrate rapid learning convergence,

be temporally stable, be robust to parameter changes and internal and
external disturbances.
 Automated reasoning:-
 The study of automated reasoning helps produce software that allows
computers to reason completely, or nearly completely, automatically.
Although automated reasoning is considered a sub-field of artificial
intelligence, it also has connections with theoretical computer science,
and even philosophy.

 Data mining:-
 Data mining (the analysis step of the "Knowledge Discovery in
Databases" process, or KDD), an interdisciplinary subfield of computer
science, is the computational process of discovering patterns in large data
sets involving methods at the intersection of artificial intelligence,
machine learning, statistics, and database systems. The overall goal of the
data mining process is to extract information from a data set and
transform it into an understandable structure for further use

 Behavior-based robotics:-
 Behavior-based robotics is a branch of robotics that bridges artificial
intelligence (AI), engineering and cognitive science. Its dual goals are:
(1) To develop methods for con- trolling artificial systems, ranging
from physical robots to simulated ones and other autonomous
software agents
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(2) To use robotics to model and understand biological sys- tems more
fully, typically, animals ranging from insects to humans. Cognitive
robotics.
 Developmental robotics:-
 Developmental Robotics (DevRob), sometimes called epigenetic
robotics, is a methodology that uses metaphors from neural
development and developmental psychology to develop the mind for
autonomous robots.
 The program that simulates the functions of genome to develop a
robot's mental capabilities is called a developmental program.

 Evolutionary robotics:-
 Evolutionary robotics (ER) is a methodology that uses evolutionary
computation to develop controllers for autonomous robots

 Chatbot:-
 Chatterbot, a chatter robot is a type of conversational agent, a computer
program designed to simulate an intelligent conversation with one or
more human users via auditory or textual methods.
 Internet Relay Chat bot, a set of scripts or an independent program that
connects to Internet Relay Chat as a client, and so appears to other IRC
users as another user.
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 Knowledge Representation:-
 Knowledge representation (KR) is an area of artificial intelligence
research aimed at representing knowledge in symbols to facilitate
inferencing from those knowledge elements, creating new elements of
knowledge.
 The KR can be made to be independent of the underlying knowledge
model or knowledge base system (KBS) such as a semantic network

American Association for Artificial Intelligence (AAAI):-

Founded in 1979, the American Association for Artificial Intelligence (AAAI) is a

nonprofit scientific society devoted to advancing the scientific understanding of the
mechanisms underlying thought and intelligent behaviour and their embodiment in
machines. AAAI also aims to increase public understanding of artificial intelligence,
improve the teaching and training of AI practitioners, and provide guidance for research
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planners and funders concerning the importance and potential of current AI developments
and future directions.

APPLICATIONS
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APPLICATIONS OF A.I
Artificial intelligence has been used in a wide range of fields including medical
diagnosis, stock trading, robot control, law, scientific discovery and toys.

Hospitals and medicine:-

A medical clinic can use artificial intelligence systems to organize bed

schedules, make a staff rotation, and provide medical information.

Artificial neural networks are used as clinical decision support

systems for medical diagnosis, such as in Concept Processing
technology in EMR software.

Other tasks in medicine that can potentially be performed by artificial

intelligence include:

 Computer-aided interpretation of medical images. Such systems

help scan digital images, e.g. from computed tomography, for
typical appearances and to highlight conspicuous sections, such
as possible diseases. A typical application is the detection of a
tumor.
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 Heart sound analysis.

Heavy industry:-

Robots have become common in many industries. They are often

given jobs that are considered dangerous to humans. Robots have
proven effective in jobs that are very repetitive which may lead to
mistakes or accidents due to a lapse in concentration and other jobs
which humans may find degrading.

Game Playing :-
 This prospered greatly with the Digital Revolution, and helped
introduce people, especially children, to a life of dealing with various
types of Artificial Intelligence
 You can also 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.
 The internet is the best example were one can buy machine and play
various games.

Speech Recognition :-
In the 1990s, computer speech recognition reached a practical level for

limited purposes. Thus United Airlines has replaced its keyboard

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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.

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 computer’s 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
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state of AI. 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.

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 (e.g., about whether there
have been previous credit card frauds at this establishment).
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FUTURE SCOPE
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FUTURE SCOPE OF A.I

 In the next 10 years technologies in narrow fields such as speech recognition will
continue to improve and will reach human levels.
 In 10 years AI will be able to communicate with humans in unstructured English
using text or voice, navigate (not perfectly) in an unprepared environment and will
have some rudimentary common sense (and domain-specific intelligence).
 We will recreate some parts of the human (animal) brain in silicon. The feasibility
of this is demonstrated by tentative hippocampus experiments in rats There are two
major projects aiming for human brain simulation, CCortex and IBM Blue Brain.
 There will be an increasing number of practical applications based on digitally
recreated aspects human intelligence, such as cognition, perception, rehearsal
learning, or learning by repetitive practice.
 The development of meaningful artificial intelligence will require that machines
acquire some variant of human consciousness.
 Systems that do not possess self-awareness and sentience will at best always be
very brittle.
 Without these uniquely human characteristics, truely useful and powerful assistants
will remain a goal to achieve. To be sure, advances in hardware, storage, parallel
processing architectures will enable ever greater leaps in functionality
 Systems that are able to demonstrate conclusively that they possess self awareness,
language skills, surface, shallow and deep knowledge about the world around them
and their role within it will be needed going forward.
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 However the field of artificial consciousness remains in its infancy.

 The early years of the 21st century should see dramatic strides forward in this area
however

CONCLUSION
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CONCLUSION

We conclude that if the machine could successfully pretend to be human to a

knowledgeable observer then you certainly should consider it intelligent. AI systems are
now in routine use in various field such as economics, medicine, engineering and the
military, as well as being built into many common home computer software applications,
traditional strategy games etc.

AI is an exciting and rewarding discipline. AI is branch of computer science

that is concerned with the automation of intelligent behavior. The revised
definition of AI is - AI is the study of mechanisms underlying intelligent behavior
through the construction and evaluation of artifacts that attempt to enact those
mechanisms. So it is concluded that it work as an artificial human brain which have an
unbelievable artificial thinking power.
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BIBLIOGRAPHY
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BIBLIOGRAPHY

 Programs with Common Sense :-

John McCarthy, In Mechanization of Thought Processes, Proceedings of the Symposium of the
National Physics Laboratory, 1959.
 Artificial Intelligence, Logic and Formalizing Common Sense :-
Richmond Thomason, editor, Philosophical Logic and Artificial Intelligence. Klüver Academic,
1989.
 Logic and artificial intelligence :-
Richmond Thomason.
In Edward N. Zalta, editor, The Stanford Encyclopedia of Philosophy. Fall 2003.
http://plato.stanford.edu/archives/fall2003/entries/logic-ai/.
 Artificial Intelligence a Modern Approach
Russell, Stuart and Norvig, Peter
The second edition of a standard (and very substantial) university-level textbook on AI.
2003

LINKS:-
 www.google.com
 www.wikipedia.com
 http://www.aaai.org/
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 http://ww0w-formal.stanford.edu/
 http://insight.zdnet.co.uk/hardware/emergingtech/
 http://www.genetic-programming.com/