Artificial Intelligence full report
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Paper Presentation On Artificial Intelligence(AI)

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 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.

Artificial intelligence (AI) :-
Artificial intelligence (AI) is defined as intelligence exhibited by an artificial entity. Such a system is generally assumed to be a computer.
Although AI has a strong science fiction connotation, it forms a vital branch of computer science, dealing with intelligent behaviour, learning and adaptation in machines. Research in AI is concerned with producing machines to automate tasks requiring intelligent behavior. Examples include control, planning and scheduling, the ability to answer diagnostic and consumer questions, handwriting, speech, and facial recognition. As such, it has become a scientific discipline, focused on providing solutions to real life problems. 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.

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hi sir plz send development robotocs total report
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A chatbot (or chatterbot, or chat bot) is a computer program designed to simulate an intelligent conversation with one or more human users via auditory or textual methods. Traditionally, the aim of such simulation has been to fool the user into thinking that the program's output has been produced by a human.
Programs playing this role are sometimes referred to as Artificial Conversational Entities, talk bots or chatterboxes. More recently, however, chatbot-like methods have been used for practical purposes such as online help, personalised service, or information acquisition, in which case the program is functioning as a type of conversational agent.
What distinguishes a chatbot from more sophisticated natural language processing systems is the simplicity of the algorithms used. Although many chatbots do appear to interpret human input intelligently when generating their responses, many simply scan for keywords within the input and pull a reply with the most matching keywords, or the most similar wording pattern, from a textual database.

Present situation:
One pertinent field of AI research is natural language processing. Usually, weak AI fields employ specialized software or programming languages created specifically for the narrow function required. For example, A.L.I.C.E. utilises a programming language called AIML which is specific to its function as a conversational agent, and has since been adopted by various other developers of, so called, Alicebots. Nevertheless, A.L.I.C.E. is still purely based on pattern matching techniques without any reasoning capabilities, the same technique ELIZA was using back in 1966. This is not strong AI, which would require sapience and logical reasoning abilities.

Jabberwacky learns new responses and context based on real-time user interactions, rather than being driven from a static database. Some more recent chatterbots also combine real-time learning with evolutionary algorithms which optimise their ability to communicate based on each conversation held, with one notable example being Kyle, winner of the 2009 Leodis AI Award.[citation needed] Still, there is currently no general purpose conversational artificial intelligence, and some software developers focus on the practical aspect, information retrieval.
Automated conversational systems have now progressed, and large companies such as Lloyds Banking Group, Royal Bank of Scotland, Renault and Citroën are already using them instead of call centers to provide a first point of contact. Chatbots can also be implemented via Twitter, or Windows Live Messenger (see World of Alice).
Popular online portals like eBay and PayPal are also using multi lingual virtual agents to offer online support to their customers. For example, PayPal uses chatterbot Louise to handle queries in English and chatterbot Léa to handle queries in French. Developed by VirtuOz, both agents handle 400,000 conversations in a month.These agent have been functional since September 2008 on PayPal websites.

Proposed work:
We want to provide the chatbot with following abilities.:
1) Understanding marathi.
2) Acoustic coversation.
3) New learning.
4) Agricultural database.
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What is AI
Artificial Intelligence is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion.

The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology. Artificial intelligence includes
games playing: programming computers to play games such as chess and checkers
Expert systems : programming computers to make decisions in real-life situations (for example, some expert systems help doctors diagnose diseases based on symptoms)
Natural language : programming computers to understand natural human languages
Robotics : programming computers to see and hear and react to other like human being

Currently, no computers exhibit full artificial intelligence (that is, are able to simulate human behavior). The greatest advances have occurred in the field of games playing. The best computer chess programs are now capable of beating humans. In May, 1997, an IBM super-computer called Deep Blue defeated world chess champion Gary Kasparov in a chess match.

In the area of robotics, computers are now widely used in assembly plants, but they are capable only of very limited tasks. Robots have great difficulty identifying objects based on appearance or feel, and they still move and handle objects clumsily.

Natural-language processing offers the greatest potential rewards because it would allow people to interact with computers without needing any specialized knowledge. You could simply walk up to a computer and talk to it. Unfortunately, programming computers to understand natural languages has proved to be more difficult than originally thought. Some rudimentary translation systems that translate from one human language to another are in existence, but they are not nearly as good as human translators.

There are also voice recognition systems that can convert spoken sounds into written words, but they do not understand what they are writing; they simply take dictation. Even these systems are quite limited -- you must speak slowly and distinctly.

In the early 1980s, expert systems were believed to represent the future of artificial intelligence and of computers in general. To date, however, they have not lived up to expectations. Many expert systems help human experts in such fields as medicine and engineering, but they are very expensive to produce and are helpful only in special situations.

What is intelligence?
Is it that which characterize humans? Or is there an absolute standard of judgement?
– Accordingly there are two possibilities:
– A system with intelligence is expected to behave as intelligently as a human
– A system with intelligence is expected to behave in the best possible manner
– Secondly what type of behavior are we talking about?
– Are we looking at the thought process or reasoning ability of the system?
– Or are we only interested in the final manifestations of the system in terms of
its actions?
Given this scenario different interpretations have been used by different researchers as
defining the scope and view of Artificial Intelligence.
1. One view is that artificial intelligence is about designing systems that are as
intelligent as humans.
This view involves trying to understand human thought and an effort to build
machines that emulate the human thought process. This view is the cognitive
science approach to AI.

What is Intellegent Behaviour
ƒ Perception involving image recognition and computer vision
ƒ Reasoning
ƒ Learning
ƒ Understanding language involving natural language processing, speech processing
ƒ Solving problems
ƒ Robotics

Why do AI?
Two main goals of AI:
To understand human intelligence better. We test theories of human intelligence by writing programs which emulate it.

To create useful “smart” programs able to do tasks that would normally require a human expert.

Limits OF AI
What can AI systems do

Today’s AI systems have been able to achieve limited success in some of these tasks.
In Computer vision, the systems are capable of face recognition
In Robotics, we have been able to make vehicles that are mostly autonomous.
In Natural language processing, we have systems that are capable of simple machine
Today’s Expert systems can carry out medical diagnosis in a narrow domain
Speech understanding systems are capable of recognizing several thousand words
continuous speech
In Games, AI systems can play at the Grand Master level in chess (world champion),
checkers, etc.
What can AI systems NOT do yet?
Understand natural language robustly (e.g., read and understand articles in a newspaper)
Surf the web
Interpret an arbitrary visual scene
Learn a natural language
Construct plans in dynamic real-time domains
Exhibit true autonomy and intelligence

Applications of AI
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.
Applications of AI
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.

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.
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A Brief History of AI
5th century BC
Aristotle invents syllogistic logic, the first formal deductive reasoning system.

16th century AD
Rabbi Loew supposedly invents the Golem, an artificial man made out of clay

17th century
Descartes proposes animals are machines and founds a scientific paradigm that will dominate for 250 years.
Pascal creates the first mechanical calculator in 1642

18th century
Wolfgang von Kempelen “invents” fake chess-playing machine, The Turk.

19th century
George Boole creates a binary algebra to represent “laws of thought”

Charles Babbage and Lady Lovelace develop sophisticated programmable mechanical computers, precursor to modern electronic computers.

20th century
Karel Kapek writes “Rossum’s Universal Robots”, coining the English word “robot”

Warren McCulloch and Walter Pitts lay partial groundwork for neural networks

Turing writes “Computing Machinery and Intelligence” – proposal of Turing test

1956: John McCarthy coins phrase “artificial intelligence”

1952-62: Arthur Samuel writes the first AI game program to challenge a world champion, in part due to learning.

1950’s-60’s: Masterman et. al at Cambridge create semantic nets that do machine translation.

1961: James Slagle writes first symbolic integrator, SAINT, to solve calculus problems.

1963: Thomas Evan’s writes ANALOGY, which solves analogy problems like the ones on IQ tests.

1965: J. A. Robinson invents Resolution Method using formal logic as its representation language.

1965: Joseph Weizenbaum creates ELIZA, one of the earliest “chatterbots”

1967: Feigenbaum et. al create Dendral, the first useful knowledge-based agent that interpreted mass spectrographs.

1969: Shakey the robot combines movement, perception and problem solving.

1971: Terry Winograd demonstrates a program that can understand English commands in the word of blocks.

1972: Alain Colmerauer writes Prolog

1974: Ted Shortliffe creates MYCIN, the first expert system which showed the effectiveness of rule-based knowledge representation for medical diagnosis.

1978: Herb Simon wins Nobel Prize for theory of bounded rationality

1983: James Allen creates Interval Calculus as a formal representation for events in time.

1980’s: Backpropagation (invented 1974) rediscovered and sees wide use in neural networks

1985: ALVINN, “an autonomous land vehicle in a neural network” navigates across the country (2800 miles).

Early 1990’s: Gerry Tesauro creates TD-Gammon, a learning backgammon agent that vies with championship players

1997: Deep Blue defeats Garry Kasparov
Modern Times (post-Cartesian)
Widespread viruses, security holes aplenty
AI-powered CRM
Faster—and many more—computers

A word about paradigms…
AI will force a dualistic view of life to change because the environment will be inseparable from it. Axiological shifts will occur in defining life, causing society to expand current definitions of life (e.g. requirement of a body). Also, the connectedness of the local environment to AI will force science away from a reductionist view of this new life and into a more complex view of interactions causing life to arise.

On the other hand, most scientists would be happy to view the brain as a vast but complex machine. As such it should then be possible to purely replicate the brain using artificial neurons. This has already been done for very simple life forms such as insects which only have a few thousand neurons in their brains. In principle, it would not be necessary to have a full scientific understanding of how the brain works. One would just build a copy of one using artificial materials and see how it behaves.

Utilitarianism supports the development of AI, but only because of the Christian value of dominion over the environment. AI promises to increase control over life, thus suffering can be reduced. Yet, if AI is developed and not forced into particular tasks, Utilitarianism may not apply.

Artificial life may be viewed as more expendable than human life, so AI will be used as cheap labor, or perhaps slaves, thus increasing profits for corporations.

We do have to take responsibility for our creations, so if the risks associated with creating a form of AI are too great, then we should not pursue that development.

We do have to take responsibility for our creations, so if the risks associated with creating a form of AI are too great, then we should not pursue that development.

Rights Based Ethics
Once AI programs achieve a modicum of sentience, should they be given rights on par with other animals?
Two sides of the argument:
1) No, because sentience is impossible to determine.
2) Yes, because sentience can be proven beyond a reasonable doubt.

Duty ethics
Consequence of developing AI is not at issue. What obligations do we have to our biological children? Once AL is created, are they to be a Frankenstein’s monster and cast off without help, or are they to be guided by their creator, like Adam & Eve in Eden.

We do have to take responsibility for our creations, so if the risks associated with creating a form of AI are too great, then we should not pursue that development.

Do we have a responsibility to our biological children similar to AI? Are the two exactly the same?

What rules should we use as categorical imperatives? AI should contain a set of rules that most people share, like “do not kill, unless in self-defense” or “do not lie, unless the suffering caused by honesty is large”.

Confucianism is, in part, similar to Kant’s duty ethics: “don't do to others what you would not want yourself“ (reciprocity)
Yì , not Lì: actions should be based on righteousness, duty and morality, not on gain or profit.
Rén: “benevolence, charity, humanity, love, and kindness”

But AI is also an advance in civilization; it makes it possible for everyone to live more comfortable lives
Thus, Confucianism seems to encourage development and application of (non-sentient) AI, so long as it does not endanger others
If the AI is sentient, however, it would have to be treated humanely without exploitation, and could not be created out of individual or corporate greed.
Though Confucianism can be seen as a religion, it is also a code of ethics, which suggests that it would be more open to considering AI to be alive and deserving rights than other spiritual mythologies.
Virtue ethics
Difficult to predict what virtues to give Artificial life because it is a complex technology. Unable to see the results of these virtues may be a problem because there are risks involved with some virtues overpowering others.

What preprogrammed “virtues” should computers have to allow them to be morally right? Can virtues make an AI entity behave morally at all?

Wisdom, compassion, courage, strength, obedience, carefulness…?

ADVANTAGES (Factual Changes)
Smarter artificial intelligence promises to replace human jobs, freeing people for other pursuits by automating manufacturing and transportations.

Self-modifying, self-writing, and learning software relieves programmers of the burdensome task of specifying the whole of a program’s functionality—now we can just create the framework and have the program itself fill in the rest (example: real-time strategy game artificial intelligence run by a neural network that acts based on experience instead of an explicit decision tree).

Self-replicating applications can make deployment easier and less resource-intensive.

AI can see relationships in enormous or diverse bodies of data that a human could not

Disadvantages (Risks)
Potential for malevolent programs, “cold war” between two countries, unforeseen impacts because it is complex technology, environmental consequences will most likely be minimal.

Self-modifying, when combined with self-replicating, can lead to dangerous, unexpected results, such as a new and frequently mutating computer virus.
As computers get faster and more numerous, the possibility of randomly creating an artificial intelligence becomes real.
Military robots may make it possible for a country to indiscriminately attack less-advanced countries with few, if any, human casualties.
Rapid advances in AI could mean massive structural unemployment
AI utilizing non-transparent learning (i.e. neural networks) is never completely predictable

Mythological Considerations
Do sentient programs have a soul?
Christianity says no because a soul is imparted by God alone, and not by Computer Scientists, yet Christianity does dictate that we control the environment around us, thus if cyberspace is part of our environment, would AI allow us to control it?
Buddhism and Taoism take a different stance.

Buddhism and Taoism

The Hua-Yen school of Buddhism offers as the metaphor for the world an infinite net, at each intersection of which lies a jewel in which exists every other jewel and where every part of the net depends for its existence on dynamic awareness of every other part. This is in line with the axiological shift that would likely result from developing Artificial Life; environment and individual life are one.

Buddhism and Taoism value life above all else, so AI would be valued just as highly as all other life, once developed. Creation of AI would be opposed because AI does not share the tenets of the 8-fold path.
Christianity, Islam, Judaism
Christianity, Islam, and Judaism are (at least in relation to AI) very similar: they all state God created man in his own image
How can man create artificial life if God is the creator of life? (“Say unto them, O Muhammad: Allah gives life to you, then causes you to die, then gathers you unto the day of resurrection...”)
If AI programs are sentient and as smart or smarter than humans, is man still the highest of worldly life?
Does sentient AI have a soul? Does it ascend to heaven when it is deleted? Or when it stops running temporarily (and then is reborn)? How do you baptize software? And so on…

Because they will not accept AI as life, Judaism, Christianity and Islam do not care about the rights and treatment of any AI.
Instead, they will focus on the dangers to humans.
Christianity concerned with orthodoxy (correct belief), while Islam and Judaism concerned with orthopraxy (correct action)
Should you release (potentially dangerous) AI software if you have tested it to your satisfaction or if you have applied a set testing protocol?

Judaism and Christianity hold that man has free will; however, Islam is more slanted toward predestination (“By no means can anything befall us but what God has destined for us”).
Digital AI doesn’t appear to have free will: all inputs and outputs to an AI program are discreet and reproducible, as are the AI program’s state and execution (its “memory” and “thought”). Given the same conditions and the same input, digital AI software will always produce the same output.
Notice, however, that this could be true for humans as well, but is unverifiable because our inputs, outputs, memories and thoughts are not easily accessible or reproducible.

Bottom line: in most ways, sentient AI doesn’t make sense in the context of these religions, and, in some cases, is contrary to their beliefs.
And thus Christianity, Islam, and Judaism would not accept any AI as sentient, and probably not even as life.

Applied Ethicist’s Stance
Macroethics-current societal values is dominated by Utilitarianism, thus AI is likely to continue.

Microethics-depending on a person’s spirituality, this may influence the codes of conduct designed into artificial life.

Mesoethics-if a company develops AI, it will produce a utilitarian creature. If research institutes develop AI, then it may contain various ethical standpoints depending on who is doing the research and development.
The Future?
Idea of Artificial Intelligence is being replaced by Artificial life, or anything with a form or body.

The consensus among scientists is that a requirement for life is that it has an embodiment in some physical form, but this will change. Programs may not fit this requirement for life yet.
Should we start caring yet?
Very sophisticated—perhaps even sentient—AI may not be far off; with sufficient computation power (such as that offered by quantum computers) it is possible to “evolve” AI without much programming effort.
Today, concerns include mutating viruses and the reliability of AI (you don’t want software directing your car into a tree).

What should happen
When programs that appear to demonstrate sentience appear (intelligence and awareness), a panel of scientists could be assembled to determine if a particular program is sentient or not.
If sentient, it will be given rights, so, in general, companies will try to avoid developing sentient AI since they would not be able to indiscriminately exploit it.
Software companies should be made legally responsible for failings of software that result in damage to third parties despite good-faith attempts at control by the user.
AI and robotics have the potentially to truly revolutionize the economy by replacing labor with capital, allowing greater production—it deserves a corresponding share of research funding!
And what is going to happen…
Most people are willing to torture and kill intelligent animals like cows just for a tastier lunch—why would they hesitate to exploit artificial life?
This is further compounded mainstream religious beliefs
Even with laws, any individual with sufficient computing power could “evolve” AI without much programming.
Licensing agreements will continue to allow careless companies to often escape responsibility for faulty software.
Bottom line: ethical considerations will be ignored; reform—if it happens—will only take place when the economic costs become too high.

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please send me a full report on Artificial Intelligence
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.ppt   ARTIFICIAL INTELLIGENCE Biologically Inspired Computing-Swarm Intelligence.ppt (Size: 777.5 KB / Downloads: 160)
ARTIFICIAL INTELLIGENCE Biologically Inspired Computing-Swarm Intelligence
What is AI?

 Artificial Intelligence is a branch of Science which deals with helping machines and finds solutions to complex problems in a more human-like fashion.
 Artificial Intelligence can help us understand this process by recreating it, then potentially enabling us to enhance it beyond our current capabilities
Parental Disciplines of AI
Biologically Inspired Computing
 A field of study that loosely knits together subfields related to the topics of connectionism, social behavior and emergence.
 The use of biology or biological processes in developing new computing technologies and new areas of computer science; and conversely, the use of information science concepts and tools to explore biology from a different theoretical perspective.
Swarm Intelligence
 Swarm intelligence (SI) is artificial intelligence based on the collective behavior of decentralized, self-organized systems.
 Swarm intelligence is the emergent collective intelligence of groups of simple autonomous agents.
 Ant Colony Optimization
 Particle Swarm Optimization
 Stochastic Diffusion Search
PSO Algorithm
 Initialization
 Population loop
 Goodness evaluation and update
 Neighborhood evaluation
 Determine vi
 Particle update
 Cycle
Future Scope
 Swarm techniques for controlling unmanned vehicles.
 The possibility of using swarm intelligence to control nanobots within the body for the purpose of killing cancer tumors.
 In future the velocity of each individual must be updated by taking the best element found in all iterations rather than that of the current iteration only
 Biologically inspired computing could allow the creation of new machines with promising characteristics such as fault-tolerance, self-replication or cloning, reproduction, evolution, adaptation and learning, and growth.
 Bio computing has the potential to be a very powerful tool.
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Artificial Intelligence
• Science and Engineering of making intelligent machines
with broader aspect of facilities and leisure and wide
communication qualities
Intelligence in artificiality
• Intelligence is the computational part of the ability to achieve goals in the world.
• This Intelligence when brought to simulation and computation through any machine creature by means of algorithms and logic give rise to Artificial Intelligence
Flashback Artificial Intelligence

• The 1956 Dartmouth conference was the moment that AI gained its name, its mission,
its first success and its major players, and is widely considered the birth of AI.
• In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of
creating machines with true intelligence.
• The Turing Test was the first serious proposal in the philosophy of artificial intelligence.
Turing Machine
• Turing Machine manipulates symbols contained on a strip of tape.
The Turing test is a proposal for a test of a machine's ability to demonstrate intelligence
Important Timelines of AI
Research World
Expert Systems

• An Expert System is software that attempts to provide an answer to a problem, or clarify uncertainties
• Expert systems are designed to take the place of human experts, while others are designed to aid them.
Expert System-Process
Natural Language Processing

• Natural Language Understanding,which investigates methods of allowing the computer to comprehend instructions given in ordinary English so that computers can understand people more easily
Related Discipline:
Linguistics - study of language and of languages
Psycholinguistics - language and the mind, models of human language processing
Neurolinguistics - neural-level models of language processing
Logic - unambiguous formal language used in representing (unambiguous) meanings
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Artificial Intelligence
S.Gokul, F.Ivin Prasanna
3rd Year B.TECH Department of Information & Technology
Adhiyamaan college of Engineering,

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Artificial Intelligence is study and design of machines and design of intelligents, where intelligent agent is a system
tool which prevents its environment and takes actions which maximize its chances of success.
This paper throws light on implementing the artificial intelligence to the word processing and the word editing
software. This can be accomplished by teaching the machine, both the logic & sense that we possess over the
language. It is a very tedious job. Yet it can be achieved through the concept of "Patternisation". It means that the
language is stored as set of rules and regulations, so that the machine can automatically generate its own sentences
and also can easily identify the incorrect ones. This is a key concept which is going to make the ultimate dream of
humans (AI) come true. These ideas are purely unique. This is a research work.

It is artificially imparting the intelligence of humans
to the machines, so that they can learn and act by
themselves like us. Major AI textbooks define the
field as "the study and design of intelligent agents",
where an intelligent agent is a system that perceives
its environment and takes actions which maximize its
chances of success.
AI has become the major plot for the fiction writers
since the start of the research. Particularly in the field
of ROBOTICS, AI finds its applications to be
extraordinary. Generally the fiction works are more
exposed than the reality about the AI. In contrast to
fiction, AI has been stuck up in a basic level problem
which is strong enough to get its hold until it is
solved properly. Apart from this hold, it has its
development jailed by many other problems of the
previous breed which is not going to let many
generations enjoy the AI.
In short, we can simply say that it is almost
impossible to replicate the actions of HUMAN
BRAIN artificially using the range of machines
present now. The central problems of AI include
mostly Brain involving activities such as Reasoning,
Knowledge, Planning, Learning, Communication,
Perception and the ability to move and manipulate
objects. General intelligence is the most typical part
to impart.

Problems of AI
We have almost picked all the basic level problems to
develop AI. The basic level problems are emphasized
because there is never a strong building without a
good basement. Let’s discuss all the materials
required for a good basement of AI.
Basic Problems
Lack Of Sense:
AI uses computers because they are the best available
tool, not because they are the object of study. Now
let’s discuss the problems keeping the MS-WORD’s
“Autocorrect” option as the plot. This highly famous
option has never reached the people as a basic and
efficient tool of AI.Coming back to the problems, the
chief problems are concerned with the language. The
stop is in making the computer to understand the
language. We can program the entire language to the
machine but can not make them understand the
language. Let us consider the following situation: We
have programmed the entire English grammar in a
computer with entire grammatical rules and
regulations. Now let us see how the computer reacts
to the following sentences.
1) Cat ate rat
2) Rat ate cat
If we instruct the computer to check the correctness
of the two sentences, the computer will surely give
out that the two sentences are correct. This is a big
drawback. We humans can easily sort out that the
second sentence is grammatically correct but not
sensibly. Because of the sense that we posses we can
understand that the sentence is wrong, but it is merely
impossible to program that sense to the machines.
This is where computers are one step behind the

For the present time being we have not yet solved
this problem even for the phrases.
1) Leave bus
2) Leave letter
So far, Scientists could not program the machine
even to find the difference in the grammatical sense
of the above sentences. It is very crucial for a
machine to understand the sense in the language.
Now let’s consider the third example.
1) It is midnight.
2) It is dark.
These 2 sentences are correct. But the problem
peeping out is that the computer cannot understand
the coherence between these 2 sentences. Machines
totally ignore that these 2 sentences have some
connection between them.
This leads us to a point that machines do whatever
they are programmed to. They don’t understand and
do. They simply convert the user data into processed
Intelligence is the linking of present events with the
experience and coming out with new ideas. Humans
naturally possess this ability. Our task as discussed
above is imparting this ability artificially to the
machines. I am mentioning this again because there is
a point to note down. The “Linking process” is the
key. Let us consider this example:
I saw TOM& ________.
Ans: ?
Your mind would have answered in a fraction of
second that the answer is JERRY without any
hesitation. Ya. This is intelligence. It becomes
possible for us to do this because of this simple
mechanism. As soon as brain receives a question, it
searches the records saved in it. There is memory of
size more than 1TB in the brain. In a fraction of
second our brain finishes analyzing all these huge
data and finds the answer by linking the given
question to each possibly related data. This is the
Having analyzed the brain mechanism, let us discuss
whether it is possible to achieve this mechanism in
the machines.
Consider our two eyes as a video camera and Brain is
the processing and storage unit. Let us assume that
the eyes capture video for 12hrs a day. Let us assume
that the videos are stored in the standard high quality
AVI format. Let us do a simple calculation.
12hr movie file in std. AVI format= 10GB (approx.)
Movie files stored in 1yr=3650GB
“Can a machine be much efficient to sort the data,
delete the unwanted, remember the most wanted,
process the bulk of data in a fraction of second and
always give us the correct information?”
“What will be the size of storage unit and the
processing speed of the machine if designed so?”
Now you can estimate the might of the brain which
does all these and also more than these and keeps
silent inside us with a negligible weight of 1.5kg.
“Can anyone replicate it artificially at least making
the machine to perform 1/100th of its work?”
For everything, the answer is NO at present.
Betty Crow’s Hook:
Two crows named BETTY and ABEL learnt to use
bent wire to fish a bucket of food from a vertical tube
(as in the picture). Then ABEL flew off with a hook.
• BETTY tried to use a single piece of wire
for a while and then failed.
• The next thing what she did was a great
example for intelligence.
• She then pushed one end of the wire into the
tape holding the tube and moved the other
end using her beak, making a hook.
• She then used the hook to carry the bucket.
• She did this correctly 9 times out of 10.
To find more, give in GOOGLE: Betty crow Hook
This was reported and shown in BBC- August 2002.
This is one of the examples portraying the
intelligence of the living organisms.
This is a simple question to be raised.
“Can a Robot be able to replicate BETTY’s mental

The answer is No. Machines just do what they are
programmed to. You can even program a machine to
do this work. But it won’t come under AI. AI is more
about making the machines to learn by themselves.
Let us consider the same event and analyze it a bit
Now let us consider the crow to be a machine. In its
first attempt with a straight wire, it could not produce
the desired output. So it either produces an error
statement or gives out improper output. There is no
chance for it to take efforts to prepare a hook. What
actually happens inside the crow is that it learns that
the output is improper. So it identifies the required
output and changes its code itself to get the required
“Can a machine edit its own code according to the
The reasons for the behavior of the crow are
1) Innate behavior.
2) Learnt adaptation.
3) Self knowledge.
Now let us see a simple way to overcome this
problem to a certain level.
Mentioning it again and again, A.I is simply making
the machines to replicate the human brain. So let us
discuss how language is covered by brain. It uses the
concept of patternsiation. For example, let us
consider a sentence.
1) Ram is a teacher.
The sentence pattern of this sentence is S+V+C. To
be straight, the brain recognizes the sentences in a
pattern like this. It finds out the error if any part of a
sentence mismatches the pattern. So our task is to
code the pattern and rules to the computer.
Rules cover an important portion in this due to the
flexibility of language. Flexibility leads to a lot of
exceptions which all can be translated to machines as
rules. Whenever there is a special case or an
exception, there must be a rule inserted to maintain
For example, let us consider the pattern S+V. In this
pattern, a sentence is generated when a subject and a
verb is fed. But the sentence generated is correct only
when the subject is animate. So we need to insert a
rule there which states that
“If subject is inanimate, sentence is wrong.
Else correct”
Machine can thereby generate and check all the
sentences of this pattern. This can be implemented to
all sentence patterns. This is a successful first step.
This is only for sentences level.

Get the report here:

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Architecture of Intelligence

We start by making a distinction between mind and cognition, and by positing that cognition is an aspect of mind. We propose as a working hypothesis a Separability Hypothesis which posits that we can factor off an architecture for cognition from a more general architecture for mind, thus avoiding a number of philosophical objections that have been raised about the "Strong AI" hypothesis. Thus the search for an architectural level which will explain all the interesting phenomena of cognition is likely to be futile. There are a number of levels which interact, unlike in the computer model, and this interaction makes explanation of even relatively simple cognitive phenomena in terms of one level quite incomplete.
I. Dimensions for Thinking About Thinking
A major problem in the study of intelligence and cognition is the range of—often implicit—assumptions about what phenomena these terms are meant to cover. Are we just talking about cognition as having and using knowledge, or are we also talking about other mental states such as emotions and subjective awareness? Are we talking about intelligence as an abstract set of capacities, or as a set of biological mechanisms and phenomena? These two questions set up two dimensions of discussion about intelligence. After we discuss these dimensions we will discuss information processing, representation, and cognitive architectures.
A. Dimension 1. Is intelligence separable from other mental phenomena?
When people think of intelligence and cognition, they often think of an agent being in some knowledge state, that is, having thoughts, beliefs. They also think of the underlying process of cognition as something that changes knowledge states. Since knowledge states are particular types of information states the underlying process is thought of as information processing. However, besides these knowledge states, mental phenomena also include such things as emotional states and subjective consciousness. Under what conditions can these other mental properties also be attributed to artifacts to which we attribute knowledge states? Is intelligence separable from these other mental phenomena?
It is possible that intelligence can be explained or simulated without necessarily explaining or simulating other aspects of mind. A somewhat formal way of putting this Separability Hypothesis is that the knowledge state transformation account can be factored off as a homomorphism of the mental process account. That is: If the mental process can be seen as a sequence of transformations: M1 -->M2 -->..., where Mi is the complete mental state, and the transformation function (the function that is responsible for state changes) is F, then a subprocess K1 --> K2 -->. . . can be identified such that each Ki is a knowledge state and a component of the corresponding Mi, the transformation function is f, and f is some kind of homomorphism of F. A study of intelligence alone can restrict itself to a characterization of K’s and f, without producing accounts of M’s and F. If cognition is in fact separable in this sense, we can in principle design machines that implement f and whose states are interpretable as K’s. We can call such machines cognitive agents, and attribute intelligence to them. However, the states of such machines are not necessarily interpretable as complete M’s, and thus they may be denied other attributes of mental states.
B. Dimension 2: Functional versus Biological
The second dimension in discussions about intelligence involves the extent to which we need to be tied to biology for understanding intelligence. Can intelligence be characterized abstractly as a functional capability which just happens to be realized more or less well by some biological organisms? If it can, then study of biological brains, of human psychology, or of the phenomenology of human consciousness is not logically necessary for a theory of cognition and intelligence, just as enquiries into the relevant capabilities of biological organisms are not needed for the abstract study of logic and arithmetic or for the theory of flight. Of course, we may learn something from biology about how to practically implement intelligent systems, but we may feel quite free to substitute non-biological (both in the sense of architectures which are not brain-like and in the sense of being un- constrained by considerations of human psychology) approaches for all or part of our implementation. Whether intelligence can be characterized abstractly as a functional capability surely depends upon what phenomena we want to include in defining the functional capability, as we discussed. We might have different constraints on a definition that needed to include emotion and subjective states than one that only included knowledge states. Clearly, the enterprise of AI deeply depends upon this functional view being true at some level, but whether that level is abstract logical representations as in some branches of AI, Darwinian neural group selections as proposed by Edelman, something intermediate, or something physicalist is still an open question.
III. Architectures for Intelligence
We now move to a discussion of architectural proposals within the information processing perspective. Our goal is to try to place the multiplicity of proposals into perspective. As we review various proposals, we will present some judgements of our own about relevant issues. But first, we need to review the notion of an architecture and make some additional distinctions.
A. Form and Content Issues in Architectures
In computer science, a programming language corresponds to a virtual architecture. A specific program in that language describes a particular (virtual) machine, which then responds to various inputs in ways defined by the program. The architecture is thus what Newell calls the fixed structure of the information processor that is being analyzed, and the program specifies a variable structure within this architecture. We can regard the architecture as the form and the program as the content, which together fully instantiate a particular information processing machine. We can extend these intuitions to types of machines which are different from computers. For example, the connectionist architecture can be abstractly specified as the set {{N}, {nI}, {nO}, {zi}, {wij}}, where {N} is a set of nodes, {nI} and {nO} are subsets of {N} called input and output nodes respectively, {zi} are the functions computed by the nodes, and {wij} is the set of weights between nodes. A particular connectionist machine is then instantiated by the "program" that specifies values for all these variables.
We have discussed the prospects for separating intelligence (a knowledge state process) from other mental phenomena, and also the degree to which various theories of intelligence and cognition balance between fidelity to biology versus functionalism. We have discussed the sense in which alternatives such as logic, decision tree algorithms, and connectionism are all alternative languages in which to couch an information processing account of cognitive phenomena, and what it means to take a Knowledge Level stance towards cognitive phenomena. We have further discussed the distinction between form and content theories in AI. We are now ready to give an overview of the issues in cognitive architectures. We will assume that the reader is already familiar in some general way with the proposals that we discussing. Our goal is to place these ideas in perspective.
B. Intelligence as Just Computation
Until recently the dominant paradigm for thinking about information processing has been the Turing machine framework, or what has been called the discrete symbol system approach. Information processing theories are formulated as algorithms operating on data structures. In fact AI was launched as a field when Turing proposed in a famous paper that thinking was computation of this type (the term "artificial intelligence" itself was coined later) . Natural questions in this framework would be whether the set of computations that underlie thinking is a subset of Turing-computable functions, and if so how the properties of the subset should be characterized.
Most of AI research consists of algorithms for specific problems that are associated with intelligence when humans perform them. Algorithms for diagnosis, design, planning, etc., are proposed, because these tasks are seen as important for an intelligent agent. But as a rule no effort is made to relate the algorithm for the specific task to a general architecture for intelligence. While such algorithms are useful as technologies and to make the point that several tasks that appear to require intelligence can be done by certain classes of machines, they do not give much insight into intelligence in general.
C. Architectures for Deliberation
Historically most of the intuitions in AI about intelligence have come from introspections about the relationships between conscious thoughts. We are aware of having thoughts which often follow one after another. These thoughts are mostly couched in the medium of natural language, although sometimes thoughts include mental images as well. When people are thinking for a purpose, say for problem solving, there is a sense of directing thoughts, choosing some, rejecting others, and focusing them towards the goal. Activity of this type has been called "deliberation." Deliberation, for humans, is a coherent goal-directed activity, lasting over several seconds or longer. For many people thinking is the act of deliberating in this sense. We can contrast activities in this time span with other cognitive phenomena, which, in humans, take under a few hundred milliseconds, such as real-time natural language understanding and generation, visual perception, being reminded of things, and so on. These short time span phenomena are handled by what we will call the subdeliberative architecture, as we will discuss later.
Researchers have proposed different kinds of deliberative architectures, depending upon which kind of pattern among conscious thoughts struck them. Two groups of proposals about such patterns have been influential in AI theory-making: the reasoning view and the goal-subgoal view.
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Presented By:-

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Views about Artificial Intelligence
 Cognitive Science approach to AI
 Turing Test
 Reasoning and Inference
 Rational agent
Limitation of Artificial Intelligence
 Can’t understand natural language robustly
 Can’t surf on the web
 Can’t interpret an arbitrary visual scene
 Can’t learn a natural language effectively
 Can’t construct plans in dynamic real time domain
Examples of Artificial Intelligence
 MS-Office’s helpful talking paperclip
 Intelligent search engine: GOOGLE
 Deep Blue
 Sony AIBO
 Mars Rover
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30-03-2011, 04:31 PM

Presented By
K.Venkata Rao

Artificial Intelligence &Computer Learning
The term artificial intelligence is used to describe a property of machines or programs: the intelligence that the system demonstrates. Among the traits that researchers hope machines will exhibit are reasoning, knowledge, planning, learning, communication, perception and the ability to move and manipulate objects. Constructing robots that perform intelligent tasks has always been a highly motivating factor for the science and technology of information processing. Unlike philosophy and psychology, which are also concerned with intelligence, AI strives to build intelligent entities such as robots as well as understand them. Although no one can predict the future in detail, it is clear that computers with human-level intelligence (or better) would have a huge impact on our everyday lives and on the future course of civilization Neural Networks have been proposed as an alternative to Symbolic Artificial Intelligence in constructing intelligent systems. They are motivated by computation in the brain. Small Threshold computing elements when put together produce powerful information processing machines. In this paper, we put forth the foundational ideas in artificial intelligence and important concepts in Search Techniques, Knowledge Representation, Language Understanding, Machine Learning, Neural Computing and such other disciplines.
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A.I. is a branch of computer science that studies the computational requirements for tasks such as perception, reasoning and learning and develop systems to perform those tasks
The field of Artificial intelligence strives to understand and build intelligent entities
Proposed by Alan Turing(1950), a British Computer Scientist.
Intelligence is defined as the ability to achieve human level performance in all cognitive tests, sufficient to fool a human interrogator.
The test was devised in response to the question,” Can a computer think ?”.
Result was +ve if interrogator can not tell if responses are coming from the M/C or Human.
 One person sits at a computer and types the questions.
 The computer is connected to two other hidden computers
 At one computer, Human reads and responds to questions.
 At the other end, computer with no Human aid runs the program to provide responses.
AI is a branch of computer science dealing with symbolic, nonalgorithmic methods of problem solving
AI is a branch of computer science that deals with ways of knowledge using symbols rather than numbers and with Heuristics, method for processing information
AI works with pattern matching methods which attempt to describe objects , events or processes in terms of their qualitative features and logical and computational Relationship
What is Intelligence ?
 To respond to situations very flexibly.
 To make sense out of ambiguous or contradictory messages.
 To recognize the relative importance of different elements of
 To find similarities between situations despite difference
 To draw distinctions between situations despite similarities which may link them.
1943 – McCulloh and Pitts, Boolean circuit model of brain.
1950 – Turing’s computing machine and intelligence.
1950’s – Early AI programs including Samuel’s checker program, Newell and Simon’s logic theorist, Gelisnters geometry engine
1956 – Dartmouth conference.
1952-69 – “Look, Ma, no hands!” era.
1958 – McCarthy moves to MIT, LISP was born.
1965 – Robinson’s complete algorithm for logical reasoning.
1966-74 – AI discovers computational complex.
Neural network research almost disappears.
1969-79 - Early development in knowledge based systems
1980-88 : Expert system industry booms.
1988-93 : Expert system industry busts.
1985-88 : Neural networks return to popularity.
1995 : Agents… Agents… Agents.
Logical AI

What a program knows about the world in general the facts of the specific situation in which it must act and it’s goal are all represented by sentences of some mathematical logical language.
Pattern Recognition
When a program makes observation of some kind, it is often programmed to compare what it sees with already stored patterns.

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The proposal of our research line is the search for alternatives to the resolution of complex problems where human knowledge should be apprehended in a general fashion.
The solution to the problems can be found in the area of Pattern Recognition, where the solution rests on the easiness with which the systems adapts to the information available, in this case coming from the object. In this sense, neural networks are extremely useful, since they are not only capable of learning with the aid of an expert, but they can also make generalizations based on the information from the input data, thus showing relations that are a priori of a complex nature.
Since we know that,
It’s easy to train a neural network with samples which contain patterns, but it is much harder to train a neural network with samples which do not.
The number of “non-pattern” samples is just too large.
An Example;
Pattern Recognition:
Within the pattern recognition area, one of the most important concepts is that of
discriminant. The existence of discriminants constitutes the essence of pattern recognition.
The main characteristic of neural networks is their ability to generalize information, as well as their tolerance to noise. Therefore, one of the computer science areas that uses them the most is Pattern Recognition
The system has been designed to be generally applicable to a variety of real time
applications as follows;
used as a black-and-white mug shot identification system;
with PC-attached cameras for computer logon from a smart-card stored
audio-visual speech recognition (visual lip reading
to enhance acoustic speech recognition)
audio-visual speech recognition (visual lip reading to enhance acoustic speech recognition)
Face Recognition Systems(Face matching, Face detections, Access controls, etc.), etc.
Pattern recognition is a technology just reaching sufficient maturity for it to
experience a rapid growth in its practical applications. Much research effort
around the world is being applied to expanding the accuracy and capabilities
of this biometric domain, with a consequent broadening of its application in
the near future. Verification systems for physical and electronic access security
are available today, but the future holds the promise and the threat of passive
customization and automated surveillance systems enabled by pattern recognition.

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