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Paper Presentation On Artificial Intelligence(AI)

ABSTRACT

INTRODUCTION

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hi sir plz send development robotocs total report

Chatbot

Abstract:
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
translation.
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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please send me a full report on Artificial Intelligence

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please send me a full report on Artificial Intelligence to my mail id sonu.rashu[at]gmail.com

presented by:
K.Preethi
SP.Rathna Janani

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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.
Techniques
 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
Conclusion
 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.

presented by:
Mohsin Pathan S.E(E&TC)
Sachin Kanungo F.E(Instrumentation

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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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Abstract
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.

Introduction
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
humans.

For the present time being we have not yet solved
this problem even for the phrases.
Example
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
output.
Intelligence
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
mechanism.
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.
http:\news.bbc.co.uk\hi\sci\tech\2178920.stm
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
process?”

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
differently.
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
output.
“Can a machine edit its own code according to the
output?”
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.
Patternisation
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
stability.
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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ARTIFICIAL INTELLIGENCE
Architecture of Intelligence
Abstract
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.

Presented By:-
AMIT RAJAWAT

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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

Presented By
K.Venkata Rao
Ch.Avinash

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Artificial Intelligence &Computer Learning
Abstract
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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INTRODUCTION
WHAT IS A.I. ?
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
TURING TEST
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.
DEFINITIONS
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
situations
 To find similarities between situations despite difference
 To draw distinctions between situations despite similarities which may page link them.
HISTORY
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.
(present)
BRANCHES
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.

SUBMITTED BY:
C. VIGNESHWARAN.,
S. RANGARAJAN,

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ABSTRACT:
INTRODUCTION:
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
Applications:
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
database;
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.
Conclusion
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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ARTIFICIAL INTELLIGENCE
INTRODUCTION
 AI is a new step, very helpful to the society.
 This field is defined as “the study and design of intelligent agents”.
 It has long been recognized by researchers in artificial intelligence that attempting to state theories about human mental processes using natural language is filled with difficulties.
There are many definitions of artificial intelligence -perhaps as many as there are researchers in the field.
1. ‘The use of computer programs and programming techniques to cast light on the principles of intelligence'.
2. The study of ideas which enable computers to do the things that make people seem intelligent.
3. The part of computer science concerned with designing systems that exhibit the characteristics we associate with intelligence in human behaviour.
QUEST FOR ARTIFICIAL INTELLIGENCE
WHAT IS AI?
TYPES OF AI
 AI is mainly classified into two parts:
 Weak AI
 Strong AI
WEAK AI
 An artificial intelligence system which is not intended to match or exceed the capabilities of human beings.
 This type of A.I. is presently incorporated into society, especially in large industries.
 It refers to technology that is able to manipulate predetermined rules and apply the rules to reach a well-defined goal.
 Weak A.I. also has a very exciting future.
 With the exponential growth of computing power at hand, scientists believe that weak A.I. breakthroughs are in the near future. One technology that is projected to emerge is the super-computer
STRONG AI
 The second form of artificial intelligence is strong A.I.
 Strong A.I. refers to technology that has the ability to think cognitively or is able to function in a way similar to the human brain.
 Its also called as “True AI”, as they are truly intelligent.
 It is found that this intelligence is never achieved being more specific there is no hopes of achieving this intelligence.
Branches of AI
 Logical AI
 Search
 Planning
 Representation
 Inference
 Common sense knowledge and reasoning
 Learning from experience
 Pattern recognition
 Genetic programming
 Epistemology
 Ontology
 Heuristics
CHARACTERISTICS OF AI
 The ability to act intelligently as a human.
 The ability to behave following “general intelligent action “.
 The ability to artificially stimulate the brain.
 The ability to actively learn and adapt as a human.
 The ability to process language and symbols.
ROBOTICS
 It is the science and technology of robots, and their design ,manufacture, and application.
 Intelligence is required for robots to be able to handle such tasks as object manipulation and navigation, with sub-problems of localization, mapping and motion planning.
ROBOTIC APPLICATIONS
• Sony launched the Artificial Intelligence robot dog (AIBO) in 1999, the pooch was regarded as a breakthrough in the robot entertainment market. Since then, AIBO (meaning “love” or “attachment” in Japanese) .
• AIBO is the first generation of AI pets designed to learn and adapt to its environment.
• These automatons can communicate over wireless networks and even photograph things they ‘see’ and post these to their personal (owner’s) websites.
• He will wag his tail when patted on the head, and, if you’re lucky, he’ll produce an affectionate high-pitched squeal.
• The robo-pup can respond to voice commands, pick up his AIBOne, and play with his balls just like a real dog.
PARO
• Paro has got to be the cutest and most lifelike of all the artificial pets.
• He is what is known as a Mental Commitment Robot – developed to interact with human beings and make them feel emotional attachment. These robots are specifically aimed to trigger subjective evaluations and have shown to have positive psychological, physiological (such as improvement in vital signs), and social effects among inpatients and caregivers young and old.
APPLICATIONS
 Computer science
 Finance
 Medicine
 Heavy industry
 Transport
 Telecommunication
 Toys and games
 Games
 Toys
 Music
Application of AI in Systems
 Speech Recognition
 Facial Recognition System
 Handwriting recognition system
Speech Recognition
 It is a technology that allows the computer to identify the understand words spoken by a person using a microphone or telephone.
 The ultimate goal of the technology is to be able to produce a system that can recognize with 100% accuracy all words that are spoken by any person.
Facial Recognition System
 Computer application for automatically identify or verifying a person from a digital image or a video frame from a video source.
Handwriting recognition system
 Ability of a computer to receive the interpret intelligent handwritten input.
 The image of the written text may be sensed “off line” from a piece of paper by optical scanning (optical character recognition).
 Alternatively the movements of the pen tip may be sensed “on line”, for example by a pen-based computer screen surface.
Advantages
 Artificial intelligence finds applications in space exploration. Intelligent robots can be used to explore space
 Painstaking activities, which have long been carried out by humans can be taken over by the robots.
 Artificial intelligence can be utilized in the completion of repetitive and time-consuming tasks efficiently.
 Intelligent machines can be employed to do certain dangerous tasks.
Disadvantages
 Concepts such as wholeheartedness and dedication in work bear no existence in the world of artificial intelligence.
 Lack a creative mind.
 Thinking machines will govern all the fields and populate all positions pre-occupied by people.
Conclusion
 Eventually, it is up to you whether to stand by artificial intelligence or warn yourself of the likely disaster that it may lead to.
 In my view, there is no ideal replacement for human beings.
 Artificial intelligence can help alleviate the difficulties faced by man but intelligent machines can never be ‘human’.

presented by:
Submitted By
Miss. Smruti S. Bele

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Artificial intelligence [AI] is exhibited by artificial entity; a system is generally assumed to be a computer. It is the Science and Engineering of making intelligent machines, especially intelligent computer programs. The term was coined in 1956 by John McCarthy at the Massachusetts Institute of Technology..
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.. 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.
This paper briefly describes how Artificial Intelligence works and the various techniques used in AI. It further describes, the greatest advances that have occurred in the field of Medicine, Military, Expert Systems, Robotics and Natural Language Processing. This paper deals with latest advances that 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.
Today, the hottest area of Artificial Intelligence is neural networks, which are proving successful in a number of disciplines such as voice recognition and natural language processing. Robotics incorporating artificial intelligence interaction with laser, ultrasound, MRI scanning, are performing delicate brain surgery more accurately than by traditional surgical approaches. A.I. was used in the investigation of Mars in July 1997. This paper reflects the potential impact of AI on our lives. Artificial Intelligence is likely to continue to creep into our lives without us really noticing.
Artificial Intelligence
1.Introduction:
Artificial Intelligence is a branch of Science which deals with helping machines find solutions to complex problems in a more human-like fashion. This generally involves borrowing characteristics from human intelligence, and applying them as algorithm in human friendly way. It is basically the ability of a machine to think for itself. It aims at getting computers to do tasks which require human intelligence. In short it can be described as:
Simple things turn out to be the hardest to automate:
*Recognizing a face.
*Navigating a busy street.
*Understanding what someone says.
2. Why Artificial Intelligence?
Motivation...
Computers are fundamentally well suited to performing mechanical computations, using fixed programmed rules. This allows artificial machines to perform monotonous tasks efficiently and reliably, which humans are ill - suited to. For more complex problems, things get more difficult. Unlike humans, computers have trouble understanding specific situations, and adapting to new situations. Artificial Intelligence aims to improve machine behavior in tackling such complex tasks.
3. How does Artificial Intelligence work?
Technology...
Over the past five decades, AI research has mostly been focusing on solving specific problems. Numerous solutions have been devised and improved to do so efficiently and reliably. This explains why the field of Artificial Intelligence is split into many branches. Some of the branches have been explained below:
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 the sequence of actions.
Pattern recognition:
The main focus in AI today is getting a computer to recognize, make senses and recreate in what it sees and hears.
The two major divisions of pattern recognition are machine vision and sound.
Pattern-Recognition-Vision:
It's goal is to get a computer to recognize pictures so that it can recognize objects in its surroundings that would be helpful in robotics.
Pattern-Recognition-Sound:
It wants to achieve a similar goal but is a primary concern with companies that want to produce a new means in which a person interacts with a computer by talking.
Ontology:
Ontology is the study of what objects are and what are they made of. It is the study of 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.
Robotics:
Robotics is the study of how to design, build, use, and work with robots. Robots are mechanical devices that can move and react to sensory input giving them some degree of autonomous control.
Robots are widely used in the industrial sector performing high-precision jobs such as painting and wielding. They are used in laboratories for repetitive tasks in chemistry and biology, and in situations, which would be dangerous for humans such as cleaning toxic waste or defusing bombs.
Three laws of robotics:
1. A robot may not injure or harm a human being or allow a human being to come to harm.
2. 2. A robot must follow the instructions given to it by a human being without violating Rule 1
3. 3. A robot must protect itself as long as such protection does not violate Rules 1 and 2.
ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs.
Artificial life:
Artificial life is a field of scientific study that attempts to model living biological systems through complex algorithms. Scientists use these models to test and experiment with a multitude of factors on the behaviour of the systems.
Artificial life: From robot dreams to reality
It is a diverse field of research, but a common theme is testing out the fundamental principles of life by building detailed working models. One of the most ambitious goals of artificial-life research is the construction of living systems out of non-living parts. Artificial life is a blanket term used to refer to human attempts at setting up systems with lifelike properties all biological organisms possess, such as self-reproduction, homeostasis, adaptability, mutational variation, optimization of external states, and so on.
Epistemology:
Epistemology is a study of knowledge that are required for solving problems in the world.
4. Who uses Artificial Intelligence?
Applications...
To be useful, a system has to be able to do more than just correctly perform some task.
-- Johan McDermott
Artificial Intelligence is helping people in every field to make better use of information to work harder not smarter. The potential applications of Artificial Intelligence are abundant. However, some of the applications of AI have been listed below:
Medicine:
NEW BLOOD TEST SPOTS CANCER:
In one of the biggest advances in cancer research in years, scientists have developed a blood test that can detect cancer with a greater than 90% accuracy. This artificial intelligence --already tested for cancers of the breast, ovary, and lung--could one day be used to detect many types cancer. 'All that's needed is a single drop of blood’… 'The computer does the rest.'...In tests on several hundred blood samples, some taken from women with ovarian cancer and others from healthy women, the test proved 'an astonishing' 100% accurate in detecting cancer, even at the earliest stages.
Artificial nose:
Scientists have endowed computers with eyes to see, thanks to digital cameras, and ears to hear, via microphones and sophisticated recognition software. Now they're taking computers further into the realm of the senses with the development of an artificial nose.
E-NOSE TO SNIFF OUT HOSPITAL SUPERBUGS:
"E-nose analyses gas samples by passing the gas over an array of electrodes coated with different conducting polymers. Each electrode reacts to particular substance by changing its electrical resistance in a characteristic way. Combining the signals from all the electrodes gives a 'smell-print' of the chemicals in the mixture that neural network software built into the e-nose can learn to recognize. As a result, it can be detected from the smell alone that what the bacterial infections are.
Military:
A NEW MODEL OF ARMY SOLDIER ROLLS CLOSER TO THE BATTLEFIELD:
The American military is working on a new generation of soldier, far different from the army it has. 'They don't feel hungry,' said Gordon Johnson of the Joint Forces Command at the Pentagon. 'They are not afraid. They don't forget their orders. They don't care if the guy next to them has just been shot. Will they do a better job than humans? Yes.' The robot soldier is coming. The Pentagon predicts that robots will be a major fighting force in American military in less than a decade, hunting and killing enemies in combat. Robots are a crucial part of the Army's effort to rebuild itself as a 21st-century fighting force, and a $127 billion project called Future Combat Systems is the biggest military contract in American history.
Through artificial intelligence, engineers and computer scientists are capable of creating machines that perform dangerous tasks in place of humans. Here, a police robot handles a live bomb.

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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 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.
INTRODUCTION :-
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.
History :-
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.
1950 - 1960:-
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.
1960 – 1970 :-
During the 1960s and 1970s Marvin Minsky and Seymour Papert publish Perceptrons, demonstrating limits of simple neural nets and Alain Colmerauer developed the Prolog computer language. Ted Shortliffe demonstrated the power of rule-based systems for knowledge representation and inference in medical diagnosis and therapy in what is sometimes called the first expert system. Hans Moravec developed the first computer-controlled vehicle to autonomously negotiate cluttered obstacle courses.
1980’s ONWARDS :-
In the 1980s, neural networks became widely used with the back propagation algorithm, first described by Paul John Werbos in 1974. The 1990s marked major achievements in many areas of AI and demonstrations of various applications. Most notably Deep Blue, a chess-playing computer, beat Garry Kasparov in a famous six-game match in 1997.
Categories of AI :-
AI divides roughly into two schools of thought:
• Conventional AI.
• Computational Intelligence (CI).
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. 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.
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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ARTIFICIAL INTELLIGENCE
include people, procedures, hardware, software, data and knowledge needed to develop computer systems and machines that demonstrated characteristics of intelligence.
Intelligence Behavior
 the ability to learn from experiences and apply knowledge acquired from experience, handle complex situations, solve problems when important information is missing, determine what is important, react quickly and correctly to a new situation, understand visual images, process and manipulate symbols, be creative and imaginative, and use heuristics.
Turing Test (developed by Alan Turing, a British Mathematician)
attempts to determine whether the responses from a computer with intelligent behavior are indistinguishable from responses from a human.
Specific characteristics of intelligent behavior:
Specific characteristics of intelligent behavior:
The difference between Natural and Artificial Intelligence
Major Branches of Artificial Intelligence
1. RoboticsInvolve developing mechanical or computer devices that perform tasks requiring a high degree of precision or that are hazardous for humans.
The Rover was a remote-controlled robot used by NASA to explore the surface of Mars.
Lucyan orangutan robot, was a pure research project to develop some novel theories about the fundamental operating principles of the brain.
2. Vision SystemsInclude hardware and software that permit computers to capture, store, and manipulate visual images and pictures.
3. Natural language processingComputers understand and react to statements and commands made in a “natural” language, such as English.
4. Learning systemComputer changes how it functions or reacts to situations based on feedback.
6. Expert SystemsConsists of hardware and software that stores knowledge and makes inferences, similar to a human expert.(will be discuss by the next reporter)

Presented by
Tanu Dixit

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What is INTELLIGENCE???
The ability to comprehend, understand and get profit from experience.
Intelligence is the computational part of the ability to achieve goals in the world. Varying kinds and degrees of intelligence occur in people, many animals and some machines.
Artificial intelligence may be defined in a number of ways
 AI refers to an algorithm or set of algorithms that can make decisions in a logical way.
 AI is the use of programs to enable machines to perform tasks which humans perform using their intelligence.
 AI is the process of inducing intelligence into the machines, artificially/externally.
 The branch of computer science that deal with writing computer programs that can solve problems creatively
What type of reasoning is connected with Artificial Intelligence???
 The AI routine for a bad guy in a game might let him figureout how to find you. Another use of AI is to have a maze or puzzle solved automatically.
 Artificial Intelligence (AI) is used in games for everything from making a computer opponent behave believably like a human opponent to having automated units perform tasks in a realistic manner.
Intelligent Behavior
 Learn from experience.
 Apply knowledge acquired from experience.
 Handle complex situations.
 Solve problems when important information is missing.
 React quickly and correctly to a new situation.
 Understand visual images.
 Process and manipulate symbols.
 Be creative and imaginative.
 Use heuristics.
GOALS
 Among the traits that researchers hope machines will eventually exhibit are:
• Reasoning
• Knowledge
• Planning
• Learning
• Communication
• Perception
 General Intelligence (or, Strong AI) is the ultimate long-term goal of researchers, although it has yet to be achieved.
REQUIREMENTS
 For a potential AI unit to be considered “alive” or “intelligent” it would have to exceed its original programming.
• The AI questioning its original programming without provocation.
 AI was programmed to go “beep” every minute and then left alone to do so, eventually it would wonder if it was necessary to beep every minute.
• The AI being able to solve problems it was not originally programmed to solve.
 This requirement shows an ability to apply deductive reasoning without connections that have been specifically laid beforehand. It further requires the AI to draw upon all of its “knowledge” and “skills”
 This idea further extends to the AI making connections that it was not specifically given; applying methods to situations where the methods weren’t originally intended to be applied, getting results, and either discarding the results as nonsensical or realizing that they are valid.
Problems of Artificial Intelligence
 Deduction, Reasoning, and Problem Solving
 Early AI researchers developed algorithms that could imitate the process of conscious, step-by-step reasoning that human beings use when they solve puzzles or make logical deductions.
 By the 80s and 90s, AI research had also developed successful methods for dealing with uncertain or incomplete information by using concepts from probability and economics.

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Operationally Speaking, AI is:
Applied Cognitive Science
Computational models of human reasoning
Problem solving
Scientific thinking
Models of non-introspective mental processes
Language comprehension, language learning
Human memory organization (STM, LTM)
HISTORY
In the 1940s and 50s, a handful of scientists from a variety of fields (mathematics, psychology, engineering, economics and political science) began to discuss the possibility of creating an artificial brain. The field of artificial intelligence research was founded as an academic discipline in 1956.
Cont......
Cont......
In 1950 Alan Turing published a landmark paper in which he speculated about the possibility of creating machines with true intelligence
If a machine could carry on a conversation (over a teletype) that was indistinguishable from a conversation with a human being, then the machine could be called "intelligent."
Cont......
This simplified version of the problem allowed Turing to argue convincingly that a "thinking machine"
When access to digital computers became possible in the middle fifties, a few scientists instinctively recognized that a machine that could manipulate numbers could also manipulate symbols and that the manipulation of symbols could well be the essence of human thought. This was a new approach to creating thinking machines.
Cont......
In 1955, Allen Newell and (future Nobel Laureate) Herbert Simon created the "Logic Theorist" (with help from J. C. Shaw). The program would eventually prove 38 of the first 52 theorems in Russell and Whitehead's Principia Mathematica, and find new and more elegant proofs for some
Cont......
Simon said that they had "solved the venerable mind/body problem, explaining how a system composed of matter can have the properties of mind."(This was an early statement of the philosophical position John Searle would later call "Strong AI": that machines can contain minds just as human bodies do.)
Cont......
The years after the Dartmouth conference were an era of discovery, of sprinting across new ground. The programs that were developed during this time were, to most people, simply "astonishing":computers were solving algebra word problems, proving theorems in geometry and learning to speak English
Cont......
Few at the time would have believed that such "intelligent" behavior by machines was possible at allResearchers expressed an intense optimism in private and in print, predicting that a fully intelligent machine would be built in less than 20 yearsGovernment agencies like ARPA poured money into the new field
Cont......
The agencies which funded AI research (such as the British government, DARPA and NRC) became frustrated with the lack of progress and eventually cut off almost all funding for undirected research into AI. The pattern began as early as 1966 when the ALPAC report appeared criticizing machine translation efforts. After spending 20 million dollars, the NRC ended all support.
Cont......
In 1973, the Lighthill report on the state of AI research in England criticized the utter failure of AI to achieve its "grandiose objectives" and led to the dismantling of AI research in that country.(The report specifically mentioned the combinatorial explosion problem as a reason for AI's failings.)
Cont......
In the 1980s a form of AI program called "expert systems" was adopted by corporations around the world and knowledge became the focus of mainstream AI research. In those same years, the Japanese government aggressively funded AI with its fifth generation computer project.
Cont......
In 1981, the Japanese Ministry of International Trade and Industry set aside $850 million dollars for the Fifth generation computer project. Their objectives were to write programs and build machines that could carry on conversations, translate languages, interpret pictures, and reason like human beings.Much to the chagrin of scruffies, they chose Prolog as the primary computer language for the project
Cont......
The business community's fascination with AI rose and fell in the 80s in the classic pattern of an economic bubble. The collapse was in the perception of AI by government agencies and investors — the field continued to make advances despite the criticism. Rodney Brooks and Hans Moravec, researchers from the related field of robotics, argued for an entirely new approach to artificial intelligence.
Cont......
The field of AI, now more than a half a century old, finally achieved some of its oldest goals. It began to be used successfully throughout the technology industry, although somewhat behind the scenes.
On 11 May 1997, Deep Blue became the first computer chess-playing system to beat a reigning world chess champion, Garry Kasparov
AI-Based Problem Solving
BASIC SEARCH METHODS

State-Space <{S}, S0, {SGj}, {Oi}>

S0: Initial State
SG: Goal State(s) to be reached
Oi: Operators O: {S} => {S}
AI-Based Problem Solving (cont.)
State-Space Navigation
Forward Search: BFS, DFS, HS,…
Backward Search: BFS-1, Backchaining,…
Bi-Directional Search: BFS2,…
Goal Reduction: Island-S, MEA…
Transformation: {S}  {S’}
Abstraction: {S}  {SA} + MEA ({SA})…
Analogy: If Sim(P,P’) then Sol(P) Sol’(P’)
More on the State Space
Useful Functions:
Succ(si) = {sk | oj(si) = sk}
Reachable(si) = {U{sk} | Succ *(si)}
Succ-1(si) = {sk | oj(sk) = si)
Reachable-1(si) = {U{sk} | (Succ-1)*(si)}
s-Path(sa0, san) = (sa0, sa1,…, san)
…such that for all sa1 exists oj(sai) = sai+1
o-Path(sa0, san) = (oj0, oj1,…, ojn-1)
…such that for all sa1 exists oj(sai) = sai+1
More on the State Space (cont.)
Useful Concepts:
Solution = o-Path(s0, sG) [or s-Path]
Cost(Solution) =  cost(oj) … often cost(oj) = 1
P is solvable if at least one o-Path(s0, sG) exists
Solutions may be constructed forward, backward or any which way
State spaces may be finite, infinite, implicit or explicit
Zero-Knowledge Search
Simple Depth-First Search
DFS(Scurr, Sgoal, S-queue)
IF Scurr = Sgoal, SUCCESS
ELSE Append(Succ(Scurr), S-queue)
IF Null(S-queue), FAILURE
ELSE DFS(First(S-queue), Sgoal, Trail(S-queue))
Depth First Search
DFS (cont.)
Problems with DFS
Deep (possibly infinite) rat holes
 depth-bounded DFS, D = max depth
Loops: Succ(Succ(..Succ(S))) = S
 Keep s-Path and always check Scurr
Non-Optimality: Other paths may be less costly
 No fix here for DFS
Worst-case time complexity (O(bmax(D,d))
DFS (cont.)
When is DFS useful?
Very-high solution density
Satisficing vs. optimizing
Memory-limited search: O(d) space
Solution at Known-depth (then D=d)
Zero Knowledge Search (cont.)
Simple Breadth-First Search
BFS(Scurr, Sgoal, S-queue)
IF Scurr = Sgoal, SUCCESS
ELSE Append(Succ(Scurr), S-queue)
IF Null(S-queue), FAILURE
ELSE BFS(Last(S-queue), Sgoal,
All-But-Last(S-queue))
Breadth-First Search
Simple BFS cont.
Problems with BFS:
Loops: Succ(Succ(…Succ(S)))=S
Pseudo-loops: Revisiting old states off-path
 Keep full visited prefix tree
Worst case time complexity O(bd)
Worst case space complexity O(bd)
When is BFS Useful?
Guarantee shortest path
Very sparse solution space
(better if some solution is close to SI)
Zero Knowledge Search (cont.)
Backwards Breadth-First Search
BFS(Scurr, Sinit, S-queue)
IF Scurr = Sinit, SUCCESS
ELSE Append(Succ-1(Scurr), S-queue)
IF Null(S-queue), FAILURE
ELSE BFS(Last(S-queue), Sinit,
All-But-Last(S-queue))

Backwards Breadth-First Search
Backward-BFS (cont.)
Problems with Backward-BFS
All the ones for BFS
Succ(Scurr) must be invertible: Succ-1(Scurr)
When is Backward-BFS useful?
In general, same as BFS
If backward branching < forward branching
Bi-Directional Search
Algorithm:
Initialize Fboundary:= {Sinit}
Initialize Bboundary:= {Sgoal}
Initialize treef:= Sinit
Initialize treeb:= Sgoal
For every Sf in Fboundary
IF Succ(Sf) intersects Bboundary
THEN return APPEND(Path(treef), Path-1(treeb))
ELSE Replace Sf by Succ(Sf) & UPDATE (treef)
6. For every Sb in Bboundary
IF Succ(Sb) intersects Fboundary
THEN return APPEND(Path(treef), Path-1(treeb))
ELSE Replace Sb by Succ-1(Sb) & UPDATE (treeb)
7. Go to 5.
Note: where’s the bug?
Bi-Directional Breadth-First Search
Bi-Directional Search (cont.)
Problems with Bi-BFS
Loops: Succ(Succ(…Succ(S))) = S
Loops: Succ-1(Succ-1(… Succ-1(S)))) = S
Pseudo-loops: Revisiting old states off-path
 Keep full visited prefix treef, trees
Succ(Scurr)must be invertible: Succ-1(Scurr)
When is Bi-BFS useful?
Space and time complexity:
O(bfd/2) + O(bbd/2) = O(bd/2) if bf = bb
Island-Driven BFS
Definition:
An island is a state known a-priori to be on the solution path between Sinit and Sgoal.
If there are k sequential islands:
BFS(Sinit, S-(goal)=
APPEND(BFS(Sinit, Sk1), BFS(Sk1, Sk2),…BFS(SIk, Sgoal))
Upper bound complexity: O(k*maxi=0:k[bdki,ki+1])
Complexity if islands are evenly spaced:
O((k+1)*bd/(k+1))
Island-Driven Search
APPLICATION AREA
FINANCE
Banks use artificial intelligence systems to organize operations, invest in stocks, and manage properties. In August 2001, robots beat humans in a simulated financial trading competition.
Financial institutions have long used artificial neural network systems to detect charges or claims outside of the norm, flagging these for human investigation.
MEDICAL FIELD
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.
INDUSTRY`S
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. Japan is the leader in using and producing robots in the world. In 1999, 1,700,000 robots were in use worldwide.
Online and telephone customer service
Artificial intelligence is implemented in automated online assistants that can be seen as avatars on web pages.It can avail for enterprises to reduce their operating and training cost.A major underlying technology to such systems is natural language processing.
Cont.....
Similar techniques may be used in answering machines of call centres, such as speech recognition software to allow computers to handle first level of customer support, text mining and natural language processing to allow better customer handling, agent training by automatic mining of best practices from past interactions, support automation and many other technologies to improve agent productivity and customer satisfaction.
Transportation
Fuzzy logic controllers have been developed for automatic gearboxes in automobiles (the 2006 Audi TT, VW Toureg and VW Caravell feature the DSP transmission which utilizes Fuzzy logic, a number of Škoda variants (Škoda Fabia) also currently include a Fuzzy Logic based controller).
Telecommunications
Many telecommunications companies make use of heuristic search in the management of their workforces, for example BT Group has deployed heuristic search in a scheduling application that provides the work schedules of 20,000 engineers.
Music
The evolution of music has always been affected by technology. With AI, scientists are trying to make the computer emulate the activities of the skillful musician. Composition, performance, music theory, sound processing are some of the major areas on which research in Music and Artificial Intelligence are focusing.
News and publishing
The company Narrative Science makes computer generated news and reports commercially available, including summarizing team sporting events based on statistical data from the game. It also creates financial reports and real estate analyses.
Other
Applications are also being developed for gesture recognition (understanding of sign language by machines), individual voice recognition, global voice recognition (from a variety of people in a noisy room), facial expression recognition for interpretation of emotion and non verbal queues. Other applications are robot navigation, obstacle avoidance, and object recognition.
Cont.....
Various tools of artificial intelligence are also being widely deployed in homeland security, speech and text recognition, data mining, and e-mail spam filtering.

[attachment=15387]
Abstract:
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.
Artificial Intelligence
Starting from a modest but an over ambitious effort in the late 50’s, AI has grown through its share of joys, disappointments and self-realizations. AI deals in science, which deals with creation of machines, which can think like humans and behave rationally. AI has a goal to automate every machine.
AI is a very vast field, which spans:
• Many application domains like Language Processing, Image Processing, Resource Scheduling, Prediction, Diagnosis etc.
• Many types of technologies like Heuristic Search, Neural Networks, and Fuzzy Logic etc.
• Perspectives like solving complex problems and understanding human cognitive processes.
• Disciplines like Computer Science, Statistics, Psychology, etc.
Requirement of an Artificial Intelligence system
No AI system can be called intelligent unless it learns & reasons like a human. Reasoning derives new information from given ones.
Areas of Artificial Intelligence
Knowledge Representation
Importance of knowledge representation was realized during machine translation effort in early 1950’s. Dictionary look up and word replacement was a tedious job. There was ambiguity and ellipsis problem i.e. many words have different meanings. Therefore having a dictionary used for translation was not enough.
One of the major challenges in this field is that a word can have more than one meaning and this can result in ambiguity.
E.g.: Consider the following sentence
Spirit is strong but flesh is weak.
When an AI system was made to convert this sentence into Russian & then back to English, following output was observed.
Wine is strong but meat is rotten.
Thus we come across two main obstacles. First, it is not easy to take informal knowledge and state it in the formal terms required by logical notation, particularly when the knowledge is less than 100% certain. Second, there is a big difference between being able to solve a problem “in principle” and doing so in practice.
Even problems with just a few dozen facts can exhaust the computational resources of any computer unless it has some guidance as to which reasoning steps to try first.
A problem may or may not have a solution. This is why debugging is one of the most challenging jobs faced by programmers today. As the rule goes, it is impossible to create a program which can predict whether a given program is going to terminate ultimately or not.
Development in this part was that algorithms were written using foundational development of vocabulary and dictionary entries. Limitations of the algorithm were found out. Later Formal Systems were developed which contained axioms, rules, theorems and an orderly form of representation was developed.
For example, Chess is a formal system. We use rules in our everyday lives and these rules accompany facts. Rules are used to construct an efficient expert system having artificial intelligence. Important components of a Formal System are - Backward Chaining i.e. trying to figure out the content by reading the sentence backward and page link each word to another, Explanation Generation i.e. generating an explanation of whatever the system has understood, Inference Engine i.e. submitting an inference or replying to the problem.
Reasoning
It is to use the stored information to answer questions and to draw new conclusions. Reasoning means, drawing of conclusion from observations.
Reasoning in AI systems work on three principles namely:
DEDUCTION: Given 2 events ‘P’ & ‘Q’, if ‘P’ is true then ‘Q’ is also true.
E.g.: If it rains, we can’t go for a picnic.
INDUCTION: Induction is a process where in , after studying certain facts , we reach to a conclusion.
E.g.: Socrates is a man; all men are mortal; therefore Socrates is mortal.
ABDUCTION: ‘P’ implies ‘Q’, but ‘Q’ may not always depend on ‘P’.
E.g.: If it rains , we can’t go for a picnic.
The fact that we are not in a position to go for a picnic does not mean that it is training. There can be other reasons as well.
Learning
The most important requirement for an AI system is that it should learn from its mistakes. The best way of teaching an AI system is by training & testing. Training involves teaching of basic principles involved in doing a job. Testing process is the real test of the knowledge acquired by the system wherein we give certain examples & test the intelligence of the system. Examples can be positive or negative. Negative examples are those which are ‘near miss’ of the positive examples.
Natural Language Processing (NLP)
NLP can be defined as:
• Processing of data in the form of natural language on the computer. I.e. making the computer understand the language a normal human being speaks.
• It deals with under structured / semi structured data formats and converting them into complete understandable data form. The reasons to process natural language are; Generally - because it is exciting and interesting, Commercially – because of sheer volume of data available online, Technically – because it eases out Computer-Human interaction.
NLP helps us in
• Searching for information in a vast NL (natural language) database.
• Analysis i.e. extracting structural data from natural language.
• Generation of structured data.
• Translation of text from one natural language to other. Example: English to Hindi.
Application Spectrum of NLP
• It provides writing and translational aids.
• Helps humans to generate Natural Language with proper spelling, grammar, style etc.
• It allows text mining i.e. information retrieval, search engines text categorization, information extraction.
• NL interface to database, web software system, and question answer explanation in an expert system.

Impact of Yoga on EmotionalIntelligence,Subjective Well -Being and Stress:A Pre and Post Analysis
Dr. Asha Hingar (Professor, Department of Psychology,
University of Rajasthan)
Dr. Neha Agarwal (Research Scholar, Department of Psychology
University of Rajasthan)
Ms. Nirmala Singh Rathore (Research Scholar, Department of Psychology, University of Rajasthan)

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Artificial Intelligence full report

[attachment=17206]

. Introduction
Artificial intelligence started as a field whose goal was to replicate human level intelligence in a machine. Early hopes diminished as the magnitude and difficulty of that goal was appreciated. Slow progress was made over the next 25 years in demonstrating isolated aspects of intelligence. Recent work has tended to concentrate on commercial aspects of "intelligent assistants" for human workers. No one talks about replicating the full gamut of human intelligence any more. Instead we see a retreat
into specialized sub problems, such as ways to represent knowledge, natural language understanding, vision or even more specialized areas such as truth maintenance systems or plan verification. All the work in these subareas is benchmarked against the sorts of tasks humans do within those areas.
Amongst the dreamers still in the field of AI (those not dreaming about dollars, that is), there is a feeling. That one day all these pieces will all fall into place and we will see "truly" intelligent systems emerge. However, I, and others, believe that human level intelligence is too complex and little understood to be correctly decomposed into the right sub pieces at the moment and that even if we knew the sub pieces we still wouldn't know the right interfaces between them. Furthermore, we will never understand how to decompose human level intelligence until we've had a lot of practice with simpler level intelligences.
In this paper I therefore argue for a different approach to creating artificial intelligence:
• We must incrementally build up the capabilities of intelligent systems, having complete systems at each step of the way and thus automatically ensure that the pieces and their interfaces are valid.
• At each step we should build complete intelligent systems that we let loose in the real world with real sensing and real action. Anything less provides a candidate with which we can delude ourselves.
We have been following this approach and have built a series of autonomous mobile robots. We have reached an unexpected conclusion © and have a rather radical hypothesis (H).
© When we examine very simple level intelligence we find that explicit representations and models of the world simply get in the way. It turns out to be better to use the world as its own model.
(H) Representation is the wrong unit of abstraction in building the bulkiest parts of intelligent systems.
Representation has been the central issue in artificial intelligence work over the last 15 years only because it has provided an interface between otherwise isolated modules and conference papers.

2. The evolution of intelligence
We already have an existence proof of, the possibility of intelligent entities: human beings.
Additionally, many animals are intelligent to some degree. (This is a subject of intense debate, much of which really centers around a definition of intelligence.) They have evolved over the 4.6 billion year history of the earth.


3. Abstraction as a dangerous weapon
Artificial intelligence researchers are fond of pointing out that AI is often denied its rightful successes. The popular story goes that when nobody has any good idea of how to solve a particular sort of problem (e.g. playing chess) it is known as an AI problem. When
an algorithm developed by AI researchers successfully tackles such a problem, however, AI detractors claim that since the problem was solvable by an algorithm, it wasn't really an AI problem after all. Thus AI never has any successes. But have you ever heard of an AI failure?


3.1. A continuing story
Meanwhile our friends in the 1890s are busy at work on their AF machine. They have come to agree that the project is too big to be worked on as a single entity and that they will need to become specialists in different areas. After all, they had asked questions of fellow passengers on their flight and discovered that the Boeing Co. employed over 6000 people to build such an airplane.