It is technology and a branch of computer science that studies and develops intelligent machines and software. Major AI researchers and 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 that maximize its chances of success. John McCarthy, who coined the term in 1955, defines it as "The science and engineering of making intelligent machines".

1. Amity Institute Of Telecom Technology
And Management
Topic
Artificial Intelligence
Submitted by:
Santanu Mukhopadhyay
B-Tech, 3rd Year (E&T) (2011-15)
Section: A, En No: A1607111013
AITTM
Amity University
Uttar Pradesh, India

2. In-house Project
Second Topic
Artificial Intelligence
In-House Project Location: Raniganj, West Bengal

3. Table of Contents
1. Introduction.
2. History of Artificial Intelligence.
3. Goals of Artificial Intelligence.
4. Types (Social and General Intelligence).
5. Approaches.
6. Approaches and its Integration.
7. AAAI and its Research Activities (Neutral Networks).
8. Languages and Evaluation Progress.
9. Applications.
10. Single & Multipurpose Projects.
11. Advantages & Disadvantages.
12. Artificial Intelligence-The Movie & it’s relation with the
concept of AI.
13. Future of Artificial Intelligence.
14. Conclusion, Bibliography & Appendix.

4. Introduction
What Is Artificial Intelligence?????Is it a
Myth or Reality????
Artificial intelligence (AI)
It is technology and a branch of computer science that studies
and develops intelligent machines and software. Major AI researchers and 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 that maximize its chances of success. John
McCarthy, who coined the term in 1955, defines it as "The science and engineering of making
intelligent machines".
AI research is highly technical and specialized, deeply divided into
subfields that often fail to communicate with each other. Some of the division is due to social
and cultural factors: subfields have grown up around particular institutions and the work of
individual researchers. AI research is also divided by several technical issues. There are subfields
which are focused on the solution of specific problems, on one of several possible approaches,
on the use of widely differing tools and towards the accomplishment of particular applications.
The central problems (or goals) of AI research include reasoning, knowledge, planning, learning,
communication, perception and the ability to move and manipulate objects. General intelligence
(or "strong AI") is still among the field's long term goals. Currently popular approaches
include statistical methods,computational intelligence and traditional symbolic AI. There are an
enormous number of tools used in AI, including versions of search and mathematical
optimization, logic, methods based on probability and economics, and many others.
The field was founded on the claim that a central property of humans, intelligence
the sapience of Homo sapiens-can be so precisely described that it can be simulated by a
machine. This raises philosophical issues about the nature of the mind and the ethics of creating
artificial beings, issues which have been addressed by myth, fiction and philosophy since
antiquity. Artificial intelligence has been the subject of tremendous optimism, but has also
suffered stunning setbacks. Today it has become an essential part of the technology industry and
many of the most difficult problems in computer science.

5. History
Thinking machines and artificial beings appear in Greek myths, such as Talos of Crete, the
bronze robot of Hephaestus, and Pygmalion's Galatea. Human likenesses believed to have
intelligence were built in every major civilization: animated cult images were worshiped
in Egypt and Greece and humanoidautomatons were built by Yan Shi, and Paracelsus. By the
19th and 20th centuries, artificial beings had become a common feature in fiction, as in Mary
Shelley's Frankenstein or Karel Čapek's R.U.R. (Rossum's Universal Robots). . It was also
widely believed that artificial beings had been created by Jabir in Hayan, Judah Loew
Mechanical or "formal" reasoning has been developed by philosophers and mathematicians
since antiquity. The study of logic led directly to the invention of the programmable digital
electronic computer, based on the work of mathematician AlanTuring and others.
Turing's theory of computation suggested that a machine, by shuffling symbols as simple as "0"
and "1", could simulate any conceivable act of mathematical deduction.
The field of AI research was founded at a conference on the campus of Dartmouth College in
the summer of 1956. By the middle of the 1960s, research in the U.S. was heavily funded by
the Department of Defense and laboratories had been established around the world. AI's founders
were profoundly optimistic about the future of the new field: Herbert Simon predicted that
"machines will be capable, within twenty years, of doing any work a man can do" and Marvin
Minsky agreed, writing that "Within a generation ... the problem of creating 'artificial
intelligence' will substantially be solved".
They had failed to recognize the difficulty of some of the problems they faced. In 1974, in
response to the criticism of Sir James Light hill and ongoing pressure from the US Congress to
fund more productive projects, both the U.S. and British governments cut off all undirected
exploratory research in AI. The next few years would later be called an "AI winter", a period
when funding for AI projects was hard to find
In 1974, in response to the criticism of Sir James
Lighthill and ongoing pressure from the US Congress to fund more productive projects, both the
U.S. and British governments cut off all undirected exploratory research in AI. The next few
years would later be called an "AI winter", a period when funding for AI projects was hard to
find. In the 1990s and early 21st century, AI achieved its greatest successes.The success was due
to several factors: the increasing computational power of computers a greater emphasis on
solving specific subproblems, the creation of new ties between AI and other fields working on
similar problems, and a new commitment by researchers to solid mathematical methods and
rigorous scientific standards. In February 2011, in a Jeopardy quiz show exhibition
match, IBM's question answering system, Watson, defeated the two greatest Jeopardy
champions, Brad Rutter and Ken Jennings, by a significant margin. The Kinect, which provides a
3D body–motion interface for the Xbox 360, uses algorithms that emerged from lengthy AI
research as does the iPhone’s Siri.

6. Goals
a. Deduction, reasoning, problem solving: Early AI researchers developed algorithms
that imitated the step-by-step reasoning that humans use when they solve puzzles or make logical
deductions. By the late 1980s and 1990s, AI research had also developed highly successful
methods for dealing with uncertain or incomplete information, employing concepts
from probability and economics.
b. Knowledge representation:Knowledge Representation is central to AI research.
Among the things that AI needs to represent are: objects, properties, categories and relations
between objects; situations, events, states and time; causes and effects; knowledge about
knowledge (what we know about what other people know); and many other, less well researched
domains. A representation of "what exist" is ontology , the set of objects, relations, concepts and
so on that the machine knows about. The most general are called upper ontologies, which
attempt to provide a foundation for all other knowledge.
c. Planning and Learning: In classical planning problems, the agent can assume that it is
the only thing acting on the world and it can be certain what the consequences of its actions may
be. However, if the agent is not the only actor, it must periodically ascertain whether the world
matches its predictions and it must change its plan as this becomes necessary, requiring the agent
to reason under uncertainty. Multi-agent planning uses the cooperation and competition of many
agents to achieve a given goal. Emergent behavior such as this is used by evolutionary
algorithms and swarm intelligence. Learning is the ability to find patterns in a stream of
input. Supervised learning includes both classification and numerical regression. Classification is
used to determine what category something belongs in, after seeing a number of examples of
things from several categories.
Types
a. SocialIntelligence:Affective computing is the study and development of systems and
devices that can recognize, interpret, process, and simulate human affects. It is an
interdisciplinary field spanning computer sciences, psychology, and cognitive science. The more
modern branch of computer science originated with Rosalind Picard's 1995 paper on affective
computing. A motivation for the research is the ability to simulate empathy. The machine should
interpret the emotional state of humans and adapt its behaviour to them, giving an appropriate
response for those emotions.
b. GeneralIntelligence: Most researcher think that their work will eventually be
incorporated into a machine with general intelligence (known as strong AI), combining all the
skills above and exceeding human abilities at most or all of them. A few believe
that anthropomorphic features like artificial consciousness or an artificial brain may be required
for such a project Many of the problems above may require general intelligence to be considered
solved.

7. Approaches
There is no established unifying theory or paradigm that guides AI research. Researchers
disagree about many issues. A few of the most long standing questions that have remained
unanswered are these: should artificial intelligence simulate natural intelligence by
studying psychology or neurology? Or is human biology as irrelevant to AI research as bird
biology is to aeronautical engineering? Can intelligent behavior be described using simple,
elegant principles (such as logic or optimization)? Or does it necessarily require solving a large
number of completely unrelated problems? Can intelligence be reproduced using high-level
symbols, similar to words and ideas? Or does it require "sub-symbolic" processing? John
Haugeland, who coined the term GOFAI (Good Old-Fashioned Artificial Intelligence).
Integrating these Approaches
An intelligent agent is a system that perceives its environment and takes actions which maximize
its chances of success. The simplest intelligent agents are programs that solve specific problems.
More complicated agents include human beings and organizations of human beings (such
as firms). The paradigm gives researchers license to study isolated problems and find solutions
that are both verifiable and useful, without agreeing on one single approach. The paradigm also
gives researchers a common language to communicate with other fields such as decision
theory and economics—that also use concepts of abstract agents. The intelligent agent paradigm
became widely accepted during the 1990s. classical approach (designing the AI), based on
symbolic reasoning - a mathematical approach in which ideas and concepts are represented by
symbols such as words, phrases or sentences, which are then processed according to the rules of
logic. a connectionist approach (letting AI develop), based on artificial neural networks, which
imitate the way neurons work, and genetic algorithms, which imitate inheritance and fitness to
evolve better solutions to a problem with every generation.
Agent & Cognitive Architectures
Researchers have designed systems to build intelligent systems out of interacting intelligent
agents in a multi-agent system. A system with both symbolic and sub-symbolic components is a
hybrid intelligent system, and the study of such systems is artificial intelligence systems
integration. A hierarchical control system provides a bridge between sub-symbolic AI at its
lowest, reactive levels and traditional symbolic AI at its highest levels.

8. ASSOCIATION FOR THE
ADVANCEMENT OF ARTIFICIAL
INTELLIGENCE (AAAI)
Founded in 1979, the Association for the Advancement of Artificial Intelligence (AAAI)
(formerly the American Association for Artificial Intelligence) is a nonprofit scientific society
devoted to advancing the scientific understanding of the mechanisms underlying thought and
intelligent behavior and their embodiment in machines. AAAI aims to promote research in, and
responsible use of, artificial intelligence. AAAI also aims to increase public understanding of
artificial intelligence, improve the teaching and training of AI practitioners, and provide
guidance for research planners and funders concerning the importance and potential of current
AI developments and future directions.
In the course of 50 years of research, AI has developed a large number of tools
to solve the most difficult problems in computer science. Afew of the most
generalof these methods are discussedbelow.
a. Searchand optimization: Many problems in AI can be solved in theory by intelligently
searching through many possible solutions: Reasoning can be reduced to performing a search.
For example, logical proof can be viewed as searching for a path that leads
from premises to conclusions, where each step is the application of an inference
rule Planning algorithms search through trees of goals and sub goals, attempting to find a path to
a target goal, a process called means-ends analysis. Robotics algorithms for moving limbs and
grasping objects use local searches in configuration space. Many learning algorithms use search
algorithms based on optimization.
Simple exhaustive searches are rarely sufficient for most real world problems: the search
space (the number of places to search) quickly grows to astronomical numbers. The result is a
search that is too slow or never completes. The solution, for many problems, is to use
"heuristics" or "rules of thumb" that eliminate choices that are unlikely to lead to the goal (called
"pruning the search tree"). Heuristics supply the program with a "best guess" for the path on
which the solution lies. Heuristics limit the search for solutions into a smaller sample size.
b. Logic:It is used for knowledge representation and problem solving, but it can be applied to
other problems as well. For example, the sat plan algorithm uses logic for planning and inductive
logic programming is a method for learning. Several different forms of logic are used in AI
research. Propositional or sentential logic is the logic of statements which can be true or
false. First-order logic also allows the use of quantifiers and predicates, and can express facts
about objects, their properties, and their relations with each other. Fuzzy logic, is a version of
first-order logic which allows the truth of a statement to be represented as a value between 0 and
1, rather than simply True (1) or False (0). Fuzzy systems can be used for uncertain reasoning

9. and have been widely used in modern industrial and consumer product control
systems.Subjective logic models uncertainty in a different and more explicit manner than fuzzy-
logic: a given binomial opinion satisfies (belief + disbelief + uncertainty = 1) within a Beta
distribution. By this method, ignorance can be distinguished from probabilistic statements that an
agent makes with high confidence.
c. Probabilistic methods for uncertain reasoning:Many problems in AI require the
agent to operate with incomplete or uncertain information. AI researchers have devised a number
of powerful tools to solve these problems using methods from probability theory and economics.
Bayesian networks are a very general tool that can be used for a large number of problems:
reasoning (using the Bayesian inference algorithm), learning (using the expectation-
maximization algorithm), planning (using decision networks) and perception (using dynamic
Bayesian networks). Probabilistic algorithms can also be used for filtering, prediction, smoothing
and finding explanations for streams of data, helping perception systems to analyze processes
that occur over time (e.g., hidden Markov models or Kalman filters).
:Neutral Networks:
The study of artificial neural networks began in the decade before the field AI research was
founded, in the work of Walter Pitts and Warren McCullough. Other important early researchers
were Frank Rosenblatt, who invented the perceptron and Paul Wer bos who developed the back
propagation algorithm. The main categories of networks are acyclic or feedforward neural
networks (where the signal passes in only one direction) and recurrent neural networks (which
allow feedback). Among the most popular feedforward networks are perceptrons, multi-layer
perceptrons and radial basis networks. Among recurrent networks, the most famous is
the Hopfield net, a form of attractor network, which was first described by John Hopfield in
1982. Neural networks can be applied to the problem ofintelligent control (for robotics)
or learning, using such techniques as Hebbian learning and competitive learning.
Languages
There are some languages which are only developed for Artificial Intelligence Research, which
are as follows
1. LIMP
2. PROLOG.

10. Evaluation Progress
In 1950, Alan Turing proposed a general procedure to test the intelligence of an agent now
known as the Turing test. This procedure allows almost all the major problems of artificial
intelligence to be tested. However, it is a very difficult challenge and at present all agents fail.
Artificial intelligence can also be evaluated on specific problems such as small problems in
chemistry, hand-writing recognition and game-playing. Such tests have been termed subject
matter expert Turing tests. Smaller problems provide more achievable goals and there are an
ever-increasing number of positive results.
One classificationfor outcomes ofan AI test is:
1. Optimal: It is not possible to perform better.
2. Strong super-human: Performs better than all humans.
3. Super-human: Performs better than most humans.
4. Sub-human: Performs worse than most humans.
For example, performance at draughts is optimal, performance at chess is super-human and
nearing strong super-human and performance at many everyday tasks (such as recognizing a face
or crossing a room without bumping into something) is sub-human.
A quite different approach measures machine intelligence through tests which are developed
from mathematical definitions of intelligence. Examples of these kinds of tests start in the late
nineties devising intelligence tests using notions from Kolmogorov complexity and data
compression. Two major advantages of mathematical definitions are their applicability to
nonhuman intelligences and their absence of a requirement for human testers an area that
artificial intelligence had contributed greatly to is Intrusion detection.

11. Applications and Applied Projects
There are a number of competitions and prizes to promote research in artificial intelligence. The
main areas promoted are: general machine intelligence, conversational behavior, data-
mining, robotic cars, robot soccer and games.
a. Platforms:A platform (or "computing platform") is defined as "some sort of hardware
architecture or software framework (including application frameworks), that allows software to
run." As Rodney Brook pointed out many years ago, it is not just the artificial intelligence
software that defines the AI features of the platform, but rather the actual platform itself that
affects the AI that results, i.e., there needs to be work in AI problems on real-world platforms
rather than in isolation.
A wide variety of platforms has allowed different aspects of AI to develop, ranging from expert
systems, albeit PC-based but still an entire real-world system, to various robot platforms such as
the widely available Roomba with open interface.
b. Philosophy:Artificial intelligence, by claiming to be able to recreate the capabilities of the
human mind, is both a challenge and an inspiration for philosophy. Are there limits to how
intelligent machines can be? Is there an essential difference between human intelligence and
artificial intelligence? Can a machine have a mind and consciousness? A few of the most
influential answers to these questions are given below.
1. Turing Polite Convention:
We need not decide if a machine can "think"; we need only decide if a machine can act as
intelligently as a human being. This approach to the philosophical problems associated with
artificial intelligence forms the basis of the Turing test.
2. The Dartmouth proposal:
"Every aspect of learning or any other feature of intelligence can be so precisely described that a
machine can be made to simulate it." This conjecture was printed in the proposal for
the Dartmouth Conference of 1956, and represents the position of most working AI researchers.
3. Newelland Simon's physical symbol system hypothesis:
"A physical symbol system has the necessary and sufficient means of general intelligent action."
Newell and Simon argue that intelligences consist of formal operations on symbols. Hubert
Dreyfus argued that, on the contrary, human expertise depends on unconscious instinct rather
than conscious symbol manipulation and on having a "feel" for the situation rather than explicit
symbolic knowledge.
4. Godel’s Incompleteness Theorem:
A formal system (such as a computer program) cannot prove all true statements. Roger
Penrose is among those who claim that Gödel's theorem limits what machines can do. (See The
Emperor's New Mind)

12. 5. Searle's Strong AI Hypothesis:
"The appropriately programmed computer with the right inputs and outputs would thereby have a
mind in exactly the same sense human beings have minds." John Searle counters this assertion
with his Chinese room argument, which asks us to look inside the computer and try to find where
the "mind" might be.
Speech Recognition Applications
1. Siri
Siri is an intelligent personal assistant and knowledge navigator which works as an application
for Apple Inc.'s iOS. The application uses a natural language user interface to answer questions,
make recommendations, and perform actions by delegating requests to a set of Web services.
Apple claims that the software adapts to the user's individual preferences over time and
personalizes results. The name Siri is Norwegian, meaning "beautiful woman who leads you to
victory", and comes from the intended name for the original developer's first child. Siri was
originally introduced as an iOS application available in the App Store by Siri, Inc., which was
acquired by Apple on April 28, 2010. Siri, Inc. had announced that their software would be
available for BlackBerry and for phones running Android, Siri has been an integral part of iOS
since iOS 5and was first supported on the iPhone 4S. Siri was added to the iPad (3rd generation)
with the release of iOS 6, and is included on the iPhone 5, iPod Touch (5th generation), iPad (4th
generation), and the iPad Mini.
2. S Voice
S Voice is an intelligent personal assistant and knowledge navigator which is only available as a
built-in application for the Samsung Galaxy S III, S III Mini, S4, S II Plus, Note II, Note 10.1,
Note 8.0, Stellar, Grand and Camera. The application uses a natural language user interface to
answer questions, make recommendations, and perform actions by delegating requests to a set of
Web services. It is based on the Vlingo personal assistant. Some of the capabilities of S Voice
include making appointments, opening apps, setting alarms, updating social network websites
such as Facebook or Twitter, and navigation, S Voice.
3. Google Now
Google Now is an intelligent personal assistant developed by Google that is available within the
Google Search mobile application for the Android and iOS operating systems. Google Now uses
a natural language user interface to answer questions, make recommendations, and perform
actions by delegating requests to a set of web services. Along with answering user-initiated

13. queries, Google Now passively delivers information to the user that it predicts they will want,
based on their search habits. It was first included in Android 4.1 (Jelly Bean), which launched on
July 9, 2012, and was first supported on the Galaxy Nexus smartphone. The service was made
available for iOS on April 29, 2013 in an update to the Google Search app. Popular Science
named Google Now the "Innovation of the Year" for 2012. Google Now is implemented as an
aspect of the Google Search application. It recognizes repeated actions that a user performs on
the device (common locations, repeated calendar appointments, search queries, etc.) to display
more relevant information to the user in the form of "cards". The system leverages Google's
Knowledge Graph project, a system used to assemble more detailed search results by analyzing
their meaning and connections.
Specialized cards currently comprise
 Activity Summary (Walking and Biking)
 Birthday
 Events
 Flights
 Gmail: Events
 Gmail: Flights
 Gmail: Hotels
 Gmail: Package Tracking
 Gmail: Restaurants
 Movies
 News
 Next Meeting
 Photo Spot Nearby
 Places
 Public Alerts
 Public Transit
 Research Topics
 Sports
 Stocks
 Traffic
 Travel: Attractions Nearby
 Travel: Time Back Home
 Translate

14. Specialized Projects (Single Purpose)
1. Brain Simulation: A human hybrid of latest neurobiology data and Numenta findings
aimed to implement human personality by means of computer program which was started in
2008 as independent research. Blue Brain Project, an attempt to create a synthetic brain by
reverse-engineering the mammalian brain down to the molecular level
2. Games:Chinook A computer program that plays English draughts; the first to win the
world champion title in the competition against humans. Deep Blue, a chess-playing computer
developed by IBM which beat Garry Kasparov in 1997.
3. Natural Language Processing: AIML & XML dialect for creating natural language
software agents. Artificial Linguistic Internet Computer Entity (A.L.I.C.E.), an award-winning
natural language processing charter bot. Eliza , a famous program by Joseph Weizenbaum , who
parodied person-centered therapy.
Multipurpose Projects
 Software Libraries, DANN, a freely available AI library implemented in Java,
implementing graph theory, ANN, GA, Markov Chains, graphical models (bayesian
networks, HMM), etc.
 ELKI, a research project and software framework with many data mining algorithms (in
particular cluster analysis and outlier detection) and index structures by the Ludwig
Maximilian University of Munich.
 FRDCSA, an attempt to package and integrate all FOSS AI systems for GNU+Linux-
based systems.
 I-X, a systems integration architecture project for the creation of intelligent systems at
Artificial Intelligence Applications Institute (AIAI), University of Edinburgh.
 OpenCog, a GPL-licensed framework for machine learning, genetic programming,
probabilistic reasoning, natural language processing and virtual world embodiment,
written in C++, Python and Scheme.
 RapidMiner, an environment for machine learning and data mining, developed by the
Dortmund University of Technology.
 Weka, a free implementation of many machine learning algorithms in Java.
 Pogamut, a free platform for Java AI development in games, developed by the Charles
University in Prague
 CILib, a library of different computational intelligence algorithms and supporting APIs,
supporting different paradigms: Swarm Intelligence, Evolutionary Computation, Neural
Networks, Game Playing (developed by the Computational Intelligence Research Group
(CIRG@UP) Department of Computer Science University of Pretoria South Africa).
 Encog, a neural network and artificial intelligence framework available for Java, .Net,
and Silverlight.

15. Advantages
 Jobs:Depending on the level and type of intelligence these machines receive in the
future, it will obviously have an effect on the type of work they can do, and how well
they can do it (they can become more efficient). As the level of AI increases so will their
competency to deal with difficult, complex even dangerous tasks that are currently done
by humans, a form of applied artificial intelligence.
 They don't stop: As they are machines there is no need for sleep, they don't get ill ,
there is no need for breaks or Facebook, they are able to go, go, go! There obviously may
be the need for them to be charged or refueled, however the point is, they are definitely
going to get a lot more work done than we can. Take the Finance industry for example,
there are constant stories arising of artificial intelligence in finance and that stock traders
are soon to be a thing of the past.
 No risk of harm: When we are exploring new undiscovered land or even planets,
when a machine gets broken or dies, there is no harm done as they don't feel, they don't
have emotions. Whereas going on the same type of expeditions a machine does, may
simply not be possible or they are exposing themselves to high risk situations.
 Act as aids:They can act as 24*7 aids to children with disabilities or the elderly, they
could even act as a source for learning and teaching. They could even be part of security
alerting you to possible fires that you are in threat of, or fending off crime.
 Their function is almost limitless: As the machines will be able to do everything
(but just better) essentially their use, pretty much doesn't have any boundaries. They will
make fewer mistake, they are emotionless, more efficient, basically giving us more free
time to do as we please.
Disadvantages
 Ver Reliance onAI: As you may have seen in many films such as The Matrix,
iRobot or even kids films such as WALLE, if we rely on machines to do almost
everything for us we become very dependent, so much so they have the potential to ruin
our lives if something were to go wrong..
 Human Feel:As they are machines they obviously can't provide you with that 'human
touch and quality', the feeling of a togetherness and emotional understanding, that
machines will lack the ability to sympathize & empathize with your situations, and may
act irrationally as a consequence.
 Inferior: As machines will be able to perform almost every task better than us in
practically all respects, they will take up many of our jobs, which will then result in

16. masses of people who are then jobless and as a result feel essentially useless. This could
then lead us to issues of mental illness and obesity problems etc.
 Misuse:There is no doubt that this level of technology in the wrong hands can cause
mass destruction, where robot armies could be formed, or they could perhaps malfunction
or be corrupted which then we could be facing a similar scene to that of terminator ( hey,
you never know).
Robotics & Ongoing Projects
Cyc is a 22 year old project based on symbolic reasoning with the aim of amassing general
knowledge and acquiring common sense. Online access to Cyc will be opened in mid-2005. The
volume of knowledge it has accumulated makes it able to learn new things by itself. Cyc will
converse with Internet users and acquire new knowledge from them.
Mind Forth shows thinking by the use of spreading activation (Open Mind and mind pixel are
similar projects.
These projects are unlikely to directly lead to the creation of AI, but can be helpful when teach in
rejecting the artificial intelligence about English language and the human-world domain.
Artificial GeneralIntelligence (AGI)
 Novamente is a project aiming for AGI (Artificial General Intelligence).
 Adaptive AI a company founded in 2001 with 13 employees.
 Other projects: Pei Wang's NARS project, John Weng's SAIL architecture, Nick
Cassimatis's Poly Scheme, Stan Franklin's LIDA, Jeff Hawkins Numenta, and Stuart
Shapiro's SNEPs.

17. Artificial Intelligence (Movie)
A.I. Artificial Intelligence, also known as A.I, is a 2001 American science fiction drama film
written, directed, and produced by Steven Spielberg, and based on Brian Aldiss's short story
"Super-Toys Last All Summer Long". The film stars Haley Joel Osment, Jude Law, Frances
O'Connor, Brendan Gleeson, and William Hurt. Set sometime in the future, A.I. tells the story of
David, a childlike android uniquely programmed with the ability to love.
Development of A.I. originally began with director Stanley Kubrick in the early 1970s. Kubrick
hired a series of writers up until the mid-1990s, including Brian Aldiss, Bob Shaw, Ian Watson,
and Sara Maitland. The film languished in development hell for years, partly because Kubrick
felt computer-generated imagery was not advanced enough to create the David character, who he
believed no child actor would believably portray. In 1995, Kubrick handed A.I. to Spielberg, but
the film did not gain momentum until Kubrick's death in 1999. Spielberg remained close to
Watson's film treatment for the screenplay. The film was greeted with generally favorable
reviews from critics and grossed approximately $235 million. A small credit appears after the
end credits, which reads "For Stanley Kubrick."
Critical Response
The film received generally positive reviews. Based on 181 reviews collected by Rotten
Tomatoes, 73% of the critics gave the film positive notices with a score of 6.6 out of 10. The
website described the critical consensus perceiving the film as "a curious, not always seamless,
amalgamation of Kubrick's chilly bleakness and Spielberg's warm-hearted optimism. The film is,
in a word, fascinating." By comparison, Meta critic collected an average score of 65, based on 32
reviews, which is considered favorable.
Producer Jan Harlan stated that Kubrick "would have applauded" the final film, while Kubrick's
widow Christiane also enjoyed A.I. However, Brian Aldiss was vocally displeased with the film,
stating, "It's crap. Science fiction has to for proposing to be logical, and it's full of lapses in
logic." Richard Corliss heavily praised Spielberg's direction, as well as the cast and visual
effects. Roger Ebert awarded the film 3 out of 4 stars, saying that it was "Audacious, technically
masterful, challenging, sometimes moving and ceaselessly watchable. But the movie's
conclusion is too facile and sentimental, given what has gone before. It has mastered the
artificial, but not the intelligence." On July 8, 2011, Ebert reviewed A.I. again when he added it
to his "Great Movies" pantheon. Leonard Maltin gives the film a not-so-positive review in his
Movie Guide, giving it two stars out of four, writing: "The intriguing story draws us in, thanks in
part to Osment's exceptional performance, but takes several wrong turns; ultimately, it just
doesn't work. Spielberg rewrote the adaptation Stanley Kubrick commissioned of the Brian
Aldiss short story "Super Toys Last All Summer Long"; the result is a curious and
uncomfortable hybrid of Kubrick and Spielberg sensibilities." However, he calls John Williams'
music score "striking". Jonathan Rosenbaum compared A.I. to Solaris (1972), and praised both

18. "Kubrick that Spielberg directed the project and Spielberg for doing his utmost to respect
Kubrick's intentions while making it a profoundly personal work." Film critic Armond White, of
the New York Press, praised the film noting that "each part of David’s journey through carnal
and sexual universes into the final eschatological devastation becomes as profoundly
philosophical and contemplative as anything by cinema’s most thoughtful, speculative artists –
Borzage, Ozu, Demy, Tarkovsky."
The film received generally positive reviews. Based on 181 reviews collected by Rotten
Tomatoes, 73% of the critics gave the film positive notices with a score of 6.6 out of 10. The
website described the critical consensus perceiving the film as "a curious, not always seamless,
amalgamation of Kubrick's chilly bleakness and Spielberg's warm-hearted optimism. The film is,
in a word, fascinating." By comparison, Metacritic collected an average score of 65, based on 32
reviews, which is considered favorable.
Producer Jan Harlan stated that Kubrick "would have applauded" the final film, while Kubrick's
widow Christiane also enjoyed A.I. However, Brian Aldiss was vocally displeased with the film,
stating,"Its crap. Science fiction has to be logical, and it's full of lapses in logic.” Richard Corliss
heavily praised Spielberg's direction, as well as the cast and visual effects. Roger Ebert awarded
the film 3 out of 4 stars, saying that it was "Audacious, technically masterful, challenging,
sometimes moving and ceaselessly watchable. But the movie's conclusion is too facile and
sentimental, given what has gone before. It has mastered the artificial, but not the intelligence."
On July 8, 2011, Ebert reviewed A.I. again when he added it to his "Great Movies" pantheon.
Leonard Maltin gives the film a not-so-positive review in his Movie Guide, giving it two stars
out of four, writing: "The intriguing story draws us in, thanks in part to Osment's exceptional
performance, but takes several wrong turns; ultimately, it just doesn't work. Spielberg rewrote
the adaptation Stanley Kubrick commissioned of the Brian Aldiss short story "Super Toys Last
All Summer Long"; the result is a curious and uncomfortable hybrid of Kubrick and Spielberg
sensibilities." However, he calls John Williams' music score "striking". Jonathan Rosenbaum
compared A.I. to Solaris (1972), and praised both "Kubrick for proposing that Spielberg direct
the project and Spielberg for doing his utmost to respect Kubrick's intentions while making it a
profoundly personal work." Film critic Armond White, of the New York Press, praised the film
noting that "each part of David’s journey through carnal and sexual universes into the final
eschatological devastation becomes as profoundly philosophical and contemplative as anything
by cinema’s most thoughtful, speculative artists , Borzage, Ozu, Demy, Tarkovsky."

19. Focusing the Concept of Artificial
Intelligence
In the late 21st century, global warming has flooded coastlines, and a drastic reduction of the
human population has occurred. There is a new class of robots called Mecha, advanced
humanoids capable of emulating thoughts and emotions. David (Osment), a prototype model
which was created by a scientist of New Jersey, is designed to resemble a human child and to
display love for its human owners. They test their creation with one of their employees, Henry
Swinton (Robards), and his wife Monica (O'Connor). The Swintons' son, Martin (Thomas), was
placed in suspended animation until a cure can be found for his rare disease. Although Monica is
initially frightened of David, she eventually warms to him and activates his imprinting protocol,
which irreversibly causes David to project love for her, the same as any child would love a
parent. He is also befriended by Teddy (Angel), a robotic teddy bear, who takes it upon himself
to care for David's well-being.
A cure is found for Martin and he is brought home; a sibling rivalry ensues between Martin and
David. Martin convinces David to go to Monica in the middle of the night and cut off a lock of
her hair, but the parents wake up and are very upset. At a pool party, one of Martin's friends
activates David's self-protection programming by poking him with a knife. David clings to
Martin and they both fall into the pool, where the heavy David sinks to the bottom while still
clinging to Martin. Martin is saved from drowning, but Henry in particular is shocked by David's
actions, becoming concerned that David's capacity for love has also given him the ability to hate.
Henry persuades Monica to return David to Cybertronics, where David will be destroyed.
However, Monica cannot bring herself to do this, and instead abandons David in the forest (with
Teddy) to hide as an unregistered Mecha. David is captured for an anti-Mecha Flesh Fair, an
event where obsolete and unlicensed Mecha are destroyed in front of cheering crowds. David is
nearly killed, but the crowd is swayed by his realistic nature and he escapes, along with Gigolo
Joe (Law), a male prostitute Mecha on the run after being framed for murder.
The two set out to find the Blue Fairy, whom David remembers from the story The Adventures
of Pinocchio. He is convinced that the Blue Fairy will transform him into a human boy, allowing
Monica to love him and take him home. Joe and David make their way to Rouge City.
Information from a holographic answer engine called "Dr. Know" (Williams) eventually leads
them to the top of Rockefeller Center in partially flooded Manhattan. David meets his human
creator, Professor Allen Hobby (Hurt), who excitedly tells David that finding him was a test,
which has demonstrated the reality of his love and desire. It also becomes clear that many copies
of David are already being manufactured, along with female versions. David sadly realizes he is
not unique. A disheartened David attempts to commit suicide by falling from a ledge into the
ocean, but Joe rescues him with the amphibicopter. David tells Joe he saw the Blue Fairy
underwater, and wants to go down to her. At that moment, Joe is captured by the authorities with
the use of an electromagnet, but sets the amphibicopter on submerge. David and Teddy take it to
the fairy, which turns out to be a statue from a submerged attraction at Coney Island. Teddy and
David become trapped when the Wonder Wheel falls on their vehicle. Believing the Blue Fairy

20. to be real, David asks to be turned into a real boy, repeating his wish without end, until the ocean
freezes in another ice age and his internal power source drains away.
Two thousand years later humans are extinct and Manhattan is buried under several hundred feet
of glacial ice. Mecha have evolved into a silicon-based, highly advanced and intelligent, alien-
looking futuristic Mechanism, with the ability to perform some form of time manipulation and
telekinesis. On their project to studying humans — believing it was the key to understanding the
meaning of existence, they find David and Teddy and discover they are functional Mechanism
who knew living humans, making them special and unique. David is revived and walks to the
frozen Blue Fairy statue, which cracks and collapses as he touches it. Having received and
comprehended his memories, the advanced Mechanism use them to reconstruct the Swinton
home and explain to David via an interactive image of the Blue Fairy (Streep) that it is
impossible to make him human. However, at David's insistence, they recreate Monica from DNA
in the lock of her hair which had been saved by Teddy. One of the futuristic Mecha tells David
that the clone can only live for single day, and the process cannot be repeated. But David keeps
insisting, so they fast forward the time to the next morning, and David spends the happiest day of
his life with Monica and Teddy. Monica tells David that she loves him, and has always loved
him, as she drifts to sleep for the last time. David lies down next to her, closes his eyes and goes
"to that place where dreams are born". Teddy enters the room, climbs onto the bed, and watches
as David and Monica lie down peacefully together.

21. Future of Artificial Intelligence
In the next 10 years technologies in narrow fields such as speech recognition will continue to
improve and will reach human levels. In 10 years AI will be able to communicate with humans
in unstructured English using text or voice, navigate (not perfectly) in an unprepared
environment and will have some rudimentary common sense (and domain-specific intelligence).
We will recreate some parts of the human (animal) brain in silicon. The feasibility of this is
demonstrated by tentative hippocampus experiments in rats. There are two major projects aiming
for human brain simulation, C Cortex and IBM Blue Brain. There will be an increasing number
of practical applications based on digitally recreated aspects human intelligence, such as
cognition, perception, rehearsal learning, or learning by repetitive practice.
The development of meaningful artificial intelligence will require that machines acquire some
variant of human consciousness. Systems that do not possess self-awareness and sentience will at
best always be very brittle. Without these uniquely human characteristics, truly useful and
powerful assistants will remain a goal to achieve. To be sure, advances in hardware, storage,
parallel-processing architectures will enable ever greater leaps in functionality. But these systems
will remain mechanistic zombies. Systems that are able to demonstrate conclusively that they
possess self-awareness language skills, surface, shallow and deep knowledge about the world
around them and their role within it will be needed going forward. However the field of artificial
consciousness remains in its infancy. The early years of the 21st century should see dramatic
strides forward in this area however.
During the early 2010's new services can be foreseen to arise that will utilize large and very large
arrays of processors. These networks of processors will be available on a lease or purchase basis.
They will be architected to form parallel processing ensembles. They will allow for
reconfigurable topologies such as nearest neighbor based meshes, rings or trees. They will be
available via an Internet or WIFI connection. A user will have access to systems whose power
will rival that of governments in the 1980's or 1990's. Because of the nature of nearest neighbor
topology, higher dimension hyper cubes (e.g. D10 or D20), can be assembled on an ad-hoc basis
as necessary. A D10 ensemble, i.e. 1024 processors, is well within the grasp of today's
technology. A D20, i.e. 2,097,152 processors is well within the reach of an ISP or a processor
provider. Enterprising concerns will make these systems available using business models
comparable to contracting with an ISP to have web space for a web site. Application specific
ensembles will gain early popularity because they will offer well defined and understood
application software that can be recursively configured onto larger and larger ensembles. These
larger ensembles will allow for increasingly fine grained computational modeling of real world
problem domains. Over time, market awareness and sophistication will grow. With this grow
will come the increasing need for more dedicated and specific types of computing ensembles.
Religious and other organizations will define and attempt to regulate the ways in which human
treat humanoid robots, since they will be considered quasi-human, sentient creatures that must be
treated with respect and not abused. Thus, the changing legal and social framework will deal
with the proper use of robots by humans as well as the proper behavior of robots toward humans,
and new sets of “post-Asimov” laws will emerge.

22. Finally, a few concluding thoughts. The rapid increase in the number and sophistication of
autonomous systems, including humanoid robots, lead to the dramatic changes in society. Robots
will assume an increasing share of human work and responsibility, thus creating a major social
problem with unemployment and the relations of humans and robots. I believe that new
frameworks for these interactions will emerge within the next 25 to 50 years. If they do not, there
may be neo-Luddite rebellions, in which humans will attempt to destroy large numbers of robots.
Those of us who design, program, and implement robots have a major responsibility to assist in
the creation and implementation of patterns of behavior and legal systems to ensure that robots
and humans co-evolve and co-exist for the benefit of society.
Robots are here to stay. They will be smarter, more versatile, more autonomous, and more like us
in many ways. We humans will need to adapt to this coming world.
There are some certain things which need to be expanded. These are:
1. Invention
2. First AI Laboratory
4. Speech Recognition
3. Chess Champion
5. Autonomous Robots
6. Humanoid Robots

23. Conclusion
Auto bot is not a real A.I., it is totally hypothetical but it—and its features—can be valuable as a model for
designing artificial intelligence. It has an adequate model of the world, made up of learned and tested
information; it has goals which direct its behavior and is able to create, modify, and improve these goals; it is
capable of problem solving; it is capable of deducing new information; and it is capable of
formulating strategies to achieve goals, and of adjusting those strategies as necessary. We can
look at Auto bot as a model for designing other A.I.s which can use the same basic architecture
and design features to approach their own tasks and problems.
While it requires an investment in some infrastructure, and in creating the A.I. itself, using Auto
bot to control traffic networks is hugely more efficient than letting humans control traffic lights
and maintenance. It just is not possible for a team of human controllers to adapt to changing
circumstances, or micromanage things like case by case optimal red light lengths, as effectively
as Auto bot. This is true of to an A.I. in any position, it will require some investment of
infrastructure to set it up with the necessary sensors to retrieve enough information to create an
adequate model of the world, but the things it is capable of makes it more than worth the
expenditure.
By studying realistically, and systematically designed A.I. like Auto bot, instead of
anthropomorphized A.I.s like HAL we can analyze and put to rest classic fears of rogue A.I.s
hell bent on destruction. Its architects, humans will be able to understand an A.I. at least as well
as they understand one another.
There should be no worry about A.I. making humans redundant. Auto bot (or any
A.I)'s friendliness super goal will cause it to value humanity and individual humans and their
right to autonomy. An A.I. coordinating a city could do so in tandem with, not instead of,
humans. It is possible for a team of people to control all of the traffic lights in a city, but people
get bored and sick and quit, and they need breaks for lunch and cannot work continuously. A
machine that controls the traffic signals can operate forever, never takes a day off, and never
needs to be paid. . only take over the jobs delegated to it. It would be more efficient, and safe, to
make Auto bot directly responsible for piloting all of the cars (as seen in I, Robot) but humans
are not currently willing to relinquish control of their cars and so Auto bot seeks to fulfill its
goals within this limitation, rather than challenging it. Humans can keep whatever jobs for
themselves that they please but for this task, and many others, automation just makes more sense.
1. A.I. is becoming more and more prominent in our society each year.
2. From its origins, A.I. has become a topic of extreme interest, and endless possibilities.
3. From expert systems usedto manage credit cards to speechrecognition systems.
4. A.I.'s presence in the business world business is not going unnoticed.
5. As time goes on its effect on the world will only become stronger.

24. 6. What will the future of AI
bring?????
7. Will it really turn out to be a
“MYTH” faking our “CONCEPT”???
Only Time will tell us…..
Wait & Watch

25. Bibliography
1. Google
2. IMDB
3. Wikipedia
4. www.aaai.org
5. www.journals.elsevier.org
6. www.sciencedaily.com
7. www.ai-class.com
8. www.a-i.com
9. www.edx.org
10. Newspapers & Magazines.

26. Appendix
1. Collecting Information: 5 days.
2. Noting down in a Notepad: 7 days.
3. Editing & Selecting Useful Information: 6 days.
4. Working in Microsoft word: 2 days.
5. Re-arranging Information: 7 days.
6. Final Touchand Printing: 3 days
Total: 30 days
Thank You