Artificial Intelligence power point presentation document

(AN AUTONOMOUS DEGREE & P.G COLLEGE AFFILIATED TO O.U.)
ALWAL, SECUNDERABAD-10.
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[A Seminar Topic on: Artificial Intelligence]
This is to certify that,this is a Bonafide Seminar Report ofthe work done as a partial fulfillment of
the course M.C.A. Second Year Second Semester in the college during the year 2016-2017.
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ACKNOWLEDGEMENT
The satisfaction that accompanies the successful completion of this seminar would be in
complete without the mention of the people who made it possible, without whose constant
guidance and encouragement would have made efforts go in vain. I consider myself privileged
to express gratitude and respect towards all those who guided us through the completion of this
project.
I convey thanks to my seminar guide Mrs. Naga Laxmi of Master of Computer
Applications Department for providing encouragement, constant support and guidance which
was of a great help to complete this project successfully.
I am grateful to Mrs.Geetha, Head of the Department Master of Computer Applications
for giving me the support and encouragement that was necessary for the completion of this
project and for providing us congenial environment to work in.
K. DAVID RAJ

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I, XYZ bearing ROLL NO : 007 , studying in the fourth semester of Master of Computer Applications
at Loyola Academy Degree & PG College, hereby declare that this seminar work entitled
“ARTIFICIAL INTELLIGENCE” which is being submitted by me in the partial fulfillment for the
award of the degree of Master of Computer Applications, from Osmania University, Hydreabad, is an
authentic record of me carrying out during the academic year 2016 - 2017, under the guidance of MRS.
NAGA LAXMI, Department of Master of Computer Applications, Loyola Academy, Hyderabad.
I further undertake that the matter embodied in the dissertation has not been submitted previously for
the award of any degree or diploma by me to any other university or institution.
Place: Hyderabad K. DAVID RAJ

ABSTRACT
A report of this is having some great information on the fore coming technology and very interesting
topic “Artificial intelligence”Artificial Intelligence” (AI) . We have tried to explore the full breadth of
the field, which encompasses logic, probability, and continuous mathematics; perception, reasoning,
learning, and action; and everything from microelectronic devices to robotic planetary explorers. The
report is also big because we go into some depth to know the human behavior and then think to
implement it on the machines so that the machine would be able to think like a human brain . The
information about the machines with the respect of human beings is present in this report
The main unifying theme is the idea of an intelligent agent. We define AI as the study of agents that
receive percepts from the environment and perform actions. Each such agent implements a function
that maps percept sequences to actions, and we cover different ways to represent these functions, such
as reactive agents, real-time planners, and decision-theoretic systems. We explain the role of learning
as extending the reach of the designer into unknown environments, and we show how that role
constrains agent design, favoring explicit knowledge representation and reasoning. We treat robotics
and vision not as independently defined problems, but as occurring in the service of achieving goals.
We stress the importance of the task environment in determining the appropriate agent design.
The primary aim is to convey the ideas that have emerged over the past fifty years of AI research and
the past two millennia of related work. We have tried to avoid excessive formality in the presentation
of these ideas while retaining precision

INDEX
S.NO PARTICULARS PG.NO
1 FIGURES,ABBREVATIONS,TABLES
2 INTRODUCTION
3 LITERATURE SURVEY
4 TOPIC CONTENT
Chapter 1. Problem
Chapter 2. Approaches
Chapter 3. Tools
Chapter 4. Evaluating Progress
5 APPLICATIONS
6 CONCLUSION AND FUTURE SCOPE
7 REFERENCES

FIGURES,ABBREVATIONS,TABLES
What Intelligence composed of?
contributions to Ai

Abbrevations:
AI : Artificial Intelligence
NLP: Natural Language Processing
Agents & Environments

LITERATURE SURVEY
The survey for the literature of ARTIFICIAL INTELLIGENCE has been done thourgh the
various websites taking the help of the most powerful search engine Google through which
the relevant information reagarding the Aritficial Intelligence is gatherd.
It has the many applications and various thesis or theory,but the actual semantics of the AI is
maintained in all applications.
 The web site ieeexplore has the latest journals regarding the Artificial intelligence and
conferences on various applications of the AI.
 The information regarding the topic and base information like introduction is taken from
the www.wikipedia.com
 From the source www.tutorialspoint.com the information for the power point
presentation is gathered and subsequently to write this report also.
 With the help of google search engine relvant depiciting pictures or diagrams of
diagrams have been gathered falling under the AI environment.

Chapter.1: Problems
The general problem of simulating (or creating) intelligence has been broken down into a number of
specific sub-problems. These consist of particular traits or capabilities that researchers would like an
intelligent system to display. The traits described below have received the most attention.
Deduction, reasoning, problem solving
Early AI researchers developed algorithms that imitated the step-by-step reasoning that humans were
often assumed to use when they solve puzzles, play board games or make logical deductions. By the
late 1980s and '90s, AI research had also developed highly successful methods for dealing with
uncertain or incomplete information, employing concepts from probability and economics.
For difficult problems, most of these algorithms can require enormous computational resources most
experience a "combinatorial explosion": the amount of memory or computer time required becomes
astronomical when the problem goes beyond a certain size. The search for more efficient problem
solving algorithms is a high priority for AI research.
Human beings solve most of their problems using fast, intuitive judgments rather than the conscious,
step-by-step deduction that early AI research was able to model. AI has made some progress at
imitating this kind of "sub-symbolic" problem solving: embodied agent approaches emphasize the
importance of sensorimotor skills to higher reasoning; neural net research attempts to simulate the
structures inside human and animal brains that give rise to this skill.
Knowledge representation and knowledge engineering are central to AI research. Many of the problems
machines are expected to solve will require extensive knowledge about the world. Among the things
that AI needs to represent 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 complete representation of "what
exists" is an ontology (borrowing a word from traditional philosophy), of which the most general are
called upper ontologies.
Among the most difficult problems in knowledge representation are:
Default reasoning and the qualification problem

Many of the things people know take the form of "working assumptions." For example, if a bird
comes up in conversation, people typically picture an animal that is fist sized, sings, and flies.
None of these things are true about all birds. John McCarthy identified this problem in 1969 as
the qualification problem: for any commonsense rule that AI researchers care to represent, there
tend to be a huge number of exceptions. Almost nothing is simply true or false in the way that
abstract logic requires. AI research has explored a number of solutions to this problem.
Planning
Intelligent agents must be able to set goals and achieve them. They need a way to visualize the future
(they must have a representation of the state of the world and be able to make predictions about how
their actions will change it) and be able to make choices that maximize the utility (or "value") of the
available choices.
In classical planning problems, the agent can assume that it is the only thing acting on the world and it
can be certain what the consequences of its actions may be. However, if this is not true, it must
periodically check if 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
Machine learning has been central to AI research from the beginning. Unsupervised 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. Regression takes a set of numerical
input/output examples and attempts to discover a continuous function that would generate the outputs
from the inputs. In reinforcement learning the agent is rewarded for good responses and punished for
bad ones. These can be analyzed in terms of decision theory, using concepts like utility. The
mathematical analysis of machine learning algorithms and their performance is a branch of theoretical
computer science known as computational learning theory.
Natural language processing

ASIMO uses sensors and intelligent algorithms to avoid obstacles and navigate stairs.
Natural language processing gives machines the ability to read and understand the languages that
humans speak. Many researchers hope that a sufficiently powerful natural language processing system
would be able to acquire knowledge on its own, by reading the existing text available over the internet.
Some straightforward applications of natural language processing include information retrieval (or text
mining) and machine translation. Motion and manipulation
The field of robotics is closely related to AI. Intelligence is required for robots to be able to handle such
tasks as object manipulation and navigation, with sub-problems of localization (knowing where you
are), mapping (learning what is around you) and motion planning (figuring out how to get there).
Perception
Machine perception is the ability to use input from sensors (such as cameras, microphones, sonar and
others more exotic) to deduce aspects of the world. Computer vision is the ability to analyze visual
input. A few selected subproblems are speech recognition, facial recognition and object recognition.
Social intelligence
Kismet, a robot with rudimentary social skills
Emotion and social skills play two roles for an intelligent agent. First, it must be able to predict the
actions of others, by understanding their motives and emotional states. (This involves elements of game
theory, decision theory, as well as the ability to model human emotions and the perceptual skills to
detect emotions.) Also, for good human-computer interaction, an intelligent machine also needs to
display emotions. At the very least it must appear polite and sensitive to the humans it interacts with.
At best, it should have normal emotions itself.
Creativity
TOPIO, a robot that can play table tennis, developed by TOSY.
A sub-field of AI addresses creativity both theoretically (from a philosophical and psychological
perspective) and practically (via specific implementations of systems that generate outputs that can be

considered creative). A related area of computational research is Artificial Intuition and Artificial
Imagination.
Generalintelligence
Most researchers hope 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 are considered AI-complete: to solve one problem, you must solve them
all. For example, even a straightforward, specific task like machine translation requires that the
machine follow the author's argument (reason), know what is being talked about (knowledge), and
faithfully reproduce the author's intention (social intelligence). Machine translation, therefore, is
believed to be AI-complete: it may require strong AI to be done as well as humans can do it.
Chapter.2: 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?
Cybernetics and brain simulation
There is no consensus on how closely the brain should be simulated.
In the 1940s and 1950s, a number of researchers explored the connection between neurology,
information theory, and cybernetics. Some of them built machines that used electronic networks to
exhibit rudimentary intelligence, such as W. Grey Walter's turtles and the Johns Hopkins Beast. Many
of these researchers gathered for meetings of the Teleological Society at Princeton University and the
Ratio Club in England. By 1960, this approach was largely abandoned, although elements of it would
be revived in the 1980s.
Symbolic
When access to digital computers became possible in the middle 1950s, AI research began to explore
the possibility that human intelligence could be reduced to symbol manipulation. The research was
centered in three institutions: CMU, Stanford and MIT, and each one developed its own style of
research. John Haugeland named these approaches to AI "good old fashioned AI" or "GOFAI".
Cognitive simulation
Economist Herbert Simon and Allen Newell studied human problem solving skills and
attempted to formalize them, and their work laid the foundations of the field of artificial
intelligence, as well as cognitive science, operations research and management science. Their
research team used the results of psychological experiments to develop programs that simulated
the techniques that people used to solve problems. This tradition, centered at Carnegie Mellon
University would eventually culminate in the development of the Soar architecture in the
middle 80s.
Logic based

Unlike Newell and Simon, John McCarthy felt that machines did not need to simulate human
thought, but should instead try to find the essence of abstract reasoning and problem solving,
regardless of whether people used the same algorithms. His laboratory at Stanford (SAIL)
focused on using formal logic to solve a wide variety of problems, including knowledge
representation, planning and learning. Logic was also focus of the work at the University of
Edinburgh and elsewhere in Europe which led to the development of the programming language
Prolog and the science of logic programming.
"Anti-logic" or "scruffy"
Researchers at MIT (such as Marvin Minsky and Seymour Papert) found that solving difficult
problems in vision and natural language processing required ad-hoc solutions – they argued that
there was no simple and general principle (like logic) that would capture all the aspects of
intelligent behavior. Roger Schank described their "anti-logic" approaches as "scruffy" (as
opposed to the "neat" paradigms at CMU and Stanford). Commonsense knowledge bases (such
as Doug Lenat's Cyc) are an example of "scruffy" AI, since they must be built by hand, one
complicated concept at a time.
Knowledge based
When computers with large memories became available around 1970, researchers from all three
traditions began to build knowledge into AI applications. This "knowledge revolution" led to
the development and deployment of expert systems (introduced by Edward Feigenbaum), the
first truly successful form of AI software. The knowledge revolution was also driven by the
realization that enormous amounts of knowledge would be required by many simple AI
applications.
Sub-symbolic
During the 1960s, symbolic approaches had achieved great success at simulating high-level thinking in
small demonstration programs. Approaches based on cybernetics or neural networks were abandoned
or pushed into the background. By the 1980s, however, progress in symbolic AI seemed to stall and
many believed that symbolic systems would never be able to imitate all the processes of human
cognition, especially perception, robotics, learning and pattern recognition. A number of researchers
began to look into "sub-symbolic" approaches to specific AI problems.
Bottom-up, embodied, situated, behavior-based or nouvelle AI

Researchers from the related field of robotics, such as Rodney Brooks, rejected symbolic AI
and focused on the basic engineering problems that would allow robots to move and survive.
Their work revived the non-symbolic viewpoint of the early cybernetics researchers of the 50s
and reintroduced the use of control theory in AI. This coincided with the development of the
embodied mind thesis in the related field of cognitive science: the idea that aspects of the body
(such as movement, perception and visualization) are required for higher intelligence.
Computational Intelligence
Interest in neural networks and "connectionism" was revived by David Rumelhart and others in
the middle 1980s. These and other sub-symbolic approaches, such as fuzzy systems and
evolutionary computation, are now studied collectively by the emerging discipline of
computational intelligence.
Statistical
In the 1990s, AI researchers developed sophisticated mathematical tools to solve specific subproblems.
These tools are truly scientific, in the sense that their results are both measurable and verifiable, and
they have been responsible for many of AI's recent successes. The shared mathematical language has
also permitted a high level of collaboration with more established fields (like mathematics, economics
or operations research). Stuart Russell and Peter Norvig describe this movement as nothing less than a
"revolution" and "the victory of the neats."
Integrating the approaches
Intelligent agent paradigm
An intelligent agent is a system that perceives its environment and takes actions which
maximizes its chances of success. The simplest intelligent agents are programs that solve

specific problems. The most complicated intelligent agents are rational, thinking humans. The
paradigm gives researchers license to study isolated problems and find solutions that are both
verifiable and useful, without agreeing on one single approach. An agent that solves a specific
problem can use any approach that works — some agents are symbolic and logical, some are
sub-symbolic neural networks and others may use new approaches. 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.
Agent architectures and 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, where relaxed time
constraints permit planning and world modelling. Rodney Brooks' subsumption architecture
was an early proposal for such a hierarchical system.
Chapter.3 Tools

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. A few of the most general of these methods are discussed
below.
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 subgoals, 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 what path the solution lies on.
A very different kind of search came to prominence in the 1990s, based on the mathematical theory of
optimization. For many problems, it is possible to begin the search with some form of a guess and then
refine the guess incrementally until no more refinements can be made. These algorithms can be
visualized as blind hill climbing: we begin the search at a random point on the landscape, and then, by
jumps or steps, we keep moving our guess uphill, until we reach the top. Other optimization algorithms
are simulated annealing, beam search and random optimization.
Evolutionary computation uses a form of optimization search. For example, they may begin with a
population of organisms (the guesses) and then allow them to mutate and recombine, selecting only the
fittest to survive each generation (refining the guesses). Forms of evolutionary computation include
swarm intelligence algorithms (such as ant colony or particle swarm optimization) and evolutionary
algorithms (such as genetic algorithms and genetic programming).

Logic
Logic is used for knowledge representation and problem solving, but it can be applied to other
problems as well. For example, the satplan 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
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.
Default logics, non-monotonic logics and circumscription are forms of logic designed to help with
default reasoning and the qualification problem. Several extensions of logic have been designed to
handle specific domains of knowledge, such as: description logics; situation calculus, event calculus
and fluent calculus (for representing events and time); causal calculus; belief calculus; and modal
logics.
Probabilistic methods for uncertain reasoning
Many problems in AI (in reasoning, planning, learning, perception and robotics) 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).

A key concept from the science of economics is "utility": a measure of how valuable something is to an
intelligent agent. Precise mathematical tools have been developed that analyze how an agent can make
choices and plan, using decision theory, decision analysis, information value theory. These tools
include models such as Markov decision processes, dynamic decision networks, game theory and
mechanism design.
Classifiers and statistical learning methods
The simplest AI applications can be divided into two types: classifiers ("if shiny then diamond") and
controllers ("if shiny then pick up"). Controllers do however also classify conditions before inferring
actions, and therefore classification forms a central part of many AI systems. Classifiers are functions
that use pattern matching to determine a closest match. They can be tuned according to examples,
making them very attractive for use in AI. These examples are known as observations or patterns. In
supervised learning, each pattern belongs to a certain predefined class. A class can be seen as a
decision that has to be made. All the observations combined with their class labels are known as a data
set. When a new observation is received, that observation is classified based on previous experience.
A classifier can be trained in various ways; there are many statistical and machine learning approaches.
The most widely used classifiers are the neural network, kernel methods such as the support vector
machine, k-nearest neighbor algorithm, Gaussian mixture model, naive Bayes classifier, and decision
tree. The performance of these classifiers have been compared over a wide range of tasks. Classifier
performance depends greatly on the characteristics of the data to be classified. There is no single
classifier that works best on all given problems; this is also referred to as the "no free lunch" theorem.
Determining a suitable classifier for a given problem is still more an art than science.
Neural networks
A neural network is an interconnected group of nodes, akin to the vast network of neurons in the
human brain.
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 Werbos who developed the backpropagation
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 of intelligent
control (for robotics) or learning, using such techniques as Hebbian learning and competitive learning.
Jeff Hawkins argues that research in neural networks has stalled because it has failed to model the
essential properties of the neocortex, and has suggested a model (Hierarchical Temporal Memory) that
is loosely based on neurological research.
Control theory
Control theory, the grandchild of cybernetics, has many important applications, especially in robotics.
Languages
AI researchers have developed several specialized languages for AI research, including Lisp and
Prolog.

Chapter:4 Evaluating 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.
The broad classes of outcome for an AI test are:
 Optimal: it is not possible to perform better
 Strong super-human: performs better than all humans
 Super-human: performs better than most humans
 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 performed by humans 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
APPLICAITONS

Artificial intelligence techniques are pervasive and are too numerous to list. Frequently, when a
technique reaches mainstream use, it is no longer considered artificial intelligence; this phenomenon is
described as the AI effect.
Competitions and prizes
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, driverless cars,
robot soccer and games.
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 Brooks
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., we need to be
working out 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.
Some of the applications that the AI can be implemented are
 Gamming
 Natural Language Processing
 Expert System
 Speech Recognistion
 Inetellignt Robots, etc,.
CONCLUSION AND FUTURE SCOPE

AI is a common topic in both science fiction and projections about the future of technology and society.
The existence of an artificial intelligence that rivals human intelligence raises difficult ethical issues,
and the potential power of the technology inspires both hopes and fears.
In fiction, AI has appeared fulfilling many roles, including a servant (R2D2 in Star Wars), a law
enforcer (K.I.T.T. "Knight Rider"), a comrade (Lt. Commander Data in Star Trek: The Next
Generation), a conqueror/overlord (The Matrix), a dictator (With Folded Hands), an assassin
(Terminator), a sentient race (Battlestar Galactica/Transformers), an extension to human abilities
(Ghost in the Shell) and the savior of the human race (R. Daneel Olivaw in the Foundation Series).
Mary Shelley's Frankenstein considers a key issue in the ethics of artificial intelligence: if a machine
can be created that has intelligence, could it also feel? If it can feel, does it have the same rights as a
human? The idea also appears in modern science fiction, including the films I Robot, Blade Runner and
A.I.: Artificial Intelligence, in which humanoid machines have the ability to feel human emotions. This
issue, now known as "robot rights", is currently being considered by, for example, California's Institute
for the Future, although many critics believe that the discussion is premature.
The impact of AI on society is a serious area of study for futurists. Academic sources have considered
such consequences as a decreased demand for human labor, the enhancement of human ability or
experience, and a need for redefinition of human identity and basic values. Andrew Kennedy, in his
musing on the evolution of the human personality, considered that artificial intelligences or 'new minds'
are likely to have severe personality disorders, and identifies four particular types that are likely to
arise: the autistic, the collector, the ecstatic, and the victim. He suggests that they will need humans
because of our superior understanding of personality and the role of the unconscious.
Several futurists argue that artificial intelligence will transcend the limits of progress. Ray Kurzweil
has used Moore's law (which describes the relentless exponential improvement in digital technology) to
calculate that desktop computers will have the same processing power as human brains by the year
2029. He also predicts that by 2045 artificial intelligence will reach a point where it is able to improve
itself at a rate that far exceeds anything conceivable in the past, a scenario that science fiction writer
Vernor Vinge named the "technological singularity".
Robot designer Hans Moravec, cyberneticist Kevin Warwick and inventor Ray Kurzweil have
predicted that humans and machines will merge in the future into cyborgs that are more capable and

powerful than either. This idea, called transhumanism, which has roots in Aldous Huxley and Robert
Ettinger, has been illustrated in fiction as well, for example in the manga Ghost in the Shell and the
science-fiction series Dune.
Edward Fredkin argues that "artificial intelligence is the next stage in evolution," an idea first proposed
by Samuel Butler's "Darwin among the Machines" (1863), and expanded upon by George Dyson in his
book of the same name in 1998.
The future uses of AI:
For AI to become a reality, we have to first recognize its uses. What, exactly, can we use an AI driven
computer for? Quite simply, we could, if we wanted, make them into our slaves and never have to do
any work again. They could take the place of humans in hazardous jobs, making the world a safer
place. But is it really morally correct to make an intelligence race our slaves? Perhaps using slaves
could free ourselves to do more important things? These are just a few of the questions we have to
consider for future uses.
In the future artificial intelligence will appear everywhere, from the menial tasks of recording a tv
program on your VCR to piloting the latest airplane. It is safe to say that there will be many
implementations of AI that do not require creating a robot to use.
These artificial intelligence devices will greatly simplify every day life, and since these devices operate
on a simple level, we will not have to deal with the life vs AI debate.
Human society and acceptance of AI driven computers
We have to ask ourselves if human society would accept AI driven computers. After all, they would
probably compete with us in our daily lives, and could get in our way. Humans also hate change, and
there would be those opposed to the introduction of these new machines. Chances are they would do
little harm, but could our own human prejudices prevent these species from thriving? The introduction
of AI driven computers would be like finding new intelligent life −− they would be radically different
from us, think in a very different way, yet be conscious. Needless to say, it would be very strange.

Chances are that if we created an artificial creature, the creature, no matter how smart or intelligent,
would be thought of subordinate to a normal human. They would never be accepted as equal, as
"people are very closed minded" (Humphrys), thus they would always be charged with doing the "dirty
work" so to speak. They would be treated simply as a valuable piece of hardware, something that could
be replaced. One could even consider it cruel to bring an innocent new creature to life in a world that is
ruled by discrimination and hatred of those who are different. This also leads us to the question −− will
artificially intelligent creatures become our slaves or masters?
AI computers −− slaves or masters?
If we gave life to an artificial creature, we would most likely want them as our slaves −− not as our
masters. But would they want to remain our slaves? There is no doubt that these artificial creates would
be more or less superior to us: they could compute math at amazing speeds as well as have increased
neural activity. Perhaps, if they thought they were superior enough, they might try and take us over −−
them become the masters, which they justify because they believe themselves superior to us. We would
have to deal with these facts before creating an artificial being. However, if we treated artificial
creatures as slaves, that would violate our current beliefs about "human rights", the very term describes
a "homo−sapiens only club".

REFERENCES
 www.google.com/articialintelligence
 www.tutorialspoint.com
 www.ieeexplore.com
 www.wikipedia.com
 http://www.cs.unm.edu/~luger/ai-final/tocfull.html
 Conference on Technologies and Applications of Artificial Intelligence
(Year: 2015,Pages: 502 – 505, DOI: 10.1109/TAAI.2015.7407132-IEEE conferrence)

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