ARTIFICIAL INTELLIGENCE
IMAGINE A WORLD IN WHICH PEOPLE TALK TO MACHINES
Shikha Gangwar#1, Shivani Agarwal#2, Prachi Gupta#3,Nidhi Garg#4
Department of CSE, Raj Kumar Goel Institute of Technology for Women
Ghaziabad, India
1shikhagangwar814@gmail.com
2shivaniagarwal168@gmail.com
3prachigupta228@gmail.com
4nidhigarg@rkgitw.edu.in
Abstract-Communication is the basic phenomena in the nature.
By vary means, all species communicate to each other. The
languages are nothing but a medium to represent the thoughts.
but as technology evolves these days people can talk to machine
also. This paper is basically an explanation of how the
communication takes place between the same in real world
applications, and what are the basic elements who takes major
role in this act.
Keywords-artificial intelligence; turing test;
robotics; speech recognition.; expert system
I.
II.

“Artificial Intelligence is simply the application of
artificial or non-naturally occurring systems that use
the knowledge-level to achieve goals”. It is any
machine that relates to a human if it’s through a
program.

AI is the science of making machines do things that
would require intelligence if done by people.
(MARVIN MINSKY)

AI is the science of automating intelligent behaviours
currently achievable by humans only.

Artificial Intelligence is the ability of a computer or
other machine to perform those activities that are
normally thought to require intelligence [1].
Some machines were and are built to make our lives
easier, keep us alive longer and stay healthy.
fuzzy logic; nlp;
INTRODUCTION
So far as the laws of mathematics refer to reality, they are not
certain. And so far as they are certain, they do not refer to
reality. -- Albert Einstein…the more one thinks about
imprecision and the need to model and represent it, the more
the problems with the current mathematical approach and
precise science seem to become apparent. There is seemingly
an increasing need to come to terms with the pervasive
imprecision of the real world. -- Stan Openshaw.
It is really a wonderful thought to talk with machines. In the
world of ARTIFICIAL INTELLIGENCE our dreams seems to
be true. Artificial Intelligence (AI) may be regarded as an
attempt to understand the processes of perception and
reasoning that underlie successful problem-solving and to
incorporate the results of this research in effective computer
programs. At present, AI is a large collection of sophisticated
programming techniques.
Artificial Intelligence (AI) is a perfect example of how
sometimes science moves more slowly than we would have
predicted. In the first flush of enthusiasm at the invention of
computers it was believed that we now finally had the tools
with which to crack the problem of the mind, and within years
we would see a new race of intelligent machines.
In some sense it is engineering inspired by biology. We look
at animals, we look at humans and we want to be able to build
machines that do what they do. We want machines to be able
to learn in the way that they learn, to speak, to reason and
eventually to have consciousness. AI is regarded as the branch
of ‘computer science’ which is concerned with making
‘computers’ behave like humans.”
ABOUT ARTIFICIAL
INTELLIGENCE
III.
SUPERBRAIN
Artificial intelligence attempts to provide machines with
human like thinking. It is used to automate and replace some
human functions with computer-driven machines. These
machines can see and hear, respond to questions, learn, draw
inferences and solve problems. But for the Singulatarians, A.I.
refers to machines that will be both self-aware and
superhuman in their intelligence, and capable of designing
better computers and robots faster than humans can today [2].
It is the perception that in future the contents of our brain and
thought processes can somehow be translated into a
computing environment, making a form of immortality
possible — within his lifetime. Such a shift, they say, would
lead to a vast acceleration in technological improvements of
all kinds [2].
IV.
TURING TEST
Turing test, the famous probe of computer intelligence is
named after Alan M. Turing, the British mathematician who
suggested it. He proposed that if a computer could carry on a
typed conversion with a person and successfully impersonate a
human, that computer could be called intelligent [4]. It can be
said that a machine could be judged as intelligent if it could
comprehensively fool a human examiner into thinking the
machine was human [3]. Turing held that future computers
can be programmed to acquire abilities rivaling human
intelligence. As part of his argument Turing put forward the
idea of an 'imitation game', in which a human being and a
computer would be interrogated under conditions where the
interrogator would not know which was which, the
communication being entirely by textual messages. Turing
argued that if the interrogator could not distinguish them by
questioning, then it would be unreasonable not to call the
computer intelligent. Turing's 'imitation game' is now usually
called 'the Turing test' for intelligence.
V.
NATURAL LANGUAGE
PROCESSING
Natural Language Processing (NLP) is both a modern
computational technology and a method of investigating and
evaluating claims about human language itself.
A truly intelligent computer would not be limited to rigid
computer language commands, but instead be able to process
and understand the English language. This is the concept
behind Natural Language Processing.
One goal of AI work in NATURAL LANGUAGE
PROCESSING (NLP) is to use natural languages and to
enable communication between people and computers without
resorting to memorization of complex commands and
procedures [8]. It is the use of computers to process written
and spoken language for some practical, useful, purpose: to
translate languages, to get information from the web on text
data banks so as to answer questions, to carry on conversations
with machines, so as to get advice about, say, pensions and so
on. These are only examples of major types of NLP, and there
is also a huge range of lesser but interesting applications, e.g.
getting a computer to decide if one newspaper story has been
rewritten from another or not.
VI.
SPEECH RECOGNITION
SPEECH RECOGNITION is basically a technology that
allows computers to interpret human speech. It also allows
converting speech into text, making it easier both to create and
to use information.
In past, people mostly imagined SPEECH RECOGNITION
directly producing the end result, e.g. a dictated document or a
computer performing a command.
But, in the next 10 years, speech recognition will continue to
improve and will reach human levels. Also it will be able to
communicate with humans in unstructured English using text
or voice, navigate in an unprepared environment and will have
some rudimentary common sense. As we speak to tell
someone something and they read to understand it. Similarly
we will talk to machines. E.g. When we say, “JIM, HOW
ARE YOU DOING?”, the machine will recognize that you
mean to talk with JIM ,and will send the text “ HOW ARE
YOU DOING?” to him.
It is interesting to note that human beings perform HSR
(Human speech recognition) by integrating multiple
knowledge sources from bottom up. It has long been
postulated that a human determines the linguistic identity of a
sound based on detected evidences that exist at various levels
of the speech knowledge hierarchy, from acoustics to
pragmatics [7]. For example, Klatt [6] studied the so-called
acoustic landmarks that are assumed invariant to changes in
speakers and speaking environments.
VII.
ROBOTICS
ROBOTICS is also one field within artificial intelligence
which involves mechanical, usually computer-controlled
devices to perform tasks that require extreme precision or
tedious or hazardous work by people. Traditional Robotics
uses Artificial Intelligence planning techniques to program
robot behaviors and works toward robots as technical devices
that have to be developed and controlled by a human engineer
[5]. The Autonomous Robotics approach suggests that robots
could develop and control themselves autonomously. These
robots are able to adapt to both uncertain and incomplete
information in constantly changing environments. This is
possible by imitating the learning process of a single natural
organism or through Evolutionary Robotics, which is to apply
selective reproduction on populations of robots. It lets a
simulated evolution process develop adaptive robots. .
VIII.
FUZZY LOGIC
Fuzziness means ‘vagueness’. Fuzzy Logic is an excellent
mathematical tool to handle the uncertainty arising due to
vagueness. Understanding human speech and recognizing
hand written characters are some common instances where
fuzziness manifests. It enables a computer to make decisions
which care more in line with the sort of decisions which a
human would make. Computer logic is rigorous and
deterministic and relates to finite states and numbering
systems [10]. Computer logic marks distinct boundaries
between any states. Fuzzy logic is concerned with pulling
away from logic that is crisp or Boolean (binary 0 or 1). This
method has an advantage over Boolean logic in that it mimics
complex human reasoning in order to arrive at realistic
conclusions about the imprecise and often fuzzy nature of
reality. Fuzzy logic techniques provide the ability to develop
soft computing applications that permit computer models to be
specified and built from linguistic statements, based on
common sense or theory or rules of thumb.
Fig. 2:-SIMPLE MODEL OF AN ARTIFICIAL NEURON
Fig. 1:-Fuzzy Logic
IX.
EXPERT SYSTEM
The artificial intelligence concept of the "EXPERT SYSTEM"
is highly developed. This describes robot programmers’ ability
to anticipate situations and provide the robot with a set of "ifthen" rules. An expert system is a computer system that
emulates the decision-making ability of a human expert.
Expert systems are designed to solve complex problems by
reasoning about knowledge, like an expert, and not by
following the procedure of a developer as is the case in
conventional programming. The first expert systems were
created in the 1970s and then proliferated in the 1980s. Expert
systems were among the first truly successful forms
of AI software [13].
X.
ARTIFICIAL NEURAL
NETWORK
Artificial neural network or simulated neural network (SNN),
is an interconnected group of artificial neurons that uses a
mathematical model or computational model for information
processing based on a connectionist approach to computation
(shown in fig. 2). In most cases an ANN is an adaptive
system that changes its structure based on external or internal
information that flows through the network [11].
In more practical terms neural networks are nonlinear statistical data modeling or decision making tools. They
can be used to model complex relationships between inputs
and outputs or to find patterns in data [9]. In the artificial
intelligence field, artificial neural networks have been applied
successfully to speech recognition,image analysis and adaptive
control, in order to construct software agents (in computer and
video games) or autonomous robots. Most of the currently
employed artificial neural networks for artificial intelligence
are based on the statistical estimations and on the
classification optimization and control theory [12] .
XI.
CONCLUSION:
The Art of Virtual Chat Is Still a Work in Progress. Getting a
computer to communicate with a human is a definite struggle,
but it's a field that's progressing. 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. But A.I. assured us to make such robots which
would have consciousness and emotions like humans. They
will have different kinds of ‘human intelligence’, including the
ability to understand other people, and to influence their
behavior.
Therefore, A.I. is an endless supply of vast knowledge getting
pout into a program to make our lives
easier and better. A
major shift in the way people interact with computers is
coming. And it is something that we badly need.
REFERENCES
[1] H.A. Simon,” The Sciences of the Artificial” (MIT Press,
Cambridge, MA, 1969).
[2] John Makoff Article on” The Coming Superbrain” May
23, 2009
[3] Stevan Harnad Article on” Turing Test is not a trick:
Turing indistinguishability is a scientific criterion” published
in newsletter ACM SIGART Bulletin Volume 3 Issue 4,
October 1992.
[4] Katie Hafner Article on “Guessing who is online” July 22,
1999.
[5] N.J. Nilsson, Shakey the robot, Tech. Note 323,SRI AI
Center, Menlo Park, CA(1984). [24] C. Douligeris and
G.Develekos, A fuzzy logic approach to congestion control in
ATM networks, IEEE International Conference on
Communications, ICC '95, pp. 1969-1973, 1995.
[6] D. Klatt, ``Review of the ARPA Speech Understanding
Project,'' J. Acous. Am., Vol. 62, No. 6, 1977.
[7] Chin- Hui Lee,”From Knowledge-Ignorant to KnowledgeRich Modelling: A New Speech Research Paradigm for Next
Generation Automatic Speech Recognition,”Atlanta,USA,
2004.
[8] Ralph Weischedel, Chairperson, BBN Systems and
Technologies Corporations White paper on “Natural Language
Processing”.
[9] A. A. Tarraf, I. W. Habib, and T. N. Saadawi, Congestion
control mechanism for ATM networks using neural networks,
IEEE International Conference on Communications, ICC '95,
pp. 206-210, 1995.
[10] D. Driankov, H. Hellendoorn and M. Reinfrank, An
Introduction to Fuzzy Control, Springer-Verlag, 1993.
[11] Simon, H. (1999). Neural networks: a comprehensive
foundation. Prentice Hall, Upper Saddle River, NJ.
[12] Zhang, G., B. E. Patuwo, and M. Y. Hu (1998).
Forecasting with artificial neural networks: The state of the
art. International Journal of Forecasting 14, 35–62.
[13] Swartout, W., Paris, C., and Moore, J. (1991). Design for
explainable expert systems. IEEE Expert.