ABSTRACT
Today in the world of computers, everything has become automated.
Computers have taken over our boring and tedious jobs. Earlier they were
designed to help humans but nowadays computer engineers are working on a new
breed of computers known as intelligent machines. These machines could simulate
the human intelligence to an extent. Although they are designed by humans,
still they are able to learn by themselves. They can learn and work in a way a
new born child does. These machines can adapt to the changing environment by
observing their surroundings and learning by their past experiences just like a
small child.
To help us and our organizations cope with the unpredictable
eventualities of an ever-more volatile world, these systems need capabilities
that will enable them to adapt readily to change. Health-care providers require
easy access to information systems so they can track health-care delivery and
identify the most recent and effective medical treatments for their patients'
conditions. Crisis management teams must be able to explore alternative courses
of action and support decision making. Educators need systems that adapt to a
student's individual needs and abilities. Businesses require flexible
manufacturing and software design aids to maintain their leadership position in
information technology, and to regain it in manufacturing.
By providing computer programs that amplify human cognitive abilities
and increase human productivity, reach, and effectiveness, we can help meet
national needs in industries like health care, education, service, and
manufacturing. This paper reviews the issues arising from the combination of
artificial intelligence techniques with those of virtual environments created
by humans.
INTRODUCTION
Computer systems are becoming commonplace; indeed, they are
almost ubiquitous. We find them central to the functioning of most business,
governmental, military, environmental, and health-care organizations. They are
also a part of many educational and training programs. But these computer
systems, while increasingly affecting our lives, are rigid, complex, and
incapable of rapid change. To help us and our organizations cope with the
unpredictable eventualities of an ever-more volatile world, these systems need
capabilities that will enable them to adapt readily to change. They need to be
intelligent. Our national competitiveness depends increasingly on capacities
for accessing, processing, and analyzing information. The computer systems used
for such purposes must also be intelligent.
Health-care providers require easy access to information
systems so they can track health-care delivery and identify the most recent and
effective medical treatments for their patients' conditions. Crisis management
teams must be able to explore alternative courses of action and support
decision making. Educators need systems that adapt to a student's individual
needs and abilities. Businesses require flexible manufacturing and software
design aids to maintain their leadership position in information technology,
and to regain it in manufacturing.
Advanced information technology can help meet these and many
other needs in our society. Advances in computer and telecommunications have
made available a vast quantity of data, and given us computational power that
puts the equivalents of mainframes on our desktops. However, raw information
processing power alone, like brute strength, is useful but insufficient. To
achieve their full impact, computer systems must have more than processing
power-they must have intelligence. They need to be able to assimilate and use
large bodies of information and collaborate with and help people find new ways
of working together effectively. The technology must become more responsive to
human needs and styles of work, and must employ more natural means of
communication.
To address the critical limitations of today's systems, we
must understand the ways people reason about and interact with the world, and
must develop methods for incorporating intelligence in computer systems. By
providing computer programs that amplify human cognitive abilities and increase
human productivity, reach, and effectiveness, we can help meet national needs
in industries like health care, education, service, and manufacturing.
Artificial intelligence (AI) is a field that studies
intelligent behavior in humans using the tools-theoretical and experimental-of
computer science. The field simultaneously addresses one of the most profound
scientific problems-the nature of intelligence-and engages in pragmatically
useful undertakings: developing intelligent systems. The concepts, techniques,
and technology of AI offer us a number of ways to discover what intelligence
is-what one must know to be smart at a particular task-and a variety of
computational techniques for embedding that intelligence in a program.
National competitiveness depends increasingly on capacities
for information analysis, decision making, and flexible design and
manufacturing. Strength in these areas was once limited by insufficient data,
lack of computational power, or inadequate control mechanisms. Many critical
limitations, however, can now be overcome only by adding intelligence to
systems.
Basic research in AI will, in the long run, contribute not
only to our scientific knowledge but also to our technological base and to a
wide variety of applications. It will provide the foundation for systems that
can search large bodies of data for relevant information; help users to
evaluate the effects of complex courses of action; and work with users to
develop, share, and effectively use knowledge about complex systems and
processes. It will make it possible to build a wide range of application
systems that assist decision makers in adapting and reacting appropriately to
rapidly changing world situations.
Today in the world of computers, everything has become
automated. Computers have taken over our boring and tedious jobs. Earlier they
were designed to help humans but nowadays computer engineers are working on a
new breed of computers known as intelligent machines. These machines could
simulate the human intelligence to an extent. Although they are designed by
humans, still they will be able to learn by themselves. They will learn and
work in a way a new born child does. These computers could learn new things
from their surroundings and past experiences just like a small child would do.
BRANCHES OF AI
·
Logical AI
·
Search
·
Pattern recognition
·
Representation
·
Inference
·
Common sense knowledge and reasoning
·
Learning from experience
·
Planning
·
Epistemology
·
Ontology
·
Heuristics
·
Genetic programming
1. Logical AI
What a program knows about the world in general the facts of
the specific situation in which it must act, and its goals are all represented
by sentences of some mathematical logical language. The program decides what to
do by inferring that certain actions are appropriate for achieving its goals.
2. Search
AI programs often examine large numbers of possibilities, e.g.
moves in a chess game or inferences by a Theorem Proving program.
3. Pattern recognition
When a program makes observations of some kind, it is often
programmed to compare what it sees with a pattern. For example, a vision
program may try to match a pattern of eyes and a nose in a scene in order to
find a face.
4. Representation
Facts about the world have to be represented in some way.
Usually languages of mathematical logic are used.
5. Inference
From some facts, others can be inferred. For example, when we
hear of a bird, we man infer that it can fly, but this conclusion can be
reversed when we hear that it is a penguin. In logical reasoning the set of
conclusions can be drawn from a set of function of the premises.
6. Common sense
knowledge and reasoning
This is the area in which AI is farthest from human-level, in
spite of the fact that it has been an active research area since the 1950s.
7. Learning from
experience
Programs can only learn what facts or behaviors their
formalisms can represent, and unfortunately learning systems are almost all
based on very limited abilities to represent information.
8. Planning
Planning programs start with general facts about the world,
facts about the particular situation and a statement of a goal. From these,
they generate a strategy for achieving the goal. In the most common cases, the
strategy is just a sequence of actions.
9. Epistemology
This is a study of the kinds of knowledge that are required
for solving problems in the world.
10. Ontology
Ontology is the study of the kinds of things that exist. In
AI, the programs and sentences deal with various kinds of objects, and we study
what these kinds are and what their basic properties are.
11. Heuristics
Heuristics are the knowledge use to make good judgments or the
strategies, tricks or rules of thumb use to simplify the solution of problem
and help us to determine how to proceed. Heuristics are usually acquired with
much experience.
12. Genetic programming
Genetic programming is a technique for getting programs to
solve a task by mating random Lisp programs and selecting fittest in millions
of generations.
TASK DOMAINS OF AI
·
Mundane tasks
Perception
Vision
Speech
Natural Language
Understanding
Generation
Translation
Commonsense
reasoning
Robot
control
·
Formal tasks
Games
Chess
Backgammon
Checkers
Go
Mathematics
Geometry
Logic
Integral calculus
Proving properties of programs
·
Expert tasks
Engeneering
Design
Fault
finding
Manufacturing
planning
Scientific
analysis
Medical
diagnosis
Financial
analysis
APPLICATIONS OF AI
Game Playing
You can buy machines that can play master level chess for a
few hundred dollars. There is some AI in them, but they play well against people
mainly through brute force computation--looking at hundreds of thousands of
positions. To beat a world champion by brute force and known reliable
heuristics requires being able to look at 200 million positions per second.
Speech Recognition
In the 1990s, computer speech recognition reached a practical
level for limited purposes. Thus United Airlines has replaced its keyboard tree
for flight information by a system using speech recognition of flight numbers
and city names. It is quite convenient. On the the other hand, while it is
possible to instruct some computers using speech, most users have gone back to
the keyboard and the mouse as still more convenient.
Understanding Natural
Language
Just getting a sequence of words into a computer is not enough.
Parsing sentences is not enough either. The computer has to be provided with an
understanding of the domain the text is about, and this is presently possible
only for very limited domains.
Computer Vision
The world is composed of three-dimensional objects, but the
inputs to the human eye and computers' TV cameras are two dimensional. Some
useful programs can work solely in two dimensions, but full computer vision
requires partial three-dimensional information that is not just a set of
two-dimensional views. At present there are only limited ways of representing
three-dimensional information directly, and they are not as good as what humans
evidently use.
Expert Systems
A ``knowledge engineer'' interviews experts in a certain
domain and tries to embody their knowledge in a computer program for carrying
out some task. How well this works depends on whether the intellectual
mechanisms required for the task are within the present state of AI. When this
turned out not to be so, there were many disappointing results. One of the
first expert systems was MYCIN in 1974, which diagnosed bacterial infections of
the blood and suggested treatments. It did better than medical students or
practicing doctors, provided its limitations were observed. Namely, its ontology
included bacteria, symptoms, and treatments and did not include patients,
doctors, hospitals, death, recovery, and events occurring in time. Its
interactions depended on a single patient being considered. Since the experts
consulted by the knowledge engineers knew about patients, doctors, death,
recovery, etc., it is clear that the knowledge engineers forced what the
experts told them into a predetermined framework. In the present state of AI,
this has to be true. The usefulness of current expert systems depends on their
users having common sense.
Heuristic
Classification
One of the most feasible kinds of expert system given the
present knowledge of AI is to put some information in one of a fixed set of
categories using several sources of information. An example is advising whether
to accept a proposed credit card purchase. Information is available about the
owner of the credit card, his record of payment and also about the item he is
buying and about the establishment from which he is buying it (e.g., about
whether there have been previous credit card frauds at this establishment).
Intelligent Simulation
System
For many tasks, on-the-job training is extremely effective,
providing the trainee with the chance to
make real, on-the-spot decisions and see the consequences. On-the-job training
is impossible, however, when a bad decision can be disastrous-for example, in
controlling a steel mill, or making diagnoses and prescribing treatment in an
operating room, or running a large company, or making battle management
decisions . Simulation systems that could portray realistic simulated worlds,
and in particular that had the capability to produce realistic simulations of
people, would enable development of training systems for such situations. These
same simulation capabilities are also important when the cost of assembling
large groups of people for training is prohibitive.
Intelligent Information
Resources
Information-resource specialist systems would support
effective use of the vast resources of the national information infrastructure.
These systems would work with their users to determine users' information
needs, navigate the information world to locate appropriate data sources-and
appropriate people-from which to extract relevant information. They would adapt
to changes in users needs and abilities as well as changes in information
resources. They would be able to communicate in human terms in order to assist
those with limited computer training. An information-resource specialist system
(IRSS) could meet a wide range of needs at home, at work, and at school. Such
systems would be tailored to individual users rather than a single project and
its needs; consequently an IRSS would be able to assist its user with a broad
range of information needs.
Intelligent Project
Coaches
Software designed to act as an intelligent, long-term team
member could help to design and to operate complex systems. An intelligent
project coach system can assist with design of a complex device (such as an
airplane) or a large software system by helping to preserve knowledge about
tasks, to record the reasons for decisions, and to retrieve information
relevant to new problems. It could help at the operational level to improve
diagnosis, failure detection and prevention, and system performance. Project
coach systems do not need to be experts themselves; rather, they could
significantly boost capability and productivity by collaborating with human
experts, assisting them by capturing and delivering organizational memory.
PROJECTS IN AI
XSPAN
Anatomies of model species, together with cell-type knowledge
defined in ontologies, are being used as the basis for describing cross-species
mappings which identify homologous tissues. Knowledge of analogous and
homologous tissues in different species is key to investigations of gene
expression.
PLINTH
PLINTH is an expertext shell. It integrates the technologies
of hypertext, semantic nets and rule-based expert systems to provide tools and
intelligent support for the authors and readers of technical documents.
GHOSTWRITER
A demonstration of how plan-based modelling and natural
language generation can be used to greatly assist human technical authors in
the production of aircraft maintenance manuals
GhostWriter was a research project between British Aerospace
Defence and Dassault Aviation, with assistance from AIAI and the Department of
AI. Its primary objective was to design, develop and demonstrate a
prototype-authoring environment so as to illustrate the kinds of proactive
support that are required by and that would benefit authors in the production
of technical documents. The prototype has been used to demonstrate the
interactive and semi-automatic production of a significant portion of a complex
maintenance procedure for the Falcon 900 aircraft in both English and French.
The resulting texts are at a level close to that produced by human authors. The
prototype has been demonstrated to members of the Technical Publications
Department at British Aerospace and has received some praise, although further
work in knowledge acquisition is required before it can be used in earnest.
AUSDA (Analysis and
Unification of Software Development Approaches)
The project aims to provide a unified approach to development
and maintenance of safety critical software, by identifying the root causes of
incidents in which the use of computer software is involved studying different
software development approaches, and identifying aspects of these which are
relevant to those root causes producing guidelines for using and improving the development
approaches studied providing support in the integration of these approaches, so
that they can be the development and maintenance of safety critical software.
SPIRIT (knowledge-based
oil well test interpretation system)
SPIRIT is a knowledge based diagnostic system for a particular
type of oil well test, which is used to determine how much oil exists in a
reservoir, where it is and how it will flow to the well. The diagnosis draws on
multiple sources of data and can handle uncertainty in the data. The prototype
system was used by sponsors and a commercial product based on the system ideas
has been released. The motive behind the SPIRIT project was the perceived need
by the project sponsors to improve the quality of well test interpretation as
it is carried out within oil companies. Existing conventional well test
interpretation software assists reservoir engineers by removing some of the
more mundane tasks as well as automating some of the manual interpretation
techniques. However, the software generally provides no guidance on the most
important stage in the analysis: that of selecting the most appropriate
mathematical model to use to analyze the pressure test data. This task requires
significant expertise and experience, beyond that of many reservoir engineers
who use the software. PSTI wished to prototype a decision support system for
reservoir engineers that encoded expert knowledge on well test interpretation.
PSTI (the major project funder) initiated the project as a
means of exploring the requirements for the next generation of well test
interpretation software. The aim was that petroleum software companies would
then exploit the results of the project to improve their well test software,
which would in turn benefit the petroleum industry as a whole. AIAI designed
and developed the knowledge based component of the prototype software system
EGRESS (A Tool For
Modelling People's Behaviour In Emergency Situations)
EGRESS is a methodology with computer support for modelling
the behaviour of people in emergency situations offshore. We believe that this
is the first time that account has been taken of people's decision-making
behaviour in this area. Our work is therefore an important first step in the
introduction of improved approaches to the evaluation of platform layout,
facilities and emergency procedures. Studies have shown that people do not
panic in emergency situations, and that decision making under these conditions
is, in fact, rational and amenable to modelling. To date, however, most evacuation
models have concentrated on modelling how people move through various floor
layouts. Although the effects of initial wait time and of negotiating obstacles
have been dealt with, the subsequent decisions that have to be made have been
largely ignored. Modelling these decisions, particularly those made immediately
after the occurrence of an incident, is critical to the validity of any
simulation exercise. This is particularly true in the offshore environment
where the population is well trained and where many individuals have unique
duties to carry out in the event of an emergency.
EGRESS will benefit safety analysts, safety managers and
safety officers who will be able to design floor plans and set up scenarios
with different facilities, populations and hazards, to determine their various
effects.
EGRESS demonstrates that it is possible to model the behaviour
of people in hazardous situations. The results of simulations, while they
should not be interpreted as full verification of the model, are very
encouraging.
EGRESS can simulate scenarios in different platform layouts
and help in the design of safer installations or in the improvement of existing
ones. The script representation is a natural one and scripts can be easily
modified by the safety analyst to reflect different roles, conditions or
procedures. In this way EGRESS can be used to evaluate the effectiveness of
existing procedures and to help in improving training.
FREQUENTLY ASKED QUESTIONS
Q. What is artificial intelligence?
A:- It is the science
and engineering of making intelligent machines, especially intelligent computer
programs. It is related to the similar task of using computers to understand
human intelligence, but AI does not have to confine itself to methods that are
biologically observable.
Q. What is intelligence?
A:- Intelligence is the computational part of the ability to
achieve goals in the world. Varying kinds and degrees of intelligence occur in
people, many animals and some machines.
Q. Isn't AI about simulating human intelligence?
A:- Sometimes but not always or even usually. On the one hand,
we can learn something about how to make machines solve problems by observing
other people or just by observing our own methods. On the other hand, most work
in AI involves studying the problems the world presents to intelligence rather
than studying people or animals. AI researchers are free to use methods that
are not observed in people or that involve much more computing than people can
do.
Q. What about other comparisons between human and computer
intelligence?
A:- All normal humans
have the same intellectual mechanisms and that differences in intelligence are
related to ``quantitative biochemical and physiological conditions''. Computer
programs have plenty of speed and memory but their abilities correspond to the
intellectual mechanisms that program designers understand well enough to put in
programs. The matter is further complicated by the fact that the cognitive
sciences still have not succeeded in determining exactly what the human
abilities are.
Whenever people do better than computers on some task or
computers use a lot of computation to do as well as people, this demonstrates
that the program designers lack understanding of the intellectual mechanisms
required to do the task efficiently.
Q. When did AI research start?
A:-- After WWII, a number of people independently started to
work on intelligent machines.
Q. Does AI aim to put the human mind into the computer?
A:- Some researchers say they have that objective, but may be
they are using the phrase metaphorically. The human mind has a lot of
peculiarities, and it is not sure anyone is serious about imitating all of
them.
Q. Does AI aim at human-level intelligence?
A:- Yes. The ultimate effort is to make computer programs that
can solve problems and achieve goals in the world as well as humans.
Q. How far is AI from reaching human-level intelligence? When
will it happen?
A:- A few people think that human-level intelligence can be
achieved by writing large numbers of programs of the kind people are now
writing and assembling vast knowledge bases of facts in the languages now used
for expressing knowledge.
However, most AI researchers believe that new fundamental
ideas are required, and therefore it cannot be predicted when human level
intelligence will be achieved.
Q. Are computers fast enough to be intelligent?
A:- Some people think much faster computers are required as
well as new ideas. But the computers of 30 years ago were fast enough if only
we knew how to program them. Of course, quite apart from the ambitions of AI
researchers, computers will keep getting faster.
Q. What about making a ``child machine'' that could improve by
reading and by learning from experience?
A:- AI programs haven't yet reached the level of being able to
learn much of what a child learns from physical experience. Nor do present
programs understand language well enough to learn much by reading.
Q. What about chess?
A:- Playing chess requires certain intellectual mechanisms and
not others. Chess programs now play at grandmaster level, but they do it with
limited intellectual mechanisms compared to those used by a human chess player,
substituting large amounts of computation for understanding. Once we understand
these mechanisms better, we can build human-level chess programs that do far
less computation than do present programs.
Q. How is AI research done?
A:- AI research has both theoretical and experimental sides.
The experimental side has both basic and applied aspects.
There are two main lines of research. One is biological, based
on the idea that since humans are intelligent, AI should study humans and
imitate their psychology or physiology. The other is phenomenal, based on
studying and formalizing common sense facts about the world and the problems
that the world presents to the achievement of goals. The two approaches
interact to some extent, and both should eventually succeed.
Q. What should I study before or while learning AI?
A:- Study mathematics, especially mathematical logic. The more
you learn about science in general the better. For the biological approaches to
AI, study psychology and the physiology of the nervous system. Learn some
programming languages--at least C, Lisp and Prolog. It is also a good idea to
learn one basic machine language. Jobs are likely to depend on knowing the
languages currently in fashion. In the late 1990s, these include C++ and Java.
Q. What organizations and publications are concerned with AI?
A. The American Association for Artificial Intelligence
(AAAI), the European Coordinating Committee for Artificial Intelligence (ECCAI)
and the Society for Artificial Intelligence and Simulation of Behavior (AISB).
The International Joint Conference on AI (IJCAI) is the main international
conference. The AAAI runs a US National Conference on AI. Electronic
Transactions on Artificial Intelligence, Artificial Intelligence, and Journal
of Artificial Intelligence Research, and IEEE Transactions on Pattern Analysis
and Machine Intelligence are four of the main journals publishing AI research
papers.
CAREERS IN AI
Fortunately for universities, most companies in the private
sector are looking for people with postgraduate degrees. So although it may be
difficult to keep people in academia once they are qualified, they must first
get that qualification. At Edinburgh ,
for example, around 20 students are taken on doctoral programs each year and a
further 90 doing their masters degrees. About 50 of the people taking masters
degrees will be doing conversion courses from art and other disciplines.
Employers are interested in people with undergraduate degrees
in AI, but this tends to be for less AI-oriented jobs. According to Sharon
Wood, a member of faculty and careers tutor at the School of Cognitive University
of Sussex
Have you got what it takes to be an AI researcher? Unlike many
areas of research, it is still relatively unexplored. And despite the benefits
there is still a price to pay for being the first on the beach. So besides just
being curious about what makes us tick you also have to be pretty independent
and prepared to do what it takes to break new ground. You need to be able to
cope with frustration over a long period. This demands people who are goal
oriented and like to think for themselves.
Opportunities...
So what sort of work is out there waiting for you? Just about
anything--or at least anything that can replace a human being to make money.
But in fact there would appear to be no real limits to where AI can be applied.
§ Pharmaceuticals
industry - drug development can be speeded by algorithms that can map protein
folds and help researchers to understand the brain chemistry better.
§ Medical
applications - imaging techniques: capture and manipulate information
intelligently for making diagnosis from the interpretation of scans.
§ Internet-based
businesses - automatic-trading systems for e-commerce services
§ Mobile
communications - voice-recognition technology and automatic text recognition
Playing with computer
games
But by far one of the largest growth industries for AI
research is the entertainment software sector, or more specifically the
computer games industry, widely reported to be bigger than Hollywood . Ostensibly the motivation comes
from companies wanting to design strategy "shoot-up" games that have
software opponents which appear realistic and intelligent, so that gamers don't
feel like they are playing a computer. But the scope goes much further. We are
sociable animals. Games companies want to exploit this by making games more
sociable and co-operative, and therefore less anti-social, so that new
audiences can be drawn in.
Team player
And on entering the private sector, the rules change, with
starting salaries ranging anywhere from the late teens to the mid-twenties. But
coming from the confines of the lonely lab you'll suddenly need to demonstrate
your ability to be a team player, says Andrew Wensley, project leader with
games distributor Eidos Interactive. "They also need to have excellent
problem solving skills, and be creative yet have good analytical skills,"
he says. "If they can display tenacity and determination, then they'll
succeed."
Slowly artificial intelligence is making its way into the
mainstream and the process is drawing in graduates from a many fields as its
full potential begins to dawn. With applications that can range from household
appliances and medical devices to data-mining systems for investment banks and
robotic rovers on Mars, AI is all around you.
FUTURE OF AI
Seeing the future
Computer face recognition already helps maintain security in
the workplace by monitoring the presence and identification of an employee at a
particular computer terminal, for example, and soon will help protect us
against international terrorism. Programs that allow machines to recognize our
speech and understand natural (human) language make it more convenient for us
to use them. And in a world exploding with e-everything, systems that weed
through, learn from and act upon vast amounts of data can become machine
experts that make humans more effective in their jobs and personal lives.
Face recognition, still early in its development, works only
when a face is positioned carefully in front of the computer’s camera. The
computer matches the face with data representing facial features and their
geometrical relationships. Researchers are working toward recognition of faces
in a larger environmental context — to identify known terrorists in an airport,
for instance.
At this point, just finding a face amid other “objects” in a
scene is complex. Computers have to string together subtle clues, says Jonathan
Connell, a computer vision researcher who helped develop technology for a
face-finding video browser. Using the browser, the computer looks for a pinkish
color that suggests the possibility of a face, regardless of a person’s race. But
color-matching alone might as easily select a brick. Connell explains,
“Consider someone wearing a tank top; bare arms could trick a browser based on
color alone.” To further refine its search, the computer looks for other clues
— for instance, dark bars where shadows from the eyes, nose, mouth and chin
appear. The browser quickly searches hours of videotape to find a specific
segment. For instance, a user who wants to locate an interview segment can make
the browser look for clips in which just two faces appear. Combined with speech
recognition technology on the audio track, it can find faces that are talking
about specific topics.
Sighting Speech
Computer vision is important to speech recognition, too.
Visual cues help computers decipher speech sounds that are obscured by
environmental noise. Lip reading can reduce confusion among the sounds that
make up words (phonemes) when other noise intrudes. In one AVST project, every
10 milliseconds the computer receives 10 to 12 values representing what’s visually
important about a speaker’s mouth — the shape of the lips, whether they’re open
or closed, the positions of the teeth and whether they’re in contact with the
lips, and what the tip of the tongue is doing. The computer recognizes possible
visemes, or word sounds that are visually distinguishable. It weighs the viseme
data with audio data representing phonemes. Finally, it combines that
information with a language model for “a final hypothesis of what was actually
said.”
The AVST group has found dramatic improvement in speech
recognition when a computer is fed a combination of audio and visual signals,
rather than relying on audio alone. (The same is true for speech recognition by
people.) Because speech recognition is being deployed as the “user interface of
choice” in a variety of pervasive environments, he says, its accuracy needs to
be improved. Working algorithms already demonstrate significant improvements in
overcoming “speech babble” noise, and in settings with more than one speaker.
Countless science fiction films provide a foresight into the
future of AI. For example, In The Terminator (1984), a computer network nukes
the human race in order to achieve supremacy. This network then manufactures
intelligent robots called 'Terminators' which it programs to annihilate human
survivors.
In The Matrix (1999) and The Matrix Reloaded (2003), a machine
enslaves humanity, using people as batteries to power its mainframe. Steven
Spielberg’s AI: Artificial Intelligence (2002) paints a more sympathetic view
of artificial life, depicting sensitive robots that are abused by brutal,
selfish human masters. HAL, the supercomputer that rebels against its human
handlers in the film 2001: A Space Odyssey (1968), is a cheeky reference to
IBM. The letters H, A and L, precede I, B and M in the alphabet.
REFERENCES
·
Artificial Intelligence by E. Rich & K.
Knight
·
Artificial Intelligence by Stuart Russell and
Peter Norvig
·
Introduction to Artificial Intelligence &
Expert System by D. W. Patterson
·
International Conference Papers
·
www.aaai.org : Official website of the American
Association for Artificial Intelligence
·
www.bbc.co.uk : Official website of English news
channel BBC
·
www.cs.cmu.edu : Official website of Carnegie Mellon University
Conclusion
In truth, we may never chat up a computer at a cocktail party.
But in smaller yet significant ways, artificial intelligence is already here:
in the cruise control of cars, the servers that route our email, and the
personalized ads clogging our browser windows. The future is all around us.
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