8/8/2019 AI TO SEND
1/9
What is AI and the applications of AI and what it has accomplished.
Artificial intelligence:
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.
Intelligence is the ability to think, to imagine, to create, memorize, understand, recognize patterns,
make choices, adapt to change and learn from experience. Artificial intelligence is a human endeavor to
create a non-organic machine-based entity, that has all the above abilities of natural organic
intelligence. Hence it is called as 'Artificial Intelligence' (AI).
It is the ultimate challenge for an intelligence, to create an equal, another intelligent being. It is the
ultimate form of art, where the artist's creation, not only inherits the impressions of his thoughts, but
also his ability to think!
AI is divided into two classes:
Strong AI and Weak AI
Strong AI makes the bold claim that computers can be made to think on a level (at least) equal to
humans and Weak AI simply states that some "thinking-like" features can be added to computers to
make them more useful tools... and this has already started to happen (witness expert systems, drive-
by-wire cars and speech recognition software).
Applications of AI
Game Playing:
The machines that can play master level chess for a few hundred dollars. There is some artificial
intelligence in them, but they play well against people mainly through brute force computation i.e.,
looking at hundreds of thousands of positions.
Speech Recognition:
In the 1990s, computer speech recognition reached a practical level for l imited purposes. Thus united
airlines have replaced its keyboard tree for flight information by a system using speech recognition of
flight numbers and city names. On 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.
8/8/2019 AI TO SEND
2/9
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 theexperts 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 establishmentfrom which he is buying it (e.g., about whether there have been previous credit card frauds at this
establishment).
Approaches to AI
Initially, researchers thought that creating an AI would be simply writing programs for each and every
function an intelligence performs! As they went on with this task, they realized that this approach was
too shallow. Even simple functions like face recognition, spacial sense, pattern recognition and language
comprehension were beyond their programming skills!
They understood that to create an AI, they must delve deeper into natural intelligence first. They tried tounderstand how cognition, comprehension, decision-making happen in the human mind. They had to
understand what understanding really means! Some went into the study of the brain and tried to
understand how the network of neurons creates the mind.
Thus, researchers branched into different approaches, but they had the same goal of creating intelligent
machines. Let us introduce ourselves to some of the main approaches to artificial intelligence. They are
divided into two main lines of thought, the bottom up and the top down approach:
8/8/2019 AI TO SEND
3/9
Neural Networks: This is the bottom up approach. It basically aims at mimicking the structure and
functioning of the human brain, to create intelligent behavior. Researchers are attempting to build a
silicon-based electronic network that is modeled on the working and form of the human brain! Our
brain is a network of billions of neurons, each connected with the other.
At an individual level, a neuron has very little intelligence, in the sense that it operates by a simple set of
rules, conducting electric signals through its network. However, the combined network of all these
neurons creates intelligent behavior that is unrivaled and unsurpassed. So these researchers created
network of electronic analogues of a neuron, based on Boolean logic. Memory was recognized to be an
electronic signal pattern in a closed neural network.
How the human brain works is, it learns to realize patterns and remembers them. Similarly, the neural
networks developed have the ability to learn patterns and remember. This approach has its limitations
due to the scale and complexity of developing an exact replica of a human brain, as the neurons number
in billions! Currently, through simulation techniques, people create virtual neural networks. This
approach has not been able to achieve the ultimate goal but there is a very positive progress in the field.
The progress in the development of parallel computing will aid it in the future.
Expert Systems: This is the top down approach. Instead of starting at the base level of neurons, by taking
advantage of the phenomenal computational power of the modern computers, followers of the expert
systems approach are designing intelligent machines that solve problems by deductive logic. It is like the
dialectic approach in philosophy.
This is an intensive approach as opposed to the extensive approach in neural networks. As the name
expert systems suggest, these are machines devoted to solving problems in very specific niche areas.
They have total expertise in a specific domain of human thought. Their tools are like those of a detective
or sleuth. They are programmed to use statistical analysis and data mining to solve problems. They
arrive at a decision through a logical flow developed by answering yes-no questions.
Chess computers like Fritz and its successors that beat chess grandmaster Kasparov are examples of
expert systems. Chess is known as the drosophila or experimental specimen of artificial intelligence.
Applications of AI
Artificial Intelligence in the form of expert systems and neural networks have applications in every field
of human endeavor. They combine precision and computational power with pure logic, to solve
problems and reduce error in operation. Already, robot expert systems are taking over many jobs in
industries that are dangerous for or beyond human ability. Some of the applications divided by domains
are as follows:
Heavy Industries and Space: Robotics and cybernetics have taken a leap combined with artificially
intelligent expert systems. An entire manufacturing process is now totally automated, controlled and
maintained by a computer system in car manufacture, machine tool production, computer chip
production and almost every high-tech process. They carry out dangerous tasks like handling hazardous
radioactive materials. Robotic pilots carry out complex maneuvering techniques of unmanned
spacecrafts sent in space. Japan is the leading country in the world in terms of robotics research and
use.
8/8/2019 AI TO SEND
4/9
Finance: Banks use intelligent software applications to screen and analyze financial data. Softwares that
can predict trends in the stock market have been created which have been known to beat humans in
predictive power.
Computer Science: Researchers in quest of artificial intelligence have created spin offs like dynamic
programming, object oriented programming, symbolic programming, intelligent storage management
systems and many more such tools. The primary goal of creating an artificial intelligence still remains a
distant dream but people are getting an idea of the ultimate path which could lead to it.
Aviation: Air lines use expert systems in planes to monitor atmospheric conditions and system status.
The plane can be put on auto pilot once a course is set for the destination.
Weather Forecast: Neural networks are used for predicting weather conditions. Previous data is fed to a
neural network which learns the pattern and uses that knowledge to predict weather patterns.
Swarm Intelligence: This is an approach to, as well as application of artificial intelligence similar to a
neural network. Here, programmers study how intelligence emerges in natural systems like swarms of
bees even though on an individual level, a bee just follows simple rules. They study relationships innature like the prey-predator relationships that give an insight into how intelligence emerges in a swarm
or collection from simple rules at an individual level. They develop intelligent systems by creating agent
programs that mimic the behavior of these natural systems!
Is artificial Intelligence really possible? Can an intelligence like a human mind surpass itself and create its
own image? The depth and the powers of the human mind are just being tapped. Who knows, it might
be possible, only time can tell! Even if such an intelligence is created, will it share our sense of morals
and justice, will it share our idiosyncrasies? This will be the next step in the evolution of intelligence.
Hope I have succeeded in conveying to you the excitement and possibilities this subject holds!
_______________________________________________________________________
HISTORY:
The field of AI research was founded at a conference on the campus of DartmouthCollege in the
summer of 1956.[27] The attendees, including John McCarthy, Marvin Minsky, Allen Newell and Herbert
Simon, became the leaders of AI research for many decades.[28] They and their students wrote
programs that were, to most people, simply astonishing:[29]computers were solving word problems in
algebra, proving logical theorems and speaking English.[30] By the middle of the 1960s, research in the
U.S. was heavily funded by theDepartment of Defense[31] and laboratories had been established
around the world.[32] 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"[33] and Marvin Minsky agreed, writing that "within a generation ... the problem of creating
'artificial intelligence' will substantially be solved".[34]
They had failed to recognize the difficulty of some of the problems they faced.[35] In 1974, in response
to the criticism of England's Sir James Lighthill and ongoing pressure from Congress to fund more
productive projects, the U.S. and British governments cut off all undirected, exploratory research in AI.
The next few years, when funding for projects was hard to find, would later be called an "AI winter".[36]
8/8/2019 AI TO SEND
5/9
In the early 1980s, AI research was revived by the commercial success of expert systems,[37] a form of
AI program that simulated the knowledge and analytical skills of one or more human experts. By 1985
the market for AI had reached over a billion dollars. At the same time, Japan's fifth generation
computer project inspired the U.S and British governments to restore funding for academic research in
the field.[38] However, beginning with the collapse of the Lisp Machine market in 1987, AI once again
fell into disrepute, and a second, longer lasting AI winter began.[39]
In the 1990s and early 21st century, AI achieved its greatest successes, albeit somewhat behind the
scenes. Artificial intelligence is used for logistics, data mining, medical diagnosisand many other areas
throughout the technology industry.[11] The success was due to several factors: the increasing
computational power of computers (see Moore's law), 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.[40]
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 anintelligent system to display.
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 becomesastronomical when the problem goes beyond a certain size. The search for more efficient problem
solving algorithms is a high priority for AI research.[43]
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.[44] 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 netresearch attempts to simulate the
structures inside human and animal brains that give rise to this skill.
Knowledge representation
Main articles: Knowledge representation andCommonsense knowledge
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;[47] situations, events, states and time;[48] causes and effects;[49]knowledge about knowledge
(what we know about what other people know);[50] and many other, less well researched domains. A
8/8/2019 AI TO SEND
6/9
complete representation of "what exists" is anontology[51] (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[52] 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.[53]
The breadth of commonsense knowledge
The number of atomic facts that the average person knows is astronomical. Research projects that
attempt to build a complete knowledge base of commonsense knowledge (e.g.,Cyc) require enormous
amounts of laborious ontological engineering they must be built, by hand, one complicated concept
at a time.[54] A major goal is to have the computer understand enough concepts to be able to learn by
reading from sources like the internet, and thus be able to add to its own ontology.[citation needed]
The subsymbolic form of some commonsense knowledge
Much of what people know is not represented as "facts" or "statements" that they could express
verbally. For example, a chess master will avoid a particular chess position because it "feels too
exposed"[55] or an art critic can take one look at a statue and instantly realize that it is a fake.[56] These
are intuitions or tendencies that are represented in the brain non-consciously and sub-
symbolically.[57] Knowledge like this informs, supports and provides a context for symbolic, consciousknowledge. As with the related problem of sub-symbolic reasoning, it is hoped that situated
AI or computational intelligence will provide ways to represent this kind of knowledge.[57]
Planning
Main article: Automated planning and scheduling
Intelligent agents must be able to set goals and achieve them.[58] 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.[59]
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.[60] 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.[61]
8/8/2019 AI TO SEND
7/9
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.[62]
Learning
Main article: Machine learning
Machine learning[63] has been central to AI research from the beginning.[64] Unsupervised learning is
the ability to find patterns in a stream of input. Supervised learning includes bothclassification 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[65]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
Main article: Natural language processing
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
Main article: Robotics
The field of robotics[68] is closely related to AI. Intelligence is required for robots to be able to handle
such tasks as object manipulation[69] andnavigation, with sub-problems of localization (knowing where
you are), mapping (learning what is around you) and motion planning (figuring out how to get
there).[70]
Perception
Main articles: Machine perception, Computer vision, and Speech recognition
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 sub problems are speech recognition, facial recognition and object recognition.
Social intelligence
Main article: Affective computing
8/8/2019 AI TO SEND
8/9
8/8/2019 AI TO SEND
9/9
- Deep Blue, the first computer to beat the human chess Grandmaster
- Fuzzy controllers in dishwashers, etc.
One persistent 'problem' is that as soon as an AI technique truly succeeds, in the minds of many it
ceases to be AI, becoming something else entirely. For example, when Deep Blue defeated Kasparov,
there were many who said Deep Blue wasn't AI, since after all it was just a brute force parallel minimax
search (!)
Top Related