Definition : What is AI ?

Artificial intelligence (AI) is the simulation of human intelligence

processes by machines, especially computer systems. These processes
include learning, reasoning and self-correction. Particular applications
of AI include expert systems, speech recognition and machine vision.
According to the father of Artificial Intelligence, John McCarthy, it is
“The science and engineering of making intelligent machines,
especially intelligent computer programs”.

The AI Problems
Before we get started with the topic, let us first get an idea about its
background. Have you ever given a thought as to how many cats does
it take to identify one cat?
In this article we will cover the five types of problems that people face
with Artificial Intelligence (AI) i.e. we will address the all important
question of – in which situation must one make use of AI (artificial
intelligence)?
Just some time ago, we conducted a strategy workshop for a bunch of
senior executives who are running a large multinational company. In
that workshop, someone asked this question – “How many cats will it
need to identify a cat?”
In this post, we will discuss the problems which can be uniquely
resolved through Artificial Intelligence. While this may not be the exact
taxonomy, but it still is pretty comprehensive. The main reason we have
added extra emphasis on Enterprise AI issues, because we believe that
this subject will have a deep impact on many mainstream applications,
but despite that a lot of media attention focuses at the more esoteric
avenues. Further, information about these concepts are available in
our Machine Learning training course.
But before we delve into AI application types, we must discuss the
main distinguishing characters between AI / Deep Learning / Machine
Learning.
The term Artificial Intelligence by definition implies that machines can
reason with the help of this feature. However, here is a better more
complete list of AI characteristics:

1. AI is capable of reasoning: they can solve complex problems

through logical deductions on their own
2. AI has knowledge: the capability to represent knowledge about
the world or our understanding of it, that there are numerous
events, entities, and varied situations that occur in the world and
such elements have properties, which can be categorised.
3. AI can plan: they have the ability to set and achieve targets. A
specific state of the planet, which we desire along with a sequence
of actions that can be undertaken which will help us, progress
towards it.
4. AI can communicate: they have the capability to comprehend
well-written and spoken language.
5. AI has its own perception: they have the ability to deduce things
about their surrounding world through the visual images, sounds
and other external sensory inputs just like us humans!
With developments in Deep Learning algorithms, AI is driven forward.
The various deep learning algorithms can detect numerous patterns
without having any prior definition of these features. And in a broader
sense, Machine Learning means the application of any algorithm which
can be applied against a set of data to discover a pattern within the
same. Such algorithms have features like supervised, unsupervised,
classification, segmentation, or regression. Moreover, while they are
very popular, there are many reasons why Deep Learning algorithms
may not make other Machine Learning algorithms.
The 5 major types of problems with AI:
Now that we have some background knowledge, we can now discuss
the five major types of problems with AI:
Domain expertise: troubles involving reasoning based on a complex
body of knowledge
This consists of tasks that are based on learning several knowledge
bodies like financial, legal, and more, and then formulating a process
where the machine will be able to simulate as an expert in the given
field.
Domain extension: problems surrounding extension of a complex body
of knowledge
In this case, the machine learns a complex body of knowledge like
information regarding the existing medication and much more, and
then suggests new ideas to the domain itself, like for instance new drugs
for curing diseases.
Complex planning: projects that require complicated planning
There are many logistics and scheduling projects, which can be done
by current (non AI) algorithms. But as optimization keeps developing
and gets more complex AI would slowly grow.
Proficient communicator: tasks that involve developing existing
communications
AI and deep learning can offer benefits to many communication modes
such as intelligent agents, automatic and much more.

History of AI

Early Days

During the Second World War, noted British computer scientist Alan
Turing worked to crack the ‘Enigma’ code which was used by German
forces to send messages securely. Alan Turing and his team created
the Bombe machine that was used to decipher Enigma’s messages.

The Enigma and Bombe Machines laid the foundations for Machine
Learning. According to Turing, a machine that could converse with
humans without the humans knowing that it is a machine would win
the “imitation game” and could be said to be “intelligent”.

In 1956, American computer scientist John McCarthy organised the

Dartmouth Conference, at which the term ‘Artificial Intelligence’ was
first adopted. Research centres popped up across the United States to
explore the potential of AI. Researchers Allen Newell and Herbert
Simon were instrumental in promoting AI as a field of computer
science that could transform the world.

Getting Serious About AI Research

In 1951, an machine known as Ferranti Mark 1 successfully used an

algorithm to master checkers. Subsequently, Newell and Simon
developed General Problem Solver algorithm to solve mathematical
problems. Also in the 50s John McCarthy, often known as the father
of AI, developed the LISP programming language which became
important in machine learning.

In the 1960s, researchers emphasized developing algorithms to solve

mathematical problems and geometrical theorems. In the late 1960s,
computer scientists worked on Machine Vision Learning and
developing machine learning in robots. WABOT-1, the first
‘intelligent’ humanoid robot, was built in Japan in 1972.

AI Winters

However, despite this well-funded global effort over several decades,

computer scientists found it incredibly difficult to create intelligence
in machines. To be successful, AI applications (such as vision
learning) required the processing of enormous amount of data.
Computers were not well-developed enough to process such a large
magnitude of data. Governments and corporations were losing faith in
AI.
Therefore, from the mid 1970s to the mid 1990s, computer scientists
dealt with an acute shortage of funding for AI research. These years
became known as the ‘AI Winters’.

New Millennium, New Opportunities

In the late 1990s, American corporations once again became interested

in AI. The Japanese government unveiled plans to develop a fifth
generation computer to advance of machine learning. AI enthusiasts
believed that soon computers would be able to carry on conversations,
translate languages, interpret pictures, and reason like people.In 1997,
IBM’s Deep Blue defeated became the first computer to beat a
reigning world chess champion, Garry Kasparov.

Some AI funding dried up when the dotcom bubble burst in the early
2000s. Yet machine learning continued its march, largely thanks to
improvements in computer hardware. Corporations and governments
successfully used machine learning methods in narrow domains.

Exponential gains in computer processing power and storage ability

allowed companies to store vast, and crunch, vast quantities of data for
the first time. In the past 15 years, Amazon, Google, Baidu, and others
leveraged machine learning to their huge commercial advantage. Other
than processing user data to understand consumer behavior, these
companies have continued to work on computer vision, natural
language processing, and a whole host of other AI applications.
Machine learning is now embedded in many of the online services we
use. As a result, today, the technology sector drives the American
stock market.
Since the invention of computers or machines, their capability to
perform various tasks went on growing exponentially. Humans have
developed the power of computer systems in terms of their diverse
working domains, their increasing speed, and reducing size with
respect to time.
A branch of Computer Science named Artificial Intelligence pursues
creating the computers or machines as intelligent as human beings.
What is Artificial Intelligence?
According to the father of Artificial Intelligence, John McCarthy, it
is “The science and engineering of making intelligent machines,
especially intelligent computer programs”.
Artificial Intelligence is a way of making a computer, a computer-
controlled robot, or a software think intelligently, in the similar
manner the intelligent humans think.
AI is accomplished by studying how human brain thinks, and how
humans learn, decide, and work while trying to solve a problem, and
then using the outcomes of this study as a basis of developing
intelligent software and systems.
Philosophy of AI
While exploiting the power of the computer systems, the curiosity of
human, lead him to wonder, “Can a machine think and behave like
humans do?”
Thus, the development of AI started with the intention of creating
similar intelligence in machines that we find and regard high in
humans.

What is AI Technique?
In the real world, the knowledge has some unwelcomed properties −

• Its volume is huge, next to unimaginable.

• It is not well-organized or well-formatted.

• It keeps changing constantly.

AI Technique is a manner to organize and use the knowledge

efficiently in such a way that −

• It should be perceivable by the people who provide it.

• It should be easily modifiable to correct errors.
• It should be useful in many situations though it is incomplete or
inaccurate.
AI techniques elevate the speed of execution of the complex program
it is equipped with.
Applications of AI
AI has been dominant in various fields such as −
• Gaming − AI plays crucial role in strategic games such as chess,
poker, tic-tac-toe, etc., where machine can think of large number
of possible positions based on heuristic knowledge.
• Natural Language Processing − It is possible to interact with
the computer that understands natural language spoken by
humans.
• Expert Systems − There are some applications which integrate
machine, software, and special information to impart reasoning
and advising. They provide explanation and advice to the users.
• Vision Systems − These systems understand, interpret, and
comprehend visual input on the computer. For example,
o A spying aeroplane takes photographs, which are used to
figure out spatial information or map of the areas.
o Doctors use clinical expert system to diagnose the patient.
o Police use computer software that can recognize the face of
criminal with the stored portrait made by forensic artist.
• Speech Recognition − Some intelligent systems are capable of
hearing and comprehending the language in terms of sentences
and their meanings while a human talks to it. It can handle
different accents, slang words, noise in the background, change
in human’s noise due to cold, etc.
• Handwriting Recognition − The handwriting recognition
software reads the text written on paper by a pen or on screen by
a stylus. It can recognize the shapes of the letters and convert it
into editable text.
Intelligent Robots − Robots are able to perform the tasks given by a
human. They have sensors to detect physical data from the real world
such as light, heat, temperature, movement, sound, bump, and
pressure. They have efficient processors, multiple sensors and huge
memory, to exhibit intelligence. In addition, they are capable of
learning from their mistakes and they can adapt to the new
environment.

LEVEL OF THE AI MODEL

‘What is our goal in trying to produce programs that do the intelligent
things that people do?’
Are we trying to produce programs that do the tasks the same
way that people do?
OR
Are we trying to produce programs that simply do the tasks the
easiest way that is
possible?
Programs in the first class attempt to solve problems that a computer
can easily solve and
do not usually use AI techniques. AI techniques usually include a
search, as no direct method is
available, the use of knowledge about the objects involved in the
problem area and abstraction on
which allows an element of pruning to occur, and to enable a solution
to be found in real time;
otherwise, the data could explode in size. Examples of these trivial
problems in the first class,
which are now of interest only to psychologists are EPAM
(Elementary Perceiver and
Memorizer) which memorized garbage syllables.
The second class of problems attempts to solve problems that are non-
trivial for a computer and
use AI techniques. We wish to model human performance on these:
1. To test psychological theories of human performance. Ex. PARRY
[Colby, 1975] – a
program to simulate the conversational behavior of a paranoid person.
2. To enable computers to understand human reasoning – for
example, programs that
answer questions based upon newspaper articles indicating human
behavior.
3. To enable people to understand computer reasoning. Some people
are reluctant to accept
computer results unless they understand the mechanisms involved in
arriving at the
results.
4. To exploit the knowledge gained by people who are best at
gathering information. This
persuaded the earlier workers to simulate human behavior in the SB
part of AISB
simulated behavior. Examples of this type of approach led to GPS
(General Problem
Solver).

THE STATE OF THE ART

International grandmaster Arnold Denker studies the pieces on the
board in front of him. He realizes there is no hope; he must resign the
game. His opponent, HITECH, becomes the first computer program
to defeat a grandmaster in a game of chess (Berliner, 1989).
"I want to go from Boston to San Francisco," the traveller says into
the microphone. "What date will you be travelling on?" is the reply.
The traveller explains she wants to go October 20th, nonstop, on the
cheapest available fare, returning on Sunday. A speech understanding
program named PEGASUS handles the whole transaction, which
results in a confirmed reservation that saves the traveller $894 over
the regular coach fare. Even though the speech recognizer gets one
out of ten words wrong,18 it is able to recover from these errors
because of its understanding of how dialogs are put together (Zue et
al., 1994).
An analyst in the Mission Operations room of the Jet Propulsion
Laboratory suddenly starts paying attention. A red message has
flashed onto the screen indicating an "anomaly" with
the Voyager spacecraft, which is somewhere in the vicinity of
Neptune. Fortunately, the analyst is able to correct the problem from
the ground. Operations personnel believe the problem might
have been overlooked had it not been for MARVEL, a real-time
expert system that monitors the massive stream of data transmitted by
the spacecraft, handling routine tasks and alerting the analysts to more
serious problems (Schwuttke, 1992).
Cruising the highway outside of Pittsburgh at a comfortable 55 mph,
the man in the driver's seat seems relaxed. He should be—for the past
90 miles, he has not had to touch the steering wheel,
brake, or accelerator. The real driver is a robotic system that gathers
input from video cameras,
sonar, and laser range finders attached to the van. It combines these
inputs with experience
learned from training runs and succesfully computes how to steer the
vehicle (Pomerleau, 1993).
A leading expert on lymph-node pathology describes a fiendishly
difficult case to the
expert system, and examines the system's diagnosis. He scoffs at the
system's response. Only
slightly worried, the creators of the system suggest he ask the
computer for an explanation of
18 Some other existing systems err only half as often on this task.
Section 1.5. Summary 27
the diagnosis. The machine points out the major factors influencing its
decision, and explains
the subtle interaction of several of the symptoms in this case. The
expert admits his error,
eventually (Heckerman, 1991).
From a camera perched on a street light above the crossroads, the
traffic monitor watches
the scene. If any humans were awake to read the main screen, they
would see "Citroen 2CV
turning from Place de la Concorde into Champs Ely sees," "Large
truck of unknown make stopped
on Place de la Concorde," and so on into the night. And occasionally,
"Major incident on Place
de la Concorde, speeding van collided with motorcyclist," and an
automatic call to the emergency
services (King et al, 1993; Roller et al., 1994).
These are just a few examples of artificial intelligence systems that
exist today. Not magic
or science fiction—but rather science, engineering, and mathematics,
to which this book provides
an introduction.