We call ourselves Homo sapiens—m an the wise—because our intelligence is so

important to us.

Intelligence
Intelligence is an umbrella term used to describe a property of the mind that
encompasses many related abilities, such as the capacities
 to reason - the action of thinking about something in a logical, sensible way
 to plan - the process of thinking regarding the activities required to achieve
a desired goal
 to solve problems - is the act of defining a problem; determining the cause
of the problem;
 to think abstractly - the ability to comprehend ideas that aren't tangible or
concrete eg predicting
 to comprehend ideas – get in a state where an idea is clear to you and you
understand it completely,
 to use language, and
 to learn - the acquisition of knowledge or skills through study, experience,
or being taught.

Intelligence can be defined as the ability for solving problems. Problem solving is
to find the “best” solution in the problem space.

Artificial Intelligence is

“The study and design of computing systems that perceives its environment and
takes actions like human beings”

AI is defined as a system that possesses at least one (not necessarily all) of the
abilities mentioned above.

We see eight definitions of AI, laid out along two dimensions. The definitions on top
are concerned with thought processes and reasoning, whereas the ones on the
bottom address behavior.
 i. Acting humanly: The Turing Test approach

Turing defined intelligent behavior as the ability to achieve human-level

performance in all cognitive tasks, sufficient to fool an interrogator.

The Turing Test, proposed by Alan Turing (1950), was designed to provide
a satisfactory operational definition of intelligence. A computer passes the
test if a human interrogator, after posing some written questions, cannot tell
whether the written responses come from a person or from a computer.

For now, we note that programming a computer to pass a rigorously applied

test provides plenty to work on. The computer would need to possess the
following capabilities:
 natural language processing to enable it to communicate
successfully in English;
 knowledge representation to store what it knows or hears;
 automated reasoning to use the stored information to answer
questions and to draw new conclusions;
 machine learning to adapt to new circumstances and to detect and
extrapolate patterns.

ii. Thinking humanly: The cognitive modeling approach

Thinking humanly is to make a system or program to think like a human. But
to achieve that, we need to know how a human thinks.

We can interpret how the human mind thinks in theory, in three ways as
follows
  Introspection method – Catch our thoughts and see how it flows.
 Psychological Inspections method – Observe a person on the action.
 Brain Imaging method (MRI (Magnetic resonance imaging) or fMRI
(Functional Magnetic resonance imaging) scanning) – Observe a person’s
brain in action.

iii. Thinking rationally: The “laws of thought” approach

It refers to the ability to think with reason. It encompasses the ability to

draw sensible conclusions from facts, logic and data. In simple words, if your
thoughts are based on facts and not emotions, it is called rational thinking

iv. Acting rationally: The rational agent approach

An agent is just something that acts

A rational agent is one that acts so as to achieve the best outcome or,
when there is uncertainty, the best expected outcome.

Acting rationally means acting to achieve one's goals, given one's beliefs or
understanding about the world.
Common Terminology

1 Agent - Agents are systems or software programs capable of autonomous,

purposeful and reasoning directed towards one or more goals. They are also
called assistants, brokers, bots, droids, intelligent agents, and software agents.

2 Autonomous Robot - Robot free from external control or influence and able to
control itself independently.

3 Backward Chaining - Strategy of working backward for Reason/Cause of a

problem.
4 Blackboard - It is the memory inside computer, which is used for
communication between the cooperating expert systems.

5 Environment - It is the part of real or computational world inhabited by the

agent.

6 Forward Chaining - Strategy of working forward for conclusion/solution of a

problem.

7 Heuristics - It is the knowledge based on Trial-and-error, evaluations, and

experimentation.

8 Knowledge Engineering - Acquiring knowledge from human experts and

other resources.

9 Percepts - It is the format in which the agent obtains information about the
environment.

1 Pruning - Overriding unnecessary and irrelevant considerations in AI systems.

0

1 Rule - It is a format of representing knowledge base in Expert System. It is in

1 the form of IF-THEN-ELSE.

1 Shell - A shell is a software that helps in designing inference engine,

2 knowledge base, and user interface of an expert system.

1 Task - It is the goal the agent is tries to accomplish.

3

1 Turing Test - A test developed by Allan Turing to test the intelligence of a

4 machine as compared to human intelligence.

A brief review of AI history

we consider the broadly common and prospering research areas in the domain of AI –
Real Life Applications of Research Areas

There is a large array of applications where AI is serving common people in

their day-to-day lives −

Sr.N Research Areas Real Life Application

o.

1 Expert Systems

Examples − Flight-tracking systems,

Clinical systems.

2 Natural Language Processing

Examples: Google Now feature, speech

recognition, Automatic voice output.

3 Neural Networks

Examples − Pattern recognition systems

such as face recognition, character
recognition, handwriting recognition.
4 Robotics

Examples − Industrial robots for moving,

spraying, painting, precision checking,
drilling, cleaning, coating, carving, etc.

5 Fuzzy Logic Systems

Examples − Consumer electronics,

automobiles, etc.

Fuzzy logic is a form of many-valued logic in

which the truth value of variables may be any
real number between 0 and 1. It is employed to
handle the concept of partial truth, where the
truth value may range between completely true
and completely false. By contrast, in Boolean
logic, the truth values of variables may only be
the integer values 0 or 1.

Summary

This topic defines AI and establishes the cultural background against which it has
developed.
Some of the important points are as follows:
• Different people approach AI with different goals in mind. Two important
questions to ask are: Are you concerned with thinking or behavior? Do you want
to model humans or work from an ideal standard?
• Philosophers (going back to 400 B.C.) made AI conceivable by considering the
ideas that the mind is in some ways like a machine, that it operates on
knowledge encoded in some internal language, and that thought can be used to
choose what actions to take.
• Mathematicians provided the tools to manipulate statements of logical certainty
as well as uncertain, probabilistic statements. They also set the groundwork for
understanding computation and reasoning about algorithms.
• Economists formalized the problem of making decisions that maximize the
expected outcome to the decision maker.
• Neuroscientists discovered some facts about how the brain works and the ways
in which it is similar to and different from computers.
• Psychologists adopted the idea that humans and animals can be considered
information processing machines. Linguists showed that language use fits into
this model.
• Computer engineers provided the ever-more-powerful machines that make AI
applications possible.
• Control theory deals with designing devices that act optimally on the basis of
feedback from the environment. Initially, the mathematical tools of control
theory were quite different from AI, but the fields are coming closer together.
• The history of AI has had cycles of success, misplaced optimism, and resulting
cutbacks in enthusiasm and funding. There have also been cycles of introducing
new creative approaches and systematically refining the best ones.
• AI has advanced more rapidly in the past decade because of greater use of the
scientific method in experimenting with and comparing approaches.
• Recent progress in understanding the theoretical basis for intelligence has gone
hand in hand with improvements in the capabilities of real systems. The
subfields of AI have become more integrated, and AI has found common ground
with other disciplines.