What do we mean by intelligence ? The dictionary gives the following
definition Intelligence : The faculty of understanding.

Understanding : To comprehend something, or to recognise its significance. This is a concept
that seems clear enough. When we apply it subjectively, it seems to correspond reasonable
accurately to our own individual experience of what it is like to be intelligent or to use our
intelligence. Unfortunately it begins to fall apartwhen we try to apply it objectively, to consider
intelligence as a faculty which might be shared by other entities, whether living or mechanical.

The main problem is that we know what it feels like to understand something, and are generally
willing to credit other people/things with sensations similar to our own.Take a simple example,
a familiar piece of machinery, the thermostat in a central heating system. It does not just
recognise when the temperature falls below or rises above a certain level, it responds by
taking the appropriate action. In a single very limited, respect it seems to possess
understanding and to demonstrate this in the clearest possible fashion, by behaving intelligently.
If the thermostat is intelligent, we devalue the word to the point where it becomes meaningless.

General intelligence has turned out to be a concept of dubious value when applied in practice,
and the whole question of using IQ tests to measure people's worth or suitability for a job
has become extremely controversial. So should we break intelligence down into separate
faculties such as perception, reason, creativity ? If so, what is the difference between
intelligence and knowledge ?

Knowledge:
    One of the few hard and fast results to come out of the first three decades of AI
research is that intelligence requires knowledge. To compensate for its one overpowering
asset, indispensability, knowledge possesses some less desirable properties, including :

     It is volumous.
     It is hard to characterise accurately.
     It is constantly changing.
     It differs from data by being organised.

There are many different ways of categorising knowledge types, one of the main distinctions
is the difference between induced knowledge and deduced knowledge.
This is best explained by means of an example.

Consider a commonplace skill which most children master between the ages of five and ten -
catching a ball. At the age of five a child may have difficulty in catching a
beach ball gently tossed at a few yards, yet a few years later he/she will probably be able
to catch a tennis ball lobbed high in the air from twenty yards away. Human beings are not
capable of mastering the technique for calculating ballistic trajectories at such an impressively
early age. The child's understanding has been gained by induction. It is as a result of watching
the trajectories of many balls and trying to catch them, that the child has been capable of
predicting the trajectory of the next ball he/she wants to catch.

A computer system on the other hand would rely on information on the projectile velocity and
trajectory to calculate the future location of a projectile using Newton's laws. This would be
dependant on a rigorous and mathematically explicit set formula programmed into the computer.
The program enables the computer to deduce the flight path of a projectile by reference to
the set of formal mathematical rules.

Few people would dispute the proposition that calculating a ballistic trajectory mathematically
requires more intelligence than being able to catch a ball (Aleksander and Burnett 1987). So
there is an important distinction to be made between knowledge and intelligence. It should
also be clear that a machine may store knowledgewithout necessarily possessing intelligence;
an intelligent machine which had no knowledge is an impossibility.

The question of how, to what extent, and in what sense can a machine be imbued with
knowledge is thus fundamental to all aspects of artificial intelligence.

Artficial intelligence:
       Well let's now see what AI means.
Although most attempts to define complex and widely used terms end in futility, it is useful to
draw at least an approximate boundary around the concept to provide a
perspective on the course. To do this we take the by no means universally accepted
Definition : Artificial Intelligence (AI) : is the study of how to make computers do
things which, at the moment, people do better (Rich, 1991). This definition is somewhat
vague due to its reference to the current state of computer science. The ability to solve problems
of one sort or another is widely used as a measure of intelligence in many different contexts.
It is rather obvious that intelligent machines are unlikely to serve any practical purpose unless
they are capable of coping with some of the myriad of simple (or not-so-simple) problems
which people overcome as a matter of routine. Problems come in a bewildering variety of shapes
and sizes. There are problems which can be solved with patience and perseverance, and others
which require flair and intuition. There are formal, abstract problems, like those involved in game
playing, the solution of which may be of little more than academic interest. There are many
problems that are practical and urgent , matters of life and death even. Some problems yield to
elementary common sense; others can only be solved with the help of obscure knowledge
(Aleksander and Burnett 1987). There are several reasons one might want to model human
performance at these sorts of tasks:

     To test psychological theories of human performance.
     To enable computers to understand human reasoning.
     To enable people to understand computer reasoning.
     To exploit what knowledge we can glean from people.
 

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