Programs Learning.


Attempts to create programs that learn are not motivated by a desire to eliminate effort of programmers & designers.

Attempts to create programs that learn are not challenge to more classic software engineering.

Attempts to create programs that learn are not motivated by the challenges of software complexity - it's solved by modern analysis & design instead.

Attempts to create programs that learn are motivated by complexity of a certain types of tasks given to be solved by a software, that hinder or make impossible to formulate correct & fully detailed algorithms that solve these problems.

Intuition & Imagination.

Program that learns can be imagined as an abstract algorithm that can be parametrized to complete. Learning is then acquiring proper parameters that make it detailed, concrete algorithm to solve the tasks given by a software constructor. Parameters acquisition uses historical data.

Hypothesis, Knowledges & Skills.

Parameters acquired during learning process are called - depending on their type & on assumed point of view - 'knowledge' or 'skill'.

Each of parameters acquired during the autonomous learning is are often called 'hypothesis', coming from 'hypothesis space' that contains every hypothese that student can use to perform task(s). This terminology emphasizes both an uncertain state of knowledge or skill acquired by a student, as well as the fact that it's autonomous subject that is acquired. Uncertain state of a skill or knowledge makes it non-infailible for the task(s) given.

The difference between knowledge or skill is fairly fluid - it has unstrict character.

It's more a skill than knowledge when we require from program to perform certain operations sequence that is acquired during the learning process; it's also called 'procedural knowledge' as well.

It's more a knowledge when we can say it's a selection for a certain case, a choice for a single decision. We can discover how to interpret certain input data, for example it's type or how it's related with other objects; it's also called a 'declarative knowledge'.

A knowledge or a skill is also called knowledge in lesser sense, knowledge with skill together is also called knowledge in wider sense.

Initial & Acquired Parameters.

We do not know perfect initial parameters for task(s), or otherwise we would not need AI; we still use initial parameters to start with learning.

During the learning process a change occurs - parameters are acquired, stored & used; this change occurs because of 'experiences', we can treat these 'experiences' as a 'training information' in this case, at least.

Source: [52].

See also, if You wish or need, ... : Learning Definition.

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