2. SYLLABUS
What is Artificial Intelligence?
The importance of A.I and related fields.
Familiarisation with Knowledge Concepts : Definition and importance of
knowledge.
Knowledge-based systems.
Representation
Organisation
Manipulation and acquisition of knowledge.
3. What is Artificial Intelligence?
Artificial intelligence (AI) refers to the simulation of human intelligence in
machines that are programmed to think like humans and mimic their actions.
The term may also be applied to any machine that exhibits traits associated with a
human mind such as learning and problem-solving.
The ideal characteristic of artificial intelligence is its ability to rationalize and take
actions that have the best chance of achieving a specific goal.
Artificial intelligence (AI), the ability of a digital computer or computer-
controlled robot to perform tasks commonly associated with intelligent beings.
4. Cont….
The term is frequently applied to the project of developing systems endowed with
the intellectual processes characteristic of humans, such as the ability to reason,
discover meaning, generalize, or learn from past experience.
The intelligence demonstrated by machines is known as Artificial Intelligence.
Artificial Intelligence has grown to be very popular in today’s world. It is the simulation
of natural intelligence in machines that are programmed to learn and mimic the
actions of humans.
These machines are able to learn with experience and perform human-like tasks. As
technologies such as AI continue to grow, they will have a great impact on our quality
of life.
5. The importance of A.I and related fields.
Below we are going to read about the vast importance of artificial intelligence:
The importance of artificial intelligence and its subsequent components have been
known for quite a long time now. They are being looked upon as tools and techniques
to make this world a better place. And it’s just not that you have to go to these fancy
tech gadgets to be able to use them. You can simply look around, and I am sure most
of your tasks are made smooth by artificial intelligence.
Its importance lies in making our lives easier. These technologies are a great asset to
humans and are programmed to reduce the human effort as much as possible. They
tend to possess the capability to work in an automated fashion. Therefore, manual
intervention is the last thing that could be asked for or seen while operating parts
associated with this technology.
6. Cont….
These machines tend to speed up your tasks and processes along with a
guaranteed level of precision and accuracy, and therefore this is what makes them
a useful and important tool. Apart from making the world an error-free place by
their simple and everyday techniques, these technologies and applications are not
only related to our general and everyday lives. It is also impacting and holds
importance for other domains as well.
7. Familiarisation with Knowledge Concepts :
Definition and importance of knowledge.
The meanings of “Knowledge” as given by the Random House Dictionary (RHD), and
words synonymous with ‘knowledge’ are:
l Acquaintance with facts or principles, as from study or investigation; general
erudition;
l Familiarity or Conversance, as with a particular subject or branch of learning;
l Acquaintance or familiarity gained by sight, experience, or report; as for example
‘knowledge of human nature’;
l The fact or state of knowing, clear and certain perception of fact or truth; l
Awareness, as of a fact or circumstance;
l That which is or may be known; information;
and l The body of truths or facts accumulated by mankind in the course of time, as for
example ‘man’s knowledge of the moon’.
8. Cont….
You’ve probably heard the old quote, “Knowledge is Power,” That’s truer than you
can ever imagine.
Few people understand how important knowledge can be.
Knowledge is what allows us to drive cars instead of ride horses, it is what helps us
survive far longer than we should, and knowledge is even what prevents us from
making the same mistakes we made in the past.
9. Knowledge-based systems.
A knowledge-based system (KBS) is a computer program that reasons and uses
a knowledge base to solve complex problems.
The term is broad and refers to many different kinds of systems.
The one common theme that unites all knowledge based systems is an attempt to
represent knowledge explicitly and a reasoning system that allows it to derive new
knowledge.
Thus, a knowledge-based system has two distinguishing features: a knowledge
base and an inference engine.
10. Cont…
Knowledge-based systems were first developed by artificial
intelligence researchers. These early knowledge-based systems were
primarily expert systems – in fact, the term is often used interchangeably with
expert systems, although there is a difference. The difference is in the view taken
to describe the system:
"expert system" refers to the type of task the system is trying to assist with – to
replace or aid a human expert in a complex task typically viewed as requiring
expert knowledge
"knowledge-based system" refers to the architecture of the system – that it
represents knowledge explicitly, rather than as procedural code.
11. Representation
Knowledge representation and reasoning (KR, KR&R) is the field of artificial
intelligence (AI) dedicated to representing information about the world in a form that a
computer system can utilize to solve complex tasks such as diagnosing a medical
condition or having a dialog in a natural language.
Knowledge representation incorporates findings from psychology about how humans
solve problems and represent knowledge in order to design formalisms that will make
complex systems easier to design and build.
Knowledge representation and reasoning also incorporates findings from logic to
automate various kinds of reasoning, such as the application of rules or the relations
of sets and subsets.
Examples of knowledge representation formalisms include semantic nets, systems
architecture, frames, rules, and ontologies.
13. Organization
A Knowledge-Based Organization may be defined as an organization that relies on
the ability of individuals to create, obtain and apply knowledge to produce
products or services.
In such organizations, learning and the continual accumulation of knowledge are
vital parts of the organization’s work. action sponsors/beneficiaries select these
organizations because of their expertise.
A knowledge organization is a management idea, describing an organization in
which people use systems and processes to generate, transform, manage, use, and
transfer knowledge-based products and services to achieve organizational goals.
14. Manipulation and acquisition of
knowledge.
Knowledge Organization
The organization of knowledge in memory is key to efficient processing Knowledge
based systems performs their intended tasks
The facts and rules are easy to locate and retrieve. Otherwise much time is wasted in
searching and testing large numbers of items in memory
Knowledge can be organized in memory for easy access by a method known as
indexing
As a result, the search for some specific chunk of knowledge is limited to the group
only
15. Knowledge Manipulation
Decisions and actions in knowledge based systems come from manipulation of the knowledge
The known facts in the knowledge base be located, compared, and altered in some way
This process may set up other subgoals and require further inputs, and so on until a final solution
is found
The manipulations are the computational equivalent of reasoning. This requires a form of
inference or deduction, using the knowledge and inferring rules.
All forms of reasoning requires a certain amount of searching and matching.
The searching and matching operations consume greatest amount of computation time in AI
systems
It is important to have techniques that limit the amount of search and matching required to
complete any given task
16. Manipulation and acquisition of
knowledge
Knowledge acquisition is the process used to define the rules and ontologies
required for a knowledge-based system.
The phrase was first used in conjunction with expert systems to describe the initial
tasks associated with developing an expert system, namely finding and
interviewing domain experts and capturing their knowledge via rules, objects,
and frame-based ontologies.
Expert systems were one of the first successful applications of artificial
intelligence technology to real world business problems.
Researchers at Stanford and other AI laboratories worked with doctors and other
highly skilled experts to develop systems that could automate complex tasks such
as medical diagnosis.
Until this point computers had mostly been used to automate highly data intensive
tasks but not for complex reasoning.
Technologies such as inference engines allowed developers for the first time to tackle
more complex problems.