1. BY
• E. BHANU PRAKASH

2. CONTENTS
HISTORY
INTRODUCTION
TYPES OF AI
KBS
ANN

3. HISTORY
 The modern history of AI can be
traced back to the year 1956 when
John McCarthy proposed the term as
the topic for a conference held at
dart.
 In the1960s and the 1970s, the
focus of AI research was primarily on
the development of Knowledge
Based Systems(KBS) or Expert
systems.

4. HISTORY
 The late 1980s and 1990s saw a
renewed interest in Neural
Networks(NN) research when
several different researcher re-
invented the Back propagation
Algorithm.
 The Back propagation algorithm
was soon applied to many learning
problems causing great excitement
within the AI community.

5. INTRODUCTION
AI is the branch of computer science which
deals with intelligence of machines where an
intelligent agent is a system that takes actions
which maximize its chances of success.
It is the study of ideas which enable
computers to do the things that make people
seem intelligent. The central principles of AI
includes such as reasoning, knowledge, planning,
learning, communication,perception and the
ability to move and manipulate objects.

6. 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.

7. What Contributes to AI?

8. LISP
PROLOG
PYTHON
JAVA

9. TYPES OF
ARTIFICIAL INTELLIGENCE

10. Knowledge Based Systems
What is Knowledge?
The data is collection of facts. The information is
organized as data and facts about the task domain.
Data, information, and past experience combined
together are termed as knowledge.

11. Knowledge Based Systems
 Knowledge Based Systems(KBS) are
also called as Expert systems.
 Expert systems (ES) are one of the
prominent research domains of AI. It is
introduced by the researchers at Stanford
University, Computer Science Department.
 The expert systems are the
computer applications developed to
solve complex problems in a particular
domain, at the level of extra-ordinary
human intelligence and expertise.

12. Knowledge Based Systems
Characteristics of Expert Systems
 High performance
 Understandable
 Reliable
 Highly responsive

13. Knowledge Based Systems
Benefits of Expert Systems
 Less Production cost
 Speed
 Less Error Rate
 Reducing Risk
 Steady response
 Availability

14. Artificial Neural Networks
The inventor of the first neurocomputer, Dr.
Robert Hecht-Nielsen, defines a neural
network as:
"a computing system made up of a
number of simple, highly interconnected
processing elements, which process
information by their dynamic state
response to external inputs.”

15. Artificial Neural Networks
Basic Structure of ANNs :
The idea of ANNs is based on the belief
that working of human brain by making the
right connections, can be imitated using
silicon and wires as living neurons and
dendrites.

16. Artificial Neural Networks

17. Artificial Neural Networks
 The human brain is composed of
100 billion nerve cells called neurons. They
are connected to other thousand cells by
Axons.
 Stimuli from external environment or
inputs from sensory organs are accepted by
dendrites.
 These inputs create electric impulses, which
quickly travel through the neural network.
 A neuron can then send the message to other
neuron to handle the issue or does not send
it forward.

18. Artificial Neural Networks
 ANNs are composed of multiple nodes,
which imitate biological neurons of human
brain.
 The neurons are connected by links and they
interact with each other.
 The nodes can take input data and perform
simple operations on the data.
 The result of these operations is passed to
other neurons. The output at each node is
called its activation or node value.

19. Artificial Neural Networks
 Each link is associated with weight.
ANNs are capable of learning, which takes
place by altering weight values. The following
illustration shows a simple ANN:

20. Artificial Neural Networks

21. TYPES OF ANN

22. Artificial Neural Networks
1. FeedForward:
 In this ANN, the information flow is
unidirectional.
 A unit sends information to other unit
from which it does not receive any
information.
 There are no feedback loops.
 They are used in pattern generation.
 They have fixed inputs and outputs.

25. APPLICATIONS OF AI
some of applications of AI:
o Gaming
o Natural language processing
o Expert Systems
o Speech recognition
o Intelligent Robots

26. THANK YOU