A
PRESENTATION
BY--
Biswajit Mondal,
Academy of
Technology,
Electronics and
Communication
Engineering.
Roll No. 071690103019
Reg. No. 071690103101019
Date: 18th February, 2010

What is Artificial Intelligence?
 It is the study of how to make computers do things which, at the
moment, people do better.
 In other words, it can be defined as the study of making of
computer with the ability to mimic or duplicate the human brain
functions.
 John McCarthy coined the term in 1956, at
Massachusetts Institute of Technology,
defines it as "the science and engineering
of making intelligent machines”

Crossbreeding of a lot of fields:
Philosophy Logic, methods of reasoning, mind as physical system,
foundations of learning, language, rationality.
Mathematics Formal representation and proof, algorithms, computation,
(un)decidability, (in)tractability
Statistics Modeling uncertainty, learning from data
Economics Utility, decision theory, rational economic agents
Neuroscience Neurons as information processing units
Psychology / How do people behave, perceive, process cognitive
Neuroscience information, represent knowledge
Computer Building fast computers
Engineering
Control Theory Design systems that maximize an objective function over time
Linguistics Knowledge representation, grammars

History of Artificial Intelligence
 1943 McCulloch & Pitts: Boolean circuit model of brain
 1950 Turing's "Computing Machinery and Intelligence"
 1956 Dartmouth meeting: "Artificial Intelligence" adopted
 1950s Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
 1965 Robinson's complete algorithm for logical reasoning
 1966—73 AI discovers computational complexity
Neural network research almost disappears
 1969—79 Early development of knowledge-based systems
 1980-- AI becomes an industry
 1986-- Neural networks return to popularity
 1987-- AI becomes a science
 1995-- The emergence of intelligent agents

The Protagonists
The various fields of AI have in common
is the creation of machines that can think.
In order to classify machines as thinking,
it is to be intelligence. Perhaps the best
way to gauge the intelligence of a machine
is British scientist Alan Turing’s test. He
stated that a computer would deserve to be
called intelligent if it could deceive a
human into believing that it was human.
Alan Turing

The Protagonists
The beginning of AI reach back before
electronics, to philosophers and
mathematicians such as Boole and
other theorizing on principle that were
used as the foundation of AI logic. AI
really begun to intrigue researchers
with the invention of computer in
1943. The technology was finally
available, or so it seemed, to simulate
intelligent behaviour.
George Boole

The Protagonists
Although the computer provided
the technology necessary for AI, It
was not until the early 1950’s that
the link between human
intelligence and machines was
really observed. Norbert Wiener
was one of the first Americans to
make observations on the principle
of feedback theory. He theorized
that all intelligent behaviour was
the result of feedback mechanism.
Norbert Wiener

The Protagonists
In 1956 John McCarthy organized a
conference to draw the talent and
expertise of others interested in
machine intelligence for a month of
brainstorming. He invited them to
Vermont for “The Dartmouth
summer research project on
Artificial Intelligence” which
brought together the founders in AI,
and served to lay the groundwork
John McCarthy for the future of AI.

Intelligent behaviour
1: Learn from experience
2: Apply knowledge acquired from experience
3: Handle complex situations
4: Solve problems when important information is missing
5: Determine what is important
6: React quickly and correctly to a new situation
7: Understand visual images
8: Process and manipulate symbols
9: Be creative and imaginative
10:Use heuristics

Artificial
intelligence
Vision Learning
systems systems
Robotics
Expert systems
Neural networks
Natural language
processing

Branches of Artificial Intelligence
 Perceptive system
• A system that approximates the way a human sees, hears, and feels
objects
 Vision system
• Capture, store, and manipulate visual images and pictures
 Robotics
• Mechanical and computer devices that perform tedious tasks with
high precision
 Expert system
• Stores knowledge and makes inferences

Branches of Artificial Intelligence
 Learning system
• Computer changes how it functions or reacts to situations based on
feedback
 Natural language processing
• Computers understand and react to statements and commands made in
a “natural” language, such as English
 Neural network
• Computer system that can act like or simulate the functioning of the
human

Fields of Artificial Intelligence:
Games playing: programming computers to play games such as chess and
checkers
Expert systems : programming computers to make decisions in
real-life situations (for example, some expert
systems help doctors diagnose diseases based on symptoms)
Natural language: programming computers to understand natural human
languages
Neural networks: Systems that simulate intelligence by attempting to
reproduce the types of physical connections that occur in
animal brains
Robotics: programming computers to see and hear and react to
other sensory stimuli

Perceptive system in game playing
Currently, no computer exhibits
full artificial intelligence (i.e., is
able to simulate human behavior).
The greatest advances have
occurred in the field of games
playing. The best computer chess
programs are now capable of
beating humans. In May, 1997, an
IBM super-computer called Deep
Blue defeated world chess
champion Gary Kasparov in a
chess match.
Deep Blue

Expert systems
In the early 1980s, expert systems were believed to
represent the future of artificial intelligence and of
computers in general. To date, however, they have not
lived up to expectations. Many expert systems help human
experts in such fields as medicine and engineering, but
they are very expensive to produce and are helpful only in
special situations.

Natural language:
Natural-language processing offers the greatest potential rewards
because it would allow people to interact with computers without
needing any specialized knowledge. Unfortunately, programming
computers to understand natural languages has proved to be more
difficult than originally thought. Some rudimentary translation
systems that translate from one human language to another are in
existence. There are also voice recognition systems that can convert
spoken sounds into written words. Even these systems are quite
limited -- you must speak slowly and distinctly.

Neural network:
Today, the hottest
area of artificial
intelligence is
neural networks,
which are
proving
successful in a
number of
disciplines such
as voice
recognition and
natural-language
processing.

Robotics:
In the area of robotics,
computers are now widely
used in assembly plants, but
they are capable only of very
limited tasks. Robots have
great difficulty identifying
objects based on appearance
or feel, and they still move
and handle objects clumsily.
Sony AIBO

Beowulf + robot =
“Beobot”

State of the Artificial Intelligence
 Deep Blue defeated the reigning world chess champion Garry
Kasparov in 1997
 Proved a mathematical conjecture (Robbins conjecture) unsolved for
decades
 No hands across America (driving autonomously 98% of the time
from Pittsburgh to San Diego)
 During the 1991 Gulf War, US forces deployed an AI logistics
planning and scheduling program that involved up to 50,000
vehicles, cargo, and people
 NASA's on-board autonomous planning program controlled the
scheduling of operations for a spacecraft
 Proverb solves crossword puzzles better than most humans

Examples of applications of AI
AUSDA: Examines the software to see if it is capable of handling
the task you need performed.
EGRESS: Program studying the human reaction to accidents. It is
trying to make a model how people’s reaction in
panic moments save life.
VOICE RECOGNIZATION: You tell the computer to do
what you want without it having to learn your voice.
SCRIPT RECOGNIZATION: With the pen accompanied by
silicon notepad you can write a little note to yourself
which magically changes into computer text.

Examples of applications of AI
The General Problem Solver: The GPS has successfully
solved a variety of problems including Deductive
reasoning, Hanoi Tower.
SAM: SAM is a program from Yale’s AI lab which is able to read
between the lines, and assume certain facts.
ELIZA: An earlier AI program that simulated the behavior of a
Rogerian therapist. ELIZA’s knowledge about
English and Psychology was coded in a set of
simple rule based on Complex and Approximate
Matching, Conflict Reasoning.

Examples of applications of AI
CYRUS: It is a MOP(memory organization packets) based
program which contains episodes taken from the
life of a particular individual. It can answer
questions that require significant amounts of
memory reconstruction.
IPP: This program accepts stories about terrorist attack and stores
them in an episodic memory.
MOPTRANS: This program uses a MOP based memory to
understand sentences in one language and translates
into another.

Examples of applications of AI
PROSPECTOR: This is a program that provides advices on
mineral exploration.
DESIGN ADVISOR: It is a system that critiques chip design.
It gives advice to the chip designer who decides to
accept or reject the advice.
NEUROGAMMON: This program is based on neural network
that learns from experience. This is one of the few
game playing program which relies heavily on
automatic learning.
There are lots of example of AI application on different aspects.

References
“ARTIFICIAL INTRLLIGENCE” by
Elaine Rich & Kevin Knight
www.wikipedia.com
www.slideshare.com
www.google.com
www.ai.mit.edu
www.techquest.com