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”

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 /

Neuroscience

How do people behave, perceive, process cognitive

information, represent knowledge

Computer

Engineering

Building fast computers

Control Theory Design systems that maximize an objective function over time

Linguistics Knowledge representation, grammars

Crossbreeding of a lot of fields:

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

History of Artificial Intelligence

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

George Boole

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.

The Protagonists

Norbert Wiener

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.

The Protagonists

John McCarthy

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

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

Robotics

Vision systems

Learning systems

Natural language processing

Neural networks

Expert systems

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

Branches of Artificial Intelligence

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

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.

Perceptive system in game playing

Deep Blue

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.

Expert systems

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.

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.

Neural network:

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

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

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