Lecture 01 Introduction to Artificial Intelligence

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Lecture 01

Introduction to Artificial Intelligence

Tooba Mehtab

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Course Objectives

• The goal of this course is to provide a detail introduction

to artificial intelligence and searching techniques.

• This course gives an overview of the key ideas such as

search, knowledge representation, rule based systems,

and learning that underlay the main subfields of artificial

intelligence and demonstrate the need for different

approaches for different problems.

• This course also covers fundamental areas of, Local Search

Algorithms, Adversarial Searching and Neural Networks.

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Course Learning Outcomes

• Analyze and solve problems by a suitable AI method

(e.g., as a search problem, machine learning, CSP,

planning, etc.) to improve efficiency.

• Design and carry out an empirical evaluation of

different algorithms on problem formalization and

state the conclusions that the evaluation supports.

• Make use of methods from artificial intelligence in

the analysis, design and implementation of computer

programs.

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Lecture Outline

• Course overview

• What is AI?

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Course Code:

CSC-411

Course Title:

Artificial Intelligence

Credit Hours:

2 Abbreviation:

AI

Prerequisite:

Object Oriented Programming

(CSC-210)

Type of Course:

Core Course

Course Description:

Introduction, Knowledge Representation, Search, Informed Search,

Search in Game Playing, Symbolic Logic, Planning, Machine Learning,

Prolog, Python, Ruled based Expert System, Introduction to Natural

Language Processing, Computer Vision, Neural Networks.

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Quizzes

10%

Assignments (Theoretical)

20%

Midterm Examination

20%

Final Examination

50%

Total 100%

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Text Book

• S. Russell, Artificial Intelligence: A Modern

Approach, Prentice Hall, (3

rd

edition)

Reference books

• Ben Coppin, “Artificial Intelligence

Illuminated”, Jones and Bartlett illuminated

Series, 2004

• Simon Haykin, “Neural Networks: A

Comprehensive Foundation”, Prentice Hall,

1999

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• https://www.youtube.com/watch?v=R0bVxbR

Cd-U

• https://www.youtube.com/watch?v=QdQL11

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Introduction to Artificial Intelligence

• Artificial Intelligence is one of the newest

sciences which emerged after the

world war II

.

• AI represents a big and open field.

• The name

Artificial Intelligence

was adopted

for the first time in

1956

. (Computational

Intelligence)

• Artificial Intelligence can be viewed as a

universal field: How to

automate

intellectual

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Introduction to Artificial Intelligence

• Goal of Artificial Intelligence:

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Introduction to Artificial Intelligence

–

Engineering point of view:

• Solve real-world problems using

knowledge

and

reasoning

• Develop

concepts

,

theory

and

practice

of building

intelligent entities

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Introduction to Artificial Intelligence

–

Scientific point of view:

• Use computers as a platform for studying intelligence

itself

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Computer science

AI

Image Processing

Symbolic learning

Machine learning

Pattern recognition

Robotics

Computer

Statistical Deep Learning

NN

vision

Learning

CNN RNN

Speech

NLP

Recognition

Computer

Vision

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Introduction to Artificial Intelligence

• What is artificial Intelligence?

–

Several definitions are available in the literature.

Thinking vs. Behavior

Model humans vs. Ideal standard (Rationality)

• Rational System = system which does the

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Introduction to Artificial Intelligence

Definitions fall into four categories:

Intelligence : Reference to judge

Human models

Rationality

Int

elli

ge

nc

e :

Go

al

to

re

ac

h

Thinking

“The exciting new effort to make computers think . . . machines with minds, in the full and literal sense.” (Haugeland, 1985)

“[The automation of] activities that we associate with human thinking, activities such as decision-making, problem solving, learning . . .” (Bellman, 1978)

-The study of mental faculties through the use of

computational models. (Charniak

and Mcdermott, 1985)

-The study of the computations that make it possible to perceive, reason and act. (Winston 1992)

Acting

-The art of creating machines that perform functions that require intelligence when performed by people. (Kurzweil, 1990) -The study of how to make computers do

things at which, at the moment, people are

better. (Rich and Knight, 1991)

- Computational intelligence is the study of the design of intelligent agents. (Poole et al.,1998).

- AI…is concerned with

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Systems that act like humans

• In this approach computer capabilities are

compared with human capabilities

• For this purpose a special test of intelligent

behavior is defined. The test is called the

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In a Turing test, the

interrogator must

determine which

respondent is the

computer and which

is the human

The Turing Test

Systems that act like humans

So when WILL we

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Systems that act like humans

• Turing (1950)

“Computing machinery and intelligence”.

• A computer passes the test if a human interrogator, after posing

some written questions, cannot tell whether the written

responses come from a person or from a computer.

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Systems that act like humans

• To pass the Turing test the

computer

must have the following

capabilities

:

–

Natural language processing

: To communicate successfully.

–

Knowledge representation

: To store what it knows or hears.

–

Automated reasoning

: to answer questions and draw conclusions using

stored information.

–

Machine learning

: To adapt to new circumstances and to detect and

extrapolate patterns.

• However ,the Turing test excludes direct

physical contact

between the machine and the interrogator. The so called the

Total Turing test

brings forward

two more requirements

:

1. Computer vision

in order to perceive objects, and

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• Captcha

–

Completely Automated Public Turing test to tell Computers and

Humans Apart

–

CAPTCHA is sometimes described as a reverse Turing test

–

Tests to identify humans from bots on the Internet, to deny

services to WebCrawler's or spammers

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Systems that think like humans

• Program think like human → How humans think?

• Requires Scientific theories of internal activities

of the brain (cognitive science and cognitive

neuroscience).

• Cognitive science

integrates

computational

models

developed in the area of artificial

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Systems that think rationally

• The

Laws of Thought

approach is based on

pattern for argument structure arising from

Aristostle’s syllogisms.

• This approach is related to

logics

, that is,

logical rules

make the mental mind of

humans

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Systems that act rationally

• Modern AI can be characterized as the

engineering of rational agents.

• An agent is simply an entity that perceives

and acts. A rational agent is an entity that

perceives, reasons and acts rationally

(correctly).

• Rational behavior

: doing the right thing

• The right thing

which is expected to maximize

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The Foundation of Artificial

Intelligence

• Philosophy

• Mathematics

• Economics

• Neuroscience

• Psychology

• Computer Engineering

• Control Theory and Cybernetics

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Linguistics Psychology

Philosophy

Electrical Engineering

Management & Management Science Computer Science

Intelligent Tutor Expert System

Natural Language Processing Automatic Programming

Machine Learning _{Speech Understanding}

Robotic

Game Playing

Neural Network

Fuzzy Logic Genetic Algorithm

Computer Vision Data Mining Affective computing

AI Tree

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• PHILOSOPHY

(428 b.c. -present)

–

Can

formal rules

be used to draw

valid conclusions

?

–

Where does

knowledge

come from?

–

How does

knowledge lead to action

?

• MATHEMATICS

(c. 800 -present)

–

What are the

formal rules

to draw valid conclusions?

(

formal logic

)

–

What can be

computed

? (

algorithms

)

–

How do we

reason

with uncertain information?

(

probability theory, fuzzy sets

, etc.)

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• ECONOMICS

(1776 -present)

• How should we make decisions so as to maximize payoff

?

• NEUROSCIENCE

(1861 -present)

• How do human brains process information? (neural

networks)

• PSYCHOLOGY

(1879 -present)

• How do humans and animals think and act? (behaviorism,

cognitive psychology, cognitive science)

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• COMPUTER ENGINEERING

(1940 -present)

❖

How can we build an efficient computer?

• CONTROL THEORY AND CYBERNETICS

(1948 -present)

❖

How can artifacts (man made objects) operate under

their own control? (

automatic

)

• LINGUISTICS

(1957 -present)

❖

How does language relate to thought?

❖

(natural language processing, knowledge representation)

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History of Artificial Intelligence

Expectations and Initial enthusiasm

(1952 – 1969) Birth of AI

(1956)

Early days

(1943-1955)

Reality Check (1966–1973) Knowledge-bas ed Systems (1969 – 1979)

AI becomes an industry (1980–Present)

Return of Neural Networks (1986–present)

1943: first piece of AI work: Warren McCulloch and Walter Pitts

Model of artificial neurons.

Mathematical learnable functions that generate “on/off” depending on inputs (logic gates) Any computable function can be computed by a network of connected neurons.

Suitably defined networks can learn.

1949: Hebbian learning

A mechanism for updating the connection strength of a neuron.

Today, neurologists have confirmed that something similar to Hebbian learning indeed is going on in our brain when we are learning.

1950: Turing test complete vision of AI in “computing machinery and Intelligence”

1951: first neural network computer

Implemented by M. Minsky and D. Edmonds

AI Adopts Sci. Methods (1987-Present)

Emergence of Intelligent Agents

(1995–Present)

Large Data Sets (2001–Present)

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History of Artificial Intelligence

1956: Dartmouth Conference

Organized by John McCarthy and colleagues for starting a new area in studying

computation and intelligence.

John McCarthy introduced the term “artificial intelligence” in the conference.

The next 20 years witnessed steady growth of the field led by the pioneers

appeared in the Dartmouth conference.

(1952 – 1969)

Birth of AI (1956)

Reality Check (1966–1973)

Knowledge-bas ed Systems (1969 – 1979)

(1995–Present)

Early days (1943-1955)

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History of Artificial Intelligence

1963: Thomas Evan’s program ANALOG

Solved analogy problems in an IQ test.

1965: ELIZA

Simulates a dialog with a computer in English on any topic.

Became popular when programmed to simulate a psychotherapist (Fedora’s Emacs).

1967: Dendral program (developed at Stanford)

- First successful program for scientific reasoning – one of the earlier rule based expert systems.

- A program that can infer molecular structures given the information provided by a mass spectrometer

Expectations and Initial enthusiasm

(1952 – 1969)

Birth of AI (1956)

(1995–Present)

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History of Artificial Intelligence

Series of disappointments and frustrations

AI was poured little buckets of “reality cold water”

Problems:

- Most early systems contain little or no knowledge of their subject matter

Example: Poor performance of earlier machine translation system (Russian ⇔English): “the spirit is willing but the flesh is weak” was translated to “the vodka is good but the meat is rotten”.

- Computational Intractability of AI problems

Theory of computational complexity was not developed. Polynomial solvable problems, NP-completeness, etc

People thought a faster machine could solve any hard problem. Expectations and

Initial enthusiasm (1952 – 1969) Birth of AI

(1956)

Reality Check (1966–1973)

(1995–Present)

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History of Artificial Intelligence

- 1971: T. Winograd’s Ph.D. thesis (MIT)

demonstrated a system that can understand English in a micro-domain (the block world).

-1972: PROLOG was developed

- 1974: MYCIN was developed by Ted Shortliffe

Expert system for medical diagnosis. Sometimes called the first expert system.

- And many other works…

(1952 – 1969) Birth of AI

(1956)

Knowledge-bas ed Systems (1969 – 1979)

(1995–Present)

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History of Artificial Intelligence

AI started to become industrially and commercially beneficial

- 1982: R1 was deployed at DEC – an expert system that saved the company around $40M / year

- Du Pont had 100 in use and an estimated 500 in development at late 90’s to early 21st century

At an international level, AI was considered a part of a country’s technological

developments

- Japan: “First Generation” project (10 year plan to build intelligence machines running in Prolog)

- USA: Microelectronics and Computer Technology Corporation (MCC) was formed in response

- Britain: Funding for AI was reinstated Expectations and

Initial enthusiasm (1952 – 1969) Birth of AI

(1956)

AI becomes an industry (1980–Present)

(1995–Present)

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History of Artificial Intelligence

- In the mid-1980s at least four different groups reinvented the back-propagation

learning algorithm first found in 1969 by Bryson and Ho.

(1952 – 1969) Birth of AI

(1956) Early days (1943-1955) Reality Check (1966–1973) Knowledge-bas ed Systems (1969 – 1979)

Return of Neural Networks (1986–present)

(1995–Present)

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History of Artificial Intelligence

- Work of the physicist John Hopfield (1982) on using techniques from statistical

mechanics.

- Connectionist models of intelligent systems competitor to the symbolic models

(Newell and Simon) and logicist approach (McCarthy). (complementary approaches

in fact).

- Several revolutions in many fields: pattern recognition, computer vision, robotics…

- Emergence of intelligent agents.

(1952 – 1969) Birth of AI

AI Adopts Sci. Methods (1987-Present)

(1995–Present)

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History of Artificial Intelligence

- The work of Allen Newell, John Laird, and Paul Rosenbloom on SOAR (Newell.

1990: Laird el al., 1987) is the best-known example of a complete agent

architecture.

-AI technologies under lie many Internet tools, such as search engines,

recommender systems, and Web site construction systems

(1952 – 1969) Birth of AI

Emergence of Intelligent Agents

(1995–Present)

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History of Artificial Intelligence

-Data rather than which algorithm sometimes more important

-Billions of words, pictures, base pairs of genomic sequences, …

-Yarowsky (1995) showed that a simple bootstrapping approach over a very large

corpus could be effective for WSD (word-sense disambiguation). [e.g. plant]

-Perhaps the knowledge bottleneck will be solved by learning methods over very

large datasets rather than by hand-coding knowledge.

(1952 – 1969) Birth of AI

(1995–Present)

Large Data Sets (2001–Present)

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History of Artificial Intelligence

-2011: IBM Watson wins Jeopardy

-2012: US state of Nevada permits driverless cars

-2014: "Deep Learning": recommendation systems, image tagging, board games,

speech translation, pattern recognition

-2016: Google AlphaGo beats the world's 2nd best Go player, Lee Se-dol

(1952 – 1969) Birth of AI

(1995–Present)

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The state-of-the-art

• Robotic Vehicles

• Speech recognition

• Autonomous planning and

scheduling

• Game playing

• Spam fighting, fraud detection

• Robotics

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• AI can help us to solve difficult, real-world problems,

creating new opportunities in business, engineering, and

many other application areas.

• The history of AI has had cycles of success, misplaced

optimism, and resulting cutbacks in enthusiasm and

funding. There have also been cycles of introducing new

creative approaches and systematically refining the best

ones.

• AI has advanced more rapidly in the past decade because

of greater use of the scientific method in experimenting

with and comparing approaches.

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