Lec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to Ai.pdf

Lec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to AiLec 1 Intro to Ai

1. AI
Ch1: Introduction
By Dr/Safynaz AbdEl-Fattah
Computer Science Department
Beni-Suef University

2. Course Description
•This course provides an introduction to Artificial
Intelligence techniques
 Introduction to AI
Introduction
Intelligent Agents
Problem Solving
Problem Solving Agents
Different Search Strategies
Local Search Strategies
Constraint Satisfaction Problems
Knowledge Representation and Reasoning
–Propositional logic, predicate logic
–Frames, Semantic web, Rule-based systems
Uncertain Knowledge Representation and Reasoning

3. Course Textbook
•Russell, S. and Norvig, P. Artificial Intelligence
A Modern Approach. Prentice Hall, 4rd
edition, 2018.

• Luger, F., George. Artificial Intelligence:
Structures and Strategies for Complex
Problem Solving. Addison-Wesley, 6th edition,
2009.

4. Chapter Outline (Introduction)
•What is AI ?
•The Foundations of AI
•The History of AI
•Fundamental Techniques of AI
•AI in Everyday Life
•Some Sub-fields of AI

5. Rise of AI
•In March 2016, AlphaGo
defeated Lee Sedol, a
South Korean 9-dan
professional Go player,
by 4-1.

6. Birth of AI

•Dartmouth Workshop: Birth of AI
•In August 1956, some scientists and mathematicians
gathered at Dartmouth College, discussing about how to
make machines simulate human learning and any other
feature of intelligence.
•The workshop ran for two months. No consensus was
reached, but they picked the name artificial intelligence
for the field.

7. What is Intelligence ?
•Intelligence may be defined as:

1. The capacity to acquire and apply knowledge.

2. The ability of thought and reason.

8. What is AI?
Charniak and McDermott
“Artificial intelligence is the study of mental faculties
through the use of computational models.”
Shapiro
“Artificial intelligence is a field of science and
engineering concerned with the computational
understanding of what is commonly called intelligent
behavior, and with the creation of artifacts that exhibit
such behavior.”
Rich and Knight
“The study of how to make computers do things at
which, at the moment, people are better.”

9. What is AI?
•Russell and Norvig view definitions of AI fall into
four categories:

10. What is AI ?
Human Performance Ideal Performance
A system is rational if it does the “right
thing,” given what it knows

11. Acting humanly: The Turing Test
approach
•The Turing Test, proposed by Alan Turing (1950), was
designed to provide a satisfactory operational
definition of 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.

12. The Turing Test approach
•Suggested major components of AI:
Natural Language Processing to enable it to
communicate successfully in English (or some other
human language);
Knowledge Representation to store information provided
before or during the interrogation;
Automated Reasoning to use the stored information to
answer questions and to draw new conclusions;
Machine Learning to adapt to new circumstances and to
detect and extrapolate patterns;
 Computer Vision to perceive objects, and
Robotics to move them about

13. Thinking humanly: The cognitive
modeling approach
•some way of determining how humans think. We need to
get inside the actual workings of human minds. There are
three ways to do this: through introspection
—trying to catch our own thoughts as they go by; through
psychological experiments
—observing a person in action; and through brain imaging
—observing the brain in action.

The interdisciplinary field of cognitive science brings
together computer models from AI and experimental
techniques from psychology to construct precise and
testable theories of the human mind.

14. Thinking Rationally: Laws of Thought
•Normative (or prescriptive) rather than descriptive.
•Aristotle: what are correct arguments/thought
processes?
•Several Greek schools developed various forms of
logic:
– Notation and rules of derivation for thoughts;
– May or may not have proceeded to the idea of
mechanization
• Direct line through mathematics and philosophy to
modern AI.

15. Acting Rationally
•Rational behavior: doing the right thing
•The right thing: that which is expected to maximize goal
achievement, given the available information
•Doesn't necessarily involve thinking - e.g., blinking reflex -
but thinking should be in the service of rational action.
•An agent is an entity that perceives and acts.
•This course is about designing rational agents.
• Abstractly, an agent is a function from percept histories
to actions.

16. An intelligent agent
•An intelligent agent is such that:
oIts actions are appropriate for its goals and circumstances.
oIt is flexible to changing environments and goals.
oIt learns from experience.
oIt makes appropriate choices given perceptual limitations and
limited resources.

•Ex: Same as building flying machines by understanding
general principles of flying (aerodynamic) vs. by
reproducing how birds fly.

17. The Foundations of AI
•Artificial intelligence is a
science and technology
based on multiple
disciplines.

•People who work in the field
would consider computer
science, linguistics, biology,
mathematics, psychology,
and engineering to be listed
as the foundations of AI.

18. The History of AI
•1943 – McCulloch & Pitts: Boolean circuit model of brain
•1950 – Turing's “Computing Machinery and Intelligence”
•1950s – Early AI programs, including Samuel's checkers
program, – Newell & Simon's Logic Theorist, Gelernter's
Geometry Engine
•1956 – Dartmouth meeting: “Artificial Intelligence”
adopted
•1965 – Robinson's complete algorithm for logical
reasoning
•1966-74 – AI discovers computational complexity – Neural
network research almost disappears

19. The History of AI
•1969-79 – Early development of knowledge-based
systems
•1980-88 – Expert systems industry booms
•1988-93 – Expert systems industry busts: “AI Winter”
•1985-95 – Neural networks return to popularity
•1988- – Resurgence of probability; general increase in
technical depth – “ Nouvelle AI ” : ALife, GAs, soft
computing
•1995- – Agents, agents, everywhere : : :
•2003- – Human-level AI back on the agenda

20. History of AI Development

21. Hierarchy of AI

22. Relationship of AI、ML and DL

23. Artificial Intelligence Vs. Machine
Learning Vs. Deep Learning

24. Machine Learning Vs. Deep Learning

25. Machine Learning?

26. Machine Learning?