introductory notes to AI,Application of AI, Need of AI

1. 1
Dr. Tarek Helmy, ICS-KFUPM
ICS-381
Principles of Artificial Intelligence
Lectures 2- 4
Introducing Artificial Intelligence
Dr.Tarek Helmy El-Basuny

2. Dr. Tarek Helmy, ICS-KFUPM 2
Introduction to Artificial Intelligence
‰ Brain Storming
 Why do we study AI?
 What is Artificial Intelligence?
‰ Characteristics of Intelligence
‰ AI is a Multi-Disciplinary Field
‰ Commonly Accepted Definitions of Artificial Intelligence
‰ Why Does Industry and the Government Care about AI?
‰ What might be involved in building a “smart” computer?
‰ Typical AI Programs
‰ Features of Using Artificial Intelligence
‰ How to Achieve AI
‰ AI Technologies and Applications
‰ AI Brief History
‰ Can a machine be truly “intelligent”?: Turing Test

3. Dr. Tarek Helmy, ICS-KFUPM 3
Why do we study AI?
Search engines
Science
Medicine/
Diagnosis
Labor
What else?
Appliances

4. Dr. Tarek Helmy, ICS-KFUPM 4
Honda Humanoid Robot
Walk Turn
Stairs
http://world.honda.com/ASIMO/

5. Dr. Tarek Helmy, ICS-KFUPM 5
Sony AIBO
http://www.aibo.com Smart/liveMarket

6. Dr. Tarek Helmy, ICS-KFUPM 6
Brain Storming: What is Artificial Intelligence?
‰ Good Question, but exactly, what is intelligence? Can we say, he is intelligent means:
 He knows a lot
 He thinks fast
 He talks much
 He learns quickly
 He memorizes well
‰ Is it learned?
‰ Are you born with it?
‰ Can we use tests to measure it?
 IQ Test!
‰ Intelligence = Knowledge + ability to perceive, feel, understand, process,
communicate, judge, and learn.
‰ What is Artificial Intelligence?
‰ There is no official agreed upon definition of Artificial Intelligence.
‰ Why?
 In practice, it is an “umbrella term”
 It is multidisciplinary subject
 Technologies enter and exit the AI “umbrella” regularly.

7. Dr. Tarek Helmy, ICS-KFUPM 7
Characteristics of Intelligence
‰ Ability to Communicate
‰ Creativity
‰ Internal Knowledge
‰ Ability to Learn
‰ Perceive World Knowledge
‰ Goal-Directed Behavior
‰ Self Awareness

8. Dr. Tarek Helmy, ICS-KFUPM 8
An Intelligent Entity
Has understanding/
intentionality
Exhibits behavior
See
Hear
Touch
Taste
Smell
INPUTS
INTERNAL
PROCESSES
OUTPUTS
Senses environment
Can Reason
Has knowledge

9. Dr. Tarek Helmy, ICS-KFUPM 9
Commonly Accepted Definitions of Artificial Intelligence
‰ Winston: “AI is the study of ideas which enable computers to do things
which make people seem intelligent.”
‰ Steven Tanimoto, “Computational techniques for performing tasks that
apparently require intelligence when performed by humans.”
‰ David Parnas, “Artificial intelligence is to artificial flowers as natural
intelligence is to natural flowers.”
‰ Luger: The branch of computer science that is concerned with automation of
intelligent behavior.
‰ Rich: “AI is the study of how to make computers do things which, at the
moment, people do better.”
‰ Fahlman: AI is the study of intelligence using the ideas and methods of
computation.”
‰ Found on the Web: AI is the reproduction of the methods or results of
human reasoning or intuition.
‰ We can define it too: AI is a field of computer science that simulates human
performance to make a computer reasons in a manner similar to humans.

10. Dr. Tarek Helmy, ICS-KFUPM 10
Why Do Industry and Government Care about AI?
‰ AI shows promise for handling many complex problems, saving lives
and resources:
• Solving information overload problems.
• Intelligent search engines
• Operating in risky environments.
• Robots
• Distributing scarce commercial knowledge.
• Data-mining software sort through massive databases, looking for
patterns that would take a human years to spot.
• Enhancing training through use of simulation.
• ES
• Adaptive Computer Based Tests

11. Dr. Tarek Helmy, ICS-KFUPM 11
Main Areas of AI
ƒ Knowledge representation
(including formal logic)
ƒ Search, especially heuristic
search (puzzles, games)
ƒ Planning
ƒ Reasoning under uncertainty,
including probabilistic reasoning
ƒ Learning
ƒ Agent architectures
ƒ Robotics and perception
ƒ Natural language processing
Search
Knowledge
rep.
Planning
Reasoning
Learning
Agent
Robotics
Perception
Natural
language
Expert
Systems
Constraint
satisfaction
...

12. Dr. Tarek Helmy, ICS-KFUPM 12
A Hierarchical Model of Intelligence
Wisdom
Knowledge
Information
Data Context
+
Vision
+
Experience
+

13. Dr. Tarek Helmy, ICS-KFUPM 13
AI is a Multi-Disciplinary Field
Control
Theory
Linguistics
Mathematics
Psychology
Artificial
Intelligence
Computer
Science
Philosophy
Computer
Engineering

14. Dr. Tarek Helmy, ICS-KFUPM 14
AI is a Multi-disciplinary
‰ Many disciplines contribute to the goal of creating/modeling intelligent
entities:
 Computer Engineering (Building fast computers)
 Psychology (Perceive, process information, represent knowledge.)
 Philosophy (Logic, methods of reasoning, mind as physical system,
foundations of learning, etc)
 Linguistics (Structure and meaning of language)
 Human Biology (How brain works)
 Mathematics (Formal representation and proof, algorithms, etc.)
 Control theory (Design systems that maximize an objective function
over time)

15. Dr. Tarek Helmy, ICS-KFUPM 15
Intelligent System Should do:
‰ How can we make computer based systems more intelligent?
‰ In practical terms, intelligent systems:
 Should have the ability to automatically perform tasks that normally
require a human expert.
 Should have more autonomy; less requirement for human intervention
or monitoring.
 Should have Flexibility in dealing with variability in the environment
in an appropriate manner.
 Are easier to use: able to understand what the user wants from limited
instructions.
 Can improve their performance by learning from experience.

16. Dr. Tarek Helmy, ICS-KFUPM 16
Typical AI Programs
‰ Intelligent entities (or “agents”) need to be able to do both “ordinary” and
“expert” tasks:
‰ Ordinary tasks - consider going shopping:
 Planning a route, and sequence of shops to visit!
 Recognizing (through vision) buses, people.
 Communicating (through natural language).
 Navigating round obstacles on the street, and manipulating objects for
purchase.
‰ Expert tasks are things like:
 Medical diagnosis.
 Equipment repair.

17. Dr. Tarek Helmy, ICS-KFUPM 17
How to Achieve AI?
‰ How is AI research done?
‰ AI research has both theoretical and experimental sides. The experimental
side has both basic and applied aspects.
‰ There are two main lines of research:
 One is biological, based on the idea that since humans are intelligent, AI
should study humans and imitate their psychology or physiology.
 The other is phenomenal, based on studying and formalizing common
sense facts about the world and the problems that the world presents to
the achievement of goals.
‰ The two approaches interact to some extent, and both should eventually
succeed. It is a race, but both racers seem to be walking. [John McCarthy]

18. Dr. Tarek Helmy, ICS-KFUPM 18
What might be involved in building a “smart” computer?
‰ What are the “components” that might be useful?
 Fast hardware?
 Foolproof [never makes error] software?
 Speech interaction?
ƒ Speech separation [segmentation/synthesis]
ƒ Speech recognition
ƒ Speech understanding
 Image recognition and understanding?
 Learning?
 Planning and decision-making?

19. Dr. Tarek Helmy, ICS-KFUPM 19
Can we build hardware as complex as the brain?
‰ How complicated is our brain?
 A neuron, or nerve cell, is the basic information processing unit
 Estimated to be on the order of 1012 neurons in a human brain
 Many more synapses (1014) connecting these neurons
 Cycle time: 10-3 seconds (1 millisecond)
‰ How complex can we make computers?
 106 or more transistors per CPU
 Supercomputer: hundreds of CPUs, 109 bits of RAM
 Cycle times: order of 10- 8 seconds
‰ Conclusion
 YES: we can have computers with as many basic processing elements as our
brain, but with
ƒ Far fewer interconnections (wires or synapses) than the brain
ƒ Much faster updates than the brain
 But building hardware is very different from making a computer behave like a
brain!

20. Dr. Tarek Helmy, ICS-KFUPM 20
Must an Intelligent System be Foolproof?
‰ A “foolproof” system is one that never makes an error:
 Types of possible computer errors
ƒ Hardware errors, e.g., memory errors
ƒ Software errors, e.g., coding bugs
ƒ “Human-like” errors
 Clearly, hardware and software errors are possible in practice
 What about “human-like” errors?
‰ An intelligent system can make errors and still be intelligent
 Humans are not right all of the time
 We learn and adapt from making mistakes
‰ Conclusion:
 NO: intelligent systems will not (and need not) be foolproof

21. Dr. Tarek Helmy, ICS-KFUPM 21
Can Computers Talk with Understanding?
‰ This is known as “speech synthesis”
 Translate text to phonetic form
ƒ e.g., “fictitious” -> fik-tish-es
 Use pronunciation rules to map phonemes to actual sound
ƒ e.g., “tish” -> sequence of basic audio sounds
‰ Difficulties
 Sounds made by this “lookup” approach sound unnatural
 Sounds are not independent
ƒ e.g., “act” and “action”
 A harder problem is emphasis, emotion, etc
ƒ Humans understand what they are saying
ƒ Machines don’t: so they sound unnatural
‰ Conclusion: NO, for complete understanding, but YES for pronouncing
and translating.

22. Dr. Tarek Helmy, ICS-KFUPM 22
The End!!
Thank you
Any Questions?

23. Dr. Tarek Helmy, ICS-KFUPM 23
Can Computers Recognize Speech?
‰ Speech Recognition:
 Mapping sounds from a microphone into a list of words.
 Hard problem: noise, more than one person talking, speech variability,..
 Even if we recognize each word, we may not understand its meaning.
‰ Recognizing single words from a small vocabulary
ƒ Systems can do this with high accuracy (order of 99%)
ƒ e.g., directory inquiries for phone companies
• Limited vocabulary (area codes, city names)
• Computer tries to recognize you first, if unsuccessful hands you over to a human
operator
• Saves millions of dollars a year for the phone companies
‰ Recognizing normal speech is much more difficult
 Speech is continuous: where are the boundaries between words?
 Large vocabularies
ƒ Can be many thousands of possible words
ƒ We can use context to help figure out what someone said
 Background noise, other speakers, accents, colds, etc
 On normal speech, modern systems are only about 60% accurate
‰ Conclusion: NO/with little accuracy, normal speech is too complex to accurately recognize,
but YES for restricted problems

24. Dr. Tarek Helmy, ICS-KFUPM 24
Can Computers Understand speech?
‰ Understanding is different to recognition:
 “Time flies like an arrow”
ƒ Assume the computer can recognize all the words
ƒ But how could it understand it?
ƒ How could a computer figure this out?
• Clearly humans use a lot of implicit common sense
knowledge in communication
‰ Conclusion: NO with full semantic, much of what we say is beyond the
capabilities of a computer to understand at present.

25. Dr. Tarek Helmy, ICS-KFUPM 25
Can Computers Learn and Adapt ?
‰ Learning and Adaptation
 Consider a computer learning to drive on the freeway
 We could code lots of rules about what to do
 We could let it drive and steer it back of course when it heads for the
embankment
ƒ Systems like this are under development.
 Machine learning allows computers to learn to do things without explicit
programming.
‰ Conclusion: YES, computers can learn and adapt, when presented with
information in the appropriate way.

26. Dr. Tarek Helmy, ICS-KFUPM 26
Can Computers “see”?
‰ Recognition v. Understanding (like Speech)
 Recognition and Understanding of Objects in a scene
ƒ Look around this room
ƒ You can effortlessly recognize objects
ƒ Human brain can map 2d visual image to 3d “map”
‰ Why is visual recognition a hard problem?
‰ Conclusion: Computers can partially “see” certain types of objects
under limited circumstances: but YES/fully for certain constrained
problems (e.g., face recognition).

27. Dr. Tarek Helmy, ICS-KFUPM 27
Can Computers Plan and Make Decisions?
‰ Intelligence
 Involves solving problems and making decisions and plans
 e.g., you want to visit your cousin in Mekah
ƒ You need to decide on dates, flights
ƒ You need to get to the airport, etc
ƒ Involves a sequence of decisions, plans, and actions
‰ What makes planning hard?
 The world is not predictable:
ƒ Your flight might be canceled or there will be a backup.
 There are a potentially huge number of details
ƒ Do you consider all flights? all dates?
• No: common sense constrains your solutions
 AI systems are only successful in constrained planning problems
‰ Conclusion: NO, real-world planning and decision-making is still beyond the
capabilities of modern computers. But YES for very well-defined, constrained
problems.

28. Dr. Tarek Helmy, ICS-KFUPM 28
Intelligent Systems in Your Everyday Life
‰ Post Office
 Automatic address recognition and sorting of mail
‰ Banks
 Automatic check readers, signature verification systems
 Automated loan application classification
‰ Telephone Companies
 Automatic voice recognition for directory inquiries
 Automatic fraud detection,
‰ Credit Card Companies
 Automated fraud detection
‰ Computer Companies
 Automated diagnosis for help-desk applications

29. Dr. Tarek Helmy, ICS-KFUPM 29
AI Applications: Consumer Marketing
‰ Have you ever used any kind of credit/ATM/store card while shopping?
 If so, you have very likely been “input” to an AI algorithm
‰ All of this information is recorded digitally
‰ Companies gather this information weekly and search for patterns
 General changes in consumer behavior
 Tracking responses to new products
 Identifying customer segments: targeted marketing, e.g., they find out that
consumers with sports cars who buy textbooks respond well to offers of new
credit cards.
 Currently a very hot area in marketing
‰ How do they do this?
 Algorithms (“data mining”) search data for patterns
 Based on mathematical theories of learning
 Completely impractical to do manually

30. Dr. Tarek Helmy, ICS-KFUPM 30
AI Applications: Identification Technologies
‰ ID cards
 e.g., ATM cards
 Can be a security risk:
ƒ Cards can be lost, stolen, passwords forgotten, etc
‰ Biometric Identification
 Walk up to a locked door
ƒ Camera
ƒ Fingerprint device
ƒ Microphone
 Computer uses your biometric signature for identification
ƒ Face, eyes, fingerprints, voice pattern

31. Dr. Tarek Helmy, ICS-KFUPM 31
AI Applications: Predicting the Stock Market
‰ The Prediction Problem
 Given the past, predict the future
 Very difficult problem!
 We can use learning algorithms to learn a predictive model from historical data
ƒ Prob (increase at day t+1 | values at day t, t-1,t-2....,t-k)
 Such models are routinely used by banks and financial traders to manage
portfolios worth millions of dollars
Time in days
?
?
Value of
the Stock

32. Dr. Tarek Helmy, ICS-KFUPM 32
AI Brief History
‰ 1950: Alan Turing: Turing test
‰ 1950: Claude Shannon publishes a paper on chess playing. Shows that a
game of chess involved about 10120 moves Æ shows the need for heuristics
‰ 1943-56: McCulloch/Pitts: Research on the structure of the brain gives a
model of neurons of the brainÆ artificial neural networks
‰ 1951: von Neumann helps Minsky and Edmonds to build the first neural
network computer.
‰ 1956: The first AI workshop sponsored by IBMÆ Birth of AI
‰ 1958: McCarthy presents a paper “Program with common sense”.
‰ 1962: Rosenblatt proves the perception convergence theorem (learning
algorithm)

33. Dr. Tarek Helmy, ICS-KFUPM 33
AI Brief History
‰ 1965: Lotfy Zadeh introduces Fuzzy sets
‰ Early 70s: shift from a general purpose, knowledge-sparse, weak methods to domain-
specific, knowledge-intensive techniques (ES)
 Mycin: rule-based expert system for diagnosis of infectious blood diseases
‰ Mid 80s: use of neural networks for machine learning.
 Generalization of single-layer network: Hopfield network, back-propagation.
 Knowledge engineering: use of Fuzzy logic improves computational power,
improves cognitive modeling, allows to represent multiple experts
‰ In 1995 The emergence of intelligent agents

34. Dr. Tarek Helmy, ICS-KFUPM 34
Can a machine be truly “intelligent”? : Turing’s Test
‰ Alan Turing's 1950 article in Computing Machinery and
Intelligence discussed conditions for considering a
machine to be intelligent
‰ Can someone tell which is the machine, when
communicating to human and to a machine in another
room? If not, can we call the machine intelligent?
‰ If the computer succeeds in fooling the judge then it has
managed to exhibit a human level of intelligence in the
task of pretending to be a woman, the definition of
intelligence the machine has shown itself to be
intelligent.
Which one’s the computer?
A B

35. Dr. Tarek Helmy, ICS-KFUPM 35
What would a computer need to pass the Turing test?
‰ Passing Turing test requires the computer to have the following capabilities:
1. NLP Æ to communicate in English with the examiner
2. Knowledge Representation Æ to store information provided during the
test
3. Automated reasoning Æ to use stored information to answer questions
and draw conclusions.
4. Machine learning Æ to adapt to new circumstances and to detect and
extrapolate patterns.
5. Computer vision Æ to recognize the examiner’s actions and various
objects presented by the examiner.
5. Robotics Æ to act upon objects as requested.

36. Dr. Tarek Helmy, ICS-KFUPM 36
AI Technologies
Previous, Today, Future
‰ Cognitive-Based AI
(has to percept, learn and reason)
 Expert Systems
 Decision Support Systems
 Natural Language
Processing
 Intelligent Agents
 Collaborative Intelligent
Agent Networks
 Fuzzy Logic
‰ Biologically-Based AI
 Neural Nets
 Genetic Algorithms
 Speech Recognition
 Computer Vision
 Evolutionary (changeable)
Programming
 Machine Learning
 Robotics

37. Dr. Tarek Helmy, ICS-KFUPM 37
The End!!
Thank you
Any Questions?