Ch 1 Introduction to AI Applications.pdf

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ARTIFICIAL INTELLIGENCE
Introduction: Chapter 1
‫اإلصطناعي‬ ‫الذكاء‬

How would you define “intelligence”?
What is the common definition of “AI”?
What are the AI sub-topics? Which topics
failed? successful? Why?
Do you know any AI real application?
Should artificial intelligence simulate natural
intelligence?
What is the relation between AI and logic?
Do you think that computers or machines will
ever be as intelligent as humans?
What is the main advantage of computers
over people and vice versa?
How far is AI from reaching human-level
intelligence? When will it happen.
Are computers fast enough to be intelligent?
Do you definitely agree that AI augmenting
human capability and capacity, or it will
damage the human life?

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What is Intelligent
There are many definitions of intelligence.
A person that learns fast or one that has a vast
amount of experience, could be called
"intelligent".
However for our purposes the most useful definition
is: systems comparative level of performance in
reaching its objectives
persons are not intelligent in all areas of knowledge, they are only
intelligent in those areas where they had experiences.

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AI Goals
• Artificial Intelligent is the part of computer science with designing
intelligent computer systems, that is, systems that have
characteristics associate with intelligence in human behaviour –
understanding language, learning, reasoning, solving
problems………………
• Scientific Goal To determine which ideas about knowledge
representation, learning, rule systems, search, and so on, explain
various sorts of real intelligence.
• Engineering Goal To solve real world problems using AI
techniques such as..
knowledge representation, learning, rule systems, search, and so
on.

Artificial Intelligence in the Movies

Why the interest in AI?
Search engines
Labor
Science
Medicine/
Diagnosis
Appliances What else?

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What is AI?
Views of AI fall into four categories:
• Thinking humanly: systems that thinks like humans, (machine
with mind). Activities as decision-making, problem solving,
learning,……
• Thinking rationally: the study of thinking faculties.
• Acting humanly: systems that acting like humans, the study of
how to make computers do things.
• Acting rationally: The study of designing intelligent agents
The textbook advocates “Acting Rationally"

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Systems that
think like humans
Systems that
think rationally
Systems that act
like humans
Systems that act
rationally
Turing test
Cognitive
science
Logic
Agents

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How do Humans do Intelligent Things?
• It seems natural to try to base our AI systems on the human nervous system.
This can be broken down into three stages that may be represented in block
diagram form as:
Receptors collect information from the environment, and effectors generate
interactions with the environment. The flow of information between them is
represented by arrows
– both forward and backward.
What we generally describe as “intelligence” is normally carried out in the central
stage
– in the brain. The brain is known to consist of an interconnected network of
neurons, and the study of neural networks is now a major sub-field of AI.

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Acting Humanly: Turing Test
• Turing (1950) "Computing machinery and intelligence":
• "Can machines think?" → "Can machines behave intelligently?"
• Operational test for intelligent behavior: the Imitation Game
• Alan Turing's discussed the conditions for considering a machine to be
intelligent. He discusses that if the machine could successfully pretend to be
human to a knowledgeable observer then you certainly should consider it
intelligent.
• Ask questions of two entities, and receive answers from both
• If you can’t tell which of the entities is human and which is a computer
program, then we should therefore consider the computer to be intelligent

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Sub-fields of Artificial Intelligence
AI now consists many sub-fields, using a variety of techniques, such as:
 Neural Networks – e.g. brain modeling, time series prediction,
classification
 Evolutionary Computation – e.g. genetic algorithms, genetic
programming
 Computer Vision – e.g. object recognition, image understanding
 Robotics – e.g. intelligent control, autonomous exploration
 Expert Systems – e.g. decision support systems, teaching
systems
 Speech Processing– e.g. speech recognition and production
Natural Language Processing – e.g. machine translation
 Machine Learning – e.g. decision tree learning, version space
learning
Most of these have both engineering and scientific aspects.

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Rational agents
• An agent is an entity that perceives and
acts
• This course is about designing rational
agents
• an agent is a function from percept
histories to actions:
[ f : P* → A]
• For any given class of environments and
tasks, we seek the agent (or class of
agents) with the best performance
• Note: Computational limitations make
perfect rationality unachievable
→ Design the best program for given
machine resources

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Examples of AI Agents
Humans Programs Robots___
senses keyboard, mouse, dataset cameras, pads
body parts monitor, speakers, files motors, limbs
Ch2 Intelligent Agents (input, output, Types, ……)

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The Roots of AI
AI has roots in a number of older sciences , particularly:
• Philosophy
• Logic/Mathematics
• Computation
• Psychology/Cognitive Science
• Biology/Neuroscience
• Evolution
• By looking at each of these in turn, we can gain a better
understanding of their role in AI, and how these underlying the
developed to play that role.

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History of AI: 1952- 1969
• Great successes!
– Solving hard math problems
– game playing
– LISP was invented by McCarthy (1958)
– McCarthy went to MIT and Marvin Minsky started lab at
Stanford (Both powerhouses in AI to this day)
History of AI: 1966 - 1973
• Reality
– Systems fail to play chess and translate Russian
– neural networks was exposed (neural networks did not return
to appear until late 1980s)

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AI History: 1969 - 1979
• Knowledge-based Systems (Expert systems)
– Problem: General logical algorithms could not be applied to
realistic problems
– Solution: accumulate specific logical algorithms
• DENDRAL – infer chemical structure
• AI History: 1987 -2000
• AI becomes a science
– More repeatability of experiments
– More development
• Intelligent Agents (1994)
– AI systems exist in real environments with real sensory inputs

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2000- Where are We Now?
– Autonomous planning: scheduling operations aboard a robot
– Game playing: Kasparov lost to IBM’s Big Blue in chess
– Autonomous Control: CMU’s NAVLAB drove from Pittsburgh to
San Francisco under computer control 98% of time
– Stanford vehicle wins 2006 DARPA Grand Challenge
CMU’s 2005 vehicle falls crashes at starting line
– Logistics: organized the time tables for any task.
– Robotics: remote heart operations.
– human genome, protein folding, drug discovery.
– stock market …………………….etc.

1. AI Application in E-Commerce
Recommendation engines , chatbots help improve the
user experience , Credit card fraud and fake reviews.
2. Applications Of Artificial Intelligence in
Education
Creating Smart Content, Voice Assistants,
Personalized Learning
3. Applications of Artificial Intelligence in Lifestyle
Autonomous Vehicles, Spam Filters, Facial
Recognition, Recommendation System
4. Applications of Artificial Intelligence in
Navigation
GPS technology can provide users with accurate,
timely, and detailed information to improve safety.
5. Applications of Artificial Intelligence in Robotics
: Carrying goods in hospitals, factories, and
warehouses, Cleaning offices and large equipment 25
Human Resource
job candidates' profiles
Healthcare
detect diseases and identify cancer cells,
analyze chronic conditions , early diagnosis.
Agriculture
computer vision, robotics
Automobiles
self-driving
.
Social Media
determine what posts you are shown.
DeepText can understand conversations better.
Marketing
• Data mining
2000- Where are We Now?

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1
Genetic Algorithms
• Basic scheme
– (1)Initialize population
– (2)evaluate fitness of each member
– (3)Selection of the best Chromosomes
– (4) Crossover
– (5) introduce random mutations in new
generation
– Continue (2)-(3)-(4) until prespecified
number of generations are complete
• Start with k randomly generated states
(population)
• Evaluation function (fitness function).
Higher values for better states.
• Produce the next generation of states by
selection, crossover, and mutation

A Simple Example
The Traveling Salesman Problem:
Find a tour of a given set of cities so that
– each city is visited only once
– the total distance traveled is minimized
Representation
Representation is an ordered list of city
numbers known as an order-based GA.
1) London 3) Dunedin 5) Beijing 7) Tokyo
2) Venice 4) Singapore 6) Phoenix 8) Victoria
CityList1 (3 5 7 2 1 6 4 8)
CityList2 (2 5 7 6 8 1 3 4)

TSP Example: 30 Cities
0
20
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120
0 10 20 30 40 50 60 70 80 90 100
y
x

Solution i (Distance = 941)
0
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0 10 20 30 40 50 60 70 80 90 100
y
x
TSP30 (Performance = 941)

Solution j(Distance = 800)
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69
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78
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62
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42
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21
35
67
60
60
40
42
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99
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y
x

Solution k(Distance = 652)
0
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y
x

Best Solution (Distance = 420)
42
38
35
26
21
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7
38
46
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58
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62
84
94
0
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0 10 20 30 40 50 60 70 80 90 100
y
x
TSP30 Solution (Performance = 420)

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Computer Vision: The world is composed of three-dimensional objects, but
the inputs to the human eye and computers' TV cameras are two dimensional.
Some useful programs can work in two dimensions, but full computer vision requires
partial three-dimensional information that is not just a set of two-dimensional views.
At present there are only limited ways of representing three-dimensional information
directly, and they are not as good as what humans evidently use.

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The Poseidon system is based on a
network of overhead and underwater
cameras installed in a public pool.
all linked to a computer system that is
going to acquire video signals in real-time
filters them extracts human body shapes
from images and assesses the movement of
these bodies.
Whenever the system detects that a body
movement pattern (or lack thereof)
resembles one of a drowning swimmer, it
sends an alert to lifeguards through
pagers that indicate the location of the
endangered person.
Computer Vision applied System

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• In the 1990s, computer speech recognition reached a practical
level for limited purposes. Thus United Airlines has replaced its
keyboard tree for flight information by a system using speech
recognition of flight numbers and city names. It is quite
convenient.
Speech recognition application
• Telephone-based Information (directions, air travel, banking, etc)
• Hands-free (in car)
• Second language ('L2') (accent reduction)
• Audio archive searching
Speech recognition

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Complex example used speech recognition

• The process of building expert systems is often called knowledge engineering.
The knowledge engineer is involved with all components of an expert system:
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Expert Systems
Building expert systems is generally an iterative process. The components and their
interaction will be refined over the course of numerous meetings of the knowledge
engineer with the experts and users. We shall look in turn at the various components.

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Goal: To create computational models of language in enough detail
that you could write computer programs to perform various tasks
involving natural language.
Scientific: to explore the nature of linguistic communication
Practical: to enable effective human-machine communication
Just getting a sequence of words into a computer is not enough.
Parsing sentences is not enough either.
The computer has to be provided with an understanding of the
domain the text is about, and this is presently possible only for
very limited domains.
Understanding Natural Language

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AI Branches
Representation Knowledge needs to be represented somehow – perhaps as a
series of if-then rules, as a frame based system, as a semantic network, or in the
connection weights of an artificial neural network.
Learning Automatically building up knowledge from the environment – such as
acquiring the rules for a rule based expert system, or determining the appropriate
connection weights in an artificial neural network.
(Detailed in next chapters)
Rules These could be explicitly built into an expert system by a knowledge
engineer, or implicit in the connection weights learnt by a neural network.
Search This can take many forms – perhaps searching for a sequence of states
that
leads quickly to a problem solution, or searching for a good set of connection
weights for a neural network by minimizing a fitness function.

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Game playing
Game playing is a search problem Defined by:
– Initial state – Successor function
– Goal test – Path cost / utility / payoff function
Characteristics of game playing:
• Initial state: initial board position and player
• Operators: one for each legal move
• Terminal states: a set of states that mark the end of the game
• Utility function: assigns numeric value to each terminal state
• Game tree: represents all possible game scenarios

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(Our) Basis of Game Playing: Search for best move
every time
Initial Board State Board State 2 Board State 3
Board State 4 Board State 5
Search for Opponent
Move 1 Moves 2
Search for Opponent
Move 3 Moves

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May, 1997: Deep Blue beats the World Chess Champion
I could feel human-level intelligence across the room
vs.
You can buy machines that can play master level chess for a few
hundred dollars. There is some IS in them, but they play well
against people mainly through brute force computation
looking at hundreds of thousands of positions. To beat a world
champion by brute force and known reliable heuristics requires
being able to look at 200 million positions per second.

What Can AI Do? From these examples
• Play a game of table tennis?
• Drive safely along a road with signals?
• Drive safely along any road?
• Buy a week's worth of groceries on the web?
• Buy a week's worth of groceries at Berkeley Bowl?
• Discover and prove a new mathematical theorem?
• Converse successfully with another person for an hour?
• Perform a complex surgical operation?
• Unload a dishwasher and put everything away?
• Translate spoken English into spoken Arabic in real time?
• Write an intentionally funny story?
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