Lect 01, 02

ARTIFICIAL INTELLIGENCE
SHAKEEL AHMAD
Lecture 1,2

Overview of Artificial
Intelligence
The Foundation of AI History of AI Applications of AI
THIS LECTURE

Course Learning Outcomes
At the end of this course:
 Knowledge and understanding
You should have a knowledge and understanding of the basic concepts of Artificial
Intelligence including Search, Game Playing, KBS (including Uncertainty), Planning
and Machine Learning.
 Intellectual skills
You should be able to use this knowledge and understanding of appropriate
principles and guidelines to synthesise solutions to tasks in AI and to critically
evaluate alternatives.
 Practical skills
You should be able to use a well known language (Python) and to construct simple
AI systems.
 Transferable Skills
You should be able to solve problems and evaluate outcomes and alternatives

Attendance
You are expected to attend all the lectures. The lecture notes (see below) cover all the topics
in the course, but these notes are concise, and do not contain much in the way of
discussion, motivation or examples. The lectures will consist of slides (Powerpoint ),
spoken material, and additional examples given on the whiteboard. In order to
understand the subject and the reasons for studying the material, you will need to attend
the lectures and take notes to supplement lecture slides. This is your responsibility. If
there is anything you do not understand during the lectures, then ask, either during or
after the lecture. If the lectures are covering the material too quickly, then say so. If there
is anything you do not understand in the slides, then ask.
In addition you are expected to supplement the lecture material by reading around the
subject; particularly the course text.
Must use text book and references.

Overview of Artificial Intelligence

What is Artificial Intelligence ?
 making computers that think?
 the automation of activities we associate with human thinking, like
decision making, learning ... ?
 the art of creating machines that perform functions that require
intelligence when performed by people ?
 the study of mental faculties through the use of computational models ?

 the study of computations that make it possible to perceive, reason
and act ?
 a field of study that seeks to explain and emulate intelligent
behaviour in terms of computational processes ?
 a branch of computer science that is concerned with the
automation of intelligent behaviour ?
 anything in Computing Science that we don't yet know how to do
properly ? (!)

 Artificial
 Produced by human art or effort, rather than originating
naturally.
 Intelligence
 is the ability to acquire knowledge and use it"
[Pigford and Baur]
 So AI was defined as:
 AI is the study of ideas that enable computers to be
intelligent.
 AI is the part of computer science concerned with
design of computer systems that exhibit human
intelligence(From the Concise Oxford Dictionary)

 What is AI ?
 Artificial intelligence is the study of how to
make computers do things which, at the
moment people do better.
 Some definitions of artificial intelligence,
organized into four categories

Definition -1
Systems that act
rationally
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
HUMAN RATIONAL
1
2 3
4
Acting humanly
Thinking humanly Thinking rationally
Acting rationally

Systems that act like humans (Acting humanly)
(Turing Machine Test)
❑The Turing Test, proposed by (Alan Turing -1950)
❑“This test represent the art of creating machines
that perform functions that require intelligence
when performed by people.” (Kurzweil-1990)
❑“This test represent the study of how to make
computers do things at which, at the moment,
people are better.” (Rich and Knight-1991)
❑The inability to distinguish computer responses
from human responses is called the Turing
test

A
B
Systems that act like humans (Acting humanly)
(Turing Machine Test ‫تيورنج‬ ‫)إختبار‬

Systems that act like humans
 You enter a room which has a computer terminal.You
have a fixed period of time to type what you want into
the terminal, and study the replies.At the other end of
the line is either a human being or a computer system.
 If it is a computer system, and at the end of the period
you cannot reliably determine whether it is a system or
a human, then the system is deemed to be intelligent.
?

 TheTuringTest approach
 a human questioner cannot tell if
 there is a computer or a human answering his question, via teletype
(remote communication)
 The computer must behave intelligently
 Intelligent behavior
 to achieve human-level performance in all cognitive tasks

 These cognitive tasks include:
 Natural language processing
 for communication with human
 Knowledge representation
 to store information effectively & efficiently
 Automated reasoning
 to retrieve & answer questions using the stored information
 Machine learning
 to adapt to new circumstances

The total Turing Test
Includes two more issues:
 Computer vision
to perceive objects (seeing)
 Robotics
to move objects (acting)

Systems that act
rationally
Systems that
think
like humans
Systems that
think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
HUMAN RATIONAL

Systems that think like humans (Thinking humanly)
(Cognitive Modeling)
❑To construct a machines program to think like a
human, it require two important things:
➢first it requires the knowledge about the actual
workings of human mind i.e.Formulate a theory of
mind/brain.
➢ Express the theory in a computer program
❑ Two Approaches
➢ Cognitive Science and Psychology (testing/ predicting
responses of human subjects)
➢ Cognitive Neuroscience (observing neurological data)

❑ After completing the study about human mind, it is
possible to express the theory as a computer
program.
❑ If the program’s inputs/output and timing behavior
matched with the human behavior, then we can say
that the program’s mechanism is working like a
human mind.

❑Humans are observed from ‘inside’
❑How humans think? 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.
➢Through brain imaging —observing the brain in action.

➢Cognitive Science
➢“The exciting new effort to make computers
think … machines with minds in the full and
literal sense” (Haugeland1985)
➢“[The automation of] activities that we
associate with human thinking, activities
such as decision-making, problem solving,
learning , mental organization…”
(Bellman1978)

❑Example: General Problem Solver (GPS) – A
problem solvers always keeps track of human mind
regardless of right answers.
❑The problem solver is contrast to other researchers,
because they are concentrating on getting the right
answers regardless of the human mind.
❑An Interdisciplinary field of cognitive science uses
computer models from AI and experimental
techniques from psychology to construct the theory
of the working of the human mind.

Systems that act
rationally
Systems that think
like humans
Systems that think
rationally
Systems that act
like humans
THOUGHT
BEHAVIOUR
HUMAN RATIONAL

Systems that think ‘rationally’
"laws of thought"
 Humans are not always‘rational’
 Rational - defined in terms of logic?
 Logic can’t express everything (e.g. uncertainty)
 Logical approach is often not feasible in terms of
computation time (needs‘guidance’)
 “The study of mental facilities through the use of
computational models” (Charniak and McDermott)
 “The study of the computations that make it possible to
perceive, reason, and act” (Winston)

Systems that think rationally
❑Rational -> ideal intelligence
➢(contrast with human intelligence)
❑Rational thinking governed by precise “laws of
thought”
➢syllogisms
➢notation and logic
❑Systems (in theory) can solve problems using such
laws

Systems that act rationally:
“Rational agent”
 Rational behavior: doing the right thing
 The right thing: that which is expected to maximize goal
achievement, given the available information
 Giving answers to questions is‘acting’.
 I don't care whether a system:
replicates human thought processes
makes the same decisions as humans
uses purely logical reasoning

Systems that act rationally
 Logic → only part of a rational agent, not all of
rationality
 Sometimes logic cannot reason a correct conclusion
 At that time, some specific (in domain) human knowledge or
information is used
 Thus, it covers more generally different situations of
problems
 Compensate the incorrectly reasoned conclusion

Systems that act rationally
 Study AI as rational agent –
2 advantages:
 It is more general than using logic only
 Because: LOGIC + Domain knowledge
 It allows extension of the approach with more scientific
methodologies

 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:

[f: P* →A]
 For any given class of environments and tasks, we seek the agent
(or class of agents) with the best performance
 Caveat: computational limitations make perfect rationality
unachievable
 → design best program for given machine resources

Systems that act rationally (Acting rationally)
“Rational Agent”

 Artificial
 Produced by human art or effort, rather than originating
naturally.
 Intelligence
 is the ability to acquire knowledge and use it" [Pigford
and Baur]
 So,AI was defined as:
 AI is the study of ideas that enable computers to be intelligent.
 AI is the part of computer science concerned with design of
computer systems that exhibit human intelligence(From the
Concise Oxford Dictionary)

From the above two definitions, we can see that AI has two
major roles:
 Study the intelligent part concerned with humans.
 Represent those actions using computers.

Goals of AI
 To make computers more useful by letting them take over
dangerous or tedious tasks from human
 Understand principles of human intelligence

Academic Disciplines important to AI.
Philosophy
Mathematics
AI
Economics
Cognitive
Science
Psychology
Control
Theory
Linguistics
Neuroscience
Computer
Engineering

Philosophy ➢ Study of human intelligence began with no
formal expression
➢ Initiate the idea of mind as a machine and its
internal operations and processes
➢ Logic, methods of reasoning
➢ foundations of learning, language rationality.
Mathematics ➢ formalizes the three main area of AI: computation,
logic, and probability
➢ Computation leads to analysis of the problems
that can be computed (complexity theory)
Economics ➢ utility, decision theory
Neuroscience ➢ neurons as information processing units.

Psychology/
Cognitive
Science
➢How do humans think and act?
➢The study of human reasoning and acting
➢Provides reasoning models for AI
➢How do people behave, perceive, process
information, represent knowledge.
Computer
Engineering
➢Building fast and efficient computers
➢The power of computer makes computation of
large and difficult problems more easily
Control
Theory
➢Design systems that maximize an objective
function over time
➢How can artifacts operate under their own control?
➢The artifacts adjust their actions
Linguistics ➢Knowledge representation, grammar
➢For understanding natural languages
➢Syntactic and semantic analysis

AI Research Areas
Artificial
Intelligence
Research Areas
Expert
Systems
Natural
Language
Processing
Neural
Networks
Fuzzy
Logic
Robotics
Optimization
Machine
Learning
Automation

Typical problems to which AI methods are applied
❑Pattern recognition
❑Optical character recognition
❑Handwriting recognition
❑Speech recognition
❑ Face recognition
❑ Computer vision
❑ Virtual reality
❑Image processing
❑Diagnosis

❑ Translation and Chatterboxes
❑ Nonlinear control and Robotics
❑ Artificial life
❑ Automated reasoning
❑ Automation
❑ Biologically inspired computing
❑ Concept mining
❑ Data mining
❑ Knowledge representation

❑ Game theory and Strategic planning
❑ Natural Language Processing(NLP)
❑Semantic Web
❑ E-mail spam filtering
❑Cognitive
❑Cybernetics
❑Hybrid intelligent system
❑Intelligent agent
❑Intelligent control

Search
 Search is the fundamental technique of AI.
 Possible answers, decisions or courses of action are structured into an abstract
space, which we then search.
 Search is either "blind" or “uninformed":
 blind
 we move through the space without worrying about what is coming next, but
recognising the answer if we see it
 informed
 we guess what is ahead, and use that information to decide where to look
next.
 We may want to search for the first answer that satisfies our goal, or we may want
to keep searching until we find the best answer.

Knowledge Representation & Reasoning
 The second most important concept in AI
 If we are going to act rationally in our environment, then we must have some way of
describing that environment and drawing inferences from that representation.
 how do we describe what we know about the world ?
 how do we describe it concisely ?
 how do we describe it so that we can get hold of the right piece of knowledge
when we need it ?
 how do we generate new pieces of knowledge ?
 how do we deal with uncertain knowledge ?

Knowledge
Declarative Procedural
• Declarative knowledge deals with factoid questions
(what is the capital of India? Etc.)
• Procedural knowledge deals with “How”
• Procedural knowledge can be embedded in
declarative knowledge

Planning
Given a set of goals, construct a sequence of actions that achieves those goals:
 often very large search space
 but most parts of the world are independent of most other parts
 often start with goals and connect them to actions
 no necessary connection between order of planning and order of execution
 what happens if the world changes as we execute the plan and/or our
actions don’t produce the expected results?

Learning
 If a system is going to act truly appropriately, then it must be
able to change its actions in the light of experience:
how do we generate new facts from old ?
how do we generate new concepts ?
how do we learn to distinguish different situations
in new environments ?

Interacting with the Environment
 In order to enable intelligent behaviour, we will have to
interact with our environment.
 Properly intelligent systems may be expected to:
 accept sensory input
vision, sound, …
 interact with humans
understand language, recognise speech,
generate text, speech and graphics, …
 modify the environment
robotics

History of AI
The birth of
artificial
intelligence
(1943–56)
➢McCulloch and Pitts, A Logical Calculus of the Ideas
Immanent in Nervous Activity, 1943
➢Alan Turing, Computing Machinery and Intelligence, 1950
➢The Electronic Numerical Integrator and Calculator
(ENIAC) project (Von Neumann)
➢Shannon, Programming a Computer for Playing
Chess,1950
➢The Dartmouth College summer workshop on machine
intelligence, artificial neural nets and automata theory,1956
The rise of
artificial
intelligence
(1956–late
1960s)
➢ Invention of LISP (John McCarthy)
➢The General Problem Solver (GPR) project (Newell and Simon)
➢Gelertner: Geometry Theorem Prover
➢Newell and Simon, Human Problem Solving, 1972
➢Minsky, A Framework for Representing Knowledge, 1975

History of AI
Reality dawns
1966—73:
➢Realization that many AI problems are intractable
➢Limitations of existing neural network methods
identified
➢Neural network research almost disappears
Adding domain
knowledge
1969—85:
➢ Development of knowledge-based systems
➢ Success of rule-based expert systems,
➢ E.g., DENDRAL, MYCIN
➢But were brittle and did not scale well in practice
Rise of
machine
learning
1986--
➢ Neural Networks return to popularity
➢Major advances in machine learning algorithms and
applications

History of AI
Role of
uncertainty
1990--
➢ Bayesian networks as a knowledge representation
framework
Major
advances
in all areas
of AI −
1990
➢Significant demonstrations in machine learning
➢Case-based reasoning
➢Multi-agent planning
➢Scheduling
➢Data mining, Web Crawler
➢natural language understanding and translation
➢Vision, Virtual Reality
➢Games

History of AI
AI as
Science
1995--
➢ Integration of learning, reasoning, knowledge
representation
➢AI methods used in vision, language, data mining,
etc
1997 ➢The Deep Blue Chess Program beats the then world
chess champion, Garry Kasparov.
2000
➢Interactive robot pets become commercially available.
MIT displays Kismet, a robot with a face that expresses
emotions. The robot Nomad explores remote regions
of Antarctica and locates meteorites.

First work in AI
1943
The name
“Artificial
Intelligence”
coined
1956
Development of
Lisp
1958
Microworlds
1963
Realization of
hurdles
AI becomes an
Industry
1981
Neural Networks
reinvented
1986
AI a booming
Industry TODAY
Paradigm Shift, GPS, People realized that all the
problems can NOT be solved with the same
approach
Paradigm Shift, Expert Systems, People
realized that software programs can act as
EXPERTS
Paradigm Shift, ANN, People realized that
software programs can LEARN

News Generation − Did you know that artificial intelligence
programs can write news stories? According to Wired, the AP,
Fox, and Yahoo! all use AI to write simple stories like
financial summaries, sports recaps, and fantasy sports
reports. AI isn’t writing in-depth investigative articles, but it
has no problem with very simple articles that don’t require a
lot of synthesis. Automated Insights, the company behind
the Wordsmith software, says that e-commerce, financial
services, real estate, and other “data-driven” industries are
already benefitting from the app.
AI applications

AI Applications
 Autonomous Planning & Scheduling:
 Autonomous rovers.
 Telescope scheduling

AI Applications
 Autonomous Planning & Scheduling:
 Analysis of data:

AI Applications
 Medicine:
 Image guided surgery

AI Applications
 Medicine:
 Image analysis and enhancement

❑ Speech Recognition − Some intelligent systems are
capable of hearing and comprehending the language in
terms of sentences and their meanings while a human talks
to it. It can handle different accents, slang words, noise in
the background, change in human’s noise due to cold, etc.
❑ Intelligent Robots − Robots can perform the tasks given by
a human. They have sensors to detect physical data from
the real world such as light, heat, temperature, movement,
sound, bump, and pressure. They have efficient processors,
multiple sensors and huge memory, to exhibit intelligence. In
addition, they are capable of learning from their mistakes,
and they can adapt to the new environment
AI applications

AI Applications
 Transportation:
 Autonomous vehicle control:
❑ Smart Cars − You probably haven’t seen someone reading
the newspaper while driving to work yet, but self-driving cars
are moving closer and closer to reality; Google’s self-driving
car project and Tesla’s “autopilot” feature are two examples
that have been in the news lately. Earlier this year,
the Washington Post reported on an algorithm developed by
Google that could potentially let self-driving cars learn to
drive in the same way that humans do: through experience.

AI Applications
 Transportation:
 Pedestrian detection:

AI Applications
Games: AI plays crucial role in strategic games such as chess,
poker, tic-tac-toe, etc., where machine can think of large number
of possible positions based on heuristic knowledge.

AI Applications
 Robotic toys:
❑ Robotics for Heavy industry − have become common in many
industries and are often given jobs that are considered dangerous to
humans.
❑ Robots have proven effective in jobs that are very repetitive which may
lead to mistakes or accidents due to a lapse in concentration and other
jobs which humans may find degrading.
❑ In 2014, China, Japan, the United States, the Republic of Korea and
Germany together amounted to 70% of the total sales volume of robots.
❑ In the automotive industry, a sector with particularly high degree of
automation, Japan had the highest density of industrial robots in the world:
1,414 per 10,000 employees.

❑ Human Performance Modeling
(HPM) is a method of quantifying human behavior,
cognition, and processes; a tool used by human
factors researchers and practitioners for both the
analysis of human function and for the development
of systems designed for optimal user experience and
interaction .
❑ Data Mining and Data warehousing (Big –Data)
AI applications

AI - (2017-2018) -Diaa Eldein Mustafa - Lecture (1)
-Introduction to Artificial Intelligence(2/2)
66
❑ Handwriting Recognition − The handwriting recognition
software reads the text written on paper by a pen or on
screen by a stylus. It can recognize the shapes of the letters
and convert it into editable text.
❑ Vision Systems − These systems understand, interpret, and
comprehend visual input on the computer. For example,
❑ A spying aero plane − takes photographs, which are used to
figure out spatial information or map of the areas.
❑ Clinical expert system − Doctors use it to diagnose the
patient.
❑ Face recognition − Police use computer software that can
recognize the face of criminal with the stored portrait made
by forensic artist.
AI applications

AI Applications
Other application areas:
 Bioinformatics:
 Gene expression data analysis
 Prediction of protein structure
 Text classification, document sorting:
 Web pages, e-mails
 Articles in the news
 Video, image classification
 Music composition, picture drawing
 Natural Language Processing

Areas of AI and Some Dependencies

Areas of AI and Some Dependencies
Machine
Learning
Computer
Vision
Expert
Systems
Robotics
NLP
Search Logic
Planning
Knowledge
Representation

Sub-fields of Artificial Intelligence
❑Neural Networks – e.g. brain modeling, time series prediction,
classification
❑ Evolutionary Computation – e.g., genetic algorithms, genetic
programming
❑ 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
❑ Planning – e.g. scheduling (air line systems, railways ), game
playing
❑Machine Learning – e.g. decision tree learning, version space
learning

Importance of AI
❑Game Playing
❑Speech Recognition
❑Understanding Natural Language
❑ComputerVision
❑Expert Systems
❑Heuristic Classification

Advantages
❑More powerful and more useful computers
❑New and improved interfaces
❑Solving new problems
❑Better handling of information
❑Relieves information overload
❑Conversion of information into knowledge
Disadvantages
❑Increased costs
❑Difficulty with software development - slow and expensive
❑Few experienced programmers
❑Few practical products have reached the market yet
Some Aspects of Artificial Intelligence

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