Artifitial intelligence (ai) all in one

 Prepared By:
 Engr: Basharat Jehan
Qualification: MS Software Engineering
Lecturer Agriculture University Peshawar Amir
Muhammad Khan Campus Mardan KP,
Pakistan
 Email id: Basharatjehan1987@gmail.com

Week 1
 Introduction to A.I.
 Scope
 Natural intelligence vs. artificial intelligence
 AI computing vs. traditional computing

Father of Artificial Intelligence

INTELLIGENCE
 Intelligence is the computational part of the
ability to achieve goals in the world. Varying
kinds and degrees of intelligence occur in
people, many animals and some machines.

Artificial intelligence (AI)
 Artificial intelligence (AI) is the intelligence
exhibited by machines or software.
 Major AI researchers and textbooks define this
field as "the study and design of intelligent
agents", where an intelligent agent is a system
that perceives its environment and takes
actions that maximize its chances of success.

 John McCarthy, who coined the term in 1955,
defines it as "the science and engineering of
making intelligent machines".

Main area’s of Research in
AI
 reasoning,
 knowledge,
 planning,
 learning,
 natural language processing
(communication),
 perception and the ability to move and
manipulate objects

Scope of AI
 Automated Reasoning
 Data Mining
 Intelligent Agents
 Robotics
 Machine Learning
 Natural Language Processing
 Pattern Recognition
 Semantic Web

12
Artificial Neural Network
Artificial neural networks
A computer representation of knowledge that
attempts to mimic the neural networks of the
human body.

WEEK 2
Application areas of AI
 Expert systems
 Natural Language Processing (NLP)
 Computer vision
 Speech recognition and generation
 Robotics
 Neural network
 Virtual reality

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
 Customer Service
 automatic voice recognition
 The Web
 Identifying your age, gender, location, from your Web surfing
 Automated fraud detection
 Digital Cameras
 Automated face detection and focusing
 Computer Games
 Intelligent characters/agents

FINANCE
 Banks use artificial intelligence systems to:
 organize operations
 invest in stocks
 and manage properties.
 In August 2001, robots beat humans in a
simulated financial trading competition.
 Financial institutions have long used artificial
neural network systems to detect charges or
claims outside of the norm, flagging these for
human investigation.

HOSPITALS AND MEDICINE
 A medical clinic can use artificial intelligence
systems to organize
 bed schedules
 make a staff rotation
 provide medical information and other
important tasks.

 Artificial neural networks are used as clinical
decision support systems for medical
diagnosis, such as in Concept
Processing technology in EMR software.

 Computer-aided interpretation of medical
images
Such systems help scan digital
images, e.g. from computed tomography,
for typical appearances and to highlight
conspicuous sections, such as possible
diseases. A typical application is the
detection of a tumor.
 Heart sound analysis

HEAVY INDUSTRY
 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.
 Japan is the leader in using and producing
robots in the world.
 In 1999, 1,700,000 robots were in use
worldwide.

TRANSPORTATION
 Fuzzy logic controllers have been developed
for automatic gearboxes in automobiles.
 For example, the 2006 Audi TT, VW Toureg
VW Caravell feature the DSP transmission
which utilizes Fuzzy Logic. A number of Škoda
variants also currently include a Fuzzy Logic
based controller.

TELECOMMUNICATIONS
MAINTENANCE
 Many telecommunications companies make
use of heuristic search in the management of
their workforces
 for example BT Group has deployed heuristic
search in a scheduling application that
provides the work schedules of 20,000
engineers.

Toys and games
 The 1990s saw some of the first attempts to mass-
produce domestically aimed types of basic Artificial
Intelligence for education, or leisure.
 Digital Revolution
 Tamagotchis and Giga Pets, iPod Touch,
 The Internet
 The first widely released robot, Furby
 Aibo, a robotic dog with intelligent features
and autonomy.
 AI has also been applied to video games, for
example video game bots, which are designed to
stand in as opponents where humans aren't available
or desired

AVIATION
 The Air Operations Division (AOD) uses AI for
the rule based expert systems.
 The AOD has use for artificial intelligence for
surrogate operators for combat and training
simulators
1. Mission management aids
2. Support systems for tactical decision making
3. Post processing of the simulator data into
symbolic summaries.

 Airplane simulators are using artificial
intelligence in order to process the data taken
from simulated flights.
 The computers are able to come up with the
best success scenarios in these situations.
 The computers can also create strategies
based on the placement, size, speed and
strength of the forces and counter forces.
Pilots may be given assistance in the air
during combat by computers.

SPEECH RECOGNITION
 In the 1990s, computer speech recognition
reached a practical level for limited purposes.
 United Airlines has replaced its keyboard tree
for flight information by a system using speech
recognition of flight numbers and city names.

Natural Language Processing
(NLP)

 is a field of computer science, artificial
intelligence, and linguistics concerned with the
interactions between computers and human
(natural) languages.
 As such, NLP is related to the area of human–
computer interaction.
 Many challenges in NLP involve natural language
understanding, that is, enabling computers to
derive meaning from human or natural language
input, and others involve natural language
generation.
Natural language
processing (NLP)

Major tasks in NLP
 Conference resolution
 Machine translation
 Named entity recognition
 Natural language
 understanding
 Speech recognition
 Topic segmentation
 Information retrieval
 Speech processing

 Automatic summarization
 Discourse analysis
 Morphological segmentation
 Natural language generation
 Optical character recognition
 Parsing
 Relationship extraction
 Sentiment analysis

History
 The first expert systems were created in the
1970s and then proliferated in the
1980s. Expert systems were among the first
truly successful forms of AI software

Expert systems
 In artificial intelligence, an expert system is a
computer system that emulates the decision-
making ability of a human expert
 Expert systems are designed to solve
complex problems by reasoning about
knowledge, represented primarily as if–then
rules rather than through
conventional procedural code

Computer vision
 Computer vision is a field that includes
methods for acquiring, processing, analyzing,
and understanding images and, high-
dimensional data from the real world in order
to produce numerical or symbolic
information, in the forms of decisions.

Applications for computer vision
 Applications range from tasks such as
industrial machine vision systems
 Computer vision covers the core technology of
automated image analysis which is used in
many fields.

APPLICATIONS OF COMPUTER VISION
INCLUDE
 Controlling processes: an industrial robot
 Navigation: by an autonomous vehicle or mobile
robot;
 Detecting events: for visual surveillance or people
counting
 Organizing information: for indexing databases of
images and image sequences;
 Modeling objects or environments: medical image
analysis or topographical modeling;
 Interaction: as the input to a device for computer-
human interaction
 Automatic inspection: in manufacturing applications.

Overview of relations between computer vision and other
fields

Speech recognition and generation
 Speech Technology
 The speech capabilities that can be added to an application
are text-to-speech synthesis (TTS) and speech recognition
(SR).
 Text-To-Speech Synthesis (TTS)
 This involves turning a string into spoken language that is
played through the computer speakers. The complexities of
turning words into phonemes, adding appropriate emphasis
and translating the result into digital audio are beyond the
scope of this paper and are catered for by a TTS engine
installed on your machine.
 The end result is that the computer talks to the user to save
the user having to read some text on the screen.

SPEECH RECOGNITION (SR)
 computer takes the user's speech and
interprets what has been said. This allows the
user to control the computer by voice, rather
than having to use the mouse and keyboard,
or alternatively just dictating the contents of a
document.

 The complex nature of translating the raw
audio into phonemes involves a lot of signal
processing.
 These details are taken care of by an SR
engine that will be installed on machine.
 SR engines are called recognisers and these
days typically implement continuous speech
recognition

Robotics
 Robotics is the branch of mechanical
engineering, electrical engineering and
computer science that deals with the design,
construction, operation, and application of
robots, as well as computer systems for their
control, sensory feedback, and information
processing. The word robotics was derived
from the word robot, which was introduced to
the public by Czech writer Karel Čapek in his
play R.U.R. (Rossum's Universal Robots),
which was published in 1920

Aspects of robotics
 Robots all have some kind of mechanical
construction, a frame, form or shape designed
to achieve a particular task.
 Robots have electrical components which
power and control the machinery.
 All robots contain some level of computer
programming code.

ARTIFICIAL NEURAL
NETWORKS
 In machine learning, artificial neural
networks (ANNs) are a family of statistical
learning algorithms inspired by biological
neural networks and are used to estimate
or approximate functions that can depend on a
large number of inputs and are generally
unknown.

• Virtual reality, is a computer-
simulated environment that can simulate
physical presence in places in the real
world or imagined worlds.
• Virtual reality can recreate sensory
experiences, which include virtual
taste, sight, smell, sound, touch, etc.
• It is also known as immersive multimedia

GAMES
 The use of graphics, sound and input
technology in video games can be
incorporated into VR
 the Virtual Boy developed by Nintendo, the
iGlasses developed by Virtual I-O, the
Cybermaxx developed by Victormaxx and the
VFX-1 developed by Forte Technologies

 There is also a new high field of view VR
headset system in development designed
specifically for gaming called the Oculus Rift

TRAINING
 The usage of VR in a training perspective is to
allow professionals to conduct training in a
virtual environment where they can improve
upon their skills without the consequence of
failing the operation.
 VR plays an important role in combat training
for the military

WEEK3
 Expert system
 Evolution of expert system
 Structure of expert system
 Types of expert system
 Main application areas of expert system

Expert system
 In artificial intelligence, an expert system is a
computer system that emulates the decision-
making ability of a human expert.

Evaluation of Expert System
 Expert systems were introduced by the
Stanford Heuristic Programming Project led by
Feigenbaum, who is sometimes referred to as
the "father of expert systems".

 The Stanford researchers tried to identify
domains where expertise was highly valued
and complex, such as diagnosing infectious
diseases (Mycin) and identifying unknown
organic molecules (Dendral).

Knowledge base
 Knowledge base – collection of information
obtained from books, magazines,
knowledgeable persons, etc.

Types of Expert Systems
 Expert systems are divided in two types based
on inference engine.
 Forward Chaining inference Engine
 Backward Chaining Inference Engine

Forward Chaining
 In artificial intelligence (AI) systems, forward
chaining refers to a scenario where the AI has
been provided with a specific problem must
"work forwards" to figure out how to solve the
set problem. To do this, the AI would look back
through the rule-based system to find the "if"
rules and determine which rules to use.

CPSC 433 Artificial Intelligence
Forward Chaining Example
If [X croaks and eats flies]
Then [X is a frog]
[Fritz croaks and eats flies]
[Fritz is a frog]
If [X is a frog]
Then [X is colored green]
[Fritz is colored green] [Fritz is colored Y] ?
Knowledge Base
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
If [X is a canary]
Then [X is colored yellow]
[Fritz is a frog]
[Fritz is colored green]
Goal
[Fritz is colored Y]?Y = green

Backward chaining
 Backward chaining (or backward
reasoning) is an inference method that can be
described (in lay terms) as working backward
from the goal(s).

CPSC 433 Artificial Intelligence
Backward Chaining Example
Knowledge Base
Then [X is a frog]
If [X chirps and sings]
Then [X is a canary]
If [X is a frog]
If [X is a canary]
Goals
[Fritz is colored Y]?
[X is a frog]
[X is a canary]
[X croaks and eats flies]
[Fritz is colored Y]
If [X is a frog]
[X is a frog]
If [X is a canary]
[X is a canary]
Then [X is a frog]
[X croaks and eats flies] [Fritz croaks and eats flies]
X = Fritz, Y = green

Week 4
 Features of expert system
 Overview of expert system’s programming
tools
 Benefits and limitations of Experts systems

Features of expert system
 • Goal driven reasoning or backward chaining - an inference
technique which uses IF THEN rules to repetitively break a
goal into smaller sub-goals which are easier to prove;
 • Coping with uncertainty - the ability of the system to reason
with rules and data which are not precisely known;
 • Data driven reasoning or forward chaining - an inference
technique which uses IF THEN rules to deduce a problem
solution from initial data;
 • Data representation - the way in which the problem specific
data in the system is stored and accessed;
 • User interface - that portion of the code which creates an
easy to use system;
 • Explanations - the ability of the system to explain the
reasoning process that it used to reach a recommendation.

 Jess is a rule engine for the Java platform that
was developed by Ernest Friedman-Hill of
Sandia National Labs.
 CLIPS is a public domain software tool for
building expert systems. The name is an
acronym for "C Language Integrated
Production System."

79
Advantages of Expert Systems
 Increased availability
 Reduced cost
 Reduced danger
 Performance
 Multiple expertise
 Increased reliability

80
Advantages Continued
 Explanation
 Fast response
 Steady, unemotional, and complete responses at
all times
 Intelligent tutor
 Intelligent database

1. DENDRAL
• First expert system
• Project began at Stanford in mid 1960's, and is still being used.
• Domain: Organic chemistry - mass spectrometry
• Task: identify molecular structure of unknown compounds
from mass spectra data

2. MACSYMA
• Developed at MIT since 1968 onwards
• Domain: high-performance symbolic math (algebra, calculus,
differential equations,...)
• Task: carry out complex mathematical derivations

3. Hearsay I and II
• Developed at Carnegie-Mellon in late 1960's
• Domain: speech understanding for simple database query
• Task: Using specific vocabulary and grammar criteria, generate
correct speech recognition

4. INTERNIST/CADUCEUS
• Developed at U of Pittsburgh in early 1970's thru mid 80’s
• Domain: diagnostic aid for all of internal medicine
• Task: medical diagnosis given interactive input

5. MYCIN
• Stanford U in mid 70's
• Domain: Medical diagnosis for bacterial and meningitis infections
• Task: interview physician, make diagnosis and therapy recommendations

6. Prospector
• Developed at SRI international in late 1970's
• Domain: exploratory geology
• Task: evaluate geological sites
.

7. PUFF
• Developed at Stanford in 1979
• Domain: Diagnosis of obstructive airway diseases using MYCIN's
inference engine and a new knowledge base
• Task: Take data from instruments and dialog, and diagnose type and
severity of disease
•

8. XCON
• Originally called R1, developed at Carnegie Mellon and DEC in late 70's
• Domain: configure computer hardware

Some other famous systems
• DELTA/CATS:
- diagnose and repair diesel locomotives
- developed in LISP, but ported to FORTRAN (a common phenomenon)
• DRILLING ADVISOR:
- diagnose oil drilling problems
- rule-based, exhaustive backward chaining with uncertainty, frames
• GENESIS:
- designs molecular genetics experiments and procedures
- was used by over 500 research scientists
• GATES:
- airline gate assignment and tracking system
- used by TWA at JFK airport
- implemented in Prolog on microcomputers
- access database for 100 daily flights, and creates gate assignment in 30 seconds
(experts took between 10 and 15 hours, with 1 hour per modification)
( possible extension: lost luggage!)

Week 5
 Robotics:
 Reasons to use a robot
 Main application areas
 Laws of robotics

Robotics
 Robotics is the branch of mechanical
engineering, electrical engineering and
computer science that deals with the design,
construction, operation, and application of
robots, as well as computer systems for their
control, sensory feedback, and information
processing.

Reasons to use a robot
 Robots are powerful machines that give us
access to places that are otherwise
inaccessible to the human population. They
protect us from danger by performing tasks
that are harmful to our health.
 Some of the first robots were used in the
1940s to handle radioactive materials.

 Since then robots have become permanent
members of the industrial workforce, including
parts handling, welding, and painting.
 Robots simulate many human functions. They
can move, sense their surroundings, and
respond to changes in the environment. Many
robots are mechanical arms attached to a
base. Robotic arms use flexible joints to
perform tasks that require very precise
movements.

 Medical robots are now so advanced that they
are being employed in brain, heart and eye
surgeries, allowing doctors to treat conditions
that were previously only possible through
treatments nearly as dangerous as the
offending condition.

Robotics Applications
 Outer Space - Manipulative arms that are controlled by a human are used to unload
the docking bay of space shuttles to launch satellites or to construct a space station
The Intelligent Home - Automated systems can now monitor home security,
environmental conditions and energy usage. Door and windows can be opened
automatically and appliances such as lighting and air conditioning can be pre
programmed to activate. This assists occupants irrespective of their state of mobility.
Exploration - Robots can visit environments that are harmful to humans. An
example is monitoring the environment inside a volcano or exploring our deepest
oceans. NASA has used robotic probes for planetary exploration since the early
sixties.
Military Robots - Airborne robot drones are used for surveillance in today's modern
army. In the future automated aircraft and vehicles could be used to carry fuel and
ammunition or clear minefields
Farms - Automated harvesters can cut and gather crops. Robotic dairies are
available allowing operators to feed and milk their cows remotely.

 The Car Industry - Robotic arms that are able to perform
multiple tasks are used in the car manufacturing process.
They perform tasks such as welding, cutting, lifting, sorting
and bending. Similar applications but on a smaller scale are
now being planned for the food processing industry in
particular the trimming, cutting and processing of various
meats such as fish, lamb, beef.
Hospitals - Under development is a robotic suit that will
enable nurses to lift patients without damaging their backs.
Scientists in Japan have developed a power-assisted suit
which will give nurses the extra muscle they need to lift their
patients - and avoid back injuries.

 Disaster Areas - Surveillance robots fitted
with advanced sensing and imaging
equipment can operate in hazardous
environments such as urban setting damaged
by earthquakes by scanning walls, floors and
ceilings for structural integrity.
Entertainment - Interactive robots that exhibit
behaviors and learning ability. SONY has one
such robot which moves freely, plays with a
ball and can respond to verbal instructions.

Laws of Robotics
 A robot may not injure a human being or, through
inaction, allow a human being to come to harm.
 A robot must obey orders given it by human
beings except where such orders would conflict
with the First Law.
 A robot must protect its own existence as long as
such protection does not conflict with the First or
Second Law.
By Isaac Asimov's was an American author and
professor of biochemistry at Boston University,

Week 6
 Types of robots
 Components of a typical robot
 Characteristics of robotics
 Robot sensors
 Robots programming tools

Main Components of robot
CONTROLLER :
Every robot is connected to a computer, which
keeps the pieces of the arm working together.
This computer is known as the controller. The
controller functions as the "brain" of the robot.
The controller also allows the robot to be
networked to other systems, so that it may
work together with other machines, processes,
or robots.

 ARM :
Robot arms come in all shapes and sizes. The
arm is the part of the robot that positions the
end-affector and sensors to do their pre-
programmed business.

 DRIVE :
The drive is the "engine" that drives the links
(the sections between the joints into their
desired position. Without a drive, a robot
would just sit there, which is not often helpful.
Most drives are powered by air, water
pressure, or electricity.

 END- EFFECTOR :
The end-effector is the "hand" connected to
the robot's arm. It is often different from a
human hand - it could be a tool such as a
gripper, a vacuum pump, tweezers, scalpel,
blowtorch - just about anything that helps it do
its job. Some robots can change end-effectors,
and be reprogrammed for a different set of
tasks.

 SENSOR :
Most robots of today are nearly deaf and blind.
Sensors can provide some limited feedback to
the robot so it can do its job. Compared to the
senses and abilities of even the simplest living
things, robots have a very long way to go.

Robotic sensing
 Robotic sensing is a branch of robotics
science intended to give robots sensing
capabilities, so that robots are more human-
like. Robotic sensing mainly gives robots the
ability to see.

 Week-7
 Natural Language Processing(N LP)
 Natural languages vs. computer languages
 Natural language understanding (NLU)
 Natural language generation (NLG)
 Domain areas of NLP
 Programming tools for NLP

BİL711 Natural Language Processing
118
What is Natural Language
Processing (NLP)
 The process of computer analysis of input
provided in a human language (natural
language), and conversion of this input into
a useful form of representation.
 The field of NLP is primarily concerned with
getting computers to perform useful and
interesting tasks with human languages.
 The field of NLP is secondarily concerned with
helping us come to a better understanding of
human language.

Forms of Natural Language
 The input/output of a NLP system can be:
 written text
 speech

Components of NLP
 Natural language generation systems convert
information from computer databases into readable
human language.
 Natural language understanding systems convert
human language into representations that are easier
for computer programs to manipulate.

Where does it fit in the CS
taxonomy?
Computers
Artificial Intelligence AlgorithmsDatabases Networking
Robotics SearchNatural Language Processing
Information
Retrieval
Machine
Translation
Language
Analysis
Semantics Parsing
…
…

Applications of Nat. Lang. Processing
 Machine Translation
 is a sub-field of computational linguistics that investigates the
use of software to translate text or speech from one natural
language to another.
 Database Access
 Information Retrieval
 Selecting from a set of documents the ones that are relevant to a
query
 Text Categorization
 Sorting text into fixed topic categories
 Extracting data from text
 Converting unstructured text into structure data
 Spoken language control systems
 Spelling and grammar checkers

NLP - Prof. Carolina Ruiz
 Input/Output data Processing stage Other data used
Frequency spectrogram freq. of diff.
speech recognition sounds
Word sequence grammar of
“He loves Mary” syntactic analysis language
Sentence structure meanings of
semantic analysis words
He loves Mary
Partial Meaning context of
x loves(x,mary) pragmatics utterance
Sentence meaning
loves(john,mary)
Natural Language Understanding

Natural language understanding
Phases
 1) Morphological Analysis: Individual words are analyzed into their components and
non word tokens, such as punctuation are separated from the words. Consider the
sentence:
Example
The man looked at the horses.
 The plural ending –s in horses is dependent on the noun horse to receive meaning
and can therefore not be a word. Horses however, is a word, as it can occur in other
positions in the sentence or stand on its own:
The horses looked at the man.
- What is the man looking at? - Horses.
 Words are thus both independent since they can be separated from other words and
move around in sentences, and the smallest units of language since they are the
only units of language for which this is possible.


 2) Syntactic Analysis: Linear sequences of
words are transformed into structures that
show how the words relate each other. Some
word sequences may be rejected if they
violate the languages rules for how words may
be combined.

Syntactic Analysis - Parsing
sentence
noun_phrase verb_phrase
proper_noun verb noun_phrase
determiner noun
“Mary” “ate” “the” “apple”

 3) Semantic Analysis: The structures created
by the syntactic analyzer are assigned
meanings.

4) Pragmatics
 Uses context of utterance
 Where, by who, to whom, why, when it was said
 Intentions: inform, request, promise, criticize, …
 Handling Pronouns
 “Mary eats apples. She likes them.”
 She=“Mary”, them=“apples”.
 Handling ambiguity
 Pragmatic ambiguity: “you’re late”: What’s the
speaker’s intention: informing or criticizing?

5) Phonology
 Phonology is a branch of linguistics
concerned with the systematic organization of
sounds in languages.

Natural Language Generation
(NLG)
 Natural Language Generation (NLG)
Systems which take information from
some database and figure out how to
present it to a human. Very little
linguistics involved.

Natural Language Generation
 Talking back! 
 What to say or text planning
 flight(AA,london,boston,$560,2pm),
 flight(BA,london,boston,$640,10am),
 How to say it
 “There are two flights from London to Boston. The first one is
with American Airlines, leaves at 2 pm, and costs $560 …”
 Speech synthesis
 Simple: Human recordings of basic templates
 More complex: string together phonemes in phonetic spelling
of each word
 Difficult due to stress, intonation, timing, liaisons between words

Programming tools for NLP
 See link
 http://www.phontron.com/nlptools.php

Week-8
 Problems in Natural Languages
 Ambiguity:
 Lexical
 Syntactic
 Semantic
 Anaphoric
 Pragmatics
 Imprecision
 Inaccuracy
 Incompleteness
 Solution of the NL problems

Ambiguity
 Ambiguity can be referred as the ability of
having more than one meaning or being
understood in more than one way.

Ambiguities in Natural Language
Processing
1) Lexical Ambiguity: is the ambiguity of a single word. A
word can be ambiguous with respect to its syntactic
 class.Eg: book, study.
 For eg: The word silver can be used as a noun, an adjective,
or a verb.
 She bagged two silver medals.
 She made a silver speech.
 His worries had silvered his hair.
 Lexical ambiguity can be resolved by Lexical category
disambiguation i.e, parts-of-speech tagging. As many words
may belong to more than one lexical category part-of-speech
tagging is the process of assigning a part-of-speech or
 lexical category such as a noun, verb, pronoun, preposition,
adverb, adjective etc. to each word in a sentence.

 2) Syntactic Ambiguity: The structural
ambiguities were syntactic ambiguities.
 Structural ambiguity is of two kinds: Scope
Ambiguity and Attachment Ambiguity.

2.1 Scope ambiguity involves operators and quantifiers.
 Consider the example:
 Old men and women were taken to safe locations.
 The scope of the adjective (i.e., the amount of text it qualifies)
is ambiguous. That is, whether the structure (old men and
 women) or ((old men) and women)?
 The scope of quantifiers is often not clear and creates
ambiguity.
 Every man loves a woman.[7]
 The interpretations can be, For every man there is a woman
and also it can be there is one particular woman who is
 loved by every man.

 2.2) Attachment Ambiguity
 A sentence has attachment ambiguity if a constituent fits more than
one position in a parse tree. Attachment ambiguity arises from
uncertainty of attaching a phrase or clause to a part of a sentence.
 The man saw the girl with the telescope.[2]
 It is ambiguous whether the man saw a girl carrying a telescope, or
he saw her through his telescope.
 The meaning is dependent on whether the preposition ‘with’ is
attached to the girl or the man.
 Buy books for children
 Preposition Phrase ‘for children’ can be either adverbial and attach
to the verb buy or adjectival and attach to the object noun books.

 3) Semantic Ambiguity: This occurs when the meaning of the words themselves
can be misinterpreted. Even after the syntax and the meanings of the individual words have
been resolved, there are two ways of reading the sentence.
 Consider the example,
 Seema loves her mother and Sriya does too.
 The interpretations can be Sriya loves Seema’s mother or Sriya likes her own mother.
 Semantic ambiguities born from the fact that generally a computer is not in a position to
distinguishing what is logical from what is not.
 The car hit the pole while it was moving.
 The interpretations can be The car, while moving, hit the pole and The car hit the pole while the
pole was moving. The first interpretation is preferred to the second one because we have a model
of the world that helps us to distinguish what
 is logical (or possible) from what is not. To supply to a computer a model of the world is not so
easy.[4]
 We saw his duck
 Duck can refer to the person’s bird or to a motion he made.
 Semantic ambiguity happens when a sentence contains an ambiguous word or phrase.

 4) Anaphoric Ambiguity: Anaphoras are the
entities that have been previously introduced
into the discourse.
 Consider the example,
 The horse ran up the hill. It was very steep (‫ڈھلوان‬
,‫سے‬ ‫تیزی‬ ).
 The anaphoric reference of ‘it’ in the two situations
cause ambiguity.
 Steep applies to surface hence ‘it’ can be hill.
Tired applies to animate object hence ‘it’ can be
horse.

Agenda
 Machine Translation
 Why Machine Translation
 History of MT
 Approaches to MT
 MT Application
 Recent Research
 Strategies of MT
 Types of MT
 Next Week Plan
 The End
(NLP) by Rahman Ali, Lect:
QACC, UOP
144
6
Septemb
er 2015

Machine Translation
QACC, UOP
145
6
Septemb
er 2015
http://iselab.cvc.uab.es/tutorials_ise/PPTs/survey/

Machine Translation
 Machine Translation (MT) is the task of
automatically converting one natural language
into another, preserving the meaning of the input
text, and producing fluent text in the output
language.
http://nlp.stanford.edu/projects/mt.shtml, Retrieval date: 28 Nov, 2010)
QACC, UOP
146
6
Septemb
er 2015

Why Machine Translation or Goals
of MT ???
 Cheap, universal access to world’s online
information regardless of original language.
QACC, UOP
147
6
Septemb
er 2015

History of Machine Translation
 The history of machine translation started way back in
the 1950s.
 The first work of translation was published in 1954 in the
Georgetown experiment involving fully automatic
translation of more than 60 Russian sentences into
English.
 The experiment was a great success and the authors
claimed that machine translation would be used in
translations within three or five years.
 However, the real progress was very slow.
(http://www.thelanguagetranslation.com/machine-translation.html)
QACC, UOP
148
6
Septemb
er 2015

(Cont..)
 The ALPAC (Automatic Language Processing
Advisory Committee) report in 1966 further
reduced the investment in Machine translation
because the report evaluated the progress in
computational linguistics in general and
machine translation in particular and was very
skeptical (disbelieving) to research done in
machine translation so far and gave more
emphasis to the need for basic research in
computational linguistics.
QACC, UOP
149
6
Septemb
er 2015

(Cont..)
 However, starting in the late 1970s and beginning 1980s, with
the impact of personal computer revolution, with the increase
in computational power, more interest began to be shown in
statistical models for machine translation.
 There was growth in the use of machine translation as a
result of the beginning of less expensive and more powerful
computers.
 With the 1990s, the importance of machine translation further
increased (for better or worse) and the use of "translation
engines" on the Internet to allow for translation of websites
and email languages.
QACC, UOP
150
6
Septemb
er 2015

(Cont..)
 Today there are many software programs
several of them online for translating source
language.
 Such software includes the SYSTRAN system
which powers both Google translate,
AltaVista's Babelfish, StarDict etc.
 These tools produce a rough translation that
gives the summary of the source text.
QACC, UOP
151
6
Septemb
er 2015

Translation process
 The human translation process may be
described as:
 Decoding the meaning of the source text; and
 Re-encoding this meaning in the target
language.

Bilingual MT
 A bilingual dictionary or translation dictionary
is a specialized dictionary used to translate words
or phrases from one language to another.
Bilingual dictionaries can be unidirectional,
meaning that they list the meanings of words of
one language in another, or can be bidirectional,
allowing translation to and from both languages.
Bidirectional bilingual dictionaries usually consist
of two sections, each listing words and phrases of
one language alphabetically along with their
translation.

Multilingual MT
 A Multilingual MT or translation dictionary is a
specialized dictionary used to translate words or
phrases from one language to several other
languages. Multilingual MT can be unidirectional,
meaning that they list the meanings of words of
one language in another, or can be bidirectional,
allowing translation to and from both languages.
Multilingual MT usually consist of two sections,
each listing words and phrases of one language
alphabetically along with their translation.

Approaches of Machine Translation
 Rule-based MT
 Example-based MT
 Statistical Based MT
 Hybrid MT
QACC, UOP
155
6
Septemb
er 2015

Rule-based Machine Translation
(RBMT
 Also known as “Knowledge-based Machine Translation”;
“Classical Approach” of MT).
 Machine translation systems that are based on linguistic
information about source and target languages basically
retrieved from (bilingual) dictionaries and grammars
covering the main semantic, morphological, and syntactic
regularities of each language respectively.
 This approach of MT make use of morphological, syntactic,
and semantic analysis of both the source and the target
languages involved in a concrete translation task.
(http://en.wikipedia.org/wiki/Rule-based_machine_translation)
QACC, UOP
156
6
Septemb
er 2015

Basic principles
 A girl eats an apple. Source Language =
English; Demanded Target Language =
German Minimally, to get a German translation
of this English sentence one needs:
 A dictionary that will map each English word to
an appropriate German word.
 Rules representing regular English sentence
structure.
 Rules representing regular German sentence
structure.

 A girl eats an apple. => Ein Mädchen isst
einen Apfel.

Example-based Machine Translation (EBMT)
 EBMT approach to machine translation is often characterized
by its use of a bilingual corpus with parallel texts as its main
knowledge base, at run-time.
 It is essentially a translation by analogy and can be viewed as
an implementation of case-based reasoning approach of
machine learning.
 (EBMT) approach was proposed by Makoto Nagao in
1984.[3][4]
(http://en.wikipedia.org/wiki/Example-based_machine_translation)
QACC, UOP
159
6
Septemb
er 2015

160/23
Example-based MT
 Long-established approach to empirical MT
 First developed in contrast with rule-based MT
 Idea of translation by analogy (Nagao 1984)
 Translate by adapting previously seen examples
rather than by linguistic rule
 “Existing translations contain more solutions to
more translation problems than any other available
resource.” (P. Isabelle et al., TMI, Kyoto, 1993)
 In computational terms, belongs in family of Case-
based reasoning approaches

161/23
EBMT basic idea
 database of translation pairs
 match input against example database
(like Translation Memory)
 identify corresponding translation
fragments (align)
 recombine fragment into target text

162/23
He buys a book on international politics
Input
Matches
He buys a notebook.
Kare wa nōto o kau.
I read a book on international politics.
Watashi wa kokusai seiji nitsuite kakareta hon o yomu.
Result
Kare wa o kau.kokusai seiji nitsuite kakareta hon
Example (Sato & Nagao 1990)

Statistical machine translation
(SMT)
 Statistical machine translation (SMT) is a
machine translation paradigm where
translations are generated on the basis of
statistical models whose parameters are
derived from the analysis of bilingual text
corpora.

164
How to Build an SMT System
 Start with a large parallel corpus
 Consists of document pairs (document and its translation)
 Sentence alignment: in each document pair automatically find
those sentences which are translations of one another
 Results in sentence pairs (sentence and its translation)
 Word alignment: in each sentence pair automatically annotate
those words which are translations of one another
 Results in word-aligned sentence pairs
 Automatically estimate a statistical model from the word-
aligned sentence pairs
 Results in model parameters
 Given new text to translate, apply model to get most probable
translation

165
Sentence alignment
 If document De is translation of document Df
how do we find the translation for each
sentence?
 The n-th sentence in De is not necessarily the
translation of the n-th sentence in document Df
 In addition to 1:1 alignments, there are also 1:0,
0:1, 1:n, and n:1 alignments
 In European Parliament proceedings,
approximately 90% of the sentence alignments
are 1:1
Modified from Dorr, Monz

166
Sentence alignment
 There are several sentence alignment algorithms:
 Align (Gale & Church): Aligns sentences based on their
character length (shorter sentences tend to have shorter
translations then longer sentences). Works well
 Char-align: (Church): Aligns based on shared character
sequences. Works fine for similar languages or technical
domains
 K-Vec (Fung & Church): Induces a translation lexicon from
the parallel texts based on the distribution of foreign-
English word pairs
 Cognates (Melamed): Use positions of cognates (including
punctuation)
 Length + Lexicon (Moore): Two passes, high accuracy,
freely available
Modified from Dorr, Monz

Corpus
 Corpus:
 corpus, plural corpora A collection of linguistic data,
either compiled as written texts or as a transcription of
recorded speech.
QACC, UOP
167
6
Septemb
er 2015

Hybrid Machine Translation (HMT)
 Hybrid machine translation (HMT) leverages
the strengths of statistical and rule-based
translation methodologies.[5]
 Several MT companies (Asia Online,
LinguaSys, and Systran) are claiming to have
a hybrid approach using both rules and
statistics.
QACC, UOP
168
6
Septemb
er 2015

Machine Translation Applications
 LinguaSys (http://www.linguasys.net/)
 provides highly customized hybrid machine translation that can go from any language
to any language. [Video: http://www.youtube.com/watch?v=lcSYwNP4CQ4]
 Asia Online [http://www.asiaonline.net/translation.aspx]
 provides a custom machine translation engine building capability that they claim gives
near-human quality compared to the "gist" based quality of free online engines. Asia
Online also provides tools to edit and create custom machine translation engines with
their Language Studio suite of products.
 Hindi to Punjabi Machine Translation System[3],
 provides machine translation using a direct approach. It translates Hindi into Punjabi. It
also features writing e-mail in the Hindi language and sending the same in Punjabi to
the recipient.
 IdiomaX,
 which powers online translation services at idiomax.com
 Toggletext
 uses a transfer-based system (known as Kataku) to translate between English and Indonesian.
QACC, UOP
169
6
Septemb
er 2015

LinguaSys
QACC, UOP
170
6
Septemb
er 2015

QACC, UOP
171
6
Septemb
er 2015

(Cont..)
 Arabic machine translation
 in multilingual framework.
 Worldlingo
 provides machine translation using both statistical based TE's and rule based TE's.
Most recognizable as the MT partner in Microsoft Windows and Microsoft Mac Office.
 Power Translator
 SDL ETS and Language Weaver
 which power FreeTranslation.com (website)
 SYSTRAN,
 which powers Yahoo! Babel Fish
 Promt,
 which powers online translation services at Voila.fr and Orange.fr
 AppTek,
 which released a hybrid MT system in 2009.[4]
QACC, UOP
172
6
Septemb
er 2015

(Cont..)
 Anusaaraka
 A free open source machine translation from English to Hindi based on Panini
grammar and uses state of the art NLP tools. Can be used online and
downloaded from
 Apertium,
 a free and open source machine translation platform (WinXLator gives this a
Windows GUI, but it is likely to be in violation of the Apertium GPL license)
 Google Translator
 A free online translator from Google. [URL: translate.google.com]
 Other translation software, most of them running under Microsoft
Windows, includes:
 Translation memory tools, such as SDL Trados, Wordfast, Deja Vu, Swordfish,
and
 localization tools, such and Alchemy CATALYST and Multilizer.
QACC, UOP
173
6
Septemb
er 2015

QACC, UOP
174
6
Septemb
er 2015

Recent Research
 Presently a large amount of research is done
into example-based machine translation and
statistical machine translation
QACC, UOP
175
6
Septemb
er 2015

Advantages of Machine
Translations
 Machine translations work at a faster rate than human translations.
 Another advantage of machine translation is that it is comparatively cheaper. It is one time
cost -the cost of the tool and its installation.
 When time is a crucial factor, machine translation can save the day. You don't have to
spend hours poring over dictionaries to translate the words. Instead, the software can
translate the content quickly and provide a quality output to the user in no time at all.
 The next benefit of machine translation is that it is comparatively cheap. Initially, it might
look like a unnecessary investment but in the long run it is a very small cost considering
the return it provides. This is because if you use the expertise of a professional translator,
he will charge you on a per page basis which is going to be extremely costly while this will
be cheap.
 Confidentiality is another matter which makes machine translation favorable. Giving
sensitive data to a translator might be risky while with machine translation your information
is protected.
 A machine translator usually translates text which is in any language so there is no such
major concern while a professional translator specializes in one particular field.
[http://www.thelanguagetranslation.com/machine-translation.html]
QACC, UOP
176
6
Septemb
er 2015

Drawback of MT
 Accuracy is not offered by the machine translation on a
consistent basis. You can get the gist of the draft or
documents but machine translation only does word to word
translation without comprehending the information which
might have to be corrected manually later on.
 Systematic and formal rules are followed by machine
translation so it cannot concentrate on a context and solve
ambiguity and neither makes use of experience or mental
outlook like a human translator can.
 [http://www.thelanguagetranslation.com/machine-translation.html]
QACC, UOP
177
6
Septemb
er 2015

Types of Machine Translation
 Monolingual
 Bilingual
 Multilingual
QACC, UOP
178
6
Septemb
er 2015

Monolingual Machine Translation
 The translation of natural language text of the
source language to the target text in the same
language is called Monolingual Machine
Translation.
 Source Text (English)……..Computer…………> Target Text
(English)
 Source Text (Urdu)……..Computer…………> Target Text (Urdu)
 Source Text (Pashto)……..Computer…………> Target Text (Pashto)
QACC, UOP
179
6
Septemb
er 2015

Bilingual Machine Translation
 The translation of natural language text written
in one natural language to the target text in the
other language is called bilingual Machine
Translation.
 Source Text (English)……..Computer…………> Target Text
(Pashto)
 Source Text (Urdu)……..Computer…………> Target Text (Chines)
 Source Text (Pashto)……..Computer…………> Target Text (Hindko)
QACC, UOP
180
6
Septemb
er 2015

Multilingual Machine Translation
 The translation of natural language text written
in one language to the target text in more than
two languages is called Multilingual Machine
Translation.
 Source Text (English)……..Computer…………> Target Text (Urdu, Pashto)
 Source Text (Urdu)……..Computer…………> Target Text (Hindko,
Japanies, Turkish)
QACC, UOP
181
6
Septemb
er 2015

Translation Unit
182
6
Septemb
er 2015
 In the field of translation, a translation unit is
a segment of a text which the translator treats
as a single cognitive unit for the purposes of
establishing an equivalence.
 The translation unit may be a
 Single word, or it may be
 Sentence
 Discourse

Translation Unit (Cont..)
 When a translator segments a text into translation units,
the larger these units are, better chance there is of
obtaining an idiomatic translation.
 This is true not only of human translation, but also in
cases where human translators use computer-assisted
translation, and also when translations are performed by
machine translation systems.
QACC, UOP
183
6
Septemb
er 2015

Word-For-Word Translation
 Transferring the meaning of each individual
word in a text to another, equivalent word in the
target language.
 Sometimes called 'Literal Translation'.
 While this is clearly appropriate for dictionaries,
it can produce very complex passages of text.
 [Translation Theory, http://www.translatum.gr/etexts/translation-
theory.htm#UnitOfTranslation, Retrieved date: 09-Jan,2011]
QACC, UOP
184
6
Septemb
er 2015

Word-For-Word Translation
(Cont..)
 Problems/limitations
 Words order in source and target language
 SOV vs SVO
 I ate the meal [English]
 Ma dody wakhwara [Pashto]
 Sometimes, no matching word in target language
 No (1-1) correspondence between the words of
source language and target language
 Poly-semantic words
QACC, UOP
185
6
Septemb
er 2015

Sentence-by-sentence Translation
 A sentence in the source language is taken as a
unit of translation and translated to the
corresponding target language.
 Most MT work focuses on sentence translation.
QACC, UOP
186
6
Septemb
er 2015

Sentence-by-sentence Translation
(Cont..)
 What does sentence translation ignore?
 Discourse properties/structure
 Inter-sentence co-reference.
QACC, UOP
187
6
Septemb
er 2015

188
William Shakespeare was an English poet and
playwright widely regarded as the greatest writer of
the English language, as well as one of the greatest
in Western literature, and the world's pre-eminent
dramatist.
He wrote about thirty-eight plays and 154 sonnets
(poems), as well as a variety of other poems.
<doc>
</doc>
. . .
<sentence>
<sentence>
<sentence>
Problems in Sentence-based MT
What is the referent of “He”?
QACC, UOP

Contents
 Referring Expression
 Referring expression
 Referent
 Types of References
 Exophora and
 Endophora
 Types of Endophora
 Anaphora and
 Cataphora
 Types or Categories of Anaphora
 Anaphoric/Cataphoric Devices
 Anaphora Resolution
 Anaphora and Ambiguity
 Reading/References
 Next Week Plan
189

Reference
 Reference is a relation between objects in
which one object designates, or acts as a
means by which to connect to or link to,
another object. The first object in this relation
is said to refer to the second object.

Referring Expression
 A natural language expression used to perform
reference is called a referring expression and the entity
that is referred to is called the referent.
 Referring expressions are words or phrases, the semantic
interpretation of which is a discourse entity (also called referent)
 Example:
 A pretty woman entered the restaurant. She sat at the table next
to mine and only then I recognized her. This was Amy Garcia, my
next door neighbor from 10 years ago. The woman has totally
changed! Amy was at the time shy…
191

Referring Expression ( Cont’)
A pretty woman entered the restaurant. She sat
at the table next to mine and only then I
recognized her. This was Amy Garcia, my next
door neighbor from 10 years ago. The woman
has totally changed! Amy was at the time shy…
192
Referent
Referring Expression

 Referring Expression: any expressions used to refer
to somebody or someone with the particular picture
in mind (Heasley 1983)

Types of References
We can summaries reference with a diagram
to make it easier to grasp:
194

Exophoric Reference
 Exophoric reference, depends on the context outside
the text for its meaning.
 In linguistics, Exophora is reference to something
extra-linguistic.
 For example
 "What is this?",
 here "this" is exophoric rather than endophoric, because it refers to
something extra-linguistic, i.e. there is not enough information in
the utterance itself to determine what "this" refers to, but we must
instead observe the non-linguistic context of the utterance (e.g. the
speaker might be holding an unknown object in their hand as they
ask that question.)
195

 "Did the gardener water those plants?", it is
quite possible that "those" refers back to the
preceding text, to some earlier mention of
those particular plants in the discussion.

Endophoric Reference
 The pronouns refer to items within the same text; it is
endophoric reference.
 Endophora is a linguistic reference to something intra-
linguistic.
 For Example:
 "I saw Sally yesterday. She was lying on the beach".
 Here, "she" is intra-linguistic, and hence endophoric, because it refers to something
(Sally, in this case) already mentioned in the text.
 (From Wikipedia, the free encyclopedia)
197

Endophora (Another Definition)
 Words or phrases like pronouns are endophora
when they point backwards or forwards to
something in the text:
 For example:
 As [he]1 was late, [Harry] 1 wanted to phone [his] 1
[boss] 2 and tell [her] 2 what had happened.
 (From Wikipedia, the free encyclopedia)
198

Types of Endophora
 1) Cataphora:
 The type of endophora in which the referring expression occur
before the referent are termed as cataphora.
 OR The type of endophora in which the pronouns link forward to a
referent (nouns) in the text that follows.
 For example:
 When [she] 1 saw the snake, [Harry] 1 cried.
 The elevator opened for [him] 1 on the 14th flour, and [Ali] 1 stepped out
quickly.
 2) Anaphora:
 The type of endophora in which the referent occurs before the
referring expression are termed as anaphora.
 OR The type of endophora in which the pronouns link backward to
a referent (nouns) in the text.
 For example:
199

Anaphora(Another Definition)
 Anaphora is a phenomenon in which certain
textual elements refer to earlier text elements
(called correlates) and share the meaning of the
correlates.
 For Example:
 1)John helped Mary.
 2) He was kind.
200
Correlate/
Referent/
AntecedentReferring Element/
Anaphor/
Anaphoric Device (AD)

Anaphoric and Cataphoric Devices
 The referring elements (pronouns) in anaphoric text that refer to
their corresponding referent ( nouns) backward are called
anaphoric devices. Also, called anaphor.
 For example:
 Bell is a powerful player but unfortunately he will not take part in the trophy
due to injury.
 The referring elements (pronouns) that refer to their corresponding
referent (nouns) forward in cataphoric text are called cataphoric
devices. Also, called cataphor.
 For example:
 As her father went abroad, Nighat took control of the organization by herself.
201
Anaphoric Device
Cataphoric Device

Types of Anaphora (On the basis of
position of anaphor and its antecedent)
 Intra-sentential/Sentence internal anaphora:
 The anaphora in which the AD and its antecedent both occurs
in the same sentence is called sentence internal.
 Reflexive pronouns
 (himself, herself, itself, themselves) are typical examples of intra-sentential
anaphora.
 Possessive pronouns
 (his, her, hers, its, their, theirs) can often be used as intra-sentential
anaphors too, and often be in the same clause as the anaphor.
 For example:
 [John] 1 took [his] 1 [hat] 2 off and hung [it] 2 on a peg.
202

Types of Anaphora (Cont..)
 Inter-sentential/Sentence external anaphora:
 The anaphora in which the AD and its antecedent
doesn’t occur in the same sentence is called sentence
external or inter-sentential anaphora.
 For example:
 [Jehansher] 1 Khan was senior player of Sqash. [He] 1 has won
several trophies.
 [John] 1 took his hat off and hung it on a peg. [He] 1 was very
tied therefore went to slept..
203

Anaphora and Ambiguity
 Many anaphors are ambiguous. Like:
 A)
 Jane told Marry she was in love (ambiguous)
 Jane informed Marry she was in love. (Here Jane is in
love)
 B)
 Jane told marry she was in danger (ambiguous)
 Jane warned Marry she was in danger.
204

Anaphora Resolution
 Anaphora Resolution == the problem of resolving what a
pronoun, or a noun phrase refers to.
 Consider the following Discourse:
1) John helped Mary.
2) He was kind.
 After anaphora resolution:
1) John helped Mary.
2) John was kind.
205

Agenda
 Natural Language Understanding (NLU)
 Ellipsis Definition
 Examples of Ellipsis
 Origin of the Word Ellipsis
QACC, UOP
206
6
Septemb
er 2015

Ellipsis
 Definition:
 The omission of a portion, of a phrase or a
sentence is called Ellipsis (Rav, L., F.).
 Example:
 He is rich, but his brother is not ᶲ.
 Bob ᶲ and Tom ate cheese.
QACC, UOP
207
6
Septemb
er 2015

Examples of Ellipsis
 George bought a huge box of chocolates but few Ǿ
were left by the end of the day.
 I have never been to Karachi but my father has Ǿ ,
and he says it was wonderful.
QACC, UOP
208
6
Septemb
er 2015
Chocolates
Been to Karachi

QACC, UOP
209
6
Septemb
er 2015
(1)
‫ل‬‫ژ‬‫و‬‫و‬‫پکښے‬ ‫څو‬‫يو‬ ‫او‬‫ل‬‫شو‬‫ټپيان‬‫خلک‬‫ډير‬ ‫السه‬‫له‬‫بريدونو‬ ‫ځانمرګې‬ ‫د‬
‫ل‬‫شو‬-
‫څو‬‫يو‬ ‫او‬‫ل‬‫شو‬‫ټپيان‬‫خلک‬‫ډير‬‫السه‬‫له‬‫بريدونو‬ ‫ځانمرګې‬‫د‬‫خلک‬‫ل‬‫ژ‬‫و‬‫و‬‫پکښے‬
‫ل‬‫شو‬-
Shows that here a noun is missing which is the possible
referring expression for the antecedent [People]

-Ǿ ‫ما‬‫کله‬‫ې‬ ‫پس‬‫ر‬‫و‬‫به‬‫ډوډۍ‬‫ه‬‫ړ‬‫يو‬‫ينې‬‫ر‬‫نو‬‫به‬ ‫کله‬
] Mirza Jahanzeb Yar, ”Gulmeena", Page-45]
‫ما‬‫کله‬‫ې‬ ‫پس‬‫ر‬‫و‬‫به‬‫ډوډۍ‬‫ه‬‫ړ‬‫يو‬‫ينې‬‫ر‬‫نو‬‫به‬ ‫کله‬‫ه‬‫ړ‬‫يو‬-
.
QACC, UOP
210
6
Septemb
er 2015
Denotes the missing verb phrased
[‫یوړه‬].

LEARNING
 Learning is the improvement of performance
with experience over time.
 Learning element is the portion of a learning AI
system that decides how to modify the
performance element and implements those
modifications.

 There are five methods of learning . They are,
 1. Rote learning
 2. Direct instruction (by being told)
 3. Analogy
 4. Induction
 5. Deduction

Rote Learning
 Rote learning is the memorization of
information based on repetition. The two
biggest examples of rote learning are the
alphabet and numbers.
 Example:- Memorizing multiplication tables,
formulate , etc.

Learning by Instruction
 This type of learning occur when a person is
instructed about a problem solution or for new
knowledge learning by an instructor.
 For example
Learning by instruction occurs when one male
imitates the song of another.

Learn by Analogy
 Analogical learning is the process of learning a
new concept or solution through the use of
similar known concepts or solutions. We use
this type of learning when solving problems on
an exam where previously learned examples
serve as a guide or when make frequent use
of analogical learning.

Inductive Learning
 Inductive Learning is the process of making
generalized decisions after observing, or
witnessing, repeated specific instances of
something. For example
 This cat is black. That cat is black A third cat is
black. Therefore all cats are black.
 This marble from the bag is black. That marble
from the bag is black. A third marble from the
bag is black. Therefore all the marbles in the
bag black.

Learning by Deduction
 In the process of deduction, you begin with
some statements, called 'premises', that are
assumed to be true, you then determine what
else would have to be true if the premises are
true.
 All men are mortal. Joe is a man. Therefore
Joe is mortal. If the first two statements are
true, then the conclusion must be true. 2
 Bachelor's are unmarried men. Bill is
unmarried. Therefore, Bill is a bachelor. 3

Artifitial intelligence (ai) all in one

Artifitial intelligence (ai) all in one

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Viewers also liked

Viewers also liked (20)

Similar to Artifitial intelligence (ai) all in one

Similar to Artifitial intelligence (ai) all in one (20)

More from jehan1987

More from jehan1987 (7)

Recently uploaded

Recently uploaded (20)

Artifitial intelligence (ai) all in one