CSNB234 Artificial Intelligence
CSNB234 Artificial Intelligence
AI in our everyday lives
Phone Assistants
Games
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CSNB234 Artificial Intelligence
AI in our everyday lives
Spam Blockers
Google Translate
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CSNB234 Artificial Intelligence
Why Learn AI?
Basic Level
• To better understand
the systems and tools
that you interact with
on a daily basis
Simple Medical Diagnosis
Career Guidance
Animal Identification
Advanced Level
• Create AI applications,
like the Google Self
Driving Car, or IBM’s
Watson
Google Self-Driving Bike And SelfDriving Car: rely on their sensors and
software to drive themselves
IBM’s Watson: a technology platform
that uses natural language processing &
machine learning to reveal insights
from large amounts of unstructured
data
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CSNB234 Artificial Intelligence
AI preparing you …
Software Engineer
Hardware Engineer
• Create shopping list
recommendation engines
• Analyzing and processing big
data
• Developing electronic
parking assistants or home
assistant robots
Computer Graphics
Cyber Security
• Creation of more intelligent
interactive characters, who
can adapt on user's input to
determine its type of
gameplay, its mood
• Detect threats, including
those are yet to be
discovered, by identifying
characteristics within families
of threats
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CSNB234 Artificial Intelligence
Outline
• Artificial Intelligence (AI) Background and
History
• AI Languages
• Overview of AI Application Areas
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CSNB234 Artificial Intelligence
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CSNB234 Artificial Intelligence
Can a machine think?
• Can be answered by the following “tests” for
machine (i.e. the program/software)
• The Alan Turing Test
– Alan Turing (father of AI)
• Revised Turing Test
– ELIZA (By Joseph Weizenbaum of MIT)
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CSNB234 Artificial Intelligence
Various AI Definitions
HUMAN PERFORMANCE
THOUGHT
PROCESSES
AND
REASONING
“The exciting new effort to make
computers think ... machines with
minds, in the full and literal sense”
(Haugeland, 1985)
“The automation of activities that we
associate with human thinking,
activities such as decision-making,
problem solving, learning ...”
(Bellman, 1978)
Systems that think like humans
BEHAVIOR
“The art of creating machines that
perform functions that require
intelligence when performed by
people” (Kurzweil, 1990)
“The study of how to make
computers do things at which, at
the moment, people are better”
(Rich and Knight, 1991)
Systems that act like humans
RATIONALITY
“The study of mental faculties
through the use of computational
models” (Charniak and McDermott,
1985)
“The study of the computations that
make it possible to perceive,
reason, and act” (Winston, 1992)
Systems that think rationally
“A field of study that seeks to explain
and emulate intelligent behavior in
terms of computational processes”
(Schalkoff, 1990)
“The branch of computer science
that is concerned with the
automation of intelligent behavior”
(Luger and Stubblefield, 1993)
Systems that act rationally
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CSNB234 Artificial Intelligence
AI Definition
• AI can be defined as a part of computer science
that concerned with the designing of intelligent
computer systems, that is, systems that exhibit
characteristics we associate with intelligence in
human behavior.
• The goal of AI is to develop computers that can
think, see, hear, walk, talk and feel.
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CSNB234 Artificial Intelligence
AI Definition
• What computer can do better than people?
– Numerical computation: Fast & accurate
– Information storage: Voluminous amounts
– Repetitive operations : Not getting bored (??)
• However, these are mechanical mindless
activities, and thus cannot be regarded as
‘intelligent’ tasks
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CSNB234 Artificial Intelligence
What people can do better than
computers?
• Activities that involve intelligence include:
Understanding
Common sense reasoning
Natural language processing and generation
Planning & Design
Learning (e.g. from mistakes, by analogy, by
experience or examples)
– Emotions
–
–
–
–
–
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CSNB234 Artificial Intelligence
What is “intelligence”?
It has the ability
– To respond to situation very flexibly
– To make sense out of ambiguous messages
– To recognize the relative importance of different
elements of a situation
Intelligent
Agent
Environment
Sensors
•
•
Actuators
•
Intelligent agent interacts with
environment
Agent receives state of
environment through its sensor
& make decision that can carry
out by actuator (based on
sensor data)
The important part: AI able to
maps sensor to actuator through
control policy/rules
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CSNB234 Artificial Intelligence
Human Intelligence vs AI
Human
Intelligence
• Natural intelligence
• Intuition, common sense,
judgment, creativity, beliefs,
etc.
• Ability to demonstrate their
intelligence by
communicating effectively
• Probable reasoning & critical
thinking
Artificial
Intelligence
• Intelligences posses by
machines
• Ability to simulate human
behavior & cognitive
processes
• Capture & preserve human
expertise
• Flexibly response – ability to
comprehend large amounts
of data quickly
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CSNB234 Artificial Intelligence
IQ
• The most widely accepted psychometric test is
the Intelligence Quotient, or IQ test
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CSNB234 Artificial Intelligence
Alan Turing
• Father of Artificial Intelligence is Alan Turing
(1912-1954)
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CSNB234 Artificial Intelligence
Turing Test
• This test was invented by Alan Turing (1912-1954)
• It was first described in his 1950 article Computing
machinery and intelligence (Mind, Vol. 59, No. 236,
pp. 433-460)
• An interrogator is connected to one person and one
machine via a terminal, and therefore can't see his
counterparts.
• The test is to find out which of the two candidates
is the machine, and which is human only by asking
them questions.
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CSNB234 Artificial Intelligence
Turing Test
• If the interrogator cannot make a
decision within a certain time
(Turing proposed 5 minutes, but
the exact amount of time is
generally considered irrelevant),
– the machine is considered to be
intelligent.
• If the computer succeeds in
fooling the interrogator, i.e. the
interrogator cannot distinguish
the machine from the human,
then
– the machine may be assumed to be
“intelligent”
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CSNB234 Artificial Intelligence
Turing Test
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CSNB234 Artificial Intelligence
Others AI Theorists
• Other AI Theorists:
– McDermott, Patrick Winston, Newell, Simon, Rosenblatt
– & more (perform an internet search)..
• Warren McCulloch (Columbia University)
– Human Brain
• Claude Shannon (Bell Lab)
– Boolean Algebra
• Norbert Wiener (MIT)
– Mathematician and philosopher
• John McCarthy (Dartmouth College)
– Computer scientist and cognitive scientist
• Marvin Minsky (MIT, graduated in Harvard University)
– AI scientist
August 9, 1927 –
January 24, 2016
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CSNB234 Artificial Intelligence
AI History
1950: Turing
Test
Result: General
problem-solving
methods
1956:
Dartmouth
Conference proposed
launch of Joint
Research on
AI.
•John McCarthy,
Marvin Minsky,
Claude Shannon
among the
attendees.
1960s: AI
established as
research field.
Focus on
knowledge bases
started. Areas of
interests are
chess games,
theorem proving
and language
translation.
1963: Newell &
Simon built
General Problem
Solver (GPS).
1965: DENDRAL
developed by
Feigenbaum at
Stanford
University.
•Lisp developed by
John McCarthy.
Result:
Knowledgebased expert
systems
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CSNB234 Artificial Intelligence
Early Birth of AI Program: Eliza
• ELIZA is an early natural language processing
computer program created from 1964 to 1966
at the MIT Artificial Intelligence Laboratory by
Joseph Weizenbaum
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CSNB234 Artificial Intelligence
AI History
1990: Intelligent agents
1972: PROLOG developed
by Alain Colmerauer at
University of Marseilles.
1970s: AI
commercialization
began. MYCIN
developed at Stanford
University, utilised
production rules
(diagnosed infectious
blood diseases).
1981: Artificial neural
networks. ICOT (Institute
for New Generation
Computer Technology)
invented Concurrent
Prolog for concurrent
programming and parallel
execution
Result: Software that
performs assigned tasks
on the users behalf
Result: Resembling the interconnected
neuronal structures in the human brain
Result: Transaction processing
and decision support systems
using AI.
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CSNB234 Artificial Intelligence
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CSNB234 Artificial Intelligence
Conventional Systems vs AI
Conventional
Systems
• Procedural
• Numerical
processing
• Algorithmic
• Rigid syntax
AI Systems
• Declarative
• Symbolic
processing
• Heuristic
programming
• More natural
syntax
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CSNB234 Artificial Intelligence
Regular Programming vs AI Programming
Regular Programming =
Algorithmic
• Input: sequence of
alphanumeric symbols
• Processing: manipulation
of the stored symbols by a
set of algorithms
• Output: sequence of
alphanumeric symbols on
such a medium as a
screen, paper, or disk
AI Programming = Heuristics
• Input: sight, sound, touch,
smell or taste
• Processing: knowledge
representation and pattern
matching, search, logic,
problem solving & learning
• Output: printed language
and synthesized speech,
manipulation of physical
objects or locomotion i.e.,
movement in space
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CSNB234 Artificial Intelligence
Symbolic Processing
It is a branch of Computer Science that deals
with symbolic, non-algorithmic methods of
problem solving.
Heuristics
• It is the branch of Computer Science that deals with ways of
representing knowledge using symbols rather than numbers and
with rules-of-thumb for processing information.
• Developed through intuition, experience & judgment.
• Do not represent (our) knowledge of design, rather, they
represent guidelines through which a system may be operated.
• Often called “Rules of thumb”.
• Characteristics
 Screening
 Filtering
 Pruning
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CSNB234 Artificial Intelligence
Heuristic Programming
Should not be confused with computer programming.
A program is a solution; programming is a procedure for obtaining a
solution.
A heuristic programming employs a practical method, not guaranteed to be
optimal, perfect, logical, or rational, but instead sufficient for reaching an
immediate goal. It is important to highlight that Heuristics are the strategies
derived from previous experiences with similar problems.
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CSNB234 Artificial Intelligence
Language Levels for AI Problem
Solving
Symbol Level
• Concerns with the
particular formalisms
used to represent
knowledge such as logic
or production rules.
• Concerns with the
structures used to
organize knowledge.
Knowledge Level
• What queries / questions
will be asked?
• How new knowledge can
be added or updated?
• What objects and
relations are necessary?
• Can the system reasons
despite of
incompleteness of
information?
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CSNB234 Artificial Intelligence
AI Systems Development
Immature but can be used (tested)
Knowledge and expertise slowly building up..
This methodology is called Rapid Prototype
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CSNB234 Artificial Intelligence
Essential Requirements for AI
Language
Support of
Symbolic
Computation
Flexibility of
Control
Late Binding &
Constraint
Propagation
Support of
Exploratory
Programming
Methodologies
Clear and Welldefined
Semantics
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CSNB234 Artificial Intelligence
Essential Requirements for AI
Language
1
Support of
Symbolic
Computation
• Implementation of a set of
operation on symbolic rather than
numeric data.
• Predicate calculus is a powerful tool
for constructing qualitative
descriptions of a domain.
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CSNB234 Artificial Intelligence
Essential Requirements for AI
Language
2
Flexibility of
Control
• Rule-based systems being the most important
paradigm for building AI programs.
• AI cannot be achieved through step-by-step execution
of a fixed sequence of instructions .
• Production rules can be fired in virtually any order
(i.e. not step-by-step) in response to a given
situation.
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CSNB234 Artificial Intelligence
Essential Requirements for AI
Language
• AI programs seldom respond to
standard software approaches such as
top-down design, stepwise
refinement.
• This is due to the nature of AI
problems that they could be started &
tested without having to completely
produce the final specification.
• In other words, most AI programs are
initially poorly specified.
• AI programming is inherently
exploratory; the program is the
vehicle through which we explore the
problem area (domain) and discover
solution strategies.
3
Support of
Exploratory
Programming
Methodologies
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CSNB234 Artificial Intelligence
Essential Requirements for AI
Language
• Often, the problems addressed by AI program (such as
Prolog program) require that the values of certain
entities to remain unknown until sufficient information
is gathered to determine the assignment.
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Late Binding &
Constraint
Propagation
• As constraints are
accumulated, the set of
possible values is reduced,
ultimately converging on a
solution.
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CSNB234 Artificial Intelligence
Essential Requirements for AI
Language
• Traditional computer languages are too complex in its
programming constructs and semantic definitions. They
are not subject to self-proof.
• This could be achieved by
developing new languages
that do not (to certain
extent) conform to the
architecture underlying
von Neumann computer
and be on the foundation
of mathematical
formalisms such as logic
(Prolog).
5
Clear and Welldefined
Semantics
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CSNB234 Artificial Intelligence
AI Languages
• In Europe and Japan, Prolog is the preferred choice
while in America, LISP is usually the way to go.
Prolog
• Good for rapid prototyping.
• Possible to write algorithms by
augmenting logical sentences with
information to control the inference
process.
Lisp
• Flexible
• Allows it to adapt as programming
styles change.
• It made complex programs easy and
fast to write.
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CSNB234 Artificial Intelligence
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CSNB234 Artificial Intelligence
Overview of AI Application Areas
Game Playing
Automated Reasoning and Theorem Proving
Expert Systems
Natural Language Understanding and Semantic Modeling
Modeling Human Performance
Planning and Robotics
Languages and Environments for AI
Machine Learning
Alternative Representations: Neural Nets and Genetic
Algorithms
• AI and Philosophy
•
•
•
•
•
•
•
•
•
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CSNB234 Artificial Intelligence
AI in Finance
Trading Agent
•
Rates, news
– Automated financial advisors and
planners that assist users in making
financial decisions
– Smart wallets that monitor and learn
users’ habits and needs and alert and
coach users
– Data-driven AI applications to make
better informed lending decisions
Stock
market
Bonds
market
Trades
Commodities
market
Personalized Financial Services
•
New Management Decision Making
– Analyze data to come up with
recommended decisions
•
Reducing Fraud and Fighting Crime
– learn and monitor users’ behavioral
patterns to identify anomalies and
warning signs of fraud attempts and
occurrences, along with collection of
evidence necessary
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CSNB234 Artificial Intelligence
AI in Robotics
Camera, mic,
touch
Motors, voice,
move wheel /
arm
• Most robots perform
repeating tasks without
ever moving an inch.
• Autonomous robots are
self supporting or in
other words self
contained. In a way
they rely on their own
brains.
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CSNB234 Artificial Intelligence
AI in Games
Game Agent
Your move
You /
Player
Its own move
• Smart objects are used
to help implement the
behaviors.
• The object specifies
how each character
interacts with it, which
has many scalability
and workflow
advantages over
centralized logic.
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CSNB234 Artificial Intelligence
AI in Medicine
Diagnostic
Agent
• Fast and accurate
diagnostics
Blood pressure,
heart signal
– ability to learn from past
cases
You /
Doctor
Diagnostics
• Reduce errors related to
human fatigue
– assisting doctors by
eliminating human error and
relieving them of time
consuming, monotonous
tasks
• Decrease in medical costs
– Patients would be asked to
submit data more frequently
via online medical records,
and the improved line of
communication could result
in less hospital visits
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CSNB234 Artificial Intelligence
AI in Website
Crawler
Web pages
List of
websites
Query
WWW
• Crawl the web
• AI understand what
words you typed in and
find the most relevant
pages
• Retrieve pages – stores
in big DB inside crawler
& analyze relevant
pages for any possible
queries
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CSNB234 Artificial Intelligence
Summary
• Artificial Intelligence (AI) Background and
History
• AI Languages
• Overview of AI Application Areas