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Artificial Intelligence
Lecture # 1
An Introduction
Objectives
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Understand the definition of artificial intelligence (AI)
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Understand the different faculties involved with intelligent
behaviour
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Examine the different ways of approaching AI
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Look at some example systems that use AI
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Trace briefly the history of AI -- ?
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Have a fair idea of the types of problems that can be currently
solved by computers and those that are as yet beyond its ability
Definition of AI
 Artificial Intelligence is a branch of Computer Science concerned
with the automation of intelligent behaviour
 Artificial Intelligence is concerned with the design of intelligence in
an artificial device.
 The art of creating machines that perform functions that require
intelligence when performed by people.
 The term was coined by McCarthy in 1956.
 There are two ideas in the definition.
 1. Intelligence
 2. artificial device
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Is it something which characterize humans? or is there an absolute standard of judgement?
What is Intelligence
 Someone’s intelligence is their ability to understand and learn
things
 Intelligence is the ability to think and understand instead of doing
things by instinct or
automatically
 Thinking is the activity of using your brain to consider a problem
or to create an idea.
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Can machines think?
Capabilities required
 Natural Language Processing
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– to enable successful communication in English
• Knowledge Representation
– to store what it knows or hears
• Automated Reasoning
– to use the stored information to answers questions
• Machine Learning
– to adapt to new circumstances and detect patterns
• ComputerVision
– to perceive objects
• Robotics
– to manipulate objects and move about
Can machines think?
Accordingly there are two possibilities:
– A system with intelligence is expected to behave as intelligently as a human
– A system with intelligence is expected to behave in the best possible
manner (Rationality)
Secondly what type of behaviour are we talking about?
– Are we looking at the thought process or reasoning ability of the system?
– Or are we only interested in the final manifestations of the system in terms
of its actions?
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Goal of AI
 Understanding and building intelligent entities
 Four approaches
– Systems that think like humans
– Systems that think rationally
– Systems that act like humans
– Systems that act rationally
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Acting humanly:
 The Turing Test
 Proposed by Alan Turing (1950)
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The Turing Test
 “The computer passes the test if a human interrogator, after posing
some written questions cannot tell whether the written responses
come from a person or not.”
 To pass the Turing test, the machine has to fool the interrogator into
believing that it is human.
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Thinking Humanly:
Cognitive Modelling
 Method must not just exhibit behavior sufficient to fool a human judge
but must do it in a way demonstrably analogous to human cognition.
This view involves trying to understand human thought and an effort to
build machines that emulate the human thought process.
 Requires to know how humans think?
 This view is the cognitive science approach to AI.
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Cognitive Modelling Approach
 “Cognitive Science brings together
computer models from
AI and experimental techniques from psychology to try to
construct precise testable theories of the working of the
mind.”
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Thinking rationally:
Laws of thought Approach
 Formalize “correct” reasoning using a mathematical model
 Syllogisms provided patterns for argument structures that yield the
correct conclusions when given correct premises.
 Syllogisms like “Socrates is human; humans are mortal; so
Socrates is mortal” …..
 These laws were supposed to govern the operation of the mind
 Initiated the field of logic
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Thinking rationally:
Laws of thought
 Logic and laws of thought deals with studies of ideal or rational thought
process and inference.
 The emphasis in this case is on the inferencing mechanism, and its
properties – i.e. how the system arrives at a conclusion, or the
reasoning behind its selection of actions is very important in this point
of view.
 The soundness and completeness of the inference mechanisms are
important here.
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Acting rationally:
Rational agents
 An agent is something that acts
– comes from Latin agere, - to do
 The rational agent is one that acts so as to achieve the best outcome
or best expected outcome if there is uncertainty.
 Making correct inferences is often part of being a rational agent,
however sometimes there is no provably correct thing to do, but
something still has to be done
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Acting rationally:
Rational agents
 This view deals with building machines that act rationally.
 The focus is on how the system acts and performs, and not so much
on the reasoning process.
 A rational agent is one that acts rationally, i.e. is in the best possible
manner.
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Typical AI problems
An “intelligent entity” is expected to perform both
 common-place tasks: (humans and animals can do easily)
 Recognizing people, objects.
 Communicating (through natural language).
 Navigating around obstacles on the streets
 Expert tasks: (requires expertise and more intelligence)
 Medical diagnosis
 Mathematical problem solving
 Playing games like chess
What If Machines are To Perform These Set of Tasks!
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Intelligent Behaviour
Capabilities required
 Natural Language Processing
– to enable successful communication in English
• Knowledge Representation
– to store what it knows or hears
• Automated Reasoning
– to use the stored information to answers questions
• Machine Learning
– to adapt to new circumstances and detect patterns
• ComputerVision
– to perceive objects
• Robotics
– to manipulate objects and move about
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Practical Impact of AI
AI systems are in everyday use:
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for identifying credit card fraud
for advising doctors
for recognizing speech
helping in complex planning tasks
there are intelligent tutoring systems that provide students with
personalized attention
Thus AI has increased understanding of the nature of intelligence and
found many applications. It has helped in the understanding of human
reasoning, and of the nature of intelligence. It has also helped us
understand the complexity of modeling human reasoning.
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What can AI systems do
Today’s AI systems have been able to achieve limited success in
some of these tasks.
 In Computer vision, the systems are capable of face recognition
 In Robotics, we have been able to make vehicles that are mostly autonomous.
 In Natural language processing, we have systems that are capable of simple machine
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translation.
Today’s Expert systems can carry out medical diagnosis in a narrow domain
Speech understanding systems are capable of recognizing several thousand words
continuous speech
Planning and scheduling systems had been employed in scheduling experiments with the
Hubble Telescope.
The Learning systems are capable of doing text categorization into about a 1000 topics
In Games, AI systems can play at the Grand Master level in chess (world champion),
checkers, etc.
What can AI systems NOT do yet?
• Understand natural language robustly (e.g., read and understand articles in
a newspaper)
• Surf the web
• Interpret an arbitrary visual scene
• Learn a natural language
• Construct plans in dynamic real-time domains
• Exhibit true autonomy and intelligence
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Abridged history of AI
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1943
McCulloch & Pitts: Boolean circuit model of brain
1950
Turing's "Computing Machinery and Intelligence"
1956
Dartmouth meeting: "Artificial Intelligence" adopted
1952—69
Look, Ma, no hands!
1950s Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
1965
Robinson's complete algorithm for logical reasoning
1966—73
AI discovers computational complexity
Neural network research almost disappears
1969—79
Early development of knowledge-based systems
1980-AI becomes an industry
1986-Neural networks return to popularity
1987-AI becomes a science
1995-The emergence of intelligent agents
Further Reading
 Searle, J. R. 1980. "Minds, Brains, and Programs".
Behavioral and Brain Sciences,Vol.3., 417-424.
 Turing, A. M. 1950. "Computing Machinery and Intelligence."
Mind,
Vol.
LIX.
433-460.
http://www.loebner.net/Prizef/TuringArticle.html.
Online:
 Descartes, R. 1637. Discourse on Method. Translated in
J.Cottingham, R.Stoothoff, and D.Murdoch, The Philosophical
Writings of Descartes, Vol.1. Cambridge University Press:
Cambridge (1985). 131-141 (Part 5).
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