Transcript Q. What is artificial intelligence?

Artificial
Intelligence
Introduction (2)
What is Artificial Intelligence ?

making computers that think?

the automation of activities we associate with human
thinking, like decision making, learning ... ?
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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 ?
What is AI?
There are no crisp definitions
Q. What is artificial intelligence?
A. It is the science and engineering of
making intelligent machines, especially
intelligent computer programs.
Q. what is intelligence?
A. 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.
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Alan M Turing, Hero
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Helped to found theoretical CS
– 1936, before digital computers existed
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Helped to found practical CS
– wartime work decoding Enigma machines
– ACE Report, 1946
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Helped to found practical AI
– first (simulated) chess program
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Helped to found theoretical AI …
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What did Turing think?
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Turing (in 1950) believed that by 2000
– computers available with 128Mbytes
storage
– programmed so well that interrogators
have only a 70% chance after 5 minutes
of being right
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“By 2000 the use of words and general
educated opinion will have altered so
much that one will be able to speak of
machines thinking without expecting to
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be contradicted”
Turing Test
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Three rooms contain a person, a
computer, and an interrogator.
The interrogator can communicate with
the other two by teleprinter.
The interrogator tries to determine which
is the person and which is the machine.
The machine tries to fool the interrogator
into believing that it is the person.
If the machine succeeds, then we
conclude that the machine can think.
The Imitation Game
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Interrogator in one
room
– computer in another
– person in a third room
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From typed responses
only (text-only), can
interrogator distinguish
between person and
computer?
If the interrogator often
guesses wrong, say
the machine is
intelligent.
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Can Machines Think?

Turing starts by defining machine &
think
– Will not use everyday meaning of the
words
 otherwise
we could answer by Gallup poll
– Instead, use a different question
 closely

related, but unambiguous
“I believe the original question to be
too meaningless to deserve
discussion”
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A sample game
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Turing suggests some Q & A’s:
Q: Please write me a sonnet on the subject of the Forth Bridge
A: Count me out on this one, I never could write poetry
Q: Add 34957 to 70764.
– (pause about 30 seconds)
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A: 105621
Q: Do you play chess?
A: Yes
Q: I have K at my K1, and no other pieces. You have only K
at K6 and R at R1. It is your move. What do you play?
– (pause about 15s)
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A: R-R8 mate
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Some Famous Imitation Games
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1960s ELIZA
– Rogerian psychotherapist
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1970s SHRDLU
– Blocks world reasoner
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1980s NICOLAI
– unrestricted discourse
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1990s Loebner prize
– win $100,000 if you pass the test
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“Chinese room”
argument [Searle
1980]
image from http://www.unc.edu/~prinz/pictures/c-room.gif
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Person who knows English but not Chinese sits in room
Receives notes in Chinese
Has systematic English rule book for how to write new
Chinese characters based on input Chinese characters,
returns his notes
– Person=CPU, rule book=AI program, really also need lots of paper
(storage)
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– Has no understanding of what they mean
– But from the outside, the room gives perfectly reasonable
answers in Chinese!
Searle’s argument: the room has no intelligence in it!
Why is AI hard?
Two usual ingredients (for standard AI)
 Representation
– need to represent our knowledge in
computer readable form
 Reasoning
– need to be able to manipulate knowledge
and derive new knowledge
– finding the successful way usually
involves search
Both of these are hard.
Knowledge Representation
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It is the problem of capturing in a
formal language suitable for computer
manipulation.
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We will study logic as a language for
AI
Representation Language
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An AI representation language must :
– Handle qualitative knowledge
– Allow new knowledge to be inferred from
set of facts and rules
Search Problem
Search is a problem-solving technique to
explores successive stages in
problem-solving process.
Search Problem
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We need to define a space to search
in to find a problem solution
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To successfully design and implement
search algorithm, we must be able to
analyze and predict its behavior.
State Space Search
One tool to analyze the search space is
to represent it as space graph, so by
use graph theory we analyze the
problem and solution of it.
Graph Theory
A graph consists of a set of nodes and a
set of arcs or links connecting pairs of
nodes.
River2
Island1
Island2
River1
Graph structure
Nodes = {a, b, c, d, e}
 Arcs
= {(a,b), (a,d), (b,c),….}
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d
b
c
e
a
Tree
A tree is a graph in which two nodes
have at most one path between them.
 The tree has a root.
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a
b
e
f
c
g
h
d
i
j
Space representation
In the space representation of a
problem, the nodes of a graph
correspond to partial problem solution
states and arcs correspond to steps in
a problem-solving process
Example
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Let the game of Tic-Tac-toe
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Strategies for search
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The strategies for state space search
are: Data-driven and goal-driven
search
Data-Driven search
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It is called forward chaining
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The problem solver begins with the
given facts and a set of legal moves or
rules for changing state to arrive to the
goal.
Goal-Driven Search
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Take the goal that we want to solve
and see what rules or legal moves
could be used to generate this goal.
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So we move backward.
Search Implementation
In both types of moving search, we
must find the path from start state to a
goal.
 We use goal-driven search if
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– The goal is given in the problem
– There exist a large number of rules
– Problem data are not given
Search Implementation
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The data-driven search is used if
– All or most data are given
– There are a large number of potential
goals
– It is difficult to form a goal