Lesson 1:
Artificial Intelligence
Artificial – usually has a negative connotation (synthetic – i.e. man made)
e.g. artificial flower :
look …maybe
feel no
smell no
vs.
artificial light

electric light

candles

kerosene
artificial motion

natural light
sunlight
natural motion

planes
walking

trains
horse

automobiles
Intelligence: Is the cognitive ability of an individual to learn from experience, to
reason well, to remember important information, and to cope with the demands of
daily living.
Intelligence might be defined broadly as facility at solving problems.
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Sequences:
1,
3,
6,
10,
15,
21,
?
28 is next
Triangular numbers
n
nth entry =
i
2
e.g. 2nd triangular # =
i  1 2  3
i 1
i 1
1,
2,
2,
3,
3,
3,
4,
4,
4,
4,
?
2,
3,
3,
5,
5,
5,
7,
7,
7,
7,
?
0,
1,
2,
?
3 … not so fast!
Another possibility …
0
=
0
1!
=
1
2!!
=
(2!)! = (2x1)! = 2! = 2
3!!!
=
(3!)!! = ((3x2)!)! = (6!)! = 720!
23,
?
And finally …
9,
14,
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Artificial Intelligence is the science of making machines do things that would
require intelligence if done by man.
Building Intelligent Systems

Knowledge Representation
o Production rules
…if cond then result
o Logic
o Frames
o Scripts
o Semantic networks
Frames and scripts utilize the prototypical nature of most events, e.g.
visits to a restaurant, dentist , etc.

Search
o Blind search – no knowledge of problem domain
A
B
D
C
E
F
G

Depth first search (dfs)
A,B,D,E,C,F,G

Breadth first search (bfs)
A, B, C, D, E, F, G
vs
o Heuristic search – employ estimates of closeness to goal.
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
Learning – improved performance via practice
Paradigms – connectionist – artificial neural networks (ANN)
e.g. learning a Boolean function – pattern classification
X1
0
0
1
1
X2
0
1
0
1
X1* X2
0
0
0
1
Feature extraction
How would you teach this to a child? Reward when correct, “punish” when
wrong.
SUPERVISED LEARNING
This scales up – male or female? What are features
here?
Evolutionary Computation
Charles Darwin – British naturalist
“I have called this principle, by which each slight variation, if useful, is
preserved, by the term natural selection” – Charles Darwin from “The Origin
of Species”, 1859
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“Darwin’s theory of evolutionary selection holds that variation within species
occurs randomly and that the survival or extinction of each organism is
determined by that organism’s ability to adapt to its environment.”
www2.lucidate.com/lucidate/library

Survival of the fittest
Natural selection occurs in nature at a rate of thousands or millions of years.
Inside a computer – evolution proceeds somewhat faster.
Genetic Algorithm – the problem is encoded as a string
Example:
3-puzzle
Start State
2
Goal
3
1
1
2
3
Operators




… where we assume it is the blank that moves
Encode these operators as binary strings…
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For example,

00

01

10

11
A solution for this puzzle (when one exists) consists of a series of moves. Such a
solution (whether successful or not so) may thus be represented by a binary
string. For example, the string:
001101
2
corresponds to:
3
00 º
1
2
11 º
1
3
2
3
1
2
01 º
1
3
// we’re getting close!
To each such string we shall attach a fitness function. What metric would you
propose?
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With a genetic algorithm (GA) we would begin with a population of such strings.
Initial Population
S1: 1110111011101110
S2: 0011011000110110
.
.
.
Sr: 0011010001000110
Selection
Much as in human reproduction, these strings are permitted to “mate” based on
their fitness.
Crossover
Strings “share genetic material”
….. a crossover point is generated randomly
Before
A: 01110011
Crossover B: 10011000
Same as A From B
After
Crossover
A’: 01110000
B’: 10011011
Same as B From A
Mutation – spontanteous variation with a small probability, say .001
A bit in a string may change 0  1 or 1  0
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Genetic Algorithms – con’t.
Initial Population
String 1
String 2
.
.
.
.String r
Fitness F0
Selection
Crossover
Mutation
Second Population
String 1'
String 2'
.
.
.
.String r’
Fitness F1
.
.
.
.
Nth Population
String 1n
String 2n
.
.
.
.String rn
Fitness Fn
It is certainly the case that Fn >> F0 and either Fn contains a string that solved the
given problem or comes very close.
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Learning Paradigms con’t.
Genetic Programming:
Proposed programs are permitted to share their code
Inductive Learning:
Generalization from a set of examples
Returning to the issue of intelligence….
How does one decide if someone (something?) is intelligent?
Are animals intelligent?
Dogs?
Cats ?
Ants ?
Dolphins ?
And if so, how would one measure it? …
Clever Hans – Berlin, circa 1900
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Intelligence – con’t.
Intelligence is the characteristic almost universally agreed upon as setting
humans apart from ( and above? ) other creatures.
The declared goal of artificial intelligence research is to teach machines to
“think”, i.e. to display those characteristics usually associated with human
intelligence.
Pivotal question: Can Machines Think?
The answer may not be a neat yes or no, but rather a highly qualified “to a
certain extent under special conditions.”
Does a person, animal, machine possess intelligence
… the answer is not binary :
Some people are smarter than others
Some animals are smarter than others
Turing rephrased this question in operational terms.
i.e. he sought to separate functionality from implementation.
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Measuring Intelligence
Alan Turing (1950) proposed two imitation games
In the first:
Man (lies)
An Interrogator
Woman (truthful)
Curtain
A series of questions is asked. The interrogator must determine if it is a
man or woman on the other side of the curtain. If a man is successful in
deceiving the interrogator, then we say that he has passed this imitation game.
What questions would you suggest?
The second…
The Turing Test for intelligence
Computer (lies)
An Interrogator
Person (truthful)
Curtain

Questions?

Loebner Prize of $10,000
Is it a computer or a human? If the computer is successful in deceiving the
interrogator then we say that it has passed the Turing Test.
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Turing Test (con’t.)
Proposed Questions:

1,000,017 …not a good idea, why not?

Are you afraid of dying?

How does the dark make you feel?

What does it feel like to be in love?
Is this a valid barometer for intelligence?
Block’s criticism of the Turing Test
English text may be encoded in ASCII inside a computer ( in fact it is! ).
Hence, a particular Turing Test which is a series of questions and answers may
be stored as a (very large) number. In fact, one could envision many instances of
the Turing Test being stored on a very large database. Passing the test could
then be accomplished by table lookup. Granted, such a computer system does
not exist at present…
But if it did, would you feel comfortable in calling this computer intelligent?
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The Chinese Room
Searle’s criticism of the Turing Test
The Chinese Room
Once again, we have an interrogator who will ask questions – this time - in
Chinese. And in a room we have an individual who does not know Chinese;
however, that person possesses a very detailed “rule book.”
To most people who do not know Chinese, the language appears as squiggles.
Questions in Chinese
Interrogator
Person
Answers in Chinese
Rule book
Does this person know Chinese?
Does the room?
What is the analogy with the Turing Test?
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...The rule
book is at
the
squiggle
level
The Chinese Room (con’t.)
Now envision instead of a single person with a rule book, a whole gymnasium of
people with “notes” that are passed to one another.
Q
Interrogator
Gymnasium with 1,000
people
A
Distributed rule book
Does the gymnasium know Chinese? …
OK – … finally picture the brain of a person who does indeed know Chinese
neurons
Q
Interrogator
A
Does an individual neuron know Chinese?
What of a collection of these neurons?
Where does the knowledge of Chinese reside?
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