Artificial Intelligence
CPSC 327
Week 1
The Astonishing Hypothesis
(with apologies to Francis Crick)
1
Towards a Definition
• The name of the field is composed of two
words:
– Artificial
• Art
• Artifact
• Artifice
• Article
– Intelligence
What do these have in common?
2
So, AI is the construction of
intelligent systems
• “Artificial Intelligence (AI) may be defined as the
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•
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branch of computer science that is concerned
with the automation of intelligent behavior.” p. 1
Key notion: behavior
Classic def. of AI doesn’t care how it’s
composed.
Intelligent systems behave intelligently.
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But what’s intelligence?
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Some indicators
• Ability to do mathematics
• Ability to design a machine
• Ability to play chess
• Ability to speak
• Ability to write an essay
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What do all of these have in
common?
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AI has concentrated on those
things that we get rewarded for in
school.
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A more precise set of criteria*
1. Intelligence must entail a set of skills to solve
2.
genuine problems valued across cultures
Potential isolation by brain damage: “to the
extend that a particular faculty can be
destroyed as a result of head trauma, its
isolation from other faculties seems likely.”
*Howard Gardner, Frames of Mind, Basic Books, 1993, pp. 62-67
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Two more criteria
3.
Existence of prodigies. That is, the skills may be plotted along a
standard normal distribution. Some people are way out on the
right side.
4.
Existence of one or more basic information processing operations
that deal with specific inputs. “One might go so far as to define a
human intelligence as a neural mechanism or computational
system which is genetically programmed to be activated or
“triggered” by certain kinds of internally or externally presented
information.” For example:
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Sensitivity to pitch relations (musicians)
Ability to see patterns among symbols (mathematicians)
Ability to imitate bodily movements (athletes)
Ability to understand emotional and power relations among a group
of people (politicians)
Ability to speak a language (all humans)
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Two More
5. Evolutionary history and evolutionary
plausibility. “A specific intelligence
becomes more plausible to the extent
that one can locate its evolutionary
antecedents.”
6. Distinctive developmental history—levels
of expertise through which every novice
passes
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Yet another
7. Support from experimental psychology.
“To the extent that various specific
computational mechanisms…work
together smoothly, experimental
psychology can also help demonstrate
the ways in which modular … abilities
may interact in the execution of complex
tasks.” Psychometric findings are also
relevant.
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Finally
8. Susceptibility to encoding in a symbol system. “Much
of human representation and communication … takes
place via symbol systems—culturally contrived systems
of meaning which capture important forms of
information. Language, picturing, mathematics are
but three of the symbol systems that have become
important the world over for human survival and
human productivity…Symbol systems may have
evolved in just those cases where there exists a
computational capacity ripe for harnessing….”
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An Historical Aside
• Newell & Simon, two AI pioneers,
formulated the Physical Symbol System
Hypothesis in their 1978 Turing Award
Lecture:
“A physical symbol system possesses the
necessary and sufficient conditions for
general intelligent action.” (about which,
more later).
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Gardner’s Seven Intelligences
These 8 criteria lead to seven intelligences
• Musical intelligence
• Logical-mathematical intelligence
• Linguistic intelligence
• Spatial intelligence (kekule’ and the Benzene ring,
artist)
• Bodily-kinesthetic (athlete, dancer, surgeon)
• Intrapersonal—access to one’s own emotional life
(novelist, shaman)
• Interpersonal—ability to read the emotional state of
others (politician, gambler, therapist)
.
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The good and bad news
• AI has had lots of success with logical
intelligence
• Less success with linguistic intelligence
• Almost no success with what comes under
the heading of common sense
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Yet another definition
• AI is the science of making machines do
the sort of things that are done by human
minds (Oxford Companion to Mind)
• Why? I mean, who cares?
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Five applications
• Build various kinds of intelligent assistants
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Monitor email
Perform hazardous tasks
Monitor correct operations of a computer network
Monitor/rewrite news
• Make computers and other appliances easier to
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use
Machine translation
Intelligent tutors
Model human cognition
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Model Human Cognition
• Another Def.
– AI is the study of mental faculties through the
use of computational models
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Good Points of this definition
1. Stays away from purely human intelligence by
talking of mental faculties
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Perceive the world
Learn, remember, control action
Create new ideas
Communicate
Create the experience of feelings, intentions, selfawareness
2. Introduces the notion of a computational
model
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Fundamental Assumption in AI
• Computational/Representational
Understanding of Mind
– Theory can best be understood in terms of
representational structures in the mind and
computational procedures that act on them
– Implication is that the material in which these
are implemented is irrelevant
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So
• Material of the brain
– Neural cells and electrical potential called
synapses
• Material of Computers
– Silicon, copper, electrical impulses organized
to implement the laws of symbolic logic
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Central Feature of AI
• Materials are irrelevant
• Intelligence implemented in silicon is still
intelligence
• Turing Test laid out the ground rules over
fifty years ago
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Physical Symbol System Hypothesis
• Allen Newell & Herbert Simon
• “A physical symbol system has the
necessary and sufficient means for general
intelligent action.”
• What is a PSS?
– A program
– A Turing Machine
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To Explain
• Symbol
– May designate anything
– If it designates something in the world, it has a
semantics
– May be manipulated according to rules and so has a
syntax
• Necessary
– Any system that exhibits general intelligence, will
prove, upon analysis, to be a physical symbol system
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Further
• Sufficient
– Any physical symbol system of large enough
size can be organized to exhibit general
intelligent action
• General Intelligent Action
– Same scope as human behavior: in any real
situation, behavior appropriate to the ends of
the system and adaptive to the demands of
the environment can occur
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Example: Language Generation
• Mary hit the ball.
– Letters are symbols for sounds
– Arranged according the rules of spelling
– To form words
– But, words refer to
• Objects: Mary, John, Ball
• Actions: hit
• Relationships: to
• These form the semantics of the sentence
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• By arranging these words according to
linguistic rules, called syntax, we get
sentences
• But how do we know the rules?
• Language spoken by native speakers is
data. Linguists tease out the regularities.
• So, a grammar is descriptive, not
prescriptive
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Simple Context Free Grammar
S  NP VP
VP  V NP (PP)
PP  P NP
NP  (det) N
det  {a, the}
N  {Mary, John, ball, bat}
P  {to, with}
V  bat
Try deriving the sentence:
Mary hit the ball to John with the bat.
Notice the recursive structure
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So we have
• Symbols
• Syntax
• Semantics
If these were sufficiently complex, we would
have a PSS that generates all English
sentences.
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The Astonishing Hypothesis
• Intelligence is, at bottom, symbol manipulation
• Convenient for computer scientists
• Hard to know which came first
– Claim then the computer
– Computer then the claim
• Western thought from Aristotle to Boole to Frege
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has paid special attention to logic
Especially interesting to learn that logic is
pattern matching, a claim that I’ll argue for
when we study proofs by resolution refutation
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Objections/Counter Objections
• Computers only do what they’re told
– Debugging programs: we often don’t know what we’ve told computers to do
– Rules given to AI program are like the axioms of an algebra. They allow the
inference of the theorems that were not anticipated
– PDP is not rule bound. Or at least, it’s difficult to specify the rules
• Can’t specify rules to govern all of behavior
– Machine learning
• Searle’s Chinese box experiment
• AI systems are brittle and not scaleable
– PDP
• Intelligence and logic are not the same thing
– PDP
– genetic algorithms
– Hidden Markov models
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AI Areas
1. Game playing
– Source of results in state space search, state space representation,
heuristic reasoning
2. Theorem Proving
– Early successes: Theorem 2.85 from Principia
– Problem: prove large number of irrelevant theorems before stumbling
onto the goal
3. Expert systems
– Domain-specific knowledge
– Rigidly hand-crafted
– Don’t learn
Common threads to all three
– Well-defined set of rules
– No outside knowledge is required
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4. NLP
• Success with parsing
• Success with speech synthesis and
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transcription
Growing success with translation
All successes are probabilistic
Language is deceptively rule-bound
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He saw her duck
“janet needed some money. She got her piggy bank
and shook it. Finally, some money came out.”
• Why did Janet get the piggy bank?
• Did Janet get the money?
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• Why did Janet shake the piggy bank?
5. Cognitive Modeling
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Forces precision
Existence proof
6. Robotics
7. Machine Learning (e.g., neural networks,
evolutionary computing, stochastic
models)
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Two Strands in AI
1.
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Strand based on logic
“The reliance on logic as a way of representing knowledge and on
logical inference as the primary mechanism for intelligent reasoning
are so dominant in Western philosophy that their “truth” often seems
unassailable. It is no surprise, then, that approaches based on these
assumptions have dominated the science of artificial intelligence from
its inception to the present day.” p. 16
But various forms of philosophical relativism have questioned the
“objective basis of language, science, and society” in the past half
century.
Examples come from philosophy of language (Wittgenstein, Grice,
Austin, Searle), phenomenology (Husserl, Heidegger, Dreyfus), logic
(Godel: In any logical system there must remain propositions that
can’t be proven from within the system), linguistics (Winograd,
Lakoff, usage-based linguists), post-modern thought (Derrida: “There
is no outside the text”).
The cumulative effect has been to call the AI project—at least as
classically conceived—into question.
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2. Strand based on biological metaphors and
stochastic modeling
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Artificial life and genetic algorithms take their
inspiration from the principles of biological
evolution. Intelligence as emergent.
– Connectionism (PDP) takes it inspiration from a
highly abstract view of neurons connected by
synapses through a feedback mechanism
– Hidden Markov models: a machine learning
technique that makes Bayesian inferences for
chains of events
• Bayes Rule: P(X|Y) = (P(Y|X) * P(X))/P(Y))
• In English: the probability that we have class today given
that today is Thursday equals the probability that today is
Thursday given that we have class times the probability
that we have class divided by the probability that today is
Thursday.
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