Artificial Intelligence CPSC 327

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Artificial IntelligenceCPSC 327

• Week 1
• The Astonishing Hypothesis
• (with apologies to Francis Crick)

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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?
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So, AI is the construction of intelligent systems

• Artificial Intelligence (AI) may be defined as
  the branch of computer science that is concerned
  with the automation of intelligent behavior. p.
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• Key notion behavior
• Classic def. of AI doesnt care how its
  composed.
• Intelligent systems behave intelligently.

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But whats 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

• Intelligence must entail a set of skills to solve
  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

• Existence of prodigies. That is, the skills may
  be plotted along a standard normal distribution.
  Some people are way out on the right side.
• 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
• Sensitivity to pitch relations (musicians)
• Ability to see patterns among symbols
  (mathematicians)
• Ability to imitate bodily movements (athletes)
• Ability to speak a language (all humans)

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Two More

• Evolutionary history and evolutionary
  plausibility. A specific intelligence becomes
  more plausible to the extent that one can locate
  its evolutionary antecedents.
• Distinctive developmental historylevels of
  expertise through which every novice passes

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Yet another

• Support from experimental psychology. To the
  extent that various specific computational
  mechanismswork 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

• Susceptibility to encoding in a symbol system.
  Much of human representation and communication
  takes place via symbol systemsculturally
  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
  productivitySymbol 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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Gardners 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)
• Intrapersonalaccess to ones own emotional life
  (novelist, shaman)
• Interpersonalability 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
• Monitor email
• Perform hazardous tasks
• Monitor correct operations of a computer network
• Monitor/rewrite news
• Make computers and other appliances easier to 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

• Stays away from purely human intelligence by
  talking of mental faculties
• Perceive the world
• Learn, remember, control action
• Create new ideas
• Communicate
• Create the experience of feelings, intentions,
  self-awareness
• 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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• 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
  has paid special attention to logic
• Especially interesting to learn that logic is
  pattern matching, a claim that Ill argue for
  when we study proofs by resolution refutation

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Objections to AI

• Computers only do what theyre told
• Debugging programs we often dont know what
  weve told computers to do
• Riddle generator
• 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, its
  difficult to specify the rules
• Cant specify rules to govern all of behavior
• Machine learning
• Searles Chinese box experiment
• AI systems are brittle and not scaleable
• PDP
• Intelligence and logic are not the same thing
• PDP
• Artificial life and genetic algorithms
• Society of agents
• The collection of many specialist talents produce
  emergent intelligent behavior

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AI Areas

• Game playing
• Source of results in state space search, state
  space representation, heuristic reasoning
• Theorem Proving
• Early successes Theorem 2.85 from Principia
• Problem prove large number of irrelevant
  theorems before stumbling on the goal
• Expert systems
• Domain-specific knowledge
• Rigidly hand-crafted
• Dont learn
• Common threads to all three
• Well-defined set of rules
• No outside knowledge is required

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• NLP
• Success with parsing
• Success with speech synthesis
• Syntax is math-like, but language is more than
  parsing
• 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?
• Why did Janet shake the piggy bank?

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• Cognitive Modeling
• Forces precision
• Existence proof
• Robotics
• Machine Learning
• Neural networks
• Evolutionary Computing

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Two Strands in AI

• 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 not 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, Turing), linguistics (Winograd,
  Lakoff), post-modern thought (Derrida).
• The cumulative effect has been to call the AI
  projectat least as classically conceivedinto
  question.

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• Strand based on biological metaphors
• Artificial life and genetic algorithms take their
  inspiration from the principles of biological
  evolution.
• Connectionism (PDP) takes it inspiration from a
  highly abstract view of neurons connected by
  synapses through a feedback mechanism. This
  approach has made a comeback since the late 80s
  after Minsky and Paperts book killed the work of
  Rosenblatt and others in the late 60s.