Artificial Intelligence

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Artificial Intelligence
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What is Artificial Intelligence (AI)?

• A good question
• First answer the more fundamental/basic question
• What is Intelligence?

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What is Intelligence?

• If you asked 10 people, you'd probably get 10
  answers
• The ability to learn from experience
• The power of thought
• The ability to reason
• The power of insight
• Intuition
• Synonymous with Knowledge

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But What is AI?

• Text defn the part of computer science that
  attempts to make computers act like human beings
• Another by Dr. Astro Teller
• A.I. is the science of how to get machines to do
  the things they do in the movies.
• So what do they do in the movies?

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YAD of AI

• YAD Yet Another Definition
• Artificial Intelligence is the study of ideas
  which enable computers to do the things which
  make people seem intelligent
• And YAD (Patrick Henry Winston)
• Artificial intelligence is the study of the
  computations that make it possible to perceive,
  reason, and act.

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The Two Themes of AI

• Hard AI make computers do what humans do
• Eventually computers will think
• Soft AI make computers more sophisticated
• Lets make computers better at solving some of our
  day-to-day problems

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What Does AI Do?

• Play Games
• Natural Language Processing
• Planning
• Diagnosis and analysis
• Pattern recognition (handwriting, voice, etc)

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Can A Machine Think?

• According to Dijkstra
• ... the question of whether Machines Can Think,
  ... is about as relevant as the question of
  whether Submarines Can Swim.
• Dijkstra's doubts aside What test can a
  computer pass to be determined intelligent?

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The Turing Test

• Proposed by guess who....
• A machine may be deemed intelligent when it can
  pass for a human being in a blind test.
• That is, if we cannot tell if a computer is a
  computer, it passes the Turing test.

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Turing Test Example
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But

• Is the Turing test adequate?
• Any potential problems with it?

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Is This Adequate?

• Possible problems
• The machine doesn't understand things
• Its success is largely dependent upon the examiner

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No Understanding

• Ex The Chinese Room Thought Experiment proposed
  by John Searle
• A thought experiment aimed at showing conscious
  computers are impossible

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Chinese Room Thought Experiment

• Take a man who does not speak Chinese and lock
  him in a room
• Give him a rule book which indicates how to
  answer questions in English
• Thus he can translate anything from Chinese to
  English, but has no understanding of Chinese

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Success Dependent Upon Examiner

• Ex Eliza - A program which for many people
  passed the Turing Test
• Simulated a psychotherapist by using simple
  pattern matching and keyword recognition

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Eliza Example

• Eliza What is bothering you?
• Patient I feel sad all the time.
• Eliza (recognizes I feel...) Why do you feel
  sad all the time?
• Patient I dont get along with my parents.
• Eliza (recognizes my parents) Tell me about
  your parents.
• Is this system Intelligent?

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Why Bother With AI?

• Because computers are stupid (but powerful)
• Wouldn't it be nice if they
• Just knew what you want
• Used more common sense
• Were more tolerant of errors
• Filled in the monotonous details of tasks because
  they're obvious
• AI helps to achieve these goals

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Knowledge Representation

• How do we encode knowledge in a computer system?
• Many possibilities including
• Natural Language
• Formal Language
• Pictorial
• Graphical

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Natural Language

• There's like, this, like dog, with a whole bunch
  of spots
• Complex semantics
• Humans can understand easily
• Computers not so much

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Formal Language

• Precise, Complete, Explicit, and most
  importantly machine-processable
• There exists a dog. The dog has spots. The
  number of spots is greater than 10
• Can use formal logic mechanisms to represent

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Pictorial

• As the saying goes A picture is worth 1K words
• Rich for humans (ex maps)
• But difficult for computers to understand
• Ex problem Face Recognition

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Graph-Based

• Use graphs to represent a knowledge network
• Use mathematical properties to analyse or
  manipulate them
• Plus easy for computers

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Evaluating Knowledge Representation Schemes

• All K.R. Schemes should have
• Adequacy (captures what you need)
• Efficient (captures what you need and no more)
• Extendible (ability to add new knowledge)
• Appropriate (good fit for domain and application)

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Games

• Early AI work focused on games (ex Chess,
  Checkers)
• Easy to represent in a computer
• Clearly defined rules
• Unmistakable goals
• (and besides, games are fun)
• Researchers tried to create programs which could
  win consistently

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Games (cont)

• Early successes include Arthur Samuel's program
  for playing checkers in 1952 which could play at
  a very strong amateur level
• More recent successes IBM's Deep Blue

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Games (cont)

• While games don't seem practical, many useful
  techniques emerged from this research including
• Solving By Searching
• Pattern recognition/matching
• Machine Learning
• Lets look at Solving By Searching

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Solving By Searching

• Intelligent Searching in the text
• Involves representing the problem as a graph, and
  finding a solution in the form of a path through
  the graph
• Nodes in the graph are states in the problem
• Edges represent valid moves from one state to
  another

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Solving By Searching (cont)

• A problem formulated this way has
• A start state
• A successor function which tells us which moves
  are valid from a particular state
• A goal test to see if we have a solution (if the
  current state is one of the solution states)

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Example Problem - 8-Queens

• From chess can we place 8 queens on a standard
  8x8 chess board such that no queen attacks another

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8-Queens

• Formulate problem as a tree, where each node
  represents a particular board configuration, and
  each child of a node is another configuration
  which can result from applying the successor
  function to the parent
• Start state?
• Successor Function?
• Goal Test?

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8-Queens As A Graph

• .......

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8-Queens

• To solve, we just pick the node which represents
  the starting configuration, and follow paths
  through the graph until we find one which has the
  desired solution
• Very simple, so what's the problem?

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8-Queens Graph

• For 8 queens on a 8x8 board, the graph has
• 64! 64 63 62 .... 2 1.8 X 104
  different configurations
• Even worse for larger boards/ of queens
• Way too big to search by brute force
• Can we do better?
• informed search strategies
• constraint satisfaction problems
• See CSC 421 for more info

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Expert Systems

• Sometimes called decision support systems,
  knowledge-based systems, or rule-based
  systems
• Systems used to help decision-makers make
  decisions in complex environments
• Ex financial decisions for banks such as credit
  checks

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Expert Systems (cont)

• Consist of
• A knowledge base (a set of facts)
• An inference engine (a mechanism for selecting
  relevant facts and reasoning from them)

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Expert Systems

• Typically encode facts into propositional forms
  and use formal logic to infer new knowledge from
  those facts
• What programming language does this sound like?
• Two main approaches to inference
• Forward chaining reason from premises to
  conclusion
• Backward chaining reason from conclusion to
  premises

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Ethical Considerations Threat to Society?

• The view of hard AI is that eventually machines
  will be more intelligent than their creators
• Fodder for many sci-fi movies/TV shows/books
• Potentially scary, will AI in the future be like
  Data or The Terminator?
• See Theodore John Ted Kaczynski

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Ethical Considerations Job Loss?

• Technological advancement can render certain jobs
  redundant
• AI (it is argued) will further this trend
• Valid concern, or fear of technology?
• Luddites

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Food For Thought

• The main lesson of thirty-five years of AI
  research is that the hard problems are easy and
  the easy problems are hard. The mental abilities
  of a four-year-old that we take for granted -
  recognizing a face, lifting a pencil, walking
  across the room, answering a question - in fact
  solve some of the hardest engineering problems
  ever conceived... As the new generation of
  intelligent devices appears, it will be the stock
  analysts and petrochemical engineers and parole
  board members who are in danger of being replaced
  by machines. The gardeners, receptionists, and
  cooks are secure in their jobs for decades to
  come.
• -- Steven Pinker, The Language Instinct