Artificial%20Intelligence

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Artificial Intelligence
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2001 A Space Odyssey
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What is Intelligence?

• No good definition
• We think of it as being human
• More than the ability to do one task well
• More than manipulating symbols

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Computers

• Calculate quickly and accurately
• Relieve us of tedious tasks
• Help us to do some tasks better
• Can entertain us (Games)
• Can provide much information (Internet)

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Artificial Intelligence

• What is AI?
• Group of related technologies used for
  development machines to emulate human-like
  qualities

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Artificial Intelligence

• Types of AI
• Virtual reality
• Robotics
• Natural language processing
• Fuzzy logic
• Expert systems
• Neural networks
• Genetic algorithms

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Artificial Intelligence

• Some early experiments failed
• A.I. scientists ridiculed

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Game Playing

• Early days of AI - Researchers thought that
  teaching computers to play games such as chess
  would enable them to understand something about
  human intelligence.
• Found it easy to have computers play games.
• Found it difficult to go beyond game playing and
  into the realm of human intelligence.

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Easy Computer Problems
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Difficult Computer Problems
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Human qualities

• Emotion
• Motivation
• Deception

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Computer Intelligence
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Computer Control
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What is IntelligenceArtificial or Not?

• The search for intelligence
• Plato (400 BC) - This Greek philosopher believed
  that ethereal spirits were rained down from
  heaven and entered the body.
• Aristotle (Platos student) - The heart must
  contain the soul and the brains function was to
  cool the blood.
• Galen - Treated fallen gladiators with spinal
  cord injuries. Noted that feeling lost in certain
  limbs sometimes came back.
• Galvani - Used Benjamin Franklins findings about
  static electricity to show that static
  electricity stimulated the nerves causing a frog
  to jump.
• Subsequently - Human nervous system found to be a
  complex network of billions of neurons.

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What is IntelligenceArtificial or Not?

• Maillardets Automaton (1805)
• Object having human form.
• Disguised as a young boy.
• Machine containing levers, ratchets, cams and
  other mechanical devices.
• Could draw several complex images.
• Because it had human form and could draw complex
  images, a certain feeling of intelligence was
  ascribed to the machine.

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Artificial Life

• What is artificial life?
• A field of study that deals with computer
  instructions that try to simulate human responses
  
• What English mathematician and computer pioneer
  created a test in 1950 to determine computer
  intelligence?
• Alan Turing

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What is IntelligenceArtificial or Not?

• Alan Turing (1912 - 1954)
• Proposed a test - Turings Imitation Game
• Tests the intelligence of the computer.
• Attempts to see of a person (Interrogator) can
  tell the difference between a human and a
  computer in answers to questions.
• If the interrogator cant tell the difference,
  the computer is considered to have intelligence.

?
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What is IntelligenceArtificial or Not?

• Claude Shannons comparison of the human brain
  and the computer
• Difference in size The brain has a million more
  parts.
• Difference in structural organization The
  seemingly random local structure of nerve
  networks differ vastly from the precise wiring of
  a computer.
• Differences in reliability The brain can operate
  reliably for decades.
• Differences in logical organization The brain is
  largely self-organizing. Digital computers do
  only a few narrowly defined tasks well.
• Differences in input-output equipment Brain is
  designed with input organs and output muscles and
  glands. Computers operate in an abstract
  environment of numbers and operations on numbers.

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Fundamental Concepts in Artificial Intelligence

• Rule-based or Expert systems - Consists of rules
  of the form IF (condition) THEN (action).
• IF (it is raining AND you must go outside)
• THEN (put on your raincoat)

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Expert Systems Components

• Knowledge Base
• Inference Engine
• User Interface

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Expert Systems

• Knowledge of experts
• Understand question (Input)
• Lookup facts and rules (Storage)
• Make decision (Processing)
• Display decision (Output)

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Expert Systems

• Expert systems are commercially the most
  successful domain in Artificial Intelligence.
• IF (some condition) THEN (some action)
• These programs mimic the experts in whatever
  field.

Auto mechanic Telephone networking Cardiologist De
livery routing Organic compounds Professional
auditor Mineral prospecting Manufacturing Infectio
us diseases Pulmonary function Diagnostic
internal medicine Weather forecasting VAX
computer configuration Battlefield
tactician Engineering structural
analysis Space-station life support
Audiologist Civil law
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Expert Systems

• Harold Cohen created an expert system called
  AAORN to create art.

Early drawings by AARON
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Expert Systems

• Intelligent Agents
• Computerized agents that might...
• respond to verbal commands as if it were human.
• be a personal assistant that would access
  electronic communications.
• take phone calls.
• make appointments.
• locate individuals by phone.
• find research material.

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Fundamental Concepts in Artificial Intelligence

• For any of these models of the human knowledge
  system to work, it must be able to make use of
  this knowledge in three different ways
• Knowledge acquisition - Must be some way of
  putting information or knowledge into the system.
• Knowledge retrieval - Must be able to find
  knowledge when it is wanted or needed.
• Reasoning with knowledge - Must be able to use
  that knowledge through thinking or reasoning.

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Fundamental Concepts in Artificial Intelligence

• Knowledge retrieval (by searching)
• Brute-force search - Searching all possible
  moves, and then selecting the best.
• Looking for a museum in a small town example
• Drive around, down every street, until you find
  one!
• Heuristic search - Uses rules of thumb,
  intuition. (The solution is not always
  guaranteed.)
• Looking for a museum in a small town example
• Look for the museum down the towns main street
  (museums are usually on the main street in
  small towns!)

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Fundamental Concepts in Artificial Intelligence

• Machine learning Writing intelligent computer
  programs that are capable of learning.
• Example Teaching a computer to play a game. The
  more the computer plays, the more strategies it
  will learn.
• Common sense
• The computer must be able to make inferences from
  the knowledge base.
• Answers to problems might not be listed.
• The computer will need to come up with its own
  answers!
• This has been a very difficult area in Artificial
  Intelligence.

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Pattern Recognition

• Humans have the ability to understand or
  recognize the relationship among various parts of
  patterns in visual object, sound patterns,
  smells, and taste.
• Pattern recognition using computers has been
  applied in many areas including
• Robot vision
• Speech recognition
• Fingerprint identification
• Handwriting identification
• Optical character recognition (OCR)
• Weather data analysis and satellite data analysis

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Pattern Recognition

• Speech-pattern recognition
• Problems - Accents, continuous speech, words that
  sound the same but are spelled differently.
• He walked to the store.
• He walked to this door.
• Some commercial products - Some commercial
  products do recognize continuous speech. (not yet
  perfect)

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Game Playing

• Major problem in game playing How to make the
  moves?
• Involves
• Processing or searching many moves (see game
  tree)
• By adding heuristics (rules of thumb /
  intuition), decisions can be made by the computer
  as to what is the best move.

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Game Playing

• Chess
• MacHack (1974) by Richard Greenblatt of MIT -
  First program to compete in a chess tournament.
• Deep Blue (1996) by IBM - Garry Kasparov,
  world-champion chess player, won over Deep Blue 4
  points to 2.
• Deep Blue (1997) by IBM - Garry Kasparov conceded
  victory to Deep Blue, 3.5 points to 2.5.
• The fastest chess-playing program processes
  200,000 positions per second.
• (Thousands of moves more than a human is capable
  of.)
• Still, the best chess players beat most computer
  programs.

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Natural Language Communication

• The semantics (meaning of the words) is the goal
  in communication with the computer.
• Eliza (1965) by Joseph Weizenbaum of MIT
• Simulation of a therapist mirroring a patients
  conversation in a positive manner.
• The program fooled people into believing it was a
  therapist.

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Natural Language Communication

• All men are alike.
• IN WHAT WAY
• Theyre always bugging us about something or
  other.
• CAN YOU THINK OF A SPECIFIC EXAMPLE
• Well, my boyfriend made me come here.
• YOUR BOYFRIEND MADE YOU COME HERE
• He says Im depressed much of the time.
• I AM SORRY TO HEAR YOU ARE DEPRESSED
• Its true, I am unhappy.
• DO YOU THINK COMING HERE WILL HELP YOU NOT TO BE
  UNHAPPY
• I need some help, that much seems certain.
• WHAT WOULD IT MEAN TO YOU IF YOU GOT SOME HELP
• Perhaps I could learn to get along with my
  mother.
• TELL ME MORE ABOUT YOUR FAMILY
• Eliza conversation

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Natural Language Communication

• Early failures in natural language translation
• Translation of English to Russian
• Typed in English (sentence to be translated...)
• The spirit is willing, but the flesh is weak.
• Translated by the program into Russian
• The vodka is strong, but the meat is rotten.
• Translation programs have come a long way since
  then!

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Neural Networks

• Neuron Basic building-block of the brain.
• There are several specialized types, but all have
  the same basic structure
• The basic structure of an animal neuron.

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Neural Networks

• Artificial models of the brain are of two
  distinct types
• Electronic Has electronic circuits that act like
  neurons.
• Software This version runs a program on the
  computer that simulates the action of the neurons.

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Neural Networks

• Neural Network
• A collection of neurons which are interconnected.
• The output of one connects to several others with
  different strength connections.
• Initially, neural networks have no knowledge.
  (All information is learned from experience using
  the network.)

Neuron 1
Input 1 Input 2 Input 3
Output from Neuron 1
Output from Neuron 2
Neuron 2
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Fuzzy Logic

• Probability that a statement is true
• Combined with other AI technologies
• Washing Machine
• Variable speed limits

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Finding Information

• Intelligent agent
• Software that performs work tasks
• Example monster.com

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Next Week

• Your PowerPoint presentation is due
• You will be able to present it in class for extra
  credit

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Exam in Two Weeks
Chapters 7 8 from the Textbook Lectures since
Exam 2
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Final Exam Week

• Final exam is scheduled Tue. Thu. 500 615
• You may take it on either day