EE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS

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EE562 ARTIFICIAL INTELLIGENCE FOR ENGINEERS

• University of Washington,
• Department of Electrical Engineering
• Spring 2005
• Instructor Professor Jeff A. Bilmes

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EE562

• General Introduction to AI for Engineers
• what does for Engineers mean? We will emphasize
  practical aspects of AI techniques, and how to
  use them for real world problems and system
  building.
• Lecturer Prof. Jeff A. Bilmes ltbilmes_at_ee.washingt
  on.edugt
• TA Winyu Chinthammit ltwinyu_at_ee.washington.edugt
• Course home page http//sssli.ee.washington.edu/c
  ourses/ee562
• Can get extra copies of syllabus, problem sets
  and labs, announcements, copies of the slides
  well be using, and other class information.
  Bookmark this page for this quarter.
• Prerequisites basic programming, algorithms and
  data structures, and basic logic and probability
  (or permission of instructor, if you are unsure
  ask me after class).
• Textbook S. Russell and P. Norvig Artificial
  Intelligence A Modern Approach Prentice Hall,
  2003, Second Edition
• excellent text, the standard in the field.
• Homework There will be 3-4 homeworks assigned
  for the quarter. They will be combination of
  standard work problems but will also involve
  significant programming assignments. They will be
  due roughly 2 weeks after assigned (but dont
  start late!!)
• Exams There will be both a midterm (May 2nd, 1.5
  hours) and a Final (June 8th, 2 hours)

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EE562

• Grading 33 homework, 33 midterm, and 33
  final.
• S/NS Must do all problem sets (need not do
  midterm/final).
  
• Class participation is also counted (attendance,
  asking and answering questions).
• Last day of class June 1st, 2005
• Holiday May 30th, Veterans day.
• Final Exam Wed, June 8th, 230-430.
• Reading This Week AIMA Chapters 1 and 2.

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Course overview

• 10 weeks, 19 1.5 hour lectures.
• Introduction and Agents (chapters 1,2)
• Search, CSP, Games (chapters 3,4,5,6)
• Logic (chapters 8,9,10)
• Learning (chapters 18,20)
• See (online) syllabus for more detailed course
  outline (we may stray from the outline depending
  on how things go).

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Outline

• Course overview
• What is AI?
• A brief history
• The state of the art

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What is AI?

• But what is intelligence?
• something not entirely well-defined that helps to
  distinguish what we call animate objects from
  what we call inanimate objects
• But when does an object become animate?
• Does learning play a role? (can an object be
  intelligent without learning?)
• Is living a necessary condition? Are there any
  non-living objects in the world you might call
  intelligent?

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What is AI?

• What tasks require intelligence?
• The easy (or seemingly mundane)
• Perception (vision, speech)
• Natural Language (understanding, generation,
  translation)
• Common sense reasoning
• rational thought, causality, etc.
• Robotics/Motor skills

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What is AI?

• What tasks require intelligence?
• The formal
• Games (chess, backgammon, checkers, go)
• Mathematics (geometry, logic, integral calculus,
  theorem proving, program correctness checkers)

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What is AI?

• What tasks require intelligence?
• The expert
• Engineering (design, fault finding, manufacturing
  planning)
• Scientific analysis and data interpretation, data
  mining, problem finding
• Medical diagnosis (doctors)
• Financial analysis (predict the stock market)
• Forensic Science
• Legal Analysis

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What is AI?

• What can Humans do? Object recognition

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Object Recognition

• Sometimes it is a continuum.
• Escher, Liberation, 1955
• What is foreground/background?
• Escher, Mosaic, 1957

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Object Recognition

• Why we need uncertainty. Is it a face, a vase, or
  both?

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What is AI?

• What can Humans do?
• Speech Recognition

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What is AI?

• Views of AI fall into four categories
• Vertical Axis Thinking ?? Acting
• Horizontal Axis Humanly ?? Rationally
• The textbook advocates "acting rationally
• this is also an engineering perspective. What is
  important to get the problem solved. Acting or
  being human? To build systems, we care only about
  acting.
• We next consider each of the four above.

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Acting humanly Turing Test

• Alan Turing (1950) "Computing machinery and
  intelligence"
• "Can machines think?" ? "Can machines behave
  intelligently?"
• Operational test for intelligent behavior the
  Imitation Game
• Needs
• natural language processing, knowledge
  representation, automated reasoning, machine
  learning, computer vision, speech recognition,
  robotics
• Turing predicted that by 2000, a machine might
  have a 30 chance of fooling a lay person for 5
  minutes
• Anticipated all major arguments against AI in
  following 50 years
• Suggested major components of AI knowledge,
  reasoning, language understanding, learning
  

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Thinking humanly cognitive modeling

• 1960s "cognitive revolution" information-processi
  ng psychology replaced orthodoxy of behaviorism
• compute as a human would compute
• Requires scientific theories of internal
  activities of the brain
• what level of abstraction? Knowledge,
  circuits, only need a model of the process,
  dont need to replicate the process (e.g.,
  neuro-)
• How to validate? Requires
• Predicting and testing behavior of human subjects
  (top-down), or
• Direct identification from neurological data
  (bottom-up)
  
• Both approaches (roughly, Cognitive Science and
  Cognitive Neuroscience) are now considered
  distinct from AI (which is more related to
  computer science)
• Both share with AI
• existing theories do not yet explain anything
  close to resembling true human-level general
  intelligence. We have a long way to go.
• So the various doctrines share a basic principal
  direction but are considered different
  (sub-)fields.

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Thinking rationally "laws of thought"

• Irrefutable (prescriptive rather than
  descriptive) reasoning processes that must occur
  (logic)
• Aristotle what are correct arguments/thought
  processes?
• Logical forms that rational thinking possesses.
• Ex Socrates is a man, all men are mortal,
  therefore Socrates is mortal.
  
• Several Greek schools developed various forms of
  logic notation and rules of derivation for
  thoughts may or may not have proceeded to the
  idea of mechanization (which is what we care
  about in this class)
• Direct line through mathematics and philosophy to
  modern AI
  
• Problems with this approach
• Not all intelligent behavior is mediated by
  logical deliberation (many intelligent people
  apparently behave irrationally)
• What is the purpose of thinking? What thoughts
  should I have out of all thoughts (logical or
  otherwise) that I could have? Hard to say

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Acting rationally rational agent

• Rational behavior doing the right thing
• but we dont care as much how it is happening as
  long as it undeniably is happening.
  
• The right thing that which is expected to
  maximize goal achievement, given the available
  information
• Doesn't necessarily involve thinking e.g.,
  blinking reflex but thinking should be in the
  service of rational action
• Aristotle (Nicomachean Ethics)
• Every art and every inquiry, and similarly every
  action and pursuit, is thought to aim at some
  good.
  

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Rational agents

• An agent is a key idea in this course.
• An agent is an entity that perceives and acts
• This course is about designing rational agents
• agents, build to in one way or another, act
  rational
  
• Abstractly, an agent is a function from percept
  histories to actions
  
• f P ? A
• Is this real intelligence? Are we deterministic?
• Practically For any given class of environments
  and tasks, we seek the agent (or class of agents)
  with the best performance in a given environment
  at a particular time.
• Caveat computational limitations make perfect
  rationality unachievable (even if we had perfect
  f)
• Some agent functions might be computationally
  intractable
• Goal design best program for given machine
  resources
  

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AI prehistory

• Philosophy Logic, methods of reasoning, mind as
  physical system, foundations of learning,
  language, rationality
• Mathematics Formal representation and proof
  algorithms, computation, (un)decidability,
  (in)tractability, probability, optimization
• Psychology phenomena of perception and motor
  control, experimental techniques, psycho-
• Economics utility, decision theory, game theory
• Linguistics knowledge representation, grammar
• Neuroscience physical substrate for mental
  activity
• Control theory design systems that maximize an
  objective function over time, temporal
  processes
• Computer building fast computing systems
  engineering
• Electrical signal processing,
  acoustics, sound Engineering

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Abridged history of AI

• 1943 McCulloch Pitts Boolean circuit
  model of brain
• 1950 Turing's "Computing Machinery and
  Intelligence"
• 1956 Dartmouth meeting "Artificial
  Intelligence" adopted
• 195269 Look, Ma, no hands!
• 1950s Early AI programs, including Samuel's
  checkers program, Newell Simon's Logic
  Theorist, Gelernter's Geometry Engine
• 196673 AI discovers computational
  complexity Neural network research almost
  disappears
• 196979 Early development of knowledge-based
  systems
• 1980-- AI becomes an industry
• 1986-- Neural networks return to popularity
• 1987-- AI becomes a science
• 1988-- Uncertain reasoning is
  acknowledged (Pearl)
• 1995-- The emergence of intelligent agents (our
  text!!)
• 2003-- Human-level AI is back to popularity

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State of the art

• Which of the following can be done by computer at
  the present time?
• Play a decent game of table tennis
• Drive safely along a curving mountain road
• Drive safely along University Avenue
• Buy a week's worth of groceries on the web
• Buy a week's worth of groceries at Whole Foods
  Market
• Play a decent game of bridge
• Discover and prove a new mathematical theorem
• Design and execute a research program in
  molecular biology
• Write an intentionally funny story
• Give competent legal advice in a specialized area
  of law
• Translate spoken English into spoken Swedish in
  real time
• Converse successfully with another person for an
  hour
• Perform a complex surgical operation
• Unload any dishwasher and put everything away
• Recognize fluently spoken conversational speech
  without mistake

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State of the art

• Which of the following can be done by computer at
  the present time?
• Play a decent game of table tennis
• Drive safely along a curving mountain road
• Drive safely along University Avenue
• Buy a week's worth of groceries on the web
• Buy a week's worth of groceries at Whole Foods
  Market
• Play a decent game of bridge
• Discover and prove a new mathematical theorem
• Design and execute a research program in
  molecular biology
• Write an intentionally funny story
• Give competent legal advice in a specialized area
  of law
• Translate spoken English into spoken Swedish in
  real time
• Converse successfully with another person for an
  hour
• Perform a complex surgical operation
• Unload any dishwasher and put everything away
• Recognize fluently spoken conversational speech
  without mistake

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State of the art

• Deep Blue defeated the reigning world chess
  champion Garry Kasparov in 1997
• Proved a mathematical conjecture (Robbins
  conjecture) unsolved for decades (1997), proved
  in the affirmative
• are all Robbins algebras boolean? Algebra that
  satisfies commutatively, associatively, and
  Robbins equation n(n(x y) n(x n(y))) x
• No hands across America (driving autonomously
  98 of the time from Pittsburgh to San Diego)
• During the 1991 Gulf War, US forces deployed an
  AI logistics planning and scheduling program that
  involved up to 50,000 vehicles, cargo, and people
  
• NASA's on-board autonomous planning program
  controlled the scheduling of operations for a
  spacecraft
• Proverb solves crossword puzzles better than most
  humans (including myself)
• Question So do these things really require
  intelligence? How does the chess program work so
  well?