neural

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CS440Introduction to Artificial Intelligence

• Fall 2003

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Todays topics

• Course administration.
• What is AI?
• AI and
• Cognitive science, philosophy, psychology,
  economics, computer science, control theory,
• History of AI.
• Applications of AI.
• Reading
• This week AIMA, Ch. 1
• Next week AIMA, Ch. 2 3

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Course administration

• InstructorVladimir PavlovicOffice 312
  CoREEmail vladimir_at_cs.rutgers.eduWeb
  www.cs.rutgers.edu/vladimirPhone
  732-445-2654Office hours Mon, 300-400
• TAZhi WeiOffice 416 HillEmail
  zhwei_at_paul.rutgers.eduPhone
  732-445-6996Office hours Thu, 200-400 PM
• Web sitehttp//www.cs.rutgers.edu/vladimir/class
  /cs440
• Mailing listcs440-fall03_at_rams.rutgers.edu

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Course administration (contd)

• Lectures Mon Wed, 430 550
• Discussion Wed, 635 730
• Classroom Arc-105
• TextbookRussell Norvig, "Artificial
  Intelligence A Modern Approach", 2nd Edition,
  Prentice Hall, 2003. Also referred to as AIMA
• PrerequisitesCS314 (Principles of Programming
  Languages). You also need a solid knowledge of
  calculus. Some knowledge of probability and
  linear algebra will be beneficial.

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Course administration (contd)

• GradingHomework 30Midterm 30Final 40
• Homework assignments
• Weekly, will include programming problems (mini
  projects).
• Programming in Java / Matlab (Lush? Lisp?)
• Assignments are due in class, on due date.
• No late homeworks accepted!
• Tests
• Midterm, around Oct. 20
• Final
• Closed book, closed notes

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What is AI?

• What is intelligence?
• The capacity to learn and solve problems
  Webster dictionary
• The computational part of the ability to achieve
  goals in the world. Varying kinds and degrees of
  intelligence occur in people, many animals and
  some machines. McCarthy Alice Bot
  (http//www.alicebot.org/)
• Ability to think and act rationally.
• What are ingredients of intelligence?

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Ingredients of intelligence

• Ability to interact with real world
• Perceive, understand, act.
• Language understanding and formation.
• Visual perception.
• Reasoning and planning
• Modeling external world
• Problem solving, planning, decision making
• Ability to deal with unexpected problems, dealing
  with uncertainty

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Ingredients of intelligence (contd)

• Learning and adaptation
• Continuous update of our model of the world and
  adaptation to it

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What is AI?

• A field that focuses on developing techniques to
  enable computer systems to perform activities
  that are considered intelligent (in humans and
  other animals). Dyer
• The science and engineering of making intelligent
  machines, especially intelligent computer
  programs. It is related to the similar task of
  using computers to understand human intelligence,
  but AI does not have to confine itself to methods
  that are biologically observable. McCarthy
• The study of how to make computer do things
  which, at the moment, people do better.
  RichKnight
• The design and study of computer programs that
  behave intelligently. Dean, Allen, Aloimonos
• The study of rational agents that exist in an
  environment and perceive and act.
  RussellNorvig

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Goals of AI

• Scientific and engineering
• Understanding of computational mechanisms needed
  for intelligent behavior
• Intelligent connection of perception and action
• Replicate human intelligence
• Solve knowledge-intensive tasks
• Enhance human-human, human-computer and
  computer-computer interaction/communication

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Some applications of AI

• Game PlayingDeep Blue Chess program beat world
  champion Gary Kasparov
• Speech RecognitionPEGASUS spoken language
  interface to American Airlines' EAASY SABRE
  reseration system, which allows users to obtain
  flight information and make reservations over the
  telephone. The 1990s has seen significant
  advances in speech recognition so that limited
  systems are now successful.
• Computer VisionFace recognition programs in use
  by banks, government, etc. The ALVINN system from
  CMU autonomously drove a van from Washington,
  D.C. to San Diego (all but 52 of 2,849 miles),
  averaging 63 mph day and night, and in all
  weather conditions. Handwriting recognition,
  electronics and manufacturing inspection,
  photointerpretation, baggage inspection, reverse
  engineering to automatically construct a 3D
  geometric model.
• Expert SystemsApplication-specific systems that
  rely on obtaining the knowledge of human experts
  in an area and programming that knowledge into a
  system.
• Diagnostic SystemsMicrosoft Office Assistant in
  Office 97 provides customized help by
  decision-theoretic reasoning about an individual
  user. MYCIN system for diagnosing bacterial
  infections of the blood and suggesting
  treatments. Intellipath pathology diagnosis
  system (AMA approved). Pathfinder medical
  diagnosis system, which suggests tests and makes
  diagnoses. Whirlpool customer assistance center.

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Some applications of AI (contd)

• Financial Decision MakingCredit card companies,
  mortgage companies, banks, and the U.S.
  government employ AI systems to detect fraud and
  expedite financial transactions. For example,
  AMEX credit check. Systems often use learning
  algorithms to construct profiles of customer
  usage patterns, and then use these profiles to
  detect unusual patterns and take appropriate
  action.
• Classification SystemsPut information into one
  of a fixed set of categories using several
  sources of information. E.g., financial decision
  making systems. NASA developed a system for
  classifying very faint areas in astronomical
  images into either stars or galaxies with very
  high accuracy by learning from human experts'
  classifications.
• Mathematical Theorem ProvingUse inference
  methods to prove new theorems.
• Natural Language UnderstandingGoogle's
  translation of web pages. Translation of
  Catepillar Truck manuals into 20 languages.
  (Note One early system translated the English
  sentence "The spirit is willing but the flesh is
  weak" into the Russian equivalent of "The vodka
  is good but the meat is rotten.")
• Scheduling and PlanningAutomatic scheduling for
  manufacturing. DARPA's DART system used in Desert
  Storm and Desert Shield operations to plan
  logistics of people and supplies. American
  Airlines rerouting contingency planner. European
  space agency planning and scheduling of
  spacecraft assembly, integration and
  verification.
• Robotics and Path planningNASAs Rover mission.
• Biology and medicineModeling of cellular
  functions, analysis of DNA and proteins.
• and

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Roomba!
Roombas (artificial) intelligence fits in 256
bytes of program space!
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Turing test (A. Turing, Computing machinery and
intelligence, 1950)

• Interrogator asks questions of two people who
  are out of sight and hearing. One is a human,
  the other one a machine.
• 30mins to ask whatever she/he wants.
• To determine only through questions and answers
  which is which.
• If it cannot distinguish between human and
  computer, the machine has passed the test!

• Predicted that in 2000 a machine would have 30
  chance of fooling a lay person for 5min.
• Suggested major components of AI (knowledge,
  reasoning, language understanding, learning)
• Anticipated arguments against AI in 50 years to
  follow

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Problems with Turing test

• Newel and Simon
• As much a test of the judge as of the machine.
• Promotes artificial con-artists, not intelligence
  (Loebner prize, http//www.loebner.net/Prizef/loeb
  ner-prize.html)

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Fundamental Issues for most AI problems

• RepresentationFacts about the world have to be
  represented in some way, e.g., mathematical logic
  is one language that is used in AI. Deals with
  the questions of what to represent and how to
  represent it. How to structure knowledge? What is
  explicit, and what must be inferred? How to
  encode "rules" for inferencing so as to find
  information that is only implicitly known? How to
  deal with incomplete, inconsistent, and
  probabilistic knowledge? Epistemology issues
  (what kinds of knowledge are required to solve
  problems).
• SearchMany tasks can be viewed as searching a
  very large problem space for a solution. For
  example, Checkers has about 1040 states, and
  Chess has about 10120 states in a typical games.
  Use of heuristics (meaning "serving to aid
  discovery") and constraints.
• InferenceFrom some facts others can be inferred.
  Related to search. For example, knowing "All
  elephants have trunks" and "Clyde is an
  elephant," can we answer the question "Does Clyde
  hae a trunk?" What about "Peanuts has a trunk, is
  it an elephant?" Or "Peanuts lives in a tree and
  has a trunk, is it an elephant?" Deduction,
  abduction, non-monotonic reasoning, reasoning
  under uncertainty.
• LearningInductive inference, neural networks,
  genetic algorithms, artificial life, evolutionary
  approaches.
• PlanningStarting with general facts about the
  world, facts about the effects of basic actions,
  facts about a particular situation, and a
  statement of a goal, generate a strategy for
  achieving that goals in terms of a sequence of
  primitive steps or actions.

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Design methodology and goals
Human
Rational
Think like humans "cognitive science" Ex. GPS Think rationally gt formalize inference process "laws of thought"
Act like humans Ex. ELIZA Turing Test Act rationally "satisficing" methods
Think
Act

• Focus not just on behavior and I/O, look at
  reasoning process. Computational model should
  reflect "how" results were obtained. GPS (General
  Problem Solver) Goal not just to produce
  humanlike behavior (like ELIZA), but to produce a
  sequence of steps of the reasoning process that
  was similar to the steps followed by a person in
  solving the same task.

• Formalize the reasoning process, producing a
  system that contains logical inference mechanisms
  that are provably correct, and guarantee finding
  an optimal solution. This brings up the question
  How do we represent information that will allow
  us to do inferences like the following one?
  "Socrates is a man. All men are mortal. Therefore
  Socrates is mortal." -- Aristotle

• Behaviorist approach. Not interested in how you
  get results, just the similarity to what human
  results are. ELIZA A program that simulated a
  psychotherapist interacting with a patient and
  successfully passed the Turing Test.

• For a given set of inputs, tries to generate an
  appropriate output that is not necessarily
  correct but gets the job done. Rational and
  sufficient ("satisficing" methods, not "optimal").

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Brief history of AI

• 1943 McCulloch Pitts Boolean circuit model of
  brain
• 1950 Turing's Computing Machinery and
  Intelligence''
• 1952-69 Look, Ma, no hands!
• 1950s Early AI programs, including Samuel's
  checkers program, Newell Simon's Logic
  Theorist, Gelernter's Geometry Engine
• 1956 Dartmouth meeting Artificial
  Intelligence'' adopted
• 1965 Robinson's complete algorithm for logical
  reasoning
• 1966-74 AI discovers computational complexity and
  Neural network research almost disappears
• 1969-79 Early development of knowledge-based
  systems
• 1980-88 Expert systems industry booms
• 1988-93 Expert systems industry busts AI
  Winter''
• 1985-95 Neural networks return to popularity
• 1988 Resurgence of probability general increase
  in technical depth and Nouvelle AI'' ALife,
  GAs, soft computing
• 1995- Agents agents everywhere

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This course

• Search,
• Knowledge representation,
• Planning,
• Uncertainty,
• Learning, and
• Examples and applications in speech and language
  modeling, visual perception, medical informatics,
  and robotics.