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https://www.youtube.com/watch?v=UdE-W30oOXo

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Brain vs. Computer

  • In AI, we compare the brain (or the mind) and the computer
    • Our hope: the brain is a form of computer
    • Our goal: we can create computer intelligence through programming just as people become intelligent by learning

But we see that the computer is not like the brain

The computer performs tasks without understanding what its doing

Does the brain understand what its doing when it solves problems?

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So What Is AI?

  • AI as a field of study
    • Computer Science
    • Cognitive Science
    • Psychology
    • Philosophy
    • Linguistics
    • Neuroscience
  • AI is part science, part engineering
  • AI often must study other domains in order to implement systems
    • e.g., medicine and medical practices for a medical diagnostic system, engineering and chemistry to monitor a chemical processing plant
  • AI is a belief that the brain is a form of biological computer and that the mind is computational
  • AI has had a concrete impact on society but unlike other areas of CS, the impact is often
    • felt only tangentially (that is, people are not aware that system X has AI)
    • felt years after the initial investment in the technology

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Importance of AI

  • Self Driving car
  • Playing chess with computers

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Artificial Intelligence

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History of AI

https://www.youtube.com/watch?v=yaL5ZMvRRqE

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Foundations of Artificial Intelligence:

    • Philosophy
  • e.g., foundational issues (can a machine think?), issues of knowledge and believe, mutual knowledge
    • Psychology and Cognitive Science
  • e.g., problem solving skills
    • Neuro-Science
  • e.g., brain architecture

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Foundations of Artificial Intelligence:

    • Computer Science And Engineering
  • e.g., complexity theory, algorithms, logic and inference, programming languages, and system building.
    • Mathematics and Physics
  • e.g., statistical modeling, continuous mathematics,
  • Statistical Physics, and Complex Systems.

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A Brief History of AI: 1950s

  • Computers were thought of as an electronic brains
  • Term “Artificial Intelligence” coined by John McCarthy
    • John McCarthy also created Lisp in the late 1950s
  • Alan Turing defines intelligence as passing the Imitation Game (Turing Test)
  • AI research largely revolves around toy domains
    • Computers of the era didn’t have enough power or memory to solve useful problems
    • Problems being researched include
      • games (e.g., checkers)
      • primitive machine translation
      • blocks world (planning and natural language understanding within the toy domain)
      • early neural networks researched: the perceptron
      • automated theorem proving and mathematics problem solving

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The 1960s

  • AI attempts to move beyond toy domains
  • Syntactic knowledge alone does not work, domain knowledge required
    • Early machine translation could translate English to Russian (“the spirit is willing but the flesh is weak” becomes “the vodka is good but the meat is spoiled”)
  • Earliest expert system created: Dendral
  • Perceptron research comes to a grinding halt when it is proved that a perceptron cannot learn the XOR operator
  • US sponsored research into AI targets specific areas – not including machine translation
  • Weizenbaum creates Eliza to demonstrate the futility of AI

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1970s

  • AI researchers address real-world problems and solutions through expert (knowledge-based) systems
    • Medical diagnosis
    • Speech recognition
    • Planning
    • Design
  • Uncertainty handling implemented
    • Fuzzy logic
    • Certainty factors
    • Bayesian probabilities
  • AI begins to get noticed due to these successes
    • AI research increased
    • AI labs sprouting up everywhere
    • AI shells (tools) created
    • AI machines available for Lisp programming
  • Criticism: AI systems are too brittle, AI systems take too much time and effort to create, AI systems do not learn

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1980s: AI Winter

  • Funding dries up leading to the AI Winter
    • Too many expectations were not met
    • Expert systems took too long to develop, too much money to invest, the results did not pay off
  • Neural Networks to the rescue!
    • Expert systems took programming, and took dozens of man-years of efforts to develop, but if we could get the computer to learn how to solve the problem…
    • Multi-layered back-propagation networks got around the problems of perceptrons
    • Neural network research heavily funded because it promised to solve the problems that symbolic AI could not
  • By 1990, funding for neural network research was slowly disappearing as well
    • Neural networks had their own problems and largely could not solve a majority of the AI problems being investigated
    • Panic! How can AI continue without funding?

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Sub Areas of AI

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What is Search?

  • We define the state of the problem being solved as the values of the active variables
    • this will include any partial solutions, previous conclusions, user answers to questions, etc

      • while humans are often able to make intuitive leaps, or recall solutions with little thought, the computer must search through various combinations to find a solution
  • To the right is a search space for a tic-tac-toe game

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Search Spaces and Types of Search

  • The search space consists of all possible states of the problem as it is being solved
    • A search space is often viewed as a tree and can very well consist of an exponential number of nodes making the search process intractable
    • Search spaces might be pre-enumerated or generated during the search process
    • Some search algorithms may search the entire space until a solution is found, others will only search parts of the space, possibly selecting where to search through a heuristic
  • Search spaces include
    • Game trees like the tic-tac-toe game
    • Decision trees (see next slides)
    • Combinations of rules to select in a production system
    • Networks of various forms (see next slides)
    • Other types of spaces

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Search Algorithms and Representations

  • Breadth-first
  • Depth-first
  • Best-first (Heuristic Search)
  • A*
  • Hill Climbing
  • Limiting the number of Plies
  • Minimax
  • Alpha-Beta Pruning
  • Adding Constraints
  • Genetic Algorithms
  • Forward vs Backward Chaining
  • We will study various forms of representation and uncertainty handling in the next class period
  • Knowledge needs to be represented
    • Production systems of some form are very common
      • If-then rules
      • Predicate calculus rules
      • Operators
    • Other general forms include semantic networks, frames, scripts
    • Knowledge groups
    • Models, cases
    • Agents
    • Ontologies

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Today: The New (Old) AI

  • Look around, who is doing AI research?
  • By their own admission, AI researchers are not doing “AI”, they are doing
    • Intelligent agents, multi-agent systems/collaboration
    • Ontologies
    • Machine learning and data mining
    • Adaptive and perceptual systems
    • Robotics, path planning
    • Search engines, filtering, recommendation systems
  • Areas of current research interest:
    • NLU/Information Retrieval, Speech Recognition
    • Planning/Design, Diagnosis/Interpretation
    • Sensor Interpretation, Perception, Visual Understanding
    • Robotics
  • Approaches
    • Knowledge-based
    • Ontologies
    • Probabilistic (HMM, Bayesian Nets)
    • Neural Networks, Fuzzy Logic, Genetic Algorithms

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