CSC384 Intro to Artificial Intelligence - Department of Computer

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                                                               CSC384: Intro to Artificial Intelligence
                                                             Instructor: Fahiem Bacchus
                                                                   – Office D.L. Pratt, Room 398B
                                                                   – Office Hours: Monday 3:00pm to 4:00pm and Friday 10:00
                                                                     am to 11:00 am.
       Intro to Artificial Intelligence                            – MWF 1:00 pm – 2:00 pm. GB 119
                                                                   – Note Fridays will be partly a tutorial, but some lectures will
                                                                     be given that day.

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              CSC384: Reference Materials                                 CSC384: Reference Materials
 Recommended Textbook:                                       Alternate Book:
   Artificial Intelligence: A Modern Approach
    Stuart Russell and Peter Norvig                          Computational Intelligence: A Logical Approach by David
                                                              Poole and Alan Mackworth.
         Edition, 2009
    3rd Editi
                                                             - Complete book is available on line!
    – Older editions are also useable---but you will  
      have to search the text for the relevant
      sections (they have renumbered the sections).
    – 2 copies on 24hr reserve in the Engineering and
      Computer Science Library                                  Online Course:
    – Sections most related to the lecture material             - Lectures are on line via you-tube (linked from the
      will be indicated in the slides                              course website).

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                         CSC384: Prerequisites                                                              CSC384: Website
 • Some probability (STA247H/STA255H/STA257H). Some                           •The course web site:
   knowledge of functional programming and logic
   programming is useful (CSC324H).                                       
 • This year the course will use Python in the assignments.                                                           information,
                                                                                    – Primary source of more detailed information
                                                                                      announcements, etc.
 • You will be responsible for any background material                              – Check the web site often.
   that you don’t know, you will have to learn on your own.                         – Updates about assignments, clarifications etc. will be
                                                                                      posted only on the web site.

                                                                              •The course bulletin board:
                                                                                    Will not be monitored.

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          CSC384: How You Will be Graded                                                                              Plagiarism
 • Course work:
      –   3 Assignments (programming, theory, short answers): 10% each           • See
      –   Two Term tests (ap o . 1 hour each): 15% each
             o e     es s (aprox.   ou eac ): 5% eac                     
      –   A Final Exam (3hrs): 40%                                                 for the meaning of plagiarism, how to avoid it, and the U of T
                                                                                   policies about it.
      –   You need a minimum of 40% on the Final to pass the course

 • Late policy for Assignments:                                                  • All assignments are to be done individually!
      – Start Early, late assignments will not be accepted.
                                                                                 • You can discuss the assignments with other students, but you
                                                                                   should not give your code (or parts of your code) to other
                                                                                   students. You should not look at another student’s code until
                                                                                   after you have handed in your assignment (and the due date
                                                                                   is past).

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                         CSC384: Email Policy                                          CSC384: Important Dates
 • I don’t answer questions about course content or the             • See the Course Information Sheet.
   assignments by email.                                            • The dates for the tests and assignments might in unusual
 • I will read short and to the point email.                          circumstances have to be slightly changed (but hopefully not).
 • Come to my office hours, talk to me before or after class
 • If you have an unavoidable scheduling conflict we can
   arrange a mutually acceptable alternative meeting time.

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                                                                                            Are these Intelligent?

     Wh t is Artificial Intelligence?
     What i A tifi i l I t lli      ?

                   What is Intelligence?

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                          What is Intelligence?                                                                Artificial Intelligence
 • Webster says:
       – The capacity to acquire and apply knowledge.
       – The faculty of thought and reason
       – …
                                                                                                 How to achieve intelligent behavior
 • What features/abilities do humans (animals? animate                                             through computational means
   objects?) have that you think are indicative or
   characteristic of intelligence?

 • Abstract concepts, mathematics, language, problem
   solving, memory, logical reasoning, planning ahead,
   emotions, morality, ability to learn/adapt, etc…

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   Classical Test of (Human) Intelligence                                                                       Human Intelligence
 • The Turing Test:                                                                   •Turing provided some very persuasive
      – A human interrogator. Communicates with a hidden
        subject that is either a computer system or a human.
           j                        p      y                                           arguments that a system passing the Turing test
                                                                                       i intelligent.
                                                                                       is i t lli  t
        If the human interrogator cannot reliably decide
        whether or not the subject is a computer, the                                      – We can only really say it behaves like a human
        computer is said to have passed the Turing test.                                   – Nothing guarantees that it thinks like a human

 • Weak Turing type tests:
                                                                                      •The Turing test does not provide much traction
                                                                                       on the question of how to actually build an
                                                                                       intelligent system.

           See Luis von Ahn, Manuel Blum, Nicholas Hopper, and John Langford.
           CAPTCHA: Using Hard AI Problems for Security. In Eurocrypt.
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                           Human Intelligence                                           Human Intelligence
 •Is imitating humans the goal?                               • In general there are various reasons why trying to mimic
                                                                humans might not be the best approach to AI:
                                                                   – Computers and Humans have a very different architecture with
 •Pros?                                                                              abilities.
                                                                     quite different abilities
                                                                   – Numerical computations
                                                                   – Visual and sensory processing
                                                                   – Massively and slow parallel vs. fast serial

                                                                                                      Computer            Human Brain
 •Cons?                                                                                                                   1011 neurons
                                                                  Computational Units                 4 CPUs, 109 gates
                                                                  Storage Units                       1010 bits RAM       1011 neurons
                                                                                                      1013 bits disk      1014 synapses
                                                                  Cycle time                          10-9 sec            10-3 sec
                                                                  Bandwidth                           1010 bits/sec       1014 bits/sec
                                                                  Memory updates/sec                  1010                1014
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                           Human Intelligence                                                         Rationality
 •But more importantly, we know very little about             •The alternative approach relies on the notion of
  how the human brain performs its higher level                rationality.
  processes. Hence
  processes Hence, this point of view provides
  very little information from which a scientific
  understanding of these processes can be built.              •Typically this is a precise mathematical notion
                                                               of what it means to do the right thing in any
 •Nevertheless, Neuroscience has been very                     particular circumstance. Provides
  influential in some areas of AI. For example, in                 – A precise mechanism for analyzing and
           sensing        processing etc.
  robotic sensing, vision processing, etc                            understanding the properties of this ideal behavior
                                                                     we are trying to achieve.
 •Humans might not be best comparison?
                                                                   – A precise benchmark against which we can measure
      – Don’t always make the best decisions
                                                                     the behavior the systems we build.
      – Computer intelligence can aid in our decision
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                                         Rationality                                                 Computational Intelligence
 • Mathematical characterizations of rationality have                                •AI tries to understand and model intelligence as
   come from diverse areas like logic (laws of thought)                               a computational process.
   and economics (utility theory how best to act
   under uncertainty, game theory how self-interested
   agents interact).                                                                 •Thus we try to construct systems whose
                                                                                      computation achieves or approximates the
 • There is no universal agreement about which                                        desired notion of rationality.
   notion of rationality is best, but since these notions
   are precise we can study them and give exact                                                                    Science.
                                                                                     •Hence AI is part of Computer Science
   characterizations of their properties, good and bad.                                   – Other areas interested in the study of intelligence lie in other
                                                                                            areas or study, e.g., cognitive science which focuses on human
                                                                                            intelligence. Such areas are very related, but their central focus
 • We’ll focus on acting rationally                                                         tends to be different.
      – this has implications for thinking/reasoning
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    Four AI Definitions by Russell + Norvig                                                                           Subareas of AI
                                                                                     •Perception: vision, speech understanding, etc.
                     Like humans              Not necessarily like humans
                                                                                     •Machine Learning, Neural networks
              Systems that think like
                                               Systems that think rationally
                                                                                     •Natural language processing

                                                                                     •Reasoning and decision making       OUR FOCUS
                                                                                           – Knowledge representation
               Systems that act like            Systems that act rationally
                                                                                                      g( g     ,p             )
                                                                                           – Reasoning (logical, probabilistic)

                    humans                              Our focus                          – Decision making (search, planning, decision theory)

                    Cognitive Science

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                          Further Courses in AI                                                  What We Cover in CSC384
 • Perception: vision, speech understanding, etc.
       – CSC487H1 “Computational Vision”
                                                                                •Search (Chapter 3, 5, 6)
       – CSC420H1 “Introduction to Image Understanding”                               – Uninformed Search (3.4)
 • Machine Learning, Neural networks                                                  – Heuristic Search (3.5, 3.6)
       – CSC321H “Introduction to Neural Networks and Machine Learning”
                                                                                      – Game Tree Search (5)
       – CSC411H “Machine Learning and Data Mining”
       – CSC412H1 “Uncertainty and Learning in Artificial Intelligence”               – Constraint Satisfaction Problems, Backtracking Search (6)
 • Robotics
       – Engineering courses                                                    •Knowledge Representation (Chapter 8, 9)
 • Natural language processing
                                                                                      – First order logic for more general knowledge (8)
       – CSC401H1 “Natural Language Computing”
       – CSC485H1 “Computational Linguistics”                                         – Inference in First-Order Logic (9)
 • Reasoning and decision making
       – CSC486H1 “Knowledge Representation and Reasoning”
             • Builds on this course

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                  What We Cover in CSC384                                                                          AI Successes
 •Classical Planning (Chapter 10)                                               • Games: chess, checkers, poker, bridge, backgammon…
       – Predicate representation of states
                                                                                • Physical skills: driving a car, flying a plane or helicopter,
                                                                                    y                    g          y g p                 p
       – Planning Algorithms                                                      vacuuming...

                                                                                • Language: machine translation, speech recognition, character
 •Quantifying Uncertainty and Probabilistic                                       recognition, …
  Reasoning (Chapter 13, 14, 16)
       – Uncertainties, Probabilities                                           • Vision: face recognition, face detection, digital photographic
                                                                                  processing, motion tracking, ...
       – Probabilistic Reasoning, Bayesian Networks
       – Decision Making under Uncertainties, Utilities and Influence           • Commerce and industry: page rank for searching, fraud detection,
         diagrams                                                                 trading on financial markets…

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                          Recent AI Successes                                                             Degrees of Intelligence
                                                                                     • Building an intelligent system as capable as humans
    • Re-integrating the diverse subfields of AI                                       remains an elusive goal.
    • Darpa Grand Challenges                                                           However,
                                                                                     • However systems have been built which exhibit various
          – Goal: build a fully autonomous car that can drive a 240 km                 specialized degrees of intelligence.
            course in the Mojave desert
          – 2004: none went further than 12 km
                                                                                     • Formalisms and algorithmic ideas have been identified
          – 2005: 5 finished
                                                                                       as being useful in the construction of these “intelligent”
          – 2007: Urban Challenge: 96 km urban course (former air force
            base) with obstacles, moving traffic, and traffic regulations: 6         • Together these formalisms and algorithms form the
            finishers                                                                  foundation o ou a e p to u de s a d intelligence as
                                                                                        ou da o of our attempt o understand e ge ce
          – 2011: Google testing its autonomous car for over 150,000 km on             a computational process.
            real roads
    • 2011: IBM Watson competing successfully against two                            • In this course we will study some of these formalisms and
      Jeopardy grand-champions                                                         see how they can be used to achieve various degrees
                                                                                       of intelligence.

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          – 1.1: What is AI?
          – 2: Intelligent Agents

    •Other interesting readings:
      – 1.2: Foundations
      – 1.3: History

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