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    Introduction 2018

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    Introduction 2018

    Uploaded by

    tammanamadeya

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    © All Rights Reserved
    Download as PPTX, PDF, TXT or read online from Scribd
    Download as pptx, pdf, or txt
    of 24

    Artificial Intelligence

    for Engineers
    EE 562
    Autumn 2018

    1
    Administrative Details
    • Instructor: Linda Shapiro, 634 CSE,
    shapiro@cs.washington.edu
    • TA: Dianqi Li, dianqili@uw.edu
    • Course Home Page:
    http://homes.cs.washington.edu/~shapiro/EE562
    • Text: Artificial Intelligence: A Modern Approach (3rd edition),
    Russell and Norvig

    2
    This Lecture
    • What is AI all about, roughly from
    Chapters 1 and 2.
    • Begin looking at the Python language we
    will use.

    3
    What is intelligence?
    • What capabilities should a machine have
    for us to call it intelligent?

    4
    Turing’s Test
    • If the human cannot tell whether the
    responses from the other side of a wall are
    coming from a human or computer, then the
    computer is intelligent.

    5
    Performance vs. Humanlike
    • What is more important: how the program
    performs or how well it mimics a human?

    • Can you get a computer to do something


    that you don’t know how to do? Like what?

    • What about creativity?

    6
    Mundane Tasks
    • Perception
    – Vision
    – Speech
    • Natural Language
    – Understanding
    – Generation
    – Translation
    • Reasoning
    • Robot Control

    7
    Formal Tasks
    • Games
    – Chess
    – Checkers
    – Kalah, Othello
    • Mathematics
    – Logic
    – Geometry
    – Calculus
    – Proving properties of programs

    8
    Expert Tasks
    • Engineering
    – Design
    – Fault Finding
    – Manufacturing planning
    • Medical
    – Diagnosis
    – Medical Image Analysis
    • Financial
    – Stock market predictions

    9
    What is an intelligent agent?
    • What is an agent?
    • What does rational mean?
    • Are humans always rational?
    • Can a computer always do the right thing?
    • What can we substitute for the right thing?

    10
    Intelligent Agents
    • What kinds of agents already exist today?

    11
    Problem Solving
    C

    Find a sequence of operations to produce the


    desired situation from the initial situation.
    12
    Game Playing
    • Given:
    – An initial position in the game
    – The rules of the game
    – The criteria for winning the game
    • WIN!

    13
    Constraint Satisfaction
    Example: Map Coloring

    14
    Reasoning
    • Given:
    – x (human(x) -> animal(x))
    – x (animal(x) -> (eats(x)  drinks(x)))
    • Prove:
    – x (human(x) -> eats(x))

    15
    Learning
    • Example: Neural Network

    16
    Natural Language Understanding
    • Pick up a big red
    block.
    • OK.
    • While hunting in
    Africa, I shot an
    elephant in my
    pajamas.
    • I don’t understand.

    17
    Computer Vision with Machine Learning

    Given: Some images and their corresponding descriptions

    
    {trees, grass, cherry trees} {cheetah, trunk} {mountains, sky} {beach, sky, trees, water}

    To solve: What object classes are present in new images

    

    ? ? ? ? 18
    Groundtruth Data Set:
    Annotation Samples
    tree(97.3), sky(99.8),
    bush(91.6), Columbia gorge(98.8),
    spring flowers(90.3), lantern(94.2), street(89.2),
    flower(84.4), house(85.8), bridge(80.8),
    park(84.3), car(80.5), hill(78.3),
    sidewalk(67.5), boat(73.1), pole(72.3),
    grass(52.5), water(64.3), mountain(63.8),
    pole(34.1) building(9.5)

    sky(95.1), Iran(89.3), Italy(99.9), grass(98.5),


    house(88.6), sky(93.8), rock(88.8),
    building(80.1), boat(80.1), water(77.1),
    boat(71.7), bridge(67.0), Iran(64.2), stone(63.9),
    water(13.5), tree(7.7) bridge(59.6), European(56.3),
    sidewalk(51.1), house(5.3)
    20
    21
    22
    Stuart Russell’s “Potted History of AI”
    • 1943 McCulloch & Pitts: neural nets model of the brain
    • 1950 Turing’s “Computing Machinery and Intelligence”
    • 1952-69 Look Ma, no hands
    • 1950s Early AI Programs: Logic Theorist, Checker Player, Geom
    • 1956 Term “Artificial Intelligence” adopted
    • 1965 Robinson’s complete algorithm for logical reasoning
    • 1966-74 AI discovers computational complexity; neural nets go
    • 1969-79 Early development of knowledge-based “expert systems”
    • 1980-88 Expert systems boom
    • 1988-93 Expert systems bust: “AI Winter”
    • 1985-95 Neural networks return
    • 1988- AI and Statistics together
    • 1995- Agents, agents everywhere
    • NOW- PROBABILITY EVERYWHERE!
    • NOW- Learning, Learning, Learning
    • NOW- DEEP Learning
    23
    Overview of Intended Topics
    1. Introduction to AI (Chs. 1-2, done)
    2. Python (Python as a Second Language, S. Tanimoto)
    3. Problem Solving by Search (Ch 3) “Big Chapter”
    4. Beyond Classical Search (Ch 4)
    5. Adversarial Search (Ch 5) “Game Playing”
    6. Constraint Satisfaction Problems (Ch 6)
    7. Learning (related to Ch 18)
    8. Computer Vision (not from book)
    9. Knowledge and Reasoning (Loosely related to Ch 7, 8, 9)
    10. Other Applications

    24

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