# Artificial Intelligence - Introduction by ecg16852

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```									Artificial Intelligence -
Introduction

Mahdi Shafiei
Faculty of Computer Science
Dalhousie University
www.cs.dal.ca/~shafiei
shafiei@cs.dal.ca

Acknowledgement: Based on the slides
provided by Russell and Norvig
Course Information
   Course homepage:
http://www.cs.dal.ca/~shafiei/courses/ai.html

   Check out course homepage for schedule, lecture notes, tutorials,
   Location and times
–   Mostly Teaching Lab 2, please check out the summer course
schedule for the class location
–   Mondays, Tuesdays, Wednesdays and Thursdays, 10:00-11:30am
and 13:00-14:30pm.
   Main text: David MacKay, Information Theory, Inference, and
Learning Algorithms, 2003
   Grading: Class participation (10%), 4 assignments (40%),
   CSCI-4150
–   Course Project (25%), Final exam – August 8, (25%)
   CSCI-2140
–   Final exam – August 4, (50%)
Course Overview

   Probability concepts
–   Random variables
–   Information theory
–   Representation of Uncertainty
   Probabilistic reasoning
   Bayesian networks
Why study AI?

Search engines
Science

Medicine/
Diagnosis
Labor
Appliances        What else?
What is AI?
   Views of AI fall into four categories:

Thinking humanly            Thinking rationally

Acting humanly             Acting rationally
Acting humanly: Turing Test
   Alan Turing's 1950 article Computing Machinery and
Intelligence discussed conditions for considering a
machine to be intelligent
– ―Can machines think?‖  ―Can machines behave
intelligently?‖
– The Turing test (The Imitation Game): Operational
definition of intelligence.
Acting humanly: Turing Test

• Computer needs to possess: Natural language processing,
Knowledge representation, Automated reasoning, and Machine
learning

• Are there any problems/limitations to the Turing Test?

   ―AI is the science and engineering of making
intelligent machines which can perform tasks
that require intelligence when performed by
humans …‖

–   Solving a differential equation
–   Brain surgery
–   Inventing stuff
–   Playing Chess
What would a computer need to pass the Turing test?

   Natural language processing: to communicate with
examiner.
   Knowledge representation: to store and retrieve
information provided before or during interrogation.
   Automated reasoning: to use the stored information
to answer questions and to draw new conclusions.
   Machine learning: to adapt to new circumstances
and to detect and extrapolate patterns.
Acting humanly: Turing Test
   Turing (1950) "Computing machinery and intelligence":
   "Can machines think?"  "Can machines behave intelligently?"
   Operational test for intelligent behavior: the Imitation Game

   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
   ―artificial flight‖: imitating birds or learning about aerodynamics
Thinking Humanly: Cognitive Science

   1960 ―Cognitive Revolution‖: information-processing
psychology replaced behaviorism
   Cognitive science brings together theories and
experimental evidence to model internal activities of the
brain
–   What level of abstraction? ―Knowledge‖ or ―Circuits‖?
–   How to validate models?
Predicting and testing behavior of human subjects (top-down)
 Direct identification from neurological data (bottom-up)
   Both approaches (roughly, Cognitive Science and Cognitive
Neuroscience) are now distinct from AI
Thinking Rationally: Laws of Thought

   Aristotle (~ 450 B.C.) attempted to codify ―right
thinking‖
What are correct arguments/thought processes?

   E.g., ―Socrates is a man, all men are mortal;
therefore Socrates is mortal‖
   Initiation of the Logic

   Several Greek schools developed various forms of
logic:
notation plus rules of derivation for thoughts.
Thinking Rationally: Laws of Thought

 Problems:
1) Uncertainty: Not all facts are certain
(e.g., the flight might be delayed).

2) Resource   limitations:
-   Not enough time to compute/process
-   Insufficient memory/disk/etc
-   Etc.
Acting Rationally: The Rational Agent
   Computer Agent
–    Autonomous control, perceiving the environment, adopting to change
and …
   Rational Agent
–    Acts so as to achieve the best (expected) outcome
   Rational behavior: Doing the right thing!
   The right thing: That which is expected to maximize the expected
return
   Provides the most general view of AI because it includes:
–    Correct inference (―Laws of thought‖)
–    Uncertainty handling
–    Resource limitation considerations (e.g., reflex vs. deliberation)
–    Cognitive skills (NLP, knowledge representation, ML, etc.)
1)   More general
2)   Its goal of rationality is well defined
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
   Economics        utility, decision theory
   Neuroscience     physical substrate for mental activity
   Psychology       phenomena of perception and motor control,
experimental techniques
   Computer         building fast computers
engineering
   Control theory   design systems that maximize an objective
function over time
   Linguistics      knowledge representation, grammar
State of the art
   Game Playing: Deep Blue defeated the reigning world chess
champion Garry Kasparov in 1997
   Theorem Proving: Proved a mathematical conjecture (Robbins
   Autonomous Control: No hands across America (driving
autonomously 98% of the time from Pittsburgh to San Diego)
   Logistics Planning: 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
   Autonomous planning and scheduling: NASA's on-board
autonomous planning program controlled the scheduling of
operations for a spacecraft
   Language Understanding and Problem Solving: Proverb solves
crossword puzzles better than most humans
   Robotics: robot assistance for microsurgery
State of the art
   Which of the following can be done at present?
–   Play a decent game of table tennis
–   Drive along a curving mountain road
–   Drive in the center of Cairo
–   Buy a week’s worth of groceries at Sobey’s
–   Buy a week’s worth of groceries on the Web
–   Play a decent game of bridge
–   Discover and prove a new mathematical theorem
–   Write an intentionally funny story
–   Give competent legal advice in a specialized area of law
–   Translate spoken English into spoken Swedish in real time
–   Perform a complex surgical operation
Statistical Artificial Intelligence

   Principles, methods, and algorithms for
learning and prediction on the basis of past
experience
–   speech recognition
–   hand-written character recognition
–   information retrieval
–   operating systems, compilers
–   fraud detection, security, defense applications
–    ...
Example

   A classification problem:
–    predict the grades for students taking this course
       Key steps:
1.   Data
2.   Assumptions
3.   Representation
4.   Estimation
5.   Evaluation
6.   Model selection
Example
   A classification problem:
–     predict the grades for students taking this course
       Key steps:
1.    data: what ―past experience‖ can we rely on?
2.    assumptions: what can we assume about the students or
the course?
3.    representation: how do we ―summarize‖ a student?
4.    estimation: how do we construct a map from students to
5.    evaluation: how well are we predicting?
6.    model selection: perhaps we can do even better?
Data

   The data we have available (in principle):
–   names and grades of students in past years ML
courses
–   academic record of past and current students
   ―training data‖

   ―test data‖
Assumptions

   There are many assumptions we can make
to facilitate predictions
–   the course has remained roughly the same over
the years
–   each student performs independently from others
Representation
   Academic records are rather diverse so we might limit
the summaries to a select few courses
    For example, we can summarize the      student (say
Peter) with a vector

   where the grades correspond to (say) 1806, 6041, and
6034.
   The available data in this representation
Estimation
   Given the training data

we need to find a mapping from ―input vectors‖ x to
―labels‖ y encoding the grades for the ML course.
   Possible solution (nearest neighbor classier):
1.   For any student x find the ―closest‖ student xi in the training
set
2.   Predict yi, the grade of the closest student
Evaluation

   How can we tell how good our predictions
are?
–   we can wait till the end of this course...
–   we can try to assess the accuracy based on the
data we already have (training data)
   Possible solution:
–   divide the training set further into training and test
sets
–   evaluate the classifier constructed on the basis of
only the smaller training set on the new test set
Model selection

   We can refine
–   the estimation algorithm (e.g., using a classier
other than the nearest neighbor classier)
–   the representation (e.g., base the summaries on a
different set of courses)
–   the assumptions (e.g., perhaps students work in
groups) etc.
    We have to rely on the method of evaluating
the accuracy of our predictions to select
among the possible refinements
Types of learning problems
A rough (and somewhat outdated) classification of learning
problems:

   Supervised learning, where we get a set of training inputs and
outputs
–   classification, regression

    Unsupervised learning, where we are interested in capturing
inherent organization in the data
–   clustering, density estimation

    Reinforcement learning, where we only get feedback in the
form of how well we are doing (not what we should be doing)
–   planning
Supervised learning : classification

   Example: digit recognition
(8x8 binary digits)

   We wish to learn the
mapping from digits to
labels
Representations / Assumptions
Supervised Learning: Regression
Unsupervised learning: data organization

   The digits again

   We'd like to understand the generation process of examples
(digits in this case)
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
   1952—69   Look, Ma, no hands!
   1950s     Early AI programs, including Samuel's checkers
program, Newell & Simon's Logic Theorist,
Gelernter's Geometry Engine
   1965      Robinson's complete algorithm for logical reasoning
   1966—73   AI discovers computational complexity
Neural network research almost disappears
   1969—79   Early development of knowledge-based systems
   1980--    AI becomes an industry