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Advanced Artificial Intelligence

Purpose of Course  showclose

This course will present advanced topics in Artificial Intelligence (AI).  We will begin by defining the term “software agent” and discussing how software agents differ from programs in general.  We will then take a look at those problems in the field of AI that tend to receive the most attention.  Different researchers approach these problems differently.  In this course, we will focus on how to build and search graph data structures needed to create software agents, an approach that you will find useful for solving many problems in AI.  We will also learn to “break down” larger problems into a number of more specific, manageable sub-problems.

In the latter portion of this course, we will review the study of logic and conceptualize the differences between propositional logic, first-order logic, fuzzy logic, and default logic.  After learning about statistical tools commonly used in AI and about the basic symbol system used to represent knowledge, we will focus on artificial neural network and machine learning, which are essential components of computational and statistical methods, and theoretical computer science.  The course will then conclude with a study of the Turing machine and a discussion of the questionable claims that human thinking is a symbol manipulation.

Learning Outcomes  showclose

Upon successful completion of this course, students will be able to:

  • Define the term “intelligent agent,” list major problems in AI, and identify the major approaches to AI.
  • Translate problems into graphs and encode the procedures that search the solutions with the graph data structures.
  • Explain the differences between various types of logic and basic statistical tools used in AI.
  • List the different types of learning algorithms and explain why they are different.
  • List the most common methods of statistical learning and classification and explain the basic differences between them.
  • Describe the components of Turing machine.
  • Name the most important propositions in the philosophy of AI.
  • List the major issues pertaining to the creation of machine consciousness.
  • Design a reasonable software agent with java code.

Course Requirements  showclose

In order to take this course you must:
 

√    Have access to a computer.

√    Have continuous broadband Internet access.

√    Have the ability/permission to install plug-ins or software (e.g., Adobe Reader or Flash).

√    Have the ability to download and save files and documents to a computer.

√    Have the ability to open Microsoft files and documents (.doc, .ppt, .xls, etc.).

√    Be competent in the English language.

√    Have read the Saylor Student Handbook.

Unit Outline show close


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  • Unit 1: Intelligent Agents and Problems Of AI  

    AI is often seen through the autonomous, rational intelligent agents paradigm, which we will emphasize in this unit.  This unit will begin by discussing what software agents are and how agents differ from programs in general.  The unit will then provide a natural taxonomy of autonomous agents and discusses possibilities for further classification before presenting those problems in AI that seem to received the most attention.  The problem of creating intelligence is then broken down into a number of specific sub-problems, which consist of particular traits that should be found in an intelligent system.  Note that different researchers approach the problems of AI from different perspectives, depending on their respective training, fields of expertise, and favored tools.

    Unit 1 Time Advisory   show close
    Unit 1 Learning Outcomes   show close
  • 1.1 Is It an Agent, or Just a Program?  
    • Reading: The University of Memphis: Stan Franklin and Art Graesser's “Is It an Agent, or Just a Program?: A Taxonomy for Autonomous Agents”

      Link: The University of Memphis: Stan Franklin and Art Graesser’s “Is It an Agent, or Just a Program?” (HTML)
       
      Instructions:  This resource covers subsections 1.1.1-1.1.5.  Read the webpage to learn about the advent of software agents.  Memorize the definitions of the AIMA, Maes, KidSim, Hayes-Roth, IBM, SodaBot, Foner, and Brustoloni Agents.  Make sure you know how to define “agency” and work to memorize Franklin's definition of an agent.  Read through the examples of the different taxonomies and classifications of agents. 
       
      About the link: Stan Franklin and Art Graesser are researchers of AI, and professors of computer science and cognitive science at the University of Memphis.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the web pages above.

  • 1.1.1 What is an agent?  
  • 1.1.2 The Essence of Agency  
  • 1.1.3 Agent Classifications  
  • 1.1.4 A Natural Kinds Taxonomy of Agents  
  • 1.1.5 Subagents and Societies of Agents  
  • 1.1.6 John Lloyd on Intelligent Agents  
    • Lecture: videolectures.net: John Lloyd’s “Intelligent Agents: Part 1”

      Link: videolectures.net: John Lloyd’s“Intelligent Agents: Part 1” (Adobe Flash and Windows Media Player)
       
      Instructions: Watch the first part of this three-part video series by John Lloyd.  As he lectures you may wish to work through the slides included on the page.  Throughout the lecture, Professor Lloyd talks about AIMA agents and presents some pertinent examples.  Please compare his thoughts with yours and Franklin's from the previous sections.  This lecture is approximately 50 minutes in length. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
       
      About the link: John Lloyd is a professor at Australian National University who shares lectures on videolectures.net.  In the lecture, he introduces the basic ideas of agents and describes some agent architectures.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (John Lloyd).

  • 1.1.7 Stan Franklin - A Cognitive Theory of Everything  
    • Lecture: Google Videos: Stan Franklin’s “A Cognitive Theory of Everything”

      Link: Google Videos:Stan Franklin’s “A Cognitive Theory of Everything” (Google Video)
       
      Instructions: Watch this video, which does an excellent job explaining how intelligent agents fit into “the big picture.”  Ask yourself whether Franklin's thoughts make sense to you.  This video is 40 minutes long.
       
      About the link: In this video, Stan Franklin presents theories of cognition at the 2006 AGIRI workshop.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the web page above.

      The Saylor Foundation does not yet have materials for this portion of the course. If you are interested in contributing your content to fill this gap or aware of a resource that could be used here, please submit it here.

      Submit Materials

  • 1.2 Overview of AI General Problems  
    • Reading: Wikipedia’s “Artificial Intelligence: Problems”

      Link: Wikipedia’s “Artificial Intelligence: Problems” (PDF)

       
      Instructions: Read this entry on the general problems arising in the field of AI.  After completing this assignment, you should know the meaning of terms such as knowledge representation, planning, learning, natural language processing, motion and manipulation, perception, social intelligence, creativity, and general intelligence.  This link covers subsections 1.2.1-1.2.9.  Note that sections 1.2.2-1.2.4 have additional resources assigned to them (see below) and require extra attention.
       
      About the link: The article above is an entry from en.wikipedia.org, which is a web-based, free-content encyclopedia project based on an openly editable model.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-Share-Alike License 3.0(HTML).  This article is a modified version of an article of the same title originally found on Wikipedia.  The Saylor Foundation has reformatted the entry and has omitted several of the original sections.You can find the original Wikipedia version of this article here(HTML).

       

  • 1.2.1 Deduction, Reasoning, Problem Solving  
  • 1.2.2 Knowledge Representation  
  • 1.2.3 Planning  
    • Lecture: videolectures.net: Jussi Rintanen’s “Planning: Part 1”

      Link: videolectures.net: Jussi Rintanen’s“Planning: Part 1” (Adobe Flash and Windows Media Player)
       
      Instructions: Watch the first part of the video by Jussi Rintanen.  You may wish to work through the slides provided on the right-hand side of the screen as Professor Rintanen lectures.  After viewing the lecture, you should understand why planning can be difficult and be able to define the term “transition systems.”  This video is about an hour long. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
       
      About the link: Jussi Rintanen is a researcher and an associate professor at NICTA Canberra Research Laboratory and The Australian National University. 
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Jussi Rintanen).

  • 1.2.4 Learning  
    • Lecture: videolectures.net: Olivier Bousquet’s “Introduction to Learning Theory: Part 1”

      Link: videolectures.net: Olivier Bousquet’s “Introduction to Learning Theory; part 1” (Adobe Flash and Windows Media Player)
       
      Instructions: Watch Olivier Bousquet’s “Part 1,” working through the provided on the right-hand side of the screen as you listen to his lecture.  After viewing the lecture, you should have a general understanding of “learning theory,” be able to differentiate between deduction and induction, and describe, in general terms, the concept of probability and Bayes' rule.  This lecture is about 1 hour long. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
       
      About the link: Olivier Bousquet works at the Max Planck Institute for Biological Cybernetics. 
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Oliver Bousquet).

  • 1.2.5 Natural Language Processing  
  • 1.2.6 Motion and Manipulation  
  • 1.2.7 Perception  
  • 1.2.8 Social Intelligence  
  • 1.2.9 General Intelligence  
  • 1.3 Approaches to AI  
  • 1.3.1 Cybernetics and Brain Simulation  
  • 1.3.2 Symbolic AI  
  • 1.3.3 Sub-symbolic AI  
  • 1.3.4 Statistical  
  • 1.3.5 Systems with General Intelligence  
    • Lecture: videolectures.net: Michael Thielscher’s "Systems with General Intelligence"

      Link: videolectures.net: Michael Thielscher’s "Systems with General Intelligence" (Adobe Flash and Windows Media Player)
       
      Instructions: Watch this video about general problems in AI, working through slides provided on the right-hand side of the screen as Thielscher lectures.  After watching the video, you should be familiar with the chess-as-an-intelligent-system example, understand what general game playing is about, and identify the major questions with which general AI is concerned.  Do not let yourself get bogged down by the details; work for a general understanding of AI.  This lecture is 53 minutes long. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
       
      About the link: In this video, Michael Thielscher of the School of Computer Science and Engineering, University of New South Wales,  talks about general intelligence and AI problems, approaches, and history.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Michael Thielscher).

  • 1.4 Agents in Code  
  • Unit 2: Solving Problems By Searching  

    This unit will teach you how to build and search data structures needed to create software agents.  We will focus on graph structures and a few classical graph search algorithms because their understanding is important for solving many problems that arise in AI.  Graphs enable logical description of the problems.  A graph search, then, represents the search for the solutions. We will begin this unit with some basic graph theory definitions and then learn how to solve some problems with a graph.  The last section of this unit has a video that will expand the understanding of the graph structures.

    Unit 2 Time Advisory   show close
    Unit 2 Learning Outcomes   show close
  • 2.1 Graphs  
  • 2.1.1 Graph Definition  
  • 2.1.2 Binary Tree  
  • 2.1.3 Example Problem: Minimum Spanning Tree  
    • Reading: planetmath.org: Cameron McLeman’s “Minimum Spanning Tree"

      Link: planetmath.org:Cameron McLeman’s “Minimum Spanning Tree” (PDF)
       
      Instructions: Read about minimum spanning trees and try to figure out how Prim's algorithm works; the solution can be found at brpreiss.com's link “Prim's Algorithm.” (HTML)  Before you check the solution, try to solve problem yourself.  After you have solved the problem (or if you have spent a couple of hours working on it, and are stumped!), study the solution. 
       
      About the link: planetmath.org is a mathematics encyclopedia with entries written and reviewed by members.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-Share-Alike License 3.0.  It is attributed to Planetmath.org and the original version can be found here

  • 2.2 Tree Search Algorithms  
  • 2.2.1 Binary Search Trees  
    • Reading: The University of Auckland in New Zealand: John Morris’s “Binary Search Tree"

      Link: The University of Auckland in New Zealand: John Morris’s “Binary Search Tree” (PDF)
       
      Instructions: Read the article to learn how to build and search binary trees.
       
      About the link: This link is provided by John Morris, a professor in the Electrical and Computer Engineering Department at the University of Auckland in New Zealand.
       
      Terms of Use: The linked material above has been reposted by the kind permission of John Morris, and can be viewed in its original form here.  Please note that this material is under copyright and cannot be reproduced in any capacity without explicit permission from the copyright holder.  

  • 2.2.2 Red-Black Trees  
    • Reading: The University of Auckland in New Zealand: John Morris’s “Red-Black Trees"

      Link: The University of Auckland in New Zealand: John Morris’s “Red-Black Trees” (PDF)
       
      Instructions: Please read the linked material above.  After reading this section, you should know how a binary tree differs from a red-black tree and understand the basics of building and searching red-black trees. 
       
      About the link: This link is provided by John Morris, a professor in the Electrical and Computer Engineering Department at University of Auckland in New Zealand.
       
      Terms of Use: The linked material above has been reposted by the kind permission of John Morris, and can be viewed in its original form here.  Please note that this material is under copyright and cannot be reproduced in any capacity without explicit permission from the copyright holder.  

  • 2.2.3 Skip List  
    • Reading: The University of Auckland in New Zealand: John Morris’s “Skip Lists"

      Link: The University ofAuckland in New Zealand: John Morris’s “Skip List” (PDF)
       
      Instructions: Please learn how to build and search a skip list by reading the linked material.
       
      About the link: This link is provided by John Morris, a professor in the Electrical and Computer Engineering Department at University of Auckland in New Zealand.
       
      Terms of Use: The linked material above has been reposted by the kind permission of John Morris and can be viewed in its original form here.  Please note that this material is under copyright and cannot be reproduced in any capacity without explicit permission from the copyright holder.  

  • 2.3 Common Search Techniques with Graphs  
  • 2.3.1 Depth-first Search  
  • 2.3.2 Breadth-First Search  
  • 2.3.3 Dijkstra's Algorithm  
  • 2.4 Search Algorithms in General  
  • 2.5 Basic Notions in Graph Theory  
    • Lecture: videolectures.net: Zoubin Ghahramani’s “Graphical Models: Parts 1-3”

      Link: videolectures.net: Zoubin Ghahramani’s “Graphical Models: Parts 1-3” (Adobe Flash and Windows Media Player)
       
      Instructions: Watch this three-part video on graph theory to develop a better understanding of how to use graphs in AI.  After viewing the first part, you should know about directed, undirected, and factor graphs, conditional independence, d-separation, and plate notation.  The second part will teach you about inference in graphical models, key ideas in belief propagation, and the junction tree algorithm. Watch the third part for fun, trying to follow along as much as possible.  The first lecture is 53 minutes; the second is 58 minutes; and the third is 1 hour and 18 minutes. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
       
      About the link: Zoubin Ghahramani is Professor of Information Engineering at Department of Engineering, University of Cambridge.  His research interests include Bayesian approaches to machine learning, artificial intelligence, statistics, information retrieval, bioinformatics, and computational motor control.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Zoubin Ghahramani).

  • 2.6 Graph Examples in Code  
    • Assignment: Artificial Intelligence Center’s “Route Finding Agent”

      Link: Artificial Intelligence Center’s “Route Finding Agent” (JAVA)
       
      Instructions: Create a route-finding agent given the environment in the form of a graph.  One possible solution can be found via the link above, under the “Route Finding Agent” section.  Study the solution code after you have already solved the problem, or if you have spent a substantial amount of time and are stuck (this problem could take you up to 12 hours to solve!)
       
      About the link: The code provided by the link is a Java implementation of search algorithms from Norvig And Russell's "Artificial Intelligence - A Modern Approach,” 3rd Edition.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the web page above.

  • Unit 3: Logical Agents And Knowledge Representation  

    Intelligent agents are supposed to make rational decisions, which are not just logically reasoned, but optimal as well, given the available information.  Accordingly, in this unit, we will review the study of logic and conceptualize the differences between propositional logic, first-order logic, fuzzy logic, and default logic.  This unit will also present an overview of common statistical tools used in AI.  In the last part of this unit, we will try to clarify our definition of knowledge representation and will discuss its roles based on research conducted at MIT.

    Unit 3 Time Advisory   show close
    Unit 3 Learning Outcomes   show close
  • 3.1 Logic Programming  
    • Reading: Wikipedia’s “Logic Programming”

      Link: Wikipedia’s “Logic Programming” (PDF)
       
      Instructions: Read this web page on logic programming. Make sure you understand the differences between abductive logic, metalogic, constraint logic, concurrent logic, and inductive logic, higher-order logic, and linear logic programming.  This reading covers subunits 3.1.1-3.1.7. 
       
      Terms of Use: The article above is released under a Creative Commons Attribution-Share Alike License 3.0 (HTML).  You can find the Wikipedia source article here (HTML).

    • Lecture: videolectures.net: Alwen Tiu’s “Introduction to Logic: Parts 1-3”

      Link: videolectures.net: Alwen Tiu’s “Introduction to Logic: Parts 1-3” (Adobe Flash and Windows Media Player)
       
      Instructions: Watch the first lecture on logic and compare it to the reading above.  In this lecture, you will learn about the syntax and semantics of propositional logic, boolean functions, satisfiability, and binary decision trees.  You will need to know the difference between conjunctive and disjunctive normal forms.  The first lecture is 56 minutes long. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
       
      Then, watch the second lecture to learn about first-order logic.  Pay particular attention to the examples. This second lecture is 39 minutes long. 
       
      Finally, watch the third lecture, which presents modal logic.  Make sure you know the differences between propositional, first-order, and modal logic.  The third lecture is 49 minutes long.
       
      About the link: Alwen Tiu is a professor at the Australian National University.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (John Lloyd).

  • 3.1.1 Abductive Logic  
  • 3.1.2 Metalogic  
  • 3.1.3 Constraint Logic  
  • 3.1.4 Concurrent Logic  
  • 3.1.5 Inductive Logic  
  • 3.1.6 Higher-Order Logic  
  • 3.1.7 Linear Logic Programming  
  • 3.2 Probabilistic Methods for Uncertain Reasoning  
  • 3.2.1 Bayesian Network  
  • 3.2.2 Hidden Markov Model  
  • 3.2.3 Other Methods for Uncertain Reasoning  
  • 3.2.3.1 Kalman Filter  
  • 3.2.3.2 Decision Theory  
  • 3.3 Knowledge Representation and Reasoning  
  • 3.3.1 Discussion on Knowledge Representation  
  • 3.3.1.1 Terminology and Perspective  
  • 3.3.1.2 What is a Knowledge Representation?  
  • 3.3.1.3 Consequences for Research and Practice  
  • 3.3.1.4 The Goal of Knowledge Representation Research  
  • 3.4 Coding Drills  
  • Unit 4: Learning  

    This unit presents an artificial neural network (NN) as the most important learning tool for machine learning.  Machine learning research tries to automatically extract information from data through computational and statistical methods.  Machine learning is closely related to not only data mining and statistics, but also theoretical computer science.  NN is a computational model based on biological neural networks.  It consists of an interconnected group of artificial neurons and processes.  Practically, neural networks are non-linear statistical data modeling tools used to model complex relationships between inputs and outputs.  After being successfully trained, NNs are able to perform classification, estimation, prediction, or simulation with new data.  The second part of this unit reviews the Gaussian and Bayesian processes used in machine learning.

    Unit 4 Time Advisory   show close
    Unit 4 Learning Outcomes   show close
  • 4.1 Machine Learning  
  • 4.1.1 Supervised Learning  
  • 4.1.2 Unsupervised Learning  
  • 4.1.3 Reinforcement Learning  
  • 4.1.4 Transduction  
  • 4.1.5 Multi-task Learning  
  • 4.1.6 Machine Learning, Probability, and Graphical Models  
  • 4.2 Neural Network  
  • 4.2.1 Introduction to Neural Networks  
    • Reading: Wolfram Mathematica’s “Introduction to Neural Networks”

      Link: Wolfram Mathematica’s “Introduction to Neural Networks” (HTML)
       
      Instructions: Read the general 2.1 section to learn about general neural networks and how they are mathematically defined. 
                             
      About the link: This entry is from Wolfram, which is a software company known for Mathematica.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the web pages above.

  • 4.2.2 Feedforward Neural Networks  
  • 4.2.3 Radial Basis Function Networks  
  • 4.2.4 The Perceptron  
    • Reading: Wolfram Mathematica’s “The Perceptron”

      Link: Wolfram Mathematica’s “The Perceptron” (HTML)
       
      Instructions: Read only the pages under the section 2.4 about Perceptron.  Be sure to understand its mathematical definition, learn the training algorithm, and study the example in figure 2.4.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the web pages above.
       

  • 4.2.5 Vector Quantization (VQ) Networks  
  • 4.2.6 Hopfield Network  
  • 4.3 Other Classifiers and Statistical Learning Methods  
  • 4.3.1 Kernel Methods  
  • 4.3.2 k-nearest Neighbor Algorithm  
  • 4.3.3 Mixture Model  
  • 4.3.4 Naive Bayes Classifier  
  • 4.3.5 Decision Tree  
  • 4.3.6 Kernels and Gaussian Processes  
    • Lecture: videolectures.net’s: Mark Girolami’s “Kernels and Gaussian Processes: Parts 1-3”

      Link: videolectures.net’s: Mark Girolami’s “Kernels and Gaussian Processes: Parts 1-3” (Adobe Flash and Windows Media Player)
                             
      Instructions: Watch the first video about machine learningand compare it to what you have learned in the readings from the sections above.  After watching this video, you should the basics of linear regression, loss function, prediction techniques. Study non-linear models, probabilistic regression, and uncertainty estimation.  This lecture is just over 1 hour long.  You may wish to work through the slides provided on the right-hand side of the screen as you work through this lecture and the two below. You can also download the PowerPoint slides in a PDF format by clicking on the links under “See Also.”
       
      Then, watch the second video lecture to learn about Bayesian regression and classification.  This second lecture is 1 hour long.
       
      Finally, watch the last lecture to learn about Gaussian processes, regression, and classification.  This third installment is just over 1 hour long.
       
      About the link: Mark Girolami is a professor at the University of Glasgow.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Mark Girolami).

  • 4.4 Machine Learning Coding Drills  
    • Assignment: Artificial Intelligence Center’s “Tic-Tac-Toe”

      Link: Artificial Intelligence Center’s “Tic-Tac-Toe Demo” (JAVA)
       
      Instructions: Code an agent that plays the Tic-Tac-Toe game.  You can choose to play the game yourself by selecting board positions or have the Agent propose moves.  One possible solution is available via the link above under the Tic-Tac-Toe Demo section.  Work towards a solution for no more than 10 hours and then check your work against the solution code.
       
      About the link: The code above is a Java implementation of algorithms from Norvig And Russell's "Artificial Intelligence - A Modern Approach,” 3rd Edition.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the web page above.

  • Unit 5: Philosophical Foundations of AI  

    In this unit, we will study the Turing machine as a definition of the intuitive notion of computability in the discrete domain.  In the theory of computation, many major complexity classes can be characterized by an appropriately restricted Turing machine.  We will also discuss the claim that human thinking is a kind of symbol manipulation.  Note that a symbol system is necessary for intelligence and that machines can be intelligent.  Finally, we will discuss the ongoing neuroscientific attempt to understand how the human brain works and examine the possible role of consciousness in the machines.  Is it possible, in theory and then in practice, to create a machine that has all the capabilities of a human being?

    Unit 5 Time Advisory   show close
    Unit 5 Learning Outcomes   show close
  • 5.1 Computing Machinery and Intelligence  
  • 5.1.1 Philosophical Issues and Turing Test  
    • Lecture: videolectures.net: John Lloyd’s “Intelligent Agents: Part 3”

      Link: videolectures.net: John Lloyd’s “Intelligent Agents: Part 3” (Adobe Flash and Windows Media Player)
       
      Instructions: Watch the third part of this video series by John Lloyd (you will need to click on the appropriate video once you navigate to the site’s landing page) and compare his interpretation of the Turing test with what you learn later in this unit.  This lecture is 50 minutes long.  You may wish to work through the slides provided on the right-hand side of the screen as you work through this lecture. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
       
      About the link: John Lloyd is a professor at Australian National University who shares lectures on videolectures.net.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (John Lloyd).         

  • 5.1.2 Computing Machinery and Intelligence  
  • 5.1.3 Turing Machine  
    • Reading: Scholarpedia: Paul M.B. Vitanyi's “Turing Machine”

      Link: Scholarpedia: Paul M.B.  Vitanyi's “Turing Machine” (HTML)
       
      Instructions: This reading is fairly challenging; read through it to the best of your abilities for a detailed description of the Turing Machine.  After you have completed this reading, you should know how to define Turing machine and summarize the Church-Turing theses.  Make sure you know what the Halting problem is.
                 
      About the link: Article above is from Paul M.B.  Vitanyi computer scientist at University of Amsterdam.
       
      Terms of Use: This material is in the public domain.

  • 5.1.4 Computability and Incompleteness  
    • Lecture: videolectures.net: Errol Martin’s “Computability and Incompleteness”

      Link: videolectures.net: Errol Martin’s “Computability and Incompleteness” (Adobe Flash and Windows Media Player)
       
      Instructions: These videos cover challenging topics mentioned earlier in this unit.  Watch the first lecture to learn about Hilbert's consistency program, Godels incompleteness theorem, attributes of computable functions, Church's thesis, and three approaches to computability, paying particular attention to the examples.  This first lecture is just over 1 hour long.  You may wish to work through the slides provided on the right-hand side of the screen as you listen to this lecture and the other installments below. You can also download the PowerPoint slides in a PDF format by clicking on the link under “See Also.”
       
      In the second video, will learn about the Halting problem, universal Turing machine, and the undecidability proof.  This second installment is 48 minutes long.  Finally, watch the last two videos in this series, which are 56 and 53 minutes long, respectively.           
       
      About the link: Errol Martin is founder of an Enterprise Architecture and Systems Consulting Company based in Canberra Australia.
       
      Terms of Use: The article above is released under a Creative Commons Attribution-NonCommercial- NoDerivatives License 3.0(HTML).  It is attributed to (Errol Martin).          

  • 5.2 Important Propositions in the Philosophy of AI  
  • 5.2.1 The Brain Can Be Simulated  
  • 5.2.2 Human Thinking Is Symbol Processing  
  • 5.3 Machine Consciousness  
    • Reading: Scholarpedia: Igor Aleksander’s “Machine Consciousness”

      Link: Scholarpedia: Igor Aleksander’s “Machine Consciousness”(HTML)
       
      Instructions: Read all the sections of the web pagethat discuss machine consciousness.  Focus on learning about early models of conciousness and neural models of conciousness. 
       
      About the link: The article above is an entry at www.scholarpedia.org, the peer-reviewed open-access encyclopedia written by scholars from all around the world.
       
      Terms of Use: Please respect the copyright and terms of use displayed on the web page above.

    • Lecture: MIT: Marvin Minsky’s "Emotion Machine”

      Link: MIT: Marvin Minsky’s "Emotion Machine" (Adobe Flash)
       
      Also available in: iTunes U
       
      Instructions: Watch this video about emotional machines.  Ask yourself whether you think one is possible and begin to think about how you would approach its creation.  This video is 1 hour and 23 minutes long.
       
      About the link: Marvin Minsky is a pioneer of artificial intelligence.

      Terms of Use: Please respect the copyright and terms of use displayed on the web page above.

  • Final Exam  

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