IDST-010-06: Artificial Intelligence: From NAND to Consciousness

Fall 2015



  • 12/11/15: Scheduled review session: Saturday, 12/19, 1 PM, 326 STM.
  • 12/9/15: Posted room for final exam: ICC 106, Monday, 12/21, 4–6 PM.
  • 11/12/15: Linked to Synopsis
  • 11/5/15: Posted Writing Assignment #2.
  • 10/30/15: Posted Professor Riesenhuber's slides on Bb.
  • 10/26/15: Posted Writing Assignment #1.
  • 10/5/15: Posted Problem Set #2.
  • 9/29/15: Updated Elements of Algorithms.
  • 9/21/15: Posted Problem Set #1.
  • 9/9/15: Posted Computer science: Enchantress of abstraction, an article about Ada Lovelace.
  • 9/4/15: Posted Mark's Favorite Definitions of AI.
  • 9/1/15: Opened Blackboard site.
  • 8/27/15: Set dates for lectures and assignments.
  • 3/30/15: Created this Web page.
  • Where, When, Who

    Class Time: TR 3:30–4:45 PM
    Classroom: Intercultural Center 208A
    Instructor: Mark Maloof
    Office: 325 St. Mary's Hall
    Mailbox: 329A St. Mary's Hall
    Office Hours: In-person (325 STM): TR 11:00 AM–12:00 PM; online: M 10:30–11:30 AM and W 3:00–4:00 PM; or by appointment. Send me an email to get the Zoom link for online office hours.


    The notion of mechanized thought traces back to Aristotle's categorical syllogisms. In the 1830s, Lady Lovelace's work on Charles Babbage's Analytical Engine, a mechanical computer that was never built, compelled her to remark that the machine “has no pretensions whatever to originate anything. It can do whatever we know how to order it to perform. It can follow analysis; but it has no power of anticipating any analytical relations or truths.” And yet in 2005, Stanley, a self-driving car, drove a 175-mile course in the Mojave Desert unaided by humans, who had only two-hours prior notice of the route. Stanley used terrain maps to plan its overall route, but as it drove, it relied on its own analysis of “analytical relations and truths” to anticipate what lay ahead, by navigating the road itself, assessing its condition, and avoiding obstacles. In 2011 on Jeopardy!, an IBM computer named Watson played as a human would and beat Brad Rutter and Ken Jennings, two former winners.

    Whether machines can think, be intelligent, and be conscious are some of the greatest questions of our time, questions that, if answered positively, will have profound societal and ethical impact. This Ignatius Seminar is a combination of computer science, artificial intelligence, neuroscience, cognitive science and philosophy. We start the seminar with voltage and proceed through logic gates, machine language, high-level programming languages, algorithms, models of computation, and the limits of computation. Building upon this foundation, we examine computational methods of reasoning logically, simulating neural networks, and learning. We conclude the seminar by turning our attention to and grappling with the philosophical and ethical implications of this work through readings and discussions supplemented by guest lectures.

    Michael Fellows said, “Computer science is no more about computers than astronomy is about telescopes,” a statement often attributed to Edsger Dijkstra. In the spirit of this statement and to encourage broad participation, the seminar intentionally eschews using programming to explore concepts, focusing instead on discussion, thought experiments, exercises, and analysis of books and articles from the primary and secondary literature. Crucially, students do not need to know how to program to be successful in this seminar. In addition to homework assignments and midterm and final exams, there is a semester project on a relevant topic of the student's choosing.

    Primary Texts:

    Learning Goals

    By the end of the semester, students will be able to:


    My seminar policies are designed to supplement the University's Undergraduate Honor System and the CS Department's Honor Policy. However, unless otherwise stated when I distribute an assignment, the following is the default for all assignments for my Ignatius Seminar Artificial intelligence: From NAND to Consciousness (IDST-010-06).

    Students must always follow proper scholarly practice for graded assignments. Typically, we do not have cite facts, common math formulae, or expressions of our own ideas, observations, interpretations, and analyses, but we must cite our reliance on the work of others. Students may be quite adept at and knowledgeable about citing and quoting material from traditional sources, such as books and articles. However, students may not realize that formulae, theorems, proofs, algorithms, and computer programs can require the same treatment as any other form of expression. For convenience, you do not need to cite conversations with me or information you obtain from class lectures and discussions, but you should cite the published course materials upon which we base our discussions. If you are unsure about what requires citation or what constitutes proper scholarly practice, please ask me during class, during office hours, or by e-mail. Students who do not acknowledge their use of resources to complete assignments may be in violation of my course policies and the university's policies on academic integrity.

    The quality of your sources is important. Judging the quality of a source is not always easy, especially early in a scholar's career. Primary, peer-reviewed sources are best, especially for this class's short papers and research project. The Web can be a valuable research and learning tool, but since anyone can write anything and post it, you must be particularly critical of such sources until improving your understanding of what constitutes good practice in your field of study. (See the library's page on evaluating Internet resources.)

    The following list details acceptable and unacceptable practices:

    I am obligated to refer all suspected cases of academic dishonesty to Georgetown's Honor Council. If you have any questions about these policies or how they apply, please discuss such concerns during class, during office hours, by e-mail, or using the seminar discussion list.

    In my experience, students at Georgetown do honest work. The small percentage of students who have submitted someone else's work as their own did so because they did not manage their time wisely. It is important to start working on assignments when I post them, to ask question in class, and to seek help from me. Indeed, as a Georgetown student, it is important to develop and strengthen your knowledge and skills of good academic practice. (Oxford has an excellent page on developing good academic practice.)

    Policies dealing with late projects, cell phones, attendance, and inclement weather.

    Assignments and Grading

    1. input grade
    2. if grade >= 94 then
    3.    return "A"
    4. if grade >= 90 then
    5.    return "A-"
    6. if grade >= 87 then
    7.    return "B+"
    8. if grade >= 84 then
    9.    return "B"
    10. if grade >= 80 then
    11.    return "B-"
    12. if grade >= 77 then
    13.    return "C+"
    14. if grade >= 74 then
    15.    return "C"
    16. if grade >= 70 then
    17.    return "C-"
    18. if grade >= 67 then
    19.    return "D+"
    20. if grade >= 64 then
    21.    return "D"
    22. else
    23.    return "F"

    Materials: Readings, Videos, and Links


    Week Date Topics and Materials
    1 9/3 Introduction, Orientation, The Ignatian University (Connor, 1990; Perez-Hernandez, 2014; Mueller and Oppenheimer, 2014)
    2 9/8 Writing and Citing, Doing Honest Work (Lecture Notes)
      9/10 What is AI? (Lecture Notes; Stanley Video; Watson Video)
    3 9/15 Digital Logic, NAND (Lecture Notes; Buffington's Videos)
      9/17 Combinatorial Circuits (Lecture Notes; Buffington's Videos)
    4 9/22 Languages, Algorithms (Lecture Notes)
      9/24 Algorithms, Turing Machines (Lecture Notes)
    5 9/29 Propositional Logic (Priest, 2001), Logical Reasoning, Resolution Proof Using If-Then Rules (Lecture Notes)
      10/1 Predicate, Modal, and Temporal Logics (Priest, 2001)
    6 10/6 Probability (Priest, 2001)
      10/8 Inverse Probability, Bayes' Rule, Decision Theory (Priest, 2001)
    7 10/13 Learning Rules (Lecture Notes)
      10/15 Learning Probabilities (Lecture Notes)
    8 10/20 Stanley (Thrun, 2010)
      10/22 Midterm
    9 10/27 Biological Neurons and Networks (O'Shea, 2006)
      10/29 Artificial Neurons and Networks (O'Shea, 2006; Merolla, et al., 2014; Lecture Notes)
    10 11/3 Writing-Assignment Discussion, Brain (O'Shea, 2006);
      11/5 Guest Lecture, Neuroscience (O'Shea, 2006); Movie Night
    11 11/10 Mind and Consciousness (Blackmore, 2005)
      11/12 Guest Lecture, Neuroethics
    12 11/17 Can Machines Think? (Turing, 1950)
      11/19 Guest Lecture, Philosophy
    13 11/24 Writing-Assignment Discussion, Symbol Systems (Newell and Simon, 1976)
      11/26 Thanksgiving
    14 12/1 Searle's Chinese Room (Searle, 1980)
      12/3 Guest Lecture, Law, Policy, and Technology
    15 12/8 The Big Picture: Reflection and Discussion (Russell and Norvig, 2010, Ch. 26; Bringsjord, et al., 2001)

    Other Interesting Links

    Copyright © 2019 Mark Maloof. All Rights Reserved. This material may not be published, broadcast, rewritten, or redistributed.