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Georgetown University
Graduate School of Art and Sciences
Communication, Culture & Technology Program

CCTP-607 "Big Ideas" in Technology (and What They Mean): AI to the Cloud
Professor Martin Irvine

Spring 2018 [New course: in development]

Description:

This course will provide a conceptual and design-oriented introduction to some of the "big ideas" in technology that everyone needs to know. The course is especially designed for students from non-technical backgrounds, but all students can benefit from learning the methods in this course.

In launching this new course, we will build much of the "content," questions, and problems to be studied from the interests and background levels of students in the course. The topics and readings are not yet determined for many of the syllabus weeks. We will allow many questions and starting points to emerge from the active engagement of students.

Every cycle of “new” technology brings responses of hype, hope, and hysteria. In this course, we will go beyond the hype and learn what our current “Big Ideas” in technology are really about: What is “artificial intelligence” or “AI”? What are “algorithms” and how are they designed? What is “The Cloud”? What is “Big Data” and what do we mean by “Data Analytics” and “Data Visualization”? What is “the Internet of Things (IoT)”? What are “smart” appliances (home security, media services, shopping)? Every day, the news media, popular discourse, marketing, and advertising are full of statements about these technologies, but treated as unfathomable “black boxes” and corporate branded products. This course will provide the methods, key concepts, and analytical tools for understanding the “Big Ideas” and what they mean.

We will combine four main methods and approaches for an interdisciplinary deblackboxing method. This integrated method works to reveal how everyone, not just technical people, can understand the meaning of the “Big Ideas” behind our technologies and find ways to participate in how they can be used. We will will combine:

(1) “Systems Thinking” to understand how a specific technology is part of a larger, interrelated system (for example, computing systems, kinds of software, networks, and social contexts);

(2) “Design Thinking” for uncovering how and why certain technologies are designed the way they are, including the history of designs and the consequences of design choices;

(3) “Semiotic Thinking” for understanding these technologies as artefacts of human symbolic thought, which includes (a) understanding how sign systems and media can be digitally encoded as “information” or data, (b) the relationship between abstract models (e.g., algorithms, code, data models) and how (or whether) they can be implemented technically, and (c) understanding the social meanings, values, and purposes of the technical systems;

(4) the “Ethics and Policy” viewpoint for evaluating the social consequences of design choices in the large-scale adoption of certain kinds of technologies, and for analyzing proposals for ethical decisions and governmental policy.

The course will include individual and group writing assignments for students to learn how to “de-hype” popular discourse and marketing language, and use our methods and analytical tools for better ways of interpreting these technologies and explaining them to others. Requirements will also include in-class group projects and a final research project on one of the “Big Ideas.”

Classroom location: Car Barn 318

Course Format

The course will be conducted as a seminar and requires each student’s direct participation in the learning objectives in each week’s class discussions. The course has a dedicated website designed by the professor with a detailed syllabus and links to weekly readings and assignments. Each syllabus unit is designed as a building block in the interdisciplinary learning path of the seminar, and students will write weekly short essays in a Wordpress site that reflect on and apply the main concepts and approaches in each week’s unit. Students will also work in teams and groups on collaborative in-class projects and group presentations prepared before class meetings.

Grades:

Grades will be based on:
(1) Weekly short writing assignments (in the course Wordpress site) and participation in class discussions (25%). Weekly short essays must be posted by 10:00AM for each class day so that students will have time to read each other's work before class for a better informed discussion in class.
(2) A group project (25%) to be presented in class after the 10th week. Presentation date and group rosters will be determined by the 10th week of the seminar.
(3) A final research project written as a rich media essay or a creative application of concepts developed in the seminar (50%). Due date: one week after last day of class.
(Final projects will be posted on the course Wordpress site, which will become a publicly accessible web publication with a referenceable URL for student use in resumes, job applications, or further graduate research) .

Professor's Office Hours
Wed. and Thurs. 12:00-2:00, and by appointment. I will also be available most days after class meetings.

Academic Integrity: Honor System & Honor Council
Georgetown University expects all members of the academic community, students and faculty, to strive for excellence in scholarship and in character. The University spells out the specific minimum standards for academic integrity in its Honor Code, as well as the procedures to be followed if academic dishonesty is suspected. Over and above the honor code, in this course we will seek to create an engaged and passionate learning environment, characterized by respect and courtesy in both our discourse and our ways of paying attention to one another.

Statement on the Honor System
All students are expected to maintain the highest standards of academic and personal integrity in pursuit of their education at Georgetown. Academic dishonesty, including plagiarism, in any form is a serious offense, and students found in violation are subject to academic penalties that include, but are not limited to, failure of the course, termination from the program, and revocation of degrees already conferred. All students are held to the Georgetown University Honor Code: see https://honorcouncil.georgetown.edu/system.

Instructional Continuity
In the event of a disruption of class meetings on campus from inclement weather or other event, we will continue the work of the course with our Web and online resources, and will arrange for online discussions and meetings with the professor by using the Google Hangout interface in our GU Google apps suite. I am also always available via email, and respond to student messages within a few hours or less.

Books and Resources

This course will be based on an extensive online library of book chapters and articles in PDF format in a shared Google Drive folder (access only for enrolled students with GU ID). Most readings in each week's unit will be to pdf text links in the shared folder, or to other online resources in the GU Library.

Required Books:

  • Peter J. Denning and Craig H. Martell. Great Principles of Computing. Cambridge, MA: The MIT Press, 2015. 
  • Luciano Floridi, Information: A Very Short Introduction. Oxford, UK: Oxford University Press, 2010.

Recommended Books:

  • Jerry Kaplan, Artificial Intelligence: What Everyone Needs to Know. Oxford University Press, 2016.

Course Online Library (Google Drive: GU student login required)

University Resources

Using Research Tools for this Course (and beyond)

  • Required: Use Zotero for managing bibliography and data for references and footnotes.
    Directions and link to app, Georgetown Library (click open the "Zotero" tab).
    You can save, organize, export and copy and paste your references with formatted metadata into any writing project.

AI Issues: News and Research Sources

Stanford University: Human-Centered Artificial Intelligence Initiative

 

Stanford "100 Years of Artificial Intelligence" Study Site

Pew Research: Public Attitudes toward Algorithms, AI and the Future

Professional Computing and AI Sources (ACM)

Orientation to Learning Goals of the Course:

  • Establishing some useful definitions, distinctions, and scope of subject matter included in "Artificial Intelligence."
  • What are we talking about when we talk about "Artificial Intelligence" and "Machine Learning"?
  • How to apply methods from design thinking, key principles of computing, semiotics, and cognitive science to deblackboxing AI, ML, "Data," and "The Cloud."

Old computer science jokes:

  • "Artificial intelligence is the science of getting computers to do what they do in Hollywood SF movies."
  • "AI works in practice, but not in theory."

Course Introduction: Requirements and Expectations

  • Format of course, requirements, participation, weekly assignments, projects, outcomes.
  • Classroom rules: how to use PCs and mobile devices: no social media or attention sinks during class.

Introduction to the Topics and Key Concepts of the Course (Presentation)

 

Learning Objectives:

  • Establishing some useful definitions, distinctions, and scope of subject matter included in "Artificial Intelligence."
  • Design concepts in computing and AI.
  • Major schools of thought, traditions, and applications of AI.
  • Reviewing some recent accessible descriptions and definitions of AI for common assumptions.
  • Introduction to concepts and methods for critical analysis of assumptions: what are some of the unexpressed ideologies and commitments in AI and views of computing technology?
  • Introduction to major applications and uses of "AI": language processing, data analytics, image analysis, pattern recognition.

Readings: Orientations

Weekly writing and reflection (link to Wordpress site)

  • Read the Instructions for the weekly writing assignment.
  • Describe 1-2 common topics or themes in these introductory books. What questions came to mind as you were reading? What did you find helpful in "deblackboxing" AI, where do the approaches and discourse continue to totalize AI and computers as "things" rather than human designs?

Learning Objectives and Main Topics:

  • Introductory steps in understanding the key concepts and design principles in computing and computing systems.
  • Learning the basics of design thinking for understanding computer systems (large or small).
  • Learning the foundational principles of computation and the development of AI as a multi- and inter-disciplinary field that combines computer science with philosophy, logic, mathematics, and cognitive science.
  • Learning how to describe computing and AI/ML systems: what is and what isn't AI or ML?

Readings

  • Peter J. Denning and Craig H. Martell. Great Principles of Computing. Cambridge, MA: The MIT Press, 2015. Chapters 1-2.
    • We will be referring to the computing principles outlined in this great book throughout the course. Read it and re-read it.
  • Jerry Kaplan, Artificial Intelligence: What Everyone Needs to Know. Oxford University Press, 2016. Chaps. 1-3.
  • Further readings to be determined.

 

Learning Objectives and Main Topics

  • Where did the key ideas in "AI" come from?
  • How do people in the "Machine Learning" (ML) practice community define ML in relation to AI in general? Why does it matter?
  • AI and ML depend on computational methods, but what deeper ideas motivate the history and development of AI?
  • What are the main unexpressed assumptions and ideologies in the practice of AI/ML, i.e., as a professional community of practice?
  • What is AI/ML about beyond operational and instrumental "success"?
  • Why do the logical, mathematical, and symbolic principles of AI/ML and computation work (as well as they do) despite few people asking what human capabilities are implemented in "cognitive technologies"?

Readings:

  • Peter J. Denning and Craig H. Martell. Great Principles of Computing. Cambridge, MA: The MIT Press, 2015. Chapters 4-6. (Go as far as you can: the more you understand these key principles, the less AI and ML seem like mysterious, unknowable black boxes.)
  • Margaret A. Boden, AI: Its Nature and Future (Oxford: Oxford University Press, 2016).
    • Finish book.
  • Ethem Alpaydin, Machine Learning: The New AI. Cambridge, MA: The MIT Press, 2016.
    • Finish book.
  • Stuart Russell and Peter Norvig, Artificial Intelligence: A Modern Approach, 3rd ed. (Upper Saddle River: Prentice Hall, 2010). (Selections: Chaps. 1-2).
    • This is the mostly used (and as the authors say, most downloaded) book on AI in computer science fields.
    • Review the Contents, and read chap. 1. This is a classic introduction written for computer science students. It is based on the logic and probability models for AI, still assumed in all computing and AI, but more attention is now given to "Neural Net" and "Deep Learning" methods, which are forms of probabilistic "learning" models. Do you best on this background and we will discuss in class.
    • This book has a companion Website hosted by the Computer Science Dept. at Berkeley. Note the resources available for study.

 

Learning Objectives and Topics:

In this week and next, students will learn the foundational concepts of "information" and "data" as used in digital electronics, computing, communications, and "data processing/data analysis."

All forms of computation and AI are based on "information [or "data"] processing" and "data analysis." But what is information (in the scientific and computational sense)? Instead of just eliding over this topic (and using vague, ordinary discourse conceptions), we need to understand two central concepts that are at the core of the design principles for computation, digital electronics, and AI/ML applications: information and/vs. data?

  • How can we define and understand the concept of "information" in computer science and all digital media and communication applications?
  • What is the difference between information and data? Why does it matter to get clear on these concepts and how they used?
  • Nutshell definitions: information is the technique of structuring physical, material signals or substrates (e.g., radio waves, binary digital electronic states in a physical medium like memory cells or Internet packets).
  • Data implies further structure: units or computational "objects" by data type (e.g., digital image: patterns of visual units in a matrix R,G,B color values; text characters ["strings" in programming languages and software]; types of numbers [integers, rationals = "floating point" numbers]). Classified, categorized, labeled "data objects" as organized in data bases. We also have "unstructured data" (a problem term, but means heaps of text strings or streams of combined data types like that in text messages, social media streams, email, and blog posts.)

Readings:

  • Martin Irvine, "Introduction to the Technical Theory of Information" (Information Theory + Semiotics)
  • Luciano Floridi, Information, Chapters 1-4. PDF of excerpts.
    [For background on the main traditions of information theory, mainly separate from cognitive and semantic/semiotic issues.]
  • James Gleick, The Information: A History, a Theory, a Flood. (New York, NY: Pantheon, 2011).
    Excerpts from Introduction and Chapters 6 and 7.
    [Readable background on the history of information theory. I recommend buying this book and getting as deeply into the issues that Gleick explains as possible.]
  • Peter J. Denning and Craig H. Martell. Great Principles of Computing, Chap. 3, "Information."

 

Learning Objectives

We will continue the study of "data" as defined and used in computing, AI, and all data sciences. Both "information" and "data" are used in general and undifferentiated was in ordinary and popular discourse, but to advance in your learning for AI and all the data science topics that we will study, we all need to be clear on the specific meanings of these terms and concepts.

Readings:

  • John D. Kelleher and Brendan Tierney, Data Science (Cambridge, Massachusetts: The MIT Press, 2018).

 

Learning Objectives

Readings:

  • To be determined by developing the seminar

Learning Objective and Main Topics:

Readings and Applications

  • Further into algorithms and understanding everyday applications (Web search, media recommendations, bank accounts, Facebook news feeds)
  • Speech recognition systems (Siri, Alexa, Google speech commands): the design principles behind the products
  • Google Translate and ML Translation Models

Learning Objectives and Main Topics:

The wide deployment of AI applications (ML, Deep Learning, etc.) by businesses and governmental entities has provoked important ethical questions about AI and all uses of data. We will explore some of the main issues and learn via case studies of AI applications, both in everyday life and behind-the-scenes "invisible" technologies that effect privacy, security, surveillance, and human agency.

Readings

Cases:

  • Face recognition
  • Autonomous ("Self-Driving") Vehicles
  • "Bias" in machine learning algorithms
  • Data, business models, privacy, personal identity

Learning Objectives:

Learning the design principles and main architecture of Cloud Computing: "Software as a Service," "Platform as a Service," and major principles of massively distributed, Web-deliverable computing services, databases, and data analytics.

Readings

Learning Objectives and Main Topics:

Readings:

  • To be determined by developing the seminar.
  • Tarleton Gillespie, Custodians of the Internet: Platforms, Content Moderation, and the Hidden Decisions That Shape Social Media (New Haven: Yale University Press, 2018). (selections)
  • Siva Vaidhyanathan, Antisocial Media: How Facebook Disconnects Us and Undermines Democracy (New York: Oxford University Press, 2018).

 

Learning Objectives:

Readings:


Learning Objective and Main Topics:

  • Topics and applications to be determined by students.

 

Readings:

  • To be determined for case studies and applications.