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

CCTP-607 Leading Ideas in Technology: AI to the Cloud
Professor Martin Irvine

Spring 2021: Online

This course will provide a conceptual and design-oriented introduction to some of the leading ideas in computing and data technologies 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.

The main learning objectives are gaining conceptual competencies and critical thinking skills for understanding, interpreting, and explaining the key design principles for (1) contemporary, networked computing systems, (2) kinds of data and data processes, (3) "artificial intelligence" (AI) and "machine learning" (ML), (4) Cloud Computing and "Big Data" systems, and (5) how these technologies are now combined and integrated. With the "design principles" approach, students will understand why certain technologies are designed the way that they currently are, and be able to distinguish between design and implementation, that is, the difference between product implementations and more universal design principles that make them possible.

Framework and Main Approaches

Every day, the news media, popular discourse, marketing, and advertising are full of statements about these technologies, but they are treated as unfathomable “black boxes” and corporate-branded products. To reverse this "blackboxing," this course will provide the methods, key concepts, and analytical tools for understanding the designs of the systems, devices, and interfaces that we use every day.

We will combine four main methods and approaches for an interdisciplinary deblackboxing method (exposing the designs and concepts behind what we can't observe when using a technology). This integrated method works to reveal how everyone, not just technical people, can understand the meaning of the ideas behind our technologies and find ways to participate in how they can be used. We 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 use our methods and analytical tools for better ways of interpreting these technologies and explaining them to others. By the end of the course, students will have achieved a competency in design thinking and systems thinking as applied to the technologies we study, and will be able to work with others in providing "de-blackboxed," clear explanations and "translations" of design principles for communicating across technical and non-technical communities.


Grading will be based on weekly assignments, class participation, and group projects (50%), and a final research project (50%).

This will be an online course for Spring 2021.

Full description of Georgetown Policies, Student Expectations, and Student Support Services: consult and download the GU syllabus document (pdf online).

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.

Students will participate in the online course by using a suite of Web-based online learning platforms and etext resources:

(1) A custom-designed Website created by the professor for the syllabus, links to readings, and weekly assignments: [this site].
(2) An e-text course library and access to shared Google Docs: most readings (and research resources) will be available in pdf format in a shared Google Drive folder prepared by the professor. Students will also create and contribute to shared, annotatable Google Docs for certain assignments and dialogue (both during synchronous online class-time, and working on group projects outside of class-times).
(3) A course discussion forum in WordPress for weekly writing assignments.
(4) Zoom video conferencing for synchronous class meetings, group discussion, and virtual office hours.


Grades will be based on:

  • 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.
    • Group discussion presentations as part of weekly assignments: You will have 2-3 group discussions to prepare for presentation in class. Choose a group "scribe" to collect together your notes (and any images, diagrams, etc. you want to use) and post in one person's "author" post. Your group post should capture the outcome of what you studied and discussed, with the key points that you think are important or interesting to present in class. List the names in your group at the top of the post. Everyone will get a group grade for that week's class discussion.
    • On weeks that you do a group study topic, that post will count as your weekly writing assignment. No individual post, just one collective post. Focus on your own discussion and what you would like to present in class, and we will have a "cross-group" discussion on the topics in class.
  • 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
To be announced. I will also be available most days after class meetings.

Georgetown Policies

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

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 video conference 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.

For all Georgetown Policies, Student Expectations, and Student Support Services:
consult and download the GU syllabus document (pdf online).

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:

  • Alpaydin, Ethem. Machine Learning: The New AI. Cambridge, MA: The MIT Press, 2016.
  • 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:

  • Newport, Cal. Deep Work: Rules for Focused Success in a Distracted World. New York: Grand Central Publishing, 2016.
  • ———. Digital Minimalism: Choosing a Focused Life in a Noisy World. New York: Portfolio, 2019.
  • Marcus, Gary, and Ernest Davis. Rebooting AI: Building Artificial Intelligence We Can Trust. New York: Pantheon, 2019.
  • Mitchell, Melanie. Artificial Intelligence: A Guide for Thinking Humans. New York: Farrar, Straus and Giroux, 2019.
  • Russell, Stuart. Human Compatible: Artificial Intelligence and the Problem of Control. New York: Viking, 2019.

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 Research and Information Sources

News and Research Sources

Stanford "100 Years of Artificial Intelligence" Study Site

AI, Ethics, and Human-Centered Design: University Research Centers

Pew Research Projects: 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 key concepts for "Data," "Artificial Intelligence," "Machine Learning," "Cloud Computing," and other key terms.
  • 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."

Course Introduction: Requirements, Expectations, Orientation

  • Format of course, requirements, participation, weekly assignments, projects, outcomes (see above).
  • Using our Web-based syllabus, discussion platform (WordPress), online etext library (shared Google Drive).
    • Why I use custom-designed websites for courses: teaching philosophy, instructional design, student access to materials.
  • Classroom rules: how to use PCs and mobile devices: no social media or attention sinks during class.

Using Research Tools for this Course (and beyond)

Shared Google Doc for In-Class Comments during Zoom meeting sessions

  • Linked here. We will use this shared doc in class instead of Zoom chat, so that we can can a live shared document that everyone can write notes and questions in. You can insert anything relevant -- from a web page, text, image, URL link, etc. You can also leave suggestions or questions that you'd like to take up in the following week.
  • Instructions: keep a web tab with this doc open on your desktop (small window is OK) so that you can go back and forth from the Zoom class session to this file as needed.
  • I will also post questions for you to consider "off line" when we pause the Zoom session for breaks or group discussion. This doc will also give us a record of weekly discussions, ideas, and questions for the whole semester.

WordPress site for weekly writing assignments

In class: Student Introductions and Interests

  • Who are we? Backgrounds and interests to be considered in developing the course.
  • Your professor's background and research interests: where I'm coming from (c. 2020).

Introduction to the Topics and Key Concepts of the Course
(we will begin in class, then study on your own)

Film Documentaries to Reflect on (time permitting, on your own):

Main Topics and Learning Objectives:

  • Establishing useful definitions, distinctions, and scope of subject matter included in our course.
  • 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 major applications and uses of "AI": language processing, data analytics, image analysis, pattern recognition.

Readings and Learning Sources

  • Prof. Irvine, "Introduction to the Course: Key Concepts, Background, and Approaches". Read first.
    • I have synthesized an introduction to computing, design, and AI into a short essay. Download and read at least twice. Don't worry: you can get it! (We will go over all the key concepts, step by step, in the course.)
  • Video Introductions:
  • Survey of book Introductions (not for reading in full, survey for overview):
    When beginning a new field, it's useful to survey introductory books for the treatment of main topics understood as belonging to the field. Here are some good places to start. We will study topics in more detail in coming weeks.
  • Margaret A. Boden, AI: Its Nature and Future (Oxford: Oxford University Press, 2016).
    • Review Contents, and survey Chaps. 1-2 (to p. 56). We will return to these topics later; just survey the contents for this week.
  • Michael Wooldridge, A Brief History of Artificial Intelligence.(New York: Flatiron, 2020).
    • Read the Introduction and Chap. 1 (to p. 24) for this week.
  • What does "Artificial" mean as used in AI theory? Major source: Herbert Simon.
    • Herbert A Simon, The Sciences of the Artificial (Cambridge, MA: MIT Press, 1996). (excerpt). Read this short excerpt for useful meanings of the terms "artificial" and "symbol system" by one of the early founders of AI and computer system theory.
    • "Artificial" (in our AI/computing context) = (1) whatever is produced by imposing a human design; (2) an artefact (human made thing) based on human symbolic thought and sets of symbols defined for a symbolic processing system; (3) some artefacts are designed as interfaces between observers and observed environments, which are different systems from those of the observers.
  • Ethem Alpaydin, Machine Learning: The New AI. Cambridge, MA: The MIT Press, 2016.
    • Read the Preface and Chap. 1 (to p. 28).
    • What is the distinction now made between "AI" and "Machine Learning" (ML)?

Prof. Irvine, Introduction: Topics and Key Concepts of the Course (Presentation)

Film documentary (view on your own for discussion): Do You Trust This Computer? (2018)

  • Optional. Download and view. This movie is an example of how a "Hollywood" approach to AI goes: hype, myths, scare tactics, fears, anxieties. Not much actual knowledge.

Shared Google Doc for Comments during Zoom meeting sessions

Weekly writing assignment (link to Wordpress site)

  • Read the Instructions for the weekly writing assignment and how to "Post" in WordPress.
  • This week provides a "top level" overview of the topics and main approaches in the course and a summary view of how AI is presented in standard books. In the following weeks, we will study the essential conceptual and technical background to our current "leading technologies" by following the key design principles that have enabled where we are today.
  • For this first week's writing, simply post some notes and questions from the readings for further discussion in class. Can you describe 1 or 2 common topics or themes in these introductory readings? What questions came to mind as you were reading? What would you like to learn more about or have better explained?

Learning Objectives and Main Topics:

  • To understand how and what we can design AI/ML and large-scale Cloud systems to do, students need to understand the basic architecture of digital computing systems from the design and conceptual viewpoint. This is an important learning step in our deblackboxing goals.
  • Your learning goal for this week is to begin clarifying computing design principles for yourself, so that you can make the next learning steps in understanding how and why all our contemporary systems -- from digital media to AI and Cloud systems -- are built up in scalable layers or levels using many kinds of interconnected subsystems (lower levels systems serving the whole system). All the combined levels and modules of a computing system -- small, large, or invisibly embedded -- depend on the fundamental design principles for digital computing and digital data. There is no magic, no mysteries -- only human design for complex systems!
  • You will also learn some of the specific terminology and computational methods used in the field of AI/ML today, specifically in the "machine learning" approaches (statistical, probabilistic, graph-based analysis of data for patterns and making predictions for new analyses).
  • The best way to approach what AI/ML means for those working in the field is as an ongoing research program, a quest to discover what can and cannot be automated for human interpretability or delegating actions with the tools in computing systems. AI is not an "it," a thing. AI/ML represents many design communities and philosophies, and many attempts at implemented designs in computing and digital data services and products. Don't confuse commercial products (digital "assistants" with speech recognition, "machine translation," face recognition, consumer preference predictions/recommender systems, etc.) with what the design principles enabling AI are.
  • With the introductions to system design and AI/ML key concepts (this week and in following units), you will also be prepared for studying the issues in ethics and policy, and the importance of the recent movement in Computer Science and Policy fields for "explainable AI" (disclosable, de-blackboxed). The most powerful ethical weapon we have for deblackboxing and explaining AI/ML and the Cloud is the truth: learning true descriptions based on design principles and key concepts that can be made accessible for everyone.

Readings and Video:
Fundamentals of Computer System Design Underlying AI and ML

  • Prof. Irvine, (Video) "Introduction to Computer System Design"
    • I made this video for CCT's Intro course (CCTP-505), but it is applicable for any introduction to the topic. You can also follow the presentation in a Google Slides version.
    • (Other videos in my "Key Concepts in Technology" series may be useful for you.)
  • Peter J. Denning and Craig H. Martell. Great Principles of Computing. Cambridge, MA: The MIT Press, 2015. [Also in pdf.]
    • Chapters 1-2 (especially chap. 2 for background); if you can, review (and re-read later) chaps. 3 and 4.
    • We will be referring to the computing principles outlined in this great book throughout the course. Even though the book is for non-specialists, much may be new to you, and will take time for the concepts to become yours to think with. That's normal and OK. Read it and re-read it as we progress through the course.
    • This book is also valuable for getting clear, precise meanings of all the technical terms in computing and data science. These terms will be our "conceptual vocabulary."
  • Ethem Alpaydin, Machine Learning: The New AI. Cambridge, MA: The MIT Press, 2016.
    • Read chaps. 2-3, focus on "Pattern Recognition."
  • John D. Kelleher, Deep Learning (Cambridge, MA: MIT Press, 2019).
    • Read Chap 1 (Introduction) and Chap. 2 (Conceptual Foundations)
    • This is a great, up-to-date book, supplementing Alpaydin (above). Do your best to focus on the design principles, and if you can't follow the math, you can follow how the math is designed to be implemented in computational processes.

Current Issue in Computer Science Ethics: "Explainable / Interpretable AI and ML"

    • ACM Video: Techniques for Interpretable Machine Learning
    • This short video introduces one approach that communities in computer science are taking to deblackbox AI/ML. You can see that "explainability" and "interpretability" depend on communicating the truth about computer systems and algorithm design. You will learn more in a few weeks how using complex ML layered networks (graphs of combined computations) can generate unpredictable results that require further redesign. We will follow up on these issues in a few weeks.

Shared Google Doc for Comments during Zoom meeting sessions

Weekly writing assignment (link to Wordpress site)

  • Continuing from last week, consider one or two of the computer system design principles (from the video and Denning reading) that help you "deblackbox" how and why computers embody these specific kinds of designs. In your thinking, try out a way to connecting the "bottom up" system design approach with a key concept in the Alpaydin and Kelleher readings. Does working through the main principles and learning precise definitions of terms help you to "deblackbox" what seems closed and inaccessible? Express all the questions that come up for you that we can explain in class.

Learning Objectives and Topics:

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

The term "information" is used in many ways, even within computer and "information" sciences, so we need to close off the whole "word fog" and focus on what the electrical engineers and designers of digital binary computer systems use this term to describe and define. We can understand the technically precise meaning of "information," just like we can learn what "matter" means in physics, as opposed to many ordinary language uses of the word.

As all the introductions to computing and AI will tell us, all forms of computation and AI are based on "information [or "data"] processing," encoded in various levels of symbol representations (for data, code, algorithms, programs, etc.). Instead of just eliding over this topic (and using vague, ordinary discourse conceptions), we need to understand two central concepts at the core of the design principles for computation and AI/ML applications: what are the design principles for information and data, and how we can keep the technical meanings of the terms clear, distinct, and useful to think with in understanding all our complex systems.

  • Capsule definitions (further explained in first reading below):
    • Information (in the electrical engineering and computer science context): for digital electrical signals, "information" is represented as a discrete unit (of time + energy + material location) that can be measured and controlled (designed, engineered) for imposing regular and predictable patterns in physical electronic substrates. The binary two-state system can be engineered to be the most regular, predictable, and interpretable. In the binary system, the binary unit ("bit") is an "information potential" used as a "value placeholder" for registering a "state" (one unit of time + energy). A bit, when "read" in a system, tells us what that unit place for a "one or the other" potential registers: voltage/no voltage, there/not there, on/off, yes/no. We then map our human symbolic meanings to bit-states for everything else we can chain together and build on top of ordered sequences of bits: units representing true/false, yes/no, 1/0, +/-. "Information theory" is "designer electronics" for providing an engineering solution to a semiotic problem: how can we impose human logic and symbolic values on electronic and physical media for creating meaningful patterns to be interpreted in all kinds of designed computational processes?
    • Data is a level up in binary structure for units that can be assigned a first level of meaning. Specific sequences of bits are assigned a first level of meaning by configuring them in "bytes" (most commonly structured in powers of 2 -- 8, 16, or 32-bit bytes). The "byte" is the minimal data unit (a level up from bits understood as minimal information units). We use many levels of byte structures for all kinds of data representations -- including metadata, data about other data. Internet packets impose a further structure on bundles of bytes for network protocol transmission -- which relies on "information theory" for error-free replication of sent and received data as physical units.
    • Data, then, implies further structure and assigned meanings. Imposing levels and layers on bit units for byte structures provides us with the "code" for all the types of symbolic representations that we use. Units of bytes become treated as computational "objects" according to their data type and digital media type. For example, different kinds of numerical representations must be assigned a data type; text characters are assigned the type "string" or "text" and represented by standard "byte code" (Unicode); digital images are interpreted as data in a format of numbers for patterns of visual units in a matrix (positions in an x/y grid) of R,G,B color values that we represent in patterns of pixels.
    • Databases are designed by imposing yet other levels of meaning with techniques for classifying and categorizing sets of kinds of data into organized "data objects," over which all kinds of computations can be performed. In our era of "big data" (massive production and storage of digital data), there are many methods for what we call "labeled data" or "structured data" (kinds of data given a database structure), and also methods for dealing with "unstructured data" -- heaps of text strings or streams of combined data types like that in text messages, social media streams, email, and blog posts.


Readings (read in this order):

  • Martin Irvine, Introduction to the Technical Theory of Information (Information Theory + Semiotics) [Introduction to the key concepts in Claude Shannon's "signals transmission mathematical model" for "information" in electronic communications systems.]
  • James Gleick, The Information: A History, a Theory, a Flood. (New York, NY: Pantheon, 2011). Excerpts from Prologues and Chaps. 6-7.
    [A popular, accessible account of the background story of information theory as defined and applied in engineering. I recommend buying this book and following the issues that Gleick explains for non-technical readers.]
  • Peter J. Denning and Craig H. Martell. Great Principles of Computing, Chap. 3, "Information."
  • Video Lessons: Bits, Information, and Logic in Computer Systems

Case Study: The Information and Data Design Levels for the Internet & Web

Presentation (In-class and self-study): The Technical Design of Information (Irvine)

Shared Google Doc for Comments during Zoom meeting sessions

Weekly writing assignment (link to Wordpress site)

Referring to at least two of the readings, choose one of these topics to focus your thinking and learning this week:

  • Describe the main features of the signal transmission theory of information and why the signal-code-transmission model is not a description of meaning (the semantic, social, and cultural significance of encoded signals)? Further, why is the information theory model essential for everything electronic and digital, but insufficient for extending to models for meanings, uses, and purposes of our sign and symbol systems?
  • Think through a case study on your own: How do we recognize the difference between E-information transmitted and received (successfully or unsuccessfully) and what a text message, an email message, social media post, or digital image means? What do senders and receivers know about the transmitted data signals that isn't a physical property of the signals? How is E-information designed as a substrate or physical medium for symbolic (meaningful) structures (text, images, music, video, etc.)?

Learning Objectives and Main Topics:

How do we encode "data" in structures of "information" to create different levels and types of digital representations?

We will continue the study of "information and "data" as defined in computing, AI, and all data sciences. To advance in learning about how AI/ML techniques are applied to "data," students need to have a basic background on the specific meanings and concepts for "digital data" in the various domains of the "data sciences."

Understanding Data Types and Data Formats for AI/ML: Digital Text and Images

Most AI and ML techniques are applied to digital text data (for Natural Language Processing) and digital image data (mostly digital photos). But how are these "data types" represented in digital form so that they are "computable" (able to be interpreted and analyzed)? To do any further study of AI/ML, students need to know how text characters (for all all languages) are encoded and differentiated (in now standard byte code sequences), and how digital images are encoded in (now) standard formats that represent formulas for patterns of geometric coordinates (x/y axis "pixel maps") and color values (in a scale of Red, Green, and Blue for each pixel). These methods of digital encoding mean that both of our main data types are "computable" -- not only as we ordinarily use them in all our digital devices and PCs, but for applying mathematical and logical techniques for pattern recognition, statistical analysis, and predictions for probable future patterns in yet new and further amounts of data.


  • Prof. Irvine, "Introduction to Data Concepts and Database Systems."
  • Video lessons: How we design computer systems for packaging bytes as "data types" and kinds of digital media "file formats" (e.g., text, images, audio) for digital processing:
  • David M. Kroenke et al., Database Concepts, 8th ed. (New York: Pearson, 2017). Excerpt.
    • This is a technical (but accessible) introduction for students beginning data science studies in a computer science or information science program. The approach includes our view of Internet-accessible data.
    • You can review the contents and survey the main sections quickly. Takeaways: Traditional databases use the model of a table (in columns and rows, like a spreadsheet) for imposing a structure of types, categories, and properties or attributes to data items. This format is what is known as "structured data."
  • John D. Kelleher and Brendan Tierney, Data Science (Cambridge, Massachusetts: The MIT Press, 2018). Read Chaps.1-2 for this week.
    • This is a view of "data science" after recent ML approaches to "unstructured data" and interactive Web-interface databases.
    • Good statement:
      "One of the biggest myths is the belief that data science is an autonomous process that we can let loose on our data to find the answers to our problems. In reality, data science requires skilled human oversight throughout the different stages of the process. Human analysts are needed to frame the problem, to design and prepare the data, to select which ML algorithms are most appropriate, to critically interpret the results of the analysis, and to plan the appropriate action to take based on the insight(s) the analysis has revealed." (33-34)

Digital Text and Images: How our Two Main Data Types are Encoded

All the World's Text Data: Unicode (background in "Introduction to Data Concepts" above)

  • See Wikipedia for background on Unicode and problem of digital text encoding.
  • The Unicode Consortium (everything is open-source and international standards-based)
    • Unicode History | Unicode Technical Site [for reference]
    • Unicode Standard [click on "current version']
    • See: Code Charts for All Languages
    • UTF-8 (Unicode Transformation Format - 8 Byte Units) is the most commonly used code format, including the character encoding and graphical rendering of the Web page in your current screen display "window." [Read the background definition of UTF, then scroll down to UTF-8.]
    • Unicode Emoji (pictographic symbols) | Emoji Charts
    • [Yes! Emoji are not sent and received as images but as bytecode definitions to be interpreted in a software context. All emoji must have Unicode byte definitions or they wouldn't work for all devices, software, and graphics renderings. See the Emoji List, and Recently Added.
    • Bytecode definitions expose systems levels: data encoding formats, device-software contexts, and screen rendering software for each device's OS and graphics hardware and software are treated as separate levels in system design.
    • Current Unicode Full Emoji Chart (with all current skin tone modifications)
    • Unicode test file of all currently defined "emojis" (v.13, 2020) (with byte code, symbol, and description)
      [Note: not all emoji characters will display in your window or software context. That's why this is a "test file" with the current code to see how it will display in software that recognizes Unicode, but may not have the recent definitions.]
  • "Han Ideographs in the Unicode Standard," Yajing Hu (CCT student, final project essay)
    [This is a good essay that looks at the background of Unicode standards for Han characters, and other Asian language families. The same issues were dealt with for Arabic and many other "non-Latin" character languages.]

Digital Images as Data: Digitizing Light, Pixels, Image Formats, and Software
How are images encoded as digital data? What are the basics that everyone can understand?

Shared Google Doc for Comments during Zoom meeting sessions

Weekly writing assignment (link to Wordpress site): choose one question to frame your thoughts, and cite references to the readings and data cases

  • Your learning goal and key "takeaway" for this week is a basic understanding of what we mean by "data" in the digital computing context, how and why "data" is a level above "information" (in chunks or sequences or binary structures and how we build representational meanings with them), and how our two main "data types" (text and images) are encoded as "digital data."
  • Choose one of the data types -- digital text or images -- and explain, as far as you can, how they are encoded as for the representations that we perceive and use with software and screens. If you can, try going on to explain how and why we need use standard "formats" (precise ways of structuring and defining) like Unicode or .jpg for systems and patterns of encoding that can be interpreted across any kind of computer platform, software, or hardware. (Hint: a data standard must be independent of any specific platform, OS, software, or hardware, and all designers and manufacturers build on the common standard.) Ask questions, and we will go over things in class.

Learning Objectives and Main Topics

  • Goal: Learning the foundational principles for Pattern Recognition in AI/ML, and how basic pattern recognition techniques are used in digital image analysis.
  • Pattern Recognition (which includes Feature Detection) is the main problem domain of AI/ML: how can we design computational systems that "recognize" (can classify, categorize, or label) the regularities or invariants (= "patterns") in all kinds of data, but specifically in selected sets of data?
  • This main problem domain for application in all kinds of data has created a large multi- and inter-disciplinary field that combines computer science with philosophy, logic, mathematics, and cognitive psychology.
  • Detecting "features" and recognizing "patterns" in all the varying kinds of properties in data is also based on logical-mathematical models for establishing statistical invariance (getting better and more finely tuned approximations to properties of data that indicate patterns, what stays the same over millions of variable instances). We will never have 100% absolutely true or false feature detection and pattern recognition, but what most methods in AI and ML are designing for is getting "close enough" approximations on which decisions can be made and future states of things can be projected.

Readings and Video Introductions:

AI/ML application case study: Pattern Recognition Using Neural Networks

  • Andrej Karpathy, “What a Deep Neural Network Thinks About Your #selfie,” Andrej Karpathy Blog (blog), October 25, 2015,
    • Karpathy (now head of AI at Tesla) provides an "inside the blackbox" view of how "Convolutional Neural Networks" (the mathematical network graphs that can continually readjust the "weights" [values] between the nodes in the probabilistic calculations) can be used to do feature detection, pattern recognition, and probabilistic inferences/predications for classifying selfie photo images.
    • All "neural net" ML techniques are based on models in mathematical graphs and matrices for multiple layers of algorithms that can work in parallel and recursively (feeding outputs back in). This provides mathematical models for implementing multiple weighted statistical calculations over indexed features (the features identified, automatically or predefined). The "nodes" in the graphs represent a part of a statistical calculation with many interim results, which can be fed back into the program, and "fine tuned" for targeted output approximations. The layered graph models are encodable as linear algebra and other kinds of statistical formulas in the programming language used for the kind of data analysis being specified. All the abstract algorithms must be encoded in a runnable program.
    • By looking a little inside the "blackbox," you can also get a sense of the ethical implications of programming criteria (the parameters defined for selecting certain kinds of features and patterns) when the ML design is automatically "tuned" for certain kinds of results.

Weekly writing assignment (link to Wordpress site)

  • This week will add further "building blocks" to understanding computing and AI/ML design principles through an introduction to pattern recognition by learning about an application in face-image analysis (Karpathy's article on "neural net" analysis of "selfies"). Study the background readings, and then discuss what you find to be the key points and issues in the Karpathy article. Apply as much as you can from our learning path so far.
  • Note: In your reading and discussion this week, don't be put off by the mathematical and technical models introduced in the readings. You will discover that the mathematical and logical models for the graph matrix algorithms (which are unfortunately termed "neural nets") are all in service of our cognitive abilities for recognizing and interpreting patterns, and then making projections (predictions) based on already "learned" patterns for analyzing new, future data.

Learning Objectives and Main Topics

Students will learn the basic principles of Natural Language Processing (NLP), which includes pattern recognition techniques for both text analysis and speech recognition in AI/ML methods. AI/ML applications use a suite of computational techniques for sorting, classifying, feature extraction, pattern recognition, translation, and text-to-speech and speech-to-text.

You will learning the basics of the linguistic and computational levels of analysis and processing involved in NLP implementations (text, speech, and both). We will focus on examples of machine translation (troublesome term) and speech recognition or speech processing.

Natural Language means the human languages acquired naturally by being born in a language community, as opposed to second-order or "artificial," "formal," "languages" developed for mathematics, sciences, computing, philosophy, and other fields. Formal "languages" are usually developed with specialized terms, notation, and symbols, and are termed "metalanguages," specialized "languages" about other languages.

NLP, as combination of linguistics and computer science, is built up with many kinds of formal "metalanguages" (from logic and mathematics) for defining, describing, and analyzing instances of natural language (spoken and written) as data. Computer programming "code" is a metalanguage for converting interpretations of data into data at another level.

Readings and Video Introductions:

Examples and Implementations

Case: Exposing Limitations of Neural Net ML Methods in NLP

  • OpenAI: New ML Language Model
    • This is the new RNN model that surprised everyone by how well the algorithms could generate well-formed "fake news" from millions of data samples of news writing.
    • Since ML is designed for pattern identification and recognition, the algorithms will provide recognized patterns (because the patterns are there -- in human-composed sentences), but the fact of a pattern has nothing to do with its meaning, the relation of a pattern to its use and its contexts of interpretation (truth values, consistency in logic, beliefs).
  • Will Knight, “An AI That Writes Convincing Prose Risks Mass-Producing Fake News,” MIT Technology Review, February 14, 2019.
  • Karen Hao, “The Technology Behind OpenAI’s Fiction-Writing, Fake-News-Spewing AI, Explained,” MIT Technology Review, February 16, 2019.
    • Note the continuing reification of "AI" as an entity in journalistic discourse.

Weekly writing assignment (link to Wordpress site)

  • Using the key concepts and descriptions of the technologies in the background readings and videos, describe the design principles of one or more levels at work in one of the NLP applications above.

Learning Objectives and Main Topics:

  • What are the main design principles and implementations of AI systems in interaction interfaces for information, digital media, and Internet/Web services that we use everyday?
  • What do we find when we de-blackbox the algorithms, computing processes, and data systems used in "virtual personal assistants" (Siri, Google Assistant and Google speech queries, Alexa, Cortana, etc.)?
  • Deblackboxing the levels and layers in speech recognition/NLP applications (Siri, Alexa, etc.) by using the design principles method.

New: Online Learning Sources for AI/ML Technologies (compiled by Prof. Irvine)

  • Good lessons and sources for learning more at your own pace.

Continuing with OpenAI's GPT-3 NLP Generative Text "Transformer" System

Readings and Background on Applications

Virtual Assistant Speech Recognition Systems

  • Survey the implementations of "Virtual Assistant" speech recognition systems below, and choose one kind of system to focus on. Try to keep focused on the Computing/AI/ML design principles involved, and not on the products (which will be black boxes).

  • "Virtual Assistants" and Recommendation Systems: enhancement and/or surveillance?
  • Amazon Lex: Amazon's description of the Natural Language Understanding (NLU) service that Amazon uses (for Alexa and product searches) and also markets as an AI product for other companies on the Amazon Web Services (AWS) Cloud platform.
  • Google Assistant: Wikipedia background [mostly business and product information]
    • Google Assistant: Google Homepage [company info and app integrations]
    • Google's Patent Application for "The intelligent automated assistant system" (US Patent Office)
      [Scroll down to read the description of the "invention", and/or download the whole document.]
      [Patents are good sources for the design principles and a company's case for why their design (intellectual property) is distinctive and not based on "prior art" (already known and patented designs).]
    • Abstract of the system in the patent (same general description as for Siri):
      "The intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact."
    • Google Assistant for Developers (Google)
  • Apple Siri: Wikipedia background [note prior development of the system before Apple]
    • Apple's Patent Application for "An intelligent automated assistant system" (US Patent Office, 2011)
      [Scroll down to read the description of the "invention", and/or download the whole document.]
      [Note the block diagrams for the layers of the system.]
      [There are now many patent lawsuits going on for "prior art" in Siri and speech recognition systems.]
    • Abstract of the system in the patent (same general description as Google):
      "An intelligent automated assistant system engages with the user in an integrated, conversational manner using natural language dialog, and invokes external services when appropriate to obtain information or perform various actions. The system can be implemented using any of a number of different platforms, such as the web, email, smartphone, and the like, or any combination thereof. In one embodiment, the system is based on sets of interrelated domains and tasks, and employs additional functionally powered by external services with which the system can interact.
    • Apple's WIPO Patent, "Intelligent assistant for home automation" (2015).
    • Apple Machine Learning Journal (1/9, April 2018): "Personalized 'Hey Siri'."
    • Apple Machine Learning Journal: "Hey Siri..." [The design of Apple's "Neural Net" Speech Recognition system]
    • An inside view of how app developers can use Siri in other apps (Apple Developer).
    • Siri uses Wolfram Alpha for "knowledge base" (or "answwer engine") answers

Presentation (for discussion in class and study on your own):
De-blackboxing the Virtual Assistant, Speech Recognition/NLP and Database System

Critique of Virtual Assistants and Recommendation Systems

Weekly writing assignment (link to Wordpress site)

  • Using the background from this week and the concepts that you've learned so far, choose one of the implementations of the speech recognition "virtual assistant" applications above and "de-blackbox" as many of the levels and layers in the design that make the system work in the way we observe it to work. Again, try to stay focused on uncovering the underlying design principles and implementations of NLP, and not on the product or company brand. You can apply what you've learned about AI/ML speech recognition/NLP systems for creating an interface "bridge" to the data and media services invoked behind the scenes. This discovery of levels and system modules will give a clearer picture of the complex system design.
  • The deblackboxing will at first be difficult because there is not much open source published information on the systems. So we need to use our conceptual design principles tools to "reverse engineer" how the systems must be designed to work they way they do. If you get stuck, you can use "reverse engineering" as a thought experiment (i.e., make a new version of a designed thing, not by using the plans of a branded product but creating a model of the required technologies that must be combined and managed to make it work): if we were going to design and build a service with a user-facing app like Siri or Alexa, what would it take to do it? Inside the black box: what unobservable (invisible) layers of technologies and design principles are required to explain what we do observe?

Learning Objectives and Main Topics:

This unit will provide a top-level overview of the ethical, legal, and government policy issues for the development and application of AI/ML in services, products, and "black boxed" applications.

The wide deployment of AI applications (ML, Deep Learning, Face Recognition, Speech Recognition systems) 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.

Frameworks for Study, Research, and Advocacy:

The starting point for ethics in relation to any technical system is a commitment to truthful, de-blackboxed, open and accessible descriptions, definitions, and explanations (see the Floridi and Cowls reading below). There is no magic, but much of the discourse for AI/ML is hype that keeps everything blackboxed and working like "magic" in the service of commercial (corporate) interests. Any definition or conception of an "ethics for AI" must begin with truthful definitions and descriptions. Here's where our three core methods for truthful de-blackboxing can come to the rescue:

  • The Complex Systems View: How is/can human agency for Ethics and Social Responsibility be included, modeled, or introduced in the design and implementation of AI/ML processes in the whole design complex systems from the beginning (not a "fix" or add-on when something goes wrong)?
  • The Design View of Complex Systems: How can the design view (and consequences for design) of AI/ML systems be communicated? AI/ML is always implemented in designs that must function in interdependent Socio-Technical Systems (composed of many Subsystems, Modules, and Levels [Layers]). Can the "Explainable" and "Intelligible" AI movement be the context for this communication?
  • The Semiotic Systems View: Computational and AI Systems are designed Semiotic-Cognitive Artefacts, for which human agency and collective human ownership needs to be clearly revealed, explained, and continually reclaimed. Explaining the underlying facts about computing systems must be part of the ethics of truth-telling about any computational technology. How can these truths be communicated so that more people can claim ownership over the designs and implementations of AI/ML, and become involved in collective agency for establishing an ethics of technology?

The Huge National and International Challenges

Though the above points are viable and well-understood directions in research and provide frameworks for debate, the major challenges for AI/ML ethics are political: how can any collective decision about ethics be put into practice in policy, regulation, and law (national and international).

Almost everything in computing (hardware, software) and data communications (Internet) is unregulated, and subject only to industry ecosystem standards and international standards agreements. Computing and data (digital media, information protocols, etc.) are global and international.

Technologies and specific versions of consumer products have no legal or judicial institution, except for intellectual property disputes. Software products, computing components, data communications hardware and software (Internet, wireless), and online transaction systems are based on many levels of international agreements and standards. How can AI/ML models, algorithms, and stored data used for commercial or governmental purposes be "controlled" or "regulated" at the level of ethics, policy, and law?

Video Introduction on AI, Ethics, and Society

Intro Readings and Backgrounds

Ethics, Policy, and Law: Industry, Corporate, and Governmental Issues

Cases: Ethics of Face Recognition Technologies and Use of Personal Data

For Further Discussion in Class: Other Applications being discussed for ethical issues:

  • "Bias" in machine learning algorithms used for business applications; personal data and privacy.
  • AI/ML and surveillance
  • Social Media business models: How can we analyze and critique the AI + data systems in business applications designed to maximize user attention, market personal data, and turn transactions (sales)?

E-Text Library for Further Research (Shared Drive: Data, Ethics, AI)

Weekly writing assignment (link to Wordpress site)

  • There are many ethical, political, and ideological issues surrounding AI/ML applications. From the readings and examples of cases, identify what you think are 1 or 2 important issues and explain why. Use your deblackboxing skills to critique what is being discussed, and also to untangle and expose false, alarmist, or misunderstood ideas about AI, data, and computing systems.

Learning Objectives:

Learning the basic design principles and main architecture of Cloud Computing:

  • "Software as a Service" (SaaS)
  • "Platform as a Service" (Paas)
  • "Infrastructure as a Service" (Iaas)
  • "Virtualization" of server systems, scalable "on-demand" memory

"The Cloud" architecture: a model for integrating the "whole stack" of networked computing.

The design principles for Cloud computing systems extend the major principles of massively distributed, Web-deliverable computing services, databases, data analytics, and, now, AI/ML modules. Today, a simpler question for the ways we use the Web and Internet data might be "what isn't Cloud Computing"?

The term "Cloud" began as an intentional, "black box" metaphor in network engineering for the distributed network connections for the Internet and Ethernet (1960s-70s). The term was a way of removing the complexity of connections and operations (which can be any number of configured TCP/IP connection in routers and subnetworks) between end-to-end data links. Now the term applies to the many complex layers, levels, and modules designed into online data systems mostly at the server side. The whole "server side" is "virtualized" across hundred and thousands of fiber-optic linked physical computers, memory components, and software modules, all of which are designed to create an end product (what is delivered and viewed on screens and heard through audio outputs) that seems like a whole, unified package to "end users."

An Internet "Cloud" Diagram: What happens "inside" a Cloud is abstracted away from the connections to and from the Cloud: only the "outputs" and connections to the Cloud as a system need to be known.

Learning the design principles of "Cloud Computing" is an essential tool in our de-blackboxing strategy. Many of the computing systems that we are studying -- and use every day -- are now integrated on platforms (a systems architecture for data, communications, services, and transactions) designed for convergence (using combinatorial principles for making different systems and technologies interoperable) for data, information, and AI/ML data analytics. For organizations and business on the supply-side of information and commercial services, subscribing to a Cloud Service provides one bundle or suite of Web-deliverable services that can be custom-configured for any kind of software, database, or industry-standard platform (e.g., the IBM, Amazon AWS, and Google Cloud services).

Internet-based (or Internet-deliverable, Internet-distributed) computing continues to scale and extend to many kinds of online and interactive services. Many services we use every day are now managed in Cloud systems with an extensible "stack" architecture (levels/layers) all abstracted out of the way from "end users" (customers, consumers) -- email, consumer accounts and transactions (e.g., Amazon, eBay, Apple and Google Clouds for data and apps), media services (e.g., Netflix, YouTube, Spotify), and all kinds of file storage (Google app files) and platforms for Websites, blogs, and news and information.

Readings & Video Introductions (read/view in this order)

Major Cloud Service Providers: Main Business Sites

Weekly writing assignment (link to Wordpress site):
choose one topic to focus your learning this week

  • Backgrounds for thinking and writing:
    • The Cloud system model provides ways to combine and integrate the "whole stack" of computing in the Internet/Web interactive client/server model. What was once modeled on distributed computing and processes in networked servers (considered as individual computers) is now virtualized in a design architecture across thousands of computers with high-speed connections that share processors, memory, and unlimited provision of storage, backup, and security. At any point of view (especially that of the end "user"), a Cloud system (like Amazon AWS and Google's Cloud) is a complex black box full of intentionally engineered subsystem black boxes that "abstract away" the complexity so that for a user/customer the screen+output "presentation layer" seems like a transparent, seamless, unified service. All the "back-end" processes and transactions are handled behind the scenes, and users only receive the results.
    • The Cloud architecture (although a well-known international standards-based model for system integration in layers and modules) is operationally available only through a subscription and build-out of services with an account on one of the major Cloud service provider companies.
  • (1) AI/ML modules and data service layers are now becoming a routine part of the Cloud "bundle" in a subscription package. Based on your background so far and this weeks readings, identify one or two main points of convergence in the design and use of AI/ML and Data systems implemented in the Cloud architecture ("Virtual Assistants," speech recognition, and Web/Mobile translation apps are all Cloud-based systems), and map out for yourself how the modules and layers/levels are designed for combination.
  • (2) Can you think through some of the consequences -- positive and negative, upside/downside -- in the convergence of the technologies on one overall "unifying" architecture (system design) provided only by one of the "big four" companies (Google, AWS, IBM, Microsoft)?

Learning Objectives and Main Topics:

In this unit, students will learn the basic principles at work in the converging "platforms" for data and databases, Internet/Web accessible data services, Cloud Computing, AI, and data analytics -- the whole bundle of which has become known as "Big Data."

"Big Data" (large quantities of stored data) is clearly connected to recent successes in ML applications. All "neural net" models are known to be "data hungry" -- that is, work best when calculating over millions of data "objects" for statistical maps of properties, features, and attributes that are the targets of data analysis.

Our current "data environment" is shaped by many kinds of technologies that are managed in multiple levels of interoperable Internet/Web-accessible data of all kinds. This includes the background layers of Internet and Web programming (for active and interactive networked client/server applications), AI/ML techniques for data, Cloud Computing for provisioning all levels of computing-and-networking-as-a-service (OS, platform, software, databases, real-time analytics, and memory storage), and Internet of Things (IoT) (IP-connected devices, sensors, and remote controllable actions).

This environment forms our biggest, complexly layered "black box", comprised of hundreds of subsystem and modular black boxes inside black boxes, all orchestrated for what we call "big data" functions. "Big data" just means massive amounts of data generated from multiple sources (human and human-designed computational agents) and stored in massive arrays of memory accessible to software processes at other levels in the whole system. The main dependencies of both AI/ML expansion and "big data" are cheap, modular memory, fast multiple core processors for parallel and concurrent processing, and fast ubiquitous IP-connected networks (both wired and wireless).

In studying a technology reified with a name (like "AI," or "Big Data") we must always immediately remove the name and consider the system of dependent technologies without which the top-level "black boxes" would be impossible. These are all complex systems, designed to scale with massive modularity and precise attention to levels of operation. Our access point to understanding this kind of system is always through opening up the design principles, and always keeping the human-community designs in focus, especially when confronting the torrents of marketing and ideological discourse from the business and technical communities for the technologies branded and marketed as products.


  • "Big Data": Wikipedia overview. [This is all over the place, but it provides a view of all the many contexts of Big Data, and how this concept overlaps with other data and AI/ML issues.]
  • "Big Data": ACM Ubiquity Symposium (2018). Read (html and pdf versions available):
  • Rob Kitchin, “Big Data, New Epistemologies and Paradigm Shifts,” Big Data & Society 1, no. 1 (January 1, 2014): 1-12. [Kitchin's summary of concerns discussed in the research in his book below.]
  • Rob Kitchin, The Data Revolution: Big Data, Open Data, Data Infrastructures and Their Consequences. London; Thousand Oaks, CA: SAGE Publications, 2014. Excerpts.
    [This is an excellent book for our focus on kinds of data, uses of data, and ethical consequences. You can survey the selected chapters to get a sense of Kitchin's approach. Also excellent bibliography of references if you want to follow up on issues in data ethics.]
  • Cathy O’Neil, Weapons of Math Destruction: How Big Data Increases Inequality and Threatens Democracy (New York: Crown, 2016). Selections.
    [This book has some valuable insights and important questions, but usually presented in a journalistic and anecdotal way (using an example to prove negative effects). The overall point is important, but "math" and "data" (as computational objects) aren't the problem. The full suite of ML models and methods (modeled in algorithms to be coded in a programming language), are designed to detect and establish patterns by closest approximation to invariants across unlimited amounts of data. The problem is faulty, incorrect, non-representative, and/or incomplete data samples over which the pattern recognizers are "trained" making predictions and decisions about new data to be analyzed. It comes down to basic statistical methods, the final outputs of which are based on inductive logic for the quantities of sampled data, and if the data being used for "normalized" patterns is wrong, then the AI/ML processes can lead to false correlations that, when used in decisions for hiring or financial credit, will continue to reinforce inequality.]

Weekly writing assignment (link to Wordpress site)

  • "Big Data" continues to be vague term, often used in marketing and journalistic hype. From what you have learned in the course, how would you explain the key concepts in "Big Data" and "data science" as applied to an implementation in a process that we use every day (review the readings from Week 6 in this context).

Learning Objectives:

This class meeting will be devoted to synthesizing (drawing together, unifying) what we have learned about the design principles and key concepts for AI/ML, and Information and Data, and underlying computational methods. In our review and "summing up," we will want to reflect on the main philosophical and conceptual foundations of AI/ML, Information, and Data, and the social and ethical consequences of of current implementations of these technologies, now and for the future.

Readings and Video:

  • Film Documentary, Coded Bias (Dir. Shalini Kantayya, 2020). Available on Netflix, and free to view on the PBS Independent Lens site (until 4/21/21/).
  • This documentary has received a lot of attention, and the face recognition issues are very important. You will find that the movie production is full of confusing talk about "algorithms," and there is little or no explanation of ML and the mathematical networks used for the statistical modeling, and only one mention of how training data sets can be improved and corrected (good people in the field are already working on this, but this work is not mentioned).
  • We can also use this documentary as a case for applying what you have learned for critique and explanations. You should now be able to help "deblackbox" the talk abut AI and explain key truths for others: (1) all the tech is based on computational design and openly understood (or learnably understandable) design principles, (2) because AI/ML/Big Data are designed systems, communities of people with responsibility for these systems can intervene and redesign for truer outcomes, and (3) we all have an ownership stake in these technologies, not only because they are active behind every computational, networked device, software, or data service that we use, but because the symbolic functions themselves -- the math, logic, and kinds of data being interpreted (language, images, video, music, personal data, etc.) -- belong to all of us as human cognitive agents in communities with others. We all "own" this. Our human identities are based on shared, collective symbolic systems for communication, information, expression, learning, and knowledge, and this includes all the logic and mathematical operations that go with our shared symbolic systems (all of which preceded our digital era).

Critique and Analysis of Current Descriptions of AI/ML

  • Zachary C. Lipton and Jacob Steinhardt, “Troubling Trends in Machine Learning Scholarship,” ArXiv:1807.03341 [Cs, Stat], July 9, 2018. Presented at ICML 2018: The Debates.
    • This is a very enlightening article. The authors present analyses of some of the rhetorical mistakes and a critique of discourse used in describing current work in AI/ML -- from an insider's view.
    • The analyses are also very relevant for anyone wanting to understand what is going on in this field, develop clear and truthful explanations, and critique non-explanations and mystification.
    • The authors are part the Machine Intelligence Research Institute (MIRI) at Berkeley. [View the site to see the kind of research going on at MIRI.]

Current Issues and Promising Directions: Combining Ethics, Design, and Policy

Confirm Your Own Learning Achievements in the Course!

  • Re-read the "Introduction to Key Concepts, Background, and Approaches" for the course.
  • (I promised that by following our learning path, step by step, you would be able to understand these key concepts in computing and AI. If you need help filling in what isn't clear yet, ask in class.)

In-class discussion: Developing plans for Final Projects

Weekly writing assignment (link to Wordpress site)

  • Use this week to review and reflect back on the main concepts and methods of the course, and think about how you could develop your own synthesis (ways of combining ideas, approaches, models, and methods) that could lead toward an approach for your final project. Use the readings and approaches to ethical and social concerns from last week and this week, if these ideas provide a way to reflect on the topics of the course in a unified way.
  • For your post, you can pose questions about a topic, method, or key concept that we have covered, and that you would like to know more about and follow up with further research. You can also discuss any "aha!" moments that you experienced when reflecting back on what you have learned in the course, and connections between concepts and/or principles of technologies that you've discovered this week.

Learning Objective:

  • Developing final research projects.

Beginning Notes, Outlines, and Bibliography for your Research

General Final Project Instructions (on Wordpress site)

  • Post any notes or bibliography references that you are working with (if ready) to discuss with your professor.
  • Final Project Research Essays by students in 607: Spring 2019 | Spring 2020

Class Discussion:

Group discussion on your final project topics, ideas, and current state of your research and writing.

  • Link to Wordpress site
  • Final Project Research Essays by students in 607: Spring 2019 | Spring 2020

  • Deadline: The target deadline for posting your essay is one week after the last day of class. Extensions are possible if you write to the professor to explain. We will be flexible in this unusual time so that you will be able to do good work, and finish the semester well. (But we all need deadlines!)

  • Use of your online project after the course: Remember that the URL that WordPress creates for your "Final Project" post is a permanent URL for you. You can post the URL as part of your "digital profile" wherever it can be useful to you (in a resume, LinkedIn, social media, internship applications, job applications, and applications for further graduate studies). You can also revise and update the post, at least through the official end of the semester when you can log in as an enrolled student, if you want to revise, enhance, or improve it for your online profile and resume.