Computing, Design & Values

Glenn Harvey (NYT, 2001)

Course Learning Objectives

Develop technical intuition and hands-on experience with computing paradigms in design including visual calculating with shape grammars, problem-solving, design knowledge and models of learning
Differentiate and explain strengths, applicability, and weaknesses of major computing paradigms for thinking spatially about “objects in the world” as well as for addressing design problem-solving scenarios
Integrate knowledge from design theory, art, mathematics, philosophy, linguistics, computer science, artificial intelligence to foster a critical (evaluative) mindset on computing concepts
Develop skills in constructive criticism of the design work done by peers incorporating verbal (in-person) and written critiques
Build familiarity and a broad basis on past and present research and scholarship in design and computation to facilitate continuous learning
Develop interest, knowledge, and skills that are necessary to undertake more advanced and specialized studies or research in the field

Course Format

Lectures and Class Activities. Lectures introduce new concepts that are often exposed through short hands-on exercises in the classroom. Class activities involve hands-on experience with a particular technical method, readings analysis and discussion (including peer-critique), project reviews and student presentations.
Reading, Writing, Discussion. Students are expected to attend lecture weekly and to participate in the analysis and discussion of assigned readings (refer to the readings lists provided under course outline). There will be class sessions where students will be required to function as lecture leaders and to lead a discussion with external guests. Up to three planned class period absences are allowed without affecting one’s grade as long as they are registered in advance with me. Remote attendance will be supported in exceptional situations.
Homework Assignments. There are four multi-step assignments, both individual and team based. The assignments involve handwritten technical exercises, reading comprehension and critical thinking, and short one or two-week projects. Late assignments will result in a reduction of one letter grade per day after the deadline unless extreme circumstances warrant an extension (must be arranged with instructor 24 hours before deadline).
Final Project. After about 2/3 of the semester, students will work individually or in teams of two on an original final project. Projects should focus on developing, implementing, extending, or applying one or more computational methods introduced in class to an original design case. Cases can be drawn from the built environment, everyday functional products, engineering design, technology and policy, and different areas of art. Various topic options that embody the themes of the course will be discussed, but students are also free to explore projects of their own interest. All projects will be documented and reported in the form of a final term paper with a mandatory in-class presentation (details will be provided in a separate project handout). See course outline for final presentation schedule and due date.
Presentations and Peer-Critique. Following the tradition of design studio education, students will be asked to participate in “crits” or reviews of their ongoing work (whether that’s for homework assignments or their final project). Assignments may ask students to give a presentation of their findings in class for peer feedback and critique. Peer feedback and ability to formulate constructive critique is an integral element of the course and part of the final grade.
Final Course Document. All students will submit a cumulative PDF document to Canvas that contains all of their homework assignments, written work, final project, or other material developed throughout the semester. A standard format for this cumulative PDF document will be provided. The purpose of the final course document is to provide students an opportunity to develop a complete “portfolio” of their work in the class for future use and reflection.

Course Schedule

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[Subject to change based on enrollment]

[Tu 01/23] Lecture 1 – Introductory lecture on modules, assignments, materials, and policies.

Module 1 – Visual Calculating and Rule-Based Design I [Weeks 1-3]

In this first module, we focus on things designed – the real-world artifacts of human design. How can we observe and understand design objects through verbal, visual, and spatial representations? What kinds of part-whole descriptions are possible, and for what purpose or meaning? Can the same design object have multiple meanings? We focus on “designed objects” in the broadest sense, to include cases from the built environment, everyday functional products, artworks, mechanical objects, material structures, and other.

[Thu 01/25] Lecture 2 – Fundamentals Part I

Introduction: Visual Calculating and Rule-Based Design in Architecture, Art, Design, Science, and Engineering
Basic Elements: Symbols vs Shapes
Part-Relation: Identity vs Embedding

Assignment 1 Released (individual); Due 02/06

[Tue 01/30] Lecture 2 – Fundamentals Part II

Shape Arithmetic
Spatial Relations
Descriptions: Labels and Decompositions
Recognition and Redescription

[Thu 02/01] Class Activity: Readings Discussion and Assignment 1 Review

Readings:

1.1. “Points, Parts, Shapes,” by A. Haridis (Prepared Reading, 2024).
1.2. Excerpts on “Pattern Recognition” from John R. Anderson, Cognitive Psychology and Its Implications, 6th Edition (W. H. Freeman and Company, 2005).
1.3. Excerpt from Chapter 13 “Seeing and Believing” in M. Minsky, The Society of Mind (Simon & Schuster, Inc., 1988)
1.4. Excerpt from “Part & Whole” by J. Koenderink (De Clootcrans Press, 2013).
(optional) 1.5. Excerpt from “Developing Musical Structures” by J. S. Bamberger (MIT Faculty Newsletter, 2004).

Module 2 – Visual Calculating and Rule-Based Design II [Weeks 3-6]

This module continues the exploration of representation and understanding, now with a focus on process and the concept of a visual algorithm. Visual algorithms are formalized through shape grammars and applications are explored both for style analysis and generative design. Related grammatical formalisms are discussed across design, engineering, and science. The module ends with a discussion on visual calculating as a framework for evaluating design objects as both art (aesthetics) and common use (utility).

[Tue 02/06] Lecture 4 – Design Grammars

Shape Rules, Recursion and Embedding
Visual Algorithms (Shape Grammars)
Stylistic Analysis and Generation

Assignment 2 released (individual); Due 02/22.

[Thu 02/08] Class Activity: Stylistic Analysis (Grammar Induction)

From Observed Instances to the Grammar that Describes Them

[Tue 02/13] Class Activity: Generation (Grammar Design)

From Shape Rules to Grammars for Designing Novel Instances

[Thu 02/15] Lecture 5 – Evaluative Frameworks

Aesthetic Value and Calculation
Design as Use (Visibility) vs Design as Art (Ambiguity)

[Tue 02/20] Class Activity: Student-led Discussion of Readings with Invited Guest

Readings:

2.1. Excerpt from The Design of Everyday Things by D. Norman (Basic Books, 2002).
2.2. Excerpt from Shapes of Imagination: Calculating in Coleridge’s Magical Realm, by G. Stiny (The MIT Press, 2022).
2.3. “What Makes Things Cool?” by D. Thompson (The Atlantic, January/February, 2017).
2.4. “The Aesthetics of the Ordinary and Familiar,” Y. Saito (Oxford University Press, 2017).
(optional) 2.5 Catalogue of Extraordinary Objects, by J. Carelman (Abelard- Schuman, 1974).
(optional) 2.6. Movie scene “Story #1: Art and Artists section” from The French Dispatch (2021) by Wes Anderson.

[Thu 02/22] Class Activity: Assignment 2 Presentations and Peer-Critique

[Required: Peer-critiques documented and submitted digitally.]

Module 3 – Problem-Solving and Learning [Weeks 6-11]

In this module, our exploration continues with more emphasis on computing using digital machines. This module is built around computational paradigms in design influenced by Artificial Intelligence (AI) research, traditionally distinguished between models of problem solving and models of learning. Of interest here is the issue of encoding/incorporating domain specific design knowledge (as in the rule-based methods covered in modules 1 and 2) in computational learning. The module includes a two-week session on Human-AI Interaction Evaluation, where students will test the effectiveness and usability of code produced by a vision language system. We end with a discussion and reflection on the two traditions of research in AI and some present critique.

[Tue 02/27] Lecture 6: Artificial Intelligence and Design: Two Paradigms

Models of Problem-Solving
Models of Learning
Revisiting The Sciences of the Artificial (Design)

Assignment 3 (multi-step team assignment); Due: 03/19.
(optional) Opening movie scene “The Dawn of Man” from 2001: Space Odyssey (1968), by Stanley Kubrick.

[Thu 02/29] Lecture 7: Learning as Discernment

Types of Learning
From Observed Instances to Feature Discernment (Embedding)
Visual/Spatial Challenges for Learning from “Design Data”

[Tue 03/05] Lecture 8: Computational Learning Part I

Neighbors and Decision Trees
Clustering

[Thu 03/07] Lecture 9: Computational Learning Part II

Modeling Neural Nets
Representation Learning

Final Project Handout: “Formulate Your Final Project”; Due: 04/11.

[Tue 03/12] No Class – Spring Recess

[Thu 03/14] No Class – Spring Recess

[Tue 03/19] Assignment 3 Presentations and Peer-Critique

[Required: Peer-critiques documented and submitted digitally.]
Assignment 4 (multi-step team assignment):Presentation: 03/28, Due: 04/02.

[Thu 03/21] Class Activity: Human-AI Interaction Evaluation

[Required: In-Class Group Work with Mandatory Documentation and Presentation.]

[Tue 03/26] Class Activity: Human-AI Interaction Evaluation (continued)

[Thu 03/28] Class Activity: Assignment 4 Presentations and Peer-Critique

[Required: Peer-critiques documented and submitted digitally.]

[Tue 04/02] Class Activity: Student-led Discussion and Analysis of Readings

Readings:

3.1. Excerpt from “Computing Machinery and Intelligence” by A. M. Turing (Mind 49, 1950).
3.2. “The Science of Design: Creating the Artificial,” Chapter 5 in The Sciences of the Artificial, 3rd Edition by H. A. Simon (The MIT Press, 1996).
3.3. “AI And the Limits of Language,” by J. Browning and Y. LeCun (Noema, August 23, 2022).
3.4. “Point 2” and “Point 3” in Chapter 4 from Art in the After-Culture by B. Davis (Haymarket Books, 2022).
3.5. “Formulating the Generative in Design, Science and Technology,” by A. Haridis (Prepared Reading, 2024).
(optional) 3.6. “Computer Science as Empirical Inquiry: Symbols and Search” by A. Newell and H. A. Simon (1975 ACM Turing Award Lecture).
(optional) 3.7. “Introduction,” Chapter 1 in Deep Learning by I. Goodfellow, Y. Bengio and A. Courville (The MIT Press, 2016).
(optional) 3.8. “The End of Programming” by Matt Welsh (Communications of the ACM, 2023).

[Thu 04/04] Class Activity: Student-led Discussion and Analysis of Readings (continued)

Module 4: Reflections and Final Projects [Weeks 12-14]

The last module reinforces key ideas and dedicates time for final project development.

[Tue 04/09] Final Projects Draft Due and In-Class Presentation

[Thu 04/11] Final Projects Work In-Progress Crit

[Tue 04/16] Final Projects Work In-Progress Crit

[Thu 04/18] Lecture 10 – Structure of effective presentations, literature review, preparing figures, writing a research paper, etc.

[Tue 04/23] Wrap-up Lecture / Final Projects Work In-Progress Crit. Last day of class for ES138.

(Spring term reading period begins.)

Final Project Presentations (Tuesday May 7, 9:00am – 12:00pm, Room in SEC TBD)

Day/Hour is set by Registrar, but open to student input.
Guest Critics [TBD].
Final Course Document – Due on Wednesday May8, at 11:59 pm