Syllabus
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Syllabus

Intelligence: Artificial and Otherwise (ATCM

Instructor:

Office: Let’s find a coffee shop or Teams

Office hours: By Appointment

Email: davidc.rheams@utdallas.edu

Lecture: ATCM 2.918

Location:

Department:

⚠️
Course Policies

πŸ“œ Course Description

The course inquires into the nature and character of intelligence understood as a cognitive process that transpires via semiosis, or a process of meaning-making. We will ask questions such as: How do we recognize intelligence? Who and/or what might be considered intelligent? What does this mean exactly, especially in the context of emerging debates regarding new technologies called "Artificial Intelligence"? In order to address these questions, we will engage materials (e.g., popular, theoretical, historical, etc.) across a variety of fields, including but not limited to: neuroscience/cognitive science, cybernetics and early AI, philosophy, literature, and aesthetics. We will analyze cultural objects produced using AI tools; we will conduct experiments with such tools, e.g., ChatGPT , Dall-E 2, etc. Ultimately, we will contemplate the implications of the variations on the "A" in "AI," e.g., "artificial," "augmented," "alternative," etc. The culminating assignment will be critical and creative.

This class is an applied philosophy of science. In other words, we seek clarity on how do we do artificial intelligence, why do we make these specific tools, and how do we plan to use them. Most importantly, the class investigates how these tools guide our decision making and finds ways that we can be both more critical and more cognizant of the tools we make.

πŸ€”
Remember that you’re an active participant in putting this class together. This isn't a class where I present information, and your job is to memorize it for future use. Instead, the goal of the class is to uncover ideas and present them in a new light. Students will be asked to help facilitate lectures and contribute to case studies throughout the semester. In addition, we will be working in groups during almost every class.

Technologies & Platforms

🧰 What You'll Get Out of This Class

  • The ability to think critically about technology
  • The ability to be reflexive and challenge your own ideas
  • A deep understanding of how we are building AI as an infrastructure

πŸ“š Readings

All readings are provided for you - no need to buy textbooks. I've provided links to all readings on the class website.

πŸ“š
Selected Texts

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Race After Technology_ Abolitionist Tools for the New Jim Code-Polity
Race After Technology_ Abolitionist Tools for the New Jim Code-Polity
Polity
2019
Ruha Benjamin
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Reassembling the Social
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Reassembling the Social
Oxford University Press
2005
Bruno Lator
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Artificial Unintelligence
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Artificial Unintelligence
MIT Press
2018
Meredith Broussard
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A Prehistory of the Cloud
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A Prehistory of the Cloud
MIT Press
2015
Tung-Hui Hu
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The Structure of Scientific Revolutions
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The Structure of Scientific Revolutions
U of Chicago Press
1962
Thomas Kuhn
The Second Self
The Second Self
MIT Press
1984
Sherry Turkle
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The Creativity Code
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The Creativity Code
Harper Collins
2019
Marcus du Sautoy
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Epistemic Cultures
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Epistemic Cultures
Harvard University Press
1999
Knorr Cetina, K. (Karin)
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Sorting Things Out
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Sorting Things Out
MIT Press
200
Geoffrey C. Bowker & Susan Leigh Star
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The Mangle of Practice
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The Mangle of Practice
U of Chicago Press
1995
Andrew Pickering

πŸ—“ Schedule

πŸ—“
Course Schedule

Monthly Calendar

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Monthly Calendar

Table View

October 2023
Today
Sun
Mon
Tue
Wed
Thu
Fri
Sat
24
25
26
27
28
29
30
AI Infrastructure and Materiality
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AI Infrastructure and Materiality
Journal 5 Due
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Journal 5 Due
Oct 1
2
3
4
5
6
7
Mid Term
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Mid Term
8
9
10
11
12
13
14
Infrastructure and Aesthetics
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Infrastructure and Aesthetics
Journal 6 Due
πŸ’―
Journal 6 Due
15
16
17
18
19
20
21
AI Infrastructure and Aesthetics
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AI Infrastructure and Aesthetics
Journal 7 Due
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Journal 7 Due
22
23
24
25
26
27
28
Human-Machine Interaction and Future Directions
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Human-Machine Interaction and Future Directions
Journal 8 Due
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Journal 8 Due
29
30
31
Nov 1
2
3
4
Peer Review: Bring your Prototype to Class!
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Peer Review: Bring your Prototype to Class!

πŸ† Grading

Breakdown

πŸ’‘
Assignment Submission: Turn in everything via eLearning. For papers, please submit your work as a Google Docs link (one that I can edit) as it is easier to give feedback in this format.

Scale

A 90%-100% B 80%-89% C 70%-79% D 60%-69% F < 60%

😒 Plagiarism

Presenting someone else’s ideas as your own, either verbatim or recast in your own words – is a serious academic offense with severe consequences. In short, don't do it.