Last updated 10/15/2024 — accessed:
NOTE: THIS SYLLABUS IS A LIVING DOCUMENT INSOFAR AS THE READING SCHEDULE WILL BE CONTINUOUSLY UPDATED. THE OTHER SECTIONS OF THE SYLLABUS ARE ALSO TENTATIVE UNTIL THE END OF THE FIRST FULL WEEK OF CLASSES.
Course Canvas site: https://ucsb.instructure.com/courses/21907
| Lecture Times/Location and Instructor Information | |
|---|---|
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(Prof.) Colin Allen
<colinallen@ucsb.edu>
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Course Description
Advances in artificial intelligence and the widespread use of computers in society confront us with many interesting philosophical questions. What is computation? Is AI really intelligence? Could computers ever think or feel? Is the Turing test a good test? Are we really computers? Are there decisions computers should never make? Do computers threaten our privacy in special ways? Should AI be regulated, and how? We will survey some of the controversies and puzzles that AI has provoked.
Learning Objectives
By the end of the course, students should have sufficient grasp of the material to be able to explain a range of ideas about the relation of computing to intelligence. They should also be able to explain the philosophical issues which are engendered by thinking about the nature of computation, the use of computing as a model or a metaphor for human mind, and the values inherent in or threatened by attempts to build systems that are artificially intelligent, or to replace human judgment with algorithmic procedures. Students should be able to articulate different perspectives on these issues and state and critically evaluate the main lines of the arguments for and against these views.
Required Materials
There is no textbook for this course. Readings will be made available via URLs linked into the schedule below.
This is an upper-division/graduate hybrid course. Students at both levels are expected to be able to read original research articles in philosophy and adjacent fields, and to extract the main lines of argument from them. If you are unsure about your capacity to do this, please talk to me during office hours before the end of the first week of the course.
Concerning note-taking and studying styles, you are advised to read the following two articles from the cognitive science of learning.
TLDR: (1) taking notes on a keyboard is less effective than taking notes by hand; (2) practicing recall via repeated testing is more effective than re-reading for long term retention.
Grading Basis
The grading basis for students taking PHIL217 for graduate credit will be discussed at the first 217-only meeting (time tba). The following applies to students taking the course as PHIL117 for undergraduate credit.
Grades will be assigned based on the following point schedule: 0-59=F 60-65=D 66-69=D+ 70-72=C- 73-76=C 77-79=C+ 80-82=B- 83-86=B 87-79=B- 90-93=A- 94-97=A 98-100=A+
There are two assessment tracks in this course that you may choose between. Both tracks assign for classroom exercises (short quizzes, etc.). There will be no makeup opportunities for missed exercises, even for excused absences, but you are allowed two free passes during the quarter.
Track 1: You will demonstrate relatively deep understanding of an issue in philosophy of AI/computing by developing a writing portfolio over the course of the quarter, culminating in an 10-12 page (2500-3000 word) essay that states and defends a philosophical thesis that you have selected from among the course topics. This essay is worth 80 points and will require sustained work according to a strict deadline and revision process described in detail below. If you miss any deadline in this process, you are automatically switched to Track 2. Combined with the in-class assignments scaled to a maximum of 20 points, the maximum score on this track is 100 points for the quarter, equivalent to an A+ grade if everything is done to a very high standard.
Track 2: You will demonstrate broad knowledge of the course material by taking a final exam based on the readings and lectures. The exam will consist of a mixture of multiple choice questions, some short format questions (one paragraph), and one or two essay questions requiring 4 or 5 paragraphs each to answer. This exam is worth 75 points. This will be combined with the in-class points in two ways, and the better score will determine your final grade: The first method scales the in-class work to 20 points which are added to the final exam points. The second method scales the in-class points to 75 (same value as the final) and rescales the total out of 95 (i.e. divide the total by 1.58 and round up to next whole number). Thus, the maximum score on this track is 95 points for the quarter, equivalent to an A grade if everything is done to a very high standard.
A goal of Track 1 is to help you improve the level of conceptual clarity and argumentative rigor in your writing. Writing well is an iterative process of drafts and revisions, and while you will be graded on the quality of the final product, you are required to follow a procedure that will produce a portfolio that allows me to see your writing unfold and give you timely feedback on your work at certain milestones. ALL of your writing (no exceptions) is to be done in a single Googledoc. You will share an editable link to this Googledoc with me according to the instructions at https://colinallen.dnsalias.org/Courses/PHILx17/gdocassignment.html and you will revise and extend your work within this Googledoc throughout the quarter. All deadlines are listed as falling on Sundays but since I won't notice until Monday morning, your actual deadline is 8 a.m the following morning which means you have some leeway if you are a night owl. For each milestone you will submit by create a named version using the milestone name below. (See the online instructions for how to create a named revision.) You may continue to revise portfolio elements after the deadlines, but the named version is required as proof of meeting the deadline.
| Due Date | "Milestone" Label | Assignment instructions |
|---|---|---|
| Oct 6 | "Template" | You will set up a Googledoc and share the link to it via the Canvas assignment. Copy and paste the template at https://docs.google.com/document/d/1LHubwyiZVveCWYwr0Mok_ahbS5OCE0D-fyRq_Wij4Fs/ into your document. Throughout the quarter you may optionally use the space provided under this portion of the assignment in the template to jot down any ideas, reactions, thoughts, or questions that occur to you while reading or in the classroom. I won't promise to respond to everything, but I will do my best to respond to things that you have highlighted for my attention. |
| Oct 13 | "Criticism" | Among the philosophical positions on AI and Computing that you have encountered in the course so far, which do you find least plausible and why? Make sure you state the view clearly as if explaining to a friend who is not taking the course before explaining why you disagree with it (1-2 paragraphs) |
| Oct 27 | "Titles" | Give two titles in the form of questions for essays on different topics that you would be interested in writing about. Your titles should be informative about the topic. For each title, write one paragraph containing a thesis stating your position with respect to the issue raised in the title, and sketch the argument you would give for that thesis. Finally, in a third paragraph briefly explaining which of these topics you would choose if you were forced to do so at this moment, and why you prefer that one over the other. |
| Nov 10 | "Proposal" | Give a title for your essay and sketch your approach to addressing it (one paragraph). Note that this does not have to be one of the titles that you considered in the previous, but it may be. I will provide feedback on the proposal and either approve it or work with you to develop a more suitable topic. |
| Nov 24 | "Draft" | Produce a 1500-2000 word draft of your final paper. I will provide feedback and a provisional grade. |
| Dec 8 | "Final" | Revise your draft in light of comments. You are aiming for 2500-3000 well-polished words, but quality matters more than quantity. |
Policy on use of ChatGPT or similar generative AI tools in this class: You may not submit any AI-generated text as your own writing. Some exercises or your paper topic may require you to interact with an AI system and report its responses for the purposes of analysis, but you must clearly attribute any text generated in this way. You may use AI-enhanced tools for basic grammar and wording suggestions without documentation, but if you use generative AI to rewrite your text you must document this by including a transcript of the interaction with the AI.
Schedule of Readings, Topics, and Major Assignments
Below is a list of things I intend for us all to read & discuss. The links go either to open access copies on the web where available or to PDFs on Canvas which will require you to log in. We will do this reading at a pace and order that is appropriate to the level of discussion in the classroom, so specific dates are only fixed for the first couple of weeks. Other items may be added to the list. The schedule will be updated continously throughout the quarter, so check back regularly. Reading ahead is, of course, always a good idea! Let me know if any of these links do not work.
| Date | Topic | Readings / Assignments |
|---|---|---|
| Week 0 | ||
| Thu 09/26 | Introduction to the Course | no reading |
| Week 1 | ||
| Tue 10/01 | Reflecting the human mind | [magazine or canvas] • Vallor 2024 "The danger of Superhuman AI is not what you think" |
| Thu 10/03 | Turing's Legacy | [SEP] • Rescorla 2020 Stanford Encycl. of Phil. entry on "Computational Theory of Mind" -- read sections 1. "Turing Machines" and 2. "Artificial Intelligence" only |
| Week 2 | ||
| Tue 10/08 | What's in the Turing Test? |
[canvas]
• Turing 1950 "Computing Machinery and Intelligence"
[arxiv or canvas] • Jones & Bergen 2024 "Does GPT-4 pass the Turing test?" -- Reflection question: Is what Jones & Bergen did really the Turing test? |
| Thu 10/10 | No lecture... do not come to class. Instead watch a talk I gave in spring 2023 about large language models. | [youtube] • Note: I make a mistake in the talk where I say that 'GPT' stands for 'general purpose transformer'; actually it stands for 'generative pretrained transformer' -- Write down at least one question about the talk and bring it to class next Tuesday. |
| Week 3 | What does the Turing Test show? | |
| Tue 10/15 | The "Chinese Room" Thought Experiment |
[canvas]
• Searle 1990 "Is the Mind's Brain a Computer Program?"
[canvas] • Churchland & Churchland 1990 "Could a Machine Think?" [canvas] • Moor 2005 "The status and future of the Turing test" |
| Thu 10/17 | The "Chinese Room" Thought Experiment | continue with same readings as Tuesday |
| Intervening Weeks | ||
| tbd | Computational Theory of Mind | [SEP] • Rescorla 2020 Stanford Encycl. of Phil. entry on "Computational Theory of Mind" sections 3. "The classical computational theory of mind" and 4. "Neural Networks" |
| tbd | Computational Theory of Mind | [canvas] • Churchland 2005 "Functionalism at Forty: A critical perspective" |
| tbd | Networks as Philosophy | [arxiv or canvas] • Millière & Buckner 2024 "A Philosophical Introduction to Language Models Part I: Continuity With Classic Debates" |
| tbd | Deep Learning Machines | [author_copy or canvas] • Buckner 2023 "From Deep Learning to Rational Machines" Chapter 1 (optional: listen to Buckner interviewed on new books in philosophy podcast) |
| tbd | Computational Neuroscience | [canvas] • McCulloch & Pitts 1943/1990 "A logical calculus of the ideas immanent in nervous activity" |
| tbd | Computational Cognitive Science |
[canvas]
• Marr 1975 Vision Chapter 1 |
| tbd | Brain as Computer |
[canvas]
• Shagrir 2006 "Why we view the brain as a computer" |
| tbd | The Brain is a computer? | [journal] • Maley 2022 "How (and why) to think that the brain is literally a computer" |
| tbd | Digital/Analog) =? Discrete/Continuous | [canvas] • Maley 2024 "The analog alternative" in Mind Design III |
| tbd | Insight and Creativity | [canvas] • Halina 2019 "Insightful Artificial Intelligence" |
| tbd | AI as Scientist? | [canvas] • Lu et al. "The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery" |
| tbd | Science without Theory? | [canvas] • Andrews in prep. "The Immortal Science of ML: Machine Learning & the Theory-Free Ideal" |
| tbd | Algorithmic Bias | [journal or canvas] • Danks & Fazelpour 2021 "Algorithmic bias: Senses, sources, solutions" |
| tbd | Domain Distortion | [canvas] • Pruss 2021 "Mechanical Jurisprudence and Domain Distortion" |
| tbd | Artificial Morality | [canvas] • Allen, Varner & Zinser 2000 "A prolegomena to any future artificial moral agent" |
| tbd | Moral Machines | [canvas] • Allen & Wallach 2012 "Moral machines: contradiction in terms or abdication of human responsibility?" |
| tbd | Machine emotions | [canvas] Allen in prep. "Appearance or reality: does AI need emotions?" |
| Week 9 | Writing | |
| Tue 11/26 | Writing Workshop (for both tracks) | |
| Thu 11/28 | THANKSGIVING | |
| Looking Ahead | ||
| Tue 12/03 | Bonus topic TBA | |
| Thu 12/05 | Final Review | |
| Finals Week | ||
| Mon 12/09 | Track 1 only: Final Paper due | |
| Wed 12/11 12:00-3:00 p.m. | Track 2 only: Final Exam -- NOTE day and time. | |
Still Relatively New to Philosophy Courses?
You may find my concise guides to reading philosophy and writing philosophy helpful. You may find them useful even if you are not so new to philosophy.
Missed Assignments
Statement about Academic Misconduct
Students in this course are obliged to comply with UCSB's Academic Integrity Policies. Any student suspected of violating this obligation for any reason during the quarter will be referred via the Academic Integrity procedures detailed at the above link. When you submit assignments with your name on them in this course, you are signifying that the work contained therein is all yours, unless otherwise cited or referenced. Any ideas or materials taken from another source for either written or oral use must be fully acknowledged. If you are unsure about the expectations for completing an assignment or taking a test or exam, be sure to seek clarification beforehand. Use of ChatGPT or similar generative AI products will be discussed in class, and may not be used unless you are explicitly given permission to do so, and never without explicit acknowledgment of its use.
Diversity and Inclusion
The Americans with Disabilities Act (ADA) is a federal anti-discrimination statute that provides comprehensive civil rights protection for persons with disabilities. Among other things, this legislation requires that all students with disabilities be guaranteed a learning environment that provides for reasonable accommodation of their disabilities. If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and UCSB's Disabled Students Program for information about accomodations and services.
The University of California, in accordance with applicable federal and state laws and University policy, prohibits discrimination against or harassment of any person at the University on the basis of race, color, national origin, religion, sex, gender identity, pregnancy, physical or mental disability, medical condition (cancer-related or genetic characteristics), ancestry, marital status, sexual orientation, citizenship, or age. For more information see https://eodp.ucsb.edu/resources/policies.
I ask that everyone in the class strive to help ensure that other members of this class can learn in a supportive and respectful environment. If there are instances of the aforementioned issues, you may contact the Title IX Office, by calling 805-893-2701 or visiting https://titleix.ucsb.edu/. You may also choose to report this to a faculty/staff member; they may also be required to communicate about such issues to the University’s Office of Diversity and Incusion. If you wish to maintain complete confidentiality, you may also contact University Counseling & Psychological Services .
Statement on Classroom Recording
To ensure the free and open discussion of ideas, students may not record classroom lectures, discussion and/or activities without the advance written permission of the instructor, and any such recording properly approved in advance can be used solely for the student’s own private use.
Materials provided for the course may be protected by copyright. United States copyright law, 17 USC section 101, et seq., in addition to University policy and procedures, prohibit unauthorized duplication or retransmission of course materials. See Library of Congress Copyright Office and the University Copyright Policy.