Last updated 09/02/2020 — accessed:
|LECTURES W 9:25-11:55 (synchronous mode at Zoom)|
This course will examine developments in Artificial Intelligence (including Machine Learning) from the perspective of philosophy of science. In module I, we will focus on acquiring or extending your technical and historical understanding of the major strands of AI/ML from the late 1950s to the present. In module II we will consider AI/ML as methods for automating scientific discovery and scientific reasoning. In module III we will consider the status of different AI/ML approaches as computational models for cognitive science. In module IV we will focus on the normative and value issues raised by AI/ML in various scientific and social applications.
Students are expected to gain and strengthen their technical understanding of various kinds of AI/ML technology, such as the ability to explain the significance of differences among supervised, unsupervised, and reinforcement learning, why multi-layer networks are more capable than a simple perceptron or simple two or three layer networks, and what role symbol-processing AI still plays in current AI systems. You will also develop your ability to apply your technical understanding to issues in philosophy of science, such as the nature of scientific discovery and scientific reasoning, and the prospects for automating such processes, as well as the role of AI/ML as scientific models of human and animal cognition. Finally, you will consider the extent to which current discourse on normative issues in AI/ML is permeated by a "value-free ideal" of science and technology.
Course Delivery / Health & Safety
This course is being taught under the Flex@Pitt model and the semester begins with the "Elevated Risk" posture.
The operating posture may change as determined by university authorities. If the university goes to "high risk", we will stay entirely online. In "guarded risk" we may continue with hybrid online/classroom meetings. For the most up-to-date information and guidance, please visit coronavirus.pitt.edu and check your Pitt email for updates before each class.
In the midst of this pandemic, it is extremely important that you abide by public health regulations and University of Pittsburgh health standards and guidelines. While in the classroom, at a minimum this means that you must wear a face covering and comply with physical distancing requirements; other requirements may be added by the University during the semester. These rules have been developed to protect the health and safety of all community members. Failure to comply with these requirements will result in you not being permitted to attend class in person and could result in a Student Conduct violation.
If you know already that participation via Zoom special difficulties for you please let me know immediately so that we may discuss options. Similarly, if you become too unwell to attend class meetings during the semester, you should let me know as soon as possible.
The Department of History & Philosophy of Science has established a teaching buddy system to ensure that classes will be covered in the case of instructor illness.
For students taking the course for a letter grade the requirements are one classroom presentation and either (i) four short papers (one per module, 3-5 pages), or (ii) one short paper (based on the first module) and a longer research paper due at the end of the semester.
Questions for the module-based papers will be announced at the 2nd meeting for each module, and will typically be due 12 days after the third meeting for that module (i.e., 09/14, 10/05 and 11/02 for the first three modules). The final paper will be due November 30 whether module-based or research-based.
Participation via Zoom
If you anticipate technological or other difficulties that will make it difficult for you to participate fully via Zoom or in person, please bring them up immediately so that we can try to find a suitable alternative.
Schedule of Readings, Topics, and Major Assignments
NOTE: SOMETIMES LINKS BREAK. IF SOMETHING CAN'T BE REACHED, PLEASE LET ME KNOW IMMEDIATELY.
|Date||Topic / Event||Reading Assignments|
|Wed 08/19||(IA) 20th C. AI/ML -- the "Ancient" History||
• Mitchell book, Part I|
• Buchanan 2005 "A (very) brief history of artificial intelligence"
|Wed 08/26||(IB) The Recent History of AI/ML||
• Mitchell book, Parts II and III|
• Lecun et al. "Deep Learning"
• Silver et al. 2016 "Mastering the game of Go..."
[see also AlphaGo explainer diagram"]
• Buckner 2019 "Deep learning: A philosophical introduction"
[OPTIONAL play with https://playground.tensorflow.org/"]
|Wed 09/02||(IC) To GPT-3 and beyond!||
• Mitchell book, Parts IV and V|
• Vaswani et al. "Attention is all you need" (Google translate)
• Brown et al. "Language Models are Few-Shot Learners" (GPT-3) [read sections 1 and 6, skim the rest]
[OPTIONAL: blog/magazine pieces on GPT-3]
|Wed 09/09||(IIA) AI for Science: Beginnings||
• Bradshaw, Langley, & Simon 1983 "Studying Scientific Discovery by Computer Simulation"|
• Slezak 1989 "Scientific Discovery by Computer as Empirical Refutation of the Strong Programme"
• Collins 1989 "Computers and the Sociology of Scientific Knowledge"
|Mon 09/14||Module I short paper due|
|Wed 09/16||(IIB) Machine discovery: Recent work||
• Korb 2004 Machine Learning as Philosophy of Science|
• [BRETT P.] Schmidt & Lipson 2009 "Distilling Free Form Laws from Experimental Data"
• Iten et al. 2020 "Discovering physical concepts with neural networks"
[OPTIONAL: Williamson 2004 "A Dynamic Interaction Between Machine Learning and the Philosophy of Science"]
|Wed 09/23||(IIC) Machine learning and Philosophy of Science||
• [JUSTIN] Pearl 2018 "Theoretical Impediments to Machine Learning With Seven Sparks from the Causal Revolution"|
• [BRENDAN] López-Rubio 2020 "The Big Data razor"
|Wed 09/30||(IIIA) Cognitive Models||
• Smolensky 1988 On the Proper Interpretation of Connectionism"|
• [JORDAN] Stinson 2020 "From Implausible Artificial Neurons to Idealized Cognitive Models: Rebooting Philosophy of Artificial Intelligence" [previously linked preprint version]
|Mon 10/05||Module II short paper due (four paper option only)|
|Wed 10/07||(IIIB) Empiricism redux?||
• [CLARA] Crosby 2020 "Building Thinking Machines"|
• [JAMES] Marcus 2018 "Innateness, AlphaZero, and Artificial Intelligence"
• Buckner 2018 "Empiricism without magic: transformational abstraction in deep convolutional neural networks"
|Wed 10/14||No Class|
|Wed 10/21||(IIIC) Biological Plausibility||
• Bengio et al. 2016 "Towards Biologically Plausible Deep Learning"|
• Crawford et al. 2016 "Biologically Plausible, Human-Scale Knowledge Representation"
[OPTIONAL Ferrucci et al. 2010 "Building Watson: An overview of the DeepQA Project"]
|Wed 10/28||(IVA) Epistemic Values in AI||
• Rudin 2018 "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead"|
• [ERIC] Lipton 2019 "The Mythos of Model Interpretability"
• [OSMAN] Zednik 2019 "Solving the Black Box Problem"
[OPTIONAL: Creel 2020 "Transparency in Complex Computational Systems"]
|Mon 11/02||Module III short paper due (four paper option only)|
|Wed 11/04 |
**11 a.m. start**
|(IVB) Ethical Values in AI||
• Buolamwini & Gebru 2018 "Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification"|
|Wed 11/11||IVB-cont'd Ethics in AI||
• [TOM] Johnson & Verdicchio 2019 "AI, agency and responsibility: the VW fraud case and beyond"|
• [DASHA] Fazelpour & Lipton 2020 "Algorithmic Fairness from a Non-ideal Perspective"
• Mitchell et al. 2019 "Model Cards for Model Reporting"
|Wed 11/18||IV-C Practical Wisdom for AI||
• [XIN HUI] Raji et al. 2020 "Closing the AI Accountability Gap"|
• Karlan & Allen (draft) "Engineered Wisdom for Learning Machines"
|Mon 11/30||Final paper due|
Statement about Academic Misconduct
Students in this course will be expected to comply with the University of Pittsburgh’s Policy on Academic Integrity. Any student suspected of violating this obligation for any reason during the semester will be required to participate in the procedural process as outlined in the University Guidelines on Academic Integrity. 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.
To learn more about Academic Integrity, visit the Academic Integrity Guide for an overview of the topic. For hands-on practice, complete the tutorial on Understanding and Avoiding Plagiarism.
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 Disability Resources and Services (DRS), 140 William Pitt Union, 412-648-7890, firstname.lastname@example.org, 412-228-5347 for P3 ASL users, as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course.
The University of Pittsburgh does not tolerate any form of discrimination, harassment, or retaliation based on disability, race, color, religion, national origin, ancestry, genetic information, marital status, familial status, sex, age, sexual orientation, veteran status or gender identity or other factors as stated in the University’s Title IX policy. The University is committed to taking prompt action to end a hostile environment that interferes with the University’s mission. For more information about policies, procedures, and practices, see Pitt's Civil Rights & Title IX Compliance pages.
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 Coordinator, by calling 412-648-7860, or e-mailing email@example.com. Reports can also be filed online: https://www.diversity.pitt.edu/make-report/report-form. 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 Inclusion. If you wish to maintain complete confidentiality, you may also contact the University Counseling Center 412-648-7930.
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.
At certain times, lectures or portions of the lectures may be recorded by the instructor. Before starting recording, it will be announced to the class. Students who do not wish to be identifiable during such recordings may remain silent and obscure their faces either by turning off their own video feed if connected via Zoom or obscuring their faces if in the classroom.
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.
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