schedule.md

Date	Topic	Readings	Speaker(s)	Weekly Check-in
1/19	Intro	Background Artificial intelligence: a guide for thinking humans (Mitchell) (chapter 1) Patterns, predictions, actions (Hardt & Recht) (chapter 1)	—	NA
1/26	Origins: AI	Required Computing Machinery and Intelligence (Turing) (1950) A Proposal for the Dartmouth Summer Research Project on Artificial Intelligence (McCarthy) (1955) Computer science as empirical inquiry: symbols and search (Newell & Simon) (1956) Optional The Dartmouth College Artificial Intelligence Conference: The Next Fifty Years (Moor) The Art of Artificial Intelligence (Feigenbaum) Artificial Intelligence Meets Natural Stupidity (McDermott)	Zach Lipton Mel Andrews Michael Feffer
2/02	Origins: Cybernetics	Required The Mechanization of the Mind (Dupuy) (2000) (Chapter 2) A logical calculus of the ideas immanent in nervous activity (McCulloch & Pitts) (1943) The Human Use of Human Beings (Wiener) (1950) (Chapter 3) Optional What Is a Number, that a Man May Know It, and a Man, that He May Know a Number? (Warren S. McCulloch) (1960)	Saujus Vaduguru Emmy Liu Mel Andrews Presenter Notes
2/9	Origins: Machine Learning	Required Statistical Modeling: The Two Cultures (Breiman) (2001) The Discipline of Machine Learning (Mitchell) (2006) Artificial Intelligence — The Revolution Hasn’t Happened Yet (Jordan)(2019)	Dhananjay Ashok Lindia Tjuatja Charvi Rastogi Presenter Notes
2/16	The Problem of Induction	Required Excerpts from An Enquiry Concerning Human Understanding (Hume) (1748) The Logic of Scientific Discovery (Popper) (1959) (Chapter 1 The Material Theory of Induction (Norton) (Prologue & Chapter 1)(2021) Optional Excerpts from A Treatise of Human Nature (Hume) (1739) The Problem of Induction(Henderson)	Ji Min Mun Patrick Fernandes Saujas Vaduguru Presenter Notes
2/23	Induction and Statistical Learning Theory	Background Reliable Reasoning: Induction and Statistical Learning Theory (Harman & Kulkarni) (2005) Required Afterward to Estimation of Dependences Based on Empirical Data (Vapnik) (2006) Human Induction in Machine Learning: A Survey of the Nexus (Spelda & Stritecky) (2021) The No-Free-Lunch Theorems of Supervised Learning (Sterkenburg) (2021) Optional Induction, Inductive Biases, and Infusing Knowledge into Learned Representations (Finlayson) (2020) No Free Lunch Theorem, Inductive Skepticism, and the Optimality of Meta-induction (Schurz) (2022)	Michael Feffer Rishabh Ranjan Mel Andrews Presenter Notes
3/2	Causation	Background Causation (Scheines) Required Causal Diagrams for Empirical Research (Pearl) (1994) Actual Causation and the Art of Modeling (Halpern & Hitchcock) (2011)	Conny Knieling Dhananjay Ashok Emmy Liu Presenter Notes
3/9	No Class
3/16	Categories & Kinds	Required The Looping Effects of Human Kinds Basic Objects in Natural Categories Excavating AI	Lindia Tjuatja Simran Khanuja Patrick Fernandes Presenter Notes
3/23	Epistemological & Methodological Considerations	Required The Predictive Reframing of machine learning applications: good predictions and bad measurements (Mussgnug) (2022) Two Dimensions of Opacity and the Deep Learning Predicament (Boge) (2022) Big Data need Big Theory Too (Coveney et. al.) (2016)	Ji Min Mun Mel Andrews Emmy Liu Presenter notes
3/30	Understanding & Knowledge	Required Climbing Towards Natural Language Understanding (Bender & Koller) (2020) Can Machine Learning Provide Understanding? (Meskhidze) (2021) Physics-Informed Machine Learning (Karniadakis et. al.) (2021)	Lindia Tjuatja Patrick Fernandes Dhananjay Ashok Presenter Notes
4/6	Generative AI, Bullshit, & Creativity	Required Creativity in Explaining Imagination. (Langland-Hassan) (2023) (Just the part of the chapter that’s about creativity in DNNs Excerpt from "On Bullshit". Frankfurt Ted Chiang “ChatGPT Is a Blurry JPEG of the Web” Optional Foundation Models Stanford	Emmy Liu Saujas Vaduguru Simran Khanuja Presenter Notes
4/13	Carnival Week Pandemonium / AGI Hootenanny	Required The Symbol Grounding Problem (Harnad) (1990) Will We Ever Have Conscious Machines? Krauss et al Could a Large Language Model be Conscious? Chalmers Optional Could large language models be conscious? A perspective from the free energy principle The Model is the Message (consciousness, Google LaMDA)--Bratton Arcas	Zach Lipton Patrick Fernandes Simran Khanuja Presenter notes
4/20	The Troubles with Explanation	Required The Mythos of Model Interpretability Lipton Woodward “Making Things Happen: A Theory of Causal Explanation” Chapter 1 The fate of explanatory reasoning in the age of big data. Cabrera	Rishabh Ranjan Saujas Vaduguru Ji Min Mun Presenter notes
4/27	Ethics I	Background Stanford Encyclopedia of Philosophy on "Justice" Required "Ideal Theory" as Ideology Inherent Trade-Offs in the Fair Determination of Risk Scores. Kleinberg et. al. Algorithmic Fairness from a Non-ideal Perspective. Fazelpour, Lipton	Simran Khanuja Michael Feffer Conny Knieling Presenter notes
5/4	Ethics II	Required Fairness in Decision-Making: The Causal Explanation Formula Disparate Causes Lily Hu Eddie Murphy and the Dangers of Counterfactual Causal Thinking about Detecting Racial Discrimination. Kohler-Hausmann	Michael Feffer Ji Min Mun Conny Knieling Presenter notes

**Topics **

• Origins — AI
• Origins — Cybernetics
• The Problem of Induction — Hume, Popper, Vapnik
• The Problem of Induction — Carnap, Adversarial/Distribution-Free Learning
• Categories & Kinds
• Causation
• Epistemological & Methodological Considerations
• Knowledge & Understanding
• Generative Models, Creativity, and Bullshit
• Privacy
• Fairness — Justice, Ideals, and Idealizations
• Fairness - Discrimination, Causal Interpretations, and Path-Specific Effects