CS109: Essentials of AI for Life and Society

Assignments

Date	Topic
Week 1	Introduction, AI100 Study Peter Stone (Computer Science) Reading: The 2021 AI100 Study Panel Report: (PDF).
Week 2	Planning and Search Joydeep Biswas (Computer Science) Reading: Beginning of LaValle, S.M., 2006. Planning algorithms. Cambridge university press. Chapter 1 OPTIONAL: Chapter 2
Week 3	Probabilistic Modeling Roberto Martin-Martin (Computer Science) Reading: Kaplan, M. and Kaplan, E., 2007. Chances are...: Adventures in probability. Penguin: (PDF). OPTIONAL: Why Our Brains Do Not Intuitively Grasp Probabilities How Randomness Rules Our World and Why We Cannot See It Humans Have Innate Grasp of Probability
Week 4	Machine Learning Fundamentals Adam Klivans (Computer Science) Reading: Shalev-Shwartz, S. and Ben-David, S., 2014. Understanding machine learning: From theory to algorithms. Cambridge university press: (PDF).
Week 5	Machine Learning Paradigms Ray Mooney (Computer Science) Reading: Pohl, J.G., 2019. The five tribes of machine-learning: A brief overview: (PDF).
Week 6	Computer Vision Kristen Grauman (Computer Science) Reading: Computer Vision, p. 1-22 (download the book following the instructions)
Week 7	Intelligent Robotics Luis Sentis (Aerospace Engineering and Engineering Mechanics) Reading: Sentis, L., 2015. Whole-body operational space control for locomotion and manipulation. Mechanical Engineering, 137(06), pp.S2-S6: (PDF). Deep Imitation Learning for Humanoid Loco-manipulation through Human Teleoperation
Week 8	Natural Language Processing (Large Language Models) Greg Durrett (Computer Science) Reading: [All] ChatGPT Is a Blurry JPEG of the Web, by Ted Chiang [Abstract, Section 1, 2, and Figure 4] TruthfulQA: Measuring How Models Mimic Human Falsehoods [Abstract and Section 1] Marked Personas: Using Natural Language Prompts to Measure Stereotypes in Language Models [Beginning up to the start of the section titled "The Waluigi Effect"] The Waluigi Effect, by Cleo Nardo OPTIONAL: The Full Story of Large Language Models and RLHF, by Marco Ramponi Can LLMs Really Reason and Plan? by Subbarao Kambhampati
Week 9	Philosophical Foundations and Relation to Computing Don Fussell (Computer Science) Reading: Turing, A.M., 2009. Computing machinery and intelligence (pp. 23-65). Springer Netherlands: (PDF). McCarthy, J., Minsky, M.L., Rochester, N. and Shannon, C.E., 2006. A proposal for the Dartmouth summer research project on artificial intelligence, august 31, 1955. AI magazine, 27(4), pp.12-12: (PDF).
Week 10	AI and Mis/disinformation Matt Lease (School of Information) Reading: Das, A., Liu, H., Kovatchev, V. and Lease, M., 2023. The state of human-centered NLP technology for fact-checking. Information processing & management, 60(2), p.103219: (PDF).
Week 11	Bias and Fairness in AI Models / Ethical Datasets Craig Watkins (Journalism & Media) Reading: Suresh, H. and Guttag, J., 2021, October. A framework for understanding sources of harm throughout the machine learning life cycle. In Proceedings of the 1st ACM Conference on Equity and Access in Algorithms, Mechanisms, and Optimization (pp. 1-9): (PDF).
Week 12	Impacts on Workplace and Economics Sherri Greenberg (LBJ School of Public Affairs) Reading: Will AI be an economic blessing or curse? History offers clues Choosing AI's Impact on the Future of Work How AI Is Changing The Future Of Work ChatGPT may be coming for our jobs. Here are the 10 roles that AI is most likely to replace 14 AI Jobs Of The Future - NOW
Week 13	AI Alignment and Existential Threats Scott Aaronson (Computer Science) Reading: 'Reform' AI Alignment with Scott Aaronson
Week 14	Current and Future Directions Joydeep Biswas, Don Fussell, and Peter Stone (Computer Science) Reading: None (just watch the lecture recording!)