Hi all, Lecture 2: Uncertainty <https://cs50.harvard.edu/summer/ai/2020/lectures/2/> is now available on the course website. While we've so far focused on information our AI can know for sure (a game's optimal move, a mine's location, etc.), we'll now transition to how AI can model uncertain events. We'll start by looking at the mathematics of probability theory. Then, we'll explore models — including Bayesian networks and Markov chains — that AI can use to deal with uncertainty. For your project this week, you'll have two choices (you only need to choose one): you can either use a Markov chain approach to implementing Google's PageRank algorithm for computing the importance of web pages, or you can use a Bayesian network approach to see how AI can be used to trace the inheritance of genetic traits through generations. A few other importance announcements for this week: - Quiz 2 <https://cs50.harvard.edu/summer/ai/2020/quizzes/2/> is now available and is due by 11:59pm ET on Wed 7/8. As a reminder, each quiz is open-book: you may use any and all non-human resources during a quiz, but the only humans to whom you may turn for help or from whom you may receive help are the course’s heads. - Project 2 <https://cs50.harvard.edu/summer/ai/2020/projects/2/> is now available as well and is due by 11:59pm ET on Sun 7/12. For this project, you can choose to complete either *Pagerank *or *Heredity*. - Sections <https://cs50.harvard.edu/summer/ai/2020/sections/> this week are an opportunity to talk about probability and uncertainty, and ask questions about this week's concepts. You're highly encouraged to attend! As always, feel free to reach out to me or any of the course staff with any questions! All the best, Brian