At the end of Session 2, I posted a bunch of resources for topics that interested people. It’s only fair that I post these for you all as well.


  • “Where Am I” by Daniel Dennett
  • “Three Arguments Against the Singularity” by Charlie Stross
  • “The Coming Technological Singularity: How to Survive in the Post-Human Era.” by Vernor Vinge (original coining of the “Singularity.”)
  • “The Singularity is Near” by Ray Kurzweil (The book that popularized the concept)
  • (Lots of papers on simulation and mind uploading)
  • (More resources in uploading)
Also, an interesting quote related to the singularity is Edsger Dijkstra’s quote, “The question of whether Machines can think[. . .]is about as relevant as the question of whether submarines can swim.”
Women and Minorities in Computer Science
Learning more about CS
    • I’d highly recommend taking CS106A, B, and CS107 before trying any of the other Stanford or MIT courses. They are, in my opinion, the best introduction to programming anywhere, going pretty deep into programming fundamentals, as well as enough of computer science fundamental concepts to understand deeply what you are trying to do.
    • CS106A (!)
    • CS106B (!)
    • CS107 (Nicknamed the “Programming Maturity” class at Stanford, CS107 solidifies lots of important skills in computer science and programming.)
    • CS229 (Machine Learning)
    • 6.005
    • 6.034 (AI)
  • Graham, Knuth, Patashnik: “Concrete Mathematics” (My favorite discrete math textbook)
  • Knuth: The Art of Computer Programming (THE tomes on theoretical CS–very theoretical, and definitely requires knowledge of the above textbook or similar before approaching)
  • Project Euler ( — algorithm puzzles; I love using these puzzles to learn new programming languages. After you solve a problem, the forums have a great community to learn about other solutions, and swap ideas)
  • Khan Academy (Free lectures for lots of topics. Perhaps not the best for a very complete and deep understanding, but excellent for wanting to learning general math, science, and engineering)
  • TopCoder (Programming competitions–note, this is usually a good site, but as a competitive site, people tend to be a bit edgier than on some other sites)
  • StackOverflow (Q&A — pretty good community)
  • Kaggle (Machine Learning/Data Mining competitions)

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