Chasing The Wind

References Adversarial collaboration [http://slatestarcodex.com/2018/09/26/adversarial-collaboration-contest-results/] Why do some people parse a space well and others get snowed? * Energy landscapes * Explore/exploit * Global optimization * Shape of space vs strategy for optimizing in it What are situations where you'll need to integrate a large data burden? * Either data that you don't understand the structure of, or just too much of it. * Quantum mechanics: different representations mastery can not translate * Recognition of form * Domain shear in programming architecture: components that haven't been designed with each other in mind. Domain Driven Design [https://en.wikipedia.org/wiki/Domain-driven_design] book. Rationality community's double cruxing [http://www.rationality.org/resources/updates/2016/double-crux]. * Umbrella topics: not thinking of the whole as the sum of its parts, e.g. Obamacare At what point do you need to do work in the space as opposed to just reading about it? * Build a thing, visit a place, have an experience? * Podcasting on a thing: what bar is needed for sufficient understanding to talk about it? * Journalism: you have to actually touch primary source, not just other journalistic products. * How? Go places, read textbooks, compute. Run numbers (as done often on Slate Star Codex [http://slatestarcodex.com/]). * Observation vs Randomized Controlled Trial: build the thing to know what biases are built in. Potential topic areas * Moral philosophy * AI risk * Visited OpenAI * Education * My dad * Jesse * Archery * Science fiction and fantasy * Physics simulation * Should do this! * Drug development Preliminary research: How do you do it? * What to do after first-depth internet search hits limits. * Make an actual research plan. * Decide if e.g. you need experts, technical documents etc. * Start drawing yourself a map. * Written out decision tree process. Avi's map: Designing a House * My goal is to learn what I would need to know to get a house that I would be happy with built. * Re-read "A Pattern Language" carefully * Find out the typical procedure for working with an architect - communication style, cost, time. * Learn what major classes of housing site/lot there are (e.g. terrain type, climate, access, zoning). * Find a set of example houses that have at least one thing I like a lot to draw parts from. * Ideally, do Habitat for Humanity or in some other way get involved in the process of building a house. * Make a list of activities and functions that are important to me for living in a house. * Locate and speak with a professional architect and ask them what I should learn before embarking on designing a house. Bugsby's map: Space * My goal is to learn about the past and present of space exploration enough to present an opinion about the future. * Skim my old Astronomy textbook. * Read a history of space exploration. * Re-read a popular technical analysis of the good and bad things about the shuttle program. * Find and read a book about the present state of space exploration. * Find some opposing viewpoint articles about private space companies. * Learn enough about SpaceX and Blue Origin to say what's different about each of them. * Read James Michener's "Space" OR re-read Kim Stanley Robinson's "Red Mars" OR read the sequel "Green Mars".

Avi and Bugsby discuss Bugsby's data science process and brainstorm a design for a tool to improve it. There is a great deal of programming jargon and philosophy. References Command-line interface [https://en.wikipedia.org/wiki/Command-line_interface](e.g. bash) Bugsby's workflow * Gather data * Process data with commands/scripts * Feed processed data to model building * Produce output which may get piped to something else Python [https://en.wikipedia.org/wiki/Python_(programming_language)] Visual programming [https://en.wikipedia.org/wiki/Visual_programming_language] XKCD - is time spent automating a task worth it [https://xkcd.com/1205/] Semantic web [https://en.wikipedia.org/wiki/Semantic_Web] GUI [https://en.wikipedia.org/wiki/Graphical_user_interface] ("gooey") "repl" (read-evaluate-print loop) command line [https://en.wikipedia.org/wiki/Read%E2%80%93eval%E2%80%93print_loop] "typed"/"typing" [https://hackernoon.com/i-finally-understand-static-vs-dynamic-typing-and-you-will-too-ad0c2bd0acc7] Caching [https://www.quora.com/How-do-I-explain-the-concept-of-caching-to-a-non-Computer-Science-person] Pipes [https://en.wikipedia.org/wiki/Pipeline_(Unix)] Macros [https://en.wikipedia.org/wiki/Macro_(computer_science)] Using Dynalist [dynalist.io]- store instructions in the form of a work log, be able to "tag" sections of it as "programs" Unit testing [https://en.wikipedia.org/wiki/Unit_testing] Users/groups/permissions [https://wiki.archlinux.org/index.php/users_and_groups] Transaction log [https://en.wikipedia.org/wiki/Transaction_log] (in the sense of database or file system) Software liberal [https://plus.google.com/110981030061712822816/posts/KaSKeg4vQtz]

We explore what a morality based on "good enough" means, what it would like, and whether we already follow it.  Discussed: genies, strong AI, and tech startups. References Effective altruism [https://en.wikipedia.org/wiki/Effective_altruism] Rationalist community [https://wiki.lesswrong.com/wiki/Rationalist_movement] Satisficing utilitarianism [https://www.princeton.edu/~ppettit/papers/1984/Satisficing%20Consequentialism.pdf] Innumeracy [https://en.wikipedia.org/wiki/Innumeracy_(book)] Consequentialism and Utilitarianism [https://plato.stanford.edu/entries/consequentialism/] Pascal's Wager [https://en.wikipedia.org/wiki/Pascal%27s_Wager] Roko's Basilisk [https://rationalwiki.org/wiki/Roko's_basilisk] Repugnant Conclusion [https://en.wikipedia.org/wiki/Mere_addition_paradox] Peter Singer [https://plato.stanford.edu/entries/principle-beneficence/#ProOveDemBen]

We explore AI Risk, the notion that computer intelligence will cross the human level, start compounding exponentially, and, if not trammelled, mulch everything we hold dear into paperclips.  Why aren't Avi and Bugsby more worried about this? References Eliezer Yudkowsky [http://yudkowsky.net/] Public figures who've expressed concern: Elon Musk [http://www.telegraph.co.uk/technology/2017/07/17/ai-biggest-risk-face-civilisation-elon-musk-says/],Bill Gates [https://www.cnet.com/news/bill-gates-is-worried-about-artificial-intelligence-too/],Hillary Clinton [https://www.theverge.com/2017/11/23/16693894/hillary-clinton-ai-america-totally-unprepared] Machine Intelligence Research Institute [https://intelligence.org/] OpenAI [https://openai.com/] Nick Bostrom - Superintelligence [https://en.wikipedia.org/wiki/Superintelligence:_Paths,_Dangers,_Strategies] Value alignment problem [http://lcfi.ac.uk/projects/the-value-alignment-problem/] Hippocratic Oath insoftware [https://www.computer.org/web/education/code-of-ethics]/civil engineering [https://www.asce.org/code-of-ethics/] Iron Law of Oligarchy [https://en.wikipedia.org/wiki/Iron_law_of_oligarchy] Superintelligence: the idea that eats smart people [http://idlewords.com/talks/superintelligence.htm] Principal-agent problem [https://en.wikipedia.org/wiki/Principal%E2%80%93agent_problem] SSC AI risk persuasion experiment [http://slatestarcodex.com/2016/10/24/ai-persuasion-experiment-results/] EY AI box escape experiment [http://lesswrong.com/lw/up/shut_up_and_do_the_impossible/]

References Master of the Senate [https://www.goodreads.com/book/show/86525.Master_of_the_Senate] Episode 3 - Legacy: the size of a science slice of the pie is shrinking [http://chasingthewind.libsyn.com/episode-3-legacy] Rationalists coordinating people on a project article [https://thezvi.wordpress.com/2017/06/24/on-dragon-army/] Technical debt [https://en.wikipedia.org/wiki/Technical_debt] Kula Sushi LA, Silicon Valley [http://kulausa.com/] Influence - Cialdini [https://www.goodreads.com/book/show/28815.Influence] Things to Hang on your Mental Mug Tree [https://www.edge.org/conversation/rory_sutherland-things-to-hang-on-your-mental-mug-tree] Chan Zuckerberg Initiative [https://chanzuckerberg.com/] Microsoft research announcement around the same time [http://www.independent.co.uk/life-style/gadgets-and-tech/news/microsoft-cancer-cure-research-solved-machine-learning-cells-programming-diseases-a7317616.html](note from Avi: Chan Zuckerberg was actually by far the more reasonable of these, the opposite of what I said in the episode.  Apologies!) Endeavors Moon landing Tennessee Valley Authority Manhattan Project Hoover Dam The Pyramids Human Genome Project Skyscrapers Civil Rights Act Affordable Care Act Standard Oil, Amazon, Google SpaceX Blue Origin Dragon Going to Mars Sea farming Growing a city (Bellevue) Starting a new country Starting a religion Starting a movement Occupy Wall Street/Tea Party Making old age not suck/fighting degeneration