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References
Adversarial collaboration
Why do some people parse a space well and others get snowed?
What are situations where you'll need to integrate a large data burden?
At what point do you need to do work in the space as opposed to just reading about it?
Potential topic areas
Preliminary research: How do you do it?
Avi's map: Designing a House
Bugsby's map: Space
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 (e.g. bash)
Bugsby's workflow
Python
Visual programming
XKCD - is time spent automating a task worth it
Semantic web
GUI ("gooey")
"repl" (read-evaluate-print loop) command line
"typed"/"typing"
Caching
Pipes
Macros
Using Dynalist - store instructions in the form of a work log, be able to "tag" sections of it as "programs"
Unit testing
Users/groups/permissions
Transaction log (in the sense of database or file system)
Software liberal
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
Rationalist community
Satisficing utilitarianism
Innumeracy
Consequentialism and Utilitarianism
Pascal's Wager
Roko's Basilisk
Repugnant Conclusion
Peter Singer
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
Public figures who've expressed concern: Elon Musk, Bill Gates, Hillary Clinton
Machine Intelligence Research Institute
OpenAI
Nick Bostrom - Superintelligence
Value alignment problem
Hippocratic Oath in software/civil engineering
Iron Law of Oligarchy
Superintelligence: the idea that eats smart people
Principal-agent problem
SSC AI risk persuasion experiment
EY AI box escape experiment
References
Master of the Senate
Episode 3 - Legacy: the size of a science slice of the pie is shrinking
Rationalists coordinating people on a project article
Technical debt
Kula Sushi LA, Silicon Valley
Influence - Cialdini
Things to Hang on your Mental Mug Tree
Chan Zuckerberg Initiative
Microsoft research announcement around the same time (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
References
xkcd - ranking people
Paul Graham cities essay
Article - The urban archipelago
Iron law of institutions
Iron law of oligarchy
Article about the significance of "again" in "Make America Great Again"
Places
The Barbican
Seguin, Texas
Sisters, Oregon
References
Bret Stephens climate change article
Arguments as soldiers
California wine growers buying land in British Columbia
States and cities offering to step in and honor US climate deals
KSR - global warming as huge engineering project
Reinsurance companies
Bloomberg climate change NGO - covering US portion of UN budget
References
Elasticity of demand
System I and II
Achieving flow
Failure mode of computer scientists in building tools: You Aren't Gonna Need It, Premature Optimization,
Dominion (again) / Isotropic
Overdamped/underdamped control. For the short version, see this Quora answer. For the math, see this.
Efficiency practices
Making (and checking) checklists
Series of many small efficiencies: bumping drawers closed with your hip, staying in touch with best balance contact point
Pipelining
Merging steps (in general, streamlining processes)
Retrospection - which behaviors over last week could have been improved?
When successfully correcting an error: what practices would have avoided that error in the first place? What would those practices have cost me, and would it be worth it?
Scheduling - both making good use of time, and avoiding schedule failures that require costly correction
Recognizing inefficient behavior patterns
Main takeaway: the balance between optimizing for a value function and checking whether you need to update the value function is the key to efficiency.
References
Goodreads
Anathem
Pythagorean
Foundation - Asimov
Doberman
Neal Stephenson Clang video
Gentlemen of the Road
The Yiddish Policeman's Union
Clown on Fire
History of vanilla
Lois McMaster Bujold
Ursula K Le Guin
Gaudy Night
Lord Peter Wimsey
Among Others
High Fidelity
Neil Gaiman and Amanda Palmer
Very Short Introductions
Einstein's Relativity
Chaos - Gleick
Anarchy, State, and Utopia
Rawls A Theory of Justice
C.S. Lewis
The Magicians - Lev Grossman
Kingkiller Chronicle
Jo Walton's close reading of the Kingkiller Chronicle
Robert Jordan
References
K-Blocks
rm -rf
Hardcore Linux
(fencing accident videos are out there but we're not linking them)
Pressure of an arrow tip - calculations, interesting comparison vs pistol
Chuck/lathe
Top roping
Hang gliding
New Republic
Plato's Republic
Electoral college
SSC reverse advice article
Manic Pixie Dream Girl
Soviet seduction department
From the publisher's feed