References & Resources for LessWrong
A list of references and resources for LW
Updated: 2011-05-24
- F = Free
- E = Easy (adequate for a low educational background)
- M = Memetic Hazard (controversial ideas or works of fiction)
Summary
Do not flinch, most of LessWrong can be read and understood by people with a previous level of education less than secondary school. (And Khan Academy followed by BetterExplained plus the help of Google and Wikipedia ought to be enough to let anyone read anything directed at the scientifically literate.) Most of these references aren't prerequisite, and only a small fraction are pertinent to any particular post on LessWrong. Do not be intimidated, just go ahead and start reading the Sequences if all this sounds too long. It's much easier to understand than this list makes it look like.
Nevertheless, as it says in the Twelve Virtues of Rationality, scholarship is a virtue, and in particular:
It is especially important to eat math and science which impinges upon rationality: Evolutionary psychology, heuristics and biases, social psychology, probability theory, decision theory.
Recommended Reading for Friendly AI Research
This post enumerates texts that I consider (potentially) useful training for making progress on Friendly AI/decision theory/metaethics.
Bloggingheads: Yudkowsky and Aaronson talk about AI and Many-worlds
Eliezer Yudkowsky and Scott Aaronson - Percontations: Artificial Intelligence and Quantum Mechanics
Sections of the diavlog:
- When will we build the first superintelligence?
- Why quantum computing isn’t a recipe for robot apocalypse
- How to guilt-trip a machine
- The evolutionary psychology of artificial intelligence
- Eliezer contends many-worlds is obviously correct
- Scott contends many-worlds is ridiculous (but might still be true)
Eric Drexler on Learning About Everything
Related to: The Simple Math of Everything, Your Strength as a Rationalist, Teaching the Unteachable.
Eric Drexler wrote a couple of articles on the importance and methods of obtaining interdisciplinary knowledge:
Note that the title above isn't "how to learn everything", but "how to learn about everything". The distinction I have in mind is between knowing the inside of a topic in deep detail — many facts and problem-solving skills — and knowing the structure and context of a topic: essential facts, what problems can be solved by the skilled, and how the topic fits with others.
This knowledge isn't superficial in a survey-course sense: It is about both deep structure and practical applications. Knowing about, in this sense, is crucial to understanding a new problem and what must be learned in more depth in order to solve it.
This topic was discussed intermittently on Overcoming Bias. Basic understanding of many fields allows to recognize how well-understood by science a problem is and to see its place in the structure of scientific knowledge; to develop better intuitive grasp on what's possible and what's not; and to adequately perceive the natural world.
The advice he gives for obtaining general knowledge feels right, even for studying the topics that you intend to eventually understand in depth:
Don't drop a subject because you know you'd fail a test — instead, read other half-understandable journals and textbooks to accumulate vocabulary, perspective, and context.
Storm by Tim Minchin
I'm sure many of you have already seen this performance. Tim Minchin's beat poem "Storm" is about the sceptical, secular understanding of the world, stupidity of quackery and supernatural, weight of dishonesty, and joy in the merely real. Contains strong language.
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