Difference between revisions of "Encoding dependence problem"

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(Created page with "one difficulty i have with anki is for knowledge that is very "encoding-dependent" (there must be a better term for this). e.g. how in arithmetizing logic, each book uses a di...")
 
 
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one difficulty i have with anki is for knowledge that is very "encoding-dependent" (there must be a better term for this). e.g. how in arithmetizing logic, each book uses a different encoding of primes and whatnot, how each book uses different methods to build up to Bew, and so forth. i was just looking at GEB, and noticed this too with the specific assembly language instructions Hofstadter gives (ADD, PRINT, JUMP on page 290). There are many sets of "complete operators" (like with propositional logic) for these things, in other words, many equivalent ways to do these things. But when you're first learning this material, when you don't know it well, it's hard to get down to the "core" of the material, to the principles that make things tick. So it's like, you don't want to memorize the artificial encoding, rather you want the encoding-invariant ideas, but to get to that you still need to first learn the encodings. But then, the question is, how do you deal with this in anki, especially if you work with multiple books?
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one difficulty i have with [[Anki]] is for knowledge that is very "encoding-dependent" (there must be a better term for this). e.g. how in arithmetizing logic, each book uses a different encoding of primes and whatnot, how each book uses different methods to build up to Bew, and so forth. i was just looking at GEB, and noticed this too with the specific assembly language instructions Hofstadter gives (ADD, PRINT, JUMP on page 290). There are many sets of "complete operators" (like with propositional logic) for these things, in other words, many equivalent ways to do these things. But when you're first learning this material, when you don't know it well, it's hard to get down to the "core" of the material, to the principles that make things tick. So it's like, you don't want to memorize the artificial encoding, rather you want the encoding-invariant ideas, but to get to that you still need to first learn the encodings. But then, the question is, how do you deal with this in Anki, especially if you work with multiple books?
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I think traditional learning avoids this problem by just sticking to a single text, and also by probably going over the main results not very closely and instead focus on problem sets.
  
 
[[Category:Spaced repetition]]
 
[[Category:Spaced repetition]]

Latest revision as of 20:02, 14 May 2021

one difficulty i have with Anki is for knowledge that is very "encoding-dependent" (there must be a better term for this). e.g. how in arithmetizing logic, each book uses a different encoding of primes and whatnot, how each book uses different methods to build up to Bew, and so forth. i was just looking at GEB, and noticed this too with the specific assembly language instructions Hofstadter gives (ADD, PRINT, JUMP on page 290). There are many sets of "complete operators" (like with propositional logic) for these things, in other words, many equivalent ways to do these things. But when you're first learning this material, when you don't know it well, it's hard to get down to the "core" of the material, to the principles that make things tick. So it's like, you don't want to memorize the artificial encoding, rather you want the encoding-invariant ideas, but to get to that you still need to first learn the encodings. But then, the question is, how do you deal with this in Anki, especially if you work with multiple books?

I think traditional learning avoids this problem by just sticking to a single text, and also by probably going over the main results not very closely and instead focus on problem sets.