The Sapir Whorf hypothesis (language defines what we can perceive and think) is mostly wrong for natural language, but true for programming. Computer languages don't differ in what they can do but in how they let us think.
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Replying to @Plinz
In my notes: "Sapir Whorf seems to be only weakly true for so-called 'human languages', but strongly true for many other representations (mathematics, many interfaces, music, PL, etc)". BTW, I don't much like the term "natural" language, tho I'll concede it has some utility
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Replying to @michael_nielsen
It might simply be because we now have a largely unified global context, and all linguistic families are required to explore a similar semantic space. This does not apply to specialized semantic areas, which don't have linguistic expressions in natural languages.
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Replying to @Plinz
Yep. I do wonder how much it has to do with Miller-style "chunks". That, roughly speaking, most "natural" languages require roughly the same number of chunks to represent a given concept. But specialized representations can greatly reduce that number.
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Replying to @michael_nielsen
That is definitely part of the story, especially since you can describe all mental behavior algorithmically, and represent algorithms using chunks. But I suspect the general answer is that expertise involves building new operators, which then get specialized symbolic descriptors.
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Replying to @Plinz
Those operators are (some of) the new chunks that I believe are acquired. But there's also just general pattern-recognition, along the lines of http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.601.2724&rep=rep1&type=pdf …
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So do you think that operators that transform a whole map (for instance represented as a lattice of grid cells) are implemented as a chunk, i.e. a single local operator with only a handful of parameters (= latent variables of the space generated by the operator)?
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