X-Git-Url: http://unremediatedgender.space/source?p=Ultimately_Untrue_Thought.git;a=blobdiff_plain;f=content%2Fdrafts%2Fsexual-dimorphism-in-the-sequences-in-relation-to-my-gender-problems.md;h=6ad81a42c9f01ccb93f0412850c56c5395b96125;hp=a16d05f59bbbf58f496e069d4a46d627b5017d05;hb=3da09bbcf027a10acee5187e03a655029fdd853e;hpb=4860088c00ac348aa063f5d1d4bc37746291f9dc

diff --git a/content/drafts/sexual-dimorphism-in-the-sequences-in-relation-to-my-gender-problems.md b/content/drafts/sexual-dimorphism-in-the-sequences-in-relation-to-my-gender-problems.md
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--- a/content/drafts/sexual-dimorphism-in-the-sequences-in-relation-to-my-gender-problems.md
+++ b/content/drafts/sexual-dimorphism-in-the-sequences-in-relation-to-my-gender-problems.md
@@ -172,7 +172,7 @@ Notably, for _images_ of faces, we actually _do_ have transformation technology!
 
 If you let each pixel vary independently, the space of possible 1024x1024 images is 1,048,576-dimensional, but the vast hypermajority of those images aren't photorealistic human faces. Letting each pixel vary independently is the wrong way to think about it: changing the lighting or pose changes a lot of pixels in what humans would regard as images of "the same" face. So instead, our machine-learning algorithms learn a [compressed](https://www.lesswrong.com/posts/ex63DPisEjomutkCw/msg-len) representation of what makes the tiny subspace (relative to images-in-general) of _faces in particular_ similar to each other. That [latent space](https://towardsdatascience.com/understanding-latent-space-in-machine-learning-de5a7c687d8d) is a lot smaller (say, 512 dimensions), but still rich enough to embed the distinctions that humans notice: [you can find a hyperplane that separates](https://youtu.be/dCKbRCUyop8?t=1433) smiling from non-smiling faces, or glasses from no-glasses, or young from old, or different races—or female and male. Sliding along the [normal vector](https://en.wikipedia.org/wiki/Normal_(geometry)) to that [hyperplane](https://en.wikipedia.org/wiki/Hyperplane) gives the desired transformation: producing images that are "more female" (as the model has learned that concept) while keeping "everything else" the same.
 
-Two-dimensional _images_ of people are _vastly_ simpler than the actual people themselves in the real physical universe. But _in theory_, a lot of the same _mathematical principles_ would apply to hypothetical future nanotechnology-wielding AI systems that could synthesize a human being from scratch (this-person-_didn't_-exist-dot-com?), or do a real-world sex transformation (PersonApp?)—and the same statistical morals apply to reasoning about sex differences in psychology and (which is to say) the brain.
+Two-dimensional _images_ of people are _vastly_ simpler than the actual people themselves in the real physical universe. But _in theory_, a lot of the same _mathematical principles_ would apply to hypothetical future nanotechnology-wielding AI systems that could, like the AI in "Failed Utopia #4-2", synthesize a human being from scratch (this-person-_didn't_-exist-dot-com?), or do a real-world sex transformation (PersonApp?)—and the same statistical morals apply to reasoning about sex differences in psychology and (which is to say) the brain.
 
 Daphna Joel _et al._ [argue](https://www.pnas.org/content/112/50/15468) [that](https://www.pnas.org/content/112/50/15468) human brains are "unique 'mosaics' of features" that cannot be categorized into distinct _female_ and _male_ classes, because it's rare for brains to be "internally consistent"—female-typical or male-typical along _every_ dimension. It's true and important that brains aren't _discretely_ sexually dimorphic the way genitals are, but as [Marco del Guidice _et al._ point out](http://cogprints.org/10046/1/Delgiudice_etal_critique_joel_2015.pdf), the "cannot be categorized into two distinct classes" claim seems false in an important sense. The lack of "internal consistency" in Joel _et al._'s sense is exactly the behavior we expect from multivariate normal-ish distributions with different-but-not-vastly-different means. (There aren't going to be many traits where the sexes are like, _four_ or whatever standard deviations apart.) It's just like how sequences of flips of a very Heads-biased and very Tails-biased coin are going to be unique "mosaics" of Heads and Tails, but pretty distinguishable with enough flips—and indeed, with the right stats methodology, [MRI scans can predict sex at 96.8% accuracy](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374327/).