From a5a7b08178cf7e40ce76ac49b8c4a9ec56bab474 Mon Sep 17 00:00:00 2001 From: "M. Taylor Saotome-Westlake" Date: Wed, 28 Oct 2020 23:44:01 -0700 Subject: [PATCH] "Sexual Dimorphism": explaining high-dimensionality, again MIME-Version: 1.0 Content-Type: text/plain; charset=utf8 Content-Transfer-Encoding: 8bit (This is one of my standard points that I'm pretty practiced at explaining—should I regret it being in the middle of a long post, rather than making it an individually linkable standalone (like I did for just the math part)?) I'm pleased with the height → faces → FaceApp → morphic-adaptation flow. --- ...ences-in-relation-to-my-gender-problems.md | 37 +++++++------------ ...exual-dimorphism-in-the-sequences-notes.md | 27 ++++++++++++++ 2 files changed, 40 insertions(+), 24 deletions(-) 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 index 12ec531..f8b633a 100644 --- 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 @@ -132,7 +132,7 @@ But, well ... I mean, um ... From the standpoint of my secret erotic fantasy, "normal, masculine man wearing a female body like a suit of clothing" is actually a _great_ outcome—the _ideal_ outcome. Let me explain. -The main plot of my secret erotic fantasy accomodates many frame stories, but I tend to prefer those that invoke the [literary genre of science](https://www.lesswrong.com/posts/4Bwr6s9dofvqPWakn/science-as-attire), and posit technology indistinguishable from magic rather than magic _simpliciter_. +The main plot of my secret erotic fantasy accomodates many frame stories, but I tend to prefer those that invoke the [literary genre of science](https://www.lesswrong.com/posts/4Bwr6s9dofvqPWakn/science-as-attire), and posit "technology" rather than "spells" or "potions" as the agent of change, even if it's all ultimately magic (where "magic" is anything you don't understand). So imagine having something like [the transporter in _Star Trek_](https://memory-alpha.fandom.com/wiki/Transporter), but you re-materialize with the body of someone else, rather than your original body—a little booth I could walk in, dissolve in a tingly glowy special effect for a few seconds, and walk out looking like (say) [Nana Visitor (circa 1998)](https://memory-alpha.fandom.com/wiki/Kay_Eaton?file=Kay_Eaton.jpg). (In the folklore of [female-transformation erotica](/2016/Oct/exactly-what-it-says-on-the-tin/), this machine is often called the ["morphic adaptation unit"](https://www.cyoc.net/interactives/chapter_115321.html).) @@ -148,42 +148,31 @@ Do I want the magical transformation technology to fix all that, too? Do I have _any idea_ what it would even _mean_ to fix all that, without spending multiple lifetimes studying neuroscience? -[TODO (working): rewrite this whole section to be more focused on _just_ explaining the math language needed to explain how the transformation mapping would work, using face and height as "easy" examples] +I think I have just enough language to _start_ to talk about what it would mean. Since sex isn't an atomic attribute, but rather a high-level statistical regularity such that almost everyone can be cleanly classified as "female" or "male" _in terms of_ lower-level traits (genitals, hormone levels, _&c._), then, abstractly, we're trying to take points from male distribution and map them onto the female distribution in a way that preserves as much structure (personal identity) as possible. My female analogue doesn't have a penis (because then she wouldn't be female), but she is going to speak American English like me and be [85% Ashkenazi like me](/images/ancestry_report.png), because language and autosomal genes don't have anything to do with sex. -I think I have just enough language to _start_ to talk about what it would mean. If sex isn't an atomic attribute, but rather a high-level statistical regularity such that people can be cleanly classified as "female" or "male" _in terms of_ lower-level traits (like hormone levels, genital shape, _&c._), then, abstractly, we're trying to take points from male distribution and map them onto the female distribution in a way that preserves as much structure as possible. My female analogue doesn't have a penis (because then she wouldn't be female), but she is going to speak American English like me and be [85% Ashkenazi like me](/images/ancestry_report.png), because language and autosomal genes don't have anything to do with sex. +The hard part has to do with traits that are meaningfully sexually dimorphic, but not as a discrete dichotomy—where the sex-specific universal designs differ in ways that are _subtler_ than the presence or absence of entire reproductive organs. We are _not_ satisfied if the magical transformation technology swaps out my penis and testicles for a functioning female reproductive system without changing the rest of my body, because we want the end result to be indistinguishable from having been drawn from the female distribution (at least, indistinguishable _modulo_ having my memories of life as a male before the magical transformation), and a man-who-somehow-has-a-vagina doesn't qualify. -The subtle part as to do with traits that are meaningfully sexually dimorphic but not as a discrete dichotomy: we're _not_ satisfied with the transformation if the magical transformation technology swaps out my penis and testicles for a functioning female reproductive system but doesn't change the rest of my body. +The "obvious" way to to do the mapping is to keep the same percentile rank within each trait, but take it with respect to the target sex's distribution. I'm 5′11″ tall, which [puts me at](https://dqydj.com/height-percentile-calculator-for-men-and-women/) the 73rd percentile for American men, about 6/10ths of a standard deviation above the mean. So _presumably_ we want to say that my female analogue is at the 73rd percentile for American women, about 5′5½″. -[...] +You might think this is "unfair": some women—about 7 per 1000—are 5′11″, and we don't want to say they're somehow _less female_ on that account, so why can't I keep my height? But if we refuse to adjust for every trait for which the female and male distributions overlap (on the grounds that _some_ women have the same trait value as my male self), we don't end up with a result from the female distribution. -I'm 5′11″ tall, which [puts me at](https://dqydj.com/height-percentile-calculator-for-men-and-women/) the 73rd percentile for men, about 6/10ths of a standard deviation above the mean. So _presumably_ we want to say that my female analogue is at the 73rd percentile for women, about 5′5½″. +The typical point in a high-dimensional distribution is _not_ typical along each dimension individually. [In 100 flips of a biased coin](http://zackmdavis.net/blog/2019/05/the-typical-set/) that lands Heads 0.6 of the time, the _single_ most likely sequence is 100 Heads, but they're only one of those and you're _vanishingly_ unlikely to actually see it. The sequences you'll actually observe will have close to 60 Heads. Each such sequence is individually less probable than all Heads, but there are vastly more of them. -[...] +Statistical sex differences are like flipping two different collections of coins with different biases, where the coins represent various traits. Learning the outcome of any individual flip, doesn't tell you which which set the coin came from, but if we look at the aggregation of many flips, we can get _godlike_ confidence as to which collection we're looking at. +A single-variable measurement like height is like a single coin: unless the coin is _very_ biased, one flip doesn't tell you much. But there are lots of things about people for which it's not that they can't be measured, but that the measurements require _more than one number_. -People are [verifiably very good at recognizing sex from (hair covered, males clean-shaven) photographs of people's faces](/papers/bruce_et_al-sex_discrimination_how_do_we_tell.pdf) (96% accuracy, which is the equivalent of _d_ ≈ 3.5), but we don't have direct introspective access into what _specific_ features our brains are using to do it; we just look, and _somehow_ know. The differences are real, but it's not a matter of any single measurement: [covering up the nose makes people slower and slightly worse at sexing faces, but people don't do better than chance at guessing sex from photos of noses alone](/papers/roberts-bruce-feature_saliency_in_judging_the_sex_and_familiarity_of_faces.pdf). +Take faces. People are [verifiably very good at recognizing sex from (hair covered, males clean-shaven) photographs of people's faces](/papers/bruce_et_al-sex_discrimination_how_do_we_tell.pdf) (96% accuracy, which is the equivalent of _d_ ≈ 3.5), but we don't have direct introspective access into what _specific_ features our brains are using to do it; we just look, and _somehow_ know. The differences are real, but it's not a matter of any single measurement: [covering up the nose makes people slower and slightly worse at sexing faces, but people don't do better than chance at guessing sex from photos of noses alone](/papers/roberts-bruce-feature_saliency_in_judging_the_sex_and_familiarity_of_faces.pdf). -[TODO: Mathematically, -Joel et al. and response—maybe in next paragraph -Beyond the Binary: https://www.pnas.org/content/112/50/15468 -http://cogprints.org/10046/1/Delgiudice_etal_critique_joel_2015.pdf +Notably, for _images_ of faces, we actually _do_ have magical transformation technology. AI techniques like [generative adversarial networks](https://arxiv.org/abs/1907.10786) and [autoencoders](https://towardsdatascience.com/generating-images-with-autoencoders-77fd3a8dd368) can learn the structure of the distribution of face photographs, and use that knowledge to [synthesize faces from scratch](https://thispersondoesnotexist.com/) and -http://zackmdavis.net/blog/2019/05/the-typical-set/ -> once you draw a boundary around a group, the mind starts trying to harvest similarities from the group. And unfortunately the human pattern-detectors seem to operate in such overdrive that we see patterns whether they're there or not; a weakly negative correlation can be mistaken for a strong positive one with a bit of selective memory. -https://www.lesswrong.com/posts/veN86cBhoe7mBxXLk/categorizing-has-consequences -[a higher-dimensional statistical regularity in the _conjunction_ of many variables](https://www.lesswrong.com/posts/cu7YY7WdgJBs3DpmJ/the-univariate-fallacy-1) -96.8% classification from MRI https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374327/ -] -[the wrists: http://unremediatedgender.space/papers/yune_et_al-beyond_human_perception_sexual_dimorphism_in_hand_and_wrist_radiographs.pdf] -[talk about mapping from one distribution to another: e.g. height] +[...] The same moral applies to sex differences in psychology. I'm a pretty weird guy, in more ways than one. I am not prototypically masculine. Most men are not like me. If I'm allowed to cherry-pick what measurements to take, I can name ways in which I'm more female-typical than male-typical. (For example, I'm _sure_ I'm above the female mean in [Big Five Neuroticism](https://en.wikipedia.org/wiki/Big_Five_personality_traits).) But "weird" represents a much larger space of possibilities than "normal", much as [_nonapples_ are a less cohesive category than _apples_](https://www.lesswrong.com/posts/2mLZiWxWKZyaRgcn7/selling-nonapples). If you _sum over_ all of my traits, everything that makes me, _me_—it's going to be a point in the _male_ region of the existing, unremediated, genderspace. In principle, you could define a procedure that maps that point to the female region of configuration space in some appropriately structure-preserving way, to compute my female analogue who is as authentically _me_ as possible while also being authentically female, down to the proportion of gray matter in her posterior lateral orbitofrontal cortex and—the love of a woman for a man. -(Note that we can already basically do this for _images_ of female and male faces, using the [latent spaces found by generative adversarial networks](https://arxiv.org/abs/1907.10786) and [autoencoders](https://towardsdatascience.com/generating-images-with-autoencoders-77fd3a8dd368), as demonstrated by the likes of [FaceApp](https://www.faceapp.com/), the _uniquely best piece of software in the world_. Doing it for _actual whole people in the real world_ and not just flat images is a task for future superintelligences, not present-day GANs, but some of same basic principles should apply.) - -[TODO: mention https://thispersondoesnotexist.com/ ; we can synthesize images from scratch] +[...] Okay. Having supplied just enough language to _start_ to talk about what it would mean to actually become female—is that what I _want_? I mean, I would definitely be extremely eager to _try_ it ... @@ -257,7 +246,7 @@ Anyway, that—briefly (I mean it)—is the story about my weird obligate sex fa Imagine my surprise to discover that, in the current year, my weird sexual obsession is suddenly at the center of [one of the _defining political issues of our time_](https://en.wikipedia.org/wiki/Transgender_rights). -All this time—the dozen years I spent reading everything I could about sex and gender and transgender and feminism and evopsych and doing various things with my social presentation (sometimes things I regretted and reverted after a lot of pain, like the initials) to try to seem not-masculine—I had been _assuming_ that my gender problems were not of the same kind as people who were _actually_ transgender, because the standard narrative said that that was about people whose ["internal sense of their own gender does not match their assigned sex at birth"](https://www.vox.com/identities/21332685/trans-rights-pronouns-bathrooms-sports), and I had never interpreted my beautiful pure sacred self-identity thing as an "internal sense of my own gender." +All this time—the dozen years I spent reading everything I could about sex and gender and transgender and feminism and evopsych and doing various things with my social presentation (sometimes things I regretted and reverted after a lot of pain, like the initials) to try to seem not-masculine—I had been _assuming_ that my gender problems were not of the same kind as people who were _actually_ transgender, because the standard narrative said that that was about people whose ["internal sense of their own gender does not match their assigned sex at birth"](https://www.vox.com/identities/21332685/trans-rights-pronouns-bathrooms-sports), and I had never interpreted my beautiful pure sacred self-identity thing as an "internal sense of my own gender", /2018/Jan/dont-negotiate-with-terrorist-memeplexes/ diff --git a/notes/sexual-dimorphism-in-the-sequences-notes.md b/notes/sexual-dimorphism-in-the-sequences-notes.md index 6afa647..933173c 100644 --- a/notes/sexual-dimorphism-in-the-sequences-notes.md +++ b/notes/sexual-dimorphism-in-the-sequences-notes.md @@ -148,3 +148,30 @@ people like me being incentivized to identify as part of a political pressure gr Yudkowsky says trans rights! + +https://www.lesswrong.com/posts/jMTbQj9XB5ah2maup/similarity-clusters +https://www.lesswrong.com/posts/cFzC996D7Jjds3vS9/arguing-by-definition + +https://www.lesswrong.com/posts/9QxnfMYccz9QRgZ5z/the-costly-coordination-mechanism-of-common-knowledge + + +as demonstrated by the likes of [FaceApp](https://www.faceapp.com/), the _uniquely best piece of software in the world_. + +(Note that we can already basically do this for _images_ of female and male faces, using the [latent spaces found by generative adversarial networks]() and [autoencoders](), + +Doing it for _actual whole people in the real world_ and not just flat images is a task for future superintelligences, not present-day GANs, but some of same basic principles should apply.) + +[TODO: Mathematically, +Joel et al. and response—maybe in next paragraph +Beyond the Binary: https://www.pnas.org/content/112/50/15468 +http://cogprints.org/10046/1/Delgiudice_etal_critique_joel_2015.pdf + + +> once you draw a boundary around a group, the mind starts trying to harvest similarities from the group. And unfortunately the human pattern-detectors seem to operate in such overdrive that we see patterns whether they're there or not; a weakly negative correlation can be mistaken for a strong positive one with a bit of selective memory. +https://www.lesswrong.com/posts/veN86cBhoe7mBxXLk/categorizing-has-consequences + +96.8% classification from MRI https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6374327/ +] +[the wrists: http://unremediatedgender.space/papers/yune_et_al-beyond_human_perception_sexual_dimorphism_in_hand_and_wrist_radiographs.pdf] + +Face editing with Generative Adversarial Networks: https://www.youtube.com/watch?v=dCKbRCUyop8 \ No newline at end of file -- 2.17.1