on spiritual poverty

My engineer brain wants to cure all material scarcity.

While climbing a long staircase up a mountain to the Savitri Temple in the holy city of Pushkar I encountered a man whom I believe lived in the forest and collected firewood to sell to the villagers. Didn’t know if he was an ascetic or perhaps an untouchable. But I did conclude, without evidence beyond educated guess, that I make more money in a day than he makes in a year… or a lifetime.

I own X number of hats and Y pairs of shoes.

Suppose we did cure all material scarcity: Everyone is fed, housed, and clothed. Everyone has smart phones and computers and transportation to the farthest reaches of the planet. We can accomplish this with little additional technological innovation, and I think it a noble goal.

Then we’ll all want art and meaning. We’ll want to convince each other we are right. We might annihilate ourselves. We’ll create new scarcity with intellectual property—we might all be clothed but some of us will get haute couture—we’ll still use our plumage to create rank.

Stars upon thars… We might annihilate ourselves.

My communist soul wants a leveling revolution. My Christian soul wants the Golden Rule to underpin material society.

But we might annihilate ourselves in attempt to create that world. And I can’t argue with the fact that capitalism has lifted more people out of poverty than communism or religion combined.

My anarchist soul just wants to love God. No popes, no trips to Mecca. Just ecstatic prayer transmitted from the RV that I live in.

I own X number of hats and Y pairs of shoes. I’m a fashion legend in my own mind. I’m more creative than most. My IQ falls in the 98th percentile.

But God can raise a 1,000 of me out of the dust. I don’t mean much.

My favorite holiday is Dia de los Muertos; reminds me that rich or poor, we all snuff it in the end.

My engineer brain wants to cure all material scarcity. But does God really care about that? We are spiritually impoverished in the Global North. Spiritual malnutrition is destroying us.

Spiritual malnutrition is destroying us.

I don’t give a rat’s ass who you fuck, what you drive, or who you pray with. I just want you to talk with God.

Axis Evil featuring Napalm Fatale @ the Che Café – 24 January 2019

My performance with special guest Queen Mab playing the Kaossilator Pro. I’m using a seven-string guitar and trying new embellishments in the guitar parts, especially in the song “Voice in the Distance”:

lace punk, satin punk, petticoat punk, oh my!

This article was first published on the fashion blog Mad Hot and Artsy on 20 November 2018. Special thanks to them for featuring my content!

To satirize my proud (and adopted) feminine modality, I developed a personal clothing style I call “lace punk”, “satin punk”, and/or “petticoat punk”. It carries steampunk, burlesque, and pin-up influences; and emphasizes poise, class, and elegance. In other words, I give the appearance of “high class”. This combination kills when combined with sound posture and a confident stride—my satire has since evolved into a robust display of raw power.

Key elements involve millinery, corsetry, lace or satin gloves, heels, and frequent appearance in gowns.

The “punk” in all this is me: Accomplished hacker. Cyberpunk. Skilled guitar shredder. Free-thinking anarchist. These outfits tweak guys’ expectations when I talk engineering circles around them—“geek chic” never celebrated femininity quite like this.

I perform under the stage name “Napalm Fatale”. Have released two albums freely available at https://napalmfatale.bandcamp.com. Wrote an important article called “This is Transgender Music” describing this work and my musical goals.

I founded the company Whole-Systems Enterprises, Inc. to pay the bills. Am extremely interested in applying artificial intelligence to the fashion industry.

how to sit with ladylike poise, class, and elegance

Everybody knows I live an absolutely prim and proper life. For that reason I insist on spreading my copious knowledge to quality ladies everywhere! Here I demonstrate the “Duchess Slant” and the “Cambridge Cross”:

The Duchess Slant and the Cambridge Cross, attributed to Kate Middleton and Diana Spencer respectively, prevent your knickers from showing while seated, particularly in front of a camera [1]. We wouldn’t want the press to snap a photograph of the royal panties now would we?

Duchess Slant

English royals prove well practiced [2]:

Legs join at the knees and ankles (not crossing), and then tilt to one side. Heels touch the floor. Hands gently rest in the lap, ideally with one folded over the other [3]. Good posture—with back not touching the chair [4]—finalizes the position.

On my side of the Atlantic, I tend add a pronounced head tilt because I think it’s cute; compare to Ms. Middleton’s very slight tilt in the picture above.

Cambridge Cross

Again with the English royals [4]:

As the name implies, ankles gently cross when performing the Cambridge Cross, but otherwise one follows the Duchess Slant technique.

Why Bother?

Besides protecting modesty, these positions also slim and lengthen leg appearance [3].

I learned to sit properly from a modeling school’s video. Unfortunately I do not know which one so I cannot cite it. The video argued that given two equally qualified candidates for a photo shoot, the one that sits better will most likely get the job.

We can expand this idea to cover any job interview, and to cover career growth in general. Carrying oneself with class and poise, including how you sit, takes you places.

What About Crossing at the Knee?

The talking heads of etiquette have not sorted out amongst themselves whether crossing at the knee, while keeping angles together, proves appropriate for a lady of class [3]. But Ms. Spencer performs this position very elegantly:

Word to the wise: Prevent your petticoat from getting between your angles as demonstrated here:

The medical community continually debates the potential health hazards of sitting with legs crosses at the knee, but the jury is out [5].

How Not to Sit

Above I emphasized that one can only properly cross legs at the knee whilst keeping angles together, as demonstrated in the last two pictures. For contrast, this image [6] shows a less elegant form which ladies must avoid:

References

  1. https://www.popsugar.com/celebrity/What-Duchess-Slant-44944499
  2. https://people.com/royals/meghan-markle-duchess-slant-kate-middleton/
  3. https://www.vogue.com.au/culture/features/what-is-the-duchess-slant-and-does-it-really-matter-in-2018/news-story/e205f9b71548f9e9a004e15c6c573ba8
  4. https://brightside.me/wonder-people/12-exclusive-rules-from-the-duchess-of-cambridge-372910/
  5. http://www.bbc.com/future/story/20151013-is-crossing-your-legs-bad-for-you
  6. http://faze.ca/is-sitting-with-your-legs-crossed-bad-for-you/

 

thinking big (part #1)

Declaring I’m a “big thinker” understates it. I channel more creativity than a single human can ever bring to fruition, so I’m now going to simply start logging ideas on a somewhat weekly to monthly basis in case others want to take them on, or collaborate with me.

So, here are this week’s big ideas, many of which I received while driving between Carmel and San Simeon on California Highway 1:

Censorship-Resistant Microblogging

For two weeks I’ve been developing a blog post in my head about using Blockchain as a tool for censorship-resistant microblogging. The article will discuss a particular platform’s solution, not survey all possible options (which I’ll do later). It will take the form of a “how to” post.

Technology that Responds to Desertification

Climate change will likely promote heavy desertification. We have two angles to take regarding technological response:

First, we can develop technology that resists the desertification process itself. Think infrastructure and environmental engineering by nation-states. However, given that I do not expect nation-states to be able to respond functionally to rapid desertification, I offer the following alternative:

Second, develop high technology for single family, tribe, and/or homestead use. I’m thinking composting systems that capture water as it is off-gassed. I’m thinking distilling water from urine. Micro-desalination for folks living right against the coast. Massively effective composting reactions from food waste. Capturing and distilling sweat. Capture water from fuel cell use (if I remember correctly how fuel cells work).

That latter social layout risks invoking the “tragedy of the commons” with regard to well water. So we need water-efficient farming technology such as hydroponics and good social compacts.

Adverse Possession

I once claimed squatter’s rights (technically “adverse possession”) to a ten-acre piece of abandoned land. Applied data science to find the ideal property.

About to start the process again, and refine the method.

I’ll give special consideration to developing a strategy that supports the needs of RV boondockers.

Fuzzy Logic-Based Women’s Body Shape Calculator

I wrote a women’s body shape calculator, where one enters their measurements and receives a classification of their body shape into one of the classic style-guide shapes (e.g., “apple”, “rectangular”). My post “artificial intelligence in fashion (part two: a first step)” introduces this tool.

However, I am unhappy with the use of crisp computation by this tool and would rather replace it with fuzzy reasoning, to reflect differences in expert opinion about what makes what body shape what. Then users will receive more informative results. For example, instead of reporting that a user’s measurements indicate a strictly “rectangular” body shape, the fuzzy reasoning-based method could declare that the same use is 90% “rectangular” and 10% “hourglass”.

Blockchain

Blockchain matters. Learn it inside and out. Learn about the “internet of money”. Learn about “discretized valuation”. Program it. “Get” it.

Expand My Social Media Footprint

People generally like what I have to say online, and I believe it provides social value. So therefore I want to expand my reach. This is not so much for my “glory” as it is to promote diverse voices in media. My agenda is to “transmit” what is given to me.

So I will apply my full data scientist brain to this task and see what happens! Perhaps it starts with measurement?

Extract More Precise Signal from the Media Firehose

My friend Epochryphal challenged me to think of improved ways to capture, sort, and process online content as it emerges. I started them off by recommending an RSS reader but we agreed the problem requires a better solution. My next move involves checking out Feedly.

Practice Navigating Without a Smart Phone

I ran into a situation where I needed a road atlas due to lack of wireless service, but didn’t have one. Not good! Such an atlas might also prove important if one is running from the law and cannot have their cellular phone turned on (to avoid tracking).

Dead reckoning is a powerful skill. Nurture it.

Revisit Development of Technology for “Off-Grid” Living

A primal value of mine. Will discuss specifics in later posts.

LSD-25 and CRISPR/Cas9

Use CRISPR/Cas9 to engineer a fungus or a mold to safely produce clean LSD-25 on a table top.

Grow a Vagina in a Petri Dish from Stem Cells

I’m not happy with current male-to-female gender affirmation surgery technique. Let’s grow a vagina and clitoris from reprogrammed stem cells and then install it.

women’s style recommendation with artificial intelligence (part #2)

In “women’s style recommendation with artificial intelligence (part #1)”, I introduced my work toward developing artificial intelligence (AI) for fashion and style recommendation. Essentially, its an expert system built on a Bayesian belief network. Now I discuss model validation and next steps in the design iteration process.

I first wanted to see if the trained network correctly returned known recommendations (“wear” or “don’t wear”) based on known clothing selections. This procedure successfully validated the code I wrote. Then I wanted to see if the model can derive new style rules. Experienced partial success on this account; I will outline a possible strategy for improving it.

The rest of this article details the processes summarized in the previous paragraph:

Consider the following trained Bayesian belief network structure:

While calculating the structure, the learning algorithm also calculated the node value probability distributions from the training set:

We first evaluate the model on three fashion rules, asking whether the selected node combination’s values are okay to wear:

  • IF body shape = “apple” AND skirt zipper = “on front” THEN wear = “No!” [1]
  • IF body shape = “apple” AND skirt zipper = “on side” THEN wear = “Yes” [1]
  • IF shoes = “flip-flops” THEN wear = “No!” [2]

(I trained the model upon 126 such rules simultaneously).

Running the inference code:

All looks good. As a control, I added “shoe = pumps” (instead of flip-flops) to the above calculation, and see that these are okay to wear as expected. (However, see the discussion below where I ran into trouble).

So now I start to derive novel new style rules from the model. Suppose we want to simply find out if it is okay to “wear” an “apple” body shape. We expect the model to report “yes”, as it does, assigning a probability to the conclusion:

However, the model cannot handle the addition of a shoe type to the “apple” body shape query above:

The problem is “fixed” when I add a style rule specifically allowing apple-shaped folks to wear pumps, but I am not happy with this. Ideal outcome would be for the inference to conclude this. I’m first going to check the dependencies encoding… which, if that solves the problem, stresses the importance of specifying dependencies well in additional to lateral relationships. For example, I might establish a “human” node, and indicate that each clothing article and feature proves appropriate for humans to wear. Then I’ll declare that each body shape associates with “human = true”.

Nonetheless, the progress reported here is significant!

I’ll keep you posted.

– Emily

References

I figured out this whole “ladylike” thing today

“A ‘lady’ is a woman who, through her mere presence, simultaneously commands power while setting others at ease.” – Emily Marie Williams

I achieved public womanhood on 14 July 2015 when I declared myself a woman before a judge and started living full-time as one. But as discussed several times on this blog, I work consistently to develop my personal concept of “lady” (a concept distinct from “woman”), and labor to assimilate this concept’s traits into my core being. (Check out the “See Also” section at the end of this post for links to my previous writings on the subject, which illustrate my progress through this exercise).

Today I experienced a breakthrough in this concept’s development, upon writing the following letter to my mom. I quoted the key innovation at the introduction to this post:

I have discovered that when I dress simultaneously classy, elegant, and ladylike; and move with casual grace while wearing heels, strangers take me more seriously. They step out of the way in stores. They open doors for me more frequently. They resolve conflicts with me more effectively.

Of course, it helps that I smile at and make eye contact with everyone I pass, and that I’m tall. And that I’m confident in my skin. Somehow I’ve discovered how to command power while simultaneously setting people at ease.

I think that last sentence is the essence of the “ladylike” concept I am striving to create for myself. I now have a vision that fits my feminist ethos and still matches my extremely gendered ideas about class.

Recently concluded that my days proceed more effectively, both in my mind and out in society, when I dress sharply.

Here is what I was wearing when I figured this out, what earned me the respect from strangers I received today that enabled me to put the pieces together:

See Also

women’s style recommendation with artificial intelligence (part #1)

Introduction

We know several basic style “rules” (ha!) based on body shape:

  • Skirts:
    • “Apple” Body Shape:
      • IF body shape is apple AND skirt has front zipper THEN don’t wear
      • IF body shape is apple AND skirt has side zipper THEN wear
      • IF body shape is apple AND skirt has no zipper THEN wear
    • “Rectangular” Body Shape:
      • IF body shape is rectangle AND skirt has front zipper THEN wear
      • IF body shape is rectangle AND skirt has front zipper THEN wear
      • IF body shape is rectangle AND skirt has front zipper THEN wear
      • IF body shape is rectangle AND skirt is A-line THEN wear
  • Pants:
    • “Apple” Body Shape:
      • IF body shape is apple AND jeans have flare THEN wear
      • IF body shape is apple AND jeans have pleats THEN don’t wear
      • IF body shape is apple AND jeans have stretch THEN wear
      • IF body shape is apple AND trousers have flare THEN wear
      • IF body shape is apple AND trousers have pleats THEN don’t wear
      • IF body shape is apple AND trousers have stretch THEN wear
    • “Rectangle” Body Shape:
      • IF body shape is rectangle AND jeans have flare THEN wear
      • IF body shape is rectangle AND jeans have pleats THEN wear
      • IF body shape is rectangle AND jeans have stretch THEN wear
      • IF body shape is rectangle AND trousers have flare THEN wear
      • IF body shape is rectangle AND trousers have pleats THEN wear
      • IF body shape is rectangle AND trousers have stretch THEN wear

We want to create an artificially intelligent system to probabilistically decide, given a query such as “I have an ‘apple’ body shape and am thinking of wearing a skirt with a zipper in front. Should I?”. To accomplish this we use these rules to train a Bayesian network, and then use the network to make inferences upon queries such as the one given above.

Training the Network

From these we derive the 13 nodes of our Bayesian network:

Node
apple
jeans.with.flare
jeans.with.pleats
jeans.with.stretch
rectangle
skirt.with.a.line
skirt.with.front.zipper
skirt.with.no.zipper
skirt.with.side.zipper
trousers.with.flare
trousers.with.pleats
trousers.with.stretch
wear

We use the rules and the nodes to produce an automatically generated graph. Put to help it along, we will apply some expert knowledge and specify some

We seed the model structure identification algorithm with some basic expert knowledge by manually specifying the following 12 causal relationships:

From To
rectangle wear
apple wear
skirt.with.front.zipper wear
skirt.with.side.zipper wear
skirt.with.no.zipper wear
skirt.with.a.line wear
jeans.with.flare wear
jeans.with.stretch wear
jeans.with.pleats wear
trousers.with.flare wear
trousers.with.stretch wear
trousers.with.pleats wear

(We will see later that the automated graph structure learning procedure adds one more edge).

We save these relationships in “output/style_edges.csv” for later import using R.

We then encode the rules in dictionaries/hashes for items co-joint in a rule. For example, we express the skirt-related rules pertaining to apple-shaped bodies in JSON as:

    {
        "wear": "Yes",
        "apple": "1",
        "skirt.with.no.zipper": "1"
    },
    {
        "wear": "Yes",
        "apple": "1",
        "skirt.with.side.zipper": "1",
    },
    {
        "wear": "No",
        "apple": "1",
        "skirt.with.front.zipper": "1",
    }

For each entry, we zero out all other nodes (expect for “wear”, which is set to “No”), and express all 19 rules as a data frame, where the index order corresponds to the node order displayed above:

0,0,0,0,1,0,0,1,0,0,0,0,Yes
0,0,0,0,1,0,1,0,0,0,0,0,Yes
0,0,0,0,1,0,0,0,1,0,0,0,Yes
0,0,0,0,1,1,0,0,0,0,0,0,Yes
0,1,0,0,1,0,0,0,0,0,0,0,Yes
0,0,0,1,1,0,0,0,0,0,0,0,Yes
0,0,1,0,1,0,0,0,0,0,0,0,Yes
0,0,0,0,1,0,0,0,0,1,0,0,Yes
0,0,0,0,1,0,0,0,0,0,0,1,Yes
0,0,0,0,1,0,0,0,0,0,1,0,Yes
1,0,0,0,0,0,0,1,0,0,0,0,Yes
1,0,0,0,0,0,0,0,1,0,0,0,Yes
1,0,0,0,0,0,1,0,0,0,0,0,No
1,1,0,0,0,0,0,0,0,0,0,0,Yes
1,0,0,1,0,0,0,0,0,0,0,0,Yes
1,0,1,0,0,0,0,0,0,0,0,0,No
1,0,0,0,0,0,0,0,0,1,0,0,Yes
1,0,0,0,0,0,0,0,0,0,0,1,Yes
1,0,0,0,0,0,0,0,0,0,1,0,No

We save this data frame as “output/style_rules.csv” for later import by R.

In R, we load the necessary libraries and the CSV files. We also ensure everything is a factor in the rules data frame:

We look at the expert-specified edges, noting the existence of 12 relationships. After running the hill climbing algorithm to derive the network structure from the prior-specified edges and the rules, we notice that now 13 edges are present:

Here is the added edge:

From To
apple rectangle

We derive the model’s parameters from the training data, and then compile it for use in inference.

Results

Suppose we have an “apple” body shape, and want to choose a skirt using this model. We try the following skirt types against the apple body shape to infer whether or not to wear a particular skirt:

The first result in the image above resoundingly rejects wearing a skirt having a front zipper when one carries and apple-shaped body. By contrast, the second result approves of skirts having side zippers for apple-shaped folks. Both results concord with the IF-THEN-ELSE rules initially specified. The third result proves interesting—we did not provide a rule for apple-shaped bodies and A-line skirts, so the model provides no conclusion.

We observe similar results for trousers: The first two outcomes match the rules, but the third provides no decision because we provided no information about whether flare and stretch may be used together in a pair of trousers for apple-shaped bodies, or for any body shape for that matter!

Issues to Resolve

As indicated in the last paragraph, in practice a pair of trousers may have both flare and the ability to stretch. Each of these traits alone proves great for apple-shaped individuals. So together I manually infer that the two together are at least okay and may be even preferable. However, the model does not derive such a conclusion. In other words, we need to add rules saying these two traits may coexist.

Also, this effort took a lot of manual “expert” specification of the initial “seed” graph structure. Ideally one would learn the final structure purely from rules. My thinking is that the rule data frame is rather sparse, making it hard to learn the structure in an automated fashion. On the other hand, I may not have chosen the best learning algorithm.

Stay tuned…

– Emily

Update 16 April 2018

I’m onto the next iteration of the model design. A visual of results so far:

artificial intelligence in fashion (part two: a first step)

In my recent post, “artificial intelligence in fashion (part one: brainstorming)“, I produced a list of big ideas on how machine learning and artificial intelligence may be applied to the fashion industry. I addressed sizing, marketing, and design activities when brainstorming this list.

This post doesn’t specifically cover an artificial intelligence solution, but it lays groundwork that I need in place to get to an AI-based style recommendation engine based on body shapes that I’d like to build. Essentially, most fashion dictums take the form of IF-THEN-ELSE rules, where the IF clause generally starts with specifying one’s body shape.



So I needed a way for many individuals at once to determine their body shape, which led to creation of a web-based body shape calculator, pictured below. Several of these already exist, but I really needed my own for my AI project for the following reasons:

  • I can include this work into larger AI software pipelines.
    • Cannot easily include others’ tools, by comparison.
  • I understand the computational method behind what I’m offering.
    • Others’ tools are black boxes.
  • The computation method I used comes from academic literature, so it is peer-reviewed.
  • I can show ads to users to generate some cash flow.

Here is a picture of the web-application I created for this task. Click here to use the application!

go to a damn tailor!

Let’s face it, transgender women often experience a difficult time finding clothes that fit perfectly. (But then, doesn’t every woman?!?). For example, my particular problems are:

  • I usually need plus-sized blouses to enable fit around my chest and shoulders, but then the blouses generally appear unflatteringly wide around my waist.
  • While I usually buy dresses that flair to diminish my lack of hips, I’ve at least one dress with too much “definition” in the fabric such that actual hips need to fill the space for it to look right.
  • At least one of my dresses expected a larger breast size than I possess.

 

To solve all three cases I’ll regularly hire a tailor rather than waste my time looking for perfection at the item’s point of initial sale.



This of course requires some experience (and common sense) to know what can be altered to suit your needs. I can’t tell you how to gain this experience except through taking risks and making mistakes (I generally buy at Goodwill so I’m only out a few bucks if the item can’t be altered to meet my requirements). Your tailor will describe to you why a request will or will not be achievable and you will learn from those discussions.

You may not even know what needs to be accomplished to improve a (mostly fitting) outfit. But your tailor will employ their experience to gain the best result for your body.

Here is the most important thing a transwoman must look for when seeking a tailor:

Are they trans-friendly? If you have a penile bulge under your panties it might show while they are working with you to specify the correct fit. They must be cool with that possibility!

But money talks—and therefore I’ve experienced no problems.

I also recommend tipping your tailor. Not sure if that’s customary, but I do it anyway.

The most important thing I want to impress upon you:  A good tailor will make a mediocre outfit stunning on you! Found this vital in my business life.

I interviewed my tailor, Karla Vega of Vista, California, for tips on how to find a good one. Here is what she told me:

Know the difference between a “seamstress” and “tailor”. Seamstresses make dresses from scratch, tailors alter and repair clothes.

Tailoring works successfully when you begin with clothes that are too big rather than too small, for obvious reasons. So buy one size up if you anticipate the need for tailoring of an item. I asked if certain parts of an item, such as a dress, proved more challenging then others to alter, and she said no. My experience confirms this as she has successfully altered the bust areas, hips, or the waist of the items I bring her as required.

Learn your body type, e.g. “pear”, so you increase your shopping prowess.

Understand that knowing how to sew does not make you a tailor. The warned of clothing swap meets where an amateur sets up an alteration table for the event and sells “tailoring” services. Her advice is too look for someone who makes tailoring their business. I asked about places like Nordstrom that offer tailoring services and she said they are just fine.

artificial intelligence in fashion (part one: brainstorming)

Brainstorming as usual:

  1. Fashion dictums involve many IF-THEN-ELSE rules. One can convert this into a decision engine (inference engine).
  2. User specifies their body shape, and a recommendation engine selects suitable clothing for them, taking into account the user’s tastes.
  3. Upload an image of a dress you want to buy, and specify the dress’s given size. At the same time, upload your measurements. The algorithm then tells you the likelihood of fit.
  4. Upload your measurements. The algorithm searches for clothes that fit well.
  5. Upload your measurements. The algorithm searches for clothes that flatter your body shape.
  6. User submits 10+ images of dresses they like, with the option to add more. Moreover, they submit their measurements. The algorithm then designs dresses for them.
  7. Automate difficult design tasks. My model here is the AI drummer in GarageBand which provides very sophisticated beats, and which I use in all my songs.
  8. Enhance design. Algorithms can produce combinations that have not been thought of before. Here I envision designer as “pilot” and algorithm as “vehicle”.
  9. Create fiber optic dresses that light up responsively to movement, such that the changes in lighting accentuate curves.



Collaborate!

If you would like to collaborate with me to these or similar ideas happen, I’m an extremely experienced data scientist and would love to work with you!  Please contact me through Facebook if you are interested.

AI-Driven Fashion Show

Holding a fashion show for AI-created styles sounds fabulous!

Next Steps

See what tools exist already. See what APIs exist. Determine if measurement statistics are known. Investigate the Computer Science and Home Economics academic literature.

What data is out there?

See Also

body shape calculator