fans control the music: using AI to measure fan enthusiasm at EDC

We invented technology to enhance the fan/performer connection. Vote for Team Ambience at EDC!

DJs and more traditional musicians require realtime audience feedback during performances. However, often we cannot see our audience—their movement, their facial expressions, etc.—during shows due to stage lighting. Therefore we cannot gauge their enthusiasm, and therefore cannot alter our performance to respond.

We at Team Ambience invented a means for measuring such audience enthusiasm using a video camera and artificial intelligence (AI). Our solution reports mood metrics and summarizes crowd movement directly to performers’ phones. (Many musicians, myself included, perform with an iPhone connected to our mic stands).

We developed our prototype from scratch while attending the Amazon- and Intel-sponsored “Hack Till Dawn – A Deep Learning Hackathon: Use Machine Learning to Revolutionize EDC Festivals” event last weekend. At this hacking competition, teams competed to win tickets to the Electric Daisy Carnival (EDC) by developing AI technologies designed to improve fan experiences at large music festivals. By strengthening performers’ ability to “read” the audience in realtime, our invention definitely enhances the fan experience by enabling performers to better craft their shows according to audience sentiment.

EDC will feature Team Ambience’s (our) solution, along with those of two additional winning teams, at three kiosks positioned throughout the festival this coming weekend. Fans will vote on their favorite idea—we certainly encourage you to vote for us!

Demonstration

Reading Audience Facial Expressions

The first video below shows us presenting different facial expressions to performers. The phone in the lower right reports our mood:

Zooming in on the phone application itself, note the change as the music changes:

Reading Audience Movement

Is the audience jumping, are they swaying? If so, how much? Consider the following video [1]:

From this we measure audience movement intensity and report it:

Additional Applications

The audience metrics described hold value not only to performers, but also to venue owners, booking agencies, and artist managers. We provide a business dashboard for such stakeholders:

Furthermore, as a composer/performer, I envision creating an AI that composes and improvises with me on-the-fly using audience feedback.

Technical Details

We used the Amazon machine learning stack, along with a DeepLens camera:

AWS DeepLens

We could not have achieved this without a complimentary AWS DeepLens camera (thanks Amazon!). This wireless video camera contains a dedicated machine learning chip, along with preloaded algorithms. Furthermore, you can add your own algorithms. The whole solution is tightly integrated with the rest of the AWS machine learning stack. Click on the image below to go to the DeepLens page. (Disclosure: I am an affiliate marketer for Amazon).

References

  1. I apologize that I do not know the source of this video.

blockchain is punk

Wide distribution of power lies at the heart of anarchistic thinking. While “punk” and “anarchy” do not necessarily imply one another, they often overlap. Punks tend to balk at centralized authority, as do anarchists. A short leap of logic concludes that we therefore dislike centralized technology.

Concentration in technology long parallels concentration in political power. For example, our abandonment of hunting and gathering in favor of agriculture let to the formation of the state. The Industrial Revolution created modern class divisions. Algorithmic credit score models, opaque and controlled by few, moderate our economic options.

Early in my mechanical engineering career, I realized that if we as a society end material scarcity—through local, distributed control of capital—we will free the world from the chains of classism. Envisioned village- or homestead-sized power power stations, local production of hydrogen to power transportation, homestead-based food production (via hydroponics and synthetic biology), and local manufacture through technologies like 3D printing. And of course, I envisioned a fully distributed data infrastructure. A place for the state exists in this vision, for example they will pave roads, but the state would hold less power in my scenario than it does in the world today.

Large businesses would also lose centralized power, though they will continue to exist and thrive. We’ll need them to produce technology to drive the transition to a (liberated) post-scarcity society—manufacturing an iPhone for instance requires significant investment and raw material sourcing infrastructure. Therefore we’ll have to reward the these businesses appropriately. But we can pilot the business models of large enterprises toward wide distribution of stake, such that the line between producers and consumers blurs.

So far I’ve only briefly mentioned the subject of data centralization, as I felt it better to explain my overall ethic in terms of wider technical matters, in terms of anthropology. Now we zero in on computing, and data in particular:

In the 1990’s I labeled myself “cyberpunk”. Our epic heroes (e.g. Case in Neuromancer) broke information free from the highly centralized control, often distributing it to the people (a la Chevette Washington in Virtual Light). I practiced these values with urgency while resisting US West as a BBS sysop and through over a decade of Linux hacking (the time period in the early 2000’s when Microsoft fought tooth-and-nail to kill open-source software). I practiced these values when I data-mined Ventura County’s online tax delinquency records to find abandoned land to park my RV on.

Most data today resides under centralized control. My bank likely runs a single database to store my account information (we are ignoring practical matters such dev/test servers and sharding). The California Department of Motor Vehicles (DMV) likely stores my truck’s title information in a single location. The United States Treasury controls the American money supply (financial transactions may be thought of as information flow—an abstraction—a numerical measurement of value—i.e., “data”).

If I sell my truck, I have to tell the DMV so they can update their records. I’d rather register the transaction on the decentralized Ethereum blockchain. And if money is data, let’s manage it as decentralized currency!

Blockchain technology dilutes concentrated power. Distributes data (and therefore power) more evenly amongst stakeholders.

This punk, this anarchist, never grew up. She simply grew practical in the short term without losing her long-term concept for the world. She regards blockchain as a bridge between this short-term practicality and the long term vision.

See also:

why we still need cyberpunk

declaring myself an arbiter of proper ladylike behavior

Today I officially declared myself an arbiter of proper ladylike behavior. Issued the announcement via Twitter and Facebook:

Obviously I’m not a perfect lady myself, having immediately mocked the whole concept by using foul language in the second sentence of the announcement tweet.

But this speaks to a fundamental issue: A proper lady will not take herself too seriously! A proper lady knows that “ladylike behavior” is an abstraction and a ruse, yet chooses to employ it anyway. It’s a means to an end, and, for those that wish to participate, one of many pathways an individual may take toward creating a more civil and empowered society (if taken within proper context).

For a transgender lady such as myself, and perhaps for all ladies, ladylike behavior exercises empowerment. It provides an assertion of identity against a world that devalues the feminine. Deep in my transgender femme brain (and I’m only speaking for myself here), becoming a woman is never enough. I need to blossom into a lady. A “proper” lady. This liberates, not oppresses!

The best thing about this process is that I get to define “proper” and define “lady”. I’m creating something that works for me within the current time and place. Certainly I draw on a multitude of others’ etiquette manuals, blog posts, and how-to videos. I tap the Kama Sutra and the Bible for ideas. But the tiara stops with me—I’m the ultimate arbiter of my intent.

However, I plan to actively influence culture with my process and conclusions. Therefore I will add my voice to the growing worldwide call for promotion of civil, polite, feminine, demure (when appropriate), and of course, “ladylike” behavior by (interested) women of all ages. Will never treat my contribution as mandate, as many fabulous women will find no interest in it. This is perfectly fine.

My perspective proves unique in that no one taught me proper ladylike behavior growing up. The result is that I still “man-spread” and chug my beer when I lose mindfulness. As a work in progress recreating my own social construction from the ground up, I assimilate ladylike behavior as a foreigner learning a new language from scratch. This is beautiful and absurd. And it means all assumptions fell off the table.

So in my derivation of ladylike behavior for this social reconstruction I’m learning a lot about it, and intend to share my findings from a position only a transgender lady can offer.

Let’s get started!

Ladylike behavior involves many “musts”. I now issue my first:

“A proper lady never wears flip-flops in public, except at the beach, the pool, or the public shower.”

Proudly developed this “rule” myself; read it in no style guide or etiquette post.

The world is ours, ladies!

Update 27 April 2018

Received the following perspective-enhancing reply to my Facebook announcement:

Just reminds me to follow what I first admonished above:  “A proper lady will not take herself too seriously!”. Also illustrates how “expertise” lies in the eye of the beholder.

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

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:

constant self-reinvention: my profound habit for creating success

Video of speech I gave at a Toastmasters meeting about my primary method for obtaining success:

a non-linear and holistic work ethic

I started working out a holistic map of my work ethic and work values, and quickly found that linearity failed to cut it. Essentially, I need to capture the interdependencies between spiritual, social, and financial wealth. More importantly, I need to illustrate the crucial balance between these factors. Enter non-linear system dynamics:

Simulation, based on ad-hoc parameterization (because there is no way to actually measure most of these variables), demonstrates that I’m at least moving in the correct direction:

Method

Used Vensim PLE to create and simulate the model.

why we still need cyberpunk

William Gibson penned Neuromancer over thirty years ago, and the 1990’s ended viciously on 9/11. With the exception of cyberfeminism, I wrote off “cyberpunk” as an ethic once we as a society stopped saying “cyber” and replaced the word with “online”.

Yesterday I traced partial assets of an individual I distrusted—and needed the straight dope from—from my laptop. Dating while transgender proves dangerous and a girl must protect herself!

In between I persistently beleaguered Microsoft as a career-long Linux hacker.

Once declared squatter’s rights on a piece of land I identified though data mining.

I walk with the Big Data devils to broadcast my signal, a means to an end. Twitter, Google, Amazon, and Facebook receive my data, and in exchange they amplify my cultural imperative.

And they know where the real value in data lies: Not in the records themselves but in the interconnections between them.

Emergent properties steered by unholy gods.

“Cyber”: Greek for “to steer”.

Steering a boat requires connecting the data: Position, velocity, acceleration, time. State variables alone won’t suffice.

When we get burned by Cambridge Analytica or the Russian Federation, we realize our individual technological vulnerability.

Propaganda is hacking: Implant bias, implant ideas, grow emergent outcomes. Seduction is a system intrusion.

Technological warfare and psychological warfare forever link.

Class war must proceed asymmetrically.

I only trust the Prophets, not the Church, not the State, not the Oligarchs.

And we can be prophets in cyberspace. We can create technology that liberates the world.

We can steer toward our own emergent outcomes.

We can end material scarcity.

Love forward. Program. Network. Build enterprise. Produce art. Write. Love forward.

Jam the system, and prepare to be jammed by the punks that follow you.

The 1990’s are dust, but the “system” still remains cybernetic control. Therefore resistance remains cyberpunk.

mathematical coolhunting

I aim to become the Timothy Leary of data scientists!

Intuitive coolhunting scales poorly. Here’s some math to help fix that problem:

Axioms of cool

Five axioms enable us to mathematically model cool:

  1. No one is intrinsically cool, individuals simply channel it.
  2. Ability to temporarily hold coolness varies by individual.
  3. Coolness naturally flows into some individuals more readily than others.
  4. Rate of coolness flow into an individual increases with the amount of cool stored within that individual’s social network.
  5. The rate at which cool leaves an individual increases as observation of cool’s presence in that individual increases.

Examining these axioms in more detail:

1. No one is intrinsically cool, individuals simply channel it

‘Cool’ flows into and out of individuals, as shown by the following stock and flow diagram:

Individuals can temporarily store some of this cool, in a manner resembling a capacitor storing electrical charge. We can for example imagine an individual’s step response to incoming cool:

We can describe this capacitive behavior in the stock and flow diagram with first-order dynamics:

2. Ability to temporarily hold coolness varies by individual

Individual capacity for storing cool differs. Given the same step input above, we might observe different responses for individuals A and B:

3. Coolness naturally flows into some individuals more readily than others

Some individuals channel cool better than others. We model this by varying the “natural” coolness input flow rate by individual:

4. Rate of coolness flow into an individual increases with the amount of cool stored within that individual’s social network

Individuals with cool friends tend to more successfully channel cool themselves. We model this by increasing influx rate according to a “coolness in social network” factor:

5. The rate at which cool leaves an individual increases as observation of cool’s presence in the individual increases

Once observed, cool tends to exit the individual it was observed in. We model this by increasing the coolness decay rate as a function of public observance of an individual’s coolness:

Source and sink of cool

Assume the universe provides an infinite source of cool. Similarly, assume existence of an infinite capacity sink for coolness that exits individuals. Also assume that everyone alive connects to this source and sink. It follows that individuals cannot “use up” the supply of cool or withhold coolness from others. Under the axioms, cool never transfers from one person to another—the relationships between individuals simply modulate the rate cool enters each person from the source and leaves each person to the sink.

Networks of cool

The last two axioms relate individual ability to receive and store coolness to the instantaneous state of their social network. To demonstrate the axioms in this social context, suppose the following friendship network exists among seven individuals:

Now suppose that Julie holds a lot of cool at a particular moment. It follows from axiom #4 that Guido’s instantaneous ability to channel cool will increase due to his connection with Julie. Similarly, if Di stores very little cool at a given time, Hardeep’s ability to receive cool will not benefit from his relationship with Di.

Hardeep’s coolness influx rate benefits from the combined cool stored within Emilio, Kaitlin, Di, and Abe. However, because of axiom #5, the fact that Emilio, Kaitlin, Di, and Abe observe Hardeep’s cool accelerates its exit from Hardeep. Due to the first-order dynamics described above, this exit of cool lags the influx of cool, giving Hardeep time to enjoy a temporary build up of coolness and time for Emilio, Kaitlin, Di, and Abe to benefit from its presence in Hardeep.

Simulating coolness networks

Using the mathematical framework developed above, we now simulate cool’s flow within the network described in the last section. Since we currently have no way to actually measure cool—and therefore parameterize the model—we run it with fictional initial conditions and examine the resulting system-level effects to see what happens.

The combined model for this friendship network is shown in the image below (sorry about the mess of arrows):

Simulating this model with arbitrarily selected initial conditions and factors yields:

A long way to go before this work is useful

As stated above, we currently have no way to measure cool, and therefore no way to validate and parameterize this model. Expect a Bayesian strategy to emerge shortly though. Until then, this work remains conjectural and exploratory.

Computation notes

Used Vensim PLE to draw and simulate the stock and flow systems, R to display the simulation output, and NetworkX to draw the example social network.

encoding fashion rules into mathematical data structures (part one)

As we build our fashion recommendation engine, we seek rules to populate it with. With few exceptions (e.g. [1]), we find these rules encoded in prose or infographic form, rather than a semantic web form suitable for computation. For example, [2] provides written advice on dressing fabulously for a “rectangular” women’s body type. The writers meant this document for a human reader, not a computer program.

However, we can’t scale a process consisting of manual extraction of rules to the level we would like to achieve in this project, so we turn to natural language processing to extract rules from texts in an automated fashion. We begin by identifying parts of speech and the syntax relationships between words in sentences. For example, consider the following two fashion rules from [2]:

  • If you are a heavy or tall rectangle, choose a big bag.
  • If you are a petite rectangle, choose a petite bag.

We then create a directed graph with words as nodes, each with an attribute indicating its part of speech, and edges indicating the syntactic relationships between the nodes (e.g., “heavy” is a modifier of “rectangle”). We also add edges to specify the direction of sentence flow. Visualizing the above two sentences in this form using Neo4j [3] yields:

Next Steps

In the next phase, we plan to automatically derive computationally useful IFTHENELSE rules from such mappings. For example, the above two sentences express in IFTHENELSE form as:

  • IF rectangle AND (heavy OR tall) THEN choose a big bag
  • IF rectangle AND petite THEN choose a petite bag

Once we form a comprehensive set of such rules, we will load them into an expert system or related system to enable fuzzy reasoning on the rules, enabling custom fashion recommendations!

After this, we will come up with a way to reconcile similar recommendations. For example, suppose we find the following two IFTHENELSE rules from two different sources:

  • IF rectangle AND (heavy OR tall) THEN choose a big bag
  • IF rectangle AND (heavy set OR tall) THEN select a big bag

These say the same thing. We will devise a way to combine them into one recommendation such that the weight (value) of the recommendation doubles due to its backing by two distinct sources.



References

  1. Vogiatzis, D. Pierrakos, G. Paliouras, S. Jenkyn-Jones, B.J.H.H.A. Possen, Expert and community based style advice, Expert Systems with Applications, Volume 39, Issue 12, 2012, Pages 10647-10655, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2012.02.178. (http://www.sciencedirect.com/science/article/pii/S0957417412004411) Keywords: Style advice; Recommender system; Fashion ontology; User modeling
  2. http://www.styled247.com/rectangle-body-shape
  3. https://neo4j.com

“cool” as information flow

“Coolness” is a measure of creative information’s degree of flow through a social network.

Individuals merely channel and contribute to this flow as it progresses through such networks, they do not contain or retain coolness within themselves.

However some individuals prove more receptive to the incoming information and more effective at transmitting it forward than others, and we often label these people as “cool”. But this focuses attention toward the wrong place—the individual—where in reality coolness only resides in the experience of its movement through the network’s nodes.

And when the network consists of many such open, creative people sending signals back and forth, we accurately describe the network as cool.

It’s a jam session. Whether I’m working on a collaborative science project or improvising with a rock band, it’s a jam session. Creative information flows through the network of participants—and we experience “cool” through the exchange.

power and the art of seduction

In my last post, “power and sexual technique”, I urged women to gain leverage in their romantic relationships by increasing their skill in bed. More importantly, I developed this idea as a strategy transgender women may employ to decrease their overall marginalization—to help close the power differential that exists when straight men sexualize us for our unique bodies.

So far so good. But as a charismatic person (and natural propagandist) I know that the real establishment of a relationship’s or sexual encounter’s power distribution occurs at the encounter’s initiation—at the point of seduction—not in bed. One skilled at seduction, even if they lean submissive within the overall dynamic, owns the situation.

I frankly enjoy seducing my way into an encounter, thereby controlling the situation initially, and then joyfully sharing this power as the night unfolds. I might even completely surrender this power depending on the lover.

So a skilled seductress wields a mainline to authority and control.

I therefore, in a manner similar to my last post, encourage women and particularly transgender women to learn the art of seduction. My intent is not manipulation, but enhancement of mutual joy and an attenuation of the patriarchy’s power. I want us to diminish our marginalization by grabbing men by their psychosexual balls.

I realize that now I need to set an example. Problem is, I don’t particular think I’m good at seduction. To remedy that I’m committing to a thorough study of the art, starting with Robert Greene’s classic “The Art of Seduction”:

Need some inspiration? I started with a video featuring the fabulous Dita Von Teese where she irresistibly (yes, I’m bisexual) explains basic technique and attitude (below). However, I consider her words just the tip of the iceberg…

…I want the ability to start wars with my seduction (a la Troy), not merely get a date! I want to force Odysseus to break the ropes binding him.

And now, Dita Von Teese:

power and sexual technique

Competence proves extremely sexy.

My firm desire (pun intended) is that all women, particularly transgender women, take complete ownership over their presence and their bodies. Presence emits power. Ownership delivers power. Here I refer to “power” in a feminine network sense, enabling women full constructive influence within the relationships they build and expand. These relationships may include friendships, business connections, or romantic partnerships.

In the case of romantic connections, whether short or longterm, sexual competency wins constructive leverage. I therefore encourage all women, and again, particularly transgender women, to study sexual technique. To set an example, I’ve embarked on the reading list given below.

Why do I focus on transgender women in particular? Because we often regard ourselves as “inferior” women and I wish that to cease immediately. Furthermore, we are often sexualized for being transgender rather than treated like human partners. That probably won’t cease but can be manipulated. My opinion is that developing skill in bed resists the former mindset, and shifts control toward transwomen in the latter situation.

Regarding the latter: Instead of thinking of myself as objectified for being trans, I think of myself as having cornered a market. This economic viewpoint empowers. I can set my base price (expectation of a partner) based on scarcity. I can then increase my price (expectation of a partner) by enhancing my sexual technique.

Become a world-class lover. Own yourself. Own your power.

My reading list:

Here is the most famous one, but you should know that sexual technique is only a small part of it. Wikipedia explains this well:

Contrary to western popular perception, the Kama Sutra is not exclusively a sex manual; it presents itself as a guide to a virtuous and gracious living that discusses the nature of love, family life, and other aspects pertaining to pleasure-oriented faculties of human life.

Finally, we must learn to seduce, a skill separate from competency in bed:

ten ways to deliver class (part #1)

“Class” strategically combines humility with knowing you stand a cut above the masses.

And now we begin writing about class.

“Class” is:

  1. Knowing when to lead and performing it gracefully.
  2. Knowing when to follow and performing it gracefully.
  3. Returning your shopping cart to the requested place.
  4. Realizing the full humanity in those who serve you (e.g., at a restaurant, etc.).
  5. Sitting up straight.
  6. Using proper grammar.
  7. Admitting and apologizing for your mistakes, and immediately working to remedy them.
  8. Dressing appropriately for an occasion.
  9. Driving politely.
  10. Counting your blessings.

More to come in future editions of this series!

flaunt those legs girl!

Transgender women typically sport great legs, due to pre-HRT (hormone replacement therapy) muscle development combined with post-HRT muscle shaping. The result stands out!

So flaunt those legs girl!

I never wear pants.

And remember that high heels will further accentuate your legs! For tips and video on successfully living in heels see my post “the trick to walking in heels…”. Confidence forms the key ingredient—you must emit badass!

Setting an Example

To encourage you, let me now walk the walk (pun intended):