Some random late-night musings on profanity

When I was a kid, I had a little plaque with a poem on it hanging up on my bedroom wall. I have no idea who wrote the poem or where it came from, but it was there on my wall for so long I memorized it.

Never say die, say “damn!”
It isn’t poetic,
it may be profane,
but we mortals have need of it,
time and again.
And you’ll find you recover from Fate’s hardest slam,
if you never say die, say “damn!”

I love profanity. I’ll admit it. Supposedly “profane” language is language that communicates quickly and effectively, with lovely immediacy. It’s shunned because it’s particularly well-suited to conveying unruly emotions–messy, untidy emotions that some folks would like to pretend don’t exist.

But they do, and “vulgar” language is singularly eloquent in expressing them.

There is tremendous nuance in vulgarity. If I call someone a hopeless fuckmuppet, that conveys a different meaning than if I say they’re a hopeless fuckwit or a hopeless fuckhead. Each of these communicates disdain, to be sure, and in a far more visceral way than saying “I rather do believe that chap is quite distressingly incompetent at going about this business of life,” but those few syllables after the vulgarity carry a great deal of subtlety and differentiation.

People who fear vulgar language fear life, for it is a fact not easily overlooked that some parts of life are vulgar.

eBook Design Illuminated

A short time ago, I was hired by Talk Science To Me to do the eBook version of Tantra Illuminated, a very lengthy academic work on the history of Tantrick religious traditions in India.

The book was large and beautifully designed, with a great deal of content from original Sanskrit sources. The design used a number of different, complex elements, including copious margin notes.

I’m in the process of blogging about the complexities of eBook design with non-English alphabets and complex layouts. Part 1 of the series is up on the Talk Science To Me blog. Here’s a teaser:

The project turned out to be far more daunting than I’d imagined, even knowing from the outset that it would likely be more complex than it first appeared. I could easily write a book on the various technical, layout and rendering challenges I encountered creating this e-book (in fact, that might be a good future project!), but we’ll just look at a few of the interesting potholes we encountered on the road to creating the e-book.

A tale of two diacritics

The text in Tantra Illuminated contains significant lengths of transliterated Sanskrit. Sanskrit uses a non-Latin alphabet for which a standard transliteration system called the International Alphabet of Sanskrit Transliteration (IAST) exists. This is the system employed by the transliterations in Tantra Illuminated.
The IAST relies heavily on Latin characters with diacritic marks. Most of these marks are supported by the majority of e-book readers, so I didn’t anticipate difficulty with the transliterations.

I was wrong.

You can comment here or over there.

#WLAMF no. 27: “Polyamory is wrong!”

If you’ve been part of any poly community online for more than…oh, about 400 milliseconds or so, you’ve unquestionably seen someone post the “polyamory is wrong” T-shirt. You know the one I mean:

Get it? You’re supposed to think at first that it’s saying polyamory is morally wrong, but really it’s just saying it’s wrong to mix Latin and Greek roots! Get it?

Except that…err, it’s totally okay to mix Latin and Greek roots. We do it all the time. In fact, even purely Latin words might have mixed roots, because the Romans had their grubby paws all over the place, and mixed words from different languages with gleeful abandon. Latin itself is about as pure as a Baptist in a tavern, and as it says in Job 14:4, “Who can bring what is pure from the impure? No one!”

But I’m not one to stand in the way of a good linguistic joke, so I most humbly propose the following additions to the canon:

I’m writing one blog post for every contribution to our crowdfunding we receive between now and the end of the campaign. Help support indie publishing! We’re publishing five new books on polyamory in 2015.

Some thoughts on machine learning: context-based approaches

A nontrivial problem with machine learning is organization of new information and recollection of appropriate information in a given circumstance. Simple storing of information (cats are furry, balls bounce, water is wet) is relatively straightforward, and one common approach to doing this is simply to define the individual pieces of knowledge as objects which contain things (water, cats, balls) and descriptors (water is wet, water flows, water is necessary for life; cats are furry, cats meow, cats are egocentric little psychopaths).

This presents a problem with information storage and retrieval. Some information systems that have a specific function, such as expert systems that diagnose illness or identify animals, solve this problem by representing the information hierarchically as a tree, with the individual units of information at the tree’s branches and a series of questions representing paths through the tree. For instance, if an expert system identifies an animal, it might start with the question “is this animal a mammal?” A “yes” starts down one side of the tree, and a “no” starts down the other. At each node in the tree, another question identifies which branch to take—”Is the animal four-legged?” “Does the animal eat meat?” “Does the animal have hooves?” Each path through the tree is a series of questions that leads ultimately to a single leaf.

This is one of the earliest approaches to expert systems, and it’s quite successful for representing hierarchical knowledge and for performing certain tasks like identifying animals. Some of these expert systems are superior to humans at the same tasks. But the domain of cognitive tasks that can be represented by this variety of expert system is limited. Organic brains do not really seem to organize knowledge this way.

Instead, we can think of the organization of information in an organic brain as a series of individual facts that are context dependent. In this view, a “context” represents a particular domain of knowledge—how to build a model, say, or change a diaper. There may be thousands, tens of thousands, or millions of contexts a person can move within, and a particular piece of information might belong to many contexts.

What is a context?

A context might be thought of as a set of pieces of information organized into a domain in which those pieces of information are relevant to each other. Contexts may be procedural (the set of pieces of information organized into necessary steps for baking a loaf of bread), taxonomic (a set of related pieces of information arranged into a hierarchy, such as knowledge of the various birds of North America), hierarchical (the set of information necessary for diagnosing an illness), or simply related to one another experientially (the set of information we associate with “visiting grandmother at the beach).

Contexts overlap and have fuzzy boundaries. In organic brains, even hierarchical or procedural contexts will have extensive overlap with experiential contexts—the context of “how to bake bread” will overlap with the smell of baking bread, our memories of the time we learned to bake bread, and so on. It’s probably very, very rare in an organic brain that any particular piece of information belongs to only one context.

In a machine, we might represent this by creating a structure of contexts CX (1,2,3,4,5,…n) where each piece of information is tagged with the contexts it belongs to. For instance, “water” might appear in many contexts: a context called “boating,” a context called “drinking,” a context called “wet,” a context called “transparent,” a context called “things that can kill me,” a context called “going to the beach,” and a context called “diving.” In each of these contexts, “water” may be assigned different attributes, whose relevance is assigned different weights based on the context. “Water might cause me to drown” has a low relevance in the context of “drinking” or “making bread,” and a high relevance in the context of “swimming.”

In a contextually based information storage system, new knowledge is gained by taking new information and assigning it correctly to relevant contexts, or creating new contexts. Contexts themselves may be arranged as expert systems or not, depending on the nature of the context. A human doctor diagnosing illness might have, for instance, a diagnostic context that behaves similarly in some ways to the way a diagnostic expert system; a doctor might ask a patient questions about his symptoms, and arrive at her conclusion by following the answers to a single possible diagnosis. This process might be informed by past contexts, though; if she has just seen a dozen patients with norovirus, her knowledge of those past diagnoses, her understanding of how contagious norovirus is, and her observation of the similarity of this new patient’s symptoms to those previous patients’ symptoms might allow her to bypass a large part of the decision tree. Indeed, it is possible that a great deal of what we call “intuition” is actually the ability to make observations and use heuristics that allow us to bypass parts of an expert system tree and arrive at a leaf very quickly.

But not all types of cognitive tasks can be represented as traditional expert systems. Tasks that require things like creativity, for example, might not be well represented by highly static decision trees.

When we navigate the world around us, we’re called on to perform large numbers of cognitive tasks seamlessly and to be able to switch between them effortlessly. A large part of this process might be thought of as context switching. A context represents a domain of knowledge and information—how to drive a car or prepare a meal—and organic brains show a remarkable flexibility in changing contexts. Even in the course of a conversation over a dinner table, we might change contexts dozens of times.

A flexible machine learning system needs to be able to switch contexts easily as well, and deal with context changes resiliently. Consider a dinner conversation that moves from art history to the destruction of Pompeii to a vacation that involved climbing mountains in Hawaii to a grandparent who lived on the beach. Each of these represents a different context, but the changes between contexts aren’t arbitrary. If we follow the normal course of conversations, there are usually trains of thought that lead from one subject to the next; and these trains of thought might be represented as information stored in multiple contexts. Art history and Pompeii are two contexts that share specific pieces of information (famous paintings) in common. Pompeii and Hawaii are contexts that share volcanoes in common. Understanding the organization of individual pieces of information into different contexts is vital to understanding the shifts in an ordinary human conversation; where we lack information—for example, if we don’t know that Pompeii was destroyed by a volcano—the conversation appears arbitrary and unconnected.

There is a danger in a system being too prone to context shifts; it meanders endlessly, unable to stay on a particular cognitive task. A system that changes contexts only with difficulty, on the other hand, appears rigid, even stubborn. We might represent focus, then, in terms of how strongly (or not) we cling to whatever context we’re in. Dustin Hoffman’s character in Rain Man possesses a cognitive system that clung very tightly to the context he was in!

Other properties of organic brains and human knowledge might also be represented in terms of information organized into contexts. Creativity is the ability to find connections between pieces of information that normally exist in different contexts, and to find commonalities of contextual overlap between them. Perception is the ability to assign new information to relevant contexts easily.

Representing contexts in a machine learning system is a nontrivial challenge. It is difficult, to begin with, to determine how many contexts might exist. As a machine entity gains new information and learns to perform new cognitive tasks, the number of contexts in which it can operate might increase indefinitely, and the system must be able to assign old information to new contexts as it encounters them. If we think of each new task we might want the machine learning system to be able to perform as a context, we need to devise mechanisms by which old information can be assigned to these new contexts.

Organic brains, of course, don’t represent information the way computers do. Organic brains represent information as neural traces—specific activation pathways among collections of neurons.

These pathways become biased toward activation when we are in situations similar to those where they were first formed, or similar to situations in which they have been previously activated. For example, when we talk about Pompeii, if we’re aware that it was destroyed by a volcano, other pathways pertaining to our experiences with or understanding of volcanoes become biased toward activation—and so, for example, our vacation climbing the volcanoes in Hawaii come to mind. When others share these same pieces of information, their pathways similarly become biased toward activation, and so they can follow the transition from talking about Pompeii to talking about Hawaii.

This method of encoding and recalling information makes organic brains very good at tasks like pattern recognition and associating new information with old information. In the process of recalling memories or performing tasks, we also rewrite those memories, so the process of assigning old information to new contexts is transparent and seamless. (A downside of this approach is information reliability; the more often we access a particular memory, the more often we rewrite it, so paradoxically, the memories we recall most often tend to be the least reliable.)

Machine learning systems need a system for tagging individual units of information with contexts. This becomes complex from an implementation perspective when we recall that simply storing a bit of information with descriptors (such as water is wet, water is necessary for life, and so on) is not sufficient; each of those descriptors has a value that changes depending on context. Representing contexts as a simple array CX (1,2,3,4,…n) and assigning individual facts to contexts (water belongs to contexts 2, 17, 43, 156, 287, and 344) is not sufficient. The properties associated with water will have different weights—different relevancies—depending on the context.

Machine learning systems also need a mechanism for recognizing contexts (it would not do for a general purpose machine learning system to respond to a fire alarm by beginning to bake bread) and for following changes in context without becoming confused. Additionally, contexts themselves are hierarchical; if a person is driving a car, that cognitive task will tend to override other cognitive tasks, like preparing notes for a lecture. Attempting to switch contexts in the middle of driving can be problematic. Some contexts, therefore, are more “sticky” than others, more resistant to switching out of.

A context-based machine learning system, then, must be able to recognize context and prioritize contexts. Context recognition is itself a nontrivial problem, based on recognition of input the system is provided with, assignment of that input to contexts, and seeking the most relevant context (which may in most situations be the context with greatest overlap with all the relevant input). Assigning some cognitive tasks, such as diagnosing an illness, to a context is easy; assigning other tasks, such as natural language recognition, processing, and generation in a conversation, to a context is more difficult to do. (We can view engaging in natural conversation as one context, with the topics of the conversation belonging to sub-contexts. This is a different approach than that taken by many machine conversational approaches, such as Markov chains, which can be viewed as memoryless state machines. Each state, which may correspond for example to a word being generated in a sentence, can be represented by S(n), and the transition from S(n) to S(n+1) is completely independent of S(n-1); previous parts of the conversation are not relevant to future parts. This creates limitations, as human conversations do not progress this way; previous parts of a conversation may influence future parts.)

Context seems to be an important part of flexibility in cognitive tasks, and thinking of information in terms not just of object/descriptor or decision trees but also in terms of context may be an important part of the next generation of machine learning systems.

Some thoughts on British linguistics

The Brits have it right.

This morning, while I was in the shower, I was thinking about snogging.

Not the practice of snogging (which, I hasten to add, I’m strongly in favor of), but the language of snogging. Which is something where we on this side of the pond have got it all wrong.

I quite like the word “snogging.” It’s a fun word. A playful word, kind of like the act itself. The American term, “making out,” is dreadfully dreary by comparison. You can see the Puritan work ethic here; only the Puritans could make it sound like a manufacturing process.

Yes, I think about language in the shower. Hush.

In many ways, I think British English gets it wrong. And I don’t want to hear any “they did it first, so that makes them right by definition;” American English is English 2.0, the bugfix release of the original. Like calling the trunk of a car the “boot” for example–I have often stored things in a trunk, but the only thing that gets kept in my boots is my foot, thank you very much. (If they’re using boots for cargo storage and transportation over there, I don’t want to know about it.)

But in the language of physical intimacy, American English kind of falls down flat, with a sort of shocked expression on its face, and then lies twitching in the gutter for a while. “Bumping uglies.” “Doing the horizontal mambo.” “Doing the nasty.” “Hot beef injection.” Ugly language for a beautiful act.

It’s not all this bad, of course; I’m kind of fond of “the act of darkness” as a sexual euphemism. And the Brits have their own ungainly words as well; “bonk,” “bugger,” and “shag” are all perfectly ridiculous in their own right.

But “snog”? Yes, I quite like that word.

Some thoughts on Shakespeare

So a couple of days ago my roommate David and I were talking about Shakespeare, who really is very good in spite of all the people who say he really is very good (as opposed to, for example, William Faulkner, who really is pretty dreadful in spite of all the people who say he really is very good).

Now, I started reading Shakespeare on my own in middle school; during recess, I’d sit in a corner of the playground with Macbeth, which probably explains a great deal abou why I m the way I am today. Though that’s a whole ‘nother subject altogether.

Anyway, the part the folks don’t seem to get about William Shakespeare is that the man was the Quentin Tarantino of his time. The way we teach Shakespeare in high school literature class is absolutely awful; we suck the joy and fun and off-color humor right out of him.

I have visions of lit classes 300 years hence subjecting Quentin Tarantino to the same sort of academic savaging:

“Now, class, today we’re going to be discussing the symbolism of the wallet owned by the hit-man Jules. His wallet had ‘Bad Mother Fucker’ written on it. As we discussed yesterday, the word ‘bad’ in the English of the time meant something that was of inferior quality, but it also had a vernacular meaning of something that was especially good, or dangerous. Today, I’d like us to turn our attention to this dual meaning, and how Mr. Tarantino played on the juxtaposition of the two meanings of the word ‘bad’ in the slogan written on the wallet.

“Tonight, when you go home, I want you to write a 600-word essay about the meaning of the two hit-men’s conversation about foot rubs in the beginning of the movie. Pay particular attention to what their conversation says about gender roles and assumptions during the late 20th century. Compare and contrast the view of gender and gender roles in the line where Jules says ‘Now look, maybe your method of massage differs from mine, but, you know, touchin’ his wife’s feet, and stickin’ your tongue in her Holiest of Holies, ain’t the same fuckin’ ballpark, it ain’t the same league, it ain’t even the same fuckin’ sport’ to the ideas about gender and gender roles later when the character Jody tells the hit-man Vincent that her tongue ring is ‘a sex thing. It helps fellatio.'”

So what does it mean to succeed?

If you’re a printer, or you’ve ever been in the print industry in any capacity at all, or you’ve ever taken a design class, or even if you’ve ever used the “random text” command in any of a large number of different programs, you’ve probably seen a piece of gibberish text that begins with the words “lorem ipsum.” Since the dawn of time, relatively speaking, that gibberish text has been used by newspaper editors, magazine layout artists, prepress people, designers, even Web designers, as “filler” text to let them see, for example, how many columns of type they need, or whatever. The whole text looks like this:

Lorem ipsum dolor sit amet, consectetuer adipiscing elit, sed diam nonummy nibh euismod tincidunt ut laoreet dolore magna aliquam erat volutpat. Ut wisi enim ad minim veniam, quis nostrud exerci tation ullamcorper suscipit lobortis nisl ut aliquip ex ea commodo consequat. Duis autem vel eum iriure dolor in hendrerit in vulputate velit esse molestie consequat, vel illum dolore eu feugiat nulla facilisis at vero eros et accumsan et iusto odio dignissim qui blandit praesent luptatum zzril delenit augue duis dolore te feugait nulla facilisi. Nam liber tempor cum soluta nobis eleifend option congue nihil imperdiet doming id quod mazim placerat facer possim assum. Typi non habent claritatem insitam; est usus legentis in iis qui facit eorum claritatem. Investigationes demonstraverunt lectores legere me lius quod ii legunt saepius. Claritas est etiam processus dynamicus, qui sequitur mutationem consuetudium lectorum. Mirum est notare quam littera gothica, quam nunc putamus parum claram, anteposuerit litterarum formas humanitatis per seacula quarta decima et quinta decima. Eodem modo typi, qui nunc nobis videntur parum clari, fiant sollemnes in futurum.

That’s the full version. That, or fragments of it, are used whenever someone has a need for nonsense text to fill in a blank area in a design, until the real text comes along.

Problem is, that nonsense text is not nonsense.

In the groundbreaking book The Selfish Gene, Richard Dawkins put forth a revolutionary new way to conceptualize evolutionary biology–at the level of the gene, not the level of the individual. A gene’s “goal,” in a manner of speaking, is to reproduce itself. Genes that do this efficiently succeed and become widespread; genes that don’t, disappear.

Many people still have a mistaken view of evolution as “survival of the fittest.” Charles Darwin never used that expression [Edit: Charles Darwin did not use the expression until the fifth edition of Origin of Species, where he adapted it as it had come to be associated with his ideas of natural selection through Spencer’s deliberate creation of an analogy between evolution and market economics using the term)–it was coined by a man named Herbert Spencer–and the term can create inaccurate perceptions of how evolution works. Evolution does not necessarily work on the level of the individual at all; for example, all the bees in a beehive save the queen are not capable of reproducing, and so have no chance to pass on their genes. However, all the bees in a beehive share their genes with the queen. So if a bee dies, but its death helps promote the survival of the hive, that particular bee’s genes survive; it is the survival of the gene, not the survival of the organism, that matters.

In many cases, the survival of the organism means the survival of the gene–in many cases, but not in all. And some genes are passed on by individuals who do not express those genes at all. Male pattern baldness and red-green color blindness in humans are passed on matrilineally–you inherit those genes from your mother–even if they are expressed primarily in men. If you were somehow to prevent every color-blind man or every bald man from reproducing, even though color blindness and male-pattern baldness are genetic, you would not remove those genes from the population.

The human genome project is attempting to map and understand every gene that makes a human being. Each of these genes codes for the production of a single protein, and each of these proteins is responsible for carrying out all the tasks necessary to build a cell and make it go. Human beings have somewhere around 25,000 genes, each made of many DNA base pairs; the ntire sequence of these genes has been mapped, and their function is being explored.

And a lot of them are gibberish.

Biologists call this “junk DNA”. It exists in all species over a certain level of sophistication. Some of it is “obsolete code”–stuff that calls for things the organism no longer needs or uses, like gills and tails in humans. Some of it has been selected against because the stuff it codes for has no or negative survival value, at least in the short run. (New research suggests that mammals carry the genes that would let them, and us, regenerate damaged organs or regrow lost limbs, but that these genes are switched off. If that’s the case, learning how to switch those genes on could revolutionize medicine…but I digress.)

Some junk DNA is old or obsolete…but some is not. A good deal of this “junk DNA” is actually the remnants of old retroviruses, viruses that eons ago infected our ancestors and then became inert, their DNA integrated with ours.

Retroviruses are strange beasts. They work in a simple but devious way. They contain a strand of RNA, not DNA, and this strand of RNA has the necessary code for creating a special enzyme called “reverse transcriptase,” bundled together with the viral code.

When the virus attacks a cell, it injects its RNA into the cell. The RNA is snapped up by tiny machines within the cell whose job it is to read pieces of RNA and build the proteins that the RNA calls for. These machines have no way to tell that a particular piece of RNA is legitimate; they read and process any RNA they see. The viral RNA, once inside the cell, gets read and processed just like any other RNA.

The viral RNA tells the machines to build reverse transcriptase. Reverse transcriptase is a molecular machine that take a piece of RNA and writes it into the cell’s own DNA. When a retrovirus infects a cell, it injects its genes. Its genes tell the cell to make reverse transcriptase, and once the cell does that, the reverse transcriptase writes the viral RNA into the cell’s DNA; the viral gene becomes a part of the cell’s gene.

Now, normally, at this point the viral gene pretty much shuts down the rest of the cell, and tells the cell to stop whatever it was doing and dedicate all its resources to making more copies of the virus. This destroys the cell, of course, but it’s how viruses reproduce.

Every now and then, though, something goes wrong. The viral RNA is injected, the reverse transcriptase is made, the viral genes get recorded into the cell’s own DNA, and then…nothing. The viral gene just sits there. It doesn’t activate, it doesn’t take over the cell, it just sits there.

But when that cell divides, the viral gene goes with it. that viral gene is now a permanent part of the cell’s DNA. Sometimes, if a gamete is infected, the viral DNA gets passed on to new offspring; it never actually does anything, it’s just along for the ride. Has it succeeded? If it never gets activated, ever produces anything, but the organism it infected becomes successful and spreads all troughout the world, then that viral gene has been spread throughout the world too, right?

In talking about ideas, philosophers often speak of “memes” rather than “genes.” A meme is an idea; memes–ideas–can spread themselves, and can grow. You can look at Christianity as a meme, for example. People who accept the idea of Christianity want to spread that idea; the idea uses their minds, just as a viral gene uses a cell, to spread and reproduce itself. Christianity is a very complex meme, which has fractured and divided into competing sub-species, and most memes are not that complex, but it’s a good example nonetheless.

So what’s the equivalent of a “junk meme?” Lorem ipsum.

Most people believe that the Lorem Ipsum text is meaningless gibberish. In fact, “lorem ipsum” is a generic term for any gibberish text in the print industry–“Oh, I don’t have Sandy’s editorial yet, so I just put some lorem ipsum into the space where her column goes for now.” But the lorem ipsum text is not meaningless, and it’s not gibberish.

In fact, the lorem ipsum text is Latin, and it comes from a work called De Finibus Bonorum et Malorum (The Extrmes of Good and Evil), a study on ethics written by the philosopher Cicero in about 45 BC. The fragment that survives as the modern gibberish text placeholder begins not only in the middle of a sentence, but in the middle of a word; the complete section of the original text begins

Neque porro quisquam est, qui dolorem ipsum quia dolor sit amet, consectetur, adipisci velit, sed quia non numquam eius modi tempora incidunt ut labore et dolore magnam aliquam quaerat voluptatem. Ut enim ad minima veniam, quis nostrum exercitationem ullam corporis suscipit laboriosam, nisi ut aliquid ex ea commodi consequatur? Quis autem vel eum iure reprehenderit qui in ea voluptate velit esse quam nihil molestiae consequatur, vel illum qui dolorem eum fugiat quo voluptas nulla pariatur?

Translated into English, the passage reads:

There is no one who who loves pain itself, who seeks after it and wants to have it, simply because it is pain, but because occasionally circumstances occur in which toil and pain can procure him some great pleasure. To take a trivial example, which of us ever undertakes laborious physical exercise, except to obtain some advantage from it? But who has any right to find fault with a man who chooses to enjoy a pleasure that has no annoying consequences, or one who avoids a pain that produces no resultant pleasure?

One could argue that, viewed from a certain perspective, this passage represents a meme that is successful almost beyond all reason, because it has been picked up and reproduced over and over and over again. But like the viral gene that becomes an inert, non-functional part of its host’s DNA, this meme is “junk;” it is completely inert, and most people do not even realize it has any meaning at all. It’s filler; it’s treated as gibberish, just like the viral gene is effectively gibberish.

But is it successful? It’s picked up and propagated all over the planet; it has spread far and wide, as inert viral genes which have become a part of the human genome have spread far and wide; but is it successful?

Weekend stuff, computer peripherals as a metaphor for relationships, and an olive branch

Saturday evening, Shelly and I went out to the Castle to do some danng,a nd hooked up with S, her other boyfriend, nekidsteve, nihilus, phyrra, and their friend and housemate. (Tried to shoot you an IM, johnnymoon, but you weren’t online and it was a really spur-of-the-moment, last-minute thing…)

The main DJ sucked. The alternate DJ ruled. There’s a life lesson in there somewhere, that I’m too lazy to dig out. Anyway, the alternate DJ played the Second Best Song to Dance To Ever Written1, which I’ve heard before but didn’t know the name of. Shelly talked to him after. Turns out the song is by a band called Wumpscut, and the song is called Wreath of Barbs. (Note: direct MP3 download; 6.3 megabytes.)

datan0de, if you aren’t familiar with this song, you should be. 🙂

In old-school computer hackerspeak, a situation can arise between a computer and a peripheral which is called “deadlock” (or, for those of you who hail from MIT, “deadly embrace”). Modern computer protocols have largely done away with it, but generally speaking it’s a situation where a computer and a peripheral stop talking to one another because each is waiting for some sort of response from the other.

There are two basic varieties of deadlock: “starvation,” in which the computer and the peripheral are each waiting for data from the other, and “constipation,” where the computer’s buffer is full and it’s waiting for a signal from the peripheral to receive the data, and the peripheral’s buffer is full and it’s waiting for a signal from the computer to receive ITS data.

It seems the same sort of thing can happen between two people, especially if some kind of problem has existed between them. ach ends up feeling marginalized by the other, and each ends up feeling that the other wants nothing to do with him–so each ends up not reaching out to the other.

Computers can be rebooted, and there’s no hard feelings. With people, it’s a bit more tricky.

So. We had a blast at the club, except for tension etween phyrra and I. I can tell she doesn’t feel comfortable around me, so I don’t try to impose on her, so she feels like I’m avoiding her, so she feels uncomfortable around me, so… starvation deadlock.

And there’s no reason it should be this way. phyrra is a warm and wonderful person who I like very much. Just so y’all know. 🙂

And, just as a bonus, I bring you, courtesy of felisdemens, English As She Is Spoke, the worse English dictionary and phrasebook ever written. From the site:

This 1883 book is without question the worst phrasebook ever written. The writer, Pedro Carolino, who was Portuguese, did not particularly speak English, nor did he have a Portuguese-English dictionary available. Instead, he worked with a French-English phrasebook and a Portuguese-French dictionary. The results, I’m sure you’ll agree, are staggering.

This text is that of a book of excerpts compiled a few years after the book was first published. Anything that looks like an error is, in fact, the way it actually appears in the book. I’ve transcribed the complete text of that book; I do not, unfortunately, have a copy of the original. I’m sure you’ll notice bits that look like typos. They’re not; that’s all part of the fun.

The phrasebook contains such useful gems as a handy list of common English colours (White, Gridelin, Cray, Musk, and Red), popular English games (Foot-ball, Pile, Bar, Mall, Gleek, Even or non even, Carousal, and Keel), handy English phrases (“Give me some good milk newly get out,” “He burns one’s self the brains”), and English idiotisms and proverbs (“He has fond the knuckle of the business,” “So much go the jar to spring that at last it break there”). This stuff predates Engrish by a good century, and is, if anything, even more bizarre. Great stuff!

1All decent, God-fearing people know, of course, that in the great cosmology of Songs to Dance To, nothing can compare to the pinnacle of human achievement, Front 242’s Headhunter v1.0.

Some thoughts to keep in mind when you’re taking over the world

Brainwashing the masses to do what you want them to do is a tricky business, as Shelly observed last night. The subconscious mind is simple and extremely literal; you don’t want to brainwash people with messages that require any sort of high-order cognitive processing, and if your subliminal messages require interpretation, God knows what kind of weird results you’ll get once the subconscious mind finishes with your message!

For example:

Good brainwashing message: “You want to go to the island.” Simple, direct, no real interpretation necessary.

Bad brainwashing message: “You want to be a good consumer.” Danger, Will Robinson! You’re leaving it up to the subconscious to decide what a ‘good consumer’ is and how to be one. There ain’t no telling what kind of behaviors you’re going to get from this one.

And for the love of God, never, ever include metaphor, analogy, or similie in your brainwashing messages! *shudders*

Here ends the public service message of the day.

More grammar-type stuff

In light of smoocherie‘s and fatesgirl‘s comment on my recent grammar rant, I started thinking, and what I thought was, “hey, if you’re not part of the solution, you’re part of the precipitate,” and then I thought about how much it’s not cool to be a chalky, powdery substance that settles to the bottom of a teacup, and then I started thinking…well, you really don’t want to know what I thought after that.

Nevertheless, they both have good points. So, in the interest of being part of the solution, I’ve decided to add a grammar cheat sheet to my Web site, so that if someone’s confused, it’s there and easily accessible. Any suggestions are welcome. 🙂

I also updated my BDSM and polyamory pages, while I was at it.