Reverting to Blogspot
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@acta-lingweenie
Reverting to Blogspot
I have finally given up on the clunky tumblr interface (and slightly alienating interaction style). Future conlanging posts will just be at my old blog: https://acta-lingweenie.blogspot.com

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Hi, sorry to bring up something from ages ago, but I was wondering if we might see some stuff released on the Usandu language for Grey Goo. It's been a while and the game looks like it might be on the way out (the game website hasn't been updated in over a year) so I wonder if it might be possible to negotiate releasing a grammar say into the public domain.
I’ll chat with my contacts there to see what they think.
hi, strange request: would it be easy for you to save the conlanger's thesaurus into .epub format, that we may carry it wherever we go? the .pdf version that's up right now is not converting in calibre and refuses to open on my nook.
I have no idea. It is written in LaTeX and might take some trickery to convert. I’ll do some digging.
Throughout her translation of the “Odyssey,” Wilson has made small but, it turns out, radical changes to the way many key scenes of the epic are presented — “radical” in that, in 400 years of versions of the poem, no translator has made the kinds of alterations Wilson has, changes that go to truing a text that, as she says, has through translation accumulated distortions that affect the way even scholars who read Greek discuss the original. These changes seem, at each turn, to ask us to appreciate the gravity of the events that are unfolding, the human cost of differences of mind.
The first of these changes is in the very first line. You might be inclined to suppose that, over the course of nearly half a millennium, we must have reached a consensus on the English equivalent for an old Greek word, polytropos. But to consult Wilson’s 60 some predecessors, living and dead, is to find that consensus has been hard to come by…
Of the 60 or so answers to the polytropos question to date, the 36 given above [which I cut because there were a lot] couldn’t be less uniform (the two dozen I omit repeat, with minor variations, earlier solutions); what unites them is that their translators largely ignore the ambiguity built into the word they’re translating. Most opt for straightforward assertions of Odysseus’s nature, descriptions running from the positive (crafty, sagacious, versatile) to the negative (shifty, restless, cunning). Only Norgate (“of many a turn”) and Cook (“of many turns”) preserve the Greek roots as Wilson describes them — poly(“many”), tropos (“turn”) — answers that, if you produced them as a student of classics, much of whose education is spent translating Greek and Latin and being marked correct or incorrect based on your knowledge of the dictionary definitions, would earn you an A. But to the modern English reader who does not know Greek, does “a man of many turns” suggest the doubleness of the original word — a man who is either supremely in control of his life or who has lost control of it? Of the existing translations, it seems to me that none get across to a reader without Greek the open question that, in fact, is the opening question of the “Odyssey,” one embedded in the fifth word in its first line: What sort of man is Odysseus?
“I wanted there to be a sense,” Wilson told me, that “maybe there is something wrong with this guy. You want to have a sense of anxiety about this character, and that there are going to be layers we see unfolded. We don’t quite know what the layers are yet. So I wanted the reader to be told: be on the lookout for a text that’s not going to be interpretively straightforward.”
Here is how Wilson’s “Odyssey” begins. Her fifth word is also her solution to the Greek poem’s fifth word — to polytropos:
Tell me about a complicated man. Muse, tell me how he wandered and was lost when he had wrecked the holy town of Troy, and where he went, and who he met, the pain he suffered in the storms at sea, and how he worked to save his life and bring his men back home. He failed to keep them safe; poor fools, they ate the Sun God’s cattle, and the god kept them from home. Now goddess, child of Zeus, tell the old story for our modern times. Find the beginning.
When I first read these lines early this summer in The Paris Review, which published an excerpt, I was floored. I’d never read an “Odyssey” that sounded like this. It had such directness, the lines feeling not as if they were being fed into iambic pentameter because of some strategic decision but because the meter was a natural mode for its speaker. The subtle sewing through of the fittingly wavelike W-words in the first half (“wandered … wrecked … where … worked”) and the stormy S-words that knit together the second half, marrying the waves to the storm in which this man will suffer, made the terse injunctions to the muse that frame this prologue to the poem (“Tell me about …” and “Find the beginning”) seem as if they might actually answer the puzzle posed by Homer’s polytropos and Odysseus’s complicated nature.
Complicated: the brilliance of Wilson’s choice is, in part, its seeming straightforwardness. But no less than that of polytropos, the etymology of “complicated” is revealing. From the Latin verb complicare, it means “to fold together.” No, we don’t think of that root when we call someone complicated, but it’s what we mean: that they’re compound, several things folded into one, difficult to unravel, pull apart, understand.
“It feels,” I told Wilson, “with your choice of ‘complicated,’ that you planted a flag.”
“It is a flag,” she said.
“It says, ‘Guess what?’ — ”
“ ‘ — this is different.’ ”
The First Woman to Translate the Odyssey Into English, Wyatt Mason
@inaneenglish
The farther I get in Wilson’s translation, the less I reach for my Fitzgerald copy to compare. Hers is so wonderful in its clarity and proof that language doesn’t have to be highly elevated to be beautiful. The Odyssey is a much more intimate poem than the Iliad, and omg Wilson digs into that so well.
Can I borrow your copy over the summer?? I want to read it so much.
Hm, I’ve never actually read the whole Odyssey, and this translation is making the idea seem appealing!
I really want to do a group read of Wilson’s Odyssey but I don’t want to be the one who organizes it. :)
SAME! Translating the Aenaid in HS put me off epic poems, but I’d LOVE to read Wilson’s Odyssey.
@acta-lingweenie thoughts?
She’s doing the Muses’ Work. Old High Translationese is a blight.
moving the cat

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Arthur Krüger (1866-1926) cover, ‘Prosit Neujahr!’ (Happy New Year), “Der Wahre Jacob”, #769, 1916 Source
What on earth sort of new year was this designer expecting?
your cat can see ghosts
In the early 1980s, linguist Bill Shipley offered an undergraduate class called “Languages of the World”. One of the assignment of that course was to create a language. As a guide, this document written by M. A. R. Barker was given to students. It comprises a text in Tsolyáni plus an interlinear, along with a grammar sketch to help the reader understand and appreciate the text.
If you want to catch a glimpse of the largest, most detailed, oldest artlang you’ve never heard of, take a look at this.
Ancient wisdom from the neural network
What happens when really old advice meets really new technology?
A recurrent neural network (like the open-source char-rnn framework used here) can teach itself to imitate recipes, paint colors, band names, and even guinea pig names. By examining a dataset, it learns to formulate its own rules about it, and can use these rules to generate new text that - according to the neural network - resembles the dataset. But since the neural network is doing all this without cultural context, or any knowledge of what the words really mean, the results are often a bit bizarre.
In this example, the dataset is a list of more than 2000 ancient proverbs, collected by reader Anthony Mandelli. Some of these are well-known, such as “You can lead a horse to water, but you can’t make it drink.” and “Where there’s a will, there’s a way.” Others are frankly a bit strange: “Where there’s muck there’s brass.” and “A curst cow has short horns.” and “Be not a baker if your head is made of butter.”
What will a neural network make of this ancient wisdom?
If you answered “Really really weird proverbs”, you are correct.
A fox smells it better than a fool’s for a day. No songer in a teacuper. A fool in a teacup is a silent for a needle in the sale. No man is the better pan on the hunder. A mouse is a good bound to receive. Do not come to the cow.
Some of them almost make sense:
A good wine makes the best sermon. A good fear is never known till needed. Death when it comes will have no sheep. An ounce of the heart comes without an exception. A good face is a letter to get out of the fire. No wise man ever wishes to be sick. A good excuse is as good as a rest. There is no smoke without the best sin. A good man is worth doing well. A good anvil does not make the most noise.
While others would be more difficult to pass off as real proverbs:
We can serve no smort. A good face is a letter like a dog. A good earse makes a good ending. Gnow will not go out. Ung. A fox smeep is the horse of the best sermon. No sweet is half the barn door after the cat. There is not fire and step on your dog and stains the best sermon. An ox is a new dogn not sing in a haystar.
One of the oddest things to emerge from the proverb-trained neural network is a strange obsession with oxen. I checked, and there were only three oxen-related proverbs in the dataset, yet they appear frequently in the neural network’s version, and usually as rather powerful creatures.
An ox can lever an enemies are dangerous and restens at home. An ox is not to be given with a single stone. An ox is never known till needed. An ox is as good as a best. An ox is not to be that wound is hot. An ox is a silent for the gain of the bush. An ox is not fill when he will eat forever.
Whatever the internal mythos the neural network has learned from these ancient proverbs, oxen are mysteriously important.
Paint colors designed by neural network, Part 2
So it turns out you can train a neural network to generate paint colors if you give it a list of 7,700 Sherwin-Williams paint colors as input. How a neural network basically works is it looks at a set of data - in this case, a long list of Sherwin-Williams paint color names and RGB (red, green, blue) numbers that represent the color - and it tries to form its own rules about how to generate more data like it.
Last time I reported results that were, well… mixed. The neural network produced colors, all right, but it hadn’t gotten the hang of producing appealing names to go with them - instead producing names like Rose Hork, Stanky Bean, and Turdly. It also had trouble matching names to colors, and would often produce an “Ice Gray” that was a mustard yellow, for example, or a “Ferry Purple” that was decidedly brown.
These were not great names.
There are lots of things that affect how well the algorithm does, however.
One simple change turns out to be the “temperature” (think: creativity) variable, which adjusts whether the neural network always picks the most likely next character as it’s generating text, or whether it will go with something farther down the list. I had the temperature originally set pretty high, but it turns out that when I turn it down ever so slightly, the algorithm does a lot better. Not only do the names better match the colors, but it begins to reproduce color gradients that must have been in the original dataset all along. Colors tend to be grouped together in these gradients, so it shifts gradually from greens to browns to blues to yellows, etc. and does eventually cover the rainbow, not just beige.
Apparently it was trying to give me better results, but I kept screwing it up.
Raw output from RGB neural net, now less-annoyed by my temperature setting
People also sent in suggestions on how to improve the algorithm. One of the most-frequent was to try a different way of representing color - it turns out that RGB (with a single color represented by the amount of Red, Green, and Blue in it) isn’t very well matched to the way human eyes perceive color.
These are some results from a different color representation, known as HSV. In HSV representation, a single color is represented by three numbers like in RGB, but this time they stand for Hue, Saturation, and Value. You can think of the Hue number as representing the color, Saturation as representing how intense (vs gray) the color is, and Value as representing the brightness. Other than the way of representing the color, everything else about the dataset and the neural network are the same. (char-rnn, 512 neurons and 2 layers, dropout 0.8, 50 epochs)
Raw output from HSV neural net:
And here are some results from a third color representation, known as LAB. In this color space, the first number stands for lightness, the second number stands for the amount of green vs red, and the third number stands for the the amount of blue vs yellow.
Raw output from LAB neural net:
It turns out that the color representation doesn’t make a very big difference in how good the results are (at least as far as I can tell with my very simple experiment). RGB seems to be surprisingly the best able to reproduce the gradients from the original dataset - maybe it’s more resistant to disruption when the temperature setting introduces randomness.
And the color names are pretty bad, no matter how the colors themselves are represented.
However, a blog reader compiled this dataset, which has paint colors from other companies such as Behr and Benjamin Moore, as well as a bunch of user-submitted colors from a big XKCD survey. He also changed all the names to lowercase, so the neural network wouldn’t have to learn two versions of each letter.
And the results were… surprisingly good. Pretty much every name was a plausible match to its color (even if it wasn’t a plausible color you’d find in the paint store). The answer seems to be, as it often is for neural networks: more data.
Raw output using The Big RGB Dataset:
I leave you with the Hall of Fame:
RGB:
HSV:
LAB:
Big RGB dataset:

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beckoning witch
Metal band names invented by neural network
The question of the day: can a computer learn to sound metal?
Thanks to HellBlazer of http://www.metal-archives.com/, who provided a dataset of over 100,000 bands, subgenres, and countries of origin, I had an opportunity to find out.
I gave the dataset to an open-source neural network framework that I’ve previously trained to generate recipes, Pokemon, knock-knock jokes, pick up lines, and D&D spells. As usual the instructions were only to learn what the dataset is like and try to make more of the same. With over 100,000 entries to chew on, the neural network managed to produce results that were… well, surprisingly metal.
I give you: Band names, so far untaken, generated by neural network
Dragonred of Blood - Death Metal - Indonesia Deathhouse - Melodic Death Metal - Brazil Vultrum - Folk/Black Metal - Germany Stäggabash - Black Metal - Canada Deathcrack - Death Metal - Mexico Stormgarden - Black Metal - Germany Vermit - Thrash Metal/Crossover,/Deathcore - United States Swiil - Progressive Metal/Shred - United States Inbumblious - Doom/Gothic Metal - Germany Inhuman Sand - Melodic Death Metal - Russia ChaosWorge le Plague - Doom Metal - Brazil Inhum the Thorg - Black Metal - Slovenia Chaosrug - Black Metal - Mexico Jazzy - Heavy Metal - United States Sux - Heavy Metal/Hard Rock - Chile Dragonsulla and Steelgosh - Heavy Metal - Tuera Verking of the Beats - Thrash Metal/Crossover Thrashcore - Netherlands Squeen - Doom Metal - Colombia Death from the Trend - Black Metal - Croatia Shuck - Death Metal - Israel Dragorhast - Heavy Metal/Hard Rock - Germany Verb - Black Metal - Norway Black Clonic Sky - Black Metal - Greece Snapersten - Folk/Melodic Black Metal - Italy Verk - Melodic Death Metal - Sweden Snee - Thrash/Death Metal - Brazil Vomberdean - Melodic Black Metal - United States Suffer the Blue - Death/Thrash Metal - Germany Sespessstion Sanicilevus - Melodic Death Metal - United States Sköpprag - Black Metal - Norway Sht - Symphonic/Heavy Metal - United States Sun Damage Omen - Symphonic Progressive Metal - France
does no one realize that robin hood was a terrible role model for young kids? i mean you are stealing from people (illegal) and those people (usually) worked hard to get their wealth. it really demotivates people to succeed when they know they can get something someone else worked for.
is this what rich people worry about lmao
who knew the sheriff of nottingham had a blog
How does someone read Robin Hood and miss the part where it’s set in feudal England. He stole from people who got their wealth by exploiting the poor, incidentally that’s all rich people to this very day.
Tune in next week when they tell you the story of Ebeneezer Scrooge, a benevolent job creator, harassed during his sleeping hours by the hellish socialist dead.
Bugs Bunny accidentally transformed the word nimrod into a synonym for idiot because nobody got a joke where he sarcastically compared Elmer Fudd to the Biblical figure Nimrod, a mighty hunter.
Etymology is ridiculous and terrifying sometimes
Story titles, invented by neural network
So Prof. Mark Reidl of Georgia Tech is the best kind of geek, and used some cool scripting to extract all the things on Wikipedia with plot summaries: movies, books, tv episodes, video games, etc. That’s a lot of plot summaries: 112,936, to be exact.
With a dataset this large, a neural network can achieve impressive results. Sure enough, when I trained this open-source neural network framework on just the titles alone, it consistently came up with titles that were both varied and (usually) plausible.
Below are some of my favorites, arranged roughly by apparent genre:
Action/Adventure
Titanic Buffalo Pirates: A Fight Dance Story The Bad Legend Conan the Pirate O Bullets Home Transformers Shurk Hat Dies! An Enemy of Bob (Homicide: Life on the Street) Cannibal Spy II American Hero: Fire of Crusty Lego Man Hunt Nancy Drew: The Last Day (film) Surf Crisis Legend of the Experience of Scarlet Freedom Damageboo American Midnight: Swear Dragon Problem
Scifi/fantasy
Under the Daleks Batman and Flancles: The Fun Tree The Legends of World Planet Bomberman’s Love The Enchanted Feed The Star Wars: The Santa Contact The Long Ninja Dove in the Air (film) The History of the Galaxy Bunny Lada City of the Stupid (film) Shy Castle Hamburger (Star Trek: The Next Generation) Swords and Batman: Summer Party ?
Kids/Family
The Boordeeple (2011 film) A Dog’s Toy Friends Boop (Adventure Time) A Dinosaur Quest Colonel Corn (video game) Scooby-Drum New Bear Borky the Pig (film) Excellent Very Broken Christmas The Great Bother Cat (film) Happy Cat in the Yaku Wonder Fireman and Halloween Rules Big Can Flower Home The Green Yaurglar Pig Scooby-Doo'Wagon Traps (video game) Book Dog (film)
Horror
Terror Dog Tree Screaming Zombies of Florence The Trunkelling A Vampire Time for Monster Murder’s Eagle Frozen Bat (film) Haunted Place The Sheep of Evil Barney’s The Devil’s Treachery Merry Scroobers: Crown of Evil The Steel-Pounted Murder King The Shadow of Life of Very Worgy (film) The Mystical Booged of California
Documentary
Market that Knave Spork at Bliss The White Soup An Indiana Office The Last Fish Show The Fish of Education
Restricted section (there were quite a few more of these)
Absilloved Lovers 2: Black Bearfly Dawn Horse Man Academy 5-R: Cowboy Sheeper Wydex Breed Bot 3: The Journey Kitchen Wild Bad Party 109 Pink Moon Indiscreet Maidman
And finally, a list of the most quintessential story titles, obtained by setting the creativity to near zero on a highly-trained network:
The Story of the Stranger (1994 film) The Last Day of the Story The Lost Princess (film) The Stranger (1994 film) The Last Star (1994 film) The Secret of the Story of the Stranger (1996 film) The Stranger (2014 film) The Story of the Stars The Story of the Stranger (1999 film) The Last Day of the Sun The Story of the Star Trek: The Secret of the Story of the Star Wars

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Vocabulary Discipline
A few years ago it was important for me to keep track of how many words were in the dictionary of a conlang I was creating. It was trivial to add some additional checks, and one thing I can now track with my LaTeX conlang documentation tools is how many examples there are in the dictionary.
In some ways knowing how many examples I’ve created has been disappointing. There should be a lot more examples in most of the languages I’ve created so far. Simply having more examples than a bare wordlist isn’t really good enough when the majority are still without examples.
This has led me recently to a new rule for myself — no new word goes into the dictionary without at least one example, more if that is warranted. Further, the example has to demonstrate the meaning. I can’t just do a string of “I see the rock, I see the cup, I see the tree,” but something relevant about the sense of the new word has to come through. I do make exceptions for certain kinds of words. In the normal course of events, strawberry, for example, is unlikely to have unanticipated shades of meaning (unless, of course, it does). And some more grammatical words get their focus in the grammar. But for the most part I try very hard not to give myself a pass.
Committing to this rule is agonizing at first. Every exception is an accusation. I suspect it will always be agonizing at the start of a new project, because you have so little vocabulary to start with that your range of sentences is pretty absurd. The examples have to explain the new word, not just use it. I do go back and change examples when it seems like the original is too ridiculous.
Months into the (slow) development of this language, I find that I am much further along than expected for the word count. The process of coming up with the examples makes me think about secondary meanings earlier, and to think more about usage that I’d normally put off. This helps guard agains the dangers of relexicalization. Plus, the examples themselves guide the development of new vocabulary and grammar. The desire for new, less monotonous, constructions may drive some of this.
This mass of sentences that can explain both vocabulary and grammar gives a richer total impression. Maybe it’s just this particular language is working out well for me, but I do think this new bit of discipline has had a significant part to play.
Dāz is a sketch I have put aside for now. I had not quite committed to this new rule in it, but it gives an impression of what the results can be, even for a very small, new language: Dāz Sketch.
goddess of pollen