Women in comics

The people in charge of the Angoulême comics festival were recently completely unable to think of any female cartoonists, so I thought I’d help by contributing a list of more than 200.

If your favorites aren’t there… tell me!  Especially if they’re non-English.  I’m especially weak on manga.

As it happened, I was already reading Deborah Elizabeth Whaley’s Black Women in Sequence, which is about black female cartoonists.  It has a whole chapter about Catwoman, so I had to read it.  (Catwoman has been played on the screen by black actresses twice, going back to 1967, so it’s not surprising she has a special meaning for black comics fans.)

The most interesting chapter is on Jackie Ormes, who had several syndicated strips in black newspapers from the late ’30s till the ’50s.  I would love to see more of her work; it’d be a fascinating glimpse into those times.  What’s striking about her elegant, smart characters is simply that they look human, and sexy, at a time when white cartoonists were producing abominations like the Spirit’s Ebony.

Anyway, Whaley’s theorycrafting doesn’t turn me on much, but the introduction to a bunch of artists is worthwhile.  (I kept wanting to ask what she thought of Jaime Hernandez, or what she might think of Ta-Nehisi Coates’s new Black Panther…)




iGot iPad

This week I cobbled together an impressive argument, to myself, which succeeded in convincing me that I needed an iPad Air. So now I have this little portable slab of computation sitting on my desk. The main expected use is as a camera. Here’s an example, otherwise known as “what every new iPad user discovers within the first hour”:

I hear there’s an app that will distort your face, too

Something else I needed, which you can see above: computer glasses. I’m near-sighted, which is supposed to mean I can see near things, but in the last few years my near vision got fuzzy with my glasses on. I learned to take them off, but the in-focus zone is now about 8 inches from the book or monitor. So now I have computer glasses, so I can sit at a comfortable (and probably healthier) distance from the screen.

I haven’t had a lot of chances to play with the gestural interface before, but I have to say: I love it. The basic gestures are intuitive, and manipulating the screen directly is a huge conceptual improvement over doing it remotely with the mouse. It’s not as great for detailed manipulation— but I learned how to make a stylus with a wet Q-tip wrapped in aluminum foil.  (Yes, that is a thing. The iPad screen works with your body’s static electricity, which is why most other objects don’t work as styluses.)

I’ve seen Apple Maps before, but their 3-D representation of major cities is pretty damn awesome.

Can you fuse the images? (I can’t.)

It’s neat that Apple has spent some ungodly number of man-hours creating 3-D models of all these buildings, including their setbacks and roof units. They could have wimped out with the Aqua Tower, above, but no, the undulations on the sides are 3-D modeled.

Sadly, they haven’t done the 3-D modeling out this far from the city.  They’ve done Evanston, though, where I went to college.

Another neat thing: it has a charger, but instead of using that, you can just hook it up to the Mac. Hey, it saves an electric outlet.

One reason I got the iPad instead of a Surface is because it talks nicely to my Mac. It can use the local WiFi, or the cable— I was able to grab the pictures easily enough, and to copy some PDFs to the iPad for reading.

Another projected use is research. I wish I’d had it back when I was researching numbers— scrawling numbers down in the library was always a hassle, to say nothing of the surprisingly tedious process of identifying what language a book represents (it’s often different from the name in Ruhlen or the Ethnologue) and whether I had its numbers already.

(The one thing I won’t use it for is phone calls, as I didn’t pick up a phone plan with it.)

Why NLP is so hard

Recently I wrote about a commercial NLP project that bit off more than it could chew. In response an alert reader sent me a fascinating paper by Ami Kronfeld, “Why You Still Can’t Talk to Your Computer”.  It’s unfortunately not online, and Kronfeld is sadly no longer with us, but it was presented publicly at the International Computer Science Institute, so I figure it’s fair game.

Kronfeld worked for NLI (Natural Language Incorporated), which produced a natlang interface to relational databases. The project was eventually made part of Microsoft SQL Server (apparently under the name English Query), but it was allowed to die away.

It worked pretty well— Kronfeld gives the sample exchange:

Does every department head in Center number 1135 have an office in Berkeley?
[Answer: “No. All heads that work for center number 1135 are not located in an office in Berkeley”]

Who isn’t?
[Answer: Paul Rochester is the head not located in an office in Berkeley that works in center number 1135]

He points out that language and relational databases share an abstract structure: they have things (nouns, entities) which have properties (adjectives, values) and relate to one another (verbs, cross-references). This sort of matchup doesn’t always occur nicely.  (E.g. your word processor understands characters and paragraphs, but it hasn’t the slightest idea what any of your words mean.)

But the interesting bit is Kronfeld’s analysis of why NLI failed. One aspect was amusing, but also insightful: we humans don’t have a known register for talking to computers. For instance, one executive sat down at the NLI interface and typed:

How can we make more money?

The IT guys reading this are groaning, but the joke’s on us. If you advertise that a program can understand English, why be surprised that people expect that it can understand English?

Curiously, people attempting to be “computery” were no easier to understand:

Select rows where age is less than 30 but experience is more than 5

This seems to be an attempt to create an on-the-fly pidgin between SQL and English, and of course the NLI program could make nothing of it.

Of course there were thousands of questions that could be properly interpreted. But the pattern was not obvious. E.g. an agricultural database had a table of countries and a table of crops.  The syntactic template S grow O could be mapped to this— look for S in the country table, O in the crops— allowing questions like these to be answered:

  • Does Italy grow rice?
  • What crops does each country grow?
  • Is Rice grown by Japan?
  • Which countries grow rice?

But then this simple question doesn’t work:

  • Does rice grow in India?

Before I say why, take a moment to guess.  We have no trouble with this question, so why does the interface?

The answer: it’s a different syntactic template.  S grows in O is actually the reverse of our earlier template— before, the country was growing things, here the rice is growing, all by itself, and a location is given in a prepositional phrase. As I said before, language is fractally complicated: you handle the most common cases, and what remains is more complicated that all the rules you’ve found so far.

Now, you can of course add a new rule to handle this case.  And then another new rule, for the next case that doesn’t fit.  And then another.  Kronfeld tells us that there were 700 separate rules that mapped between English and the database structure.  And that’s one database.

So, the surprising bit from Kronfeld’s paper is not “natural language is hard”, but that the difficulty lives in a very particular area: specifying the semantics of the relational database. As he puts it:

I realized from the very start that what was required for this application to work was nothing short of the creation of a new profession: the profession of connecting natural language systems to relational databases.

So, that’s a way forward if you insist on having a natlang interface for your database!  NLP isn’t just a black box you can tack on to your program. That is, parsing the English query, which is something you could reasonably assign to third-party software, is only part of the job.  The rest is a detailed matchup between the syntactic/semantic structures found, and your particular database, and that’s going to be a lot more work than it sounds like.



80 Days

This is a thoroughly charming game, and it’s just $10… if you haven’t picked it up yet, why not?

Valeting: Unsurpassed

It’s based of course on Jules Verne’s Le Tour du monde en quatre-vingts jours, first published in 1873. It’s not technically sf, but it’s in the ballpark– it’s like a 19C Wired, besotted with the transformative power of technology. Phileas Fogg is able to make his tour thanks to three recent events: the opening of the Suez Canal and the completion of railroad routes across India and America.  (I just re-read the first few chapters of Le Tour, which are amusing in an arch 19C way.  Passepartout signs up with Fogg because he is the most dependable, boring rentier in London… and only a few hours later Fogg returns with the news that they are departing that very night for Dover.)

Anyway, the game!  I’ve been marveling at the cleverness of its gameplay. It’s literally an open world– you can get to every part of the globe– but most of the cities and routes start out hidden. Knowledge becomes the hidden treasure of the game: as you explore and talk and buy and sell, you discover new routes, as well as new reasons to go to the places mentioned.

I’ve played the game through twice– each playthrough taking about 2.5 hours– and each was entirely different, as I took different routes.  Which is another enormously clever bit!  I’ll play a really good game several times, but usually it’s almost exactly the same experience.  Here, nothing at all need be the same, and it makes sense… of course a trip through Europe and Russia will be different from one through Africa and Oceania. There are 169 cities total, and it’s hard to visit more than about 30 in one trip, so the game is highly replayable.

At heart, the game is a text adventure.  Wait, come back!  This is actually a good thing.  It allows far more imagination and variety than could be done in 3-d models or even drawings.  In form, you get short descriptions (rarely more than a screenful), and choose the continuation. You can choose to be adventurous or fearful, friendly or disdainful; often you can stay out of trouble if you wish, sometimes you can’t. (You are Passepartout, not Fogg; Fogg will offer clues but he’s generally barely functional as a companion.)   I should also add that the game makes good use of the map and illustrations of your inventory, conveyances, cities, and traveling companions, so there is plenty of visual interest.

You get ‎£4000 to start with, but fares and hotels cost money, and you’ll need more. There’s a nice mechanism for this: you can buy items in various cities, and sell them for a profit later on, usually in a specific city.  Items may also be acquired to make the journey easier, to make negotiations on departure time easier, or to reveal routes.

Fogg is delicate and suffers through travel, so another mechanic is keeping him healthy.

My first playthrough went swimmingly: I got back to London in 66 days, and made enough money to afford a £5000 steamship ride back home. Highlights of the trip included getting engaged and delivering a baby (not to the same woman).  My second playthrough was a much closer shave: 76 days, and I almost ran out of money in Portugal, tantalizingly close to the final destination.  The trading had not worked out well on my route; I had to sell everything I had and do some extra work in the hotels, and we made it back with £31 in pocket.  (It’s possible to get emergency funds from banks, but this takes extra days. I also learned afterwards that you don’t have to sleep in a hotel.)

The writer,  Meg Jayanth, deserves a lot of credit for making every route interesting. You are of course not restricted to the route from the book. You’re also not restricted, well, to our reality. Realizing that the frazzled citizens of 2015 are not as enthused as those of 1872 by steamships and railroads, the developers have gone full steampunk… plus it’s a revisionist steampunk where Zulus, Haitians, and Maoris are as likely as Europeans to be building massive steam contraptions, walking cities, automata, and so on.  And why not?  If you’re going to upgrade the technology, you’d might as well downgrade the colonialism.

There’s reams of adventure created for the game, but there are also nods to Verne’s other novels… on my second playthrough, where I tried to keep to the southern hemisphere, I ended up being abducted by Captain Nemo.

I can’t even think of anything to complain about. There are times I didn’t end up where I expected to go, but that’s fine– it wouldn’t be true to Verne if the unexpected never happened. The game is just challenging enough (as my almost-failed 2nd playthrough showed), but it’s also not very punishing, so you can take chances and explore.





Wáng Wéi

In my China book I included a poem by the Táng poet 王维 Wáng Wéi, 鹿柴 Lù Chái (Deer Park), one of the most-translated poems from Chinese.

Prompted by my friend Adrian’s thoughts on the poem (with his own translation), I thought I’d provide the glosses and grammatical notes so you can create your own version.

(The poem may actually be Lù Zhài. 柴 is sometimes read zhài, but my dictionaries don’t have this reading, apparently rare.)


Kòng shān bú jiàn rén

empty mountain not see person

An empty mountain. No one is seen

“Kòng shān” is a topic here, giving the setting. The subject is omitted— this lack of overt viewpoint is one of the features of Chinese poetry.  Rén can of course be either “person” or “people”. Chinese verbs have no tense, which helps create a timeless mood in poetry. (They have aspect, but with the limited characters per line available, I believe it’s rarely used in poems.)


Dàn wén rén yǔ xiǎng

however hear person words/speak sound

Yet we hear the sound of voices.

OC is comfortable with N +  N, or as here, N + N + N phrases— “person speech sounds”. Again, no explicit subject— my “we” is an interpolation, to avoid an awkward passive. 


Fǎn jǐng rù shēn lín

return brightness/view/situation enter deep/thick forest

Evening light penetrates the deep forest

The first word is difficult. It just means ‘return’, but what is returning light? My friend Ran suggested “the setting sun is described as returning its light because as it moves forward toward the horizon, any sort of light it casts is perceived as being opposite to its direction of motion.”  To me the sentence suggests the edge of the forest, where low evening light temporarily lights up the forest floor. In any case translators all seem to agree, for whatever reason, that we’re talking about dusk!


Fù zhào qīng tái shàng

repeat/again shine dark.green moss above/on

It shines again on the green moss.

Easy part first: “X shàng” is the OC way of saying “on X”; versions that use the meaning “rise” are a real stretch.  I’m not sure why “again” is specified, unless it’s what I mentioned above— a daily cycle of low light illuminating the forest floor. (Or moss in the trees— see the picture in Adrian’s posting.)

If you missed it: lines 2 and 4 in the original rhyme.  Lines 1/3 do not, either in Mandarin or in Táng Chinese.

Business Report

Now that I have one more title, it was time to convert my book sales records to the exciting technology of 1997— Excel!

This allows me to do some more data wrangling, such as this chart of sales for the last month:


(I had to label it myself, though. Separate legends for pie charts are dumb and should fail the designers out of Tufte school.)

If you’re curious, the all-time sales chart looks very similar, but the older titles get bigger slices. Total sales for the LCK are now over 8,000.  That’s a lot of conlangs inflicted on a  world which may or may not be ready for them.

For fiction fans, the good news is that Against Peace and Freedom sales just reached 201, which is the level I set for creating an Incatena conlang. So that will happen sometime this year.  The bad news is that, as you can see on the pie chart, fiction sales are crappy, which is why I mostly write non-fiction books.  The next one is probably going to be a manual on Quechua.

More data wrangling: Google ads are barely worth it, rarely reaching $10 a month.  I mostly keep them because I like having the search engine widget.

Also: print is far from dead. In fact it’s 62% of sales. But it varies by book: people apparently prefer to read fiction, and about China, on their Kindles.

My best month is always December, for obvious reasons. January is usually pretty good, presumably because if people didn’t get the books they wanted for Christmas, they buy it for themselves.  (Go do it now!)  The months in between tend to form a saddle shape, with the depressing low point around May or June.

Thanks to everyone who bought books!  Happy reading!