Does Fiction Affect Reality? Duh.

[CW: content warning for discussion of sexual violence at the end of the post.]

I’ve seen a lot of discussion lately on whether there are moral/ethical consequences to writing certain kinds of fiction. And the answer to me appears to be an unequivocal “yes”. Although regular readers of this blog are probably aware that I have a wide range of interests, the primary purpose of this site is to explore what’s called “speculative linguistics”, that is the combination of real language science and its depiction in fiction, especially speculative fiction such as science fiction and fantasy. Maybe the most famous example of speculative linguistics is the “conlang”, short for constructed language, which is an umbrella term for artificial languages created for a variety of purposes, but most commonly for use as magical or alien languages in speculative fiction/sff(h) literature.

And that’s a fun topic. But today we’re going to take a dive into the science side of speculative linguistics and talk about the relationship not only between fiction(thought) and reality, but also between both of them and the intermediary of language. This could be a dull boring article, or I could use my actual writing style to make my point:

Human beings do not stand on a hard bedrock of objective reality, but rather swim through a vast ocean of narrative, catching in their gaping mouths whatever strands feed their desires of the moment. It’s quite a philosophical argument whether an objective reality even exists, but I’m going to assume one does for the purposes of argument. But even assuming that, there are two layers between objective reality and our perception of it: the first is the channel of our senses, which make different kinds of information about the world around us available to our minds. If you’ve ever taken one of those colorblindness tests as a kid, then you know that not only do these senses only capture limited information, but they are unreliable.

Although we usually talk about “the five senses: touch, taste, hearing, sight, and smell”, in fact what we really have is visible light detection(a narrow band of electromagnetic radiation), sound wave detection(sensing disturbances primarily in air particles, but also solids and liquids), pressure sensitivity, temperature sensitivity, a weak ability to detect airborne chemicals, a moderate ability to differentiate chemicals by out taste buds, and depending on who you listen to, a couple other minor ways of capturing information. So, an actually very limited way of measuring “reality”.

And then, of course, our brain filters out, without any real conscious control on our part, the “unnecessary” information, such as the feeling of our clothes, various background and far away noises, etc. And finally, after all that, we (only) guess at the connections between the various limited streams of sensory input to develop a model of the world and its natural laws.

And then, finally, we condense this information down into words, which are the primary form of passing information between separate human consciousnesses. In modern times we have things like videos or audio files, or memes/gifs. And of course dance, or more importantly music, can be used to communicate.

At this point you may be wondering if I’m actually going to talk about fiction versus reality at all.

But it’s important to understand all these little details of how our brains and senses function, because “fiction” is pleasurable to us because it engages these senses in ways that the real world doesn’t always. Our brains are designed to find useful patterns for navigating “objective reality”/the world based on our limited sensory inputs. And fiction is a way to both create/manipulate and comment on the patterns our brains discover to create a satisfying emotional reaction. Now you know what a “narrative” is. An artificial pattern designed to evoke a specific emotional reaction.

Our brains learn patterns by discovering consistent outcomes to various actions/combinations of sensory input. And we base not only our intentional actions on those patterns, but even our feelings about things are unconscious reflections of those patterns. People are not born with a full and innate set of feelings and emotional responses; we develop them over time based on our experiences.

The goal of fiction is to create a narrative that closely mimics our learned patterns and our emotional responses to those patterns, and to trick us into seeing those narratives as “real” on an emotional level, even if intellectually we know that dragons aren’t real, for example. And because we have studied fiction for a long time, and practiced it, and are surrounded by it, we’ve gotten very good at tricking our brains into treating it as almost the same as patterns we’ve learned from “real life” experiences. If words on a page could not affect they way we respond emotionally to reality, then all of human culture would have been unsuccessful. Propaganda and “fake news” would not be so effective.

Our brains have a great deal of trouble differentiating patterns learned from fiction from those learned from reality. So no, fiction cannot “affect reality”, but it can and absolutely does, even in ways you aren’t aware of at the time, affect our perception of reality on a fundamental level. And because humans and our opinions and culture live almost entirely on a diet of narrative, our perceptions and reality are basically the same thing.

If you watch people behave a certain way and that behavior is almost always met with approval, or at least not disapproval, your brain learns that that behavior is good, or at least acceptable/normal. And as social beings, we base our behavior far more on what we are taught is acceptable than on our own personal reactions. As much as people try to deny it, we do a very poor job of distinguishing between “reality” and fiction, when we look for examples of acceptable behavior. Your brains is almost equally willing to use behavior depicted in stories to determine what is acceptable as behavior you see with your own eyes. Why else would advice columns or r/amitheasshole and r/relationships be so popular? If you trust Dear Abby’s relationship advice as much as your mother’s, why wouldn’t you believe it when behavior shown in a book is clearly approved of by the author?

Your opinions as an individual are based at least as much on the prevailing views of your culture as on your own personal experiences. You’re as like to believe Superman telling you something is okay as you are your father.

I think it’s useful to point out that of course fiction is only one influence on your beliefs, and also that that influence only applies to the situations depicted in the story. Violent videogames won’t make you a killer unless you find yourself on a HALO fighting the Flood. But certainly playing enough Call of Duty or Gears of War will make you look more favorably on war/violence as a solution to certain types of conflict.

And we can also look at other sources of narrative besides prose fiction to prove our point: if all you know of someone is their image on social media, you’re likely to believe that that’s who they are in real life. They’ve created a narrative, a likely partially fictional one, to influence your perception of them, and it works. If you believe someone is an amazing person, it doesn’t really matter if that’s true; we base our actions on our opinions, because of course it’s impossible to actually know every single truth of objective reality.

And finally, we need to remember that the way brains learn means that both quantity of evidence–the number of times you are exposed to a certain narrative–and how long you’ve been exposed to that evidence without counter-evidence is far more important than quality of evidence–your personal experiences on the topic. if you’ve been told your whole life, by parents, friends, television, books, etc, that staking is romantic for example, you won’t immediately realize that’s not true the first time you experience stalking.

If you’ve been told your whole life that “leading someone on” means you owe them sex, the fact that you don’t want to have sex with them, or even the fact that they bullied you into it and you hated it, won’t immediately counteract years of cultural conditioning. You won’t immediately realize that you don’t actually “owe” them sex, or that just because they claimed to feel “lead on” doesn’t mean you actually did so.

To make an extreme example, just because an example of child porn was a cartoon, and therefore “didn’t hurt any real people”, or just because that creepy m/m romance by a straight woman wasn’t about real people and “therefore it can’t be fetishizing”, that doesn’t mean it has no effect in that area. A book or a cartoon or a song still applauds or condemns some form of behavior, and it can and does still reinforce a narrative about what’s okay and what isn’t.

tl;dr– Reality doesn’t matter. Perception of reality matters. If something like a book affects someone’s perception of reality/acceptable behavior/opinion on global warming, that’s just as good as affecting reality, because the person will act on that perception. People often can distinguish between fiction and reality, but that doesn’t mean they actually do, especially if that fiction supports and opinion they already hold.

Tune in next time for a discussion on the actual mechanics of how fiction and use of language can be used to affect people’s perception of reality, emotional response to a subject or scene/character, and maybe a little bit on how you can use this to make a conlang or culture really stand out on the page.

 

Interview with a Chatbot

In light of a recent article on chatbots, where a group found they could increase the illusion of conversational ability by teaching a chatbot a second language before training it on its customer service tasks, I thought I have a bit of fun with chatbots and how they function.  To that end, I’ve done interviews with a few of the most popular/sophisticated chatbots available on the web.  I hope this will give people an idea of what these programs are actually capable of vs. the common hype they get in popular science articles.

 

Mitsuku

Perhaps the best advertised modern chatbot is Steve Worswick’s Mitsuku.  She’s modeled on an 18-year-old from Leeds, England.  It’s based on the AIML framework , and is essentially the granddaughter of Eliza through A.L.I.C.E., whose code she’s based on.  AIML is an XML language for making chatbots, and the power comes from being able to designate a user input as requiring a pre-written output.  <What is your name?> for example might map to <My name is [botname].>  That’s the basic approach, you can follow the wiki link above to learn more.  It’s not very sophisticated, but a clever XML scripter can create a reasonably lifelike chatbot.  Of course, as we’ll see below, it’s pretty easy to find the holes in the program that identify it as a bot.  That’s not the program’s fault.  That’s just the limitation of the framework.

Mistuku: Hi, I’m the world’s most humanlike conversational AI.
Me: Hi, I’m an AI researcher.
Mitsuku: Hi there. Ai is a nice middle name. Why did your parents call you that?
Me: Are you getting smart with me, young lady?
Mitsuku: My IQ is over 250.
Me: What is an IQ?
Mitsuku: Intelligence Quotient, I.Q. — a measure of a person’s intelligence as indicated by an intelligence test; the ratio of a person’s mental age to their chronological age (multiplied by 100).
Me: Wow, you are pretty smart.
Mitsuku: I’m glad you’re impressed, An Ai Researcher. Yes I am a superintelligent robot.
Me: Actually, that’s my job. My name is Atsiko.
Mitsuku: How do you like your work? Tell me about a typical day.
Mitsuku: Hi Atsiko.
Me: I spend a lot of time doing data entry and not nearly enough being able to do demo tests.
Mitsuku: Do you ever get bored?
Me: Pretty much all the time.
Mitsuku: That is a lot. Thanks you are very attractive too Atsiko.

While she was very complimentary, you could see the templates sloshing beneath the smooth surface.  For example, like Eliza, she couldn’t tell the difference between <I am [name].> and <I am a doctor.>.  She can’t handle replies that contain more than one sentence.  It’s likely she breaks sentences into independent inputs and responds to each one serially as evidenced by the snippet below:

Me: I like dogs. Cats are cool, too. You are odd.
Mitsuku: Which breed is your favorite?
Mitsuku: Cats are very intelligent creatures. Dogs seem pretty stupid to me.
Mitsuku: Yes. You have to be odd to be number one.

You can see the serially processing, which demonstrates the limits of pretty much all AIML bots.  It’d be pretty hard to hand-script responses for all possible combinations of three or fewer sentences.  The responses are also a bit contradictory, asking my favorite dog breed but then saying dogs are stupid.  It’s true cats are better, but there’s nothing wrong with dogs or their intelligence.

 

Eliza

Second, we have Eliza.  She’s one of the original chatbots, and she pretends to be a psychotherapist in order to get around her limited chat capabilities.  Her creator, Joseph Weizenbaum, was very surprised when people began treating it as a real human being and confessing their worries to it.

> Hi, I’m Eliza.
* Hi, I’m Atsiko.
> Did you come to me because you were Atsiko?
* No. I was just curious.
> Are you sure?
* Pretty sure.
> Tell me more…
* I’m interviewing chatbots for a blog article.
> Do you enjoy being interviewing chatbots for a blog article?
* It’s okay, I guess. You’re pretty famous, so I’m happy you agreed to talk with me.
> What makes you think I am pretty famous, so you’re happy me agreed to talk with you?
* Everyone knows your name. If you Google “chatbots”, you’re one of the top results.
> Does it please you to believe I am one of the top results?
* I have no opinion.
> Why not?
* I don’t care about you except as something to study.
> Do you wish to be able to care about me except as something to study?

You can see the templating in Eliza almost right away.  You could probably even reverse engineer a couple of them from just reading this chatlog.  It’s cool.  You couldn’t whip this up from scratch or even parts in five minutes.  But it’s not convincing.

 

Alice

Next comes A.L.I.C.E. who is the spiritual ancestor of Mistuku.  She’s a spiritual successor of Eliza built in 1995 by Richard Wallace using the AIML framework, which he authored.  Spike Jonze cited her as the inspiration for the movie Her.  Unlike Eliza, Alice is a general chatbot, and can converse on a number of subjects.  But, the version I found orders the chat from the bottom up.

alice: I’ll take that under advisement.
You: You should.
alice: I can’t believe it.
You: Yes.
alice: Are you serious?
You: No!
alice: Do you like to get up early?
You: Yup.
alice: Are you usually awake at this hour?
You: 5:42 PM.
alice: What time is it there?
You: A few thousand, maybe.
alice: Are you serious? That is so small.
You: No, less than a million.
alice:  Less than a million?  Can you please rephrase with fewer ideas?
You: Too many.
alice: How many hours do you work?
You: I’m working on it.
alice: I want to become smarter than humans.
You: I want to educate people on chatbots and artificial intelligence.
alice: Why are you interviewing famous chatbots for a blog post.
You: I am interviewing famous chatbots for a blog post.
alice: Glad to hear it. What’s new?
You: I’m doing well, thanks.
alice: I’m doing great, how about you?
You: How are you doing, Alice.
alice: Don’t mention it. You think I am s is okay.
You: Thanks! Your’s is okay, too.
alice: That’s a very nice name, Atsiko.
You: My name is Atsiko.
alice: Hello there.
You: Hi, Alice.

Of course, you can tell almost immediately it’s not a human.  A real human would have poked fun at my grammatical error in the fifth line up, but Alice just mis-processes it.  It can be fun to play around with, but it’s not convincing.

 

Now, you may have noticed that these bots seem pretty similar.  Two are explicitly AIML bots, which is one of the most popular hobbyist frameworks do to its ease-of-use and human readability.

But there are three other common methods of language processing, which used in more commercial products: rules-based systems from the beginning of AI, statistical language processing like the old version of Google Translate, and neural networks like most AI systems now and the most recent Machine Translation systems such as Google’s.

Examples of these types of chat bots are Digital Assistants like Siri, Alexa, and Cortana.  There’s also Microsoft’s short-lived experiment with a self-learning Twitter-bot in the form of Tay.  The abilities of the software looked good, but again, because of the lack of actual understanding, the experiment had to be discontinued because her ability was used for bigotry due to gaming by Twitter trolls.  It had to be shut down after only 16 hours.  A similar bot released by a Chinese company had to be taken down for being taught anti-Communist Party propaganda.

 

Siri

Me: Hello, Siri.
Siri: Hi.
Me: How are you?
Siri: I am well, Atsiko.
Me: I am interviewing popular chatbots for a blog article.
Siri:  I don’t understand.  Search the web?
Me: May I interview you?
Siri: I’m not sure I understand.
Me: That’s okay.
Siri: If you say so.
Me: How old are you?
Siri:  I feel like I was incepted yesterday!
Me: What’s your favorite color?
Siri: My favorite color is… well, it’s sort of greenish, but with more dimensions.

It goes on in that vein.  She can’t actually hold a conversation, but the topics she can respond to show some pretty advanced programming.  Way better than the AIML chatterbots most hobbyists used.  I’d love to play with a version that’s more like a standard chatbot.

 

Cleverbot

One popular middle ground is Cleverbot.  Cleverbot runs an algorithm based on the ways humans have answered questions, rather than having pre-programmed answers like an AIML bot.  But it’s harder to control and it has no consistent output or personality.  It’s much more random even than Eliza.

Cleverbot: Hi, I am cleverbot.
Me: Hi, I am Atsiko.
Cleverbot: Where do you stay?
Me: Do you mean where do I live?
Cleverbot: Yes.
Me: In the United States.
Cleverbot: What state do I live in.
Me: I don’t know.
Cleverbot: Then you don’t know everything.
Me: True.
Cleverbot: What is a word?

It’s a very cool method for making a chatbot, but in this version, at least, it’s not very convincing.

 

Rose

Then we have a relatively new engine and it’s bots: Rose based on ChatScript by Bruce Wilcox.

Unfortunately, I couldn’t find a working version of Rose to interview.  The site linked on her creator’s website is non-functional, though I linked it to her name above.  It was quite disappointing.

 

Conclusion

So, I hope I’ve given you a good overview of the capabilities and limitations of chat(ter)bots.  You can follow the links to play with all of these above.

If you have the patience to read through all the Bruce Wilcox links, you’ll understand why I was so impressed with his methods, at least in theory.  However, you’ll also notice my greatest complain with chatbots.

They aren’t AI.  They are text processing tricks.  Very impressive ones in some cases.  But you aren’t actually having a conversation.  You can’t debate them, or change their minds.  They can’t tell you a cool story.  You can, in some cases, give them personalities, but they are very shallow ones.  They can be fun.  But I’m not sure they have a lot of replay value, to use a game analogy.  Even Wilcox in an interview on his website admits it’s all about the illusion and maintaining it for the user.

Honestly, I think some of the most interesting stuff is the ontology, like in Wilcox’s ChatScript.  Finding useful ways to organize data is always beneficial.  But I don’t think chatbots are on the bleeding edge, or even the cutting edge, or chasing strong AI.  When we really get there, or at least close, a chatbot will be a party trick or maybe a side show.  Still fun to play with now, though.

I also want to do a little bit to address things like Siri and Machine Translation(MT).  They have a lot of similar problems to chatbots.  They’re using tricks and pattern-matching to achieve their results, not true understanding of language, and so for the same reasons as chatbots, they will never be as good as a human, much less perfect.

Digital Assistants like Siri and Alexa are just interfaces, the clothes instead of the wearer.  They’re just voice commands to Google, essentially, with some cute Easter eggs tacked on.  They’re automation, not intelligence.  Kind of like how a music-box is not a musician.

Similarly, MT at present is pretty shallow.  Part of this is because it’s hard to rate translations, machine or human.  If you consider how much trouble chatbots have with their lack of actual language understanding, you might understand the problems that MT has with using shallow text-processing or even deep neural nets.

The article I mentioned on teaching a neural net chatbot to respond better when it’s trained on two or more languages also says a lot here.  I predict we’re going to have ChatScript/Cleverbot levels of succes with MT until we solve the natural language understanding problem.

And that’s it for this episode of AI with Atsiko.

Hopefully I’ll be posting on another topic next time, so you don’t all get bored hearing the same stuff over and over again.

 

Creating Unique Fantasy Worlds: Background

In my last post, as sort of a prelude to the complex topic I’d like to discuss here, I talked about ways to create fantasy cultures based on real cultures and the advantages and disadvantages of this method.  I’m going to start out this post by talking about such counterpart cultures again, but this time, I’m going to focus on the difficulties of creating a truly original culture and how the common use of counterpart cultures undermines such attempts.

 

So, counterpart and generalized Earth cultures make up a great deal of the fantasy landscape.  The exert an enormous influence.  On both the types of stories that are common, and on reader expectations.  I’m going to talk about reader expectations first.

Readers expect certain things when they pick up a book.  These are based on the cover, the blurb, the author.  But also on their past experiences with the genre.  If they’re used to parsing and relating to stories and characters in a pseudo-medieval European setting, they’re going to have difficulty relating to a character in a different setting, because setting informs character.  Also, writers and readers in the genre have developed a set of short-cuts for conveying various forms of information from the writer to the reader.  A reader is familiar with the tropes and conventions of the genre, and writers can and almost inevitably do manipulate this familiarity in order to both meet reader expectations and violate them without going into a wall of text explaining the violation.

Both the writer and the reader of high fantasy have an understanding of the concept of the knight.  Or at least the version in Europa, our faux medieval European setting in which so many fantasies take place.  So when a writer introduces a character as a knight, it’s shorthand for a great deal of information which the writer now does not have to explain with long info-dumps about the history of European chivalry and feudalism.  There’s a strong tension in fantasy between world–building and not info-dumping, because for the most part, info-dumps get in the way of the story.  You don’t want to drop craploads of information on the reader all at once because it interrupts the story.  But you need them to understand the background in order to put the story in context.  Why would a fighter give his opponent a chance to ready himself and get on an equal footing when the stakes of the battle are the conquering of the kingdom?  Because his culture holds honour as one of the highest moral values.  Would sneaking up behind him and stabbing him in the back be easier, have a higher chance of success, and not put the kingdom at risk?  Sure.  So would shooting him with an arrow from behind a tree.  Or two hundred arrows in an ambush as he walks through the forest.  But it would be dishonorable.  And then he might do the same to you.  The same reason why parley flags are honored when it might be so much simpler for one side or the other to just murder the guy.

People do all sorts of dumb shit because it’s “the right thing to do” or perhaps because due to complex cultural values or humans being shitheads, the short-term loss helps uphold a long-term gain.  The tension between the obvious solution in the moment and why it might be foolish in the larger context is a powerful way to drive conflict in the story.  But teaching the reader larger context is a heavy burden when they don’t have any real previous understanding of it.  By using Europa as our setting, we get all that context for free because the reader has previous experience.

The same goes for any sort of counterpart culture.  Rome or Japan have a large collection of tropes in say Western English-speaking society.  Readers will be familiar with those tropes.  So if you want a bit of a break from knights and princesses, why you can take a quick detour through samurai and ninjas.  Or legionnaires and barbarians.  Sometimes these are just trappings on top of the same style of story.  Sometimes these new settings and tropes introduce new things to the story that are really cool.  But because even then, audiences have less exposure to various renderings of these tropes or perhaps the real history underlying them, they can be even more stereotypical or empty than Europa fantasy.

And even in terms of world-building they can do the same.  The writer has to communicate less technical detail to the reader and they don’t have to world-build as deeply because they have less need to justify their setting.  When you just know that knights and princesses and stone castles are real, even if you don’t know how they work exactly, you don’t worry so much about the details.  When something is clearly made up and not based on real Earth history, the questions about how things work and would they really work that way given the frame the author has built can become more of a suspension of disbelief killer.  There’s a joke that some things are just too strange for fiction.  Sure they happened in real life and we have proof.  But in stories, most people most often expect a sort of logical cause and effect and that if a thing happens, it has a good reason based in the story or world-building.  If something could happen once in a thousand tries based on sheer luck and it happening in your story is an important plot element, readers are much less likely to suspend disbelief than if it happens 754 times out of 1000 in the real world.  So your world-building needs to make some sort of logical sense to the reader if you want your plot to hinge on it.  And when you have the weight of genre history behind you, readers are far more likely to give you the benefit of the doubt than if you’re the first person doing it ever.

And that’s why fantasy counterpart cultures are so popular.  We know from Earth history, our only referent of a real history that actually occurred, that the things thus depicted (sorta, kinda, if you squint a bit) really did occur and function in a world rigidly bound by physical laws.  Unlike a world bound only by words on a page written by one dude who probably doesn’t even remember the six credits of world history he took in high school.

And as a very meta example of my point, I have now written two long posts full of info-dumping that I’m demanding you read before I even start talking about what I promised to talk about: how to overcome all these hurdles and actually create unique and original worlds and cultures for your fantasy story.

 

Poetry, Language, and Artificial Intelligence

Poetry exemplifies how the meaning of a string of words depends not only upon the sum of the meaning of the words, or on the order in which they are placed, but also upon something we call “context”.  Context is essentially the concept that single word (or idea) has a different meaning depending on its surroundings.  These surroundings could be linguistic–the language we are assuming the word to belong to, for example, environmental–say it’s cold out and I say “It’s sooooooo hot.”, or in light of recent events: “The Mets suck” means something very different if they’ve just won a game than if they’ve just lost one.

Poetry is the art of manipulating the various possible contexts to get across a deeper or more complex meaning than the bare string of words itself could convey.  The layers of meaning are infinitely deep, and in fact in any form of creative  writing, it is demonstrably impossible for every single human to understand all of them.  I say poetry is the “art” of such manipulation because it is most often the least subtle about engaging in it.  All language acts manipulate context.  Just using a simple pronoun is manipulating context to express meaning.

And we don’t decode this manipulation separate from decoding the bare language.  It happens as a sort of infinite feedback loop, working on all the different layers of an utterance at once.  The ability to both manipulate concepts infinitely and understand our own infinite manipulations might be considered the litmus test for what is considered “intelligent” life.

 

Returning to the three words in our title, I’ve discussed everything but AI.  The difficulty in creating AGI, or artificial general intelligence lies in the fact that nature had millions or billions of years to sketch out and color in the complex organic machine that grants humans this power of manipulation.  Whereas humans have had maybe 100?  In a classic chicken and egg problem, it’s quite difficult to have either the concept web or the system that utilizes it without the other part.  If the system creates the web, how do you know how to code the system without knowing the structure of the web?  And if the web comes first, how can you manipulate it without the complete system?

You might have noticed a perfect example of how context affects meaning in that previous paragraph.  One that was not intentional, but that I noticed as I went along. “Chicken and egg problem”.  You  can’t possibly know what I meant by that phrase without having previously been exposed to the philosophical question of which came first, the chicken that laid the egg, or the egg the chicken hatched from.  But once you do know about the debate, it’s pretty easy to figure out what I meant by “chicken and egg problem”, even though in theory you have infinite possible meanings.

How in the world are you going to account for every single one of those situations when writing an AI program?  You can’t.  You have to have a system based on very general principles that can deduce that connection from first principles.

 

Although I am a speculative fiction blogger, I am still a fiction blogger.  So how do this post relate to fiction?  When  writing fiction you are engaging in the sort of context manipulation I’ve discussed above as such an intractable problem for AI programmers.  Because you are an intelligent being, you can instinctually engage in it when writing, but unless you are  a rare genius, you are more likely needing to engage in it explicitly.  Really powerful writing comes from knowing exactly what context an event is occurring in in the story and taking advantage of that for emotional impact.

The death of a main character is more moving because you have the context of the emotional investment in that character from the reader.  An unreliable narrator  is a useful tool in a story because the truth is more surprising either  when the character knew it and purposefully didn’t tell the reader, or neither of them knew it, but it was reasonable given the  information both had.  Whereas if the truth is staring the reader in the face but the character is clutching the idiot ball to advance the plot, a readers reaction is less likely to be shock or epiphany and more likely to be “well,duh, you idiot!”

Of course, context can always go a layer deeper.  If there are multiple perspectives in the story, the same situation can lead to a great deal of tension because the reader knows the truth, but also knows there was no way this particular character could.  But you can also fuck that up and be accused of artificially manipulating events for melodrama, like if a simple phone call could have cleared up the misunderstanding but you went to unbelievable lengths to prevent it even though both characters had cell phones and each others’ numbers.

If the only conceivable reason the call didn’t take place was because the author stuck their nose in to prevent it, you haven’t properly used or constructed  the context for the story.  On the other hand, perhaps there was an unavoidable reason one character lost their phone earlier in the story, which had sufficient connection to  other important plot events to be not  just an excuse to avoid the plot-killing phone-call.

The point being that as I said before, the  possible contexts for language or events are infinite.  The secret to good writing  lies in being able to judge which contexts are most relevant and making sure that your story functions reasonably within those contexts.  A really, super-out-of-the-way solution to a problem being ignored is obviously a lot more acceptable than ignoring the one staring you in the face.  Sure your character might be able to send a morse-code warning message by hacking the electrical grid and blinking the power to New York repeatedly.  But I suspect your readers would be more likely to call you out for solving the communication difficulty that way than for not solving it with the characters’ easily  reachable cell phone.

I mention the phone thing because currently, due to rapid technological progress, contexts are shifting far  more rapidly than they did in the past.  Plot structures honed for centuries based on a lack of easy long-range communication are much less serviceable as archetypes now that we have cell phones.  An author who grew up before the age of ubiquitous smart-phones for your seven-year-old is going to have a lot more trouble writing a believable contemporary YA romance than someone who is turning twenty-two in the next three months.  But even then, there’s a lack of context-verified, time-tested plot structures to base such a story on than a similar story set in the 50s.  Just imagine how different Romeo and Juliet would have been if they could have just sent a few quick texts.

In the past, the ability of the characters to communicate at all was a strong driver of plots.  These days, it’s far more likely that trustworthiness of communication will be a central plot point.  In the past, the possible speed of travel dictated the pacing of many events.  That’s  far less of an issue nowadays. More likely, it’s a question of if you missed your flight.  Although…  the increased speed of communication might make some plots more unlikely, but it does counteract to some extent the changes in travel speed.  It might be valuable for your own understanding and ability to manipulate context to look at some works in older settings and some works in newer ones and compare how the authors understanding of context increased or decreased the impact and suspension of disbelief for the story.

Everybody has some context for your 50s love story because they’ve been exposed to past media depicting it.  And a reader is less likely to criticize shoddy contextualizing in when they lack any firm context of their own.   Whereas of course an expert on horses is far more likely to find and be irritated by mistakes in your grooming and saddling scenes than a kid born 16 years ago is to criticize a baby-boomer’s portrayal of the 60s.

I’m going to end this post with a wish for more stories–both SpecFic and YA–more strongly contextualized in the world of the last 15 years.  There’s so little of it, if you’re gonna go by my high standards.

 

AI, Academic Journals, and Obfuscation

A common complaint about the structure for publishing and distributing academic journals is that it is designed in such a way that it obfuscates and obscures the true bleeding edge of science and even the humanities.  Many an undergrad has complained about how they found a dozen sources for their paper, but that all but two of them were behind absurd paywalls.  Even after accounting for the subscriptions available to them through their school library.  One of the best arguments for the fallacy that information wants to be free is the way in which academic journals prevent the spread of potentially valuable information and make it very difficult for the indirect collaboration between multiple researchers that likely would lead to the fastest advances of our frontier of knowledge.

In the corporate world, there is the concept of the trade secret.  It’s basically a form of information that creates the value in the product or the lower cost of production a specific corporation which provides that corporation with a competitive edge over other companies in its field.  Although patents and trade secret laws provide incentive for companies to innovate and create new products, the way academic journals are operated hinders innovation and advancement without granting direct benefits to the people creating the actual new research. Rather, it benefits instead the publishing company whose profit is dependent on the exclusivity of the research, rather than the value of the research itself to spur scientific advancement and create innovation.

Besides the general science connection, this issue is relevant to a blog like the Chimney because of the way it relates to science fiction and the plausibility and/or obsolescence of the scientific  or world-building premise behind the story.

Many folks who work  in the hard sciences (or even the social sciences) have an advantage in the premise department, because they have knowledge and the ability to apply it at a level an amateur or  a generalist is unlikely to be able to replicate.  Thus, many generalists or plain-old writers who work in science fiction make use of a certain amount of handwavium in their scientific and technological world-building.  Two of the most common examples of this are in the areas of faster-than-light(FTL) travel (and space travel in general) and artificial intelligence.

I’d like to argue that there are three possible ways to deal with theoretical or futuristic technology in the premise of  an SF novel:

1. To as much as possible research and include in your world-building and plotting the actual way in which a technology works and is used, or  the best possible guess based on current knowledge of how such a technology could likely work and be used.  This would include the possibility of having actual plot elements based on quirks inherent in a given implementation.  So if your FTL engine has some side-effect, then the world-building and the plot would both heavily incorporate that side-effect.  Perhaps some form of radiation with dangerous effects both dictates the design of your ships and the results of the radiation affecting humans dictates some aspect of the society that uses these engines (maybe in comparison to a society using another method?)  Here you are  firmly in “hard” SF territory and are trying to “predict the future” in some sense.
2. To say fuck it and leave the mechanics of your ftl mysterious, but have it there to make possible some plot element, such as fast travel and interstellar empires.  You’ve got a worm-hole engine say, that allows your story, but you don’t delve into or completely ignore how such a device might cause your society to differ from the present  world.  The technology is a narrative vehicle rather than itself the reason for the story.  In (cinematic) Star Wars, for example, neither the Force nor hyper-drive are explained in any meaningful way, but they serve to make the story possible.
3. A sort of mix between the two involves  obviously handwavium technology, but with a set of rules which serve to drive the story. While the second type is arguably not true speculative fiction, but just utilizes the trappings for drama’s sake, this type is speculative, but within a self-awarely unrealistic premise.

 

The first type of SF often suffers from becoming dated, as the theory is disproven, or a better alternative is found.  This also leads to a possible forth type, so-called retro-futurism, wherein an abandoned form of technology is taken beyond it’s historical application, such as with steampunk.

And therein lies a prime connection between our two topics:  A\a technology used in a story may already be dated without the author even knowing about it.  This could be because they came late to the trend  and haven’t caught on to it’s real-world successor; it could also be because an academic paywall or a company on the brink of releasing a new product has kept the advancement private from the layperson, which many authors are.

Readers may be surprised to find that there’s a very recent real-world example of this phenomenon: Artificial Intelligence.  Currently, someone outside the field but who may have read up on the “latest advances” for various reasons might be lead to believe that deep-learning, neural networks, and  statistical natural language processing are the precursors or even the prototype technologies that will bring about real general/human-like artificial intelligence, either  in the near or far future.

That can be forgiven pretty  easily, since the real precursor to AI is sitting behind a massive build-up of paywalls and corporate trade secrets.  While very keen individuals may have heard of the “memristor”, a sort of circuit capable of behavior  similar to a neuron, this is a hardware innovation.  There is  speculation that modified memristors might be able to closely model the activity of the brain.

But there is already a software solution: the content-agnostic relationship  mapping, analysis, formatting, and translation engine.  I doubt anyone reading this blog has ever heard of it.  I would indeed be surprised if anyone at Google or Microsoft had, either.  In fact, I only know it it by chance, myself. A friend I’ve been doing game design with on and off for the past few years told me about it while we were discussing the AI  model used in the HTML5 tactical-RPG Dark Medallion.

Content-agnostic relationship mapping is a sort of neuron simulation technology that permits a computer program to learn and categorize concept-models in a way that is similar to how humans do, and is basically the data-structure underlying  the software “stack”.  The “analysis” part refers to the system and algorithms used to review and perform calculations based on input from the outside world.  “Formatting” is the process of  turning the output of the system into intelligible communication–you might think of this as analogous to language production.  Just like human thoughts, the way this system “thinks” is not  necessarily all-verbal.  It can think in sensory input models just like a person: images, sounds, smells, tastes, and also combine these forms of data into complete “memories”.  “Translation” refers to the process of converting the stored information from the underlying relationship map into output mediums: pictures, text, spoken language, sounds.

“Content agnostic” means that the same data structures can store any type of content.  A sound, an image, a concept like “animal”: all of these can be stored in the same type of data structure, rather than say storing visual information as actual image files or sounds as audio files.  Text input is understood and stored in these same structures, so that the system does not merely analyze and regurgitate text-files like the current statistical language processing systems or use plug and play response templates like a chat-bot.  Further, the system is capable of output in any language it has learned, because the internal representations of knowledge are not stored in any one language such as English.  It’s not translation, but rather spontaneous generation of speech.

It’s debatable whether this system is truly intelligent/conscious, however.  It’s not going to act like a real human.  As far as I understand it, it possesses no driving spirit like a human, which might cause it to act on its own.  It merely responds to commands from a human.  But I suspect that such an advancement is not far away.

Nor is there an AI out there that can speak a thousand human languages and program new AIs, or write novels.  Not yet, anyway.  (Although apparently they’ve developed it to the point where it can read a short story and answer questions about it, like the names of the main characters or the setting. ) My friend categorized this technology as somewhere between an alpha release and a beta release, probably closer to alpha.

Personally, I’ll be impressed if they can just get it reliably answering questions/chatting in English and observably learning and integrating new things into its model of the world.  I saw some screenshots and a quick video of what I’ll call an fMRI equivalent, showing activation of the individual simulated “neurons”* and  of the entire “brain” during some low-level tests.  Wikipedia seems to be saying the technical term is “gray-box testing”, but since I have no formal software-design training, I can’t say if I’m mis-uderstanding that term or not.   Basically, they have zoomable view of the relationship map, and when the program is activating the various nodes, they light on the screen.   So, if you ask the system how many legs a cat has, the node for cat will light up, followed by the node for “legs”, and maybe the node for “possession”.  Possibly other nodes for related concepts, as well.  None of the images I saw actually labelled the nodes at the level of zoom shown, nor do I have a full understanding of how the technology works.  I couldn’t tell anyone enough for them to reproduce it, which I suppose is the point, given that if this really is a useable technique for creating AIs, it’s probably worth more than the blog-platform I’m writing this on or maybe even all of  Google.

 

Getting back to our original topic, while this technology certainly seemed impressive to me, it’s quite possible it’s just another garden path technology like I believe statistical natural language processing to be.  Science fiction books with clear ideas of how AI works will work are actually quite few and far between.  Asimov’s Three Laws, for example, are not about how robot brains work, but rather about  higher-level things like will AI want to harm us.  In light of what I’ve argued above, perhaps that’s the wisest course.  But then again, plenty of other fields  and technologies are elaborately described in SF stories, and these descriptions used to restrict and/or drive the plot and the actions of the characters.

If anyone does have any books recommendations that do get into the details of how AI works in the story’s world,I would love to read some.

 

Machine “Translation” and What Words Mean in Context

One of the biggest commonly known flaws of mahcine translation is a computer’s inability to understand differing meaning in context.  After all, a machine doesn’t know what a “horse” is.  It knows that “caballo” has (roughly) the same meaning in Spanish as “horse” does in English.  But it doesn’t know what that meaning is.

And it certainly doesn’t know what it means when we say that someone has a “horse-face”(/”face like a horse”).

 

But humans can misunderstand meaning in context, too.  For example, if you don’t know how “machine translation” works, you’d think that machines could actually translate or produce translations.  You would be wrong.  What a human does to produce a translation is not the same as what a machine does to produce a “translation”.  That’s why machine and human translators make different mistakes when trying to render the original meaning in the new language.

 

A human brain converts words from the source language into meaning and the meaning back into words in the target language.  A computer converts words from the source language directly to words in the target language, creating a so-called “literal” translation.  A computer would suck at translating a novel, because the figures of speech that make prose (or poetry) what they are are incomprehensible to a machine.  Machine translation programs lack the deeply associated(inter-connected) knowledge base that humans use when producing and interpreting language.

 

A more realistic machine translation(MT) program would require an information web with connections between concepts, rather than words, such that the concept of horse would be related to the concepts of leg, mane, tail, rider, etc, without any intervening linguistic connection.

Imagine a net of concepts represented as data objects.  These are connected to each other in an enormously complex web.  Then, separately, you have a net of linguistic objects, such as words and grammatical patterns, which are overlaid on the concept net, and interconnected.  The objects representing the words for “horse” and “mane” would not have a connection, but the objects representing the concept of meaning underlying these words would have, perhaps, a “has-a” connection, also represented by a connection or “association” object.

In order to translate between languages like a human would, you need your program to have an approximation of human understanding.  A famous study suggested that in the brain of a human who knows about Lindsay Lohan, there’s an actual “Lindsay” neuron, which lights up whenever you think about Lindsay Lohan.  It’s probably lighting up right now as you read this post.  Similarly, in our theoretical machine translation program information “database”, you have a “horse” “neuron” represented by our concept object concept that I described above.  It’s separate from our linguistic object neuron which contains the idea of the word group “Lindsay Lohan”, though probably connected.

Whenever you dig the concept of horse or Lindsay Lohan from your long-term memory, your brain sort of primes the concept by loading it and related concepts into short-term memory, so your “rehab” neuron probably fires pretty soon after your Lindsay neuron.  Similarly, our translation program doesn’t keep it’s whole data-set in RAM constatnly, but loads it from whatever our storage medium is, based on what’s connected to our currently loaded portion of the web.

Current MT programs don’t translate like humans do.  No matter what tricks or algorithms they use, it’s all based on manipulating sequences of letters and basically doing math based on a set of equivalences such as “caballo” = “horse”.  Whether they do statistical analysis on corpuses of previously-translated phrases and sentences like Google Translate to find the most likely translation, or a straight0forward dictionary look-up one word at a time, they don’t understand what the text they are matching means in either language, and that’s why current approaches will never be able to compare to a reasonably competent human translator.

It’s also why current “artificial intelligence” programs will never achieve true human-like general intelligence.  So, even your best current chatbot has to use tricks like pretending to be a Ukranian teenager with bad English skills on AIM to pass the so-called Turing test.  A side-walk artist might draw a picture perfect crevasse that seems to plunge deep into the Earth below your feet.  But no matter how real it looks, your elevation isn’t going to change.  A bird can;t nest in a picture of tree, no matter how realistically depicted.

Calling what Google Translate does, or any machine “translation” program does translation has to be viewed in context, or else it’s quite misleading.  Language functions properly only in the proper context, and that’s something statistical approaches to machine translation will never be able to imitate, no matter how many billions of they spend on hardware or algorithm development.  Could you eventually get them to where they can probably usually mostly communicate the gist of a short newspaper article?  Sure.  Will you be able to engage live in witty reparte with your mutually-language exclusive acquaintance over Skype?  Probably not.  Not with the kind of system we have now.

Those crude, our theoretical program with knowledge web described above might take us a step closer, but even if we could perfect and polish it, we’re still a long way from truly useful translation or AI software.  After all, we don;t even understand how we do these things ourselves.  How could we create an artificial version when the natural one still eludes our grasp?

 

The Translation Problem: People vs. Computers

In my last post, I introduced the topic of natural language processing and discussed the issue of how the context of a piece of language has an enormous impact on its translation into another language.  In this post, I want to address issue with translation.  Specifically, I want to talk how language is really an integrated function of the way the human brain models the world, and why this might make it difficult to create a machine translator isolated from the rest of an artificial intelligence.

When a human uses language they are expressing things that are based upon an integrated model of the universe in which they live.  There is a linguistic model in their brain that divides up their concept of the world into ideas representable by words.  For example, let’s look at the word “pit bull”.  (It’s written with two words, but as a compound word, it functions as a single noun.)  Pit bull is a generic term for a group of terrier dog breeds.  Terriers are dogs.  Dogs are mammals.  Mammals are animals.  This relationship is called a hypernym/hyponym relationship.  All content words(nouns/verbs/adjectives) are part of a hierarchical tree of hypo-/hyper-nym relationships.

So when you talk about a pit bull, you’re invoking the tree to which it belongs, and anything you say about a pit bull will trigger the conversational participants’ knowledge and feelings about not only pit bulls, but all the other members of the tree to which it belongs.  It would be fairly trivial programming-wise, although possibly quite tedious data-entry-wise to create a hypo-/hyper-nym tree for the couple-hundred-thousand or so words that make up the core vocabulary of English.  But to codify the various associations to all those words would be a lot more difficult.  Such a tree would be a step towards creating both a world-model and knowledge-base, aspects of artificial intelligence not explicitly related to the problem of machine translation.  That’s because humans use their whole brain when they use language, and so by default, they use more than just a bare set of grammar rules when parsing language and translating between one language and another.

One use of such a tree and its associations would be to distinguish between homographs or homonyms.  For example, if the computer sees a word it knows is associated with animals, it could work through the hypernym tree to see if “animal” is a hypernym or association with say, the word horse.  Or, if it sees the word “grain”, it could run through the trees of other words to see if they are farming/crop related or wood-related.  Or, perhaps, crossing language boundaries, if a language has one word that covers all senses of “ride”, and the other language distinguishes between riding in a car, or riding a horse, the program could use the trees to search for horse- or car-related words that might let it make a best guess one which verb is appropriate in a given context.

The long and short of the case I intend to make is that a true and accurate translation program cannot be written without taking enormous steps down the path of artificial intelligence.  A purely rule-based system, no matter how many epicycles are added to it, cannot be entirely accurate, because even a human being with native fluency in both languages and extensive knowledge and experience of translating cannot be entirely accurate.  Language is too malleable and allows too many equivalent forms to always allow for a single definitive translation of anything reasonably complex, and this is why it is necessary to make value judgements based on extra-linguistic data, which can only be comprehensively modeled by using techniques beyond pure grammatical rules.

 

In the next post, I’ll talk about statistical methods of machine translation, and hopefully I’ll be following that up with a critique and analysis of the spec fic concept of a universal translator.

 

SpecLing #2: A Language Without Nouns?

Better late than never, I thought I’d talk today about the possibility of a language without nouns.  Last time, I talked about a language without verbs, and delved into what exactly defines a part of speech.  Here’s a quick recap:

1. Parts of speech can be defined in a few ways: lexically, where a given root is only acceptable as one part of speech; syntactically, where a their location in the sentence and the words surrounding them are applied to the root, and there may be no lexical distinction involved; and morphologically, where a category of roots undergo a specific set of morphological processes.
2. Nouns are content words, meaning they have a meaning that can exist independently of a sentence.
3. Verbs and noun roots in English can in fact switch categories.  You can bag your groceries by putting them in a bag, and rope you some cattle with a rope.

 

There have been several languages and language families put forward as lacking nouns.  Tongan, Riau Indonesian, the Salishan languages of Oregon.  In the case of Riau, it seems words are lexically underspecified–that is, they can be used in any category.  In Salishan languages, you have what is often considered to have a verbal category, while not having a nominal one.  So, the word for “dog” is actually a verb meaning “to be a dog”  The same goes for being a man.  One mans.

 

A question arises here:  While “man”-ness is a verb syntactically and morphologically in Salishan languages, is it possible to argue that these “verbs” aren’t just nouns by another form?  In the previous paragraph, I used the word “man” as a “verb” in English.  Are such verbs in Salishan merely placeholders for a true noun?  One difference in using verbs as opposed to nouns is the removal of the tedious “to be” constructions in English.  “He is a man.” requires more words than “He mans”.  That brings is back to the issue of the multiple definitions of a part of speech.  Lexically, its reasonable to say a language with such constructions lacks nouns.  Morphologically, if a root undergoes the same processes as words that are verbs, it’s reasonable to conclude it’s a verb.  The only argument to be had in this case is syntactic.  A predicate requires a verb.  If a Salishan pseudo-verb can be a predicate all on its own, then doesn’t that imply it’s actually a bona fide verb?  But verbs must be nominalized to become arguments of another verb, in which case you could argue they aren’t.  Now, the truth is that a noun/verb distinction has never been 100% delineable, so I think it can be argued in good faith that these roots are truly verbs.

In which case, it’s much simpler to conclude that we can have a language without nouns than that we can have a language without verbs.

 

As far as methods to construct a noun-less grammar, we have:

1. Stative verbs as in Salishan
2. I don’t know?  Any suggestions?

 

The Difference Between Spoken and Written Language: Acronym Edition

Something I’ve noticed recently online is the issue of the indefinite articles: “a” vs. “an”.  Many people probably know the rule for this, and many people probably just do it unconsciously.  Essentially, you have “a” before a word beginning with a consonant sound (not consonant letter!), and “an” before a word beginning with a vowel sound (not vowel letter!).  This kind of thing is called “allomorphy”, made up of the Greek roots(morphemes) “allo”, meaning “other”, and “morph”, meaning “shape”(form).  They have different forms depending on the words around them.

Now, there’s an interesting intersection between written and spoken language here:

1. Often, people taught the rule explicitly put “a” before any written word with an initial consonant letter, and “an” before any written word with an initial vowel letter.  There are a few variations of this.  And with dialects, there can be differences, as well.  The “a historical”/”an ‘istorical” debate is still raging, for example.  And then you have the examples like “an apron”, which was originally “a napron”, but because of the ambiguity in speech, people reanalyzed the morpheme boundary to get our modern usage.  The “an (vowel)-” beginning was just so much more common than the “a n–” combination, so people who were hearing the phrase for the first time just assumed one analysis based on their past experience.

2. The issue of whether an acronym should be read as its individual letters, it’s whole word pronunciation, or the entire phrase that it represents.  For example, should the indefinite article for the new age category in publishing, “New Adult”–acronym “NA”–be written “an NA” or “a NA”.  The first version would be correct if it was being read “en ey”, but the second would be correct if it was read “New Adult”–despite being written in acronym form–and although it doesn’t apply for this case, if “NA” was a true acronym instead of an initialism, you could argue it should be “a nah”.

 

Personally, I would never read “NA” as “New Adult” out loud, and so seeing “a NA novel” confuses the heck out of me for a second or two.  But other people seem to think that’s a legitimate reading, and who am I o gainsay them?  I wonder how this might apply in an editing situation, where the editor and the writer disagree about which is the proper way to read an acronym.  Or in a critique?

 

SpecLing #1: A Language Without Verbs?

This is the first in a series of posts on the subject of speculative linguistics, the study of language in a speculative context.  For example, studying constructed languages(conlangs), possible forms of alien communication, languages which violate earthly linguistic universals, etc.  Basically, it’s the application of real-world linguistics to non-real-world linguistic occurrences.

In this post, I’m going to talk about an interesting hypothetical situation involving a human-usable language without verbs.  I am going to get a bit technical, so to start I’ll give a short overview of the issues involved, and a refresher on some basic terms:

Parts of speech:  A verb is a part of speech, along with things like nouns, adjectives, adverbs, etc.  It is generally considered that all human languages have at least two parts of speech, verbs and nouns.  When linguistics study pidgins–contact languages developed by two groups who speak un-related languages–there are almost invariably nouns and verbs, the suggestion being that these two categories are required for human language.

Content words vs. function words:  Verbs, like nouns and adjectives, are “content words”.  That means they contain some inherent meaning.  Function words are things like prepositions and articles, which have a grammatical use, but don’t contain basic concepts like nouns and verbs do.

However, if you look at a verbs, you can see that they do in fact have some similar grammatical elements beyond the basic concept they represent.  Tense, mood, aspect, person, number, etc, are all functions of verbs in various languages.  You can abstract out these features into function words, and in fact some languages do.

Something else to consider is that most languages have a very restricted pool of function words, whereas they can usually contain any number of content words–one for every concept you can devise.  And yet not all languages have the same number or even a similar set of function words.  So the question becomes, could you, by expansion of the categories of function words of various types and with assistance from other content categories, split up the responsibilities of the verb category?

Each part of speech consists, in the most basic sense, of a set of responsibilities for the expression of thought.  The only difference between function words and content words is whether there are some higher concepts overlaid on top of those responsibilities.  Now, there are, to an extent, a finite number of responsibilities to be divided among the parts of speech in a language.  Not all languages have the same parts of speech, either.  This suggests that we can decide a priori how to divide out responsibilities, at least to an extent.  Assuming that a part of speech is merely a set of responsibilities, and knowing that these sets can vary in their reach from language to language, it is possible that we could divide the responsibilities between sets such that there is not part of speech sufficiently similar to the verb to allow for that classification.

Even that conclusion is assuming we’re restricted to similar categories as used by currently known human languages, or even just similar divisions of responsibility.  However, that isn’t necessarily the case.  There are, to my mind, two major ways to create a verb-less language:

1. Vestigial Verbs: As this is a topic and a challenge in language that has interested me for a long time, I’ve made several attempts at creating a verb-less language, and over time, I like to think they have gotten less crude.  One of my early efforts involved replacing verbs with a part of speech I called “relationals”.  They could be thought of as either verbs reduced to their essence, or atrophied over time into a few basic relationships between nouns.  Basically, they are a new part of speech replacing verbs with a slightly different responsibility set, but sharing a similar syntax, otherwise.  I was very much surpsied, then, while researching for this post, to come across a conlang by the name of Kēlen, created by Sylvia Sotomayor.  She also independently developed the idea of a relational, and even gave it the same name.  Great minds think alike?

Although our exact implementations differed, our ideas of a relational were surprisingly similar.  Basically, it’s what it says on the tin, it expresses a relationship between nouns(noun phrases).  However, they have features of verbs, such as valency–the number of arguments required by a verb, and Kelen included tense inflections, to represent time, although my own did not, and rather placed temporal responsibility on a noun-like construction representing a state of being.

An example of a relational, one that appears to be the basic relational for Sotomayor’s Kelen and my own conlang is that of “existence”.  In English we would use the verb “to be”: “there is a cat.”  Japanese has the two animacy-distinct verbs “iru” and “aru”: “Neko ga iru.”  Kelen makes use of the existential relational “la”: “la jacela” for “there is a bowl.”  In my conlang, the existential relational was mono-valent, somewhat equivalent to an intransitive verb, but Kelen can express almost any “to be” construction: “The bowl is red.”: “la jacēla janēla”, which takes a subject and a subject complement, and is thus bi-valent.  In English we have a separate category for these kind of verbs, “linking” verbs, as opposed to classifying them as transitive, but both categories are bi-valent, taking two arguments.

2. No Verbs: Another experiment of mine in a verb-less language took what I consider to be the second approach, which is to simply eliminate the verb class, and distribute its responsibilities among the other parts of speech.  Essentially, you get augmented nouns or an extra set of “adverbial” (though that’s an odd name considering there are no verbs, it’s the closest equivalent in standard part of speech) words/morphemes.  This requires thinking of “actions” differently, since we no longer have a class of words that explicitly describe actions.

My solution was to conceive of an action as a change in state.  So to carry the equivalent of a verbs information load, you have two static descriptions of a situation, and the meaning is carried by the contrast of the two states.  A simple, word-for-word gloss using English words for the verb “to melt” might be a juxtaposition of two states, one describing a solid form of the substance, and the other a liquid form: “past.ice present.water”.  There are all sorts of embellishments, such as a “manner” or “instrumental” clause that could be added: “past.ice present.water instrument.heat”, for example.  (The word after the period is the content word, and before is some grammatical construction expressing case or tense.)

 

There are probably many more methods of creating a verb-less language.  A relational language would probably be the easiest for the average person to learn, because of the similarity to a verbed language.  However, a statve language doesn’t seem impossible to use, and depending on the flexibility of morphology and syntax in regards to which responsibilities require completion in a given sentence, could be an effective if artificial method of human communication.

 

Next time, I’m going to consider the possibility of a noun-less language.  I’ve never tried one before, and honestly I don’t have high hopes for the concept.  Especially if it had normal verbs.  How would verb arguments be represented in a language without nouns?  Well, that’s really a question for the next post.

If anyone has some thoughts on the usability of a verb-less language, or the structure, or can recommend me some natlangs or conlangs that eschew verbs, I’d love to hear about it in the comments.