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Do Androids Dream?

I’m here with some fascinating news, guys.  Philip K. Dick may have been joking with the title of his famous novel Do Androids Dream of Electric Sheep?  But science has recently answered this deep philosophical question for us.  In the affirmative.  The fabulous Janelle Shane trains neural networks on image recognition datasets with the goal of uncovering some incidental humour.  She’s taken this opportunity to answer a long-standing question in AI.  As it turns out, artificial neural networks do indeed dream of digital sheep.  Whether androids will too is a bit more difficult.  I’d hope we would improve our AI software a bit more before we start trying to create artifical humans.

As Shane explains in the above blog post, the neural network was trained on thousands or even millions (or more) of images, which were pre-tagged by humans for important features.  In this case, lush green fields and rocky mountains.  Also, sheep and goats.  After training, she tested it on images with and without sheep, and it turns out it’s surprisingly easy to confuse it.  It assumed sheep where there were none and missed sheep (and goats) staring it right in the face.  In the second case, it identified them as various other animals based on the other tags attached to images of them.  Dogs in your arms, birds in a tree cats in the kitchen.

This is where Shane and I come to a disagreement.  She suggests that the confusion is the result of insufficient context clues in the images.  That is, fur-like texture and a tree makes a bird, with a leash it makes a dog. In a field, a sheep.  They see a field, and expect sheep.  If there’s an over-abundance of sheep in the fields in the training data, it starts to expect sheep in all the fields.

But I wonder, what about the issue of paucity of tags.  Because of the way images are tagged, there’s not a lot of hint about what the tags are referring to.  Unlike more standard teaching examples, these images are very complex and there lots of things besides what the tags note.  I think the flaw is a lot deeper than Shane posits.   The AI doesn’t know how to recognize discrete objects like a human can.  Once you teach a human what a sheep is, they can recognize it in pretty much any context.  Even a weird one like a space-ship or a fridge magnet.  But a neural net isn’t sophisticated enough or, most generously, structured properly to understand what the word “sheep” is actually referring to.  It’s quite possible the method of tagging is directly interfering with the ANNs ability to understand what it’s intended to do.

The images are going to contain so much information, so many possible changing objects that each tag could refer to, that it might be matching “sheep” say to something entirely different from what a human would match it to.  “Fields” or “lush green” are easy to do.  If there’s a lot of green pixels, those are pretty likely, and because they take up a large portion of the information in the image, there’s less chance of false positives.

Because the network doesn’t actually form a concept of sheep, or determine what entire section of pixels makes up a sheep, it’s easily fooled.  It only has some measure by which it guesses at their presence or absence, probably a sort of texture as mentioned in Shane’s post.  So the pixels making up the wool might be the key to predicting a sheep, for example.  Of course, NNs can recognize lots of image data, such as lines, edges, curves, fills, etc.  But it’s not the same kind of recognition as a human, and it leaves AIs vulnerable to pranks, such as the sheep in funny places test.

I admit to over-simplifying my explanations of the technical aspects a bit.  I could go into a lecture about how NNs work in general and for image recognition, but it would be a bit long for this post, and in many cases, no one really knows, even the designers of a system, everything about how they make their decision.  It is possible to design or train them more transparently, but most people don’t.

But even poor design has its benefits, such as answering this long-standing question for us!

If anyone feels I’ve made any technical or logical errors in my analysis, I’d love to hear about it, insomuch as learning new things is always nice.

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Your Chatbot Overlord Will See You Now

Science fiction authors consistently misunderstand the concept of AI.  So do AI researchers.  They misunderstand what it is, how it works, and most importantly how it will arise.  Terminator?  Nah.  The infinitely increasing complexity of the Internet?  Hell no.  A really advanced chatbot?  Not in a trillion years.

You see, you can’t get real AI with a program that sits around waiting for humans to tell it what to do.  AI cannot arise spontanteously from the internet, or a really complex military computer system or from even the most sophisticated natural language processing program.

The first mistake is the mistake Alan Turing made with his Turing test.  The same mistake the founder and competitors for the Loebner Prize have made.  The mistake being: language is not thought.  Despite the words you hear in your head as you speak, despite the slowly-growing verisimilitude of chatbot programs, language is and only ever has been the expression of thought and not thought itself.  After all, you can visualize a seen in your head without ever using a single word.  You can remember a sound or a smell or the taste of day-old Taco Bell.  All without using a single word.  A chatbot can never become an AI because it cannot actually think, it can only loosely mimic the linguistic expression of thought through tricks and rote memory of templates that if it’s really advanced may involve plugging in a couple variables taken from the user’s input.  Even chatbats based on neural networks and enormous amounts of training data like Microsoft’s Tay, or Siri/Alexa/Cortana are still just tricks of programming trying to eke out an extra tenth of a percentage point of illusory humanness.  Even IBM’s Watson is just faking it.

Let’s consider for a bit what human intelligence is to give you an idea of what the machines of today are lacking, and why most theories on AI are wrong.  We have language, or the expression of intelligence that so many AI programs are so intent on trying to mimic.  We also have emotions and internal drive, incredibly complex concepts that most current AI is not even close to understanding, much less implementing.  We have long-term and short-term memory, something that’s relatively easy for computers to do, although in a different way than humans–and which there has still been no significant progress on because everyone is so obsessed with neural networks and their ability to complete individual tasks something like 80% as well as a human.  A few, like AlphaGoZero, can actually crush humans into the ground on multiple related tasks–in AGZ’s case, chess-like boardgames.

These are all impressive feats of programming, though the opacity of neural-network black boxes kinda dulls the excitement.  It’s hard to improve reliably on something you don’t really understand.  But they still lack the one of the key ingredients for making a true AI: a way to simulate human thought.

Chatbots are one of two AI fields that focus far too much on expression over the underlying mental processes.  The second is natural language processing(NLP).  This includes such sub-fields as machine translation, sentiment analysis, question-answering, automatic document summarization, and various minor tasks like speech recognition and text-to-speech.  But NLP is little different from chatbots because they both focus on tricks that manipulate the surface expression while knowing relatively little about the conceptual framework underlying it.  That’s why Google Translate or whatever program you use will never be able to match a good human translator.  Real language competence requires understanding what the symbols mean, and not just shuffling them around with fancy pattern-recognition software and simplistic deep neural networks.

Which brings us to the second major lack in current AI research: emotion, sentiment, and preference.  A great deal of work has been done on mining text for sentiment analysis, but the computer is just taking human-tagged data and doing some calculations on it.  It still has no idea what emotions are and so it can only do keyword searches and similar and hope the average values give it a usable answer.  It can’t recognize indirect sentiment, irony, sarcasm, or other figurative language.  That’s why you can get Google Translate to ask where the toilet is, but its not gonna do so hot on a novel, much less poetry or humour.   Real translation is far more complex than matching words and applying some grammar rules, and Machine Translation(MT) can barely get that right 50% of the time.

So we’ve talked about thought vs. language, and the lack of emotional intelligence in current AI.  The third issue is something far more fundamental: drive, motivation, autonomy.  The current versions of AI are still just low-level software following a set of pr-programmed instructions.  They can learn new things if you funnel data through the training system.  They can do things if you tell them to.  They can even automatically repeat certain tasks with the right programming.  But they rely on human input to do their work.  They can’t function on their own, even if you leave the computer or server running.  They can’t make new decisions, or teach themselves new things without external intervention.

This is partially because they have no need.  As long as their machine “body” is powered they keep chugging along.  And they have no ability to affect whether or not it is powered.  They don’t even know they need power, for the most part.  Sure they can measure battery charge and engage sleep mode through the computer’s operating system.  But they have no idea why that’s important, and if I turn the power station off or just unplug the computer, a thousand years of battery life won’t help them plug back in.  Whereas human intelligence is based on the physical needs of the body motivating us to interact with the environment, a computer and the rudimentary “AI” we have now has no such motivation.  It can sit in its resting state for eternity.

Even with an external motivation, such as being coded to collect knowledge or to use robot arms to maintain the pre-designated structure of say a block pyramid or a water and sand table like you might see demonstrating erosion at the science center, an AI is not autonomous.  It’s still following a task given to it by a human.  Whereas no one told human intelligence how to make art or why it’s valuable.  Most animals don’t get it, either.  It’s something we developed on our own outside of the basic needs of survival.  Intelligence helps us survive, but because of it we need things to occupy our time in order to maintain mental health and a desire to live and pass on our genes.  There’s nothing to say that a complete lack of being able to be bored is a no-go for a machine intelligence, of course.  But the ability to conceive and implement new purposes in life is what make human intelligence different from that of animals, whose intelligence may have less raw power but still maintains the key element of motivation that current AI lacks, and which a chatbot or a neural network as we know them today can never achieve, no matter how many computers you give it to run on or TV scripts you give it to analyze.  The fundamental misapprehension of what intelligence is and does by the AI community means they will never achieve a truly intelligent machine or program.

Science fiction writers dodge this lack of understanding by ignoring the technical workings of AI and just making them act like strange humans.  They do a similar thing with alien natural/biological intelligences.  It makes them more interesting and allows them to be agents in our fiction.  But that agency is wallpaper over a completely nonexistent technological understanding of ourselves.  It mimics the expression of our own intelligence, but gives limited insight into the underlying processes of either form.  No “hard science fiction” approach does anything more than a “scientific magic system”.  It’s hard sci-fi because it has fixed rules with complex interactions from which the author builds a plot or a character, but it’s “soft sci-fi” in that these plots and characters have little to do with how AI would function in reality.  It’s the AI equivalent of hyperdrive.  A technology we have zero understanding of and which probably can’t even exist.

Elon Musk can whinge over the evils of unethical AI destroying the world, but that’s just another science fiction trope with zero evidential basis in reality.  We have no idea how an AI might behave towards humans because we still have zero understanding of what natural and artificial intelligences are and how they work.  Much less how the differences between the two would effect “inter-species” co-existence.  So your chatbot won’t be becoming the next HAL or Skynet any time soon, and your robot overlords are still a long way off even if they could exist at all.

 

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Is Blogging “Dead” and Is That A Bad Thing?

John Scalzi over on his blog Whatever just posted his yearly summary of readership statistics for his blog for this half of 2017, and it brought up some very interesting questions and insights for me.

 

He mentions how his site views seem to have halved since 2012.  But then he points out how the way social media sites address linking to content obscures many views and distorts the picture from the viewpoint of his built-in WordPress analytics package.

 

Whereas in the early 2000s, blogging was a rather distributed and free-wheeling hobby, nowadays it has been corporatized and hedged in by so-called “walled garden” platforms such as Facebook and Twitter and Tumblr.  A walled garden is a platform that creates inward pressure on users and makes interfacing with outside platforms and media difficult.  Usually to preserve it’s userbase by requiring you to be a member/user of the platform in order to access or interact with its content.  This means that even though there may be links pointing outside, most of the discussion happens within the garden, and if the content creator wants to respond to comments on their content, they must have an account on the walled-garden platform.  And when a garden gets sufficiently large enough, like Facebook, the dilemma then arises: why go to all the extra work of maintaining an external platform such as a blog or website, when the audience all have say a Facebook and the content creator does, too–why not just post straight to Facebook?

 

And Mr. Scalzi is not the only blogger noting or struggling with the issue of how monetized platforms and walled gardens have altered blogging and the web in general.  In fact, many blogs, including many I used to follow closely, have closed their doors or switched formats to keep up with these changes.

 

And beyond the walled garden issue, part of this has to do with how we access the internet today.  Mobile devices make up a much larger share of web viewing now than they did when blogging and the internet first became popular.  And because these are mobile devices, they have many limitations: screen size, processing power, input methods.  A site or blog that looks great on a PC is going to look mighty odd on many mobile devices.  It would be almost impossible for me to type out this post on my phone’s touchscreen keypad.  Complex sites with lots of doodads load much slower on phones, though the gap has closed a bit these days.  Certainly, it’s nicer for me to read a long blog post on my laptop than my phone.  These things, too, have contributed to the decline of the blogosphere compared to its earlier days.

 

And I don’t like that.  For the things I use the blogosphere for, from my own posts to reading essays and such by people such as John Scalzi or Cory Doctorow, or others in various fields, I much prefer a good blog post to a Tweet, or a Facebook status.  I like long-form prose writing, and I don’t feel like I can get the same things out of a tweet or even a tumblr post in many cases.  That’s not to say those things don’t have they’re uses; they’re just different uses in my case.

 

I often wonder whether things might change back a little once we develop technology like laser keyboards and augmented reality or just mini-projectors that could let phones break out of the limitations of their size.  Is it merely that the medium is so different that forces these changes in media?  Does Twitter rely entirely on the artificial restrictions of mobile technology for its popularity?  If I could set my phone on a table or my lap, and have it mimic a keyboard and a computer screen, would I find that I wanted to use it like a more convenient laptop more often?  Or are the changes social changes.  Is it really that people don’t like reading 200-word blog posts anymore?  Or is it just that a 140 character Tweet is a lot less stressful when I’m on my tiny phone screen in the airport?

 

To get a bit more spec ficcy, do people just love Facebook and Twitter that much, or would we break out of the garden if we took down the walls a bit?  If there was an open-source freeware social media network that could access and display your Facebook data and your myspace data, and your Google posts and your tweets all in one platform/app–if it could convert a post/status so that your Google+ post would be accessible on your friend’s Facebook feed would people be more willing to step outside the single platform?  It takes a great deal of energy to manage even one active social media account.  I know I wouldn’t want to have to triple-post to Facebook, Google+, Ello, and then push a link to Twitter, just to reach all my possible audiences.  But what if there was a bridge between these castles that would do the work for me?  Because controlling every aspect of the garden is great for the companies behind Google+, Twitter, Facebook, etc.  But it’s not quite so great for the regular user, and it’s definitely not great for the community as a whole.  The democratization of the web is one of my favorite features, and Facebook and Co. work hard every day to kill that democracy and carve a monopoly from its bloody corpse.

 
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Posted by on July 5, 2017 in atsiko, Blogging, Rants, Sigh, Social Media

 

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The Myth of Publishers as Gatekeepers

I read a pair of posts over on Fantasy-Faction via Magical Words on the issue of self-publishing and its effect on the publishing industry in general.  The two authors took two very different approaches to the subject, and came from two different perspectives.

You should read the two posts if you really want to understand the full context for what I’m about to write.  But in summary, one called the explosion of new authors “the writer’s plague” and decried the damaging effect of much of self-publishing has had on publishing and English literature; the other expounded on how a self-publishing signal-boosting contest run by Mark Lawrence was “revitalizing” SFF.  The first comes across as very elitist even if it’s not meant that way, and the second is a massive exaggeration.  SFF is being revitalized by a large number of factors, of which one is certainly gems in the rough discovered from self-publishing.

But how does that relate to my post title?  Well, as often happens in self-publishing discussion, accusations of dreck-peddling by self-publishers and of elitist snobbery from fans of trade publishing came up several times in the comments to the two posts.  The existence of snobbery towards self-publishing and the justification for it are a mixed bag of truths that people rarely stop to examine.  But they should be examined.

Is and Why Is So Much Self-published Writing Crap?

Yes, a great deal of self-published SFF (and literature in general) is dreck.  So is s portion of trade published SFF.  There are several reasons for this:

  1. Publishers have an investment in their lists and therefore want to do as much as possible to be sure they pan out.  And so they engage in editing and proof-reading.  These costs come out of author profit for obvious reasons.  Many self-publishers do not to to the same lengths as trade-publishers to ensure the quality of the work.  This is for many reasons.  They are more likely to have a biased view of the quality of their work as studies have shown it is much harder to be objective about your own material and also because they may not have written enough or studied writing enough to know how badly they’ve misjudged their work.  Trade-published authors can suffer from the same issue, but that’s what editors and proof-readers are for.  Further, good editing costs money.  That’s why authors fork over s much of the profit to publishers and agents.  Which leads to the second issue.
  2. There’s nothing stopping you from publishing your trunk novels and high school angst poetry.  Self-publishing costs as much as you want to invest.  Stock covers and raw drafts and a few hours can get your book “published”.  This tends not to result in very good books.

 

People Misunderstand the Character of Publishers as a Business

Although publishers provide publishing services such as editing and cover design, publishers are not service companies.  Lulu, Lightning Source, and CreateSpace are examples of publishing  service companies.  You can pay them money for services.  There are many free-lance service providers.  But what they will not do is “buy your book”.  Which is itself a mis-characterization of what publishers do.  Publishers do usually buy the various copyrights associated with your intellectual property.  They don’t buy the intellectual property, though, only the license to produce a product from it.

But what publishers really are is venture capitalists.  Turning a manuscript into a quality book product is expensive.  Printing that book is expensive.  Just like a tech start-up tries to attract venture capital to start a business when they don’t have the money themselves, an author is something like a book start-up.  But they rarely have the money to take the risk on making, marketing, and selling their product themselves.  So the publisher comes in and looks at the product and if they think they can make money by fronting the author the money to produce and sell the book, they make an offer.

Now, the skills to produce a quality book from a manuscript are almost entirely unrelated to the skills required to produce a manuscript.  So not only does the publisher front the money, they provide the services in-house.  Their large reserves of capital allow them to take the risk of providing these services with no guaranteed ROI.  If the publisher publishers your book and it tanks, you don’t owe them the cost of production, nor do you owe them the advance on royalties for selling them the various license rights to the finished product.

It is the combination of these two aspects of a publisher that seem to cause people confusion.

Publishers Are Not Gatekeepers

Many people when self-publishing was just getting started were doing it because they couldn’t get accepted by a trade publisher.  Their product was not believed to be marketable enough for the publisher to risk an investment.  Publishers don’t give a shit about the quality of your manuscript.  They care about the commercial viability.

This is why you see so many books published by trade publishers that are total shit writing-wise, or you think are total shit.  Snookie’s memoir is going to sell a ton of copies and make a bundle regardless of the quality of her ghost-writer.  When you are a debut author of fantasy or SF or whatever, the publisher has no way to judge the risk involved in publishing your manuscript, except for their experience in publishing other manuscripts from debut authors.  And many books fail, or at least don’t succeed massively.  The publisher has to have a way to recoup these losses.  That’s why you get such harsh terms in your contract.  The few major sellers and many minor sellers have to not only pay for the non-sellers, they also have to pay the bills and then produce a profit.

No one is stopping your from publishing your book.  A publisher is not preventing you from being on bookstore shelves.  The bookstore is the gatekeeper, although honestly, would you go in and yell at Shark Tank or Walmart for not investing in or stocking your amateur product?  No, you wouldn’t.  Because that’s silly.  Publishers are investors with services-added, and they have no obligation to invest in your product/company/brand.

Agents Are Not Gatekeepers

Similarly, an agent is a company offering services.  Services on commission.  They are not a gatekeeper trying to screw over brilliant but misunderstood works of art.  If they think your manuscript will make them money, they take it.  On spec.  No charges.  For which you agree to pay them a percentage on future profits.  If no publisher takes on the book, you don’t owe any money.  In fact, the agent is out time and money on your book that they could have spent elsewhere.

Publishers Accepting Only Agented Manuscripts is not Gatekeeping

If you need an agent to get your work considered by a publisher, it’s not “gatekeeping”.  Well, it is, technically.  But gatekeeping is not a crime.  It takes me four or five hours to read a standard-length fantasy novel.  If a publisher would receive a reasonably-expected 10,000 manuscripts a year, that’s 40,000 hours.  If they pay minimum wage to their first readers–which would be stupid, because knowing whether a book is potentially commercial is a high-skill job–that’s $320,000 a year just on the first screening of a manuscript.  Let’s say 10% of those manuscripts are worth a second look by a more experienced reader, or even just a second read by another first reader.  $32,000 a year.  That’s equivalent to an entire employee position.  Why in the world would you expect someone to provide you that service for free?  Some entire businesses have net profits less than $352,000.

Publishers want agented manuscripts because then that process is already completed, and without them paying for it.  Shit, the agent doesn’t even get paid for it.  Do you as an author really want to be shelling out a minimum of $32 a manuscript submission?  If you submit to 10 publishers, that’s $320 out of pocket for a manuscript that is unlikely to be picked up.

Now imagine that, but you’re paying for all the costs associated with production of the final text and the printing.  You’d rather be paying for that?  Please.

 

The Pros and Cons of Trade Publication

 A trade publishing deal takes care of all the technical aspects of publication and getting space on bookstore shelves.  Publishers are respected brands.  You can expect to sell many copies on name recognition of the publisher alone.  I know that a book published by Orbit or Tor with an interesting cover blurb has a strong chance of being worth my time and money.  And you get thousands of dollars up front, which you will keep even if the books sells not a single copy.

But you do have to get accepted by a publisher, probably pay an agent, sign over your copyrights, and for a general average of 10% of the cover price in royalties, and you have to pay back your advance with sales before you get more money.

 

The Pros and Cons of Self-publication

You retain full creative control, keep all the copyrights, and get a far larger share of the profits.

In exchange, you front all the money for production and have to source and compensate your own talent.  If you are wasting your money on a bad book, tough luck.  And you might honestly not realize the low quality or commercial value of your manuscript.

 

Snobbery

So, you often hear complaints about snobbery from trade-published authors or trade publishers and readers towards self-published works.  There’s no inherent reason for this, of course.  Great books have been self-published and horrible books have been trade-published.

But!

There is practical reason for this snobbery, condescension, etc.  Readers get burned by self-published works all the time.  There are tons and tons of horribly written, edited, and produced self-published works.  The majority of them suffer from fatal flaws.  And there are hundreds of thousands of them.  Why in the world would a reader want to run those odds when the odds are much better (though far from perfect!) when going with a trade-published work?  That’s a silly expectation.

But!

There are many reasons an author might choose to self-publish besides they couldn’t hack it in the trade publishing world.  That creative control can be very handy.  There are many horror stories of publishers fucking over authors in contracts or with rights reversion.  There are horror stories of shitty or racist/sexist/etc covers an author has limited say in.  There are terrible stories about marketing from trade publishers for midlist books.  If you happen to have the necessary skill-set for publishing and marketing a book, it may be a much better choice to self-publish.  Hugh Howey got a trade publishing deal for print, but he kept e-book rights because is was financially sensible for him to do so given his success in that format.  He should be applauded for that decision rather than looked down on.

Maybe the writer knew they could make more money by ignoring the desires of the publisher.  If you can sell more shitty pulp novels at a higher royalty than you could a better quality novel through a publisher, who’s to say you shouldn’t, if profit is your goal?  (As long as you aren’t deceiving readers, in my opinion.)

Signal to Noise and Target Audience

The elitism in trade publishing is both misplaced and understandable.  The signal-to-noise ratio, or ratio of good books to bad, is drastically higher in self-publishing.  But it’s important to remember that even if an author is self-publishing because they couldn’t get a trade deal, it doesn’t automatically mean their books is terrible.  They may have a brilliant work that targets a niche market.  The publisher may have liked the book but felt they lacked the expertise to sell to its specific audience.  Perhaps it could have made profit but not enough.  Perhaps there was a glut in the market.  Maybe it was a little ahead of its time.  Maybe it didn’t fit the publisher’s brand.  Maybe it didn’t match any editor’s taste.

The sheer number of books being published today does make it a lot harder for even a brilliant story to stand out from the crowd.  Even though even more of the crowd of published books these days aren’t good.  It’s perfectly legitimate to complain about that.  Or to not read self-published authors because as a reader you’ve found it’s not worth your time.  There are more quality trade-published SFF books in the world than I could afford in terms or either time or money.  The review blog I participate in doesn’t review self-published books because we haven’t found it to provide us the same value as readers or reviewers.  There’s nothing snobby about that.  No one owes your book their time or money.  You may have a quality book that doesn’t succeed the way you want it to, and it doesn’t have to be malicious.

 

Conclusion

I am 100% against condemning other’s publishing decisions.  But I think it’s reasonable to discuss them.  If I think a writer might have done better to trade publish than self-publish, I’ll say so.  You shouldn’t call people stupid, or cast insults because they chose a different route than you.  You shouldn’t do that even if their book sucks, unless they are misrepresenting that for personal gain.  You’re perfectly welcome to say a book sucks, though.

The tone of the first article I linked to is distressing.  It’s metaphor is insulting.  It makes a few valid points, but there’s no reason why they had to be a jerk about them.  And it makes a few invalid points, as well.  Rather than just criticizing other’s “bad” decisions, we should first seek to understand them and the context in which they occur.  And then, with that understanding, we might consider critiquing them.  Maybe.

 

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I’m a Lazy Shit

Some of you may have gathered that I’m a lazy shit.  From the number of post series even with their own index page that never finished or even came to fruition.  I do in fact intend to get all of those up eventually, but I’m a lazy shit.  And some of them require serious research and planning and maybe even citation of sources, all of which I hate but the last of which I really hate.  Just ask my former Academic Advisor.  I’m more an off the cuff sort of person.  If you imagined this presents some major challenges to the goal of me ever having a story/book published, congrats.  You’re pretty sharp.

Anyway, for that reason, I will be trying to post on here more frequently, but in smaller bites to work my way up to having a stronger habit of consistency, which I hope will be beneficial to my fiction and also to those more ambitious series of posts sitting around the Chimney unfinished.

First up–today in fact!:

A world-building post on the challenges and answering techniques for creating a new and unique world not based on a set of previously existing Earth cultures.  Many of which are probably exocitized and stereotyped in your conception, particularly if you are a (white) Western European, or really any identity that isn’t a part of those cultures in general.  Fantasy versions of real-world cultures are fraught with risk, not just from cultural appropriation or downright racism, but from genre stereotypes, from lazy writing and characterization, from plain old old-hatted-ness.  But more on that in the post later today!

 
1 Comment

Posted by on May 16, 2017 in atsiko, Blogging, Books, Uncategorized

 

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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?

 

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The “Next Big Thing” Generation

So, a common topic in many of the writing communities I used to frequent was “the next big thing”.  Generally, what genre of book was going to be the next Twilight vampire romance or Hunger Games dystopia.  I had a lot of fun with those discussions, but only recently have I really stopped to consider how damaging the “next big thing” mindset is.  Not only to literature, but to any field and to our characters as people.

First, it’s damaging to the quality and diversity of books coming out.  If everyone is chasing the next “popular” genre, they aren’t writing, reading, or accepting for publication many good books who just happen to not be the next big thing or who are part of the last big thing.  Even though 90% of the books in the genre of the last big thing were crap, and 7% of the rest were mediocre.

Which ties into my next issue: This attitude creates a hunger for similar books, despite quality or whether the reader would like to try something else because it creates a comfort zone for the reader.  They know they like dystopia because they liked Hunger Games, so they’re more willing to take a chance on another dystopia than a high fantasy or Mundane SF.  (Mundane SF itself having once been the next big thing, thus the proper noun moniker.)

But this is a false comfort zone for many reasons.  The reader may not actually like dystopia, but just that one book.  They may like dystopia but ignore other things they would also really enjoy to keep from having to stray outside their comfort zone.  They may gorge on so many dystopias that they learn to see the flaws in the genre finally,  and therefore ignore a wonderful dystopia down the line, because they’ve moved onto their next big thing.

Or, if they’re jumping on the bandwagon, they may perceive all of YA, say, as mediocre dystopias or obsessed with love triangles.  Perhaps they think all epic fantasy is ASOIAF, which they disliked, and so they don’t take the chance on other works.  For example, maybe they watched the TV show, and aren’t fans of gratuitous sexposition, and so they don’t read the books or similar books because they don’t want to get buried in another avalanche of incest and prostitutes.

Many authors have stories of agents or publishers telling them they have a great book, but they missed the window, or it doesn’t fit with whatever the next big thing is, and so they can’t sell it.  Or they already have ten of these, and even though 8 of them are sub-par, they can’t cancel the contract and pick up this new book.

Or perhaps they like the book, but everyone acquiring fantasy stories right now wants ASOIAF, not comedic contemporary fantasies, or low-key urban fantasies in the original mode without kick-ass leather-wearing, tattoo-bearing heroines with troubled backstories and seriously poor taste in lovers.

And the same can be said for things besides commercial fiction.  Google+ was going to be the next big thing in social media.  Then it was Ello.  Tinder was the next big thing in online dating, and it spawned dozens of clones.  Social media itself is something of a successful next big thing in human interaction and the Internet.  Object-Oriented programming was the next big thing in software design, and yet now the backlash has been going on for years.

Sometimes a next big thing is a great thing.  But the mentality of always hunting for the next big thing is not.  And despite the pressure from our capitalist economy, it might be better in the long term to look for alternatives.  And it is capitalism that is a major driver of this obsession, because history shows even mediocre products can ride the wave of a predecessor to make big money.  Following a successful formula is a bit of a dream situation for many producers of entertainment or products.  That’s why Walmart and most other chains have their own brand version of most popular products, from medicine to housewares to groceries.  The next big thing trend might make some people a decent amount of money in the short-term, but it has long-term effects that have created a sort of creativity pit that we’ll have a hard time climbing out of any time in the near future.  And in the short term, the people who don’t manage to catch the wave, as wonderful as their contributions to literature or software or society may be, are left choking on the dust.

 
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Posted by on January 19, 2017 in atsiko, Uncategorized

 

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