There are winds I love. They have color and texture and taste, so these are the winds I follow. To whom do these winds belong? There are so many, they have names, they have gods, they belong to their cultures.  The scirocco, the berg, the foehn, the karaburan. They race across plains and twist around mountains. They froth up the white caps, and drag you off to far away lands.  These winds, these external winds, they all come with internal components.  You think the wind blows only around the objects? No. It cannot be so. They must also blow through. They create, they have meaning. They rustle your soul.

Pay attention. Forever, we can demand this. Pay attention. But to what? The winds, I say. Watch the way they blow and what they mean, the winds from Africa, they bring the desert, and the winds of the desert are the winds that tell me the stories I most want to hear. Not just the African deserts, the Gobi and the Altai plains, the western deserts, the empty quarter.  Thousands of years of history, buried under a sand, a sand lifted by the winds, tiny particulates, moved across the surface of the planet, washed across our shores, breathed into your lungs, to become part of you.

These are the stories I have to tell you, if you wish to sit, to listen. The gods of sand and sea, the winds that bring them into your soul, into your being. And the winds you were born into, which were born in to you.  Each of us has our winds, and these are the stories you want to know, these are the stories of who you are, these are the stories I can see when I watch you pause, breathe, when I see the winds as they flow.

Before Akathesia was where I wrote about language, AI, and other assorted more technical things, it was a home for travels and fiction or anything else that I wrote. Then, for a few years, I wrote mostly about language and technology, because the movement in machine translation and the absence of any type of cultural translation was both frustrating to me, and what I perceived as a very bad path for humanity. To remove culture from language, or to try, or worse yet, to not realize that is what you are doing, by allowing algorithms to translate or decide, or use statistical weighting to choose the meaning of a word is such a diminishment of humanity.

I think all my writing was for naught, as it seems to continue that way and what we might consider culture now, in 2021, well, I haven’t a clue what comes next, especially when looking at the United States as well as populist-leaning nations.

So I’ve decided to rebirth this, and go back to what I used to write, of other things. I am going to leave the algos here, whereas last time, I took it all away. Instead, a break from the past few years, and perhaps a more interesting future view. See you soon.

E.

Google announced that instead of solving bias issues in their algorithms, they will simply stop using words that make that bias explicit.

It is unclear how opting to not label an image with a word that denotes gender is going to solve any problem that is currently caused by the assumption of gender based on visuals.

So many forms in the modern world require us to tick gender, and we know that is has impact. Think of the to-do over the Apple Card and the men who received better terms and higher credit, based on being male. It was revealed that this was embedded in the algorithm. So sure, we can go back and remove gender markers from algorithms, but the question is, what are we trying to do here?

Men tend to have higher insurance rates, as they have more car accidents and deaths-by-misadventure. Which makes one wonder to what end we’ve all been reduced to maths, and where it is valuable and where it is not.

Either way, short of never calling out gender, and allowing it to have no impact on any calculations of any sort, refusing to allow an AI to choose the gender of a human in a photo seems a stop-gap measure.

I am reminded of a linguistics paper I was reading, on the gender of animals in nature, and the prevalence, in English, to call most animals ‘he’ unless we have clear evidence to the opposite. An interesting bias, given we don’t have a gendered language, such as a romance language, where this would be the default.

Duolingo, for example, always defaults male, in every sentence, and does not explain that, in the early levels of their romance language apps. If you see a cat and type gatta instead of gatto, it’s an error. Which is a pretty hefty bias, in my opinion. It would be lovely if they just swapped the default sentences, not just about cats, to the feminine.

I admit, I spend more time thinking about the evolutionary pathways that machines may take with language, if given enough autonomy, that I rarely write about the types of communications that exist in the world around us.

David G. Haskell’s beautiful article, Five Practice for Listening to the Language of Birds, reminds me that paying attention to the world is not enough.

I love that he writes of this as augmented reality:

The practice of listening to other species is the original “augmented reality.” In opening our minds to the language of species, we experience connection and meaning that far transcend anything offered by electronic simulacra. Why so deep? Because attending to the tongues of other species is our inheritance, bequeathed by a lineage of ancestors extending back hundreds of millions of years. Every one of these grandmothers and grandfathers lived in attentive relationship with the sounds of other species, the diverse conversation of the living Earth.

Though at the same time, I wish he wouldn’t because this is simply reality. Perhaps more pleasurable is the phrase, “ecological polylinguists” which is exactly what we are, and what other species are as well. Perhaps the greatest difference is that many humans pretend we are not, that the languages of other species, of the earth herself, are not as relevant as the languages we speak.

Language is not the only way in which we do not hear the communications of our planet. Scent is another crucial pathway of information. And I like to believe that the magnetic field system, which we humans are not good at, but other species are, is another source of enormously important knowledge.

The MIT Technology Review recently posted an article touting the success of machine learning in the translation of long-last languages. Think Linear-A or Linear-B. These are languages found on ancient tablets, from the Minoan civilizations. Like many ancient languages, they were untranslated for a long time, almost two millennia, in this case.

In the non-machine-assisted manner, you have a man of a different culture and a different time looking at the language pattern and what is known of the culture, and interpreting the symbols in a manner that is considered translation. Language IS culture, so if you don’t know the culture, you probably cannot really translate the language. If you think of English today, and the different varieties, and the jargons, one can see that you might have words you recognize, but you may, in context, have no idea what they mean. And this is in a world you largely recognize — though maybe not.

I often point to the images of the Sumerian gods, when asked about translation, and when asked about machine translation, I did the same. Take the following image, and imagine, if you will, that this person lived on Earth, had wings, carried pail, wore a great skirt, and had two magical flower watches. Place yourself within your six-year old mind and the stories you could tell about what this person could do. Now imagine you are a British gentleman from the 1800s. What stories would he tell? And as you are, today, now? We’ve thrown out so many possible interpretations of the image, because we don’t believe in things now, that may have been believed then. And we certainly don’t all believe the same things, today.

So back to machine translation, and machine learning. Someone has to input the culture, or, alternately, ignore that there may be a variance. This article is about statistical analysis of a language structure, and makes the assumption that it can apply to more than one, and that this is meaningful, and that the output may approximate a reality we cannot know.

Another way to think about this, I translate for you, something from English to French, and I tell you it is true in both languages, because I know it to be true, because it matches the pattern.

Scholarly article is here. They speak of ‘decipherment of lost languages’ in which they are looking for cognates. They seem to assume success with their correct translation of 63.7% of the cognates. Last year I did some linguistic research for the McCain Institute, on the meaning of human rights. In our research, done in the US, across demographics, people couldn’t agree on the meaning of human rights, equality, equity, justice, freedom, and other such words. Yes, these are concepts that can be complex, but the degree of variation in meaning was surprising, and I’ve been doing this kind of research for a long time. Part of the issue seems to be that the meanings are changing very fast right now, combined with the media and fake news and all these other pressures on language and on culture. A corpus analysis of usage in the media shows enormous variation in the past few years, in usage, structure, and domains. So what I am saying, is we can’t do this for English from 2019 to 2009.

Yet here is research, saying they’ve derived grammar from probabilities, so here we have a statistical approach to meaning. And as anyone that ever reads this knows, I don’t agree that we can model culture mathematically, nor that we can understand or know the past when there were no living speakers to tell us what concepts meant. Our view is reductive, especially post-enlightenment.

Here’s my fellow, crossing the space-time barrier with his fancy beard and his magic pine cone.