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.
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.
While I haven’t been writing, I have still been watching. The world of AI continues to be interesting, with updates and changes, though we are still arguing sentience and intelligence, still using dirty and biased data sources, and still arguing if the demise of humans is on the way, at the hands of these machines too bright and too amoral to let us live.
A recent article that suggests that certain humans do have sensory ability to perceive the magnetic field of the earth got me thinking about whether or not electromagnetism could be a communications network/pathway for the many, many species who can sense it, many in very sophisticated manners. What if there is a whole world happening we’ve never seen? What would it be to tap into it? Which makes me wonder, as well, as a world and species that have evolved on a planet with terrestrial magnetism, what happens if we evolve without it? What would a non-magnetic sentience look like? Could this be what the AIs could be? Not that I actually care about artificial intelligences, specifically, rather I am curious about language evolution in non-human species, about transmission of information, language, and culture, and the ways in which we are encoding the past into a future by our unthinking choices in our view of the present. Granted, on that last bit, we think more now than two years ago or five years ago or ten years ago, but if all of our data comes from the textual residue of the past few hundred years, well, not good, I think, just not good.
I’ve gone farther backwards, this time, in two directions, digging in to the origins of language, of machine language, of language machines, and the possibilities of future languages, as yet undone.
The origin of human language is always complicated, and no one has ever found an answer that can be agreed up. The French decreed that evolutionary linguistics was a forbidden topic in the 18th century, and there was quite a pause before people took it up again.
And in a bookshop last week I found Minsky’s 1968 Semantic Information Processing, which has me reading the past. Symbolic logic, cybernetics, minimal self-organizing systems, artificial intelligence, machine modeling of human behavior. It’s interesting what we have chosen to bring to the present with us. And how much Chomsky there is. Not surprising, but this is no longer a Chomskian world, I would say.
Because my interests combine language and culture, I care less about why humans have language and more about what that means, both day to day in usage, and over time. The current American world, in this respect, is astonishing. Watching cable news, day by day, the words morph and shift in the mouths of speakers, meaning the opposite one day, then again the next. The speed is astonishing. In both my education and my life I can never recall language moving so quickly, and being so forcefully used to cleave.
But then, we all know, human memory is faulty.
I found myself going back to quotes about language, famous ones, and wondering what they would mean if uttered by or applied to, languages spoken between machines. To make this non-threatening, apply these thoughts to C3P0 and R2D2.
“Language, that most human invention, can enable what, in principle, should not be possible. It can allow all of us, even the congenitally blind, to see with another person’s eyes.”
That is Oliver Sacks. We give machines the ability to modify what we teach them. They modify language, then, is this no longer, the most human invention? Or have is the machine human, does it become human, once it has language?
In earlier work I did, on dying and dead languages, it is a breach of human rights to outright kill a language. This is meant for minority populations. If two machines speak a modified language together, and a human kills it off, by re-programming, by shutting down the machines, however–is this a breach of human rights?
Remember, there was a day when half the humans on this planet did not qualify as human.
We we travel from the other side, what rights does a language have, what rights does a machine have, we do find violations. But how to validate these violations? How to even understand if they apply, outside of the thought exercise?
The mission of the new MIT – IBM Watson AI Lab seems to assume that ethics are a form of applicable math, that they have natural laws, and can be understood and applied without undue complexity. The mission here
The collaboration aims to advance AI hardware, software, and algorithms related to deep learning and other areas; increase AI’s impact on industries, such as health care and cybersecurity; and explore the economic and ethical implications of AI on society.
is followed by a note that there will be calls for proposals from those affiliated with either of the institutions, in these key areas:
AI algorithms: Developing advanced algorithms to expand capabilities in machine learning and reasoning. Researchers will create AI systems that move beyond specialized tasks to tackle more complex problems and benefit from robust, continuous learning. Researchers will invent new algorithms that can not only leverage big data when available, but also learn from limited data to augment human intelligence.
Physics of AI: Investigating new AI hardware materials, devices, and architectures that will support future analog computational approaches to AI model training and deployment, as well as the intersection of quantum computing and machine learning. The latter involves using AI to help characterize and improve quantum devices, and researching the use of quantum computing to optimize and speed up machine-learning algorithms and other AI applications.
Application of AI to industries: Given its location in IBM Watson Health and IBM Security headquarters in Kendall Square, a global hub of biomedical innovation, the lab will develop new applications of AI for professional use, including fields such as health care and cybersecurity. The collaboration will explore the use of AI in areas such as the security and privacy of medical data, personalization of health care, image analysis, and the optimum treatment paths for specific patients.
Advancing shared prosperity through AI: The MIT–IBM Watson AI Lab will explore how AI can deliver economic and societal benefits to a broader range of people, nations, and enterprises. The lab will study the economic implications of AI and investigate how AI can improve prosperity and help individuals achieve more in their lives.
The last one is where they note that ethics lives, and it does not seem integral to the research in all areas.
Also to note, there is no nuanced or even truly noted inclusion on the difficulties of ethics, of cultural relativity, nor about the cultures both of the teams of creation, but where these advances will be put into the world.
And of course at the end, they note that a key aspect is commercialization.
Another example of enormous dollars being put to something which will fundamentally changes the ways in which humans and machines function in the world, without seeming to desire to understand how that will change the world, and what this means. The ‘delivery of … benefits’ sounds secondary to the creation of commercial enterprises and new technologies. The good will be an added benefit, if it comes, and the structure of the technology is not focused on beginning with clean data and understanding of sociological contexts in which it is going. Engineering-heavy organizations, those who dictate the future of the 21st century.
Humanoid form and flexibility – SecondHands will feature an active sensor head, two redundant torque controlled arms, two anthropomorphic hands, a bendable and extendable torso, and a wheeled mobile platform.
Match this image:
It is the wheeled mobile platform. To me, this robot should not have legs, it should look more like the maid from The Jetsons.
Alibaba Group Holding Ltd. put its deep neural network model through its paces last week, asking the AI to provide exact answers to more than 100,000 questions comprising a quiz that’s considered one of the world’s most authoritative machine-reading gauges. The model developed by Alibaba’s Institute of Data Science of Technologies scored 82.44, edging past the 82.304 that rival humans achieved.
What is notable to me is that in this instance, these questions can only have one answer, to be correct.
The quiz itself is based on wikipedia articles. Remember when you would never let your students use wikipedia as a source?
As the Bloomberg article notes, NLP ‘mimics’ human comprehension. The underlying belief is that the machines can answer objective questions.
“That means objective questions such as ‘what causes rain’ can now be answered with high accuracy by machines,” Luo Si, chief scientist for natural language processing at the Alibaba institute, said in a statement.
Functionality and thus comprehension and correctness is based on a binary model of knowledge, and is using wikipedia for the source of correct. Much about that sentence is complicated, from my perspective. The binary model of correctness allows for no nuance, and is based on those who have the power to control the narrative. No alternate views, no other models.
It reminds me of taking standardized tests, when none of the answers seemed exactly correct, and I spent my test taking time trying to imagine which one the test makers believed to be correct. I was forced to fit into the culture of the creators of the exam. Extending this out to what it means that machines ‘know’ and allowing them to provide authoritative answers seems reductive, dangerous, and seems to be moving ahead apace.
As we sit here this morning, I am reading of Berber languages, and W is reading of Sumer and Akkadian.
Berber, and the Tamasheq variant that particularly interests me, has had a long life as an oral language. Sumerian is one of the first known written languages, and while there is a sample of someone reading Gilgamesh in Akkadian, it was a language that was dead long ago, and we modern humans really do not know what it sounds like. The recreation, however, is beautiful.
Many of the languages which die off are oral languages, the last speakers die, and thus the language goes with it. This has long been a concern of linguists, and the popular press doesn’t seem to make it through a year without a piece about it as well.
The rise of social media based on images, the use of video, and the use of emoji are all interesting language shifts at play now. It is difficult to make any long ranging assumptions, but that makes it no less interesting than to watch younger demographics (in particular) prefer to engage with English in its oral form. Not just the in-person conversations that have always existed (and it may be argued that the in-person is diminishing) but the endless youtube videos and channels with millions of followers. The language variations spoken by many of these English speakers are certainly not the written language that we find in standard texts, lexically and grammatically. The use of emoji shifts English to a pictographic language, rather than a symbol corresponding to a sound, it corresponds to a concept or an idea.
There are thus, interesting ideas about the future of the visual language, both photographic and iconic/ideographic, which I am not going to touch at this time.
What I wonder however, if there will be an orality of language that is prioritized in the future, that shifts the current power and status dynamic in which unwritten languages are a lesser language, an archaic form from a culture which has ‘failed to develop’ despite the many ways in which the more oral languages do have advantages in a cultural context.
Imagining a world in which oral English is how stories are shared, that this access to the storytellers is required, beyond books, to belong, to understand, is a world we have, perhaps, never lived in, not in the modern English that we speak now. It would have been centuries since English was predominately oral, and it was an earlier version of English. Back to the time of the bards, except this time around, our bards will be digital.