Monthly Archives: November 2017

Is the future oral?

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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.

 

 

Is being bilingual only about the lexicon?

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To start, each constructs bilingual dictionaries without the aid of a human teacher telling them when their guesses are right. That’s possible because languages have strong similarities in the ways words cluster around one another. The words for table and chair, for example, are frequently used together in all languages.

So how does the computer know that table and chair are often used together? What about cultures that do not have chairs, but do have tables?  How is the computer mapping co-occurances that have a significant variation by culture, or simply do not exist?

This article uses Chinese and Arabic as the example languages for mapping. The underlying cultural principles are rather different, for community, behaviors, constructs, and as these are mapping IN language, does one become more like the other? Does the machine create an Arabic with Chinese sentiments? [The papers use French and English, which are much more similar, culturally and linguistically.]

Treating language like math is not going to turn out well. Though I haven’t yet read the papers that support these assumptions.

Back to my regularly repeated statement: machine translation and language that does not address the significant cultural components of language, as communication, as culture, as transfer mode of ideology, will, in the end, create a different or new culture, and now would be a very good time to be paying more attention to this.

http://www.sciencemag.org/news/2017/11/artificial-intelligence-goes-bilingual-without-dictionary

 

 

An AI is an AI is an AI…or not.

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Every morning, of late, when I read the news, there is a slew of headlines of what AI has done for us lately.

Just this morning, I read:

Robert Wickham of Salesforce is the source of the last statement, that AI will be the new electricity, once we are done oohing and ahhing. Or being afraid that we will all lose our jobs.

AI, however, is not like electricity. It is not so straight forward. While it may, eventually, be ubiquitous and unconsidered, so far we cannot provide a single and clear definition for what it is, and thus these reductive metaphors create greater confusion than clarity.

In each article ‘AI’ describes something different. Deep learning, neural networks, robotics, hardware, a combination, etc. Even within deep learning or neural networks, the meanings can be different, as can the nuts and bolts. Most media and humans use ‘AI’ as shorthand for whatever suits their context. AI, without an agreed upon definition, but the lack of clarity, differentiation, and understanding does make it very difficult to discuss in a nuanced manner.

There is code, there is data, there is an interface–for inputs and outputs, and all of these are (likely) different in each instantiation.  Most of the guts are proprietary, in the combination of code and data and training. So we don’t necessarily know what makes up the artificial intelligence.

Even code, as shorthand to a layperson, as the stuff that makes computers do what they do, is a broad and differentiated category. In this case, like language, it is used for a particular purpose, so this reduction is perhaps not as dangerous. We’ve never argued that code is going to take over the world, or that rogue code is creating disasters.  As compared to algorithms, a few years ago, and AI, now.

So much of this lumping is a problem? We lump things, such as humans or cats, into categories based on like attributes, but we do have some ways to differentiate them. These may not be useful categories, nationality, breed, color, behavior, gender.  (Even these are pretty fraught of late, so perhaps our categorization schemes for mammals needs some readdressing.)  On the other side, we could consider cancer, an incredibly reductive title for very a broad range of…well of what? Tumor types? Mechanisms? Genetic predispositions? There are discussions, given recent research, as to whether cancer should be a noun, perhaps it is better served as a verb. Our bodies cancer, I am cancering, to show the current activity of internal cellular misbehavior.

What if we consider of this  on the intelligence side, how do we speak of intelligence, artificial or otherwise? For intelligence, like consciousness, in humans, we do not have clear explanations for what it is or how it works. So not perhaps the simplest domain to borrow language from, and apply it to machines.  Neural networks is one aspect, modeled on human brains, but it is limited to the structural pathways, a descriptor of how information travels and is stored.

The choice to use AI to represent such a broad range of concepts, behaviors, and functions concerns me. Even in the set of headlines above, it is difficult to know what is being talked about, from a continuum of visible outputs to robots who speak.  If we cannot be more clear about what we are discussing it is incredibly complicated to make clear decisions, about functions, about inputs, about outputs, data, biases, ethics, and all the things which have broad impacts on society.

I haven’t seen clear work on how we should use this language, and though I worked with IBM Watson for a while on exactly this concern, I can’t say I have a strong recommendation for how we categorize not just what we have now, but, as importantly, what is being built and what will exist in the future. The near future.

I’ll work on this later, as in soon, ways in which to talk about these parts in a public context that are clearer, and allow for growth of creations into a systems model.  Check back!