Terminology Glosses: AI and paradigm shifts

Last century’s young people were faced with solid ideologies that had liquefied by the turn of the millennium, only to vaporize into the intangibility of a virtual world in the next few decades. On several occasions, the idea of a paradigm shift has surfaced, more or less explicitly, between the lines of this column. We have always observed such shift through the lens of language, with awareness of the dual role played by language. Language always reflects and, at the same time, always explains change.

Yet when it comes to artificial intelligence, the change is so striking and extraordinary that the language is not entirely codified yet and even less so are the new concepts developing around such change. For terminologists, this is a very intriguing moment. Change — and this time change involves the very essence of human beings — is accompanied by new terms and concepts that define new scenarios. Strictly linked to artificial intelligence are terms like:

cyborg and The Universal Declaration of Cyborg Rights.

biohacking (or grinding): in one of its nuances, the practice of altering one’s body by implanting cybernetic devices.

germinal improvement: improvements humans want to add to their selves not only for themselves, but also for all the future generations.

genomic editing: a technology that uses molecular tools to modify the DNA inside a cell.

designer baby: a baby created based on the taste and needs of the parents.

transhumanism: a cultural movement that promotes the use of science and technology to improve human beings with a particular emphasis on physical and cognitive transcendence in addition to esthetical improvement.

posthumanism: a philosophical approach that studies “the ethical implications of expanding the circle of moral concern and extending subjectivities beyond the human species” according to Wikipedia.

Languages typically grow following concrete to abstract patterns: the first to appear are usually words that denote objects, then abstractions and so on. Word formation may happen through prefixation and suffixation. If a new noun derives from an adjective, this is called a deadjectival noun. The more of these traits we find in the new words, the closer we are to real neologism. At the other end of the spectrum, we find new combinations of existing words. The lexical universe of artificial intelligence gives us an important sign of how deep the semantic change is by means of words like transhumanism and posthumanism: neologisms representing abstractions or, in other words, new notions and thoughts as opposed to new things.

Consider posthumanism, for example, a deadjectival noun formed by adding the prefix post- and the suffix -ism to the adjective human. This word by itself testifies that the field is already beyond the level of naming objects: -ism really is the suffix of philosophy. When appended to a stem, it can mean doctrine, system or method, and it is used in terms like realism, pragmatism, rationalism, postmodernism and so forth.

However, is artificial intelligence already applicable to the language industry? At the end of 2017, ScienceMag published “Artificial intelligence goes bilingual—without a dictionary,” an article that looked at machine translation from the angle of unsupervised machine learning. Two studies that had been presented, but not peer reviewed at the time, showed that “neural networks can learn to translate with no parallel text.” They could also use unsupervised machine learning to build bilingual dictionaries and translations at the sentence level. The training strategies were slightly different: the first study used back translation, which we are all more or less familiar with, whereby a sentence in language X is translated in language Y and then retranslated in language X to see the differences. The other study used a strategy called denoising. In this case the sentence was translated, then some noise was added to it by removing or rearranging words and then the sentence was translated back in the source language. According to the article, the results obtained with the studies were not very competitive, but they were still better than word for word translation.

In the domain of education, language learning and assistance with languages, artificial intelligence is starting to be used productively in a variety of domains. Among the others, Glossika, a linguistic company, in its webpages states that their smart algorithm automatically adjusts to the learner’s level, learning speed and schedule by using a learning-method based on repetitions. Capiche, on the other end, is a “new artificial intelligence and crowd-based innovation… that aims to support refugees with a mobile translation and search service.” It is the first AI-system to integrate crowd sourcing and to use the data collected and interpreted to “enhance the machine-learning system.” This is what it provides: real time translation service, initially in four languages: German, English, Arabic and Persian; legal review of documents on the crowd; and other services.  

As for our ideal termbase, this time we would have to pick from a large selection of new words and specialized terms, but, as always, terminologists need to be cautious with neologisms. In short, my pick for today are the terms artificial intelligence (noun, full form) and its acronym AI defined as: “An area of computer science emphasizing the creation of intelligent machines that work and react like humans,” based on Techopedia.