Our machines, PCs, tablets, smartphones, etc., from which we have become inseparable in work, but also in our social communication, in training or in entertainment, they are particularly skilled in treating huge quantities of standardized and structured data like tables in a database. They are able to process this data with a much higher speed and precision than we would have been able to do.
But men do not communicate with each other with structured data or binary systems.
Human language is dated in its complete form to about 40.000 years ago and written language has existed for more than seven thousand years. This allows us to express our ideas, the events that involve us, but also our emotions, in a practically infinite way. And this has given the wonderful and complex human language some characteristics that the language of the machine does not have. Or at least it doesn't own yet.
One of the peculiarities of our language lies precisely in its 'ambiguity'. I can say to my interlocutor: 'You're cute', and the message is very simple. But I can say exactly the same thing on the content level using emotional and relational elements that can be irony or sarcasm, and the meaning of an apparently identical communication will be completely different. This the machine does not know yet.
The mother tongue of a computer, the machine language, largely unknown to the majority of the people who use it, is profoundly different from the human one. The machine knows no irony or feeling, which are proper (and so important) to human communication.
However the center of attention of current research concerning the NLP it is precisely aimed at this distance between human language and that of our machines, with the aim of increasingly reducing the distance between their languages. For this reason, increasingly complex algorithms are built, capable of reading the text of human language more and more deeply in the infinite subtleties that each mother tongue has.
We already speak with effective communication applications, such as Siri, Cortana, Alexa, Ok Google, using a reciprocal language that is getting closer and closer to our own natural language.
Much still needs to be done along this path, but the current results are much more than encouraging.
A subfield of the Natural Language Processing, called Natural Language Understanding, has become particularly important in research, due to its great potential in cognitive applications and in the field of artificial intelligence. NLU goes beyond the structural understanding of language to interpret intention, resolve the communicative context, and understand its possible 'ambiguous' aspects. NLU algorithms have to tackle the extremely complex problem of semantic interpretation, that is, understanding the intended meaning of spoken or written language, with all the subtleties, context and inferences, and overcoming the obstacle of language barriers.
Even Digital Learning can and must equip itself by adopting solutions that exploit the potential of these tools. From the elimination of language barriers - a non-trivial topic in the localization of courses - to intelligent applications and systems that analyze reporting, create dedicated training courses, respond to voice commands, every possible development is an opportunity to propose e-learning solutions innovative.