AI and Natural language processing in this modern world

The concept of artificial intelligence is becoming more and more common, not only in the technological field but also in everyday life. It basically refers to the ability to simulate human intelligence processes developed by computer software, with reasoning, learning, and self-correcting capabilities. A few years ago, this seemed futuristic, but we can already see it reflected in our daily lives, such as voice commands like Alexa, chatbots, and other technologies with which we can communicate with just our smartphone.

On the other hand, we find natural language, which is nothing more than the language we humans use, it can be spoken, written, or gestural. This language requires many neural connections and brain and body processes to be able to express ourselves and understand others spontaneously. And the so-called formal language is the one used by sciences such as mathematics or computing and is based on the union of previously specified symbols.

Now, these 3 concepts, although they seem very basic but different, can be united.

What is “Natural Language Processing”?

NLP, or Natural Language Processing, focuses on how artificial intelligence can understand and imitate the natural language of human beings. Achieving this has been a challenge for those who have pursued it, but they have finally come up with several ways to develop NLP:

Probabilistic model: to carry out this model, data are first collected and the frequency of occurrence of certain linguistic units in a context is calculated so that the appropriate unit can be predicted.

Logical model: in contrast to the previous model, in this case, the patterns are previously defined by the linguists, so that, by combining them with the stored dictionary information, the response patterns will be configured.

 Processing natural language requires different techniques, and by following both models and certain algorithms, PLN allows artificial intelligence to perform tasks such as:

– Language detection: one of the most basic tasks artificial intelligence has when processing natural language.

– Relationship identification: to know what to answer next.

– Content categorization: in such a way that they summarize all the information based on natural language, facilitating its search and indexing.

– Syntactic analysis: to be able to answer correctly.

– Lemmatization: is the automatic elimination of prefixes and suffixes to stay with the root word, which facilitates word searches and helps a faster response.

– Contextualization: structuring the information based on the context that has been previously defined.

– Sentiment analysis: identifying the mood of the interlocutor based on language that has been used.

– Documentation summarization: capable of automatically summarizing large amounts of text.

– Translation: translating into several languages.

– Speech to text and vice versa: transforming spoken language into written text and vice versa almost immediately.

In general, these tasks break messages into elementary pieces to explore how these pieces together have new meanings and add value to communications.

What is the relationship of NLP with Artificial Intelligence?

Natural Language Processing is one of the branches of Artificial Intelligence. Artificial Intelligence relies on this processing to be able to give effective answers and to have conversations getting closer and closer to human language.

One of the most visible faces of this area is the Chatbots, especially we can talk about personal assistants like Siri, Cortana, or Alexa.

Are there bugs in this Natural Language Processing?

These technologies are very recent, and it is common that they need improvements, especially in the ability to reproduce this natural language. The lack of cadence in conversations or errors in understanding certain forms of speech is tiny errors that have been progressively solved. Let’s remember that Artificial Intelligences can learn, so they are gradually correcting their mistakes.

Precisely because of this great capacity for learning and efficiency in the processes where AI intervenes, it can make, for example, the repetitive administrative tasks associated with management positions, such as the creation and modification of work shifts or the preparation of analytical reports, be carried out more efficiently, impartially, and profitably than if they were done by the managers themselves.

But employees should not see AI as an enemy. The great advantage would be that these workers, now freed from these necessary tasks that add little value to their jobs, can devote themselves more freely to processes that do require human mediation, and in which machine intelligence cannot yet replace human capabilities.

Who would have thought 20 years ago that we would now be talking to our computing devices? And even more importantly, how far will we go? There is already talk of the metaverse and the interrelation with artificial intelligence and natural language processing – it will be very interesting to see how these technologies adapt to each other!


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