Natural Language Processing: a trend for the future of AI

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According to Wikipedia, Natural Language Processing (NLP) is a subfield of linguistics, computer science, and artificial intelligence concerned with the interactions between computers and human language, in particular how to program computers to process and analyze large amounts of “natural language” data.

In other words, NLP is a technique used to enable computers to process human language.

In the history of Artificial Intelligence, one of the most important dimensions used to measure the “intelligence” of a machine has been the linguistic one. According to the Turing’s test, an intelligent machine should be able to respond to human request in a natural way, hiding its artificial nature from its human counterpart.

Today, NLP techniques are employed by many instruments used on a daily basis. Behind every virtual assistant, chatbot, or simply in the auto-completion of Google’s search bar, there are NLP algorithms that read and process the human language.

If handling numerical information could be considered relatively simple for a machine, extracting data from natural language is far more complex. The human language has an “unstructured” nature, which relies on a set of grammar and syntax rules that can be completely different for every country or linguistic region. Therefore, the final goal of NLP is to create a defined “structure” in the language in order to solve ambiguities and process words and their meaning as numerical data, thus enabling document analysis and speech recognition.

Nevertheless, the actual implementation of these techniques is relatively recent and has great room to grow in the following years, considering the number of problems that could be fixed with this kind of solutions. Besides better virtual assistants, NPL can be used for enhancing the precision of digital translators, creating engines to automatically generate text or to manage archives and extract information from documents and books.

But these are only few examples, Spam and Fraud Detection algorithms can also benefit from NLP, as advanced linguistic tools can be used to detect malwares and other threats. Here at Dataskills, we managed to create NLP tools to detect duplicates and fake users, thereby allowing a fast maintenance of your company’s data, which will ultimately improve in terms of Data Quality.

From a business perspective, it’s important to carefully monitor trends in NLP, to discover possible application in your company, which may solve problems and facilitate processes.

Recent breakthroughs in NLP technologies

In this regard, 2020 has been a crucial year for NLP as the technology has experienced several breakthroughs. OpenAI, an organization founded by Elon Musk to conduct research in the field of Artificial Intelligence, released a new model for text generation called GPT-3 (Generative Pre-trained Transformer 3). The model has been trained on a dataset of 570Gb of human-generated content, mainly web-scraped from Reddit. GPT-3 is able to perform several tasks that involve the generation of structured language, such as answering to questions, writing short essays, taking notes, translating texts and even writing code. Today, GPT-3 is still unavailable to the general public and has to be improved, but in the following years this technology could potentially disrupt many industries.

NLP is particularly useful also for Business Intelligence. Some BI tools already offer the possibility to translate questions written in natural language (“How much was the amount of travel expenses in 2019?”) into SQL queries that will interrogate the Data Warehouse and immediately show the desired metrics through the appropriate visual element.

Eventually, NLP can be used by businesses to perform sentiment analysis and to improve customer satisfaction. There are already NLP tools to analyze tweets, reviews and other texts that involve a specific company or product. In the following years, as these tools will be gradually adopted by the vast majority of companies, access to NLP technologies would probably become a mandatory instrument to compete in most industries.

Although the human creativity is still necessary for the most important tasks, there is a bunch of standard routines which involve text generation that could be completely automatized in the following years. For these reasons Natural Language Processing represents a trend that should be monitored closely to discover new business opportunities.


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