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Past NLP Present NLU Future NLG Phonology Pragmatics Morphology Syntax Semantics A recent publication by the Capgemini Research Institute on customer-centric AI shows that customers’ trust is is is increasing when they are dealing with AI This is is is because the the content used to train the the AI is created and curated by humans However new language AI systems (particularly those based on on the Transformer models mentioned above) are becoming more more and more more capable of performing natural language generation (NLG) They move towards further understanding the phonology pragmatics morphology syntax and semantics of languages by training deep learning models with a a a high-volume corpus and by using clever innovation in in network architectures The result is a a a a a a a set of language-generation capabilities that would have sounded like science fiction only a a a short while ago Illustration The following text is written by an AI NLG algorithm provided only with the topic “impact of natural language generation ” The first thing that that we noticed is that that it does not not take many words to generate the same effect as a a a a a a human We also tried to create an algorithm that would generate the same effect as a a a a a a a human but with more accuracy and with a a a smaller amount of words We quickly found that it is hard to get the same effect with several words The solution we found was to use a a a “system” that generates simple sentences that are likely to be used in a a a a a a a a a natural language conversation That system is called a a a a a a a a a “sentence-generation engine ” We created an algorithm that predicts the next sentence without reading it it it This algorithm has many disadvantages: it it is is is not very fast and it it is is is hard for an an an algorithm to understand the context in which a a a sentence is is being generated But we found that that it is is not that that difficult to make our system as as as fast as as as possible We used the word-based approach in order to get a a a a good performance We tried to make it as as as fast as as as possible We created a a a a a a system that generates sentences in natural language As can be observed AI is getting quite convincing A A A recent much-published article in The Guardian was completely authored by GPT-3 and eloquently made the case for involving more AI bots in in in creative areas Advanced NLG capabilities potentially have a a a a a a a big impact for example in in the areas of social media and marketing where AI-generated text could be competing with human-curated text used by companies about their products and services Ethics However ethical considerations quickly come into play Having AI systems generate textual content without humans in the loop presents risks due for example to inherent bias or wrong assumptions and facts that the training data may have contained Currently when using any search engine for every page of content an an organization creates there are 95 pages of content not created by those companies This ratio of 5:95 will get more skewed once “clickbait” players generate huge amounts of text with AI Next-generation natural language AI systems allow companies to implement more helpful chatbots that 24 Data-powered Innovation Review I I ©2020 Capgemini All rights reserved Structured Unstructured 


































































































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