Computers and humans have never spoken the same language. Over and on speech recognition, we also need computers to understand the semantics of the human written language. We need this capability because we’re building artificial intelligence (AI)-enabled chatbots that now form layers of intelligence in Robot Process Automation (RPA) systems and beyond.
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Formally known as Natural Language Understanding (NLU), the early attempts (as recently as the 1980s) to provide computers with the human ability to interpret text was a terrible thing. This is a huge disappointment for both the developers trying to get these systems working and the users who come into contact with these systems.
Computers are excellent at long division, but really bad at knowing the difference between whether humans are referring to soccer divisions, parliamentary dividing corridors, or actually long divisions for math. learn. This is because math is formulaic, universal, and unchanging, but human language is ambiguous, contextual, and dynamic.
Hence, understanding a typical sentence requires a quality that is normally not programmable – or so we think.
Solve human semantics with math
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But just in the last few years, software developers in Natural Language Understanding (NLU) have made great progress over the past few decades in overcoming that barrier, reducing the language barrier between children. people and AI by solving semantics with math.
“Such progress is in no small part coming from huge leaps in the NLU models, including the landmark BERT framework and branches like DistilBERT, Roberta and ALBERT. Backed by hundreds of these models, modern NLU software can deconstruct complex sentences to refine their essential meanings, ”said Vaibhav Nivargi, CTO and co-founder of Move works.
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Move works software combines AI with Natural Language Processing (NLP) to understand and interpret user requests, challenges, and problems before using a higher level of AI to help take action. appropriate to satisfy the needs of the user.
Nivargi explains that what’s important here is that we can now also build chatbots that use Machine Learning (ML) to go one step further: handle user requests by themselves and troubleshoot sentences. questions are written in natural language. So now AI can not only communicate with employees on their terms, but it can even automate many of the daily tasks that make work like work – thanks to new reading comprehension capabilities. was discovered this.