Chatbots Are Machine Learning Their Way To Human Language Posted on : Aug 20 - 2020

Computers and humans have never spoken the same language. Over and above speech recognition, we also need computers to understand the semantics of written human language. We need this capability because we are building the Artificial Intelligence (AI)-powered chatbots that now form the intelligence layers in Robot Process Automation (RPA) systems and beyond.

Known formally as Natural Language Understanding (NLU), early attempts (as recently as the 1980s) to give computers the ability to interpret human text were comically terrible. This was a huge frustration to both the developers attempting to make these systems work and the users exposed to these systems.

Computers are brilliant at long division, but really bad at knowing the difference between whether humans are referring to football divisions, parliamentary division lobbies or indeed long division for mathematics. This is because mathematics is formulaic, universal and unchanging, but human language is ambiguous, contextual and dynamic.

As a result, comprehending a typical sentence requires the unprogrammable quality of common sense — or so we thought.

Solving human semantics with mathematics

But in just the last few years, software developers in the field of Natural Language Understanding (NLU) have made several decades’ worth of progress in overcoming that obstacle, reducing the language barrier between people and AI by solving semantics with mathematics.

“Such progress has stemmed in no small part from giant leaps forward in NLU models, including the landmark BERT framework and offshoots like DistilBERT, RoBERTa and ALBERT. Powered by hundreds of these models, modern NLU software is able to deconstruct complex sentences to distill their essential meaning,” said Vaibhav Nivargi, CTO and co-founder of Moveworks.

Moveworks’ software combines AI with Natural Language Processing (NLP) to understand and interpret user requests, challenges and problems before then using a further degree of AI to help deliver the appropriate actions to satisfy the user’s needs.

Nivargi explains that crucially here we can also now build chatbots that use Machine Learning (ML) to go a step further: autonomously addressing users’ requests and troubleshooting questions written in natural language. So not only can AI now communicate with employees on their terms, it can even automate many of the routine tasks that make work feel like work - thanks to this newfound capacity for reading comprehension. View More