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.
But in just the last few years, software developers in the field of 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.
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 here, any solution worth its salt must tackle the fundamental challenges of natural language, which is ambiguous, contextual and dynamic,” said Nivargi.
Technologies like these show that we’ve started to build chatbots with semantic intuitive intelligence, but there is still work to do.
- AI May Predict Which Viruses Spread from Animals to Humans
- How low-code development could boost AI adoption
- Digital is not Strategy. Strategy is Strategy.
- Employees want more AI to boost productivity, study finds
- Survey Says: Digital Transformation Isn’t Optional