AI SPECIAL .....AI’s next challenge: Understanding the nuances of language

AI’s next challenge: Understanding the nuances of language

Language is a uniquely human capability and the manifestation of our intelligence. But through artificial intelligence — specifically natural language processing, or NLP — we are providing machines with language capabilities, opening up a new realm of possibilities for how we’ll work with them.

Today you can walk into a dark living room and ask Alexa to turn the smart lights up to a pleasant 75 per cent brightness. But to maintain an excellent humanmachine relationship, machines must be able to hold more contextual and natural conversations — something that remains a challenge.

Sentiment analysis

In particular, machines need to understand the intricacies of language and how we as humans communicate in order to make use of it. Advances in sentiment analysis, question answering, and joint multitask learning are making it possible for AI to understand humans and the way we communicate.

With sentiment analysis, for example, AI can understand certain things about a given statement, such as whether a brand mention or film review is positive, negative or neutral. The AI can also figure out things like the attitude and intentions of the speaker. At Salesforce, the Einstein AI services allow brands to get realtime analysis of sentiment in emails, social media and text from chat in order to provide better customer experiences.

Answering questions

As NLP gets better at parsing the meaning of text, the intelligence of digital assistants helping to manage our lives will also improve. Applications like Siri and Google Assistant already provide good answers to common questions and execute simple commands. Computers are getting better at guessing our meaning through a deeper understanding of semantics, plus a smarter use of contextual data. With NLP, we can figure out how to learn each of these layers of context so that AI can process all of it at once and not miss vital information.

Semantic parsing

More intuitive, conversational and contextual interfaces also require an AI model that learns continuously — integrating new tasks with old tasks and learning to perform more complex ones in the process. This is true of AI in general, but particularly true when it comes to language. As researchers continue to improve, AI will continue to grow smarter as they take on more complex tasks. Though NLP has been around for some time now, it’s still early days. The hope, though, is that as NLP improves, it will allow AI to change everything about how we interact with our machines.

— The New York Times