technical insights
Conversational AI-Design considerations Biswajit Biswas Tata Elxsi
Introduction Conversation bot design is the most happening thing when it comes to AI computing and an essential thing to consider for making products smart and digitally inclusive. With the rapid progress in AI and specifically in NLP computing, language interpretation has improved considerably making a near-normal conversation possible since the time Siri was first introduced in iPhone 4s in 2011. Today we see, chatbots have proliferated as part of the web application extension. In fact, adding a voice or chat interface is the fastest way to qualify an application AI-ready, the chatbot also is the strategy for the mobile-first digital economy. Some of the well-known use cases where AI-powered conversational bots have vastly improved the user experience are into following: Pre-sales bots: Conversational bots which can help or guide a prospective customer to make a purchase decision, can convert a window shopper into a buyer are in great use. Many customers have inhibition to interact with a real human but may find it perfectly normal to interact with a bot. Co-pilot: In-car voice assistants are already in great use. They help reducing driver distraction by assisting in non-critical driving tasks, for example, cabin comfort control, infotainment, navigation assist. Voice-assisted- maintenance: A voice bot is a great help for technicians on the shop-floor which can guide them step by step to deal with a fault repair which otherwise takes referring to user guides, manuals, and technical drawings. Virtual doctor: A robot health care assistant can converse with the patient for the first level interaction and guide to the next step to Doctor has been in the use already. During the recent pandemic time of Covid-19, this has been put to great use to isolate suspecting patients with symptoms at the same time enforcing social distance.
Conversational AI – Technical background and recent advances To build an intelligent Conversational Agent, understanding user intent is a key. 32 | Telematics Wire | July 2020
There are many parts to this challenge, too many variables to solve. Human comprehension of language is complex, and not everything of it is verbal. As a human listener, we also consider many things like the speaker’s facial expressions, hand, and body movement, which is also called ‘body language’ that is unfortunately not under the purview of the NLP computing domain. Language understanding has following key parts and each of them needs to be solved separately to figure out the holy grail: l Understanding semantics (lexical) l Understanding syntax l Understanding context (both short and long term) There have been several shallow and deep learning techniques that have been very successful to solve some of the language understanding problems. If we were to pick up three most important advancement, which has leapfrogged the NLP success, those would be: l Word embedding l Recurrent Neural Networks or RNN l Attention Word embedding: In a typical dictionary of any language, words are arranged alphabetically or in some order which doesn’t