When you consider the idea of having to anticipate the 1,700 ways a person might ask one straightforward question, it’s clear why rules-based bots often provide frustrating and limited user experiences. Companies that implement scripted chatbots or virtual assistants need to do the tedious work of thinking up every possible variation of a customer’s question and match the scripted response to it. First and foremost, these bots cannot provide the correct response if a customer uses a phrase or synonym that differs even slightly from what has been pre-programmed, whereas conversational AI chatbots can understand the intent and meaning of the words given by the customers. Scripted chatbots have multiple disadvantages compared to conversational AI. In the US, search volume for conversational AI has never been higher. However, over the last 3 years, interest in Conversational AI has grown exponentially. According to data from Google Trends, interest in “conversational AI” was practically non-existent from 2005 through 2017. The concept of Conversational AI has been around for decades, but it wasn’t always something that was wildly talked about. Why Conversational AI is Becoming So Critical Today?.This article will highlight the key elements of conversational AI, including its history, popular use cases, how it works, and more. Whether you’re messaging with a chatbot, responding to an automated email or speaking with a virtual assistant, computers are working hard behind the scenes to interpret what is being said (sometimes called “intent classification”), determine the appropriate response, and respond in a way that’s natural and easily understandable to humans. Whenever computers have conversations with humans, there’s a lot of work engineers need to do to make the interactions as human-like as possible. Not just in business, but for entertainment purposes as well. Conversational AI is growing more prevalent every day.
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