Kylie.ai Blog

This blog post was originally posted by Larry Hardesty on MIT News.

Several recent studies and surveys’ across several sources has shown that implementing an artificial intelligence strategy can be a challenging maze to navigate. With a lack of extensive business cases and A.I. implementation examples it can create a needle in the haystack scenario for business leaders not only looking for relevant A.I. solutions, but also successfully creating A.I. initiatives.

In 2016 Amazon inaugurated the Alexa Competition dedicated to “accelerating the field of conversational AI”, with the winner to be determined in late 2017. As part of the competition, university research teams attempted to build a socialbot that could converse coherently and engagingly with humans on popular topics for 20 minutes.

With A.I. being such a buzzword recently, so many misconceptions are being built about the technology. From articles talking about how Facebook had to shut down an AI after the system created its language, to the widely known Microsoft’s bot that became racist, A.I. is developing an illusion of being uncontrollable.

We grade the intelligence of a software based on how well it can mimic a human being. There is even a test developers refer to in order to assess if the computer can trick a human into believing that it's human. The Turing Test. If you can’t distinguish between a human being’s reply and a computer’s, the computer is considered intelligent.

In today’s world, a chatbot that sounds like a human being belongs to one of two categories:

Rule-Based Chatbots

Consider the last time you sat through a long automated phone menu where you press 1 for Spanish, 2 for account info, 3 for ... etc. A rule-based chatbot is the same technology in chat/conversation format. When you start engaging with a chatbot, it offers you a few options, you select 1, 2, or 3 and the chatbot responds based on your choice. This chatbot has a pre-programed answer that guides the conversation to flow in a structured manner.