There are simply too many technologies that really go into making a bot comprehend a question, then reason and finally respond to it. Unlike the human brain that has answers to most questions, intelligent systems have multiple answers to every question, it can comprehend and the cycle of responding with the right answer is often as key as being able to understand. This post is really about what kind of technologies and software architecture powers the QnA element of chatbots.
Text Analytics or often known as Text Mining is the discovery of new or existing facts applying the science of natural language processing and statistical learning techniques. This post explains how NLP based text analytics systems anatomically look like .
In a post last January, I had termed 2017 to be the year of the banking bots and chatbots , virtual assistants and robo advisors did seem to get their fair share of the spotlight. Throughout the year chatbots and their use cases occupied a fair share of attention and saw very senior business leadership take interest and action to enable NLP based intelligent systems become part of their ecosystem. And over and again , the basics of the technology or how-it-works was asked . Presenting a small primer to enable anyone to be able to understand the nuances.
Since its inception around 1984 ,the Electronic Bill Presentment and Payments (EBPP) business has seen a continuous evolution in terms of business models , technology components and services offered by bank . Governments have taken key initiatives to ensure that clearing houses and regulatory bodies support the growth of homegrown EBPP Models. It has emerged as a key change driver driver in the way corporates manage the end to end billing and collection cycle and paved way for a new wave of consumer-centric digital payments and product innovation . Read on for a primer in EBPP