If you're investing in a conversational interface technology platform, you probably want some idea of how it works. There's no magic involved, but conversational AI makes use of some very interesting statistical methods and a lot of computing power to navigate the difficulties of human language and meaning. Here is a look at this topic that someone outside of the tech or stats industry can understand.
How a Machine Gets a Clue
At the core of the vast majority of conversation AI systems is some type of natural language processing system. NLP is based on a fairly simple idea that requires tons of computation to implement. The gist of nearly every sentence is contained within a few clues, and those clues can be assigned mathematical scores.
Some of the clues in a sentence are pretty obvious to a person once they've learned how to read at even an elementary level. If you see a question mark at the end of a sentence in English, then you already have a big clue someone is asking a question. For that matter, seeing an upside-down question mark at the beginning of the sentence informs you that you might be dealing with someone who speaks Spanish.
Learning How to Respond Like a Human
As you grow up, you learn from trial and error how to respond to others in various situations. You likely make mistakes, but you learn from them. Over a lifetime, positive and negative feedback do, in fact, teach you how to respond to cues from others.
For a conversational interface technology platform, this process is called training. The machine will be given a large body of real-world interactions from humans. Each interaction will include a set of cues and responses.
Someone might wonder how to activate their account and ask the AI system. A response would then follow. Repeat the process to train the conversational AI on how to deal with potentially thousands of issues.
Finding the Right Words to Say
Just like you do, the AI has to find the right words to say when someone discusses a topic with it. The AI accomplishes this by looking at a list of responses that might be even remotely right. It will then trim the list down to a handful of responses that have been scored as highly on-topic for the conversation. Just like you would, it then supplies the best answer it can come up with.