If you have never interacted with a chatbot before, you may be inclined to think of it as a simple piece of software with pre-programmed responses - like some sort of text-based game from the eighties, in which you have to carefully input just the right combination of words in order to make a request.
This, of course, is not at all the case. That’s largely thanks to something called NLP, or natural language processing. NLP is an integral part of what gives artificial intelligence its intelligence — it’s the aspect of bots that allows them to understand just what it is we humans are trying to say.
has come quite a long way from its origins, and strides are continually being made to improve upon the functionality. But in the context of today’s chatbots, NLP is the factor that allows a chatbot to understand your intent, and not simply take your literal input at face value. Recent bot-building platforms, such as SnatchBot, employ powerful proprietary NLP engines that claim the ability to hold human-like conversations and 'learn' over time and interactions.
Here’s a simple example of how NLP works. Let’s say that you have an online store that sells t-shirts, and you have created a very basic chatbot to help with your sales. Your bot does not have NLP functionality, and is therefore only programmed with scripted default responses.
A user interacting with your bot sends it a message: “I would like to purchase one blue t-shirt.” That’s an easy request for a bot to understand and reply to with a pre-programmed response. You would likely have programmed the bot for all synonyms of 'purchase' as well, so that if a user said, “I would like to buy a blue t-shirt,” your bot would still understand and respond accordingly.
But then a customer sends this message: “I want a t-shirt.” Your bot is confused; it doesn’t recognize the input without a direct purchase request. The consumer has gone “off-script”, and the chatbot doesn’t understand what they’re asking because you did not predict every potential permutation of a request for a shirt.
While that may seem like an oversimplified example, it was an issue with the default-response, linear-scripted bots of yesteryear. Today, however, chatbots that employ NLP derive intent from a user’s input—they are able to break down the input into meaning and determine what the most likely response should be.
NLP is actually not all that different from how our own brains work. Consider the question, “How are you doing today?” versus, “How’s it going?” While the sentence structure is different, our brains recognize that “it” in the latter question is not referring to a physical thing, but rather our state of being at the time.
And much like a human mind, chatbots use machine-learning capabilities in conjunction with NLP so that they can be 'trained' through repeated interactions to better respond to input. Highly conversational bots become more accurate over time because they 'learn' in an intuitive manner from past interactions.
In the not-so-distant past, employing NLP in a bot was limited to skilled coding professionals and the cost was significant. However, modern bot-building platforms are providing a number of pre-made NLP functions that allow enterprises to create intelligent chatbots with very little technical skills. As artificial intelligence rolls steadily towards the future, we can anticipate seeing continually smarter, more intuitive bots and a broader variety of use cases. Regardless of how complex your industry may seem – even in cases such as healthcare
and digital marketing
chatbots already started to join the workforce.