Artificial intelligence (AI) is the driving force behind emerging conversational technologies. Businesses are beginning to strengthen their customer relations with conversational AI technologies by deploying various chatbot development frameworks. One such bot framework, Wit.ai is gaining momentum with its machine learning algorithms to empower chatbots and virtual assistants across applications and IoT devices. As an emerging chatbot development company, we at Oodles AI are exploring new business opportunities for conversational chatbot development using Wit.ai.
In this article, we explore how artificial intelligence services can be combined with Wit.ai to develop business-oriented applications.
How Machine Learning is Embedded in Wit.ai
The core functionality of Wit.ai is based on two major ML technologies, i.e. Natural Language Processing (NLP) and Natural Language Understanding (NLU). While NLP enabled Wit.ai to break customer queries into actionable ‘entities’, NLU extracted meaning out of these entities. However, in April 2016, Wit.ai released an entirely new mechanism called Bit Engine, for building NLP-based chatbots or virtual agents.
The new setup of Wit.ai facilitates the development of cognitive chatbots based on the concept of ‘Stories’. Stories provide the essential conversational flow to human-chatbot interactions using ‘Actions’. Though much of this new paradigm shift in Wit.ai works similar to Watson’s intent and entity mechanism. Application integration of Wit.ai is made simpler with the support of popular programming languages such as Python, Ruby, Go, and Node.js.
For now, let’s look at the four main pillars that keep a chatbot interaction going in Wit.ai today-
1) User says
The first step is to identify and input the exact query or command you expect your user to raise.
“I need a 30-minute appointment for a haircut tomorrow at 7 pm”
It prompts Wit.ai to extract the following “Entities” form the text-
Intent Haircut appointment
wit/duration 30 minutes
wit/datetime 01/24/2020, 7:00 PM
The Understanding tab in Wit.ai enables us to add the variants of this text or input in order to train the chatbot with human-like language.
2) Bot Sends
This section defines the message that a chatbot should send to the user. It may be an answer for a query or a prompt to fetch further information.
3) Jump
As a chatbot developer, it is important to maintain the flow of the conversation. The Jump section enables developers to jump at any point in the user-chatbot conversation and create bookmarks for important exit points.
4) Bot executes
It is the final control wherein Wit.ai embeds ‘Action’ into the chatbot interface. Here, developers can instruct the bot to execute certain actions wherever required. However, this action runs parallel to the code that is built in the bot’s backend to fulfill the user’s command.
Learn more: AI powered Conversational Chatbot Development Using Wit.ai