Chat-bots are Conversational Artificial Intelligence tools which are capable of performing the duties of a human being such as replacing personal assistant and taking care of many manual tasks like booking tickets, managing calendars, placing food orders, making reservations etc. These have helped solve myriad business problems across many industries such as E-Commerce, Banking, Healthcare, Legal, Telecom, Logistics, Travel, Retail, Auto, Leisure, Finance, Sports, Insurance, Entertainment, Media and many others and help you save time and increase efficiency. Chat-bots can usually interact through speech and text. Text-based chat-bot communicates via conventional conversation i.e. text messages.
How does a Chat-bot work?
Chat Bots are built using machine learning and artificial intelligence algorithms and NLP concepts. AI helps induce human intelligence into Chat-bots. Machine Learning (ML) is a branch of AI which lets Chat-bots learn based on their experience of their interaction with the environment and users. Natural Language Processing (NLP) helps Chat-bots contextually understand the human inputs. So a chat-bot typically accept and interpret the input and provide a relevant response to the output. This might seem simple on the surface but is very complicated in practice.
Interpreting the input:
In order to understand what the user said, bot uses pattern matching of user input and classification of the intents with Natural Language Processing (NLP). Pattern matching is simple and easy but still hard to maintain with flexible inputs at a large scale. Extraction of intent from what is being said is done using machine learning to interpret the inputs and is harder to implement. Here comes the concept of entities, intents, and actions.
Entities are specific mappings of the spoken word combinations in the human discourse to standard phrases explaining their meaning. Intents are the prediction of workflow and map the user input to a corresponding bot action. Actions are the steps that bot is capable of performing as a result of an intent. This can also be seen as key/value mapping i.e. keeping track of current implications of entities and differentiating the meanings/intents of phrases.
Preparing for output:
We have had a look at a bot’s language comprehension method, let’s see what is the typology of bots when it comes to responding to an input. Chatbots are purpose built and their way of responding differs from each other. A bot can either be strictly specialized in one domain known as the closed domain bot, e.g. weather forecasting or could just be a general conversationalist known as an open domain bot.
Responses of Chatbots can be of two types. They either retrieve the predefined response for the user or will generate the response corresponding to the input.
A retrieval based response can either be static or dynamic. A static response is more like a template filling, where to every input, the machine has a corresponding answer and Dynamic response is knowledge-based, it returns the list of possible responses with the scoring of relevance. The machines answers with the response which has the best score.
This has to be kept in mind that closed domain bots are only designed only to handle a finite set of queries and we cannot engage in general conversations with these. Open domain bots, on the other hand, are mainly focused on imitating real-life conversations which can be about any topic. They do not need to have a clear understanding of what the input is so they do not retrieve entities and intents, nor do they need to keep track of the context. Their basic focus is to entertain or to answer general FAQ-style questions.
The code is very complex and can not be used easily for large applications.
Its highly dependent on Microsoft services and can not be hosted on non-Microsoft platforms.
Development can only be done on either Node.Js or C# platforms.