Why conversational chatbot are future

Introduction

Conversational bots or chatbots are applications that can have a conversation with a human. Conversational bots work on rule-based algorithms. They use the latest technology in Natural Language Processing, Machine Learning and Artificial Intelligence to understand, interpret and respond to questions as well as handle requests from customers or assistant users. Conversational bots provides enhancements for websites, mobile apps and other interface components by providing intelligent interfaces for different types of user requests such as search queries, booking tickets, placing orders etc.

Types of conversational chatbots:

Conversational chatbots can be classified into two categories:

o Rule-Based Conversational Bots

o Learn able Intelligence Conversational Chatbot.

Rule-based conversational bots:

Rule-based conversational bots also called as conventional chatbots. A rule-base chatbot is a computer program that uses if… then rules to decide how to respond to users based on user input. Rule-Based Conversational Bots are divided into three types:

· Keyword driven bot

o The most basic of the three models of the rule-driven conversation, keywords driven bots require users to use specific keywords (such as “make table reservation”) and will generate a response based on those parameters alone. This type of bot will be able to respond to simple questions but isn’t advanced enough to understand free form natural language sentences and phrases. It also can’t deal with non text based inputs such as images and audio.

· Pattern matching bot

o The next step up from Keyword driven is to use pattern matching, in which the user’s input is matched with patterns and triggers for various response phrases. These bots can understand words, short phrases and even basic sentences, but they don’t understand meaning. They only match pre-defined patterns. This makes them better at understanding commands or requests than key words alone, but still not that good at handling free form natural language input such as “I’m looking for a pizza with cheese on it”.

· Natural Language Understanding (NLU) bot

NLU bots attempt to understand sentences written in plain English and extract the meaning behind those sentences. This means these bots can interpret and respond to a wider variety of input, such as “I would like a table for 3 near the window please” as well as complex sentences such as “My brother has been living in Paris for 17 years”. Some NLU chatbots use external sources that define intents and entities or they might build their own internal ontologies that let them understand concepts at various levels. These types of chatbots are capable of understanding very complex natural language phrases but they are also more expensive to develop and maintain than other types of conversational chatbots.

Learn able Intelligence Conversational Chatbot:

Machine Learning is a set of techniques used by programmers to enable applications to become more accurate at predicting responses over time. It involves feeding an application large amounts of data from varied sources and then allowing the algorithms to adjust themselves accordingly to the information. In a way, it is similar to how humans learn new things via experience.

Machine Learning Chatbots:

Machine learning-based conversational chatbots are capable of learning from their mistakes without being explicitly programmed where to apply each type of error correction. Machine learning doesn’t require any human intervention to build a long term relationship with users for better responses. Unlike Keyword driven bots and Pattern matching bots, these types of chatbots rely heavily on machine learning so they can understand complex sentences, slang words etc.,

A simple example would be when you type in “What time does your restaurant close?” A keyword driven bot will interpret your input as asking about the restaurant’s state. A pattern matching bot would understand that you are looking for a time information, but a machine learning-based chatbot would actually take meaning from the sentence and answer the question based on its context. Currently this is still very basic technology, but it shows great promise in providing users with more natural conversations over time.

No matter what type of conversation bots you develop or which one you go with to create your voice apps, they can help provide valuable services for your customers and help increase user satisfaction within your service or product. Smart assistants such as Google Assistant and Siri have revolutionized how we look at software applications and these types of conversational chatbots will only continue to grow and evolve.

Till next time, Keep rocking with the BotZ !!!