Chatbot Architecture: Types, Function & User Interaction

One of the primary needs of today’s businesses is instant and open communication. Many companies have realized the importance of advanced technologies like Artificial Intelligence and Chatbots for robust communication with their customers. 

So much so, that in the near future advanced Chatbots are set to replace humans. This is possible because of a Chatbot’s ability to “learn” by discovering patterns in data.

Two Types of Chatbots

 Primarily Chatbots can be of two kinds: 

  • Command-based do not provide a very diverse set of functionalities they are hardcoded to certain specific commands and their responses. These cannot respond to a new query that it has never encountered before. 
  • AI-based Chatbots are advanced versions of command-based Chatbots. These come with added features like humanoid behavior, speed, and improvisation. Similar to human minds, these Chatbot’s ability to process and respond grows with experience, making it more interactive.

Check out: Top 4 Examples of Artificial Intelligence in Day-to-Day Life

Workings of Chatbot

First and foremost, let us delve into the intricacies that allow Robot to give responses like an actual human with improvisation through learning. 

A Chatbot’s AI consists of two components – 

  • Natural language processing (NLP) – It enhances the capability to imitate human behaviour and decrease the time taken to give response
  • Machine learning added with some deep learning – It implements different algorithms which are sequenced in complex ways to give a response. 

Chatbot Architecture

The Chatbot architecture is designed to respond to a query. It helps the Chatbot look for data patterns in input, and then save these inputs for future references, thus constituting the learning process.

Source

User interaction with Chatbots

Chatbots can either have a graphical user interface, that is, screen-based interaction or voice user interface, that is, voice-activated. Either way, it is a conversational user interface in which the user provides a data input expecting a response.

1. Chatbots analyze the text

After receiving a query, Chatbots contextualize the intent (what customer meant to ask) and entity (what the users say or type) and consequently return the most appropriate response to the query.

Natural Language Processing comes into play at this point. It enables the Chatbot to react interactively, providing a human touch to it. Conventionally, NLP, along with deep learning, detects the language, tries to run some algorithms to find out the context of the query, splits the text in the pre-processing phase and provides the output after modelling the input. Broadly NLP involves:

  • Natural Language Understanding (NLU) that helps in converting the text to machine-understandable language 
  • Natural Language Generation (NLG) to convert that structured data back to the text, hence, helping in guessing out the actual intent of the customer.

Chatbots also carry on sentimental analysis, which specifies the mood of the user through different stages either in binary form or a sequence of different moods.

2. Chatbots give an answer

Chatbots generate a response to a query in two ways: 

  • Give a new response using machine learning algorithms. ML tools use the input to analyze the complex structured data and then create a response that is high-on accuracy. 
  • Select a sensible response from a database or API solutions supplied by various plugins. A database with conditions, preset, with the correct response for a variety of inputs given previously is used. The machine finds out the patterns from the data and makes decisions accordingly with minimum human intervention. 

After either of the processes, the Chatbots respond to the query in the form of text, image, sound, etc. Further, dialogue management is utilized to creating relevant paths to ensure answers are more suitable and the feedback mechanism promotes learning.  

3. The bots can learn from humans

Though the main purpose of Chatbots is to provide responses to queries, that is not the end of the process. These: 

All of the above are designed to promote deep learning using layered algorithms called artificial neural networks (a human replication of the brain). Each layer consists of interconnected artificial neurons where the connections are classified and stored based on past events, which further helps in handling new queries.

4. The more information the Chatbots get, the more it learns 

From all the information above we can perceive that the more input Chatbots get, as a result of the interaction, the more accurate, fast and sensible will be the response. This is the importance of ML algorithms which makes the system capable of giving responses without humans providing it for each and every input possible. 

Summing up

Chatbots have revolutionized the customer services of several businesses from e-commerce. Amazon’s Alexa and Apple’s Siri are the prime examples of the ability of these robots to interact and satisfy customer queries and demands. But, that is not all. A small business can today use the services these intelligent machines offer to respond to their customers all through the day and night. You can integrate them with your telephones, websites, messengers, and more. 

According to Christi Olson, the head of evangelism for search at Bing, “The Chatbots of the future don’t just respond to questions. They talk. They think. They draw insights from knowledge graphs. They forge emotional relationships with customers”

Today, the advancements in Chatbots architecture make them accessible to brands as well as small who can use them for a variety of tasks, though the primary function remains customer service efficiency. 

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