Corpus is usually huge data with a lot of human interactions. Please refer the below figure to understand the architectural interface: Corpus or Training DataĬorpus means the data that could be used to train the NLP model to understand the human language as text or speech and reply using the same medium. Application Database for processing actions to be performed by the chatbot.Corpus or training data for training the NLP model.The deep learning model for Natural Language Processing.Chat window/ session/ or front end application interface.Typical chatbot architecture should consist of the following: Can help a warehouse executive in locating the stocked product.Assist the visually impaired person to describe surroundings.Virtual email, complaints, or content distributor.There is a possibility of introduction of master bots and eventually a bot OS. They have the ability to maintain the system, task, and people contexts. Future chatbot: Future chatbots can communicate at multiple levels with automation at the system level.
They have the ability to maintain both system and task contexts. Current chatbot: Current chatbots are driven by back and forth communication between the system and humans.Traditional chatbot: Traditional chatbots are driven by system and automation, mainly through scripts with minimal functionality and the ability to maintain only system context.There are many types of chatbots available depending on the complexity, a few of them can be majorly classified as follows: These bots can be further classified in two types: Retrieval Based or Generative Self-learning bots are the ones that use some Machine Learning-based approaches and are definitely more efficient than rule-based bots.The bots can handle simple queries but fail to manage complex ones. The rules defined can be very simple to very complex. In a Rule-based approach, a bot answers questions based on some rules on which it is trained on.There are mainly two approaches used to design the chatbots, described as follows: Voice-based chatbot: In a voice or speech-based chatbot, a bot answers the user’s questions via a human voice interface.Text-based chatbot: In a text-based chatbot, a bot answers the user’s questions via text interface.There are many types of chatbots available, a few of them can be majorly classified as follows: These two categories can be further broken down to 4 analytics models namely, Efficiency, Expert, Effectiveness, and Innovation. For each type of activity, the respective artificial intelligence solution broadly falls under two categories: “Data Complexity” or “Work Complexity”. To understand the best application of Bot to the company framework, you will have to think about the tasks that can be automated and augmented through Artificial Intelligence Solutions. The first step is to identify the opportunity or the challenge to decide on the purpose and utility of the chatbot. Identifying opportunities for an Artificial Intelligence chatbot Let’s have a look at the basics of creating an Artificial Intelligence chatbot: The challenge here is not to develop a chatbot, but to develop a well functioning one. We practically will have chatbots everywhere, but this doesn’t necessarily mean that all will be well-functioning. Also, 80% of businesses are expected to have some sort of chatbot automation by 2020 ( Outgrow, 2018). As per a report by Gartner, Chatbots will be handling 85% of the customer service interactions by the year 2020. Today, almost all companies have chatbots to engage their users and serve customers by catering to their queries. These advancements have led us to an era where conversations with chatbots have become as normal and natural as with another human. It all started when Alan Turing published an article named “Computer Machinery and Intelligence”, and raised an intriguing question, “Can machine think?”, and ever since, we have seen multiple chatbots surpassing their predecessors to be more naturally conversant and technologically advanced.
The first chatbot was created by Joseph Wiesenbaum in 1966, named Eliza. For example, a chatbot can be employed as a helpdesk executive. They are simulations which can understand human language, process it and interact back with humans while performing specific tasks.