What Is a Chat API?
What is a chat API? How can you choose the best one for your application? And how can you integrate the selected one into your own application? Find out here.
A chatbot, in its basic form, is a computer program that takes a human natural language as an input, processes it, and generates human interpretable results.
Wikipedia classifies a chatbot as a software application used to conduct an online chat conversation via text or text-to-speech, instead of providing direct contact with a human agent.
To break it down for you, a chatbot takes human input such as text or voice, structures, synthesizes, and processes that input to produce a human simplified response.
The earliest chatbot was developed before the introduction of personal computers. It was built by Joseph Weizenbaum in 1966 and was called Eliza by the MIT Artificial Intelligence Laboratory.
Eliza evaluated the incoming input keywords and then activated them according to a set of criteria. Multiple chatbots still utilize this way of producing output.
Following then, other virtual helpers have been developed. The first to introduce chat engaging aides was Siri by Apple. The idea became popular and shortly after Google debuted its Google Android Assistant. After then, Cortana came into being from Microsoft.
Keeping this in mind, intelligent speakers were created that enabled speech interaction between human beings and chatbots. Another category of chat interface is Amazon's Alexa & Google Home.
The following are areas where chatbots are predominantly used for:
Before the advent of chatbots, the traditional system used for supporting customers was the helpdesk. The helpdesk system was used for providing timely support for the users inquiring about an institution, product, or service.
This traditional system takes in a pool of incoming calls from well-meaning users around the world and distributes them across an array of available support agents.
Regardless of the empathetic gestures, these companies have towards their customers in resolving their unique issues, the helpdesk system was faced with an inevitable challenge. And this problem is associated with delays and insufficient resources to cater to all user's complaints. Below are the predicaments faced by the helpdesk system to cater to all their customers.
The helpdesk system was helpful, but not enough to cater to the growing need of the ever-increasing population of our world. This limitation often results in an outburst of quarrels and dissatisfaction on the part of the customers. Because of this challenging situation of the helpdesk system, businesses, products, and services often bag a negative review from their customers who have had a bad experience resolving their problems with the helpdesks.
The most common causes of the annoying situations faced by the customers are as given below:
While developing a computer program to handle a chunk of the helpdesk limitations, it was discovered that 90% of the complaints coming from the customers are often common. As such, a computer program was needed to be developed to help customers resolve the problems linked to the most frequent questions asked on the platform, and this was how the chatbot came about.
The criteria was that the chatbots must be able to take user input (text or voice) in a human natural language, process, and return a human-understandable response. With this, chatbots were developed and deployed on both web and mobile platforms.
There are generally two types of chatbots: the traditional or rule-based chatbots and the conversational or AI-based chatbots.
Instead of AI, a rule-based chatbot utilizes a tree flow design pattern to help visitors with their questions. In other words, the chatbot will help you to get to the right resolution through follow-up questions. All structures and replies are predefined so that the dialogue is under your control.
An AI-based chatbot simulates a user conversation with a natural language through messaging applications. It uses machine learning and natural language processing (NLP) to deliver near human-like conversational experience.
For example, a user named Mark wishes to order an item on an e-store, the conversation will flow in this manner for an AI-based chatbot.
Chatbots in artificial intelligence (AI) make up for a cozy niche. They have several similar components with AI, ML, and DL, to be more exact.
Artificial intelligence (AI) is intelligence shown by machines, as opposed to the natural intelligence shown by humans or animals.
Machine Learning (ML) is a branch of artificial intelligence and informatics discipline that uses data and algorithms to gradually improve the accuracy of the learning process and replicate how people learn.
Deep learning (DL) is an artificial intelligence (AI) mechanism that mimics the functioning of the human brain in data processing and decision-making processes.
Modern chatbots use technologies such as natural language processing (NLP), natural language understanding (NLU), and automatic voice recognizing (ASR) in artificial intelligence, machine learning, and deep learning.
Natural language processing (NLP) or linguistic processing describes how computers processes and analyze vast quantities of natural language input, and is a topic of linguistics, computer science, and artificial intelligence.
Natural language understanding (NLU) or natural-language interpretation is a subtopic of natural language processing in AI that deals with machine reading comprehension.
Automatic speech recognition (ASR) is the use of computer hardware and software-based techniques to identify and process human voices. It is used to identify the words a person has spoken or to authenticate the identity of the person speaking in the system.
A basic chatbot will take natural input from the user, this could be text or voice data, and send it into the system. The natural language processing (NLP) of the chatbot immediately goes to work in synthesizing the input data. In reality, this natural language processing is composed of unstructured data which is then structured by the natural language understanding layer.
The natural language understanding finds out the actual user intent and queries the knowledge base of the chatbot system thereby returning appropriate answers.
Within the chatbot system, if user input is text-based, NLU is used to structure the data. Whereas, if it's a voice input, the automatic speech recognizer (ASR) is used.
A chatbot framework is a development platform shipped with the necessary software codes and libraries for building a chatbot from scratch.
With a chatbot framework, you can build your own chatbot from scratch and some of these frameworks require only a fragment of coding skills. Below is the list of chatbot frameworks and their respective links.
In conclusion, we have learned quite a lot about chatbots in this tutorial. We have covered the basic nitty-gritty of chatbots, what they are, how they operate, and how they can make your life and business better.
Now, you must be wondering: are chatbots and in-app chat the same? The answer is — no, they aren't. While chatbots have a platter of benefits and are an invaluable part of customer service operations, in-app chat is the 1-on-1 or real-time bidirectional conversation that takes places in an app or product. If you want to know more about the differences between chatbots and in-app chat, check out this article!
CometChat is dedicated to always sensitize you to new technologies, especially as regards web and mobile communications. You can also check our latest products such as the ChatAPI or the ChatSDK on our site.
About the author
Gospel Darlington is a remote full-stack web developer, prolific in Frontend and API development. He takes a huge interest in the development of high-grade and responsive web applications. He is currently exploring new techniques for improving progressive web applications. Gospel currently works as a freelancer and spends his free time coaching young people on how to become successful in life. His hobbies include inventing new recipes, book writing, songwriting, and singing.
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