How does Machine Learning in AI make chatbots more responsive as per user queries?
AI chatbots powered by machine learning (ML) is the next big thing in customer communication and support. You can use Machine Learning to improve your chatbot’s natural language processing (NLP). That allows it to understand and respond to a user’s query in whatever tone, language, or accent in which it was posed. However, using ML algorithms is not as easy as deciding to do so. There are several factors that you will have to consider before choosing an appropriate model for your chatbot.
In this blog, I’ll explain how ML makes chatbots smarter and more responsive. But first, get a brief on what is a chatbot? And how ML makes it more appropriate.
A Chatbot is a software program designed to simulate conversation with human users. It is often used for simple customer service and marketing functions.
The chatbot is a conversational AI that you can use to chat with. It learns from what you say and it also remembers your conversation with it. Isn't it cool? It's like talking to a real human. Well, not exactly. A chatbot is designed to make your life easier by providing the information you may want at that point of time. However, like humans, they learn better through practice. By practicing more and more with the same or different people, they learn to be smarter and more helpful (in the case of a business).
How ML makes chatbots smarter and more responsive
Machine Learning (ML) is the other essential technology for a well-functioning chatbot. Machine learning is a technology that gives systems the ability to learn from experience and improves their decision-making ability. In other words, interacting with users helps bots to expand their knowledge about possible responses.
ML in AI make chatbots more responsive as per user queries.
Most chatbots today communicate in a simple and direct way. A typical example would be a chatbot that has to answer politely to any statement made by the user. A typical statement like "Can you web search for me?" would lead to a response like "No, I cannot. But I can book you an appointment with a human assistant." A smart chatbot makes use of context analysis. It analyses each sentence based on the time, day, data, and tone of the sentence.
With the help of ML in AI, they can analyse context more deeply, make a smart response and establish better connections with the end-users.