We grade the intelligence of a software based on how well it can mimic a human being. There is even a test developers refer to in order to assess if the computer can trick a human into believing that it's human. The Turing Test. If you can’t distinguish between a human being’s reply and a computer’s, the computer is considered intelligent.
In today’s world, a chatbot that sounds like a human being belongs to one of two categories:
Consider the last time you sat through a long automated phone menu where you press 1 for Spanish, 2 for account info, 3 for ... etc. A rule-based chatbot is the same technology in chat/conversation format. When you start engaging with a chatbot, it offers you a few options, you select 1, 2, or 3 and the chatbot responds based on your choice. This chatbot has a pre-programed answer that guides the conversation to flow in a structured manner.
Rule-based chatbots can sometimes be effective for collecting basic information or routing questions, however these interactions mostly end with the person frustrated and requesting to talk to a human anyway. The chatbot cannot actually figure out what your problem is and find a solution for it. Issues that would require some back-and-forth cannot be resolved.
Machine Learning (ML) Based Chatbots
ML-based chatbots fill in the gaps left by traditional rule-based chatbots. This superior AI can identify the intent of the customer from conversations, just like humans, and then take the user through a natural conversation to extract key information to resolve the customer's problem.
Based on the AI's understanding of the scenario, it will infer context, refer to past conversations and offer resources or links to help with queries.
The best part?
It’s always learning.
This means that an ML-enabled chatbot will gradually make better decisions and improve itself, with every interaction.