Embedded Files
Chatbots rely on a combination of keyword scanner applications, natural language processing (NLP) systems, and machine learning algorithms to present their ‘intelligence’ within a virtual audio or text communication with humans.
NLP:
NLP:
The Chatbot pulls keywords from the human user and feeds these to a data base, which provides pre-set correspondences for the chatbot to provide as replies. The chatbot uses NLP software to apply conversational cadences that mimic human conversation (Mai-Hanh Nguyen, 2017).
Machine Learning:
Machine Learning:
Machine learning allows the chatbot to feed data into “if-then” pattern and inference algorithms that help the chatbot adapt responses to novel exchanges. Nguyen (2017) describes machine learning algorithms as, “neural networks, consisting of different layers for analyzing and learning data. Inspired by the human brain, each layer consists of its own artificial neurons that are interconnected and responsive to one another. Each connection is weighted by previous learning patterns or events and with each input of data, more "learning" takes place”.
Taken altogether, the chatbot appears to ‘learn’ over time by “discovering new patterns in the data without any prior information or training, then extracting and storing the patterns” in order to respond appropriately if a similar correspondence arises in the future. When questions come up that a chatbot cannot answer, these get fed to a human operator who can either respond to directly or update the data base for future use by the chatbot.
Learn More:
Learn More:
For a more technical examination of these aspects of Chatbot intelligence systems access the links highlighted in this section. Or view this Techrepublic video that explains the behind the scenes programing that allow the chatbot to interact intelligently. Or, view the video on Machine Learning Basics:
Google Sites
Report abuse