Vector embeddings help machines interpret data in ways that were once unimaginable, says Reven Singh, sales engineer at InterSystems South Africa.
Demystifying vector embeddings : Unlocking the power of data in a new dimensionVisit our press officeData truly isn’t just about numbers or spreadsheets anymore. With artificial intelligence and machine learning at the forefront of innovation, data is taking on new forms and uses, particularly when it comes to helping machines understand and process human language, images and more.
A "vector" in this context refers to a list of numbers, and "embedding" refers to the process of mapping complex information into this numeric form. For example, when a machine reads the word "cat", it might translate it into a series of numbers – its vector. Another word, like "dog", will have a different vector, but one that is still close enough in value to indicate a relationship .
For instance, if you wanted to compare the meanings of different words, vector embeddings would position "king" and "queen" close to each other because they have similar semantic meanings , but they would place "king" further from "dog" because the meanings are less related. Take the example of chatbots, which are becoming increasingly common across customer service departments in the country. To provide meaningful responses, these bots need to understand what customers are asking. By using vector embeddings, chatbots can analyse the words a customer types, compare them to similar queries from past interactions and deliver a relevant answer. This not only improves customer satisfaction but also reduces the need for human intervention in routine queries.
Reven Singh Vector Embeddings Artificial Intelligence Machine Learning Natural Language Processing NLP Chatbots
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