Powering Semantic Similarity Search in Computer Vision with State of the Art Embeddings


A whopping 90% of data created since the dawn of human civilization was produced in the past two years! The rate of data creation continues to increase with the proliferation of digital technologies such as social media and the internet of things (IoT) together with ever-faster wireless communication technologies such as 5G. However, most new data created is "Unstructured," such as text, images, audio, and video [Source]. Unstructured data gets its name because it does not have an inherent structure, unlike a table of rows and columns. Instead, unstructured data contains information in one of several possible formats. For example, e-commerce images, customer reviews, social media posts, surveillance videos, speech commands, etc., are rich sources of information that do not follow the traditional tabular data format. Recent advances in Artificial Intelligence (AI) and Machine Learning (ML) technologies have created a way to extract useful information from unstructured data sources in a scalable way by the use of "embeddings."

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