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Words are no longer sufficient in delivering the search results users are looking for, particularly in relation to image search. Text and languages pose many challenges in describing visual details and providing the necessary context for optimal results. Machine Learning technology opens a new world of search innovation that has yet to be applied by businesses.
In this session, we will share a technical presentation detailing his research on composition aware search, which is a recently launched AI technology allowing users to more precisely find the image they need within Shutterstock's collection of more than 150 million images. This technology makes heavy use of modern machine vision, natural language processing and information retrieval techniques. While the company released a number of AI search enabled tools in 2016, this research and technology expands on existing tools to identify the networks that localize and describe regions of an image as well as the relationships between things. The goal of this research was to improve the future of search using visual data, contextual search functions, and AI. A combination of multiple machine learning technologies led to this breakthrough.
As Director of Deep Learning and Content Research at Shutterstock, Bryan Minor, Ph.D., leads efforts to develop AI based products and tools for media search and discovery on Shutterstock content with an emphasis on Deep Learning. He is an Internet algorithm expert with experience as Chief Data Scientist at envision.ai and Chief Scientist at Acquisio. Previously, he formed ServiceOps to service algorithm development software projects. Bryan has a BS in Physics from Central Washington University, and both a Master's in Nuclear Science and a Ph.D. in Physics from US Air Force Institute of Technology.