In 2015, Google reported $68 billion in advertising revenue, which was roughly 90% of their total revenue for the year. Despite being so vital to the financial fitness of many tech companies, there is disparity in the experience of online advertising. Deep learning is changing that experience. As a computer vision firm applying our technology to advertising, I will discuss how GumGum is using deep learning for a multitude of purposes including content safety, reduction of redundant processing, and general image understanding. Further, I will share some highly specific - and occasionally peculiar - image recognition use cases to which we employed deep learning techniques to afford the user a more organic experience, such as serving ads for lipstick only on pages which have images of people with “bold” lips. I will describe the problems we have attacked with deep learning, both supervised and unsupervised, our battle with statistics at scale, and how we see deep learning dramatically benefiting both consumers and marketers in the long-term.
Cambron Carter works in research and development at GumGum. He is responsible for designing computer vision and machine learning solutions for a wide variety of applications related to images and video. Cambron previously conducted research in medical image analysis where he worked on the early detection of malignant, pulmonary nodules from chest CT. He holds B.S. degrees in physics and electrical engineering and a M.Eng. in electrical engineering from the University of Louisville.