Deep learning techniques are gaining in popularity in many facets of embedded vision, and this holds true for AR and VR. Will they soon dominate every facet of vision processing? This talk explores this question by examining the theory and practice of applying deep learning to real world problems for Augmented Reality, with real examples describing how this shift is happening today quickly in some areas, and slower in others. Today it’s widely accepted for image recognition tasks that Deep Learning techniques involving Convolutional Neural Networks (CNNs) are dominating. Other application solutions use hybrid approaches. Other applications are still holding out using classical embedded vision techniques. These themes are then explored further through specific real world examples of gesture tracking (which is moving to CNN), stereo depth (hybrid approach), SLAM (moving toward a hybrid approach), and ISP (imaging pipelines holding with traditional algorithms). The talk contrasts the mix of algorithms being deployed today with a prediction of the mix we expect to find in AR/VR headsets 3 years from now.