This document discusses AI vision and a hybrid approach using both edge and server-based analytics. It outlines some of the challenges of vision problems where data is analog, complex, and data-heavy. A hybrid approach is proposed that uses edge devices for initial analysis similar to the ventral stream, while also using servers for deeper correlation and inference like the dorsal stream. This combines the strengths of edge and server-based computing on platforms like Intel that support both CPUs and GPUs to efficiently solve real-world vision problems. Several case studies are provided as examples.