Project Management in AI 16 RSIP Vision’s CEO Ron Soferman has launched a series of lectures to provide a robust yet simple overview of how to ensure that computer vision projects respect goals, budget and deadlines. This month David Menashe tells us about another aspect of Project Management with Deep Learning: Fitting Talents into Projects. It’s another handy tip by RSIP Vision for Project Management in Computer Vision. Project Management in AI One of the most creative tasks at project inception is deciding who within your team will do which part of the project . A good team includes a number of talents, each specialized in different disciplines. For example: a physicist or electrical engineer , a computer scientist , an algorithm developer and a mathematician. We can try to fit each one of them with the project at hand. The computer scientist is probably better suited for supervising the overall structure of the program and defining data structure and classes; the physicist or engineer is better suited to define the physical interface with the devices (LiDAR, specialized cameras and the like); the mathematician is better suited to tackle those parts of the problem which require a more mathematical approach, such as graph theory ; algorithm developers will use their experience in classical computer vision algorithms to find the right algorithmic fit to other parts of the problem; an AI researcher will complement all this by addressing the deep learning core of the project. However, we still need to be flexible in assigning tasks to the different resources. For instance, a mathematician can also complete computer science tasks; a computer scientist can interface with physical hardware; fitting talents into projects should not be a rigid process: rather, it should be flexible according to the time priorities of each resource. The physicist will probably take responsibility for the integration of the physical hardware, but others will help as possible, according to time considerations.