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Adding Visual AI or Computer Vision to Your Embedded System

Key Things Every Engineering Manager Should Know

Visual AI and computer vision are bringing compelling capabilities to many types of systems -- making them safer, easier to use, more efficient and more capable.  But visual AI and computer vision are quite different from traditional embedded technologies, and for many product development groups, these new technologies bring unfamiliar challenges and unexpected risks.

How can you gain confidence that your requirements can be met with today’s technology?  What metrics should you use to assess accuracy?  Will you need to collect and label your own training data?  Will you need a much more powerful (and expensive and power-hungry) processor?  How will you keep your algorithms up to date as their environment changes?  

Join Jeff Bier and Phil Lapsley from the Edge AI and Vision Alliance, who will take you on a quick course in managing visual AI projects for embedded systems.  We’ll cover how data is now your best friend, and maybe also your worst nightmare (and what you can do to stay on its good side); we’ll look at the iterative nature of AI/CV projects and how that differs from traditional development; we’ll talk about the importance of requirements and real-world versus laboratory conditions; we’ll touch on important issues of bias; we’ll provide an overview of how to think about accuracy; and we’ll give tips on how to talk to your management about a development process that is likely a bit different than what they’re used to.

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