The Challenges of Running Computer Vision on the Edge

Artificial intelligence (AI) is the field of making computers able to act intelligently, to make decisions in real environments that will have favorable outcomes. This is obviously a broad, and somewhat vague, definition, and there are many fields within this umbrella term. One example of such a field is that of computer vision, in which computers can process images as a human would, and make inferences about what is in an image so that computer programs can then use that information to make decisions that have favorable outcomes. It is becoming more common to see artificial intelligence applications such as computer vision integrated into new business models and products. Computer vision has many real-world applications, analyzing traffic patterns, detecting changes in posture, counting the number of persons in an area, etc. Learning how to build any computer vision application requires a steep learning curve, and deploying it to the edge adds an extra layer of complication.