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Artificial Intelligence at the Edge: Exploring Opportunities and Challenges


Technological innovations are driving widespread deployment of AI-based computer vision for industrial IoT applications, resulting in safety, quality, and efficiency improvements.

Once confined to data centers, artificial intelligence (AI) is now on the network edge. Hardware and software advances are making AI at the edge even easier to implement, enabling increased performance and greater flexibility; however, success requires the right technology ecosystem and the right technology partner.

Artificial Intelligence at the Edge in Reach

Before exploring benefits and industry successes in edge AI, it helps to review why easily implementing these machine-learning technologies are now within reach. Firstly, AI is biologically inspired. For example, when fed data, an artificial neural network adjusts the weight of its neurons relative to one another through feedback and feed-forward mechanisms. This activity creates an inference about the input, a model that can produce an assessment. In practice, an AI system can process and identify a variety of images.

To keep reading and learn more about the opportunities AI at the Edge can bring, download the white paper.