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.
Machine learning is more attainable due to increased input data, more powerful computing platforms, and more capable software.
For the first item, imaging produces megabits per picture and a multitude of cameras worldwide aid AI inference systems in completing tasks, such as quality assurance, product flow monitoring, traffic control, security, etc.
For computing platforms, available industrial- grade GPUs (graphics processing units) can boost neural net performance 27-fold or more in image evaluation when compared to general- purpose or commercial central processing units (CPUs). Regarding software, algorithms are more accurate and capable than ever before.
With the combination of these three elements, end users
no longer need an army of data scientists and engineers to implement effective, successful AI solutions.
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