Typical Artificial Intelligence workflows are well understood. The classic example is image recognition distinguishing between an image of a cat or a dog. The first step is performed by data scientists who create a model they train using large sets of tagged data. The models are iteratively refined by the data scientists to achieve higher and higher levels of accuracy. Once the refined model is trained it can be deployed and new images can be presented to it for inferencing. The AI inferencing process results in a never seen before image being classified as either a cat or a dog.
When you take a step back and look at today’s high-performance computing and AI technological ecosystem, it is a field dominated by the power of GPUs in compute acceleration. For the layperson, the term “Graphical Processing Unit” would not stand out as the core component in processing massive datasets at unprecedented speeds – with no graphics involved. The transition from CPU to GPU core computing in HPC and AI/ML applications, and the further development of GPU technologies since, is the fundamental driving force behind today’s most cutting-edge applications. Modern AI spans practically all industries including piloting the most sophisticated autonomous vehicles, detecting previously unseeable fraud patterns in the financial industry, and increasing productivity through automation in mining, oil and gas, and farming.
Independent of the administration in office, one of the top priorities for today and the next couple of decades is the deployment of Artificial Intelligence (AI) throughout all the branches of the armed forces. Many of these applications are AI Transportables requiring performance without compromise in harsh and challenging environments. This post will cover some of the driving factors, paint the picture of tomorrow and discuss how we will continue to protect our freedom with military superiority.
