Data center-hosted artificial intelligence is rapidly proliferating in both government and commercial markets, and while it's an exciting time for AI, only a narrow set of applications is being addressed, primarily limited to neural networks based on convolutional approach. Other categories of AI include general AI, symbolic AI and bio-AI, and all three require different processing demands and run distinctly different algorithms. Virtually all of today's commercial AI systems run neural network applications. But much more control-intensive and powerful AI workloads using symbolic AI, bio-AI and general AI algorithms are ill-suited to GPU/TPU architectures. Today, commercial and governmental entities that need AI solutions are using workarounds to achieve more compute power for their neural net applications, and chief among them is specialty processors like Google TPUs and NVIDIA GPUs, provisioned in data centers specifically for AI workloads.