The industry as a whole is beginning to realize the intimate connection between Artificial Intelligence and its less heralded, yet equally viable, knowledge foundation. The increasing prominence of knowledge graphs in almost any form of analytics--from conventional Business Intelligence solutions to data science tools--suggests this fact, as does the growing interest in Neuro-Symbolic AI. In most of these use cases, graphs are the framework for intelligently reasoning about business concepts with a comprehension exceeding that of mere machine learning. However, what many organizations still don't realize is there's an equally vital movement gaining traction around AI's knowledge base that drastically improves its statistical learning prowess, making the latter far more effectual. In these applications graphs aren't simply providing an alternative form of AI to machine learning that naturally complements it.

The industry as a whole is beginning to realize the intimate connection between Artificial Intelligence and its less heralded, yet equally viable, knowledge foundation. The increasing prominence of knowledge graphs in almost any form of analytics--from conventional Business Intelligence solutions to data science tools--suggests this fact, as does the growing interest in Neuro-Symbolic AI. In most of these use cases, graphs are the framework for intelligently reasoning about business concepts with a comprehension exceeding that of mere machine learning. However, what many organizations still don't realize is there's an equally vital movement gaining traction around AI's knowledge base that drastically improves its statistical learning prowess, making the latter far more effectual. In these applications graphs aren't simply providing an alternative form of AI to machine learning that naturally complements it.

They're the most effective means of preparing data for statistical AI, creditable knowledge graph platforms utilize supervised and unsupervised learning to accelerate numerous processes, and their smart inferences are a form of machine intelligence. Coupling knowledge graphs with high performance computing enables organizations to not only avail themselves of sophisticated techniques to optimize AI, but also employ it at the scale and speed of contemporary data demands. According to Katana Graph CEO Keshav Pingali, "There is a need for high performance graph computing…in two ways. One is the volume of data, and the other is time to insight." Scaling knowledge graphs with high performance computing is a means of rapidly analyzing the tremendous data quantities organizations routinely contend with for informed, low latent action across numerous use cases including "intrusion detection, fraud detection, and Anti-Money Laundering," Pingali noted.

Early this month, IBM announced that it shall be rolling out an AIOps product at its Think Digital Conference. The product is expected to help CIOs plan better as IBM's flagship AI, Watson, caters to anomalies in the process and deals with outages. The company said that AIOps will be able to self-detect, diagnose and respond to IT anomalies in real-time. For instance, AI will be able to correct any anomalies in the operations. "Our work is centered around cognitive-AI driven enterprise planning," Gopal Pingali, global vice president of the Global Technology Services Labs, IBM, said.