Contributor
Jelani Harper is an editorial consultant servicing the information technology market. He specializes in data-driven applications focused on semantic technologies, data governance, and analytics.
An inordinate amount of some of the most vital aspects of Artificial Intelligence—from data engineering to data science, data preparation to machine learning—rely on one indispensable prerequisite: data modeling. Without …
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The dynamic software agents of Robotic Process Automation represent many things to the modern enterprise. They’re simultaneously a digital workforce of tireless, ubiquitous employees, a cogent means of actuating statistical …
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The edge computing paradigm has long been considered at the forefront of the Internet of Things. The ability to process data at the fringe of enterprise networks in today’s distributed …
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The motion towards cloud computing is swiftly reaching a crescendo. It’s the most viable architecture for accommodating the scale of statistical Artificial Intelligence, one of the most effective mediums for …
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Knowledge graphs may not be as lauded as machine learning, as well known as Natural Language Processing, or as futuristic as their synthesis in conversational AI applications, but they’re an …
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The rapid retrieval of data—at scale—required for many cognitive computing use cases depends on several factors, only one of which is the vaunted computational power many attribute to the current …
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Several of the most advanced applications of Robotic Process Automation (RPA)—including employing bots to discover new automation processes or mimicking human employees with a digital workforce—hinge on deployments of computer …
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One of the most significant developments about the current resurgence of statistical Artificial Intelligence is the emphasis it places on knowledge graphs. These repositories have paralleled the contemporary pervasiveness of …
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