Knowledge Architect/Overview/Computing

Knowlwdge from a computing viewpoint
Definition [H]

Better understanding the nature, structure, and operation of knowledge has many ramifications, including in business knowledge management, in content sharing security, the effective development of the knowledge economy and its progressive take over of the industrial economy, as the main source of wealth, and possibly nowhere as much as computing, including AI.

Considering all these ramifications requires separate space-times, still with the importance of today's AI, knowing that intelligence is the ability to manage knowledge, effective artificial intelligence still needs to effectively understand and process knowledge, rather than just information.

The computing ramifications and impacts of better understanding knowledge are so determining that they are at least briefly introduced here :

Better understanding knowledge can allow humans to enable computers to process and manage knowledge, to develop more useful and friendly systems, applications and interfaces. Providing systems with better understanding of how humans think and operate, enables more efficient human-computer collaboration.

The fact that security, classification, authorization, and context management are so natural and intrinsic to knowledge enables systems and application to play a much smarter, richer, and useful role in securing content. More so, it enables extensive secure knowledge and content sharing, as well as new breeds of applications and business models.

Knowledge Management

Generic Processing
By processing knowledge rather than just information, new data processing design models become possible, as generic applications can be built into content streaming pipelines where all information flows and gets dynamically dispatched and channeled to relevant processing.

In fact, applications can now be developed and broken into transforms (processing units, application components) in the pipelines where each application component is designed to manage specific knowledge resource types (data driven), ignoring content for which other transforms are set up in the pipelines, providing much better separation of concerns, as well as asynchronous data-driven processing.

With metadata integrated into knowledge, applications can now be free from typically custom detailed format dependency, as well as from much synchronicity requirements. More so, parallel processing is enabled, increasing performance while reducing interdependence.

With more fluid, dynamic, modular, natural, and generic processing and applications, knowledge computing enables a new era of smart and effective applications and systems, as well as foundations for a new economy, the knowledge economy.

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