This paper describes human hierarchies-of-understanding (HoUs) as frameworks and yardsticks for (1) understanding the effects of semantic technologies on individuals and on society, (2) for designing educational courseware, (3) for designing process flows and content properties in decision support systems, and (4) for measuring the efficiency with which humans use cognitive resources, to include cognitive-cybernetic resources, such as information technology and artificial intelligence. Hierarchies of understanding are comprised of layers of understanding (data, information, knowledge, wisdom, vision). The Vision HoU informs courseware design into the age of ubiquitous artificial intelligence (U.A.I.), an age that may transform societies in turbulent ways in the 2020s. Allocation-of-understanding (AoU) is a speculative distribution of human understanding across layers of data, information, knowledge, wisdom, and vision. The Vision HoU and AoU together illustrate how semantic technologies are facilitating deep human understanding since the beginning of electronic computing, and how such trends can continue into the 2020s. This work concludes by discussing how to design suitable courseware for the age of ubiquitous A.I. (U.A.I.), so that humans play to their cognitive strengths (purpose, creativity, goal-setting, and wise decision-making) while leaving the mundane processing of data, information, and knowledge to semantic technologies and A.I.
[1]
Kevin Warwick.
Natural-born cyborgs: Minds, technologies and the future of human intelligence
,
2003
.
[2]
Rama Devi Tella.
Knowledge Management - Emerging Perspectives : A New Challenge for the Library Profession in the Digital Environment
,
2007
.
[3]
Ilkka Tuomi,et al.
Data is more than knowledge: implications of the reversed knowledge hierarchy for knowledge management and organizational memory
,
1999,
Proceedings of the 32nd Annual Hawaii International Conference on Systems Sciences. 1999. HICSS-32. Abstracts and CD-ROM of Full Papers.
[4]
Scott Alan Carpenter.
New Methodology for Measuring Information, Knowledge, and Understanding versus Complexity in Hierarchical Decision Support Models
,
2008
.
[5]
R. Ackoff.
From Data to Wisdom
,
2014
.
[6]
H. Cleveland.
Information as a resource
,
1983
.