Page 178 - Artificial Intelligence for the Internet of Everything

164 Artificial Intelligence for the Internet of Everything

gives rise to new information, allowing the learning process to continue.
Information, then, creates value not in a linear value chain of process steps
but, rather, in a never-ending value loop. Whether information is viewed
discretely or from a continuous variable perspective the question remains:
what is the probability that new value can be derived?
From a systems standpoint, as well as a system-augmented human
decision-making perspective, machine learning and AI is implied in the
never-ending value-loop. This notion is consistent with and supported by
the decision science literature, which generally views decision making as
an ongoing process (Simon, 1960). If value, derived from plentiful IoT
information is created in a nonlinear loop (Baker, Song, & Jones, 2017), then
information’s relationship to the decision is inseparable. Further, given an
abundance of supply-side information, VoI would decrease proportionally
to its decision relevance. This is logical, because if everyone has perfect
information in a bidding situation, VoI would correlate highly with its per-
ceived potential for modification (Sa ´nchez-Ferna ´ndez & Iniesta-Bonillo,
2007). The decision-transactional basis of Howard’s on theoretical VoI
and thereby our own extension, may introduce contextual bias in this sense,
so we provide some discussion here on the nature of sensed information and
VoI from the sensor network literature.
Bisdikian, Kaplan, and Srivastava (2013) have conducted a significant
amount of research in quality of information (QoI), VoI, and the relation-
ship between them. Their findings are relevant here because their work
helps define the differences between quantitative and qualitative character-
istics of information. Moreover, they provide these definitions from the
perspective of sensor networks making the application to IoT direct, if
on a somewhat smaller scale. The work of Bisdikian et al. is a departure
from the information theoretic perspective taken in our work and instead
provide a descriptive characterization of QoI and VoI. Their definition
casts VoI as a function of QoI, where QoI is use-independent facts about
information (e.g., percentage of error, age, resolution) and VoI is use-
dependent qualitative judgments (e.g., trustworthiness, completeness,
readability). Figs. 9.9 and 9.10 show the semantic taxonomy from Bisdi-
kian et al., whereitisevident that ourtreatment of VoI isconsistentwith
the qualitative judgment.
The semantic descriptions of QoI and VoI suggest that AI’s use of the
IoT would concentrate primarily on QoI because VoI characteristics are