Page 132 - Artificial Intelligence for the Internet of Everything

118 Artificial Intelligence for the Internet of Everything

Weiser, Gold, and Brown (1999) defines a smart environment as “the
physical world that is richly and invisibly interwoven with sensors, actuators,
displays, and computational elements, embedded seamlessly in the everyday
objects of our lives, and connected through a continuous network.” We will
generalize this portrayal to emphasize real-time data that enables one to build
real-time models. In this context, we will argue that there is real-time data
that comes from sources other than sensors.
Stankovic (2014) sees a “… significant qualitative change in how we
work and live.” We will expose some of those changes and further refine
his assessment. He continues by stating that “We will truly have systems-
of-systems that synergistically interact to form totally new and unpredictable
services.” We agree with this assessment and shed light on the kinds of
services we may expect.
This chapter continues to develop the themes of the book The Internet
of Things,by Greengard (2015), Precision,by Chou (2016), and the paper
“Network of ‘Things’,” by Voas (2016). From a perspective of analyzing
the impact of IoT, this paper continues to refine the ideas presented in
the book How IoT Is Made by McDonald, Pietrocarlo, and Goldman (2015).
Greengard (2015) is focused on a contemporary version of IoT.
In particular he focuses on automation that results from real-time data. This
automation is true even in his extended example entitled 2025: A Day in the
Life, pp. 180–186.
McDonald et al. (2015) argue that it is pertinent for companies to join
the IoT space as it offers vast new opportunities for revenue streams and
for optimizing operations. It furthermore exposes what the authors call
the “democratization” of information. This book does not address the bigger
picture that evolves when IoT devices act and interact. We go beyond
this book with a nuanced discussion of how, where, and by whom data is
generated, where it is stored, and who ought to own it.
Chou (2016), similar to McDonald et al. (2015), is focused on IoT for
industry and makes a case for companies to join the IoT to develop new
business models and revenue streams that take advantage of the data that
is generated by smart devices. This book does not address the bigger picture
that evolves when IoT devices act and interact.
Tucker (2014) and Siegel (2016) focus on big-data and predictive
analysis. Predictive analysis can reveal things that may be shocking to individ-
uals (see Duhigg, 2012). While predictive analysis will lead to automation,
we focus on the automation that results when models that learn specifics
about someone or something’s behavior are empowered to act.