Industrial Intelligence: AI’s Implications on Security, Seamlessness and Services for the IIoT
open-source software libraries reduce
complex applications into a few lines of
quick-to-write code. Data are abundant,
with billions of diverse, distributed
connected devices uploading information to
centralized repositories. Platforms for data
sharing allow information to be moved,
fused, and aggregated with ease.
parameters to be counted on fingers, at slow
data rates allowing for human oversight.
These systems have their place where
predictability is key, input data are
controlled and the process limits can ensure
safety. However, these systems are not
extensible – binary logic cannot make use of
new forms of data, and may fail when
presented with a scenario outside the design
specifications.
As a result of this new technological frontier,
AI has the power to turbocharge connected
devices’ data exhaust to make products and
platforms smarter and more powerful than
ever. We are entering the first AI revolution
where technology can keep up with our
dreams.
AI addresses the problem by imbuing
physical objects and their digital duplicates
with a degree of self-awareness. In this way,
AI cultivates another IoT: the Intelligence of
Things.
Self-learning models will allow devices to
monitor
themselves,
their
users,
environments, processes and outputs. These
models can identify impending failures,
minimizing downtime, optimize process
efficiency to limit work-in-progress, or even
identify tampering. Because these models
use rich input data and continually learn,
they evolve to become better over time.
B UILDING T OWARDS A N I NTELLIGENT
I NTERNET OF T HINGS
It is our view that the Internet of Things
describes products and services supporting
connectivity, sensing, inference, and action.
Connectivity is well understood, with
technologies and protocols ranging from 5G
and LoRa to 6LoPan and MQTT. Sensing has
been commoditized with the advent of low-
cost, low-power MEMS sensors. Actuation is
implemented through text message