Accelerating Performance with the Artificial Intelligence of Things
computer vision on radiographs, CT scans
and MRIs to identify nodules and other areas
of concern on the human brain and liver.
This detection process uses deep learning
techniques such as convolutional neural
networks (CNN) to analyze visual imagery.
necessary. For monitoring, diagnosing and
acting on individual pieces of equipment,
such as home automation systems, it makes
sense to do the analysis as close to the
device as possible. Sending locally sourced,
locally consumed data to a faraway data
center causes needless network traffic,
delayed decisions and drain on battery
powered devices.
The clinic then uses a completely different AI
technology – natural language processing –
to build a patient profile based on family
medical history, medications, prior illnesses
and diet; it can even account for IoT data,
such as pacemaker data. Combining natural
language data with computer vision, the tool
enables valuable medical staff to be much
more efficient.
With the exponential increase in IoT devices
and their data volumes – along with demand
for low latency – we have seen a trend to
move analytics from traditional data centers
toward devices on the edge – the “things” –
or to other compute resources close to edge
and cloud – to the fog.
Much of the value of the AI-empowered IoT
is the promise to act now. Make customers
the right offer before they look away. Detect
the suspicious transaction before it is
approved. Help that self-driving car
maneuver through the busy intersection
without crashing into other moving vehicles.
Do it now. Latency matters.
A concept just a few years old, fog
computing shifts data processing, real-time
analytics, security and networking functions
from a centralized cloud to network nodes
and gateways closer to the IoT devices or
services. Fog computing or “fogging”
enables the data to be processed locally.
Only the results, exceptions or alerts are sent
to a centralized data center. Faster results,
less bandwidth.
Clearly, many types of sensors and devices
cannot wait for data or commands from the
cloud. And for other uses, it just isn’t
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June 2019