EDITOR’S QUESTION
Towards the trusted Edge
Issues around personal privacy will continue to be debated around
the world. While technologies such as dynamic anonymisation and
masking can be used on the Edge to protect privacy, attitudes and
regulation are inconsistent across regions and countries. The need
to navigate the international legal framework will be on-going for
companies in the surveillance sector. Many organisations are still
failing to undertake even the most basic firmware upgrades, yet with
more processing and analysis of data taking place in the device itself,
cybersecurity will become ever more critical.
T
Regulation: Use cases verse technology
The world on the Edge
We are seeing a growing momentum towards computing at the
‘Edge’ of the network. More of the devices that are connected to the
network require or would benefit from the ability to analyse received
data, make a decision and take appropriate action.
Autonomous vehicles are an obvious example. Whether in relation to
communications with the external environment or through sensors
detecting risks, decisions must be processed in a split second. It is the
same with video surveillance.
If we are to move towards the proactive
rather than reactive, more processing of data
and analysis needs to take place within the
camera itself.
Processing power in dedicated devices
Dedicated and optimised hardware and
software, designed for the specific application,
is essential with the move towards greater
levels of Edge Computing. Connected devices
will need increased computing power and
be designed for purpose from the ground up
with a security first mindset. The concept of
embedded AI in the form of Machine and
Deep Learning computation will also be more
prevalent moving forwards.
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INTELLIGENTCIO
Attitudes towards appropriate use technology cases and the
regulations around them differ around the world. Facial recognition
might be seen as harmless and even desirable. However, when used
for monitoring citizens and social credit systems it is regarded as
much more sinister and unwanted. The technology is exactly the
same but the case is vastly different. Regulations are struggling to
keep pace with advances in technology. It’s a dynamic landscape
that the industry will need to navigate, and where business ethics will
continue to come under intense scrutiny.
“
MANY
ORGANISATIONS
ARE STILL FAILING
TO UNDERTAKE
EVEN THE MOST
BASIC FIRMWARE
UPGRADES.
Network diversity
As a direct result of some of the regulatory
complexities, privacy and cybersecurity
concerns, we’re seeing a move away from
the open Internet of the past two decades.
While public cloud services will remain part
of how we transfer, analyse and store data,
hybrid and private clouds are growing in use.
Openness and data sharing was regarded as
being essential for AI and Machine Learning,
yet pre-trained network models can now
be tailored for specific applications with a
relatively small amount of data. For instance,
we’ve been involved in a recent project where
a traffic monitoring model trained with only
1,000 photo examples reduced false alarms in
accident detection by 95%.
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