AI definitely needs QA monitoring

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AI
definitely needs QA monitoring
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AI definitely
needs QA monitoring
In 2017, there were reports on how the worlds
leading robotics and artificial intelligence
pioneers called on the United Nations to ban the
development and use of killer robots and weapons
such as drones, tanks and automated machine guns.
A group of 116 specialists from across 26
countries led by Teslas Elon Musk and Alphabets
Mustafa Suleyman had called a ban on autonomous
weapons. This proves that even the big guns in
automation are worried about robots running amok
in the war field! Almost a year back even
Facebook had abandoned an experiment after two
artificially intelligent programs started
interacting with each other in a strange language
only they understood. Ensuring a stringent
surveillance and monitoring plan is one of the
key focus areas for any Artificial Intelligence
(AI) related activity or applications. All these
inventions closely impact our regular routine and
peaceful coexistence. Hence, if anything goes
wrong, it can probably endanger lives or our
well-being in some way. Cybersecurity is as well
a perspective to look at the ongoing boom around
AI. What you need is a robust Quality Assurance
and Testing plan! AI can be implemented across
various operational areas AI and facial
recognition technology can help the police to
identify suspects, whereas in the healthcare,
radiographers can leverage AI to examine
radiographs effectively and within time limits.
It can be and has been implemented for
conservation activities like tracking endangered
species in the wild, monitoring remote glaciers,
and much more. All this can happen if the device
and application is well tested and configured for
any unforeseen occasion.
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AI definitely needs QA monitoring
What can go wrong with AI? John-David Lovelock,
research vice president at Gartner states, AI
promises to be the most disruptive class of
technologies during the next 10 years due to
advances in computational power, volume, velocity
and variety of data, as well as advances in deep
neural networks (DNNs). He further mentions, In
the early years of AI, customer experience (CX)
is the primary source of derived business value,
as organizations see value in using AI techniques
to improve every customer interaction, with the
goal of increasing customer growth and retention.
CX is followed closely by cost reduction, as
organizations look for ways to use AI to increase
process efficiency to improve decision making and
automate more tasks. AI is indisputably loaded
with opportunities and potential. Whether it
delivers as expected, is a phase we are yet to
see and believe. Lets take the reverse route. It
makes sense to establish what could go wrong with
AI and then estimate how any such situation can
be salvaged. Data the rider and roller for
AI Any new technology will work as per the data
provided. Whether its a virtual assistant or a
smart home device, it will function on the basis
of the information that it sources from its
virtual environment or any external source. Any
leak or flaw in the data can result in disruption
or breach within the system. Hence, it is
critical to ensure quality of the data and test
the application along with the data sources.
Erroneous data can impact the quality of your
applications performance, particularly, its
accuracy.
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AI definitely needs QA monitoring

According to a survey conducted by Forrester,
only 17 percent of respondents say that their
biggest challenge was that they didnt have a
well-curated collection of that to train an AI
system. For instance, with AI for Facial
Recognition, the accuracy of the application will
depend on the way the data fragment is fed and
the application is trained. It can even result in
a bias, where a man could be recognized in some
way and a woman in a particular way, resulting in
inaccuracy. There are chances of racial bias as
well. Hence, it is important to test and confirm
the data that is being used to train the
applications and devices for the respective
operations. Read Full Blog at
https//www.cigniti.com/blog/artificial-intellige
nce-needs-qa-monitoring/
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