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EDITOR’S QUESTION
JAMES PETTER, EMEA VP,
PURE STORAGE
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A
rtificial intelligence (AI) is starting
to change how many businesses
operate. The ability to accurately
process and deliver data faster than any
human could is already transforming how
we do everything from studying diseases
and understanding road traffic behaviour to
managing finances and predicting weather
patterns. For organisations like Global
Response, AI represents an opportunity
to reinvent existing business models. With
the help of storage industry leaders, Global
Response has begun development on a
state-of-the-art call centre system that allows
for the real-time transcription and analysis of
customer support calls.
This will allow for a superior customer
experience and faster solutions, both
increasingly important as consumer
expectations shift heavily towards
personalised experience. Similarly, Paige.AI
is an organisation focused on revolutionising
clinical diagnosis and treatment in oncology
through the use of AI.
Pathology is the cornerstone of most cancer
diagnoses. Yet most pathologic diagnoses
rely on manual, subjective processes,
developed more than a century ago. By
leveraging the potential of AI, Paige.AI aims
to transform the pathology and diagnostics
industry from highly qualitative to a more
rigorous, quantitative discipline.
With so much on offer and at stake, the
question is no longer simply what AI is
capable of, but rather where AI can best be
used to deliver immediate business benefits.
According to a recent 2018 report from
PwC, AI is expected to contribute US$320
billion to the Middle East economy by
2030, with an annual growth rate between
20% to 34% across the region. This is not
surprising given findings from our recent
Evolution report, which revealed that 45%
www.intelligentcio.com
of IT decision makers in the Middle East
are planning on increasing their IT budget
for AI and machine learning projects in the
next financial year. A further 46% are also
planning on investing in AI skills/personnel in
the same timeframe.
For those looking to implement AI or
machine learning projects, the compute
bottleneck that used to hold back projects
like these has largely been eliminated. The
application of graphics processing unit
(GPU) technology from the likes of NVIDIA,
has played a big part in this.
scale linearly and non-disruptively in order to
grow capacity and performance.
For legacy storage systems, meeting these
requirements is no mean feat. As a result,
data can easily end up in infrastructure siloes
at each stage of the AI pipeline – comprised
of ingest, clean and transform, explore,
train – making projects more time intensive,
complex and inflexible.
As a result, the challenge for many projects is
now providing the data fast enough to feed
the data analysis pipelines central to AI. Bringing together data into a single
centralised data storage hub as part of a
deep learning architecture enables far more
efficient access to information, increasing
the productivity of data scientists and
making scaling and operating simpler and
more agile for the data architect.
It is critical that organisations also carefully
consider the infrastructure needed to
support their AI ambitions. To innovate and
improve AI algorithms, storage has to deliver
uncompromised performance across all
manner of access patterns – small to large
files, random to sequential and low to high
concurrency – all with the ability to easily Modern all-flash based data platforms
are ideal candidates to act as that central
data hub. It’s the only storage technology
capable of underpinning and releasing
the full potential of projects operating
in environments that demand high
performance compute capabilities such as AI
and deep learning.
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