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FEATURE: MACHINE LEARNING
Using supervised Machine Learning
to reach a desired solution
A
s well as utilising ML for cybersecurity
purposes, it is also widely used across
a number of industries and can help
to decipher between documents and data
within the workplace.
We spoke with Sascha Eder, COO, NewtonX,
who describes how supervised Machine
Learning technology works and Machine
Learning’s role within society.
NewtonX, an AI-Powered Knowledge Access
Platform, connects clients with experts using
a unique set of proprietary automation tools.
Where traditional expert networks rely on
a finite number of pre-onboarded experts,
NewtonX leverages a proprietary search
algorithm to identify and onboard the best
possible experts in the world for each specific
request – whether or not those experts
are already part of the NewtonX network.
Supervised Machine Learning refers to a
certain type of algorithm which is able to learn
through a supervised process. The algorithm is
fed labelled training data in successive rounds
to progressively reach a state where it can
recognise this data independently. It’s called
supervised learning because during training,
the human ‘supervisor’ knows the output that
they want the machine to reach and corrects
the algorithm as it tries different paths to
reach the desired output.
The inspiration behind the
supervised ML technology
The inspiration derived from the fact we
were involved in an industry that consisted of
indexing data (knowledge, skills, experience),
but accessing this data was still mostly
manual. We realised we could automate
aspects of the process and leverage Machine
Learning to make our search engine
increasingly precise. Our first step in pursuing
this was building the NewtonX knowledge
graph which is a data model we designed to
be able to map the knowledge of each of our
NewtonX experts. This data architecture uses
nodes and vectors to represent knowledge
rather than a traditional database or tagging
system to improve precision. That allowed us
to structure data in a way where each area
of expertise is defined by a set of nodes and
vectors which then define complex knowledge.
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Creating the technology
Myself and my team started it very
manually which often, in most successful ML
algorithms, is the underlying process – you
manually create the supervised set of data
that you then manually define. We looked
at roughly 5,000 CVs and defined all the
different areas we thought were relevant,
including job titles, industries and areas of
expertise. We initially focused on areas of
technology, for example, general IT, cloud
computing and mobile IT. We defined
all these categories and sub-categories
and every single profile we studied, we
categorised based on all the different
variables. We put them into relationships
and built a database around defining these
profiles and then started using a broader
database of 50-100,000 CVs that we tried
to map to the previously defined profiles
we’d created. This is based on where we
built the algorithm that was automatically
defining which category the profile would fall
into. We would then have a feedback loop
where we’d go back in and see what level of
precision was used and whether the profile
was categorised correctly.
The main industries utilising
the offering
The main industry we’re working with
currently is the consulting industry –
management and strategy consultants – to
help them receive a lot of information to
their product line. We work with private
equity, asset management and hedge funds
which need information to make investment
decisions. We also work with more traditional
market research firms that are gathering
reports, running surveys or doing in-depth
interviews with their clients. So, in summary,
companies where a large amount of data
handling is required.
A successful client deploying
the technology
We’re currently working with a large
investment bank that has a massive
employee base, with thousands of very
experienced people and a lot of finance
specialists. The bank was struggling to
harness the power of these different
experiences and connect employees to each
other in real-time to share their expertise. We
are currently working with the company to
rollout our technology to its own employee
base. The client is now able to access real-
time expertise within its employee base.
Machine Learning enabling
technological development
One benefit of ML is the automation of
functions that previously only humans
were capable of. People can now focus on
higher value tasks that are more difficult to
automate, in order to provide higher value
for clients, which in turn increases company
value. In DevOps, for instance, ML enables the
automation of tech cases, optimisation of load
balances and improvement of IT infrastructure.
ML’s role within society
moving forward
One of the more popular cases where
Machine Learning is impacting society is
autonomous driving. A common complex
problem the automotive industry attempts
to solve is extremely unpredictable
environments and decisions often need to
be made in micro seconds. Another example
is the finance industry – by using ML, you
can gather much more information about
people that would qualify them for a certain
credit type and so forth.
More generally, nearly all industries are being
disrupted by ML, meaning we’ll be able to
make better decisions, predictions and have
improved transparency and applications
for society. Despite the benefits of ML, it’s
important not to disregard the potential for
a high level of abuse. Concern surrounding
the amount of data which needs to be
leveraged and required to run ML operations
is something people might not want to
reveal. There is an undeniable impact on
society, whether it be good or bad, in terms
of there being a lot of automation, decision-
making and new industries emerging
that weren’t around 10 years ago, like
autonomous driving. n
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