NC1801

FEATUREAI
AI: A PRACTICAL GUIDE
ARTIFICIAL INTELLIGENCE IS EVERYWHERE, BUT WHAT DOES AI
MEAN EXACTLY AND, CRITICALLY, WHAT OPPORTUNITIES DOES IT
PROMISE? OPENING OUR NEW FEATURE ON AI, PRACTITIONER
PIERS STOBBS, CHIEF DATA OFFICER AT MONEYSUPERMARKET
GROUP, OFFERS SOME PRACTICAL INSIGHT
WHAT IS AI?
You'd be hard pressed to have missed the
acronym AI recently. Artificial Intelligence
crops up in topics as disparate as
healthcare (reading scans), space
exploration (finding planets) and board
games (winning at Go). This very broad
term means different things to different
people, often starting with the spectre of
Terminator-style super intelligent robots
enslaving the human race. But for many
practitioners AI is no more powerful than
the statistical techniques we have used
for decades.
In his thought-provoking book Life 3.0
respected MIT professor Max Tegmark
defines intelligence as "the ability to
accomplish complex goals". He then
defines General Intelligence, often
characterised as human-level general
intelligence, as the ability to accomplish
virtually any goal, with Narrow
Intelligence being restricted to a few
specific goals. Clearly, today's AI
examples are very much in the narrow
intelligence camp. Although there have
been some impressive developments
towards broader capabilities, particularly
by DeepMind and others, the general
consensus is that we are still some way off
artificial general intelligence.
CAN AI HELP?
If today's reality is the application of
narrow AI to specific tasks then there are
many use cases that can build great
advantage. To understand if AI can help a
given problem, characterise the problem
along two dimensions: how obvious is the
answer and how many times do you have
to perform it? If the answer to the problem
is relatively obvious to a human and is a
task that is completed frequently, then it is
likely that AI will help. A classic example
is the amazing progress made by the
Google Brain team in image tagging.
For humans, it is relatively obvious what
objects appear in an image, and we can
easily assign tags. However, at the rate of
a picture every second of every day, it
would take over 30,000 person years to
tag the trillion or so images in Google's
index. Automation might be useful here!
The accuracy achieved by the Google
team using their Deep Learning approach
is astonishing, and it's accessible to
anyone using their API.
IMPLEMENTING AI?
The first thing to think about when
considering AI is data. AI, in this context
supervised machine learning, works by
learning from examples. To build an AI
model you need what is called a data
training-set. Think of this as a really big
spreadsheet where each row contains a
specific example, perhaps information
about a specific customer and the correct
answer such as, did that customer purchase
a specific product? Without a decent set of
data you cannot build an AI model.
You also need to be very clear about the
question you are trying to answer and
how you will act on the model's output.
One popular use case for AI is in churn
prediction to identify which customers are
most likely to stop using your service.
However, it is not always clear what you
should do with the data once obtained.
If you have relevant data and are very
clear about the problem you are
addressing, then it's all about iterating.
Get something simple up and running
and gradually improve it, testing and
measuring all the time. You do not need
the latest and greatest approaches to be
successful. Often it's better to start simply
and establish a baseline to avoid wasting
time spinning your wheels.
Remember to be creative. AI can be
used internally as well as externally. At
Moneysupermarket we have had great
success building an anomaly detection
system to monitor the thousands of data
streams we collect. This is leading to
much speedier detection of potential
issues and is improving data quality. NC
10 NETWORKcomputing JANUARY/FEBRUARY 2018 @NCMagAndAwards
WWW.NETWORKCOMPUTING.CO.UK