Unformatted text preview: THE EXECUTIVE GUIDE TO ARTIFICIAL INTELLIGENCE How to identify and implement applications
for AI in your organization ANDREW BURGESS The Executive Guide to Artificial Intelligence Andrew Burgess The Executive Guide
How to identify and implement
applications for AI in your organization Andrew Burgess
London, United Kingdom ISBN 978-3-319-63819-5 ISBN 978-3-319-63820-1 (eBook)
Library of Congress Control Number: 2017955043
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The registered company address is: Gewerbestrasse 11, 6330 Cham, Switzerland This book is dedicated to my wonderful wife, Meg, and our two amazing
children, James and Charlie. Foreword I remember well the AI work I did whilst at college studying computer
science, how different and fascinating it was and still is. We were set a very
open challenge to write an AI programme on any subject. I decided to
write mine so that it could tell you if the building in a photo was a house,
a flat or a bungalow. Somewhat impractical, but a great learning experience for me, particularly in understanding how AI is different from traditional software.
Although my college days were a number of years ago, since that time the
concept of computers learning has always intrigued me and I have since wondered how long it will take for AI to have a truly widespread impact. In recent
years, we’ve seen massive improvements in processing power, big data collection via sensors and the Internet of Things, cloud services, storage, ubiquitous
connectivity and much more. These technological leaps mean that this is the
right time for AI to become part of the ‘here and now’ and I strongly believe
we will see a dramatic increase in the use of AI over the next few years.
The AI in use today is known as narrow AI because it can excel at thousands
of relatively narrow tasks (e.g. doing internet searches, playing Go or looking
for fraudulent transactions). Things will certainly get even more exciting when
‘general AI’ can outperform humans at nearly every task we do, but we simply
don’t know when this might be, or what the world will then look like. Until
then, what excites me most is how we can apply AI now to solve our day-today problems at home and work.
So why is AI important and how can we use it? Firstly, if you are impatient
(like I am), doing small manual, repetitive tasks on computers simply takes
too much time. I want the computer to do a better job of anticipating my
needs and to just get on with it. If I could, I would prefer to talk to Alexa or
vii viii Foreword Google Assistant and just tell the computer what to do. For example, I would
love to be able to ask Alexa to buy the most convenient train ticket and take
the money out of my account. Compare this to buying a train ticket on any
website, where after something like 50 key strokes you might have bought a
ticket. I don’t think future generations, who are becoming increasingly impatient, will put up with doing these simple and time-consuming tasks. I see my
children and future generations having more ‘thinking time’ and focusing on
things that are outside the normal tasks. AI may in fact free up so much of my
children’s time that they can finally clean up their bedrooms.
In the workplace, how many of the emails, phone calls and letters in a call
centre could be handled by AI? At Virgin Trains, we used AI to reduce the time
spent dealing with customer emails by 85% and this enabled our people to focus
on the personable customer service we’re famous for. Further improvements will
no doubt be possible in the future as we get better at developing conversational
interfaces, deep learning and process automation. One can imagine similar
developments revolutionising every part of the business, from how we hire people to how we measure the effectiveness of marketing campaigns.
So, what about the challenges of AI? One that springs to mind at Virgin is
how to get the ‘tone of voice’ right. Our people are bold, funny and empathetic, and our customers expect this from us in every channel. Conversational
interfaces driven by AI should be no different.
Today it may be a nuisance if your laptop crashes, but it becomes all the
more important that an AI system does what you want it to do if it controls
your car, your airplane or your pacemaker. With software systems that can
learn and adapt, we need to understand where the responsibility lies when
they go wrong. This is both a technical and an ethical challenge. Beyond this,
there are questions about data privacy, autonomous weapons, the ‘echo chamber’ problem of personalised news, the impact on society as increasing numbers of jobs can be automated and so on.
Despite these challenges, I am incredibly excited about the future of technology, and AI is right at the heart of the ‘revolution’. I think over the next
five to ten years AI will make us more productive at work, make us more
healthy and happy at home, and generally change the world for the better.
To exploit these opportunities to the full, businesses need people who
understand these emerging technologies and can navigate around the challenges. This book is essential reading if you want to understand this transformational technology and how it will impact your business.
John Sullivan, CIO and Innovation at Virgin Trains Acknowledgements I would like to thank the following people for providing valuable input, content and inspiration for this book:
Andrew Anderson, Celaton
Richard Benjamins, Axa
Matt Buskell, Rainbird
Ed Challis, Re:infer
Karl Chapman, Riverview Law
Tara Chittenden, The Law Society
Sarah Clayton, Kisaco Research
Dana Cuffe, Aldermore
Rob Divall, Aldermore
Gerard Frith, Matter
Chris Gayner, Genfour
Katie Gibbs, Aigen
Daniel Hulme, Satalia
Prof. Mary Lacity, University of Missouri-St Louis
Prof. Ilan Oshri, Loughborough University
Stephen Partridge, Palgrave
Mike Peters, Samara
Chris Popple, Lloyds Bank
John Sullivan, Virgin Trains
Cathy Tornbaum, Gartner ix x Acknowledgements Vasilis Tsolis, Congnitiv+
Will Venters, LSE
Kim Vigilia, Conde Naste
Prof. Leslie Willcocks, LSE
Everyone at Symphony Ventures Contents 1 Don’t Believe the Hype 1 2 Why Now? 11 3 AI Capabilities Framework 29 4 Associated Technologies 55 5 AI in Action 73 6 Starting an AI Journey 91 7 AI Prototyping 117 8 What Could Possibly Go Wrong? 129 9 Industrialising AI 147
10 Where Next for AI? 165
Index 177 xi List of Figures Fig. 2.1 Fig. 2.2 Fig. 2.3 Fig. 3.1 Fig. 3.2 Fig. 3.3 Fig. 4.1 Fig. 4.2 Fig. 4.3 Fig. 6.1 Fig. 6.2 Fig. 6.3 Fig. 6.4 Fig. 9.1 Basic neural network
Training a neural network
A trained neural network
The AI framework
Human in the loop
Training through a human in the loop
Crowd-sourced data training
Aligning with the business strategy
AI maturity matrix
AI heat map first pass
AI heat map
AI Eco-System 21
148 xiii 1
Don’t Believe the Hype Introduction
Read any current affairs newspaper, magazine or journal, and you are likely to
find an article on artificial intelligence (AI), usually decrying the way the
‘robots are taking over’ and how this mysterious technology is the biggest risk
to humanity since the nuclear bomb was invented. Meanwhile the companies
actually creating AI applications make grand claims for their technology,
explaining how it will change peoples’ lives whilst obfuscating any real value
in a mist of marketing hyperbole. And then there is the actual technology
itself—a chimera of mathematics, data and computers—that appears to be a
black art to anyone outside of the developer world. No wonder that business
executives are confused about what AI can do for their business. What exactly
is AI? What does it do? How will it benefit my business? Where do I start? All
of these are valid questions that have been, to date, unanswered, and which
this book seeks to directly address.
Artificial Intelligence, in its broadest sense, will have a fundamental impact
on the way that we do business. Of that there is no doubt. It will change the
way that we make decisions, it will enable completely new business models to
be created and it will allow us to do things that we never before thought possible. But it will also replace the work currently being done by many knowledge workers, and will disproportionally reward those who adopt AI early and
effectively. It is both a huge opportunity and an ominous threat wrapped up
in a bewildering bundle of algorithms and jargon.
But this technological revolution is not something that is going to happen in
the future; this is not some theoretical exercise that will concern a few businesses.
© The Author(s) 2018
A. Burgess, The Executive Guide to Artificial Intelligence,
1 2 1 Don’t Believe the Hype Artificial Intelligence is being used today in businesses to augment, improve and
change the way that they work. Enlightened executives are already working out
how AI can add value to their businesses, seeking to understand all the different
types of AI and working out how to mitigate the risks that it inevitably brings.
Many of those efforts are hidden or kept secret by their instigators, either because
they don’t want the use of AI in their products or services to be widely known,
or because they don’t want to give away the competitive advantage that it
bestows. A persistent challenge for executives that want to get to grips with AI is
where to find all the relevant information without resorting to fanciful articles,
listening to vendor hyperbole or trying to understand algorithms. AI is firmly in
the arena of ‘conscious unknowns’—we know that we don’t know enough.
People generally experience AI first as consumers. All our smartphones
have access to sophisticated AI, whether that is Siri, Cortana or Google’s
Assistant. Our homes are now AI enabled through Amazon’s Alexa and Google
Home. All of these supposedly make our lives easier to organise, and generally
they do a pretty good job of it. But their use of AI is actually pretty limited.
Most of them rely on the ability to turn your speech into words, and then
those words into meaning. Once the intent has been established, the rest of
the task is pretty standard automation; find out the weather forecast, get train
times, play a song. And, although the speech recognition and natural language understanding (NLU) capabilities are very clever in what they achieve,
AI is so much more than that, especially in the world of business.
Artificial Intelligence can read thousands of legal contracts in minutes and
extract all the useful information out of them; it can identify cancerous
tumours with greater accuracy than human radiologists; it can identify fraudulent credit card behaviour before it happens; it can drive cars without drivers;
it can run data centres more efficiently than humans; it can predict when
customers (and employees) are going to desert you and, most importantly, it
can learn and evolve based on its own experiences.
But, until business executives understand what AI is, in simple-enough
terms, and how it can help their business, it will never reach its full potential.
Those with the foresight to use and exploit AI technologies are the ones that
need to know what it can do, and understand what they need to do to get
things going. That is the mission of this book. I will, over the course of the ten
chapters, set out a framework to help the reader get to grips with the eight
core capabilities of AI, and relate real business examples to each of these. I will
provide approaches, methodologies and tools so that you can start your AI
journey in the most efficient and effective way. I will also draw upon interviews and case studies from business leaders who are already implementing
AI, from established AI vendors, and from academics whose work focuses on
the practical application of AI. Introducing the AI Framework 3 Introducing the AI Framework
My AI Framework was developed over the past few years through a need to be
able to make sense of the plethora of information, misinformation and
marketing-speak that is written and talked about in AI. I am not a computer
coder or an AI developer, so I needed to put the world of AI into a language
that business people like myself could understand. I was continually frustrated by the laziness in the use of quite specific terminology in articles that
were actually meant to help explain AI, and which only made people more
confused than they were before. Terms like Artificial Intelligence, Cognitive
Automation and Machine Learning were being used interchangeably, despite
them being quite different things.
Through my work as a management consultant creating automation strategies for businesses, through reading many papers on the subject and speaking
to other practitioners and experts, I managed to boil all the available information down into eight core capabilities for AI: Image Recognition, Speech
Recognition, Search, Clustering, NLU, Optimisation, Prediction and
Understanding. In theory, any AI application can be associated with one or
more of these capabilities.
The first four of these are all to do with capturing information—getting
structured data out of unstructured, or big, data. These Capture categories are
the most mature today. There are many examples of each of these in use today:
we encounter Speech Recognition when we call up automated response lines;
we have Image Recognition automatically categorising our photographs; we
have a Search capability read and categorise the emails we send complaining
about our train being late and we are categorised into like-minded groups
every time we buy something from an online retailer. AI efficiently captures
all this unstructured and big data that we give it and turns it into something
useful (or intrusive, depending on your point of view, but that’s a topic to be
discussed in more detail later in the book).
The second group of NLU, Optimisation and Prediction are all trying to
work out, usually using that useful information that has just been captured,
what is happening. They are slightly less mature but all still have applications
in our daily lives. NLU turns that speech recognition data into something
useful—that is, what do all those individual words actually mean when they
are put together in a sentence? The Optimisation capability (which includes
problem solving and planning as core elements) covers a wide range of uses,
including working out what the best route is between your home and work.
And then the Prediction capability tries to work out what will happen next—
if we bought that book on early Japanese cinema then we are likely to want to
buy this other book on Akira Kurosawa. 4 1 Don’t Believe the Hype Once we get to Understanding, it’s a different picture all together.
Understanding why something is happening really requires cognition; it
requires many inputs, the ability to draw on many experiences, and to conceptualise these into models that can be applied to different scenarios and
uses, which is something that the human brain is extremely good at, but AI,
to date, simply can’t do. All of the previous examples of AI capabilities have
been very specific (these are usually termed Narrow AI) but Understanding
requires general AI, and this simply doesn’t exist yet outside of our brains.
Artificial General Intelligence, as it is known, is the holy grail of AI researchers
but it is still very theoretical at this stage. I will discuss the future of AI in the
concluding chapter, but this book, as a practical guide to AI in business today,
will inherently focus on those Narrow AI capabilities that can be implemented
You will already be starting to realise from some of the examples I have
given already that when AI is used in business it is usually implemented as a
combination of these individual capabilities strung together. Once the individual capabilities are understood, they can be combined to create meaningful
solutions to business problems and challenges. For example, I could ring up a
bank to ask for a loan: I could end up speaking to a machine rather than a
human, in which case AI will first be turning my voice into individual words
(Speech Recognition), working out what it is I want (NLU), deciding whether
I can get the loan (Optimisation) and then asking me whether I wanted to
know more about car insurance because people like me tend to need loans to
buy cars (Clustering and Prediction). That’s a fairly involved process that
draws on key AI capabilities, and one that doesn’t have to involve a human
being at all. The customer gets great service (the service is available day and
night, the phone is answered straight away and they get an immediate response
to their query), the process is efficient and effective for the business (operating
costs are low, the decision making is consistent) and revenue is potentially
increased (cross-selling additional products). So, the combining of the individual capabilities will be key to extracting the maximum value from AI.
The AI Framework therefore gives us a foundation to help understand what
AI can do (and to cut through that marketing hype), but also to help us apply
it to real business challenges. With this knowledge, we will be able to answer
questions such as; How will AI help me enhance customer service? How will
it make my business processes more efficient? And, how will it help me make
better decisions? All of these are valid questions that AI can help answer, and
ones that I will explore in detail in the course of this book. The Impact of AI on Jobs 5 Defining AI
It’s interesting that in most of the examples I have given so far people often
don’t even realise they are actually dealing with AI. Some of the uses today,
such as planning a route in our satnav or getting a phrase translated in our
browser, are so ubiquitous that we forget that there is actually some really
clever stuff happening in the background. This has given rise to some tongue-
in-cheek definitions of what AI is: some say it is anything that will happen in
20 years’ time, others that it is only AI when it looks like it does in the movies.
But, for a book on AI, we do need a concise definition to work from.
The most useful definition of AI I have found is, unsurprisingly, from the
Oxford English Dictionary, which states that AI is “the theory and development of computer systems able to perform tasks normally requiring h...
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