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The Age of AISarah Finch & Tariq Khatri
disruptionhub.com
/The age of AI
2 / The age of AI www.disruptionhub.com / 3
Contents
The age of Artificial Intelligence 4
AI in business - the who, what, where and why 5
AI talent – mind the gap 6
The Opportunities of Ubiquitous AI 8
AI and ethics – the ghost in the intelligent machine 12
Looking to the future 14
Conclusion 16
About the authors
Sarah Finch
Sarah is Staff Writer & Content Editor
at D/SRUPTION. An experienced writer,
editor, and lifelong generalist, she
provides regular insights in the fields
of disruptive technology and business
innovation. Particular areas of interest
are the philosophical, ethical and geo-
political implications of these topics.
Sarah has an MA in Philosophy &
French from the University of Oxford, a
Masters in Creative Writing from Oxford
Brookes University and an MA Medieval
Studies from the University of York.
Tariq Khatri, MD Machinable
Tariq is co-founder of AI and machine
learning company Machinable where he
helps clients keep abreast of the latest
developments in machine learning
research. Machinable develops digital
analytics solutions that improve the
performance of people-intensive
businesses. He has a DPhil in Physics
from Oxford and an MSc in Machine
Learning from UCL.
www.disruptionhub.com / 5
/The age of AI
4 / The age of AI
Artificial intelligence is one of the most
transformative technologies of our times.
AI is a general term – used to describe
computer functions which can replicate
the thinking or work done by humans.
What can be accurately described as
artificial intelligence is a matter of some
debate, but this eBook will consider such
characteristics as planning, learning and
perception, applied by machines to
solve problems.
From the smart assistants on our mobile
phones to the search engines we use on
the web, AI now underpins many aspects
of our everyday lives. With AI mostly
designed to integrate seamlessly into our
normal activity, we often don’t even notice
it is there. What we might experience is
simply that information is a little bit easier
to retrieve or services are better suited to
our individual preferences.
In line with the growth of AI in the
consumer sphere, business use of AI has
also exploded, with applications such
as chatbots, advanced analytics and
intelligent process automation beginning
to find their way into the mainstream.
In the past, AI was the preserve of large
technology companies such as Facebook,
Amazon and Google, who used it to great
effect to understand customer data and
deliver the services that people really
wanted. Today, these tech giants also offer
AI as-a-service to other companies, in
a rapidly growing market. Main players
include Amazon, Google, IBM, Microsoft,
Oracle, Salesforce and SAP – to name a few.
Buying AI solutions off the shelf is an
ideal way for businesses to access AI
without the need for highly skilled in-
house experts. Cloud-based AI services
and enterprise software with embedded AI
are popular options for companies seeking
AI advantage without large technical
investment. Enterprise software with
embedded AI, such as SAP’s Leonardo
(which can be used to improve customer
experience), and Salesforce’s Einstein
(which helps sales teams prioritise
accounts according to likeliness to buy)
are pushing AI towards mass adoption as
they require no special skills from end
users. However, the generic and widely
available nature of these tools means that
genuine competitive advantage may not
always be possible.
Into the cloud
For businesses with more technical nous
on their side, AI-based development
tools can optimise the work of in-house
data scientists and experts. According to
Deloitte, 49 per cent of companies that
deploy AI today are using cloud-based
development services, such as those offered
by Amazon Web Services and Google Cloud.
These solutions offer bespoke AI solutions
at scale without the need to develop
proprietary AI systems from scratch.
Popular uses of such technology include
applying machine learning to data sets for
advanced analytics, conversational AI for
customer support chatbots, and intelligent
robotic process automation.
The age of Artificial IntelligenceAI in business - the who, what, where and why
Companies that deploy AI via cloud based development services
As the scope and strengthof AI applications hasskyrocketed over the pastfew years, the technologyis finding its way intomore generalisedbusiness use
As the scope and strength of AI applications
has skyrocketed over the past few years,
the technology is finding its way into more
generalised business use. Terms such as
‘ubiquitous AI’ and ‘the democratisation of AI’
have come to describe widened access to this
field, with non technical experts now able to
access AI solutions through platform services
and AI-integrated software packages.
It’s no exaggeration to say that all industries
can benefit from the use of AI – whether
this be to better understand client behaviour,
optimise manufacturing processes, or suggest
pathways for future business development. AI
is forging a path into all areas of our lives as
both consumers and business leaders.
If your business isn’t yet exploring its
potential benefits, it’s time to get on board.
However, as with all new technologies, there
are certain considerations to
bear in mind.
www.disruptionhub.com / 7
/The age of AI
6 / The age of AI
Although outsourcing some aspects
of AI may be an ideal option for many
businesses, for others it is necessary due to
a lack of available talent. The skills gap is
a well known feature of many technology
fields today, and AI is no exception. In
fact, while many business leaders now
understand the importance of AI a dearth
of skilled workers is one of the biggest
factors impeding widespread adoption.
To counter this growing issue, many
large companies are taking the matter
into their own hands. By undertaking its
own AI talent development programme,
Microsoft hopes not only to upskill and
reskill 15,000 workers by 2022, but also
to create standards and credentials for AI
skills. In a similar effort to standardise the
data scientist – a key AI role – IBM has
created a new certification for data science
employees. Such measures are a welcome
step towards developing the AI talent
pipeline, but they will not succeed without
unified efforts from governments and
academic institutions.
It’s an unfortunate fact that employees
with AI skills – even when you can manage
to find them – are overwhelmingly white
and male. According to Google’s 2018
diversity report, a measly 21 per cent of
all technical roles in the company are held
by women, but this drops to just 10 per
cent in the specific AI field of machine
intelligence. Regrettably, figures are even
worse for employees from diverse ethnic
backgrounds, and these trends are seen
across the board in the AI industry.
While all companies should aim to
support diversity, failing to do so in AI has
particularly negative consequences. As the
use of AI – such as image classification
and facial recognition – comes to underpin
more of the world’s systems, we are at
risk of enshrining bias into dominant
structures of our society. Whether it
originates in the data that programmes
use, or whether it’s unintentionally built
into algorithms themselves, AI consistently
exhibits bias to the detriment of
underrepresented groups. Tackling a lack
of diversity in AI talent is one of the most
impactful ways of countering this problem.
AI talent – mind the gap Although outsourcingsome aspects of AI may be an idealoption for manybusinesses, for othersit is necessary due to alack of available talent
The amount of technical roles held by women at Google
8 / The age of AI www.disruptionhub.com / 9
ccessibility to AI tools has
already become much more
straightforward even in
the last several years. The
advanced mathematical and statistical
techniques that were once the preserve
of highly specialised academics are
increasingly available to mainstream
programmers. Examples abound with cloud
providers now offering programmable
interfaces for face recognition, sentiment
analysis, speech-to-text and other machine
learning algorithms. Open-source software
packages now facilitate widespread usage
of advanced Bayesian analyses – one of
the building blocks of modern machine
learning. Anyone with a modicum of
programming skill can now code up a
passable image-recognising robot using an
open-source software package, trained in
an open-source simulation environment
using publicly available data.
If this “democratisation” of AI continues –
and all of the new (and future) AI use cases
effectively become to some degree “plug-
and-playable” for any moderately sized
organisation – what opportunities are
there and what might the future hold?
Predicting the future impact of technology
is an exercise fraught with danger. We have
a tendency to overestimate the utility of
new tech and underestimate the enduring
nature of human behavioural likes and
dislikes. With this in mind, here are some
thoughts on what such a future might
look like. Firmer than stabs in the dark,
they’re more like sneaking suspicions.
(And, yes, for this thought experiment
we’ll leave aside for now all of the other
real challenges to achieving “Ubiquitous
AI” that the practical-minded will be
clamouring to raise).
1. Re-engineered
Tim Harford wrote (see http://www.bbc.
co.uk/news/business-40673694) of how it
took over forty years from the invention
of the first usable light bulb in the 1870s
before substantial productivity gains
were achieved from the introduction of
electricity into manufacturing. Realising
these gains required overcoming the
capital costs needed to rearchitect steam-
powered factories arranged on the logic
of the driveshaft to ones organised on the
logic of a production line. He similarly
describes how gains from the introduction
of computers took time because “You
couldn’t just take your old systems and
add computers. You needed to do things
differently,” (through decentralisation,
outsourcing, streamlining supply
chains, etc.).
The same will be true of AI. To take a
granular example – beginning a new
banking relationship can be painful for
businesses large and small. Banks are
obliged, in order to be compliant with
stringent anti-money laundering (AML)
regulation, to perform numerous identity
checks and risk assessments. Much
of this work – which involves identity
verification with trusted public sources,
screening global media for adverse events,
performing litigation and bankruptcy
checks, etc. – is today performed manually.
The very first AI “point” solutions are
starting to appear – each automating one
isolated manual step. However, it is only
once the entire process becomes automated
end-to-end that we can contemplate gains
so great that banks might conceivably be
able to at least partially risk score all target
corporate customers in advance of doing
business with them – even potentially
adjusting their sales efforts and pricing
to reflect different levels of AML risk. If
they were able to do so, we would see more
lending decisions made more efficiently
and, arguably, more businesses financed.
In the context of AI, organisations will
need to move beyond merely incorporating
“point” (typically pre-existing vendor)
solutions and develop the competence to
fundamentally rethink processes, and even
their original reason for being.
2. More intermediated
There are reasons to believe that a world
of ubiquitous AI necessarily means a
world of greater intermediation, not
less. A widespread use of more natural
interfaces (voice, gesture, feel, or whatever
the future holds) will bring with it less
tolerance for hearing, feeling, or sensing
a multiplicity of answers or offers to
our requests. The entities that own the
interfaces will inevitably, regulators
notwithstanding, have a greater degree of
control over the selection (and access price)
of content provided even than today. This
will be as true in B2B as B2C. In time, AI
solution proliferation may mean that it
will make more sense for a farm to select
an agricultural equipment provider, for
example, with the best possible in-house
and third party AI solution portfolio (and
best possible environment to attract more)
than to work with a provider offering only
in-house developed solutions.
In this intermediated environment, the
demands for clean “data furnishing”
between organisations will grow. The
quality of the data an organisation can
The Opportunities ofUbiquitous AIDr Tariq Khatri, MD Machinable
Predicting the future impactof technology is an exercisefraught with danger. We havea tendency to overestimatethe utility of new tech andunderestimate the enduringnature of human behaviourallikes and dislikes
AI and machine learning expert Tariq Khatri considers the future of freely available AI.
EXPERT VIEW
10 / The age of AI www.disruptionhub.com / 11
make available to another will become as
important a driver of distribution success
as the quality of the product or service
itself. If your food product doesn’t come
along with its expected accompanying
dataset (dimensions, perishability,
promotion details, ingredients, calorie/
fat/sugar content, recipe options, carbon
footprint, quality certifications...) then the
smart retailer of the future won’t be able to
incorporate it into their AI-enabled
shop experience and your product won’t
cut the mustard.
3. Emancipated
Many are concerned about the impact of
widespread AI adoption on employment.
For better or worse, we’re now at or very
near a point where both individuals
and institutions are making important
decisions (such as parents guiding
the future studies of their children,
or governments and think-tanks
contemplating the need for a universal
income) in part based on anticipated “man
vs. machine” futures.
I think there’s a more optimistic future
in store for us. The short term impact
of “point” AI solutions may be one of
fewer required man-hours of labour in
the original role, but this ignores the
amount of effort required to engineer, sell
and support these solutions. The amount
of data cleansing and processing, for
instance, that goes into developing a single
AI solution can be vast. Setting up and
maintaining a production AI solution in a
context of changing client requirements
and circumstances is equally resource-
intensive. Multiply this by the number
of as-yet-unforeseen new use cases that
“Ubiquitous AI” will engender over the
coming years and in the medium-term the
employment gain will surely be positive.
AI democratisation will in time help to
alleviate the talent scarcity problem of
today – in the future (and to some extent
already) you won’t need a team of in-
house PhDs to develop an AI solution. At a
societal level, employment will be created
for much wider swathes of the population
than are presently involved in AI.
Economic growth is not a “man vs.
machine” zero-sum game. The economist
Ha-Joon Chang wrote that “the washing
machine has changed the world more
than the internet has” as the arrival of
household appliances made it possible
for far more women to join the labour
market and thereby grow production
and consumption. In time, I hope that
AI will be seen to have had a similarly
emancipative effect on the workforce –
replacing, on aggregate, highly repetitive
tasks which have low productivity
with more stimulating and more
productive ones.
4. Artisanal
The scope of applicability of AI today
remains bounded not only by data
availability (you can’t train a model to
detect a medical condition if you don’t
have labelled data of historical cases,
and typically lots of it) but also by severe
limitations in the techniques of AI.
Generally, AI can work well on “narrow
tasks” in “clean” environments. In this
context, “narrow” means performing a
task with a single objective (rather than
multiple diverse objectives – as is the aim
of “general” AI). “Clean” can mean various
things, including, but by no means limited
to, the requirement that the environment
can be realistically simulated so that the
machine can be trained without vast
quantities of real world data.
Not only will AI scope will remain bounded
for the foreseeable future (and certainly I
suspect during my lifetime), but many (if
not most) real world AI solutions will –
whilst being extraordinarily useful – also
be imperfect for some time to come. “Next
best product” recommendation engines
have been around for a good while now, but
unsatisfactory implementations abound.
The phenomenon of Google’s Pixel ear-
buds – which promise real-time language
translation, but struggle with complex
sentences and difficult speaker accents –
will be common to many AI solutions for
a very long time (their notable ability to
learn notwithstanding).
The scope boundary and imperfection
phenomenon mean that there will remain
substantive roles and responsibilities
for humans – most especially in high
consequence decision making. Looking
further out, as machines slowly expand
their generative reach – perhaps one day
originating reasonably compelling film
scripts, architecture, orchestral music,
custom-designed prosthetic limbs – there
will be a large premium on the human
designed... On content and objects that are
perceived as retaining an ineffable human-
ness. The market for the artisanal in the
broadest sense will grow substantially as
AI gains ubiquity.
An unremarkable future
So what will a future world of Ubiquitous AI
look and feel like?
Potentially unimaginable. What I do
know is that societal norms compared
across decades can change radically but,
paradoxically, not in ways particularly
noticeable on the day to day timescales
in which we live our lives. Last summer I
inexcusably interrupted a tennis match to
point out that a drone was hovering above
us, the first I had seen. The other players
looked at me as if I was some kind of dark
age simpleton. Drones are, within the space
of so little time since their introduction,
fast becoming unremarkable. A future
world of Ubiquitous AI will feel as ordinary
and unexceptional to those that live in it
as it seems fantastic to us. We should be
reassured by this.
EXPERT VIEW
www.disruptionhub.com / 13
/The age of AI
12 / The age of AI
people who label data, clean up databases,
and moderate content for AI services.
Often referred to as ‘ghost workers’ due
to the hidden nature of their employment,
these taskforces are the backbone of tech
companies. In digital assistants such as
Google Assistant or Amazon Alexa, for
example – which exemplify the widespread
adoption of AI – teams of human linguists
are required to listen to and transcribe
voice recordings in order to train the
software. What’s more, it might come as a
surprise, but sometimes, what we are led
to believe is AI is in fact actually a person.
Around 25 per cent of Google Duplex calls
originate with a human pretending to be
an AI, and 15 per cent of those which begin
with automation have a human intervene
at some point. In this era of AI, the ghost in
the machine is frequently all too real.
There are several important ethical themes
surrounding the growing use of AI. Fears
that artificially intelligent machines will
take jobs away from people remain, in spite
of evidence that AI automation will actually
create jobs in new areas of business. Trust
in AI is undermined due to the frequent
use of ‘black box’ models, which prevent
people from understanding how an AI has
made its decisions. This leads to calls for
AI programmes to inform people’s decision
making, but to always stay under human
control. This should also help to mitigate
another key area of concern in AI – who
bears responsibility when things go wrong.
In the context of greater consumer
interaction with AI, businesses have a duty
to inform their customers about what kinds
of product they are using. Take chatbots,
for example. In one of the most common
forms of AI, intelligent, automated
assistants are now being deployed by
businesses for customer services purposes.
Chatbots can provide faster responses and
more accurate information than a human
operator, but should a business inform its
clients when they are not actually speaking
to a real person?
This question is rapidly growing
in importance as AI becomes more
sophisticated, and the lines between
human and computer are blurred. At
Google’s developers conference in May
2018, the company unveiled Google
Duplex – an AI service that works with
Google Assistant to make automated
phone calls for its user. Speaking with an
artificial - but very human-like – voice,
Google Duplex can make and cancel dinner
reservations and appointments with a real
person on the other end of the line. The
AI’s use of common features of speech such
as pauses and words like ‘um’ are designed
to make the programme sound realistically
human.
Hey, Google?
While it might be impressive, listening
to a recording of Google Duplex in
action is understandably unsettling, and
the technology raised serious security
concerns. When the company eventually
launched the product in select US cities in
November 2018, notable alterations from
the launch included the voice telling the
receiver of the call that they are speaking
to Google, and that the conversation will be
recorded.
Although artificial intelligence is machine
based by nature, it requires humans to
create it. This gives rise to another ethical
grey area around the working conditions of
AI and ethics – the ghost in the intelligent machine
In the context ofgreater consumerinteraction with AI,businesses have aduty to inform theircustomers aboutwhat kinds of productthey are using
The percentage of Google Duplex calls that actually originate with a human
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/The age of AI
14 / The age of AI
Even though a high proportion of business
leaders are now aware of – and keen to
harness – the benefits of AI, the future is
not all plain sailing. We’ve already seen
how a lack of technical talent and diversity
is hampering AI development, but there’s
one more major area of concern:
compute power.
A study by AI research organisation OpenAI
found that the amount of compute used
in the largest AI training runs has been
increasing exponentially. Since 2012,
required compute power has doubled
every 3.5 months – a significantly faster
rate than the 18 month doubling time of
Moore’s law during this period. This figure
explains the rapid improvements in AI
that we have recently seen, but they also
indicate potential pitfalls ahead. With the
amount of compute needed to power AI
rising so quickly, the corresponding cost
of AI development may exclude all but the
largest industry players.
OpenAI’s research suggests that the
historic trend of growing AI capability
will continue – at least in the short term.
Hardware solutions exist such as using
AI-specific chips and repurposing existing
machinery to do the same number of
operations for less economic cost. However,
rising demand for compute power is still
likely to affect the industry ecosystem.
Dominance by a select group of powerful
companies – to an even greater extent
than in the present day – will probably be a
feature of the AI sector in the future.
Given the unprecedented influence that AI
will have on our lives, this is something
worth thinking about...
Looking to the futureA study by AI researchorganisation OpenAI found that the amount of computeused in the largest AI training runshas been increasing exponentially
Since 2012, compute requirements in AI training runs have doubled every 3.5 months
www.disruptionhub.com / 17
/The age of AI
For businesses seeking to understand their operations, get to grips
with their customers, automate processes, personalise their products,
manage their machinery, improve efficiency – in short – to explore
new business avenues and do existing things better, AI really is
the perfect tool. With entry level AI options in integrated software
packages, today’s AI solutions are available to all companies,
regardless of size or industry.
This means that we will continue to see AI play a major role in
business. However, the talent gap will frustrate many medium to
large companies, which – after convincing leadership that AI is
a necessity – will struggle to fill their vacancies. To ensure that
the talent gap doesn’t begin to cripple the AI industry, business,
governments and academia must come together to find a solution.
The aim of attracting more workers into the field can also be squared
with dramatically improving its diversity levels. Committing to
develop the entire AI talent ecosystem, including the often low
paid and poorly treated ‘ghost workers,’ is a necessary step towards
securing a strong and ethical AI industry. This is a vision of AI which
will benefit our world in the future.
16 / The age of AI
Conclusion
/The age of AI
18 / The age of AI www.disruptionhub.com / 19
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