The Age of AISarah Finch & Tariq Khatri

disruptionhub.com

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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.

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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.

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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

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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

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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

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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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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

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/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

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