artificial intelligence - implementation-hub · 2018. 8. 20. · turing test was devised by alan...

Artificial IntelligenceAre you AI-ready? How will its adoption transform business and what will the expansion of AI mean for society?

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ForewordArtificial intelligence (AI) offers organisations more

opportunities to become smarter, faster – to gain

a competitive advantage. But like other disruptive

technologies, it’s not a panacea for underlying business

problems. You need to know why you want to apply AI.

So rather than asking, “What can AI do for me?” you

should focus on where problems and opportunities lie

and use AI to exploit them.

Whether that’s reducing costs through time-saving

automation, improving customer experience with new

insight or predicting which clients will call you next

and what they’ll ask.

While there’s no one way to resolve a problem with AI,

data is vital. Generate it, gather it or buy it, data is the

lifeblood of AI. But using complex algorithms to find

hidden patterns in data isn’t a solution in itself. Real

impact can only come from determining causation

from correlation, then understanding and exploiting it.

To begin on the AI path, you need a flexible vision of

where you want to end up, as the technology evolves

quickly. More important than high-end computers are

eager and enthused people. Finally, you need a desire

to scale up so when you’ve proven a concept, you fully

exploit it.

So think big, start small and scale fast. And realise that

ultimately, people are the key to making AI a success.

Artificial Intelligence isn’t human intelligence, so

human characteristics will become more valued.

In fact, AI could drive us to be more human.

Dr Lee Howells AI and automation expert PA Consulting

ContentsForeward 3

Executive summary 4

What is Artificial Intelligence? 5

Glossary 6

The future has not been written 10

Investment landscape 11

How business is embracing AI 12

Impacting the workplace 16

The race to regulation 18

Is your business AI-ready? 20

What does AI mean for society? 22

Case studies 24

Key takeaways and considerations 27

Sources 28

/Artificial Intelligence

Are you AI-ready? How will AI’s adoption

transform business and what will its

expansion mean for society?

In this special report, D/SRUPTION draws

on industry trends, statistical evidence

and expert observations to explore the

most challenging questions surrounding

Artificial Intelligence today.

But first thing’s first. What does AI mean?

We start with a concise definition of AI and

a glossary of need-to-know terms, then

quickly go through AI’s development in

a visual timeline.

In the main body of this report,

D/SRUPTION tracks the investment

landscape to understand why AI funding

has increased, where this funding is

coming from and how the market might

develop. Through an examination of

different industry sectors and real-world

examples, we discuss AI’s advantages and

potential applications. We also consider the

AI is an umbrella term that spans a range

of techniques, tools and technologies. The

term was first coined by John McCarthy

in 1956 and refers to a machine’s ability to

replicate human intelligence. While there

is no single theory of intelligence, it can be

understood as the acquisition of knowledge

through thoughts, experiences and senses.

Now that AI has become a catch-all phrase

for both current and future capabilities, it is

important to distinguish between narrow

AI and AGI – Artificial General Intelligence.

effect of AI adoption on the world of work,

including what possible actions employees

can take in order to cope with or mitigate

its disruptive impact.

Then there are the regulatory implications

of AI that have added yet another layer of

complexity to AI adoption, contributing to

growing tensions between governments

and corporations. Can big businesses be

trusted to develop AI applications that

benefit society? D/SRUPTION offers six key

considerations for organisations when

applying an AI strategy and looks at the

implications for society generally. While

the end point of AI’s trajectory is still hotly

debated, expert opinions demonstrate a

notable level of agreement.

Three case studies then demonstrate how

industry leaders are already using AI to

their advantage. After looking at them,

we pose a series of questions that every

organisation should ask as it develops its

own AI vision.

Narrow AI refers to artificially intelligent

tools that achieve a single aim. In its

current form, narrow AI is used to augment

human ability.

AGI, on the other hand, can perform any

task that a human can, with comparable or

higher accuracy. AGI is the technology that

is often associated with an uncertain future

in which AI overtakes human intelligence.


Executive summary

What is Artificial Intelligence?

ArtificialIntelligencetimeline


4 / Artificial Intelligence / Summer 2018

Swiss physician, astrologer and alchemist Paracelsus (1493-1541) claims to have created an artificial man using alchemy

Spanish engineer Leonardo Torres y Quevedo (1852-1936) builds a chess automaton able to play a king and rook endgame

Mathematician John von Neumann (1903-1957) publishes a paper that introduces game theory, the mathematical study of conflict and cooperation in rational, intelligent decisions

Alan Turing (1912-1954) publishes Computing Machinery and Intelligence. This paper questions the ability of machines to think and proposes the Imitation Game (the Turing Test) as a testing method

The first working AI program is written at the University of Manchester. It is capable of playing draughts and chess

American computer scientist John McCarthy (1927-2011) coins the term ‘Artificial Intelligence’

The General Problem Solver is demonstrated at Carnegie Mellon University

John McCarthy invents the Lisp programming language

MIT professor Joseph Weizenbaum (1923-2008) builds ELIZA, an interactive program for English dialogue

1500 1915 1944 1950 1951 1956 1957 1958 1965


Glossary



The AI winter sees commercial and scientific applications decline due to a lack of government investment. AI research continues under different names, including informatics, and machine learning

The Lighthill report advises British governments against investment in AI, leading in part to the ‘AI winter’ of the 1970s

The Fifth Generation Computer Systems (FGCS) project begins in Japan

Ian Horswill creates Polly at the MIT Artificial Intelligence Laboratory. It is the first robot capable of navigating using vision

Checkers-playing computer program Chinook wins the USA National Tournament

IBM’s Deep Blue beats reigning world chess champion Gary Kasparov

IBM’s Watson defeats Jeopardy! champions Rutter and Jennings

In 2011, Apple develops Siri, a mobile-dwelling AI that uses natural language processing to answer questions, make suggestions and perform tasks. In 2012 Google releases its answer to Siri, Google Now, followed in 2014 by Microsoft’s Cortana

Poker AI Libratus beats four human opponents at the Rivers Casino in Pittsburgh, using perception, reasoning and deception

Google’s AutoML creates its own daughter AI called NASNet that can recognise images in videos with the highest accuracy level to date (82.7 per cent)

1970s1973 1982 1993 1994 1997 2011 2011-2014 2017

Algorithm A set of steps – usually computerised –

taken to solve problems. Algorithms can perform

calculation, data processing and automated reasoning.

Artificial General Intelligence (AGI) AGI learns without

supervision. It can perform tasks to at least the same

accuracy as a human. Although not currently possible,

AGI is expected to eventually outperform human

intelligence in every application.

Artificial Neural Networks (ANN) Artificial replicas of

the biological networks seen within brains. ANNs are

a type of machine learning that takes inspiration from

neuron activity to solve problems that are too complex

for traditional programming. Instead of neurons, ANNs

use interconnected nodes to simulate the nervous

system. In their current state, neural networks are far

less powerful than living brains, although they can still

perform such complicated tasks as playing chess.

Automation The process of performing a task without

human assistance. Automation is often used in

conjunction with AI but, at a basic level, uses sensors

and automatic control systems that aren’t necessarily

artificially intelligent. Alongside AI, automation has

fuelled fears over the future of employment levels.

Capsule networks In 2017, British computer scientist

Geoffrey Hinton introduced a new type of neural

network he called “capsule networks”. These are

capable of gathering more information than traditional

networks by using capsules, and are expected to

improve deep learning capabilities.

Convolutional Neural Networks (CNN) Neural networks

used in image recognition and classification, as well as

Natural Language Processing (NLP). CNNs are made up

of neurons that are trained to make classifications by

extracting features from input data, such as an image.

Deep learning Also known as a ‘deep neural networks’,

deep learning uses algorithms to understand data and

datasets. It’s a subfield of machine learning that has

enabled practical applications in image recognition,

speech recognition, natural language processing

and the environmental awareness necessary for

autonomous vehicles. Deep learning feeds data to a

computer via artificial neural networks, aiming to solve

any problem that requires thought.

Expert system Some of the first successful forms of

AI. Expert systems are computer systems that replicate

human decision-making, using reasoning to solve

complex problems.

Conversational interfaces powered

by AI. They live in apps and mainly

handle customer queries.

Chatbot Autonomous systems are able to function without

human intervention using machine learning

techniques. Current examples include autonomous

robots and self-driving vehicles.

Autonomous




Machine learning The methods and algorithms used to

improve the performance of data collecting software.

Although the term is sometimes used interchangeably

with Artificial Intelligence, machine learning is actually

a statistical approach to creating AI. It’s a process of

learning from examples which allows machines to

adapt to new data without reprogramming. Machine

learning methods include pattern recognition, natural

language processing and data mining.

Narrow AI In contrast to AGI, narrow AI carries out

a single task. Narrow AI describes the artificially

intelligent tools used by organisations today to augment

the human workforce.

Natural Language Processing (NLP) Through NLP,

machines are able to understand human language.

The way people communicate is typically full of

nuances and colloquialisms that are hard for software

to comprehend, yet tech giants are already heavily

invested in improving voice search. Google, for

example, aims to reach human level accuracy in the

NLP systems used in Google Home devices.

Pattern recognition A branch of machine learning

that assigns a label to an input value. Systems can

be trained with labelled data (supervised learning) or

discover patterns on their own without ready-made

labels (unsupervised learning). Image recognition is an

example of pattern recognition, in which an algorithm

identifies features in an image.

Predictive analytics This uncovers patterns in

structured and unstructured data to discern the

likelihood of future events. The technique uses a

combination of methods that include machine learning,

data mining and predictive modelling, to make

predictions based on current and historical information.

Prescriptive analytics In the same way that predictive

analytics predicts what might happen through

analysing data, prescriptive analytics uses this

information to offer a relevant course of action. For

example, if a supply chain is likely to handle higher

demand for a certain item, prescriptive analytics would

advise an increase in production.

Technological Singularity The Technological

Singularity is the prediction made by American writer,

inventor and futurist Ray Kurzweil (below) that AI will

eventually outpace human intelligence.

Turing Test Also known as the Imitation Game, the

Turing Test was devised by Alan Turing (above) in

the early 1950s as a way to determine whether any AI

developed could ever pass as human. The most simple

iteration of the test involves three players. If Player C,

the human interrogator, is unable to work out whether

Players A or B is the machine, then the machine has

passed the test. As of yet, no AI has ever passed the

Turing Test.

Glossary

Neural NetworksInformation processing systems modelled

on human and animal biology. They

imitate neurons in the brain via connected

nodes, enabling computers to learn from

observational data.

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The future hasnot been written

Investment landscape



All eyes are currently on AI and it can

never be watched too closely, if the

concerns of sceptics like technology

billionaire Elon Musk are any indication.

The business tycoon has repeatedly warned

of the dangers of AI, even going so far as to

suggest that the creation and installation

of a safety “kill switch” for all AI-powered

machines would only further antagonise

any super-powered system.

On the other side of the AI debate sits

another billionaire – Jeff Bezos. The

Amazon CEO wants the field of AI to

keep expanding and believes there is

no institution that can’t be made better

through machine learning technology.

Before either Musk’s gloomy vision or

Bezos’ optimistic predictions are realised,

AI has a lot of growing up to do. We are

still far from the development of a super

powerful Artificial General Intelligence

(AGI). However, AI that can learn and

complete specific tasks to the same skill

level as humans is already shaping many

parts of industry and government. How we

all approach the next generation of AI will

play a major part in shaping how humans

work and live in the future.

The market for AI is in an important

transitional phase. Corporations and

venture capitalists certainly view AI

technology as a top spending priority,

and 2017 saw venture capital in AI

double[1]. It would seem that companies

are overcoming their initial wariness

surrounding the technology and are now

vying to implement the most effective

solutions. AI has become a buzzword in

business, so using (or attempting to use)

AI has become increasingly important to

remaining competitive.

Regardless of the industrial sector,

businesses are currently locked in an

AI arms race. Governing bodies are

also competing to boost AI adoption in

everything from research to commercial

applications. The battleground is currently

dominated by China and the US and,

according to KPMG, China accounted for

five of the world’s biggest venture capital

investments in the fourth quarter of 2017.

CB Insights research also shows that China

has surpassed the US in equity funding by

10 per cent. In other words, the US lead in

AI is no longer so certain.

As of April 2018, the European Commission

has announced plans to invest a further

€1.5bn[2] in AI to catch up with the US and

Asia. This increased funding is likely to

fuel the development of AI-associated

solutions and strategies, building trust

and confidence in the technology. Statista

research, published in September 2017,

shows that worldwide market revenue has

grown exponentially and suggests that this

will continue.

Although investment has grown, market

forecasts vary widely. According to

Tractica, predictions for 2025 range from

$644m to $126bn. Statista’s research sits

somewhere in the middle at $60bn.

Photo: Steve Jurvetson, Flickr

Jeff Bezos, CEO, Amazon

“There is no institution that can’t be made better

through the application of machine learning”

2016

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[1] https://bit.ly/2DBg5I8 [2] https://reut.rs/2r3UD6G

Source: Statista

Worldwide AI market revenue & predicted growth

www.disruptionhub.com / 1312 / Artificial Intelligence / Summer 2018

[3] https://bit.ly/2IWKDUd [4] http://www.croptix.solutions/

Retail

Major corporations such as Amazon and Ocado offer

clear examples of scenarios in which AI is the right

solution for consumer-facing companies. Amazon has

pursued a number of AI-powered projects, including

deep learning for recommendations, smart robotics

and, of course, its flagship AI venture, Alexa. Through

Amazon Web Services, the ecommerce leader sells

its machine learning platform to external parties,

generating revenue as well as more data. Data is

paramount to a successful AI vision, which largely

explains why big tech companies, with their keen

interest in consumer data, dominate the market.

Ocado, for example, uses AI to categorise customer

enquiries and connect its warehouse robots.

Aerospace

Lockheed Martin has applied AI to real-time,

autonomous systems that track engine health and

connect unmanned vehicles. The company’s Artificial

Intelligence Laboratory[3] develops AI solutions that

monitor and manage both piloted and pilotless aircraft,

automatically detecting system failures or threats

before assessing their impact.

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Despite the rush to invest, no one really knows just

how transformative AI will be. According to Andrew

Burgess, AI advisor and strategist for Symphony

Ventures, AI’s business impact will go far beyond

the cost-saving scenarios envisaged by the more

conservative voices.

“AI is about providing greater insights and more

informed decisions,” says Burgess. “AI extracts value

from the data within your business. If you look at

Google, they used AI on their own data centres to

predict heating demand and they were able to reduce

the cooling requirements of those centres by 40 per

cent. If you expand that to tens of thousands of data

centres, not only are you saving money but you’ve got

the environmental benefits as well.”

AI looks set to reimagine central operations and has

disrupted supply chains and end-to-end processes

across many sectors. This has been encouraged by

convergence and the open source movement and

been augmented by collaboration with other new

technologies such as Big Data and the IoT. By fuelling

AI with appropriate data, it is altering many sectors…

How business is embracing AI



Healthcare

Sometimes, data can be as much of a hindrance as

a help. This is especially the case for industries that

handle sensitive information, such as healthcare.

“Healthcare is certainly interested in AI and how they

can use it to improve services,” explains Lee Howells,

Artificial Intelligence and automation expert at PA

Consulting. “The problem with healthcare adopting

any new technology is that they have to be very, very

careful. It’s not that they might lose money, it’s that

somebody might die. That’s a very different problem

to deal with.”

In one innovative project, healthtech startup Woebot

Labs[5] has developed an AI-powered chatbot that uses

clinical techniques such as CBT to monitor patients’

mental health[6].

[5] https://woebot.io/ [6] https://bit.ly/2sife8t

Manufacturing

Deep learning and machine learning enable the

interrogation of data flows from machinery in order

to examine history and performance. This way,

the algorithm can predict how likely the machine

is to break down and when that might happen. By

recognising such patterns, AI provides insight as well

as foresight, in that the manufacturer is able to carry

out maintenance and repairs on machinery before

faults occur, yet also evaluate the specific reasons for

each failure.

Financial services

The financial services sector has been one of the

quickest off the mark when it comes to adopting AI.

Deutsche Bank uses AI to monitor dealers’ calls with

clients in order to identify fraud or noncompliance.

The AI listens to every call, mitigating huge amounts

of risk. AI is also being trialled to help combat money

laundering, especially in the important but time

intensive area of Know Your Customer (KYC).

Technology

AI is enabling developments in computing by bringing

far greater capabilities to the cloud. To meet growing

demand for cheap computing power and data

storage, the cloud has become artificially intelligent.

This has given rise to a range of cloud computing

services provided by the likes of Amazon, Google, and

Microsoft, the tech giants at the top of the AI pack.

Through these vendors, AI itself has become a service.

Agriculture

Machine learning is being utilised to provide remote

monitoring and alerts, to recommend optimal levels

of water and pesticides, and improve crop yields

with data forecasts. In emerging economies, AI is

equipping smallholders with the predictive tools to

navigate volatile natural environments. CROPTIX[4], a

Pennsylvania State University spin-off, uses predictive

analytics to detect potential disease in plants,

preventing crop failures.

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Impacting the workplace



In time, there will be AI for everything. This

is both exciting and unsettling because

once AI can do anything, what will be the

point of having human workers?

Until a few years ago, certain jobs were

considered to be ‘safe’ from automation

and it was the blue collar workers carrying

out repetitive manual labour that were

expected to be replaced by automation and

robots. Yet AI is starting to prove that it’s

every bit as good at carrying out repetitive

tasks in the white collar world as it is on

the factory floor. AI can already write news

articles, settle legal disputes and diagnose

diseases. Any job can and will be impacted

as AI becomes more mature and prevalent

in business.

However, walk into any corporation’s

headquarters and the most obvious thing

you will notice is still… people. So many

industries (hospitality, healthcare, and

retail, to name a few) rely heavily on the

face-to-face connections between humans.

AI may be able to replace certain roles but

there is likely to be an even greater need

for human employees who can collaborate

successfully with the machine.

Fewer roles or newer roles?

Without doubt, many roles within the

workforce are set to change over the

next few years, so businesses will have

to navigate this period carefully through

reskilling and recruitment. Any employee

able to delegate certain tasks to an

algorithm can then move beyond their

traditional role, becoming more creative,

and making more valuable contributions.

Employers who recognise the value of

reskilling will foster a collaborative

relationship between human workers and [7] http://www.jfgagne.ai/talent/

AI. Instead of replacing people, AI will

provide them with invaluable tools.

Employees, much like the supply chains

they work on, are becoming increasingly

connected. Robotic Process Automation –

RPA – is a class of software that mimics

human operators to carry out rules-based,

repetitive actions. RPA is often confused

with AI and while they are different, they

can be used in conjunction to great effect.

Humanyze offers ‘people analytics’, a term

coined by company CEO Ben Waber, to

track employees via biometric ID badges.

People analytics enables employers to

understand their workers by recognising

patterns in data. Whether or not

‘understand’ is synonymous with ‘control’

is another discussion entirely.

The scramble for talent

Perhaps the most important of the new

generation of jobs created will be those that

create AI, run the programs and determine

whether or not it is appropriate to use AI

in the first place. The difficulty, of course,

is recruiting and retaining these talented

individuals. Estimates of the number

of people with the expertise to create

machine learning systems vary wildly,

from thousands to hundreds of thousands.

Recent research by Jean-François

Gagné, based on LinkedIn and academic

conferences, suggests that there are 22,000

PhD-educated researchers[7] worldwide

capable of working in AI, with just over

3,000 actively looking for work.

Regardless of which estimates are most

accurate, demand for AI skills clearly

exceeds supply. Organisations are therefore

locked in an ongoing battle for AI talent,

often poaching experts from competitors

or academic institutions. After finding that

attracting individuals was not their strong

suit, the AI enthusiasts at Amazon adopted

a strategy of acquisition. In 2013, the

company bought Ivona, a text-to-speech

startup that literally gave Echo a voice.

Creating a successful and harmonious

human and AI workforce will take more

than smart programmers, however. It will

involve a wholesale review of corporate

culture and governance so that both

humans and machines have trust in work

being produced, in the way they are being

managed and in the way that the company

values their input.


The raceto regulation



Across the world, governments are

demonstrating an interest in AI. In many

ways, this reflects a growing realisation

of just how disruptive AI technologies will

be, along with a recognition of regulatory

needs. In a recent report[8] by the UK House

of Lords’ Select Committee on AI, members

considered the economical, financial

and social implications of AI. The report

concluded that the UK was in a strong

position to become a global AI leader,

providing it could continue to uphold

a healthy startup ecosystem, dynamic

academic research and to design unbiased

AI. The report’s recommendations include

a growth fund for SMEs working on AI

and greater efforts to shape data culture.

Regulators, however, are generally viewed

as hindering rather than enabling

this development.

This is especially the case when it comes

to taxation. Business leaders certainly

think that it is a hindrance to growth but

how to measure impact. I would say that

we should never ever solely rely on private

companies to self regulate, or trust them to

always consider the wider impacts of what

they do.”

Too much power?

If we look at who controls AI, we are drawn

back to major vendors. What can be done to

ensure that these companies continue their

development in a way that benefits wider

society? Taxation applied equally and fairly

would be one such way. Another would be

the compilation of universal standards.

This is not necessarily something that

can or should be done by governments

alone. Collaboration between the private,

public and non-governmental sectors will

be crucial in developing acceptable and

useful AI standards. The Partnership on

AI,[10] for example, is a non-governmental

organisation set up to study and formulate

the best practices on AI technology for

everyone’s benefit. The Partnership

combines the expertise of developers,

regulators and representatives from various

industry sectors to create an open platform

for discussion.

As AI is gradually distributed throughout

society, such need for controls will become

even greater. But a serious obstacle for

globally focused regulators is the existence

of cultural differences between countries.

This is as much a problem on an individual

as an organisational level. To encourage

‘ethical’ or ‘moral’ AI, regulators are faced

with the challenge of creating a universal

code of conduct. Perhaps this is where

corporations will lead governments, by

adopting collaborative strategies and

supporting the Open Data movement.

recognise that it is unavoidable in order to

fairly distribute wealth. Friction between

governments and corporations[9] is hardly

anything new but the wider application

of AI could bring a plethora of underlying

tensions to the fore. Taxation is just one

point of contention but governing bodies

have also conflicted with tech companies

over data privacy, and undoubtedly view

their power as a threat.

The ultimate aim of AI regulators is to

encourage the benevolent development and

application of AI. In order to function in the

real world, highly capable AI also needs to

be ethical.

According to Harry Armstrong, Head of

Technology Futures at Nesta, AI’s ethical

value is determined by how it is used.

“There is definitely a willingness and

a keenness by big companies to use AI

ethically,” he says. “But there is a certain

lack of knowledge about ethics, and about

[8] https://bit.ly/2vhDmfr [9] https://bit.ly/2smCkKB [10] https://bit.ly/2IXO90F

The ultimate aim of AI regulators is to encourage the benevolent development and application of

AI. In order to function in the real world, highly capable AI also needs to be ethical.

Is your business AI-ready?Businesses are beginning to recognise the

extensive benefits that AI can offer but

taking full advantage of them requires a

deliberate reorganisation of infrastructure,

processes and corporate culture. Incumbent

businesses, especially those from

traditional industries, are particularly risk

averse. Getting over this initial hurdle

requires research, as well as a clear vision

of what problems need to be solved.

Dr Tariq Khatri, managing director of

machinable (www.machinable.com), works

with senior management teams across

sectors to identify and realise machine

learning opportunities. While his clients

are keen to solve an array of problems

within their operations using AI, he finds

himself repeatedly pointing out that AI

may not always be the best solution.

“Businesses that haven’t used analytics

much are thinking about how to use AI,”

says Khatri. “Part one of the answer is

that, more often than not, you don’t need

advanced models to solve the question.

Part two is that the problem can’t always

be solved with analytics only. I would say

for most of our clients that the answer is

20 per cent machine learning, 30 per cent

yesterday’s data science and 50 per cent

just good business management practices.”

How then can businesses make themselves

AI-ready if technology is just one part

of the equation? Here are five key

considerations:

1. Create a healthy data culture

At the most basic level, organisations

must make a conscious effort to create

a healthy data culture. Employees need

to understand the importance of data,

how it should be stored and the insights

that it can bring. The better the data,

the better the decisions. Companies can

achieve this by training their workforces

to think differently about data. Given the

availability of Massive Open Online Courses

from sites such as Coursera, this can be

done with minimal resource expenditure.

2. Choose the right vendor

The accessibility of AI through cloud

computing services has widened the scope

for business applications but choosing

the right vendor is still crucial. External

providers will compete to offer ease of

use, algorithmic ability and user-friendly

interfaces. Companies should prioritise

choosing an appropriate vendor, perhaps

even working with multiple vendors to cut

time-to-market.

3. Match overall objectives to AI vision

Before an organisation can realise a

successful AI strategy, it has to build

a clear vision of how AI is going to fit

within operations. Deciding if AI is the

right technology to invest in depends on

what problems the organisation wants

to solve. Technology should not be used

for technology’s sake, only to inform

important decisions.

4. Get the balance right

Businesses should consider the complex

interplay between humans and machines.

Once AI has been implemented,

management teams will need to maximise

the potential of working relationships

between existing employees and AI

systems. This might require the creation

of new roles, including a new generation

of Chief AI Officers.

5. Build AI trust in the organisation

As AI becomes more proficient in

understanding business functions, its

algorithms and thinking will become

more opaque and, in many cases, develop

beyond human understanding. Such a

level of machine learning will raise serious

questions around trust in AI – especially

when crucial decisions are being made

from data processes that human employees

don’t fully understand. For executives and

employees to have faith in their own AI

systems, companies will need to develop

governance structures that considers the

actions of machines in the same way that it

does its human workforce.

Each of these conscious changes will have a

domino effect as AI gradually infiltrates company

infrastructure. Friction points will be removed,

which will streamline supply chains, connect

different branches of the business and allow data-

driven decisions based on in-depth knowledge. The

end result? Insight and foresight will facilitate the

construction of coherent, cooperative systems.

For the most part, these are not the systems that

businesses use today. But by choosing the right

vendor, marrying overall objectives with an AI vision,

addressing data culture, augmenting the workforce

with AI tools and shaping an inclusive system of

governance, operational efficiency can reach an

entirely new level.




What does AI mean for society?

ILLO

The real societal value of AI relies on the

technology being able to answer relevant,

real-world questions. AI integration will

reduce friction and meaningless labour.

It will take passengers from A to B in self

driving vehicles, gather constant data from

connected items to inform predictive and

prescriptive analytics, reallocate certain

job roles, integrate the physical and digital

worlds, and transform computational

ability. Artificially intelligent robots and

services will go from novelty to necessity.

More people will want – and need – to

use AI. However, whether or not wider

adoption will be socially beneficial depends

on governance and regulation. Ironically, it

is not technological capability that presents

the biggest obstacle.

At present, encouraged by Hollywood

blockbusters, society largely fears AI. Many

companies can create a highly sophisticated

AI solution but will reap little reward if

nobody is willing to use it. Addressing

such attitudes is a necessary prerequisite

of accelerated adoption, but how can it be

done and who is responsible?

The education system would be a good

place to start. By changing culture

through education, fear can be replaced

by understanding. Coding, for instance,

has now become part of the British school

curriculum. Open conversation can also

demystify AI technologies. Socio-political

debate will characterise the ongoing

development of AI solutions and services

as cultures clash. Russia and China, for

example, have entirely different standards

when it comes to data protection. Bringing

divided opinions under a single body of

legislation is a huge dilemma faced by AI

enthusiasts everywhere.

Jobs vs technology

Perhaps the biggest hurdle to AI acceptance

lies in its impact on the world of work. As

business gradually transitions towards

automated and tech-enabled professions,

uncertainty over unemployment will only

increase. Some jobs will be entirely lost to

automation and their previous occupiers

may not have the resources or opportunity

to reskill. How governments adapt their

social systems to accommodate and reward

the growing number of citizens who no

longer work in the traditional sense will be

critical to the widespread acceptance of AI.

So to revisit the debate between the

wary Elon Musk and the unashamedly

enthusiastic Jeff Bezos, which Silicon Valley

behemoth do we side with? The answer,

as it often does when weighing up two

extremes, lies somewhere in the middle.

Certainly, AI continues to pose considerable

challenges for every organisation. However,

in the words of David Sharp, Head of

Technology 10x at Ocado, “You should be

more fearful about not implementing AI.”




To revisit the debate between the wary Elon Musk and the enthusiastic Jeff Bezos, which Silicon Valley behemoth do we side with?


[11] https://on.bp.com/2wQrdyX [12] https://bit.ly/2H1FokC

Case study Case study

Beyond Limits is an AI startup that

is commercialising industrial grade

AI technology developed by NASA for

deep space exploration. Following a

$20m investment[11] from BP Ventures,

the company’s cognitive computing

software is now moving to the energy

sector to offer operational insight and

process automation.

Says Paul Stone, Technology Director

at BP, “An important challenge that

Beyond Limits can help BP to solve

is codifying knowledge and to make

systems behave more human-like so

they can adapt to changing situations.

Up until now, our systems have been

quite rigid in terms of their objectives.

If the environment or situation

changes, they become less effective.”

Stone explains that Beyond Limits’

previous work in space exploration

has a number of parallels with the

oil and gas industry, including, for

example, communication difficulties,

time delays, harsh environments and

problems with equipment. Building

systems that behave and make

decisions in a more human-like way

helps to address such challenges by

mimicking the creative problem-

solving capabilities of humans and

combining these with the speed and

accuracy of computers.

“For instance,” says Stone, “If the

cognitive computing system is looking

at data and decides that some is

missing, it can make a confidence-

based decision in the absence of all

the information, or it can look to use

data from an analogous situation.

Becoming more ‘human-like’ means

that computer systems will be able to

make decisions using imprecise data

and in ambiguous situations.”

www.beyond.ai

As a public video sharing site, YouTube

needs to be constantly vigilant about

the content it publishes. In order to do

this in the face of a constant deluge

of uploads, the website has relied on a

combination of machine learning and

human agents to flag inappropriate

or unsuitable videos. At the start of

2017, eight per cent of videos that were

tagged for extremism were removed

with under 10 views. Once machine

learning capabilities were added in

June, over half of the videos removed

for extremism had received less than

10 views. In other words, machine

learning techniques mean that far

fewer inappropriate videos reach any

viewers at all.

According to Google’s quarterly

Transparency Report, YouTube

removed 8,284,039 videos between

October and December 2017. Of this

number, almost 6,700,000 were

identified by automated tagging

systems. The vast majority (76 per

cent) were removed before anybody

had a chance to view them. The

remaining videos were tagged by the

Trusted Flagger programme and by

YouTube users themselves.

Instead of replacing employees,

machine learning algorithms enhance

the work that humans do to put out

relevant and appropriate content. Now

that more consumers are foregoing

TV[12] in favour of online viewing,

applying AI to entertainment and

content will help to ensure a positive

user experience.

www.youtube.com

6,685,731

1,131,962

402,33563,938 73

IndividualTrustedFlagger

Automated flagging

User NGO Government agency

Source: https://transparencyreport.google.com/youtube-policy/overview

Methods by which videoswere removed from YouTubeOctober - December 2017

Key takeaways and considerations

How can you make sure that your employees understand the importance of data and how it fits into an overall AI vision?

Have you chosen the right vendor to offer ease of use, algorithmic ability and a user-friendly interface?

Does AI complement or confuse your business’ overall objectives?

Do you have an appropriate management team to navigate the complex interplay between humans and machines?

Have you created a governance structure that enables you and your employees to trust AI to make crucial decisions?


[13] https://bit.ly/2kyBjLR

Case study

Online grocery company Ocado has

been quick to adopt AI to enhance

various parts of its business. One of

the ways that the technology has been

applied is as an administrative aid for

responding to customer enquiries. By

‘triaging’ emails – making decisions

about the order of treatment –

machine learning can distinguish

between the most and least urgent

messages. For example, if a customer

changes their delivery slot to be an

hour later on the day of delivery, this

needs to be handled immediately so

that the order can be successfully

processed and received.

“A machine learning algorithm can

learn how to do this by reading lots of

emails that a human has categorised

as being urgent or not urgent,” says

David Sharp, Head of Technology 10x

at Ocado. “Over time, we expect more

and more parts of Ocado to be using

machine learning and AI.”

Ocado also uses AI to flag suspicious

activity or anomalies in transaction

data. In March, the company

announced the development of the

first fraud detection system for online

groceries[13]. The machine learning

algorithm, created using TensorFlow

and Google Cloud, can recognise if an

order has been delivered but not paid

for. Since the system was introduced,

Ocado’s fraud detection precision has

improved by 15 per cent.

www.ocado.com

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CBInsights Report Top AI Trends

to Watch in 2018

https://bit.ly/2L7YuYq

Jean-François Gagné, Global AI

Talent Report 2018

http://www.jfgagne.ai/talent/

House of Lords Select Committee

on AI, April 2018, AI in the UK: Ready

Willing and Able

https://bit.ly/2vhDmfr

KPMG, Venture Pulse Q4 2017

https://bit.ly/2DBg5I8

McKinsey Report, June 2017, Is AI the

next digital frontier?

https://mck.co/2iCPq53

The Economist, Special AI Report,

March 2018, GrAIt Expectations

https://econ.st/2s4ZQvw

University of Washington paper,

December 2006, The History of AI

https://bit.ly/2y1KCgw

Sources

Mental health Woebot https://bit.ly/2sife8t

AI cheat sheet https://bit.ly/2LHBzEw

Government and Big Tech https://bit.ly/2smCkKB

D/SRUPTION resources


disruptionhub.com

This report was produced in partnership with D/SRUPTION’s founding partners:

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