artificial intelligence (ai) - statista · impact of artificial intelligence on the gross domestic...

October2018

ARTIFICIAL

INTELLIGENCE

(AI)

Economic Impact of AI

▪ GVA, GDP & jobs

▪ Market size

AI Investment Today

▪ Venture capital and funding

▪ Major deals

Leading Companies

▪ Investments

▪ Patents & papers

Applications & Industry Impact

▪ Major use cases

▪ Industry & sector impact

01

02

03

04

05

06

Agenda

Leading Countries

▪ Experts, papers, patents

▪ Funding & companies

AI Technologies & Enablers

▪ AI types & categories

▪ AI frameworks/computing & semiconductors

07 Summary & Outlook

2

Digital transformation, transhumanism, Internet of Things, connected world, smart everything, Industry 4.0, nanotechnology,

biotechnology, quantum computing, big data, 5G, automation, smart robots…. Is your head spinning yet? Don’t worry, we got one more

for you: artificial intelligence (AI). Welcome to the cognitive era.

Today the world is in an age of fundamental change that by some is considered the Fourth Industrial Revolution and also referred to as

the cognitive era. Artificial intelligence is at the center of this development as fully fledged AI has the potential to disrupt every industry in

the economy and basically all aspects of human life within the next 20 to 50 years.

Currently, artificial intelligence is in an era of exploration where new technologies and ideas are emerging constantly. It is transitioning

from the development of underlying theoretical concepts (e.g. machine and deep learning, neural networks) to having a real-life impact

across a multitude of industries, verticals and products. This includes fields such as health care, retail & e-commerce, transportation,

finance, national security, energy smart cities and much more.

Virtual digital assistants such as the Google Assistant and Apple’s Siri are already part of the consumer market’s mainstream.

Autonomous driving is expected to fundamentally transform transportation; and applications such as robot-assisted surgery, virtual

nursing assistants, and medical imaging will have a strong impact on the healthcare market

This dossier provides insights into the potential economic impact of AI and current investment trends, including which countries are in

the lead, major companies, and the enabling technologies. Major use cases and applications of AI across several industries and

verticals are also covered.

Executive summary

3

Artificial intelligence timeline

1943 Warren McCulloch / Walter Pitts conceive the first neural network

1950 Turing test: test of a machine‘s capability to exhibit intelligent behavior equivalent to, or indistinguishable from, that of a human

1956 John McCarthy coins the term Artificial Intelligence (Dartmouth Summer Research Project on Artificial Intelligence (DSRPA)

1959 First definition of machine learning by Arthur Samuel („Field of study that gives computers the ability to learn without being explicitly programmed“)

1974-1980 First „AI winter“ – lack of progress leads to substantial cuts in funding

1975 Kunihiko Fukushima develops the first true multilayered neural network

1987-1993 Second „AI winter“ period – market for specialized AI hardware collapses; funding is cut once again as ambitious goals are not met

1997 IBM‘s Deep Blue defeats Gary Kasparov, the world‘s chess champion at the time

2006 Geoffrey Hinton shows how neural networks can be improved by adding more layers to the network (deep learning).

2009 Andrew Ng describes how GPUs can be used to accelerate the mathematical calculations required by convolutional neural networks (CNNS)

Mid-2010s Beginning of the cognitive era

2011 IBM‘s Watson Q&A machine wins Jeopardy!

2011 Apple introduces first virtual digital assistant Siri to the market

2014 Amazon launches Alexa

2016 AlphaGo (Google DeepMind) beats Lee Sedol

2017 China‘s Ministry of Industry and Information Technology (MIIT) publishes its Next Generation Artificial Intelligence Development Plan

2018 „Edmondde Belamy“ is the first work of art created by AI sold at an auction (price $432.5k)

2020/21Major car manufacturers (like Daimler, BMW, Ford, Honda, Toyota, Volvo, Hyundai, Renault-Nissan) aim to have highly-automated cars ready for the

market

2025 Quantum computing

4

Economic Impact of AI

▪ GVA, GDP & jobs

▪ Market size

01

Artificial intelligence will have major economic impact

Creating a reliable forecast that estimates the economic impact of artificial intelligence using

numbers alone is impossible. Evaluation and comparison of industry forecasts show that the

overall market for artificial intelligence is projected to generate tens of billions in revenue by the

mid 2020s.

Forecasts on the impact of artificial intelligence (in dollar value) may differ, but one thing everyone

agrees on is that artificial intelligence will be a disruptive force – not only across every industry and

sector, but for society as a whole. Projections show that by 2030 artificial intelligence has the

potential to enhance the gross domestic product by ten percent or more. This is mainly through

product enhancements and productivity gains.

China and the United States are set to benefit the most from the continuous advancement of

artificial intelligence and its neighboring technologies. For example, increased productivity from

labor substitution in the United States is projected to increase the country’s GDP by 15 percent by

2030.

The effect of artificial intelligence will be felt strongly in the job market, impacting employment

across many industries. For instance, around 70 percent of jobs in the transportation and logistics

industry in North America are at high risk of automation by 2030. Additionally, the share of

machine working hours is forecast to increase by ten or more percent across most work tasks and

activities.

“The new spring in AI is the most

significant development in

computing in my lifetime. Every

month, there are stunning new

applications and transformative

new techniques. But such

powerful tools also bring with

them new questions and

responsibilities.”

– Sergey Brin, Google co-

founder and President of

Alphabet

6

Market size and revenue comparison for artificial intelligence worldwide from 2016 to 2025 (in billion U.S. dollars)

Artificial intelligence (AI) market size/revenue comparisons for 2016 to 2025

Note: Worldwide; 2018

Source(s): Grand View Research; MarketsandMarkets; IDC; Tractica; Frost & Sullivan; Statista; UBS

0

50

100

150

200

250

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ma

rke

t siz

e in

bill

ion

U.S

. d

olla

rs

IDC (September 2018) Tractica (June 2018) MarketsandMarkets (February 2018)

Grand View Research (July 2017) Frost & Sullivan (November 2017) Rethink (July 2018)

Allied Market Research (September 2018) UBS (January 2018)

7

Potential aggregate economic impact of artificial intelligence worldwide in the future (in trillion U.S. dollars)

Global potential aggregate economic impact of artificial intelligence in the future


Source(s): McKinsey

0.4 0.4

0.3

0.2

0.3 0.3

0.2 0.2 0.2 0.2 0.2 0.2

0.1 0.1 0.1 0.1 0.1 0.1 0.1

0.8

0.5 0.5 0.5

0.4 0.4

0.3 0.3 0.3 0.3 0.3

0.2

0.3

0.2 0.2 0.2 0.2

0.1

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Ecn

om

ic im

pa

ct in

tri

llio

n U

.S.

do

llars

Low estimate High estimate

8

Projected increase of GDP due to artificial intelligence by industry sector in 2030

Impact of artificial intelligence on GDP worldwide as share of GDP 2030


Source(s): PwC; Statista estimates

12%

9%

7%6.5% 6.5% 6.5%

6%

8.5%

6%

5.5%5.5% 5.5%

4%4%

0%

5%

10%

15%

20%

25%

Other public and personalservices

Consumer goods,accomodation and food

services

Technology, media andtelecommunications

Energy, utilities and mining Manufacturing andconstruction

Transport and logistics Financial and professionalservices

GD

P in

cre

ase

GDP gains associated with product enhancements GDP gains associated with productivity

9

Impact of artificial intelligence on the gross domestic products (GDPs) worldwide in 2030, by region (in percent/trillion U.S. dollars)

Increase of GDPs globally due to artificial intelligence 2030


Source(s): PwC

26.1

14.5

9.9

11.510.4

5.4 5.6

7

3.7

1.80.7 0.9 0.5

1.2

0

5

10

15

20

25

30

China North America Northern Europe Southern Europe Developed Asia Latin America Rest of world

GD

P g

row

th in

pe

rce

nt a

nd

tri

llio

n U

.S.

do

llars

Regions

GDP growth due to AI in % GDP growth due to AI in trillion U.S. dollars The mature economies of the United States,

Europe, and Asia (Japan, South Korea,

Australia, Singapore etc.) are forecast to

profit from the development and application

of artificial intelligence on somewhat the

same level. China is projected to maximize

the potential of artificial intelligence due to

its more dynamic overall economic growth

and the Chinese’s government strong focus

on it as a national strategy (more details on

China‘s AI policy in country chapter).

10

Incremental GDP increase based on impact of artificial intelligence by economic driver in the United States by 2030

Economic impact of AI on GDP in the United States by 2030, by driver

Note: United States; 2018

Source(s): ITU; McKinsey

4%

15%

10%

2%

6%

37%

-8%

-7%

21%

-15% -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% 40%

Augmentation

Increased productivity from labor substitution

Innovation / market extension

Global data flows

Wealth creation / reinvestment

Gross impact

Transition costs

Negative externalities

Net impact

GDP increase

11

Share of jobs at high risk of automation by 2030, by region and industry sector

Share of jobs at high risk of automation by region and industry by 2030



0%

10%

20%

30%

40%

50%

60%

70%

80%

North America Northern Europe Southern Europe China Developed Asia Latin America

Sh

are

of jo

bs a

t ri

sk

Energy, utilities and mining Manufacturing and construction Consumer goods, accomodation and food services

Transport and logistics Technology, media and telecommunications Financial and professional services

Other public and personal services

12

19% 19%

23%

28%

31%29%

34%36%

47%

28% 29%31%

44% 44%46% 46%

55%

62%

0%

10%

20%

30%

40%

50%

60%

70%

Reasoning anddecision making

Coordinating,developing,

managing andadvising

Communicating andinteracting

Administering Performing physicaland manual work

activities

Identifying andevaluating job-releant

information

Performing complexand technical

activities

Looking for andreceiving job-related

information

Information and dataprocessing

Sh

are

of w

ork

ing

ho

urs

2018 2022

Note: Worldwide; November 2017 to July 2018; 313 Respondents; companies

Source(s): World Economic Forum

Ratio of machine working hours 2018 to 2022, by task

Ratio of machine working hours by task 2018-2022

13

Change of hours worked in 2030 compared to 2016 in the United States and Western Europe, by skill level

Change in amount of hours worked by skill set in 2030 compared to 2016

Note: Austria, Denmark, Finland, Germany, Greece, Italy, Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States; 2018

Source(s): ILO; McKinsey

-16%

-17%

7%

22%

52%

-11%

-14%

9%

26%

60%

-30% -20% -10% 0% 10% 20% 30% 40% 50% 60% 70%

Physical and manual skills

Basic cognitive skills

Higher cognitive skills

Social and emotional skills

Technological skills

Percentage change in working hours

Western Europe United States

Automation is considered to be the

main driving change factor for the job

market of the future, as the adoption of

automation and artificial intelligence will

transform the entire workplace.

There are strong growth opportunities

for technological skills such as basic

digital skills and advanced IT skills and

programming. Basic cognitive and

physical skills (e.g. inspecting &

monitoring skills; basic data input and

processing skills) on the other hand are

projected to decline.

The transportation and logistics industry

is projected to be impacted the most in

terms of workforce change.

14

Leading Countries

▪ Experts, papers, patents

▪ Funding & companies

02

Both China and the United States are ranked as the top countries for artificial intelligence in all

the major categories we looked at (number of companies and experts, funding, patent

applications and papers published on artificial intelligence). Historically, the majority of funding

has been invested in the United States. However, China is making a strong push for the global

lead. In 2017, almost 28 billion U.S. dollars were poured into the Chinese artificial intelligence

market.

One main difference between the United States and China is the role of the government. China’s

government has outlined its artificial intelligence plans in a national strategy and is streamlining

the development more strongly than the United States. For example, four out of five artificial

intelligence startups with funding of more than one billion U.S. dollars are located in China.

However, the number of startups venturing into this field is around 3.5 times higher in the United

States than China.

This shows that the United States are the more innovative country for artificial intelligence, while

China is more concentrated in larger artificial intelligence companies.

Other countries where investment and development of artificial intelligence is at a significant

level are Canada, Japan, Israel, India and the three biggest economies in Europe – Germany,

the UK and France. French president Emmanuel Macron, for example, announced government

investments of more than 1.5 billion U.S. dollars for his country in March 2018.

United States and China leading the way in artificial

intelligence

„By 2020 they will have

caught up. By 2025 they

will be better than us. And

by 2030 they will dominate

the industries of AI. Just

stop for a sec. The Chinese

government said that.“

Eric Schmidt – Former

Google CEO & Executive

Chairman of Alphabet

16

Artificial intelligence performance benchmark by country as of 2018

AI country performance benchmark 2018

Note: Worldwide, Australia, Canada, China, France, Germany, Japan, South Korea, Netherlands, Russia, United Kingdom, United States; 2018

Source(s): Capgemini

36

31

48

11

37

10

10

28

13

14

12

10

68

56

69

34

52

12

23

35

40

18

30

18

59

57

56

26

46

53

51

56

63

46

29

19

76

52

32

66

22

18

22

14

20

18

10

16

98

70

29

95

45

87

71

28

19

37

16

29

0 50 100 150 200 250 300 350

United States

Germany

United Kingdom

China

France

Japan

Republic of Korea

Canada

Netherlands

Australia

Poland

Russian Federation

Benchmark score

Personnel Monetary Impact Competitiveness Research & Education Technology

18

Number of artificial intelligence companies worldwide as of June 2018, by country

Number of AI companies worldwide 2018, by country

Note: Worldwide; as of June 2018

Source(s): CISTP

2,028

1,011

392

285

152

121

120

111

55

53

40

40

40

33

31

0 500 1,000 1,500 2,000 2,500

United States

China

United Kingdom

Canada

India

Israel

France

Germany

Sweden

Spain

Netherlands

Japan

Switzerland

Poland

Australia

Number of companies

As of June 2018, there were 4,925

artificial intelligence enterprises

worldwide. The establishment of new

artificial intelligence companies has

slowed worldwide since 2015 when the

number of newly established enterprises

worldwide reached 847.

19

Number of artificial intelligence startups worldwide in 2018, by country

Number of AI startups by country 2018


Source(s): Roland Berger

1,393

383

362

245

131

113

109

106

82

55

45

42

39

35

28

0 200 400 600 800 1,000 1,200 1,400 1,600

United States

China

Israel

United Kingdom

Canada

Japan

France

Germany

India

Sweden

Finland

South Korea

Spain

Singapore

Switzerland

Number of startups

Total number of true artificial intelligence

startups worldwide is 3,465.

The number of startups exceed the

number of total AI enterprises for some

countries – indicating that the AI

ecosystem of some countries (e.g.

Israel) might have been growing more

dynamically than others over the past

year.

20

Number of artificial intelligence (AI) experts/talents worldwide by country 2018


Source(s): Institute for Science and Technology Policy (China) (Tsinghua University)

0 5,000 10,000 15,000 20,000 25,000 30,000

United States

China

India

Germany

United Kingdom

France

Iran**

Brazil

Spain

Italy

Canada

Turkey**

Australia

Japan

Number of experts/talents

AI talent Top AI talent

Estimates on the number of global AI professionals/experts/talents

range from 200 thousand to 1.9 million worldwide.

The United States lead all countries with 14 percent of global AI

talent and around 44 percent of AI professionals.

The AI talent pool of the United States, Europe, Japan and Australia

is more advanced in terms of expertise, skill level and experience

compared to China and India.

While 18 percent of US AI talent is considered top-level, this is true

for only around 5 to 6 percent of AI talent in China. In the US, the

share of top-level talent is 25 percent, up from 14 percent share of

total talent, whereas China’s share of top-level talent is only 5

percent compared to a 9 percent share of overall global AI talent.

Around 70 percent of AI professionals in the US have more than ten

years of experience compared to almost 40 percent in China.

US leads world in artificial intelligence talent – both in quantity & quality

21

Number of artificial intelligence professionals by country in 2017 (in 1,000s)

AI professionals by country worldwide 2017


Source(s): LinkedIn

850

150

140

80

50

50

50

30

30

30

20

20

17

16

15

0 100 200 300 400 500 600 700 800 900

United States

India

United Kingdom

Canada

France

Australia

China

Germany

Netherlands

Italy

Spain

Brazil

Singapore

United Arab Emirates

South Africa

Number of AI professionals in thousands Linkedin estimated the number of AI

professionals at around 1.9 million in

2017, based on entries in their database

and desk research.

Compared to the more narrow definition

used for „AI talents“, the lead of the

United States for AI workforce is even

more substantial at a 40 percent share

China ranks only in seventh place for

number of AI professionals. The data

might not show the full picture though, as

Linkedin does not have the same

penetration in all of the markets shown in

the graph.

22

Number of artificial intelligence patents granted by trademark/patent office worldwide from 2000 to 2016

Artificial intelligence patents granted by patent/trademark office 2000-2016

Note: Worldwide; 2000 to 2016

Source(s): RIETI

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

USPTO (United States)

SIPO (China)

PCT (International)

JPO (Japan)

EPO (Europe)

Number of patents granted

Biological Knowledge Mathematical Other

23

Number of artificial intelligence patents granted to universities by country from 2000 to 2016

Artificial intelligence patents granted to universities 2000-2016

Note: Worldwide, China, Japan, United States; 2000 to 2016

Source(s): RIETI

725

241

93

118

0 100 200 300 400 500 600 700 800

Chinese university

U.S. university

Japanese university

ROW universities


24

Number of papers published in the field of artificial intelligence worldwide from 1997 to 2017, by country

AI-related paper publications worldwide 1997-2017, by country


Source(s): CISTP

369,588

327,034

96,536

94,112

85,587

75,128

72,261

61,782

61,466

58,582

52,175

46,138

45,884

34,028

27,552

0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000

United States

China

United Kingdom

Japan

Germany

India

France

Canada

Italy

Spain

Republic of Korea

Taiwan, China

Australia

Iran

Brazil

Number of publications

25

Number of papers published in the field of artificial intelligence in China and worldwide from 1997 to 2017 (in 1,000s)

AI-related paper publications in China and worldwide 1997-2017


Source(s): Statista estimates; CISTP

23.529.5

24.5

3337.5

49.554.5

61

72

89.5 89.593.5

102

83.586

99.5

106

120

135.5

146

134.5

2 4 6 7.5 912.5

2219

2227

17.521

26.5 2630

3539

36.5

0

20

40

60

80

100

120

140

160

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Num

be

r o

f p

ub

lica

tio

ns in

th

ou

sa

nd

s

Global China

26

United States – Still in the artificial intelligence driver’s seat?

Projected increase of GDP due to artificial intelligence by industry sector in North America in 2030

Note: North America; 2018


10%

7%

6.5%

4.5%

4%

5%

3%

11.5%

9%

7%

5.5%

5%

4%

5%

0% 5% 10% 15% 20% 25%

Health, education and other public and personal services

Consumer goods, accomodation and food services

Technology, media and telecommunications

Financial and professional services

Manufacturing and construction

Energy, utilities and mining

Transport and logistics

GDP increase

Productivity Consumption

AI policy/strategy

Trump administration

New Select Committee on AI to advise White

House in 2018.

Main government goals to maintain global

leadership in AI:

• Support national AI R&D ecosystem (e.g.

public-private partnerships)

• Develop US workforce to benefit from AI

• Remove barriers to innovation (regulations)

Obama administration

Three separate reports released in 2016 outlining

a possible foundation for future US strategy:

• Preparing for the Future of Artificial

Intelligence: Recommendations regarding

regulations, public R&D, automation, fairness,

ethics and security

• National Artificial Intelligence Research and

Development Strategic Plan: Outline for

publicly funded AI R&D strategy

• Artificial Intelligence, Automation, and the

Economy: Deals with impact of automation,

possible policies to increase benefit from AI

and mitigate costs

27

Artificial intelligence funding investment in the United States from 2012 to 2018 (in million U.S.

dollars)

Artificial Intelligence funding United States 2012-2018

Note: United States; 2012 to 2018

Source(s): CB Insights; PwC

282

595

1,118

2,489

3,203

3,921

5,012

4,218

0

1,000

2,000

3,000

4,000

5,000

6,000

2011 2012 2013 2014 2015 2016 2017 Q1 & Q2 2018

Fu

nd

ing

in

mill

ion

U.S

. d

olla

rs

Funding and investment in artificial

intelligence in the United States has

consistently risen over the years.

Data for the first two quarters of 2018 point

towards a new record high in AI funding for

the whole year in the United States, as AI

funding for Q1 and Q2 2018 alone already

amounted to around 85 percent of overall

funding for the full year of 2017.

Funding across all industries in the United

States was 27.5 billion USD in Q3 2018,

an increase of about 15 percent from Q2

2018. AI funding is expected to grow at

least in line with overall funding as well.

For 2018, overall AI funding in the United

States is forecast to amount to between

nine and ten billion USD based on overall

funding growth and growth rates of AI

funding over the past eight quarters.

28

In July 2017, the State Council of China released the Next Generation Artificial Intelligence Development Plan outlining the country’s

strategic approach and policy for AI up to 2030:

• 2020: Keeping pace with developments in artificial intelligence and narrow/close the gap to the United States; China as a global

innovation leader. Focus on big data intelligence, autonomous intelligence systems (estimated core AI industry size 150 billion

yuan / roughly 21.5 billion USD)

• 2025: Make major breakthroughs in basic AI technologies; initial AI laws and regulations; broader use of AI across all sectors –

medicine, city infrastructure, manufacturing, agriculture (estimated core AI industry size 400 billion yuan / roughly 58 billion USD)

• 2030: World leader in AI; major breakthroughs in core technologies. Focus in social governance, national defense construction,

industrial value chain (estimated core industry size 1,000 billion yuan / roughly 144 billion USD)

• Baidu, Alibaba, Tencent and iFlytek appointed by Chinese government as “national champions” to lead development and

innovation of AI

• The state-owned electric utility monopoly State Grid Corporation of China holds by far the most AI patents in the country with

4,246. Baidu has the most amongst enterprises with 542 and the Chinese Academy of Sciences System the most in the

academic world with 897 patents

• 2.1 billion USD investment in a technology park dedicated to artificial intelligence to be built in Beijing

• Government funding of around 430 million USD for AI-related research projects in a six month period in early 2018 alone

China on its way to global artificial intelligence dominance

29

Government funding of artificial intelligence related projects in China in the six month period

ending April 2018, by focus (in million U.S. dollars)

Artificial intelligence related project funding in China six months ending April 2018

Note: China; 2015 to 2018

Source(s): SCMP; Various sources (National Science and Technology Information System)

128.7

74.6

72

57.3

55.2

27.2

13.9

0 20 40 60 80 100 120 140

Smart cars

Cloud and big data

Smart robotics

Quantum and high performance computing

Strategic technologies

Leading electronic materials

Smart medical devices

Funding in million U.S. dollars

30

Size of the artificial intelligence market in China from 2015 to 2020 (in billion U.S. dollars)

Artificial intelligence market size in China 2015-2020

Note: China; 2015 to 2018.

Source(s): CMN; CISTP; Statista estimates

1.62.1

3.4

6.2

10.2

14.3

0

2

4

6

8

10

12

14

16

2015 2016 2017 2018 2019 2020

Ma

rke

t siz

e in

bill

ion

U.S

. d

olla

rs

Considering the goal of the Chinese

government to make the country the

global leader in AI by 2030, the market is

projected to grow strongly over the next

few years. The GDP in China is forecast

to increase by at least ten percent

across all industries and sectors.

31

Projected increase of GDP due to artificial intelligence by industry sector in China in 2030

Impact of artificial intelligence on GDP in China as share of GDP 2030

Note: China; 2018


23%

14.5%

13.5%

14%

12%

11%

8.5%

22.5%

14%

12.5%

11.5%

11.5%

10.5%

8.5%

0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%

Health, education and other public and personal services

Consumer goods, accomodation and food services

Technology, media and telecommunications

Financial and professional services

Manufacturing and construction

Energy, utilities and mining

Transport and logistics

GDP increase

Productivity Consumption

32

Number of newly founded artificial intelligence companies in China from 2000 to 2017

Number of AI start-ups in China 2000-2017

Note: China; 2000 to 2017

Source(s): CAICT; Statista estimates; CISTP

1510 10 10 10

1510

15 1520

30 30

6257

128

228

192

98

0

50

100

150

200

250

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017

Num

be

r o

f co

mp

an

ies

The average age of artificial intelligence

enterprises in China is 5.5 years.

75 percent of AI companies in China are

located in Beijing, Shanghai and

Guangdong.

33

Highest valued artificial intelligence companies in China in 2018 (in billion U.S. dollars)

Artificial intelligence unicorn companies in China by value 2018

Note: China; 2018

Source(s): CMN

15

5

4.5

2.5

2

2

2

1.5

1

1

1

1

1

1

0 2 4 6 8 10 12 14 16

DJI

UBTech

SenseTime

Cambrion

Cloudwalk

YITU

Face++

Horizon Robotics

iCarbonX

Pony.ai

Unisound

Tongdun

Mobvoi

Orbbec

Estimated worth in billion U.S. dollars

34

Investment & Funding

▪ Venture capital & funding

▪ Major deals

03

Artificial intelligence has, once again, entered an exciting period - with hopes for breakthrough

developments and products. We are in what has been termed an “AI spring”, as investments

and funding into artificial intelligence have grown strongly over the past few years. For example,

the growth in global funding of AI startups amounted to more than 460 percent from 2012 to

2017. Furthermore, the overall artificial intelligence market is forecast to grow annually by more

than 100 percent each year up to 2025.

In 2017, AI investment and financing reached almost 40 billion U.S. dollars globally - up from

around 31 billion U.S. dollars the year before. Almost 70 percent of investments went to China,

as the country is making a strong push to reach its goal of becoming the world‘s leader in AI.

Globally, the majority of investment and funding goes into all purpose AI companies that are not

focused on a specific industry or product. Startups are a huge part of the artificial intelligence

ecosystem, as funding of AI startups amounted to more than 15 billion U.S. dollars in 2017.

Trends show that an increasing number of startups are moving to more mature funding phases.

Only around a quarter of investment and funding projects in the first quarter of 2018 were in the

seed/angel phase compared to more than 40 percent in the previous year. Full year data for

2018 will highlight if this trend solidifies itself.

Investment in AI is at an all-time high

“AI is the new electricity. I

can hardly imagine an

industry which is not going

to be transformed by AI. A

clear path to an AI-powered

society includes wide

adoption of AI to drive

industrial and technological

endeavors.”

Andrew Ng – Co-Chairman

& Co-Founder of Coursera;

Former Chief Scientist at

Baidu

36

Growth of the artificial intelligence market worldwide from 2017 to 2025

Artificial intelligence market growth worldwide 2017-2025


Source(s): Tractica

150% 152% 154%

143%

151%146%

140%

133%127%

0%

20%

40%

60%

80%

100%

120%

140%

160%

2017 2018 2019 2020 2021 2022 2023 2024 2025

Ye

ar-

on

-ye

ar

gro

wth

37

Global artificial intelligence investment and financing from 2013 to Q1 2018 (in billion U.S. dollars)

AI investment and financing worldwide 2013-2018

Note: Worldwide; 2013 to Q1 2018

Source(s): CAICT; Statista estimates

4.5

13.2

28

31.3

39.2

10.1

0

5

10

15

20

25

30

35

40

45

2013 2014 2015 2016 2017 Q1 2018

Fu

nd

ing

in

bill

ion

U.S

. d

olla

rs

38

Available data for the first quarter of

2018 points towards AI investments

leveling off at 2017 values. Global AI

investments in the first quarter of 2018

were at about a quarter of total 2017

spending.

AI startup funding is also on pace to

match, but not strongly exceed, 2017

levels.

Artificial intelligence startup funding worldwide from 2011 to 2018 (in billion U.S. dollars)

AI funding worldwide 2011-2018, by quarter

Note: Worldwide; 2011 to Q3 2018.

Source(s): Venture Scanner; Statista estimates

0

2

4

6

8

10

12

14

16

2011 2012 2013 2014 2015 2016 2017 2018

Fu

nd

ing

in

bill

ion

U.S

. d

olla

rs

Q1 Q2 Q3 Q4

39

Share of global artificial intelligence investment and financing projects from 2013 to Q1 2018, by stage of funding

Distribution of AI investment and financing projects 2013-2018, by stage of funding



0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2013 2014 2015 2016 2017 Q1 2018

Sh

are

of p

roje

cts

Seed/Angel Series A Series B Series C Series D Series E Series F Other series

40

Share of artificial intelligence startup funding count (deals) worldwide from 2012 to 2017, by stage of funding

AI funding count share worldwide 2012-2017, by stage of funding



0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2012 2013 2014 2015 2016 2017

Sh

are

of fu

nd

ing

co

un

t

Seed A B C D Late stage

41

Share of artificial intelligence funding amounts worldwide from 2012 to 2017, by stage of funding

AI funding amount share worldwide 2012-2017, by stage of funding



15%12.5%

11% 11.5%

8%3.5%

40%

28.5%

22%

27.5%29.5%

19%

26%

31.5%

31.5%23.5%

28.5%

29.5%

15.5%

18%

29.5%

20.5%

21%

25%

4%

2.5%

6.5%

8%

13.5%20.5%

7% 9.5%2.5%

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

100.0%

2012 2013 2014 2015 2016 2017

Sh

are

of fu

nd

ing

am

ou

nts

Seed A B C D Late stage

42

Number of newly founded artificial intelligence companies worldwide from 2000 to 2017

Number of AI start-ups worldwide 2000-2017



6035 35 30

4565

50

85100

130

180

245

385

450

650

845

740

320

10

0

100

200

300

400

500

600

700

800

900

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 July 2018

Num

be

r o

f co

mp

an

ies

43

Number of artificial intelligence startup company acquisitions worldwide 2013-2017

Acquisitions of AI startup companies worldwide 2013-2017


Source(s): CB Insights

22

39

45

80

115

0

20

40

60

80

100

120

140

2013 2014 2015 2016 2017

Num

be

r o

f a

cq

uis

itio

ns

The growth of investment in AI over the

past few years has led to more

acquisitions of startups as well.

Intel is one of the most active investors

in AI with more than one billion USD

invested in AI startups through its Intel

Capital division.

44

Number of artificial intelligence investments by investor as of November 2018

Number of AI investments by investor as of November 2018

Note: Worldwide; as of November 2018

Source(s): Website (index.co)

50

45

34

29

28

25

22

22

22

21

21

21

21

21

20

0 5 10 15 20 25 30 35 40 45 50

Intel Capital

500 Startups

NEA

Battery Ventures

Y Combinator

Madrona Venture Group

Horizon Ventures

Accel

Bloomberg Beta

Data Collective

Techstars

First Round Capital

Sequoia Capital

Kima Ventures

vesna

Number of investments

45

Artificial intelligence funding worldwide cumulative through September 2018 (in billion U.S. dollars), by category

AI funding worldwide cumulative through September 2018, by category

Note: Worldwide; Cumulative through September 2018


19.5

9.4

6.5

5.4

4

3.1

3

2.5

2

0.8

0.4

0 2 4 6 8 10 12 14 16 18 20

Machine learning applications

Machine learning platforms

Computer vision platforms

Smart robots

Recommendation engines

NLP

Computer vision applications

Virtual assistants

Speech recognition

Gesture control

Video recognition

Funding in billion U.S. dollars

46

Share of global artificial intelligence investment and financing projects from 2013 to Q1 2018, by category

Distribution of AI investment and financing projects worldwide 2013-2018, by category


Source(s): CAICT

53%

11%

10%

8%

6%

4%

4%

3%

2%

1%

0% 10% 20% 30% 40% 50%

AI+

Computer Vision

Big Data & Data Services

Smart Robot

Natural Language Processing

Autonomous Vehicles

Speech

Basic Hardware

Unmanned Aerial Vehicle

Augmented & Virtual Reality

Share of total number

47

Leading Companies

▪ Investments

▪ Patents & papers

04

Artificial intelligence is not limited in its possibilities of use, meaning companies from all spheres

are engaged in the market. Naturally, major players from the tech industry are leading the way;

having already invested billions of dollars into research, development and the artificial

intelligence ecosystem overall. Amongst these are well-known US-based internet and tech giants

like Google, Amazon, Facebook, Apple, Intel, IBM and Microsoft.

Google is at the forefront of AI investment and development. Over the past two decades the

company has invested billions of dollars in AI R&D and the acquisition of startups in the industry.

Most notably they acquired Deepmind, which Google bought for 500 million U.S. dollars in 2014.

Using AI patents and published papers on AI topics as an indicator for innovation and “AI

readiness” of companies Microsoft, IBM and Samsung consistently show up in the Top-5 of such

rankings.

Chinese companies like Tencent, Baidu and Alibaba are rapidly closing in on the leading US-

based tech giants though as some of the best-funded AI startups are also located in China.

Toutiao, Bytedance, SenseTime and NIO have each raised more than 2.5 billion U.S. dollars in

funding. Today there are more than ten unicorn AI companies in China with a value of one billion

U.S. dollars or more each. The state-owned electricity utility company State Grid Corporation of

China also has a strong presence in the market as it leads all companies in China in terms of

patents and paper publications.

US companies heavy hitters in AI with China catching up

“If the internet was the

appetizer, then AI is the

main course. The internet

changed a lot of our daily

lives, but did not have

much impact on the 2B

industries. I think AI will

change that.”

Robin Li Yanhong – CEO

of Baidu

49

Artificial intelligence startup acquisitions spending of tech companies from 1998 to 2017 (in million U.S. dollars)

Technology companies by AI startup acquisitions spending 1998-2017

Note: Worldwide; January 2018

Source(s): Website (techrepublic.com)

3,900

871

786

776

690

680

629

191.7

60

32.8

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

Google

Amazon

Apple

Intel

Microsoft

Uber

Twitter

AOL

Facebook

Salesforce

Acquisition spending in billion U.S. dollars

Some of the major tech companies are

using a two-part approach to AI by

investing into their own AI research and

development departments but also

investing in and acquiring startups from

the AI ecosystem.

The acquisition of AI startups provides

the companies with potential products

and use cases. However, more

importantly they are able to acquire AI

professionals and experts which are in

high demand all over the world.

50

Number of artificial intelligence startups acquired from 2010 to June 2018, by company

AI startup acquisitions by company 2010-2018

Note: Worldwide; 2010 to June 2018

Source(s): Fortune; CB Insights

14

13

6

5

5

5

5

4

4

0 2 4 6 8 10 12 14

Alphabet/Google

Apple

Facebook

Amazon

Intel

Microsoft

Meltwater

Twitter

Salesforce

Number of acqusitions

51

Artificial intelligence patent applications of leading technology companies from 1999 to 2017

Global AI-related patent applications by company 1999-2017



4,167

3,360

2,650

2,404

1,413

1,246

1,167

1,149

1,132

930

0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500

Microsoft

IBM

Google

Samsung

AT&T

Baidu

State Grid

Toshiba

Fujitsu

NEC

AI patent applications

Some of the major players in the tech

industry are leading the charge in AI

foundation work and innovation, as

companies like IBM, Microsoft, Google and

Samsung have applied for the most

patents and published the majority of

papers on AI topics over the past 10 to 20

years.

Quantity in AI papers and patents are

indicative of these companies being in the

lead today but this does not necessarily

mean that they are the ones to cash in first

or that they will be at the forefront in five

years, as more money than ever is

invested in AI research especially in China.

52

Number of published artificial intelligence patents in DWPI database by company worldwide from 1997 to 2017

AI patents published in DWPI database worldwide 1997-2017, by company


Source(s): CISTP

7,276

5,356

5,255

3,794

3,569

3,090

2,932

2,868

2,757

2,716

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000

IBM

Microsoft

Samsung Electronics

State Grid Corporation of China

Canon

Sony

NEC Corporation

Fujitsu Limited

Google

Mitsubishi Electric

Number of patents

53

Number of artificial intelligence patents granted by company worldwide from 2000 to 2016

Artificial intelligence patents granted by company 2000-2016


Source(s): RIETI

1,057

466

450

255

212

195

192

154

119

94

93

88

86

77

69

0 200 400 600 800 1,000 1,200

IBM (US)

Microsoft (US)

Qualcomm (US)

NEC (Japan)

Sony (Japan)

Google (US)

Siemens (Germany)

Fujitsu (Japan)

Samsung (Korea)

NTT (Japan)

Hewlett- Packard (US)

Yahoo (US)

Toshiba (Japan)

D-wave (Canada)

Hitachi (Japan)


54

Number of papers published in the field of artificial intelligence worldwide from 1997 to 2017, by company

AI-related paper publications worldwide 1997-2017, by enterprise


Source(s): CISTP

5,105

4,710

2,825

1,548

1,383

1,324

1,229

1,181

1,168

1,136

957

923

869

841

816

0 1,000 2,000 3,000 4,000 5,000 6,000

IBM

Microsoft

Siemens AG

Samsung

Google

Intel

Philips

Microsof Research Asia

General Electric

Siemens

NEC Corporation

Philips Research

Nokia

State Grid Corporation of China

Honda


55

Artificial intelligence startups ranked by total equity funding as of November 2018 (in million U.S. dollars)

Ranking of most well-funded AI startup companies worldwide as of November 2018

Note: Worldwide; as of November 2018

Source(s): CB Insights; Statista; CrunchBase; various sources

3,100

3,000

2,600

2,500

1,000

968.6

940

790

782.8

720

609

607

601

546

497.3

0 500 1,000 1,500 2,000 2,500 3,000 3,500

Toutiao (China), 2012

ByteDance (China), 2012

SenseTime (China), 2014

NIO (China), 2014

Argo AI (United States), 2017

Dataminr (United States), 2009

UBTech Robotics (China), 2012

Zoox (United States), 2014

Tanium (United States), 2007

Affirm (United States), 2012

Indigo (United States), 2014

Megvii Technology (China), 2011

OakNorth (United Kingdom), 2013

CloudWalk Technology (China), 2015

Kreditech (Germany), 2012

Total funding in million U.S. dollars

Some of the best funded companies in

artificial intelligence today are located in

China.

With a high number of AI companies and

enterprises in the United States, funding

and investment money is more spread out

when compared to China. The majority of

AI funding in China is more concentrated

with fewer but bigger companies,

indicative of a more concentrated market

space.

56

Highest valued artificial intelligence companies in China in 2018 (in billion U.S. dollars)

Artificial intelligence unicorn companies in China 2018, by value

Note: China; 2018

Source(s): CMN

15

5

4.5

2.5

2

2

2

1.5

1

1

1

1

1

1

0 2 4 6 8 10 12 14 16

DJI

UBTech

SenseTime

Cambrion

Cloudwalk

YITU

Face++

Horizon Robotics

iCarbonX

Pony.ai

Unisound

Tongdun

Mobvoi

Orbbec

Estimated worth in billion U.S. dollars

57

AI Technologies & Enablers

▪ AI types & categories

▪ AI frameworks

▪ Computing & semiconductors

05

Artificial intelligence mainly functions as an umbrella term for different technologies and concepts. One subset of AI that has been one

of the main focus areas of AI research and investment over the past ten years is machine learning.

Pattern recognition – a subfield of machine learning, natural language processing, learning systems, and neural networks, to name a

few, have attracted tenth of billions in funding money over the past ten years. Further development of AI not only hinges on

investments and research in the field itself but also on various adjacent hardware, software and service technologies.

Deep learning artificial intelligence systems, for example, need huge amounts of data and sufficient computing power to improve

through reinforcement learning. In 2016, Google Deepmind‘s AlphaGo used 1,202 CPUs and 176 GPUs in computing power when it

beat world champion Lee Sedol in the Chinese board game of Go.

Example systems and products:

• Availability of / access to large sets of data for AI systems to learn from (big data)

• Semiconductors / computer chips (CPU, GPU, VPU, SPU & AI chips) & sensors

• Computing power & AI frameworks (environments)

• 5G mobile network (low latency in autonomous driving)

Artificial intelligence technologies – a wide field of

opportunites & categories

59

External investment in artificial intelligence-focused companies worldwide in 2016, by technology category (in billion U.S. dollars)

External investment in AI-focused companies worldwide 2016, by category


Source(s): McKinsey; PitchBook; Dealogic; S&P Capital IQ

7

3.5

0.9

0.5

0.5

0.2

5

2.5

0.6

0.3

0.3

0.1

0 1 2 3 4 5 6 7 8

Machine learning

Computer vision

Natural language

Autonomous vehicles

Smart robotics

Virtual agents

External investment in billion U.S. dollars

High end Low end

What actually is…?

Computer vision is an interdisciplinary

field of computer science dealing with

enabling computers to see, identify and

process images. It aims at giving

computers a high level of

understanding of these images similar

to humans.

Natural language processing (NLP)

is a subfield of AI that helps computers

understand, interpret and manipulate

human language. It combines

linguistics with computer science to

identify and understand spoken

language as well as text based

language.

60

Number of artificial intelligence publications worldwide from 2007 to 2017, by topic

AI-related publications worldwide 2007-2017, by topic


Source(s): IP Pragmatics

63,666

53,539

29,941

26,470

23,486

22,626

20,089

19,709

18,529

16,649

15,848

14,629

13,855

13,580

13,430

0 10,000 20,000 30,000 40,000 50,000 60,000 70,000

Pattern recognition

Learning systems

Machine learning

Neural networks

Natural language processing systems

Learning algorithms

Data mining

Feature extraction

Semantics

Image processing

Pattern recognition, automated

Pattern recognition, visual

Non-human

Decision support systems

Decision making


What actually is…?

Pattern recognition is a branch of machine

learning (supervised/unsupervised) that is

used to recognize patterns and regularities

in data sets. It makes use of computer

algorithms to discover these regularities and

then classifies the data into different

categories. Pattern recognition is the basis

for computer-aided diagnosis systems in

medical science. Other applications include

speech recognition, image recognition and

classification of text into categories.

Artificial neural networks (ANN) is an AI

technique that mimics or tries to replicate

the workings of the human brain. It is one of

the main concepts/frameworks used for

machine and deep learning. Developed

neural networks can extract meaning from

complicated data and detect trends and

patterns too complex for humans to identify.

61

Share of global artificial intelligence enterprises in 2018, by category

Distribution of AI enterprises worldwide 2018, by category


Source(s): CAICT

49%

12%

11%

8%

7%

5%

4%

3%

1%

1%

0% 10% 20% 30% 40% 50% 60%

AI+ - specific industry verticals

Big Data & Data Services

Computer Vision

Smart Robot

Natural Language Processing

Basic Hardware

Speech

Autonomous Vehicles

Unmanned Aerial Vehicle

Augmented & Virtual Reality

Share of total number

Artificial intelligence companies are

almost evenly split amongst ones that

focus on one specific industry vertical,

such as business intelligence and

healthcare, and those that are focused

on a specific type of horizontal AI

application.

62

46%

41%

4%

6%

4%

7%

15%

96% 43% 38%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

2017 2020 2023

Ma

rke

t sh

are

Sound processor Embedded sound processing unit Vision processor Embedded vision processing unit


Source(s): Yole Développement

Artificial intelligence hardware market share worldwide in 2017, 2020 and 2023, by product

AI hardware market share by product worldwide 2017-2023

63

Semiconductor sales revenue worldwide from 1987 to 2019 (in billion U.S. dollars)

Semiconductor industry sales worldwide 1987-2019


Source(s): WSTS; SIA

0

50

100

150

200

250

300

350

400

450

500

1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Sa

les in

bill

ion

U.S

. D

olla

rs

64

Semiconductor unit shipments worldwide from 2000 to 2018 (in billions)

Semiconductor unit shipments worldwide 2000-2018


Source(s): IC Insights

397.4

467.1

556.2

623.7

705.6

815.3

868.8

986.2

1,075.1

0

200

400

600

800

1,000

1,200

2000 2004 2006 2007 2010 2014 2016 2017 2018

Unit s

hip

me

nts

in b

illio

ns

65

Estimated size of the artificial intelligence semiconductor market in the United States from

2017 to 2022 (in billion U.S. dollars)

AI semiconductor industry revenue in the U.S. 2017-2022

Note: United States; 2016 to 2018

Source(s): SIA

3

6

12

19

26

33

0

5

10

15

20

25

30

35

2017 2018 2019 2020 2021 2022

Reve

nu

e in

bill

ion

U.S

. d

olla

rs

The global market for AI specific chips is forecast to increase from

4.52 billion USD in 2017 to more than 90 billion USD by 2025. Major

semiconductor manufacturers as well as a variety of startups are

currently developing chips specifically for AI purposes.

CPUs (Central Processing Unit) are chips used for general

computing purposes.

GPUs (Graphic Processing Unit) are programmable chip

processors originally designed for display functions (images, videos,

animations). They have been adopted for use in AI as they can

perform parallel operations on multiple sets of data.

FPGA (Field Programmable Gate Arrays) is a semiconductor chip

that can be configured and programmed by the user. FPGAs are

good at processing small-scale but intensive data.

ASIC (Application-Specific Integrated Circuit) chips are built for a

specific purpose or application. They are tailored towards that one

specific use but cannot be customized after production.

NPU (Neuromorphic Processing Unit) are a type of newly

developed chip category mimicking the architecture of the human

brain. These type of chips are still in the early stages of

development.

66

Optoelectronics / optical semiconductor revenue worldwide from 2008 to 2019 (in billion U.S. dollars)

Global optical semiconductors market revenue 2008-2019


Source(s): WSTS

17.917.04

21.723.09

26.227.57

29.87

33.2631.99

34.8135.99

38.02

0

5

10

15

20

25

30

35

40

2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019

Sa

les in

bill

on

U.S

. d

olla

rs

67

CMOS image sensors sales revenue worldwide from 2007 to 2022 (in billion U.S. dollars)

CMOS image sensor sales worldwide 2007-2022


Source(s): IC Insights; Statista estimates

44.5

3.94.5

5.9

7.17.4

8.9

9.910.5

12.5

13.7

15.2

16.1

17.4

19

0

2

4

6

8

10

12

14

16

18

20

2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022

Sa

les r

eve

nu

e in

bill

ion

U.S

. d

olla

rs

CMOS image sensors are projected to become one of the most

important means to acquire image data for artificial intelligence

applications. They could be considered the eye of artificial

intelligence bringing vision to these systems.

Today there are two main technologies in use for CMOS image

sensors:

FSI (front side illuminated) – limited in the fields of use compared

to BSI because the pixel size is reduced with higher resolutions.

The manufacturing process of FSI sensors is more simple, lower in

cost and has a higher yield compared to BSI.

BSI (back side illuminated) – more mature/advanced technology

than FSI. Solution for applications in need of high resolution with

limited optical and pixel size. BSI sensors have a high sensitivity

and a strong low-light performance. Exemplary fields of application

are surveillance, factory automation and smartphones.

68

Worldwide revenue of the supercomputer market from 2015 to 2017 (in billion U.S. dollars)

Supercomputer revenue worldwide 2015-2017


Source(s): Hyperion Research

3.3

4.1

4.8

0

0.5

1

1.5

2

2.5

3

3.5

4

4.5

5

2015 2016 2017

Reve

nu

e in

bill

ion

U.S

. d

olla

rs

69

Deep learning artificial intelligence framework power scores 2018

Ranking of artificial intelligence deep learning frameworks 2018


Source(s): Website (Towards Data Science)

96.77

51.55

22.72

17.15

12.02

8.37

4.89

3.65

2.71

1.18

1.06

0 10 20 30 40 50 60 70 80 90 100

TensorFlow

Keras

PyTorch

Caffe

Theano

MXNET

CNTK

DeepLearning4J

Caffe2

Chainer

FastAI

Power score

Deep learning uses neural networks and

large data sets (big data). The concept is

in many aspects inspired by the human

brain. Based on existing information and

the neural network, the system is capable

of connecting what it has already learned

with new content and information to

continuously learn more. As a result, the

system has the ability to make predictions

and decisions.

Software frameworks are platforms for

developing software applications. They

provide generic functionalities that can be

selectively changed by additional user-

written code.

70

Artificial Intelligence frameworks by number of commits and contributors on GitHub as of November 2018

Popularity/usage of artificial intelligence frameworks worldwide 2018

Note: Worldwide; as of November 16, 2018

Source(s): GitHub

43,768

28,052

26,623

16,675

15,994

14,709

8,846

4,907

4,152

1,336

1,723

330

237

192

189

838

641

748

270

132

0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000

TensorFlow

Theano

DeepLearning4J

Chainer

Microsoft Cognitive Toolkit (CNTK)

PyTorch

Apache MXNet

Keras

Caffe

Torch

Number of commits/contributors

Commits Contributors

Google’s open source TensorFlow AI

framework/library has become the

most used and popular one since its

initial release in November 2015.

71

Forecast number of mobile 5G subscriptions worldwide from 2019 to 2022 (in millions)

Forecast number of 5G mobile subscriptions worldwide 2019-2022

Note: Worldwide; December 2017

Source(s): 5G Americas

0.4211

84

400

0

50

100

150

200

250

300

350

400

450

2019 2020 2021 2022

Su

bscri

ptio

ns in

mill

ion

s

72

Applications & Industry Impact

▪ Major use cases

▪ Industry & sector impact

06

Artificial intelligence is set to impact every industry and many aspects of everyday life in the future.

There are still many milestones to be reached to achieve full AI maturity and “strong AI“. However,

some fields such as virtual digital assistants and chatbots are already making an impact.

Amazon Alexa, Google Assistant, Apple’s Siri and Microsoft Cortana are well-known examples of

virtual digital assistants (VDA) making use of AI technology. These VDAs answer questions,

provide news and weather updates and let the user control other devices in their home.

All major automotive manufacturers are now developing self-driving autonomous vehicles and plan

to release partly, if not fully, automated cars to the market in the mid-2020s.

In e-commerce and retail, artificial intelligence can help companies with warehouse automation,

identifying target groups for their products and predicting sales more accurately.

In healthcare, the use cases for artificial intelligence are manifold as well. Medical imaging, cancer

detection, diagnostic scans, robot-assisted surgery, health monitoring, drug discovery and virtual

nursing assistants.

Where will artificial intelligence impact be felt first?

“Whenever I hear people

saying AI is going to hurt

people in the future I think,

yeah, technology can generally

always be used for good and

bad and you need to be careful

about how you build it … if

you’re arguing against AI then

you’re arguing against safer

cars that aren’t going to have

accidents, and you’re arguing

against being able to better

diagnose people when they’re

sick.”

- Mark Zuckerberg – Facebook

CEO

74

Share of artificial intelligence startups worldwide in 2018, by industry

Share of AI startups by industry 2018


Source(s): Roland Berger

25%

14%

12%

9%

7%

6%

6%

3%

3%

3%

2%

1%

1%

1%

1%

0% 5% 10% 15% 20% 25%

General/Cross-Sectoral (B2B services)

Communication (B2B services)

Sales/Marketing (B2B services)

Healthcare/BioTech

Other

Defense/Security (B2B services)

FinTech

Human Resources (B2B services)

Entertainment

Transportation

Education

Travel

Other (B2B services)

Energy

Automotive

Share of startups

More than 60 percent of AI startups

are applying to major functions in

cross-cutting sectors

(communications, marketing, HR,

security, e-commerce, legal, etc.) and

are therefore considered B2B

services.

75

0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000 9,000

Machine/vehicular object detection/identification/avoidance

Static image recognition, classification, and tagging

Patient data processing

Algorithmic trading strategy performance improvement

Localization and mapping

Predictive maintenance

Prevention against cybersecurity threats

Converting paperwork into digital data

Intelligent recruitment and HR systems

Medical image analysis

Market in million U.S. dollars


Source(s): Tractica

Cumulative revenue of top 10 use cases/segments of artificial intelligence market worldwide, between 2016 and 2025 (in million U.S. dollars)

Top 10 artificial intelligence use cases by cumulative revenue worldwide 2016-2025

76

Cognitive/artificial intelligence systems spending worldwide by use case in 2018 (in billion U.S. dollars)

Artificial intelligence and cognitive system spending by use case 2018


Source(s): IDC

2.9

1.9

1.7

1.7

0 0.5 1 1.5 2 2.5 3 3.5

Automated customer service agents

Automated threat intelligence and prevention systems

Sales process recommendation and automation

Automated preventive maintenance

Spending in billion U.S. dollars

With a projected total market size of 24

billion U.S. dollars, spending on all other

use cases combined amounts to 15.8

billion USD.

77

Projected spending on artificial intelligence by industry group worldwide in 2020 (in billion U.S. dollars)

Artificial intelligence spending by industry group worldwide 2020


Source(s): IDC; Website (Finch Capital)

11

10

9

8

9

0 2 4 6 8 10 12

Financials

Public Sector

Distribution & Services

Manufacturing & Resources

Other

Spending in billion U.S. dollars

78

Adoption plans for intelligent automation technologies in organizations worldwide as of 2018

Intelligent automation adoption plans in enterprise worldwide 2018

Note: Worldwide; 2018; 590 Respondents; Global 2000 enterprise leaders including 100 C-level executives

Source(s): HfS Research ; KPMG

5%

5%

5%

6%

4%

6%

5%

14%

23%

17%

23%

16%

20%

20%

28%

25%

31%

31%

36%

30%

34%

30%

28%

30%

24%

28%

29%

27%

23%

19%

17%

15%

15%

15%

13%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Smart Analytics (including predictive and prescriptive analytics)

Computer Vision

Machine Learing (ML)

NLP (extract & interpret, compare & comply, retrieve and recomment)

Artificial Intelligence (AI) (e.g. IBM Watson-type reasoning apps)

Cognitive/smart virtual assistants (chatbots++)

Robotic Process Automation (RPA)

Share of respondents

Unsure No plans Piloting right now Moving to production Scaled-up and industrialized

79

There are deep-learning based methods in development and trial that will be able to detect

illness, including cancer, much better than human average performance. Artificial intelligence

applications will be used to diagnose scans, e.g. for heart disease and lung cancer.

The medical imaging segment of healthcare offers many opportunities for machine and deep

learning applications. Computer vision models can be trained on a large number of medical

images which are matched with validated patient diagnoses. These systems can then help

doctors to process a greater amount of patient cases and lower the number of diagnostic

mistakes.

AI can also be put to use in liquid biopsy. Here, cells and bacteria from patients‘ blood can be

analyzed for (potential) diseases and cancer. This will lead to earlier detection of such diseases

and improve the patients‘ chances to maintain their health or recover more quickly and effectively.

The use of chatbots for remote patient treatment is another field of application for AI in healthcare

and medicine. Virtual nursing assistants and digital consultations have the potential to reduce

unnecessary hospital and physician visits and lessen the burden on medical professionals.

The global health artificial intelligence market is set for strong growth and projected to reach more

than six billion U.S. dollars in value by 2021.

Healthcare – ready for AI today

Major use cases in healthcare

• Medical imaging analysis

• Cancer detection

• Diagnostic scans

• Chronic illness prediction

• Preliminary diagnosis

• Health monitoring

• Drug discovery / creation

• Robot-assisted surgery

• Digital consultation

• Virtual nursing assistants

• Medication management

• Dosage error reduction

• Liquid biopsy

• Workflow and administrative tasks

80

507633.8

811.1

1,065.1

1,438.4

2,002.7

2,882

4,298.2

6,662.2

0

1,000

2,000

3,000

4,000

5,000

6,000

7,000

2013 2014 2015 2016 2017 2018 2019 2020 2021

Reve

nu

e in

mill

ion

U.S

. d

olla

rs

Note: Worldwide

Source(s): IP Pragmatics; Frost & Sullivan

Revenue from artificial intelligence systems in healthcare worldwide from 2013 to 2021 (in million U.S. dollars)

Global healthcare artificial intelligence market revenue 2013-2021

81

40

20

18

17

16

14

13

5

3

2

0 5 10 15 20 25 30 35 40

Robot-assisted surgery

Virtual nursing assistants

Administrative workflow assistance

Fraud detection

Dosage error reduction

Connected machines

Clinical trial participant identifier

Preliminary diagnosis

Automated image diagnosis

Cybersecurity

Value in billion U.S. dollars

Note: Worldwide

Source(s): IP Pragmatics

Forecast value of the artificial intelligence healthcare market by application worldwide in 2026 (in billion U.S. dollars)

Global healthcare artificial intelligence market value 2026, by application

82

The automotive industry is at the beginning of a major transformation process, in large parts due

to artificial intelligence. And it is happening in two major ways, both at the same time. For one, the

development of autonomous driving and vehicles is fundamentally changing and challenging the

business model of automotive manufacturers. And secondly, the production process in automotive

is undergoing fundamental changes through the use of robotics and automation, both of which are

affected and influenced by developments in AI.

When moving to an autonomous, self-driving eco-space in transportation, revenue will be

generated from managing vehicle fleets (cars, trucks, buses, drone delivery systems) and the

accompanying services, and not so much by the sales of vehicles itself anymore.

To make autonomous driving work, advanced technological capabilities such as automated

vehicle guidance, braking, cameras and sensors for collision avoidance and lane-changing

systems are needed. AI is used in this context to analyze information in real time.

One of the main needs for autonomous vehicles to work on a full scale is the new mobile network

standard 5G, which has a considerably lower latency compared to current mobile networks. This

is important for the communication between vehicles, network, infrastructure, pedestrians etc. as

all decisions have to be made within the split of a second.

AI in automotive – twofold challenge for the future

“So there will be a shared

autonomy fleet where you buy

your car and you can choose to

use that car exclusively. You can

choose to have it used only by

friends and family… or other

drivers who are rated five star.

You can choose to share it

sometimes but not other times.

That’s 100 percent what will

occur. It’s just a question of

when.”

– Elon Musk, Tesla co-founder

and CEO

83

Technical feasibility is by far not the only

challenge for autonomous driving. Today

even minor accidents involving

autonomous vehicles make headline

news. People are mostly concerned

about safety and not being in control of

the situation at all times.

These attitudes are bound to change,

though, if the technology improves to the

point where it is considered safe by all

standards.

Age also plays an important role in

regards to how open people are to

autonomous vehicles. Around 70 percent

of 14- to 17-year-olds are willing to be

passengers in self-driving cars. This

share declines with each age group.

71%

61%

53%

37%

0% 10% 20% 30% 40% 50% 60% 70% 80%

14-17

18-34

35-54

55+

Share of respondents

Willingness to be a passenger in a self-driving vehicle, by age

Consumers are concerned about safety and loss of control

Note: Worldwide; October to November 2017; 14-55 years; 21,000 respondents; online consumers

Source(s): Accenture; Harris Interactive

84

0.1% 0.2% 0.3% 0.5%0.8%

1.7%

3%

5%

8%

12%

0%

2%

4%

6%

8%

10%

12%

14%

2021 2022 2023 2024 2025 2026 2027 2028 2029 2030

Ma

rke

t p

en

etr

atio

n r

ate

Note: Worldwide

Source(s): UBS; Intel; Nvidia

Projected autonomous vehicle market penetration worldwide between 2021 and 2030

Autonomous vehicles: global market penetration 2021-2030

87

1,161

472

243

188

80

71

50

30

23

10

5

0 200 400 600 800 1,000 1,200

Robotaxi service

In-car time monetization

AV production & sale

Fleet management

Sensor modules

L2-3 ADAS options

AV Op. System

Semis: compute

Semis: sensors

Maps

Semis: memory

Market size in billion U.S. dollars

Note: Worldwide

Source(s): UBS

Size of the global market for autonomous vehicles in 2030, by segment (in billion U.S. dollars)

Autonomous vehicles: global market size by segment 2030

88

90

44

34

19

0 10 20 30 40 50 60 70 80 90 100

Telematics

Cleaning and charging

Maintenance and repair

Financial services

Revenue in billion U.S. dollars

Note: Worldwide

Source(s): UBS

Global autonomous vehicle fleet revenue split in 2030, by segment (in billion U.S. dollars)

Autonomous vehicle fleet revenue split by segment 2030

89

Smart speaker unit shipments worldwide from 2016 to 2019 (in millions)

Global smart speaker unit shipments 2016-2019

Note: 2016 to 2018

Source(s): TrendForce

6.57

27.66

62.25

95.25

0

10

20

30

40

50

60

70

80

90

100

2016 2017 2018 2019

Unit s

hip

me

nts

in m

illio

ns

91

0

5,000

10,000

15,000

20,000

25,000

2016 2017 2018 2019 2020 2021 2022 2023 2024 2025

Ma

rke

t in

mill

ion

U.S

. d

olla

rs

Software Services Hardware

Note: Worldwide; 2016 to 4th quarter 2017

Source(s): Tractica

Size of the enterprise virtual digital assistant (VDA) market worldwide from 2016 to 2025, by segment (in million U.S. dollars)

Enterprise virtual digital assistant (VDA) market size worldwide by segment 2016-2025

92

Summary & Outlook07

Artificial intelligence has experienced both periods of growth and innovation as well as stagnation and frustration. Today, AI is in the

midst of another “spring“ period. Investment into AI technologies and companies has grown strongly over the past few years and is at

an all-time high. Where exactly AI is headed and what is going to look like in the near future is almost impossible to predict. However,

it is an accepted fact among experts, scientists, company executives, and politicians that AI will be one of the most disruptive

technologies impacting all industries, sectors, and society as a whole.

The economic impact of artificial intelligence is forecast to amount to several trillion U.S. dollars worldwide and will have a strong

impact on the gross domestic product worldwide. The effect will be much greater in developed countries such as the United States,

Korea, Japan and throughout Europe. China‘s economy and society are also set to be affected strongly by AI. The country is placing a

heavy emphasis on becoming the world‘s leader in artificial intelligence. With its current “New Generation Artificial Intelligence

Development Plan“ China aims to become the dominant player in the field by 2030. Other countries with investments to stake claim in

the market include India and Israel.

All of the major tech and internet companies are investing into artificial intelligence research, as well as full data for funding money.

Many tech and non-tech companies are investing in the field, and company executives recognize the potential of AI. However, there is

still uncertainty regarding what the real-life implications will actually look like.

There are also other uncertainties about potential (technological) developments such as quantum computing, which has the potential

to accelerate the development of artificial intelligence.

On the path to stronger artificial intelligence – breakthrough

on the horizon?

94

Algorithm An algorithm is a set of rules (unambiguous specification) to be followed when solving problems. It is used for data

processing, calculation and other mathematical and computer operations.

Artificial intelligence (AI) is an umbrella term for several computer science technologies thriving to emulate and eventually improve on

the human brain. „Strong AI“ systems exhibit human intelligence by approximating, mimicking and replicating human thinking. Artificial

intelligence systems make use of sensors, cameras and other input to gather information and rely on huge sets of structured and

unstructured data to learn from and improve the system‘s capabilities.

Artificial neural networks (ANN) is an AI technique that mimics or tries to replicate the workings of the human brain. It is one of the

main concepts/frameworks used for machine and deep learning. Developed neural networks can, for example, extract meaning from

complicated data and detect trends and patterns too complex for humans to identify.

Cognitive era refers to a concept based on artificial intelligence and advanced cognitive systems. Technology in the cognitive era is

able to form hypotheses and make judgments based on the synthesis of big data. It has the ability to understand unstructured data,

reason about it by considering arguments and generating hypotheses and learn from experts, interaction and the intake of data.

Computer vision is an interdisciplinary field of computer science dealing with enabling computers to see, identify and process images.

It aims at giving computers a high level of understanding of these images similar to humans.

Glossary

95

Deep learning uses neural networks and large data sets (big data). In many aspects, the concept is inspired by the human brain.

Based on existing information and the neural network, the system is capable of connecting what it has already learned with new content

and information to continuously learn more. As a result, the system has the ability to make predictions and decisions.

Machine learning is a subset of AI focusing on the ability of machines to learn for themselves from sets of data. These systems take in

information and process it, changing algorithms as they learn more about the processed data. Machine learning systems are able to

learn from this process and make predictions or determinations based on the results.

Natural Language Processing (NLP) is a subfield of AI that helps computers understand, interpret and manipulate human language. It

combines linguistics with computer science to identify and understand spoken language as well as text-based language.

Pattern recognition is a branch of machine learning (supervised/unsupervised) that is used to recognize patterns and regularities in

data sets. It makes use of computer algorithms to discover these regularities and then classifies the data into different categories.

Pattern recognition is the basis for computer-aided diagnosis systems in medical science. Other applications include speech

recognition, image recognition and classification of text into categories.

Reinforcement learning uses some form of conditioning where the AI system gets “rewarded” for a positive/correct result or “punished”

for a false one. It allows the machines to learn by interacting with its environment.

Glossary

96

Smart/AI intelligent robots are artificial intelligence systems that can learn from their environment and build on this knowledge to

improve their capabilities. They can be considered a bridge between AI and robotics as they are robots controlled by AI

systems/programs.

Software frameworks are platforms for developing software applications. They provide functionalities that can be selectively changed

by additional user-written code.

Supervised learning With this method, training data sets (characteristics, patterns, dimensions etc.) are provided to the system for

„correct answers“ are already in the data sets so that the system can use the examples when classifying new sets of data. Credit card

fraud detection is an example of a supervised learning algorithm.

Unsupervised learning is not using training data sets but unclustered data and therefore has no corresponding output variables on

which to base the classification of data. The goal for unsupervised learning is to model the underlying structure or distribution in the data

to learn more about it.

Virtual digital assistants (VDA) are software application programs that make use of natural language processing to interact with a

user. They deliver voice- or text-based information to the user and can perform tasks or services. They are sometimes referred to as

“chatbots”. Apple’s Siri, Google Assistant, Amazon Alexa, and Microsoft Cortana are examples of VDAs.

Glossary

97

Sources

98

451 Research

5G Americas

ABI Research

Accenture

China Academy of Information and Communications Technology

Capgemini

CB Insights

China Money Network

Cisco Systems

CISPT

CIRP

Consumer Technology Association

Crunchbase

Deloitte

DHL

Forbes

Fortune

Frost & Sullivan

Gartner

GitHub

Grand View Research

Harris Interactive

Hyperion Research

IC Insights

IDC

Intel

International Federation of Robotics

International Labour Organization

International Telecommunications Union

IP Pragmatics

J.P. Morgan

Kleiner Perkins Caufield & Byers

KPMG

LinkedIn

McKinsey

National Institution for Transforming India

Nvidia

Politics + AI (Medium.com)

Price Waterhouse Coopers

RIETI

Roland Berger

Semiconductor Industry Association

South China Morning Post

Tractica

Trendforce

Tsinghua University

Towards Data Science

UBS

Venture Scanner

World Economic Forum

WSTS

Yole Development

June 2017

E-M ail: [email protected]

Released: December 2018

Imprint

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DisclaimerStatista, Inc. shall not be liable for any loss, injury, claim, liability, or damage of any kind resulting in any way from (a) any errors in or omissions from the Statista, Inc. industry report(s) orany Materials available or not included therein, (b) the unavailability or interruption of the Statista, Inc. industry report(s) or any features thereof or any Materials, (c) An Authorized User orMember's use of the Statista, Inc. industry report(s), Online Services, or Materials, (d) the loss or corruption of any data or equipment in connection with the Statista, Inc. industry report(s), (e)the content, accuracy, or completeness of Materials, all regardless of whether you received assistance in the use of the Statista, Inc. industry report(s) from a Covered Party, (f) any delay orfailure in performance beyond the reasonable control of a Covered Party, or (g) any content retrieved from the Internet even if retrieved or linked to from within the Statista, Inc. industryreport(s).

Authors, Imprint, and Disclaimer

Arne Holst

Senior Researcher

Arne Holst is the Statista research specialist for technology and telecommunications with more than seven years of experience in the industry. He is an expert for top tech trend topics like digital transformation, IoT, AI, VR, Industry 4.0 and smart everything.

mailto:[email protected]

http://www.statista.com/

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