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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
Note: Worldwide; 2018
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
Note: Worldwide; 2018
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
Note: Worldwide; 2017
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
Note: Worldwide; 2018
Source(s): PwC; Statista estimates
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
17
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
Note: Worldwide; 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
Note: Worldwide; 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
Note: 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
Number of patents granted
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
Note: Worldwide; 1997 to 2017
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
Note: Worldwide; 1997 to 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
Source(s): PwC; Statista estimates
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
Source(s): PwC; Statista estimates
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
Note: Worldwide; 2016 to 2017
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
Note: Worldwide; 2013 to Q1 2018
Source(s): CAICT; Statista estimates
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
Note: Worldwide; 2012 to 2017
Source(s): Venture Scanner; Statista estimates
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
Note: Worldwide; 2012 to 2017
Source(s): Venture Scanner; Statista estimates
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
Note: Worldwide; 2000 to 2017
Source(s): CAICT; Statista estimates
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
Note: Worldwide; 2013 to 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
Source(s): Venture Scanner; Statista estimates
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
Note: Worldwide; 2013 to Q1 2018
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
Amazon
Apple
Intel
Microsoft
Uber
AOL
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
Amazon
Intel
Microsoft
Meltwater
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
Note: Worldwide; 1999 to 2017
Source(s): CAICT; Statista estimates
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
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
Note: Worldwide; 1997 to 2017
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
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
Note: Worldwide; 2000 to 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)
Number of patents granted
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
Note: Worldwide; 1997 to 2017
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
Intel
Philips
Microsof Research Asia
General Electric
Siemens
NEC Corporation
Philips Research
Nokia
State Grid Corporation of China
Honda
Number of publications
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
Note: Worldwide; 2016
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
Note: Worldwide; 2007 to 2017
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
Number of publications
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
Note: Worldwide; 2018
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
Note: Worldwide; 2017
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
Note: Worldwide; 1987 to 2017
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
Note: Worldwide; 2000 to 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
Note: Worldwide; 2008 to 2017
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
Note: Worldwide; 2018
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
Note: Worldwide; 2015 to 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
Note: Worldwide; 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
Note: Worldwide; 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
Note: Worldwide; 2016
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
Note: Worldwide; 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
Note: Worldwide; 2018
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
85
86
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
90
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
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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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.