ARTIFICIAL INTELLIGENCE (AI) - October 2018 - Statista
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ARTIFICIAL
INTELLIGENCE
(AI)
October 2018
Agenda
01 Economic Impact of AI 05 AI Technologies & Enablers
▪ GVA, GDP & jobs ▪ AI types & categories
▪ Market size ▪ AI frameworks/computing & semiconductors
02 Leading Countries 06 Applications & Industry Impact
▪ Experts, papers, patents ▪ Major use cases
▪ Funding & companies ▪ Industry & sector impact
03 AI Investment Today 07 Summary & Outlook
▪ Venture capital and funding
▪ Major deals
04 Leading Companies
▪ Investments
▪ Patents & papers
2
Executive summary 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. 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)
Major car manufacturers (like Daimler, BMW, Ford, Honda, Toyota, Volvo, Hyundai, Renault-Nissan) aim to have highly-automated cars ready for the
2020/21
market
2025 Quantum computing
4
01 Economic Impact of AI
▪ GVA, GDP & jobs
▪ Market size
Artificial intelligence will have major economic impact Creating a reliable forecast that estimates the economic impact of artificial intelligence using “The new spring in AI is the most numbers alone is impossible. Evaluation and comparison of industry forecasts show that the significant development in overall market for artificial intelligence is projected to generate tens of billions in revenue by the computing in my lifetime. Every mid 2020s. month, there are stunning new Forecasts on the impact of artificial intelligence (in dollar value) may differ, but one thing everyone applications and transformative agrees on is that artificial intelligence will be a disruptive force – not only across every industry and new techniques. But such sector, but for society as a whole. Projections show that by 2030 artificial intelligence has the powerful tools also bring with potential to enhance the gross domestic product by ten percent or more. This is mainly through them new questions and product enhancements and productivity gains. responsibilities.” China and the United States are set to benefit the most from the continuous advancement of – Sergey Brin, Google co- artificial intelligence and its neighboring technologies. For example, increased productivity from founder and President of labor substitution in the United States is projected to increase the country’s GDP by 15 percent by Alphabet 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. 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
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)
250
200
Market size in billion U.S. dollars
150
100
50
0
2016 2017 2018 2019 2020 2021 2022 2023 2024 2025
Note: Worldwide; 2018
Source(s): Grand View Research; MarketsandMarkets; IDC; Tractica; Frost & Sullivan; Statista; UBS
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
Low estimate High estimate
0.9
0.8
0.8
Ecnomic impact in trillion U.S. dollars
0.7
0.6
0.5 0.5 0.5
0.5
0.4 0.4 0.4 0.4
0.4
0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3 0.3
0.3
0.2 0.2 0.2 0.2 0.2 0.2 0.2 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 0.1
0.1
0
Note: Worldwide; 2018
Source(s): McKinsey
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
GDP gains associated with product enhancements GDP gains associated with productivity
25%
20% 8.5%
15%
GDP increase
6%
5.5%
12% 5.5% 5.5%
10% 4%
4%
9%
7%
6.5% 6.5% 6.5%
5% 6%
0%
Other public and personal Consumer goods, Technology, media and Energy, utilities and mining Manufacturing and Transport and logistics Financial and professional
services accomodation and food telecommunications construction services
services
Note: Worldwide; 2018
Source(s): PwC; Statista estimates
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
GDP growth due to AI in % GDP growth due to AI in trillion U.S. dollars The mature economies of the United States,
30 Europe, and Asia (Japan, South Korea,
Australia, Singapore etc.) are forecast to
26.1 profit from the development and application
of artificial intelligence on somewhat the
GDP growth in percent and trillion U.S. dollars
25
same level. China is projected to maximize
the potential of artificial intelligence due to
20 its more dynamic overall economic growth
and the Chinese’s government strong focus
on it as a national strategy (more details on
15
14.5 China‘s AI policy in country chapter).
11.5
9.9 10.4
10
7
5.4 5.6
5 3.7
1.8
0.7 0.9 1.2
0.5
0
China North America Northern Europe Southern Europe Developed Asia Latin America Rest of world
Regions
Note: Worldwide; 2017
Source(s): PwC
10Incremental 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
GDP increase
-15% -10% -5% 0% 5% 10% 15% 20% 25% 30% 35% 40%
Augmentation 4%
Increased productivity from labor substitution 15%
Innovation / market extension 10%
Global data flows 2%
Wealth creation / reinvestment 6%
Gross impact 37%
Transition costs -8%
Negative externalities -7%
Net impact 21%
Note: United States; 2018
Source(s): ITU; McKinsey
11Share 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
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
80%
70%
60%
Share of jobs at risk
50%
40%
30%
20%
10%
0%
North America Northern Europe Southern Europe China Developed Asia Latin America
Note: Worldwide; 2018
Source(s): PwC; Statista estimates
12Ratio of machine working hours 2018 to 2022, by task
Ratio of machine working hours by task 2018-2022
2018 2022
70%
62%
60%
55%
50% 47%
46% 46%
44% 44%
Share of working hours
40%
36%
34%
31% 31%
28% 29% 28% 29%
30%
23%
19% 19%
20%
10%
0%
Reasoning and Coordinating, Communicating and Administering Performing physical Identifying and Performing complex Looking for and Information and data
decision making developing, interacting and manual work evaluating job-releant and technical receiving job-related processing
managing and activities information activities information
advising
Note: Worldwide; November 2017 to July 2018; 313 Respondents; companies
Source(s): World Economic Forum
13Change 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
Western Europe United States
Percentage change in working hours
-30% -20% -10% 0% 10% 20% 30% 40% 50% 60% 70% Automation is considered to be the
main driving change factor for the job
-16% market of the future, as the adoption of
Physical and manual skills automation and artificial intelligence will
-11%
transform the entire workplace.
There are strong growth opportunities
-17%
Basic cognitive skills
for technological skills such as basic
-14% digital skills and advanced IT skills and
programming. Basic cognitive and
physical skills (e.g. inspecting &
7%
Higher cognitive skills monitoring skills; basic data input and
9% processing skills) on the other hand are
projected to decline.
22% The transportation and logistics industry
Social and emotional skills is projected to be impacted the most in
26% terms of workforce change.
52%
Technological skills
60%
Note: Austria, Denmark, Finland, Germany, Greece, Italy, Netherlands, Norway, Spain, Sweden, Switzerland, United Kingdom, United States; 2018
Source(s): ILO; McKinsey
1402 Leading Countries
▪ Experts, papers, patents
▪ Funding & companiesUnited States and China leading the way in artificial intelligence Both China and the United States are ranked as the top countries for artificial intelligence in all „By 2020 they will have the major categories we looked at (number of companies and experts, funding, patent caught up. By 2025 they applications and papers published on artificial intelligence). Historically, the majority of funding will be better than us. And has been invested in the United States. However, China is making a strong push for the global by 2030 they will dominate lead. In 2017, almost 28 billion U.S. dollars were poured into the Chinese artificial intelligence the industries of AI. Just market. stop for a sec. The Chinese One main difference between the United States and China is the role of the government. China’s government said that.“ 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 Eric Schmidt – Former intelligence startups with funding of more than one billion U.S. dollars are located in China. Google CEO & Executive However, the number of startups venturing into this field is around 3.5 times higher in the United Chairman of Alphabet 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. 16
17
Artificial intelligence performance benchmark by country as of 2018
AI country performance benchmark 2018
Personnel Monetary Impact Competitiveness Research & Education Technology
Benchmark score
0 50 100 150 200 250 300 350
United States 36 68 59 76 98
Germany 31 56 57 52 70
United Kingdom 48 69 56 32 29
China 11 34 26 66 95
France 37 52 46 22 45
Japan 10 12 53 18 87
Republic of Korea 10 23 51 22 71
Canada 28 35 56 14 28
Netherlands 13 40 63 20 19
Australia 14 18 46 18 37
Poland 12 30 29 10 16
Russian Federation 10 18 19 16 29
Note: Worldwide, Australia, Canada, China, France, Germany, Japan, South Korea, Netherlands, Russia, United Kingdom, United States; 2018
Source(s): Capgemini
18Number of artificial intelligence companies worldwide as of June 2018, by country
Number of AI companies worldwide 2018, by country
Number of companies
0 500 1,000 1,500 2,000 2,500 As of June 2018, there were 4,925
United States 2,028
artificial intelligence enterprises
worldwide. The establishment of new
China 1,011 artificial intelligence companies has
United Kingdom 392 slowed worldwide since 2015 when the
number of newly established enterprises
Canada 285
worldwide reached 847.
India 152
Israel 121
France 120
Germany 111
Sweden 55
Spain 53
Netherlands 40
Japan 40
Switzerland 40
Poland 33
Australia 31
Note: Worldwide; as of June 2018
Source(s): CISTP
19Number of artificial intelligence startups worldwide in 2018, by country
Number of AI startups by country 2018
Number of startups
0 200 400 600 800 1,000 1,200 1,400 1,600 Total number of true artificial intelligence
United States 1,393
startups worldwide is 3,465.
China 383
The number of startups exceed the
number of total AI enterprises for some
Israel 362 countries – indicating that the AI
United Kingdom 245 ecosystem of some countries (e.g.
Israel) might have been growing more
Canada 131
dynamically than others over the past
Japan 113 year.
France 109
Germany 106
India 82
Sweden 55
Finland 45
South Korea 42
Spain 39
Singapore 35
Switzerland 28
Note: Worldwide; 2018
Source(s): Roland Berger
20US leads world in artificial intelligence talent – both in quantity & quality
Number of artificial intelligence (AI) experts/talents worldwide by country 2018
AI talent Top AI talent
Number of experts/talents
Estimates on the number of global AI professionals/experts/talents
0 5,000 10,000 15,000 20,000 25,000 30,000 range from 200 thousand to 1.9 million worldwide.
United States The United States lead all countries with 14 percent of global AI
talent and around 44 percent of AI professionals.
China
The AI talent pool of the United States, Europe, Japan and Australia
India
is more advanced in terms of expertise, skill level and experience
Germany compared to China and India.
United Kingdom While 18 percent of US AI talent is considered top-level, this is true
France
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
Iran** total talent, whereas China’s share of top-level talent is only 5
Brazil percent compared to a 9 percent share of overall global AI talent.
Spain Around 70 percent of AI professionals in the US have more than ten
years of experience compared to almost 40 percent in China.
Italy
Canada
Turkey**
Australia
Japan
Note: Worldwide; 2018
Source(s): Institute for Science and Technology Policy (China) (Tsinghua University)
21Number of artificial intelligence professionals by country in 2017 (in 1,000s)
AI professionals by country worldwide 2017
Number of AI professionals in thousands Linkedin estimated the number of AI
0 100 200 300 400 500 600 700 800 900 professionals at around 1.9 million in
United States 850
2017, based on entries in their database
and desk research.
India 150
Compared to the more narrow definition
United Kingdom 140
used for „AI talents“, the lead of the
Canada 80 United States for AI workforce is even
France 50 more substantial at a 40 percent share
Australia 50 China ranks only in seventh place for
China 50 number of AI professionals. The data
might not show the full picture though, as
Germany 30
Linkedin does not have the same
Netherlands 30 penetration in all of the markets shown in
Italy 30 the graph.
Spain 20
Brazil 20
Singapore 17
United Arab Emirates 16
South Africa 15
Note: Worldwide; 2017
Source(s): LinkedIn
22Number of artificial intelligence patents granted by trademark/patent office worldwide from
2000 to 2016
Artificial intelligence patents granted by patent/trademark office 2000-2016
Biological Knowledge Mathematical Other
Number of patents granted
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)
Note: Worldwide; 2000 to 2016
Source(s): RIETI
23Number of artificial intelligence patents granted to universities by country from 2000 to 2016
Artificial intelligence patents granted to universities 2000-2016
Number of patents granted
0 100 200 300 400 500 600 700 800
Chinese university 725
U.S. university 241
Japanese university 93
ROW universities 118
Note: Worldwide, China, Japan, United States; 2000 to 2016
Source(s): RIETI
24Number 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
Number of publications
0 50,000 100,000 150,000 200,000 250,000 300,000 350,000 400,000
United States 369,588
China 327,034
United Kingdom 96,536
Japan 94,112
Germany 85,587
India 75,128
France 72,261
Canada 61,782
Italy 61,466
Spain 58,582
Republic of Korea 52,175
Taiwan, China 46,138
Australia 45,884
Iran 34,028
Brazil 27,552
Note: Worldwide; 1997 to 2017
Source(s): CISTP
25Number 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
Global China
160
146
140 135.5 134.5
120
120
Number of publications in thousands
106
102 99.5
100 93.5
89.5 89.5
83.5 86
80
72
61
60 54.5
49.5
37.5 39 36.5
40 33 35
29.5 30
24.5 27 26.5 26
23.5 22 22 21
19 17.5
20
12.5
7.5 9
4 6
2
0
1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Note: Worldwide; 1997 to 2017
Source(s): Statista estimates; CISTP
26United 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
Productivity Consumption
GDP increase
0% 5% 10% 15% 20% 25% AI policy/strategy
Trump administration
Health, education and other public and personal services 10% 11.5% New Select Committee on AI to advise White
House in 2018.
Main government goals to maintain global
leadership in AI:
Consumer goods, accomodation and food services 7% 9%
• Support national AI R&D ecosystem (e.g.
public-private partnerships)
• Develop US workforce to benefit from AI
Technology, media and telecommunications 6.5% 7% • Remove barriers to innovation (regulations)
Obama administration
Three separate reports released in 2016 outlining
Financial and professional services 4.5% 5.5% a possible foundation for future US strategy:
• Preparing for the Future of Artificial
Intelligence: Recommendations regarding
Manufacturing and construction 4% 5% regulations, public R&D, automation, fairness,
ethics and security
• National Artificial Intelligence Research and
Development Strategic Plan: Outline for
Energy, utilities and mining 5% 4% publicly funded AI R&D strategy
• Artificial Intelligence, Automation, and the
Economy: Deals with impact of automation,
Transport and logistics 3% 5% possible policies to increase benefit from AI
and mitigate costs
Note: North America; 2018
Source(s): PwC; Statista estimates
27Artificial intelligence funding investment in the United States from 2012 to 2018 (in million U.S.
dollars)
Artificial Intelligence funding United States 2012-2018
6,000
Funding and investment in artificial
intelligence in the United States has
consistently risen over the years.
5,012
5,000 Data for the first two quarters of 2018 point
towards a new record high in AI funding for
4,218 the whole year in the United States, as AI
funding for Q1 and Q2 2018 alone already
3,921
Funding in million U.S. dollars
4,000 amounted to around 85 percent of overall
funding for the full year of 2017.
3,203 Funding across all industries in the United
3,000 States was 27.5 billion USD in Q3 2018,
an increase of about 15 percent from Q2
2,489
2018. AI funding is expected to grow at
least in line with overall funding as well.
2,000 For 2018, overall AI funding in the United
States is forecast to amount to between
nine and ten billion USD based on overall
1,118
funding growth and growth rates of AI
1,000 funding over the past eight quarters.
595
282
0
2011 2012 2013 2014 2015 2016 2017 Q1 & Q2 2018
Note: United States; 2012 to 2018
Source(s): CB Insights; PwC
28China on its way to global artificial intelligence dominance
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
29Government 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
Funding in million U.S. dollars
0 20 40 60 80 100 120 140
Smart cars 128.7
Cloud and big data 74.6
Smart robotics 72
Quantum and high performance computing 57.3
Strategic technologies 55.2
Leading electronic materials 27.2
Smart medical devices 13.9
Note: China; 2015 to 2018
Source(s): SCMP; Various sources (National Science and Technology Information System)
30Size of the artificial intelligence market in China from 2015 to 2020 (in billion U.S. dollars)
Artificial intelligence market size in China 2015-2020
16
Considering the goal of the Chinese
14.3 government to make the country the
global leader in AI by 2030, the market is
14
projected to grow strongly over the next
few years. The GDP in China is forecast
12 to increase by at least ten percent
across all industries and sectors.
Market size in billion U.S. dollars
10.2
10
8
6.2
6
4 3.4
2.1
2 1.6
0
2015 2016 2017 2018 2019 2020
Note: China; 2015 to 2018.
Source(s): CMN; CISTP; Statista estimates
31Projected 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
Productivity Consumption
GDP increase
0% 5% 10% 15% 20% 25% 30% 35% 40% 45% 50%
Health, education and other public and personal services 23% 22.5%
Consumer goods, accomodation and food services 14.5% 14%
Technology, media and telecommunications 13.5% 12.5%
Financial and professional services 14% 11.5%
Manufacturing and construction 12% 11.5%
Energy, utilities and mining 11% 10.5%
Transport and logistics 8.5% 8.5%
Note: China; 2018
Source(s): PwC; Statista estimates
32Number of newly founded artificial intelligence companies in China from 2000 to 2017
Number of AI start-ups in China 2000-2017
250
The average age of artificial intelligence
228 enterprises in China is 5.5 years.
75 percent of AI companies in China are
located in Beijing, Shanghai and
200 192
Guangdong.
Number of companies
150
128
98
100
62
57
50
30 30
20
15 15 15 15
10 10 10 10 10
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017
Note: China; 2000 to 2017
Source(s): CAICT; Statista estimates; CISTP
33Highest valued artificial intelligence companies in China in 2018 (in billion U.S. dollars)
Artificial intelligence unicorn companies in China by value 2018
Estimated worth in billion U.S. dollars
0 2 4 6 8 10 12 14 16
DJI 15
UBTech 5
SenseTime 4.5
Cambrion 2.5
Cloudwalk 2
YITU 2
Face++ 2
Horizon Robotics 1.5
iCarbonX 1
Pony.ai 1
Unisound 1
Tongdun 1
Mobvoi 1
Orbbec 1
Note: China; 2018
Source(s): CMN
3403 Investment & Funding
▪ Venture capital & funding
▪ Major dealsInvestment in AI is at an all-time high Artificial intelligence has, once again, entered an exciting period - with hopes for breakthrough “AI is the new electricity. I developments and products. We are in what has been termed an “AI spring”, as investments can hardly imagine an and funding into artificial intelligence have grown strongly over the past few years. For example, industry which is not going the growth in global funding of AI startups amounted to more than 460 percent from 2012 to to be transformed by AI. A 2017. Furthermore, the overall artificial intelligence market is forecast to grow annually by more clear path to an AI-powered than 100 percent each year up to 2025. society includes wide In 2017, AI investment and financing reached almost 40 billion U.S. dollars globally - up from adoption of AI to drive around 31 billion U.S. dollars the year before. Almost 70 percent of investments went to China, industrial and technological as the country is making a strong push to reach its goal of becoming the world‘s leader in AI. endeavors.” Globally, the majority of investment and funding goes into all purpose AI companies that are not Andrew Ng – Co-Chairman focused on a specific industry or product. Startups are a huge part of the artificial intelligence & Co-Founder of Coursera; ecosystem, as funding of AI startups amounted to more than 15 billion U.S. dollars in 2017. Former Chief Scientist at Trends show that an increasing number of startups are moving to more mature funding phases. Baidu 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. 36
Growth of the artificial intelligence market worldwide from 2017 to 2025
Artificial intelligence market growth worldwide 2017-2025
160% 154%
150% 152% 151%
146%
143%
140%
140% 133%
127%
120%
Year-on-year growth
100%
80%
60%
40%
20%
0%
2017 2018 2019 2020 2021 2022 2023 2024 2025
Note: Worldwide; 2016 to 2017
Source(s): Tractica
37Global artificial intelligence investment and financing from 2013 to Q1 2018 (in billion U.S.
dollars)
AI investment and financing worldwide 2013-2018
45
Available data for the first quarter of
2018 points towards AI investments
40 39.2 leveling off at 2017 values. Global AI
investments in the first quarter of 2018
were at about a quarter of total 2017
35
spending.
31.3
AI startup funding is also on pace to
30
Funding in billion U.S. dollars
28 match, but not strongly exceed, 2017
levels.
25
20
15 13.2
10.1
10
4.5
5
0
2013 2014 2015 2016 2017 Q1 2018
Note: Worldwide; 2013 to Q1 2018
Source(s): CAICT; Statista estimates
38Artificial intelligence startup funding worldwide from 2011 to 2018 (in billion U.S. dollars)
AI funding worldwide 2011-2018, by quarter
Q1 Q2 Q3 Q4
16
14
12
Funding in billion U.S. dollars
10
8
6
4
2
0
2011 2012 2013 2014 2015 2016 2017 2018
Note: Worldwide; 2011 to Q3 2018.
Source(s): Venture Scanner; Statista estimates
39Share 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
Seed/Angel Series A Series B Series C Series D Series E Series F Other series
100%
90%
80%
70%
60%
Share of projects
50%
40%
30%
20%
10%
0%
2013 2014 2015 2016 2017 Q1 2018
Note: Worldwide; 2013 to Q1 2018
Source(s): CAICT; Statista estimates
40Share 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
Seed A B C D Late stage
100%
90%
80%
70%
Share of funding count
60%
50%
40%
30%
20%
10%
0%
2012 2013 2014 2015 2016 2017
Note: Worldwide; 2012 to 2017
Source(s): Venture Scanner; Statista estimates
41Share 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
Seed A B C D Late stage
100.0% 2.5%
4% 6.5% 9.5% 13.5%
7%
15.5% 20.5%
2.5% 29.5%
90.0%
18% 8%
21%
80.0% 20.5%
26%
25%
70.0% 31.5%
Share of funding amounts
28.5%
60.0% 31.5%
23.5%
40%
50.0% 29.5%
40.0%
28.5%
27.5%
29.5%
30.0% 22%
20.0% 19%
15%
10.0% 12.5%
11% 11.5%
8%
3.5%
0.0%
2012 2013 2014 2015 2016 2017
Note: Worldwide; 2012 to 2017
Source(s): Venture Scanner; Statista estimates
42Number of newly founded artificial intelligence companies worldwide from 2000 to 2017
Number of AI start-ups worldwide 2000-2017
900
845
800
740
700
650
600
Number of companies
500
450
400 385
320
300
245
200 180
130
100
100 85
60 65
45 50
35 35 30
10
0
2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 July 2018
Note: Worldwide; 2000 to 2017
Source(s): CAICT; Statista estimates
43Number of artificial intelligence startup company acquisitions worldwide 2013-2017
Acquisitions of AI startup companies worldwide 2013-2017
140 The growth of investment in AI over the
past few years has led to more
acquisitions of startups as well.
120 115 Intel is one of the most active investors
in AI with more than one billion USD
invested in AI startups through its Intel
100 Capital division.
Number of acquisitions
80
80
60
45
39
40
22
20
0
2013 2014 2015 2016 2017
Note: Worldwide; 2013 to 2017
Source(s): CB Insights
44Number of artificial intelligence investments by investor as of November 2018
Number of AI investments by investor as of November 2018
Number of investments
0 5 10 15 20 25 30 35 40 45 50
Intel Capital 50
500 Startups 45
NEA 34
Battery Ventures 29
Y Combinator 28
Madrona Venture Group 25
Horizon Ventures 22
Accel 22
Bloomberg Beta 22
Data Collective 21
Techstars 21
First Round Capital 21
Sequoia Capital 21
Kima Ventures 21
vesna 20
Note: Worldwide; as of November 2018
Source(s): Website (index.co)
45Artificial intelligence funding worldwide cumulative through September 2018 (in billion U.S.
dollars), by category
AI funding worldwide cumulative through September 2018, by category
Funding in billion U.S. dollars
0 2 4 6 8 10 12 14 16 18 20
Machine learning applications 19.5
Machine learning platforms 9.4
Computer vision platforms 6.5
Smart robots 5.4
Recommendation engines 4
NLP 3.1
Computer vision applications 3
Virtual assistants 2.5
Speech recognition 2
Gesture control 0.8
Video recognition 0.4
Note: Worldwide; Cumulative through September 2018
Source(s): Venture Scanner; Statista estimates
46Share 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
Share of total number
0% 10% 20% 30% 40% 50%
AI+ 53%
Computer Vision 11%
Big Data & Data Services 10%
Smart Robot 8%
Natural Language Processing 6%
Autonomous Vehicles 4%
Speech 4%
Basic Hardware 3%
Unmanned Aerial Vehicle 2%
Augmented & Virtual Reality 1%
Note: Worldwide; 2013 to Q1 2018
Source(s): CAICT
4704 Leading Companies
▪ Investments
▪ Patents & papersUS companies heavy hitters in AI with China catching up Artificial intelligence is not limited in its possibilities of use, meaning companies from all spheres “If the internet was the are engaged in the market. Naturally, major players from the tech industry are leading the way; appetizer, then AI is the having already invested billions of dollars into research, development and the artificial main course. The internet intelligence ecosystem overall. Amongst these are well-known US-based internet and tech giants changed a lot of our daily like Google, Amazon, Facebook, Apple, Intel, IBM and Microsoft. lives, but did not have Google is at the forefront of AI investment and development. Over the past two decades the much impact on the 2B company has invested billions of dollars in AI R&D and the acquisition of startups in the industry. industries. I think AI will Most notably they acquired Deepmind, which Google bought for 500 million U.S. dollars in 2014. change that.” 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 Robin Li Yanhong – CEO rankings. of Baidu 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. 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
Acquisition spending in billion U.S. dollars
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500 Some of the major tech companies are
using a two-part approach to AI by
Google 3,900 investing into their own AI research and
development departments but also
Amazon 871 investing in and acquiring startups from
the AI ecosystem.
Apple 786 The acquisition of AI startups provides
the companies with potential products
Intel 776 and use cases. However, more
importantly they are able to acquire AI
Microsoft 690 professionals and experts which are in
high demand all over the world.
Uber 680
Twitter 629
AOL 191.7
Facebook 60
Salesforce 32.8
Note: Worldwide; January 2018
Source(s): Website (techrepublic.com)
50Number of artificial intelligence startups acquired from 2010 to June 2018, by company
AI startup acquisitions by company 2010-2018
Number of acqusitions
0 2 4 6 8 10 12 14
Alphabet/Google 14
Apple 13
Facebook 6
Amazon 5
Intel 5
Microsoft 5
Meltwater 5
Twitter 4
Salesforce 4
Note: Worldwide; 2010 to June 2018
Source(s): Fortune; CB Insights
51Artificial intelligence patent applications of leading technology companies from 1999 to 2017
Global AI-related patent applications by company 1999-2017
AI patent applications
0 500 1,000 1,500 2,000 2,500 3,000 3,500 4,000 4,500
Some of the major players in the tech
industry are leading the charge in AI
Microsoft 4,167
foundation work and innovation, as
companies like IBM, Microsoft, Google and
IBM 3,360 Samsung have applied for the most
patents and published the majority of
Google 2,650 papers on AI topics over the past 10 to 20
years.
Samsung 2,404
Quantity in AI papers and patents are
indicative of these companies being in the
AT&T 1,413 lead today but this does not necessarily
mean that they are the ones to cash in first
Baidu 1,246 or that they will be at the forefront in five
years, as more money than ever is
State Grid 1,167 invested in AI research especially in China.
Toshiba 1,149
Fujitsu 1,132
NEC 930
Note: Worldwide; 1999 to 2017
Source(s): CAICT; Statista estimates
52Number 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
Number of patents
0 1,000 2,000 3,000 4,000 5,000 6,000 7,000 8,000
IBM 7,276
Microsoft 5,356
Samsung Electronics 5,255
State Grid Corporation of China 3,794
Canon 3,569
Sony 3,090
NEC Corporation 2,932
Fujitsu Limited 2,868
Google 2,757
Mitsubishi Electric 2,716
Note: Worldwide; 1997 to 2017
Source(s): CISTP
53Number of artificial intelligence patents granted by company worldwide from 2000 to 2016
Artificial intelligence patents granted by company 2000-2016
Number of patents granted
0 200 400 600 800 1,000 1,200
IBM (US) 1,057
Microsoft (US) 466
Qualcomm (US) 450
NEC (Japan) 255
Sony (Japan) 212
Google (US) 195
Siemens (Germany) 192
Fujitsu (Japan) 154
Samsung (Korea) 119
NTT (Japan) 94
Hewlett- Packard (US) 93
Yahoo (US) 88
Toshiba (Japan) 86
D-wave (Canada) 77
Hitachi (Japan) 69
Note: Worldwide; 2000 to 2016
Source(s): RIETI
54Number 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
Number of publications
0 1,000 2,000 3,000 4,000 5,000 6,000
IBM 5,105
Microsoft 4,710
Siemens AG 2,825
Samsung 1,548
Google 1,383
Intel 1,324
Philips 1,229
Microsof Research Asia 1,181
General Electric 1,168
Siemens 1,136
NEC Corporation 957
Philips Research 923
Nokia 869
State Grid Corporation of China 841
Honda 816
Note: Worldwide; 1997 to 2017
Source(s): CISTP
55Artificial 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
Total funding in million U.S. dollars
0 500 1,000 1,500 2,000 2,500 3,000 3,500
Some of the best funded companies in
Toutiao (China), 2012 3,100 artificial intelligence today are located in
ByteDance (China), 2012 3,000 China.
SenseTime (China), 2014 2,600 With a high number of AI companies and
enterprises in the United States, funding
NIO (China), 2014 2,500
and investment money is more spread out
Argo AI (United States), 2017 1,000 when compared to China. The majority of
AI funding in China is more concentrated
Dataminr (United States), 2009 968.6
with fewer but bigger companies,
UBTech Robotics (China), 2012 940 indicative of a more concentrated market
Zoox (United States), 2014 790
space.
Tanium (United States), 2007 782.8
Affirm (United States), 2012 720
Indigo (United States), 2014 609
Megvii Technology (China), 2011 607
OakNorth (United Kingdom), 2013 601
CloudWalk Technology (China), 2015 546
Kreditech (Germany), 2012 497.3
Note: Worldwide; as of November 2018
Source(s): CB Insights; Statista; CrunchBase; various sources
56Highest valued artificial intelligence companies in China in 2018 (in billion U.S. dollars)
Artificial intelligence unicorn companies in China 2018, by value
Estimated worth in billion U.S. dollars
0 2 4 6 8 10 12 14 16
DJI 15
UBTech 5
SenseTime 4.5
Cambrion 2.5
Cloudwalk 2
YITU 2
Face++ 2
Horizon Robotics 1.5
iCarbonX 1
Pony.ai 1
Unisound 1
Tongdun 1
Mobvoi 1
Orbbec 1
Note: China; 2018
Source(s): CMN
5705 AI Technologies & Enablers
▪ AI types & categories
▪ AI frameworks
▪ Computing & semiconductorsArtificial intelligence technologies – a wide field of opportunites & categories 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) 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
High end Low end
External investment in billion U.S. dollars What actually is…?
0 1 2 3 4 5 6 7 8
Computer vision is an interdisciplinary
7 field of computer science dealing with
Machine learning
5 enabling computers to see, identify and
process images. It aims at giving
3.5 computers a high level of
Computer vision
2.5 understanding of these images similar
to humans.
0.9
Natural language Natural language processing (NLP)
0.6
is a subfield of AI that helps computers
understand, interpret and manipulate
0.5
Autonomous vehicles human language. It combines
0.3 linguistics with computer science to
identify and understand spoken
0.5 language as well as text based
Smart robotics
0.3 language.
0.2
Virtual agents
0.1
Note: Worldwide; 2016
Source(s): McKinsey; PitchBook; Dealogic; S&P Capital IQ
60Number of artificial intelligence publications worldwide from 2007 to 2017, by topic
AI-related publications worldwide 2007-2017, by topic
Number of publications
0 10,000 20,000 30,000 40,000 50,000 60,000 70,000 What actually is…?
Pattern recognition 63,666
Pattern recognition is a branch of machine
Learning systems 53,539
learning (supervised/unsupervised) that is
Machine learning 29,941 used to recognize patterns and regularities
in data sets. It makes use of computer
Neural networks 26,470
algorithms to discover these regularities and
Natural language processing systems 23,486 then classifies the data into different
Learning algorithms 22,626
categories. Pattern recognition is the basis
for computer-aided diagnosis systems in
Data mining 20,089 medical science. Other applications include
Feature extraction 19,709 speech recognition, image recognition and
classification of text into categories.
Semantics 18,529
Image processing 16,649 Artificial neural networks (ANN) is an AI
technique that mimics or tries to replicate
Pattern recognition, automated 15,848
the workings of the human brain. It is one of
Pattern recognition, visual 14,629 the main concepts/frameworks used for
Non-human 13,855
machine and deep learning. Developed
neural networks can extract meaning from
Decision support systems 13,580 complicated data and detect trends and
Decision making 13,430 patterns too complex for humans to identify.
Note: Worldwide; 2007 to 2017
Source(s): IP Pragmatics
61Share of global artificial intelligence enterprises in 2018, by category
Distribution of AI enterprises worldwide 2018, by category
Share of total number
0% 10% 20% 30% 40% 50% 60%
Artificial intelligence companies are
AI+ - specific industry verticals 49% almost evenly split amongst ones that
focus on one specific industry vertical,
Big Data & Data Services 12%
such as business intelligence and
healthcare, and those that are focused
on a specific type of horizontal AI
Computer Vision 11%
application.
Smart Robot 8%
Natural Language Processing 7%
Basic Hardware 5%
Speech 4%
Autonomous Vehicles 3%
Unmanned Aerial Vehicle 1%
Augmented & Virtual Reality 1%
Note: Worldwide; 2018
Source(s): CAICT
62Artificial intelligence hardware market share worldwide in 2017, 2020 and 2023, by product
AI hardware market share by product worldwide 2017-2023
Sound processor Embedded sound processing unit Vision processor Embedded vision processing unit
100%
96% 43% 38%
90%
80%
70%
60% 15%
Market share
7%
50%
4%
46% 6%
40%
41%
30%
20%
10%
4%
0%
2017 2020 2023
Note: Worldwide; 2017
Source(s): Yole Développement
63Semiconductor sales revenue worldwide from 1987 to 2019 (in billion U.S. dollars)
Semiconductor industry sales worldwide 1987-2019
500
450
400
350
Sales in billion U.S. Dollars
300
250
200
150
100
50
0
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
Note: Worldwide; 1987 to 2017
Source(s): WSTS; SIA
64Semiconductor unit shipments worldwide from 2000 to 2018 (in billions)
Semiconductor unit shipments worldwide 2000-2018
1,200
1,075.1
986.2
1,000
868.8
815.3
800
Unit shipments in billions
705.6
623.7
600 556.2
467.1
397.4
400
200
0
2000 2004 2006 2007 2010 2014 2016 2017 2018
Note: Worldwide; 2000 to 2018
Source(s): IC Insights
65Estimated 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
35 The global market for AI specific chips is forecast to increase from
33
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
30 currently developing chips specifically for AI purposes.
CPUs (Central Processing Unit) are chips used for general
26
computing purposes.
25
GPUs (Graphic Processing Unit) are programmable chip
Revenue in billion U.S. dollars
processors originally designed for display functions (images, videos,
animations). They have been adopted for use in AI as they can
20 19 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
15
good at processing small-scale but intensive data.
12
ASIC (Application-Specific Integrated Circuit) chips are built for a
10 specific purpose or application. They are tailored towards that one
specific use but cannot be customized after production.
6 NPU (Neuromorphic Processing Unit) are a type of newly
5 developed chip category mimicking the architecture of the human
3 brain. These type of chips are still in the early stages of
development.
0
2017 2018 2019 2020 2021 2022
Note: United States; 2016 to 2018
Source(s): SIA
66Optoelectronics / optical semiconductor revenue worldwide from 2008 to 2019 (in billion U.S.
dollars)
Global optical semiconductors market revenue 2008-2019
40
38.02
35.99
34.81
35
33.26
31.99
29.87
30
27.57
26.2
Sales in billon U.S. dollars
25
23.09
21.7
20
17.9
17.04
15
10
5
0
2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019
Note: Worldwide; 2008 to 2017
Source(s): WSTS
67CMOS image sensors sales revenue worldwide from 2007 to 2022 (in billion U.S. dollars)
CMOS image sensor sales worldwide 2007-2022
20 CMOS image sensors are projected to become one of the most
19
important means to acquire image data for artificial intelligence
18 17.4 applications. They could be considered the eye of artificial
intelligence bringing vision to these systems.
16.1
16 15.2 Today there are two main technologies in use for CMOS image
sensors:
13.7
Sales revenue in billion U.S. dollars
14 FSI (front side illuminated) – limited in the fields of use compared
12.5 to BSI because the pixel size is reduced with higher resolutions.
12 The manufacturing process of FSI sensors is more simple, lower in
10.5 cost and has a higher yield compared to BSI.
9.9
10 BSI (back side illuminated) – more mature/advanced technology
8.9
than FSI. Solution for applications in need of high resolution with
8 7.4 limited optical and pixel size. BSI sensors have a high sensitivity
7.1
and a strong low-light performance. Exemplary fields of application
5.9 are surveillance, factory automation and smartphones.
6
4.5 4.5
4 3.9
4
2
0
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022
Note: Worldwide; 2018
Source(s): IC Insights; Statista estimates
68Worldwide revenue of the supercomputer market from 2015 to 2017 (in billion U.S. dollars)
Supercomputer revenue worldwide 2015-2017
5 4.8
4.5
4.1
4
3.5 3.3
Revenue in billion U.S. dollars
3
2.5
2
1.5
1
0.5
0
2015 2016 2017
Note: Worldwide; 2015 to 2017
Source(s): Hyperion Research
69Deep learning artificial intelligence framework power scores 2018
Ranking of artificial intelligence deep learning frameworks 2018
Power score
0 10 20 30 40 50 60 70 80 90 100
Deep learning uses neural networks and
TensorFlow 96.77 large data sets (big data). The concept is
in many aspects inspired by the human
Keras 51.55 brain. Based on existing information and
the neural network, the system is capable
PyTorch 22.72 of connecting what it has already learned
with new content and information to
Caffe 17.15 continuously learn more. As a result, the
system has the ability to make predictions
Theano 12.02 and decisions.
Software frameworks are platforms for
MXNET 8.37
developing software applications. They
CNTK
provide generic functionalities that can be
4.89
selectively changed by additional user-
DeepLearning4J 3.65
written code.
Caffe2 2.71
Chainer 1.18
FastAI 1.06
Note: Worldwide; 2018
Source(s): Website (Towards Data Science)
70Artificial Intelligence frameworks by number of commits and contributors on GitHub as of
November 2018
Popularity/usage of artificial intelligence frameworks worldwide 2018
Commits Contributors
Number of commits/contributors Google’s open source TensorFlow AI
0 5,000 10,000 15,000 20,000 25,000 30,000 35,000 40,000 45,000
framework/library has become the
43,768 most used and popular one since its
TensorFlow
1,723 initial release in November 2015.
Theano
28,052
330
DeepLearning4J
26,623
237
Chainer
16,675
192
Microsoft Cognitive Toolkit (CNTK)
15,994
189
PyTorch
14,709
838
Apache MXNet
8,846
641
Keras
4,907
748
Caffe
4,152
270
Torch
1,336
132
Note: Worldwide; as of November 16, 2018
Source(s): GitHub
71Forecast number of mobile 5G subscriptions worldwide from 2019 to 2022 (in millions)
Forecast number of 5G mobile subscriptions worldwide 2019-2022
450
400
400
350
300
Subscriptions in millions
250
200
150
100 84
50
11
0.42
0
2019 2020 2021 2022
Note: Worldwide; December 2017
Source(s): 5G Americas
7206 Applications & Industry Impact
▪ Major use cases
▪ Industry & sector impactWhere will artificial intelligence impact be felt first?
Artificial intelligence is set to impact every industry and many aspects of everyday life in the future. “Whenever I hear people
There are still many milestones to be reached to achieve full AI maturity and “strong AI“. However, saying AI is going to hurt
some fields such as virtual digital assistants and chatbots are already making an impact. people in the future I think,
Amazon Alexa, Google Assistant, Apple’s Siri and Microsoft Cortana are well-known examples of yeah, technology can generally
virtual digital assistants (VDA) making use of AI technology. These VDAs answer questions, always be used for good and
provide news and weather updates and let the user control other devices in their home. bad and you need to be careful
about how you build it … if
All major automotive manufacturers are now developing self-driving autonomous vehicles and plan you’re arguing against AI then
to release partly, if not fully, automated cars to the market in the mid-2020s. you’re arguing against safer
In e-commerce and retail, artificial intelligence can help companies with warehouse automation, cars that aren’t going to have
identifying target groups for their products and predicting sales more accurately. accidents, and you’re arguing
against being able to better
In healthcare, the use cases for artificial intelligence are manifold as well. Medical imaging, cancer
diagnose people when they’re
detection, diagnostic scans, robot-assisted surgery, health monitoring, drug discovery and virtual
sick.”
nursing assistants.
- Mark Zuckerberg – Facebook
CEO
74Share of artificial intelligence startups worldwide in 2018, by industry
Share of AI startups by industry 2018
Share of startups
0% 5% 10% 15% 20% 25%
More than 60 percent of AI startups
General/Cross-Sectoral (B2B services) 25% are applying to major functions in
Communication (B2B services) 14% cross-cutting sectors
(communications, marketing, HR,
Sales/Marketing (B2B services) 12%
security, e-commerce, legal, etc.) and
Healthcare/BioTech 9% are therefore considered B2B
services.
Other 7%
Defense/Security (B2B services) 6%
FinTech 6%
Human Resources (B2B services) 3%
Entertainment 3%
Transportation 3%
Education 2%
Travel 1%
Other (B2B services) 1%
Energy 1%
Automotive 1%
Note: Worldwide; 2018
Source(s): Roland Berger
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