China plans to become a leading player in AI in the next few decades - this ambition, together with numerous social and ethical challenges, turns Chinese AI into a highly vibrant, but also controversial topic. This report analyzes Chinese online texts pertaining to AI in the period of December 10, 2018 to January 5, 2019.

1. Artificial Intelligence in China
Competitors
Markets
Consumers
Industries
Partners
A quantitative snapshot of common topics from the Chinese Web
(Dec 2018 - Jan 2019)

2. Popular EV/PHEV modelssOverview
China plans to become a leading player in AI in the
next few decades - this ambition, together with
numerous social and ethical challenges, turns
Chinese AI into a highly vibrant, but also
controversial topic. The present report analyzes
Chinese online texts pertaining to AI in the period
of December 10, 2018 to January 5, 2019. The
data is drawn from Anacode’s Social Technology
Index, which includes a variety of data such as
social media, business news and specialized
technology portals.The analysis is based on a
fine-grained representation of the AI domain which,
among others, models the major technologies,
players and application areas.
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Data basis: Web data from Anacode China Social Technology Index, Dec 2018 - Jan 2019. 1

3. Popular EV/PHEV modelssArtificial Intelligence in context
This chart shows the various contexts in which AI is
discussed on the Chinese Web. The most
prominent area is Careers & Education. This can
be explained two tendencies: on the one hand,
education is one of the main application areas of
AI; city-wide experiments in AI-based education
turn it into a hot discussion topic (cf. slides 6 and
7). On the other hand, China is currently making
huge efforts to systematize and scale training and
education in AI itself. This is reflected in numerous
online tutorials, course descriptions and reviews of
the various offerings. Naturally, AI is also an
attractive career path for engineering and computer
science graduates.
Beyond education and HR, infrastructural areas
relating to Smart Cities, Finance and Energy make
up another large proportion of the data. Pure B2C
applications such as Games and Home Appliances
make up a relatively small proportion of the data.
Data basis: Web data from Anacode China Social Technology Index, Dec 2018 - Jan 2019. 2

4. Popular EV/PHEV modelssResearch and education institutions
As seen on the previous slide, education and the
supply of AI talent are a central topic and a critical
component in the implementation of a successful AI
strategy. According to McKinsey¹, China needs to
build three layers of talent:
● Top scientists pushing the boundaries of
fundamental AI technology
● Developers who can create AI applications
for real-world contexts
● Large base of workers who work alongside
AI systems on a day-to-day basis
The chart shows the distribution of Chinese
universities in the Web data. Qinghua University
clearly stands out - the school launched an AI
institute in July 2018 and hired Google’s AI lead
Jeff Dean as advisor.² Beijing university also
announced a new campus with focus on AI. In total,
34 Chinese universities currently have specialized
AI institutes.³References:
[1] McKinsey. 2017. Artificial Intelligence: Implications for China.Retrieved from https://mck.co/2Ri33ag.
[2] Qinghua University. 2018. 清华与谷歌联合举办人工智能学术研讨会. Retrieved from https://bit.ly/2RHuD0b.
[3] 程瑶. 2018. 北大将在昌平建新校区以人工智能为特色. Retrieved from
http://www.xinhuanet.com/local/2018-11/12/c_1123696834.htm.
Data basis: Web data from Anacode China Social Technology Index, Dec 2018 - Jan 2019. 3

5. Popular EV/PHEV modelssFundamental technologies and algorithms
From a technological perspective, there are two
main approaches to AI:
- Machine Learning: the machine learns from
large quantities of data.
- Rule-based approach: a human expert
teaches the machine by coding rules on a
specific domain.
The chart shows that Machine Learning with its
subdomain Deep Learning and the Neural Network
as its principal tool stand out and clearly win over
rule-based approaches such as Expert Systems.
Traditional “shallow” learning algorithms, such as
Support Vector Machines and different types of
regression, only make up a small proportion of the
data. While still being widely used in practical
applications, these methods attract less interest in
the research and coding community and thus … .
Data basis: Web data from Anacode China Social Technology Index, Dec 2018 - Jan 2019. 4

6. Popular EV/PHEV modelssZooming in on the Neural Network
References:
[1] Yoav Goldberg. 2016. A primer on neural network models for natural language processing. J. Artif. Int. Res. 57, 1 (September 2016), 345-420.
[2] Alibaba. 2019. 达摩院2019十大科技趋势. Retrieved from https://www.iyiou.com/p/89119.html.
[3] Ian Goodfellow et al. 2014. Generative adversarial nets. In Proceedings of the 27th NIPS.
Neural Networks come in different architectures,
depending on the structure of the layers and the way in
which data travels through the network.¹ The chart
illustrates the distribution of different network types in
the data.
Graph Neural Networks (GNNs) have been around for
some years; interest was revived in the past months by
Chinese scientists. Alibaba names the GNN as one of
the main research trends for 2019.² Convolutional and
Recurrent Neural Networks, incl. the highly efficient
LSTM architecture, are more traditional architectures
and widely used in NLP and image recognition.
Finally, the Generative Adversarial Network (GAN) is a
relatively new architecture proposed by the university of
Montreal.³ GANs detect, learn and reproduce the natural
features of a data set. By virtue of their generative
capacity, GANs have been especially intriguing for
creative tasks such as the creation of paintings and
videos, and thus are an important milestone in moving
the AI frontier.
Data basis: Web data from Anacode China Social Technology Index, Dec 2018 - Jan 2019. 5

7. Popular EV/PHEV modelssThe BAT race
Baidu, Alibaba and Tencent are the core players in China’s AI arena. By virtue of their resources, these companies can tap
into the combined potential of algorithmic research, huge data quantities and powerful hardware and computation resources.
The following chart compares the top 5 application areas of Baidu with the offerings of Alibaba and Tencent:
Data basis: Web data from Anacode China Social Technology Index, Dec 2018 - Jan 2019. 6

8. Popular EV/PHEV modelssAI players beyond BAT
This chart shows prominent AI players beyond BAT and
classifies them by application.
Yixue Education has generated a lot of discussion in the
past months with provoking hypotheses about the
interplay between AI and human creativity. In December
2018, Yixue started an experiment in Weifang
(Shandong), during which most schools in the city
implement the company’s adaptive AI-based learning
technology of the company.
Megvii is the owner of the widely used face recognition
platform Face++, which is also used by the Chinese
government and police. The company is considering an
IPO on the Hong Kong Stock Exchange in 2019.
UBTECH, the third player in the ranking, is B2C robotics
startup that secured USD 280M funding in May 2018.
References:
[1] 奎文新闻. 2018. 全市首批乂学教育松鼠AI实验基地在奎文区试点启动. Retrieved from https://kuaibao.qq.com/s/20181225B0GDDF00?refer=cp_1026.
[2] Bloomberg. 2019. Alibaba-Backed AI Startup Said to Mull Up to $1 Billion IPO. Retrieved from https://bloom.bg/2ClPvAo.
[3] Ian Goodfellow et al. 2014. Generative adversarial nets. In Proceedings of the 27th NIPS.
Data basis: Web data from Anacode China Social Technology Index, Dec 2018 - Jan 2019. 7

9. Popular EV/PHEV modelssContact
Anacode GmbH
Kurfürstendamm 76
10709 Berlin
info@anacode.de
www.anacode.de
Dr. Janna Lipenkova
+49 (152) 098 17 228