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Artificial Intelligence and Machine Learning China Region Special Section: Hot Topics

The Future of Artificial Intelligence in China

  1. Introduction
  2. Technical Giants Commit to AI Research
  3. Leading Startups Founded by Professors
  4. The Future of AI Research in China
  5. References
  6. Authors
  7. Footnotes
chip with map of China

China’s research efforts in artificial intelligence (AI) began later than the U.S. and Europe. Early contributions in the 1970s included automated theorem proving, logic reasoning, search, and knowledge engineering. For example, Wen-tsün Wu is a pioneer in automated theorem proving. He received the State Preeminent Science and Technology Award in 2000, an honor bestowed on only 25 Chinese scientists across all fields to date. Bo Zhang and Ruqian Lu received the Life Achievement Award from the China Computer Federation (CCF) for their fundamental contributions respectively on problem solving and knowledge engineering.

With the establishment of basic research funding to include AI research and development (R&D) in 1986, two agencies—the National Natural Science Foundation of China (NSFC), which supports basic research, and the 863 Program (State High-Tech Development Plan) for applied research—began funding diverse AI-related research topics, such as hardware and software for intelligence, human-computer interaction (HCI), intelligent application systems, neural networks, genetic algorithms, machine learning, natural language processing, computer vision, and robotics.

Figure. The first Smart China Expo was held last August in Chongqing and featured cutting-edge technologies from the fields of AI, robotics, 5G, and more.

In the late 1980s and 1990s, an emphasis on research in Chinese natural language processing took hold. Xuan Wang, a pioneer in applying AI to Chinese character printing and layout processing, became another recipient of the State Preeminent Science and Technology Award in 2001. He created the Founder Group, one of the largest computer companies on Mainland China in the late 1990s. Other AI-related companies started during that period include iFlyTek (Chinese voice synthesis and recognition), Hanvon (handwriting recognition), and TRS (Chinese full-text retrieval system).

After 2000, China’s Ministry of Science and Technology (MOST), NSFC, other central government agencies, and local governments including Beijing, Shenzhen, and Hangzhou, increased funding tremendously to facilitate the new AI boom. The financial boost enabled Chinese researchers to attend international conferences and become deeply involved and integrated into international research communities. It is now common to see China’s researchers attending top conferences, and their success includes having their research published extensively in leading AI conferences and journals, such as AAAI, IJCAI, ICML, NIPS, CVPR, ACL, PAMI, Artificial Intelligence, and more. For example, 23% of the accepted papers for the AAAI 2017 conference were from China, rising from only 10% in 2012.a For the IJCAI 2017 conference, nearly one-third of both submitted and accepted papers came from China.b

Important technical contributions from the region have been made in machine learning, computer vision, natural language processing, robotics, and more. For example, in machine learning, extensive work has been done on ensemble learning,8 transfer learning,4 artificial neural networks, evolutionary computing,6 and probabilistic machine learning.9 In computer vision, much progress has been made on Markov’s random field modeling for image analysis,3 handwritten character recognition,1 facial recognition, and so on.2 Finally, progress in AI hardware has enhanced accelerators for deep neural networks.7

With this increasing impact and recognition, more researchers from China have been invited to serve the community, in roles such as program chairs or area chairs for leading conferences, and as associate editors for top journals. For example, Qiang Yang from the Hong Kong University of Science and Technology (HKUST) is the president of the IJCAI Board of Trustees (2017–2019), and Zhi-Hua Zhou from Nanjing University is serving as a program co-chair for AAAI 2019. As the local community is growing fast, many top conferences such as IJCAI, ICML, and ICCV are now held (or will be) in China.

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Technical Giants Commit to AI Research

In addition to government-supported academic research, industry has also been very active in AI exploration.

China’s technical giants, such as Baidu, Alibaba, Tencent, and Huawei, are actively investing in AI research and related development. These corporations have established their own worldwide AI labs, typically directed by world-renowned AI scientists such as Andrew Ng, who led Baidu Lab from 2014–2017. Moreover, these companies have branches throughout China, the U.S., and Europe.

AI research in industry labs is generally more business oriented. They focus on inventing and developing AI algorithms and systems to optimize not only their current businesses, such as online advertisement, payments, social networking, and gaming, but also new businesses such as smart city, healthcare, and auto-drive technologies. For example, Alibaba’s ET Brain project uses AI to reduce traffic jams. It has been reported that traffic delays have been reduced by 15.3% by controlling 128 traffic signals in a select area of Hangzhou, where Alibaba’s corporate headquarters is based. Moreover, ambulance response times in the same area were cut in half.

In addition to specific applications, corporate giants are also trying to build their own ecosystems. For example, Baidu launched DuerOS, a system that allows users to embed many AI functionalities, such as voice, natural language processing, and image recognition into devices. It also released open source platforms, such as Apollo for autonomous driving, and PaddlePaddle for deep learning.

International companies such as Microsoft, IBM, and Intel, also have built research labs in China with active AI research. They not only have very high-quality and impactful research, such as the Dual Learning theory proposed by Tieyan Liu at Microsoft Research Asia, but also feed China’s AI industry with many high-quality researchers and technical managers.

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Leading Startups Founded by Professors

The AI boom has given rise to smaller AI-focused companies, including Cambricon (AI chips), iFlytech (voice), SenseTime and MegeView (computer vision), and UBTECH (robotics). Researchers from academic institutions and universities founded many of these firms. Cambricon was founded by Tianshi Chen and Yunji Chen, both researchers at the Institute of Computing Technology, Chinese Academy of Science. They are pioneers in AI processor architecture and won the best paper awards at ACM’s premier computer architecture conferences, ASPLOS and MICRO. Xiaoou Tang, a professor at the Chinese University of Hong Kong who has won best paper awards at top computer vision conferences like CVPR and ICCV, founded SenseTime—now the most valuable AI startup in the world, with a valuation of over $4.5 billion.

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The Future of AI Research in China

AI research is young, but growing up fast in China. While still short on groundbreaking works and highly influential researchers, we are optimistic China’s fast-growing economy and the aging population will drive strong demand for novel AI techniques, and ensure the successful future of China’s AI research.

Recognizing the strong demand for AI, the Chinese government is planning support for AI education, research, and applications. In 2017, NSFC’s Information Science Department reorganized its five information science areas (electronic engineering, computer science, automation, semiconductors, and optoelectronics) to incorporate the new sixth area, artificial intelligence.

AI research is young, but growing up fast in China.

The AI 2.0 proposal from the China Academy of Engineering5 triggered the launch of a 15-year New Generation Artificial Intelligence Development Plan in July 2017. The plan is focused on a forward-looking blueprint for basic theories and common key technologies, including big data intelligence, swarm intelligence, cross-media intelligence, hybrid enhanced intelligence, and autonomous systems, and their applications in manufacturing, urbanization, healthcare, and agriculture, as well as AI hardware and software platforms, policies and regulations, and ethical concerns. Another R&D project related to AI is the so-called “Brain Science and Brain-Inspired Research,” comparable to Europe’s Human Brain Project, the BRAIN Initiative in the U.S., and other state-level projects. It is expected to be approved this year and should run for 15 years.

Last but not the least, a positive feedback loop between academia and industry has been established, which we believe will trigger more fundamental breakthroughs in AI in the future.

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    1. Ding, X. and Wang, Y. Character Recognition: Principles, Methods and Practice. Tsinghua University Press, 2017.

    2. Gao, W. and Chen, X. Computer Vision—Algorithms and System. Tsinghua University Press (in Chinese), 1999

    3. Li, S.Z. Markow Random Field Modeling in Image Analysis. Springer Science & Business Media, 2001.

    4. Pan, S.J. and Yang, Q. A survey on transfer learning. IEEE Trans. on Knowledge and Data Engineering 20, 10 (2009).

    5. Pan, Y. Heading toward artificial intelligence 2.0. Engineering, 2016, 409–413.

    6. Yan, P. and Zhang, C. Artificial Neural Network and Evolutionary Computing. Tsinghua University Press, 2005.

    7. Zhang, S. et al. Cambricon-X: An accelerator for spare neural networks. In Proceedings of the of the 49th Annual IEEE/ACM International Symposium on Microarchitecture, 2016.

    8. Zhou, Z.-H. Ensemble Methods: Foundations and Algorithms. Chapman & Hall/CRC, Boca Raton, FL, 2012.

    9. Zhu, J., Chen, J., Hu, W. and Zhang, B. Big learning with Bayesian methods. National Science Review 4, 4 (2017), 627–651.

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