Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Perspective
  • Published:

Towards a new generation of artificial intelligence in China

Nature Machine Intelligence volume 2, pages 312–316 (2020)Cite this article

Subjects

Abstract

Artificial intelligence has become a main driving force for a new round of industrial transformation around the world. Many countries including China are seizing the opportunity of the AI revolution to promote domestic economic and technological development. This Perspective briefly introduces the New Generation Artificial Intelligence Development Plan of China (2015–2030) from the point of view of the authors, a group of AI experts from academia and industry who have been involved in various stages of the plan. China’s AI development plan outlines a strategy for science and technology as well as education, tackling a number of challenges such as retaining talent, advancing fundamental research and exploring ethical issues. The New Generation Artificial Intelligence Development Plan is intended to be a blueprint for a complete AI ecosystem for the country.

This is a preview of subscription content, access via your institution

Access options

Buy this article

  • Purchase on Springer Link
  • Instant access to full article PDF
Buy now

Prices may be subject to local taxes which are calculated during checkout

Similar content being viewed by others

References

  1. China issues guideline on artificial intelligence development. Gov.cn http://english.gov.cn/policies/latest_releases/2017/07/20/content_281475742458322.htm (2017).

  2. Pan, Y. Heading toward artificial intelligence 2.0. Engineering 2, 409–413 (2016).

    Article  Google Scholar 

  3. Pan, Y.-h. Special issue on artificial intelligence 2.0. Front. Inform. Technol. Electron. Eng. 18, 1–2 (2017).

    Article  Google Scholar 

  4. Pan, Y.-h. Special issue on artificial intelligence 2.0: theories and applications. Front. Inform. Technol. Electron. Eng. 19, 1–2 (2018).

    Article  Google Scholar 

  5. Zhuang, Y.-t., Wu, F., Chen, C. & Pan, Y.-h. Challenges and opportunities: from big data to knowledge in AI 2.0. Front. Inform. Technol. Electron. Eng. 18, 3–14 (2017).

    Article  Google Scholar 

  6. Peng, Y.-x et al. Cross-media analysis and reasoning: advances and directions. Front. Inform. Technol. Electron. Eng. 18, 44–57 (2017).

    Article  Google Scholar 

  7. Li, W. et al. Crowd intelligence in AI 2.0 era. Front. Inform. Technol. Electron. Eng. 18, 15–43 (2017).

    Article  Google Scholar 

  8. Zheng, N.-n et al. Hybrid-augmented intelligence: collaboration and cognition. Front. Inform. Technol. Electron. Eng. 18, 153–79 (2017).

    Article  Google Scholar 

  9. Zhang, T. et al. Current trends in the development of intelligent unmanned autonomous systems. Front. Inform. Technol. Electron. Eng. 18, 68–85 (2017).

    Article  Google Scholar 

  10. Fisk, P. Meituan Dianping: China’s everything-app to “eat better, live better”. Gamechangers https://www.thegeniusworks.com/gamechanger/meituan-dianping/ (2019).

  11. China’s consumer credit balance expected to exceed 10t yuan by 2020. China Banking News http://www.chinabankingnews.com/2019/01/21/chinas-consumer-credit-balance-expected-to-exceed-10t-yuan-by-2020/ (2019).

  12. The Mobile Economy 2020 (GSMA Intelligence, 2020).

  13. Global Status Report on Road Safety 2018 (World Health Organization, 2018).

  14. Chen, W. et al. Cancer statistics in China, 2015. CA: Cancer J. Clin. 66, 115–132 (2016).

  15. Bi, W. L. et al. Artificial intelligence in cancer imaging: clinical challenges and applications. CA: Cancer J. Clin. 69, 127–157 (2019).

  16. LeCun, Y., Bengio, Y. & Hinton, G. Deep learning. Nature 521, 436–444 (2015).

    Article  Google Scholar 

  17. Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H. & Aerts, H. J. W. L. Artificial intelligence in radiology. Nat. Rev. Cancer 18, 500–510 (2018).

    Article  Google Scholar 

  18. Bejnordi, B. E. et al. Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. JAMA 318, 2199–2210 (2017).

    Article  Google Scholar 

  19. Papandreou, G. et al. Weakly-and semi-supervised learning of a deep convolutional network for semantic image segmentation. In Proc. IEEE Int. Conf. Computer Vision 1742–1750 (IEEE, 2015).

  20. Bonawitz, K. et al. Towards federated learning at scale: system design. Preprint at https://arxiv.org/abs/1902.01046 (2019).

  21. Song, J. et al. IEEE Trans. Neural Netw. Learn. Syst. https://doi.org/10.1109/tnnls.2020.2989364 (2020).

  22. Growing IoT in China (GSMA, 2019).

  23. Zhuang, Y. et al. The next breakthroughs of artificial intelligence: the interdisciplinary nature of AI. Engineering 6, 245–247 (2020).

    Article  Google Scholar 

  24. Wu, W., Huang, T. & Gong, K. Ethical principles and governance technology development of AI in China. Engineering 6, 302–309 (2020).

    Article  Google Scholar 

  25. Amini, A. Soleimany, A. P., Schwarting, W., Bhatia, S. N. & Rus, D. Uncovering and mitigating algorithmic bias through learned latent structure. In Proc. 2019 AAAI/ACM Conf. AI, Ethics, and Society 289–295 (ACM, 2019).

  26. Papernot, N., Abadi, M., Erlingsson, U., Goodfellow, I. J. & Talwar, K. Semi-supervised knowledge transfer for deep learning from private training data. In 5th Int. Conf. Learning Representations (ICLR, 2017).

  27. China AI Development Report 2018 (Tsinghua Univ., 2018).

  28. Lv, Y.-G. Artificial intelligence: enabling technology to empower our society. Engineering 6, 205–206 2020).

    Article  Google Scholar 

  29. Roberts, H. et al. The Chinese approach to artificial intelligence: an analysis of policy and regulation. Preprint at https://doi.org/10.2139/ssrn.3469784 (2019).

Download references

Acknowledgements

We thank H. Shum and Z. Zhang for discussions and comments. We also thank the strategic consulting research project of the Chinese Academy of Engineering ‘AI 2.0 in China’, and the disruptive information technology research group of the Department of Information and Electronic Engineering at the Chinese Academy of Engineering. This paper is partly supported by AI Young Scientists Alliance, STCSM(Xuhui), SHEITC, NSFC (61625107).

Author information

Author notes

  1. These authors contributed equally: Fei Wu, Cewu Lu, Mingjie Zhu.

Authors and Affiliations

  1. Zhejiang University,, Hangzhou, China

    Fei Wu, Xi Li, Yueting Zhuang & Yunhe Pan

  2. Shanghai Jiao Tong University, Shanghai, China

    Cewu Lu

  3. CraiditX, Shanghai, China

    Mingjie Zhu & Kai Yu

  4. Imsight Medical Technology, Hong Kong, China

    Hao Chen

  5. Tsinghua University, Beijing, China

    Jun Zhu

  6. ByteDance, Beijing, China

    Lei Li

  7. Nanjing University, Nanjing, China

    Ming Li

  8. Hong Kong University of Science and Technology, Hong Kong, China

    Qianfeng Chen

  9. Momenta, Beijing, China

    Xudong Cao

  10. Meituan, Beijing, China

    Zhongyuan Wang

  11. University of Science and Technology of China, Hefei, China

    Zhengjun Zha

  12. Zhejiang Laboratory, Hangzhou, China

    Yunhe Pan

Authors
  1. Fei Wu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cewu Lu
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Mingjie Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hao Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Jun Zhu
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Kai Yu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Lei Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  8. Ming Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  9. Qianfeng Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  10. Xi Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  11. Xudong Cao
    View author publications

    You can also search for this author in PubMed Google Scholar

  12. Zhongyuan Wang
    View author publications

    You can also search for this author in PubMed Google Scholar

  13. Zhengjun Zha
    View author publications

    You can also search for this author in PubMed Google Scholar

  14. Yueting Zhuang
    View author publications

    You can also search for this author in PubMed Google Scholar

  15. Yunhe Pan
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Yunhe Pan.

Ethics declarations

Competing interests

The authors declare no competing interests.

Additional information

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Wu, F., Lu, C., Zhu, M. et al. Towards a new generation of artificial intelligence in China. Nat Mach Intell 2, 312–316 (2020). https://doi.org/10.1038/s42256-020-0183-4

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s42256-020-0183-4

This article is cited by

Search

Advanced search

Quick links

Nature Briefing AI and Robotics

Sign up for the Nature Briefing: AI and Robotics newsletter — what matters in AI and robotics research, free to your inbox weekly.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing: AI and Robotics