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Boosting AI to Give Productivity ‘Leap of Force’

May 07-2024   



New-generation AI has been designated as a strategic lever for China to seize the initiative in global tech competition and a strategic resource for its technology to leapfrog in development, for its industry to optimize and upgrade, and for its productivity to achieve sweeping improvement. In a media commentary, Prof. Huang Zhuo and co-author Zhou Ding offered proposals for solving bottlenecks in China’s AI development. Prof. Huang is NSD Deputy Dean, and Zhou Ding is Assistant to Director of PKU Changsha Institute for Computing and Digital Economy (ICODE).

 

As China’s AI industry has advanced to a more sophisticated stage, the two authors identified three major clogging points that are hampering the development of new-quality productive forces. Firstly, some of the country’s AI first-mover technologies are still in catch-up phase, as its AI large models trail the most advanced ones in the world when it comes to core technologies such as overall structures, data input and output, and training algorithms. Secondly, AI application R&D faces insufficient supply of computing power. China only has a limited number of smart computing centers, large data centers, and domestically produced high-performance computing hardware and equipment. Thirdly, AI industry’s development is in need of more productive factors. The number of China’s leading AI talents take up 14% of the global total, and both the number and value of investments in the industry remain relatively small.

 

Accordingly, the two authors laid out policy advice to remove blockages and allow AI to empower industries and drive the transformation and upgrade of all links and arenas in the economy and society. The first concerns the construction of AI industry R&D system, which should comprise top-tiered research universities, national innovation platforms, technologically leading enterprises, and new research institutes. As such they will cover the distance from basic research all the way to production, thereby forming an abundant fountainhead for new-quality productive forces. Secondly, leveraging the advantages of the ‘whole-nation mechanism’ and building a nationwide computing grid to achieve AI technological breakthroughs. This will give birth to core momentum for new-quality productive forces. Next, forge AI application scenarios in order to provide physical materials for new-quality productive forces. Based on China’s vast domestic market and stratospheric amount of data, one priority is to develop AI applications for various industries to reap vertically generated benefits. Lastly, it is advisable to consolidate factor safeguards for new-quality productive forces by strengthening the conjoint forces across the AI industry, including joint programs by universities, research institutes and enterprises to develop AI talents, improving the talent incentivization mechanism, and encouraging government-backed industry investment funds to provide diverse equity financing for AI firms.

Boosting AI to Give Productivity ‘Leap of Force’

May 07-2024   



New-generation AI has been designated as a strategic lever for China to seize the initiative in global tech competition and a strategic resource for its technology to leapfrog in development, for its industry to optimize and upgrade, and for its productivity to achieve sweeping improvement. In a media commentary, Prof. Huang Zhuo and co-author Zhou Ding offered proposals for solving bottlenecks in China’s AI development. Prof. Huang is NSD Deputy Dean, and Zhou Ding is Assistant to Director of PKU Changsha Institute for Computing and Digital Economy (ICODE).

 

As China’s AI industry has advanced to a more sophisticated stage, the two authors identified three major clogging points that are hampering the development of new-quality productive forces. Firstly, some of the country’s AI first-mover technologies are still in catch-up phase, as its AI large models trail the most advanced ones in the world when it comes to core technologies such as overall structures, data input and output, and training algorithms. Secondly, AI application R&D faces insufficient supply of computing power. China only has a limited number of smart computing centers, large data centers, and domestically produced high-performance computing hardware and equipment. Thirdly, AI industry’s development is in need of more productive factors. The number of China’s leading AI talents take up 14% of the global total, and both the number and value of investments in the industry remain relatively small.

 

Accordingly, the two authors laid out policy advice to remove blockages and allow AI to empower industries and drive the transformation and upgrade of all links and arenas in the economy and society. The first concerns the construction of AI industry R&D system, which should comprise top-tiered research universities, national innovation platforms, technologically leading enterprises, and new research institutes. As such they will cover the distance from basic research all the way to production, thereby forming an abundant fountainhead for new-quality productive forces. Secondly, leveraging the advantages of the ‘whole-nation mechanism’ and building a nationwide computing grid to achieve AI technological breakthroughs. This will give birth to core momentum for new-quality productive forces. Next, forge AI application scenarios in order to provide physical materials for new-quality productive forces. Based on China’s vast domestic market and stratospheric amount of data, one priority is to develop AI applications for various industries to reap vertically generated benefits. Lastly, it is advisable to consolidate factor safeguards for new-quality productive forces by strengthening the conjoint forces across the AI industry, including joint programs by universities, research institutes and enterprises to develop AI talents, improving the talent incentivization mechanism, and encouraging government-backed industry investment funds to provide diverse equity financing for AI firms.