ChinAI #78: China as a Major Manufacturing Power — Who Will Do the Manufacturing?
A Shortage of 20 Million Senior Technicians and automation in manufacturing
|Jeffrey Ding||Jan 7, 2020|| 4|
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Feature Translation: 大国智造谁来造 (China as a) Major Manufacturing Power — Who Will Do the Manufacturing?)
Context: Last November, a few representatives from the Alibaba ecosystem rang the gong to celebrate the company’s IPO for Hong Kong-based institutional investors. Yuan Wenkai, representing one of those partners (4PX Express logistics warehouse), stood third from the right. A former tally clerk who graduated from a run-of-the-mill Guangdong vocational school, Yuan is now an expert in automation management who increased the sorting capacity of the 4PX logistics warehouse by 20,000 orders per hour.
The core argument in this week’s longform article from jiqizhineng (synced): China still lacks a lot of technical staff like Yuan Wenkai (20 million senior technicans) if it wants to achieve a manufacturing transformation.
“The quality of the technicians will limit the effectiveness of not just automation technology but even those artificial intelligence technologies that can currently be implemented.” Supporting anecdotes: robotics startups can’t even give away free robots as part of product promos to manufacturing companies because they don’t have the technicians that can implement and debug the robot, let alone compile robot programs. In a shiitake mushroom sorting line, the help of experienced technicians and the factory manager were crucial to improve the algorithm’s recognition rate.
Regarding China’s efforts to raise its industrial manufacturing level, the US-China binary is worn out. As was the case with a previous ChinAI issue on machine vision in quality inspection in the production chain, this piece frames China’s main competitors as Germany and Japan: On the proportion of senior technicians in the entire industrial workforce: Japan - 40%; Germany - 50%. China - 5%. On the diffusion rate of welding robots: Japan - 70%; Germany - 70%. China - 20-30%.
Most importantly, this piece contests and complicates the notion of what talent matters most when it comes to realizing the potential of AI technologies, especially if we take the diffusion, maintenance, and large-scale adoption of technology as our focus rather than invention, innovation, and R&D: For instance, the recent CSET report on AI talent (which I’ll heap praise on in four links to forward section) takes a) AI PhD graduates and b) personnel with AI skills who work at AI employers as its proxies for AI talent. To be sure, the Ilya Sutskevers of the world are important but so are the Yuan Wenkais, the Ma Menglis (ChinAI #41) who label ladders at a data annotation company, and the CNC machine tool operators who are not PhD graduates but will play crucial roles in applying machine learning to train machine tools. In fact, in many contexts, they may be more important than the star AI researchers. Based on published catalogues of shortages in skilled trades, this jiqizhineng article claims that for many Chinese companies and regions seeking to leverage robotics and automation technology to transform their manufacturing industry, “the demand for CNC machine tools operators and equipment maintenance electricians ranked ahead of AI engineers.”
Along the same vein, yes the Tsinghuas and Beidas of the world are important but so is Shenzhen Technology University (China’s first university of applied technology) and technical colleges in Jiangsu and Guangdong that will not appear on the rankings of elite universities: the last section of this week’s translation highlights how Chinese companies, educational institutions, and local governments seek to imitate the German “dual education system,” of vocational education and company apprenticeships.
DISCUSSED IN THE FULL TRANSLATION (in the style of Believer magazine): the first intelligent shiitake mushroom sorting line in China, monthly salaries of full-time Didi drivers, the Lewis Turning Point and other causes of the talent shortage
ChinAI Links (Four to Forward)
Must-listen: Heartland Mainland Podcast
Holly He and Matt Sheehan, of MacroPolo, spent the past year trekking around Iowa to dig into U.S.-China ties at the grassroots. Episode 1 of the podcast looks at the impact Chinese students have had at Iowa's largest universities. A highlight of 2019 was getting to show Holly and Matt around my hometown (Iowa City) and rep my alma mater (University of Iowa). At around the 12:40 mark, you’ll hear a little bit about how I changed my perspective on the challenges faced by Chinese students. Give it a listen and rating!
Remco Zwetsloot, James Dunham, Zachary Arnold, and Tina Huang have published the most comprehensive, data-backed, and careful assessment of the U.S. AI talent landscape to date. The finding that stay rates among international graduates in AI are persistently high is particularly important: “Around 90 percent of international AI PhD students take a job in the United States after graduating, and more than 80 percent stay in the country for at least five years” AND “Stay rates are highest—exceeding 90 percent—among students from Taiwan, India, Iran, and China, and lower—around 75 percent—among students from European countries.” (p. iv)
This has implications for on the weight that U.S. policymakers put on technology transfer concerns, according to the authors:
A prominent 2018 report by the Defense Innovation Unit notes that 25 percent of graduate students in STEM fields are Chinese and that “nearly all [of them] will take their knowledge and skills back to China” because they “do not have visas to remain in the U.S.,” the implication being that U.S. universities are educating the country’s competitors without much benefit to the United States. As this report shows, that is not the case—with the vast majority of Chinese graduate students in fact staying in the United States— despite longstanding efforts by the Chinese government to draw them back.
Should Read: CSET Report on AI Safety, Security, and Stability Among Great Powers
Based on the authors’ own experiences participating in a number of Track 1.5 and Track 2 dialogues involving issues related to AI and U.S.-China relations, Elsa Kania and Andrew Imbrie provide an extremely sensible, thoughtful, and pragmatic roadmap for engagement on AI safety and security among the U.S., China, and Russia.
Specifically, they “present and evaluate several measures in AI safety and security that could prove feasible and mutually beneficial for future bilateral and multilateral interactions. These measures are intended to prevent or correct misperceptions, enhance mutual transparency on policies and capabilities, and contribute to providing safeguards against inadvertent escalation. By pursuing such initiatives in the near term, the United States can improve its capacity to leverage the benefits of AI, while mitigating the risks and managing the shifting terrain in today’s geopolitics, particularly among the United States, China, and Russia.”
Josh Horwitz of Reuters gives really useful context for the political reasons behind Alibaba’s strategy of dual listing in Hong Kong. This quote by a former Alibaba senior executive who declined to be named was a particularly insightful nugget: “Investors in the Hong Kong stock market are less influenced by the political atmosphere and have a more objective view of the richness of the Alibaba economy.”
Thank you for reading and engaging.
These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is a Rhodes Scholar at Oxford, PhD candidate in International Relations, Researcher at GovAI/Future of Humanity Institute, and Research Fellow at the Center for Security and Emerging Technology.
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