What I learned from a year of translating Chinese articles about AI

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(Image credit: Bigstock/Freer Law)

A version of this article by Jeffery Ding originally appeared in the ChinAI newsletter, which publishes translations of writings on AI policy and strategy from Chinese thinkers.

1. There is a language asymmetry in the Chinese-speaking community’s understanding of the global AI landscape and that of the English-speaking community.

Big developments covered in Western outlets—the publication of the Malicious Use of AI report, any breakthrough made by Deepmind or OpenAI, an op-ed about human-centered AI by Fei-fei Li—are translated within a day or two and analyzed in Chinese outlets. This short turnaround time is a product of a China’s vigorously competitive and quickly expanding science and technology media landscape. Many of my translations this year drew from outlets such as xinzhiyuan, Leiphone and jiqizhixin, many of which are outpacing their Western outlets in content and scale.

2. Western observers consistently overinflate Chinese AI capabilities. While some of this exaggeration is a product of media sensationalism or deliberate overestimation on the part of interest groups, another significant factor behind the overinflation is a misunderstanding of what is happening at the technical level of AI development in Chinese companies.

In a year that featured the rise of the “AI arms race” meme and headlines like “China’s tech giants spending more on AI than Silicon Valley,” few people dug underneath the hood to see what China’s so-called AI giants, such as Tencent, were actually doing regarding AI at the technical level. One exception was a Chinese-language essay by Li Guofei, a widely respected thinker in China’s investment community, which drew on interviews with Tencent insiders. It revealed that Tencent’s algorithms “still give a very imprecise profile of users” because “Tencent’s customer data is scattered in various departments and has become the ‘private property’ of departments” (e.g. WeChat’s advertising algorithms are not under the purview of the WeChat department but are actually under another department which does not have access to the data of the WeChat team). Moreover, Li wrote that the number of Tencent engineers solely dedicated to doing work on improving algorithms is “pitifully few.”

Another piece by a writer for Huxiu, a Chinese-language platform for sharing news and thinkpieces on S&T issues, argued that “Only Baidu and Huawei are Really Doing AI.”It found that China’s four tech giants (Baidu, Alibaba, Tencent and Huawei) had promoted a top-heavy AI industry with few companies producing the foundational technologies—deep learning frameworks and chips—that underpin AI development.

3. In addition to AI’s significance for economic growth and military security, the Chinese government sees AI as a tool to improve social governance, which makes public security applications a large driver of China’s AI development. This also means that some Chinese AI companies are involved in China’s mass surveillance of Xinjiang.

According to a report by Yiou intelligence, a consulting firm that publishes reports in Mandarin on China’s industry, security + AI companies accounted for the highest proportion of companies in Yiou’s list of top 100 AI companies.

Two of China’s most successful facial recognition startups, Sensetime and Megvii, also called Face++, are involved in China’s efforts to securitize Xinjiang. At the 2017 China-Eurasia Security Expo, Megvii was announced as an official technical support unit of the Public Security Video Laboratory in Xinjiang. Under the backdrop of the “Silk Road Economic Belt,” expos like these enable the export of China’s surveillance technology to Central Asian countries and beyond, as nearly 100 government agencies, experts and procurement companies attended.

It is also important to be precise about the technical capabilities of the security systems actually in implementation, as there are limits to continuous real-time location tracking due to limitations of facial recognition technology, camera costs and computing power.

4. In a world of globalizing innovation where AI talent flows across borders and AI firms set up R&D centers around the world, taking a techno-nationalist approach toward understanding China’s AI landscape will miss much of the story. The seeds of China’s AI development are rooted in Microsoft Research Asia in Beijing, Microsoft’s largest center outside of its headquarters, as a key training ground and hub. 

MSRA, which celebrated its 20th anniversary last year forces us to question what does it mean to be an “American” or “Chinese” tech company. On the one hand, it has played a key role in China’s AI rise by both attracting initial overseas talent and then cultivating domestic talent. It has “trained more than 4,800 Chinese interns and more than 500 of them are now active in various large companies in China’s IT industry, including Baidu, Tencent, China Mobile, Alibaba, Lenovo, etc.

At the same time, MSRA has been essential for Microsoft. Zhou Ming’s story, fleshed out further in the last half of the translation, embodies this level. He taught at Tsinghua University in China for 8 years before joining MSRA as one of the first researchers, and 20 years later, he’s still making immense contributions for Microsoft.

5. Chinese people—including regular netizens, data protection officers and philosophy professors—care about AI-related ethics issues, including privacy. It is perfectly reasonable to highlight differences in Chinese notions of AI ethics or the degree to which privacy is important to Chinese consumers, but it is absolutely dehumanizing to say Chinese people don’t care about privacy.

Chinese tech giants clash fight over user privacy violations, as evidenced by Tencent asking the Ministry of Industry and Information Technology to intervene in a dispute between Tencent and Huawei on alleged user privacy infringements of the Honor Magic phone. The Nandu Personal Information Protection Research Center has assessed 1,550 websites and apps for the transparency of their privacy policies.

Finally, Chinese thinkers are engaged on broader issues of AI ethics, including the risks of human-level machine intelligence and beyond. Zhao Tingyang, an influential philosopher at the Chinese Academy of Social Sciences, has written a long essay on near-term and long-term AI safety issues. Professor Zhihua Zhou, who leads an impressive lab at Nanjing University, argued in an article for the China Computer Federation that even if strong AI is possible, it is something that AI researchers should stay away from.

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