ChinAI #51: China's AI "National Team"

A "Collective Work Report" on five AI innovation open platforms

Welcome to the ChinAI Newsletter!

These are Jeff Ding's (sometimes) weekly translations of writings on AI policy and strategy from Chinese thinkers. I'll also include general links to all things at the intersection of China and AI. Please share the subscription link if you think this stuff is cool. Here's an archive of all past issues. *Subscribers are welcome to share excerpts from these translations as long as my original translation is cited.

I'm a grad student at the University of Oxford where I'm based at the Center for the Governance of AI, Future of Humanity Institute.

A “Collective Work Report” of the five major “National Team” members — BAT, iFlytek, and Sensetime

In November 2017, the Ministry of Science and Technology (MOST) assigned Baidu (autonomous driving), Alibaba (smart cities), Tencent (medical imaging), and iFlytek (intelligent voice) to lead the development of four national AI open innovation platforms; in September 2018, Sensetime (intelligent vision) was selected as the fifth member of this “national team” (guojiadui). This week’s feature translation is on a Leiphone report from an AI Expo held in Suzhou on May 10th, where each of the companies reported on the progress of their respective platforms (a “collective work report” of sorts). This fits with the Chinese government’s recognition of the importance of openness in spurring research and diffusion in the AI field, related to a previous white paper on AI Open Source Software, which we covered in a previous issue.

I find these “national team” platforms super interesting. Of course, they can tell us about leading Chinese companies in various domains of AI. But the contrast between a “national team” vs. “national champion” model may also be significant. For one, all five were already a) strong, b) independently self-sufficient, and c) hybrid firms backed with a lot of foreign capital before they got this “national platform” assignment. These aren’t your father’s “national champions” propped up with government funding. Some have argued that this “national team” moniker is just a PR move for both sides — MOST bureaucrats and these companies get to share in the glory in helping China advance its AI dream. Relatedly, there’s a lot of competition within each of these company’s “turf” — in Sensetime’s states in its progress report that it will compete in areas (autonomous driving, smart cities) in which Baidu and Alibaba are building national innovation platforms. And everybody’s going after smart cities.

A few tidbits from the translation about each company’s platform progress:

iFlytek: iFLYOS is its open platform, with 920,000 registered developers as of Dec 2018; its speech recognition capabilities now cover 23 dialects (some Chinese dialects are hard problems for speech recognition because there’s not enough data); it claims to have 100,000+ personal voice banks and 90+ customized corporate voice banks on the platform

Sensetime: SenseParrots is its core technology platform, which it positions as a competitor to Berkeley’s Caffe2, Facebook’s PyTorch, and Google’s TensorFlow. We don’t get many statistics on SenseParrots before the report goes on to talk about Sensetime’s forays in augmented reality, autonomous driving, and smart cities.

Baidu: Apollo is its open source platform for autonomous driving, now in its seventh version as Apollo 3.5. There’s a “1 to 3” data exchange principle in which partners open some data to Baidu, and Baidu will open up 3 times of its data to give back. It has a “Dual Hundred Plan” that will invest 10 billion RMB (100 yi) to 100 autonomous driving-related businesses. It highlighted deep collaborations with carmakers Neolix, Ku Wa Saodi Che (酷哇扫地车), and FAW-Hongqi.

Alibaba: Feitian (飞天) is the cloud computing platform for the city brain. I’m not as familiar with this domain but I’m not sure where the “open innovation” happens. My intuition is there’s not really a big open source aspect of city brains and Alibaba is positioning general cloud services as the “open” component, but others should correct me on this. Notable that Alibaba Cloud and its DAMO Academy seem to be taking the lead on this effort.

Tencent: Miying is Tencent’s AI imaging product. Similar to the Alibaba case, I’m not sure what the “open innovation” aspect of Miying is — Tencent reports on its vast repositories of medical knowledge datasets that its feeding into Miying but it’s not clear that these are open to other companies to benefit from. Anyways, report makes clear that Tencent is investing a lot of talent and resources into Miying, which focuses on five major diseases: colorectal, lung, breast, cervical cancer, and various diseases of the fundus [eye].

READ FULL TRANSLATION: A “Collective Work Report” of the five major “National Team” Members — BAT, iFlytek, and Sensetime

This Week's ChinAI Links

Tech Scroll Asia, a FT and Nikkei joint newsletter is putting out some good content.

Helen Toner, director of strategy at Georgetown’s Center for Security and Emerging Technology shares her observations from the last few years of talking with AI scientists and policymakers in the US and China on an episode of the Rationally Speaking podcast. She references a review essay that Helen, Remco, and I co-authored in Foreign Affairs: "Beyond the AI Arms Race: America, China, and the Dangers of Zero-Sum Thinking"

I got linked to this podcast from the excellent Chinese Effective Altruism (EA) newsletter, which gives updates (in both Mandarin and English) on AI safety/EA-related issues in China. Also give some love to AI safety researcher Rohin Shah’s Alignment Newsletter (now featuring Chinese translations by Xiaohu Zhu) — alignment refers to the problem of building powerful AI systems that are aligned with their operators.

Richard Lester, MIT’s Associate Provost and Professor Nuclear Science & Engineering, writes a wise and measured letter about MIT’s relationship to China. Many, not all, of these insights could be applied to the US-China relationship.

Thank you for reading and engaging.

Shout out to everyone who is commenting on the translations - idea is to build up a community of people interested in this stuff. You can contact me at or on Twitter at @jjding99

ChinAI #50: FT Follow-up, Chinese Americans caught in the midst of Geopolitical Competition

Welcome to the ChinAI Newsletter!

These are Jeff Ding's (sometimes) weekly translations of writings on AI policy and strategy from Chinese thinkers. I'll also include general links to all things at the intersection of China and AI. Please share the subscription link if you think this stuff is cool. Here's an archive of all past issues. *Subscribers are welcome to share excerpts from these translations as long as my original translation is cited.

I'm a grad student at the University of Oxford where I'm based at the Center for the Governance of AI, Future of Humanity Institute.

FT Follow-up

If I I could rewind time and rewrite last week’s issue, I would have focused on the larger systemic issues/context surrounding why reporting on these issues is so hard rather than targeting the reporting itself. As many people reminded me, the debate format can sometimes optimize for "“winning” over more productive deliberations. The goal should have been to build each other up to do better rather than take each other apart over mistakes. In parts of my rebuttal, my sarcastic (and let’s be honest — douchey) tone was unfair to both the journalists and commentators, and detracted from the substance. I apologize to all injured parties and promise to learn from my mistakes. That doesn’t mean I won’t express my strong opinions on issues (see my musings on Chinese Americans as strategic assets in the last section of this issue), and I stand by all the substantive arguments in my rebuttal. It just means I need to recognize the immense privilege of having a platform like ChinAI, as well as the privilege of being a male in this space (when I go in on an issue, I don’t need to worry about being labeled “emotional” or “ranty”), and do better on all these fronts.

One last clarification on the reporting details from last week, and then we’re moving on. I respect the FT reporters and staff for defending their reporting, as Madhumita did in her responses to some of my points, which I linked at the bottom of last week’s post. Thanks to FT for correcting the article to include links to the coauthored papers. Madhumita informed me that one of the papers (the one on how facial features, dress, and voice collectively affect the human sense of beauty) originally shared with me was not one of the three FT was reporting on. Instead it was this paper on machine reading comprehension.

I do want to address one point of substance: why am I harping on the technical content of these papers? I think it links to bigger issue with AI+politics/tech+politics research — or what I call the “technology abstraction problem.” An exercise to illustrate this: take a sample of AI+politics articles/papers that claim AI has revolutionized X, and replace AI with “high-level statistics.” The best policy research on AI should use the word artificial intelligence in an abstract sense as few times as possible. AI has become too prone to hype and it’s too broad of a concept to be analytically coherent or useful, encompassing anything from subfields of fuzzy mathematics to research on decentralized drone warfare. Analysts should rigorously force themselves to specify what claims they are making about “AI” in terms of the domain and technological layer they are talking about.

4 million Chinese Americans as Strategic Resources for the U.S. and China

This week’s two translations are not on spectacular research analysis or incredibly well-reported investigative pieces but rather they are “thermometer” pieces that give a good sense of the overall temperature of Chinese public/influencer opinion on a particular subject. The subject in question is the position of Chinese Americans as a strategic asset for the U.S. and China in the competition over scientific and technological development.

The first piece, by Dong Jielin, an associate researcher at China Institute for Science and Technology Policy (CISTP) of Tsinghua University, who received her PhD in Physics from Carnegie Mellon University. She states that Chinese Americans “Are very important strategic resources for China and the United States,” and if the “ ‘friendly-to-China’ Americans and the ‘friendly-to-America’ Chinese are purged clean, and the two sides are divided clearly into two camps, then the bridge is broken and there will be no one to repair it.” She reviews the history of Chinese Americans in the United States and highlights the cases of American physicist Xiao Xiaxing and U.S. Meteorological Administration expert Chen Xiafen as instances of discrimination toward Chinese Americans in wrongful charges of espionage. It’s a short piece so I’d recommend reading it in full, but one tidbit I found particularly interesting. Dong references that many people in China accuse Chinese immigrants in the U.S. of causing frictions in the U.S.-China relationship due to their misconduct (e.g. illegal tech transfer) and Dong calls for Chinese Americans to “strengthen their legal awareness and compliance.”

READ FULL TRANSLATION: Chinese Americans: The Sacrificial Lamb of Great Power Contestation?

The second piece shows how some Chinese thinkers and media view U.S. targeting of Chinese American scientists and engineers as a strategic opportunity. Usually in ChinAI issues, I feature the “cream of the crop” Chinese reporting/analysis, so "these “thermometer”-type pieces give a truer sense of how there are still a lot of flaws in the media environment. Titled “The United States has extensively restricted ethnic Chinese talents, the backbone of technology has returned to China, and the edge in AI has been reversed,” this piece gets basic facts wrong and spreads some scary rumors.

One passage states, “Washington believes that all Chinese-American talents are inherently suspect as ‘spies.’ The White House even uses its power to go to major high-tech companies and other companies involved in key technologies, and ordered that them to remove all these Chinese talents within a certain time period to ensure that US technology will not flow out.” Unless I’ve been living under a rock (to be fair, have not been checking Twitter all that much lately), this is a false rumor, presumably, because the author wants to push the narrative that the US is no longer friendly to Chinese-American scientists and engineers and encourage them to move back to China.

READ FULL TRANSLATION: The United States has extensively restricted ethnic Chinese talents, the backbone of technology has returned to China, and the edge in AI has been reversed.?

Reflections on Chinese Americans as “Strategic Resources” in the US-China Tech Competition

Chinese Americans are not strategic assets. We are people. Translating these two pieces reminded me of this video of the announcement of the 2008 Nobel Prize in Chemistry which was awarded to Roger Tsien along with two othersI would strongly recommend watching it alongside my breakdown of what happens. At 13:40, a journalist from Chinese news agency Xinhua asks, "Are you Chinese? Can you speak Chinese?" Roger's response is pure grace. Born in New York to Chinese immigrants, he responds in Mandarin, "I can speak a bit." Xinhua follows-up by asking what his achievement means to Chinese scientists. “Well I can't say I’m really a Chinese scientists. I grew up entirely in America, but if this should make Chinese people feel good and proud and it will inspire a lot of young people to do science in China, that's a good thing." It's an eloquent reply that takes claim of his own identity but also one that acknowledges why the reporter is asking the question: he recognizes that no Chinese citizen of the PRC had won a Nobel Prize in science, and that aspiring Chinese scientists may draw inspiration from someone who looks like him winning.

Still, what I can't shake about this clip is the drawn-out "ummm" after he's being asked if he's a "Chinese scientist.” I wonder what was flashing through his mind in that "um." Was he thinking about the astronomical odds that his mom faced in immigrating to the U.S. when the Chinese Exclusion Act was in force? Was he recalling how a NJ developer wouldn't sell to his family because they were Chinese? Was he thinking about how for so many Chinese Americans, no matter how good your English is, how high you climb (even getting a Nobel prize), there will be some people who only see you as Chinese? In a world where science is more politicized, increasingly framed in the context of international competition, where you hear whispers about people of Chinese ethnicity not having the U.S. national interest in mind, Roger's story reminds us that Chinese Americans have agency to claim their own identities, that we who have Chinese faces and family but U.S. passports and roots contain multitudes like everyone else, but inhabit multiple identities more often than everyone else. That it matters to read Roger's story to see a great Chinese American scientist who did work for the world. Roger passed away in 2016. He was a fine pianist, a gifted amateur photographer, and loved by family, friends, and colleagues. He was a person, not an asset.

I think this links back to why I was so mad about those FT pieces. I wonder if FT saw the humanity in the Chinese American researchers involved in the collaborations, if they would have tried to get the views of people who would have defended the collaborations or at least added more context. One of the researchers named in the FT pieces was Thomas Huang, who, like me, was born in Shanghai. In 1949, his family moved to Taiwan where Huang studied electronics before going on to get a Doctor of Science at Massachusetts Institute of Technology (MIT). I don’t pretend to have any insight into Professor Huang’s political views, but let’s just say that most people with families who moved to Taiwan at the time when Mao proclaimed the founding of the People’s Republic of China are usually not schills for the Chinese Communist Party. Huang has taught at the University of Illinois at Urbana-Champaign, where he was named as a Swanlund Chair, the highest endowed title, for his contributions to one of those midwestern public research universities (shoutout to the University of Iowa, my alma mater) that are the engines of America’s competitiveness and economic strength.

Again, as I emphasized in last week’s newsletter, Huang’s collaboration with Cloudwalk on re-ID research is concerning. What I’m trying to get at here is that as we can critique personal actions, government policies, and the CCP, let’s not lose sight of the humanity of Chinese people. Recently, I was at one of those meetings where a bunch of powerful “influencers” get together and talk about “influencer” things. I gave a brief on China’s AI development alongside a Chinese American friend. After the briefing, I was surprised by how many people came up to me and confused me with my friend (we look nothing alike). It reminded me of a passage from a Vulture profile of Korean American actor John Cho, which has been stuck in my head ever since July 31, 2016, when I shared it in my previous (now-temporarily-defunct) newsletter project, the New Chimericans, a collaborative effort with my good friend Laura Wang that looked at Asian American issues. John makes this comment on Asian American representation in media:

“I’ve seen many instances where we’re seen as a little less than human, or maybe a little more than human — like ultrahuman, rather than subhuman. What is wrong with film representation? Some of it is mechanical, surprisingly. I’ve thought about why Asian stars — from Asia, I mean — look so much better in their Asian films than they do in their American films, and now I can answer that to some extent. There’s an eye, and it’s not a malicious eye, which is a way that the people working the camera and behind the scenes view us. And then they process it and they put it on film. And it’s not quite human. Whereas Asian films, they are considered fully human. Fully heroic, fully comic, fully lovely, fully sad, whatever it is. And it’s this combination of lighting, makeup, and costume.

If you don’t think of a person as fully human, you sort of stop short and go, That’s good enough. Do you remember Doug Liman’s film Go? I remember Taye Diggs in that movie, and he was charcoal black. I was surprised to see him in “How Stella Got Her Groove Back” — I realized that Go was not an accurate representation of his skin tone whatsoever. And I’ve met him. He was carelessly lit. Why is that? Why is one carelessly lit? The white people were carefully lit.”

How do we tell stories, write analysis, and understand the role of Chinese Americans in U.S.-China strategic competition that sees Chinese Americans as "carefully lit” human beings. Recently, there were reports that the U.S. State Department’s policy planning staff is composing a memo in the style of George Kennan’s “X Article” that argues the coming conflict with China is “the first time that we will have a great power competitor that is not Caucasian.” As Professor Ward points out in analysis for the Washington Post, the argument Trump’s State Department is making is not about ideology or civilization. “It is about race. China — unlike Russia — is not predominantly white, and thus must be dealt with differently.”

I think Chinese Americans can play a valuable role in dissecting these dangerous modes of thought. We represent an America that is not “Caucasian.” We have a “strategic in-betweenness.” Many of us are high-skilled, Western-educated Chinese natives who can move back and forth between the countries. But we can also leverage this “strategic in-betweenness” to question the humanity behind arguments like the ones coming out of the U.S. State Department about race-based great power politics.

This Week's ChinAI Links

Chinese phrase of the Week:  泛泛之辈 (fan4fan4zhi1bei4) - a mediocre person.

Human Rights Watch report on a system that “surveils and collects data on everyone in Xinjiang. The system is tracking the movement of people by monitoring the “trajectory” and location data of their phones, ID cards, and vehicles; it is also monitoring the use of electricity and gas stations of everybody in the region.” Also, read this account of women fleeing Xinjiang and telling their stories.

Show some love for the American Mandarin Society newsletter - they are a great resource for those looking to keep up their Mandarin abilities.

Check out and subscribe to DigiChina’s must-read monthly digest: an exclusive from April’s edotopm — translated excerpts from Zhang Shu, a researcher at the China Information Technology Security Evaluation Center (CNITSEC), analyzing U.S. “containment” strategies in technology and arguing China should prepare for a “long-term competition by promoting openness.

ChinaLawTranslate is an invaluable resource for unofficial translations on Chinese legal documents. See this new translation of Measures for Determination of Violations of Laws and Regulations in APPs' Collection and Use of Personal Information (Draft)

Thank you for reading and engaging.

Shout out to everyone who is commenting on the translations - idea is to build up a community of people interested in this stuff. You can contact me at or on Twitter at @jjding99

ChinAI #49: Rebuttal to FT Articles on Western-Chinese AI collaborations

Also, security vulnerabilities in "pay-by-face" authorizations in Wechat Pay and Alipay as well as Wandering Earth connection to AI rebellions

Welcome to the ChinAI Newsletter!

These are Jeff Ding's (sometimes) weekly translations of writings on AI policy and strategy from Chinese thinkers. I'll also include general links to all things at the intersection of China and AI. Please share the subscription link if you think this stuff is cool. Here's an archive of all past issues. *Subscribers are welcome to share excerpts from these translations as long as my original translation is cited.

I'm a grad student at the University of Oxford where I'm based at the Center for the Governance of AI, Future of Humanity Institute.

ChinAI – Let’s Debate!

***Back from a hiatus with a jam-packed issue. Welcome to all the new subscribers – we’re up to 4000+. Introducing a new segment of ChinAI where I’ll make a case for or against some proposition. High school policy debate was the single most formative activity in my life, and I truly believe testing one’s arguments in direct opposition to others can result in more effective policy. I’ll always try to keep the arguments directed at an idea rather than the person behind the idea. That’s why in what follows I’m blunt and sarcastic at times toward Financial Times reporting on Western-Chinese AI research partnerships not because I don’t respect its reporting (FT is second only to the New York Times in terms of China coverage in my opinion) but rather because I think “iron sharpens iron” should apply to argumentation.

**I think there should be public debates on issues like these in policy debate format (with cross-examination, multiple rebuttals). You can watch the tape on me back in the glory days of my policy debate career (“Google me, Chuck”) – and I’m sure there are countless people smarter than me who would be up for more structured debates about these issues.  

 *We previewed a prototype of this segment in the previous newsletter, where I laid out five lessons learned from a year of translating Chinese writings on AI-related issues. Other outlets are welcome to excerpt from these rantings and ramblings as MIT tech review did or contact me about re-publishing them with light editing as technode did (I also did a podcast with technode on the subject).

This week’s proposition: The recent Financial Times (FT) pieces on Microsoft working with a Chinese military university on AI and Western AI researchers partnering with Chinese surveillance firms — which have led to Republican senators Cruz and Rubio publicly calling out Microsoft’s China work — were deeply flawed. Specifically, they:

1)    contained uninformed reporting on technical details of the co-authored papers

2)    overhyped the relevance and significance of these collaborations with respect to their potential to aid China’s military and surveillance efforts, and

3)    failed to provide appropriate context to prevent the misuse of this reporting by others who want to question all collaborations with Chinese counterparts and promote the meme of a US-China Cold War.

Points 1 and 2: Uniformed and overhyped reporting

The first FT piece highlighted three papers co-written by academics at Microsoft Research Asia and researchers with affiliations to China’s National University of Defense Technology. As of May 5, nearly a month after the article was first published, the article did not even link to the actual papers themselves, so we have no way to even properly judge their significance and relevance for surveillance and censorship (Note: I sent a heads-up about this post to Madhumita, Yuan, and Christian, so links to articles may be edited into the article now; I’ll provide rebuttals from Madhumita from our exchange at the end of this post so as to hopefully give fair representation to FT’s views but welcome further open exchanges). It’s a seemingly minor thing to point out about the piece, but the three papers are the central component of piece so there’s no excuse for not even linking to them in the original article. This is reflective of possible haste and lack of attention to detail in the publication process.

As Miles Brundage, a research scientist on OpenAI’s policy team, who has probably read more AI articles on arxiv than anyone, wrote on Twitter, “The recent FT story on China/AI and the reaction to it tells us more about the state of AI journalism than it does about China/AI: - overhyping the significance of specific papers… Panicked coverage of cherrypicked (public) papers that say things one could have already known about from other public sources, all packaged together into a scary narrative that makes this random batch of papers sound like the most important thing ever.”

The only details we are given about the three papers include that one of them describes a “new AI method to recreate detailed environmental maps by analyzing human faces,” which can have a “variety of vision applications” (cue ominous vibes). From my read of it — and I’m happy to entertain push back as I’m not nearly as qualified as reading AI arxiv articles as others — this paper is focused on increasing photorealism in augmented reality apps like Snapchat, with limited applications for, say, matching a person in real life better to a surrounding environment.

We’re also told that Microsoft’s Beijing lab collaborated on “two other papers with NUDT researchers, including in the area of machine reading.” (a very awkward phrasing that we’ll investigate later). First, let’s look at the machine reading comprehension article (which was shared with me by Yuan as, again, these weren’t linked in the original article) which was on building systems that can abstain from answering “unanswerable questions” given a particular passage. For example, if given a passage that gave background on Normandy, a region in France, previous models would respond to the “What is France a region of” (an unanswerable question in the context of the given passage) with “Normandy,” but the correct answer is “no answer.” If you’ve been following ChinAI, specifically issue #45 on China’s NLP landscape, one could easily deduce that the likely application of this research would not be for boosting censorship or military operations (unless one believes that the Communist Party and the PLA are very concerned with making sure not to answer unanswerable questions) but rather for something like, I don’t know, maybe say, improving Microsoft’s Xiaoice, its hugely popular chatbot and one of its crown jewel AI-based products? Cue next week’s FT headline: BREAKING NEWS THAT YOU HAVE TO CLICK BELIEVE: CHINESE MILITARY UNIVERSITY AIDS AND ABETS US TECH GIANT

Now, let’s circle back to that awkward phrasing. Curiously, the FT piece doesn’t ever specify what that third paper is about. It’s on the subject of how facial features, dress, and voice collectively affect the human sense of beauty. Um. Excuse me, as I need to brush up on how a better sense of human beauty is the next key revolution in military affairs and surveillance.

At least in a FT follow-up piece on Western AI researchers partnering with Chinese surveillance firms, there are links to the articles. These include ones on 3D scene reconstruction for robots and machine comprehension. The former is about reconstruction models of large indoor environments, so I am not sure how FT concludes this has “applications in unmanned drones, and autonomous underwater vehicles” (emphasis mine). The latter is about reducing the time costs by training more compact neural language models on mobile devices – this could enable model training on decentralized data, which may actually undercut surveillance/censorship applications, in contrast to what FT cites experts as saying. Also, from my read of things, none of the corpuses used even contain Chinese-language data.

Please, please, please: before writing these pieces, talk to people who regularly read and can understand AI articles on arxiv (not me please). Jack Clark, policy director for OpenAI, provides an essential weekly ImportAI newsletter as an invaluable service toward this end – he highlights what he views as significant AI research and lists affiliations for pretty much everything he covers, giving a sense of how multi-disciplinary and multi-country AI research is. Do the work to find people like Miles and Jack and get their opinions, in order to make informed, specific claims about the connection between the technical details of the paper to the dual-use applications.

Tech journalism in these times is a damn hard job and a lot of faithful public servants are doing their best. In one sense, the issue here was FT found people who could speak to the technical side of things but not the China side of things. They also found people who could speak to the China side of things but not the technical side. They didn’t find anyone who could speak to both. Those people who can are, unfortunately, few in number. I don’t count myself among this camp but there are good folks (including many cited in these articles) working hard to get there, and I think in some newsletters we get close.

Also, maybe read the articles closely enough to get to the end. If they did, the FT reporters would have found some interesting tidbits in the “acknowledgements” sections such as the fact that one piece, Fu et al., was sponsored by IARPA (an organization within the Office of the Director of National Intelligence) and another, Shi et al., was supported by the National Science Foundation. Either these bodies deemed the research appropriate or the review process was not stringent enough. Did anyone look closely enough to ask?  

Point 3: Failure to Provide Appropriate Context to Prevent Misuse of Reporting

This is not to say that the piece does not raise valid and important concerns. The Fu et al. paper on person re-ID involving a researcher at IBM and coauthors at CloudWalk is an example of concerning collaboration that could enable the Chinese government’s adoption of high-tech surveillance, especially in targeting and persecuting Uighurs in Xinjiang. And the commentators in both pieces are right to push for more oversight and due diligence when it comes to academic collaborations linked to human rights abuses.

But it’s also important not to swing too far in the other direction and problematize all academic collaborations. In my opinion, FT has a duty to understand the context on the issues their reporting concerns. There is a climate of hostility against international researchers in the US now, with many university professors being warned to stay away from international students, and this goes from the President on down. Trump himself has noted of Chinese international students in the U.S., “almost every student that comes over to this country is a spy.” FT’s reporting failed to provide the context to guard against how others have used it to problematize all US-China academic collaboration and push forward the dangerous meme of a US-China Cold War.

The FT piece raises several important issues facing a world that has seen significant increases in international research collaborations but one that is still separated into nation-states. One issue is what level of engagement, if any, should Western researchers have with researchers at military institutes of a strategic rival. Microsoft makes the point that everything their researchers did was transparent and in compliance with US and local laws. I think many would be surprised to know that the U.S. Army conducts international S&T collaborations through its international technology centers in Tokyo, Chile, and the UK. A subtly different issue is what level of engagement, if any, should Western researchers have with researchers at research institutions that receive Chinese-military funding. Does every researcher at Johns Hopkins University, which receives substantial funding from the DoD, become a member of “US military-funded academia?” FT calls out Microsoft Research’s “long-running links to Chinese military-funded academia” but again doesn’t link to where they are getting this evidence from (Who is doing the editing for these pieces? Am I just misunderstanding journalistic practice? Isn’t there an obligation to link to external sources and/or quote experts as evidence for claims like these?)

A third issue is research collaborations pertaining to facial recognition and surveillance. In one of the FT pieces Elsa Kania, a fellow at the think-tank Center for a New American Security said, “When research pertains to facial recognition or person re-identification, considering the known abuse of these technologies by the Chinese government, no [organisation] that values ethics or human rights should collaborate with Chinese counterparts, let alone those that are known to profit from marketing surveillance capabilities to China’s party-state.” I disagree with Elsa on one aspect of this point. I think there’s some space for collaboration with Chinese counterparts in research that pertains to facial recognition, but it’s a topic worthwhile for debate. For instance, does that mean international collaboration on research that pertains to making “pay-with-face” mobile repayment systems more secure (the subject of this week’s feature translation) is also off-limits?  

FT didn’t address the subtleties of any of these issues. Writing things in a way to score political points by citing Senators and naming and shaming people will not convince AI researchers to more strictly scrutinize collaborations nor help with the situation in Xinjiang. FT failed to get anyone to present opposing arguments, of which there are many. Here’s a brief sampling: a) allowing collaborations on emerging technologies with Chinese military-affiliated folks may actually increase US net assessment capabilities to track Chinese progress in these domains; b) stricter regulations could send a broader chilling effect that cuts against the openness of the US academic enterprise and especially the unique openness ethos of the ML research community, one of the key driving forces of our technological dominance; c) it is really really hard to prevent AI advances from diffusing – so we should focus our efforts (as the consensus has been with respect to export controls) on “running faster” -- none of the info contained in any of these articles was groundbreaking and could have easily been found in any number of other articles.

These were important pieces that, understandably and predictably, caused waves. There was a version of this story that a) limited over-hyping of specific papers by better technical understanding, b) provided more context about how to judge the relevance and significance of these papers to potential applications, and c) still emphasized lack of oversight in academic collaborations with implications for human rights abuses. In this case, it’s unfortunate the FT failed to take the time to tell that version.

I reached out to Madhumita, who led FT’s reporting on the pieces, with excerpts of an incomplete draft of this section. She was kind and responsive enough to provide rebuttals to some of my points and give me permission to cite them. They are linked here in this Google doc.

ChinAI Translations of the Week

1. Fascinating longform article about how someone lost all their money because a roommate scanned their face while they were sleeping and transferred all the money out of their mobile payment system. Goes into the vulnerabilities of the “pay by face” functions of Alipay and Wechat pay

READ FULL TRANSLATION: How safe do you think it is to "pay by face" on mobile phones? We measured an accuracy rate of less than 70% | Exclusive In-Depth Survey

*Too much writing this week, so we’ll come back next week to dive deeper into this case as well as finish out the second part of the translation.

2. Speaking of NUDT, how does one of the researchers connect the recent Chinese sci-fi blockbuster “Wandering Earth” to the need for early-warning systems of AI rebellions as well as calling out the loss of control of military drones by the US military on the Iraqi battlefield?

READ FULL TRANSLATION: In “The Wandering Earth” Do We Encounter a Defection by MOSS? Artificial intelligence is advancing rapidly, intelligent command and control systems need early warning of rebellion

Phew, don’t worry – my neck doesn’t usually feel good enough for this much typing each week. Here’s this week’s ChinAI links:

I really think the world’s collective understanding of China’s AI development would improve so much if we looked outside DC and Silicon Valley for China expertise. While these places attract really talented and knowledgeable individuals and benefit from network effects, they are also bubbles vulnerable to groupthink. Two organizations come to mind but there are countless others. MERICs in Berlin, which recently put out a great paper on how China’s Digital Rise will affect Europe; and MacroPolo in Chicago, which recently published a data-packed piece on why American should open, rather than close, its doors to Chinese AI talent.

Speaking of orgs based outside of DC and SV, my home base GovAI recently published two technical reports on AI governance:

1.     Stable Agreements in Turbulent Times: A Legal Toolkit for Constrained Temporal Decision Transmission (2019) - Cullen O’Keefe – on crafting long-term agreements, ex ante, in the face of radical changes from Advanced AI.

2.     Standards for AI Governance: International Standards to Enable Global Coordination in AI Research & Development (2019) - Peter Cihon - The case for further engagement in the development of international standards for AI R&D are detailed in this report. It explains the global policy benefits of AI standards, outlines the current landscape for AI standards around the world, and offers a series of recommendations to researchers, AI developers, and other AI organizations. Peter’s piece is especially relevant in light of the US government’s recent call for comments on federal involvement in AI standards-related activities.

FT reports that Chinese facial recognition unicorn SenseTime has sold out of a joint venture with Xinjiang security company Leon – marking “the first time a major Chinese technology has opted out of operations in the region.” ChinAI first flagged this partnership in a previous issue in September.

New York Times on how China’s targeting of Uighurs is not limited to Xinjiang; facial recognition technology is used to identify Uighurs in eastern cities like Hangzhou and Wenzhou and across the coastal province of Fujian.

Thank you for reading and engaging.

Shout out to everyone who is commenting on the translations - idea is to build up a community of people interested in this stuff. You can contact me at or on Twitter at @jjding99

ChinAI #48: Year 1 of ChinAI

Thanks for being a part of Year 1

Welcome to the ChinAI Newsletter!

*It’s been a little over the year since I started ChinAI with an email to a small group of colleagues and friends with chapter translations of a book co-authored by Tencent and a Chinese gov’t think tank on a National Strategic Initiative on AI. Since then, we’ve grown to 2800+ subscribers, and somehow nearly 50% of you actually open the email every week. ChinAI translations have been featured on the Financial Times, MIT Technology Review, Axios China, and other outlets. Thanks to Miles Brundage and Caroline Daniel for their generous pumping up of the newsletter, to all the contributors who suggested edits and added comments to the Google doc translations, to Graham Webster and the team at New America’s DigiChina initiative for collaborating with us on joint translations, and special thanks to Cameron Hickert, lisbeth at China Digital Times, and Karson Elmgren for contributing translations of their own. As we go from weekly to (sometimes) weekly translations, my hope is that other researchers and translators can use this as a platform to share their work. Lastly, none of this would be possible without the support of the great team at the Center for the Governance of AI at Oxford’s Future of Humanity Institute — kinda unbelievable that they trust this grad student to rant about random stuff every week.

These are Jeff Ding's (sometimes) weekly translations of writings on AI policy and strategy from Chinese thinkers. I'll also include general links to all things at the intersection of China and AI. Please share the subscription link if you think this stuff is cool. Here's an archive of all past issues. *Subscribers are welcome to share excerpts from these translations as long as my original translation is cited.

What I Learned from a Year of ChinAI

*No new translations this week; instead, I’m doing a year in review post of what I learned from a year of translating articles, blogs, government white papers from Chinese thinkers on AI-related issues. If any publishing outlets would like to publish this section (with minor edits) please hit me up with a pitch and I’ll get back within a week’s timespan. All of the bolded and underlined text links to a ChinAI translation on the subject.


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 S&T (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 terms of output of content and scale.

Andrew Ng referenced a similar language asymmetry with regards to AI technical research in an article in The Atlantic, “The language issue creates a kind of asymmetry: Chinese researchers usually speak English so they have the benefit of access to all the work disseminated in English. The English-speaking community, on the other hand, is much less likely to have access to work within the Chinese AI community. ‘China has a fairly deep awareness of what’s happening in the English-speaking world, but the opposite is not true,’ says Ng.” I think the growth of ChinAI in this past year is proof that there is a demand from the English-speaking world for a deeper awareness of what’s happening in the Chinese-speaking world, and I hope more people and organizations work to rebalance this language asymmetry.

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 to drive momentum for their own agenda, 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 Tecent’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, the number of Tencent engineers solely dedicated to doing work on improving algorithms is “pitifully few” according to Li, and each unit has its own algorithm engineers so there’s also a lot of low-level, redundant development of algorithms.

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 divided the 190 major AI companies that make up China’s AI ecosystem into three layers (application, technology, and foundation), but 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 (e.g. 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 complicit with China’s mass surveillance of Xinjiang, an effort that disproportionately targets ethnic Uyghurs.

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. AI startups like Mininglamp are positioning themselves as the “Palantir of China” by integrating their products with public security departments and collaborating with the police to crack cases related to the production and sales of fake vaccines.

Two of China’s most successful facial recognition startups, Sensetime, and Megvii (Face++), are involved in China’s efforts to securitize Xinjiang. At the 2017 China-Eurasia Security Expo, Megvii (Face++) 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. The 2018 edition of expo featured the announcement of a joint venture company called Tang Li Technology by Sensetime and Leon Technology, a security systems integrator company that claims it is responsible for 50% of "safe city" projects in Urumqi, the capital of Xinjiang, as well as the maintenance of surveillance infrastructure for the border between Xinjiang and neighboring countries. 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 constraints to compute 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 (MSRA) 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, complicates the assumption of nations as impermeable containers of AI development, forcing 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. Over 100 people teach at leading universities in China, such as Tsinghua University, Peking University, University of Science and Technology of China, and the Chinese Academy of Sciences” (from my translation of a Renwu (People) magazine feature).

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. For instance, he’s the principal researcher on Microsoft XiaoIce, an enormously popular social chatbot in China, and also leads development on Xiaona, the Chinese version of the Cortana digital assistant.

5. Chinese people — including regular netizens, data protection officers, philosophy professors — care about AI-related ethics issues, including privacy. Let’s dispel once and for all with this fiction that there are no discussions of AI ethics happening in China. 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. After a yearlong investigation, China’s Shandong Province brought a major case in July of 2018 on infringements of personal information against 57 individuals and 11 big data companies, which revealed a debate over how to interpret a new national personal information protection specification. The Nandu Personal Information Protection Research Center has assessed 1550 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, including the prospect of superintelligence. 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.

This Week's ChinAI Links

Many of my favorite issues were linked in the above review, but I’ll use this week’s four ChinAI links to highlight a few translations from this past year worth your time:

In 2007, when Princeton professor Fei-fei Li first started annotations for Imagenet, she hired a group of Princeton undergraduates for $10/hour. Ten years later, this experiment has evolved into an industry of data workshops found throughout the small fourth, fifth-tier towns of Henan, Shandong, Hebei, and other areas. This piece follows the moving stories of Ma Mengli, a worker in a data annotation company Qianji Shuju, and one of the company’s founders, Liu Yangfeng. I hope more people can get beyond the flattened big picture of China’s development and see the human people like Ma Mengli.

These articles from zhishi fenzi ("知识分子") - a fascinating media platform dedicated to discussing the state of science in China, discuss China’s science and technology talent with a focus on the natural sciences as well as talent policies. I also give a brief reflection on my own story and the term Zhonghua Minzu (Chinese nation).

If measured by the increase in the number of people who knew about X company from the beginning of the year to the end of the year, Huawei may have ranked first in 2018 by this metric. This issue covered its AI strategy, particularly in the security field.

Chinese commentators do often take a techno-national approach to AI development, here’s Saidong, who has a background in the semiconductor field (studied in a lab at Peking University), on an industrial strategy for “China chips.”

Thank you for reading and engaging.

Shout out to everyone who is commenting on the translations - idea is to build up a community of people interested in this stuff. You can contact me at or on Twitter at @jjding99

ChinAI #47: The Sensenet Data Leak - What Actually Happened

Welcome to the ChinAI Newsletter!

These are Jeff Ding's (sometimes) weekly translations of writings on AI policy and strategy from Chinese thinkers. I'll also include general links to all things at the intersection of China and AI. Please share the subscription link if you think this stuff is cool. Here's an archive of all past issues. *Subscribers are welcome to share excerpts from these translations as long as my original translation is cited.

I'm a grad student at the University of Oxford where I'm based at the Center for the Governance of AI, Future of Humanity Institute.

An AI Data Leak Case Misunderstood by Multiple Parties: 5 Key Clues to Get to the Truth

Last month, a data leak revealed that a Chinese facial-recognition company called Sensenets had collected 6.7m GPS locations of 2.6 million people (almost all in Xinjiang) in one database in a 24-hour period, according to security researcher Victor Gevers who found the database. Obviously this discovery struck a chord, and it was covered in high-level forums such as the Financial Times and Washington Post editorial board. This week’s 6000-word+ translation of an investigative report by Chinese S&T media platform jiqizhixin represents the best Chinese-language reporting on the case. It’s not without flaws — any mention of Xinjiang is glaringly absent — but it sheds light on two key areas: 1) the growing reflexivity of Chinese media sources, as the author calls out “publicity stunt” reports and news reports that generated collective panic about the incidence, 2) the impressive quality of Chinese reporting on tech issues (again with the caveat that key aspects of the story are off-limits), especially in understanding the technical details, helped in large part by access to interviews with insiders.

Specifically, this report, which draws on interviews with researchers involved in cooperative projects between Chinese university laboratories and the public security system, well-known security AI company engineers, security engineers, and public security system personnel, answering five basic questions — giving insights that I haven’t seen in any English-language coverage of the case:

  1. What was the nature of the leaked data? There was confusion in Chinese reporting about the leaks including “face recognition images” data (stills from security camera footage with frames around faces of interest), but this was not the case. In fact, the data leaked was even more sensitive including personal identity information.

  2. Where did the data come from? A researcher involved in cooperative projects between Chinese university laboratories and the public security system believes that because there was ID card data involved, “there is a large probability that this flowed out of the public security system.” An engineer for a well-known security AI company in China, said that there’s also a possibility the data came from other sources (hotels, banks, etc.) that collect user identity information, which then gets sold in underground markets. Another key distinction: sometimes AI companies/research labs only get access to train their algorithms on a dataset but don’t get to directly copy and take the original data — this seems to be a case where Sensenet and the relevant public security bureau negotiated the data arrangement in “You open the data to us, we will guarantee the security of these data” terms.

    Notably, the reporter investigates the connection between Sensenet and Sensetime, which had previously invested in Sensenet and provided algorithmic support, but withdrew its investment in November 2018.

  3. How was the leaked database accessed? Sensenet used a MongoDB, a NoSQL method, to secure the database which is known to be very vulnerable and easily broken into. One researcher said while Sensenet is a smaller company with weak security protection, most AI algorithm companies’ databases do not physically isolate their database (i.e. they allow external IP access and critical datasets are not air-gapped). They recount another instance when a student at their lab was an intern at an AI company and used a single command to remotely send the company’s database to the university lab.

  4. What are the implications of collecting “location information captured by cameras in the past 24 hours?” There are two scenarios to digest: First is the case where cameras are tracking in real-time location information matched to the pedestrian’s actual identity information; the second, is that the location information corresponds to a some pedestrian only identified by some code (pedestrian A, B, C, etc). The first scenario can only be achieved through checkpoint monitoring (e.g. at airports), whereas the security system as a whole can only realize the second scenario due to camera costs, lack of demand from public security systems themselves, and technical challenges of facial recognition with non-cooperative subjects (i.e. not being asked to turn and face the camera).

    Notably, the reporter also writes, “even if the public security can get our ‘location information based on the cameras we have passed in the past 24 hours,’ there is some controversy over whether the public security system has the right to monitor the life trajectory of each of us, and what places we have passed each day; compared with identity information, which is information necessary to maintain law and order, and there is constant need to register (the identity information). But the monitoring of the former (real-time location in the past 24 hours) is very likely to violate our privacy.” PLEASE STOP with the notion that Chinese people don’t care about privacy.

  5. How can the security of sensitive data in the AI security domain be improved? A variety of methods are proposed: physical separation of intranet from external networks (method employed by Huawei), multiple levels of approval to access/copy and take data, establishment of a special security research team to actively conduct attack/defense experiments on different levels of the AI company. China has a long way to go in this sphere: “During the course of interviews, several experienced security practitioners felt that compared with traditional established IT companies, the new generation of Internet companies are too slack in user data, and the artificial intelligence companies that inherited the genes of these Internet companies are even worse in their awareness of sensitive data.”

READ FULL TRANSLATION: An AI Data Leak Case Misunderstood by Multiple Parties: 5 Key Clues to Get to the Truth

This Week's ChinAI Links

Chinese phrase of the Week:  抽丝剥茧 (chou1si1bao1jian3) -- to spin silk from cocoons — fig. to conduct a painstaking investigation of an incident

Georgetown's new Center for Security and Emerging Technology is looking to hire 2 full-time Chinese to English translators.

I wish everyone over-inflating China’s AI capabilities would read more articles like this one from Technode on why China is not prepared for a widespread AI rollout, especially in places outside first-tier cities like Beijing. A year ago, my report Deciphering China’s AI Dream called out over-inflation of China’s AI capabilities as one of the four common myths associated with China’s AI development.

Another flawed tendency is to see the global politics of AI as a two-agent US-China game. A useful corrective is to check out this survey of the EU’s AI ecosystem by Charlotte Stix here, and subscribe to her excellent EuropeanAI newsletter.

Currently available open access, read Julian Gerwitz’s article, “The Futurists of Beijing: Alvin Toffler, Zhao Ziyang, and China's 'New Technological Revolution" which examines the Chinese Communist Party’s modernization policies in the post-Mao period, particularly the Party’s response to the global rise of information technology and the surprising influence of American writer Alvin Toffler and futurist ideas.

Thank you for reading and engaging.

Shout out to everyone who is commenting on the translations - idea is to build up a community of people interested in this stuff. You can contact me at or on Twitter at @jjding99

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