ChinAI #73: Dispatch from the Beijing Academy of AI Conference (BAAI) 2019

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Welcome to the ChinAI Newsletter!

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As I’ve mentioned in passing in previous issues, last year I struggled with severe neck pain. I don’t think it’s something that will ever completely go away, but I’m grateful for the days now where it doesn’t consume every waking moment, and I’m especially grateful that it made me realize that I couldn’t churn out 10,000 word translations every week anymore, and needed to rely on a community of ChinAI contributors. The payoff is a much more balanced lifestyle and the chance to feature amazing contributions/translations like this week’s.

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Feature Translation: Beijing Academy of AI (BAAI) Conference 2019

The BAAI, established in Nov 2018 as an implementing body for the Beijing Zhiyuan Action Plan (a Beijing-specific AI plan), hosted its first global AI summit last week. A total of 1500 people attended the conference, including more than 100 top experts from the U.S., UK, Japan, Canada, Singapore, the Netherlands, and China. A ChinAI contributor who wishes to remain anonymous attended the BAAI Conference last week, and wrote up the following dispatch:


  • BAAI was set up by the Beijing Municipal Science and Technology Commission and Haidian District government, with the support of the Ministry of Science and Technology and leading academic and industry players such as Peking University, Tsinghua, the Chinese Academy of Sciences, Baidu, Xiaomi and Megvii.

  • More analysis on BAAI can be found in Thomas Lehmann’s piece in the recent DigiChina report “AI Policy and China.” ChinAI #52 covered BAAI’s Beijing AI ethics principles.

  • The conference featured a range of technical presentations and conversations, as well as discussions on AI ethics and governance. The write-up presents excerpts from opening session speeches/slides from: Vice Minister of Science and Technology Li Meng and Vice Mayor of Beijing Yin Yong; Huang Tiejun, dean of BAAI Zhiyuan Research Institute, who gave an overview of BAAI’s first year of work; Gao Wen, Director of the Peng Cheng Lab (PCL) in Shenzhen.

MAIN TAKEAWAYS from anonymous ChinAI contributor:

  • Immense efforts are being made by local governments to implement the national New Generation AI Development Plan and address shortcomings in China’s AI ecosystem. The work of BAAI and PCL is ambitious and wide-ranging, bringing together the resources and expertise of leading figures and organizations across academia and industry.

  • To address shortcomings in top talent and fundamental breakthroughs, BAAI aims to provide funding (~USD 71,000 per year) to 300 AI scholars identified over three years. The dean of BAAI said in his conference speech that the youngest of the current scholars is not young enough: “The two largest contributors in the fields of computing and artificial intelligence, Turing and Gödel, made groundbreaking contributions when they were 24 years old. Therefore, we hope that more young students in the future will be able to take the lead and in the future become the backbone of China's artificial intelligence innovation.” As an aside, the UK recently announced a Turing Fellows program that similarly aims to support top AI talent.

  • PCL is trying hard to reduce China’s dependency on American platforms and frameworks. PCL’s CloudBrain aims to provide shared access to an exascale supercomputer combined with an open source framework and machine learning tools. Its Zhihui code hosting site was framed in large part as a response to the risk of the US cutting off access to GitHub. However, I am sceptical that these projects will deliver significant results. As an article by Helen Toner and Lorand Laskai in a recent report by DigiChina said, “The network effects that arise because researchers want to use the same frameworks as their collaborators (and because frameworks with more users are generally better maintained over time) mean it could be increasingly difficult for a Chinese company to come from behind and dethrone established frameworks.” Exchange ‘frameworks’ for ‘code hosting platforms’ here and the same logic applies.

REFLECT FURTHER on BAAI’s and the Shenzhen-based Peng Cheng Lab (PCL) actual value-add — Jeff’s thoughts:

What is BAAI actually contributing? In his speech, the dean of BAAI’s Zhiyuan Research Institute claims that Zhiyuan Institute released the world's largest object detection data set [Objects365]. The ICCV paper that introduces this dataset is a Megvii paper (supervising author is the chief scientist at Megvii). They acknowledge support from the National Key R&D Program of China, Beijing S&T Program, and BAAI. The other two are established funding schemes, but I’m not sure what BAAI is bringing to the table here (maybe just more funding or some sort of coordinating power?). My cynical take is that it’s more of a “PR-based, let the bureaucrats believe they are doing cool AI things” contribution. BAAI claims they are planning 10 joint AI labs with industry (two already established with Megvii and Jingdong). Again what is BAAI contributing to the joint lab? Why wouldn’t Megvii and JD just partner with Tsinghua or a university instead? To be clear, I’m not saying BAAI is not doing good work on the AI ethics and safety side — we’ve covered that in a previous issue — I’m just skeptical of their value-add to leading-edge technical research.

Gao Wen, director of PCL, gives more detail on hardware deficiencies in his slides: Xilinx and Altera have nearly 90% of the FPGA market, NVIDIA have nearly 70% of the global GPU market; Boston Dynamics robot Atlas relies on the company’s huge edge in high-precision sensors and motion control algorithms. It’s not just chips, it’s also high-end sensors (e.g. analog and radio frequency sensors).

PCL is the more interesting body to me: It’s a Provincial Research Lab opened in 2018 by the Guangdong Provincial Government and funded and managed by the Shenzhen Municipal Government. In partnership with the Harbin Institute of Technology (Shenzhen), PCL also cooperates with local industry and research institutions such as the Shenzhen branches of Peking and Tsinghua Universities, the National Supercomputing Center in Shenzhen, Huawei, Tencent, and ZTE.

PCL’s CloudBrain (p. 8-10 of full translation) brings real value-add on through access to compute: CloudBrain 1 is a large-scale cluster system with 100 Petaflops of computing power, including NVIDIA GPUs, Huawei GPUs, and Cambrian AI chips. A machine of 1000 Petaflops will probably be built next year, which can be used by universities, research institutes, and SMEs for training models. The goal of 1000 Petaflows (an exaflop) is generally considered a big milestone for compute over the next few years, which the DOE is heading towards.

Still, I think the importance of these supercomputing benchmarks is often overstated. It often depends on whether the “supercomputer” is defined as a fully integrated supercomputer with low latency or a bunch of distributed compute. CloudBrain seems to fit the latter case: at present, the distributed computing resources connected to CloudBrain 1 include Zhongshan University’s Tianhe-2 supercomputer in Guangzhou, Hefei Brain-Inspired Computing Center’s server cluster (which they spent nearly 100 million RMB on), and PCL’s supercomputing center in Shenzhen.

***The full translation contains the anonymous ChinAI contributor’s notes from the conference, extracts from speeches, and translations of presentation slides:

FULL TRANSLATION: BAAI 2019 Conference Write-Up

ChinAI Links (Four to Forward)

Must-read: The Early History of AI in China (1950s-1980s) by Jieshu Wang

Jieshu, PhD student in the Human and Social Dimensions of Science and Technology Program at ASU, takes us through the tumultuous development of cybernetics and AI in China, including a fascinating section on how Qian Xuesen (father of China’s space program) was convinced that AI was associated with “exceptional human body functions (e.g. ESP, telepathy). Jieshu argues that the establishment of the Chinese Association for Artificial Intelligence in 1981, the first official research institute in China dedicated specifically to AI, was critical to the development of AI in China. The piece is a welcome reminder for us to not get caught up in the presentist view of AI in China.

Should Read: Scary China by Yuan Yang in Chinese Storytellers Newsletter

Reflecting on the development of the “Scary China” narrative, Yuan (FT’s China tech correspondent) writes “We as journalists should take some responsibility for the rise of this meme. As an example, I recall that it was early 2017 when the phrase “AI arms race” started to gain prominence. Although I can’t remember exactly how the phrase entered the collective consciousness of our English-speaking newsrooms, I can guess at the reason we all reached for it: it sounded scary. Scary things are important, and important things are newsworthy.” The question she poses to the Chinese Storytellers group: How do we create narratives that extend our emotional palettes beyond fear?

Should Read: AI Principles: Recommendations on the Ethical Use of Artificial Intelligence by the Department of Defense

Longtime readers of ChinAI know that I’m not the biggest fan of what I lovingly call the military industrial complex. But this Defense Innovation Board (DIB) document on AI principles is worthy of praise, drawing on a 15-month study that involved public listening sessions at major universities. The 74-page supplementary document draws attention to emergent effects (unintended escalation and speed) of AI systems, which are especially important in my opinion. Full disclosure: I participated in a stakeholder interview session, providing input to the DIB on this initiative.

Should Read: Assessing the State of AI R&D in the US, China, and Europe — Part 1: Output Indicators

Stefan Torges provides a clear-eyed comparison between the state of AI R7D in the U.S. and China, finding that for almost all indicators the US institutions are the global leaders. I particularly liked the comparisons between the assumptions and methodologies driving various indicators. Stefan also draws from my testimony before the U.S.-China Economic and Security Review Commission, which provides a systematic framework to assessing the fuzzy concept of “national AI capabilities.”

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.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at or on Twitter at @jjding99

ChinAI #72: An Overview of China's AI Industry in the form of a 50+ page PPT deck

We get reacquainted with excellent work by the Qianyan Chanye Research Institute

Welcome to the ChinAI Newsletter!

Our subscription drive continues: we’re so close to 100 subscribers — if we hit that mark, I’ll give subscribers access to an organizational document with the complete collection of ChinAI translations to date (grouped by theme/category).

As always, the archive of all past issues is here and please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors).

Feature Translation: Qianzhan Chanye Report on China’s AI Industry

CONTEXT: Based on what I’ve read, Qianzhan’s (a consultancy/research institute) 50-page slide deck is the best open-source, overview-style report on China’s AI industry. I’d wager it’s probably better than the closed-source reports from Goldman, government departments, etc. I’m going to keep harping on this point: If you’re following China’s tech sector, you will miss a lot if you’re not looking at the output of orgs like Qianzhan, CCID, and other Chinese research firms, as these places are only going to scale up and get better.

  • I only had time this week to do a quick scan of the first 18 slides and did a few quick-and-dirty translations of interesting sections (if there’s no comment on the slide that means I probably didn’t do any translations on it).

  • ****I’ll circle back to this every now and then to translate more chunks, but if you’re keen to help out with the whole thing, let me know! In a previous issue, an anonymous ChinAI contributor and I translated a 5G PPT slide deck that went viral in China and became recommended reading for all Huawei staff. There’s been lots of good discussion happening in the comments of that translation, and I hope the same happens for this one.


  • Three-layer (Foundation-Technology-Application) division of China’s AI industry : Slide 5’s specification of the different layers of AI is the first clear sign that this is good stuff. Representative companies in the foundation layer (slide 6) includes usual suspects like Cambricon, Horizon Robotics in chips; Baidu, Haiyun Shuju in big data; BAT in cloud computing, Huawei in 5G.

  • What stood out about the representative companies in various layers of China’s AI industry (slide 6-8): Datatang is listed as a key big data company. We analyzed their involvement in a turning point case in personal information protection in ChinAI #19. Ultrapower shows up in more verticals than Alibaba and Tencent in the foundation layer — we did a ChinAI Company Profile of them in ChinAI #63.

  • Ping An is the most important company in China’s AI landscape that you’ve never heard of: nominally an insurance company but transforming quickly into a tech company, they are everywhere in the foundation (including cloud computing) and application layers. However, they don’t show up in any of the verticals in the technology layer.

  • Slide 18 on China’s AI talent Training System was particularly instructive:

It’s a nice overview of industry-academia joint AI institutes. We covered the iFlytek-Chongqing University of Posts and Telecommunications one in ChinAI #7. These types of schools may be just as important as the Tsinghuas and Pekings for the diffusion of AI advances across the whole country.

TRANSLATION IN PROGRESS: Qianzhan Chanye Report on China’s AI Industry

ChinAI Links (Four to Forward)

Must-read: DigiChina Special Report: AI Policy and China — Realities of State-Led Development (Edited by Graham Webster)

Big ups to Graham Webster and the Stanford-New America DigiChina team for an excellent report on China’s AI Policy. Each one of the pieces are worth a read, and I was impressed by the consistent effort to translate and analyze Chinese-language sources throughout. I learned a lot in particular from Siodhbhra Parkin’s piece on how AI can better serve people with disabilities in China (p. 33 of the report).

Must-listen: ChinAI Pod #2: Reframing Superintelligence with Eric Drexler

Nick Bostrom’s book Superintelligence is what got me really interested in AI in the first place; after joining FHI a couple years later, Eric Drexler’s report Reframing Superintelligence significantly shifted my view of superintelligence in a way that lived up to the report’s title. It was an honor to talk with the founding father of nanotechnology about his framework of Comprehensive AI Services.

If you like what you hear, pass it on! If you don’t like what you hear but are willing to take a chance on hearing more, let me know in a public setting so that I get the feedback and also promo for the pod. We should be on all the places you get your podcasts now (just search “ChinAI,” but if we’re not, you can paste this link into your podcast app and the feed should pop up:

Should-read: Translations related to China’s AI Policy by Center for Security and Emerging Technology

Ben Murphy, Chinese STEM Translation Lead at CSET, is doing fine work with these full text translations. A few notes from some of the translations that CSET has published. I plan to circle back every month or so and highlight what’s interesting from the translations.

1.Translation of Project to Strengthen Development of the Defense Technology Industry at the Grassroots Level: Guidelines for Basic Research and Cutting-Edge Technology Projects (2018)a notice to Chinese universities and research institutes on emerging technologies (including AI-related domains) that the PLA prioritizes, from SASTIND (a civilian agency that funds research in support of PLA requirements)

  • AI features heavily in three of the six themes: intelligent detection/identification and autonomous control technology; brain-machine intelligence and biological interdisciplinary technologies; highly reliable information security and new types of technology

  • Nothing in first 2 themes was that surprising (cloud-based intelligent target recognition and tracking, intelligent decision-making for virtual battlefield environments, intelligentized exoskeleton technology); the third theme was more interesting to me and included key tasks such as “intelligent mining technology” for firmware vulnerabilities as well as big data analysis-based intelligent fixed decryption technology for electronic documents

2.Translation of Tianjin Municipal Action Plan for Military-Civil Fusion Special Projects in Intelligent Technology (2018) — Wow this is a rich document with a lot of material in the translator’s footnotes

  • There’s a story/report waiting to be written about Tianjin’s technology policy (remember: Tianjin was one of the first cities to set up a big AI fund. “While many Chinese local governments have published military-civil fusion plans, Tianjin’s is among the most detailed,” per CSET’s summary.

  • A lot of the initiatives in here sound very ambitious (“build an authoritative IoT perception testing and authentication center for the state, military, and industrial sectors in Tianjin’s Binhai New District!”), but I’m also reminded of the empty promise of the Tianjin Eco-city.

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.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at or on Twitter at @jjding99

ChinAI Pod #2: Reframing Superintelligence with Eric Drexler


Welcome to the second episode of the ChinAI podcast, hosted by Jeff Ding. Our guest today is Eric Drexler, a senior research fellow at the Future of Humanity Institute. He joins the ChinAI podcast to discuss the latest FHI technical report “Reframing Superintelligence: Comprehensive AI Services as General Intelligence.”  In contrast to conventional views of superintelligence as an agent with unbounded capabilities across multiple domains, Eric reframes superintelligence as embodied within a bounded framework of Comprehensive AI Services (CAIS). We discuss how this alternative model may lessen some of the classic risks associated with artificial general intelligence (e.g. the paperclip maximizer) but also bring under-explored risks to the fore (e.g. supercharged addiction).

Often described as the founding father of nanotechnology, he provides a unique perspective on the pathway to superintelligence — one of an extremely perceptive systems engineer. We also examine a perspective on superintelligence from a prominent Chinese philosopher.

*****Timestamps: Briefing Checklist (1:00); Debate the Guest (20:00); Footnote Fever (31:00); Trust the Process (37:50)

ChinAI # 71: What I Got Wrong re: Entity List & Chinese AI Startups

Plus, what does the French Ministry of the Armies Report on AI say about China?

Reflections on the Entity List Ban

In one episode of the Sinica podcast (I forget which one it is now), one of the hosts — I think it was Kaiser — said that studying China is a continual process of saying I don’t know in better ways. That comment was probably from at least five years ago, but it still resonates with me. Using that approach as a guide, I reflect on my comments for the WSJ article I shared last week, and look at what I got wrong and what I can do better in the future.

CONTEXT: A couple of weeks ago, the U.S. added eight Chinese companies — including facial recognition startups Sensetime, Megvii, and Yitu — to a trade blacklist that cuts off their access to American-sourced products and technology without a license. Here’s how the WSJ and I interpreted the impact on these companies:

A major challenge for these companies now will be to secure access to cutting-edge processors, where the market is currently dominated by American companies. That includes powerful graphics-processing units made by Nvidia Corp., of Santa Clara, Calif. The chips are used by AI companies to crunch the massive amounts of data that feed algorithms underpinning AI programs. Nvidia is widely seen as a leader in the field without a clear non-U.S. alternative.

“There’s a reason everybody uses Nvidia GPUs—they’re the best in class,” said Jeffrey Ding, a researcher at the Centre for the Governance of AI at Oxford University. “Even if you switch, it’s going to have a cost on your efficiency; it’s going to have a cost on your margin.”

WHAT I GOT WRONG: I got two things wrong here: 1) Because Nvidia uses TSMC’s Taiwanese fabs to their manufacture chips, firms relying on Nvidia chips will likely not be affected by the ban; 2) AI companies do not necessarily use Nvidia GPUs because they are more efficient; they use them because of architecture lock-in. I thank Doug Fuller and Cooper Pellaton for their criticism on these two points and follow-on comments.

1) The Entity List’s Impact on Nvidia chip sales to Chinese AI firms:

As Doug pointed out in this thread, the hype over the effects of a chip ban on these companies is overblown. My understanding was that even if chips were made at TSMC fabs in Taiwan, they would still fall under the ban if more more than 25% of the content (including IP and software) came from the United States.

Doug replies, “The weird thing is that manufacturing outside of the US automatically makes these manufactured goods fall outside of the 25%. I have to look up the legalese again AND it ain't clear but that is the interpretation both the DOC [Department of Commerce] and its commercial ‘foes’ [U.S. suppliers affected by the ban] have arrived at.” Thanks to Doug for letting me quote him for this post. The legalese here is that “U.S. origin” is based on a 1990s era concept of inputs to physical goods, which does not account for software and IP.

In an internal letter to staff cited in this excellent South China Morning Post article covering the effects of the entity list ban, Megvii’s CEO Yin Qi wrote, “The specific impact is that we can't directly buy products subject to US export regulations, such as x86 servers and GPUs made in the country.” Doug’s interpretation is that since Nvidia’s chips aren’t made in the United States, they do not fall under the ban.

While I think there’s still a lot of uncertainty surrounding what falls under the ban and what doesn’t, I should have made sure I was much more confident about how Nvidia’s GPU sales would be affected before making my comments.

2) Why do firms use Nvidia? We need to get a deeper story than my broad claims of better efficiency. Thanks to Cooper for writing me a 1500-word exchange on this topic and letting me quote him. He’s a student at Georgia Tech who’s worked at Cigna and Alibaba.

The key here is understanding architecture lock-in, specifically CUDA (compute unified device architecture), which is a software layer created by Nvidia that enables machine learning researchers to use a GPU for general purpose processing. Essentially what interface libraries like CUDA do is turn high-level machine learning (ML) operations into low-level instructions that can be run on a card at training time.

According to Cooper, when ML became mainstream, Nvidia’s CUDA interface layer was better at doing this type of interfacing, so ML researchers could turn their workloads into massively parallelizable, runnable jobs with CUDA bindings (using Nvidia’s middleman implementations) and not “worry about micro-architectural changes that come with each year’s revision to physical GPUS.”

Again, the story here is not about efficiency. Cooper writes, “In fact, Intel has surpassed the performance of most GPUs with their Stratix lineup (Stratix 9 was the first to give them a run for their money, Stratix 10 can match). Xilinix has also been pushing hard in this space…NVIDIA is good, but people don’t buy NVIDIA cards because they’re good — they’re big, hot, and power hungry. People buy them because of vendor and library lock-in.”

What Nvidia has done in the past does not guarantee its future. Cooper argues that while lack of access to lots of GPUs might hurt Chinese researchers in the short term, there is now a large effort to make the interfacing easier (porting models to different underlying architectures and libraries). He concludes, “Researchers realize they need to mitigate this dependency on specific-provider GPUs for training and there are a number of efforts to make the transition effort easier. Will these bear fruit in the short term? Likely not. But in 5-10 years as libraries become further abstracted, and workload patterns are better established I would reckon that NVIDIA is fighting a mighty battle against the provider of some XPU with a fundamentally new (surrounding) architecture.”

3) To complicate things even further:

The previous two points illustrate how hard it is to implement economic statecraft in the context of a) complicated, global supply chains, b) uncertainty surrounding the underlying technology. In many cases policies that seem to work or make us feel good on the surface level will ultimately be counterproductive for American interests. As Doug points out, “The export ban actually encourages offshoring as currently written and interpreted.” The entity list actions have hammered home the possible consequences of being dependent on a sole-source supplier for GPUs, and now more firms and researchers around the world (including in the U.S.) are working on alternatives.

Missing among all of this is how the entity list action will actually affect the situation of people suffering in Xinjiang (see the ChinAI links for firsthand accounts). I find it pretty disheartening that so much of this has been framed as just another chip for Trump to play around with in trade deal negotiations, rather than as a coherent, principled formulation of how the U.S. wants to enforce a reasonable standard against the complicity of U.S. companies in human rights abuses.

We’ve only scratched the surface of how complicated this stuff is. I’ve limited the analysis in this reflections on AI software firms (Megvii, Sensetime) access to hardware for training algorithms. We haven’t even talked about hardware firms affected by the ban (Hikvision, Dahua), access to hardware for inference (running AI algorithms), etc. What we can do is continue saying I don’t know in better and better ways.

Feature Translation: China-related Excerpts from the French Ministry of the Armies report on AI

I went through the French Ministry of the Armies (formerly French Ministry of Defence) report on Artificial Intelligence, released last month, and translated the portions that mentioned China. See our GovAI summer fellow Ulrike Franke’s broader analysis of France’s new military AI strategy here:

There’s a strong emphasis on the two AI superpowers narrative. Note as well the emphasis on sovereignty and ensuring France is free from this duopoly. Excerpts from the report:

  • Two "superpowers": the United States and China, out of reach of the others, controlling an immense mass of data, having an ecosystem articulated around powerful integrating companies with global reach (GAFA and BATX) and being able still to multiply their domination thanks to their scientific and financial means;

  • “Outside the European continent, other major states want to free themselves from the duopoly over AI exerted by China and the United States.” (Hors continent européen, d’autres États importants souhaitent s’affranchir du duopole IA exercé par la Chine et les États-Unis)

  • As underlined by the strategic review of defense and national security "... the mastery of artificial intelligence will represent a stake of sovereignty, in an industrial environment characterized by rapid technological innovations and today dominated by foreign companies." The global artificial intelligence ecosystem is dominated by major US and Chinese digital players who are developing internal capabilities and buying up many promising companies.

To be honest, I was underwhelmed by the China-specific portions of the report.

Re: the two AI superpowers claim, I’ve argued elsewhere that this is a) a nonsensical claim without specifying what aspect of the abstract catchall term AI that we are talking about, b) not true based on any reasonable specification of AI, and c) a dangerous meme. In the comments of the full translation, I also point out how there’s not very in-depth analysis or understanding of context of cross-country differences in civil military fusion.

Note: my French background consists of barely surviving AP French, doing a summer stint at the U.S. Embassy in Senegal, and watching a bunch of French-language movies and TV shows that somehow always feature Gerard Depardieu.


ChinAI Links (Four to Forward)

Must-read: Weather Reports: Voices from Xinjiang by Ben Mauk for The Believer

“Weather Reports” collects Ben’s interviews of former detainees in Xinjiang’s camps, and the “weather code” they use to check in on how their relatives back in China (if the weather is good so is the health of their relatives).

Context from the piece: These firsthand accounts were recorded in Almaty, Kazakhstan’s largest city, in collaboration with the volunteer-run human rights group Atajurt. In spring 2019, Kazakhstan’s government curtailed Atajurt’s activities as part of a crackdown on public criticism of China…The speakers here have nevertheless chosen to use their own names and the names of their relatives, despite the risks of censure in Kazakhstan and of reprisal for family members still in China. In doing so, they hope to pressure the Chinese government to reconsider its policy of mass detention in Xinjiang.

Must-read: Social Credit Scores in Xiamen and Fuzhou

In the Berkman Klein Center’s collection on Medium, Dev Lewis and a group of Yenching Scholars interviewed officials in Xiamen and Fuzhou, two cities in Fujian which are two of a handful of Chinese cities with their own city-level personal credit scores. h/t to Johanna Castigan for recommending this to me.

Key points:

  1. The data comes from public not private sources - Fuzhou’s platform collects data from over 630 sources, including government ministries, public bodies, state-owned enterprises, but people’s scores are not impacted at all by data from the private sector (online purchases or social media posts).

  2. AI plays no role for now - neither of these models use machine learning based technologies such as predictive scoring, though both governments claim to be bringing in ML. This lines up with Shazeda Ahmed’s comments in a previous issue of ChinAI: “I've been cautious about how I talk about technology in relation to the social credit system because even though I've spotted a few vague, aspirational policy references to ‘implement big data and AI into the credit system,’ thus far the applications I've seen are limited and fragmented.”

  3. A very rudimentary system: Dev states that implementation so far reveals a very basic attempt with numerous gaps and question marks, but a far cry from the western media picture of an all-encompassing score enabled by mass surveillance.”

Go deeper: The post cites Chinese legal scholar Xin Dai’s important paper “The Reputation State: China’s Social Credit Project,” in which he frames the social credit system as an effort of China’s developmental state to tackle its persistent governance problems with reputation-based schemes.

Should-read: MIT Tech Review Interactive and DeepMind Technical Paper on fairness in COMPAS pretrial risk assessment tool

Trained on historical defendant data to find correlations between factors like someone’s age and past criminal record, the COMPAS tool is used in the US court system to help judges determine whether a defendant should be kept in jail or be allowed out while waiting trial. It has come under fire for being biased against African American defendants. The MIT Tech Review interactive, by Karen Hao and Jonathan Stray, nicely illustrates how satisfying different definitions of fairness in this context is impossible.

Silva Chiappa and William S. Isaac point out that debates over the COMPAS tool should also consider patterns of unfairness underlying the training data. In their paper they outline a method (causal Bayesian networks) to measure the unfairness in a dataset.

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.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at or on Twitter at @jjding99

ChinAI #70: CASICloud and the Industrial Internet

Plus, how does 5G fit into all of this?

Welcome to the ChinAI Newsletter!

Our subscription drive continues until we get to 100 subscribers (at around 90 right now — thanks to all new subscribers!). I’m making a brief trip to DC on October 23-25: if you’re a subscriber, let me buy you a drink as a token of appreciation. If you’re not, I will aggressively ignore your emails (jk but not really).

As always, the archive of all past issues is here and please please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay support access for all AND compensation for awesome ChinAI contributors like Victoria Wu — a ChinAI subscriber who helped with this week’s feature translation.

Feature Translation: CASICloud and China’s Industrial Internet

I like to choose topics where the information arbitrage ratio (relative importance divided by number of people paying attention) is very high. China’s efforts to build an industrial Internet has a very very high information arbitrage ratio. On this topic, we’ve previously translated a case study of CASICloud in an AI Open Source Software White Paper and also covered the unsexy details of how computer vision affects quality inspections on production lines for making cutting tools.

THE CONTEXT: A snapshot of China’s industrial Internet landscape by way of a Leiphone interview with Xu Shan, deputy GM of CASICloud, one of the key players and a subsidiary of China Aerospace Science & Industry Corporation which is a large [Fortune 500] state-owned enterprise develops missiles, aerospace products, etc.


  • Taking the buzz out of the industrial Internet buzzword: a term by General Electric in late 2012, the Industrial Internet of Things refers to a system of industrial devices connected with communications technologies that enables advanced analytics, machine-to-machine coordination, etc. Two of the big players are GE’s Predix and Siemen’s MindSphere.

  • Need to knows about CASICloud: established in June 2015, started working on a cloud platform for the industrial internet in 2015 and released the INDICS platform in 2017, has support of CASIC — one of the top performing high-tech SOEs and key behind-the-scenes player behind a lot of the weapons/equipment showcased during the National Day military parade.

  • Disaggregating the notion of a single industrial Internet: CASICloud works with 28 SOEs including State Grid/China Unicom to build a national integrated industrial Internet, but it also has deployed regional industrial Internet service platforms including for Guizhou and Changzhou as well as regional platforms for single industries (e.g. a Sichuang Heavy Equipment cloud platform)

  • A government-guided but many obstacles: CASICloud’s main way to land these industrial Internets is to form a joint venture company with the local government, and very interestingly, CASICloud claims to handle data processing through the National Engineering Lab for Industrial Big-data Application Technology. There are still many issues with data islands and companies that want to hold on to their legacy information systems.

  • An indicator of how important this stuff is: In 2017, Gao Hongwei, Chairman of CASIC, and Joe Kaeser, CEO of Siemens, signed an agreement to allow both CASICloud and Siemens to build applications on the other platform. The signing ceremony was attended by Xi Jinping and Angela Merkel.

DISCUSSED IN THE FULL TRANSLATION (in the style of Believer magazine): how 5G and TSN are crucial to closing the loop on the industrial Internet system, the differences among IaaS, PaaS, SaaS layers and all the acronyms in the world, and just a lot of really dense and technical but interesting things about the industrial Internet.

FULL TRANSLATION: How many steps does it take to transfer the digitized capabilities of national-level aerospace equipment to the manufacturing industry?

ChinAI Links (Four to Forward)

Should-read: The Turnaround by Jayadevan, a journalist who has covered technology for Indian publications for 10 years

One recent podcast episode of The Turnaround covered the Indian gaming sector, which is growing rapidly and catching the attention of the Chinese VC/start-up world. In a follow-up issue, Jayadevan reflects on a previous ChinAI issue on the diverging trajectories of China and India in the information revolution and the link to online games.

Should-read: Expanded U.S. Trade Blacklist Hits Beijing’s Artificial-Intelligence Ambitions

The WSJ’s Dan Strumpf and Yoko Kubota analyze the effects of the U.S. decision to add 8 Chinese companies to the entity list, with good details on the percentages of companies’ revenues from Xinjiang, revenues likely to be affected by the blacklisting, and possible indirect effects.

Should-read: Global Defence-Industry League: Where is China?

The International Institute for Strategic Studies’s Meia Nouwens and Lucie Béraud-Sudreau calculated how much defense-related revenue that eight of ten Chinese SOEs involved in defense production generated in 2016; their calculations placed CASIC 11th in worlds top defense companies by total arms sales.

I really wanted to find a profile that comprehensively unpacked all of CASIC’s subsidiaries and research institutes but was unable to — if anyone has recommendations, please send them my way.

Should-read: The Next Word

John Seabrook’s piece for The New Yorker on predictive fed text from the end of each section of the section of the article into the New Yorker’s AI — a full-strength version of GPT-2 fine-tuned on all nonfiction work published in the New Yorker since 2007 along with some digitized classics dating back to the 1960s — and then generated the predicted text that follows each section in the article.

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.

Check out the archive of all past issues here & please subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (everyone gets the same content but those who can pay for a subscription will support access for all).

Any suggestions or feedback? Let me know at or on Twitter at @jjding99

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