Greetings from a land where even the homecoming king cries…

…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).

Browsing PaperWeekly Together

One of the issues I (and a lot of folks in this space, I think) struggle with is how much technical knowledge we need to accumulate. As someone without a technical background, I’m always faking’ it until I make it. But what does making it look like?

While technical fluency is the holy grail, I think folks who study politics of tech can reasonably shoot for better levels of technical literacy. Open source courses, playing around with trying to implement some very simple ML techniques (like my attempt to build a classifier for Taylor Swift songs), and weekly newsletters that keep you updated on the state of the AI field are all helpful. Thebatch, deeplearning weekly, and my personal favorite — Jack Clark’s Import AI — are example resources.

This week we’re going to look at one Chinese newsletter/platform called Paperweekly that’s also in this mold. I think it’s a really interesting model for up-skilling/keeping updated on ML research, and for those interested in China’s AI landscape, one advantage of following Paperweekly is you can double dip and brush up on both your Mandarin and code literacy. So, let’s read it together…

Here’s what my version of the homepage looks like:

To access the site you have to give an email address and then wait 24 hours for a check. When you register, you can list your interests. I put down NLP, so the homepage gives me recommended arxiv articles on NLP that I can read. I scrolled down a little to some of the more well-known pieces in this field — the first one in the screenshot above is the well-known Microsoft paper on NMT achieving human parity in Chinese-English news. Note that the site-runners recommend some articles, but most recommendations come from users (like the third one in the screenshot). Let’s click on the 2nd article in the screenshot — “Chinese Text in the Wild”:

There’s an abstract summary (with authors, date, arxiv link) in the main body. Then, the right column has (from top to bottom): the open source PyTorch implementation of the code, links to collections of articles that feature this article, and also links to related articles.

Second section is the paper rankings. Let’s say I wanted to see the most viewed papers of all time under the tab of data mining:

I can also just get the data mining papers that received the most views from just this past month:

Next is the “Discover” tab, which gives notes/explainers on different ML subjects. I sorted by NLP-related subjects. The first one that comes up explains the mechanics of a “billingual expert model” — basically the construction of a model to find errors in neural machine translation. I believe its’s trying to break down this article accepted to AAAI 2019.

The next tab is “today’s arxiv” which gives me five arxiv articles (I think also targeted at NLP since that was what I selected as my interest) that I can recommend or not. Finally, the last tab (论文集) lets people build collections of articles around a particular topic. Some of the most popular are collections of papers on qa & chatbots, attention, GANs, etc.

Feature Translation — A Complete Explanation of Masking in NLP

So, let’s take a peek into an example Paperweekly article. We’re all intensely familiar now with the role of masks in preventing covid transmission, but what about the role of masks in NLP models? (how’s that for a transition sentence that endeavors to make really nitty gritty technical details relatable to the average Jeff?)

Context: Short and effective explainer written on May 8 by Hai Chenwei, a Master’s student at Tongji University. It’s a nice simple explainer of the function of masking, with summaries of the key articles, figures of Chinese-language use cases, and example code implementations.

Key takeaways: Masking is a function that does two main things:

• 1) Helps NLP models handle input sequences of variable length (e.g. really short sentences). It goes through how masking differs in RNNs vs. attention mechanisms. For a really good explainer of the concept of “attention” in neural machine translation, see this article.

• 2) Prevent the disclosure of the (Accurate) Label. “In language models, it is often necessary to predict the next word from the previous word, but if you want to apply ‘self attention’ in LM (language models) or use contextual information at the same time, you need to use a mask to ‘cover’ so as to prevent disclosing the label information that you want to predict. Different papers have different masking methods.” Hai then goes through how the masking methods differ across transformers, BERT, and XLNet.

We’ll definitely be coming back to Paperweekly in the upcoming weeks. If folks do some exploring of this platform their own and want to contribute to translations, let me know!

FULL(ish) TRANSLATION: A Complete Explanation of Masking in NLP

Jeff Jots

Last week Nesta published a collection of essays (link downloads a PDF) on China’s “AI Powered State.” I wanted to give a quick breakdown of my essay in this collection, titled “Promoting Nationally, Acting Locally: China’s Next Generation AI approach.” Hopefully we’ll come back to this and give some breakdowns on the other essays which are all well-worth reading. In my article, my goal was to investigate why Hangzhou and Hefei, which often rank as top AI clusters alongside the first-tier cities (BJ, SZ, SH, GZ), have built such successful industrial ecosystems.

The key point: Efforts of local governments play a crucial role — arguably more important than the central government — in fostering AI development. This is the case with overall S&T policy too. I write, “Provincial and local governments play an outsized role – one only increasing in significance – in implementing innovation policy. Their proportion of China’s overall fiscal expenditures on science and technology rose from 48 per cent during 2007–2011 to 59 per cent in 2015–2016. (ref. 4)”

Key Similarities between Hangzhou and Hefei:

• Both Hangzhou and Hefei have a key ‘anchor tenant’ tech company (Alibaba for Hangzhou and iFlytek for Hefei) and an elite university (Zhejiang University for Ali and USTC for iFlytek) that glue the ecosystem together

• Both had established plans to spur AI development before the July 2017 national AI plan was announced. I examine Hangzhou Future Sci-Tech City’s AI Town initiative and Hefei’s China Speech Valley initiative. In places where there is already a decently strong technology base, anchored by players mentioned above, then local initiatives like S&T parks can help circulate knowledge and boost agglomeration effects.

Key Differences

• Compared to Hangzhou, China Speech Valley’s international linkages are not as strong and there is less appeal for graduate returnees to work in Hefei. Overall, Hangzhou boasted the highest growth in attracting returning graduates from international universities from 2017 to 2018, according to a report by recruitment site Boss Zhipin.

• Partly due to the above point, Hangzhou has a more comprehensive coverage of AI subdomains compared to Hefei, which is specializing in intelligent speech.

Dive really deep

• One of the main issues with doing research on AI policy is trying to get to the substance behind some of the outlandish announced figures reported in the media (anybody remember the over-hyping of Tianjin’s $15 billion AI fund?). Well, Hangzhou AI Town published an audit report of its 2019 spending, so we can actually get a sense of project-level spending when we get really local.

• Per the report: Hangzhou AI Town disbursed 43 million RMB in funding in 2019, separated into research and development (R&D) funds, subsidies for office fees and cloud services funds. Across the three categories, the 123 accepted project proposals spanned an extensive range of subdomains (from sign language translation using computer vision to predictive analytics of smart city data) and parts of the AI stack (from open source software to end devices). Alibaba Cloud was the sole supplier or co-supplier for 25 of the 27 projects in the cloud services category.

Read the full article in the Nesta collection of essays.

ChinAI Links (Four to Forward)

Must-read: Data Security and U.S.-China Tech Entanglement

Was doing some research on data security standards this week, and came across this really fantastic Lawfare article by Samm Sacks I had missed from last month. It provides a framework for how US should approach complicated data security issues related to China. It incorporates a really rare sort of triple-threat combination of info from contacts on ground, technical chops, and smart strategy. Based on her full testimony before the Senate Judiciary Committee:

Must-read: Policy.AI

Curated by Rebecca Kagan, a no-nonsense, info-packed, comprehensive newsletter by CSET that gives biweekly updates on AI, emerging technology & security policy. Caught up on the May 13 issue recently, and here’s some nuggets you can expect in each issue:

• “Machine Learning Spotlight — Limitations of AI COVID-19 Diagnostic Tools: Preliminary research suggests that most models using AI for COVID-19 diagnosis and prognosis are at high risk of bias and likely misleading. The researchers reviewed 31 models that use AI for online diagnosis, diagnosis based on CT scans and case prognosis prediction. According to their assessment, the reported performance of all 31 models was “probably optimistic.” Given the lack of external validation of most models, the authors recommend against relying on any current AI-based COVID-19 diagnostic tools. More:Google’s Medical AI Was Super Accurate in a Lab. Real Life Was a Different Story. | AI Software Gets Mixed Reviews for Tackling Coronavirus”

• Also includes an “In Translation” section which includes CSET’s translations of important foreign language documents on AI. Waaay back in March 2018, the first issue of ChinAI featured my excerpted translation of an AI Standardization White Paper. Now, CSET has published a full translation, edited by Ben Murphy, that makes some really valuable info more accessible (See, for example, p. 37 breakdown of ISO/IEC JTC 1 and AI standardization-related subcommittees; and p. 74-75 on use cases of iFlytek’s intelligent speech tech in high school/college exam entrance exams)

Should-read: China NewSpace Newsletter

I’m really excited about others adopting the ChinAI model to other topics. Check out Cory Fitz’s new newsletter on Newspace. Here’s the description: “I created this newsletter because two of my passions, China and space, have finally come together. The private space industry in China is young and growing rapidly; it was only in 2014 that the State Council began encouraging private investment in space…To better understand these developments, the China NewSpace newsletter will bring you translations of interesting Chinese-language blog posts, articles, etc…as well as a roundup of news in the Chinese private space industry.”

Should-read: A Forgotten Story of Soviet AI

It’s funny — I first came across this piece via a Chinese translation of a platform I follow. Sergei Ivanov retells a forgotten story of Soviet AI dating back to 1955, through the lens of Alexander Kronrod who founded the first AI lab in the USSR.

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 PhD candidate in International Relations at the University of Oxford and a researcher at the Center for the Governance of AI at Oxford’s Future of Humanity Institute.

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 chinainewsletter@gmail.com or on Twitter at @jjding99

Greetings from a land that is always in the process of becoming…

…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).

Full Translation: Respect the Latecomer Wave — Cloudwalk Receives 1.8 billion RMB in financing from national funds

Context: Cloudwalk, one of the big four Chinese computer vision startups (Megvii, Sensetime, and Yitu are the others), raised an additional 1.8 billion RMB in financing this past week, putting its valuation at more than 25 billion RMB. This piece by Wu Xin for jiqizhixin (Synced) examines why Cloudwalk is “a member of the national team unlike the others (与众不同国家队).” We discussed the “National Team” concept in a past ChinAI issue; this article shows how there’s important distinctions among the national team members themselves in terms of ties to the state.

Key Takeaways:

• Much of Cloudwalk’s financing this round came from government funds such as the China Internet Investment Fund (established by the Central Cyberspace Affairs Commission and the Ministry of Finance in China)

• It’s not just about facial recognition and surveillance: per jiqizhixin, Cloudwalk is the largest AI supplier in China’s financial industry (serving banks including three of the “big four” state-owned commercial banks of China) — with nearly half of the market share. This involves facial recognition for identity authentication, facial scans to pay, precision marketing, etc. Part of a broader “smart business” vertical which has become the largest source of revenue for Cloudwalk in the past two years.

• How is it unlike the other “national team” members? It was incubated in the Chongqing Research Institute of the Chinese Academy of Sciences, participated in a national strategic guiding technology (category A) project in which it was responsible for face recognition research and application, plus “it is also the only AI unicorn that has been invited to formulate national-level standards, public security bureau standards, and industry standards in facial recognition.”

• Why is it unlike the other members of the “national team?” Short answer comes from the preface of the article: “With the advent of the “new infrastructure” era, “purely domestically funded enterprises (纯内资)” are expected to stand out from the competition.” Also: “In the opinion of some people in industry circles, the "purely domestically funded" company style retains more national autonomy for original AI technology and massive amounts data, and also provides a protective screen for some security and financial projects that involve national security.”

• Where companies list give you some sense of their genetics: Wu Xin writes, “There are rumors that Cloudwalk will be listed on the Sci-Tech innovation board (STAR Market), and the listing time may be at the end of 2020.” This Star Market, a pet project of President Xi Jinping’s, is meant to encourage investment in domestic tech companies and make it easier for mainland investors to trade in these companies, “after complaints that Chinese mega stars like Alibaba, parent company of the South China Morning Post, chose to list in the US rather than at home.” See this SCMP article for more on the STAR Market.

READ FULL TRANSLATION: Respect the Latecomer Wave: Cloudwalk receives 1.8 billion RMB in financing from national funds

Reflections: Mark Cohen on trajectories

I got a lot of good feedback from folks after last week’s musings on trajectories and U.S.-China relations. I want to start featuring more reflections from ChinAI readers — especially welcome those that directly rebut/disagree with my own. This week, Mark Cohen, Distinguished Senior Fellow and Director of the Berkeley Center for Law and Technology, offers his thoughts on discursive bandwagoning in the context of intellectual property issues. One of the points he emphasizes — how China’s lack of transparency fuels these trajectories — is really important and was lacking from my post last week. Mark runs the excellent ChinaIPR blog and previously served as Senior Counsel for the U.S. Patent and Trademark Office. Thanks to Mark for offering his views (very lightly edited by me):

****

Excellent article on discursive bandwagoning. I would probably go a bit further into how pernicious and self-destructive this type of "engagement" has become, be a bit less harsh as there are many well-meaning people who just don't have an adequate understanding of what is going on, recognize that some of the narratives can be accurate — albeit overbroad and perhaps harmful — and also recognize that we may all have become victims of it and that there can be competing contradictory bandwagon memes.

This phenomenon can be tracked not only in social media but also especially in trade/tech contexts through business surveys (US-China Business Council, American Chambers of Commerce/EU Chamber business surveys, etc).  Generally, I think these surveys show a desire to support whatever priorities the administration has identified. They also create a vicious cycle of feeding into the meme - for example, forced tech transfer or trade secret theft, were rarely important issues before the administration identified them, and they they became important memes as the administration pushed forward with retaliation/trade war, and now they have sunk back down to a lower category.  See The Trump Administration and China IP Diplomacy: Old Wine In a New Bottle?

Moreover terms evolve.  "IP Theft" was an ill-defined concept and originally meant pervasively tolerated trademark and copyright infringement.  It later came to include trade secret theft, and later cyber intrusions/market barriers and ultimately also included patent infringement - despite various legal and definitional impediments to using it in those ways (e.g., there is no criminal remedy in most countries for patent infringement).  These terms tend to expand and contract over time. I have described these repeating, ill-defined memes as an "echo chamber."  See: https://chinaipr.com/2019/05/12/the-600-billion-dollar-china-ip-echo-chamber/

The danger has always been however that broad-brushed approaches lose nuance, are not a useful substitute for well-conceived strategies, and tend to exhaust the public's attention with a hyperbolic perspective.  We also victimize ourselves and deprive ourselves of meaningful avenues to make progress: "collaboration" vs "confrontation" or "decoupling" - when did this become a binary? Why don't we just do what suits our national interest based on prevailing circumstances? Many academics are also thinking along these lines, and they risk their insights becoming compromised by false choices. See: https://chinaipr.com/2019/07/17/collaboration-or-confrontation-beyond-the-false-dichotomy-in-us-china-ip-relations/

I also think part of the fault for this rests with China. Lack of transparency and lack of confidence in Chinese data have often created suspicion.  China's trumpeting of its own data has led to the outright rejection of any data by foreigners, and reliance by the foreign community on qualitative (anecdotal) information in lieu of ascertaining reliable data.  The result is that China has also contributed to the impoverishment of discourse around it, or to more polarized perspectives.  It is not surprising that the Section 301 Report that launched US-China trade war similarly is a data-free zone. (From my testimony before the U.S.-China Congressional Commission: "In seeking to address the impact of Chinese industrial policies on protection of IP, I believe that we should increasingly utilize big data type analyses, which are also left out of the 301 Report.”

I can't speak for the AI issues, but this has certainly been my experience on IP issues. At the US Embassy, some Chinese friends said I was the "the mouse in the accordion, receiving pressure from both sides" 风箱里的老鼠，两边受气.  That is probably the right place to be.

***

ChinAI Links (Four to Forward)

Must-read: A Guide to Writing the NeurIPS Impact Statement — by Center for the Governance of AI

The NeurIPS conference, a premier venue for machine learning research, now requires submissions to include a statement of the “potential broader impact of their work, including its ethical aspects and future societal consequences.” This (unofficial guide) provides some helpful suggestions on how to write the statement. By a team of GovAI researchers and machine learning researchers (Carolyn Ashurst, Markus Anderljung, Carina Prunkl, Jan Leike, Yarin Gal, Toby Shevlane, Allan Dafoe)

Should-read: The AI Powered State: China's approach to public sector innovation — Nesta Essay Collection

China is innovating with AI in public services at breathtaking speed. Nesta’s new essay collection explores how China is using AI in public services, with a focus on practical applications and the ethics of AI - and reflects on what policymakers in other parts of the world can learn from China’s experience. My piece in the collection, titled “Promoting Nationally, Acting Locally: China’s Next Generation AI approach,” highlights the emergence of AI ecosystems in Hangzhou and Hefei. Thanks to Hessy Elliott convening and organizing this collection.

Should-read: To Repress or to Co-opt? Authoritarian Control in the Age of Digital Surveillance

Xu Xu, a PhD candidate at PSU, in the American Journal of Political Science (pay-walled) leverages variation in the implementation of China’s Golden Shield project to study the consequences of digital surveillance in dictatorships. From the abstract:

I first develop an informational theory of repression and co-optation. I argue that digital surveillance resolves dictators’ information problem of not knowing individual citizens’ true anti-regime sentiments. By identifying radical opponents, digital surveillance enables dictators to substitute targeted repression for nonexclusive co-optation to forestall coordinated uprisings. My theory implies that as digital surveillance technologies advance, we should observe a rise in targeted repression and a decline in universal redistribution. Using a difference-in-differences design that exploits temporal variation in digital surveillance systems among Chinese counties, I find that surveillance increases local governments’ public security expenditure and arrests of political activists but decreases public goods provision. My theory and evidence suggest that improvements in governments’ information make citizens worse off in dictatorships.

Should-attend: Two Webinars this Week

1. GovAI is launching our Governance and Economics of AI webinar series on May 20th 1700-1815 BST (0900-1015 PT, 1200-1315 ET. The first one features Joseph Stiglitz, Diane Coyle, and Daron Acemoğlu in a discussion about COVID-19 and the economics of AI. Registration here. More information about this and future events here.

2. I’ll be doing a webinar this Thursday May 21 (1000-1100 PT, 1300-1400 ET) with the Tech Buzz China team. The pitch: come for discussion about the “unsexy” aspects of China’s AI landscape and basically anything folks want to ask about during the Q&A. Registration here. More information about this and the awesome work that Tech Buzz China is doing here.

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 PhD candidate in International Relations at the University of Oxford and a researcher at the Center for the Governance of AI at Oxford’s Future of Humanity Institute.

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 chinainewsletter@gmail.com or on Twitter at @jjding99

Welcome to the ChinAI Newsletter!

Greetings from a land where it’s been a little over two years since the start of ChinAI. We’re now at nearly 7,000 readers on the weekly email list, with 140 paying subscribers who support ChinAI under a Guardian/Wikipedia-style tipping model. A special thanks to everyone who contributed translations this year and also my home base at the Center for the Governance of AI at Oxford’s Future of Humanity Institute.

Recently, Substack announced an option for creators to add options to customize paid subscription offerings. I’ve decided to open up an additional subscription option for readers who are able/willing to subscribe at whatever amount they’d like greater than the regular plans. I’ve set the suggested amount at $150.00 for this supporter subscription plan, which lets you join the “Gambara Group” — named after Veronica Gambara (1485-1550), an Italian poet who was a great patron of writers and artists in the early Italian Renaissance. If you’re not a subscriber yet, you can either join the Gambara Group or subscribe at the regular amount. *Note: there’s currently not an easy way for existing subscribers to switch their plan to the Gambara Group tier, but Substack says it’s in the works.

As is the case with the existing subscribers, there’s no exclusive content for the Gambara Group. In the words of the managing director of membership for The Guardian, “This is not just a paywall under another name.” You are paying because you fundamentally agree with and want to support the idea of ChinAI as an open library and platform for deliberation.

When deliberating over this additional tipping option, I was thinking about Li Jin’s piece on 100 True Fans and the passion economy. Li’s insight is that platforms like Substack now enable random bloggers like me to cater to 100 True Fans (out of a much larger free audience base) who are willing to pay higher prices for exclusive content. Even though ChinAI is also supported by a small group of paying subscribers, I don’t want ChinAI to take the 100 True Fans approach but instead to keep the commons/library model. I still look to The Guardian and Wikipedia as alternative pathways — “PBS as a service” in the words of Tim Carmody at Kottke (one of the oldest blogs on the web). He writes:

The most economically powerful thing you can do is to buy something for your own enjoyment that also improves the world. This has always been the value proposition of journalism and art. It’s a nonexclusive good that’s best enjoyed nonexclusively.

Anyways. This is a prediction for 2018 and beyond. The most powerful and interesting media model will remain raising money from members who don’t just permit but insist that the product be given away for free. The value comes not just what they’re buying, but who they’re buying it from and who gets to enjoy it.

The bigger those two pools get — the bigger the membership, and the bigger the audience — the better it gets for everyone. This is why we need more tools, so more people can try to do it. PBS as a service.

I don’t want to overpromise. To be honest, this time next year, I’m not even sure if we’ll keep this going, or if we’ll have pivoted into a newsletter about China’s rap scene (an article on the history of Xinjiang hip hop has been sitting in my on-deck circle for translations for a while now), so if you’re looking for a long-term investment, I’d suggest real estate (that’s not real advice — I had to google “good long-term investments” to write this sentence). But if you’re up for deep dives into bureaucratic white papers/detailed slide decks on China’s AI scene, longform profiles of people who work in data annotation factories, the occasional rants/rebuttals that often foster regret, the rare insightful reflection, and the even rarer super dorky podcast episode, then you might as well come along for the ride. Thanks to the ones who brought us to the dance — you know who you are even though I can’t shoutout everyone personally. Let’s keep dancing and see where the night takes us.

…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).

Reflections - Trajectories

We’ll return to regularly scheduled programming on Taihe translations next week, but I wanted to take some time and space at the 2-year mark to throw out some ideas about this concept of “technological trajectories” from the academic literature on innovation studies/economics of technical change, and reflect on how this concept can usefully capture some trends in how “China-watchers,” China analysts, and foreign policy folks think about China and technology. Unfortunately, this is all relatively US-centric, but I’d welcome thoughts/reflections from people more plugged into other communities.

Let’s start with Giovanni Dosi’s seminal article (cited 10,000+ times per Google Scholar) on “technological trajectories,” which he defines as “the pattern of ‘normal’ problem solving activity (i.e. of ‘progress’) on the ground of a technological paradigm.” In other words, technological trajectories capture a regularity in the interaction between a broad range of economic, institutional, and social factors that characterize technological development within a particular paradigm. There are three key features to highlight about these trajectories:

1. Some technological developments have an internal logic of their own (e.g. solar technology may be “more decentralizing in both a technical and political sense,” whereas nuclear power is more centralizing.

2. But technological trajectories are not determined by technical properties alone. They are also shaped by how that technology interacts with a whole range of other actors (institutions, social forces, etc.)

3. Crucially, we can observe a regular pattern in those interactions.

Technological trajectories come in various forms. As Dosi notes, “There might be more general or more circumscribed as well as more powerful or less powerful ‘trajectories.’” One relatively narrow type is a performance trajectory, which maps the rate at which technological development is progressing in a particular industry. A representative example is Moore’s Law, which states that the number of transistors on a microchip doubles about every two years. Moore’s Law has become embedded in the semiconductor technology roadmap which sets the baseline assumptions for how a range of institutions, firms, and other political actors plan their development of semiconductor technology.

We can see analogues of performance trajectories in the “China-watching” space. The classic example is the rise of China’s “new assertiveness” meme back around the turn of the last decade (2009-2011), deconstructed in a must-read piece by Alastair Iain Johnston. The “new assertiveness” meme refers to how “it ha[d] become increasingly common in U.S. media, pundit, and academic circles to describe the diplomacy of the People’s Republic of China (PRC) as newly or increasingly assertive.”

Figure 2 of his piece gives a nice snapshot of this meme’s performance trajectory:

Johnston spent the bulk of the piece expertly debunking the meme, but what I’m most interested is in the parallels between this performance trajectory in “China-watching” and the performance trajectories in technological development. To that end, Johnston concludes, “the new assertiveness meme may reflect an important but understudied feature of international relations going forward—that is, the speed with which discursive bandwagoning (or herding, to use a different metaphor) in the online media and the pundit blogosphere creates faulty conventional wisdoms.” Similar to Moore’s law and the semiconductor roadmap, the “new assertiveness” meme took on a logic of its own, one that became embedded in the assumptions of those that followed the discursive bandwagoning of the online media and pundit blogosphere.

We can identify similar performance trajectories in analysis of China’s technological development. The “New Cold War” meme and “AI arms race” meme are good examples. Let’s look back on this one exchange on Twitter that is so fitting for my point that I sometimes wonder if it happened at all. In ChinAI #54, I called out how Paul Mozur, the leading China tech journalist for the New York Times, and Paul Triolo, head of geo-technology for Eurasia Group, were downright giddy about “being right” and “starting” the US-China Tech Cold War arms race meme. Without any sense of absurdity or irony, Triolo refers to Mozur as “the Wozniak” of the meme and himself as taking on “the Jobs role.” Like Jobs and Wozniak, they see themselves as the pioneers of this performance trajectory — just swap out iPhones for the “New Cold War” meme as the product they’re selling.

We can see a similar trajectory for the “AI arms race” meme. With my coauthors, I wrote in a Foreign Affairs article that before 2016 barely any articles mentioned the phrase “AI arms race,” whereas in November 2018 (when we were writing), “an article on the subject gets published virtually every week, and Googling the term yields more than 50,000 hits.” A search for that specific phrase now turns up 90,000+ hits.

I’m not going to debunk each of these memes in detail here. You can start with ChinAI #53 for my case. Rather, just like with the “new assertiveness meme,” what’s more interesting to me is how this trajectory unfolds. How do we prevent the next Moore’s Law for AI Dummies? This past year, I’ve spilled a lot of ink on rebutting specific individuals, but I think that was a flawed approach on my part if one takes this broader view of trajectories. Few individuals wake up and plan their day around how to best spread dangerous and inaccurate memes. But we all passively absorb assumptions from institutions like the New York Times and the Eurasia Group, we all crave the #clout that comes from jumping on the discursive bandwagon, and we all draw on existing concepts in the literature for our own research proposals. Eviscerating an individual’s points is a quick happiness hit; reforming institutions and adding brakes to mechanisms that facilitate discursive bandwagoning brings that abiding joy.

That was just the easy stuff — stay with me here. Let’s now turn to broader technological trajectories. There are some technological trajectories that extend beyond just the rate of development in a particular industry to larger political implications. Consider a trajectory of electric systems. The following draws heavily from Allan Dafoe’s 2015 article on Technological Determinism, which I’d suggest everyone interested in the impact of technology to read. Before World War I, British systems of producing and distributing electric power were much smaller than those in the U.S. and Germany because the British valued local control and smallness of scale. However, under the pressures of WWI, British electric systems were networked and enlarged, contrary to “prevailing British political values.” (Hughes, Networks of Power, 79). In this whole story, Allan identifies the key factor of “military–economic adaptationism—in which economic and military competition constrain sociotechnical evolution to deterministic paths.”

What, then, are the broader technological trajectories of the “China-watching space” toward China’s AI development? The “U.S.-China great power competition” trajectory is a clear candidate. The “technology” is the tendency to view everything related to China’s technological development through the lens of U.S.-China great power competition. The “military-economic adaptive pressures” are many. AI Superpowers sells better than National AI capabilities are very arbitrary and fuzzy to measure, so other countries beside the U.S. and China still have significant AI capabilities depending on the measures one chooses. There’s also this underlying desire in many of us to be part of a great challenge or fight in our lifetimes, and the prospect of a U.S.-China power transition has taken on that mantle for some. And to be clear, there is the very reality of increasing economic and military competition between the two countries.

So what do you, as an aspiring, romantically realist up-and-comer interested in China’s AI development, who has somehow stumbled upon a platform of sorts, do in the face of this trajectory? You find yourself engaged in the U.S. national security community’s campaign to “Make America Technologically Great Again (MATGA),” which doubles as a glorified dick-measuring contest in which everything is about achieving technological dominance over China. You spend a lot of time thinking about better frameworks for comparing “national AI capabilities” and challenging the notion of what it means to be more technologically dominant, but all you do is provide a better ruler, if you will. And well, you’ve already spent two years on a PhD topic that is firmly rooted in the U.S.-China great power competition trajectory. So you tell yourself that you’ll start to research the issues you really care about after you finish the dissertation — and, of course, after the follow-on projects that will spring from it.

When powerful people propose that the U.S. should not allow any Chinese international students to study science and technology, you reason back in the language of the great power trajectory — this will only undermine US competitiveness with China! — as opposed to how this is contrary to prevailing American political values. You tell yourself that your think tank has to first establish credibility by adhering to this vision of national security limited to this great power trajectory before it expands to tackling a broader vision. You tell your spouse you just need to grind a couple more years at this law firm or consultancy or investment bank before you pursue something that you really believe in. But you never do. After all, there’s a reason they’re called career trajectories.

Or maybe…just maybe, you start to change your trajectory.

ChinAI Links (Four to Forward)

Must-read: Death of a Quantum Man for The Wire China by Shen Lu

There has been so much brilliant coverage of the U.S.-China trade/tech war, and some of the coverage has captured the tensions Chinese American face caught in the middle. But I feel like something has been missing from the coverage. It's something that John Cho, a Korean American actor, talked about in a Vulture profile re: how Asian stars look so much better in their Asian films than in their American films. The reason: relative to the Asian films, Asian stars in American films were carelessly lit, whereas the white people were carefully lit. John reflects, "If you don’t think of a person as fully human, you sort of stop short and go, That’s good enough." I've been searching for a story that depicts the Chinese American experience in the midst of U.S.-China geopolitical competition in a way that sees us as "carefully lit" human beings. I think Shen Lu's piece is the best I’ve seen in terms of capturing that.

Should-Read: My three favorite translations from this past year

I chose these three, in part, because they all challenged the trajectories that shape coverage of China’s AI landscape. I want to do more of these next year:

• ChinAI #77: A Strong Argument Against Facial Recognition in the Beijing Subway — Tsinghua Professor Lao Dongyan makes a strong, detailed case against the encroaching reach of facial recognition technology.

• ChinAI #66: Autumn Chrysanthemums on the Bridge (poetry generated by Huawei’s AI Poet “Yuefu”): Special thank you to Ru-Ping Chen who helped with some amazing translations of classical Chinese poetry composed by Huawei’s AI Poet.

• ChinAI #58: Making Knives Better & Landscape of China's Intelligent Manufacturing — On machine quality inspection in cutting tool production lines, featuring companies and voices in smart manufacturing from outside Beijing, Shanghai, Guangzhou, and Shenzhen; and comparisons between China and countries other than the U.S. (Germany, Switzerland, Japan).

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 PhD candidate in International Relations at the University of Oxford and a researcher at the Center for the Governance of AI at Oxford’s Future of Humanity Institute.

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 chinainewsletter@gmail.com or on Twitter at @jjding99

Greetings from a land where crabapples are in bloom and where the people are deliberating over whether to walk away from Omelas…

…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).

Edits to Browsing Taihe (a mini Defense One?) Together

Got a lot of good feedback on last week’s issue introducing Taihe. I wanted to highlight two pieces of constructive criticism from ChinAI readers:

1. One reader made a very useful and obvious (in a good way: in the sense that once made clear, it’s obvious to all that this is right) suggestion that I should just reach out to the Taihe folks and run a Q&A by them. This checks against making these exercises too “voyeuristic.” I was able to get in touch with some of the Taihe editors this week, and have sent some questions, so hopefully we’ll be able to share some answers in next week’s post. I am going to try and take this obvious and also respectful step of reaching out to the platforms and authors we feature.

2. Another reader took exception, with good reason, to my hot take from last week: “Here’s the reason I wanted to throw out Defense One as a possible analogue for sites like Taihe. I think if I wanted to get a good sense of what people in the U.S. military and defense industry were thinking, I would read readouts and articles in Defense One as opposed to the 300+ page reports of government defense strategies.” This reader, much more well-versed on US defense strategy than me, made a strong case for actually reading the documents (which are not 300+ pages) that guide US policy: the National Defense Strategy and the National Security Strategy. The goal here, as the reader astutely put it, is to “build off the key policy literature, creating a win-win for your research and higher-quality addition for the U.S. defense policy community!”

I wanted to use the second edit as a jumping off point for being more nuanced about how I see the value of outlets like Taihe. Again, using Defense One as an imperfect analogy, what I’m trying to get at here is that outlets like Defense One may provide some different form of value-add to learning about the US defense industry/technology base than government strategy documents. The implication being: analysts of China's defense industry may get some value out of Taihe that could supplement what they get out of PLA military academic journals or official strategy documents. Instead of framing these types of sources in a mutually exclusive way, I should have framed them as complementary and mutually beneficial.

The broader thesis I’m trying to prove with this series on Taihe, though, goes back to the original vision of ChinAI:

While traditional media and China specialists can provide important insights on [insert topic here] through on-the-ground reporting and extensive background knowledge, ChinAI takes a different approach: it bets on the proposition that for many of these issues, the people with the most knowledge and insight are Chinese people themselves who are sharing their insights in Chinese. Through translating articles and documents from government departments, think tanks, traditional media, and newer forms of “self-media,” etc., ChinAI provides a unique look into the intersection between the country that is changing the world and the technology that is changing the world.

This is a vision that builds off the work of so many other platforms, like the amazing work of China Digital Times and DigiChina. It’s also one that others are adopting as well. I’m excited about work Jordan Schneider is doing with ChinaTalk, the translation work that CSET is doing, and Politico’s new initiative (led by David Wertime) which also seems to be very much translation-based. The overall purpose is to slowly chip away at the massive language asymmetry that characterizes the current Chinese-English transmission pipeline. We’re seeing the consequences of this with how US media is “rediscovering” issues (at substantial delay) related to COVID-19 that were already covered by their Chinese counterparts — a very tangible way to represent the costs of this language asymmetry.

At the risk of being overly direct and self-referential about this: if you tell me you are an expert on China’s ___X___, you have to be able to list at least five Wechat public accounts that are focused to covering X that you follow regularly. Let’s call it the Ding Dare. If you can’t do that, I’m sorry, but you’re just not doing it right.

More specifically, this series on Taihe is making the case that we can learn a lot from newer forms of self-media and new-style think tanks even on issues one might think are relatively closed-off — like, say, the inner workings of China’s military industrial complex or, say, this week’s feature translation…

Thanks to everyone who gave feedback, and I hope to use future feedback as a way to “edit” posts.

Feature Translation: 90% of military industrial enterprises cannot introduce formal capital

Context: last week, we looked at an article from one of Taihe’s five main verticals (Transformers). This week we’re going through a 2018 article from a vertical focused on the defense industry. The article’s headline starts with a startling stat: 90% of companies in China's defense industry cannot introduce formal capital (e.g. VC funding, institutional investment as opposed to informal investors such as family or friends). Using that as a starting point and the provocative question — Can China Produce a Lockheed Martin? — as a landing point, this article provides an in-depth look at the state of civil-military integration from the anchor point of financing.

Key Takeaways — This is just going to be a list of really really interesting stats:

• >14,000 equity investment funds in China; the ones involved in military affairs can be divided into four types: 1. central-led ones (e.g. CASIC-led VC fund); 2. local government-led CMI funds (e.g. Xi’an High-tech Investment fund); 3. M&A funds led by listed companies (e.g. Tianhai Defense and Ruiye Digital Assets have a joint M&A fund); 4. private or market-oriented institutions that also invest in defense (e.g. Fortune VC, Harvest Capital, etc.)

• According to Zero2IPO's 2016 Top 10 VC/PE Firms in Military Industry, rough statistical estimates based on the public data regarding these top ten firms show that the total amount of capital invested in the field of military equipment in 2016 did not exceed RMB 5 billion. Far lower than hotspot fields like health, electronics, etc.

• Why such little investment so far? One military leader who supervised equipment-related tasks told Taihe: The essence of military enterprises bringing in capital is that private capital participates in the defense industry. From a macro perspective, military enterprises are not short of money, due to the huge support from military and national defense expenditures, especially related to the core projects required by the military, so it is difficult for private capital to participate in the whole process. At the most, the military will make use of certain technologies and equipment from private companies, which means the private companies serve as an outsourcing partner. Of course, some civil-military dual-use projects with prospects for broad applications are more likely to absorb private capital to jointly develop and produce, so as to benefit from mutual gains. This is probably the key entry point for grasping civil-military integration.

• Hence, there is a popular saying in military investment circles: "There is no venture capital in the defense industry [军工无创投]."

However — the recent push for more civil-military integration has made some progress:

• Since 2009, 68% of private companies involved in the defense industry have come from the field of informatization; as of the end of 2017, among the units that are qualified for scientific research and production of military products, private units accounted for nearly 41%, and the pace of civilian participation in the military has accelerated significantly.

• The State Administration for Science, Technology and Industry for National Defense’s new licensing catalogue for military equipment has been reduced by more than 60% to make it easier for private companies to navigate.

But — can a Chinese private company ever move beyond being a 2nd, 3rd, 4th-tier parts supplier, or even a builder of subsystems, to get the level where they are winning bids for large-scale complete systems such as the J-20 stealth fighter or the 055 class of destroyer. In other words, where is China’s Lockheed Martin?

• Yu Chuanxin, Secretary General of the Civil-Military Integration Research Center of the Academy of Military Sciences, says: “I am optimistic that there will be a large number of privately-run enterprises that are globally competitive in the next 5 to 10 years. ‘China’s Lockheed Martin’ will definitely appear.”

• “There are also some investors who believe that this is just a beautiful wish. It is a long process to obtain full trust of the military, and the possibility of properly dealing with all the sections is also very low. Of the current private military companies that are listed, strong leaders such as Wuhan Guide Infared (高德红外) have also undergone many difficulties on their road to building complete systems.”

There’s more where that come from. See FULL TRANSLATION: Outlook: 90% of companies in the military industry cannot introduce formal capital

ChinAI Links (Four to Forward)

Must-read: Global China: Technology — a special Brookings/CSET collaboration

A pretty incredible convening of the minds on the important topic of China’s growing technological reach. It’s hard to even pick out specific papers form this set, but I’d recommend Elsa Kania’s piece on “AI weapons” in Chinese military innovation, Remco Zwetsloot’s piece on China’s approach to tech talent competition, and Saif M. Khan and Carrick Flynn’s piece on maintaining China’s dependence on democracies for advanced computer chips.

Should-read: AI Governance in 2019

A year in review with observations from 50 global experts, convened by Li Hui and Brian Tse for the Shanghai Institute for Science of Science. Special shoutout to my colleagues Allan Dafoe and Markus Anderljung for their piece on the rapid growth in the field of AI governance.

Should-read: How to navigate the tradeoff between effective contract tracing and privacy

For the WashPost, GovAI researchers Toby Shevlane, Ben Garfinkel, and Allan Dafoe discuss how far we can go in reducing the trade-off between privacy and security, in the context of digital contact tracing methods. They describe the concept of “structured transparency,” which refers to the opportunity to achieve both high levels of privacy and effectiveness through the careful design of information architectures — the social and technical arrangements that determine who can see what, when and how.

Should-watch: PBS 5-hour Series on Asian Americans

Premiers May 11 and May 12 — definitely a timely and important watch. The series is going to dive in to the history of the Asian American experience, one that has to be disaggregated given the diversity that underlies this umbrella term, but also one that captures some commonalities in terms of the experience of exclusion and the status of a perpetual foreigner. H/t to my mom for sharing this with me.

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 PhD candidate in International Relations at the University of Oxford and a researcher at the Center for the Governance of AI at Oxford’s Future of Humanity Institute.

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 chinainewsletter@gmail.com or on Twitter at @jjding99

Greetings from a land where words like these exist, from Mohsin Hamid’s Exit West: “and when she went out it seemed to her that she too had migrated, that everyone migrates, even if we stay in the same houses our whole lives, because we can’t help it. We are all migrants through time…”

…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).

Browsing Taihe (a mini Defense One?) Together

Today, we’re kicking off a new series on “Taihe Industry Observer” (钛禾产业观察). Note: they don’t have a website, as it’s all through a Wechat public account, so you have to search those characters in Wechat to find it. Other content aggregators regularly push out their posts, so you can also search those characters on the web and find the aggregator sites.

In issue #60, we did a similar exercise for Leiphone (which I compared to China’s MIT Tech Review), a new media platform founded in 2011 that is now a leading portal for general science & tech coverage. We’ll hit some key points on what browsing Taihe looks like and then dive into a recent article in the feature translation. Next week, we’ll go through another article — if anyone wants to help out translating or adding expertise/context to translations, especially folks with a much better sense of China’s defense industry than me, hit me up!

• Taihe (钛禾) mashes the character for Titanium and the character for grain (esp. rice) together, which gives the English name Titanium Rice. Not gonna lie, that’s pretty badass. Maybe Titanium Rice is trying to convey this sense of “high-tech” industries being essential to China’s economic and defense system, which is a main theme of Taihe’s coverage.

• Taihe describes themselves as a “冷门号” (a platform that focuses on relatively uncovered/unpopular field) that mainly writes about the defense industry and the high-end manufacturing industry (军工和高端制造行业). I’m uncertain when they first launched, but I think in just the last few years. I first started tracking them back in January 2019 when we featured their article on 8 future applications of AI in Chinese public security bureaus in a previous issue of ChinAI. At the time, they published 20 original articles; fast forward a year and a bit later, and they have 41 original articles — all seem to be relatively well-researched longform analyses. They brand themselves as a "new-model think tank of national strategic core S&T industries.”

• Interesting backstory from these two screenshots above: Apparently Taihe’s 2018 year in review post was completely scrubbed from the web — it had racked up over a million views because a couple of influencers (Big Vs -- people with “verified” status on Weibo [China’s Twitter]) had shared it, and the post had generated a lot of discussion. Taihe doesn’t disclose why it was censored (其中原因就不多讲了) -- My guess is that it was a pretty critical take on either state-owned enterprises, Chinese military, or the party? Taihe claims their article was mainly framed in a constructive wayand commit to being a 正能量媒体 (positive media). Thus, when reading it’s important to have a critical view to decipher when the “positivity” slips too far into propaganda.

• Below is what Taihe’s “homepage” looks like (home screen of the Wechat public account). At the bottom left corner, I’ve clicked to open the tab for Taihe’s main services, which includes think tank services but also investing services and supply-demand linkage services. Apparently they advise aerospace, AI, new materials, etc. companies in the A-round to Pre-IPO stage. Here’s where it gets really interesting. Under “supply-demand linkage services,” they welcome folks who want have tech or products that that have “对接方面的需求” (which I interpret as they connect buyers and suppliers of tech). The connections feature some heavy hitters, as they claim to have a tight cooperative relationship with a lot of heavy hitters: Ministry of Industry and Information Technology, the State Administration for Science, Technology and Industry for National Defense, the Equipment Development Department of the Central Military Commission, etc.

• Now let’s take a look at the second tab, their past articles; the third tab is just contact info. There are five categories for their articles: 1) Industry-Finance Think Tank; 2) Great/Major Power Industries; 3) Transformers (first screenshot below gives the five recent articles); 4) City Dynamism; 5) Taihe Defense (second screenshot below gives the five recent articles). This week’s feature translation is the 11th in the Transformers series. Here’s the series description the editor wrote: *In 2020, Taihe will continue to write its series of posts on "Transformers - Seeking the Pioneers of Great Power Science and Technology for the Next Twenty Years.” We will continue to search for representative industry sample cases for in-depth research, tracking and reporting (including but not limited to companies, funds, technology incubators, etc.). We welcome everyone to recommend industry materials and research topics. If your topic is adopted, gifts will be awarded.

Here’s the reason I wanted to throw out Defense One as a possible analogue for sites like Taihe. I think if I wanted to get a good sense of what people in the U.S. military and defense industry were thinking, I would read readouts and articles in Defense One as opposed to the 300+ page reports of government defense strategies. I wonder if Taihe and other sites like it could fill a similar role. Defense One launched in 2013, so it’s been around for 7 years; in that time, how many other sites like Taihe have sprouted up? Obviously discussing military modernization and other issues is much more sensitive in China (as Taihe has encountered firsthand), and mirror-imaging bias is a thing, but I’m just curious.

I’m a newbie to this subfield of China’s defense industry, so this take may be completely off-base. I also know we have some regular readers who are much more equipped to make these comparisons, so I welcome everyone’s feedback. Maybe I’ll come back after a couple weeks and reflect on how I’m overselling Taihe’s importance. Anyways, regardless of its overall significance, the content so far has been interesting, so let’s keep reading:

Feature Translation: Who can become the "new SOEs" for this new digital infrastructure?

I’m not going to go too much into the content of the piece — those interested can read the full translation linked above. What interests me more is how the piece is written:

• It’s just written in a very accessible way. The piece starts with: “Skinnydippers are not allowed to enter the water” and ends by closing the loop on the analogy. There’s a nice reference to “A well-known saying in the age of Internet entrepreneurship: It is only when the tide recedes that we know who was swimming naked.” If you scroll down to the bottom of some of Taihe’s posts you’ll see they get a lot of reads and I think the style of writing influences that.

• They seem to have access to a lot of high-profile people from defense, industry, strategic investors, and academia. The piece gets quotes from Fortune VC managing director, the chairman of CASIC (key part of military-industrial complex), Beida Prof Lu Feng. It also cites data from the government procurement center of central state organs: as of March 31 (2020), Alibaba Cloud ranked first in the central state organs cloud computing procurement market, with a market share of more than 50%.

• Length and quality — It’s 3000+ words in the English translation — a lot of good figures and jam-packed with stats throughout, though I had some pushback on some of the claims and research (in the comments of the full translation doc)

• Interesting anecdote: Apparently, on Single’s Day (11/11) in 2014, Jack Ma defined Alibaba as a "national enterprise" in an interview on the prestigious CCTV show "Dialogue.” Usually SOE is translated as 国有企业 -- Jack used the term 国家企业 according to Taihe. I think 国家企业 can still be translated as SOE, but I use national enterprise as the context of Jack Ma was saying was that he was comparing Alibaba to Apple in the US and Benz in Germay -- so maybe like a national flagbearer company?

ChinAI Links (Four to Forward)

Must-read: Ideas and Ideologies Competing for China’s Political Future

2017 report from Kristin Shi-Kupfer, Mareike Ohlberg, Simon Lang, and Bertram Lang of MERICS that provides a really fantastic breakdown of online pluralism in China. I see Taihe and other platforms we’ve covered in the past (e.g. Saidong) as fitting under the “Industrialists” bucket that they identify. According to MERICS, industrialists are sometimes called the Industry Party (工业党), and they see technological advancement as key to global leadership. They put forward a very techno-nationalist view of the world.

Must-read: Maintenance and Care — by Shannon Mattern for Places Journal

“In many academic disciplines and professional practices — architecture, urban studies, labor history, development economics, and the information sciences, just to name a few — maintenance has taken on new resonance as a theoretical framework, an ethos, a methodology, and a political cause…

This is necessarily a collective endeavor. In 2016, the historians of technology Andrew Russell and Lee Vinsel roused a research network called The Maintainers. Playing off Walter Isaacson’s book, The Innovators: How a Group of Hackers, Geniuses and Geeks Created the Digital Revolution, the Maintainers adopted a humorous tagline: “how a group of bureaucrats, standards engineers, and introverts made digital infrastructures that kind of work most of the time.”

We previously recommended Shannon’s piece as a must-read: Networked Dream Worlds: Is 5G solving real, pressing problems or merely creating new ones?

Should-read: Understanding AI Technology

By Greg Allen of the DoD’s JAIC (Joint Artificial Intelligence Center) — this primer aims to be “A concise, practical, and readable overview of Artificial Intelligence and Machine Learning technology designed for non-technical managers, officers, and executives” — I think it’s really important to have efforts like these that widen the knowledge base as it relates to AI.

Should-read: American Mandarin Society newsletters, syllabi, etc.

For those looking to brush up on Mandarin in this time, AMS has a really wonderful array of resources. Their recent newsletter has some cool reading recs on China’s cybersecurity law as well as a link to a short 5-min video by The Paper 澎湃新闻 (in Chinese with subtitles), which introduces the two Chinese PhD students behind the development and maintenance of John Hopkins University's COVID19 tracking maps. Maybe Sen. Tom Cotton should find the time to watch this one.

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 PhD candidate in International Relations at the University of Oxford and a researcher at the Center for the Governance of AI at Oxford’s Future of Humanity Institute.

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 chinainewsletter@gmail.com or on Twitter at @jjding99