ChinAI #62: Global AI Industry Stats - the View from China

Plus, a very meaty ChinAI (Four to Forward) Section this week

Welcome to the ChinAI Newsletter!

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 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 and collaborators like Joy from this week and others like Charles and Lorand from past weeks.

Feature Translation: CAICT Report on the Global AI Industry

CAICT is a research institute under the Ministry of Industry and Information Technology and one of the co-authors (alongside Tencent Research Institute) of the 500-page book on AI strategy that first launched this newsletter. My collaborator this week is Joy Dantong Ma of MacroPolo. Joy found this report and did the bulk of translating, including some of the key graphs. Her analysis: This report dissects the AI industry into four aspects: company, capital, academic papers, and conferences. It then assesses all major stakeholders, including both institutions and countries, across these aspects. What I find most fascinating is the depth and timeliness of understanding CAICT has on the global landscape. A case in point: in the company section, the report listed 17 unicorns in China, the majority of which are seldom talked about even though China+AI has become such a hot topic. The report also listed out unicorns in the US - Avant, Uptake, Dataminr - that many of us in the US might have never heard of. 

*Also, highly relevant is a project on Chinese AI companies that Joy and I and Matt Sheehan worked on back in December 2018, which goes beyond the abstract catchall of AI and drills down into specific verticals (e.g. autonomous vehicles, voice & speech recognition, business intelligence, etc.)

Anyways, back to the report’s key findings:

 1. As of the end of March 2019, there were 5,386 active artificial intelligence (AI) companies in the world. The US, China, the United Kingdom, Canada, and India rank as the top 5 globally in terms of the amount of AI companies.

2. There are 41 AI unicorns globally, including 17 in China, 18 in the US, 3 in Japan, and 1 each in India, Germany and Israel.

3. Since Q2 2018, global AI investment has gradually declined. The total amount of global investment in AI in Q1 2019 was US$12.6 billion — down 7.3% from the previous quarter, and flat year-on-year. China's AI financing totaled US$3 billion, 55.8% down year-on-year, accounting for 23.5% of total global financing, down 29% from the same period in 2018.

4. Statistics on AI academic papers in the past 10 years: China ranks first in terms of the total number of papers published, while the number of highly cited papers is lower than that in the US.

  • Chinese research institutes such as the Chinese Academy of Sciences and Tsinghua University are among the upper echelon of AI academic research institutions.

  • Google and Microsoft published the most amount of papers in top AI conferences globally.

FULL TRANSLATION: Global Artificial Intelligence Industry Data Report (April 2019)

ChinAI Links (Four to Forward)

This week’s must-read is a report by Dongwoo Kim (research fellow at Asia Pacific Foundation of Canada) comparing AI policies across China, Japan, and Korea — with an eye toward Canada’s interests. The report emphasizes that Japan and Korea are reliable partners for cooperation in the space of AI (5th and 7th largest trading partner), and that Canada could help bridge the gap between China (2nd largest trading partner) and the West. Also, some really good stuff on Japan’s Society 5.0 and its Strategic Council for AI Technology’s policies as well as Korea’s 30-year “Master Plan” for an intelligent information society.

Had a great time talking about AI race rhetoric, Jessica Newman’s excellent China AI Policy primer, Peter Thiel, relative/absolute gains with Lucas Perry on the Future of Life Institute’s AI Alignment Podcastsuper impressed by how FLI produces their podcasts — they have a transcript of the entire podcast, detailed time stamps, and long block quotes as key points. Reminds me of a16z’s podcast about podcasting where they discuss how to improve tools for engaging with podcasts. Jade Leung, my boss and the person who makes GovAI run, was on the AI Alignment Podcast last month to discuss GovAI’s research agenda and what ideal governance in this space looks like.

Based on a public records request to HK’s Government Logistics Department which revealed tenders for facial recognition software, this is really excellent reporting by Rosalind Adams of Buzzfeed on how facial recognition is actually being used by HK authorities: 1) it’s likely that no gov depts have used or tested automated facial recognition as part of its CCTV systems, 2) according to the Immigration Department its facial data has not been shared with the Hong Kong Policy Force. HK has contracted with French company Idemia for facial recognition technology to process Hong Kong ID cards (US State Department works w/ same company on same process). However, while automated facial recognition isn’t being deployed through CCTV, faces are being weaponized amidst the protests, as Paul Mozur reports in this NYT piece.

Rather than centralizing project selection which is what initiatives like the Joint Artificial Intelligence Center do, Eric Lofgren argues we should decentralize the Pentagon’s budget by mission type to ensure AI projects “receive funding at the speed of relevance.” His framework is an important one to consider: “Military capabilities may never benefit from a single general AI application. Instead, they benefit from a variety of narrow AI applications. It seems that the effort spent developing an app for autonomous flight does not contribute much to an app for ground vehicles, let alone automating logistics, target recognition, command and control, or any number of other applications. Each app requires its own data inputs, metric selection, and training.” This was published in War on the Rocks as a response to Eric Schmidt and Robert Work’s call for ideas for the Nat Sec Commission on AI.

Thank you for reading and engaging.

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

ChinAI #61: A Backlash to Social Credit Blacklists?

Plus, note collections on language asymmetry in AI + McGregor's book The Party

Welcome to the ChinAI Newsletter!

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 and subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (those who can pay support access to all content for all).

Two quick plugs: going to be trying out public note-taking as a use-case for Twitter — see the two continuing threads below:

Feature Translation: Credit Blacklists, Not the Solution to Every Social Problem

This week’s translation is a joint work with Ryan Soh - a first-time contributor to ChinAI and an emerging stock market analyst who was previously a Chinese language student at Tsinghua University’s Inter-University Program. Please give him some love on Twitter (hyperlink in name above)!

We were tipped off to this piece by this August 1 issue of the excellent American Mandarin Society newsletter: “A July 30 opinion piece in the 钱江晚报, posted on people.cn, used a case in Shandong to push back on the idea of an all-encompassing social credit system. While there are many critics of the concept of such a system outside of China, little attention has been paid to discussions within China. This article is worth a read for how such a potentially sensitive topic can be approached within Chinese.” The case involved a teacher placed on a social credit blacklist for beating pupils with textbooks.

Ryan’s main points to emphasize: This article is interesting because it is in the Chinese public domain, and so up for grabs: a. Public discussion & input is seen as beneficial, rather than perfunctory. b. It indicates the issues are still being figured out. c. Therefore, rushing to judgement is presumptuous.

Shout-outs to Shazeda Ahmed (a PhD candidate at Berkeley and summer fellow at Upturn) and Jeremy Daum (a fellow at Yale Law’s China Center and creator of China Law Translate) for taking a look at the full translations. I’m trying to involve folks to play an “editing role” for each issue of the newsletter (I outlined why I think newsletters need editors in this refections segment from two weeks ago), and I can’t think of two better people for an issue on social credit blacklists. Jeremy shepherded us to the the second translated piece from The Paper (*both translations are included on the same Google doc this week) which contains more details on the implementation process of the blacklists; and an extra shout-out to Shazeda who helped us clarify terms of art in the translation and also was kind enough to do a full “sanity check” on my analysis — which follows:

Three main takeaways from my end:

1) I think many readers will be surprised to see such open criticism of the social credit system — both translations referenced broad public concern and controversy over the teacher being placed on the social credit blacklist. I think our collective surprise may be partly a product of the “techno-orientalist” lens through which we view China. The “techno-orientalist” frame dehumanizes Chinese people (Chinese people don’t care about privacy) and warps our understanding of China and Chinese people as vehicles where we project our own technological desires and anxieties. Xiaowei Wang, creative director at Logic Magazine, discusses this concept in the context of Jeremy’s work and Shazeda’s piece for Logic here.

2) There is a great deal of confusion and misunderstanding about the “social credit system” in China as well. Shazeda emphasizes that the consensus among law scholars in China is that social credit is meant to be a tool of administrative law, but this Qianjiang Evening News commentator thinks these will conflict, “What kind of mess would it be, if administrative and law enforcement offenses resulted in a loss to creditworthiness?” This confusion is understandable because “credit system” (信用体系) could refer to the national or provincial level social credit systems (administrative law vehicles), which differs from "People’s Bank of China’s credit information evaluation system, blacklists for those deemed to be untrustworthy by the People’s Court, etc. The second article in the full translation highlights these different systems.

3) These articles are notably tech-free. There’s nothing about using a bajillion parameters to calculate someone’s “social credit score.” Shazeda comments, “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.” A related takeaway is that this is a nascent and evolving implementation process, as emphasized at the end of the article from The Paper.

SEE BOTH FULL TRANSLATIONS: Qianjiang Evening News: Credit Blacklists, Not the Solution to Every Social Problem; and The Paper: Wulian County Bureau of Education and Sports further explain recent teacher sanctions

ChinAI Links (Four to Forward)

We have to start understanding China’s tech scene in context that involves countries other than the U.S. - one platform consistently doing an awesome job at covering this broader landscape is FT’s Tech Scroll Asia, edited by James Kynge. Check out this recent edition on the Japan-Korea tech row that’s threatening supply chains.

I’ve been consistently impressed by FT’s China+tech reporting, and I think they have the best team in the game, so here’s a second FT link: Yuan Yang, a China tech correspondent, has a column for FT magazine that is consistently sublime. The most recent one recounted her experience getting her face scanned in Xinjiang and also included details like “among the Han not everyone felt as warmly about the cameras. A shopkeeper cited them as one reason his clientele had been dwindling – the ever-present security on the streets had made it more inconvenient for people to come out, he said.” Also, see a previous column on the Anti-996 campaign that stirred China’s tech world, which included some cool anecdotes from her friends about absurd work practices.

In MacroPolo’s continuing series on China’s AI talent, Joy Dantong Ma takes an interesting cross-section of the global AI talent pool (authors who have had papers accepted to the NeurIPS (previously known as NIPS) conference. One main finding: “When it comes to retention, of the 2,800 Chinese accepted to NeurIPS over the last decade, about three-quarters of them (>2,000) are currently working outside of China, according to my estimates (see Figure 2). And of the 2,000+ AI talent that have left China, about 1,700 (85%) came to the United States.” While I think this finding largely makes sense, I would like to have seen more explanation of the methodology (Note: Joy said, “The approach I took was indeed sampling. There were over 15,000 authors over the past decade so it wasn’t within my resources to track down all of them. After getting the sample…what I did was to analyze each location individually in a hypergeometric distribution so it is more robust”). I would also be curious if this trend has shifted in the past couple years.

Of course it’s the smartest VCs that will be among the first to take advantage of the enormous "information/knowledge arbitrage” of translating Chinese S&T media, which ChinAI is built on. See these excellent pieces by Andreesen Horowitz’s team (Connie Chan with help from Avery Segal) that feature translated excerpts — one from a speech given by the “father of Wechat” and one of field notes from an interview by Ren Zhengfei, Huawei’s CEO, given to Chinese media in Shenzhen.

ChinAI #60: Leiphone (China's MIT Tech Review?) - Let's Read it Together

Plus, What Peter Thiel's NYT oped gets wrong about US-China AI competition (hint: a lot)

Welcome to the ChinAI Newsletter!

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 and subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (those who can pay support access to all content for all).

Feature Translation: Browsing Leiphone Together

*The Google Doc link (click heading) is almost the same as the newsletter body text for this issue (though there might be a few additional annotations on the Google Doc) - I’m including it mostly as a space for people to add comments and ask questions

Longtime readers of ChinAI will know that one of my favorite sources is Leiphone. This Chinese new media platform, founded in 2011 and based in Shenzhen, has emerged as a leading portal for science & tech coverage. Past ChinAI issues have covered Leiphone’s fascinating interview with the head of Baidu Security on the “Long Front” of AI cybersecurity, its AI Impact Factors Database which aims to track every paper publication/ competition result/ development project/ corporate activity in the AI field, and Huawei’s move into the AI+Security Industry.

Instead of featuring a translation of one article from a random Chinese site that most readers will forget by the following week, I want to change things up and have us browse Leiphone’s home page together. We’ll translate some excerpts of interesting articles along the way but the primary point of this exercise is to use Leiphone to give folks a sense of the scale/depth/breadth of Chinese S&T media coverage — it’s a nice reminder that the translations featured in ChinAI are just a drop in the bucket. Okay — let’s browse together!

Screenshot of Leiphone’s home page (https://www.leiphone.com) taken at 9:30AM August 4, 2019 (US Central Time). I’ve divided their home page into five sections, which we’ll go through in order. Formatting note: everything that’s a direct translation of Leiphone content is italicized; everything else are my annotations.

1)Leiphone: Understanding Intelligence & the Future (Header/Navigation Bar)

First row tabs: AI Research Institute; AI Investment Research Group; Activities Center; Top Academic Conferences (new!); Special Topics; Love Playing with Machines

Second row tabs: Industry, AI, Smart Driving, AI+, Finance Science and Tech, Future Medicine, Cybersecurity, Smart Cities, Robotics, Industrial Cloud, Smart Hardware, Internet of Things, Global AI and Robotics Conference)

The links in the first row of the header/navigation bar all go to separate branch sites. For instance, the "Top Academic Conferences" tab goes to an entire portal dedicated to covering academic conferences and articles/coding tutorials/open source software updates; The "Love Playing with Machines" tab goes to a different branch site for folks interested in playing with smart hardware products.

The links in the second row keep you within the main site but just filter the articles by the topic. Note the last tab (GAIR) — a relatively well-known AI conference in China — which Leiphone has hosted annually since 2016.

2)Industry Information (Vertical)

  • “Didi Chuxing open sources ‘Delta,’ a training platform for natural language understanding model | ACL 2019

  • “Douyin’s On-line Group Chat Function Combines Forces with Duoshan to Check WeChat”

  • “IDC Q1 Report on China’s Cloud Services: China becomes the World’s Second Largest Public Cloud Market.”  

  • “Rootcloud CTO Zhen Liu Leaves Position, the Rivers and Lakes of the Industrial Internet are Turbulent”

This section has mostly short (5-6 paragraph) pieces on industry trends. Some, like the article on Didi’s new training platform, read like edited press releases. But others, such as the last one on Rootcloud, contain some good analysis. Here’s the last paragraph from the Rootcloud article: “According to Leiphone’s understanding, although startups in the industrial Internet field are constantly emerging with hundreds of millions of financing, there is still truly demand for digitization from manufacturing enterprises (that is not being filled). However, the entire industry is still in its infancy, and the business models of many industrial Internet companies are not clear enough. Their technical capabilities are unable to meet the fragmented needs of the digital transformation of manufacturing, and there is also a big bubble in the capital market. There is still a long way to go before the manufacturing industry really enters the industrial Internet era.”

3)Banner Images 

  • Under the pressure of the United States, Huawei shipped 181 million mobile phones in the first half of the year. Hongmeng will soon debut

  • 2019 10 Top-level Python Libraries you have to Know

  • Research on micro-expressions using deep learning: difficulties, progress, and trends | CNCC 2019

  • Complete Hardware Guide for Deep Learning

  • 2019 China National Computer Congress

These five slides functions as links to featured articles. Interestingly, the complete hardware guide one goes to one of the separate branch sites (Leiphone’s AI Research Institute portal); the last one goes to the site for the CNCC.

4) Featured Longreads

  • “Explaining the Global Smartphone Market: Can Huawei Replace Samsung as the World’s Number One?”

  • “Apple’s Payments of $4.7 Billion did not Stop Qualcomm’s Plunging Stock Price”

  • “Putting NB-IoT Under the 5G (umbrella) Expands the 5G Vertical into a Very Vast Road.”

  • “30 Years of Technological Accumulation, More than 300 Patents, this Chinese female AI scientist Chooses to Return Home and Start from Zero.”

  • “Sony & LG’s Mobile Phone Business: The Former Kings, Now in the Bronze Age.”

  • “DingTalk’s Hospital of the Future at the End of One Year: How to Build a Hospital’s Digital Base?”

This section is pretty incredible — all written within the past two days — all 4000+ Chinese characters in length (6000/7000 word range if translated into English). Again, each one of these would be candidates for a weekly ChinAI feature translation and each would deepen/widen our knowledge pool about China’s tech scene — the knowledge arbitrage in this space is incredible and I would encourage people to take the ChinAI model and run with it

Let’s take a look at an excerpt from the article (link to original Mandarin) about the Chinese female AI scientist:

  • Context: Pensees-AI, a company that focuses on computer vision, IoT tech, and industrial applications, announced the opening of a research institute in Singapore, with a technical committee that includes a broad range of Singapore professors (including the former VP of the National University of Singapore). Pensees-AI also signed a memorandum of cooperation with AI Singapore (AISG), a national AI programme launched by Singapore’s National Research Foundation (NRF). The bulk of the article focuses on Shengmei Shen who will lead the Pensees R&D institute in Singapore. She who was formerly the Assistant Director of Panasonic’s R&D Center in Singapore, where she had worked since 1992. Her team won more than a dozen top-level international competitions in computer vision, including the US NIST IJB-A facial recognition challenge and Microsoft’s MS-Celeb-1M Challenge.

Direct translation of some passages about Pensees-AI’s current Singapore research team — which reflects how Chinese companies (unlike Peter Thiel) seem to get that diverse, global teams produce results:

At present, there are nearly 30 people at Shengmei Shen’s Singapore Institute, which is located in the town of Shenzhou, and they come from more than a dozen different countries. Shengmei Shen dubbed the team “The United Nations."

Although the team's "United Nations" attributes come from the missions of multinational companies intermingled with Singapore's geopolitical position, she believes that this background is very beneficial to team building and scientific research. First of all, people of different backgrounds have different advantages in ideas/thought processes, and team members can form beneficial complementarities. In addition, the cohesiveness of the "United Nations" team is not based on the same cultural background but rather on the same values, and it is easier to generate deep mutual recognition.

As for role this "United Nations" will play, Shengmei Shen revealed that at present, it is necessary to base (their work) on the product and business model of Pensees, and to integrate Pensee’s development direction with R&D and innovation.

5) Recommended Topics and Industry Express (News)

First, the recommended topics section includes promotions for major conferences and events. When I took the screenshot, the slide featured the World Artificial Intelligence Conference, which takes place in Shanghai later this year.

Second, the Industry Express (News) section is similar to Industry Information Verticals section. The two articles in the screenshot:

  • “Alibaba Cloud takes top three in the world’s container market, first in China, and enter into the quadrant of strong performers.”

  • ‘Ant Financial’s Xiandong Jing: If Xiang Hu Bao works well then the insurance industry will be better.”

Maybe the main difference between this Industry Express (News) section and the Industry Information vertical is that this section summarizes work from other publications whereas the Industry Information vertical features mostly original Leiphone work. For instance, the interview with Xiandong Jing, CEO of Ant Financial, came from the just-published cover article in the magazine Chinese Entrepreneur

Debate Segment: Thiel’s Messy Spiel

Somehow The New York Times published a Google hitpiece/Palantir marketing pitch by Peter Thiel as an oped last Thursday. In a piece that meanders from questioning Silicon Valley’s “cosmopolitanism” (which for some reason is put in quotes) to critiquing those who are worried about AI’s risks for all of humanity to defending a zombie “Cold War mentality” against China, Thiel’s main argument is that Google is helping the Chinese military instead of America by opening an AI lab in Beijing.

Thiel gets SIX major things wrong (and counting! See my Twitter thread for some good back and forth on some of these) in his oped:

1. He gets basic facts wrong: Thiel argues that China's constitution “mandates that all research done in China be shared with the People’s Liberation Army." This is just not true. As Lorand Laskai outlined, military-civil fusion certainly does incentivize research-sharing and “aims to create a commercial market for private firms to compete for PLA contracts” but Thiel’s claim takes this to an unfounded extreme.

2. No disclosure of conflicts of interest: In the piece Thiel hypes AI's potential to help armies gain an intelligence advantage (that might have been an appropriate time to mention his company Palantir won a decade-long, $876 million contract to do just this for the U.S. army last year or that Palantir has at least 29 active contracts, worth a combined $1.5 billion, with the U.S. federal government). As @ConMijente pointed out, this read like a marketing pitch targeted at government officials who dole out billions in defense contracting.

3. Thiel has a very confused conception of AI: He first says that AI is a military technology at its core. He claims that, as was the case with nuclear fission, “the first users of the machine learning tools being created today will be generals rather than board game strategists.” Um what? Never mind the fact that there are already many first adopters of machine learning tools across a wide variety of commercial verticals (translation services, predictive services in finance, etc.) You could actually take Thiel’s statement and completely reverse it and it would be true. The first users of the machine learning tools being created today will be board game strategists rather than generals. Top chess and Go players use machine learning-backed engines to improve; top generals are taking steps to make sure machine learning tools are robust before adopting them into mission-critical operations.

4. Thiel can’t be this ignorant about AI; it seems like he is deliberately trying to give an ambiguous conception of AI to take a shot at those who are concerned about the risks posed by AI for all of humanity. He waffles back to saying AI is dual-use in the middle of the piece and then says this ambiguity is "strangely missing from the narrative that pits a monolithic 'AI' against all of humanity." It’s hard not to take that as a direct shot at my home base, the Future of Humanity Institute, directed by Nick Bostrom who wrote Superintelligence (which warned about the risks of artificial general intelligence). Thiel is randomly attacking a straw man in the “terminator” AI meme. As one of OpenAI’s earliest backers, Thiel should know that there are more nuanced views on AGI’s arrival. Take for example: this reframing of superintelligence by FHI's Eric Drexler, who is widely regarded as the founding father of the nanotech field, in which he outlines a trajectory toward comprehensive, superintelligent-level AI services.

The broader point here is that Thiel seems to not get the basic idea that AI can be many things at the same time. We can recognize that AI like other general-purpose tech (e.g. steam engines or electricity) can empower both civil & military applications AND also see that intelligent agents pose unique risks. The risks of AI exceeding human-level intelligence are just one subset of unique risks posed by AI (others include accident risks from increased automation), but it's definitely not to be dismissed. A wide range of AI experts take it seriously.

5) I'd argue Google's efforts to open AI labs in China aren't "cosmopolitan"/anti-US but lean more toward being self-serving/good for US innovation given global talent flows. Thiel and many others who think that US should not be involved in any offshore R&D in China ignore insights from vast body of lit on the globalization of innovation and tech flows. Let's take a look at one of the key papers from this lit (Eaton & Kortum 1999). Drawing from international patent data from the five leading research nations at the time (US, Japan, Germany, UK, and France), Eaton and Kortum find that 40% of U.S. productivity growth came from research performed in the four other industrial leaders. They also show, through a counterfactual experiment why tech isolationism would be such a stupid tactic for the US to adopt: "cutting off the United States from the rest of the world would cause its productivity to fall far behind the other four."

The lesson here is that Google, other tech companies (e.g. Microsoft Research Asia in Beijing), and 1000s of MNCs w/ R&D labs in China aren't doing this work out of charity or some deeply-buried desire to help the Chinese military; rather they want to be plugged into global innovation networks and adopt tech advances from abroad into home bases. Now, are Thiel and others right to point out some of the negative externalities (e.g. indirect leakage to enable some Chinese mil. developments, building up talent that move to Chinese competitors who could overtake in the long-run?) Sure, but let's have an open debate with real arguments backed by empirics instead of ad hom attacks on the patriotism of companies like Google.

6) In one respect, I agree with Thiel. There’s one strand of the zombie “Cold War” mentality never seems to die: the old playbook of leveraging the exaggerated fears of Cold War competition with a rival in order to advance a totalizing technocracy that wields complete control over society. The historian Walter McDougall’s warning, issued about the dangers of America’s post-Sputnik techno-nationalist turn, still rings true today:

The social mechanisms required to tap the full technological potential of a nation, particularly in the context of cold war competition, mean we have to pay a price for our advances in science and technology and the price is usually a sacrifice in human values. I believe it is inevitable, as long as international competition is the primary engine moving history, and technology is brought to bear in the competition, that we will move more and more toward management of people by a huge bureaucracy, by technocracy

I don’t want the U.S. to compete with China in AI over who can build better tools to censor, repress, and surveil dissidents and minority groups. But that’s exactly what Palantir is trying to do in the U.S. with its efforts to create huge, unaccountable data troves to help policing systems and ICE deportations.

I want us to compete with China in AI over who can build the better industrial Internet of Things and the privacy-preserving algorithms that will help sustain a more trustworthy AI ecosystem.

ChinAI Links

I don’t have an overly intellectual justification to recommend this week’s must-watch nor is it even loosely related to China’s AI scene, but please go watch The Farewell, directed by Lulu Wang. One of these days mediocre movies about Chinese Americans will get traction but until then we need movies like The Farewell to be extremely excellent in every way and it seems like it even exceeds that high bar.

Using Microsoft’s Building Footprints, the New York Times built a map of every building in the United States.

Great piece by Christopher Magoon on mental health stigma in China and the potential of virtual reality applications to help. My one quibble was with this line “This fact has led some experts to believe China’s AI medical technology will soon surpass the U.S.’s in terms of sophistication and adoption—if it has not already done so” which I just don’t think can be supported — the degree of electronic health records and standardization of health care data in China just cannot compare to the U.S. and other developed countries. I’ve come to question any claim that China is leading in some vertical of AI — very few actually come with comparative metrics that distinguish between different parts of the AI value chain, very few talk about actual commercialization/adoption/diffusion, and almost every one just cherrypicks a couple of anecdotes.

Check out Deeptracelab’s weekly newsletter on the evolution of synthetic media technologies, weaponized misinformation, and new cybersecurity threats. Their most recent issue investigated Chinese deepfake pornography.

ChinAI #59: Is Winter Coming for Hikvision?

Plus, what are the risks in the rise of newsletters, Substack's big announcement, and the need for editors?

Welcome to the ChinAI Newsletter!

***Two meta-updates:

1) The goal: every ChinAI issue is a joint effort where my collaborator helps with translation and/or commentary and (most importantly) plays a quasi-editorial role (*more on why I think newsletters may need editors later on). Toward that end, part of the subscription fees will go toward compensating contributors for their work ($100-200 each issue depending on the length and quality of the translation + analysis + editing). If you’ve got a translation/topic to pitch, or you’re interested in learning more about how to contribute, just reply to this email!

2) I want the main incentive for readers to subscribe to be to support access to good content for all (in the mold of The Guardian or Wikipedia), but folks have also pointed out that it would be nice for subscribers to have certain perks (some have suggested making the archive only available to subscribers). A middle ground I’m exploring that still guarantees access to each issue for everyone but gives a little extra to subscribers is to put together a master library of all the Google doc translations, curated by category and shared with subscribers.

The good news is we’re at 40 subscribers & 5400+ free email list readers. I rely completely on word-of-Tweet/email for promotion, so thanks for spreading the word. I’m playing around with options (right now it’s $9 for a monthly subscription and $50 for a yearly subscription) and would appreciate feedback.

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.

Feature Translation: is Winter Coming for Hikvision?

Is winter coming for Hikvision, the world’s largest manufacture of video surveillance products and solutions? In this week’s feature translation, one Chinese investor (he holds shares in Hikvision and goes by the handle Blitzbear) argues the Night King is already inside Hikvision’s gates.

I’m very grateful to Charles Rollet, a freelance journalist who also works as a researcher with IPVM (the leading publication covering the video surveillance industry) for adding his comments throughout the Google doc full translation and letting me include some of his analysis in the key takeaways below. Check out his fantastic portfolio — which includes many in-depth stories on Hikvision.

If you’re like me, then Hikvision is probably one of those Chinese companies you hear mentioned in the news — for their role in the repression of Uyghurs in Xinjiang or for being one of two Chinese companies (Dahua is the other) targeted by a Congressional ban on U.S. federal agencies purchasing new equipment — but you don’t really know much about beyond that.

Enter our (relatively knowledgeable but probably biased) guide —Blitzbear, a Chinese investor who used to hold a decent chunk of shares in Hikvision before selling most of them and holding on to a small part to “use as an observation deck.” He spent a weekend reading Hikvision’s half-year report and came away very disappointed with Hikvision’s performance. Key takeaways:

— Blitzbear highlights two main issues: 1. Can Hikvision find commercialization pathways for intelligent security in video surveillance? 2. What’s the deal with Hikvision’s cash flow and all the loans it’s taking on?

— On the first question, Blitzbear argues that Hikvision is not making the transition from selling products and “eyes” (cameras were 47.7% of total revenue this period) to selling cloud intelligence services and “brains” that can connect cameras and hardware to the cloud computing models via 5G networks — especially in projects that can be replicated in different data application scenarios.

— A key structural trend that supports the first point: high-end cameras that support facial recognition and other AI services are becoming cheaper, so Hikvision can’t just rely on these cameras anymore and they need to get on the AI track. As Charles notes, “At the U.S.’ largest security camera trade show, I saw $59 facial recognition cameras for sale with an $8 per month app subscription.”

— On the second question, Blitzbear highlights Hikvision’s massive increase in short-term debt (an increase of 3.4 billion RMB throughout 2018 and an additional 1.1. billion in 2019 so far). Interestingly, some of its long-term loans came through a public-private partnership (PPP) or Design-Build-Finance-Operate-Transfer (DBFOT) scheme. In laypersons terms, Hikvision goes to the Chinese (subnational) government entity who is looking to do a surveillance project and says, “Since you will be paying me later, you might as well first lend me the money, and I will take it to make and build the surveillance infrastructure, and then you can pay me less later.”

— Crucially, in this period, Hikvision signed six projects according to this scheme (around 1 billion RMB in total pledged loans) and all were surveillance projects in Xinjiang. See Ipvm’s excellent coverage of Hikvision’s projects in Xinjiang, which include cameras for re-education camps and hundreds of mosques. Blitzbear highlighted one such project signed this year with Luopu, a sparsely populated rural county of about 280,000 (almost entirely Uyghur); I dug into Hikvision’s interim report to find the other five, which are listed in the full translation.

— Charles also added some critiques of this piece. This was one of the benefits of getting his “editing” because I often get too attached to each week’s piece (from spending time translating and getting into the head of the author). Charles writes, “A huge omission here is that Hikvision faces much more direct threats, in terms of possible US sanctions over its Xinjiang activities. This could put them in a Huawei-like situation.” Another flaw in the piece, according to Charles: “When you hear talk of Hikvision's financial issues, always keep in mind that Hikvision's controlling shareholder is the Chinese government, so it's unlikely it would be allowed to flounder like a normal private firm,” and “Chinese government video surveillance spending is still huge; whether that gravy train ever stops or slows down is an issue unaddressed by this article.”

FULL TRANSLATION: Has Hikvision's Cold Winter Come, I found the Answer in its Semi-Annual Report

Reflections/Debate Segment

This week’s proposition: every newsletter that gets a substantial degree of traction should consider getting an “editor” or explore substitutes for “editing” functions.

I operate ChinAI through Substack, a newsletter publishing platform which recently raised $15.3 million in a round led by Andreessen Horowitz. This awesome news (at least for people who use Substack) got me thinking about the bigger picture of how newsletters fit into the media landscape. It’s obvious that more and more people are writing and reading newsletters (one recent survey found that nearly 60 percent of American adults subscribe to an email newsletter of some sort). As Substack frequently points out, newsletters allow writers to build a direct relationship with an audience that trusts them more than social media and traditional media channels.

But how do we ensure that this trust is deserved or can last? At their core, many of the most influential newsletter are essentially personal blogs delivered to your inbox. While I’ve laid out some of the benefits above, there are also costs to concentrating all the functions of a media source/platform into one individual’s hands. Let’s take a look at two “editing” functions that are often loss when you go from a traditional media platform to a newsletter. I divide these into editing before publishing (EBP) and editing after publishing (EAP).

Let’s consider a case where a newsletter is taken to be the go-to source for all things on a subject (e.g. Bill Bishop’s Sinocism which I’ve criticized in the past and which, fittingly enough, was the first newsletter launched on Substack. All the valuable editing that happens before any article publishes on, say the Washington Post or other traditional media outlets, is lost in newsletter content. I know firsthand that this type of editing would be really helpful for ChinAI, as evidenced by: i) a lot of past typos, ii) a lot of past ass-holeish comments that I wish I could take back, iii) all the dogs that didn’t bark (e.g. personal biases that aren’t questioned & mistakes that haven’t been found yet). Crucially, newsletters also get much less editing after publishing, especially ones that are under a paywall (which is Substack’s existing model). Many traditional media sources have public editors that can critique their posts and, of course, most of their posts are open for everyone to see, comment on, and critique. This is not the case for personal, paid newsletters.

My model for ChinAI is to be very open and upfront about my own fallibility and personal biases. That’s why we make every ChinAI post open for all so that people can go back and call me out for everything I’m writing. That’s why we’re going to incorporate more collaborators to play an “editing” role in the future. Again, I’ve said I plan to redefine what it means to be a gatekeeper in the “China-watching” space. One addendum: let’s also redefine the model of making newsletters along the way.

ChinAI Links “Four to Forward”

Great piece by Olivia Carville of Bloomberg on the immense implementation challenges for federal agencies to remove surveillance cameras made by Hikvision and Dahua. At least 1,700 of these cameras are still operating in places where they’ve been banned. This is a conservative estimate because a) only a small percentage of offices actually know what cameras they’re operating and b) two cameras running identical Hikvision firmware could carry completely different labels as both companies have U.S.-based warehouses that repackage cameras. This is why when you do industrial policy you should probably HAVE A PLAN in place to implement it properly, especially given the complexity of tech supply chains. A recent Reuters article details the widespread misunderstandings and confusions with the NDAA as well as other new regulations that have restricted the role of Chinese companies in the U.S. marketplace.

This week’s must-read is another War on the Rocks article by Peter Mattis and Matt Schrader. It’s a balanced thoughtful take that recognizes the U.S. needs to address China’s systematic theft of IP and exploitation of the openness of Western institutions but needs to do so without alienating the Chinese diaspora many of whom say they have already experienced discrimination amidst the growing U.S.-Sino tensions. Two aspects that I really liked in this article: 1) a reference to America’s problematic history with enforcement actions directed at Asian-Americans and 2) a call for raising the “China literacy” of U.S. officials and American society. Hopefully ChinAI can contribute to #2.

This week’s sobering read was a NYT piece by Ana Swanson. “The Committee on the Present Danger, a long-defunct group that campaigned against the dangers of the Soviet Union in the 1970s and 1980s, has recently been revived with the help of Stephen K. Bannon, the president’s former chief strategist, to warn against the dangers of China. Once dismissed as xenophobes and fringe elements, the group’s members are finding their views increasingly embraced in President Trump’s Washington, where skepticism and mistrust of China have taken hold…An increasing number of people in Washington now view the decoupling of the two economies as inevitable — including many of the members of the Committee on the Present Danger (Bannon, Senator Ted Cruz, and Newt Gingrich were at an inaugural meeting in April).” Again this is why in issues #53, 54, and 55 I came down so hard against these New Tech Cold War Warriors, because they are either intentionally or subconsciously promoting the memes that pave the way for us to repeat the worse McCarthyist excesses of the Cold War.

Whoo okay that was a lot, let’s take a deep Exhalation and read Ted Chiang’s wonderful collection of short stories. One chapter in Exhalation on “digients” is especially meditative for those interested in ethical implications of transformative AI.

Thank you for reading and engaging.

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

ChinAI #58: Making Knives Better & Landscape of China's Intelligent Manufacturing

I'm bringin' non-sexy AI back (yeah)

Welcome to the ChinAI Newsletter!

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 and subscribe here to support ChinAI under a Guardian/Wikipedia-style tipping model (those who can pay support access to all content for all).

Feature Translation: Bringing Non-Sexy AI Back — The Landscape of Intelligent Manufacturing in China

I know I say this every week, but this is one of the favorite translations we’ve done at ChinAI. With an eye toward how China can close the gap with Germany and Switzerland!!!, it’s a deep dive into the nitty gritty production-floor level details behind slogans like “industrial intelligent transformation” and “smart manufacturing.” The author is jiqizhineng’s co chief-editor utada (宇多田, a pen name and a reference, I think, to the Japanese American singer-songwriter), and many of the insights come from Zhou Tao, the CEO of Shuzhilian — a "data industry chain integrated services” company based in Chengdu, China.

Three meta-points, which correspond to the bolded phrases in the paragraph above:

1) Other countries outside the U.S. and China exist and matter in the world. If you are only reading and following people who see China solely through the lens of competition with the U.S., it’s time to take off the blinders. This piece highlights the gap between China’s industrial manufacturing level with that of Germany, Switzerland, and Japan, specifically as it relates to manufacturing techniques/craftsmanship/workflow (工艺).

2) This piece dives into the unsexy details of key aspects of the production line (anybody care about making knives?) and how under-appreciated aspects of the manufacturing workflow (“how about twenty paragraphs on machine visual quality inspection?”), a nice reprieve from analysis that use “AI,” “chips & other sexy buzzwords of the day to serve as a catchall for anything and everything (see my Twitter rant on how the tech abstraction problem plagues analysis of these concepts).

3) This piece features voices and companies from outside Beijing, Shanghai, Guangzhou, Shenzhen — It draws a lot on insights from Zhou Tao of Shuzhilian (数之联), which works to improve machine visual inspection, a process built on a) cameras and optical sensors to collect data on objects passing through the production line, b) machine learning models that identify the type, location, and size of defects.

KEY TAKEAWAYS:

  1. THE BIG PICTURE: When the author was in Switzerland, a friend recommended a less-than-$20 peeler knife that “can rapidly dispatch any cutting tool you have used” (see photo at top of the issue) How come China cannot make it? The author writes, “When many people say that they want to chase Germany, Switzerland and Japan, they are not only talking about achieving ‘a higher production efficiency,’ or ‘slightly reduced costs.’ What we have to chase are improvements in 工艺 (techniques) and then quality improvements.”

  2. ONE WAY TO SLICE THE PROBLEM: reducing the amount of defects in production lines that now span over 100 devices. According to Zhou Tao, "The defect rate for an important product on a Chinese leading enterprise’s production line is about 1%, while the defect rate of similar products on German, South Korean and Swiss production lines can be .2 or .3%."

  3. DIG DEEPER: The core problem in knife tool production is the very severe nature of non-standardized data. A common knife has thousands of different pieces and when placed in different production and processing environments, the data that machine visual inspection models are dependent on is also different. The piece makes a distinction between simpler problems (e.g. using a Hall effect sensor to check for wear and tear of large knives) vs. thornier problems which require multi-dimensional data such as torque, vibration, etc. as well as higher-order data models.

  4. WHAT TO LOOK FOR: A few high-end Chinese enterprises have truly carried out (a transition) to so-called digital factories. "For example, companies such as Foxconn, BOE, Tianma, COMAC, SAIC, etc., have production lines, especially in newly built factories, where the entire management and processing workflow have been opened up and linked together, generating multi-dimensional data…all categorized, recorded, and kept in good condition and high quality." The key to more widespread upgrading of the manufacturing chain rests with the mid- and low-end factories.

READ FULL TRANSLATION: We Always Shout about Chasing Germany and Switzerland in Manufacturing, but We Can’t Delude Ourselves into Relying on “Intelligentization” to Take the Lead

ChinAI Links (Four to Forward)

In my ramblings last week, I laid out the notion of “info/knowledge arbitrage” — I think the notion applies to highlighting underrepresented voices in China-related analysis. Aside from the notion that we should support more female and people of color voices in this space because equity has intrinsic value, there’s also a more instrumental and calculative reason we should do it. There are systemic factors that make it so good work from, say, female historians are not getting recognition, so the smart people who are looking for an info/knowledge edge should be reading more of their work to take advantage of this market inefficiency. For instance, the best thing I listened to this week was a UPenn Center for the Study of Contemporary China interview with Professor Shelley Rigger on Taiwan and the Global Order.

This week’s must-read is a War on the Rocks piece by Laura Schousboe, a PhD fellow at the Royal Danish Defence College. In line with the “Bringing non-sexy AI back” theme of this week’s issue, she brilliantly outlines the pitfalls (e.g. presentism, “ignoring the boring stuff”) of writing about revolutionary defense technology. H/t to Uhlrike Franke, a summer Governance of AI Fellow at FHI, for pointing me to this piece.

I really enjoyed Episode 43 of Tech Buzz China, a podcast co-hosted by Ying-Ying Lu and Rui Ma, on the gaming live streaming industry in China — not just because I have watched more hours of League of Legends live streaming than I care to disclose but also because it dives deep into the history of Chinese esports and livestremaing platforms such as Douyu.

From this week’s feature translation "In the process of collecting data, although everyone thinks that these data are important assets, they have not thought out clearly where they can be used…(data’s) value is completely determined by the application scenario." Data is another buzzword that falls prey to the tech abstraction problem. Matt Sheehan provides an illuminating five-dimensional framework for understanding data as an input in this MacroPolo analysis.

Lingering Bits and Pieces

*Last week’s translation, with Jordan Schneider, was previously featured in an abridged version done by Jordan for Technode.

Chinese Phrase of the Week: 隔行如隔山 (ge2hang2 ru2 ge2shan1) - someone from a different industry knows no more of the secrets of the craft than they know of another country

Thank you for reading and engaging.

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

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