ChinAI #164: SenseTime's AI Ethics for "Balanced Development"

More on SenseTime's AI Governance mechanisms

Nov 22

Greetings from a world where…

pumpkin pie does not suck

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

Feature Translation: AI Ethics for Balanced Development

CONTEXT: On November 11, 2021, SenseTime released its “AI Sustainable Development Report 2021-2022: AI Ethics for Balanced Development” report (link to original in Mandarin). Previously, when breaking down SenseTime’s IPO Prospectus (ChinAI #154), I criticized the underwhelming AI ethics section for not naming members of their AI Ethics Council, boilerplate language, and not addressing ethnic profiling. Let’s take a look at section five of this new report, titled “AI ethics and governance practices.”

Key Takeaways:

Still no disclosure of who sits on SenseTime’s much-hyped AI ethics committee, which was formed January 2020. All we learn is that this 6-member group must have at least 1/3 of its members from external organizations (e.g., universities, think tanks); currently, 2 are external, and 4 are senior managers at SenseTime. There have been important conversations about the lack of diversity in AI ethics boards at U.S. companies. When it comes to China’s leading AI start-up, that conversation is a nonstarter because we don’t even know who’s on the board.
SenseTime reveals more details from its AI ethical review program, which evaluates whether ongoing or existing products meet its ethical standards. They have constructed a “global AI ethical risk database,” which incorporates more than 100 incidents (both positive and negative) associated with AI ethics. Since 2019, SenseTime reports rejecting 10% of product project proposals for not following its ethical codes. Those interested in this topic should look at the societal risks section (p. 41 of the pdf), which contains some terms that I can’t comprehend (e.g., 种群替代)
SenseTime will continue to play a leading role in domestic and international AI standard-setting forums. Below is a translated table of their involvement, from p. 46 of the report:

ChinAI Links

Behind on my reading this week, so forgive a few self-plugs:

Should-listen: Interview with The Gradient

I had a great conversation about some of my previous writing on China’s AI development with Andrey Kurenkov of The Gradient, a digital magazine that covers the latest research in AI. Their platform is a great way to keep informed of latest technical trends and debates in AI.

Should-watch: China’s Race for AI Supremacy

I gave some comments in a Bloomberg video about U.S.-China rivalry in AI, alongside Eric Schmidt and Robin Li. Includes some fun shots of the office where I work now.

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 postdoctoral fellow at Stanford's Center for International Security and Cooperation, sponsored by Stanford's Institute for Human-Centered Artificial Intelligence.

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

ChinAI #163: Prospects for a Chinese Facial Recognition Law?

Professor Weijun Liu posts on Caijing's Elaw Portal

Jeffrey Ding

Nov 15

Greetings from a world where…

magic and science collide in the world of Arcane

Feature Translation: Prospects for face data protection legislation

Context: Weijun Liu is a Professor at the People’s Public Security University of China (PPSUC). Founded in 1948, PPSUC not only trains people in criminology and policing but also develops technological applications for public security, in partnership with companies like Hikvision and many public security bureaus across the country. Liu published this thought-provoking piece in Caijing ELaw, a new(ish) independent content platform focused on internet governance under the umbrella of Caijing Magazine, a respected business platform known for publishing investigative, critical pieces.

Key Takeaways:

Why does China need a facial recognition law?

The Data Security Law (DSL), Personal Information Protection Law (PIPL), and other existing laws are plagued by “poor operability.” What’s their guidance for on-the-ground decisions like how to store sensitive facial data? Should it be encrypted? Should it be stored separately through physical isolation? “Obviously, the current, overly crude legislation can hardly answer these questions clearly,” states Liu.

Liu also articulates two additional, more complex reasons for a facial recognition law:

Liu gestures at the need to ensure existing efforts to protect privacy and data security will extend to government actors, not just commercial firms. Regarding the necessity of a facial recognition law, he writes: “For government departments that are responsible for the application of facial recognition technology, it is necessary to set procedural rules specifically for how facial recognition technology applications are developed for these departments to perform their duties, under the framework of the PIPL and other laws and regulations.”
Liu wants public security bureaus to get ahead of future controversies related to facial recognition. Here’s the most interesting passage, in my opinion, from the article: “It is conceivable that if the regulations are passed early, video surveillance in public spaces and the application of corresponding technologies can be regulated, and controversial issues such as separate lanes for facial recognition security checks in subways and the installation of facial recognition equipment in residential building elevators may not become the focal point of public opinion.” Liu’s rationale: the legislative environment and public discourse surrounding facial recognition have changed drastically since 2016, which was when the Ministry of Public Security last issued draft regulations for managing video image information systems.

What would a facial recognition law include? The general idea is to take legal principles in existing laws and transformed them into operational specifications for the use of facial recognition technology. Liu outlines a few planks:

Clearly stipulate scenarios where facial recognition is prohibited, including a whitelist/blacklist system for application scenarios. One line that caught my eye: “Without a legal basis, do not allow the use of facial information to carry out analysis activities on aspects such as emotions, psychological activities, and religious beliefs, etc.”
Establish a filing and annual evaluation system for facial recognition technology applications. Liu writes, “Face recognition technology application entities with a certain scale (based on the amount of face data processing or revenue scale, etc.) should undergo regular security assessments.”
Require processing of face data to be done locally (at the end terminal) and only allow transmission through public networks if it is for greater legal benefits. Delete collected or generated face data immediately after the face recognition function is complete.
Strengthen protections for the face information of special subjects. These include minors, as well as party and government leaders. For the latter group, as Liu writes, “the main purpose of protection is to prevent counterfeiting of faces and the use of face recognition technology for trajectory tracking.”

Let me close by saying 6 things that can be true at the same time:

This is only one article, and we should be careful about drawing bold conclusions.
Previous ChinAI issues (ChinAI #77) that featured outspoken voices against Chinese tech-enabled surveillance were early indicators of substantial backlash against facial recognition systems.
This is one piece of evidence that public backlash on facial recognition applications by private companies could also constrain Chinese government surveillance. There are boundaries between these two domains but they are still interconnected.
China’s party-state system will make it difficult for sustained checks on government power, and this piece does not directly address facial recognition-enabled ethnic profiling.
Even if you are very skeptical about any checks on China’s “digital authoritarianism,” you should at least be open to the possibility that debates like the ones unfolding in this article are taking place.
There are probably many articles like this one out there that will never be translated. I would wager that the vast majority of Western experts on China’s digital governance have never heard of Caijing Elaw (and neither did I until a couple weeks ago) — and it’s been around since January 2020.

A lot more interesting details in the FULL TRANSLATION: Focus on front-end governance — Prospects for face data protection legislation

ChinAI Links (Four to Forward)

Should-listen: Casey Johnston on Longform Podcast

Johnston is a journalist and editor who writes about working out and lifting weights. During her interview with Longform, this analogy really resonated with me:

I feel more comfortable lately with a sort of beloved-local-restaurant level of success. What's nice about Substack is that we've come to this place, that I hope lasts, where we can have this sort of local-restaurant relationship with writers, or I can have that with readers, where I don't have to be part of this big machine in order to do something that I really like.

I used to care a lot about how many people read ChinAI, but now I also feel really comfortable with a local-restaurant relationship with readers.1 No need to come every week, but do come back and try the new dishes every now and then. And tips (in the form of paid subscriptions) are always welcome!

Should-read: overlooked debate over data localization policies in China

Tom Nunlist, an analyst at Trivium, summarized a recent op-ed by a CAICT researcher arguing against restrictive data localization policies. Interestingly, he points out the deeper context that surround this op-ed: the bureaucratic infighting and internal debates over data localization that exist within the Chinese government.

Tom Nunlist @freefader

There's an overlooked debate re: data security policy in China. Yesterday, a researcher at CAICT - a think tank under MIIT that supports research and rollout of ICT policy - published an op/ed arguing that current global views on data localization are seriously flawed. 1/

Should-read: U.S. Companies Aid China’s Bid for Chip Dominance Despite Security Concerns

For The Wall Street Journal, Kate O’Keeffe, Heather Somerville, and Yang Jie report:

U.S. venture-capital firms, chip-industry giants and other private investors participated in 58 investment deals in China’s semiconductor industry from 2017 through 2020, more than double the number from the prior four years, according to an analysis of deals data by New York-based research firm Rhodium Group done at the Journal’s request.

Should-read: Public Debate on Facial Recognition Technologies in China

Tristan Brown, Alexander Statman, and Celine Sui published a cool case file in August 2021 for the MIT Case Studies in Social and Ethical Responsibilities of Computing:

China’s ascent on the global stage in the fields of artificial intelligence (AI) and facial recognition has been widely noted in Western-language scholarship and media. Much of the attention has focused on the applications of these technologies in government security systems and their geopolitical implications. Here, we seek to explore the private and domestic uses of facial recognition. What dynamics inform popular debates about the use and applications of these technologies in China, and how do they fit into a more global picture? We present a series of cases from the past three years in which facial recognition software attracted media attention in legal, commercial, and educational settings. Acknowledging that China is far too large and diverse for there to be just one dynamic at play, we propose that while debates about facial recognition have indeed become more common, there is still broad-based public support for uses that promise increased security or convenience. The state has been selectively receptive to limited critique, but typically in a manner that preserves its active role in shaping the contours of public discussion.

Thank you for reading and engaging.

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

I recognize that I’m in a privileged position to do this as a side hustle.

ChinAI #162: The Misfires — How BAT All Stumbled in Medical AI

Wendy Liu translates the first article in the Leiphone series on medical AI

Jeffrey Ding

Nov 8

Greetings from a world where…

75 Washington Post reporters worked 9+ months to provide the definitive account of what happened before, during, and after the January 6th attack on the Capitol

Feature Translation: The Misfires — How BAT All Stumbled in Medical AI

Thanks to Wenmiao “Wendy” Liu for contributing this week’s feature translation. She’s a product delivery manager at Kyros.AI, an innovative AI platform for education management, and former ML geophysicist at Schlumberger Oilfield Services. Previously, she translated the July AI Development Monthly Report by SciToutiao (ChinAI #152)

CONTEXT: This is the first article in the “Medical Weak Points of the Giants” series by Leiphone, which covers how large AI companies have struggled with medical AI applications in China. Last week was IBM. Baidu, Alibaba, and Tencent are up this week.

Back in 2017, the BAT companies all laid out ambitious medical imaging strategies. Baidu released an AI system for 24-hour medical consultations. Alibaba launched the Doctor You AI system for medical imaging diagnosis. At the time, the company’s VP of health, Ko Yan, said: “Doctor You will soon enter many medical institutions across the country to serve as the best assistant for doctors. We expect medical AI to take away half of doctors’ workload within 10 years.” Tencent launched Miying, an AI medical imaging system for early cancer diagnosis. China’s Ministry of Science and Technology designated Tencent to lead a national open innovation platform in medical imaging.

KEY TAKEAWAYS:

What happened next? The most interesting stories happen after the press release:

Despite plenty of talent and resources, BAT companies are in a “rather embarrassing situation”: they can’t get regulatory approvals for medical products. Specifically, they need a Class III permit (highest risk level of products which covers medical imaging AI products for diagnostic support) from the National Medical Product Administration (NMPA), a Chinese regulatory body.
In contrast, smaller players have received approvals. According to the article, as of 2021, 15 AI products have successfully passed the certification process from the NMPA.

The buzz around the BAT companies’ ambitions in medical AI has died down. Search Alibaba Health’s “Doctor You,” and the latest news will be from 2017 and 2018. English language coverage is also mostly from 2017; see for example, this SCMP article.

It take time to get medical AI software approved:

Beijing Keya Medical, one of the upstarts that succeeded where the BAT didn’t, received approval for a software system that analyzes coronary arteries based on heart imaging scans. Cao Kun, who leads their medical R&D division, shared the timeline of certification with Leiphone:

“In 2016, we started the R&D and obtained the registration test report; In 2017, the prospective clinical trial was completed and submitted for registration; In early 2018, we entered the expedited channel of examination and approval and obtained the EU CE certification; In 2019, we completed a retrospective clinical trial; In January 2020, we obtained the first registration certificate of NMPA's Class III AI medical devices.”

Referencing comments by a vice minister involved in supervision of medical devices at a major AI conference in 2021, the article concludes:

“The signal from the national level is clear: for medical AI, strict regulation will protect the interests of patients and thus eliminate the risks faced by products. This strict evaluation mentality, coupled with the evaluation criteria that have never been set, is a tough waiting period for medical AI companies that live on financing and lack stable cash flows.”

Why have the BAT companies failed to get approvals for medical AI products?

The full article cites a lot of factors, including: shifts in strategic focus, a cold wave of investment in medical imaging AI, leadership issues, lack of domain-specific expertise in getting medical product approvals, etc.
This could change in the future: One employee who works on the regulatory side for a medical AI company added, “Several BAT products have passed through the expedited channel. As far as I know, Tencent has 3 products on the list. Moreover, these products are not in the already crowded lung nodules and coronary artery racetrack. We should hear about them soon.”

FULL TRANSLATION: The Misfires: How BAT All Stumbled in Medical AI

ChinAI Links (Four to Forward)

Should-read: Medtech AI & Software Regulation in China: 5 Things to Know

It seems like coverage of AI regulation in China often fluctuates between two extremes: it’s either a lawless land where anything goes or a government stronghold that stifles any space for private sector initiatives. In trying to give more color to the middle ground in the medical AI space for this ChinAI issue, I benefited greatly from this explainer.

Should-read: Nov 1 issue of latitude(s)

From Karin Fischer’s weekly newsletter about global higher education: A federal-government probe of academic espionage is casting a chill among scientists of Chinese descent, with fears of government scrutiny of their research leading many to cut off critical collaboration with colleagues in China.

Those are the findings of a new study by Jenny J. Lee and Xiaojie Li of the University of Arizona, who surveyed nearly 2,000 professors, postdocs, and graduate students at leading American research universities about the China Initiative.

Forty percent of Chinese or Chinese American scientists reported feeling racially profiled by the U.S. government, according to the survey, which was supported by the Council of 100, a group of prominent Chinese Americans. And half of Chinese scientists — the authors use “Chinese” as shorthand to refer to students and professors of Chinese descent, regardless of nationality — said they felt “considerable fear or anxiety” that they were being “surveilled” by federal authorities. As a result, a quarter of Chinese researchers said they planned to pull back from future projects in China.

Should-read: China Information Operations Newsletter 05

Edited by Hannah Bailey, a researcher at the Programme on Democracy and Technology at Oxford University, the latest issue of the China Information Operations Newsletter links to important topics, including China’s removal of Caixin from media sources approved for domestic republishing, inauthentic Twitter amplification of a conspiracy theory that covid was imported to Wuhan via Maine lobsters, and Xi Jinping’s heavy reliance on propaganda.

Should-read: How the PRC Blocked Out Foreign Tech Products Claiming Security Risks

For IPVM, Charles Rollet reviews the history of how the Chinese government has pursued a “domestication strategy” in video surveillance and other IT domains. Includes translations of webpages, articles, and comments by key players.

Thank you for reading and engaging.

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

ChinAI #161: IBM Watson Leaves China in Defeat

The Inside Story

Jeffrey Ding

Nov 1

Greetings from a world where…

“it's no fun being the last fucking eunuch in the forbidden city” — Succession, Season 3, Episode 3

Feature Translation: IBM Watson in China

CONTEXT: Watson for Oncology, a clinical decision-support system for cancer treatments, has ceased sales in mainland China. Watson Health first entered China in 2016 and experienced a “honeymoon period.” By 2018, nearly 80 hospitals across 22 Chinese provinces and 43 Chinese cities used Watson for Oncology. At that time, according to one source, the Greater China region accounted for half of Watson Health’s revenue in a quarter.

Three years later, why did IBM Watson “leave China in defeat” (败走中国)?1 The fourth article in a very interesting Leiphone series — “The Medical Weak Points of the Giants” — tackles this question. The author, Yuchen Li, is currently drafting the last article of the series, titled “Why BAT Can’t Sell Medical Cloud.” *I welcome contributors to translate other articles in this series, and I always compensate translation work, thanks to those who financially support ChinAI.

KEY TAKEAWAYS:

The role of policy uncertainty and the geopolitical environment: changing U.S.-China relations made hospitals worry about the introduction of Watson, since its home base is in the states. The article references “invisible” restrictions on Watson introducing other services like imaging products, which further enhanced the difficulty of commercialization.
A range of other factors: With cases like these, it’s always hard to parse out the impact of government policies/pressure vs. alternative explanations. The article outlines many other factors, including stubbornness when working with local partners, leadership issues, and workplace culture.

Let’s unpack three additional factors further:

Public relations mismanagement: Many complaints about IBM not defending Watson Health against rumors and negative press about their products. The article states, “Unable to fight the fire of public opinion, the IBM Watson Health’s China marketing department is very passive, and most of their work only involves supporting exhibitions, which makes employees somewhat frustrated. On one occasion, Baiyang Intelligent, the agent of Watson for Oncology, went to the upper level management, hoping to speak on behalf of Watson for Oncology, but the response was still ‘no’.” Kang Ming (pseudonym), a former employee of Watson Health, attributes this to IBM’s roots in selling to big business and government clients, which makes them not value consumer-facing PR and self-media.
Unstandardized nature of cancer treatment in China: This was one of the drivers for Watson’s initial entry into China. IBM commissioned a survey by a consulting company. They found that in some areas of northeast China, doctors’ treatment plans for cancer patients were only consistent with standard guidelines for 30% of the time. The implication is that there should be a lot of potential for decision-support systems like Watson for Oncology. Two issues: 1) It’s a steep cost to create more standardized hospital information systems. One current project in a top hospital in southern China runs hundreds of millions of dollars. 2) Chinese doctors opposed Watson’s entry. Some doctors have linked interests with pharmaceutical companies and propose treatments outside of the guidelines (pharma rebates paid to doctors for oncology drugs are not insignificant).
Inherent issues with applying natural language processing-based solutions to clinical applications like cancer treatment: Cancer treatment is a highly individualized process that requires a lot of complex reasoning based on many indicators. A Chinese scholar who has studied NLP for many years said to Leiphone: “Watson’s problem is an inherent problem in the natural language processing industry—there is no structure. The structure is the connection between two things, it’s the problem of knowledge expression. No. There is no reasoning with structure, and there is no intelligence without reasoning. It is troublesome to solve this problem. The BERT model has no structure. It can only say that it has seen a lot of data, and it can guess. As for the scheme recommended by Watson, it is also based on calculation and probability. And the result of this probability has no clinical significance.”

ChinAI Links (Four to Forward)

Must-read: Harnessed Lightning — How the Chinese Military is Adopting Artificial Intelligence

By Ryan Fedasiuk, Jennifer Melot, and Ben Murphy, this CSET report analyzes 343 AI-related equipment contracts, selected from a broader set of procurement records published by PLA units and state-owned defense enterprises in 2020. This is one of the most detailed assessments of how the Chinese military is applying AI in practice. There’s so much to digest here, including examples of Chinese suppliers that “make a business out of sourcing foreign data or components and reselling them to sanctioned Chinese defense companies or PLA units.” One funny detail in the appendices: The authors also used an AI research assistant to check their manual coding of the 343 contracts into seven different application categories. On the first run through, this AI research assistant disagreed with the authors’ initial coding half of the time. Still, speaks to the extent to which the report tried to double-check results.

Should-read: GovAI Relaunching as a Nonprofit

Centre for the Governance of AI (GovAI) @GovAI_

Today, we’re relaunching as a nonprofit. Our mission remains the same: We are building a global research community, dedicated to helping humanity transition to a world with advanced AI. But we’ll also be exploring new ways to expand our impact.

The Centre for the Governance of AI has Relaunched | GovAI BlogThe Centre for the Governance of AI (GovAI) has relaunched as a nonprofit organisation. Our mission remains the same: We are building a global research community, dedicated to helping humanity...governance.ai

Includes details about reorganization of GovAI, hiring opportunities, fellowship opportunities, an inaugural GovAI conference, and continuing seminar series!

Should-read: China’s Hypersonic Weapons Tests Don’t Have to be a Sputnik Moment

In an excellent piece for War on the Rocks, Sanne Verschuren argues:

China’s recent tests with hypersonic weapons systems — and the added layer of fractional orbital bombardment systems — are not a Sputnik moment. The technology is far less dangerous than it is often portrayed. However, these hypersonic tests fit in a broader pattern of the nuclear powers advancing their nuclear arsenals in ways that make the world less safe. Rather than trying to outbid China in a costly arms race, U.S. policymakers should start a conversation around the strategic implications of missile defense and rein in the ever-expanding U.S. missile defense mission.

Should-read: Gettysburg

This was the piece that made me think the most this week.

Thank you for reading and engaging.

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

Other Watson products continue to be promoted in the Chinese market, including those related to clinical trials and drug discovery knowledge bases.

ChinAI #160: Sensetime's listing: a precautionary measure?

Plus, more plagiarism drama in Chinese CS Academia

Jeffrey Ding

Oct 25

Greetings from a world where…

Shohei Ohtani had the most extraordinary — if not greatest — season in baseball history

Feature Translation: A Theory For Why Sensetime Chose Now to Go Public

CONTEXT: Back in August, Sensetime, China’s largest AI start-up, filed to list on the Hong Kong exchange (ChinAI #154). This piece (original Mandarin) provides more context for Sensetime’s decision to list, including an intriguing explanation for why the choice could be seen as a precautionary measure. It’s written by Yang Jingzi for 放大灯 (enlarging light), a S&T media platform under guokr, a popular Chinese S&T education community.

KEY TAKEAWAYS:

Facial recognition has become an indispensable part of daily life in China, as evidenced by the emergence of the four computer vision dragons — Sensetime, Megvii, Yitu, and Cloudwalk. The article also points out that the price of a face photo on the black market is .002 RMB.
Why did Sensetime choose now to go public? The piece first debunks two possible explanations: a) Sensetime is short of funding; b) it needs to raise its popularity/name recognition. On the first factor, Sensetime’s losses are overestimated because Hong Kong’s financial disclosure standards differ from the mainland, which means direct comparisons between the four CV dragons are futile. Sensetime also has a healthy amount of cash on hand. Nor is Sensetime in need of a reputation boost.

Instead, the piece argues that Sensetime chose to list now because it felt pressured by structural trends in China’s AI ecosystem: “The time to go public is fleeting, and Sensetime didn’t dare to miss it.” Here’s the supporting evidence:

Investment hotspots have shifted away from computer vision and AI toward healthcare and advanced manufacturing. According to IT Juzi data, financing events in computer vision peaked in 2015 , and there’s been a sharp decline since (graph below). *Note the caption on this graph says “computer data” financing rounds (2013-2021), but I read it as a typo for “computer vision.” The Chinese word for data (shuju) and for vision (shijue) are very similar.

The policy environment around facial recognition is also tightening. Yang cites legal cases that have raised public awareness about protecting facial information, as well as Tsinghua University Law School professor Lao Dongyan’s comments about the risks of facial recognition (see ChinAI #77 for a translation of some of her critiques). China’s Personal Information Protection Law will also come into force in November, raising the level of scrutiny on facial image collection and analysis.

Without reading the minds of Sensetime’s directors, we’ll never know if the decision to list was a case of “now or never.” Still, this article makes a convincing case that Sensetime’s decision can be analyzed in the context of overall trends in China’s computer vision industry.

ChinAI Links (Four to Forward)

Should-read: Three Cases of Academic Misconduct (in Mandarin)

Jiqizhixin reports on three cases of academic misconduct from the past couple months:

1) A paper published at the 2021 International Conference on Computer Vision, co-authored by graduate students at Hong Kong University of Science and Technology and Nankai University, plagiarized a paper published at the 2021 International Conference on Machine Learning. See more details in English here.

2) Three students, including a Beijing Institute of Technology master’s student, copied a NeurIPS 2020 paper submission word-for-word and uploaded it to arxiv. This news event became the No.1 hot topic in Zhihu Science.

3) Latest case to blow up: Two researchers from the Shanghai Key Lab of Intelligent Information Processing, and School of Computer Science, at Fudan University (an elite Chinese university), have been accused of plagiarism. Their 2017 paper published in the Chinese journal Computer Applications and Software bears striking similarities with a 2008 paper by IBM and University of Michigan researchers.

Should-read: White Paper on Trustworthy Artificial Intelligence

Let me plug again this CSET translation, co-authored by China Academy of Information and Communications Technology and JD.com, because I recommended it before getting a chance to take notes on it. Here’s a few points that caught my eye:

“AIIA (China’s AI Industry Alliance) also released the first batch of commercial AI system trustworthiness assessment results in 2020, involving 16 AI systems of 11 enterprises, providing an important reference for user selection; among the latest AI legislation proposals issued by the EU is a proposal that an authoritative third-party organization carry out trustworthiness assessments and other such measures.” *Would love to see those assessment results.
“Research on trustworthy artificial general intelligence (AGI) must be laid out in advance. At present, whether it is AI governance or trustworthy AI, most work is carried out for weak AI technology and applications. AGI and even superintelligence have not garnered sufficient attention. Once these emerge, they will be major events tied to the destiny of humankind and require a forward-looking layout, such as exploring the development path of AGI through the development of cutting-edge technologies such as super deep learning (超级深度学习) and quantum machine learning. At the same time, we must also carry out research related to trustworthiness when exploring strong AI.” *Important section about AGI.

Should-read: Yiqin Fu’s newly discovered Chinese-language resources

Includes an interesting anecdote about Tencent’s text-to-speech capabilities.

Yiqin Fu @yiqinfu

Newly discovered Chinese-language resources: 1) The podcast 非洲历险记 interviews CN entrepreneurs in Africa (think Opay, Transsion, etc.). One e-commerce co started out selling diapers in Kenya but pivoted to makeup. It worked there but failed when they expanded to Rwanda.

Should-listen: Four years on Substack: A conversation with Bill Bishop, Substacker #1

To mark the four-year anniversary of the first-ever Substack publication, Bill Bishop’s newsletter about China, Hamish McKenzie of Substack interviewed Bill about his story:

I had known Bill for almost a decade from my previous life as a reporter and was a regular reader of Sinocism. Around the time that we came up with the idea for Substack, Bill had been telling his readers that he was planning to introduce a paywall for the newsletter. I jumped into his inbox and suggested that he be Substack’s first publisher. Happily, he agreed!
Chris and I promptly flew to Washington, D.C., where Bill had recently relocated after 10 years in Beijing, and started figuring out how we could build the first version of the product around his needs. By October 2017, Bill was ready to launch, and on the 15th of the month he enabled paid subscriptions. By the end of that day, he had brought in six figures of revenue, heralding the arrival of two businesses at once: his own, and Substack’s.

Thank you for reading and engaging.

Any suggestions or feedback? Let me know at chinainewsletter@gmail.com or on Twitter at @jjding99

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