ChinAI #140: 2020 China Computer Vision Talent Survey Report

Greetings from a world where…

My dissertation is signed, sealed, and submitted (but not yet defended)….now, to face the gauntlet that is the academic job market

…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). As always, the searchable archive of all past issues is here.

Feature Translation: 2020 China Computer Vision Talent Survey Report

Context: Deloitte together with Extreme Mart (Shenzhen-based AI developer ecosystem) and China Society of Image and Graphics, jointly released this report back in February 2021. It’s only available in simplified Chinese, for full download link, see here.

How they got their data: 12,000 computer vision (CV) students, employees, and researchers browsed and visited the survey site —> 3,169 questionnaires collected in the week that it was available —> screened down to 1,578 high-quality questionnaires (864 students, 635 employed in industry, and 79 teaching or researching in universities/institutes). These questionnaires were supplemented by in-depth interviews with 23 CV talents as well as 11 companies about their talent needs in the CV-related industrial chain. Keep in mind this is not a representative sample of the total population of CV talents in China. Nonetheless, it’s a rich source of data.

This week’s translation is jiqizhineng’s summary of the report’s main findings.

Key Takeaways: 

  • Why care about the CV talent base? Among many AI-related domains, computer vision (CV) is the largest application direction in the Chinese market, accounting for 35% of China’s AI market applications. However, according to this report, the current supply-demand ratio of CV talent is only 0.09, which is extremely scarce.

  • More than 70% of CV talents are concentrated in first-tier and “new first-tier cities”  (cities like Nanjing, Wuhan, Hangzhou, etc. that are getting close to the first-tier cities of BJ, SH, GZ, SZ in terms of attractiveness). In addition, 90 percent of CV talents pick first-tier cities as the cities they intend to advance their careers in the future. There’s very limited brain drain: Only .27% of respondents said they intended to advance their careers in Hong Kong, Macau, Taiwan, and international cities.

  • How about their academic backgrounds? While half of CV talents studied computer science, more than 40 percent studied non-CS majors, such as electrical engineering and mathematics. Here’s a very interesting data point: Among those surveyed, almost 7 percent studied the new AI major. Longtime readers may remember that momentum to set up this new major accelerated in 2018 (ChinAI #7), with the 35 universities first offering the major in April 2019.

  • What about their software development habits? Specifically, the survey asked CV talents about what types of machine learning frameworks that they have used in their research or work. Image below shows the responses divided by the CV students (left) and employees in the CV field (right). Pytorch and Tensorflow lead the way in terms of popular usage, whereas just 6.5% of CV personnel have used frameworks developed by Chinese firms and organizations. Again, another illuminating data point that relates to past ChinAI coverage of China’s efforts to build its own AI open source software (ChinAI #22, ChinAI #133)

  • Still a lot of issues with talent supply, including the speed of advance in the field itself. Colleges and universities are offering 1-2 general courses in computer vision (58 percent of those surveyed), but they can’t match the needs of students in certain subfields that are developing quickly (e.g. image matting, target tracking). The article states, “Although the number of computer vision talents in China has reached 200,000, talents who can truly meet the demands of industry and society and reach the target level are still scarce.”

A few other intriguing findings:

  • In terms of factors that CV talents considered when deciding where to live, government talent policies ranked 3rd (after salary levels and employment opportunities). Note: these are likely provincial- and local- level talent policies.

  • Average annual salary of CV algorithm researchers in 2020 was about 330,000 RMB. There are other types of CV positions, including software engineers and AI product managers. But for the highest earning positions, algorithm researchers, many companies or research institutes require candidates to publish in the top conferences in the field of computer vision (CVPR, ICCV, ECCV, etc.) and machine learning (NeurIPS, ICML, etc.)

  • There is a looooooot more to digest in the report, as this is only the translation of jiqizhineng’s readout.

FULL TRANSLATION: 2020 China Computer Vision Talent Survey Report, jiqizhineng’s readout

ChinAI Links (Four to Forward)

Must-read: Cooperative AI — machines must learn to find common ground

Allan Dafoe, director at GovAI, and collaborators from DeepMind, Microsoft, and UToronto, published a Nature commentary on Cooperative AI (see thread below for summary). Builds on their NeurIPS workshop on Cooperative AI , and they are launching a Cooperative AI Foundation to continue efforts in this area.

Must-read: Writing through the Cracks

In the always-excellent Chinese Storytellers, Yi-Ling Liu brings together a group of writers based in China who “steer away from grand narratives, and pay attention to the personal, the particular and the human.” Xuandi Wang talks about visiting hippie communes in Fuzhou; Qin Chen shares her experience reporting on LGBTQ issues; Jaime Chu explores subcultures in China, Huang Chenkuang writes a monthly column on the life stories of ordinary folks in Beijing.

Should-read: NBR Asia Policy 16.2

My essay on China’s growing role in international standards-setting organizations is featured in this issue of Asia Policy, published by The National Bureau of Asian Research. It’s part of a roundtable on China’s rising influence in IT and innovation featuring really great insights from Emily de La Bruyère and Elsa Kania, and many others.

Should-listen: BBC The Real Story on EU AI Regulations

Recent edition of this BBC podcast features good debate and discussion on EU’s AI regulations. I come on at the end to talk about AI regulations and evolving discussions of AI ethics in the Chinese context.

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 Predoctoral 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 #139: Japan's View of the Future

Translating Chinese Translations of Japanese White Papers

Greetings from a world where…

people are always meeting at borders

…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). As always, the searchable archive of all past issues is here.

Feature Translations: Japan’s Endeavors to Forecast the Technological Future

Context: The National Academy of Innovation Strategy (NAIS — 中国科协创新院) is a division under China Association for Science and Technology (CAST), a professional organization that promotes scientific literacy in China. Way back in March 2018, ChinAI covered a CAST analysis of Chinese netizens’ opinions on AI. One of the NAIS’s functions is to keeps tabs on science popularization and innovation strategies of other countries, with a particular interest in Japan. This week’s two feature translations are both NAIS articles about Japanese technological forecasting exercises:

1) Japan’s “2020 Science and Technology White Paper” by Ministry of Education, Culture, Sports, Science and Technology (link to coverage in Mandarin by NAIS)

  • Released in June 2020, the white paper includes results from a technology foresight survey (link to original Japanese) from the Japan Institute of Science and Technology Policy (NISTEP), which has conducted this exercise every five years since 1971!

  • The NISTEP survey describes 37 new technologies expected to materialize by 2040. I’ve included the first ten in the translated table below:

2) NISTEP Cluster Predictions (link to coverage in Mandarin by NAIS)

  • NISTEP’s foresight survey also tried to identify specific scientific and technological domains that Japan should try to focus on in the future. First, a group of 74 experts from industry, academia, and government came up with a list of 702 scientific and technological topics. Then, NISTEP used co-occurrence and hierarchical cluster analysis to come up with 32 themes across these 702 topics. After subjecting these 32 themes to more data analysis, expert meetings, and a forecasting conference in June 2019, NISTEP identified 16 future-facing fields, half of which were interdisciplinary and half of which were specific research fields.

  • Here are the 8 specialized research fields of the future, according to NISTEP:

Key Takeaways:

  • ChinAI is all about seeking out information arbitrages here. Where’s the valuable information that very few people access? For anyone interested in global technology policy, I’d recommend publications from Japanese organizations like NISTEP. Sometimes white papers will have provisional English translations, and even if you don’t read Japanese, you can copy and paste stuff into Google Translate to at least get a general idea. In my own research in progress, for instance, I’ve benefited greatly from a Japanese-language analysis of co-authorship networks among researchers from different nationalities in publications at AI conferences.

  • Chinese researchers at NAIS write, “Summarizing and thinking about the technology foresight work of other countries is of great benefit to promoting China’s scientific and effective development of technology foresight and technical planning.” China does not see the U.S. as the sole model of innovation; see ChinAI#121, for analysis on Germany as another source of inspiration.

  • “Real-time translation and interpretation system for all languages” is number 5 in the list of 37 future technologies. To be sure, technological forecasting is somewhat akin to throwing darts at a moving board while blindfolded. But the prominence of translation and communication technologies in NISTEP’s forecasting exercises speaks to the our relative lack of attention to the newsworthiness of translation.

ChinAI Links (Four to Forward)

Should-read: How China’s Food Delivery Apps Exploit Drivers

Really enjoyed reading Yi-Ling Liu’s Rest of World piece on how China’s food delivery system exploits its workers, which makes an insightful point that white-workers are increasingly subject to these types of systems. As she notes, “This is built on the powerful & compelling work of investigative reporting by Chinese publication 人物, which can be read in translation by @jjding99” (ChinAI #111.)

Should-read: China sets hopes on blockchain to close cyber security gaps

I’ve had a few folks ask if I’m going to cover blockchain (unfortunately out of my wheelhouse). Instead, I’d recommend this MERICs analysis by Kai von Carnap, which includes a lot of detailed mini-case studies of blockchain development in China. It also has an informative slide deck that provides context on China’s approach to blockchain.

Should-read: Age of Invention by Anton Howes

I’ve really enjoyed poring through the past issues of Age of Invention, by Dr. Howes, a historian of innovation, who examines the causes of Britain’s Industrial Revolution, through the lives of individual innovators who made it happen. See, for instance, this issue on “ideas behind their time,” or the “inventions that could have been invented centuries, if not millennia, before they actually were.”

Should-read: How a Chinese Surveillance Broker Became Oracle’s “Partner of the Year”

Building on her previous reporting in The Intercept, Mara Hvistendahl digs deeper into a claim by an Oracle spokesperson that Oracle doesn’t sell surveillance tech directly to the Chinese police. Her latest finds that Oracle has worked with brokers that localize Oracle’s tech for surveillance and repression.

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 Predoctoral 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 #138: Splinternet Superstitions

How China's IPv6 Rollout Challenges Splinternet Narratives

Greetings from a world where…

heat waves been fakin’ me out

…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). As always, the searchable archive of all past issues is here.

Feature Translation: China’s Progress on Large-scale Deployment of IPv6

In September 2018, Eric Schmidt, former Google chief executive, prophesied of an Internet splintered into two, with one led by the United States and the other led by China. One month later, the New York Times editorial board — citing Schmidt’s comments but doing the bare minimum to avoid regurgitating them — claimed there would be three Internets! Don’t forget about the one led by the EU, they remind us.

Here’s a modest proposal. What if we did the following two things:

  • Clearly conceptualize the Internet as a collection of technological layers rather than a singular technology

  • Measure the level of fragmentation across different levels using data rather than relying on impressionistic judgements

Fortunately, I can save you some work because the Daylight Lab at UC Berkeley’s Center for Long-Term Cybersecurity did just that. They divided the Internet stack into five layers:

  1. Link layer: the Ethernet protocol transfers data within a local network

  2. Network layer (focus of this week’s feature translation): facilitates data transfer between networks. IP addresses, for instance, are essential components of this layer. Because of the extraordinary growth of Internet-linked devices, IP addresses based on IPv4, the previous standard, are becoming scarce. So, countries are starting to switch to IPv6, a new IP protocol. Daylight Lab considers “greater IPv6 adoption as lower fragmentation, as countries remaining on IPv4 will experience exhaustion and possible outages.”

  3. Transport layer: ensures persistent connections, protection against network interference events (e.g. throttling, state-launched attacks)

  4. Application layer: includes web browsing and stuff that “standardizes communications across transport layer to create specific-purpose functionality”

  5. Regulation layer: includes data laws like those that restrict the use of encryption and cross-border data flows

After measuring proxies for fragmentation (e.g. IPv6 adoption rates) across all five layers, Daylight Lab’s findings challenge the received wisdom that “the Internet is largely bi-polar, split between ‘free’ and ‘closed’ Internets, and countries’ profiles will be similar within these basic groups.”

They specifically explored the narrative that “China’s model of the Internet has set a precedent, one which other Belt & Road countries follow” by examining data on Internet fragmentation in Laos, Indonesia, Mongolia, Pakistan, Djbouti, Argentina, and Sudan. These are all countries that have received substantial Chinese infrastructure investments, making them the most likely candidates to fit the Splinternet narrative. However, they found that these countries rank lower than China on many fragmentation indicators, including data locality, website locality, and network interference.

Context: All of that tees up this week’s feature translation of an article on the rollout of IPv6 on China. The June 2020 piece (link to original Mandarin) is by two researchers at the China Academy of Information and Communications Technology, a think tank under the Ministry of Industry and Information Technology that takes on dual roles as a brain trust and regulatory body.

Key Takeaways:

  • In November 2017 China issued an action plan on large-scale deployment of IPv6, which set the following benchmark: “By the end of 2025, the scale of China’s IPv6 network, users, and traffic ranks first in the world.” Chinese government entities and centrally-owned companies have led the way in IPv6 adoption: 86 percent of the websites of provincial-level governments and centrally-owned companies are accessible via IPv6. In addition, 78 of the top 100 websites and applications (by number of users) can be accessed via IPv6.

  • IPv6 penetration in China is growing and is probably higher than the Daylight Lab’s metrics. Daylight Lab uses Google data (the % of users that access Google over IPv6) to measure IPv6 adoption (2.25% adoption rate in China) but since Google is not used by many in China, this is probably not representative. According to data from the National IPv6 Development Monitoring Platform, as of November 2019, the number of active IPv6 users in China reached 273.9 million, which is around 30% of the Internet-using population. Again, according to the framework above, adoption of IPv6 at scale results in a more connected Internet, which goes against the Splinternet narrative, at least on one of the five layers.

  • China’s deployment of IPv6, however, still trails the leading countries: not all active users are always accessing websites over IPv6, so China’s true IPv6 penetration rate probably trails the U.S. rate (45%) by a significant margin. In addition, many of the aforementioned IPv6-accessible websites only support IPv6 access on their homepages, and IPv4 is still used for the links that actually generate the most traffic (e.g. streaming media, pictures, etc.) According to the CAICT researchers, other barriers to the diffusion of IPv6 in China include: issues with cloud service platforms and home wireless routers. Another example that cuts against the “China steals and scales U.S. tech faster and better” narrative.

  • What’s the connection to AI? IPv6 is an important infrastructural layer on which future AI applications will be built. As the article states, “with the rapid development of the Internet of Things, the Internet of Vehicles, and the Industrial Internet, China’s future demand for IPv6 addresses is still relatively large.” I want to stress that Internet protocols are very much out of my wheelhouse, so please share feedback, comments, and corrections on all these points.

FULL TRANSLATION: How is China’s Progress on Large-scale Deployment of IPv6

Thank you for reading and engaging.

*I’m behind on my reading, so no Four to Forward this week, but I’ll catch up next issue.

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 Predoctoral 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 #137: Year 3 of ChinAI

Reflections on the newsworthiness of machine translation

Greetings from a world where…

ChinAI is now three years old, which means it should be able to walk in a straight line, but it rarely does. Thanks to all the readers, especially the 170+ paying subscribers who support ChinAI under a Guardian/Wikipedia-style tipping model. If you’ve been meaning to subscribe but just haven’t gotten around to it, no time like the present.

As always, the searchable archive of all past issues is here.

Reflections - Making Translation Newsworthy

What is newsworthy? This question should haunt everyone with a platform.

Last month, Stanford HAI published the AI Index Report 2021, a 222-page report on the state of AI, put together by an all-star team supported by a lot of data and strong connections to technical experts. What was newsworthy in this report? According to The Verge, “Artificial intelligence research continues to grow as China overtakes U.S. in AI journal citations.” In fact, the article takes its cue from what the report authors themselves deemed significant, given that “China overtakes the U.S. in AI journal citations” features as one of the report’s eight key takeaways.

Dig deeper into the data, however, and you’ll uncover alternative takeaways. Look at the cross-national statistics on average field-weighted citation impact (FWCI) of AI authors, for example, which gives a sense of the quality of the average AI publication from a region. Interestingly enough, the U.S. actually increased its relative lead in FWCI over China over the past couple years. According to the 2019 version of the AI Index, the FWCI of US publications was about 1.5 times greater than China’s; in 2021, that gap has widened to almost 2 times greater (p. 24).

So, working off the same materials as released in the AI index, here’s another way one could have distilled key takeaways: “The U.S. increases its lead over China in average impact of AI publications.” Or, if you wanted to be cheeky: “China lags behind Turkey in average impact of AI publications.” Just as newsworthy, in my opinion.

However, what I found most newsworthy about the AI Index went beyond horserace reporting about “who’s winning the AI race?!”Instead, I was most intrigued by the rise of commercially available machine translation (MT) systems, covered on page 64. According to data from Intento, a startup that assesses MT services, there are now 28 cloud MT systems with pre-trained models that are commercially available — an increase from just 8 in 2017. But wait … there’s more: Intento also reports an incredible spike in MT language coverage, with 16,000+ language pairs supported by at least one MT provider (slide 33 of Intento’s “State of Machine Translation” report).

Thanks to the State of MT report, we can also get a sense of the greater significance of translation advances. Let’s take Microsoft’s neural machine translation (NMT) service, priced at $10 per million symbols, for a ride. Recall that Microsoft achieved a milestone in 2018 for the first MT system reach human parity for translating news articles from Chinese to English. For $10 we can translate about 200,000 words, using an average of 4.79 symbols per word. How does one even grasp the potential of 200,000 words? That’s four times the number of Chinese characters in Wang Shuo’s great novella <<动物凶猛>>, one of so many Chinese language classics that have not yet been translated to English. These are, of course, very rough calculations — NMT works much better for news than novels, and the process would require a lot of post-editing — but they do open a window into the possibilities of NMT.

Somehow, these incredible advances in translation are not relevant to the effect of AI on U.S.-China relations, at least based on existing discussions. Compare the complete dearth of Twitter discussions centered on the following keywords: U.S., China, and “machine translation” against what you get when you replace “machine translation” with “facial recognition.” Consider another reference point, the recently published 756-page report by the National Security Commission on Artificial Intelligence (NSCAI). Sixty-two of those pages mention the word “weapon” at least once. Only nine pages mention the word “translation,” and most do not substantively discuss translation (e.g. the word appears in a bibliographic reference for a translated text).

Yet, I could make a convincing case that translation is more significant than targeting for U.S. national security. Think about the potential of improved translation capabilities for the intelligence community. Another obvious vector is the effect of translation on diplomacy. To the NSCAI’s credit, it notes how advances in MT could “transform the way we communicate across geographic and cultural barriers, enabling business, diplomacy, and free exchange of ideas” (p. 36). Consider also how MT could increase economic interdependence, which could have a pacifying effect on conflict escalation. Professor Steve Weber explores this in “The 2020s political economy of machine translation,” a working paper which analogizes MT to the container shipping revolution. Finally, recall the possibility of more translated books, which could also have security implications. After all, empirical studies have found that students have reduced threat perceptions of their host country after studying abroad, and what is reading, if not a study abroad experience.

So, how will AI advances affect U.S. national security and U.S.-China relations? Are the U.S. and China DECOUPLING in strategic technologies like AI? The maddeningly beautiful thing about studying AI + politics is that no one really knows. If you take translation as the key frame of reference for AI as opposed to weapons targeting or facial recognition, the answer to these questions is very different (#coupledforlife). This is made all the more complicated by the fact that AI is a general-purpose technology, with potential applications that far exceed translation, facial recognition, and weapons targeting. So, if you really wanted the true answer to the above questions, you would have to somehow predict the net impact of the distribution of all still-evolving AI applications.

Our answers to these questions and our determination of what is newsworthy about China’s development of AI, therefore, say more about us — our biases, our interests, our social influences — than they do about the Truth of the matter. I am no exception. Unlike journalists, translators do not have to pretend to be neutral. ChinAI is a product of my own biases, interests, and social influences.

That includes my fears. I vigorously pushback against overhyping China’s technological prowess in part because I fear how threat inflation could affect Asian Americans like me. I am quick to rebut claims of decoupling in part because I worry about what it means for my family’s ability to visit loved ones in China.

Newsworthiness doesn’t just have to be driven by our fears. And so, ChinAI is also a product of my dreams. Here’s one: One day, my kids will learn to read Wang Shuo’s 动物凶猛 in Spanish.


ChinAI Links (Four to Forward)

My four favorite issues of ChinAI from year 3 were all collaborations with contributors:

  • ChinAI #98: Techlore - The Historical Rise of TSMC & Samsung in Semiconductors — Joy Dantong Ma found an epic poem of sorts about the history of Taiwan Semiconductor Manufacturing Company.

  • ChinAI #104: Tencent 2020 AI White Paper — Caroline Meinhardt, a GovAI summer fellow, helped shed light on how Tencent is thinking about AI applications, including deepfakes.

  • ChinAI #112: The Human Cost of 30-min Food Deliveries — Gabriele, San, and many other anonymous contributors helped translate a 15,000-word longform article on how drivers get trapped by food delivery system algorithms

  • ChinAI #129: An Emotional Mess — Shazeda Ahmed guest-edited an issue about emotion recognition in China, featuring a translated article about backlash to emotion recognition applications in classrooms and her report (co-written with Vidushi Marda) on China’s emotion recognition landscape

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 Predoctoral 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 #136: China's Tech Industry and Carbon Neutrality

Plus, Working in National Security While Asian

Greetings from a world where…

the Hawkeyes are still dancing

…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). As always, the searchable archive of all past issues is here.

Feature Translation: China’s Tech Industry and Carbon Neutrality

Context: Last week, Leiphone published a fascinating piece on state-of-play re: China’s tech industry and the momentum toward carbon neutrality (碳中和). Many Chinese media have proclaimed 2021 to be the “first year of the carbon neutral era;” the government work report released at the Two Sessions included carbon neutrality for the first time.

Key Takeaways:

  • According to a reports by Tianfeng Securities Research Institute and Greenpeace, the tech industry’s carbon emissions have continued to rise in recent years. Specifically, it ranks high in terms of Scope 3 emissions, which are indirect emissions that occur in a company’s value chain. By 2040, the information communications technology (ICT) industry is expected to account for more than 14 percent of global greenhouse gas (ghg) emissions.

  • Data centers were responsible for 87% of Tencent’s carbon emissions, and as a whole, China’s data centers consumed 2% of the country’s total electricity usage in 2018.

  • After reviewing commitments by U.S. tech giants, the piece states that Chinese tech companies lag behind in terms of clear timetables to reach carbon neutrality, though some have made notable moves. These include: Ant Group’s recently announced goal of net-zero emissions by 2030, Alibaba and Tencent’s use of liquid cooling tech to reduce heat emissions in data centers, and Baidu’s use of photovoltaic cells to power its data centers.

  • Given the significant environmental costs of training large AI models, this is definitely an area to track for the future. Announcements are nice, but transparent, verifiable action is better. As of a January 2021 Greenpeace report (in Mandarin), Chindata Group (秦淮数据) was the only Chinese firm that had committed to achieving 100% renewable energy by an end date.

ChinAI Links (Four to Forward)

Should-read: Asian Americans at State Department confront discrimination

Ryan Heath for Politico on assignment restrictions for Asian American diplomats:

One former diplomat subject to restrictions was Rep. Andy Kim (D-N.J.), a Korean-American born in Boston who told MSNBC Wednesday that even though he had ‘top secret security clearance’ and had served in Afghanistan, “One day I was told by the State Department that I was banned from working on anything related to the Korean Peninsula.”

Kim said he was shocked because he had never applied to work on any issues related to the Korean Peninsula. He labeled the decision xenophobic and said that what hurt most was “this feeling that my country didn’t trust me.”

A statement signed by over 100 Asian Americans working in national security and diplomacy, argued Thursday that the increasing U.S. focus on competition with China, has exacerbated “discrimination, and blatant accusations of disloyalty simply because of the way we look.”

Related: see Amy Chang’s Twitter thread sharing her personal experience as an Asian American women working on U.S.-China relations:

Should-read: Key Concepts in AI Safety

The first in CSET’s series on “AI safety” by Tim Rudner and Helen Toner, which goes over how to develop machine learning systems in a safe and reliable way. They introduce some key concepts, including specification, which refers to “defining a system’s goal in a way that ensures its behavior aligns with the human operator’s intentions.”

Should-read: The AI Wolf Refuses to Play the Game (Mandarin)

Speaking of misspecified AI systems, a funny (and slightly disturbing) example that went viral last week, as reported by xinzhiyuan (AI Era): researchers had set up a wolf vs. sheep game. Instead of trying to eat as many sheep as possible, which was the intent of the game, the wolf chose to run into a rock and kill itself because that somehow gave more points than not catching any sheep.

Should-read: China sours on facial recognition tech

For Protocol, Zeyi Yang covers a 10-minute investigative segment aired on the CCTV annual consumer rights gala, which “revealed that facial recognition security cameras located at chain stores nationwide have been picking up shoppers' personal information without their knowledge or consent. The revelations ignited a furious backlash against the companies. . . It's another instance of grassroots pushback against surveillance tech in China, a global leader in surveillance research as well as in deployment. The central irony went unremarked: that Beijing has become both the critic and perpetrator of mass surveillance.

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

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