ChinAI #122: A River (doesn't) Run Through It
ChinAI #122: A River (doesn't) Run Through It
China's largest North-South gap in past 40 years
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Feature Translation: Only One Northern City in China’s Top 10 by GDP
Context: 智谷趋势 (zgtrend): one of Hurun China’s Top 50 most influential finance self-media. A previous around the horn issue of ChinAI featured one of their articles on China’s growing influence in ICT standards, though they have been called out for click-baity tendencies (see: Zhihu thread). This piece was published last week, and has been read 100k+ times.
Key Takeaways:
Beijing is the only northern city remaining in the China’s top 10 cities (in GDP) — something that has never happened in the past 100 years. For the first time since the reform and opening period, Nanjing’s total GDP has entered the top ten. Wuhan, also in the south, somehow retained its top ten position. In the north, Tianjin fell to its lowest ranking since the end of the Qing Dynasty. The below table depicts a huge South-North gap between the top 10 cities in the south vs. northern counterparts:
See also this map of how China’s “economic center of gravity” has evolved over time — from work by Bai Xue, an Associate Professor of Economic Geography at Beijing Normal University. Note how the trend of north-south imbalance is more obvious than the see-saw of east-west:
Why does this matter? 1. Regional disparities are an issue of great concern for top-levels of government; 2. Diversified regional strength provides resilience — northern ports provide energy security via Russian oil along Arctic route, relieving dependency on Strait of Malacca; 3. Piece says regional balance is “related to the historical process of the rejuvenation of great powers” — China’s economy is becoming less reliant on foreign trade and more on “internal circulation” (内循环), which requires leveraging the demand of an integrated domestic market
On the third point, the article argues: “The United States has a deeper understanding of this point than China…the United States is the first country in the world to benefit from internal circulation.” Part of this is geographic fortune: America’s two main rivers (Mississippi and Missouri Rivers) connect the north and the south but they also intersect, with multiple tributaries traversing east and west in a diagonal manner. In contrast, China’s Yangtze River and Huang He (Yellow River) remain parallel throughout, and they do not intersect like the Mississippi and Missouri Rivers.
How did this happen? Surface-level explanation: after China’s accession to the WTO, there was a reshuffling of cities’ relative economic strength. Southern coastal areas were better connected to the world’s main shipping lanes. Deeper explanation: the northern economy could not adjust to this pattern — “Before 2008, industry and foreign trade determined the strength of cities. After 2008, it shifted to be driven by domestic demand.” It’s less about SOEs and massive industrial investments and more about a city’s consumption capacity (population agglomeration), as well as service industries.
More in FULL TRANSLATION: An unexpected change in the territory map of Chinese cities, this year there will be only one TOP 10 northern city
ChinAI Links (Four to Forward)
Must-read: The 2020s Political Economy of Machine Translation
We need more work about AI + politics that moves beyond simplistic, nationalist narratives. Think back to the last time you read something that even considered the possibility that AI could have a positive effect in bringing people together. Go ahead, I’ll wait. In the meantime, I’ll be rereading this paper posted on arXiv by Steven Weber, Associate Dean and Professor at the School of Information and Professor in the Department of Political Science at UC Berkeley:
This paper explores the hypothesis that the diversity of human languages, right now a barrier to ‘interoperability’ in communication and trade, will become significantly less of a barrier as machine translation technologies are deployed over the next several years. I argue that machine translation will become the 2020’s analogy for ideas to what container shipping did for goods trade in the second half of the 20th century. But as with container shipping or railroads in the 19th century, this new boundary-breaking technology does not reduce all boundaries equally, and it creates new challenges for the distribution of ideas and thus for innovation and economic growth. How we develop, license, commercialize, and deploy machine translation will be a critical determinant of its impact on trade, political coalitions, diversity of thought and culture, and the distribution of wealth.
H/t to Allan Dafoe for sharing.
Should-read: Probabilities towards death: bugsplat, algorithmic assassinations, and ethical due care
An incisive Critical Military Studies article by John Emery, a Stanton Postdoctoral Fellow, who is with me at Stanford CISAC this year. He argues that the military’s outsourcing of collateral damage estimates to algorithms has undermined ethical due care. Evidence from two cases of “algorithms of militarism”:
Bugsplat: US military’s collateral damage estimation tool in 2003 Iraq War, which had four fundamental flaws: 1) an arbitrary ceiling of 30 civilian casualties 2) a lack of empirical data on civilian casualties 3) systematic overestimations 4) automation bias and black box algorithms
SKYNET machine learning algorithm: used NSA dragnet of SIM card metadata to determine targeting for drone strikes; per leaked slides, NSA machine-learning algorithm rates people’s “terroristness” based on 80 different properties. John quotes General Michael Hayden (former director of the NSA and CIA), who has bluntly stated “We kill people based on metadata”
John draws a lot from interviews conducted by Sarah B. Sewall for her book: Chasing Success: Air Force Efforts to Reduce Civilian Harm
Should-read: Corporate Reporting in the Era of Artificial Intelligence
Increasingly, machine readers are the audience for corporate disclosures. In the NBER working paper “How to Talk When a Machine Is Listening: Corporate Disclosure in the Age of AI,” Sean Cao, Wei Jiang, Baozhong Yang and Alan L. Zhang find that companies have adjusted certain words in their corporate filings to appeal to financial ML algorithms.
Should-read: Inside A Xinjiang Detention Camp
By Megha Rajagopalan and Alison Killing: “This massive detention center, the size of 13 football fields, is a cog in the largest-scale detention of ethnic and religious minorities in the world since World War II, in which 1 million or more Muslims, including Uighurs, Kazakhs, and others, have been rounded up and detained in China’s western region of Xinjiang. Publicly, China has claimed that Muslim detainees have been freed. Yet an ongoing BuzzFeed News investigation, based on dozens of interviews with survivors and thousands of satellite images, has exposed how China has built a vast and permanent infrastructure for mass detention in Xinjiang, marking a radical shift away from the government’s makeshift use of preexisting public buildings at the beginning of the campaign. Using the same techniques that revealed the scale of China’s expanding network of detention centers, BuzzFeed News can now expose the inner workings of one such compound. The Mongolküre facility is one of at least 260 newly built sites bearing the hallmarks of long-term detention centers capable of holding hundreds of thousands of people in total servitude to the state.”
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.
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