Plus, Takeaways from the 2020 Beijng Academy of AI Conference
|Jeffrey Ding||Jul 6|| 2|
Greetings from a land beautiful enough for hyphenated Americans…
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Feature Translation: The Demise of “Technology Neutrality” (Part 1)
CONTEXT: The news that India was blocking 59 Chinese apps, including TikTok, Wechat, and Weibo sparked a lot of discussions in Chinese media. This piece (original Mandarin) on the evolution of “technology neutrality” over the years was one of the most thought-provoking. It’s written by Liu Bohan for 放大灯 (enlarging light), which is a new S&T media platform under guokr, a popular Chinese S&T education community.
Some glaring gaps in high-level framing: to motivate the essay, the author calls out how “highly nationalistic Indians once again trampled on the corpse of technology neutrality” but doesn’t mention China’s treatment of int’l tech companies — a point made in comments sections of the Huxiu version of the article
It’s very interesting how the author views recent U.S. tech developments against the backdrop of technology neutrality: 1) discusses Facebook’s attempt to maintain a neutral attitude re: hate speech on the platform, 2) is very sympathetic to Yann Lecun who suffered a “tortuous interrogation” over his comments regarding algorithmic discrimination, attributing it to bias in the dataset. Reviews how tech neutrality was established in cases involving Sony’s Betamax and Napster’s p2p sharing, but concludes that “proponents of technology-as-value-laden have gradually won an overwhelming victory in the Western intellectual community.”
According to the author, in China, the contest over technology neutrality has been slower to embark down the same road: there is still a basis of technological optimism, which has been expressed in the various slogans of the times such as “Mr. Science” (“赛先生”) supported by the May Fourth New Culture Movement and later "Science and technology are the primary productive forces" — a saying by Deng Xiaoping in 1988.
A challenge for readers: I only translated the first half of this lengthy essay this week, but next week will fill in the gaps on why technology neutrality has also been doomed in China, which covers the TD-SCDMA (3G standard), Qvod, and He Jiankui cases. One call for help/challenge for ChinAI readers and contributors: I hit an absolute dead end on the 2nd to 6th paragraphs in part 3 on Chinese views toward technology in the late 19th century. If anyone wants to give it a shot just comment in the doc. Here’s a taste of that nasty (but probably super interesting) 2nd paragraph:
FULL TRANSLATION: The Demise of “Technology Neutrality” (Part 1)
4 Takeaways from the 2020 BAAI Conference
*Huge shoutout to Kwan Yee, a Summer Research Fellow at the Future of Humanity Institute and an incoming Yenching Scholar — below are her thoughts:
The Beijing Academy of Artificial Intelligence (BAAI) hosted its annual BAAI Conference from 21-24th June. BAAI was established in 2018 by the Beijing Municipal Science and Technology Commission and Haidian District government, with the support of some of the most influential academic and industry players in AI such as Peking University, Tsinghua University, the Chinese Academy of Sciences, Baidu, Xiaomi and Megvii. BAAI serves as an experimental hub for cooperation between the academic and corporate sectors while also receiving support from the government including funding and government data. The 2020 BAAI Conference was held as a live broadcast this year and joined by around 30,000 online viewers. 4 major takeaways from the conference:
The Chinese take on privacy: Speakers emphasized the need to build safe, reliable, and trustworthy AI while challenging a Western, personal consent-oriented conception of privacy. Speakers were optimistic that privacy regulations will drive Chinese developers to innovate better privacy protection technologies and embed such protections into their products; to this end, Yang Qiang presented his work on federated learning and highlighted the need to keep humans-in-the-loop when developing AI. Further, some suggested that the U.S. was lagging behind in privacy requirements and relying on unsustainable, corner-cutting growth as a result. Zhang Bo, the dean of Tsinghua’s AI lab, stressed the need to take into account public as well as private interest perspectives when considering issues of privacy and criticized Western definitions of privacy that sacrifice the public interest in favour of personal consent.
Going beyond DL: AI scientists discussed possible futures in AI development and the need to move beyond the current deep learning paradigm to attain further breakthroughs towards human-level AI. Reviewing the past 6 decades of AI development with Bart Selman and John Hopcroft, Zhang Hongjiang observed the retreat of GOFAI techniques amidst the deep learning revolution and questioned the futures of AI paradigms. Zhang Bo and Bart Selman pointed to the scaling limitations of deep learning to access higher cognitive functions such as language and causal reasoning. Both scientists predicted that a ‘hybrid’ approach between deep learning and GOFAI techniques would be required, while John Hopcroft suggested looking towards other disciplines such as neuroscience. Further, Zoubin Grahamani expected machine learning to progress within a framework of probabilistic modelling, and Yi Wu encouraged contemplating the development of intelligence from an evolutionary perspective when introducing OpenAI’s paper on Emergent Tool Use from Multi-Agent Interaction.
Challenges and opportunities for Chinese AI development: Speakers acknowledged the ‘publish-or-perish’ hurdle facing young Chinese researchers and pointed to opportunities for advancing basic AI research in China. Speakers advised early-career AI researchers to choose their research topics wisely and look beyond the current trends to identify where they can contribute. While Zhang Bo pointed out that China had made vast progress from barely being able to publish on AI at all to the Chinese academic field now being oversaturated with AI papers, he noted that unlike their Western counterparts, Chinese students lacked economic and career affordances to take risks with their research. In a fireside chat moderated by Zhang Hongjiang, John Hopcroft and Alan Kay suggested adjusting grantmaking metrics so that researcher funding will not be so heavily conditioned on the quantity of their publications. Bart Selman took the example of deep learning when around 2010, only 5-10 people were working on the topic and it was because of the continued investment into deep learning research and, more broadly, a diversified portfolio of AI methods that enabled a huge breakthrough in the field.
BAAI launches the AI4SDGs think tank:The Research Center for AI Ethics and Sustainable Development, housed in the Beijing Academy of Artificial Intelligence, is leading the AI4SDGs Think Tank to promote the use of AI technologies for the UN Sustainable Development goals. The nonprofit is self-described as “an online open service for everyone, a global repository and an analytic engine of AI projects and proposals that impacts UN Sustainable Development Goals” and has an associated Research Program that is currently open to applications. Kaifu Lee is on the board of the think tank.
ChinAI (Four to Forward)
In all honesty I’m falling behind on reading, so send me recommendations please!
Should-read: China & AI: What the World Can Learn And What It Should Be Wary of
Hessy Elliott for thequint summarizes China’s AI development through the lens of the good (fast-paced and pragmatic approach to AI development and implementation), the bad (use of AI to enable surveillance and detention of ethnic minorities), and the unexpected (the important role of local AI ecosystems and decentralized policies on AI development). A useful readout of the Nesta essay collection she organized.
Should-read: Translation: China’s ‘Data Security Law [Draft]’
For DigiChina, Emma Rafaelof et al. translate a draft Data Security Law for public comment, which “marks a significant evolution in China’s data protection regime” and “is set to specify new responsibilities and authorities for government offices and private actors.” Article 19 discusses how to regulating data based on different levels of importance as it pertains to economic and social development.
Should-read: Data-driven Covid Management in China
Very balanced, informative MERICS report on digital solutions China has used in combatting Covid. It highlights how some contract tracing and data sharing tools have been successful to some extent, but also notes some of the drawbacks that aren’t discussed enough: “However, the swift roll out of data-driven solutions to manage public health also highlighted several kinds of risks. Technological solutions like the QR health codes proved only partially functional or serviceable. Personal data has been misused by companies to collect data for their own commercial interest. Local cadres have also abused personal data in the drive to detect infected people and reduce new cases.”
*Last week I participated in a MERICS Webinar: China as an AI superpower? Quantifying China’s AI progress against the US and Europe — good people and good conversations. Here’s the video.
By Pei Li and Josh Horowitz for Reuters: once you do this sort of thing for a while you start to notice which journalists covering China tech actually have good, well-placed sources, and it shows in this article about the competition heating up between Tencent and Alibaba in cloud services. Insights from two Tencent sources in the company’s cloud division for this story that gives a good overview of current state of play.
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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