Greetings from a land where 2.5 generations ago there was astonishingly widespread belief in world government…

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Feature Translation: Tencent’s 2020 White Paper on AI

This week’s joint translation is with Caroline Meinhardt, a GovAI summer fellow, who initially discovered this white paper. In last week’s issue, she offered four takeaways from the World AI Conference in Shanghai. Make sure to read her other work for MERICs on China’s “new infrastructure” concept and how China is turning to European suppliers to cut its reliance on US technology as well as the hidden challenges of China’s booming medical AI market for China Business Review.

CONTEXT: Introduced on July 10 at the WAIC, the White Paper provides a glimpse at how Tencent is thinking about AI. Five chapters on the macro-environment, technical research, AI applications in combatting Covid, new innovations for the economy, and institutional safeguards. We’ve translated the excerpts we found the most interesting, but the full translation gives the entire table of contents.

Our KEY TAKEAWAYS:

Preface and Chapter 1, The Macro Picture: “ubiquitous AI.” Si Xiao, head of Tencent’s Research Institute, describes"ubiquitous" in the first sense in infrastructure construction. “Guided by the favoring winds of ‘new infrastructure,’ artificial intelligence technology will gradually transform into fundamental service installations like the Internet and electricity.” “Ubiquitous" in the second sense in more diverse application scenarios and larger audiences (adoption sectors).
Chapter 2, directions for technical research: A) in general ML — increase the efficiency of small-sample learning && develop “offline reinforcement learning” (RL algorithms that learn from a fixed batch of data without exploration); B) in computer vision — enhance defense against adversarial attacks && deepfake recognition and counterfeit-detection technology to curb AI abuse
Deep synthesis chapter (4.3): chapter overwhelmingly focuses on the positive applications of what it calls deepfake synthesis. Tells a kind of evolution story from the original crude applications (e.g. deepnudes) to more and more creative, innovative, and socially impactful applications across entertainment, e-commerce, content creation etc --> the phrase “emerging from the shadow of pornographic face swap videos to usher in an era of commercialization” is used in both the first and last paragraph. Examples of deepfake synthesis include an ALS patient speech synthesis program to an interactive Martin Luther King exhibition.
AI for FEW (Food, Energy, and the Environment) chapter (4.5): Interesting details on Tencent’s agriculture AI projects and how they actually improve efficiency — includes an example of collaboration with a European university (in the Netherlands). This seems to be somewhat of a priority for Tencent.
Chapter 5, Institutional safeguards: Argues that establishing a multi-level governance system is key but cautions against hasty regulation that’s too strict/inflexible. Seems to us that Tencent is being unusually blunt about discouraging strict regulation — they’re not even pretending. Also emphasis on China’s strong system of pilot zones that provide the space for cutting-edge development/experimentation. Calls for int’l cooperation and for the tech industry to shift from its current “technocentric” model to a technohumanitarian collaboration model (技术人文协作模式).”

FULL TRANSLATION: Tencent 2020 White Paper on AI

ChinAI (Four to Forward)

Must-read: Intermingled (State and Private Companies) Censorship of the 19th National Communist Party Congress on WeChat

For The China Quarterly, Lotus Ruan and a team at Citizen Lab shine a light on how censorship actually gets implemented on company platforms:

lotus @lotus_ruan

Just out in @chinaquarterly: my @citizenlab colleagues and I examine the relationship between political events and info control on WeChat thru a longitudinal analysis of keyword censorship related to China's 19th National Communist Party Congress (NCPC19)

The Intermingling of State and Private Companies: Analysing Censorship of the 19th National Communist Party Congress on WeChat | The China …The Intermingling of State and Private Companies: Analysing Censorship of the 19th National Communist Party Congress on WeChat - Lotus Ruan, Masashi Crete-Nishihata, Jeffrey Knockel, Ruohan Xiong, Jakub Dalekcambridge.org

Should-read: Image “Cloaking” for Personal Privacy

The SAND Lab at UChicago released a new algorithm and software tool called the "Fawkes" project. It provides an “image cloaking” service to protect against unregulated 3rd party facial recognition services (e.g. clearview.ai, “has now downloaded over 3 billion photos of people from the Internet and social media, using them to build facial recognition models for millions of citizens without their knowledge or permission.” Link contains a very informative FAQ page for those who want to quickly digest key points.

Should-read: Banning TikTok is a Terrible Idea

For SupChina, Samm Sacks lays out a more sensible approach for balancing data security/censorship issues with the U.S.’s broader goal to “offer an alternative to Chinese cyber sovereignty with a vision for internet governance to help better secure data online and prevent the spread of disinformation. These are problems that are bigger than TikTok and must be dealt with in a separate lane from the U.S.-China conflict. Let’s leave the creation of national walls in cyberspace to Beijing.”

Should-read: Messier than Oil:
Measuring Data Advantage in Military AI

CSET issue brief abstract: “Both China and the United States seek to develop military applications enabled by artificial intelligence. Will China be able to accelerate this effort thanks to its access to vast quantities of private-sector data and robust surveillance? …in assessing whether the United States or China has a “data advantage” in the military AI realm. Authors Husanjot Chahal, Ryan Fedasiuk and Carrick Flynn find that commercial data, while useful, will prove less relevant for military operational AI than generally thought, and that emerging technical approaches might reduce the role of big data in AI competitiveness.”

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

ChinAI #104: Tencent 2020 AI White Paper

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