Plus, a very meaty ChinAI (Four to Forward) Section this week
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Welcome to the ChinAI Newsletter!
These are Jeff Ding's (sometimes) weekly translations of Chinese-language musings on AI and related topics. Jeff is a Rhodes Scholar at Oxford, PhD candidate in International Relations, Researcher at GovAI/Future of Humanity Institute, and Research Fellow at the Center for Security and Emerging Technology.
Check out the archive of all past issues here and 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 and collaborators like Joy from this week and others like Charles and Lorand from past weeks.
CAICT is a research institute under the Ministry of Industry and Information Technology and one of the co-authors (alongside Tencent Research Institute) of the 500-page book on AI strategy that first launched this newsletter. My collaborator this week is Joy Dantong Ma of MacroPolo. Joy found this report and did the bulk of translating, including some of the key graphs. Her analysis: This report dissects the AI industry into four aspects: company, capital, academic papers, and conferences. It then assesses all major stakeholders, including both institutions and countries, across these aspects. What I find most fascinating is the depth and timeliness of understanding CAICT has on the global landscape. A case in point: in the company section, the report listed 17 unicorns in China, the majority of which are seldom talked about even though China+AI has become such a hot topic. The report also listed out unicorns in the US - Avant, Uptake, Dataminr - that many of us in the US might have never heard of.
*Also, highly relevant is a project on Chinese AI companies that Joy and I and Matt Sheehan worked on back in December 2018, which goes beyond the abstract catchall of AI and drills down into specific verticals (e.g. autonomous vehicles, voice & speech recognition, business intelligence, etc.)
Anyways, back to the report’s key findings:
1. As of the end of March 2019, there were 5,386 active artificial intelligence (AI) companies in the world. The US, China, the United Kingdom, Canada, and India rank as the top 5 globally in terms of the amount of AI companies.
2. There are 41 AI unicorns globally, including 17 in China, 18 in the US, 3 in Japan, and 1 each in India, Germany and Israel.
3. Since Q2 2018, global AI investment has gradually declined. The total amount of global investment in AI in Q1 2019 was US$12.6 billion — down 7.3% from the previous quarter, and flat year-on-year. China's AI financing totaled US$3 billion, 55.8% down year-on-year, accounting for 23.5% of total global financing, down 29% from the same period in 2018.
4. Statistics on AI academic papers in the past 10 years: China ranks first in terms of the total number of papers published, while the number of highly cited papers is lower than that in the US.
Chinese research institutes such as the Chinese Academy of Sciences and Tsinghua University are among the upper echelon of AI academic research institutions.
Google and Microsoft published the most amount of papers in top AI conferences globally.
ChinAI Links (Four to Forward)
This week’s must-read is a report by Dongwoo Kim (research fellow at Asia Pacific Foundation of Canada) comparing AI policies across China, Japan, and Korea — with an eye toward Canada’s interests. The report emphasizes that Japan and Korea are reliable partners for cooperation in the space of AI (5th and 7th largest trading partner), and that Canada could help bridge the gap between China (2nd largest trading partner) and the West. Also, some really good stuff on Japan’s Society 5.0 and its Strategic Council for AI Technology’s policies as well as Korea’s 30-year “Master Plan” for an intelligent information society.
Had a great time talking about AI race rhetoric, Jessica Newman’s excellent China AI Policy primer, Peter Thiel, relative/absolute gains with Lucas Perry on the Future of Life Institute’s AI Alignment Podcast — super impressed by how FLI produces their podcasts — they have a transcript of the entire podcast, detailed time stamps, and long block quotes as key points. Reminds me of a16z’s podcast about podcasting where they discuss how to improve tools for engaging with podcasts. Jade Leung, my boss and the person who makes GovAI run, was on the AI Alignment Podcast last month to discuss GovAI’s research agenda and what ideal governance in this space looks like.
Based on a public records request to HK’s Government Logistics Department which revealed tenders for facial recognition software, this is really excellent reporting by Rosalind Adams of Buzzfeed on how facial recognition is actually being used by HK authorities: 1) it’s likely that no gov depts have used or tested automated facial recognition as part of its CCTV systems, 2) according to the Immigration Department its facial data has not been shared with the Hong Kong Policy Force. HK has contracted with French company Idemia for facial recognition technology to process Hong Kong ID cards (US State Department works w/ same company on same process). However, while automated facial recognition isn’t being deployed through CCTV, faces are being weaponized amidst the protests, as Paul Mozur reports in this NYT piece.
Rather than centralizing project selection which is what initiatives like the Joint Artificial Intelligence Center do, Eric Lofgren argues we should decentralize the Pentagon’s budget by mission type to ensure AI projects “receive funding at the speed of relevance.” His framework is an important one to consider: “Military capabilities may never benefit from a single general AI application. Instead, they benefit from a variety of narrow AI applications. It seems that the effort spent developing an app for autonomous flight does not contribute much to an app for ground vehicles, let alone automating logistics, target recognition, command and control, or any number of other applications. Each app requires its own data inputs, metric selection, and training.” This was published in War on the Rocks as a response to Eric Schmidt and Robert Work’s call for ideas for the Nat Sec Commission on AI.
Thank you for reading and engaging.
Shout out to everyone who is commenting on the translations - idea is to build up a community of people interested in this stuff. You can contact me at email@example.com or on Twitter at @jjding99