ChinAI #81: AI and coronavirus
Trying (somewhat unsuccessfully) to separate the wheat from the chaff
|Feb 4, 2020||3|
Welcome to the ChinAI Newsletter!
Greetings from Cyberspace where the weary giants of flesh and steel still take roost..
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Feature Translation: What is AI doing in the midst of the epidemic?
This week’s translation comes from naojiti [脑极体] (a tech media platform based in Tianjin) — h/t to Jordan Schneider for introducing me to this public account. Check out his ChinaEconTalk newsletter, which features translations of Chinese media about tech, business, and political economy.
I think it’s an interesting, if at times overly dramatic and techno-utopian account of what tech companies and scientific research institutes are doing, in AI and related domains, to combat coronavirus. At the very least, it’s better than uncritically consuming Global Times content about the use of drones to warm people from going outside, as well as some reporting from Leiphone on what tech companies are doing to combat coronavirus, which just reads like a list of press release excerpts.
Let’s take naojiti’s good overview of the main areas where AI could help combat the coronavirus as a starting point to separate some of the wheat from the chaff:
Diagnosis of the coronavirus: the article mentions computer vision to complete early CT scanning work (my initial reaction is that there’s not enough good data to train/validate a model to detect coronavirus in a way that distinguishes it from pneumonia — lancet article for more); nucleic acid detection is a stronger diagnostic test and this field is becoming more and more closely connected with AI in recent years
New drugs: naojiti reference the notion that AI can increase the success rate of a) finding whether any existing treatments could have a therapeutic effect and b) developing new drugs. Impossible to judge effectiveness at the moment, as developing a vaccine could take more than a year.
Tracking the spread: spatiotemporal data can help with building infection models and tracing the path of the epidemic. Comments at the bottom of the article call out the importance of Baidu’s data in this respect. From my read of this article and a few others it seems like Baidu is doing the most substantive stuff related to AI.
Baidu’s role: “Baidu is also opening up its AI technology along with supporting computing resources on the scale of hundreds of millions (RMB) to disease control units, scientific research units, etc. to support a series of anti-epidemic work such as screening and R&D of cures for new diseases such as the novel coronavirus.” The article also says Baidu is working on long-term solutions to identify illegal wildlife trade, in partnership with International Fund for Animal Welfare. Baidu has opened up LinearFold, its RNA prediction algorithm, to research centers around the world, claiming to reduce the prediction time of a virus’s RNA secondary structure from 55 minutes to just 27 seconds. I’m skeptical but don’t know enough to make any judgements. I stared at the article introducing LinearFold for a long time and I don’t think any deep learning is involved — it’s a dynamic programming approach drawing inspiration from computational linguistics.
Computing power: authors identify computing power as the most central resource. Tencent, Baidu, and the National Supercomputing Center in Shenzhen have all opened up their compute resources for use.
Smart cameras and facial recognition: The authors write, “integrated applications of widespread smart cameras and facial recognition algorithms can automatically identify people who do not wear masks in public places and discourage them.” I think this stuff is overhyped — Abacus News had a good story about how facial recognition is actually failing with respect to identifying people because masks are covering half their face.
The piece concludes with raising some good food for thought re: the utility of AI in this crisis and future ones:
Should computing power be organized in “material reserves” that can be called upon? My opinion, not naojiti: many governments are thinking (or should be thinking) about a strategic stockpile of computing power in the context of economic statecraft, but more governments should be thinking about allocation of compute resources in times of public health emergencies, natural disasters, etc.
Along the same lines, should there be clearer standards for authorizations of sharing and using data in times of crisis?
Not from this article but I was struck by Professor Hotez’s comments in a Guardian article about the missed opportunity in our response to Sars almost 20 years ago: “What’s so tragic is that once Sars was gone, the investor enthusiasm for a Sars vaccine was zero. If the global health community had followed through and produced and stockpiled a vaccine, something might have been ready to go now.”
ChinAI Links (Four to Forward)
A compilation of brilliant work by Chinese reporters covering the Coronavirus. H/t to Shen Lu for sharing and volunteers from internetarchive who are now working on translating the headlines.
Should-read: Is China Ready for Intelligent Automation —CSIS China Power Team
A very cool compilation of metrics that assess factors related to China’s automation readiness, drivers of automation, government initiatives related to automation, and the labor force complements necessary for an automated economy —not surprising given the consistently good analysis from the ChinaPower team led by Bonnie Glaser
Should-read: The Secret History of Facial Recognition
Longread by Shaun Raviv for Wired reminds us that everything new is old again — contemporary facial recognition systems in some ways have returned to Woody Bledsoe’s facial-recognition research in the 1960s, which was linked to front companies for the CIA (the aforementioned weary giants of flesh and steel that are still very much welcome among us in Cyberspace).
Should-read: State of AI Report 2019
I should have read Nathan Benaich and Ian Hogarth’s State of AI Report more closely. Regarding UIPath, one of the five leading American companies I highlighted in last week’s issue on robotic process automation, Ian flagged to me that there is a case to be made that UIPath is not a “US company.” It was founded in Romania and raised various first rounds of funding from European investors. Another nice example that challenges the techno-national narrative that packages firms neatly in national containers.
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, Researcher at GovAI/Future of Humanity Institute, and non-resident Research Fellow at the Center for Security and Emerging Technology.
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).
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