ChinAI #126: Alibaba’s AI Lab Fizzles Out
Yet another example of hype meeting reality in China's AI scene
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Feature Translation: Alibaba’s AI Lab Fizzles Out
Context: Usually I try to optimize the newsletter toward topics that have a very high ratio of importance to “boringness” (open source microservices, anyone?). This week’s feature translation is a little more “gossipy,” but it’s another high-profile example of an important point I’ve tried to highlight again and again: Western observers consistently overinflate Chinese AI capabilities.
Last week, news about the shutting down of Alibaba’s AI lab reached No. 1 in hot posts on Maimai (脉脉), a Chinese career networking platform and a rival to LinkedIn (screenshot below):
An Alibaba employee wrote: “Alibaba’s AI labs have already fizzled out, and Alibaba’s official website and DAMO Academy have deleted the webpages about Alibaba’s AI labs.” In fact, Daniel Zhang, Alibaba’s CEO, may have shut down the AI labs as early as 2019. To be clear, Alibaba’s R&D arm, DAMO Academy, will continue to do AI research, and Alibaba claims this is just part of restructuring. More details in this week’s feature translation (links to original in Mandarin), from AI科技评论(aitechtalk), a platform that focuses on in-depth reports on developments in the AI industry and academia.
Aitechtalk pinned Alibaba’s response to the piece: “The AI labs have not closed. In the last round of structural changes, it has been integrated into Alibaba Cloud Intelligence as a whole, led by Professor Tan Ping. Alibaba will continue to increase its investment in artificial intelligence research.” Is this big tech’s version of the excuse: I wasn’t fired, I’m resigning to spend more time with family?
In 2017 speech, Jack Ma said this about Alibaba AI labs (including DAMO Academy as a whole): “More than 90% of what is researched cannot just be in the laboratory, but must be in the market. Only in this way can this lab walk a long road.” Alibaba AI labs did produce some important breakthroughs, such as the Tmall Genie smart speaker, which generated millions in sales. But Tmall Genie was the exception not the rule, and many of the lab’s products did not pan out.
The piece makes the case that these types of research failures are normal. If AI labs were judged on their ability to commercialize products, not many would score well. Citing two international examples to underscore this point, the article presents a graph of DeepMind’s annual losses and also references Element AI’s (Canadian AI startup) disappointing sale.
Some fun in a scathing comments section:
As you can see from the top of the first screenshot below, this week’s feature translation has been read almost 60,000 times (seven of my WeChat friends had already read it before I opened it).
The top-rated comment from 隐形轰炸鸡 (Invisible Superchicken) compares Alibaba’s 90% rule for market-oriented research to Huawei’s approach to research: “Huawei’s 2012 labs (Jeff’s note: this is Huawei’s R&D arm) simultaneously carry out research oriented towards advanced technologies in 2035 and beyond, which may not necessarily enter the market. The product R&D and marketing departments shall not interfere with these research directions.” The second-rated comment also praises Huawei’s approach to R&D.
In the first comment from the next screenshot (below), Bios writes: “Is this to save money to do community group buying? (note: this is when a designated community leader coordinates food orders, a phenomenon which has shaken up e-commerce in China recently) Deepmind burns money, but what Alphafold2 has accomplished has pushed the entire industry forward a big step, a big step in the basic direction of medicine. Quoting Professor Zhang Yang, who is well known in the industry, the ability of the industry to gather talents and resources is the envy of academia.”
E’fei writes, “China’s most advanced AI technology is all used on precision-targeted sales and big data-enabled price discrimination.”
ChinAI Links (Four to Forward)
Back in 2016, Andrew Maynard wrote for Slate on his exhaustion with how nanotechnology became “brand nanotechnology” — a 14-letter fast-track to funding and the source of an “endless cycle of nanohype.” You could take Maynard’s text, find and replace all references to “nanotechnology” with “AI” and republish it today.
For DigiChina, Alexa Lee analyzes China’s draft Personal Information Protection Law (PIPL), which “represents a third way between the sectoral U.S. approach, which applies different rules for specific industries or classes of consumers, and the European Union’s comprehensive General Data Protection Regulation (GDPR) framework, which enshrines fundamental rights across contexts. With the draft law, China’s evolving data governance regime emphasizes consumer privacy while also prioritizing national security through data localization measures, cross-border data flow restrictions, and continued surveillance and law enforcement powers.”
Should-read: 10 AI Failures in 2020 by Synced
Fourth installment of Synced’s year-end compilation, which includes some examples from China I hadn’t come across before reading.
Should-read: Is Substack the Media Future We Want?
A piece I’m still grappling with after reading a week ago. Anna Wiener writes for The New Yorker: “But Substack’s founders have acknowledged that, for the majority of writers, a newsletter will be a side hustle. In most cases, subscription fees will generate not a salary but something closer to tips. In a recent blog post on Medium, Hunter Walk, a venture capitalist, compared a newsletter to a stock-keeping unit, or sku, a term of art in inventory management. “The biggest impact of someone like Casey [Newton] unbundling himself” from the Verge, Walk wrote, “is that he is now an entrepreneur with a product called Casey. His beachhead may very well be a paid newsletter . . . but the newsletter is just one sku. . . . There could be a podcast sku. A speaking fee sku. A book deal sku. A consulting sku. A guest columnist sku. And so on.” Lisa Gitelman, a media historian and professor at New York University, said, of Substack, “They obviously want to call it a democratizing gesture, which I find a little bit specious. It’s the democracy of neoliberal self-empowerment. The message to users is that you can empower yourself by creating.”
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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***1.17.21 NOTE: This issue has been edited to correct a transliteration of a name, as well as a translation of community group buying. H/t to ChinAI reader Giuseppe Baldini for bringing this to my attention.