China Ahead on AI Strategy, Behind on Data Access
Policy makers continue to bemoan the fact that the U.S., unlike its economic rivals, lacks a coherent government strategy for accelerating AI development and adoption. While the Trump administration has taken some early steps toward forging a strategy based on coordinating federal and industry research, critics argue that further delays place the U.S. at risk of falling behind in the global AI race.
“The question is whether or not countries need a national strategy to actually capture the value” of AI,” said Joshua New, a senior policy analyst with the Washington-based Center for Data Innovation. “The answer seems to be a resounding, ‘Yes’.”
By contrast, Beijing launched an aggressive AI strategy last year that “set a very ambitious goal for AI industry development in China between now and 2030,” according to Xiaomeng Lu, international public policy manager for technology consultant Access Partnership. Lu nevertheless stressed that institutional roadblocks remain, especially strict controls on access to government data that severely limit AI researchers who need lots of local data for model training and other development steps.
Lu told a recent panel discussion the global AI race that China’s strategy includes three components: technology advancement, industry expansion and establishing an AI policy roadmap. Beijing’s initial goal is catching up with the rest of the world in AI development by 2020. “By 2030, China wants to be the leading nation in AI,” Lu said.
Meanwhile, China hopes to build a $150 billion industry by 2030 generating $1.5 trillion in in annual economic growth from AI technology.
While the first two goals remain “aspirational,” she added that China may be in the best position to establish regulations and technical standards for AI over the next decade. Lu noted, however, that China’s previous roadmaps for technologies like semiconductors and an indigenously developed operating system have fallen short in previous five-year plans. Still, China has achieved greater success developing cutting-edge supercomputers.
Lu nevertheless predicted that achieving its AI technology development and adoption milestones over the next five to ten years remains “a challenge”
“The policy makers have to work with the private sector players to make these things happen,” she added.
Which raises the issue of how much public data developers can access when developing AI applications for the huge Chinese market. It remains difficult for developers and researchers to “get access to this big wealth of data sitting in government agencies.” Chinese AI researchers face not only lack of access to domestic data but also government “firewalls” that prevent them from tapping into international data sets, Lu added.
Noting the frustration of Chinese data scientists, she concluded: “Only the free flow of data can generate value. That applies to the [Chinese] data localization regulation that is taking shape.”