Chances are you’ve seen the stories, with headlines like “AI-driven technologies reshape city life in Beijing” or “Robots serving up savory food at Chinese artificial intelligence eateries” splashed across the page, a photo of a robot ominously beckoning you to believe one message: China is winning the artificial intelligence (AI) race in its quest to become the global superpower.
You would be wrong.
Since 2017, China has made an aggressive push to position itself as a global AI superpower, with a government plan investing billions of dollars in the field. But upon digging deeper, it’s not difficult to find that the US remains at the forefront of the AI race, with more investment sources, a larger workforce, more thorough research papers, and more advanced chipsets.
“There are countless industries where they said ‘We want to become world leaders,’ and it did not work–they basically burned billions,” said Georg Stieler, managing director of Stieler Enterprise Management Consulting China, referencing China. “You need an institutional framework and cultural foundations so that many independent actors can coordinate their work. China’s still not there yet.”
Here is the inside story of how China fooled the world into believing it is winning the AI race, when really it is only just getting started.
On your mark, get set, AlphaGo
Two moments in recent history catalyzed China’s grand AI plans.
The first came in March 2016, when AlphaGo–a machine learning system built by Google’s DeepMind that uses algorithms and reinforcement learning to train on massive datasets and predict outcomes–beat world champion Lee Sedol at the game.
“That was a watershed moment, because it was broadcast all throughout China,” said Jeffrey Ding, the China lead for the Center for the Governance of AI at the University of Oxford’s Future of Humanity Institute. “If you look at Baidu Trends, which is similar to Google Trends in that you can track the history of a term, the search history for ‘artificial intelligence’ spikes up after that match.”
The win highlighted how rapidly AI was advancing, said Elsa B. Kania, adjunct fellow with the Center for a New American Security’s Technology and National Security Program, focused on Chinese defense innovation in emerging technologies in support of the AI and Global Security Initiative. And since the game of Go is roughly approximate to warfare in terms of strategizing and tactics, “the success of AI in Go could imply that you could develop an AI system to seek decisions regarding warfare,” Kania said.
Threats from the US
The second moment that kicked off China’s grand AI plans came later that year, when former US President Barack Obama’s administration released three reports: Preparing for the Future of Artificial Intelligence, the National Artificial Intelligence Research and Development Strategic Plan, and Artificial Intelligence, Automation, and the Economy.
“There was a similar spike in the Baidu Trends data after that–some of the Chinese policy makers thought that the US was much further ahead in terms of AI planning and recognizing the strategic value of this technology than them,” Ding said.
The reports received more attention in China than in the US, Kania said. “Those plans were taken as an indication that the US was about to launch its own major national strategy in AI, which has not quite materialized since, but a lot of those ideas and policies have shown up to varying degrees in Chinese plans and initiatives that have come out since,” Kania said.
In July 2017, the Chinese government under President Xi Jinping released a development plan for the nation to become the world leader in AI by 2030, including investing billions of dollars in AI startups and research parks.
Meanwhile, in the US, President Donald Trump released a long-awaited American AI Initiative executive order in February 2019. The order calls for heads of implementing federal agencies that perform or fund AI R&D to prioritize this research when developing budget proposals for FY 2020 on. However, it does not provide new funding to support these measures, or many details on how the plans will be implemented.
Cutting through the hype
Despite the ambitious plan and the hyped headlines, China is not as far along in its AI ventures as its state media would lead you to believe, Stieler said.
“There are a lot of half-truths and clear exaggerations that I see every day,” Stieler said. “Things that don’t work in the West also don’t work in China yet.”
These are the key elements of AI development where China lags behind the US, despite rampant media coverage.
Chinese companies are quick to apply new technologies and test their commercial viability, Stieler said, but the different building blocks involved are not all domestic.
China’s biggest roadblock to AI dominance is in its chip market, as high initial costs and a long creation cycle have made processor and chip development difficult, Ding said. China is still largely dependent on America for the chips that power AI and machine learning algorithms.
“China has been heavily reliant upon the import of the hardware required for AI, and is deeply dependent on semiconductors and struggles to develop specialized chips of its own,” Kania said. “So far, China has poured a lot of money into that industry without a lot of results.”
However, there are motivations for China to become more self-dependent in this area, particularly considering political tensions between the nation and the US, Kania said. In February 2019, Chinese chip maker Horizon Robotics announced that it was now valued at $3 billion, and expected progress in the coming year for third-generation processor architecture.
Some of the fear of China’s growing AI dominance has stemmed from research stating that the number of AI research papers from China has outpaced those from the US and other nations in recent years. A December 2018 study from information analytics firm Elsevier found that between 1998 and 2017, the US published 106,600 AI research papers, while China published 134,990.
However, “When you measure the quality of the papers by self-citations, and when you apply an index that takes into consideration the reputation of the journals where the articles have been published, suddenly the number of Chinese papers drops, and falls below the numbers of the US,” Stieler said. “The quality of the papers is still higher in the US.”
The US also has a structural advantage for research due to the number of top universities, Ding said. “Stanford, Carnegie Mellon, and MIT attract some of the best and brightest Chinese researchers, who then end up working in the US,” he added.
While five of the top 10 global machine learning talent-producing universities are in China, their graduates are not staying there, according to a 2018 Diffbot report. Four of these schools–Tsinghua University, Peking University, Shanghai Jiao Tong University, and the University of Science and Technology of China–produced a total of 12,521 graduates in recent years; however, only 31% of these graduates stayed in China, while 62% left for the US, the report found.
“If there is an arms race in AI right now, the battlefield is talent,” Kania said. The war for talent is occurring both among major tech companies and between a number of Chinese government initiatives trying to recruit students and researchers, she added. “The US has a major advantage here, because the majority of the world’s top universities and critical mass of talent remain in the US,” Kania said.
Global distribution of machine learning talent is heavily centered in the US, according to the Diffbot report. While there are about 720,000 people skilled in machine learning across the globe, nearly 221,600 of them–representing 31% of the total talent pool–live in the US. That means America is home to more top AI talent than the rest of the top 10 nations combined, including India, the UK, and Canada.
While China is rapidly scaling up AI education initiatives to build a more robust workforce of engineers and researchers, it’s still too early to know if it will be successful, Kania said.
“Certainly, China has the potential to become a major leader in AI, both technologically and in terms of building up the pool of top AI talent,” Kania said. “That’s motivated Google and others to start to explore setting up offices in China, and ways to access that market and that talent.”
AI startups in China raised nearly $5 billion in venture capital (VC) funding in 2017, compared to $4.4 billion in the US, according to an ABI Research report.
“Even though China has outpaced the US in terms of funding, the US still sees higher numbers of investment deals,” said ABI Research analyst and report author Lian Jye Su. While the US raised its money from 155 investments, China’s came from only 19 investments–indicating that investment in the East is more concentrated on certain sectors, Jye Su said.
People can view the AI race from two perspectives, Jye Su said: Technology and implementation. In terms of technology, the US still leads, in terms of being home to major companies like Google, Amazon, Facebook, and Microsoft, whose AI development frameworks and tools are widely used in the industry.
However, China has the edge over the US when it comes to implementation, Jye Su said. “The Chinese government has made it a priority to accelerate the development, adoption, and deployment of AI technologies in key areas, such as smart cities, industrial manufacturing, and healthcare,” he said. “Investors value the commercial viability and market potential of Chinese startups.”
Data and regulations advantages
China’s major advantage in AI research and implementation is the sheer quantity of data created by its population of 1.4 billion and far more lax regulations on that data than exist in the US.
“China has approximately 20% of the world’s data, and could have 30% by 2030,” Kania said. “Because data is the fuel for the development of AI, particularly for machine learning, that could provide China a critical advantage.”
While certain elements of AI, like facial recognition, require massive quantities of data, others require more advanced algorithms, which the US has an advantage over China on, Kania said.
US tech companies also have access to a greater variety and diversity of data than Chinese companies, due to their more global presence, Kania added. “As the broader globalization of Chinese tech companies occurs, it may give them more access to different sources of data along the way, too,” she said. “Data is an advantage for China, but one that also has limitations.”
It’s difficult to tell whether in the future AI will still require such massive amounts of data, or if the development of new algorithms and techniques will be more important, Kania said.
Major Chinese tech companies like WeChat have created an ecosystem around a flow of data that could take advantage of the AI boom, collecting user data on payments, interests, and messages, Ding said.
And China has made a major push to apply facial recognition to policing and surveillance, with an estimated 200 million surveillance cameras set up nationwide that use the technology to identify and arrest criminal suspects. By 2020, the nation plans to give all of its citizens a personal score based on their behaviors captured using facial recognition, smartglasses, and other technologies.
“There’s less of a willingness to do that in the US,” Ding said. “At the same time, some of these Chinese facial recognition companies just have better tech, and have been at the leading edge of some major competitions in computer vision–so it’s not just surveillance that is the application realm of facial recognition technology, it’s also being used in securities, finance, and payments. This is a multifaceted story.”
Many have raised serious concerns about how China is developing and deploying AI in terms of potential abuses to human rights and threatening the future of democracy, Kania said. “Surveillance technology is becoming very pervasive in China, but also diffusing to other countries that might see these options as quite attractive,” she added.
While US tech companies are wary about working on military and surveillance applications, Chinese companies and universities are often eager to support the Chinese government and military on such applications, Kania said.
However, though Chinese companies have succeeded in applying facial recognition in these realms, it doesn’t mean they can apply related AI technologies to autonomous driving or smart manufacturing, where the needs are more specific, Stieler said.
“There are not so many AI use cases I’m seeing here [in China] beside facial recognition and voice recognition,” Stieler said. “They have by far the largest data pool, but without logistics, they will drown in it.”
An interdependent system
Ultimately, AI is an umbrella term–the US and China are each ahead in certain elements of the technology, but are both still extremely limited in its implementation, Kania said. Both nations remain extremely interdependent upon each other in developing this technology, so one making progress is not necessarily a loss for either.
“Understanding US-China competition and collaboration in AI requires understanding that it’s not necessarily a zero-sum game,” Ding said. “There’s a lot of mutual interdependencies and cross-border investment. It’s an interwoven system where we should be trying to emphasize the mutual interdependencies and check our worst impulses to compete in a zero-sum way.”
While there are many reasons to celebrate the synergies among US and Chinese AI development, and much room for cooperation, it remains to be seen whether trade tensions and geopolitical competition will begin to jeopardize that–particularly in terms of military technology developments, Kania said.
US tech companies should keep an eye on China’s AI work, but avoid taking claims that seem outrageous too seriously, Stieler said.
“Take it with a grain of salt, but serve it carefully, because the aspirations are there,” Stieler said. “Somebody who has a bold idea and knows the right people will have enough capital to try it out.”