China’s AI industry lives and dies by BAT. That stands for Baidu, Alibaba, and Tencent, the three Chinese tech giants that are loosely equivalent to Google, Amazon, and Facebook. These three are not just developing and deploying AI themselves. Their deep pockets have also funded a broad range of AI companies, focused on everything from smart cities to finance to education.
Last week, Chinese media outlet Huxiu.com published a graphic that visualizes the full extent of their involvement across China’s AI industry. (The graphic is quite complicated, so I translated it into three bar charts below. Here is also an English translation of the article it originated from, courtesy of Jeffrey Ding, who writes the ChinAI newsletter.) It revealed that BAT invests in 53% of the nation’s 190 major AI companies. This may not surprise those of you who closely follow China’s AI ecosystem. But it’s quite a different topology for those more familiar with Silicon Valley’s.
Looked at one way, the landscape shows how intense the competition is between the three. While they each have a main pillar of expertise—Alibaba in e-commerce, Tencent in social networking, and Baidu in search and information indexing—they are also challenging one another head-on across dozens of industries.
Looked at another way, the scale of BAT’s involvement shows just how integral they are to China’s bid to be a global leader in AI by 2030. Their expertise and funding set the direction and pace of the technology’s development, but their weaknesses also affect the robustness of China’s ambitions.
As the graphic highlights, BAT’s investments have promoted a top-heavy AI industry: lots of companies dedicated to AI applications with far fewer dedicated to developing the technologies that underpin it, including the algorithms and advanced silicon chips behind the breakthroughs in machine vision, natural language processing, and other AI capabilities.
Experts have warned about this top-heaviness before. China’s astronomical rise in AI leadership is currently buoyed by its abundance of data and lax views on privacy. In the short term, those conditions make it fertile ground for highly profitable machine-learning applications. But the country still lags behind the US in its efforts to expand existing AI capabilities through fundamental research. In the long term, that could place a ceiling on how much China will continue to benefit from the technology’s revolution.
US veterans are helping DeepMind solve its health data shortage. Nearly four million adult Americans are hospitalized each year for acute kidney injury, a life-threatening complication. The US Department of Veterans Affairs (VA), a federal agency that provides healthcare services to military veterans, thinks AI can lower that number. The idea is if software can predict those most at risk of developing the disease, doctors will be better equipped to prevent it from happening.
In collaboration with DeepMind, the VA offered the company 700,000 medical records from US veterans over a 10-year period to help train its algorithms. The records were encrypted and sanitized and remain under the VA’s control. If the algorithms prove effective in making predictions on historical data, the collaborators will likely test the system on live patients at the VA clinic.
The project is a notable example of how AI could transform healthcare—by using machine-learning to identify the patients who'd benefit most from preventative care. But such efforts are usually hindered by a lack of training data because of the strong privacy protections specific to the industry.
Fortunately for DeepMind, the VA has millions of electronic health records, representing one of the most comprehensive collections in the US. It highlights an interesting path forward for AI researchers working in this arena: to partner directly with organizations willing to proffer up their medical data troves.