China’s reliance on US-origin platforms for deep learning raises questions about country’s AI push
- This is the second instalment in a four-part series examining the brewing US-China war over the development and deployment of artificial intelligence technology
- Dependence on US frameworks for deep learning seen as significant gap in China’s AI ecosystem, potentially hampering efforts to close the AI tech gap with the US by 2030
When engineer Kuang Kaiming was assigned to a team developing artificial intelligence (AI) technology for a Shanghai start-up, the company went with two leading open-source software libraries, Google’s TensorFlow and Facebook’s Pytorch.
The decision to adopt US core technology over Chinese alternatives was telling of China’s weakness in basic AI infrastructure, despite the country’s success in producing AI companies that are commercially successful.
Kuang’s company, whose AI product detects abnormalities in X-rays, is by no means alone. Nearly all small- to mid-sized Chinese AI companies rely on the US-originated open-source platforms, which also include MXNet and Caffe, because building an in-house framework from scratch requires a large investment of time and dedicated resources, as well as top-tier talent, to ensure the framework runs smoothly and covers a variety of use-cases.
Established open-source platforms like TensorFlow and Pytorch offer a host of tools and libraries designed for machine learning and deep learning, techniques that teach computers to learn by example.
Essentially, they democratise deep learning, allowing almost anyone to feed data into these models and start training their own AI systems without having to create their own from scratch.
China’s AI national champion, search giant Baidu, introduced its PaddlePaddle open-source AI platform in 2016, only a year after TensorFlow was launched, but it failed to gain traction among global AI programmers.
