Experts confident nation can close Sino-U.S. AI gap(5)

Smarter approach

Though China has quickly developed AI large models and related areas, Wu Hequan, an academician at the Chinese Academy of Engineering, said that greater efforts are needed to close the gaps in computing power and key underlying technologies.

AI large model training requires a huge amount of computation, he said, so expanding the supply of computing power has become increasingly important in the global race for AI leadership.

As the U.S. continues to restrict exports to China of high-end processors, such efforts are more urgent than ever.

China aims to boost its aggregate computing power by more than 30 percent by 2025. The nation's computing power last year touched 197 Eflops — a measure of the performance of a supercomputer — and it ranked second globally behind the U.S., Ministry of Industry and Information Technology data showed.

Wang Xiaochuan, founder of Baichuan AI, has said that OpenAI is currently attempting to connect 10 million graphics processing units. By comparison, China has far less amount of GPUs, an indication that core resources needed in the country's AI development are lacking, Wang said.

Zhang Feng, deputy director of the Institutes of Science and Development of the Chinese Academy of Sciences, pointed out that China also faces security challenges in its development of AI large models.

"More efforts are needed to strengthen the safety evaluation of large model technology, develop security specifications and standards applicable to large models, and conduct security assessments and vulnerability detection on large model data privacy protection," she said.

Zhang said the lack of openness in regard to high-quality data sets was also a problem. "Due to unclear data ownership and sharing specifications, it is difficult to publicly obtain large-scale, high-quality data, which will hinder the collaborative development of AI large model technology and applications," Zhang said.

She added that China should make coordinated efforts to construct large-scale data sets, promote greater openness of high-quality public data, and gradually open up such data to leading companies for their use and further development.