By KhabarAsia.com
Data, knowledge, computing power, and algorithms form the backbone of artificial intelligence (AI) development. Yet, when faced with constraints in computing power, how can China leverage its unique strengths to propel the future of AI? At the forefront of this discussion is Academician Zhang Bo of the Chinese Academy of Sciences, who offers profound insights into overcoming these challenges.
At 89 years old, Zhang Bo was one of the pioneers in AI research in China, beginning his studies in 1978. Today, he continues his groundbreaking work as the honorary director of the Institute for Artificial Intelligence at Tsinghua University in Beijing.
According to Zhang, the key to advancing AI in the face of limited computing power lies in optimizing algorithms and harnessing the vast amounts of data available in China. “We need to focus on creating more efficient algorithms that can do more with less,” he explains. “By refining our algorithms, we can reduce the reliance on massive computing power and still achieve significant advancements in AI.”
Zhang emphasizes the importance of tapping into China’s rich datasets to train AI models. “Our population and diverse industries generate an enormous amount of data daily. Properly utilized, this data can compensate for some of the limitations in computing power,” he notes.
He also highlights the role of collaborative research and knowledge sharing among academic institutions and tech companies. “By fostering a collaborative environment, we can pool resources and expertise to tackle the computing power bottleneck more effectively,” says Zhang.
Zhang’s insights offer a roadmap for China to continue its rapid growth in AI development despite challenges. His decades of experience and continued passion for AI serve as an inspiration for new generations of researchers and innovators in the field.
As China navigates the complexities of AI advancement, leaders like Zhang Bo demonstrate that innovation and strategic thinking can unlock new possibilities, even when faced with resource constraints.
Reference(s):
cgtn.com
