Author(s)

Chengbo Jin ¹

Affiliation(s)

¹ Heilongjiang Weizhong Investment Group Co., Ltd, Harbin, 150000, China

Corresponding Author

Chengbo Jin

ABSTRACT

With the rapid development of science and technology, artificial intelligence (AI) has become a new driving force to promote economic and social development with its powerful capabilities of data processing, autonomous learning and decision support. This study analyzes the mechanism of AI promoting high-quality economic development, and discusses its specific implementation path, which provides useful guidance and suggestions for practice. It is found that AI has played a positive role in promoting high-quality economic development through its characteristics of permeability, synergy, substitution and creativity. The wide application of AI not only improves production efficiency and reduces operating costs, but also promotes the development of emerging industries and injects new vitality into economic growth. In order to effectively apply AI technology to empower high-quality economic development, this study puts forward some paths, such as strengthening basic research and key technology research and development, optimizing industrial development environment, deepening the integrated application of AI and traditional industries, and cultivating and expanding AI industry. In the concrete implementation, it is suggested that the government, enterprises and research institutions should make joint efforts to strengthen technology research and development, optimize the policy environment, and promote industrial integration and innovative application. At the same time, we should pay attention to the security and privacy protection of AI technology to ensure the sustainable development of technology.

KEYWORDS

High-quality Economic Development; Artificial Intelligence; Industrial integration; Artificial intelligence industry

CITE THIS PAPER

Chengbo Jin, Research on the Path of Artificial Intelligence Empowering High-quality Economic Development. Journal of Artificial Intelligence Practice (2024) Vol. 7: 34-40. DOI: http://dx.doi.org/10.23977/jaip.2024.070305.

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