Collaborative promotion of AI industrial innovation
Researchers apply an AI tea-leaf picker in Shengzhou City, Zhejiang Province, Mar. 15. Photo: CFP
The fourth industrial revolution has accelerated the growth of an innovation ecology, and Artificial Intelligence (AI) is at its core. AI is rewriting the global industrial competition landscape. One new competition rule is that technical standards, with intellectual property rights (IPRs) at its core, come first.
China’s AI has achieved breakthroughs in several fields, with a gigantic market for application. China’s IPRs are in a relatively weak position as compared to that of more advanced economies. “Technology monopoly,” “standard confinement,” and “broken alliance” trends are forming bottlenecks which constrain AI industrial innovation. Only through continuous support of collaborative interactions of technical standards, IPRs, and AI industrial innovation, can we have the initiative to promote the high-quality development of China’s digital economy.
Innovation connotations
Digital industrial innovation is a creative destruction process, creating new business forms by introducing a new combination of data production factors and digital production conditions to the economic system and forming new production functions. As a typical representative of the digital industry, the AI industry’s innovation is reflected in the organic connection of the standard chains, innovation chains, and industrial chains throughout multiple fields. The complex system of multi-dimensional interactions, diversified structures, and multi-stage symbiosis is formed through the synergy of a technical standards-led mechanism, the exclusive mechanism of IPRs, and the complementary mechanism of alliance carriers. Firstly, based on R&D cooperation of AI technologies, a horizontal technological innovation chain is formed among core enterprises, universities, research institutes, sci-tech intermediaries, competitors, and financial institutions. Secondly, collaborative interaction relationships among innovation environments allow the upstream and downstream basic layer, intelligent layer, and application layer of the industrial chains to participate in an AI innovation group and form a vertical collaborative industrial chain through interactive data and innovation of extended business forms. Finally, as an important link leading the development direction of innovation chains and industrial chains, standard chains help to form a technology standardization strategy with “intellectual property licensing, collaborative R&D, and technical standard cooperation” as the core and to form an innovation development model built based on technological innovation and product platforms.
Based on the characteristics of a multi-functional combination of AI innovation chains, the multi-stage evolution of standard chains, a multi-agent interaction in industrial chains, and the synergy of environmental subsystems, the AI industry can be divided into three core subsystems: a basic layer, an intelligence layer, and an application layer.
Among these, the basic layer’s core technologies and products include relevant infrastructure and basic technologies. The intelligent layer’s core technologies and products mainly rely on data resources and computing platforms from the basic layer, and use algorithms for machine learning, to realize perceptual intelligence and cognitive intelligence. The application layer’s core technologies and products represent a continuous integration of AI, terminals, and vertical industries, reshaping the traditional robotics industry and other fields, and generating products, solutions, and general technology platforms that are embedded in different application scenarios.
New business forms
In the process of digital industrialization innovation, the products or services which make up the AI industry’s basic layer and intelligent layer have formed two new business forms. First is the new business form of intellectual property-driven personalized customization. This requires the use of internet platforms and intelligent factories to directly convert user needs into production scheduling, to realize user-centered personalized customization and on-demand production. Second is a technical standards-led networked collaborative new business form. When the synergy between technical standards and IPRs is strong, digital technology develops to a more general level. At this time, market competition reshapes the production process. Therefore, new forms of networked collaboration led by technical standards are gradually emerging, and new models such as collaborative R&D, crowd sourced design, and supply chain collaboration have formed further with the help of the internet, big data, and industrial cloud platforms.
The products or services in the application layer of the AI industry have created two additional new business forms in the process of industrial digital innovation. The first is a new business form of intelligent production driven by technical standards. The second is the new business form of service-oriented extension led by the synergy of technical standards and IPRs. By adding intelligent modules to products, we can realize product networking and operation data collection, provide diversified intelligent services by using big data analysis, and finally realize user value creation.
IPRs & standards
First, we should improve the quality of AI patents and strengthen diversified construction of China’s standards alliances. We should strengthen the global layout of high-quality patents, improve the market value of patents, pay attention to the cultivation of patent portfolios, especially high-value patent portfolios, and improve the strategic value of patents.
Second, we should create a national technological standard innovation base to promote the coordinated development of technologies, standards, and industries. At present, the Standardization Administration of the P.R.C. has approved the launch of more than 30 national technical standard innovation bases. To develop China’s AI industry, we should begin overall planning at the national level, establish core alliance organizations, actively promote the industrialization of sci-tech achievements, and promote the deep integration and development of China’s new generation of information technology and manufacturing industry.
Third, we should accelerate the development of AI open-source technologies and strive to create a sound AI open-source ecology, focusing on AI technologies such as computer vision, machine learning, natural language processing, robotics, and speech recognition. We should follow open-source principles, jointly build a China-led open source community, promote the formulation and improvement of open source standards, and form an AI core ecosphere. We should also improve the financial support mechanism, establish an AI technological innovation system using algorithms’ frameworks as the core, and further improve the IPRs system.
Fourth, the protection mechanism of digital industrial innovation promoted by collaboration of IPRs and standards needs further improvement. We should start with research on public governance demands and institutional systems for the coordinated development of technical standards and IPRs, analyze the defects and deficiencies of existing systems and policies, actively summarize the successful experiences and failures of developed and developing countries, and grasp relevant policy trends and implementation effects of major countries in a timely manner. We should build a “five-pronged” support system encompassing the government, systems, market, platforms, and culture and a “six-pronged” guarantee mechanism including capital, talent, technology, law, service, finance and taxation. These systems support the coordinated promotion of digital industrial innovation through technical standards and intellectual property rights to provide a theoretical basis and decision-making support for related departments to further formulate policies to guide, support, and safeguard digital industrial development.
Wang Liying (professor) and Zhao Chunmiao are from Zhejiang University of Technology. Li Ping is director of the Institute of Quantitative & Technological Economics, CASS.
Edited by ZHAO YUAN