Digital News

　　Currently, artificial intelligence (AI) is moving from a technological concept to practical industrial applications, emerging as a key force in reshaping the global competitive landscape. As the intelligent era rapidly approaches, how can we seize the opportunities to achieve transformation and upgrading in the next round of industrial revolution? At the recently held National Conference on New Quality Productive Forces and Intelligent Industry Development, numerous experts and scholars shared their insights and recommendations, exploring new pathways for industrial upgrading in the age of intelligence.

　　Driving Profound Transformation with Cutting-Edge Technologies

　　A new round of sci-tech revolution and industrial transformation is reshaping the global productivity landscape at an unprecedented speed and scale.

　　In the opinion of Yang Mengfei, Chairman of the Chinese Association of Automation and a researcher at the China Academy of Space Technology, breakthroughs in cutting-edge technologies such as AI, big data, blockchain, biotechnology, and new energy are driving profound changes in production methods, organizational structures, and industrial models. Automation technology, as a fundamental pillar of innovation and development, has been deeply integrated into both economic and social development, as well as national strategic needs. It has become a critical engine powering the growth of new quality productive forces and continues to provide strong momentum for industrial upgrades, technological innovation, and social progress.

　　According to this year’s Report on the Work of the Government, under the AI Plus initiative, China will work to effectively combine digital technologies with its manufacturing and market strengths, support the extensive application of large-scale AI models, and vigorously develop new-generation intelligent terminals and smart manufacturing equipment, including intelligent connected new-energy vehicles, AI-enabled phones and computers, and intelligent robots. China will also promote broader application of 5G technology, accelerate the innovation-driven development of the industrial Internet, optimize the layout of computing resources across the country, and foster internationally competitive digital industry clusters.

　　“AI, as the driving force behind the new round of sci-tech revolution and industrial transformation, is both a vital engine for developing new quality productive forces and a key pillar in accelerating the construction of a manufacturing powerhouse,” stated Professor Qian Feng from East China University of Science and Technology.

　　With the AI Plus initiative being included in the government work report for two consecutive years, AI technology is rapidly integrating into various industries. Among these, manufacturing stands out as a key sector for the practical application and implementation of AI products.

　　According to the introduction, AI has seen widespread adoption across multiple areas within the industrial sector. In industrial R&D and design, AI can be applied in customized product design, modeling and simulation, industrial software, and process design. In industrial production and manufacturing, AI is utilized in product quality monitoring, industrial code generation, industrial robot control, and product packaging, among others. For example, in product quality monitoring, AI improves the efficiency and accuracy of industrial quality monitoring through deep learning, real-time monitoring and alerts, algorithmic analysis, and the integration of intelligent systems. In industrial management, AI can be leveraged for raw material procurement, intelligent production and manufacturing management, warehousing and logistics, and supply chain management. In industrial product services, AI can be used in areas such as intelligent marketing, customer services, and intelligent products.

　　There is a broad consensus in the industry that utilizing AI to empower new industrialization is a crucial path for realizing high-quality development in manufacturing. Recently, the Ministry of Industry and Information Technology held a special session to discuss and promote the development of the AI industry and its role in empowering new industrialization. It emphasized the need for systematic planning and coordinated efforts to advance tasks related to strategy, planning, policy, and standards, in order to create a favorable ecosystem for the development of the AI industry and the empowerment of new industrialization, while fully unleashing innovation potential.

　　Seizing the Opportunity to Build an “Industrial Brain”

　　How can we seize the strategic opportunities created by the accelerated application of AI in industry and speed up its deep integration with the manufacturing sector? Qian Feng believes that accelerating the development of industrial embodied intelligence systems, or the “industrial brain,” is a crucial step in driving the deep transformation and upgrading of traditional manufacturing industries. In his view, it is essential to accelerate the development of an “industrial brain” that integrates all production factors across the full lifecycle of manufacturing and enables rapid demand-supply sensing, precise regulation of manufacturing processes, and efficient allocation of factors. This will achieve collaborative optimization across the industrial, supply, and value chains, foster innovative allocation of production factors, and enable real-time, precise control of manufacturing processes. Additionally, it will provide intelligent management for safe and eco-friendly operation and maintenance, while supporting the intelligent design of new materials and products. The ultimate goal is to ensure efficient resource and energy utilization in manufacturing, promote green and low-carbon production, enhance product value and quality, and maximize the overall value of the industrial value chain.

　　“This will offer high-quality scientific and technological support for the next generation of intelligent manufacturing and digital transformation, serve as a key driving force for the development of new quality productive forces, and inject new momentum into the process of new industrialization,” said Qian Feng.

　　To address the current challenges in the development of China’s “industrial brain,” including the need to overcome key core technological barriers, insufficient AI support and empowerment for the manufacturing industry, and the lack of innovation in mechanisms and talent development, industry experts have proposed solutions.

　　First, we should focus on meeting industry demands by making breakthroughs in key core technologies and enhancing the high-quality technological supply for the “industrial brain.” We should accelerate the research and development of core technologies such as industrial software and industrial operating systems, promote integrated innovation of technologies like the industrial metaverse, blockchain, and privacy computing, and improve policies for the application of first units, first batches, and first versions. We should promote the integration of AI general technologies with industrial mechanisms, knowledge, and scenarios while accelerating the development of key industrial AI technologies such as semi-customized FPGA chips, highly compatible compilers, and training and inference frameworks. Additionally, we must expedite the establishment of “industrial data spaces” for key industries, facilitating the circulation and sharing of industrial data. By leveraging technologies like the industrial metaverse, AI, and the Internet of Things, we can achieve real-time acquisition of industrial data and build high-quality industrial corpora.

　　Second, we should focus on the transformation and upgrading of key industries to build an AI-powered “industrial brain.” This involves accelerating the development of large-scale models for vertical industries that integrate both general and specialized applications in manufacturing, leveraging disruptive AI innovations such as DeepSeek to empower upstream and downstream enterprises, and facilitating the rapid conversion of AI innovation achievements into industrial applications. Additionally, we must seize the opportunity presented by the large-scale equipment upgrades to expedite the creation of “industrial sub-brains” at various levels, ranging from equipment and production lines to workshops, factories, enterprises, and entire industries. These sub-brains should be agile enough to adapt to both internal and external environmental changes while incorporating embodied intelligence. By capitalizing on China’s diverse manufacturing strengths, we can enhance technological empowerment, strengthen supply chain capabilities, foster platform development, and integrate ecosystems, thereby expanding the scope and depth of intelligent manufacturing.

　　Third, we should strengthen policy and institutional guarantees to support the deep integration of technological and industrial innovation via the “industrial brain.” It is recommended to establish major technological innovation and application projects for the “industrial brain” and encourage leading manufacturing enterprises to collaborate with national-level AI and manufacturing laboratories, national technology innovation centers, universities, and research institutions to form innovation consortiums. Additionally, efforts should be made to promote the open-source development of the “industrial brain” technology framework, algorithm models, and component tools, thereby creating an application ecosystem. Universities should be guided to establish interdisciplinary programs in AI and manufacturing, strengthen the training of multidisciplinary talents for industrial intelligence, and build public service platforms for innovation incubation, application testing, and technology verification in industrial intelligence.

　　Addressing the Challenges of AI Technology Application

　　Experts highlight that the manufacturing industry still faces several challenges in applying AI technology, such as insufficient spatiotemporal data samples, difficulty in generating reliable data, and obstacles in the autonomous evolution of systems. To address these issues, there is a need to organically integrate processes, equipment, and knowledge systems, while fully combining specialized and vertical domain knowledge and technologies with general large models, ultimately building a “large model that merges general and specialized applications.”

　　“With the advancement of large AI model technologies, research institutions both at home and abroad have begun competing over model parameters and computing power, leading to critical reflections within the academic community on the current path of AI development,” said Professor Chen Junlong from the South China University of Technology. On one hand, the research and application of large models are still heavily reliant on foreign high-performance computing ecosystems, making it challenging to ensure the autonomy and security of these technologies. On the other hand, in most industrial intelligent scenarios, the utilization of large model performance does not justify the computational costs incurred, creating a greater need for more efficient and lightweight models. To address this issue, Chen Junlong, drawing on the current state of research on both large and small models, proposed an approach centered on the collaborative innovation of large and small models.

　　Based on current technological trends and industry practices, AI development is heavily dependent on the sustainability of energy supply. Energy has become the core bottleneck and ultimate barrier to breakthroughs in AI technology. In this regard, Zhang Chenghui, Vice Chairman of the Chinese Association of Automation and Professor at Shandong University, summarized the development of new energy system control both domestically and internationally. He highlighted the landmark control theories and technological achievements formed during China’s large-scale application of new energy, which have provided key theoretical insights and engineering demonstrations for such applications. He also proposed a new paradigm for the deep integration of computing power, intelligence, and electricity, referred to as the Meta-Energy System, to drive the upgrading and digital transformation of the power system.