The paper analyzes and elaborates on the layout of China’s artificial intelligence (AI) industry. It systematically examines the current state of China’s AI development from the perspectives of foundational support, application empowerment, and ecosystem building. The study concludes that China’s AI industry should be driven by application-led development. It further proposes that the “AI+” initiative should be advanced in an orderly and effective manner by clearly identifying suitable industries, thoroughly assessing the current state and challenges of implementation, and precisely calibrating the intensity of its rollout.
This paper systematically studies the key applications of “artificial intelligence (AI)+” technology in core scenarios such as IPv6 address resource management, intelligent traffic scheduling, and cyberspace operation and maintenance. It proposes technical solutions such as dynamic prediction and resolution, health-aware scheduling, semantic planning management, and digital identity tagging, and has been implemented in scenarios such as smart parks, industrial control, campus networks, and e-commerce platforms. Practice has shown that the “AI+” technology can significantly reduce the time for locating IPv6 address conflicts, significantly improve traffic scheduling efficiency, which provides a practical technical solution for building an intelligent network management system.
The deep convergence of artificial intelligence (AI) and robots has become a decisive catalyst for the next leap in robots technology, giving rise to new forms of intelligent agents. Among these, embodied intelligent robots stand out due to their core emphasis on physical embodiment and environmental interaction. Focusing on this specific form enabled by “AI+”, this paper offers a comprehensive survey of the conceptual evolution and current development of embodied intelligence robots, highlighting how AI reshapes perception, cognition, decision-making, execution, and data foundations. By examining key technologies, namely, multimodal perception, large language models, and deep reinforcement learning, and demonstrating their deployment in industrial manufacturing, healthcare, and household services, this paper illustrates the concrete achievements of “AI+” empowered embodied intelligence robots. The paper also identifies practical bottlenecks, including high computational demands and limited algorithmic generalization and robustness, and discusses future directions such as more efficient model architectures, cross-modal synergies, and broader domain expansion. These insights aim to provide references for both technological innovation and industrial adoption of embodied intelligence robots.
Data annotation, as a key link in the foundational layer of the artificial intelligence industry, directly affects the performance of artificial intelligence (AI) algorithm models and the implementation of application scenarios, and is the core productive force of high-quality AI datasets. This paper systematically reviews the connotation definition, industrial chain structure, development model and policy environment of the data annotation industry, presents a detailed summary of the application practices regarding how the data annotation industry empowers the “AI+” initiative across key sectors, deeply analyzes the industrial transformation brought about by the technological innovation of large models such as DeepSeek, and summarizes the core problems existing at present, such as the lack of top-level design, talent bottlenecks, and insufficient technological collaboration. This study proposes that the high-quality development of the data annotation industry should be promoted through paths such as strengthening the demonstration effect of national-level annotation bases, improving the technical level of data annotation, continuously advancing the application of “AI+” in key industries, constructing a collaborative innovation ecosystem, improving the standard system, and deepening international cooperation.
Currently, artificial intelligence (AI), as a representative of emerging industries, has become a major driving force in the global technological wave. From its early stages of simple logical reasoning to the current era of autonomous deep learning, complex task processing, and intelligent decision-making support, AI has shaped a new paradigm for the development of the modern era, profoundly impacting various fields such as society, economy, and culture. As a key sector for national energy reserves and public welfare, the energy industry is accelerating its transformation from “manual” to “intelligent” driven by AI. Emerging technologies such as automated production, remote monitoring, data analysis, production simulation, and intelligent transportation are gradually becoming the core drivers for improving productivity and upgrading the industrial structure.
The integration of Artificial Intelligence (AI) with research and development is reshaping the paradigms of both scientific inquiry and industrial research and development, heralding a new era of research methodologies. This paper systematically examines five core technological areas within the "AI for research and development" framework, namely research data, research computing power, research models, research agents, and automated laboratories. It explores their definitions, connotations, current development status, and future trends, while discussing how AI drives scientific research toward autonomous intelligence. The findings indicate that the convergence of research data governance, heterogeneous computing resource management, specialized and multimodal research models, the autonomous research capabilities of intelligent agents, and the wide-area collaboration of automated laboratories collectively supports a novel intelligent research paradigm characterized by an "imagination-execution" closed loop. This paradigm provides robust momentum for accelerating scientific discoveries and facilitating their industrial translation.
Based on the tracking analysis of business scenarios and typical cases of artificial intelligence (AI) application in information and communication enterprises, this paper comprehensively analyzes the positive effects of AI in improving the operational efficiency of information and communication enterprises, expanding the development space of the industry and building industrial ecology. However, promoting the integrated development of AI and information and communication industry still faces challenges such as weak technology, insufficient application, insufficient elements and incomplete governance system. The next step is to promote the deep integration of AI and information and communication industry by strengthening technological innovation, accelerating application cultivation, strengthening the supply of elements and improving the governance system.
In recent years, generative Artificial Intelligence (AI) has promoted a new round of technological revolution into an explosive phase. The platform economy, as an emerging industrial organization and resource hub arising from the previous technological revolution centered on the internet, continues to play a significant role. Based on the analytical framework constructed from the perspectives of technical economics, polycentric governance, and collaborative governance theories, it is evident that AI governance, while drawing lessons from internet governance experiences, must also address new governance challenges. This entails appropriately relaxing regulatory stringency in line with the development stage of the new technological revolution, adjusting governance structures and optimizing responsibility distribution in accordance with the evolution of industrial organizations, and shifting the governance focus to the supply side as the methods of value creation upgrade. In this regard, China should leverage the relationship between foundation models and fine-tuned models to optimize the regulatory chain, explore the use of industrial funds as a tool to promote the inclusive development of AI, and establish a risk management system based on the different roles of participants in the industrial ecosystem. These measures will contribute to forming an AI governance system characterized by the synergistic coupling of industrial chain, regulatory chains, and multi-stakeholder governance chains.
With the rapid advancement of artificial intelligence technology, particularly groundbreaking progress made by large models in domains such as natural language processing and computer vision, the development paradigms, architectural designs, interaction mechanisms, and deployment methods of traditional software are undergoing an unprecedented transformation. This paper aims to explore in depth the impact of artificial intelligence technologies—epitomized by large models—on the evolution of software forms. It systematically analyzes the intrinsic mechanisms through which large models drive software toward intelligent evolution, as well as the core characteristics exhibited by new software forms. Focusing on the requirements for intelligence grading, this paper proposes a software intelligence maturity model and corresponding implementation strategies. Additionally, it elaborates on the technical bottlenecks, security risks, ethical dilemmas, and engineering challenges confronting software evolution in the era of large models, and prospect its future development directions, thereby providing references for theoretical research and practical exploration in the intelligent evolution of software.
With the rapid development of artificial intelligence (AI) technology, defect inspection for industrial products is evolving from traditional manual visual inspection and image processing methods towards intelligent and generalized approaches. This paper systematically reviews the types of industrial product defect detection and the path of technological development, makes an in-depth analysis of the technological evolution from traditional image processing to deep learning and then to the basic model stage, focuses on the current key technological breakthroughs in the areas of data augmentation, few-shot learning, model lightweight and cloud-edge-end collaboration. Through practical application cases in industries including consumer electronics, steel, new energy batteries, tires, and textiles, the paper demonstrates the significant effects of efficiency improvement, cost reduction and quality assurance brought by AI-enabled defect detection, providing technical support and practical reference for the intelligent transformation of the manufacturing industry.
As a key application form of next-generation artificial intelligence in the public sector, domain-specific large models for government services are increasingly becoming critical enablers for enhancing administrative efficiency and public service delivery. Building upon years of experience in digital governance, Shanghai has actively explored practical approaches for deploying large model technologies across core e-government scenarios such as “One Network for All Services”, “One Network for Unified Management”, and “One Network for Coordinated Operations”. The aim is to study the application and practice of Shanghai’s government vertical big models, analyze the core challenges faced in the application process, and provide reference practical experience and path for promoting the construction of government big models throughout the country.
The gradual penetration of artificial intelligence (AI) technology into the medical, health, and elderly care fields brings hope for the construction of an integrated medical, health, and elderly care service ecosystem featuring comprehensive functionality, continuous services, personalized solutions, complementary resources, and inclusive access. From a systematic research perspective, this paper proposes a framework for an integrated medical, health, and elderly care service ecosystem centered around an intelligent integrated medical, health, and elderly care service platform. It reveals the directional research and judgment of AI empowering integrated medical, health, and elderly care services, and specifically points out the path for the construction of an intelligent integrated medical, health, and elderly care service ecosystem. Finally, suggestions are put forward to accelerate the construction of this ecosystem.
In recent years, the breakthroughs in artificial intelligence technology, particularly in large language models (LLMs), have been profoundly reshaping the global industrial landscape and social governance models. As a core driver of the new technological revolution and industrial transformation, large language models, with their powerful capabilities in general understanding, generation, reasoning, and interaction, have opened up new avenues for the development of smart cities. By analyzing the deep integration mechanism between large language models and smart cities, this study focuses on how large language models can empower the modernization of urban governance systems, the intelligence of public services, and the efficiency of industrial economies. It also examines key driving factors such as algorithm innovation, computational power support, data element circulation, and policy environment, aiming to provide forward-looking references and practical guidance for urban managers and policymakers.