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With the release of the "New Generation Artificial Intelligence Development Plan," the Chinese government began to establish a comprehensive strategy for AI development, outlining the overall vision, strategic goals, and key tasks for advancing artificial intelligence in China up to 2030. This systematic planning marks a significant step in positioning AI as a core driver of future economic and technological growth.
From a national strategy perspective, the integration of AI with manufacturing is expected to become the primary approach for promoting smart industry transformation. Looking at the industrial landscape, the AI sector has already formed a structured ecosystem, primarily consisting of three layers: foundational support, technology-driven innovation, and application-based scenarios. This article will focus on analyzing the AI industry chain from these three levels, providing insights into how each layer contributes to the broader AI ecosystem.
**Basic Support Layer: Dominated by Global Tech Giants**
At the foundational level, AI relies heavily on hardware infrastructure such as GPUs, FPGAs, neural network chips, and sensors. These components are essential for enabling high-performance computing required for deep learning and large-scale data processing. Currently, this space is dominated by international tech leaders like NVIDIA, Intel, and Google, which have established strong footholds through continuous R&D investment and advanced chip technologies. For instance, NVIDIA's Tesla Volta processor offers over 100 TFlops of performance, while Intel continues to innovate in FPGA-based AI solutions. Google's TPU chips further support its TensorFlow platform, reinforcing its position in the AI hardware market.
Despite the dominance of these global players, several Chinese startups such as Star Micro, Cambrian, and Xijing Science & Technology are making strides, though they still lag behind in terms of scale and technical capabilities.
**Technology-Driven Layer: Algorithms and Open Source Lead the Way**
The technology layer serves as the core of AI development, driving innovation through algorithms and computational power. This layer builds upon the foundational infrastructure to develop applications across various domains, including perception and cognitive intelligence. At the perceptual stage, AI systems utilize sensors, search engines, and human-machine interactions to collect and process data—such as speech recognition, image analysis, and biometric identification. At the cognitive stage, machine learning models simulate human thinking processes, enabling predictive analytics and intelligent platforms.
In China, companies like Kuaishou, Geling Shen, Horizon, SenseTime, and others are rapidly advancing in areas like computer vision and natural language processing. Their efforts reflect the growing momentum in the domestic AI technology sector, with many firms focusing on open-source platforms and collaborative innovation.
**Scene Application Layer: Deep Integration with Real-World Industries**
The application layer represents the final stage where AI is integrated into real-world industries, transforming traditional sectors through smart solutions. From consumer electronics to healthcare, AI is being deployed in diverse scenarios, enhancing efficiency and user experience. Consumer-level applications include smart robots, drones, and IoT devices, while industrial applications span autonomous vehicles, smart manufacturing, and robotic automation.
Major players like IBM, Baidu, and Google are leading the charge in deploying AI across multiple industries. IBM’s Watson is revolutionizing sectors from healthcare to finance, Baidu’s “Deep Speech†and self-driving initiatives are reshaping transportation, and Google’s AlphaGo and surgical robots are pushing the boundaries of AI capabilities. In the home appliance industry, companies like Midea, Gree, and Changhong are integrating AI to create smarter, more connected living environments.
**Conclusion: Weak AI is Here, Strong AI is Coming**
While AI has entered a phase of rapid development, most current systems remain within the realm of weak AI, excelling in specific tasks but lacking true general intelligence. The path to strong AI requires breakthroughs in cognitive computing and deeper integration with real-world applications. As AI continues to evolve, it will increasingly rely on collaboration between technology and industry, ultimately achieving meaningful and widespread adoption.
This ongoing journey highlights not only the technological advancements but also the transformative potential of AI in shaping the future of work, life, and society.