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With the release of the "New Generation Artificial Intelligence Development Plan," the Chinese government has taken a strategic approach to developing artificial intelligence, setting a comprehensive roadmap for AI advancement in the country up to 2030. This plan outlines the overall vision, strategic goals, and key tasks, along with the necessary support measures, to ensure a structured and sustainable development of AI.
Looking at the national strategy, it's clear that the integration of AI with manufacturing will become a central driver for smart manufacturing in the coming years. From an industrial perspective, the AI industry structure is taking shape, consisting of three main layers: basic support, technology-driven, and scene application. The author will explore and analyze the AI industry chain through these three levels.
**Basic Support Layer: Dominated by International Tech Giants**
In the field of AI, traditional chip architectures are no longer sufficient to meet the demands of large-scale parallel computing required for deep learning. This has led to the need for new underlying hardware that can efficiently process data and speed up computations. The basic layer is primarily hardware-centric, including components like neural network chips, sensors, GPUs/FPGAs, and middleware for performance acceleration. These hardware elements serve as the foundation for AI applications.
Currently, this sector is dominated by global tech leaders such as NVIDIA, which leads in ultra-large-scale deep-network acceleration with its Tesla processors. Intel focuses on FPGA-based solutions and has developed brain-mimicking AI chips. Google introduced the second-generation TPU to support its TensorFlow framework. While there are several Chinese startups like Star Micro, Cambrian, and Xijing Science and Technology, they still lag behind in terms of industrial scale and R&D capabilities.
**Technology-Driven Layer: Algorithms and Open Source Trends**
The technology layer is the core of AI development, playing a crucial role in determining the level of product intelligence. Here, algorithms and computing power are the main drivers. This layer relies on the computing platforms and data resources from the basic layer to perform large-scale recognition training, machine learning modeling, and the development of application technologies across different fields.
This stage includes two phases: sensory intelligence and cognitive intelligence. Sensory intelligence involves connecting people with information through sensors, search engines, and human-machine interactions, enabling tasks like speech and image recognition. Cognitive intelligence then processes the collected data using deep learning techniques to produce insights, such as predictive APIs and AI platforms. These advancements allow AI to develop "seeing" and "hearing" capabilities, paving the way for more advanced products.
Domestic companies like KTX, Geling Shen, Hitomi, Jietong Huasheng, Horizon, SenseTime, and others are rapidly advancing in this space, focusing on computer vision, speech recognition, and natural language processing.
**Scene Application Layer: Deep Integration with Real-World Applications**
The application layer is where AI integrates with traditional industries, enabling real-world use cases. As breakthroughs occur in speech, semantics, and computer vision, AI is being deployed across various sectors.
This layer can be divided into consumer-level applications and industrial applications. Consumer-level AI includes smart robots, drones, and intelligent devices, while industrial applications integrate AI into external industries. Companies have launched numerous AI-powered services, such as smart customer service, virtual assistants, and industrial robots.
IBM’s Watson has been pioneering AI across multiple industries, while Baidu's "Baidu Brain" focuses on autonomous driving. Google has made significant strides in areas like AlphaGo and self-driving cars. Microsoft continues to lead in language and vision technologies. In the home appliance sector, companies like Changhong, Midea, and Gree are embracing AI to build smarter homes and manufacturing systems.
**Conclusion: Weak AI Is Expanding, Strong AI Is Closer Than Ever**
Although AI is experiencing a surge in industrial growth, most current systems—such as deep learning networks and machine intelligence—are still limited to weak AI, operating within specific domains. True strong AI, capable of independent thinking, remains a distant goal. To reach that stage, AI must enhance its cognitive abilities, which is currently a major focus of research and development.
AI started as a technological innovation but is now evolving through deep integration with real-world industries. Only through this integration can AI achieve true commercialization and widespread adoption. The future of AI looks promising, with continuous progress bringing us closer to a more intelligent world.