Since the concept of artificial intelligence was born, its technological applications have gone through a long evolution, including the initial stage, reflection stage, application development stage, downturn stage, and steady stage.
In 2016, the artificial intelligence AlphaGo defeated world Go champion Lee Sedol with a score of 4:1. AlphaGo was like the butterfly that flapped its wings in South America, triggering a global "tornado" of artificial intelligence. In the following years, the development of information technologies such as big data, cloud computing, the internet, and the Internet of Things propelled the rapid advancement of AI technologies represented by deep neural networks, achieving multiple technological breakthroughs and ushering in a new wave of explosive growth for artificial intelligence. In 2023, pre-trained large models crossed the technological singularity. In 2024, artificial intelligence is driving technological waves represented by AIGC, digital humans, multimodality, large AI models, and intelligent decision-making. The application of artificial intelligence in industries has also reached unprecedented depth and breadth. In the field of industrial manufacturing, "AI drives industry, software defines manufacturing" has become a widespread consensus across the industry. As the "brain" of smart manufacturing, industrial AI not only promotes the shift in manufacturing production paradigms and changes manufacturing methods but also deeply integrates IT and OT into one, reshaping the business models and industrial ecosystems of manufacturing. To address the new developments, challenges, and trends of AI in industrial manufacturing, Zhu Jintong, General Manager of the Industrial Operations Solutions Division of AI-driven industrial intelligence solution provider GETECH, conducted an industry dialogue with EO Network.
Three Layers of Leap, AI Transformed Zhu Jintong believes that "overall, in the past few decades, the application of AI in manufacturing has undergone three major stages of role evolution. These three stages are: the辅助 stage, the integration stage, and the dominance stage. AI has completed the leap from an辅助 tool to a core driving force." 1.辅助 Stage: In the initial stage, AI mainly used classification and inductive reasoning to analyze, judge, and make simple decisions about real-world problems. At this stage, AI's main roles were generally data analysis, quality control, production planning and scheduling assistance, etc. Its core value lay in improving production efficiency and product quality while optimizing resource allocation and reducing labor costs. 2. Integration Stage: With the advancement of AI technology, AI began to possess self-perception, self-decision-making, and collaboration capabilities. At this point, AI became more deeply integrated with manufacturing processes, techniques, and rhythms, becoming an indispensable "key" in the production process. In the integration stage, AI mainly played roles in intelligent production systems, quality control systems, supply chain management systems, etc., maximizing the advantages of automation and intelligence to achieve efficiency, stability, and reliability in the production process. 3. Dominance Stage: The emergence of large models was an important milestone in the field of AI, opening a new chapter in the AI era. Through the叠加 of "skills" such as NLP optimization, simplified model development, multi-task learning, pre-training and fine-tuning, reasoning, and transfer learning capabilities, AI's autonomous consciousness became stronger, enabling it to independently make decisions, control, and optimize production processes, completing highly automated, customized, and intelligent production, and achieving partial "liberation" of "humans." AI-dominated production not only achieves leapfrog upgrades in processes but also brings core value through innovation and transformation of discrete manufacturing itself, enabling product design optimization, flexible production, and agile manufacturing to achieve a high degree of unity between steady and agile states, meeting consumers' personalized needs while strengthening enterprises' core competitiveness and moats. Zhu Jintong stated, "Objectively speaking, there is still uncertainty in current industrial AI applications, but industrial manufacturing itself pursues deterministic results and has high requirements for precision. Especially in advanced manufacturing and high-end manufacturing, the cost of trial and error is too high, which directly leads to an irreconcilable矛盾 between the rapid implementation of AI and the cautious decision-making of manufacturing." Taking the most widely applied machine vision as an example, in factory defect detection, there is still the possibility of misjudgment and missed judgment. This衍生 another issue: the engineering of AI. AI engineering refers to the process of transforming AI theories, models, and technologies into products and services that can stably operate, be maintained, and expanded in real environments. However, the real scenario is often that the people who develop industrial software, those who develop AI algorithm models, and those who use them for business implementation are different. This requires a high degree of cooperation and collaboration in the work process. Through team collaboration, challenges such as the gap between research and production, the complexity of the technology stack, and the generalization ability of models can be bridged. Of course, misjudgment and missed judgment in defect detection may sometimes not be caused by the technology itself but by the special attributes of the scene, such as light, position, etc. To solve problems like light and position, an expert knowledge base composed of the know-how of frontline senior engineers is needed to overcome them. Therefore, industrial intelligence service providers with a manufacturing background possess core market competitiveness. In the interview, Zhu Jintong believed, "GETECH has always firmly believed that 'to develop good industrial software, industrial genes are needed.' This is also the greatest confidence of GETECH in adhering to industrial AI: 'from industry, to industry.'"
AI is a Powerful Engine for Manufacturing Transformation and Upgrading From the perspective of事物 development, whether it is factories, cities, or parks, they all comply with the law of entropy increase. That is, over time, the system will add various functions and modules, gradually growing into a complex system, with more and more management objects appearing. At this time, the requirement for "centralized management"能力 "rises with the tide." Once a critical value appears, the existing system needs to be broken, and professional division of labor management is required, with some people managing production, some managing equipment, and some managing processes. However, the problem that arises simultaneously with professional division of labor is the "sense of fragmentation" of the system. Thus, the consequence of this cycle is increasing complexity and fragmentation. At this time, new solutions are needed, such as the emergence of standardized SOPs. Everyone operates according to the rules within the same SOP logic, doing some entropy reduction work to balance the system. The biggest role of AI's emergence is that this "super brain" will exponentially raise the critical threshold of centralized management, greatly expanding the radius and capability of management. Secondly, AI will everywhere act as an "optimizer," slowing down entropy increase and speeding up entropy reduction. This increase and decrease will greatly improve the current困境. Zhu Jintong revealed, "From the perspective of the factory, one of the most important investments in the factory is equipment, followed by raw materials, energy consumption, processes, etc. All of the above require software to manage. Similarly, when more and more software cannot be managed, AI is needed to empower them." This is also the significance of GETECH's persistent strategy of "AI + industrial software + intelligent equipment"三大支柱 layout, carrying out AI empowerment around scenarios such as manufacturing production, equipment, quality, energy and carbon, logistics, and supply chain. GETECH has two very core focuses: one is self-developed industrial software, and the other is深耕 intelligent equipment. Software + equipment are two wheels, and AI is the powerful engine that drives the two wheels to run faster. The core of such a strategic layout is to solve the engineering problems of AI and the困境 of entropy increase.
Simply understood, a pure AI company serving an industrial customer for product development or scene implementation would have to start from scratch, from 0 to 1. At this time, AI is more of an "add-on" and cannot integrate into it. In stark contrast, GETECH, within the entire system, bases itself on intelligent equipment, directly embeds AI and industrial software into it, and completes高度 integration with surrounding systems. At this time, for optimization projects around yield, energy consumption, supply chain, etc., it has incomparable leading advantages in the richness and accuracy of data collection, the response speed, concurrency capability, compatibility, stability, and scalability of instruction execution. Zhu Jintong summarized, "Relying on its self-developed Dongzhi Industrial Application Intelligent Platform, GETECH builds a one-stop solution from underlying algorithm models to middle-layer core products to upper-layer application scenarios, continuously consolidating the core capabilities of 'device connection + data modeling + application empowerment,' and steadily advancing the三大支柱 strategy of 'AI + industrial software + intelligent equipment.' This is the optimal solution for切入 industrial manufacturing scenes currently." Six Major Trends in the Development of Industrial AI Zhu Jintong stated in the interview with EO Network that in the theory of total quality management, "man, machine, material, method, environment" are the five major factors affecting product quality. Analyzing the impact of industrial AI on these five factors, the impact on people is undoubtedly the greatest. According to GETECH's frontline practices over the past six years, the most outstanding effects of AI products at the implementation level are mainly reflected in four aspects: 1. Optimization and management around energy and carbon. GETECH launched the Multi-agent AI Energy and Carbon Brain for energy and carbon management, based on cutting-edge technologies such as large models, AI algorithms, big data, and cloud computing, to achieve digital management of corporate energy input, energy consumption, and carbon emission三大环节. 2. Quality management. Through machine vision technology, it provides integrated hardware and software services for defect detection of glass or panels. 3. Equipment recipe control in the semiconductor field. GETECH hands over the historical batch data反控优参 to AI,联动 RPA to simulate engineers operating equipment, achieving a self-perceiving and self-driving closed loop, helping semiconductor production achieve yield improvement. 4. Logistics automation. Based on the coordination of AI algorithms and automated logistics handling hardware and software, it can achieve optimal OHT overhead crane handling sequence, the fastest path, and the highest efficiency. On the surface, these four aspects revolve around business and production sites, using data analysis to make production plans, scheduling, dispatching efficiency, as well as processes, rhythms, working conditions, etc., "leap to mind," but ultimately they all serve "people." For example, in semiconductor manufacturers, most equipment is imported from Japan, South Korea, and Europe, and these equipment do not open API interfaces to domestic users. When key parameters in the production process (temperature, time, pressure, angle deviation, etc.) issue warnings, they can only rely on the experience of the factory's "veteran masters" to make fine adjustments. In many factories, veteran masters are both key and隐含 risks. GETECH innovatively uses AI to联动 RPA, with RPA simulating people to operate control computers. At this time, AI completes the entire chain of operations from detection, warning, decision-making, optimization, to automatic adjustment. As AI accumulates more experience, it becomes a new veteran master, more stable and inheritable. GETECH achieves a full AI closed loop of perception, knowledge, and action through AI + RPA innovation,彻底 liberating "people." Zhu Jintong analyzed that besides the impact on people, industrial AI in manufacturing also shows the following five trends: 1. In the future, AI Agent may fundamentally change manufacturing. The uncertainty of manufacturing will always require people and AI to solve, so AI Agent will coexist with people in the form of advisors. The future is a human-machine coexistence production mode. In real production scenes, there are actually many forms similar to Agent. Taking MES (Manufacturing Execution System) as an example, current MES is only trigger-based, meaning that when problems occur, lights will turn on, but people are still needed to complete the final problem handling. If MES can be combined with large models, it can achieve unmanned factory production methods. People change from executors to supervisors, completing the role transformation. 2. Only by treating AI as a变革动力 can its maximum value be truly realized. The success of AI applications in manufacturing does not entirely depend on the technology itself. In fact, enterprise management and attitude are very important influencing factors. Today, many enterprises regard AI as a novelty, a tool, and few enterprises directly position AI as a "change driving force" and take action. Enterprise change involves organization (people who do things), processes (methods of doing things), and IT technology (tools for doing things). AI can solve organizational problems to a certain extent, but更多的是解决流程 and IT technology problems. If AI is not treated as a direct change driving force, or if转型升级 is not围绕 AI, it is difficult to truly make good use of AI and maximize its value. 3. In the short term, small models are more effective than large models. A deterministic conclusion is that, according to current industry application statistics, small models dominate. Why can't generative AI become mainstream? When large models emerged, GETECH internally激烈讨论 what product form large models would eventually appear in? The most likely form is Chatbot, which is closely related to the role large models承担. However, most scenes in factories are not human dialogue scenes but machine-to-machine "dialogues." Besides scene attributes, small models are more suitable for production manufacturing in terms of high reliability, low latency, cost advantages, rapid deployment, efficient integration, flexibility and scalability, low data requirements and strong generalization ability, and reduced energy consumption. 4. Safe, autonomous, and controllable industrial AI is a reflection of soft power. Whether it is large central state-owned enterprises or large private enterprises, establishing digital technology companies has become a "standard." For the entire industry, this is common prosperity. Building a safe, autonomous, and controllable industrial AI system is definitely the general direction. Only in this way can core soft power be mastered in one's own hands. Cooperating with this, the construction of computing power platforms, professional models, and data governance will become配套. Behind this, the value of AI for industries evolves from scene-driven to data and model-driven, bringing huge wealth and assets to enterprises while also cultivating more fertile soil for AI. 5. The出海 of industrial software is a general trend, and AI capability is crucial. Under the阵势 of全民出海 "destroying the rotten and breaking the brittle," the report card of the software industry's出海 is also very bright. In the first 10 months of this year, my country's software business exports reached 46.34 billion US dollars, a year-on-year increase of 5.2%, reaching the peak of the past year. Why is AI capability crucial for the software industry's出海? First, for the software industry itself, "出海造船" is the mainstream path choice. The experience and wisdom沉淀 in the AI capability system become the "ballast stone" for出海. Second, compared to the advantage of domestic labor costs, the high labor costs abroad urgently need AI to improve quality, efficiency, and reduce costs, thereby reducing dependence on labor. In addition, going global with industrial AI capability, while adapting to global market demands, is also an inevitable choice to enhance the competitiveness of Chinese AI globally and seize the discourse power of industrial AI.
