1. Introduction: The AI Wave Is Here
In today’s rapidly advancing technological era, generative artificial intelligence is undoubtedly the most shining star. From ChatGPT, which can create stories effortlessly, to Midjourney, which can draw exquisite paintings, and AI voice synthesis tools that realistically reproduce human voices, they are infiltrating every aspect of our lives at an unprecedented speed, fundamentally changing the way we live, work, and entertain ourselves.
According to the “Generative Artificial Intelligence Application Development Report (2024)” released by the China Internet Network Information Center, as of June 2024, the user scale of generative artificial intelligence products in our country has reached an astonishing 230 million. The number of related enterprises exceeds 4,500, and the core industry scale approaches 600 billion yuan. Such a huge user base and industrial scale inevitably raise the question: what kind of industrial ecosystem supports the burgeoning field of generative artificial intelligence? Today, let us delve into the mysteries of the generative artificial intelligence industry ecosystem.
2. Infrastructure Layer: The Cornerstone of AI Development
In the grand landscape of generative artificial intelligence, the infrastructure layer serves as the cornerstone of a towering building, providing indispensable foundational support for the operation and development of the entire industry. This layer mainly encompasses key elements such as data centers (intelligent computing centers), big data resources, and computing resources.
(1) Data Centers (Intelligent Computing Centers)
Data centers, especially intelligent computing centers, can be regarded as the “super brains” of artificial intelligence, with computing power exceeding imagination. Taking ByteDance’s Volcano Engine as an example, its intelligent computing center has a computing power scale reaching trillions of floating-point operations per second, such powerful computing capabilities can process massive amounts of data and train complex models in a short period, providing solid support for personalized recommendations and content creation in applications like Douyin and Toutiao.
Heterogeneous computing power is also a major highlight. It integrates various types of computing chips such as CPU, GPU, and FPGA, just like an elite team with different strengths, allowing different types of tasks to find the most suitable “executor.” In image recognition tasks, GPUs excel at parallel processing of large amounts of data, enabling rapid analysis of image features; while CPUs play a dominant role in logic control and general computing. The collaboration of the two greatly enhances computing efficiency.
Data centers are also the core hub for data-driven processes. They act as a huge “data warehouse” that collects, stores, and manages massive amounts of data, which are like precious “ores” waiting to be mined and refined. The algorithm library is a collection of meticulously designed and optimized algorithms, serving as tools for mining “ores,” capable of extracting valuable information from vast amounts of data, providing robust support for training artificial intelligence models.
(2) Big Data Resources
Big data resources are the “fuel depot” for generative artificial intelligence, continuously providing energy for models. Data collection is the first step to obtaining this “fuel,” collecting data from various corners through sensors, web crawlers, user behavior records, and more. In the e-commerce sector, platforms collect users’ browsing records, purchasing behaviors, reviews, etc., to better understand user needs and provide precise product recommendations.
Distributed storage systems and data processing frameworks are the “super factories” for processing these massive amounts of data. Distributed storage systems scatter data across multiple nodes, not only increasing data storage capacity but also enhancing data reliability and availability. Data processing frameworks, such as Apache Hadoop and Apache Spark, can efficiently process large-scale data in parallel, significantly shortening data processing time.
Once data is obtained, it also needs to be trained, validated, and optimized. Training is like the process of allowing the model to “learn knowledge” by continuously inputting data and adjusting the model’s parameters, enabling it to better understand the patterns and rules within the data. Validation is like an “exam” for the model’s learning outcomes, ensuring that the model performs well on new data. Optimization involves further improving the model based on validation results to enhance its performance and accuracy.
(3) Computing Resources
Computing resources are the “power engine” of generative artificial intelligence, determining the model’s operating speed and processing capacity. AI chips are the core of computing resources, among which GPUs (graphics processing units) are most widely used in the field of artificial intelligence. Initially designed for graphics rendering tasks, their powerful parallel computing capabilities make them very suitable for large-scale matrix operations in artificial intelligence models, thus becoming the preferred chip for deep learning training.
NPU (Neural Processing Unit) is a chip specifically designed for neural network computations, optimized for the structure and algorithms of neural networks, enabling more efficient processing of data within neural networks, playing an important role in edge computing and mobile AI applications. TPU (Tensor Processing Unit) is a chip developed by Google to accelerate deep learning algorithms, capable of rapidly processing tensor operations, significantly improving the training and inference speed of deep learning models.
Distributed computing frameworks connect multiple computing nodes together, forming a powerful computing cluster. By doing so, large-scale computing tasks can be decomposed into smaller tasks, distributed across different nodes for parallel processing, thereby fully utilizing computing resources and improving computing efficiency. Deep learning frameworks like Google’s TensorFlow and Baidu’s PaddlePaddle support distributed computing, enabling efficient model training on large-scale clusters.
To enhance computing power utilization efficiency, scientists are continuously exploring new technologies and methods. By optimizing algorithms and scheduling strategies, they rationally allocate computing resources to avoid waste and idleness; adopting virtualization technology to virtualize physical computing resources into multiple logical resources, improving resource utilization and flexibility.
3. Technical Platform Layer: The Core Engine of Innovation
The technical platform layer is the “strongest brain” of the generative artificial intelligence industry ecosystem, gathering a series of advanced technologies and tools that provide comprehensive support for model development, training, and optimization. This layer mainly includes model training technologies and toolchain support.
(1) Model Training Technology
Model training technology is one of the core technologies of generative artificial intelligence, determining the performance and effectiveness of models. This technology encompasses several key links, each playing a crucial role in the model’s final performance.
1. Data Preprocessing
Data preprocessing is the first step in model training and is crucial. This step is like carefully preparing ingredients for a feast; only high-quality ingredients can cook delicious dishes. Data cleaning removes “impurities” from the data, such as duplicate data, erroneous data, and missing values. In image recognition datasets, there may be some incorrectly labeled images, and data cleaning will identify and correct these errors, thereby improving data accuracy.
Normalization involves unifying data to a specific range, such as normalizing image data pixel values to between [0, 1]. The benefit of this is that it can accelerate the model’s convergence speed and improve training efficiency. For example, in housing price prediction data, the ranges of housing area and price may differ significantly; normalization can help the model better learn the relationships between these data. Data augmentation involves transforming original data by rotating, flipping, cropping images, etc., to increase data diversity, thereby enhancing the model’s generalization ability. When training image classification models, data augmentation allows the model to learn features of images from different angles and lighting conditions, enabling it to classify new images more accurately.
2. Model Structure Design
Model structure design is like constructing a building; different structural designs determine the building’s functions and performance. Recurrent Neural Networks (RNNs) are particularly adept at handling sequential data, such as in natural language processing tasks like machine translation, where they can understand semantics based on prior content and generate reasonable translations. RNNs can remember previous information through cyclically connected hidden layers, thus better handling dependencies within sequences.
Variational Autoencoders (VAEs) are a type of generative model that can learn data distributions and generate new data. In the field of image generation, VAEs can learn the features of a large number of images and then generate new images similar to the training data. The Transformer architecture has achieved tremendous success in natural language processing and other fields in recent years, introducing self-attention mechanisms that allow models to better capture long-distance dependencies in data. When processing a long article, the Transformer can quickly find other words related to the current vocabulary, thus better understanding the overall meaning of the article. The self-attention mechanism calculates attention weights between different positions, dynamically focusing on different parts of the input data, greatly improving model performance.
3. Loss Function Design
The loss function is an important tool for measuring the difference between model predictions and actual results; it acts like a ruler for evaluating model performance. Mean Squared Error (MSE) is commonly used in regression tasks, calculating the average of the squares of the differences between predicted values and actual values, providing an intuitive reflection of the model’s prediction error magnitude. In predicting stock price trends, MSE can help measure the degree of deviation between predicted prices and actual prices.
Mean Absolute Error (MAE) is also used for regression tasks; it calculates the average of the absolute differences between predicted values and actual values, being relatively insensitive to outliers. In scenarios where outliers are less of a concern, such as predicting average sales of products, MAE can more accurately reflect model performance. Cross-entropy loss is commonly used in classification tasks; it measures the difference between predicted probability distributions and actual distributions, effectively guiding the model’s optimization in classification tasks. In image classification tasks, cross-entropy loss can help the model learn how to more accurately determine the category to which an image belongs. Different loss functions are suitable for different tasks and scenarios; selecting the appropriate loss function is crucial for improving model performance.
4. Optimization Algorithms
Optimization algorithms are the “accelerators” of model training, determining how model parameters are updated, thus affecting the model’s convergence speed and final performance. The Gradient Descent method is the most basic optimization algorithm, which updates parameters in the opposite direction of the gradient of the loss function concerning model parameters, gradually reducing the value of the loss function. This is like walking down a hill, always moving in the steepest direction to reach the foot of the mountain as quickly as possible.
The Momentum method introduces a momentum term based on the gradient descent method, akin to descending a hill with a certain inertia, allowing for faster convergence and reduced oscillation. In complex terrains, the Momentum method can help us find the path down the hill more quickly. The Adam algorithm combines the Momentum method and adaptive learning rate methods, adjusting the learning rate adaptively based on parameter gradients, suitable for most deep learning tasks. The Adam algorithm is like an intelligent navigation system that can automatically adjust speed based on road conditions to reach the destination as quickly as possible. Different optimization algorithms have their pros and cons, and the appropriate algorithm should be chosen based on specific situations to improve training efficiency and model performance.
(2) Toolchain Support
Toolchain support provides a one-stop solution for model development, training, and deployment, greatly improving development efficiency and model quality. Starting from the data preprocessing stage, various tools help us clean, label, and augment data. In data labeling, there are dedicated image labeling tools, such as LabelImg, which facilitate the labeling of objects in images, providing accurate labeled data for model training.
During the model training phase, there are many powerful deep learning frameworks, such as TensorFlow and PyTorch, which provide rich functions and tools, making model construction and training simpler and more efficient. Using TensorFlow, one can quickly build a neural network model and utilize its built-in optimizer for training. Model evaluation and optimization tools can help us assess model performance, identify existing issues, and make targeted optimizations. By analyzing indicators such as model accuracy and recall rate, we can understand the model’s performance in different aspects, thus improving the model.
In the application deployment phase, there are also corresponding tools to deploy trained models into practical application scenarios, such as deploying image recognition models into mobile applications for real-time image recognition functionality. The support of these toolchains makes the development and application of generative artificial intelligence more convenient and efficient.
4. Application Scenario Layer: AI Empowering Various Industries
When the spark of generative artificial intelligence meets the dry wood of various industries, it ignites a blazing fire of innovative development. In the application scenario layer, generative artificial intelligence is bringing profound changes to various industries at an astonishing speed, creating unprecedented value.
(1) Entertainment and Media
In the film production field, generative AI has become a powerful assistant for directors. iQIYI uses generative AI tools for script evaluation, increasing the reading efficiency of the development team by 9 times, allowing for quicker filtering of quality IPs. Moreover, AI can automatically generate realistic movie scenes based on script descriptions, making the pre-production process more efficient. In animation and special effects production, AI can create complex animations and effects, reducing reliance on manual production and significantly lowering production costs. “Avatar: The Way of Water” utilized generative AI technology to create numerous complex underwater scenes and creatures, elevating the film’s visual effects to a new height.
In game development, AI can automatically generate game plots, levels, and characters, providing players with a richer and more diverse gaming experience. The game “Nirvana in Fire” developed by NetEase uses AI technology to generate a massive amount of game plots and tasks, allowing players more exploration and choice in the game. The creation of virtual characters also relies on generative AI; from virtual idols to brand ambassadors, AI-created virtual characters are vivid and can interact with fans, bringing new opportunities for brand marketing. For instance, the virtual idol “Luo Tianyi” has not only held multiple concerts but also collaborated with numerous brands, becoming a beloved virtual idol among young people.
(2) Education and Training
Generative AI has brought revolutionary changes to the education sector. In personalized teaching, AI can tailor personalized learning plans for each student based on their learning conditions, interests, and cognitive levels. For students who enjoy mathematics, AI can provide more challenging math problems and extension materials; for students struggling with language learning, AI can develop targeted learning plans to help them improve their language skills.
In terms of intelligent tutoring, AI tutoring systems act like dedicated online teachers, able to answer students’ questions in real time and provide detailed problem-solving ideas and guidance. iFLYTEK’s AI learning machine analyzes students’ learning data to provide personalized learning suggestions and tutoring, helping students improve their learning efficiency. AI can also generate various teaching resources, such as courseware, exercises, and case analyses, enriching teaching content and improving teaching quality.
(3) Healthcare
In the healthcare field, generative AI demonstrates enormous potential. In medical image analysis, AI can quickly and accurately identify abnormalities in medical images, such as tumors and lesions, assisting doctors in making more accurate diagnoses. Google’s deep learning model has surpassed human experts in detecting diabetic retinopathy through analysis of fundus images.
In surgical planning and navigation, AI can provide precise surgical plans and navigation guidance for doctors based on patient data, improving surgical success rates. In disease diagnosis, AI can assist doctors in diagnosing and predicting diseases by combining patients’ medical history, symptoms, and test results. IBM Watson Health uses AI to provide imaging-assisted diagnosis for breast cancer, significantly shortening the time taken for doctors to make diagnoses. AI can also develop personalized treatment plans and medication recommendations based on individual patient circumstances, enhancing treatment effectiveness.
(4) Intelligent Manufacturing
In the intelligent manufacturing sector, generative AI is reshaping production processes. In production process optimization, AI can analyze production data in real-time to identify bottlenecks and issues in the production process, proposing optimization solutions to improve production efficiency. In automotive manufacturing, AI can monitor and analyze various aspects of production lines, adjusting production rhythms in real time to avoid delays.
In intelligent quality control, AI can use image recognition and data analysis technologies to conduct real-time inspections of products, promptly detecting quality issues and improving product quality. In electronics manufacturing, AI can inspect solder joints on circuit boards to ensure that solder quality meets standards. AI can also achieve personalized production and services based on customer needs, meeting diverse customer demands.
(5) Fintech
In the fintech sector, generative AI plays a key role. In risk management, AI can analyze vast amounts of financial data to identify potential risk factors, providing financial institutions with more accurate risk assessments and warnings. In investment decision-making, AI can provide personalized investment advice based on market conditions and data analysis, helping investors make more informed investment decisions.
In fraud prevention, AI can monitor and analyze transaction data in real-time to promptly detect fraudulent activities, protecting the interests of financial institutions and customers. Ant Financial’s risk control system utilizes AI technology to monitor transaction risks in real time, effectively identifying and preventing fraudulent activities, safeguarding users’ funds.
5. Support and Assurance Layer: The Backbone of Steady Development
Behind the booming development of the generative artificial intelligence industry, the support and assurance layer plays an indispensable role, providing comprehensive security for the stable advancement of the industry. This layer mainly includes key elements such as policies and regulations, talent cultivation, investment drives, and ecosystem construction.
(1) Policies and Regulations
Policies and regulations serve as the “guiding baton” for industrial development, ensuring the healthy growth of generative artificial intelligence. The “Interim Measures for the Administration of Generative Artificial Intelligence Services,” effective from August 15, 2023, is an important regulation in this field. It clarifies the requirements for algorithm design and filing, training data, models, etc., emphasizing the protection of user privacy and trade secrets, as well as supervision, inspection, and legal responsibilities. This measure encourages technological innovation while proposing a regulatory approach of “inclusive prudence and graded classification” for generative artificial intelligence services, standardizing industry behavior while allowing room for corporate innovation, promoting orderly industry development.
(2) Talent Cultivation
Talent is the core driving force behind the development of generative artificial intelligence. To meet the urgent demand for talent in the industry, higher education reform is in full swing. An increasing number of universities are offering artificial intelligence-related majors, optimizing curriculum design, and strengthening practical teaching to cultivate students’ innovation and practical abilities. Universities across the country are establishing innovation and entrepreneurship bases and encouraging students to participate in various competitions to stimulate their interest and enthusiasm for artificial intelligence, cultivating a large number of outstanding talents.
Industry-academia-research collaboration is also an important avenue for cultivating AI professionals. Enterprises possess rich practical experience and real project capabilities, while universities and research institutions have strong research capabilities and talent reserves. Through industry-academia-research collaboration, resources can be shared, and advantages can be complementary, jointly cultivating high-quality talents who understand both theory and practice. Major internet companies actively participate in university competitions, providing technical support to students and helping them apply theoretical knowledge to real projects, fostering students’ innovation and practical abilities.
(3) Investment Drives
Investment acts as the “booster” for industrial development, injecting continuous financial momentum into generative artificial intelligence. Venture capital favors innovative startups, providing them with early-stage funding support to help these companies grow rapidly. ByteDance received substantial venture capital support early on, enabling it to continually explore and innovate in the field of artificial intelligence, launching popular products like Douyin and Toutiao.
Mergers and acquisitions drive industry integration and upgrading. By acquiring companies with advanced technologies or quality resources, enterprises can quickly enhance their technological strength and market competitiveness. Google’s acquisition of DeepMind greatly improved its technological capabilities in artificial intelligence, laying a solid foundation for subsequent technological research and product innovation. Government-guided funds also play an important role by directing social capital toward the artificial intelligence sector, promoting industry clustering and development. Many regions have established artificial intelligence industry guidance funds, focusing on supporting foundational research, technology development, and application demonstrations, providing strong financial backing for industry development.
(4) Ecosystem Construction
Ecosystem construction serves as the “incubator” for industrial development, creating a favorable environment for the innovative growth of generative artificial intelligence. Open-source communities provide developers with an open and shared platform where they can exchange experiences, share code, and collaboratively develop projects. Baidu’s PaddlePaddle open-source platform has attracted numerous developers, promoting the rapid development and application of artificial intelligence technologies.
Industry alliances gather enterprises, universities, and research institutions from the upstream and downstream of the industry chain, strengthening cooperation and communication to jointly promote technological innovation, standard formulation, and market expansion. The China Artificial Intelligence Industry Development Alliance actively organizes member units to conduct technical seminars, project collaborations, and other activities, facilitating coordinated industry development. Innovation centers provide enterprises with one-stop services for research and development, testing, and incubation, accelerating the transformation and application of scientific and technological achievements. Various local artificial intelligence innovation centers offer a favorable development environment and resource support for startups, helping them grow and thrive rapidly.
6. Conclusion and Outlook: Sailing Towards the Future with AI
Looking back at the industrial ecosystem of generative artificial intelligence, from the solid foundation of the infrastructure layer to the innovation-driven technical platform layer, and then to the extensive empowerment of the application scenario layer, as well as the support and assurance layer’s safeguarding role, all aspects are closely connected and advancing together, forming a vibrant and potential-filled organic whole.
With continuous technological advancement, generative artificial intelligence is expected to demonstrate its powerful capabilities in more fields. In the future, we anticipate seeing smarter medical diagnostic systems that can predict diseases in advance, providing patients with more precise treatment plans; more personalized educational services that offer tailored learning paths based on each student’s unique needs and learning styles; and more realistic virtual worlds that allow people to immerse themselves in various wonderful scenarios and stories.
Under the guidance of policies and regulations, the industry will develop more normatively and healthily. Strengthened talent cultivation will inject a continuous stream of fresh blood into the industry. Ongoing investment drives will promote the rise and development of more innovative enterprises. Continuous improvements in ecosystem construction will create a more open and collaborative innovative environment.
The future of generative artificial intelligence is filled with infinite possibilities; it will fundamentally change our lives and society. Let us seize this historical opportunity, actively engage in the wave of this technological revolution, contribute to the development of generative artificial intelligence, and jointly create a better future.