2024-2030 China Generative AI Industry Market Operation Situation and Development Trend Research Report

2024-2030 China Generative AI Industry Market Operation Situation and Development Trend Research Report

Report No.: 1756668

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The "2024-2030 China Generative AI Industry Market Operation Situation and Development Trend Research Report" released by Boyan Consulting consists of 14 chapters. Firstly, the market development environment of the generative AI industry and the overall operation situation of generative AI are introduced, and then the current situation of the market operation of the generative AI industry is analyzed, and then the competition pattern of the generative AI market is introduced. Subsequently, the report analyzes the business status of key enterprises in generative AI, and finally analyzes the development trend and investment forecast of the generative AI industry. If you want to have a systematic understanding of the generative AI industry or want to invest in the generative AI industry, this report is an indispensable and important tool for you.

The data of this research report mainly use national statistical data, General Administration of Customs, questionnaire survey data, and data collected by the Ministry of Commerce. Among them, the macroeconomic data mainly comes from the National Bureau of Statistics, some industry statistics mainly come from the National Bureau of Statistics and market research data, the enterprise data mainly comes from the National Bureau of Statistics large-scale enterprise statistical database and stock exchanges, etc., and the price data mainly comes from various market monitoring databases.

Table of contents of the text

Chapter 1: Overview of Generative AI

1.1 Definition of Generative AI

1.2 History of generative AI

1.3 Generative AI Workflows

1.3.1 Model training

1.3.2 Model Selection

1.3.3 Generating Data

1.3.4 Evaluation of generated results

1.3.5 Adjusting the Model

1.4 Advantages of Generative AI

1.4.1 New content may be created

1.4.2 Efficiency and productivity can be improved

1.4.3 The quality of the generated content can be improved

1.4.4 New applications and uses can be realized

Chapter 2 Overview of the Development of Generative AI Technologies

2.1 Overview of the development of generative AI technology

2.2 Generative Adversarial Networks (GANs)

2.2.1 Concept and Introduction

2.2.2 GAN neural network composition

(1) Generator

(2) Discriminator

2.2.3 GAN Generator Workflow

2.2.4 GAN Discriminator Workflow

2.3 扩散模型（Diffusion Models）

2.3.1 Concept and Introduction

2.3.2 Comparison of GAN and diffusion models

2.4 文生图技术（Text to Image）

2.4.1 Concept and Introduction

2.4.2 Development History

2.5 Generative AI Correlation Techniques

2.5.1 Computer Science

2.5.2 Internet Technology

2.5.3 Machine Learning Methods

2.6 Generative AI research hotspots

2.6.1 Pre-training techniques

2.6.2 Graph neural network technology

(1) Graph convolutional neural network

(2) Space-based graph convolutional neural network

Chapter 3: Analysis of the Generative AI Industry Chain and Business Model

3.1 Generative AI industry chain model

3.2 Generative AI business models

3.2.1 Mode 1: Ecological Builder - Ecological Application of the Whole Industry Chain Scenario as a breakthrough

3.2.2 Mode 2: Technical algorithm driver - technical layer Scenario application as a breakthrough

3.2.3 Mode 3: Application Focuser - Scenario Application

3.2.4 Mode 4: Pioneer in the Vertical Domain - Killer Application Gradually build a vertical ecosystem

3.2.5 Mode 5: Infrastructure Providers - Start from infrastructure and expand to the downstream of the industrial chain

3.3 Analysis of the development of China's generative AI industry

3.3.1 Market status of the generative AI industry

3.3.2 Generative AI industry financial analysis

3.3.3 Competitive landscape of the generative AI industry

Chapter 4: Upstream Composition and Major Players of the Generative AI Industry Chain

4.1 The main components of the upstream of the generative AI industry chain

4.1.1 Data Supply

4.1.2 Data analysis and annotation

4.1.3 Creator Ecology

4.1.4 Underlying Mating Tools

4.4.5 Research on related algorithms and models

4.2 Major players in the upstream of the generative AI industry chain

Chapter 5 Composition and Major Players of the Midstream of the Generative AI Industry Chain

5.1 The main components of the midstream of the generative AI industry chain

5.1.1 Content Design

5.1.2 Content Creation Tools

5.1.3 Operational Efficiency

5.1.4 Personalized Marketing

5.5.5 Data combing

5.2 Major players in the midstream of the generative AI industry chain

Chapter 6 Downstream Composition and Major Players of the Generative AI Industry Chain

6.1 The main components of the midstream of the generative AI industry chain

6.1.1 Content Creation and Distribution Platforms

6.1.2 Third-Party Distribution Channels

6.1.3 Content terminal production

6.1.4 Third-Party Content Service Providers

6.6.5 AIGC内容检测

6.2 Major players in the downstream of the generative AI industry chain

Chapter 7 Application of Generative AI Technology

7.1 The main application areas of generative AI technology are currently available

7.1.1 Entertainment media and content creation

7.1.2 Code Software Domain

7.1.3 Biomedicine

7.2 Applications of generative AI in other industries and technologies

7.2.1 Automotive technology

7.2.2 Supply Chain Technology

7.2.3 Electronic Commerce

7.2.4 Fintech

7.2.5 Medical information technology

7.2.6 Digital Wellbeing

7.2.7 Games

7.2.8 Agricultural science and technology

7.2.9 Food technology

7.2.10 Climate technology

7.2.11 企业SaaS

7.2.12 AI and machine learning

7.2.13 Information Security

7.2.14 Internet of Things

7.2.15 Cryptocurrencies/Web3

7.2.16 Insurtech

Chapter 8: Generative AI Phenomenal Application – ChatGPT

8.1 ChatGPT简介

8.2 ChatGPT Key Features

8.3 ChatGPT发展趋势

8.3.1 Machine Learning

8.3.2 Neural Networks

8.3.3 Transformer算法

8.4 Development of GPT algorithms

8.5 ChatGPT vs. InstructGPT

8.5.1 ChatGPT与InstructGPT的相同点

8.5.2 Differences between ChatGPT and InstructGPT

Chapter 9 ChatGPT Applications and Potential

9.1 Applications of ChatGPT

9.1.1 ChatGPT opens up a large number of application scenarios

9.1.2 ChatGPT is expected to be a catalyst for the next generation of search engines

9.2 Room for improvement in ChatGPT

9.2.1 may write answers that seem reasonable but are incorrect or absurd

9.2.2 Be sensitive to adjustments to the wording of the input or to try the same prompt multiple times

9.2.3 Models are often too verbose and overuse certain phrases

9.2.4 The model rejects inappropriate requests and sometimes responds to harmful instructions or behaves in biased manners

Chapter 10 ChatGPT's technical line

10.1 Based on GPT-3.5, GPT-4 is expected to improve more significantly

10.1.1 ChatGPT is the workhorse model based on GPT-3.5

10.1.2 GPT-4 is expected to become a multimodal artificial intelligence

10.2 GPT-4 is expected to become a multimodal artificial intelligence

10.3 Leading NLP models

10.4 RLHF and TAMER are important architectural supports

Chapter 11 ChatGPT Infrastructure

11.1 ChatGPT's core infrastructure, the AI Supercomputing Center

11.1.1 The concept and basic unit of computing power

11.1.2 Overview of the giant's layout of AI supercomputing centers

11.2 AI servers, the key hardware of the next-generation AI data center

11.2.1 Data center industry chain analysis

11.2.2 Total investment structure and hardware investment structure of data centers in China

11.2.3 The scale and growth rate of computing power in mainland China from 2019 to 2023

11.2.4 Internal structure of computing power in mainland China from 2019 to 2023

11.2.5 Global China AI Server Market Size

11.3 GPU, the "heart" of AI computing power

11.3.1 AI chips are the "heart" of AI computing power

11.3.2 Market structure of AI chips

11.3.3 Advantages of AI chips

11.3.4 Global and China AI chip market size

11.3.5 Market Outlook for Accelerated Servers

Chapter 12 Overview of OpenAI's development

12.1 OpenAI Company Profile

12.2 History of OpenAI

12.3 OpenAI's Organizational Structure and Operating Structure

12.4 Commercialization of OpenAI

12.4.1 OpenAI's business model is API interface fees

12.4.2 OpenAI's main business overview and product matrix

12.5 OpenAI's core products

12.5.1 Core product – DALL E 2

12.5.2 Core Product – Whisper

Chapter 13 Research on Key Enterprises in the Generative AI Industry

13.1 Inspur Electronic Information Industry Co., Ltd

13.1.1 Basic situation of enterprise development

13.1.2 Analysis of business conditions

13.1.3 Enterprise Generative AI Business Situation

13.1.4 Analysis of the core competitiveness of enterprises

13.1.5 Analysis of enterprise development strategy

13.2 Changsha Jingjia Microelectronics Co., Ltd

13.2.1 Basic situation of enterprise development

13.2.2 Analysis of business conditions

13.2.3 Enterprise Generative AI Business Situation

13.2.4 Analysis of the core competitiveness of enterprises

13.2.5 Analysis of enterprise development strategy

13.3 iFLYTEK Co., Ltd

13.3.1 Basic information of enterprise development

13.3.2 Analysis of business conditions

13.3.3 Enterprise Generative AI Business Situation

13.3.4 Analysis of the core competitiveness of the enterprise

13.3.5 Analysis of enterprise development strategy

13.4 Haiguang Information Technology Co., Ltd

13.4.1 Basic situation of enterprise development

13.4.2 Analysis of business conditions

13.4.3 Enterprise Generative AI Business Situation

13.4.4 Analysis of the core competitiveness of enterprises

13.4.5 Analysis of enterprise development strategy

13.5 Cambrian Technology Co., Ltd

13.5.1 Basic situation of enterprise development

13.5.2 Analysis of business conditions

13.5.3 Enterprise Generative AI Business Situation

13.5.4 Analysis of corporate financing

13.5.5 Analysis of enterprise development strategy

13.6 Cloudwalk Technology Group Co., Ltd

13.6.1 Basic situation of enterprise development

13.6.2 Analysis of business conditions

13.6.3 Enterprise Generative AI Business Situation

13.6.4 Analysis of the core competitiveness of enterprises

13.6.5 Analysis of enterprise development strategy

13.7 Beijing Haitian AAC Technology Co., Ltd

13.7.1 Basic situation of enterprise development

13.7.2 Analysis of business conditions

13.7.3 Enterprise Generative AI Business Situation

13.7.4 Analysis of the core competitiveness of enterprises

13.7.5 Analysis of enterprise development strategy

13.8 Tors Information Technology Co., Ltd

13.8.1 Basic situation of enterprise development

13.8.2 Analysis of business conditions

13.8.3 Enterprise Generative AI Business Situation

13.8.4 Analysis of corporate financing

13.8.5 Analysis of enterprise development strategy

13.9 360 Security Technology Co., Ltd

13.9.1 Basic situation of enterprise development

13.9.2 Analysis of business conditions

13.9.3 Enterprise Generative AI Business Situation

13.9.4 Analysis of corporate financing

13.9.5 Analysis of enterprise development strategy

13.10 Baidu Group Co., Ltd

13.13.1 Basic situation of enterprise development

13.13.2 Analysis of business conditions

13.13.3 Enterprise Generative AI Business Situation

13.13.4 Analysis of corporate financing

13.13.5 Analysis of enterprise development strategy

Chapter 14 Generative AI Industry Development Prospects and Market Space Measurement

14.1 Generative AI Industry Trends

14.1.1 Multimodal Language Processing Fusion

14.1.2 Generative AI applications are maturing

14.1.3 Growing demand for generative AI

14.1.4 Promote the ubiquity of smarter, low-cost robots and virtual assistants

14.1.5 Fundamentally build a self-renewing metaverse

14.2 Challenges in the Generative AI Industry

14.2.1 Better Algorithms

14.2.2 In-depth analysis of language

14.2.3 Interdisciplinarity

14.2.4 Data Quality Issues

14.2.5 Compute Resource Limitations

14.2.6 Interpretability Issues

14.2.7 Multimodal and Cross-Modal Generation Problems

14.2.8 Legal and Ethical Issues

14.3 Drivers for the development of the generative AI industry

14.3.1 The evolution of generative AI elements has ushered in a transformative development of the industry

14.3.2 The growth of intelligent demand in traditional industries has led to an increase in the demand for language processing

14.4 Restraints for the development of the generative AI industry

14.4.1 There are technical challenges with generative AI

14.4.2 Generative AI models are not very versatile

14.5 Generative AI Industry Investment Risks

14.5.1 Generative AI technology innovation and development are less than expected

14.5.2 User acceptance is lower than expected

14.5.3 Industry policy and regulatory risks

14.6 Generative AI Industry Market Space Forecast 2024-2030