In the context of the intelligent transformation of mainstream media, generative artificial intelligence technology has become a key driving force for this transformation. This article aims to analyze the new model of human-machine relationships in the AIGC era and deeply analyze the functions and status of AIGC technology in promoting the intelligent transformation of mainstream media, proposing transformation paths such as intelligent collection, intelligent production, intelligent distribution, intelligent review, and intelligent feedback.
In the context of intelligence, AIGC technology, as an emerging media production method, has increasingly highlighted its status and role in media integration.As intelligent technology continues to penetrate the media industry, intelligence will become an important path and inevitable direction for the digital transformation of mainstream media.During this process, how will the human-machine relationship change?This article explores how AIGC promotes the intelligent transformation of mainstream media from the perspective of human-machine collaboration by outlining the development process of AIGC in the media field.
With the continuous advancement of media intelligence, the important role of AIGC has attracted widespread attention in academia. AIGC is a new production method that automatically generates content using artificial intelligence technology, following professional generated content (PGC) and user-generated content (UGC). It amplifies the advantages of automatic content generation and AI autonomous learning, possessing unique attributes such as high content diversification, strong autonomous learning capability, wide operational range, and significant production efficiency.
With the continuous optimization of algorithms and the improvement of computing power, the AIGC production model has undergone a gradual development from simple content generation to complex scenario simulation. In the process of applying artificial intelligence technology to content production, we can divide its development into three main stages: early exploration, deep learning, and generative AI.
The first is the early exploration stage: during this period, artificial intelligence technology initially entered the field of content creation, capable of automatically generating articles based on specific topics, styles, and depth requirements, such as rule-based automatic writing and image recognition technologies.
Then came the deep learning technology stage: with the rapid development of deep learning technology, the AIGC production model ushered in a new leap. The emergence of technologies such as Generative Adversarial Networks (GANs) enables artificial intelligence to imitate the human creative process to a certain extent, such as generating new musical works and voice synthesis, bringing new content creation methods to broadcasting and film industries.
Finally, there is the generative AI stage: in this stage, the AIGC production model achieves higher autonomy and creativity, capable of converting text content into images or audio, or converting audio content into text, thereby providing content creators with a more diverse and creative toolkit.
Opportunities and Challenges of AIGC for Media
As AIGC gradually enters the media field and injects significant development momentum into the industry, its important role in promoting new changes in media integration has sparked widespread discussion in academia.
In terms of innovative content production, many scholars believe that AIGC greatly enhances the efficiency of news creation, and its continuous innovation capability profoundly impacts all aspects of news production, promoting the innovation process of journalism.
AIGC also plays an important role in enhancing user interaction and experience. A collaboration between Lehigh University and Microsoft Research pointed out that the Sora system effectively enhances the coverage of news reports and audience engagement by rapidly generating news reports or explanatory video content.
AIGC effectively promotes industry communication and reshapes the competitive landscape of the industry. Under the promotion of the AIGC production model, the competitive situation in the media industry has undergone significant changes. On one hand, media platforms with resources and technology demonstrate strong capabilities in content creation, review, and dissemination, forming a clear head effect. These platforms occupy a large market share through large-scale production and high-quality content output, continuously expanding their industry influence and voice. On the other hand, small and medium-sized media platforms adopt differentiated strategic paths in the face of fierce competition, focusing on specific fields or target user groups, providing specialized and customized content services. They rely on keen market insight and strong innovation capabilities to quickly identify market opportunities and launch innovative products and services, thus standing out in their respective market segments.
Although AIGC plays a crucial role in promoting progress and transformation in the media industry, the issues of user data security and privacy protection involved in its actual application and promotion also pose challenges that cannot be ignored. This issue not only relates to ethical considerations in technology but is also a key factor in ensuring the sustainable development of AIGC in the media field, which must attract high attention from both academia and industry. In this regard, Professor Yu Guoming and others proposed that while AIGC technology activates individual rights and increases the entropy of social knowledge, it simultaneously weakens the authority of mainstream media in interpreting the authenticity of news. Cui Yan believes that AIGC has multiple advantages in optimizing news production processes, but faces challenges in user privacy protection and maintaining information authenticity. In response, researchers such as Chen Lidan suggest that although AIGC has significant advantages in improving efficiency, reducing costs, and creating new content forms, traditional news dissemination institutions still have the responsibility to uphold the transparency and reliability of news reporting to protect the credibility established in the era of mass communication. It can be seen that the news industry will gradually form a complex network structure of human-machine interaction and collaboration. This “human-machine integration” cooperative model signifies the reshaping of the relationship between humans and media, as well as social relationships, while also revealing that the collaborative development of humans and machines has become an irreversible trend. We believe that “going with the flow” is the inevitable choice for mainstream media to achieve intelligent transformation, and promoting the collaborative development of humans and machines is the only way for deep integration in media.
Human-Machine Collaboration Reshapes the New Pattern of News Production
The relationship between humans and algorithm technology has fallen into a binary opposition. When technology is objectified as the opposite of humans, it focuses the core of the human-machine relationship on the various social issues brought about by technology. With the continuous advancement of the technological revolution, based on the “isomorphism” characteristics of “humans” and “machines,” the human-machine relationship has evolved adaptively under the influence of the technological revolution, transforming from the traditional “bifurcation” model to a progressive complex relationship of “dependence,” “permeation,” and “embedding.” Machines can act as independent subjects of content creation, forming a collaborative relationship with human creators to jointly promote news production. Although the relationship between humans and machines has achieved binary unity from the perspective of technology philosophy, in the process of human-machine collaboration promoting news production, “human-centered” remains an important premise, and the dominant position of humans in the subject relationship is beyond doubt.
Human-Machine Collaboration of Production Subjects.At the level of production subjects, the penetration of artificial intelligence technology encourages journalists to break traditional occupational boundaries, thus shaping journalists with comprehensive media literacy. Before the empowerment of artificial intelligence in news production, journalists in different roles such as news editors, text reporters, photographers, and editors had clear divisions of labor, each responsible for specific production links. However, as artificial intelligence technology integrates into various links of news production, these occupational boundaries gradually diminish, allowing journalists to leverage the power of technology to become true versatile talents. For example, news reporters can use automatic content generation models to sort the first-hand materials collected and generate news articles, thereby diminishing the role of traditional news editors; news editors can also utilize natural language processing large models for material and data retrieval, effectively saving the workload of information collection.
Human-Machine Collaboration in Content Production.Supported by the three core technologies of data, algorithms, and computing power, AIGC technology can act as an independent subject participating in content production and exhibiting outstanding creative advantages. However, compared with professional content production and user-generated content by human subjects, AIGC is relatively limited in subjective creativity, personalized expression, and emotional depth. In this context, the complementary interaction of human-machine collaborative work plays a significant role, greatly facilitating the production process of high-quality news content. With the embedding of AIGC, news content production can be completed through a “humanoid” intelligent machine assistance. By inputting instructions, AI can quickly analyze the user’s intentions and goals, effectively filter out redundant information, and rapidly generate massive audio, video, or multimedia content. In media asset management, artificial intelligence technology can assist media organizations in achieving automated efficient retrieval, classification, and management of audio and video resources.
Human-Machine Collaboration in Production Processes.With the application of artificial intelligence technology, various stages of news production have achieved integration and collaborative work. The traditional news production process involves three core links: information collection, content production, and news distribution, while the deep intervention of artificial intelligence technology is gradually breaking down the boundaries between these links. By utilizing artificial intelligence technology, information collection and content production can be integrated within automatic content generation models, allowing journalists to directly obtain a complete news article that combines information gathering and content arrangement from the model without direct intervention. With the continuous advancement of artificial intelligence technology, the news distribution link is also expected to be further integrated into the realm of artificial intelligence. After the content automatic generation model completes the tasks of information collection and content production, artificial intelligence will be able to directly push the generated content to target audiences through algorithm technology, thus achieving collaborative work throughout the entire news production process.
Application of AIGC in Mainstream Media Business Processes
In the context of comprehensive media transformation driven by big data and intelligent technology, intelligent logic has deeply penetrated various links of mainstream media news production. How to accelerate the media integration process and quickly form a new type of mainstream media with communication power, guiding power, influence, and credibility is the core goal of media integration transformation. The evolution of AIGC technology from the perspective of human-machine collaborative relationships provides a path for the intelligent transformation of mainstream media.
Intelligent Topic Selection and Interviewing.The application of big data and artificial intelligence technology has significantly enhanced the information collection and data analysis capabilities of the media, becoming an important support for topic planning and all-media interviewing. In terms of intelligent collection, the AI editing department of People’s Daily has launched a “multi-modal search” function that covers intelligent text search, image search, video search, multilingual search, semantic search, etc., effectively improving the information collection efficiency of editors and reporters. In news interviewing, Xinhua News Agency has developed two tools known as “interview artifacts”: the “Intelligent Interview Terminal” APP and the “Sound Box.” The “Intelligent Interview Terminal” APP can adapt to different mobile reporting scenarios, allowing a single mobile phone to handle multimedia articles; the “Sound Box” has language recognition, 3D printing, and other functions, can adapt to various interview devices, and can complete voice transcription with one click, greatly enhancing interviewing efficiency.
Intelligent Production and Multimedia Content Creation.In the news production and creation link, artificial intelligence technology is driving profound changes in the media industry. The widespread use of rich intelligent multimedia editing tools, virtual anchors, AIGC, and other technologies liberates journalists from tedious tasks such as text recording, image processing, audio and video editing, and news dubbing, allowing them to focus more on the creativity and depth of reporting, thus creating more valuable news products. Based on big data and artificial intelligence technology, media has generally adopted visual methods to present text content and data information, and create creative posters to enhance the attractiveness and expressiveness of news reports. In multi-modal generation scenarios, based on AIGC technology, media has also launched political cartoon creation platforms and poetry painting platforms, further expanding the expression forms and dissemination channels of news content. In terms of video, intelligent video production tools are continuously emerging, effectively reducing the difficulty of video processing and increasing production speed. For example, the AI cloud editing platform of China Central Radio and Television Station can intelligently process 12 live broadcast input signals in three stages of “finding,” “selecting,” and “editing,” producing a short video in about 90 seconds. New media video articles can also be transferred for large-screen dissemination, thus forming a bidirectional dissemination support pattern of large screens supporting small screens and small screens feeding back large screens.
Intelligent Distribution and Personalized Push.In recent years, mainstream media has actively explored new algorithm models such as “Party Media Algorithm” and “General Station Algorithm,” aiming to integrate mainstream value orientation into algorithm recommendation mechanisms and promote the healthy and orderly development of the communication ecosystem. The “General Station Algorithm” established by China Central Radio and Television Station has created a content funnel model from segmented content to explosive content, while also establishing a traffic ladder model from highly active users to less active users, gradually expanding the audience push range for high-quality content that may become hotspots or hits, thus effectively testing the communication power of content. This model uses algorithms to mine potential hotspots, accurately matching user preferences with limited content, effectively assisting new user attraction and old user retention, achieving dual growth in the number and activity of users at the general station. Since 2021, the “General Station Algorithm” has achieved significant results in multiple business segments, with related indicators showing significant growth compared to before the application of algorithms, reflecting the positive role of algorithm models in enhancing content communication power and user engagement.
Intelligent Review and Proofreading.Intelligent review technology can effectively detect errors or violations in text, video, audio, and image content through multi-dimensional precise identification, thereby improving content quality and communication safety. The “Smart Assistant” system developed by the People’s Daily’s content cognition national key laboratory integrates cutting-edge technologies such as big data and artificial intelligence, and utilizes natural language processing, machine learning, and deep transfer learning technologies to achieve machine-assisted identification and prompts for irregular content in manuscripts, images, and videos, providing correction suggestions. This system can replace part of the traditional proofreading work, assisting content creators in discovering and correcting issues, thereby greatly improving proofreading and review efficiency and accuracy. The “Smart Assistant” has been successfully applied in the intelligent review business of the People’s Daily and has achieved good results in multiple fields.
Intelligent Feedback and Public Opinion Monitoring.The application of artificial intelligence technology in the media field enables real-time evaluation and comprehensive assessment of user feedback through massive data processing and ultra-large-scale computing, providing content producers and operators with effective means to timely understand the communication effects and paths of their content. At the same time, artificial intelligence technology also provides important bases for public opinion management, false information governance, copyright protection, and other fields, and gives rise to new business models. The all-media information inspection and warning platform “Smart” developed by CCTV has video monitoring capabilities and can customize personalized feature libraries on demand, comprehensively perceiving changes in content trends across all media platforms.
With the rapid development and iteration of artificial intelligence technology, when constructing a media development model oriented towards new quality productivity, mainstream media must drive AIGC from the perspective of human-machine collaboration to promote its intelligent transformation. This not only means that mainstream media should actively embrace new technologies but, more importantly, deeply integrate new technologies with new production factors into the production process to achieve efficient allocation and utilization of production factors. However, the application of technology also raises challenges in ethics, privacy, and data authenticity. As human-generated content and machine-produced content gradually form the world we rely on, the human-machine collaboration in the news production process will become closer. Therefore, news media need to continuously innovate technology and conduct ongoing ethical scrutiny to ensure that AIGC technology can play a positive and key role in promoting the development of the news industry.
This article is supported by the Fundamental Research Funds for the Central Universities.
(Author Zhao Zizhong is the dean and professor of the School of New Media at Communication University of China, Wang Zhe is a doctoral student at the School of Advertising of the same university, and Zhou Yanchi is a master’s student at the School of New Media.)
The original article was published in the “News Front” November (Lower Issue)..
New Media Editor: Cao Yafang
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