“The media industry is transitioning from integrated media to intelligent media.” During the “Intelligent Integration Future - 2023 Artificial Intelligence Achievement Exhibition”, Professor Guo Quanzhong from the School of Journalism and Communication at Minzu University of China stated in an interview with People’s Daily Online that integrated media is the integration of media forms at the same level, while intelligent media is the evolution of media forms to a higher level. Currently, many mainstream media have actively participated in it.

“Media has both opportunities and challenges in the booming development of artificial intelligence today” Guo Quanzhong said, firstly, artificial intelligence technology can establish a direct connection between media and users, thereby providing users with better experiences and content products. Secondly, the iteration of artificial intelligence technology, especially algorithm recommendations, intelligent distribution, marketing strategy customization, helps in the production, distribution, and operation of media content, bringing huge new opportunities for the development of media integration.

Regarding the challenges that artificial intelligence brings to the media industry, Guo Quanzhong stated that they mainly focus on two aspects including conceptual transformation and research and development funding. Currently, many media outlets have not yet regarded technology as a core driving force, and their main focus is still on news content, resulting in insufficient emphasis on technology. In addition, training large models requires a significant investment in technology, manpower, and equipment, which is relatively expensive for many mainstream media and requires seeking solutions.

Data is one of the important elements and resources for current development. Specifically, how can mainstream media do well in data related products?

Guo Quanzhong summarized that in order for mainstream media to do well in data products, the first step is to solve the problem of data sources, so as to truly understand user needs and provide corresponding services. In the continuous development of generative artificial intelligence today, there is also an important issue with big models, which is value alignment. This also requires a large amount of data for training, truly making AI well and matching correct values.