AIGC-Driven: Essential Data 'Fuel'
AIGC, which stands for Artificial Intelligence Generated Content, has surged in popularity. In September 2022, AI-generated art took the world by storm, and two months later, ChatGPT made a dramatic entrance, showcasing the immense power of AIGC. The year 2022 has been hailed as the "Year of AIGC."
With the rise of AIGC, numerous startups have joined the AIGC track. At the same time, major internet companies both domestically and internationally are racing to position themselves in the AIGC field. Currently, AIGC has achieved innovative development in industries such as AIGC+media, AIGC+e-commerce, AIGC+film, and AIGC+entertainment.
The potential of AIGC is enormous, with application scenarios continuously expanding across multiple domains. To gain a competitive edge in the future market, various industries are actively exploring integration with AIGC and implementing application scenarios. When enterprises plan their AIGC strategies, these issues cannot be ignored:
Data Quality
AIGC technology generates various types of content such as images, articles, and videos through methods like data pre-training models and generative adversarial networks. AIGC does not generate content out of thin air; it requires a large amount of high-quality data to support the training of models. Data from different fields and scenarios have their own characteristics and certain differences. Content generated through publicly available datasets is more universal but lacks field specificity.
Data Sources
The demand for data in AIGC model training is substantial. Uncertain data sources can lead to content of unclear or mixed origins, making it difficult to determine the ownership of the generated content, which may involve copyright disputes. Data is the crucial "fuel" that drives AIGC.
When enterprises layout AIGC, they must first address data issues, including not only structured databases but also various unstructured data such as text, images, and audio-visual materials. In the development of AIGC, an unstructured data middleware can serve as a high-quality data "fuel pool" with field, scenario, and business characteristics.
The content managed by the unstructured data middleware covers a wealth of content such as documents, images, and audio-visual materials.
For enterprises, these data have field specificity and are closely related to business scenarios, representing high-quality field data.
When AIGC trains algorithms based on this data, the generated content is more relevant to the enterprise. Moreover, the volume of enterprise unstructured data is large and growing rapidly, with new unstructured data being generated every day.
Based on this, the training data for AIGC is inexhaustible, and the algorithm models can be updated in real-time with data increments.
Because the data stored in the enterprise unstructured data middleware comes from daily office work, the source and ownership of the data are clear. Using this as the training data for AIGC ensures that the source of the generated content is guaranteed, eliminating copyright risks.
Data is an essential foundation for AIGC and directly affects the quality and effectiveness of the generated content. Enterprises must address the issue of data sources when laying out AIGC capabilities.
Supporting AIGC training models cannot rely solely on public datasets; it is essential to build an enterprise user dataset.
Unstructured data, which accounts for a large proportion of enterprise data and has industry and business characteristics, with clear data sources and ownership, is an important part of the user dataset.
Building an unstructured data middleware is the best choice for providing data support for AIGC.
