The development of AI large models is advancing rapidly, bringing tremendous changes to various industries. However, behind the glamour, AI large models also face many challenges, with data being the core issue.
Data Quality: Garbage In, Garbage Out
The training of AI large models relies on massive amounts of data, but the quality of data varies greatly. If the input data itself has biases, errors, or noise, then the trained model will naturally "learn badly," and the output results will also be "garbage." Therefore, data quality directly determines the performance and reliability of AI large models.
Data Sources: Facing Depletion
As AI large models continue to develop, their demand for data is also increasing. However, the total amount of data on Earth is limited. When all the data in the world has been trained, AI will face the predicament of "no rice to cook." Therefore, how to find new data sources, or how to use existing data more efficiently, has become an urgent problem to be solved in the AI field.
Data: The "Fuel" and "Nutrients" of AI
Data is the "fuel" of AI, and also the "nutrients" for the healthy growth of AI. Without high-quality data, AI large models cannot operate and develop normally. Therefore, when enterprises consider introducing AI technology, they should not blindly pursue the scale and complexity of the model, but should focus on the preparation and management of data.
Enterprise Data Preparation Work
Enterprise data preparation work is a complex and huge project, mainly including the following two aspects:
Structured Data: Structured data is usually stored in databases, business systems, and other systems, with a clear structure and format, which is easy to process and analyze. For example, customer information, sales data, financial data, etc.
Unstructured Data: Unstructured data includes images, text, documents, audio, video, etc., distributed in different systems and platforms, with the characteristics of dispersion and isolation, which is difficult to process structurally. For example, user comments on social media, customer service records, product manuals, etc.
Challenges and Opportunities of Unstructured Data
Compared with structured data, unstructured data is more difficult to process, but it also contains greater value. How to effectively extract, clean, integrate and utilize unstructured data has become the key for enterprises to gain a competitive advantage in the AI era.
Baklib: A Sharp Tool for Unstructured Data
There are many tools for processing unstructured data on the market, and Baklib is one platform worthy of attention. Baklib's original "Resource Library - Knowledge Library - Experience Library" three-tier architecture can well achieve AI data preparation work:
Resource Library: Centrally store and manage various unstructured data, such as text, images, documents, audio, video, etc.
Knowledge Library: Structure the data in the resource library and extract useful information and knowledge.
Experience Library: Apply the knowledge in the knowledge library to various scenarios to provide users with personalized experiences.
Conclusion: AI Data Ready is the Key
Enterprises must first do a good job of AI Data Ready preparation to successfully introduce AI technology. Only with high-quality, diversified, and easy-to-manage data can AI large models exert their true potential and bring greater value to enterprises.
