About AI and Data Structuring

If AI develops further in the future, structuring data will become important.

As AI develops further, structuring data will become increasingly important.

Modern AI is often based on machine learning algorithms using large amounts of data. These algorithms take data as input and use it to understand patterns and trends and make predictions about unknown data. Therefore, the way data is structured has a direct impact on the performance of AI.

Structured data is divided into distinct fields or columns, each of which is assigned a distinct data type. This allows data to be easily analyzed, queried, and relationships to be identified. It also improves data quality and avoids false predictions and inaccurate results.

Conversely, unstructured data, for example in the form of natural language, can be difficult for AI to handle. Because processing unstructured data is a sophisticated challenge for AI, data structuring may become increasingly important.

In short, as AI evolves further, data structuring will become increasingly important, thereby improving AI performance.

Do you think that the development of natural language processing will enable us to understand the meaning of unstructured data at the same level as structured data in the future?

Yes, the development of natural language processing may lead to a future where even unstructured data can be understood at the same level of meaning as structured data.

Natural language processing is a technology that understands information contained in natural language and converts it into a form that can be processed by computers. With the development of natural language processing, it is now possible to extract information from unstructured data, such as text and speech, and process it in the same way as structured data.

For example, natural language processing technology can be used to analyze the level of satisfaction with products and services from review sentences and tweets. In the medical field, it is also possible to extract information on diagnosis and treatment of diseases from unstructured data such as medical records.

However, natural language processing technology is not yet perfect, and challenges remain in accuracy and reliability. In addition, a huge amount of training data is required to analyze using natural language processing technology. Therefore, the idea that data structuring is important will remain persistent.