In the field of artificial intelligence, AI large models have become a hot technology, triggering extensive discussions and controversies. As we all know, both the general-purpose underlying large model and the large model relying on giants have their unique advantages and application prospects. However, as far as the correct development direction of AI large models is concerned, whether to focus on the development of general-purpose low-level large models, or rely on giant large models to build upper-layer applications, this issue has always attracted widespread attention in the industry and academia.
As a high-tech technology, the AI large model has attracted much attention. Its correct direction is that there is indeed a dispute between the general-purpose low-level large model and the construction of upper-level applications relying on the giant's large model. The answer may not be one or the other, but a combination of the advantages of both.
The development of a common underlying large model can promote the progress and innovation of AI technology. These underlying large models have strong generalization capabilities and intelligent processing capabilities through training massive amounts of data. They can provide general functions such as natural language processing, image recognition, machine translation, etc., and provide basic support for various application scenarios. The open source and sharing of the general underlying large model also promotes the participation and contribution of global developers, forming a vibrant community and promoting the rapid development of AI technology.
However, relying on the giant's large model to build upper-level applications also has its advantages. Giant companies have huge data resources, computing power and technical strength, and can conduct more in-depth research and development, and transform the results into practical applications. These companies also have extensive corporate and market channels, and can quickly promote and implement AI technology. By developing upper-level innovative applications on the large-scale model infrastructure provided by giants, we can focus more on solving specific problems and meeting our needs, and quickly achieve commercialization.
Therefore, the correct direction is to comprehensively utilize the general underlying large model and rely on the giant's large model to build upper-layer applications. The general underlying large model provides basic intelligence capabilities and algorithm models, while the giant's large model provides deeper and more professional domain knowledge and technical support. Entrepreneurs can develop innovative upper-level applications with the help of large-scale model infrastructure provided by giants, and provide us with valuable solutions in combination with the needs of specific industries or scenarios.
At the same time, the power of socialization is also indispensable. Innovation comes from talents and tentacles at all levels, including grassroots staff and contributions from entrepreneurs, small and micro enterprises, and technology developers. Their ideas and practices can enrich the application fields and scenarios of AI large models, making them more flexible, agile and creative.
To sum up, the correct development direction of AI large models includes not only the promotion of general-purpose underlying large models, but also the construction of upper-layer applications relying on giant large models. These two paths are not mutually exclusive, but can complement each other and promote each other.