In the rapid development of artificial intelligence, solving complex AI tasks
is a crucial technology in intelligent mobile networks. Despite the good
performance of specialized AI models in intelligent mobile networks, they are
unable to handle complicated AI tasks. To address this challenge, we propose
Systematic Artificial Intelligence (SAI), which is a framework designed to
solve AI tasks by leveraging Large Language Models (LLMs) and JSON-format
intent-based input to connect self-designed model library and database.
Specifically, we first design a multi-input component, which simultaneously
integrates Large Language Models (LLMs) and JSON-format intent-based inputs to
fulfill the diverse intent requirements of different users. In addition, we
introduce a model library module based on model cards which employ model cards
to pairwise match between different modules for model composition. Model cards
contain the corresponding model's name and the required performance metrics.
Then when receiving user network requirements, we execute each subtask for
multiple selected model combinations and provide output based on the execution
results and LLM feedback. By leveraging the language capabilities of LLMs and
the abundant AI models in the model library, SAI can complete numerous complex
AI tasks in the communication network, achieving impressive results in network
optimization, resource allocation, and other challenging tasks.

在人工智能的快速发展中，解决复杂的人工智能任务是智能移动网络中的关键技术。为了应对这一挑战，我们提出了系统性人工智能（SAI），它是一个利用大型语言模型（LLMs）和基于 JSON 格式意图的输入连接自设计的模型库和数据库来解决人工智能任务的框架。