To output the "seatbelt" for AI.

In-depth analysis of the causes of AI "talking nonsense" requires consideration from three dimensions: technology, data, and regulation. In terms of technology, current mainstream large language models are better at "imitation" rather than "understanding." In terms of data, the quality of training data directly affects the output quality of AI, and issues such as data pollution and cultural biases in reality have already affected output content. On the regulatory side, the lack of industry standards and ethical norms has led to some AI products with clear flaws entering the market. To address this issue, it is necessary to establish a comprehensive governance system. Technology developers should establish stricter data cleaning mechanisms, introduce fact-checking modules, and improve the error correction capabilities of models. Regulatory authorities need to accelerate the formulation of AI content governance standards and establish a hierarchical regulatory framework. Industry organizations should promote the establishment of unified ethical guidelines and technical standards.