In recent years, large language model (LLM) technology has developed rapidly, and generative AI has demonstrated impressive creative capabilities. However, its intrinsic mechanisms and cognitive abilities still need to be further explored. This article will discuss a study on the understanding ability of generative AI models. Through experimental comparative analysis, this study reveals the performance differences of such models in different situations, providing a valuable reference for us to understand the limitations of AI.
Generative AI models such as GPT-4 and Midjourney have demonstrated compelling generative capabilities. However, research has found that these models have challenges in understanding the content they generate, which differs from human intelligence. Specifically, the researchers found through experiments that these models performed well in selective experiments, but often made errors in interrogative experiments. This research calls for caution when delving deeper into artificial intelligence and cognition, as models can create content but cannot fully understand it.Overall, this study reminds us that although generative AI has made significant progress in content creation, its ability to understand the content it generates is still limited. Future research needs to further explore the cognitive mechanism of AI to promote the healthy development of AI technology and avoid potential risks. We need to view AI’s capabilities more carefully and continuously strive to bridge the gap between AI and human intelligence.