The relationship and difference between general artificial intelligence (AGI) and large language model (LLM) is an important issue in the current field of artificial intelligence. AGI is defined as a system that fully understands and simulates human intelligence, not just performs well on specific tasks. Although the large language model has made significant progress in natural language processing, it still has many limitations, such as the creation of "illusions" and the lack of causal inference ability. The existence of these problems highlights the need to build a model that truly understands the world.
The core of AGI is its ability to perform deep cognition and reasoning, not just superficial processing of data. Although large language models can generate smooth text, they are still insufficient in understanding complex causal relationships and conducting logical reasoning. This gap makes the implementation of AGI a more challenging goal that needs to go beyond the current technical framework.
A major problem with the large language model is that it is prone to "illusion", that is, to generate content that does not match the facts. This phenomenon suggests that models have fundamental flaws in understanding the world and conducting reasoning. In contrast, AGI should have stronger causal inference capabilities and be able to extract deep structures and laws from the data to make more accurate predictions and decisions.
The key to implementing AGI is to build a model that can perform causal inference. This model not only needs to understand the correlation between data, but also needs to reveal the causal relationship behind it. In this way, AGI can better simulate human cognitive processes, thereby making smarter decisions in complex environments.
In general, the difference between AGI and large language models is whether they have real understanding. Although the large language model performs well in handling language tasks, it still needs to be improved in understanding and reasoning. Building a model that can make causal inference and understand the world is an important direction for realizing AGI and a key goal for the future development of artificial intelligence.