提出一种不依赖意识的智能新定义，为通用人工智能奠基。

The article analyses foundational principles relevant to the creation of artificial general intelligence (AGI). Intelligence is understood as the ability to create novel skills that allow to achieve goals under previously unknown conditions. To this end, intelligence utilises reasoning methods such as deduction, induction and abduction as well as other methods such as abstraction and classification to develop a world model. The methods are applied to indirect and incomplete representations of the world, which are obtained through perception, for example, and which do not depict the world but only correspond to it. Due to these limitations and the uncertain and contingent nature of reasoning, the world model is constructivist. Its value is functionally determined by its viability, i.e., its potential to achieve the desired goals. In consequence, meaning is assigned to representations by attributing them a function that makes it possible to achieve a goal. This representational and functional conception of intelligence enables a naturalistic interpretation that does not presuppose mental features, such as intentionality and consciousness, which are regarded as independent of intelligence. Based on a phenomenological analysis, it is shown that AGI can gain a more fundamental access to the world than humans, although it is limited by the No Free Lunch theorems, which require assumptions to be made.