We integrate foundational theories of meaning with a mathematical formalism of artificial general intelligence (AGI) to offer a comprehensive mechanistic explanation of meaning, communication, and symbol emergence. This synthesis holds significance for both AGI and broader debates concerning the nature of language, as it unifies pragmatics, logical truth conditional semantics, Peircean semiotics, and a computable model of enactive cognition, addressing phenomena that have traditionally evaded mechanistic explanation. By examining the conditions under which a machine can generate meaningful utterances or comprehend human meaning, we establish that the current generation of language models do not possess the same understanding of meaning as humans nor intend any meaning that we might attribute to their responses. To address this, we propose simulating human feelings and optimising models to construct weak representations. Our findings shed light on the relationship between meaning and intelligence, and how we can build machines that comprehend and intend meaning.

翻译：我们将意义的基础理论与通用人工智能的数学形式体系相结合，提供了一种关于意义、交流与符号涌现的全面机制解释。这一综合对于通用人工智能以及关于语言本质的更广泛争论具有重要性，因为它统一了语用学、逻辑真值条件语义学、皮尔斯符号学以及一种可计算的能力认知模型，从而处理了传统上难以用机制解释的现象。通过考察机器在何种条件下能够生成有意义的话语或理解人类的意义，我们确立了当前一代语言模型并不具备与人类相同的对意义的理解，也不意图传达任何我们可能赋予其回应的意义。为解决此问题，我们提出模拟人类感受并优化模型以构建弱表征。我们的研究揭示了意义与智能之间的关系，以及我们如何构建能够理解并意图传达意义的机器。