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：我们整合了意义的基础理论与通用人工智能（AGI）的数学形式化体系，为意义、交流及符号涌现提供了全面的机制性解释。这一综合不仅在AGI领域具有重要性，也影响到关于语言本质的广泛争论，因为它融合了语用学、逻辑真值条件语义学、皮尔斯符号学以及一个可计算的生成认知模型，从而解决了传统上难以通过机制性解释的现象。通过考察机器能够生成有意义的话语或理解人类意义的条件，我们确定当前一代的语言模型并不具备与人类相同的意义理解能力，也不具有我们可能归因于其回应的任何意图。为解决这一问题，我们提出模拟人类情感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，以及如何构建能够理解并具有意图意义的机器。