  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.
