Abstract
Accepted for full oral presentation at the 16th Conference on
Artificial General Intelligence, taking place in Stockholm, 2023.
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.