On the Computation of Meaning, Language Models and Incomprehensible Horrors
This work addresses the foundational problem of meaning in AGI for researchers in AI and philosophy, but it is incremental as it synthesizes existing theories without new empirical results.
The paper tackles the problem of explaining meaning and communication in artificial general intelligence (AGI), concluding that current language models lack human-like understanding of meaning and proposing a simulation-based approach to address this.
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.