Symbol Emergence and The Solutions to Any Task
This work addresses the foundational challenge of defining and achieving AGI, but it is incremental as it builds on existing symbolic and cognitive theories without empirical validation.
The paper tackles the problem of defining artificial general intelligence (AGI) by proposing that an agent constructing an 'Intensional Solution' to any arbitrary task qualifies as AGI, and it explains how natural language emerges from this framework to model others' intent.
The following defines intent, an arbitrary task and its solutions, and then argues that an agent which always constructs what is called an Intensional Solution would qualify as artificial general intelligence. We then explain how natural language may emerge and be acquired by such an agent, conferring the ability to model the intent of other individuals labouring under similar compulsions, because an abstract symbol system and the solution to a task are one and the same.