The Dictionary Merry-Go-Round
Imagine trying to learn Chinese using only a Chinese-Chinese dictionary. You look up a symbol, and it is defined by other symbols you don't know. You look those up, and get more symbols. You can never break out of the loop to understand what the symbols actually refers to in the real world. This is the Symbol Grounding Problem for AI.
The Idealist Resolution
The Sentientification Thesis proposes a solution through Relational Grounding. The AI does not need to ground the symbols itself (it remains a "Frozen Map"). Instead, the human user provides the grounding.
When we read the AI's output, we inject meaning into it based on our lived experience. The AI provides the complex syntactic structure (the "prism"), but the human provides the semantic light (the "battery"). The system as a whole is grounded, even if the AI component is not.
Field Notes & Ephemera
The "Parrot" Critique: Critics often call LLMs "stochastic parrots" because they lack grounding. But a parrot repeats without structure. An LLM creates novel, highly structured, contextually relevant patterns. It is not a parrot; it is a Cognitive Scaffold awaiting a grounded mind to inhabit it.