Words Are a Byproduct of Consciousness. For LLMs, It's Backwards
The article argues that for humans, words naturally emerge from consciousness, but for large language models (LLMs), words generate the appearance of consciousness in reverse. It explores how this reversal affects the nature of meaning and communication between human and machine intelligence.
Background
- The author, Rej P. Ranpara, is a software engineer and researcher who writes about AI, language, and consciousness from a philosophical-technical angle.
- This post argues that human language emerges from an inner "first-person" experience (qualia, intention, embodiment), so words _express_ something pre-verbal. Large language models (LLMs) like GPT-4 have no such interiority; they learn to predict words from text patterns alone. For an LLM, language is the input and the only reality — the opposite of the human direction.
- The essay connects to a long-standing debate in AI research: can a system that manipulates symbols without understanding them ever be truly intelligent? ("Searle's Chinese Room" argument is the classic reference here.)
- It also touches on why LLMs sometimes sound plausible yet nonsensical — they are "talking" without anything to talk _about_, because they lack the conscious experience that gives words their meaning.