Why Text-Based LLMs May Not Lead to AGI: A Language Perspective
The Fundamental Limitation of Natural Language
Natural languages evolved organically through human usage rather than being engineered for logical precision. This presents several inherent challenges when using language models as a path to Artificial General Intelligence (AGI):
1. Evolution vs. Design
Natural languages developed through:
- Cultural evolution and social conventions
- Geographic isolation and regional variations
- Historical accidents and practical needs
- Informal consensus among speakers
Rather than through:
- Systematic logical design
- Optimization for clarity
- Elimination of ambiguity
- Formal specification
2. The Ambiguity Problem
Natural languages contain multiple forms of ambiguity:
Lexical Ambiguity: Words having multiple meanings
- “Bank” can mean a financial institution or the edge of a river
- “Run” can be a verb of movement or a noun describing a sequence
- “Light” can be:
A noun (source of illumination)
An adjective (not heavy)
A verb (to ignite)
A cognitive state (enlightened)
Structural Ambiguity: Phrases that can be interpreted differently
- “The chicken is ready to eat” (Is the chicken going to eat or be eaten?)
- “Flying planes can be dangerous” (The act of flying planes or planes that are flying?)
- “I saw a man with a telescope”
Did I use a telescope to see the man?
Did I see a man who had a telescope?
Contextual Ambiguity: Meaning dependent on broader context
Sarcasm and irony
- “Great job!” (Could mean terrible job)
- “What a lovely day” (During a storm)
Cultural references
- “It’s their Waterloo” (Requires knowledge of history)
- “They jumped the shark” (Requires knowledge of TV culture)
Implicit knowledge
It’s cold” could mean:
Turn up the heat
Close the window
Bring a jacket
The food needs reheating
Why This Blocks AGI Development
- Fundamental Uncertainty
- Every input potentially has multiple valid interpretations
- No way to definitively resolve ambiguity without perfect context
- Context itself is often ambiguous
2. Logical Reasoning Barriers
- Cannot build reliable logical chains on ambiguous premises
- Same input can lead to multiple valid but contradictory conclusions
- No way to verify logical consistency across interpretations
3. Training Limitations
- Training data contains inherent ambiguities
- Models learn correlations without true understanding
- Cannot develop precise reasoning from imprecise foundations
This suggests that achieving AGI through current language-based approaches faces fundamental limitations that cannot be solved by simply scaling up models or improving architectures.
