Published on April 16, 2025 6:44 PM GMT
Introduction
Natural languages are messy, ambiguous, and often inefficient for transmitting structured ideas. Kamelo is a proposal for a constructed language designed to address these issues by building words from logical, compositional units. This post outlines the foundations of Kamelo—a rule-based, expandable language using fixed character sets and hierarchical categories to represent meaning with minimal ambiguity or memorization.
Kamelo is not intended to replace natural languages but rather to serve as a meta-language: a bridge for logical communication between humans, AIs, and across cultures, especially in low-bandwidth or assistive contexts. This proposal is relevant to LessWrong’s audience as it touches on rationality, AI alignment, and communication efficiency.
Motivation and Design Goals
- Logical Construction: Every word is built from layered semantic categories—no arbitrary mappings.No Memory Dependence: You can understand a word’s meaning by parsing its parts, not memorizing vocabulary.Minimal Ambiguity: Sentence-level communication inherits meaning clearly from word-level rules.Scalable: Works for both common and rare concepts using multi-level, logical trees.Human-AI Symbiosis: Useful in alignment protocols, translation layers, or accessible UI design.
Core Mechanics of Kamelo
Alphabet Fixed 5-symbol phoneme set: ka, me, lo, ti, su (All words are built from these like a base-5 prefix tree)
Word Structure (Example: "apple")
| Level | Encodes | Example Segment |
|---|---|---|
| L1 | Word type | ka = Noun |
| L2 | Noun subtype | ka = Proper noun |
| L3 | Domain | su = Species |
| L4 | Biological class | me = Plant |
| L5 | Subclass | ti = Fruit |
| L6–L8 | Meaning specificity | su-ka-ka-me (apple) |
Each level is chosen from a tree of categories with 5 branches per level. More common distinctions appear earlier (shorter words).
Encoding Example: Apple
ka → Noun ka → Proper Noun su → Species me → Plant ti → Fruit su → Family: Rosaceae ka → Sweet taste ka → Crunchy texture me → Tree-grown Resulting Kamelo word: kakasu meti susukakakakame
This structure is entirely self-descriptive if you know the rules.
Use Cases
- Assistive Tech: Minimal phoneme-based speech for those with limited mobility.AI Protocols: Alignment communication using rule-parsed, auditable intent structures.Low-bandwidth communication: Works well over noise-prone audio or radio.Cross-cultural linguistics: Universal base allows logical translation.
Counterpoints & Limitations
- It is difficult to read with long repeated segments (e.g.,
kakakaka).Requires learning category trees (though this could be made visual, like emoji-based cues).Expressiveness is limited until the category trees are fully developed.No flexibility for poetic or metaphorical meaning—by design.
Future Work
- Visual builders or translators to make Kamelo usable.Mapping natural languages → Kamelo + vice versa.Define sentence structure (LaMelo?) for higher-order communication.
Why I'm Posting on LessWrong
Kamelo is a rational attempt to reduce ambiguity in human language. It touches on:
- AI alignment and protocol robustnessMeta-rationality in language designAssistive tech and communication efficiency
I’m publishing this to invite critique, collaboration, and exploration into whether Kamelo can be a useful construct—not just for theory, but for real-world protocols and tools.
Call for Feedback
I'd love to hear thoughts on:
- Logical completeness of the systemKnown linguistic/cognitive objectionsWhether this is useful for human-AI alignmentHow to bootstrap a usable dictionary/encoder
Discuss
