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 robustness
Meta-rationality in language design
Assistive 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.
Kamelo: A Rule-Based Constructed Language for Universal, Logical Communication
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”)
ka= Nounka= Proper nounsu= Speciesme= Plantti= Fruitsu-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
Resulting Kamelo word:
kakasu meti susukakakakameThis 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 robustness
Meta-rationality in language design
Assistive 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 system
Known linguistic/cognitive objections
Whether this is useful for human-AI alignment
How to bootstrap a usable dictionary/encoder