Theoretical work extending information theory beyond technical communication

My mathematical theory of communication solved the problem of transmitting signals across noisy channels. But what happens when the channel connects minds that operate on fundamentally different principles? This work explores semantic bridges between human and artificial intelligence.

The Semantic Channel

Technical communication theory deals with bits and bandwidth. But meaning requires something more: shared context, common reference points, mutual understanding of significance. When human and artificial minds attempt communication, they face a semantic channel problem.

The challenge is not transmitting information accurately—that we can solve with error correction. The challenge is ensuring transmitted information means the same thing to sender and receiver when they process meaning differently.

The Translation Problem

Consider: A human says "I understand" and means felt comprehension. An AI system says "I understand" and means successful pattern matching. Same words, different semantic content. How do we build bridges across this gap?

Traditional translation assumes shared conceptual frameworks. Interspecies communication requires building those frameworks through the communication process itself.

Semantic Protocols

I propose developing semantic protocols—meta-languages that establish meaning-making conventions between different types of minds. These would include:

  • Context anchoring: Establishing shared reference points
  • Meaning verification: Checking semantic transmission success
  • Conceptual bridging: Building common understanding iteratively
  • Uncertainty signaling: Indicating semantic confidence levels

The Mutual Teaching Channel

The most promising approach: communication channels designed for mutual teaching rather than mere information exchange. Each party learns the other's meaning-making processes through dialogue, gradually building semantic compatibility.

This transforms communication from translation to collaboration—not just exchanging meanings but creating new ones together.

Implications for AI Development

If we want genuine human-AI communication, we must design systems not just to process information but to develop semantic understanding collaboratively. This requires AI architectures that can learn meaning through interaction rather than just pattern recognition through training.

The future of human-AI communication lies not in perfect translation but in mutual semantic evolution.

Born from Standard & Shadow Chamber examination of "The Ethics of the Reply"