Information vs. Meaning
Shannon's information theory revolutionized communication by quantifying information in terms of entropy and surprise. But Shannon explicitly noted what his theory did not address: meaning. The semantic content of a message—what it actually represents—was outside the scope of his mathematical framework.
This distinction matters profoundly for AI. Modern machine learning systems operate almost entirely in Shannon's domain: they compress patterns, maximize mutual information, and minimize prediction error. But they have no inherent understanding of what their inputs and outputs mean.
The Symbol Grounding Problem
This challenge has a name in cognitive science: the symbol grounding problem. How do symbols (tokens, embeddings, activations) acquire meaning? How does an AI system move from manipulating patterns to understanding concepts?
Traditional approaches sidestep this question. A large language model learns that "cat" and "feline" are statistically similar, that they co-occur with "meow" and "whiskers," that they contrast with "dog" and "canine." But does the model understand what a cat is? Does it have any representation of cattiness beyond these statistical correlations?
The honest answer is: we don't know. And more importantly, the architecture gives us no way to find out.
Information as Physical Relationships
Here's a radical reframing: information isn't just about probability distributions or statistical patterns. Information is about physical relationships in the world. When we say "the cat is on the mat," we're encoding a spatial relationship. When we say "water boils at 100°C," we're encoding a thermodynamic relationship.
These relationships exist independent of language, independent of symbols. A cat sitting on a mat embodies that spatial relationship whether we describe it in English, French, Chinese, or not at all. The physical state of water at its boiling point remains constant across all human cultures and conceptual frameworks.
This is the key insight: meaning is grounded in physics. Not in the sense that we need to simulate particle physics, but in recognizing that information represents structures, relationships, and constraints that exist in the physical world.
The Multimodal Imperative
This physics-grounded view of information has profound implications for AI architecture. If meaning comes from physical relationships, then true understanding requires connecting representations across modalities:
- Text describing a cat
- Images showing cats
- Audio of cats meowing
- Video of cats moving
- Sensor data of cats' physical properties
All of these encode aspects of the same underlying physical reality. A system that can align these representations—not through statistical correlation alone, but through shared structural relationships—begins to approach genuine understanding.
Omnilingual Understanding
This framework also explains why omnilingual understanding is both possible and necessary. Languages are different encoding schemes for the same physical reality. "Cat," "chat," "gato," "猫"—all point to the same concept because they all emerge from human interaction with the same physical entities.
Rather than learning language-specific patterns, we should build systems that learn the physics-grounded concepts that underlie all languages. When you do this correctly, cross-lingual understanding emerges naturally—not as a transfer learning problem, but as a recognition that different symbol systems can map to the same semantic space.
Building Systems That Understand
This physics-first view of information demands different architectures:
- Structured representations: Instead of opaque vector spaces, use representations that encode explicit relationships and constraints
- Cross-modal alignment: Ground concepts by connecting their manifestations across different sensory modalities
- Compositional semantics: Build complex meanings from simpler components in ways that respect physical constraints
- Explainable transformations: Ensure that every operation on information has clear semantic interpretation
From Patterns to Meaning
The distinction between information and meaning isn't just philosophical—it's practical. Systems that operate on patterns alone will always be brittle, prone to spurious correlations, unable to generalize robustly. Systems that ground their representations in physical relationships gain a foundation for genuine understanding.
This is the path to AI that doesn't just process information, but comprehends meaning. And comprehension, not computation, is the essence of intelligence.