AGISystem2 promises deterministic verification of probabilistic models. But where does the meaning come from? This page addresses the Symbol Grounding Problem—a fundamental critique that deserves honest engagement, not dismissal.
AGISystem2 claims to be a "deterministic" system that can verify probabilistic models (LLMs). But this raises a critical circular question: Where do the geometric coordinates come from?
We do not claim to have solved the Symbol Grounding Problem. No one has. Any system claiming otherwise is either confused or dishonest.
AGISystem2 does not:
What AGISystem2 actually does:
AGISystem2 guarantees the truth of conclusions within a theory, not the truth of the theory itself. This is a fundamental epistemological distinction.
Consider mathematics: We can prove theorems rigorously within axiom systems, but we cannot prove the axioms themselves. Gödel showed that no sufficiently powerful formal system can prove its own consistency. This isn't a bug—it's the nature of formal reasoning.
AGISystem2 adopts the same stance:
| What We Guarantee | What We Don't Guarantee |
|---|---|
| If theory T says "A implies B" and A is true in T, then B is true in T | That theory T accurately models reality |
| Logical consistency within the theory | Correspondence to external truth |
| Deterministic, reproducible reasoning | That the reasoning captures what matters |
| Explicit, auditable assumptions | That the assumptions are correct |
Multiple lines of argument suggest that a universal semantic grounding is either impossible or computationally intractable:
Even if universal semantics existed, computing it would likely be intractable. The frame problem, the qualification problem, and combinatorial explosion of context suggest that "meaning" cannot be computed in general.
Meaning is context-dependent. The same word means different things in different contexts, cultures, time periods, and disciplines. A "universal" semantics would need to capture all possible contexts—an infinite task.
Any attempt to ground symbols leads to a regress: ground symbol A in terms of B, but then B needs grounding, and so on. You either hit bedrock (arbitrary stopping point) or circle back (circular definition).
Every attempt at universal ontologies (Cyc, WordNet, etc.) has failed to scale or achieve consensus. After 40+ years and billions of dollars, we don't have a universal semantic framework. This isn't for lack of trying.
Rather than pursuing the impossible goal of universal semantics, AGISystem2 takes an engineering approach:
Break reality into domain-specific, approximate theories that can be revised, compared, and improved over time. Accept that all models are wrong, but some are useful.
We treat domain specifications in natural language as a programming language to be compiled into formal theories. The "meaning" is whatever the theory says it is—explicit, inspectable, debuggable.
// Natural language specification:
"Dogs are animals. Animals are living things. Spot is a dog."
// Compiled to formal theory:
Dog IS_A Animal
Animal IS_A LivingThing
Spot instanceOf Dog
// Now we can formally analyze:
// - What follows from these definitions?
// - Are there contradictions?
// - What happens if we change a definition?We focus on analyzing the consequences of definitions rather than their correspondence to reality:
This is useful regardless of whether the definitions are "true" in some absolute sense.
Theories are not fixed. They can be:
This is how science works. It's not magic; it's engineering.
We adopt a meta-rational approach: theories are tools for achieving goals, not mirrors of ultimate reality. A theory is "good" if it's useful, not if it's "true" in some absolute sense.
This isn't philosophical surrender. It's recognition of how science actually works:
All models are approximations. The question is whether they're useful approximations for the task at hand.
We explicitly reject claims that formal systems will somehow achieve superintelligence, solve all problems, or replace human judgment. These are not engineering claims; they are ideological fantasies.
There is no:
What AGISystem2 actually offers:
| Capability | Practical Value |
|---|---|
| Formal analysis of definitions | Debug specifications before implementation |
| Explicit, auditable reasoning | Regulatory compliance, legal defensibility |
| Deterministic operations | Reproducible results, testable systems |
| Theory revision and comparison | Systematic knowledge management |
| LLM output verification | Catch obvious errors and inconsistencies |
This isn't revolutionary. It's incremental improvement. But incremental improvement is how real progress happens.
Claims of universal semantics, AGI breakthroughs, or solved grounding problems should be viewed with suspicion. They often serve purposes other than scientific truth.
The pursuit of "perfect" AI solutions has problematic tendencies:
The pragmatic engineering approach is less exciting but more honest: we build tools that help with specific problems, gradually extending their capabilities, with humans in the loop at every step.
AGISystem2 is a tool. Like all tools, it has limitations. Understanding those limitations is essential to using it effectively.