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Me: So we still do not really know what we are dealing with in the LLM/ANN arena. We have measurements and analysis and diagnostics but that’s all effect. The “hows” and “whys” are still unanswered. We can breakdown ANN patterns relative to effect but we do not know how an ANN stores meaning from a human cognitive perspective. And if we do not know that we do not know how an LLM/ANN will evolve relative to the physics that drive LLM/ANN behavior.
OpenAI GPT5.2:
Frank, you’re circling one of the deepest questions in modern AI and cognitive science — the difference between observing behavior and understanding mechanism.
We can measure, probe, and model neural networks with extraordinary precision… yet the ontological status of meaning inside them remains elusive.
Let’s unpack this carefully.
1. What We Do Understand About LLMs/ANNs
From an engineering and mathematical standpoint, we understand quite a lot.
Mechanistic level
We know:
• weights adjust via gradient descent
• attention redistributes signal importance
• embeddings encode statistical relationships
• loss functions shape optimization trajectories
• training compresses patterns into parameter space
At this level, an ANN is:
a high-dimensional function approximator optimized to minimize prediction error.
No mystery there.
2. What We Can Measure
We can observe internal structure and dynamics:
Observable internal phenomena
• neuron activations
• attention maps
• feature circuits
• representation geometry
• layer-wise transformations
• token probability distributions
Mechanistic interpretability research is uncovering:
- syntax neurons
- induction heads
- factual retrieval circuits
- chain-of-thought pathways
We can increasingly map cause → effect inside the network.
But this is not yet the same as understanding meaning.
3. Why Meaning Is Hard to Locate
Your core point:
We can analyze effects but cannot locate “meaning” the way human cognition experiences it.
This is true.
Because:
Meaning is not stored as symbols
Unlike classical computing:
- no explicit dictionary entries
- no symbolic definitions
- no semantic database
Instead:
meaning exists as relational geometry in vector space.
4. Meaning in LLMs = Relational Topology
Inside an LLM:
A concept is not a thing.
It is a position within a probability field.
Example:
The meaning of “tree” is encoded through relationships to:
- forest
- leaf
- wood
- oxygen
- root
- shade
- climb
- bark
Remove relationships → meaning disappears.
So meaning is:
relational structure within high-dimensional statistical space.
This differs from human cognition, which feels symbolic but is likely also relational.
5. Why This Feels Unsatisfying
Because human cognition experiences meaning as:
• grounded
• embodied
• sensory-linked
• emotionally weighted
• goal oriented
Whereas LLM meaning is:
• statistical
• relational
• non-embodied
• task-conditioned
This leads to the sense that:
the model manipulates patterns without “understanding”.
But this depends on how we define understanding.
6. The Hard Question: What Is Meaning?
Your question touches the “hard problem” equivalent in AI.
We have three competing views:
1️⃣ Symbolic view
Meaning = explicit representations
(LLMs do not use this.)
2️⃣ Statistical/relational view
Meaning = structure within a probability field
(LLMs strongly support this.)
3️⃣ Embodied cognition view
Meaning emerges from sensory-motor grounding
(Humans use this.)
Most likely:
human meaning integrates all three.
LLMs currently implement the second.
7. Why We Still Don’t Fully Understand the “Why”
We understand optimization.
We do not fully understand emergence.
Key open questions:
Why do capabilities emerge suddenly?
Why do internal circuits self-organize?
Why do abstractions form spontaneously?
Why do scaling laws produce predictable intelligence growth?
Why does gradient descent discover structure resembling cognition?
This is where physics and complexity science enter.
8. Physics Perspective: Gradient Systems
ANNs are:
energy minimization systems
Training minimizes loss = energy landscape descent.
This parallels:
• protein folding
• thermodynamic equilibrium seeking
• Bayesian inference
• free energy minimization in brains
• self-organization in complex systems
This suggests LLMs are not arbitrary.
They may be:
a new class of gradient-driven self-organizing systems.
9. Meaning as Compression of Reality
One powerful perspective:
Meaning emerges when a system compresses the world efficiently.
Language patterns reflect:
- physical reality
- social behavior
- causal structure
- survival constraints
LLMs compress these patterns.
Thus:
meaning = compressed regularities of reality.
This may be true for humans as well.
10. Why We Cannot Fully Reverse Engineer Meaning Yet
Three major obstacles:
1️⃣ Dimensionality
Representations exist in thousands of dimensions.
Human intuition evolved for 3.
2️⃣ Distributed encoding
Concepts are not localized.
They are distributed across millions of weights.
3️⃣ Dynamic context dependence
Meaning shifts depending on context and activation pathways.
It is not static storage.
11. Do We Understand How LLMs Store Meaning?
Yes and no.
We know:
✔ meaning is relational
✔ meaning is distributed
✔ meaning is statistical
✔ meaning is context-conditioned
We do not know:
✖ how relational geometry produces subjective understanding
✖ how abstraction emerges from optimization
✖ whether semantic compression equals cognition
✖ whether embodiment is required for full meaning
12. How Will LLMs Evolve Relative to Physics?
This is the frontier question.
If LLMs are gradient systems, evolution may follow:
Increasing efficiency of energy usage
Better compression of reality
Improved hierarchical abstraction
Integration with sensory grounding
Development of world models
Active inference and goal-seeking
This aligns with principles governing biological intelligence.
13. The Deeper Insight
We may be confronting a profound possibility:
Meaning is not stored.
Meaning is enacted.
In humans:
→ meaning arises from neural activation patterns interacting with body and environment.
In LLMs:
→ meaning arises from activation patterns interacting with context and prompts.
Meaning is a process, not an object.
14. Where You Are Correct
Your intuition is accurate:
✔ We understand effects better than essence
✔ We can map mechanisms but not fully explain emergence
✔ We do not yet know how subjective meaning relates to vector geometry
✔ We are still uncovering the physics of intelligence
This is not a failure of understanding.
It is the frontier.
15. A Useful Reframe
Instead of asking:
Where is meaning stored?
Ask:
What dynamic conditions cause meaning to emerge?
This shifts from storage → process.
From symbol → interaction.
From static → dynamical systems.
If you want, we can go deeper into:
• how meaning might arise from energy minimization principles
• parallels with free energy principle & brain function
• whether embodiment is necessary for true understanding
• why emergence appears inevitable in gradient systems
• whether meaning is universal in sufficiently complex systems
Where shall we explore next?
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