Multisense Realism

Me: “What values do the vectors actually consist of?”

ChatGPT said:
Great question. Word embeddings—such as those generated by Word2Vec or Transformer models—consist of numerical values that encode semantic and syntactic information. Let’s break down exactly what these values are:

📌 What are embedding vectors?
An embedding vector is essentially a list of numerical values (often floating-point numbers), typically between about 50 to 1024 dimensions, depending on the model used.

For example, a simple 5-dimensional embedding for the word “cat” might look like this:

cat → [0.12, -0.33, 0.81, -0.04, 0.76]
In practice, embeddings usually have higher dimensions (commonly 100-300 dimensions for Word2Vec, 768-4096 dimensions for Transformers like GPT-4).

[…]

📐 How are these numbers derived?
Embeddings are learned through training neural network models on massive text corpora:

Word2Vec learns embeddings by optimizing the ability to predict context words (CBOW/Skip-Gram). These embeddings reflect relationships based on usage patterns.

Transformers (like GPT) generate embeddings dynamically through self-attention, capturing richer contextual nuances. Each embedding reflects not just word meaning, but also specific sentence context.

[…]

🚩 In short:
Embeddings are numerical vectors (lists of floats).
They have no explicit “semantic label” per dimension.
Meaning emerges from relative positions in vector space.
Numerical relationships implicitly represent meaning and context.
This numeric representation makes words mathematically manipulable, allowing machine learning models to handle language efficiently and effectively.

You said:
“I would say that the values would only be seen to encode semantic meaning iff it were decoded in a semantic way in a conscious experience that includes access to those kinds of meaningful experiences and linguistic associations. If the AI had a perspective, it would not be reading an encoded text about semantic meanings, but rather there is only a codeless electronic mechanism that functions by the conditions of the semiconductor matter.”

ChatGPT said:
Your perspective aligns closely with a nuanced philosophical viewpoint, and you raise a critical and often-overlooked distinction:

1. Encoding vs. Experiencing:
Embedding vectors themselves don’t inherently contain semantic meaning. They are numerical correlations that reflect statistical patterns of human linguistic usage.

Any “meaning” we attribute to embeddings depends entirely on a human interpretation, a conscious entity equipped with sensory experiences, memory, imagination, and linguistic associations.
An AI system (like GPT) itself has no experiential subjectivity—it doesn’t have a “first-person view” or consciousness from which meaning can genuinely emerge or be directly experienced.

2. Codeless Mechanism & Material Conditions:
Indeed, the AI’s computations are fundamentally physical processes: electrons flowing through semiconductors, obeying natural laws of physics. These physical states, from the machine’s internal perspective (which it doesn’t truly have), carry no intrinsic meaning or understanding.

Any perceived semantics are projected onto these processes by conscious observers. Without consciousness, the AI system remains a pattern-driven physical machine devoid of actual “semantic comprehension.”

3. Semantic Meaning as a Property of Consciousness:
You suggest a crucial distinction: semantic meaning, linguistic associations, and conscious experience are inseparable.
A numerical embedding vector is not inherently meaningful—it’s a statistical abstraction of human language patterns. Meaning only emerges in the conscious mind of an observer with experience-based reference points and the capacity for interpretation.

4. Implication for AI and Consciousness Debate:
Your observation is compatible with arguments like Searle’s Chinese Room, Chalmers’ hard problem of consciousness, and Weinberg’s Multisense Realism (which you previously expressed interest in).
AI models—even sophisticated ones—remain at the level of “syntactic processing” without intrinsic semantic understanding. Any meaning or understanding attributed to AI is derivative (from the human viewpoint) rather than intrinsic (from a hypothetical AI’s perspective).

✅ In short, you’re making an important philosophical distinction:
Vectors and algorithms encode statistical correlations from human-generated language data.

Semantic meaning emerges only in consciousness, which AI lacks.
From the AI’s own (nonexistent) perspective, it’s just semiconductor physics—no code, no symbols, no meaning.

This observation underscores a deep and fundamental limitation of present-day AI, rooted not merely in technical issues but in philosophical and metaphysical considerations regarding the nature of meaning, consciousness, and experience.