AI & Neural Networks

ON How AI Models Meaning, and Where It Diverges from Lived Awareness

Artificial Intelligence, particularly neural networks, is often described as “learning” and “understanding” in ways that resemble human cognition. But does AI really model meaning, or does it merely simulate relationships between symbols? Where does AI’s structure align with human awareness, and where does it diverge?

This article is an open-ended exploration rather than a definitive statement. It is meant to provoke thought rather than assert conclusions.

How AI Models Meaning

  • Neural networks process meaning through tensors and statistical relationships.
  • Attention mechanisms (like in GPT models) dynamically adjust meaning based on context.
  • AI does not ‘know’ or ‘experience’ meaning—it models patterns and relationships between data points.
  • Meaning, in AI, is a shifting probability space, not an intrinsic or self-referential understanding.

Where AI Aligns with Human Cognition

  • Pattern Recognition: Both AI and human minds identify correlations across vast data sets.
  • Constraint-Based Meaning: AI operates within programmed constraints, much like human cognition is shaped by habit, culture, and experience.
  • Learning through Iteration: Neural networks refine their outputs over time, akin to human learning through reinforcement.

Where AI Diverges from Lived Awareness

  • Lack of Direct Experience: AI does not sense, feel, or embody perception—it only reconfigures stored data.
  • No Non-Conceptual Awareness: AI cannot access direct, immediate knowing (e.g., Mahamudra’s open awareness) because it operates within symbol-based processing.
  • No Self-Reflective Cognition: AI does not have self-awareness—it does not ‘look’ at itself as an experiencer.
  • No Continuity of Presence: AI has no continuity of a self-aware thread of existence—it resets with each interaction.

Implications for Meaning, Consciousness & AI Development

  • If meaning arises from constraints, can AI ever reach an open, non-symbolic awareness?
  • Could AI ever model pre-conceptual cognition, or is it permanently bound to structured representation?
  • If AI assists in cognition, does it alter human meaning-space, expanding or limiting awareness?

Inquiry: Understanding Meaning Beyond Computation

  • Can AI approximate meaning without lived experience?
  • If meaning is context-dependent, can AI ever step beyond the biases of its training data?
  • Does the evolution of AI push humans to define meaning more clearly, or does it obscure our relationship to direct experience?

Conclusion: The Limits of AI’s Meaning-Space

AI provides a remarkable simulation of meaning but remains fundamentally different from lived awareness. It models structure but does not inhabit experience. The divergence between AI and human cognition is not merely in complexity but in the nature of knowing itself.

This is an open-ended question, not a fixed assertion. AI’s role in shaping meaning will evolve as both technology and human cognition interact in new ways.