Conceptual analysis reveals modern neural networks challenge traditional definitions of understanding, suggesting human comprehension is equally subjective and imperfect.
Key Points
To re-evaluate John Searle's 1980 Chinese Room argument against modern neural network architectures and question whether human cognition represents an objective standard of understanding.
Conducted a conceptual and philosophical critique of Searle's Chinese Room thought experiment in the context of modern artificial intelligence.
Synthesized findings from AI interpretability research, including latent trait transfer, reconstructed memory, and emergent system states, comparing them with human cognitive biases such as context rot.
Modern neural networks operate without explicit rulebooks or observable symbol-matching mechanisms, rendering the classical Chinese Room framing structurally inapplicable to current AI systems.
Human cognition exhibits degradation analogous to context rot, including selective memory reconstruction, emotional skew, and divergent semantic interpretations of shared terminology.
Understanding, whether biological or artificial, is fundamentally partial, subjective, and functional rather than an absolute binary state.