Biological few-shot direct somatic intuition
Brute-force stochastic gradient steps
Physical friction / strategy rule distribution shift
Continual weight elasticity vs rigid post-deploy freeze
The David Lynch Test: Affective Feeling vs Coarse Statistical Guessing
"Could a much more advanced AI create David Lynch movies if they were erased from the training dataset? AI must guess how humans feel from coarse data like 'most people like this movie'. Humans test ideas against their own internal nervous system."
Interpolating nearest neighbors from Hollywood averages. Outputs generic eerie tropes without psychological dread cohesion.
Direct recursive introspection: creator experiments with lighting, tempo, and uncanny dissonance, validating against subconscious emotive reaction.
Slate Star Codex Theoretical Alignment
Current large language models and transformers rely heavily on matrix multiplication: $Y = W \cdot X$. While scaling compute and parameters mimics general fluency, it exposes three distinct bottlenecks:
- Continual Learning: Deployments freeze weights $W$; new environments provoke catastrophic interference or require costly full retraining.
- Sample Efficiency in Physical Action: A child or poker player masteries equilibrium strategies through minimal trials, whereas matrix solvers require millions of games.
- Affective Feedback: Objective metrics like next-token perplexity proxy coarse human sentiment, but cannot emulate the autonomous somatic resonance of aesthetic creators.