This article proposes viewing free will not as a binary state of being an algorithm, but as a model parameter akin to the standard deviation (σ) in a variational autoencoder (VAE). Unlike a language model's temperature or an RL agent's epsilon, which are global and user-set, a VAE's μ and σ are input-dependent and learned. The author suggests that the KL divergence term in VAEs, which balances generalization with overfitting, represents the learned equilibrium between flexibility and constraint, thereby formalizing free will as a per-dimension, learned property. AI
IMPACT Proposes a novel conceptual framework for understanding free will through machine learning principles.
RANK_REASON The item is a philosophical exploration of free will using machine learning concepts, not a release or research finding.
- algorithm
- Epsilon
- free will
- language model
- Less Wrong
- machine learning
- reinforcement learning
- temperature
- variational auto-encoder
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