How I Found Soul in Equations

2025-11-02 · Source: The Serious Computer Vision Blog · Field: Technology & Digital — Artificial Intelligence & Machine Learning, AI Ethics & Philosophy · Depth: Intermediate, medium

Summary

Li Yang Ku presents a logical argument, rooted in reinforcement learning (RL) policy equations, for the existence of human souls as the basis for free will. The author begins by defining the parameterized policy equation, \(\pi(a_t|o_t; \theta_t, \phi)\), which describes an agent's actions given observations, learnable parameters (\(\theta_t\)), and fixed hyperparameters (\(\phi\)). The argument then posits that if human behavior is similarly modeled, \(\pi_{human}(a_t|o_t; \theta_t^h, \phi^h)\), free will cannot exist, as all actions would be determined by past experiences (\(\theta_t^h\)) or innate characteristics (\(\phi^h\)). To resolve the paradox of living without free will, the author introduces a "true-self" or "soul" term, \(\sigma\), into the human policy equation: \(\pi_{human}(a_t|o_t; \theta_t^h, \phi^h, \sigma)\). This \(\sigma\) must be time-independent and identical across all humans to qualify as free will, leading to the conclusion that believing in a soul is the only logical path to defining a life purpose function.

Key takeaway

For AI Ethicists and Research Scientists grappling with the philosophical implications of AI and human consciousness, this analysis suggests that a belief in a universal, time-independent "soul" (\(\sigma\)) is a logical imperative for defining human purpose and free will within a reinforcement learning framework. You should consider how this conceptualization of \(\sigma\) impacts discussions on AI sentience, consciousness, and the ethical boundaries of creating human-like agents, as it implies fundamental limitations for replicating free will in artificial systems.

Key insights

Reinforcement learning policy equations can logically support the existence of a soul as the basis for human free will.

Principles

• Free will requires a time-independent, identical component across all humans.
• Deterministic systems preclude genuine free will.
• Human behavior can be modeled using RL policy equations.

Method

The author extends the RL policy equation \(\pi(a_t|o_t; \theta_t, \phi)\) to human behavior, then introduces a \(\sigma\) term representing a soul to account for free will, which must be time-independent and identical across individuals.

In practice

• Consider the implications of \(\sigma\) for AI ethics and human-robot interaction.
• Explore how the "true-self" concept aligns with psychological models like IFS.

Topics

• Reinforcement Learning
• Free Will
• Human Behavior Modeling
• Philosophy of AI
• Soul Concept

Best for: AI Engineer, AI Ethicist, Research Scientist

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Editorial summary, takeaway, and curation by AIssential. Original article published by The Serious Computer Vision Blog.