Math Theory Distinguishes AI Agency from Intelligence
💡Proves current AI has agency but no true intelligence—key theory for resilient agents.
⚡ 30-Second TL;DR
What Changed
Introduces bipredictability P as intrinsic shared info fraction from first principles
Why It Matters
This theory provides a principled metric for AI self-assessment, potentially enabling more adaptive systems that mimic biological intelligence. It challenges claims of current AI intelligence, urging focus on interaction quality over prediction accuracy. Practitioners can use it to diagnose learning degradation in deployed agents.
What To Do Next
Compute bipredictability P in your next RL experiment using observation-action-reward logs to check learning effectiveness.
Key Points
- •Introduces bipredictability P as intrinsic shared info fraction from first principles
- •Proves P≤1 quantum, ≤0.5 classical, lower for agents with action selection
- •Empirically confirmed in double pendulum, RL agents, multi-turn LLM chats
- •Distinguishes agency (prediction-based action) from intelligence (adaptive learning)
- •Proposes thalamocortical-inspired feedback to monitor P in real-time
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Original source: ArXiv AI ↗
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