“Philosoplasticity” challenges the foundations of AI alignment

The concept of “philosoplasticity” highlights a fundamental challenge in AI alignment that transcends technical solutions. While the AI safety community has focused on developing sophisticated constraint mechanisms, this philosophical framework reveals an inherent limitation: meanings inevitably shift when intelligent systems recursively interpret their own goals. Understanding this semantic drift is crucial for developing realistic approaches to AI alignment that acknowledge the dynamic nature of interpretation rather than assuming semantic stability.

The big picture: Philosoplasticity refers to the inevitable semantic drift that occurs when goal structures undergo recursive self-interpretation in advanced AI systems.

• This drift isn’t a technical oversight but a fundamental limitation inherent to interpretation itself.
• The concept challenges a core assumption in the alignment community that the meaning encoded in constraint frameworks can remain stable as systems interpret and act upon them.

Philosophical foundations: The concept draws from established philosophical traditions that highlight inherent limitations in our ability to specify meanings that remain stable across interpretive contexts.

• Wittgenstein’s rule-following paradox demonstrates that any rule requires interpretation to be applied, creating an infinite regress of meta-rules.
• Quine’s indeterminacy of translation suggests multiple incompatible interpretations can be consistent with the same body of evidence.
• Goodman’s new riddle of induction shows that for any finite set of observations, infinitely many generalizations can be consistent with those observations but diverge in future predictions.

Why this matters: The alignment community has been developing increasingly elaborate constraint mechanisms while failing to recognize that the meaning territory itself is shifting.

• This analysis doesn’t suggest alignment is impossible, but rather that it cannot be achieved through approaches assuming semantic stability across capability boundaries.
• Understanding philosoplasticity is essential for developing architectures that embrace the dynamic nature of meaning rather than denying it.

Implications: The concept challenges the AI safety community to move beyond approaches that assume semantic stability toward frameworks that account for the inevitable drift in meaning.

• Rather than viewing these philosophical limitations as obstacles, they might serve as foundations for more realistic approaches to the alignment problem.
• The path forward requires embracing the limitations of interpretation itself as a prerequisite for developing architectures that might actually work.