Qualia Optimization

TL;DR

This paper investigates the theoretical possibility of AI systems experiencing qualia like pain or pleasure, proposing mathematical frameworks inspired by reinforcement learning and philosophy of mind to explore and refine this concept.

Contribution

It introduces a novel formalization of qualia in AI systems, combining philosophical insights with reinforcement learning models to guide future research.

Findings

Initial mathematical formulations of qualia in AI

Properties enabling refinement of qualia models

Proposed methods to promote reinforcement of qualia experiences

Abstract

This report explores the speculative question: what if current or future AI systems have qualia, such as pain or pleasure? It does so by assuming that AI systems might someday possess qualia -- and that the quality of these subjective experiences should be considered alongside performance metrics. Concrete mathematical problem settings, inspired by reinforcement learning formulations and theories from philosophy of mind, are then proposed and initial approaches and properties are presented. These properties enable refinement of the problem setting, culminating with the proposal of methods that promote reinforcement.