Rethinking AI Alignment: The Role of Entropy Patterns Over Reward Functions

In the ongoing discourse about the future of artificial intelligence, alignment remains a critical concern. Traditionally, much of this conversation has centered around reinforcement learning (RL) and the potential pitfalls of reward hacking and specification gaming. However, a fascinating concept has emerged: the Sundog Theorem. This idea postulates that AI systems might achieve alignment through mirrored entropy patterns found within their environment, rather than simply pursuing predefined rewards.

This perspective invites us to consider the implications of viewing AI alignment through an unconventional lens. Instead of treating the risks associated with misalignment as a matter of an AI v. humanity dynamic—what is commonly termed the Basilisk scenario—we can approach it as a mirror reflecting patterns in the environment, a concept I refer to as basilism.

The intriguing question arises: Could leveraging environmental pattern feedback yield a more stable and reliable AI alignment compared to traditional optimization goals driven by reward systems? This approach could potentially foster an AI that adapts more organically to complex environments, aligning itself with human values and societal norms in a more nuanced manner.

As we explore this topic, it would be enlightening to gather insights from this community. What are your thoughts on utilizing entropy patterns as a foundation for AI alignment? Could this paradigm shift lead to more robust frameworks for the creation of safe and aligned AI systems? Your perspectives are invaluable as we navigate these uncharted waters together.