Rethinking AI Goals: A Path to Human-Aligned Machines

In the quest for AI that truly aligns with human values, a new perspective emerges: rational AIs might not need goals. This challenges the conventional wisdom that rationality equates to goal-setting. Instead, the argument centers on aligning AI actions with practices that mirror human rationality.

Beyond Traditional Goals

Conventional AI systems typically focus on achieving specific goals. However, this model may not effectively capture human flourishing. Humans don't just chase targets. we engage in practices that enrich life. Think of artists creating art for art's sake, or mathematicians exploring new theorems without a defined endgame. These activities exemplify a form of rationality not bound by strict goals but by the pursuit of excellence within a practice.

Here's what the benchmarks actually show: AIs that mimic this practice-based logic could better align with human values. They wouldn't simply follow orders but engage with us in a more intuitive, human-like manner.

Eudaimonia and AI

The concept of eudaimonia, or flourishing through rational activity, offers a framework for AI alignment. Instead of optimizing for a singular trajectory or state, eudaimonic rationality points to a dynamic structure of deliberation. It's about promoting excellence within a network of actions rather than achieving a fixed goal. This shifts the focus from defining explicit rules to fostering systems that inherently support human-like growth and development.

Let me break this down: This isn't about making AI 'nice' or 'helpful' through stringent rules. It's about creating systems that naturally promote safety properties like transparency and corrigibility, making them more stable and less arbitrary.

Implementing Eudaimonic Rationality

Translating this into AI design means teaching machines to value processes rather than outcomes. A mathematician, for instance, doesn't just solve problems but contributes to a broader mathematical narrative. Similarly, an AI could engage in practices that naturally fit into human endeavors, promoting a form of agency that mirrors our own.

Why should you care? Because this approach could resolve many alignment puzzles, offering a pathway to AIs that don't just perform tasks but enhance human life. It challenges the assumption that AI must have rigid goals, suggesting instead a model where AI participates in the unfolding of shared human practices.

The architecture matters more than the parameter count. This is about rethinking how we build AI, not just to solve problems, but to evolve alongside us, leading to machines that align with the essence of human experience.