少点错误 05月05日 20:22
Why “Solving Alignment” Is Likely a Category Mistake
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文章指出,AI对齐并非一个可以通过技术手段一劳永逸解决的问题,而是一个需要持续管理、维护和协商的动态过程。文章首先质疑了将AI对齐简化为一套静态目标的假设,并探讨了对齐对象的选择困境,如个人用户、公司利益、民主共识等,每种选择都存在固有缺陷。随后,文章通过类比亲子关系、婚姻、地缘政治和经济等复杂系统,强调了AI对齐的持续性,而非一次性解决方案。最后,文章呼吁用“成功驾驭AI共同进化”等更合适的术语来描述这一动态、关系性和不可预测的过程。

🎯**AI对齐并非技术难题,而是一个需要持续管理和协商的动态过程**:文章反驳了将AI对齐视为可通过技术手段一次性解决的观点,认为这是一种严重的误解,忽略了其复杂性和演变性。

🤔**对齐对象的选择困境**:文章探讨了将AI与不同对象(如个人用户、公司利益、民主共识等)对齐的潜在问题,指出每种选择都存在固有的缺陷和伦理挑战。

🔄**AI对齐的持续性**:通过类比亲子关系、婚姻、地缘政治和经济等复杂系统,文章强调了AI对齐是一个需要不断调整和适应的持续过程,而非静态的解决方案。

💡**“成功驾驭AI共同进化”:更准确的描述**:鉴于当前术语的误导性,文章建议使用“成功驾驭AI共同进化”等更合适的术语来描述这一动态、关系性和不可预测的过程。

Published on May 5, 2025 4:26 AM GMT

A common framing of the AI alignment problem is that it's a technical hurdle to be overcome. A clever team at DeepMind or Anthropic would publish a paper titled "Alignment is All You Need," everyone would implement it, and we'd all live happily ever after in harmonious coexistence with our artificial friends.

I suspect this perspective constitutes a category mistake on multiple levels. Firstly, it presupposes that the aims, drives, and objectives of both the artificial general intelligence and what we aim to align it with can be simplified into a distinct and finite set of elements, a simplification I believe is unrealistic. Secondly, it treats both the AGI and the alignment target as if they were static systems. This is akin to expecting a single paper titled "The Solution to Geopolitical Stability" or "How to Achieve Permanent Marital Bliss." These are not problems that are solved; they are conditions that are managed, maintained, and negotiated on an ongoing basis.

The Problem of "Aligned To Whom?"

The phrase "AI alignment" is often used as shorthand for "AI that does what we want." But "we" is not a monolithic entity. Consider the potential candidates for the entity or values an AGI should be aligned with:

    Individual Users: Aligning to individual user preferences, however harmful or conflicting? This seems like a recipe for chaos or enabling malicious actors. We just experienced an example of how this can go wrong with the GPT-4o update and subsequent rollback, wherein positive user feedback resulted in an overly sycophantic model personality (that was still rated quite highly by many users!) with some serious negative consequences.Corporate/Shareholder Interests: Optimizes for proxy goals (e.g., profit, engagement) which predictably generate negative externalities. Subject to Goodhart's Law on a massive scale.Democratic Consensus: Aligning to the will of the majority? Historical precedent suggests this can lead to the oppression of minorities. Furthermore, democratic processes are slow, easily manipulated, and often struggle with complex, long-term issues.AI Developer Values: Aligning to the personal values of a small, unrepresentative group of engineers and researchers? This introduces the biases and blind spots of that specific group as the de facto global operating principles. We saw how this can go wrong with the Twitter Files - imagine if that was instead about controlling the values of AGI.Objective Morality/Coherent Extrapolated Volition: Assumes such concepts are well-defined, discoverable, and technically specifiable—all highly uncertain propositions that humanity has failed on thus far. And if we’re relying on AGI to figure this one out, I’m not sure how we could “check the proof” on this one, so we’d have to assume that the AGI was aligned…and we’re right back where we started.

This isn't merely a matter of picking the "right" option. The options conflict, and the very notion of a stable, universally agreed-upon target for alignment seems implausible a priori.

The Target is Moving

The second aspect of the category mistake is treating alignment as something you achieve rather than something you maintain. Consider these analogous complex systems:

These examples illustrate what Dan Hendrycks (drawing on Rittel & Webber's 1973 work) has identified as the "wicked problem" nature of AI safety: problems that are "open-ended, carry ambiguous requirements, and often produce unintended consequences." Artificial general intelligence belongs squarely in this category of problems that resist permanent solutions.

The scale of the challenge with AGI is amplified by the potential power differential. I struggle to keep my ten-year-olds aligned with my values, and I'm considerably smarter and more powerful than they are. With AGI we're talking about creating intelligent, agentic systems, but unlike children they will be smarter, think faster, and be more numerous than us. We will change, they will change, the environment will change. Maintaining alignment will be a continuous, dynamic process.

This doesn't mean we should abandon alignment research. We absolutely need the best alignment techniques possible. But we should be clear-eyed about what success looks like: not a solved problem, but an ongoing, never-ending process of negotiation, adaptation, and correction. Perhaps given the misleading nature of the current nomenclature, using a different phrase such as Successfully Navigating AI Co-evolution would better capture the dynamic, relational, and inherently unpredictable nature of integrating AGI successfully with humanity.



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