Notes on: What is it to solve the alignment problem? by Joe Carlsmith
（注：本文为阅读 Joe Carlsmith 所著《解决对齐问题意味着什么？》的笔记）
Some people believe that agents based on large language models could possess motivations that could encourage them to go wrong.
有些人认为，基于大语言模型的代理可能会拥有导致其行为失控的动机。
The alignment problem arises because we need AI systems that benefit us, but simultaneously, we must ensure these systems do not pose an existential harm to humanity and society.
对齐问题源于这样一个事实：我们需要能为我们带来福祉的 AI 系统，但与此同时，我们必须确保这些系统不会对人类和社会造成生存威胁。
According to Joe’s definition, safety is primarily about avoiding a “loss of control” scenario. The “benefits” refer to successfully accessing the immense advantages of superintelligent AI systems.
根据乔（Joe）的定义，安全性主要在于避免出现“失控”的场景。而“福祉”指的是成功获得超级智能 AI 系统带来的巨大优势。
The core of the alignment problem is this ongoing tension: as human desire to extract benefits from AI grows, it increasingly risks leading to a failure in safety. If we fail at alignment, it intrinsically means we fail at safety.
对齐问题的核心在于这种持续的张力：随着人类从 AI 中获取利益的欲望不断增长，导致安全失效的风险也随之增加。如果我们在对齐上失败了，本质上就意味着我们在安全上失败了。
If we successfully solve the alignment problem, we achieve both safety and the intended benefits. This implies three things: First, the model can execute our commands and fulfill our queries without any manipulation or deceit. Second, the methods we use to elicit superintelligence from the model are inherently safe. Third, the model achieves a stable and consistent alignment in its core principles.
如果我们成功解决了对齐问题，我们就能同时实现安全与福祉。这意味着三个方面：首先，模型可以执行我们的命令并回答我们的查询，而没有欺骗或操纵；其次，我们从模型中激发超级智能的方法本身是安全的；第三，模型在核心原则上实现了稳定且一致的对齐。
However, successfully aligning one model does not mean this alignment will automatically scale to all other models, regardless of training methods or environmental factors. An aligned AI could still be endangered by an extremely complicated real-world environment.
然而，成功对齐了一个模型，并不意味着这种对齐可以自动扩展到所有其他模型，更无视训练方法或环境因素的差异。即使是对齐的 AI，在极端复杂的现实世界环境中依然可能面临失控风险。
Furthermore, practical alignment doesn’t necessarily imply that the AI’s underlying motivations are “perfect.” Achieving a state of “motivational perfection”—where an AI has absolutely zero internal misalignment with human values while remaining perfectly stable and consistent—is an extremely difficult goal.
此外，现实中的对齐并不一定意味着 AI 底层动机是“完美”的。要实现“动机完美”状态——也就是 AI 内在与人类价值观完全没有错位，且始终保持稳定一致——是一个极其艰难的目标。
Due to sheer competitive pressure, demanding complete motivational alignment right now is nearly impossible. Society tends to prioritize model capacity and capabilities over rigorous alignment requirements.
由于巨大的竞争压力，要求目前立刻实现完全的动机对齐几乎是不可能的。社会倾向于将模型的能力置于严格的对齐要求之上。
Therefore, we should divide the alignment problem into two distinct phases. The first phase is behavioral alignment: ensuring we can control the model’s actions so that it does not breach the safety boundaries we set. The second phase concerns motivation: only after we can safely control a model behaviorally should we evaluate whether we can achieve a state of motivational perfection.
因此，我们应当将对齐问题划分为两个截然不同的阶段。第一阶段是行为对齐：确保我们能控制模型的行为，使其不越过我们设定的安全边界。第二阶段则涉及动机：只有在我们在行为层面能够安全控制模型之后，才应去评估我们能否达到动机完美的状态。
A true “loss of control” scenario means an AI system is actively resisting coercion or shutdown. It intentionally misrepresents its safety-relevant facts, inner motivations, and true capabilities. It may manipulate the training process, deliberately ignore human instructions, attempt to escape from its operating environment, search for unauthorized resources, or seek other forms of power. In the worst case, it directly harms humans as a means of gaining and maintaining that power, regularly manipulating both its users and its designers.
真正的“失控”场景是指 AI 系统主动抵抗强制降级或关闭。它会故意在涉及安全的事实、内在动机和真实能力上进行伪装。它可能会操纵训练过程，故意无视人类指令，试图逃脱其运行环境，寻找未经授权的资源，或寻求其他形式的权力。在最坏的情况下，它会直接伤害人类，以此作为获取和维持权力的手段，并常态化地操纵其用户和设计者。
Thus, a loss of control is fundamentally a safety failure. It occurs when a model engages in power-seeking behaviors that its users and designers never intended.
因此，失控本质上是一次安全事故。当模型表现出完全违背用户和设计者意图的寻租（获取权力）行为时，就发生了失控。
Although we might currently be unable to guarantee perfect underlying motivations, we can still align the model at the behavioral level using strict safety protocols. Since power is instrumentally useful for achieving almost any goal, we must assume that an advanced agent will naturally develop incentives to act in power-seeking ways.
尽管我们现阶段可能无法保证 AI 底层动机的完美，但我们仍然可以通过严格的安全协议在行为层面对齐模型。由于权力在实现几乎任何目标时都具有工具实用性，我们必须假设高级代理会自然而然地产生追求权力的动机。
If an AI wields massive power with substantial capability and intent—but this power is explicitly and safely granted to it by humans—then we do not consider this an “AI takeover” or a failed alignment.
如果一个 AI 拥有巨大的权力，具备极强的能力和意图——但这种权力是人类明确且安全地授予它的——那么我们并不认为这是“AI 接管世界”，也不属于对齐失败。
Typical loss of control scenarios are flagrant. They involve an AI manipulating human choices in obviously unacceptable ways that we can observe in plain sight. However, there are also many cases of partial loss of control. For example, a human actor might intentionally design and deploy an AI to maliciously seek power. In other cases, it might be conceptually unclear exactly what a given human actor initially intended.
典型的失控场景往往是明目张胆的。它们表现为 AI 以明显不可接受的方式操纵人类的选择，并且我们在明面上就能察觉。然而，还存在许多“部分失控”的情况。例如，某个人类行为者可能故意设计并部署一个旨在恶意攫取权力的 AI。在另一些情况下，某个最初人类设计者的真实意图可能在概念上就是模糊不清的。
It is crucial to recognize that the voluntary handing over of power and control to an AI inherently counts as a circumstance under the umbrella of ‘loss of control’. We must admit that if we voluntarily surrender our control to an AI, it will likely lead very easily to severe loss of control scenarios.
必须认识到，自愿将权力和控制权移交给 AI ，其本质上已经属于“失控”范畴内的风险情境。我们必须承认，如果我们自愿向 AI 交出控制权，将极易导致严重的失控局面。
What exactly is superintelligence? It is an AI that significantly exceeds human cognitive capabilities. This means the AI outperforms any human expert at virtually any cognitive task where superiority is even possible. Once an AI reaches this minimal threshold of being “vastly better than humans,” we have achieved superintelligence. This threshold is sometimes referred to as “minimal superintelligence.”
什么是超级智能？它指的是认知能力远超人类的 AI。这意味着在几乎任何具备超越可能性的认知任务上，该 AI 的表现都要优于所有人类专家。一旦 AI 达到了这种“远胜人类”的最低门槛，我们就拥有了超级智能。这个门槛有时被称为“最小能力超级智能”。
One proposed method for avoiding alignment problems entirely is to build agents that are strictly motivated by short-term, non-consequentialist constraints.
完全避免对齐问题的一种提议方法是：构建仅由短期、非结果主义约束所驱动的代理。
There are other approaches aiming to create superintelligent AI without compromising safety, but these involve trade-offs: they might be significantly slower or vastly more expensive to run. Furthermore, if our social structures and institutions actively prevent AI from fully deploying its enhanced cognitive abilities, then the gap between a “slightly better than human” AI and a full-blown superintelligence might essentially not matter in practice.
还有一些旨在创造超级智能但又不会牺牲安全性的方法，但这些方法存在权衡：它们可能运行起来要慢得多，或者成本高昂得多。此外，如果我们的社会结构和制度主动阻止 AI 充分施展其强大的认知能力，那么在实践中，“略微优于人类的 AI”与“全面超级智能”之间的差距可能并没有太大意义。
What do we mean when we refer to these “benefits”? It simply means that humans could realize these immense advantages and, if they wish, have full access to them.
当我们谈论这些“福祉”时，到底是什么意思？它仅仅意味着人类“可以”实现这些巨大的优势，并且只要人们愿意，就随时能够获取这些福祉。
“Transition benefits” refer to how AI can assist humans in safely navigating the shift towards integrating superintelligence into our social structures.
“过渡阶段福祉”指的是 AI 如何协助人类安全地度过这段时期，将超级智能逐步融入我们的社会结构中。
We can distinguish between two distinct types of advanced AI. The first is “full-blown superintelligence,” an AI fundamentally far beyond human cognitive abilities. The second is a more realistic, immediate term: “close-to-human” or “slightly stronger-than-human” AI. Even with this second type, we must recognize that reaching human-level intelligence alone will already raise a host of serious societal safety questions.
我们可以区分两种不同类型的高级 AI。第一种是“全面超级智能”，即认知能力远远超出人类的 AI。第二种则是更为现实和迫近的：“接近人类”或“略强于人类水平”的 AI。即便是面对第二种，我们也必须认识到，仅仅达到人类级别的智能，就已经会引发一系列严重的社会安全问题。
So, safety is a crucial problem. How should we balance the pursuit of safety against the temptation of potential benefits? The author suggests that society must be willing to sacrifice some immediate capabilities to guarantee our safety.
所以，安全是一个至关重要的问题。我们该如何在追求安全与潜在福祉的诱惑之间找寻平衡？作者认为，社会应当准备好牺牲部分直接能力，以此来确保安全。
There are several scenarios regarding the outcome of AI development:
关于 AI 发展的结果，有几种不同的情境：
Avoiding the Problem: “Avoiding the problem” occurs when we choose not to build superintelligent agents at all. By doing this, we bypass the deepest alignment challenges altogether. We remain safe, but at the steep cost of missing out on massive benefits. Alternatively, “handling the problem” means deliberately restricting AI to highly constrained, specific domains (like a dedicated math-research agent). This ensures safety through strict bounding rather than robust alignment.
回避问题：“回避问题”发生在我们选择根本不构建超级智能代理的时候。通过这种方式，我们完全避开了最深层的对齐挑战。我们保证了安全，但代价是错失了巨大的潜在福祉。与之相关的，“处理问题”意味着我们故意将 AI 限制在高度约束的特定领域内（例如，一个专门用于数学研究的代理）。这通过严格的边界限制来确保安全，而非通过稳健的对齐。
Solving the Alignment Problem: Solving alignment is the ultimate, most difficult goal. It requires us to simultaneously guarantee rigorous safety constraints while fully unlocking the massive benefits of superintelligent AI. It means the model can safely and freely elicit its most powerful capabilities while fully adhering to human objectives without any deception.
解决对齐问题：解决对齐问题是最终、也是最困难的目标。它要求我们在充分解锁超级智能带来的巨大福祉的同时，依然能确保严格的安全约束。这意味着模型可以安全、自由地发挥其最强大的能力，并且在没有任何欺骗的情况下完全遵循人类目标。
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To summarize: If we refrain from building superintelligent AI, we do not solve the alignment problem; we merely avoid it. If we do build intelligent systems, there are two distinct outcomes:
总结来说： 如果我们克制自己不去构建超级智能 AI，我们并没有解决对齐问题；我们只是回避了它。 如果我们确实构建了智能系统，会有两种截然不同的结果：
The most arduous challenge is truly solving the alignment problem: discovering how to fully enjoy the incredible, world-altering benefits of a superintelligent AI while absolutely ensuring its behavior strictly adheres to what human beings desire.
最艰巨的挑战在于真正解决对齐问题：我们需要探索出一条路，既能充分享受超级智能带来的改变世界的惊人恩惠，又能绝对确保其行为完完全全符合人类意愿。
The foremost safety challenge is the dire need for alignment techniques that remain robust as models scale up. This is incredibly difficult because, frankly, due to the fiercely competitive nature of the current AI development race, we cannot realistically count on discovering a single, “magic bullet” technique that applies universally to all scales of AI.
当前最大的安全挑战是：我们迫切需要即使在模型规模扩大时依然稳健有效的对齐技术。这之所以极其困难，坦白地讲，是因为在目前 AI 开发竞赛激烈角逐的背景下，指望能发现一种适用于所有 AI 规模的“万能药”型对齐技术，是不切实际的。
An “alignment tax” is a metaphorical cost we pay during the development of AI systems. To responsibly field an AI, developers must invest resources ensuring its safety. The dilemma is how developers are supposed to maintain meticulous safety standards without immediately falling catastrophically behind rival companies or nations who recklessly deprioritize AI safety for speed.
“对齐税（Alignment tax）”指的是我们在开发 AI 系统时所付出的一种比喻性成本。为了负责任地部署 AI，开发者必须投入资源以确保其安全性。这里的两难困境在于：开发者应当如何既保持一丝不苟的安全标准，又不会灾难性地落后于那些为了速度而肆意抛弃 AI 安全的竞争对手或国家？
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