Machines of
Loving Grace¹
仁爱机器¹

How AI Could Transform the World for the Better
人工智能如何让世界变得更美好

October 2024 2024 年 10 月

I think and talk a lot about the risks of powerful AI. The company I’m the CEO of, Anthropic, does a lot of research on how to reduce these risks. Because of this, people sometimes draw the conclusion that I’m a pessimist or “doomer” who thinks AI will be mostly bad or dangerous. I don’t think that at all. In fact, one of my main reasons for focusing on risks is that they’re the only thing standing between us and what I see as a fundamentally positive future. I think that most people are underestimating just how radical the upside of AI could be, just as I think most people are underestimating how bad the risks could be.
我经常思考和谈论强大人工智能的风险。我担任首席执行官的 Anthropic 公司就如何降低这些风险进行了大量研究。正因为如此，人们有时会得出这样的结论：我是一个悲观主义者或 "空想家"，认为人工智能大多会很糟糕或很危险。我完全不这么认为。事实上，我关注风险的一个主要原因是，它们是我们与我认为从根本上讲是积极的未来之间的唯一障碍。我认为大多数人都低估了人工智能可能带来的巨大好处，就像我认为大多数人都低估了人工智能可能带来的巨大风险一样。

In this essay I try to sketch out what that upside might look like—what a world with powerful AI might look like if everything goes right. Of course no one can know the future with any certainty or precision, and the effects of powerful AI are likely to be even more unpredictable than past technological changes, so all of this is unavoidably going to consist of guesses. But I am aiming for at least educated and useful guesses, which capture the flavor of what will happen even if most details end up being wrong. I’m including lots of details mainly because I think a concrete vision does more to advance discussion than a highly hedged and abstract one.
在这篇文章中，我试图勾勒出这一趋势可能呈现的样子--如果一切顺利，一个拥有强大人工智能的世界可能会是什么样子。当然，没有人能准确无误地预知未来，而强大的人工智能所带来的影响很可能比过去的技术变革更加难以预测，因此本文难免会包含一些猜测。但我的目标是至少做出有根据的、有用的猜测，即使大多数细节最终是错误的，也能捕捉到将要发生的事情的味道。我之所以包含大量细节，主要是因为我认为具体的愿景比高度对冲和抽象的愿景更能推动讨论。

First, however, I wanted to briefly explain why I and Anthropic haven’t talked that much about powerful AI’s upsides, and why we’ll probably continue, overall, to talk a lot about risks. In particular, I’ve made this choice out of a desire to:
不过，首先，我想简要解释一下为什么我和人类学没有过多地谈论强大人工智能的好处，以及为什么我们总体上可能会继续过多地谈论风险。特别是，我之所以做出这样的选择，是出于以下愿望：

• Maximize leverage. The basic development of AI technology and many (not all) of its benefits seems inevitable (unless the risks derail everything) and is fundamentally driven by powerful market forces. On the other hand, the risks are not predetermined and our actions can greatly change their likelihood.
  最大化杠杆作用。人工智能技术的基本发展及其带来的许多（并非全部）好处似乎是不可避免的（除非风险使一切脱轨），而且从根本上说是由强大的市场力量推动的。另一方面，风险并不是预先确定的，我们的行动可以极大地改变风险发生的可能性。
• Avoid perception of propaganda. AI companies talking about all the amazing benefits of AI can come off like propagandists, or as if they’re attempting to distract from downsides. I also think that as a matter of principle it’s bad for your soul to spend too much of your time “talking your book”.
  避免宣传。人工智能公司在谈论人工智能的所有神奇优势时，可能会让人觉得他们是宣传家，或者是在试图转移人们对人工智能缺点的注意力。我还认为，从原则上讲，花太多时间 "高谈阔论 "不利于你的灵魂。
• Avoid grandiosity. I am often turned off by the way many AI risk public figures (not to mention AI company leaders) talk about the post-AGI world, as if it’s their mission to single-handedly bring it about like a prophet leading their people to salvation. I think it’s dangerous to view companies as unilaterally shaping the world, and dangerous to view practical technological goals in essentially religious terms.
  避免夸夸其谈。许多人工智能风险公众人物（更不用说人工智能公司的领导者了）谈论后人工智能世界的方式常常令我反感，好像他们的使命就是单枪匹马地实现后人工智能世界，就像先知带领他们的人民走向救赎一样。我认为，将公司视为单方面塑造世界的人是危险的，从宗教角度看待实用技术目标也是危险的。
• Avoid “sci-fi” baggage. Although I think most people underestimate the upside of powerful AI, the small community of people who do discuss radical AI futures often does so in an excessively “sci-fi” tone (featuring e.g. uploaded minds, space exploration, or general cyberpunk vibes). I think this causes people to take the claims less seriously, and to imbue them with a sort of unreality. To be clear, the issue isn’t whether the technologies described are possible or likely (the main essay discusses this in granular detail)—it’s more that the “vibe” connotatively smuggles in a bunch of cultural baggage and unstated assumptions about what kind of future is desirable, how various societal issues will play out, etc. The result often ends up reading like a fantasy for a narrow subculture, while being off-putting to most people.
  避免 "科幻 "包袱。虽然我认为大多数人都低估了强大人工智能的潜力，但一小部分人在讨论激进的人工智能未来时，往往会用一种过于 "科幻 "的语气（例如上载的大脑、太空探索或一般的赛博朋克氛围）。我认为，这会导致人们不那么认真地对待这些说法，并使其带有一种不真实感。说白了，问题并不在于所描述的技术是否可能或有可能实现（主要文章对此进行了详细讨论）--更重要的是，"氛围 "的内涵包含了一堆文化包袱，以及对什么样的未来是理想的、各种社会问题将如何解决等未加说明的假设。其结果往往是读起来像一种狭隘亚文化的幻想，而对大多数人来说却令人反感。

Yet despite all of the concerns above, I really do think it’s important to discuss what a good world with powerful AI could look like, while doing our best to avoid the above pitfalls. In fact I think it is critical to have a genuinely inspiring vision of the future, and not just a plan to fight fires. Many of the implications of powerful AI are adversarial or dangerous, but at the end of it all, there has to be something we’re fighting for, some positive-sum outcome where everyone is better off, something to rally people to rise above their squabbles and confront the challenges ahead. Fear is one kind of motivator, but it’s not enough: we need hope as well.
尽管有上述种种担忧，但我确实认为，讨论一个拥有强大人工智能的美好世界会是什么样子，同时尽力避免上述陷阱，是非常重要的。事实上，我认为最关键的是要有一个真正鼓舞人心的未来愿景，而不是只是一个救火计划。强大的人工智能所带来的许多影响都具有对抗性或危险性，但归根结底，我们必须为之奋斗，必须取得一些积极的结果，让每个人都过得更好，必须让人们超越争吵，直面未来的挑战。恐惧是一种动力，但它还不够：我们还需要希望。

The list of positive applications of powerful AI is extremely long (and includes robotics, manufacturing, energy, and much more), but I’m going to focus on a small number of areas that seem to me to have the greatest potential to directly improve the quality of human life. The five categories I am most excited about are:
强大人工智能的积极应用不胜枚举（包括机器人、制造、能源等），但我将重点关注少数几个在我看来最有可能直接改善人类生活质量的领域。我最感兴趣的五个领域是

1. Biology and physical health
   生物与身体健康
2. Neuroscience and mental health
   神经科学与心理健康
3. Economic development and poverty
   经济发展与贫困
4. Peace and governance 和平与治理
5. Work and meaning 工作与意义

My predictions are going to be radical as judged by most standards (other than sci-fi “singularity” visions²), but I mean them earnestly and sincerely. Everything I’m saying could very easily be wrong (to repeat my point from above), but I’ve at least attempted to ground my views in a semi-analytical assessment of how much progress in various fields might speed up and what that might mean in practice. I am fortunate to have professional experience in both biology and neuroscience, and I am an informed amateur in the field of economic development, but I am sure I will get plenty of things wrong. One thing writing this essay has made me realize is that it would be valuable to bring together a group of domain experts (in biology, economics, international relations, and other areas) to write a much better and more informed version of what I’ve produced here. It’s probably best to view my efforts here as a starting prompt for that group.
从大多数标准（科幻小说中的 "奇点 "愿景除外² ）来看，我的预测都将是激进的，但我是认真和真诚的。我所说的一切都很可能是错误的（重复我上面的观点），但我至少试图将我的观点建立在对各个领域的进步可能加快的程度以及这在实践中可能意味着什么的半分析性评估的基础之上。我很幸运在生物学和神经科学领域拥有专业经验，而且我在经济发展领域也是一个见多识广的业余爱好者，但我相信我肯定会有很多地方做错。写这篇文章让我意识到，如果能把生物学、经济学、国际关系和其他领域的专家聚集在一起，写出比我的文章更好、更有见地的版本，那将是非常有价值的。也许最好的办法是把我在这里所做的努力看作是给这个小组的一个起始提示。

Basic assumptions and framework
基本假设和框架

To make this whole essay more precise and grounded, it’s helpful to specify clearly what we mean by powerful AI (i.e. the threshold at which the 5-10 year clock starts counting), as well as laying out a framework for thinking about the effects of such AI once it’s present.
为了让整篇文章更加精确和有根有据，我们有必要明确指出强大人工智能的含义（即 5-10 年开始计时的门槛），并为思考人工智能出现后的影响制定一个框架。

What powerful AI (I dislike the term AGI)³ will look like, and when (or if) it will arrive, is a huge topic in itself. It’s one I’ve discussed publicly and could write a completely separate essay on (I probably will at some point). Obviously, many people are skeptical that powerful AI will be built soon and some are skeptical that it will ever be built at all. I think it could come as early as 2026, though there are also ways it could take much longer. But for the purposes of this essay, I’d like to put these issues aside, assume it will come reasonably soon, and focus on what happens in the 5-10 years after that. I also want to assume a definition of what such a system will look like, what its capabilities are and how it interacts, even though there is room for disagreement on this.
强大的人工智能（我不喜欢 AGI 这个词）³ 将会是什么样子，以及何时（或是否）会出现，这本身就是一个巨大的话题。我曾公开讨论过这个问题，而且可以写一篇完全独立的文章（我可能会在某个时候写）。很明显，很多人都怀疑强大的人工智能是否会很快诞生，有些人则怀疑人工智能是否会诞生。我认为最早可能在 2026 年就能实现，不过也有可能需要更长的时间。但在这篇文章中，我想抛开这些问题，假设它很快就会出现，并专注于之后 5-10 年发生的事情。我还想假定这样一个系统会是什么样子，它的功能是什么，以及它是如何交互的，尽管在这一点上还存在分歧。

By powerful AI, I have in mind an AI model—likely similar to today’s LLM’s in form, though it might be based on a different architecture, might involve several interacting models, and might be trained differently—with the following properties:
通过强大的人工智能，我想到了一个人工智能模型--形式上可能类似于今天的LLM，尽管它可能基于不同的架构，可能涉及多个相互作用的模型，而且可能以不同的方式进行训练--具有以下特性：

• In terms of pure intelligence⁴, it is smarter than a Nobel Prize winner across most relevant fields – biology, programming, math, engineering, writing, etc. This means it can prove unsolved mathematical theorems, write extremely good novels, write difficult codebases from scratch, etc.
  就纯智能⁴ 而言，在生物、编程、数学、工程、写作等大多数相关领域，它都比诺贝尔奖获得者更聪明。这意味着它可以证明尚未解决的数学定理，写出非常出色的小说，从头开始编写高难度的代码库，等等。
• In addition to just being a “smart thing you talk to”, it has all the “interfaces” available to a human working virtually, including text, audio, video, mouse and keyboard control, and internet access. It can engage in any actions, communications, or remote operations enabled by this interface, including taking actions on the internet, taking or giving directions to humans, ordering materials, directing experiments, watching videos, making videos, and so on. It does all of these tasks with, again, a skill exceeding that of the most capable humans in the world.
  除了是一个 "可与之交谈的智能物体 "之外，它还拥有人类虚拟工作的所有 "界面"，包括文本、音频、视频、鼠标和键盘控制以及互联网访问。它可以通过这些界面进行任何操作、通信或远程操作，包括在互联网上进行操作、为人类提供指导、订购材料、指导实验、观看视频、制作视频等。同样，它完成所有这些任务的技能也超过了世界上能力最强的人类。
• It does not just passively answer questions; instead, it can be given tasks that take hours, days, or weeks to complete, and then goes off and does those tasks autonomously, in the way a smart employee would, asking for clarification as necessary.
  它不会只是被动地回答问题；相反，它可以接受需要数小时、数天或数周才能完成的任务，然后以聪明员工的方式自主完成这些任务，并在必要时请求澄清。
• It does not have a physical embodiment (other than living on a computer screen), but it can control existing physical tools, robots, or laboratory equipment through a computer; in theory it could even design robots or equipment for itself to use.
  它没有实体（除了活在电脑屏幕上），但可以通过电脑控制现有的实体工具、机器人或实验室设备；理论上，它甚至可以设计机器人或设备供自己使用。
• The resources used to train the model can be repurposed to run millions of instances of it (this matches projected cluster sizes by ~2027), and the model can absorb information and generate actions at roughly 10x-100x human speed⁵. It may however be limited by the response time of the physical world or of software it interacts with.
  用于训练模型的资源可重新用于运行数百万个模型实例（这与预计的集群规模相匹配，约为 2027 年），并且该模型可以大约 10 倍至 100 倍于人类的速度吸收信息并生成操作⁵ 。不过，它可能会受到物理世界或与之交互的软件的响应时间的限制。
• Each of these million copies can act independently on unrelated tasks, or if needed can all work together in the same way humans would collaborate, perhaps with different subpopulations fine-tuned to be especially good at particular tasks.
  这一百万个拷贝中的每一个都可以独立完成不相关的任务，如果需要，也可以以人类合作的方式一起工作，也许不同的亚群经过微调后，特别擅长特定的任务。

We could summarize this as a “country of geniuses in a datacenter”.
我们可以将其概括为 "数据中心中的天才之国"。

Clearly such an entity would be capable of solving very difficult problems, very fast, but it is not trivial to figure out how fast. Two “extreme” positions both seem false to me. First, you might think that the world would be instantly transformed on the scale of seconds or days (“the Singularity”), as superior intelligence builds on itself and solves every possible scientific, engineering, and operational task almost immediately. The problem with this is that there are real physical and practical limits, for example around building hardware or conducting biological experiments. Even a new country of geniuses would hit up against these limits. Intelligence may be very powerful, but it isn’t magic fairy dust.
显然，这样一个实体有能力以非常快的速度解决非常棘手的问题，但要想知道它有多快，却并非易事。在我看来，有两种 "极端 "的观点都是错误的。首先，你可能会认为世界会在几秒或几天的时间内瞬间发生巨大变化（"奇点"），因为卓越的智能会不断自我完善，几乎立即解决所有可能的科学、工程和操作任务。但问题是，这存在着实际的物理和实践限制，例如在制造硬件或进行生物实验方面。即使是一个由天才组成的新国家，也会遇到这些限制。智能可能非常强大，但它不是神奇的仙尘。

Second, and conversely, you might believe that technological progress is saturated or rate-limited by real world data or by social factors, and that better-than-human intelligence will add very little⁶. This seems equally implausible to me—I can think of hundreds of scientific or even social problems where a large group of really smart people would drastically speed up progress, especially if they aren’t limited to analysis and can make things happen in the real world (which our postulated country of geniuses can, including by directing or assisting teams of humans).
其次，反过来说，你可能会认为技术进步已经饱和，或其速度受到现实世界的数据或社会因素的限制，而比人类更高的智能只会带来很小的影响⁶ 。在我看来，这种说法同样难以置信--我可以想到数以百计的科学甚至社会问题，在这些问题上，一大群真正聪明的人将大大加快进步的速度，尤其是如果他们不局限于分析，并且能够在现实世界中创造奇迹的话（我们假设的天才之国就可以做到这一点，包括指导或协助人类团队）。

I think the truth is likely to be some messy admixture of these two extreme pictures, something that varies by task and field and is very subtle in its details. I believe we need new frameworks to think about these details in a productive way.
我认为，真相很可能是这两种极端情况的混乱混合体，因任务和领域而异，在细节上非常微妙。我认为，我们需要新的框架，以富有成效的方式思考这些细节。

Economists often talk about “factors of production”: things like labor, land, and capital. The phrase “marginal returns to labor/land/capital” captures the idea that in a given situation, a given factor may or may not be the limiting one – for example, an air force needs both planes and pilots, and hiring more pilots doesn’t help much if you’re out of planes. I believe that in the AI age, we should be talking about the marginal returns to intelligence⁷, and trying to figure out what the other factors are that are complementary to intelligence and that become limiting factors when intelligence is very high. We are not used to thinking in this way—to asking “how much does being smarter help with this task, and on what timescale?”—but it seems like the right way to conceptualize a world with very powerful AI.
经济学家经常谈论 "生产要素"：如劳动力、土地和资本。劳动/土地/资本的边际收益 "这一短语抓住了这样一个概念，即在特定情况下，特定要素可能是也可能不是限制性要素--例如，空军既需要飞机也需要飞行员，如果你没有飞机了，雇佣更多的飞行员也无济于事。我认为，在人工智能时代，我们应该讨论智力的边际收益⁷ ，并试图找出与智力互补的其他因素，当智力非常高时，这些因素就会成为限制因素。我们不习惯用这种方式来思考问题--问 "更聪明对完成这项任务有多大帮助，在什么时间范围内？"但这似乎是概念化一个拥有非常强大的人工智能的世界的正确方式。

My guess at a list of factors that limit or are complementary to intelligence includes:
我猜测，限制智力或与智力互补的因素包括：

• Speed of the outside world. Intelligent agents need to operate interactively in the world in order to accomplish things and also to learn⁸. But the world only moves so fast. Cells and animals run at a fixed speed so experiments on them take a certain amount of time which may be irreducible. The same is true of hardware, materials science, anything involving communicating with people, and even our existing software infrastructure. Furthermore, in science many experiments are often needed in sequence, each learning from or building on the last. All of this means that the speed at which a major project—for example developing a cancer cure—can be completed may have an irreducible minimum that cannot be decreased further even as intelligence continues to increase.
  外部世界的速度。智能代理需要在世界上进行交互式操作，以便完成任务和学习⁸ 。但是，世界的运行速度太快了。细胞和动物的运行速度是固定的，因此对它们进行实验需要一定的时间，而这些时间可能是不可还原的。硬件、材料科学、任何涉及与人交流的东西，甚至我们现有的软件基础设施都是如此。此外，在科学领域，许多实验往往需要依次进行，每一个实验都要借鉴或建立在上一个实验的基础上。所有这些都意味着，一个重大项目--例如开发癌症治疗方法--的完成速度可能有一个不可还原的最小值，即使智能不断提高，也无法进一步降低。
• Need for data. Sometimes raw data is lacking and in its absence more intelligence does not help. Today’s particle physicists are very ingenious and have developed a wide range of theories, but lack the data to choose between them because particle accelerator data is so limited. It is not clear that they would do drastically better if they were superintelligent—other than perhaps by speeding up the construction of a bigger accelerator.
  需要数据。有时缺乏原始数据，再多的智慧也无济于事。今天的粒子物理学家非常聪明，提出了各种各样的理论，但由于粒子加速器的数据非常有限，他们缺乏数据来对这些理论进行选择。目前还不清楚，如果他们是超级智能人，是否会做得更好--除了加快建造更大的加速器之外。
• Intrinsic complexity. Some things are inherently unpredictable or chaotic and even the most powerful AI cannot predict or untangle them substantially better than a human or a computer today. For example, even incredibly powerful AI could predict only marginally further ahead in a chaotic system (such as the three-body problem) in the general case,⁹ as compared to today’s humans and computers.
  内在复杂性。有些事情本质上是不可预测或混乱的，即使是最强大的人工智能也无法比人类或当今的计算机更好地预测或解开它们。例如，在一般情况下，⁹ 与当今的人类和计算机相比，即使是无比强大的人工智能也只能在混沌系统（如三体问题）中略微提前预测。
• Constraints from humans. Many things cannot be done without breaking laws, harming humans, or messing up society. An aligned AI would not want to do these things (and if we have an unaligned AI, we’re back to talking about risks). Many human societal structures are inefficient or even actively harmful, but are hard to change while respecting constraints like legal requirements on clinical trials, people’s willingness to change their habits, or the behavior of governments. Examples of advances that work well in a technical sense, but whose impact has been substantially reduced by regulations or misplaced fears, include nuclear power, supersonic flight, and even elevators.
  来自人类的限制。如果不触犯法律、伤害人类或扰乱社会，很多事情就无法完成。一个与人类保持一致的人工智能不会想做这些事情（如果我们有一个不一致的人工智能，我们又回到了风险的话题）。许多人类社会结构效率低下，甚至是有害的，但却很难在尊重法律规定的前提下改变，比如临床试验的法律要求、人们改变习惯的意愿或政府的行为。核能、超音速飞行和甚至是电梯等都是在技术意义上运行良好，但其影响却因法规或错误的恐惧而大大降低的进步。
• Physical laws. This is a starker version of the first point. There are certain physical laws that appear to be unbreakable. It’s not possible to travel faster than light. Pudding does not unstir. Chips can only have so many transistors per square centimeter before they become unreliable. Computation requires a certain minimum energy per bit erased, limiting the density of computation in the world.
  物理定律。这是更鲜明的第一点。有些物理定律似乎牢不可破。比光速更快的旅行是不可能的。布丁无法搅拌。芯片在变得不可靠之前，每平方厘米只能有那么多晶体管。计算需要 每擦除一个比特所需的最低能量，这就限制了世界上的计算密度。

There is a further distinction based on timescales. Things that are hard constraints in the short run may become more malleable to intelligence in the long run. For example, intelligence might be used to develop a new experimental paradigm that allows us to learn in vitro what used to require live animal experiments, or to build the tools needed to collect new data (e.g. the bigger particle accelerator), or to (within ethical limits) find ways around human-based constraints (e.g. helping to improve the clinical trial system, helping to create new jurisdictions where clinical trials have less bureaucracy, or improving the science itself to make human clinical trials less necessary or cheaper).
根据时间尺度，还有进一步的区别。在短期内是硬约束的东西，在长期内可能变得更容易被智能所改变。例如，智能可能被用来开发一种新的实验范式，使我们能够在体外学习过去需要进行活体动物实验的知识，或用来制造收集新数据所需的工具（如更大的粒子加速器），或（在道德限度内）找到绕过人类限制的方法（如帮助改进临床试验系统，帮助创建新的司法管辖区，使临床试验的官僚主义减少，或改进科学本身，使人体临床试验的必要性降低或成本降低）。

Thus, we should imagine a picture where intelligence is initially heavily bottlenecked by the other factors of production, but over time intelligence itself increasingly routes around the other factors, even if they never fully dissolve (and some things like physical laws are absolute)¹⁰. The key question is how fast it all happens and in what order.
因此，我们应该设想这样一幅图景：智能最初受到其他生产要素的严重瓶颈制约，但随着时间的推移，智能本身会越来越多地绕过其他要素，即使它们永远不会完全消失（而且有些东西，如物理定律是绝对的）¹⁰ 。问题的关键在于这一切发生的速度和顺序。

With the above framework in mind, I’ll try to answer that question for the five areas mentioned in the introduction.
根据上述框架，我将尝试回答导言中提到的五个领域的问题。

1. Biology and health 1.生物与健康

Biology is probably the area where scientific progress has the greatest potential to directly and unambiguously improve the quality of human life. In the last century some of the most ancient human afflictions (such as smallpox) have finally been vanquished, but many more still remain, and defeating them would be an enormous humanitarian accomplishment. Beyond even curing disease, biological science can in principle improve the baseline quality of human health, by extending the healthy human lifespan, increasing control and freedom over our own biological processes, and addressing everyday problems that we currently think of as immutable parts of the human condition.
生物学可能是科学进步最有可能直接、明确地改善人类生活质量的领域。在上个世纪，一些最古老的人类疾病（如天花）终于被消灭了，但还有许多疾病依然存在，战胜它们将是一项巨大的人道主义成就。除了治愈疾病之外，生物科学原则上还可以通过延长人类的健康寿命、增强对自身生物过程的控制和自由度，以及解决我们目前认为是人类生存条件不可改变的部分的日常问题，来改善人类的基本健康质量。

In the “limiting factors” language of the previous section, the main challenges with directly applying intelligence to biology are data, the speed of the physical world, and intrinsic complexity (in fact, all three are related to each other). Human constraints also play a role at a later stage, when clinical trials are involved. Let’s take these one by one.
用上一节的 "限制因素 "语言来说，将智能直接应用于生物学的主要挑战是数据、物理世界的速度和内在复杂性（事实上，这三者相互关联）。在涉及临床试验的后期阶段，人为限制因素也会发挥作用。让我们逐一分析。

Experiments on cells, animals, and even chemical processes are limited by the speed of the physical world: many biological protocols involve culturing bacteria or other cells, or simply waiting for chemical reactions to occur, and this can sometimes take days or even weeks, with no obvious way to speed it up. Animal experiments can take months (or more) and human experiments often take years (or even decades for long-term outcome studies). Somewhat related to this, data is often lacking—not so much in quantity, but quality: there is always a dearth of clear, unambiguous data that isolates a biological effect of interest from the other 10,000 confounding things that are going on, or that intervenes causally in a given process, or that directly measures some effect (as opposed to inferring its consequences in some indirect or noisy way). Even massive, quantitative molecular data, like the proteomics data that I collected while working on mass spectrometry techniques, is noisy and misses a lot (which types of cells were these proteins in? Which part of the cell? At what phase in the cell cycle?).
细胞、动物甚至化学过程的实验都受到物理世界速度的限制：许多生物实验涉及培养细菌或其他细胞，或仅仅是等待化学反应的发生，这有时需要几天甚至几周的时间，而且没有明显的加速方法。动物实验可能需要数月（甚至更长），人体实验通常需要数年（长期结果研究甚至需要数十年）。与此有关的是，数据往往缺乏--不是数量上的缺乏，而是质量上的缺乏：总是缺乏清晰、明确的数据，无法将感兴趣的生物效应从其他一万种干扰因素中分离出来，或无法对特定过程进行因果干预，或无法直接测量某种效应（而不是以某种间接或嘈杂的方式推断其后果）。即使是海量、定量的分子数据，比如我在研究质谱技术时收集的蛋白质组学数据，也存在很多噪音和遗漏（这些蛋白质存在于哪种类型的细胞中？细胞的哪个部分？处于细胞周期的哪个阶段？）

In part responsible for these problems with data is intrinsic complexity: if you’ve ever seen a diagram showing the biochemistry of human metabolism, you’ll know that it’s very hard to isolate the effect of any part of this complex system, and even harder to intervene on the system in a precise or predictable way. And finally, beyond just the intrinsic time that it takes to run an experiment on humans, actual clinical trials involve a lot of bureaucracy and regulatory requirements that (in the opinion of many people, including me) add unnecessary additional time and delay progress.
造成这些数据问题的部分原因在于其内在的复杂性：如果您看过显示人体新陈代谢生化过程的图，您就会知道要分离出这一复杂系统中任何部分的影响都非常困难，而要以精确或可预测的方式干预该系统则更加困难。最后，除了在人体上进行实验所需的固有时间外，实际的临床试验还涉及大量的官僚主义和监管要求（在包括我在内的许多人看来），这些要求增加了不必要的额外时间，延误了进展。

Given all this, many biologists have long been skeptical of the value of AI and “big data” more generally in biology. Historically, mathematicians, computer scientists, and physicists who have applied their skills to biology over the last 30 years have been quite successful, but have not had the truly transformative impact initially hoped for. Some of the skepticism has been reduced by major and revolutionary breakthroughs like AlphaFold (which has just deservedly won its creators the Nobel Prize in Chemistry) and AlphaProteo¹¹, but there’s still a perception that AI is (and will continue to be) useful in only a limited set of circumstances. A common formulation is “AI can do a better job analyzing your data, but it can’t produce more data or improve the quality of the data. Garbage in, garbage out”.
有鉴于此，许多生物学家长期以来一直对人工智能和 "大数据 "在生物学中的价值持怀疑态度。从历史上看，数学家、计算机科学家和物理学家在过去 30 年中将他们的技能应用到生物学领域取得了相当大的成功，但并没有产生最初所希望的真正变革性影响。阿尔法折叠（其创造者当之无愧地获得了诺贝尔化学奖）和阿尔法蛋白¹¹ 等重大革命性突破减少了一些怀疑，但人们仍然认为，人工智能（并将继续）只在有限的情况下有用。一种常见的说法是："人工智能可以更好地分析数据，但无法生成更多数据或提高数据质量。垃圾进，垃圾出"。

But I think that pessimistic perspective is thinking about AI in the wrong way. If our core hypothesis about AI progress is correct, then the right way to think of AI is not as a method of data analysis, but as a virtual biologist who performs all the tasks biologists do, including designing and running experiments in the real world (by controlling lab robots or simply telling humans which experiments to run – as a Principal Investigator would to their graduate students), inventing new biological methods or measurement techniques, and so on. It is by speeding up the whole research process that AI can truly accelerate biology. I want to repeat this because it’s the most common misconception that comes up when I talk about AI’s ability to transform biology: I am not talking about AI as merely a tool to analyze data. In line with the definition of powerful AI at the beginning of this essay, I’m talking about using AI to perform, direct, and improve upon nearly everything biologists do.
但我认为，这种悲观的观点是对人工智能的错误思考。如果我们关于人工智能进步的核心假设是正确的，那么正确看待人工智能的方式就不是将其视为一种数据分析方法，而是将其视为一个虚拟的生物学家，执行所有生物学家所做的任务，包括在现实世界中设计和运行实验（通过控制实验室机器人或简单地告诉人类运行哪些实验--就像首席研究员对其研究生所做的那样），发明新的生物学方法或测量技术，等等。正是通过加快整个研究过程，人工智能才能真正加速生物学的发展。我想重复这一点，因为当我谈到人工智能改变生物学的能力时，这是最常见的误解：我不是把人工智能仅仅当作分析数据的工具。根据本文开头对强大人工智能的定义，我所说的是利用人工智能来执行、指导和改进生物学家所做的几乎所有工作。

To get more specific on where I think acceleration is likely to come from, a surprisingly large fraction of the progress in biology has come from a truly tiny number of discoveries, often related to broad measurement tools or techniques¹² that allow precise but generalized or programmable intervention in biological systems. There’s perhaps ~1 of these major discoveries per year and collectively they arguably drive >50% of progress in biology. These discoveries are so powerful precisely because they cut through intrinsic complexity and data limitations, directly increasing our understanding and control over biological processes. A few discoveries per decade have enabled both the bulk of our basic scientific understanding of biology, and have driven many of the most powerful medical treatments.
为了更具体地说明我认为加速可能来自哪里，生物学进步中令人惊讶的一大部分来自于真正极少数的发现，这些发现通常与广泛的测量工具或技术¹² 有关，它们允许对生物系统进行精确但通用的或可编程的干预。这些重大发现每年可能只有 ~1 项，它们共同推动了生物学领域 >50% 的进步。这些发现之所以如此强大，正是因为它们突破了内在的复杂性和数据限制，直接增加了我们对生物过程的理解和控制。每十年的几项发现，既促成了我们对生物学的大部分基础科学认识，也推动了许多最有力的医学治疗。

Some examples include: 这方面的例子包括

• CRISPR: a technique that allows live editing of any gene in living organisms (replacement of any arbitrary gene sequence with any other arbitrary sequence). Since the original technique was developed, there have been constant improvements to target specific cell types, increasing accuracy, and reducing edits of the wrong gene—all of which are needed for safe use in humans.
  CRISPR：一种允许对生物体内任何基因进行活体编辑（用任何其他任意序列替换任何任意基因序列）的技术。自最初的技术问世以来，CRISPR一直在不断改进，以针对特定的细胞类型，提高准确性，减少错误基因的编辑--所有这些都是在人类身上安全使用所必需的。
• Various kinds of microscopy for watching what is going on at a precise level: advanced light microscopes (with various kinds of fluorescent techniques, special optics, etc), electron microscopes, atomic force microscopes, etc.
  各种显微镜可以精确地观察正在发生的事情：先进的光学显微镜（采用各种荧光技术和特殊光学技术等）、电子显微镜、原子力显微镜等。
• Genome sequencing and synthesis, which has dropped in cost by several orders of magnitude in the last couple decades.
  基因组测序和合成，其成本在过去几十年中下降了几个数量级。
• Optogenetic techniques that allow you to get a neuron to fire by shining a light on it.
  光遗传技术，通过对神经元进行光照，使其发射。
• mRNA vaccines that, in principle, allow us to design a vaccine against anything and then quickly adapt it (mRNA vaccines of course became famous during COVID).
  mRNA疫苗，原则上，我们可以设计出针对任何疾病的疫苗，然后迅速对其进行改造（mRNA疫苗当然是在COVID期间出名的）。
• Cell therapies such as CAR-T that allow immune cells to be taken out of the body and “reprogrammed” to attack, in principle, anything.
  细胞疗法，如CAR-T，可将免疫细胞从体内取出并 "重新编程"，原则上可攻击任何东西。
• Conceptual insights like the germ theory of disease or the realization of a link between the immune system and cancer¹³.
  疾病的病菌理论或免疫系统与癌症之间联系的认识¹³ 等概念性见解。

I’m going to the trouble of listing all these technologies because I want to make a crucial claim about them: I think their rate of discovery could be increased by 10x or more if there were a lot more talented, creative researchers. Or, put another way, I think the returns to intelligence are high for these discoveries, and that everything else in biology and medicine mostly follows from them.
我之所以不厌其烦地列出所有这些技术，是因为我想对它们提出一个至关重要的主张：我认为，如果有更多有才华、有创造力的研究人员，这些技术的发现率可以提高10倍或更多。或者换一种说法，我认为，这些发现的智力回报率很高，生物学和医学中的其他一切大多都源于这些发现。

Why do I think this? Because of the answers to some questions that we should get in the habit of asking when we’re trying to determine “returns to intelligence”. First, these discoveries are generally made by a tiny number of researchers, often the same people repeatedly, suggesting skill and not random search (the latter might suggest lengthy experiments are the limiting factor). Second, they often “could have been made” years earlier than they were: for example, CRISPR was a naturally occurring component of the immune system in bacteria that’s been known since the 80’s, but it took another 25 years for people to realize it could be repurposed for general gene editing. They also are often delayed many years by lack of support from the scientific community for promising directions (see this profile on the inventor of mRNA vaccines; similar stories abound). Third, successful projects are often scrappy or were afterthoughts that people didn’t initially think were promising, rather than massively funded efforts. This suggests that it’s not just massive resource concentration that drives discoveries, but ingenuity.
我为什么这样想呢？因为当我们试图确定 "智力回报 "时，我们应该习惯性地询问一些问题的答案。首先，这些发现一般都是由极少数研究人员做出的，而且往往是由同一个人反复做出的，这说明他们是有技巧的，而不是随机搜索的（后者可能说明长时间的实验是限制因素）。其次，这些发现往往 "本可以 "比发现时间更早：例如，CRISPR 是细菌免疫系统中的一种天然成分，早在上世纪 80 年代就已为人所知，但又过了 25 年，人们才意识到它可以重新用于一般的基因编辑。此外，由于缺乏科学界对有前途的方向的支持，这些项目往往被推迟了很多年（参见关于mRNA疫苗发明者的简介；类似的故事比比皆是）。第三，成功的项目往往是零敲碎打的，或者是人们最初并不认为有前途的事后想法，而不是获得大量资助的努力。这表明，推动发现的不仅是大量资源的集中，还有独创性。

Finally, although some of these discoveries have “serial dependence” (you need to make discovery A first in order to have the tools or knowledge to make discovery B)—which again might create experimental delays—many, perhaps most, are independent, meaning many at once can be worked on in parallel. Both these facts, and my general experience as a biologist, strongly suggest to me that there are hundreds of these discoveries waiting to be made if scientists were smarter and better at making connections between the vast amount of biological knowledge humanity possesses (again consider the CRISPR example). The success of AlphaFold/AlphaProteo at solving important problems much more effectively than humans, despite decades of carefully designed physics modeling, provides a proof of principle (albeit with a narrow tool in a narrow domain) that should point the way forward.
最后，尽管这些发现中有一些具有 "序列依赖性"（你需要先有 A 发现，才能有工具或知识来进行 B 发现）--这可能会再次造成实验延误--但许多发现，也许是大多数发现，都是独立的，这意味着许多发现可以同时并行地进行。这些事实以及我作为生物学家的一般经验都强烈地告诉我，如果科学家们更聪明、更善于将人类拥有的大量生物知识联系起来，那么就会有数以百计的这类发现等待着我们去发现（再看一下CRISPR的例子）。AlphaFold/AlphaProteo成功地解决了一些重要问题，尽管经过数十年精心设计的物理建模仍比人类有效得多，这提供了一个原理证明（尽管是在一个狭窄的领域使用一个狭窄的工具），应该为我们指明了前进的方向。

Thus, it’s my guess that powerful AI could at least 10x the rate of these discoveries, giving us the next 50-100 years of biological progress in 5-10 years.¹⁴ Why not 100x? Perhaps it is possible, but here both serial dependence and experiment times become important: getting 100 years of progress in 1 year requires a lot of things to go right the first time, including animal experiments and things like designing microscopes or expensive lab facilities. I’m actually open to the (perhaps absurd-sounding) idea that we could get 1000 years of progress in 5-10 years, but very skeptical that we can get 100 years in 1 year. Another way to put it is I think there’s an unavoidable constant delay: experiments and hardware design have a certain “latency” and need to be iterated upon a certain “irreducible” number of times in order to learn things that can’t be deduced logically. But massive parallelism may be possible on top of that¹⁵.
因此，我猜测强大的人工智能至少可以将这些发现的速度提高 10 倍，这样我们就可以在 5-10 年内实现未来 50-100 年的生物学进步。¹⁴ 为什么不是 100 倍？也许这是可能的，但在这里，序列依赖性和实验时间都变得很重要：要在 1 年内取得 100 年的进展，需要很多事情第一次就做对，包括动物实验和设计显微镜或昂贵的实验室设施等。实际上，我对我们可以在 5-10 年内取得 1000 年进展的想法持开放态度（也许听起来很荒谬），但对我们能否在 1 年内取得 100 年进展持非常怀疑的态度。另一种说法是，我认为存在不可避免的持续延迟：实验和硬件设计有一定的 "延迟"，需要重复一定的 "不可还原 "次数，才能学习到逻辑上无法推导的东西。但在此基础上，大规模并行可能是可行的¹⁵ 。

What about clinical trials? Although there is a lot of bureaucracy and slowdown associated with them, the truth is that a lot (though by no means all!) of their slowness ultimately derives from the need to rigorously evaluate drugs that barely work or ambiguously work. This is sadly true of most therapies today: the average cancer drug increases survival by a few months while having significant side effects that need to be carefully measured (there’s a similar story for Alzheimer’s drugs). This leads to huge studies (in order to achieve statistical power) and difficult tradeoffs which regulatory agencies generally aren’t great at making, again because of bureaucracy and the complexity of competing interests.
那么临床试验呢？虽然临床试验存在很多官僚主义和缓慢的问题，但事实上，临床试验的缓慢最终是因为需要严格评估那些几乎不起作用或作用不明确的药物。如今，大多数疗法都是如此：一般的抗癌药物只能提高几个月的存活率，但副作用却很大，需要仔细衡量（老年痴呆症药物也有类似情况）。这就需要进行大量的研究（以达到统计功率）和艰难的权衡，而监管机构通常并不擅长做出这样的权衡，这同样是由于官僚主义和利益竞争的复杂性造成的。

When something works really well, it goes much faster: there’s an accelerated approval track and the ease of approval is much greater when effect sizes are larger. mRNA vaccines for COVID were approved in 9 months—much faster than the usual pace. That said, even under these conditions clinical trials are still too slow—mRNA vaccines arguably should have been approved in ~2 months. But these kinds of delays (~1 year end-to-end for a drug) combined with massive parallelization and the need for some but not too much iteration (“a few tries”) are very compatible with radical transformation in 5-10 years. Even more optimistically, it is possible that AI-enabled biological science will reduce the need for iteration in clinical trials by developing better animal and cell experimental models (or even simulations) that are more accurate in predicting what will happen in humans. This will be particularly important in developing drugs against the aging process, which plays out over decades and where we need a faster iteration loop.
如果效果非常好，审批速度就会快得多：有一个加速审批通道，当效应大小较大时，审批就会容易得多。用于 COVID 的 mRNA 疫苗在 9 个月内就获得了批准，比通常的速度快得多。尽管如此，即使在这样的条件下，临床试验的速度仍然太慢--可以说mRNA疫苗本应在约2个月内获得批准。但是，这种延迟（一种药物的端到端时间约为 1 年）与大规模并行化以及需要进行一些但不是太多的迭代（"几次尝试"）相结合，完全有可能在 5-10 年内实现根本性转变。更乐观的是，人工智能支持的生物科学有可能通过开发更好的动物和细胞实验模型（甚至模拟）来减少临床试验中的迭代需求，从而更准确地预测人体中会发生的情况。这对于开发抗衰老药物尤为重要，因为衰老过程长达数十年，我们需要更快的迭代循环。

Finally, on the topic of clinical trials and societal barriers, it is worth pointing out explicitly that in some ways biomedical innovations have an unusually strong track record of being successfully deployed, in contrast to some other technologies¹⁶. As mentioned in the introduction, many technologies are hampered by societal factors despite working well technically. This might suggest a pessimistic perspective on what AI can accomplish. But biomedicine is unique in that although the process of developing drugs is overly cumbersome, once developed they generally are successfully deployed and used.
最后，关于临床试验和社会障碍这一话题，值得明确指出的是，在某些方面，生物医学创新在成功应用方面有着不同寻常的强记录，这与其他一些技术形成了鲜明对比¹⁶ 。正如导言中提到的，许多技术尽管在技术上运行良好，但却受到社会因素的阻碍。这可能会让人对人工智能所能取得的成就产生悲观的看法。但生物医学的独特之处在于，虽然药物的开发过程过于繁琐，但一旦开发成功，一般都能成功部署和使用。

Although predicting what powerful AI can do in a few years remains inherently difficult and speculative, there is some concreteness to asking “what could humans do unaided in the next 100 years?”. Simply looking at what we’ve accomplished in the 20th century, or extrapolating from the first 2 decades of the 21st, or asking what “10 CRISPR’s and 50 CAR-T’s” would get us, all offer practical, grounded ways to estimate the general level of progress we might expect from powerful AI.
尽管预测强大的人工智能在几年内能做什么本质上仍然是困难的和猜测性的，但提出 "未来 100 年人类在没有辅助的情况下能做什么 "的问题还是有一些具体性的。只要看看我们在 20 世纪取得的成就，或者从 21 世纪的前 20 年进行推断，或者问问 "10 个 CRISPR 和 50 个 CAR-T "会给我们带来什么，所有这些都提供了切实可行、脚踏实地的方法来估计我们可能期望从强大的人工智能中获得的总体进展水平。

Below I try to make a list of what we might expect. This is not based on any rigorous methodology, and will almost certainly prove wrong in the details, but it’s trying to get across the general level of radicalism we should expect:
下面，我试图列出一份我们可能期待的清单。这并非基于任何严谨的方法论，而且几乎肯定会在细节上证明是错误的，但它试图表达我们应该期待的激进主义的一般水平：

• Reliable prevention and treatment of nearly all¹⁷ natural infectious disease. Given the enormous advances against infectious disease in the 20th century, it is not radical to imagine that we could more or less “finish the job” in a compressed 21st. mRNA vaccines and similar technology already point the way towards “vaccines for anything”. Whether infectious disease is fully eradicated from the world (as opposed to just in some places) depends on questions about poverty and inequality, which are discussed in Section 3.
  可靠地预防和治疗几乎所有¹⁷自然传染病。鉴于 20 世纪在防治传染病方面取得的巨大进步，设想我们能够在压缩的 21 世纪或多或少地 "完成这项工作 "并不激进。传染病能否从世界上彻底消除（而不仅仅是在某些地方）取决于贫困和不平等问题，这将在第 3 节中讨论。
• Elimination of most cancer. Death rates from cancer have been dropping ~2% per year for the last few decades; thus we are on track to eliminate most cancer in the 21st century at the current pace of human science. Some subtypes have already been largely cured (for example some types of leukemia with CAR-T therapy), and I’m perhaps even more excited for very selective drugs that target cancer in its infancy and prevent it from ever growing. AI will also make possible treatment regimens very finely adapted to the individualized genome of the cancer—these are possible today, but hugely expensive in time and human expertise, which AI should allow us to scale. Reductions of 95% or more in both mortality and incidence seem possible. That said, cancer is extremely varied and adaptive, and is likely the hardest of these diseases to fully destroy. It would not be surprising if an assortment of rare, difficult malignancies persists.
  消灭大多数癌症。过去几十年来，癌症死亡率每年下降约2%；因此，按照人类科学目前的发展速度，我们有望在21世纪消灭大多数癌症。某些亚型癌症已经基本治愈（例如，某些类型的白血病采用了CAR-T疗法），而让我更为兴奋的可能是那些选择性极强的药物，它们能够在癌症萌芽阶段就将其作为靶点，防止其生长。人工智能还能使治疗方案非常精细地适应癌症的个体化基因组--这在今天是有可能实现的，但在时间和人类专业技术方面都非常昂贵，而人工智能应该能让我们扩大规模。将死亡率和发病率降低 95% 或更多似乎是有可能的。尽管如此，癌症的种类繁多，适应性极强，很可能是最难完全消灭的疾病。如果各种罕见、棘手的恶性肿瘤持续存在，也不足为奇。
• Very effective prevention and effective cures for genetic disease. Greatly improved embryo screening will likely make it possible to prevent most genetic disease, and some safer, more reliable descendant of CRISPR may cure most genetic disease in existing people. Whole-body afflictions that affect a large fraction of cells may be the last holdouts, however.
  有效预防和治愈遗传病。大幅改进的胚胎筛查将有可能预防大多数遗传疾病，而CRISPR的一些更安全、更可靠的后代可能会治愈现有人群中的大多数遗传疾病。不过，影响大部分细胞的全身性疾病可能是最后的顽疾。
• Prevention of Alzheimer’s. We’ve had a very hard time figuring out what causes Alzheimer’s (it is somehow related to beta-amyloid protein, but the actual details seem to be very complex). It seems like exactly the type of problem that can be solved with better measurement tools that isolate biological effects; thus I am bullish about AI’s ability to solve it. There is a good chance it can eventually be prevented with relatively simple interventions, once we actually understand what is going on. That said, damage from already-existing Alzheimer’s may be very difficult to reverse.
  预防阿尔茨海默氏症。我们一直很难弄清阿尔茨海默氏症的病因（它与β-淀粉样蛋白有一定关系，但实际细节似乎非常复杂）。这似乎正是可以通过更好的测量工具来解决的问题，这些工具可以隔离生物效应；因此，我看好人工智能解决这一问题的能力。一旦我们真正了解了问题所在，就很有可能通过相对简单的干预措施来预防它的发生。尽管如此，已经存在的阿尔茨海默氏症造成的损害可能很难逆转。
• Improved treatment of most other ailments. This is a catch-all category for other ailments including diabetes, obesity, heart disease, autoimmune diseases, and more. Most of these seem “easier” to solve than cancer and Alzheimer’s and in many cases are already in steep decline. For example, deaths from heart disease have already declined over 50%, and simple interventions like GLP-1 agonists have already made huge progress against obesity and diabetes.
  改善对大多数其他疾病的治疗。这是一个包括糖尿病、肥胖症、心脏病、自身免疫性疾病等其他疾病的总括类别。这些疾病中的大多数似乎比癌症和老年痴呆症更 "容易 "解决，而且在许多情况下已经在急剧下降。例如，心脏病导致的死亡人数已经下降了 50%，而像 GLP-1 激动剂这样简单的干预措施已经在防治肥胖症和糖尿病方面取得了巨大进展。
• Biological freedom. The last 70 years featured advances in birth control, fertility, management of weight, and much more. But I suspect AI-accelerated biology will greatly expand what is possible: weight, physical appearance, reproduction, and other biological processes will be fully under people’s control. We’ll refer to these under the heading of biological freedom: the idea that everyone should be empowered to choose what they want to become and live their lives in the way that most appeals to them. There will of course be important questions about global equality of access; see Section 3 for these.
  生物自由。在过去的 70 年中，节育、生育、体重管理等方面都取得了进步。但我认为，人工智能加速发展的生物学将极大地扩展其可能性：体重、外貌、生殖和其他生物过程将完全由人们控制。我们将在生物自由的标题下讨论这些问题：我们认为，每个人都应有权选择自己想成为的人，并以最适合自己的方式生活。当然，还有一些关于全球机会平等的重要问题，请参见第 3 部分。
• Doubling of the human lifespan¹⁸. This might seem radical, but life expectancy increased almost 2x in the 20th century (from ~40 years to ~75), so it’s “on trend” that the “compressed 21st” would double it again to 150. Obviously the interventions involved in slowing the actual aging process will be different from those that were needed in the last century to prevent (mostly childhood) premature deaths from disease, but the magnitude of change is not unprecedented¹⁹. Concretely, there already exist drugs that increase maximum lifespan in rats by 25-50% with limited ill-effects. And some animals (e.g. some types of turtle) already live 200 years, so humans are manifestly not at some theoretical upper limit. At a guess, the most important thing that is needed might be reliable, non-Goodhart-able biomarkers of human aging, as that will allow fast iteration on experiments and clinical trials. Once human lifespan is 150, we may be able to reach “escape velocity”, buying enough time that most of those currently alive today will be able to live as long as they want, although there’s certainly no guarantee this is biologically possible.
  人类寿命延长一倍¹⁸.这看似激进，但20世纪的预期寿命延长了近2倍（从约40岁延长到约75岁），因此 "压缩后的21世纪 "会将预期寿命再次延长一倍，达到150岁，这是 "趋势"。显然，减缓实际衰老过程所需的干预措施与上个世纪为防止（主要是儿童）过早死于疾病所需的干预措施有所不同，但变化的幅度并非前所未有¹⁹ 。具体来说，已经有药物可以将大鼠的最长寿命延长 25-50%，而且不良反应有限。而一些动物（如某些种类的乌龟）的寿命已经达到了 200 岁，所以人类显然还没有达到某个理论上的上限。据估计，人类最需要的可能是可靠的、非古德哈特式的人类衰老生物标志物，因为这将有助于快速迭代实验和临床试验。一旦人类寿命达到 150 岁，我们或许就能达到 "逃逸速度"，从而争取到足够的时间，让现在活着的大多数人都能想活多久就活多久，尽管我们无法保证这在生物学上是否可行。

It is worth looking at this list and reflecting on how different the world will be if all of it is achieved 7-12 years from now (which would be in line with an aggressive AI timeline). It goes without saying that it would be an unimaginable humanitarian triumph, the elimination all at once of most of the scourges that have haunted humanity for millennia. Many of my friends and colleagues are raising children, and when those children grow up, I hope that any mention of disease will sound to them the way scurvy, smallpox, or bubonic plague sounds to us. That generation will also benefit from increased biological freedom and self-expression, and with luck may also be able to live as long as they want.
看完这份清单，我们不妨反思一下，如果从现在开始的 7-12 年后全部实现（这与积极的人工智能时间表一致），世界将会变得多么不同。不言而喻，这将是一场难以想象的人道主义胜利，人类困扰千年的大多数祸患将被一举消除。我的许多朋友和同事都在养育孩子，当这些孩子长大成人时，我希望任何疾病在他们听来都会像坏血病、天花或鼠疫在我们听来那样可怕。这一代人还将受益于生物自由度的提高和自我表达能力的增强，幸运的话，他们也许还能想活多久就活多久。

It’s hard to overestimate how surprising these changes will be to everyone except the small community of people who expected powerful AI. For example, thousands of economists and policy experts in the US currently debate how to keep Social Security and Medicare solvent, and more broadly how to keep down the cost of healthcare (which is mostly consumed by those over 70 and especially those with terminal illnesses such as cancer). The situation for these programs is likely to be radically improved if all this comes to pass²⁰, as the ratio of working age to retired population will change drastically. No doubt these challenges will be replaced with others, such as how to ensure widespread access to the new technologies, but it is worth reflecting on how much the world will change even if biology is the only area to be successfully accelerated by AI.
除了一小部分对强大的人工智能抱有期望的人之外，很难高估这些变化会给所有人带来多大的惊喜。例如，美国数以千计的经济学家和政策专家目前正在讨论如何保持社会保障和医疗保险（Medicare）的偿付能力，以及更广泛地说，如何降低医疗保健（主要是 70 岁以上的老人，尤其是癌症等绝症患者）的成本。如果这一切得以实现，这些计划的状况可能会得到根本改善²⁰ ，因为工作年龄人口与退休人口的比例将发生巨大变化。毫无疑问，这些挑战将被其他挑战所取代，比如如何确保新技术的普及，但值得反思的是，即使生物学是人工智能成功加速发展的唯一领域，世界也将发生巨大变化。

2. Neuroscience and mind 2.神经科学与心智

In the previous section I focused on physical diseases and biology in general, and didn’t cover neuroscience or mental health. But neuroscience is a subdiscipline of biology and mental health is just as important as physical health. In fact, if anything, mental health affects human well-being even more directly than physical health. Hundreds of millions of people have very low quality of life due to problems like addiction, depression, schizophrenia, low-functioning autism, PTSD, psychopathy²¹, or intellectual disabilities. Billions more struggle with everyday problems that can often be interpreted as much milder versions of one of these severe clinical disorders. And as with general biology, it may be possible to go beyond addressing problems to improving the baseline quality of human experience.
在上一节中，我重点介绍了物理疾病和一般生物学，没有涉及神经科学或心理健康。但神经科学是生物学的一个分支学科，心理健康与身体健康同样重要。事实上，心理健康对人类福祉的影响甚至比身体健康更直接。由于成瘾、抑郁、精神分裂症、低功能自闭症、创伤后应激障碍、心理变态²¹ 或智力障碍等问题，数亿人的生活质量非常低下。还有数十亿人在日常问题中挣扎，而这些问题往往可以被解释为这些严重临床疾病的轻微版本。就像普通生物学一样，我们有可能不只是解决这些问题，而是改善人类体验的基本质量。

The basic framework that I laid out for biology applies equally to neuroscience. The field is propelled forward by a small number of discoveries often related to tools for measurement or precise intervention – in the list of those above, optogenetics was a neuroscience discovery, and more recently CLARITY and expansion microscopy are advances in the same vein, in addition to many of the general cell biology methods directly carrying over to neuroscience. I think the rate of these advances will be similarly accelerated by AI and therefore that the framework of “100 years of progress in 5-10 years” applies to neuroscience in the same way it does to biology and for the same reasons. As in biology, the progress in 20th century neuroscience was enormous – for example we didn’t even understand how or why neurons fired until the 1950’s. Thus, it seems reasonable to expect AI-accelerated neuroscience to produce rapid progress over a few years.
我为生物学制定的基本框架同样适用于神经科学。这一领域是由少数通常与测量或精确干预工具有关的发现推动前进的--在上述发现中，光遗传学是神经科学的一项发现，最近的清晰度和扩张显微镜也是同样的进步，此外，许多普通细胞生物学方法也直接应用于神经科学。我认为这些进步的速度同样会因人工智能而加快，因此 "5-10 年内取得 100 年进步 "的框架适用于神经科学的方式与适用于生物学的方式相同，原因也相同。与生物学一样，20 世纪神经科学的进步也是巨大的--例如，我们甚至直到 20 世纪 50 年代才了解神经元是如何或为什么发射的。因此，期待人工智能加速神经科学在几年内取得快速进展似乎是合理的。

There is one thing we should add to this basic picture, which is that some of the things we’ve learned (or are learning) about AI itself in the last few years are likely to help advance neuroscience, even if it continues to be done only by humans. Interpretability is an obvious example: although biological neurons superficially operate in a completely different manner from artificial neurons (they communicate via spikes and often spike rates, so there is a time element not present in artificial neurons, and a bunch of details relating to cell physiology and neurotransmitters modifies their operation substantially), the basic question of “how do distributed, trained networks of simple units that perform combined linear/non-linear operations work together to perform important computations” is the same, and I strongly suspect the details of individual neuron communication will be abstracted away in most of the interesting questions about computation and circuits²². As just one example of this, a computational mechanism discovered by interpretability researchers in AI systems was recently rediscovered in the brains of mice.
在这幅基本图景之外，我们还应该补充一点，那就是在过去几年中，我们已经（或正在）学习到的有关人工智能本身的一些知识很可能有助于推动神经科学的发展，即使这项工作仍然只能由人类来完成。可解释性就是一个明显的例子：虽然生物神经元表面上的运行方式与人工神经元完全不同（它们通过尖峰和通常的尖峰率进行通信，因此存在人工神经元所不具备的时间因素，而且与细胞生理和神经递质有关的大量细节也会大大改变它们的运行方式），但 "如何通过分布式的、训练有素的、由简单单元组成的网络来执行组合操作 "这一基本问题仍然存在、我强烈怀疑，在有关计算和电路的大多数有趣问题中，单个神经元通信的细节将被抽象掉²² 。仅举一例，可解释性研究人员在人工智能系统中发现的一种计算机制最近在小鼠大脑中被重新发现。

It is much easier to do experiments on artificial neural networks than on real ones (the latter often requires cutting into animal brains), so interpretability may well become a tool for improving our understanding of neuroscience. Furthermore, powerful AI’s will themselves probably be able to develop and apply this tool better than humans can.
在人工神经网络上做实验要比在真实的神经网络上做实验容易得多（后者往往需要切开动物的大脑），因此可解释性很可能成为提高我们对神经科学理解的一种工具。此外，强大的人工智能本身也可能比人类更好地开发和应用这一工具。

Beyond just interpretability though, what we have learned from AI about how intelligent systems are trained should (though I am not sure it has yet) cause a revolution in neuroscience. When I was working in neuroscience, a lot of people focused on what I would now consider the wrong questions about learning, because the concept of the scaling hypothesis / bitter lesson didn’t exist yet. The idea that a simple objective function plus a lot of data can drive incredibly complex behaviors makes it more interesting to understand the objective functions and architectural biases and less interesting to understand the details of the emergent computations. I have not followed the field closely in recent years, but I have a vague sense that computational neuroscientists have still not fully absorbed the lesson. My attitude to the scaling hypothesis has always been “aha – this is an explanation, at a high level, of how intelligence works and how it so easily evolved”, but I don’t think that’s the average neuroscientist’s view, in part because the scaling hypothesis as “the secret to intelligence” isn’t fully accepted even within AI.
除了可解释性之外，我们从人工智能中学到的关于智能系统如何训练的知识应该（虽然我不确定是否已经）在神经科学领域引起一场革命。当我在神经科学领域工作时，很多人都专注于我现在认为是错误的学习问题，因为当时还不存在扩展假说/痛苦教训的概念。一个简单的目标函数加上大量数据就能驱动极其复杂的行为，这种想法使得理解目标函数和架构偏差变得更有趣，而理解新兴计算的细节就不那么有趣了。近年来，我没有密切关注这一领域，但我隐约感觉到计算神经科学家们仍未完全吸收这一教训。我对缩放假说的态度一直是 "啊哈--这是从高层次上解释了智能是如何工作的，以及它是如何如此轻易地进化的"，但我不认为这是一般神经科学家的观点，部分原因是缩放假说作为 "智能的秘密 "甚至在人工智能领域也未被完全接受。

I think that neuroscientists should be trying to combine this basic insight with the particularities of the human brain (biophysical limitations, evolutionary history, topology, details of motor and sensory inputs/outputs) to try to figure out some of neuroscience’s key puzzles. Some likely are, but I suspect it’s not enough yet, and that AI neuroscientists will be able to more effectively leverage this angle to accelerate progress.
我认为，神经科学家应该尝试将这一基本见解与人类大脑的特殊性（生物物理限制、进化历史、拓扑结构、运动和感官输入/输出的细节）结合起来，试图找出神经科学的一些关键谜题。有些问题可能已经解决了，但我认为这还远远不够，人工智能神经科学家将能更有效地利用这一角度来加快进展。

I expect AI to accelerate neuroscientific progress along four distinct routes, all of which can hopefully work together to cure mental illness and improve function:
我预计人工智能将沿着四条不同的路线加速神经科学的进步，所有这些都有望共同治愈精神疾病并改善功能：

• Traditional molecular biology, chemistry, and genetics. This is essentially the same story as general biology in section 1, and AI can likely speed it up via the same mechanisms. There are many drugs that modulate neurotransmitters in order to alter brain function, affect alertness or perception, change mood, etc., and AI can help us invent many more. AI can probably also accelerate research on the genetic basis of mental illness.
  传统的分子生物学、化学和遗传学。这与第 1 节中的普通生物学基本相同，而人工智能很可能通过相同的机制加速这一过程。有许多药物可以调节神经递质以改变大脑功能、影响警觉性或感知、改变情绪等，而人工智能可以帮助我们发明更多的药物。人工智能或许还能加速对精神疾病遗传基础的研究。
• Fine-grained neural measurement and intervention. This is the ability to measure what a lot of individual neurons or neuronal circuits are doing, and intervene to change their behavior. Optogenetics and neural probes are technologies capable of both measurement and intervention in live organisms, and a number of very advanced methods (such as molecular ticker tapes to read out the firing patterns of large numbers of individual neurons) have also been proposed and seem possible in principle.
  精细神经测量和干预。这是指测量大量单个神经元或神经元回路在做什么，并进行干预以改变其行为的能力。光遗传学和神经探针是既能测量又能干预活体生物的技术，还有一些非常先进的方法（如用分子滴答带读出大量单个神经元的发射模式）也已被提出，而且原则上似乎是可行的。
• Advanced computational neuroscience. As noted above, both the specific insights and the gestalt of modern AI can probably be applied fruitfully to questions in systems neuroscience, including perhaps uncovering the real causes and dynamics of complex diseases like psychosis or mood disorders.
  高级计算神经科学。如上所述，现代人工智能的具体洞察力和主旨或许可以卓有成效地应用于系统神经科学中的问题，包括揭示精神病或情绪障碍等复杂疾病的真正原因和动态。
• Behavioral interventions. I haven’t much mentioned it given the focus on the biological side of neuroscience, but psychiatry and psychology have of course developed a wide repertoire of behavioral interventions over the 20th century; it stands to reason that AI could accelerate these as well, both the development of new methods and helping patients to adhere to existing methods. More broadly, the idea of an “AI coach” who always helps you to be the best version of yourself, who studies your interactions and helps you learn to be more effective, seems very promising.
  行为干预。由于我一直专注于神经科学的生物学方面，因此没有过多提及，但精神病学和心理学在 20 世纪当然已经开发出了大量的行为干预方法；按理说，人工智能也可以加速这些方法的开发，既可以开发新方法，也可以帮助患者坚持使用现有方法。更广义地说，"人工智能教练 "的想法似乎非常有前景，它总是帮助您成为最好的自己，研究您的互动，并帮助您学习如何提高效率。

It’s my guess that these four routes of progress working together would, as with physical disease, be on track to lead to the cure or prevention of most mental illness in the next 100 years even if AI was not involved – and thus might reasonably be completed in 5-10 AI-accelerated years. Concretely my guess at what will happen is something like:
我猜测，这四条进展路线协同工作，即使不涉及人工智能，也会像治疗身体疾病一样，在未来 100 年内治愈或预防大多数精神疾病，因此，在人工智能加速发展的 5-10 年内就有可能完成。具体来说，我对未来的猜测如下

• Most mental illness can probably be cured. I’m not an expert in psychiatric disease (my time in neuroscience was spent building probes to study small groups of neurons) but it’s my guess that diseases like PTSD, depression, schizophrenia, addiction, etc. can be figured out and very effectively treated via some combination of the four directions above. The answer is likely to be some combination of “something went wrong biochemically” (although it could be very complex) and “something went wrong with the neural network, at a high level”. That is, it’s a systems neuroscience question—though that doesn’t gainsay the impact of the behavioral interventions discussed above. Tools for measurement and intervention, especially in live humans, seem likely to lead to rapid iteration and progress.
  大多数精神疾病可能是可以治愈的。我不是精神疾病方面的专家（我在神经科学领域的时间都花在了制作探针来研究神经元的小群体上），但我猜想，像创伤后应激障碍、抑郁症、精神分裂症、成瘾等疾病都可以通过上述四个方向的某种组合来找出原因并进行非常有效的治疗。答案很可能是 "生化方面出了问题"（尽管可能非常复杂）和 "神经网络高层出了问题 "的某种结合。也就是说，这是一个系统神经科学问题--尽管这并不能否认上文讨论的行为干预措施的影响。测量和干预工具，尤其是活人的测量和干预工具，似乎有可能带来快速的迭代和进步。
• Conditions that are very “structural” may be more difficult, but not impossible. There’s some evidence that psychopathy is associated with obvious neuroanatomical differences – that some brain regions are simply smaller or less developed in psychopaths. Psychopaths are also believed to lack empathy from a young age; whatever is different about their brain, it was probably always that way. The same may be true of some intellectual disabilities, and perhaps other conditions. Restructuring the brain sounds hard, but it also seems like a task with high returns to intelligence. Perhaps there is some way to coax the adult brain into an earlier or more plastic state where it can be reshaped. I’m very uncertain how possible this is, but my instinct is to be optimistic about what AI can invent here.
  非常 "结构性 "的条件可能更加困难，但并非不可能。有些证据表明，心理变态与明显的神经解剖学差异有关--心理变态者的某些大脑区域较小或不发达。精神变态者也被认为从小就缺乏同情心；无论他们的大脑有什么不同，可能一直都是这样。一些智障者可能也是如此，或许还有其他情况。重组大脑听起来很难，但似乎也是一项智力回报率很高的任务。也许有某种方法可以把成年人的大脑哄骗到更早或更具可塑性的状态，在这种状态下，大脑可以被重塑。我很不确定这种可能性有多大，但我本能地对人工智能在这方面的发明持乐观态度。
• Effective genetic prevention of mental illness seems possible. Most mental illness is partially heritable, and genome-wide association studies are starting to gain traction on identifying the relevant factors, which are often many in number. It will probably be possible to prevent most of these diseases via embryo screening, similar to the story with physical disease. One difference is that psychiatric disease is more likely to be polygenic (many genes contribute), so due to complexity there’s an increased risk of unknowingly selecting against positive traits that are correlated with disease. Oddly however, in recent years GWAS studies seem to suggest that these correlations might have been overstated. In any case, AI-accelerated neuroscience may help us to figure these things out. Of course, embryo screening for complex traits raises a number of societal issues and will be controversial, though I would guess that most people would support screening for severe or debilitating mental illness.
  有效遗传预防精神疾病似乎是可能的。大多数精神疾病都是部分遗传的，而全基因组关联研究正在开始在确定相关因素方面取得进展，这些因素通常有很多。也许可以通过胚胎筛查来预防大多数此类疾病，这与身体疾病的情况类似。不同之处在于，精神疾病更有可能是多基因疾病（许多基因共同作用），因此由于其复杂性，在不知情的情况下针对与疾病相关的阳性特征进行选择的风险会增加。但奇怪的是，近年来的全球基因组研究似乎表明，这些相关性可能被夸大了。无论如何，人工智能加速的神经科学可能会帮助我们弄清这些问题。当然，针对复杂性状的胚胎筛查会引发一系列社会问题，也会引起争议，不过我想大多数人都会支持针对严重或衰弱性精神疾病的筛查。
• Everyday problems that we don’t think of as clinical disease will also be solved. Most of us have everyday psychological problems that are not ordinarily thought of as rising to the level of clinical disease. Some people are quick to anger, others have trouble focusing or are often drowsy, some are fearful or anxious, or react badly to change. Today, drugs already exist to help with e.g. alertness or focus (caffeine, modafinil, ritalin) but as with many other previous areas, much more is likely to be possible. Probably many more such drugs exist and have not been discovered, and there may also be totally new modalities of intervention, such as targeted light stimulation (see optogenetics above) or magnetic fields. Given how many drugs we’ve developed in the 20th century that tune cognitive function and emotional state, I’m very optimistic about the “compressed 21st” where everyone can get their brain to behave a bit better and have a more fulfilling day-to-day experience.
  我们不认为是临床疾病的日常问题也将得到解决。我们中的大多数人都有日常的心理问题，这些问题通常不会被认为是临床疾病。有些人容易发怒，有些人注意力不集中或经常昏昏欲睡，有些人恐惧或焦虑，或者对变化反应迟钝。如今，已经有一些药物可以帮助人们提高警觉性或集中注意力（咖啡因、莫达非尼、利他林），但与以前的许多其他领域一样，可能还有更多的药物可以帮助人们提高警觉性或集中注意力。可能还有更多这样的药物存在而未被发现，也可能有全新的干预方式，如定向光刺激（见上文的光遗传学）或磁场。鉴于我们在 20 世纪已经开发出了许多调整认知功能和情绪状态的药物，我对 "压缩后的 21 世纪 "非常乐观，在那里，每个人都能让自己的大脑表现得更好一些，拥有更充实的日常体验。
• Human baseline experience can be much better. Taking one step further, many people have experienced extraordinary moments of revelation, creative inspiration, compassion, fulfillment, transcendence, love, beauty, or meditative peace. The character and frequency of these experiences differs greatly from person to person and within the same person at different times, and can also sometimes be triggered by various drugs (though often with side effects). All of this suggests that the “space of what is possible to experience” is very broad and that a larger fraction of people’s lives could consist of these extraordinary moments. It is probably also possible to improve various cognitive functions across the board. This is perhaps the neuroscience version of “biological freedom” or “extended lifespans”.
  人类的基线体验可以更好。更进一步说，许多人都经历过启示、创造性灵感、同情、满足、超越、爱、美或冥想平静的非凡时刻。这些体验的特征和频率因人而异，在同一个人的不同时期也大相径庭，有时也会被各种药物触发（但往往有副作用）。所有这些都表明，"可能体验的空间 "是非常广阔的，人们生活中的更大一部分可能都是由这些非凡的时刻组成的。全面提高各种认知功能或许也是可能的。这或许就是神经科学版的 "生物自由 "或 "延长寿命"。

One topic that often comes up in sci-fi depictions of AI, but that I intentionally haven’t discussed here, is “mind uploading”, the idea of capturing the pattern and dynamics of a human brain and instantiating them in software. This topic could be the subject of an essay all by itself, but suffice it to say that while I think uploading is almost certainly possible in principle, in practice it faces significant technological and societal challenges, even with powerful AI, that likely put it outside the 5-10 year window we are discussing.
在人工智能的科幻描述中经常出现的一个话题是 "思维上传"，即捕捉人脑的模式和动态并将其实例化到软件中。这个话题本身就可以成为一篇文章的主题，但我只想说，虽然我认为上载原则上几乎肯定是可能的，但在实践中，它面临着巨大的技术和社会挑战，即使是强大的人工智能也不例外，这很可能使它超出我们正在讨论的 5-10 年时间窗口。

In summary, AI-accelerated neuroscience is likely to vastly improve treatments for, or even cure, most mental illness as well as greatly expand “cognitive and mental freedom” and human cognitive and emotional abilities. It will be every bit as radical as the improvements in physical health described in the previous section. Perhaps the world will not be visibly different on the outside, but the world as experienced by humans will be a much better and more humane place, as well as a place that offers greater opportunities for self-actualization. I also suspect that improved mental health will ameliorate a lot of other societal problems, including ones that seem political or economic.
总之，人工智能加速发展的神经科学有可能极大地改善甚至治愈大多数精神疾病的治疗方法，并极大地扩展 "认知和精神自由 "以及人类的认知和情感能力。这将与上一节所述的身体健康的改善一样彻底。也许世界在外表上不会有明显的变化，但人类所经历的世界将变得更加美好、更加人性化，也将为人类提供更多自我实现的机会。我还认为，心理健康的改善将改善许多其他社会问题，包括那些看似政治或经济的问题。

3. Economic development and poverty
3.经济发展与贫困

The previous two sections are about developing new technologies that cure disease and improve the quality of human life. However an obvious question, from a humanitarian perspective, is: “will everyone have access to these technologies?”
前面两节是关于开发治疗疾病和提高人类生活质量的新技术。然而，从人道主义的角度来看，一个显而易见的问题是："是否每个人都能获得这些技术？

It is one thing to develop a cure for a disease, it is another thing to eradicate the disease from the world. More broadly, many existing health interventions have not yet been applied everywhere in the world, and for that matter the same is true of (non-health) technological improvements in general. Another way to say this is that living standards in many parts of the world are still desperately poor: GDP per capita is ~$2,000 in Sub-Saharan Africa as compared to ~$75,000 in the United States. If AI further increases economic growth and quality of life in the developed world, while doing little to help the developing world, we should view that as a terrible moral failure and a blemish on the genuine humanitarian victories in the previous two sections. Ideally, powerful AI should help the developing world catch up to the developed world, even as it revolutionizes the latter.
研制出一种疾病的治疗方法是一回事，而在世界上根除这种疾病则是另一回事。更广义地说，许多现有的卫生干预措施尚未在世界各地得到应用，因此，一般的（非卫生）技术改进也是如此。另一种说法是，世界许多地方的生活水平仍然极度低下：撒哈拉以南非洲的人均 GDP 约为 2,000 美元，而美国约为 75,000 美元。如果人工智能进一步提高了发达国家的经济增长和生活质量，而对发展中国家的帮助却微乎其微，那么我们应该将其视为可怕的道德失败，是对前两节中真正的人道主义胜利的玷污。理想情况下，强大的人工智能应该帮助发展中世界赶上发达国家，甚至在彻底改变后者的同时。

I am not as confident that AI can address inequality and economic growth as I am that it can invent fundamental technologies, because technology has such obvious high returns to intelligence (including the ability to route around complexities and lack of data) whereas the economy involves a lot of constraints from humans, as well as a large dose of intrinsic complexity. I am somewhat skeptical that an AI could solve the famous “socialist calculation problem”²³ and I don’t think governments will (or should) turn over their economic policy to such an entity, even if it could do so. There are also problems like how to convince people to take treatments that are effective but that they may be suspicious of.
我对人工智能能够解决不平等和经济增长问题的信心不如对它能够发明基础技术的信心，因为技术对智能的回报如此明显（包括绕过复杂性和缺乏数据的能力），而经济则涉及来自人类的大量限制，以及大量内在复杂性。我对人工智能能否解决著名的"社会主义计算问题"²³ 持怀疑态度，而且我不认为政府会（或应该）将其经济政策交给这样一个实体，即使它能做到这一点。还有一些问题，比如如何说服人们接受有效但他们可能会怀疑的治疗。

The challenges facing the developing world are made even more complicated by pervasive corruption in both private and public sectors. Corruption creates a vicious cycle: it exacerbates poverty, and poverty in turn breeds more corruption. AI-driven plans for economic development need to reckon with corruption, weak institutions, and other very human challenges.
私营和公共部门普遍存在的腐败现象使发展中世界面临的挑战变得更加复杂。腐败会造成恶性循环：加剧贫困，而贫困反过来又会滋生更多的腐败。人工智能驱动的经济发展计划需要考虑到腐败、机构薄弱以及其他非常人性化的挑战。

Nevertheless, I do see significant reasons for optimism. Diseases have been eradicated and many countries have gone from poor to rich, and it is clear that the decisions involved in these tasks exhibit high returns to intelligence (despite human constraints and complexity). Therefore, AI can likely do them better than they are currently being done. There may also be targeted interventions that get around the human constraints and that AI could focus on. More importantly though, we have to try. Both AI companies and developed world policymakers will need to do their part to ensure that the developing world is not left out; the moral imperative is too great. So in this section, I’ll continue to make the optimistic case, but keep in mind everywhere that success is not guaranteed and depends on our collective efforts.
不过，我确实看到了乐观的重要理由。疾病已经被消灭，许多国家已经从贫穷走向富裕，很明显，这些任务中涉及的决策显示出智能的高回报（尽管存在人为限制和复杂性）。因此，人工智能有可能比现在做得更好。此外，还有一些有针对性的干预措施可以绕过人类的限制，而人工智能可以专注于这些措施。但更重要的是，我们必须尝试。人工智能公司和发达国家的政策制定者都需要尽自己的一份力量，确保发展中国家不会被排除在外；这在道义上势在必行。因此，在本节中，我将继续提出乐观的观点，但无论在哪里都要牢记，成功并非必然，它取决于我们的共同努力。

Below I make some guesses about how I think things may go in the developing world over the 5-10 years after powerful AI is developed:
以下是我对强大的人工智能发展 5-10 年后发展中世界的一些猜测：

• Distribution of health interventions. The area where I am perhaps most optimistic is distributing health interventions throughout the world. Diseases have actually been eradicated by top-down campaigns: smallpox was fully eliminated in the 1970’s, and polio and guinea worm are nearly eradicated with less than 100 cases per year. Mathematically sophisticated epidemiological modeling plays an active role in disease eradication campaigns, and it seems very likely that there is room for smarter-than-human AI systems to do a better job of it than humans are. The logistics of distribution can probably also be greatly optimized. One thing I learned as an early donor to GiveWell is that some health charities are way more effective than others; the hope is that AI-accelerated efforts would be more effective still. Additionally, some biological advances actually make the logistics of distribution much easier: for example, malaria has been difficult to eradicate because it requires treatment each time the disease is contracted; a vaccine that only needs to be administered once makes the logistics much simpler (and such vaccines for malaria are in fact currently being developed). Even simpler distribution mechanisms are possible: some diseases could in principle be eradicated by targeting their animal carriers, for example releasing mosquitoes infected with a bacterium that blocks their ability to carry a disease (who then infect all the other mosquitos) or simply using gene drives to wipe out the mosquitos. This requires one or a few centralized actions, rather than a coordinated campaign that must individually treat millions. Overall, I think 5-10 years is a reasonable timeline for a good fraction (maybe 50%) of AI-driven health benefits to propagate to even the poorest countries in the world. A good goal might be for the developing world 5-10 years after powerful AI to at least be substantially healthier than the developed world is today, even if it continues to lag behind the developed world. Accomplishing this will of course require a huge effort in global health, philanthropy, political advocacy, and many other efforts, which both AI developers and policymakers should help with.
  保健干预措施的分布。我最看好的领域可能是在全世界推广卫生干预措施。疾病实际上已经通过自上而下的运动被根除了：天花在 20 世纪 70 年代被完全消灭，脊髓灰质炎和麦地那龙线虫也几乎被根除，每年的病例不到 100 例。数学上复杂的流行病学建模在疾病根除运动中发挥着积极作用，而且比人类更聪明的人工智能系统很有可能在这方面做得比人类更好。物流配送或许也可以大大优化。作为GiveWell的早期捐赠者，我了解到的一件事是，一些健康慈善机构比其他慈善机构更有效；希望人工智能加速的努力会更加有效。此外，一些生物技术的进步实际上使分发的后勤工作变得更加容易：例如，疟疾一直难以根除，因为每次感染这种疾病都需要治疗；而只需注射一次的疫苗则使后勤工作变得更加简单（事实上，目前正在开发此类疟疾疫苗）。甚至更简单的分配机制也是可能的：一些疾病原则上可以通过针对其动物载体来根除，例如释放感染了细菌的蚊子来阻止其携带疾病的能力（蚊子随后会感染所有其他蚊子），或者简单地使用基因驱动来消灭蚊子。这需要一次或几次集中行动，而不是必须单独治疗数百万蚊子的协调行动。总的来说，我认为 5-10 年是一个合理的时间期限，可以让人工智能驱动的大部分（也许是 50%）健康益处传播到世界上最贫穷的国家。一个好的目标可能是，在强大的人工智能出现 5-10 年后，发展中国家即使继续落后于发达国家，但至少要比现在的发达国家更健康。当然，要实现这一目标，需要在全球健康、慈善事业、政治宣传和许多其他方面做出巨大努力，人工智能开发者和政策制定者都应该为此提供帮助。
• Economic growth. Can the developing world quickly catch up to the developed world, not just in health, but across the board economically? There is some precedent for this: in the final decades of the 20th century, several East Asian economies achieved sustained ~10% annual real GDP growth rates, allowing them to catch up with the developed world. Human economic planners made the decisions that led to this success, not by directly controlling entire economies but by pulling a few key levers (such as an industrial policy of export-led growth, and resisting the temptation to rely on natural resource wealth); it’s plausible that “AI finance ministers and central bankers” could replicate or exceed this 10% accomplishment. An important question is how to get developing world governments to adopt them while respecting the principle of self-determination—some may be enthusiastic about it, but others are likely to be skeptical. On the optimistic side, many of the health interventions in the previous bullet point are likely to organically increase economic growth: eradicating AIDS/malaria/parasitic worms would have a transformative effect on productivity, not to mention the economic benefits that some of the neuroscience interventions (such as improved mood and focus) would have in developed and developing world alike. Finally, non-health AI-accelerated technology (such as energy technology, transport drones, improved building materials, better logistics and distribution, and so on) may simply permeate the world naturally; for example, even cell phones quickly permeated sub-Saharan Africa via market mechanisms, without needing philanthropic efforts. On the more negative side, while AI and automation have many potential benefits, they also pose challenges for economic development, particularly for countries that haven't yet industrialized. Finding ways to ensure these countries can still develop and improve their economies in an age of increasing automation is an important challenge for economists and policymakers to address. Overall, a dream scenario—perhaps a goal to aim for—would be 20% annual GDP growth rate in the developing world, with 10% each coming from AI-enabled economic decisions and the natural spread of AI-accelerated technologies, including but not limited to health. If achieved, this would bring sub-Saharan Africa to the current per-capita GDP of China in 5-10 years, while raising much of the rest of the developing world to levels higher than the current US GDP. Again, this is a dream scenario, not what happens by default: it’s something all of us must work together to make more likely.
  经济增长。发展中世界能否不仅在健康方面，而且在经济方面迅速赶上发达国家？这方面有一些先例：在 20 世纪的最后几十年里，东亚的几个经济体实现了持续约 10% 的实际 GDP 年增长率，从而赶上了发达国家。人类的经济规划者做出了导致这一成功的决策，他们并没有直接控制整个经济体，而是拉动了几个关键杠杆（例如出口导向型增长的产业政策，以及抵制依赖自然资源财富的诱惑）；"人工智能财政部长和中央银行行长 "有可能复制或超越这一 10% 的成就。一个重要的问题是，如何让发展中国家的政府在尊重自决原则的前提下采用这些方法--有些政府可能会对此充满热情，但另一些政府很可能会持怀疑态度。乐观的一面是，上一要点中的许多健康干预措施可能会有机地促进经济增长：根除艾滋病/疟疾/寄生虫会对生产力产生变革性影响，更不用说一些神经科学干预措施（如改善情绪和集中注意力）会给发达国家和发展中国家带来的经济效益了。最后，非健康领域的人工智能加速技术（如能源技术、无人机运输、改良建筑材料、更好的物流配送等）可能只是自然而然地渗透到世界各地；例如，甚至手机也通过市场机制迅速渗透到撒哈拉以南非洲，而无需慈善机构的努力。消极的一面是，虽然人工智能和自动化有许多潜在的好处，但它们也给经济发展带来了挑战，尤其是对那些尚未实现工业化的国家。如何确保这些国家在自动化程度不断提高的时代仍能发展和改善经济，是经济学家和政策制定者需要应对的重要挑战。总体而言，一个梦幻般的场景--或许是一个目标--是发展中国家每年20%的GDP增长率，其中各10%来自人工智能驱动的经济决策和人工智能加速技术的自然传播，包括但不限于健康领域。 如果实现这一目标，撒哈拉以南非洲将在 5-10 年内达到中国目前的人均 GDP 水平，而发展中世界其他大部分地区的 GDP 水平也将高于美国目前的水平。再说一遍，这只是一个梦想，而不是自然而然会发生的事情：我们所有人都必须共同努力，使之更有可能实现。
• Food security ²⁴. Advances in crop technology like better fertilizers and pesticides, more automation, and more efficient land use drastically increased crop yields across the 20th Century, saving millions of people from hunger. Genetic engineering is currently improving many crops even further. Finding even more ways to do this—as well as to make agricultural supply chains even more efficient—could give us an AI-driven second Green Revolution, helping close the gap between the developing and developed world.
  粮食安全²⁴ 。农作物技术的进步，如更好的化肥和杀虫剂、更高的自动化程度以及更有效的土地利用，在整个 20 世纪大幅提高了农作物产量，使数百万人免于饥饿。基因工程目前正在进一步改善许多农作物。如果能找到更多的方法来实现这一目标，同时提高农业供应链的效率，那么人工智能驱动的第二次绿色革命就会到来，从而帮助缩小发展中国家与发达国家之间的差距。
• Mitigating climate change. Climate change will be felt much more strongly in the developing world, hampering its development. We can expect that AI will lead to improvements in technologies that slow or prevent climate change, from atmospheric carbon-removal and clean energy technology to lab-grown meat that reduces our reliance on carbon-intensive factory farming. Of course, as discussed above, technology isn’t the only thing restricting progress on climate change—as with all of the other issues discussed in this essay, human societal factors are important. But there’s good reason to think that AI-enhanced research will give us the means to make mitigating climate change far less costly and disruptive, rendering many of the objections moot and freeing up developing countries to make more economic progress.
  减缓气候变化。发展中国家对气候变化的感受将更为强烈，这将阻碍其发展。我们可以预期，人工智能将带来减缓或防止气候变化技术的改进，从大气碳清除和清洁能源技术到实验室种植肉类，以减少我们对碳密集型工厂化养殖的依赖。当然，正如上文所讨论的，技术并不是限制气候变化进展的唯一因素--就本文讨论的所有其他问题而言，人类社会因素也很重要。但我们有充分的理由相信，人工智能增强型研究将为我们提供手段，使减缓气候变化的成本和破坏性大大降低，从而使许多反对意见变得毫无意义，并使发展中国家得以腾出手来，取得更大的经济进步。
• Inequality within countries. I’ve mostly talked about inequality as a global phenomenon (which I do think is its most important manifestation), but of course inequality also exists within countries. With advanced health interventions and especially radical increases in lifespan or cognitive enhancement drugs, there will certainly be valid worries that these technologies are “only for the rich”. I am more optimistic about within-country inequality especially in the developed world, for two reasons. First, markets function better in the developed world, and markets are typically good at bringing down the cost of high-value technologies over time²⁵. Second, developed world political institutions are more responsive to their citizens and have greater state capacity to execute universal access programs—and I expect citizens to demand access to technologies that so radically improve quality of life. Of course it’s not predetermined that such demands succeed—and here is another place where we collectively have to do all we can to ensure a fair society. There is a separate problem in inequality of wealth (as opposed to inequality of access to life-saving and life-enhancing technologies), which seems harder and which I discuss in Section 5.
  国家内部的不平等。我主要把不平等作为一种全球现象来讨论（我确实认为这是其最重要的表现形式），但当然国家内部也存在不平等。对于先进的医疗干预措施，尤其是大幅延长寿命或增强认知能力的药物，肯定会有人担心这些技术 "只为富人服务"。我对国内不平等现象比较乐观，尤其是在发达国家，原因有二。首先，发达国家的市场运作更好，而市场通常善于随着时间的推移降低高价值技术的成本²⁵ 。其次，发达国家的政治体制更能顺应其公民的需求，并且有更大的国家能力来执行普及计划--我预计公民会要求获得能从根本上改善生活质量的技术。当然，这并不意味着这些要求一定会成功--这也是我们必须竭尽全力确保社会公平的另一个地方。财富的不平等（相对于获得拯救生命和提高生命质量的技术的不平等）是一个单独的问题，这个问题似乎更难解决，我将在第5节中讨论这个问题。
• The opt-out problem. One concern in both developed and developing world alike is people opting out of AI-enabled benefits (similar to the anti-vaccine movement, or Luddite movements more generally). There could end up being bad feedback cycles where, for example, the people who are least able to make good decisions opt out of the very technologies that improve their decision-making abilities, leading to an ever-increasing gap and even creating a dystopian underclass (some researchers have argued that this will undermine democracy, a topic I discuss further in the next section). This would, once again, place a moral blemish on AI’s positive advances. This is a difficult problem to solve as I don’t think it is ethically okay to coerce people, but we can at least try to increase people’s scientific understanding—and perhaps AI itself can help us with this. One hopeful sign is that historically anti-technology movements have been more bark than bite: railing against modern technology is popular, but most people adopt it in the end, at least when it’s a matter of individual choice. Individuals tend to adopt most health and consumer technologies, while technologies that are truly hampered, like nuclear power, tend to be collective political decisions.
  退出问题。发达国家和发展中国家都担心的一个问题是，人们选择放弃人工智能带来的好处（类似于反疫苗运动，或更普遍的卢德运动）。最终可能会出现不良的反馈循环，例如，最不能做出正确决策的人选择放弃那些能提高他们决策能力的技术，从而导致差距不断扩大，甚至产生一个乌托邦式的底层社会（一些研究人员认为，这将破坏民主，我将在下一节进一步讨论这个话题）。这将再次给人工智能的积极进步抹上道德污点。这是一个难以解决的问题，因为我认为从道德上讲，强迫人们是不允许的，但我们至少可以尝试提高人们的科学认识--也许人工智能本身就可以帮助我们做到这一点。一个充满希望的迹象是，从历史上看，反科技运动一直都是吠声大于咬声：对现代科技的抨击很受欢迎，但大多数人最终还是会采用现代科技，至少在个人选择的情况下是这样。个人倾向于采用大多数健康和消费技术，而真正受到阻碍的技术，如核能，往往是集体的政治决定。

Overall, I am optimistic about quickly bringing AI’s biological advances to people in the developing world. I am hopeful, though not confident, that AI can also enable unprecedented economic growth rates and allow the developing world to at least surpass where the developed world is now. I am concerned about the “opt out” problem in both the developed and developing world, but suspect that it will peter out over time and that AI can help accelerate this process. It won’t be a perfect world, and those who are behind won’t fully catch up, at least not in the first few years. But with strong efforts on our part, we may be able to get things moving in the right direction—and fast. If we do, we can make at least a downpayment on the promises of dignity and equality that we owe to every human being on earth.
总的来说，我对迅速将人工智能的生物进步带给发展中国家的人们感到乐观。我对人工智能也能带来前所未有的经济增长速度并使发展中国家至少超过发达国家现在的水平充满希望，尽管我并不自信。我对发达国家和发展中国家的 "选择退出 "问题感到担忧，但我认为随着时间的推移，这个问题会逐渐消失，而人工智能可以帮助加快这一进程。这不会是一个完美的世界，落后的国家不会完全赶上，至少在最初几年不会。但是，只要我们努力，也许就能让事情朝着正确的方向发展，而且是快速发展。如果我们这样做了，我们至少可以为我们欠地球上每一个人的尊严和平等的承诺支付一笔首付款。

4. Peace and governance 4.和平与治理

Suppose that everything in the first three sections goes well: disease, poverty, and inequality are significantly reduced and the baseline of human experience is raised substantially. It does not follow that all major causes of human suffering are solved. Humans are still a threat to each other. Although there is a trend of technological improvement and economic development leading to democracy and peace, it is a very loose trend, with frequent (and recent) backsliding. At the dawn of the 20th Century, people thought they had put war behind them; then came the two world wars. Thirty years ago Francis Fukuyama wrote about “the End of History” and a final triumph of liberal democracy; that hasn’t happened yet. Twenty years ago US policymakers believed that free trade with China would cause it to liberalize as it became richer; that very much didn’t happen, and we now seem headed for a second cold war with a resurgent authoritarian bloc. And plausible theories suggest that internet technology may actually advantage authoritarianism, not democracy as initially believed (e.g. in the “Arab Spring” period). It seems important to try to understand how powerful AI will intersect with these issues of peace, democracy, and freedom.
假设前三个部分一切顺利：疾病、贫困和不平等现象显著减少，人类经验的基线大幅提高。但这并不意味着人类痛苦的所有主要原因都已解决。虽然技术进步和经济发展是一种趋势，导致民主与和平，但这是一种非常松散的趋势，经常（最近）出现倒退。在20世纪初，人们以为他们已经把战争抛在了脑后；但随后又发生了两次世界大战。三十年前，弗朗西斯-福山（Francis Fukuyama）写下了"历史的终结"和自由民主的最后胜利；但这一切尚未发生。二十年前，美国的政策制定者们认为，与中国的自由贸易会使中国变得更加富裕，从而实现自由化；但这一切都没有发生，我们现在似乎要与一个卷土重来的独裁集团进行第二次冷战。有一些似是而非的理论认为，互联网技术实际上可能有利于专制主义，而不是最初认为的民主（例如在 "阿拉伯之春 "时期）。试图理解强大的人工智能将如何与这些和平、民主和自由问题交织在一起似乎很重要。

Unfortunately, I see no strong reason to believe AI will preferentially or structurally advance democracy and peace, in the same way that I think it will structurally advance human health and alleviate poverty. Human conflict is adversarial and AI can in principle help both the “good guys” and the “bad guys”. If anything, some structural factors seem worrying: AI seems likely to enable much better propaganda and surveillance, both major tools in the autocrat’s toolkit. It’s therefore up to us as individual actors to tilt things in the right direction: if we want AI to favor democracy and individual rights, we are going to have to fight for that outcome. I feel even more strongly about this than I do about international inequality: the triumph of liberal democracy and political stability is not guaranteed, perhaps not even likely, and will require great sacrifice and commitment on all of our parts, as it often has in the past.
遗憾的是，我没有看到任何强有力的理由来相信人工智能会优先或结构性地促进民主与和平，就像我认为人工智能会结构性地促进人类健康和减轻贫困一样。人类冲突是对抗性的，人工智能原则上既可以帮助 "好人"，也可以帮助 "坏人"。如果有的话，一些结构性因素似乎令人担忧：人工智能似乎可以更好地进行宣传和监视，而这正是专制者的主要工具。因此，作为个体行动者，我们有责任让事情朝着正确的方向发展：如果我们希望人工智能有利于民主和个人权利，我们就必须为这一结果而奋斗。在这一点上，我的感受甚至比对国际不平等问题的感受更强烈：自由民主和政治稳定的胜利并不能得到保证，或许甚至不太可能，这需要我们所有人做出巨大的牺牲和承诺，就像过去经常发生的那样。

I think of the issue as having two parts: international conflict, and the internal structure of nations. On the international side, it seems very important that democracies have the upper hand on the world stage when powerful AI is created. AI-powered authoritarianism seems too terrible to contemplate, so democracies need to be able to set the terms by which powerful AI is brought into the world, both to avoid being overpowered by authoritarians and to prevent human rights abuses within authoritarian countries.
我认为这个问题包括两个部分：国际冲突和国家内部结构。在国际方面，当强大的人工智能诞生时，民主国家在世界舞台上占据上风似乎非常重要。由人工智能推动的独裁主义似乎太可怕了，因此民主国家需要能够制定将强大的人工智能带入世界的条件，既要避免被独裁者压倒，又要防止独裁国家内部侵犯人权。

My current guess at the best way to do this is via an “entente strategy”²⁶, in which a coalition of democracies seeks to gain a clear advantage (even just a temporary one) on powerful AI by securing its supply chain, scaling quickly, and blocking or delaying adversaries’ access to key resources like chips and semiconductor equipment. This coalition would on one hand use AI to achieve robust military superiority (the stick) while at the same time offering to distribute the benefits of powerful AI (the carrot) to a wider and wider group of countries in exchange for supporting the coalition’s strategy to promote democracy (this would be a bit analogous to “Atoms for Peace”). The coalition would aim to gain the support of more and more of the world, isolating our worst adversaries and eventually putting them in a position where they are better off taking the same bargain as the rest of the world: give up competing with democracies in order to receive all the benefits and not fight a superior foe.
我目前认为实现这一目标的最佳途径是通过 "协约战略"²⁶ 来实现，在该战略中，民主国家联盟将通过确保其供应链的安全、快速扩展以及阻止或延迟对手获得芯片和半导体设备等关键资源，从而在强大的人工智能方面获得明显的优势（哪怕只是暂时的优势）。该联盟一方面将利用人工智能来实现强大的军事优势（大棒），同时还将向越来越多的国家提供强大人工智能的好处（胡萝卜），以换取这些国家对联盟促进民主战略的支持（这有点类似于"原子促进和平"）。该联盟的目标是获得世界上越来越多国家的支持，孤立我们最糟糕的对手，并最终将他们置于与世界其他国家一样的境地：放弃与民主国家竞争，以获得所有好处，而不是与更强大的敌人作战。

If we can do all this, we will have a world in which democracies lead on the world stage and have the economic and military strength to avoid being undermined, conquered, or sabotaged by autocracies, and may be able to parlay their AI superiority into a durable advantage. This could optimistically lead to an “eternal 1991”—a world where democracies have the upper hand and Fukuyama’s dreams are realized. Again, this will be very difficult to achieve, and will in particular require close cooperation between private AI companies and democratic governments, as well as extraordinarily wise decisions about the balance between carrot and stick.
如果我们能做到这一切，我们将拥有这样一个世界：民主国家在世界舞台上处于领先地位，并拥有经济和军事实力来避免被专制国家削弱、征服或破坏，并有可能将其人工智能优势转化为持久优势。乐观地说，这可能会导致一个 "永恒的 1991 年"--一个民主国家占据上风、福山的梦想得以实现的世界。同样，这将很难实现，尤其需要私营人工智能公司与民主政府之间的密切合作，以及在胡萝卜加大棒之间做出非常明智的决定。

Even if all that goes well, it leaves the question of the fight between democracy and autocracy within each country. It is obviously hard to predict what will happen here, but I do have some optimism that given a global environment in which democracies control the most powerful AI, then AI may actually structurally favor democracy everywhere. In particular, in this environment democratic governments can use their superior AI to win the information war: they can counter influence and propaganda operations by autocracies and may even be able to create a globally free information environment by providing channels of information and AI services in a way that autocracies lack the technical ability to block or monitor. It probably isn’t necessary to deliver propaganda, only to counter malicious attacks and unblock the free flow of information. Although not immediate, a level playing field like this stands a good chance of gradually tilting global governance towards democracy, for several reasons.
即使一切顺利，每个国家内部的民主与专制之间的斗争问题仍然存在。显然，我们很难预测这里会发生什么，但我确实有些乐观，如果全球环境中民主国家控制着最强大的人工智能，那么人工智能实际上可能在结构上有利于各地的民主。特别是，在这种环境下，民主政府可以利用其优势人工智能赢得信息战：他们可以反击专制国家的影响和宣传行动，甚至可以通过提供信息渠道和人工智能服务，创造一个全球自由的信息环境，而专制国家则缺乏阻止或监控的技术能力。这可能并不需要进行宣传，只需要反击恶意攻击，疏通信息自由流动的渠道。虽然不能立竿见影，但这样的公平竞争环境很有可能使全球治理逐渐向民主倾斜，原因有以下几点。

First, the increases in quality of life in Sections 1-3 should, all things equal, promote democracy: historically they have, to at least some extent. In particular I expect improvements in mental health, well-being, and education to increase democracy, as all three are negatively correlated with support for authoritarian leaders. In general people want more self-expression when their other needs are met, and democracy is among other things a form of self-expression. Conversely, authoritarianism thrives on fear and resentment.
首先，在所有条件相同的情况下，第 1-3 部分中生活质量的提高应该会促进民主：从历史上看，它们至少在一定程度上促进了民主。我尤其期望心理健康、幸福感和教育的改善能促进民主，因为这三者都负相关与支持专制领导人相关。一般来说，当人们的其他需求得到满足时，他们会希望有更多的自我表达，而民主正是自我表达的一种形式。相反，专制主义则在恐惧和怨恨中茁壮成长。

Second, there is a good chance free information really does undermine authoritarianism, as long as the authoritarians can’t censor it. And uncensored AI can also bring individuals powerful tools for undermining repressive governments. Repressive governments survive by denying people a certain kind of common knowledge, keeping them from realizing that “the emperor has no clothes”. For example Srđa Popović, who helped to topple the Milošević government in Serbia, has written extensively about techniques for psychologically robbing authoritarians of their power, for breaking the spell and rallying support against a dictator. A superhumanly effective AI version of Popović (whose skills seem like they have high returns to intelligence) in everyone’s pocket, one that dictators are powerless to block or censor, could create a wind at the backs of dissidents and reformers across the world. To say it again, this will be a long and protracted fight, one where victory is not assured, but if we design and build AI in the right way, it may at least be a fight where the advocates of freedom everywhere have an advantage.
其次，只要专制者无法审查信息，自由信息就很有可能真的会削弱专制。而未经审查的人工智能也能为个人带来破坏专制政府的强大工具。专制政府的生存之道是剥夺人们的某种常识，不让他们意识到 "皇帝没有穿衣服"。例如，曾帮助推翻塞尔维亚米洛舍维奇政府的斯尔达-波波维奇（Srđa Popović）就撰写了大量文章，介绍从心理上剥夺专制者权力的技巧，以及打破咒语、团结支持反对独裁者的技巧。如果每个人的口袋里都有一个超人效率的人工智能版波波维奇（他的技能似乎具有很高的智力回报率），独裁者又无力阻止或审查，那么全世界的持不同政见者和改革者就会如坐针毡。我再说一遍，这将是一场旷日持久的斗争，胜负难料，但如果我们以正确的方式设计和制造人工智能，这场斗争至少会让世界各地的自由拥护者占得先机。

As with neuroscience and biology, we can also ask how things could be “better than normal”—not just how to avoid autocracy, but how to make democracies better than they are today. Even within democracies, injustices happen all the time. Rule-of-law societies make a promise to their citizens that everyone will be equal under the law and everyone is entitled to basic human rights, but obviously people do not always receive those rights in practice. That this promise is even partially fulfilled makes it something to be proud of, but can AI help us do better?
与神经科学和生物学一样，我们也可以追问如何让事情 "比正常情况更好"--不仅仅是如何避免专制，而是如何让民主政体比现在更好。即使在民主国家内部，不公正现象也时有发生。法治社会向其公民承诺，法律面前人人平等，每个人都有权享有基本人权，但很显然，人们在实践中并不总能得到这些权利。这一承诺甚至部分得到了履行，这值得我们骄傲，但人工智能能否帮助我们做得更好呢？

For example, could AI improve our legal and judicial system by making decisions and processes more impartial? Today people mostly worry in legal or judicial contexts that AI systems will be a cause of discrimination, and these worries are important and need to be defended against. At the same time, the vitality of democracy depends on harnessing new technologies to improve democratic institutions, not just responding to risks. A truly mature and successful implementation of AI has the potential to reduce bias and be fairer for everyone.
例如，人工智能能否使决策和程序更加公正，从而改善我们的法律和司法系统？如今，在法律或司法领域，人们大多担心人工智能系统会成为歧视的原因，这些担忧非常重要，需要加以防范。与此同时，民主的活力取决于利用新技术来改善民主制度，而不仅仅是应对风险。真正成熟和成功的人工智能有可能减少偏见，对每个人都更加公平。

For centuries, legal systems have faced the dilemma that the law aims to be impartial, but is inherently subjective and thus must be interpreted by biased humans. Trying to make the law fully mechanical hasn’t worked because the real world is messy and can’t always be captured in mathematical formulas. Instead legal systems rely on notoriously imprecise criteria like “cruel and unusual punishment” or “utterly without redeeming social importance”, which humans then interpret—and often do so in a manner that displays bias, favoritism, or arbitrariness. “Smart contracts” in cryptocurrencies haven’t revolutionized law because ordinary code isn’t smart enough to adjudicate all that much of interest. But AI might be smart enough for this: it is the first technology capable of making broad, fuzzy judgements in a repeatable and mechanical way.
几个世纪以来，法律体系一直面临着这样的困境：法律的目标是公正，但其本身却带有主观性，因此必须由带有偏见的人来解释。试图将法律完全机械化的做法并不奏效，因为现实世界是混乱的，并不能总是用数学公式来概括。相反，法律体系依赖的是"残酷和不寻常的惩罚"或"完全没有可挽回的社会意义"等臭名昭著的不精确标准，然后由人类来解释--而且解释的方式往往表现出偏见、偏袒或武断。加密货币中的"智能合约"并没有给法律带来革命性的变化，因为普通代码还不够智能，无法对所有利益做出裁决。但人工智能可能足够聪明：它是第一种能够以可重复的机械方式做出广泛、模糊判断的技术。

I am not suggesting that we literally replace judges with AI systems, but the combination of impartiality with the ability to understand and process messy, real world situations feels like it should have some serious positive applications to law and justice. At the very least, such systems could work alongside humans as an aid to decision-making. Transparency would be important in any such system, and a mature science of AI could conceivably provide it: the training process for such systems could be extensively studied, and advanced interpretability techniques could be used to see inside the final model and assess it for hidden biases, in a way that is simply not possible with humans. Such AI tools could also be used to monitor for violations of fundamental rights in a judicial or police context, making constitutions more self-enforcing.
我并不是建议我们真的用人工智能系统取代法官，但将公正性与理解和处理纷繁复杂的现实情况的能力结合起来，感觉应该会对法律和司法产生积极的影响。至少，此类系统可以与人类一起辅助决策。在任何此类系统中，透明度都是非常重要的，可以想象，成熟的人工智能科学可以提供这种透明度：可以对此类系统的训练过程进行广泛研究，先进的可解释性技术可以用来观察最终模型的内部，并评估其是否存在隐藏的偏见，而这是人类根本无法做到的。此类人工智能工具还可用于监测司法或警察部门侵犯基本权利的情况，从而使宪法得到更好的自我执行。

In a similar vein, AI could be used to both aggregate opinions and drive consensus among citizens, resolving conflict, finding common ground, and seeking compromise. Some early ideas in this direction have been undertaken by the computational democracy project, including collaborations with Anthropic. A more informed and thoughtful citizenry would obviously strengthen democratic institutions.
与此类似，人工智能也可用于汇总意见，推动公民达成共识，解决冲突，寻找共同点，寻求妥协。计算民主项目已经朝着这个方向提出了一些早期想法，其中包括与Anthropic的合作。一个更知情、更有思想的公民群体显然会加强民主体制。

There is also a clear opportunity for AI to be used to help provision government services—such as health benefits or social services—that are in principle available to everyone but in practice often severely lacking, and worse in some places than others. This includes health services, the DMV, taxes, social security, building code enforcement, and so on. Having a very thoughtful and informed AI whose job is to give you everything you’re legally entitled to by the government in a way you can understand—and who also helps you comply with often confusing government rules—would be a big deal. Increasing state capacity both helps to deliver on the promise of equality under the law, and strengthens respect for democratic governance. Poorly implemented services are currently a major driver of cynicism about government²⁷.
人工智能还有一个明显的机会，可以用来帮助提供政府服务，如医疗福利或社会服务，这些服务原则上人人都可以享受，但实际上往往严重缺乏，而且在某些地方比其他地方更严重。这包括医疗服务、车管所、税收、社会保障、建筑法规执行等等。如果有一个深思熟虑、信息灵通的人工智能，其工作就是以你能理解的方式向你提供政府依法赋予你的一切权利，同时还能帮助你遵守往往令人困惑的政府规定，那将是一件大事。提高国家能力既有助于实现法律面前人人平等的承诺，又能加强对民主治理的尊重。目前，服务执行不力是导致人们对政府不满的主要原因²⁷ 。

All of these are somewhat vague ideas, and as I said at the beginning of this section, I am not nearly as confident in their feasibility as I am in the advances in biology, neuroscience, and poverty alleviation. They may be unrealistically utopian. But the important thing is to have an ambitious vision, to be willing to dream big and try things out. The vision of AI as a guarantor of liberty, individual rights, and equality under the law is too powerful a vision not to fight for. A 21st century, AI-enabled polity could be both a stronger protector of individual freedom, and a beacon of hope that helps make liberal democracy the form of government that the whole world wants to adopt.
所有这些想法都有些模糊，正如我在本节开头所说，我对它们的可行性并不像我对生物学、神经科学和减贫方面的进展那样充满信心。它们可能是不切实际的乌托邦。但重要的是要有远大的愿景，要愿意梦想远大并勇于尝试。让人工智能成为自由、个人权利和法律平等的保障者，这样的愿景太强大了，我们不能不为之奋斗。21 世纪的人工智能政体既可以成为个人自由更有力的保护者，也可以成为希望的灯塔，帮助自由民主成为全世界都希望采用的政府形式。

5. Work and meaning 5.工作与意义

Even if everything in the preceding four sections goes well—not only do we alleviate disease, poverty, and inequality, but liberal democracy becomes the dominant form of government, and existing liberal democracies become better versions of themselves—at least one important question still remains. “It’s great we live in such a technologically advanced world as well as a fair and decent one”, someone might object, “but with AI’s doing everything, how will humans have meaning? For that matter, how will they survive economically?”.
即使前面四个部分一切顺利--我们不仅减轻了疾病、贫困和不平等，而且自由民主成为政府的主要形式，现有的自由民主国家也变得更好--至少还有一个重要问题依然存在。有人可能会反对说："我们生活在这样一个技术先进、公平正义的世界里真是太好了"，"但是，如果人工智能什么都能做，人类还有什么意义？至于经济方面，他们又将如何生存？

I think this question is more difficult than the others. I don’t mean that I am necessarily more pessimistic about it than I am about the other questions (although I do see challenges). I mean that it is fuzzier and harder to predict in advance, because it relates to macroscopic questions about how society is organized that tend to resolve themselves only over time and in a decentralized manner. For example, historical hunter-gatherer societies might have imagined that life is meaningless without hunting and various kinds of hunting-related religious rituals, and would have imagined that our well-fed technological society is devoid of purpose. They might also have not understood how our economy can provide for everyone, or what function people can usefully service in a mechanized society.
我认为这个问题比其他问题更难。我的意思并不是说我对这个问题的看法一定比对其他问题的看法悲观（尽管我确实看到了挑战）。我的意思是说，它更模糊，更难以提前预测，因为它涉及到社会组织方式的宏观问题，而这些问题往往只能随着时间的推移以分散的方式解决。例如，历史上的狩猎采集社会可能会认为，如果没有狩猎和各种与狩猎相关的宗教仪式，生活就没有意义，也会认为我们这个衣食无忧的科技社会没有目标。他们可能也不理解我们的经济如何能够养活所有人，或者在一个机械化的社会中，人们能够发挥什么有益的作用。

Nevertheless, it’s worth saying at least a few words, while keeping in mind that the brevity of this section is not at all to be taken as a sign that I don’t take these issues seriously—on the contrary, it is a sign of a lack of clear answers.
尽管如此，我们还是应该至少说几句话，同时要记住，本节的简短并不意味着我不认真对待这些问题，恰恰相反，这表明我缺乏明确的答案。

On the question of meaning, I think it is very likely a mistake to believe that tasks you undertake are meaningless simply because an AI could do them better. Most people are not the best in the world at anything, and it doesn’t seem to bother them particularly much. Of course today they can still contribute through comparative advantage, and may derive meaning from the economic value they produce, but people also greatly enjoy activities that produce no economic value. I spend plenty of time playing video games, swimming, walking around outside, and talking to friends, all of which generates zero economic value. I might spend a day trying to get better at a video game, or faster at biking up a mountain, and it doesn’t really matter to me that someone somewhere is much better at those things. In any case I think meaning comes mostly from human relationships and connection, not from economic labor. People do want a sense of accomplishment, even a sense of competition, and in a post-AI world it will be perfectly possible to spend years attempting some very difficult task with a complex strategy, similar to what people do today when they embark on research projects, try to become Hollywood actors, or found companies²⁸. The facts that (a) an AI somewhere could in principle do this task better, and (b) this task is no longer an economically rewarded element of a global economy, don’t seem to me to matter very much.
关于意义问题，我认为，仅仅因为人工智能可以做得更好，就认为自己承担的任务毫无意义，这很可能是一个错误。大多数人在任何事情上都不是世界上最好的，这似乎并没有让他们感到特别困扰。当然，今天他们仍然可以通过比较优势做出贡献，并可能从他们创造的经济价值中获得意义，但人们也非常喜欢那些不产生经济价值的活动。我花很多时间玩电子游戏、游泳、在户外散步、和朋友聊天，所有这些活动产生的经济价值都是零。我可能会花上一天的时间去努力提高电子游戏的水平，或者更快地骑自行车爬山，而对我来说，某个地方的某个人在这些事情上做得更好并不重要。无论如何，我认为意义主要来自人际关系和联系，而不是经济劳动。人们确实希望获得成就感，甚至是竞争感，在后人工智能世界中，人们完全有可能花费数年时间，通过复杂的策略来尝试一些非常困难的任务，就像今天人们开始研究项目、试图成为好莱坞演员或创立公司时所做的那样²⁸ 。在我看来，以下事实并不重要：(a) 某处的人工智能原则上可以更好地完成这项任务；(b) 这项任务不再是全球经济中具有经济回报的要素。

The economic piece actually seems more difficult to me than the meaning piece. By “economic” in this section I mean the possible problem that most or all humans may not be able to contribute meaningfully to a sufficiently advanced AI-driven economy. This is a more macro problem than the separate problem of inequality, especially inequality in access to the new technologies, which I discussed in Section 3.
在我看来，经济这一块其实比意义这一块更难。在本节中，我所说的 "经济 "是指这样一个可能的问题：大多数或所有人类可能无法为足够先进的人工智能驱动的经济做出有意义的贡献。与我在第 3 节中讨论的不平等问题相比，这是一个更为宏观的问题，尤其是在获取新技术方面的不平等。

First of all, in the short term I agree with arguments that comparative advantage will continue to keep humans relevant and in fact increase their productivity, and may even in some ways level the playing field between humans. As long as AI is only better at 90% of a given job, the other 10% will cause humans to become highly leveraged, increasing compensation and in fact creating a bunch of new human jobs complementing and amplifying what AI is good at, such that the “10%” expands to continue to employ almost everyone. In fact, even if AI can do 100% of things better than humans, but it remains inefficient or expensive at some tasks, or if the resource inputs to humans and AI’s are meaningfully different, then the logic of comparative advantage continues to apply. One area humans are likely to maintain a relative (or even absolute) advantage for a significant time is the physical world. Thus, I think that the human economy may continue to make sense even a little past the point where we reach “a country of geniuses in a datacenter”.
首先，在短期内，我同意这样的观点，即比较优势将继续保持人类的相关性，并在事实上提高他们的生产力，甚至可能在某些方面使人类之间的竞争更加公平。只要人工智能只能胜任某项工作的 90%，那么另外 10%的工作就会使人类变得非常重要，从而提高报酬，并在事实上创造出大量新的人类工作，补充和放大人工智能所擅长的工作，从而使 "10%" 扩大到继续雇用几乎所有人。事实上，即使人工智能能100%地比人类做得更好，但它在某些任务上仍然效率低下或成本高昂，或者如果人类和人工智能的资源输入有显著差异，那么比较优势的逻辑仍然适用。人类有可能在相当长的时间内保持相对（甚至绝对）优势的一个领域是物理世界。因此，我认为人类经济即使在我们达到 "数据中心中的天才之国 "的地步之后仍有意义。

However, I do think in the long run AI will become so broadly effective and so cheap that this will no longer apply. At that point our current economic setup will no longer make sense, and there will be a need for a broader societal conversation about how the economy should be organized.
不过，我确实认为，从长远来看，人工智能将变得如此广泛有效和廉价，以至于这种情况将不再适用。到那时，我们目前的经济结构将不再有意义，社会将需要就如何组织经济进行更广泛的讨论。

While that might sound crazy, the fact is that civilization has successfully navigated major economic shifts in the past: from hunter-gathering to farming, farming to feudalism, and feudalism to industrialism. I suspect that some new and stranger thing will be needed, and that it’s something no one today has done a good job of envisioning. It could be as simple as a large universal basic income for everyone, although I suspect that will only be a small part of a solution. It could be a capitalist economy of AI systems, which then give out resources (huge amounts of them, since the overall economic pie will be gigantic) to humans based on some secondary economy of what the AI systems think makes sense to reward in humans (based on some judgment ultimately derived from human values). Perhaps the economy runs on Whuffie points. Or perhaps humans will continue to be economically valuable after all, in some way not anticipated by the usual economic models. All of these solutions have tons of possible problems, and it’s not possible to know whether they will make sense without lots of iteration and experimentation. And as with some of the other challenges, we will likely have to fight to get a good outcome here: exploitative or dystopian directions are clearly also possible and have to be prevented. Much more could be written about these questions and I hope to do so at some later time.
虽然这听起来有些疯狂，但事实上，人类文明在过去曾成功地经历过重大的经济转变：从狩猎采集到农耕，从农耕到封建主义，从封建主义到工业主义。我猜想，人类将需要一些新的、更奇怪的东西，而这种东西今天还没有人能够很好地设想出来。它可以简单到为每个人提供大量的全民基本收入，尽管我怀疑这只是解决方案的一小部分。它可以是一个由人工智能系统组成的资本主义经济，然后根据人工智能系统认为对人类有意义的奖励（基于最终来自人类价值观的判断），向人类分配资源（大量资源，因为整个经济蛋糕将是巨大的）。也许经济运行的基础是Whuffie积分。又或者，人类终究会以某种常规经济模型未曾预料到的方式继续具有经济价值。所有这些解决方案都存在大量可能的问题，如果不进行大量的反复试验，就无法知道它们是否有意义。与其他一些挑战一样，我们很可能需要努力争取一个好的结果：剥削或乌托邦式的发展方向显然也是可能的，必须加以防止。关于这些问题，我还可以写更多，希望以后有机会再写。

Taking stock 总结

Through the varied topics above, I’ve tried to lay out a vision of a world that is both plausible if everything goes right with AI, and much better than the world today. I don’t know if this world is realistic, and even if it is, it will not be achieved without a huge amount of effort and struggle by many brave and dedicated people. Everyone (including AI companies!) will need to do their part both to prevent risks and to fully realize the benefits.
通过以上不同的主题，我试图勾勒出一个世界的愿景：如果人工智能一切顺利，这个世界是可信的，而且比现在的世界要好得多。我不知道这个世界是否现实，即使现实，如果没有许多勇敢而敬业的人付出巨大的努力和奋斗，也不可能实现。每个人（包括人工智能公司！）都需要尽自己的一份力，既要防范风险，又要充分实现利益。

But it is a world worth fighting for. If all of this really does happen over 5 to 10 years—the defeat of most diseases, the growth in biological and cognitive freedom, the lifting of billions of people out of poverty to share in the new technologies, a renaissance of liberal democracy and human rights—I suspect everyone watching it will be surprised by the effect it has on them. I don’t mean the experience of personally benefiting from all the new technologies, although that will certainly be amazing. I mean the experience of watching a long-held set of ideals materialize in front of us all at once. I think many will be literally moved to tears by it.
但这是一个值得为之奋斗的世界。如果所有这一切真的在 5 到 10 年内发生--大多数疾病被治愈、生物和认知自由的增长、数十亿人摆脱贫困并分享新技术、自由民主和人权的复兴--我猜想，每一个看到这一切的人都会惊讶于这一切对他们的影响。我指的不是从所有新技术中获益的亲身经历，尽管那肯定会令人惊叹。我指的是观看一套长期坚持的理想一下子在我们面前具体化的体验。我想，许多人会为此感动得热泪盈眶。

Throughout writing this essay I noticed an interesting tension. In one sense the vision laid out here is extremely radical: it is not what almost anyone expects to happen in the next decade, and will likely strike many as an absurd fantasy. Some may not even consider it desirable; it embodies values and political choices that not everyone will agree with. But at the same time there is something blindingly obvious—something overdetermined—about it, as if many different attempts to envision a good world inevitably lead roughly here.
在撰写这篇文章的过程中，我注意到一种有趣的紧张关系。从某种意义上说，本文所阐述的愿景是极其激进的：它不是几乎所有人都期望在未来十年发生的事情，可能会让许多人觉得是荒谬的幻想。有些人甚至会认为它并不可取；它所体现的价值观和政治选择并不是每个人都会同意的。但与此同时，它又有一些显而易见的东西--一些过度确定的东西--就好像许多不同的设想美好世界的尝试都不可避免地会大致导向这里。

In Iain M. Banks’ The Player of Games²⁹, the protagonist—a member of a society called the Culture, which is based on principles not unlike those I’ve laid out here—travels to a repressive, militaristic empire in which leadership is determined by competition in an intricate battle game. The game, however, is complex enough that a player’s strategy within it tends to reflect their own political and philosophical outlook. The protagonist manages to defeat the emperor in the game, showing that his values (the Culture’s values) represent a winning strategy even in a game designed by a society based on ruthless competition and survival of the fittest. A well-known post by Scott Alexander has the same thesis—that competition is self-defeating and tends to lead to a society based on compassion and cooperation. The “arc of the moral universe” is another similar concept.
在伊恩-M-班克斯（Iain M. Banks）的《游戏玩家》²⁹ 中，主人公--一个名为 "文化"（Culture）的社会的成员--来到了一个压抑的军国主义帝国，在这个帝国中，领导权是通过错综复杂的战斗游戏中的竞争来决定的。不过，这个游戏非常复杂，玩家在游戏中的策略往往反映了他们自己的政治和哲学观。主人公在游戏中成功击败了皇帝，这表明即使在一个以残酷竞争和适者生存为基础的社会所设计的游戏中，他的价值观（文化的价值观）也是一种制胜策略。斯科特-亚历山大（Scott Alexander）的一篇著名文章也提出了同样的论点--竞争是自取灭亡，而竞争往往会导致建立一个以同情和合作为基础的社会。道德宇宙的弧线"是另一个类似的概念。

I think the Culture’s values are a winning strategy because they’re the sum of a million small decisions that have clear moral force and that tend to pull everyone together onto the same side. Basic human intuitions of fairness, cooperation, curiosity, and autonomy are hard to argue with, and are cumulative in a way that our more destructive impulses often aren’t. It is easy to argue that children shouldn’t die of disease if we can prevent it, and easy from there to argue that everyone’s children deserve that right equally. From there it is not hard to argue that we should all band together and apply our intellects to achieve this outcome. Few disagree that people should be punished for attacking or hurting others unnecessarily, and from there it’s not much of a leap to the idea that punishments should be consistent and systematic across people. It is similarly intuitive that people should have autonomy and responsibility over their own lives and choices. These simple intuitions, if taken to their logical conclusion, lead eventually to rule of law, democracy, and Enlightenment values. If not inevitably, then at least as a statistical tendency, this is where humanity was already headed. AI simply offers an opportunity to get us there more quickly—to make the logic starker and the destination clearer.
我认为，文化价值观是一种制胜策略，因为它们是无数个具有明确道德力量的微小决定的总和，往往能将所有人拉到同一阵线。人类对公平、合作、好奇心和自主性的基本直觉是难以辩驳的，而且具有累积性，而我们更具破坏性的冲动往往不具备这种累积性。如果我们能够预防疾病的发生，那么儿童就不应该死于疾病，这一点很容易争论，而每个人的孩子都应该平等地享有这一权利，这一点也很容易争论。由此不难得出结论，我们应该团结起来，运用我们的智慧来实现这一结果。几乎没有人不同意，人们在不必要的情况下攻击或伤害他人就应该受到惩罚。同样直观的是，人们应该对自己的生活和选择拥有自主权并承担责任。这些简单的直觉如果符合逻辑，最终会导致法治、民主和启蒙价值观。如果不是不可避免，那么至少作为一种统计趋势，这就是人类本来的方向。人工智能只是提供了一个让我们更快到达目的地的机会--让逻辑更清晰，目的地更明确。

Nevertheless, it is a thing of transcendent beauty. We have the opportunity to play some small role in making it real.
然而，这是一种超凡脱俗的美。我们有机会扮演一个小小的角色，让它成为现实。

Thanks to Kevin Esvelt, Parag Mallick, Stuart Ritchie, Matt Yglesias, Erik Brynjolfsson, Jim McClave, Allan Dafoe, and many people at Anthropic for reviewing drafts of this essay.
感谢 Kevin Esvelt、Parag Mallick、Stuart Ritchie、Matt Yglesias、Erik Brynjolfsson、Jim McClave、Allan Dafoe 以及《人类学》杂志的许多人对本文草稿的审阅。

To the winners of the 2024 Nobel prize in Chemistry, for showing us all the way.
向2024年诺贝尔化学奖得主致敬，他们为我们指明了前进的方向。