Sam Altman：智能的奇点，温柔地到来，未来的十年，人类会怎样？
Sam Altman：智能的奇点，温柔地到来，未来的十年，人类会怎样？
The Gentle Singularity 温柔的奇点
We are past the event horizon; the takeoff has started. Humanity is close to building digital superintelligence, and at least so far it’s much less weird than it seems like it should be.
我们已经超越了事件视界;起飞已经开始。人类即将构建数字超级智能，至少到目前为止，它远没有看起来那么奇怪。
Robots are not yet walking the streets, nor are most of us talking to AI all day. People still die of disease, we still can’t easily go to space, and there is a lot about the universe we don’t understand.
机器人还没有走在街上，我们大多数人也没有整天与 AI 交谈。人们仍然死于疾病，我们仍然无法轻易进入太空，宇宙还有很多我们不了解的地方。
And yet, we have recently built systems that are smarter than people in many ways, and are able to significantly amplify the output of people using them. The least-likely part of the work is behind us; the scientific insights that got us to systems like GPT-4 and o3 were hard-won, but will take us very far.
然而，我们最近构建的系统在许多方面都比人更智能，并且能够显著放大使用它们的人的产出。工作中最不可能的部分已经过去了;让我们了解 GPT-4 和 o3 等系统的科学见解来之不易，但会带我们走得很远。
AI will contribute to the world in many ways, but the gains to quality of life from AI driving faster scientific progress and increased productivity will be enormous; the future can be vastly better than the present. Scientific progress is the biggest driver of overall progress; it’s hugely exciting to think about how much more we could have.
AI 将以多种方式为世界做出贡献，但 AI 推动更快的科学进步和生产力的提高对生活质量的收益将是巨大的;未来可能比现在好得多。科学进步是整体进步的最大驱动力;想到我们可以拥有更多，真是令人兴奋。
In some big sense, ChatGPT is already more powerful than any human who has ever lived. Hundreds of millions of people rely on it every day and for increasingly important tasks; a small new capability can create a hugely positive impact; a small misalignment multiplied by hundreds of millions of people can cause a great deal of negative impact.
从某种意义上说，ChatGPT 已经比任何曾经活过的人类都更强大。数亿人每天都依赖它来完成越来越重要的任务;一个小的新功能可以产生巨大的积极影响;一个小的错位乘以数亿人，就会造成很大的负面影响。
2025 has seen the arrival of agents that can do real cognitive work; writing computer code will never be the same. 2026 will likely see the arrival of systems that can figure out novel insights. 2027 may see the arrival of robots that can do tasks in the real world.
2025 年见证了可以进行真正认知工作的代理的到来;编写计算机代码永远不会相同。2026 年可能会看到能够得出新见解的系统的到来。2027 年可能会看到可以在现实世界中执行任务的机器人的到来。
A lot more people will be able to create software, and art. But the world wants a lot more of both, and experts will probably still be much better than novices, as long as they embrace the new tools. Generally speaking, the ability for one person to get much more done in 2030 than they could in 2020 will be a striking change, and one many people will figure out how to benefit from.
更多的人将能够创建软件和艺术。但世界对两者的需求要多得多，只要他们接受新工具，专家可能仍然会比新手好得多。一般来说，一个人在 2030 年完成比 2020 年多得多的能力将是一个惊人的变化，许多人会弄清楚如何从中受益。
In the most important ways, the 2030s may not be wildly different. People will still love their families, express their creativity, play games, and swim in lakes.
在最重要的方面，2030 年代可能没有太大的不同。人们仍然会爱他们的家人，表达他们的创造力，玩游戏，在湖里游泳。
But in still-very-important-ways, the 2030s are likely going to be wildly different from any time that has come before. We do not know how far beyond human-level intelligence we can go, but we are about to find out.
但从仍然非常重要的方面来看，2030 年代可能会与以往任何时候都大不相同。我们不知道我们能超越人类水平的智能走多远，但我们即将找到答案。
In the 2030s, intelligence and energy—ideas, and the ability to make ideas happen—are going to become wildly abundant. These two have been the fundamental limiters on human progress for a long time; with abundant intelligence and energy (and good governance), we can theoretically have anything else.
在 2030 年代，智能和能源——想法以及实现想法的能力——将变得非常丰富。长期以来，这两者一直是人类进步的根本限制因素;凭借丰富的智慧和精力（以及良好的治理），我们理论上可以拥有其他任何东西。
Already we live with incredible digital intelligence, and after some initial shock, most of us are pretty used to it. Very quickly we go from being amazed that AI can generate a beautifully-written paragraph to wondering when it can generate a beautifully-written novel; or from being amazed that it can make live-saving medical diagnoses to wondering when it can develop the cures; or from being amazed it can create a small computer program to wondering when it can create an entire new company. This is how the singularity goes: wonders become routine, and then table stakes.
我们已经生活在令人难以置信的数字智能中，在最初的一些震惊之后，我们大多数人已经习惯了它。很快，我们从惊讶于 AI 可以生成一个写得很漂亮的段落，到想知道它什么时候可以写出一本写得很漂亮的小说;或者从惊讶于它可以做出挽救生命的医学诊断，到想知道它何时可以开发出治疗方法;或者从惊讶它可以创建一个小型计算机程序到想知道何时可以创建一家全新的公司。奇点就是这样：奇迹成为例行公事，然后是赌注。
We already hear from scientists that they are two or three times more productive than they were before AI. Advanced AI is interesting for many reasons, but perhaps nothing is quite as significant as the fact that we can use it to do faster AI research. We may be able to discover new computing substrates, better algorithms, and who knows what else. If we can do a decade’s worth of research in a year, or a month, then the rate of progress will obviously be quite different.
我们已经从科学家那里听说，他们的工作效率比 AI 之前高出两到三倍。高级 AI 很有趣，原因有很多，但也许没有什么比我们可以使用它来更快地进行 AI 研究这一事实更重要的了。我们也许能够发现新的计算基础、更好的算法，谁知道还能发现什么。如果我们能在一年或一个月内完成十年的研究，那么进展速度显然会大不相同。
From here on, the tools we have already built will help us find further scientific insights and aid us in creating better AI systems. Of course this isn’t the same thing as an AI system completely autonomously updating its own code, but nevertheless this is a larval version of recursive self-improvement.
从这里开始，我们已经构建的工具将帮助我们找到进一步的科学见解，并帮助我们创建更好的 AI 系统。当然，这与 AI 系统完全自主更新自己的代码不是一回事，但无论如何，这是递归自我改进的幼虫版本。
There are other self-reinforcing loops at play. The economic value creation has started a flywheel of compounding infrastructure buildout to run these increasingly-powerful AI systems. And robots that can build other robots (and in some sense, datacenters that can build other datacenters) aren’t that far off.
还有其他自我强化的循环在起作用。经济价值创造已经开始了复合基础设施建设的飞轮，以运行这些日益强大的 AI 系统。可以构建其他机器人的机器人（从某种意义上说，可以构建其他数据中心的数据中心）并不遥远。
If we have to make the first million humanoid robots the old-fashioned way, but then they can operate the entire supply chain—digging and refining minerals, driving trucks, running factories, etc.—to build more robots, which can build more chip fabrication facilities, data centers, etc, then the rate of progress will obviously be quite different.
如果我们必须用老式的方式制造第一百万个人形机器人，但随后它们可以作整个供应链——挖掘和提炼矿物、驾驶卡车、经营工厂等——以制造更多的机器人，从而建造更多的芯片制造设施、数据中心等，那么进度显然会大不相同。
As datacenter production gets automated, the cost of intelligence should eventually converge to near the cost of electricity. (People are often curious about how much energy a ChatGPT query uses; the average query uses about 0.34 watt-hours, about what an oven would use in a little over one second, or a high-efficiency lightbulb would use in a couple of minutes. It also uses about 0.000085 gallons of water; roughly one fifteenth of a teaspoon.)
随着数据中心生产实现自动化，智能成本最终应该会趋同到接近电力成本。（人们经常好奇 ChatGPT 查询使用多少能源;平均查询使用大约 0.34 瓦时，大约是烤箱在一秒多一点内使用量，或者一个高效灯泡在几分钟内使用量。它还使用大约 0.000085 加仑的水;大约是十五分之一茶匙。
The rate of technological progress will keep accelerating, and it will continue to be the case that people are capable of adapting to almost anything. There will be very hard parts like whole classes of jobs going away, but on the other hand the world will be getting so much richer so quickly that we’ll be able to seriously entertain new policy ideas we never could before. We probably won’t adopt a new social contract all at once, but when we look back in a few decades, the gradual changes will have amounted to something big.
技术进步的速度将继续加快，人们几乎能够适应任何事情。会有一些非常困难的部分，比如所有类别的工作岗位都会消失，但另一方面，世界将变得更加丰富，如此之快，以至于我们将能够认真接受我们以前从未有过的新政策想法。我们可能不会一下子采用新的社会契约，但当我们在几十年后回顾时，逐渐的变化将产生重大变化。
If history is any guide, we will figure out new things to do and new things to want, and assimilate new tools quickly (job change after the industrial revolution is a good recent example). Expectations will go up, but capabilities will go up equally quickly, and we’ll all get better stuff. We will build ever-more-wonderful things for each other. People have a long-term important and curious advantage over AI: we are hard-wired to care about other people and what they think and do, and we don’t care very much about machines.
如果以史为鉴，我们会找出新的事情要做，新的事情要想，并迅速吸收新的工具（工业革命后的工作变动就是一个很好的近期例子）。期望值会提高，但功能也会同样迅速地提高，我们都会得到更好的东西。我们将为彼此创造更美好的事物。与 AI 相比，人类有一个长期的重要和奇特的优势：我们天生就关心其他人以及他们的想法和行为，而我们不太关心机器。
A subsistence farmer from a thousand years ago would look at what many of us do and say we have fake jobs, and think that we are just playing games to entertain ourselves since we have plenty of food and unimaginable luxuries. I hope we will look at the jobs a thousand years in the future and think they are very fake jobs, and I have no doubt they will feel incredibly important and satisfying to the people doing them.
一千年前的自给自足的农民会看着我们许多人的所作所为，说我们有假工作，认为我们只是在玩游戏来娱乐自己，因为我们有充足的食物和难以想象的奢侈品。我希望我们在一千年后看待这些工作，认为它们是非常虚假的工作，我毫不怀疑它们会让从事这些工作的人感到极其重要和满意。
The rate of new wonders being achieved will be immense. It’s hard to even imagine today what we will have discovered by 2035; maybe we will go from solving high-energy physics one year to beginning space colonization the next year; or from a major materials science breakthrough one year to true high-bandwidth brain-computer interfaces the next year. Many people will choose to live their lives in much the same way, but at least some people will probably decide to “plug in”.
新奇迹的实现速度将是巨大的。今天甚至很难想象到 2035 年我们会发现什么;也许我们会从前一年解决高能物理学到明年开始太空殖民;或者从前一年的材料科学重大突破到明年真正的高带宽脑机接口。许多人会选择以大致相同的方式生活，但至少有些人可能会决定 “插入”。
Looking forward, this sounds hard to wrap our heads around. But probably living through it will feel impressive but manageable. From a relativistic perspective, the singularity happens bit by bit, and the merge happens slowly. We are climbing the long arc of exponential technological progress; it always looks vertical looking forward and flat going backwards, but it’s one smooth curve. (Think back to 2020, and what it would have sounded like to have something close to AGI by 2025, versus what the last 5 years have actually been like.)
展望未来，这听起来很难理解。但可能会经历它会让人印象深刻但可控。从相对论的角度来看，奇点是一点一点发生的，而合并是缓慢发生的。我们正在攀登指数级技术进步的漫长弧线;它向前看总是垂直的，向后看起来总是平坦的，但它是一条平滑的曲线。（回想一下 2020 年，到 2025 年接近 AGI 的声音听起来会是什么样子，与过去 5 年的实际情况相比。
There are serious challenges to confront along with the huge upsides. We do need to solve the safety issues, technically and societally, but then it’s critically important to widely distribute access to superintelligence given the economic implications. The best path forward might be something like:
除了巨大的好处外，还有严峻的挑战需要面对。我们确实需要在技术和社会上解决安全问题，但考虑到经济影响，广泛分发超级智能的访问至关重要。最好的前进路径可能是这样的：
解决对齐问题，这意味着我们可以有力地保证我们让 AI 系统长期学习并采取行动，以实现我们集体真正想要的东西（社交媒体信息流是错位 AI 的一个例子;为这些提供支持的算法在让您继续滚动并清楚地了解您的短期偏好方面令人难以置信， 但它们是通过利用你大脑中的东西来做到这一点的，这些东西会凌驾于你的长期偏好之上）。
然后专注于使超级智能便宜、广泛可用，并且不要过于集中在任何人、公司或国家。社会具有弹性、创造力和快速适应能力。如果我们能够利用人们的集体意志和智慧，那么尽管我们会犯很多错误，有些事情会真的出错，但我们会很快学习和适应，并能够使用这项技术来获得最大的优势和最小的劣势。在社会必须决定的广泛范围内，给用户很大的自由似乎非常重要。世界越早开始讨论这些广泛的界限是什么以及我们如何定义集体联盟越好。
We (the whole industry, not just OpenAI) are building a brain for the world. It will be extremely personalized and easy for everyone to use; we will be limited by good ideas. For a long time, technical people in the startup industry have made fun of “the idea guys”; people who had an idea and were looking for a team to build it. It now looks to me like they are about to have their day in the sun.
我们（整个行业，不仅仅是 OpenAI）正在为世界构建大脑。它将非常个性化，每个人都易于使用;我们将受到好主意的限制。长期以来，创业行业的技术人员一直在取笑 “创意家伙”;他们有一个想法并正在寻找一个团队来构建它。现在在我看来，他们即将在阳光下度过他们的一天。
OpenAI is a lot of things now, but before anything else, we are a superintelligence research company. We have a lot of work in front of us, but most of the path in front of us is now lit, and the dark areas are receding fast. We feel extraordinarily grateful to get to do what we do.OpenAI
现在有很多东西，但首先，我们是一家超级智能研究公司。我们面前有很多工作要做，但眼前的大部分路现在都已经亮了，黑暗区域正在迅速消退。我们非常感激能够做我们所做的工作。
Intelligence too cheap to meter is well within grasp. This may sound crazy to say, but if we told you back in 2020 we were going to be where we are today, it probably sounded more crazy than our current predictions about 2030.
情报太便宜了，无法衡量，完全触手可及。这听起来可能很疯狂，但如果我们在 2020 年告诉你，我们会达到今天的水平，这听起来可能比我们目前对 2030 年的预测更疯狂。
May we scale smoothly, exponentially and uneventfully through superintelligence.
愿我们通过超级智能顺利、指数级、平安无事地扩展。
