Sam Altman 最新雄文： 《温和的奇点》

文章作者:Sam Altman

文章来源:X平台

文章编译:Vivian, MetaEra

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.

我们已经越过了「事件视界」,起飞已然开始。人类正接近构建数字超级智能(superintelligence),而且,至少到目前为止,这一切并未如想象中那般奇异,反而显得出奇地平缓。

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.

机器人尚未上街,大多数人也还未整日与 AI 交流。人们仍因疾病去世,我们依然难以自由进出太空,对于宇宙,我们仍知之甚少。

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.

但我们最近已经构建出在许多方面比人类更聪明的系统,并能够显著增强使用者的产出效率。那些最不可思议的部分已经成为过去;支撑 GPT-4 和 o3 等系统的科学洞察来之不易,却将带我们走得更远。

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 将以多种方式推动世界发展,但它通过加速科学进步和提升生产力所带来的生活质量提升将尤为巨大;未来可能远胜于现在。科学进步是整体进步的最大引擎;想象我们可以实现的更多成果,令人振奋不已。

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.

从某种宏观意义上讲,ChatGPT 已经比人类历史上任何个体都更强大。每天有数亿人依赖它,并用于越来越重要的任务;一个微小的新功能可以带来极大的积极影响;但若有细微的「错误对齐(misalignment)」,在数亿人规模下也可能造成严重的负面后果。

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 年我们已见证具备真实认知能力的 AI 智能体出现;编程的方式将彻底改变。2026 年我们可能会看到能自主发现新见解的系统,2027 年可能会出现能在现实中执行任务的机器人。

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.

将有更多人能够创作软件和艺术。但世界对这两者的需求也将水涨船高。只要专家拥抱新工具,他们仍将远胜于新手。总体来看,到了 2030 年,个人的产出能力相比 2020 年将实现显著提升,这一转变将为许多人带来新的机遇。

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.

在最核心的方面,2030 年代或许并不会与现在大相径庭。人们依旧会爱家人、释放创意、玩游戏、在湖中游泳。

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.

但在其他依然重要的方面,2030 年代极可能与历史上的任何时期大相径庭。我们尚不知能走多远超越人类智能的边界,但我们即将得知答案。

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——思想,以及实现思想的能力——将变得极其丰富。这两者长期以来一直是人类进步的根本限制;有了丰富的智能和能源(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.

实际上我们已经与令人惊叹的数字智能共处。在初期震撼之后,大多数人已渐趋习惯。从惊叹 AI 能生成优美段落,到好奇它何时能写出一整部小说;从惊叹它能诊断疾病,到期待它能研发治愈方法;从惊叹它能写出程序,到设想它何时能创建一家公司。这就是奇点演化的过程:奇迹将逐渐变成日常,随后成为「入局的基本筹码(table stakes)」。

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.

科学家们反馈,他们的生产力较 AI 出现前提升了两到三倍。先进 AI 值得关注的原因很多,但或许最关键的是它能加速 AI 自身的研究。我们可能将发现新的计算载体、更优算法,甚至更多未知突破。如果能用一年,甚至一个月完成过去十年的研究,进步速度将发生实质性改变。

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.

从现在开始,我们所构建的工具将帮助我们获得更多科学洞见,助力构建更强大的 AI 系统。当然,这还不等同于 AI 完全自主更新自身代码,但这无疑是递归式自我改进的雏形。

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.

有其他自我增强的循环机制在运作。经济价值的创造启动了基础设施建设的「飞轮」,以支撑这些日益强大的 AI 系统。而能够制造其他机器人的机器人(以及从某种意义上说,能建造其他数据中心的数据中心)离现实并不遥远。

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.

如果我们不得不以传统方式制造出第一百万个人形机器人,但之后它们可以运营整个供应链——挖掘和提炼矿物、驾驶卡车、运营工厂等——来制造更多机器人,这些机器人可以建造更多的芯片制造设施、数据中心等,那么进步的速度显然会大不相同。

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.)

随着数据中心生产的自动化,智能的成本最终应该会趋近于电力的成本。(人们经常好奇ChatGPT查询消耗多少能量;平均每次查询消耗约0.34瓦时,大约是烤箱在一秒多一点的时间内消耗的能量,或者高效灯泡在几分钟内消耗的能量。它还消耗约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,人们将继续能够适应几乎任何事物。会有非常困难的部分,比如整类工作岗位的消失,但另一方面,世界将如此迅速地变得更加富裕,以至于我们将能够认真考虑以前从未有过的新的政策理念。我们可能不会一下子采纳一个新的社会契约,但当我们几十年后回顾时,这些渐进的变化将汇聚成巨大的成就。

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.

如果历史可以作为参考,我们将找到新的事情去做,新的东西去追求,并迅速吸收新的工具(工业革命后的工作变革就是最近的一个好例子)。期望会提高,但能力也会同样迅速地提高,我们都将得到更好的东西。我们将为彼此建造越来越美妙的东西。与人工智能相比,人类有一个长期的重要而有趣的优势:我们天生就关心其他人以及他们所想所做的事,而我们并不太在乎机器。

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所做的事情,说我们有「假工作」,并认为我们只是在玩游戏自娱自乐,因为我们有充足的食物和难以想象的奢侈品。我希望我们能在一千年后看待未来的工作,并认为它们是非常「假」的工作,我毫不怀疑它们会给从事这些工作的人带来难以置信的重要感和满足感。

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」.

新奇迹实现的速率将是巨大的。今天甚至很难想象到2035年我们会发现什么;也许我们会从一年解决高能物理问题到下一年开始太空殖民;或者从一年取得重大材料科学突破到下一年实现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「接入」。

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,我们可能难以理解这一切。但经历这一切可能会让人感到印象深刻但又可以应对。从相对论的角度来看,奇点是在一点一滴中发生的,融合也是在慢慢进行的。我们正在攀登指数级技术进步的长弧; 向前看总是垂直的,向后看总是平坦的,但它其实是一个平滑的曲线。(回想一下2020年的情况,想象一下到2025年拥有接近通用人工智能会是什么样子,然后再看看过去5年实际的情况是怎样的。)

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:

伴随着巨大的优势,也有严峻的挑战需要面对。我们确实需要从技术和社会层面解决安全问题,但考虑到经济影响,广泛分配超级智能的访问权限至关重要。最佳前进路径可能如下:

1. Solve the alignment problem, meaning that we can robustly guarantee that we get AI systems to learn and act towards what we collectively really want over the long-term (social media feeds are an example of misaligned AI; the algorithms that power those are incredible at getting you to keep scrolling and clearly understand your short-term preferences, but they do so by exploiting something in your brain that overrides your long-term preference).
2. Then focus on making superintelligence cheap, widely available, and not too concentrated with any person, company, or country. Society is resilient,创意, and adapts quickly. If we can harness the collective will and wisdom of people, then although we’ll make plenty of mistakes and some things will go really wrong, we will learn and adapt quickly and be able to use this technology to get maximum upside and minimal downside. Giving users a lot of freedom, within broad bounds society has to decide on, seems very important. The sooner the world can start a conversation about what these broad bounds are and how we define collective alignment, the better.



1. 解决对齐问题,这意味着我们可以可靠地保证人工智能系统能够学习并长期朝着我们集体真正想要的方向行动(社交媒体信息流就是未对齐人工智能的一个例子;驱动这些信息流的算法在让你不断滚动方面表现出色,并且清楚地理解你的短期偏好,但它们通过利用你大脑中某种覆盖你长期偏好的东西来实现这一点)。
2. 然后专注于让超级智能变得廉价、广泛可用,并且不要过于集中在任何个人、公司或国家手中。社会具有韧性、创造力,并且适应迅速。如果我们能够利用集体的意志和智慧,那么尽管我们会犯很多错误,有些事情会非常糟糕,但我们将迅速学习和适应,并能够利用这项技术获得最大的好处和最小的坏处。在社会必须决定的广泛范围内给予用户很大的自由,这似乎非常重要。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对齐,就越好。

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)正在为世界构建一个大脑。它将极其个性化,并且每个人都易于使用;我们将受到好想法的限制。长期以来,创业行业的技术人员一直嘲笑「点子男」;那些有想法并正在寻找团队来构建它的人。现在在我看来,他们即将迎来他们的辉煌时刻。

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现在涵盖了很多领域,但首先我们是一家超级智能研究公司。我们面前还有很多工作要做,但我们前进的道路现在已经照亮了,那些黑暗的区域正在迅速消失。我们为能做我们现在所做的事情感到非常感激。

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.

智能技术唾手可得。这听起来可能有些疯狂,但如果我们在2020年告诉你,我们现在的情况会是这样的话,那可能比我们现在对2030年的预测还要疯狂。

May we scale smoothly, exponentially and uneventfully through superintelligence.

愿我们能够顺利、快速且平稳地迈向超级智能。