將 Sam Altman 的這篇 Blog 翻譯成繁體中文，方便閱讀，用中英文對照的方式，覺得中文怪怪的時候，可以再回頭去看英文的原文。 繁體中文 generated by Perplexity。廢話不多說，開始看吧。
原文在這裡：這裡。要用聽的來這裡 : 我是這裡。要看影片的可以看看這裡：我也是這裡。
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.
我們已經越過了事件視界；起飛已經開始。人類距離建造數位超級智慧已經很接近了，而且到目前為止，這一切比想像中要不那麼奇怪。
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.
機器人還沒有在街上行走，大多數人也還不是整天和 AI 對話。人們依然會因疾病而死，我們仍無法輕鬆前往太空，對宇宙的許多事物依然一知半解。
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，我們最近已經建造出在許多方面比人類更聰明的系統，並且能大幅提升使用者的產出。最難以置信的部分已經過去；讓我們能擁有像 GPT-4 和 o3 這樣系統的科學洞見得來不易，但將帶領我們走得更遠。
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 將以多種方式貢獻世界，但 AI 推動更快科學進步和提升生產力所帶來的生活品質提升將是巨大的；未來可以比現在好得多。科學進步是整體進步的最大動力；想像我們還能擁有多少令人振奮的成就。
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.
在某種意義上，ChatGPT 已經比歷史上任何一個人都更強大。每天有數億人依賴它，且用於越來越重要的任務；一項小小的新能力就能帶來巨大的正面影響；而一點點的偏差，乘以數億人，則可能造成極大的負面影響。
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 年已經出現能夠執行真正認知工作的代理人；寫電腦程式的方式將徹底改變。2026 年很可能會出現能夠發現新見解的系統。2027 年則可能會出現能在現實世界執行任務的機器人。
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.
更多人將能夠創作軟體和藝術。但世界對這兩者的需求也會更大，只要專家們擁抱新工具，他們很可能仍然遠勝新手。總體來說，到了 2030 年，一個人能完成的事情將遠超 2020 年，這將是令人矚目的改變，許多人也會學會如何從中受益。
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.
在最重要的層面上，2030 年代也許並不會有天翻地覆的變化。人們依然會愛家人、展現創意、玩遊戲、在湖裡游泳。
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.
但在其他同樣重要的層面上，2030 年代很可能會與以往任何時代截然不同。我們不知道能超越人類智慧多遠，但很快就會知道了。
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.
到了 2030 年代，智慧和能量——也就是創意與實現創意的能力——將會極度豐富。這兩者長久以來一直是人類進步的根本限制；擁有充足的智慧和能量（以及良好的治理），理論上我們就能擁有其他一切。
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.
我們已經與令人難以置信的數位智慧共處，經過最初的震驚後，大多數人都已經習慣。很快地，我們從驚嘆 AI 能寫出漂亮段落，變成期待它何時能寫出一部精美小說；從驚嘆它能做出救命醫學診斷，變成期待它何時能研發出治療方法；從驚嘆它能寫出小型程式碼，變成期待它何時能創建新公司。這就是奇點的進程：奇蹟變成日常，然後成為基本門檻。
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.
我們已經聽到科學家說，他們的生產力是 AI 出現前的兩到三倍。先進的 AI 有許多有趣之處，但也許最重要的是我們能用它來加速 AI 研究。我們或許能發現新的運算基礎、改良演算法，甚至更多未知的事物。如果我們能在一年、甚至一個月內完成十年的研究，進步的速度顯然會截然不同。
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.
從現在開始，我們已經建造的工具將協助我們獲得更多科學洞見，並幫助我們創造更好的 AI 系統。當然，這還不是 AI 完全自主更新自身程式碼，但這已經是遞迴自我改進的初期形態。
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.
還有其他自我強化的循環正在發生。經濟價值的創造已經啟動了基礎設施建設的飛輪效應，以運行這些越來越強大的 AI 系統。而能建造其他機器人的機器人（某種程度上，能建造其他資料中心的資料中心）也不再遙遠。
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.
如果我們必須用傳統方式製造第一百萬個人形機器人，但之後它們能操作整個供應鏈——挖礦、提煉、開卡車、運作工廠等——來製造更多機器人，這些機器人又能建造更多晶片廠、資料中心等，那麼進步的速度顯然會完全不同。
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.)... 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.
隨著資料中心生產自動化，智慧的成本最終應該會接近電力成本。（人們常好奇 ChatGPT 查詢耗多少能量；平均一個查詢約 0.34 瓦時，約等於烤箱一秒多一點的用電，或高效能燈泡幾分鐘的用電。也會用掉約 0.000085 加侖水，大約是十五分之一茶匙。）……技術進步的速度將持續加快，人們也會持續展現出對幾乎任何事物的適應力。會有很艱難的部分，比如整個職業類別的消失，但另一方面世界會迅速變得更富裕，讓我們能嚴肅討論以前從未考慮過的新政策。我們或許不會一下子採納新的社會契約，但幾十年後回顧，這些漸進的變化將累積成巨大的轉變。
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.
如果歷史可以作為借鏡，我們會找到新的事物去做、學會新的渴望，並迅速吸收新工具（工業革命後的職業轉變就是很好的例子）。期望會提升，但能力也會同樣快速提升，我們都會獲得更好的東西。我們會為彼此創造越來越美好的事物。人類相較於 AI 有一個長期且有趣的優勢：我們天生在意他人及其想法和行為，而對機器則不太在意。
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.
一千年前的自給自足農民看到我們現在的工作，會說我們做的是假工作，覺得我們只是在玩遊戲自娛，因為我們有充足的食物和難以想像的奢侈品。我希望我們將來看到一千年後的工作時，也會覺得那些是很假的工作，但我毫不懷疑，對於那些從事這些工作的人來說，這些工作會顯得極其重要且令人滿足。
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”.
新奇蹟誕生的速度將會極為驚人。如今我們很難想像到了 2035 年我們會發現什麼；也許某年解決高能物理問題，隔年就開始太空殖民；或某年有重大材料科學突破，隔年就有真正的高頻寬腦機介面。許多人會選擇過著和現在差不多的生活，但至少有些人可能會選擇「接入」新世界。
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.)
展望未來，這一切聽起來很難理解。但實際經歷時，可能會覺得既令人驚嘆又能應付。從相對論的角度看，奇點是一點一滴發生，融合也是慢慢進行。我們正攀登著技術指數成長的長弧線；往前看總覺得是垂直的，往回看又像是平坦的，但其實是一條平滑的曲線。（想想 2020 年時，如果有人說 2025 年會有接近 AGI 的東西，和過去五年實際發生的事相比，會有多不可思議。）
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:
除了巨大的好處，還有嚴峻的挑戰需要面對。我們確實需要在技術和社會層面解決安全問題，但考慮到經濟影響，讓超級智慧的使用權廣泛分配極為重要。最佳的前進路徑可能如下：
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).
解決對齊問題，也就是我們能有力保證 AI 系統能長期學習並朝著我們集體真正想要的目標行動（社群媒體動態就是 AI 未對齊的例子；這些演算法非常擅長讓你一直滑動，並明確掌握你的短期偏好，但卻是透過利用你大腦中會壓倒長期偏好的機制來達成的）。
Then focus on making superintelligence cheap, widely available, and not too concentrated with any person, company, or country. Society is resilient, creative, 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.
接著要讓超級智慧變得便宜、普及，且不會過度集中在任何個人、公司或國家。社會具有韌性、創造力並能快速適應。如果我們能利用人類的集體意志和智慧，雖然會犯很多錯，有些事情會出大問題，但我們會快速學習和適應，並善用這項技術以獲得最大好處、最小壞處。在社會決定的廣泛界限內，給用戶大量自由顯得非常重要。世界越早開始討論這些界限是什麼、如何定義集體對齊，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.
我們（整個產業，不只是 OpenAI）正在為世界建造一個大腦。它將極度個人化，人人都能輕鬆使用；我們將只受限於好點子。長期以來，新創圈的技術人總是嘲笑「點子王」——只有點子，卻要找團隊來實現的人。現在看來，他們的時代即將到來。
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.... 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.
OpenAI 現在身兼多重角色，但最根本的身份是超級智慧研究公司。我們還有很多工作要做，但前方的道路大多已經點亮，黑暗的區域正迅速消退。我們對能做這些事感到無比感激……智慧便宜到不值一提的時代近在咫尺。這聽起來或許很瘋狂，但如果 2020 年時我們說今天會到這個地步，可能比我們現在對 2030 年的預測還要瘋狂。
May we scale smoothly, exponentially and uneventfully through superintelligence.
願我們能順利、指數式且平穩地邁向超級智慧的未來。
