讀完〈The Gentle Singularity〉讓我有點驚訝，沒想到 Altman 的文筆這麼柔軟，和新聞裡那個科技大亨的形象很不一樣。
整篇文章讓人心暖暖的，誠實又溫和：）
中英文對照部分使用 ChatGPT-4o 翻譯整理。
「人們仍會愛家人、展現創造力、玩遊戲、在湖裡游泳。」
—〈溫和的奇點〉Sam Altman
Sam Altman blog 原文：
https://blog.samaltman.com/the-gentle-singularity
作者簡介：山姆·阿特曼 Sam Altman
山姆·阿特曼（Sam Altman, 1985/4/22 生於芝加哥），美國企業家與投資人，自 2019 年起擔任 OpenAI 執行長（2023 年曾短暫被免職，但迅速復職）。
在 Altman 領導下，OpenAI 推出 ChatGPT、GPT‑4、最新的 GPT‑4o、GPT‑4.5 等突破性模型，並與蘋果、微軟合作緊密。他也倡導 AI 安全管治、普及與去集中化，並呼籲建立共同的倫理邊界。
The Gentle Singularity - Sam Altman
溫和的奇點 - 山姆·阿特曼
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.
我們已經跨過了事件視界，起飛已經開始。人類距離打造數位超級智慧已經非常接近，而到目前為止，這件事看起來遠比我們原本預期的「奇怪」程度要來得溫和。
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.
街道上還沒出現會走路的機器人，大多數人也還不是整天在跟 AI 對話。人們仍然會因疾病死亡，我們依然無法輕易前往太空，對宇宙的理解也還有許多空白。
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.
但我們最近打造出的系統，在許多面向上都比人類更聰明，並且能大幅提升使用者的產出效率。最難的部分已經過去了——讓我們抵達 GPT-4 和 o3 這類系統的科學洞見，是經過極度艱難的努力換來的，但這些突破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 將在世界各地產生各種影響，但透過 AI 加速科學進展與提高生產力，帶來的生活品質提升將是巨大的。未來有機會比現在好上非常多。科學進步一直是人類整體進步的最大驅動力，想像我們可能達成的成果，真的讓人非常興奮。
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.
某種意義上，ChatGPT 已經比歷史上任何一位人類都還要強大。數億人每天依賴它處理各式各樣、越來越重要的任務。一個微小的新能力，就可能帶來極大的正面效益；但一點點的偏差，乘上數億使用者，也可能造成非常大的負面影響。
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，我們已經迎來能夠執行真正認知工作的 AI agent；寫程式這件事，將從此不同。2026 年，很可能會出現能夠推導新洞見的系統；2027 年，則可能會出現能在真實世界中完成任務的機器人。
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.
更多人將能夠創造軟體與藝術作品。但世界對這些東西的需求也在增加，專業人士如果願意擁抱新工具，仍然會比新手強得多。整體來看，一個人在 2030 年所能完成的工作量，將遠超過 2020 年，這會是一個驚人的改變，也會讓許多人受惠。
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.
從某些最根本的層面來看，2030 年代也許不會和現在差太多。人們仍會愛家人、展現創造力、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.
但從某些同樣重要的角度來看，2030 年代將會與歷史上的任何時代截然不同。我們還不知道人類智慧的極限在哪裡，但我們很快就會知道了。
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.
在 2030 年代，「智慧」與「能源」——也就是「點子」與「實現點子的能力」——都將變得極度豐富。這兩者長久以來都是人類進步的主要限制因素；而當智慧與能源都變得充足（再加上良好的治理），我們理論上就有可能擁有其他一切。
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。經歷最初的震撼後，大多數人很快就習慣了。我們很快會從「哇！AI 居然能寫出這麼漂亮的段落！」轉變為「它什麼時候能寫出整本小說？」從「它能診斷病症，真神奇！」變成「那它什麼時候能開發出治療方法？」從「它會寫小程式」變成「它什麼時候能創建一整家公司？」這就是奇點的節奏：奇蹟變成日常，而後成為門檻（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.
現在就已經有科學家表示，AI 讓他們的生產力提升兩到三倍。高階 AI 有很多吸引人的地方，但也許最關鍵的是：我們可以用 AI 來加速 AI 的研究。我們有可能發現新的計算基礎材料、更好的演算法，甚至未知的其他東西。如果我們能在一年內完成十年的研究量，甚至在一個月內，那麼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.
從現在起，我們已經打造出來的工具，將幫助我們發現更多科學洞見，也協助我們創造出更好的 AI 系統。當然，這和「AI 自主地更新自己的程式碼」還不完全一樣，但某種程度上，這已經是自我優化（recursive self-improvement）的幼蟲期版本。
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.
還有其他正在發酵的自我強化循環。AI 所創造的經濟價值，已經啟動了一個基礎建設的正向飛輪，用來支撐這些越來越強大的 AI 系統。讓機器人生產機器人（某種意義上，也讓資料中心打造資料中心）這件事，其實已經不算遙遠了。
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.
即使我們得用「老方法」先製造出第一百萬個人形機器人，只要這些機器人之後能操作整個供應鏈——從挖礦、精煉、運輸、到工廠作業等等——接著再生產出更多機器人、晶片廠、資料中心…… 那麼，進步的速度就會呈現爆炸性增長。
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.)
當資料中心的建造過程也被自動化之後，智慧的成本最終將趨近於電力成本。（很多人會好奇 ChatGPT 一次查詢到底耗多少電：平均一次查詢大約耗 0.34 瓦時，差不多是一台烤箱開一秒鐘，或是一顆高效率燈泡亮兩分鐘左右。至於耗水量，大約是 0.000085 加侖，也就是差不多 1/15 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.
科技進步的速度將持續加快，而人類也會如過往一樣，展現出強大的適應能力。會有一些非常艱難的部分，比如整個職業類別消失，但另一方面，世界也將快速變得更加富裕，使得我們能認真討論以往根本無法想像的政策選項。我們大概不會在一夕之間徹底改寫社會契約，但當我們回顧幾十年後的變化，這些逐步的小調整，總量可能會非常巨大。
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.
如果歷史可以作為參考，我們總是會發明出新的事物、新的需求，也會很快習慣新工具。（產業革命後的就業轉變，就是一個很接近的例子。）人們的期待會提升，但能力也會同步增強，我們將能擁有更多更棒的事物，也會為彼此創造出越來越精彩的世界。人類對 AI 有一個長期且本質上的優勢：我們天生就會關心他人，以及他人所想與所做的事，但我們並不太在意機器本身。
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.
如果你讓一位一千年前的自耕農來看現代人的工作，他們可能會覺得我們的工作根本是假的，覺得我們只是在玩耍，因為我們早就不愁吃穿，過著他們無法想像的奢侈生活。我希望一千年後的人類在看他們自己的工作時，也能讓我們感覺：「好像有點假」，但我毫不懷疑，那些工作對當事人而言，會是極為重要且充滿成就感的。
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”.
未來將充滿源源不絕的新奇事物。很難想像到了 2035 年我們會發現什麼——也許某一年我們突破了高能物理，隔年就開始太空殖民；或者某年材料科學大突破，下一年我們就有真正高頻寬的腦機介面。許多人還是會選擇照常過日子，但也會有一些人，選擇「插上去」。
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，這些變化聽起來可能令人難以消化。但實際體驗起來，可能會讓人覺得震撼又可控。從「相對論式的觀點」來看，奇點是逐步發生的，而人機融合也是緩慢進行的。我們正沿著科技進步的長曲線往上爬；當你向前看，它總像是垂直的；當你回頭看，又覺得它幾乎是平的；但事實上，它一直是一條平滑的曲線。（想想 2020 年，如果那時有人說我們將在 2025 年接近通用人工智慧（AGI），聽起來可能超級瘋狂。但現在回頭看過去五年的發展，實際上我們真的快到了。）
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:
當然，這一切背後也存在嚴峻的挑戰，與龐大的潛在好處並存。我們的確需要解決安全問題，不論是技術上的，還是社會制度上的。而一旦安全問題得到處理，如何讓超智慧的存取權普及化就成為關鍵，畢竟這會帶來巨大的經濟與權力影響。最理想的路徑可能長這樣：
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 AI 對齊問題（alignment problem）——也就是要能夠穩定地讓 AI 系統學習、並朝著我們「長期真正想要的東西」行動。（舉例來說，社群媒體演算法其實就是錯誤對齊的例子：它們非常擅長抓住你「短期的偏好」，讓你不停滑手機，但這種方式，是透過操弄你的大腦來壓過你「長期的選擇」。）
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.
讓超智慧變得便宜、普及、不集中。它不應只被某個人、某家公司或某個國家所壟斷。人類社會有極強的彈性、創造力與適應力。只要我們能動員起足夠的集體智慧與意志，即使會犯很多錯、甚至出現災難性的問題，我們還是有機會快速學習、快速修正，讓科技發揮最大效益，並將負面影響降到最低。讓使用者擁有高度自由，在一個由社會決定的廣泛邊界內行動，是非常重要的。越早啟動「什麼是社會共識的邊界」與「我們如何定義集體對齊」的全球對話，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）正在打造一個「世界的大腦」。它將極度個人化，而且人人都能輕鬆使用；未來的限制將不再是技術，而是「好點子」。長久以來，創投圈常拿「只有點子、找不到團隊的創業者」開玩笑，說他們不實際。但現在看來，這些「點子人」的時代要來了。
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 現在做的事情很多，但最核心的身份，仍是一家超智慧研究公司。我們前面還有很多工作要做，但大部分的路已經被照亮，那些未知的黑暗角落也正在快速消退。我們對自己能參與這樣的工作，懷抱極大的感恩與敬畏。
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.
「智慧便宜到不像話」的時代，已經近在咫尺。這聽起來也許很瘋狂——但如果我們在 2020 年就告訴你，幾年後會走到今天這一步，那時候聽起來會比我們現在預測 2030 年的情況還要更瘋狂。
May we scale smoothly, exponentially and uneventfully through superintelligence.
願我們平穩、指數式、無事故地邁向超智慧時代。
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