Thank you for that kind introduction, but I hate hearing about myself.

謝謝你的好意介紹，但我討厭聽人介紹自己。

And just as a show, well, maybe if you could just applaud.

就像表演一樣，也許你可以鼓鼓掌。

How many of you know who NVIDIA is?

有多少人知道英偉達是誰？

And how many of you know what a GPU is?

有多少人知道 GPU 是什麼？

Okay, good, I don't have to change my speech.

好的，很好，我不用改變我的演講了。

Ladies and gentlemen, President Rosenbaum, esteemed faculty members, distinguished guests, proud parents, and above all, the 2024 graduating class of Caltech.

女士們，先生們，羅森鮑姆校長，尊敬的教職員工，尊貴的來賓，自豪的家長們，最重要的是，加州理工學院 2024 屆畢業班的同學們。

This is a really happy day for you guys.

對你們來說，今天真是個高興的日子。

You got to look more excited.

你得看起來更興奮些。

You know you're graduating from Caltech.

你知道你就要從加州理工學院畢業了。

This is the school of the great Richard Feynman, Linus Pauling, and someone who is very influential to me and our industry, Carver Mead.

這是偉大的理查德-費曼（Richard Feynman）、萊納斯-鮑林（Linus Pauling）以及對我和我們行業影響深遠的卡佛-米德（Carver Mead）的學校。

Yeah, this is a very big deal.

是啊，這可是件大事。

Today is a day of immense pride and joy.

今天是一個無比自豪和喜悅的日子。

It is a dream come true for all of you, but not just for you, because your parents and families have made countless sacrifices to see you reach this milestone.

這對你們所有人來說都是夢想成真，但不僅僅是你們，因為你們的父母和家人為見證你們達到這個里程碑做出了無數犧牲。

So let's take this moment and congratulate them, thank them, and let them know you love them.

是以，讓我們藉此機會祝賀他們，感謝他們，讓他們知道你們愛他們。

You don't want to forget that because you don't know how long you're going to be living at home.

你不想忘記這一點，因為你不知道自己還要在家裡住多久。

You want to be super grateful today.

今天，你要心存感激。

As a proud parent, I really loved it when my kids didn't move out, and it was great to see them every day, but now they've moved out and it makes me sad.

作為一名自豪的家長，我非常喜歡孩子們沒有搬出去的日子，每天都能看到他們真是太好了，但現在他們搬出去了，這讓我很難過。

So hopefully you guys get to spend some time with your parents.

希望你們能多陪陪父母。

Your journey here is a testament of your character, determination, willingness to make sacrifices for your dreams, and you should be proud.

你們在這裡的旅程證明了你們的品格、決心和為夢想做出犧牲的意願，你們應該感到自豪。

The ability to make sacrifices, endure pain and suffering, you will need these qualities in life.

做出犧牲、忍受痛苦和折磨的能力，這些都是你在生活中需要的品質。

You and I share some things in common.

你和我有一些共同點。

First, both chief scientists of NVIDIA were from Caltech.

首先，英偉達公司的兩位首席科學家都來自加州理工學院。

And one of the reasons why I'm giving this speech today is because I'm recruiting.

我今天發表演講的原因之一是，我正在招募人員。

And so I want to tell you that NVIDIA is a really great company.

所以我想告訴大家，英偉達是一家非常偉大的公司。

I'm a very nice boss, universally loved, come work at NVIDIA.

我是個很好的老闆，人見人愛，來英偉達工作吧。

You and I share a passion for science and engineering, and although we're separated by about 40 years, we are both at the peaks of our career.

你和我都對科學和工程學充滿熱情，雖然我們相差約 40 歲，但都處於事業的巔峰期。

For all of you who have been paying attention to NVIDIA and myself, you know what I mean.

對於所有關注英偉達™（NVIDIA®）和我本人的人來說，你們都知道我的意思。

It's just that in your case, you'll have many, many more peaks to go.

只是就你的情況而言，你還有很多很多的山峰要爬。

I just hope that today is not my peak.

我只希望今天不是我的巔峰。

Not the peak.

不是峰值

And so I'm working as hard as ever to make sure that I have many more peaks ahead.

是以，我將一如既往地努力工作，以確保我的事業再攀高峰。

Last year, I was honored to give the commencement address at Taiwan University, and I shared several stories about NVIDIA's journey and the lessons that we learned that might be valuable for graduates.

去年，我有幸在臺灣大學畢業典禮上發表演講，分享了幾個關於英偉達發展歷程的故事，以及我們學到的可能對畢業生有價值的經驗。

I have to admit that I don't love giving advice, especially to other people's children.

我必須承認，我不喜歡給別人提建議，尤其是給別人的孩子。

And so my advice today will largely be disguised in some stories that I've enjoyed and some life experiences that I've enjoyed.

是以，我今天的建議在很大程度上將以我喜歡的一些故事和我喜歡的一些生活經歷來偽裝。

I'm the longest-running tech CEO in the world today, I believe.

我相信，我是當今世界上經營時間最長的科技公司首席執行官。

Over the course of 31 years, I managed not to go out of business, not get bored, and not get fired.

在 31 年的時間裡，我沒有倒閉，沒有感到厭倦，也沒有被解僱。

And so I have the great privilege of enjoying a lot of life's experiences, starting from creating NVIDIA from nothing to what it is today.

從創建 NVIDIA 開始，從一無所有到今天的成就，我有幸享受了人生的許多經歷。

And so I spoke about the long road of creating CUDA, a programming model.

是以，我談到了創建 CUDA（一種編程模型）的漫長道路。

The programming model that we dedicated over 20 years to invent, and that is revolutionizing computing today.

我們花了 20 多年時間發明的編程模型，正在徹底改變當今的計算方式。

I spoke about a very quite public canceled Sega game console project we worked on.

我談到了我們參與的一個非常公開的世嘉遊戲機取消項目。

And where intellectual honesty, something that I know Richard Feynman cares very deeply about and spoke quite often about, where intellectual honesty and humility saved our company.

我知道理查德-費曼（Richard Feynman）非常重視知識上的誠實，並經常談到這一點，知識上的誠實和謙遜拯救了我們的公司。

And how a retreat, a strategic retreat, was one of our best strategies.

務虛會，戰略務虛會，是我們最好的戰略之一。

All of these are counterintuitive lessons that I spoke about at the commencement.

所有這些都是我在開學典禮上談到的反直覺的教訓。

But I encouraged the graduates to engage with AI, the most consequential technology of our time.

但我鼓勵畢業生們接觸人工智能這一當代最具影響力的技術。

And I'll speak a little bit more about that later, but all of you know about AI.

我稍後會詳細談談這個問題，但你們都知道人工智能。

It's hard not to be immersed in it and surrounded by it, and a great deal of discussion about it.

你很難不沉浸其中，不被它包圍，不對它進行大量討論。

And of course, I hope that all of you are using it and playing with it, with surprising results and some magical, some disappointing, and some surprising.

當然，我也希望你們所有人都在使用它、玩弄它，並取得令人驚喜的結果，有些神奇，有些令人失望，有些令人驚訝。

But you have to enjoy it, you have to engage it, because it's advancing so quickly.

但你必須享受它，你必須參與它，因為它發展得太快了。

It is the only technology that I've known that is advancing on multiple exponentials at the same time.

這是我所知道的唯一一種同時以多種指數形式發展的技術。

And so the technology is changing very, very quickly.

是以，技術變化非常非常快。

So I advised the students at the Taiwan University to run, don't walk, and engage the AI revolution.

是以，我建議臺灣大學的學生們不要走，要跑，投入到人工智能革命中去。

And yet, one year later, it's incredible how much has changed.

然而，一年之後，變化之大令人難以置信。

And so today, what I wanted to do is share with you my perspective, from my vantage point, of some of the important things that are happening that you're graduating into.

所以今天，我想從我的角度，與你們分享我對你們即將畢業的一些重要事情的看法。

And these are extraordinary things that are happening that you should have an intuitive understanding for, because it's going to matter to you, it's going to matter to the industry.

這些正在發生的非同尋常的事情，你應該有一個直觀的認識，因為這對你和整個行業都很重要。

And hopefully, you take advantage of the opportunity ahead of you.

希望你們能抓住眼前的機會。

The computer industry is transforming from its foundations, literally from studs.

計算機行業正在從根基開始轉型，簡直就是從螺柱開始。

Everything is changing from studs on up.

從螺栓開始，一切都在改變。

And across every layer and soon, every industry will also be transformed.

各行各業也將很快發生變革。

And the reason for that is quite obvious, because computers today are the single most important instrument of knowledge.

原因顯而易見，因為計算機是當今最重要的知識工具。

And it's foundational to every single industry and every field of science.

它是每個行業和每個科學領域的基礎。

If we are transforming the computer so profoundly, it will, of course, have implications in every industry.

如果我們正在對計算機進行如此深刻的改造，當然會對各個行業產生影響。

And I'll talk about that in just a little bit.

我稍後再談這個問題。

And as you enter industry, it's important you know what's happening.

當你進入這個行業時，瞭解正在發生的事情非常重要。

Modern computing traces back to the IBM System 360.

現代計算可以追溯到 IBM System 360。

That was the architecture manual that I learned from.

這是我學習的建築手冊。

It's an architecture manual that you don't need to learn from.

這是一本不需要學習的建築手冊。

A lot better documentation and better descriptions of computers and architecture has been presented since.

從那時起，關於計算機和建築的文檔和描述就更加完善了。

But the System 360 was incredibly important at its time.

但在當時，360 系統的重要性令人難以置信。

And in fact, the basic ideas of the System 360, the architecture of it, the principal ideas and architecture and strategy of the System 360 are still governing the computer industry today.

事實上，System 360 的基本思想、架構、主要思想、架構和戰略至今仍在支配著計算機行業。

And it was introduced a year after my birth.

而它是在我出生一年後推出的。

In the 80s, I was among the first generation of VLSI engineers who learned to design chips from Mead and Conway's landmark textbook.

上世紀 80 年代，我是第一代 VLSI 工程師，他們從 Mead 和 Conway 的里程碑式教科書中學會了芯片設計。

And I'm not sure if it's still being taught here.

我不確定這裡是否還在教這門課。

It should be in the introduction of VLSI systems.

這應該是在介紹超大規模集成電路系統。

Based on Carver Mead's pioneering work here at Caltech on chip design methodologies and textbook that revolutionized IC design.

該書基於 Carver Mead 在加州理工學院的開創性工作，研究芯片設計方法，並編寫了一本徹底改變集成電路設計的教科書。

And it enabled our generation to design super giant chips and ultimately the CPU.

它使我們這一代人能夠設計出超大型芯片，並最終設計出中央處理器。

The CPU led to exponential growth in computing.

中央處理器使計算能力呈指數級增長。

The performance, the incredible technology advances, that's called Moore's Law, fueled the information technology revolution.

性能、令人難以置信的技術進步，即所謂的摩爾定律，推動了信息技術革命。

The industrial revolution that we are part of, that my generation was part of, saw the mass production of something the world had never seen before.

我們這一代人所參與的工業革命，見證了世界上前所未有的大規模生產。

The mass production of something that was invisible, easy to copy, the mass production of software.

大規模生產看不見、很容易複製的東西，即大規模生產軟件。

And it led to a $3 trillion industry.

這導致了一個 3 萬億美元的產業。

When I sat where you sat, the IT industry was minuscule.

當我坐在你的位置上時，IT 行業還微不足道。

And the concept that you could make money selling software was a fantasy.

賣軟件就能賺錢的想法簡直是天方夜譚。

And yet today, it's one of the most important commodities, most important technologies and product creations that our industry produces.

然而今天，它已成為我們行業最重要的商品、最重要的技術和產品創造之一。

However, the limits of Dennard scaling, of transistor scaling, and instruction level parallelism have slowed CPU performance.

然而，由於 Dennard 擴展、半導體擴展和指令級並行性的限制，CPU 性能有所下降。

And the slowed CPU performance gains is happening at a time when computing demand continues to grow exponentially.

CPU性能提升放緩的同時，計算需求卻在持續成倍增長。

This exponentially growing gap between demand of computing and the capabilities of computers, if not addressed, computing energy consumption and cost, inflation, would eventually stifle every industry.

計算需求與計算機能力之間的差距呈指數級增長，如果不加以解決，計算能耗和成本、通貨膨脹最終將扼殺每一個行業。

We see very clear signs of computing inflation as we speak.

在我們說話的時候，我們看到了非常明顯的計算通脹跡象。

And after two decades of advancing NVIDIA's CUDA, NVIDIA's accelerated computing offers a path forward.

二十年來，英偉達™（NVIDIA®）的CUDA不斷進步，英偉達™（NVIDIA®）的加速計算提供了一條前進之路。

That's the reason why I'm here.

這就是我來這裡的原因。

Because finally the industry realized of the incredible effectiveness of accelerated computing at precisely the time that we're witnessing computing inflation after several decades.

因為在幾十年後的今天，當我們目睹計算膨脹時，業界終於意識到加速計算的驚人功效。

By offloading time-consuming algorithms to a GPU that specializes in parallel processing, we routinely achieve 10, 100, sometimes 1,000-fold speedups saving money, cost, and energy.

通過將耗時的算法卸載到專門從事並行處理的 GPU 上，我們通常能實現 10 倍、100 倍，有時甚至 1000 倍的速度提升，從而節省資金、成本和能源。

We now accelerate application domains from computer graphics, ray tracing, of course, to gene sequencing, scientific computing, astronomy, quantum circuit simulations, SQL data processing, and even pandas, data science.

現在，我們加速的應用領域從計算機圖形學、光線追蹤，當然還有基因測序、科學計算、天文學、量子電路仿真、SQL 數據處理，甚至熊貓、數據科學。

Accelerated computing has reached a tipping point.

加速計算已達到臨界點。

That is our first great contribution to the computer industry, our first great contribution to society, accelerated computing.

這是我們對計算機行業的第一個偉大貢獻，也是我們對社會的第一個偉大貢獻--加速計算。

It now gives us a path forward for sustainable computing where cost will continue to decline as computing requirement continues to grow.

現在，它為我們提供了一條可持續計算的前進道路，隨著計算需求的不斷增長，成本將持續下降。

A hundred-fold, a hundred-fold of anything in time or cost or energy savings that accelerated computing opened surely would trigger a new development somewhere else.

加速計算所帶來的時間、成本或能源的百倍、百倍的節省，肯定會引發其他地方的新發展。

We just didn't know what it was until deep learning came to our consciousness.

我們只是不知道它是什麼，直到深度學習出現在我們的意識中。

A whole new world of computing emerged.

一個全新的計算世界出現了。

Jeff Hinton, Alice Krzyzewski, and Ilya Suskever used NVIDIA CUDA GPUs to train AlexNet and shocked the computer vision community by winning the 2012 ImageNet Challenge.

Jeff Hinton、Alice Krzyzewski 和 Ilya Suskever 使用英偉達™（NVIDIA®）CUDA GPU 訓練 AlexNet，並贏得了 2012 年 ImageNet 挑戰賽，震驚了計算機視覺界。

This was the big moment, the big bang of deep learning, a pivotal moment that marked the beginning of the AI revolution.

這是一個重要時刻，是深度學習的大爆炸，是標誌著人工智能革命開始的關鍵時刻。

Our decisions after AlexNet transformed our company is something that's worth taking note of.

我們在 AlexNet 改變我們公司之後做出的決定值得注意。

Our decisions after AlexNet transformed our company and likely everything else.

我們在 AlexNet 之後做出的決定改變了我們的公司，也可能改變了其他一切。

We saw the potential of deep learning and believed, just believed through principle thinking, believed through our own analysis of the scalability of deep learning.

我們看到了深度學習的潛力，並且相信，只是通過原則性思維相信，通過我們自己對深度學習可擴展性的分析相信。

We believed the approach could learn other valuable functions.

我們相信，這種方法還能學習到其他有價值的功能。

That maybe deep learning is a universal function learner.

也許深度學習是一種通用功能學習器。

And how many problems are difficult or impossible to express using fundamental first principles?

又有多少問題難以或無法用基本的第一性原理來表達呢？

And so when we saw this, when we saw this, we thought this is a technology we really have to pay attention to because its limits are potentially only limited by model and data scale.

是以，當我們看到這一點時，我們認為這是一項我們必須關注的技術，因為它的侷限性可能只受到模型和數據規模的限制。

However, there were challenges at the time.

不過，當時也遇到了一些挑戰。

This is 2012, shortly after 2012.

現在是 2012 年，2012 年後不久。

How could we explore the limits of deep learning without having to build these massive GPU clusters?

我們如何才能探索深度學習的極限，而無需構建這些龐大的 GPU 集群？

At the time we were a rather small company.

當時，我們還是一家相當小的公司。

Building these massive GPU clusters could cost hundreds and hundreds of millions of dollars.

建造這些大規模 GPU 集群的成本可能高達數億美元。

And if we didn't though, there was no assurance that it would be effective if we scaled.

但如果我們不這樣做，就無法保證擴大規模後會有效。

However, no one knew how far deep learning could scale.

然而，沒有人知道深度學習的規模能有多大。

And if we didn't build it, we'd never know.

如果我們不建造它，就永遠不會知道。

This is one of those, if you build it, will they come?

如果你建造了它，他們會來嗎？

Our logic is if we don't build it, they can't come.

我們的邏輯是，如果我們不建，他們就不會來。

And so we dedicated ourselves based on our first principle beliefs and our analysis.

於是，我們根據自己的第一原則信念和分析，奉獻出了自己的力量。

And we got ourselves to the point where we believed this was going to be so effective and when the company believes something, we should go act on it.

我們相信這樣做會非常有效，當公司相信某件事情時，我們就應該付諸行動。

So we dove deep into deep learning and over the next decade, systematically reinvented everything.

是以，我們潛心研究深度學習，並在接下來的十年中系統地重塑了一切。

We reinvented every computing layer, starting with the GPU itself.

從 GPU 本身開始，我們重塑了每個計算層。

The invention of the modern GPU, which is very different than the GPU of the past that we invented in the first place, and we went on to invent just about every other aspect of computing, the interconnects, the systems, the networking, and of course, software.

現代 GPU 的發明與我們最初發明的過去的 GPU 有很大不同，我們接著發明了計算的幾乎所有其他方面、互連、系統、網絡，當然還有軟件。

We invested billions.

我們投資了數十億美元。

We invested billions into the unknown.

我們向未知領域投入了數十億美元。

Thousands of engineers for a decade worked on deep learning and advancing and scaling deep learning without really knowing how far we could really take the technology.

數以千計的工程師十年來一直致力於深度學習，並不斷推進和擴展深度學習，但卻不知道我們到底能將這項技術推進到什麼程度。

We invested billions.

我們投資了數十億美元。

And we designed and built supercomputers to explore the limits of learning, of deep learning in AI.

我們設計並建造了超級計算機，以探索人工智能深度學習的極限。

Then in 2016, we announced DGX-1, our first AI supercomputer, and I delivered the first one to a startup in San Francisco, a startup nobody knew anything about, a group of friends of mine who were working on artificial intelligence, a company called OpenAI.

然後在 2016 年，我們發佈了第一臺人工智能超級計算機 DGX-1，我將第一臺超級計算機交付給了舊金山的一家初創公司，一家無人知曉的初創公司，我的一群朋友，他們正在研究人工智能，一家名為 OpenAI 的公司。

In 2022, 10 years after AlexNet, and about a million-fold increase in computing later, a million-fold.

2022 年，也就是 AlexNet 推出 10 年後，計算能力增加了約 100 萬倍。

If you could just imagine, what would it be like if your laptop was a million-fold more capable?

如果你能想象一下，如果你的筆記本電腦性能提高一百萬倍，那會是什麼樣子？

A million-fold later, OpenAI launched ChatGPT, and AI went mainstream.

一百萬倍之後，OpenAI 推出了 ChatGPT，人工智能成為主流。

During this decade, NVIDIA transformed ourselves from a graphics company that many of you probably first knew us as, that builds GPUs, to now an AI company that builds massive data center-scale supercomputers.

在這十年間，NVIDIA 從一家圖形公司轉型為一家人工智能公司，前者製造 GPU，後者製造大規模數據中心級超級計算機。

We transformed our company completely.

我們徹底改變了公司。

We also transformed computing completely.

我們還徹底改變了計算方式。

The fundamental way of doing computing today has been radically changed.

如今，進行計算的基本方式已經發生了根本性的變化。

The computing stack now uses GPU to process large language models that are trained on computers, rather than CPUs that are processing instructions written by programmers.

現在，計算堆棧使用 GPU 處理在計算機上訓練的大型語言模型，而不是處理程序員編寫的指令的 CPU。

We are now creating software that no humans can write.

我們現在正在創造人類無法編寫的軟件。

We are now creating software that does things that no humans can imagine, even just 10 years ago.

我們現在所創造的軟件，其功能是人類無法想象的，即使是在 10 年前也是如此。

Computers are now intention-driven, rather than instruction-driven.

現在，計算機是由意圖驅動的，而不是由指令驅動的。

Tell a computer what you want, and it will figure out how.

告訴計算機你想要什麼，它就會想出辦法。

Like humans, AI applications will understand the mission, reason, plan, and orchestrate a team of large language models to perform tasks.

與人類一樣，人工智能應用程序將理解任務、推理、規劃，並協調大型語言模型團隊執行任務。

Future applications will do and perform very similar to the way we do things, assemble teams of experts, use tools, reason and plan, and execute our mission.

未來的應用程序將以與我們非常相似的方式開展工作、組建專家團隊、使用工具、進行推理和規劃以及執行任務。

Software and what software can do has been completely changed.

軟件和軟件的功能已經完全改變。

Even our industry, as it's being changed and transformed, created yet another industry, an industry the world's never seen before.

就連我們的行業也在不斷變化和轉型，創造出了另一個行業，一個世界上從未見過的行業。

An industry is forming right in front of our eyes.

一個產業正在我們眼前形成。

AI's input and output are tokens.

人工智能的輸入和輸出都是代幣。

For all the engineers in the room, you know what I mean.

在座的工程師們都明白我的意思。

These are floating-point numbers that embed intelligence.

這些浮點數蘊含著智能。

Companies are now building a new type, a new type of data center that didn't exist before that specialized in producing intelligence tokens, essentially AI factories.

現在，各家公司正在建設一種新型數據中心，一種以前不存在的專門生產智能代幣的新型數據中心，本質上就是人工智能工廠。

Like AC generators that Nikola Tesla invented of the past industrial revolution, we now have AI token generators, and they will be the factories of a new industrial revolution.

就像尼古拉-特斯拉在過去的工業革命中發明的交流發電機一樣，我們現在有了人工智能令牌發電機，它們將成為新工業革命的工廠。

There's large industries producing energy, electricity.

這裡有生產能源和電力的大型工業。

We now have a large industry producing something invisible called software.

我們現在有一個龐大的產業，生產一種看不見的東西，叫做軟件。

In the future, in the very near future, we'll have industries that are producing, manufacturing intelligence tokens, AI generators.

在未來，在不久的將來，我們就會有生產、製造智能代幣、人工智能生成器的行業。

A new computing model has emerged, and a new industry has emerged, all because we reasoned from first principles, formed our belief about the future, and we acted on them.

一個新的計算模式已經出現，一個新的行業已經崛起，這一切都是因為我們從第一性原理出發進行推理，形成了我們對未來的信念，並根據這些信念採取了行動。

The next wave of AI is robotics, where AI, in addition to a language model, also has a physical world model.

人工智能的下一個浪潮是機器人技術，在機器人技術中，人工智能除了語言模型外，還有物理世界模型。

We work with hundreds of companies building robots, robotic vehicles, pick-and-place arms, humanoid robots, and even entire gigantic warehouses that are robotic.

我們與數百家公司合作製造機器人、機器人車輛、拾取和放置機械臂、仿人機器人，甚至整個巨大的機器人倉庫。

But unlike our AI factory strategy and our experience there, which was really formed through reasoning and deliberate action, our robotics journey resulted from a series of setbacks.

但是，我們的人工智能工廠戰略和我們在那裡的經驗確實是通過推理和深思熟慮後形成的，與此不同的是，我們的機器人之旅源於一系列挫折。

As you know, NVIDIA invented the GPU.

眾所周知，英偉達™（NVIDIA®）公司發明了 GPU。

This was before we invented AI factories.

這還是在我們發明人工智能工廠之前。

Our first great contribution to the computer industry was reinventing computer graphics through programmable shaders.