英伟达CEO Jensen Huang 在加州理工学院2024年毕业典礼的讲话：Commencement speech at CALTEC 2024

NVIDIA CEO Jensen Huang commencement speech at CALTEC 2024

Link: https://www.youtube.com/watch?v=1Egl4c5IIeI

Speaker: Jensen Huang

Data: June 2024

Summary

Here is a summary of the speech, including the key stories and their meanings:

Introduction:

The speaker opens with humor, mentioning their discomfort with hearing their own accolades and then engaging the audience by asking about Nvidia and GPUs.

Addressing the Graduates:

1. Congratulations to the Graduates and Families:

   • The speaker acknowledges the hard work and sacrifices of the graduates and their families, emphasizing the importance of gratitude and spending time with family.

2. Shared Passion for Science and Engineering:

   • The speaker highlights the common passion for science and engineering, and humorously mentions recruiting for Nvidia.

3. First Principles and Belief in AI:

   • Story of Nvidia's Journey with AI:
     • The speaker discusses Nvidia's transformation from a graphics company to an AI company, emphasizing their belief in deep learning and investing in it despite uncertainties.
     • Key Points:
       • Development of CUDA programming model.
       • Challenges faced and overcome in building AI supercomputers.
       • Collaboration with OpenAI and the creation of ChatGPT.
     • Meaning: Belief in potential and first principles can drive transformative innovation and success.

4. Setbacks and Resilience:

   • Story of Nvidia's Shifting Markets:
     • The speaker recounts how Nvidia faced setbacks in various markets (e.g., AMD, Intel, mobile devices) and pivoted to robotics where there were no customers initially.
     • Meaning: Resilience and agility in the face of setbacks lead to new opportunities and strengths.

5. Craft and Dedication:

   • Story of the Gardener at the Silver Temple:
     • The speaker shares a profound experience with a gardener meticulously caring for a moss garden, teaching the value of dedication to one's craft.
     • Meaning: Dedication to perfecting a craft over a lifetime brings fulfillment and mastery.

Final Advice:

1. Belief and Dedication:

   • The speaker encourages the graduates to believe in something unconventional, reason through it, and dedicate themselves to making it happen.

2. Resilience and Character:

   • Setbacks should be seen as opportunities, and enduring pain and suffering builds resilience and character.

3. Finding a Craft:

   • The importance of finding a craft to dedicate one's life to and continuously honing skills.

4. Prioritization:

   • Prioritizing tasks and life to ensure time for important things.

Conclusion:

The speaker congratulates the graduates again and urges them to go out and make an impact.

---

The speech is a blend of personal anecdotes, professional experiences, and philosophical insights, aiming to inspire the graduates to pursue their passions, remain resilient, and prioritize their goals.

这篇演讲是由英伟达(NVIDIA)的创始人兼CEO黄仁勋在2024年加州理工学院的毕业典礼上发表的。演讲主要包括以下几个故事和含义：

1. 谦逊与感恩

故事 ：黄仁勋表达了对自己的介绍和掌声感到不自在，并邀请观众为他们所熟知的英伟达和GPU鼓掌。他也感谢了校长、教授、来宾、家长和毕业生，并强调了感恩家长的付出。
含义：这个开场部分展示了他的谦逊和对支持他的人们的感激之情，强调了感恩的重要性。

2. 坚韧与牺牲

故事 ：黄仁勋讲述了自己和英伟达的成长历程，从创建公司、开发CUDA编程模型、经历失败项目到最终取得成功。他还提到了对深度学习的坚定信念，即使在资源有限的情况下，也勇敢地进行大量投资和创新。
含义：他通过这些故事强调了坚持梦想、勇于冒险和持续创新的重要性，同时也表达了在面对挑战时要保持坚韧和灵活。

3. AI革命与计算转型

故事 ：黄仁勋谈到了AI的发展历程，特别是深度学习的崛起。他回顾了从2012年AlexNet获奖到2022年OpenAI发布ChatGPT这十年间的巨大变化，并提到了英伟达在推动AI进步方面的贡献。
含义：他希望毕业生们能够理解并参与到AI革命中，因为这是当今最具影响力的技术之一。计算领域的转型对所有行业都有深远影响，掌握这些技术是未来成功的关键。

4. 应对挫折与寻找机会

故事 ：黄仁勋讲述了英伟达在面对市场挑战时的一系列应对措施，从与AMD和Intel的合作到进入移动设备市场，再到最终专注于机器人技术。他通过这些挫折展示了公司的适应能力和创新精神。
含义：他通过这些经历传递了在面对失败时要迅速调整策略，寻找新机会的重要性。适应性和韧性是成功的重要品质。

5. 热爱工作与人生优先级

故事 ：黄仁勋描述了他在京都银阁寺看到一位园丁用竹子镊子精心照顾苔藓的场景，这位园丁已经照顾花园25年，并表示他有足够的时间完成这项工作。他也分享了自己每天优先处理最重要的工作，以确保日常工作已经取得成功。
含义：这个故事强调了热爱和专注于自己的工作的重要性，以及如何通过合理安排优先级来高效工作和生活。

总体总结

黄仁勋在演讲中分享了自己的职业生涯和公司成长中的多个故事，传递了以下核心信息：

• 谦逊和感恩是成功的基础。
• 坚韧和牺牲是实现梦想的重要品质。
• AI和计算领域的变革是未来的关键，鼓励毕业生参与其中。
• 面对挫折时，要迅速调整策略，寻找新机会。
• 热爱工作和合理安排优先级是实现长期成功的关键。

最后，他祝贺了2024届毕业生，并鼓励他们相信自己的梦想，勇敢追求，不断创新，并在面对挑战时保持坚韧和灵活。

Vocabulary

accolades：美 ['ækəleɪdz] 嘉奖；（accolade的复数）

CalTech：加州理工学院

cringe: 美 [krɪndʒ] 畏缩；蜷缩；尴尬；谄媚；难为情；

applaud: 美 [əˈplɔːd] 鼓掌表同意；赞扬；表示强烈赞同

esteemed: 美 [ɪˈstiːmd] 令人尊敬的

esteemed faculty members: 尊敬的教职员工

distinguished: 美 [dɪˈstɪŋɡwɪʃt] 杰出的；著名的；卓越的；尊贵的；

distinguished guests: 尊贵的客人们

proud parents

today is a day of immense pride and joy

testament：美 [ˈtestəmənt] 证据；证明；见证

commencement：美 [kəˈmensmənt] 毕业典礼

give the commencement address： 毕业典礼演讲

my advice today will largely be disguised in some stories

counterintuitive lessons：反直觉的教训

engage with：参与；涉入；与...互动；与...建立联系

consequential：重要的；重大的；有重要意义的；

engage with AI the most consequential technology 参与人工智能这一最重要的技术

Your journey here is a testament of your character, determination, and willingness to make sacrifices for your dreams, and you should be proud. 你的旅程证明了你的性格、决心和为梦想做出牺牲的意愿，你应该感到自豪。

we are both at the peaks of our careers：在职业生涯的顶峰

it's hard not to be immersed in it and surrounded by it 很难不沉浸其中，不被它包围

vantage point：有利位置；制高点；优势点

share with you my perspective from my vantage point of some of the important things与你分享我对一些重要事情的看法

tipping point：引爆点；爆发点；忍受极限

Accelerated computing has reached a tipping point 加速计算已经达到了一个临界点

our robotics journey resulted from a series of setbacks

we developed agility and a culture of resilience 我们发展了敏捷性和弹性文化

shake it off：摆脱（不愉快的感觉或经历）；忘掉（困难或不快）；不受（失败等）影响继续前进；

swiftly：迅速地；敏捷地

Swiftly shake it off. 迅速摆脱它。

renowned for：以xxx闻名

exquisite：美 [ɪkˈskwɪzɪt] 极美的；精致的；精美的

moss：苔藓

It's renowned for its exquisite moss garden. 它以精致的苔藓花园而闻名。

Kyoto：美 [kiˈ(j)oʊdoʊ] 京都

quintessential：英 [ˌkwɪntɪˈsɛnʃ(ə)l] 典型的；典范的；

suffocatingly：美 [sʌfəˌketɪŋli] 令人窒息地

hot and humid：闷热潮湿

sticky：湿热的；闷热的；黏的；黏性的

radiating：美 [ˈreɪdieɪtɪŋ] 辐射；发射；（radiate的现在分词形式）

The day we visited was a quintessential Kyoto summer day---suffocatingly hot and humid, sticky heat radiating from the ground, the air thick and still. 我们参观的那一天是典型的京都夏日------闷热潮湿，地面散发出黏糊糊的热量，空气厚重而静止。

squatting：美 [sk'wɒtɪŋ] 蹲；蹲伏；（squat的现在分词形式）

dedicate to： 致力于；专用于某目的（或活动）；奉献（时间、精力等）

craft：手艺；工艺；技艺

This gardener had dedicated himself to his craft and his life's work. 这位园丁把自己奉献给了他的手艺和他一生的工作。

Transcript

[Applause]

It really makes me cringe listening to all that. Thank you for that kind introduction, but I hate hearing about myself. Just to show, maybe you could just applaud how many of you know who Nvidia is? And how many of you know what a GPU is? Okay, good, I don't have to change my speech.

Ladies and gentlemen, President Rosenbaum, esteemed faculty members, distinguished guests, proud parents, and above all, the 2024 graduating class of Caltech, this is a really happy day for you guys. You got to look more excited; you're graduating from Caltech. This is the school of the Great Richard Feynman, Linus Pauling, and someone who is very influential to me and our industry, Carver Mead. This is a very big deal.

Today is a day of immense pride and joy. 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. So, let's take this moment and congratulate them, thank them, and let them know you love them. You don't want to forget that because you don't know how long you're going to be living at home. You want to be super grateful. Today, 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 it makes me sad. So hopefully you guys get to spend some time with your parents.

Your journey here is a testament of your character, determination, and willingness to make sacrifices for your dreams, and you should be proud. The ability to make sacrifices, endure pain and suffering, you will need these qualities in life. You and I share some things in common. First, both Chief Scientists of Nvidia were from Caltech, and one of the reasons why I'm giving the speech today is because I'm recruiting. And so I want to tell you that Nvidia is a really great company. I'm a very nice boss, universally loved. Come work at Nvidia.

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 careers. For all of you who've been paying attention to Nvidia and myself, you know what I mean. It's just that in your case you'll have many, many more peaks to go. I just hope that today is not my peak. And so I'm working as hard as ever to make sure that I have many, many more peaks ahead.

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. I have to admit that I don't love giving advice, especially to other people's children, and so my advice today will largely be disguised in some stories that I've enjoyed and some life experiences that I've enjoyed. I'm the longest-running tech CEO in the world today, I believe, and over the course of 31 years I managed not to go out of business, not get bored, and not get fired. 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.

I spoke about the long road of creating CUDA, a programming model that we dedicated over 20 years to invent and that is revolutionizing computing today. I spoke about a very quite public cancelled Sega game console project we worked on 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 and how a strategic retreat was one of our best strategies. All of these are counterintuitive lessons that I spoke about at the commencement. I encouraged the graduates to engage with AI, the most consequential technology of our time, and I'll speak a little bit more about that later. But all of you know about AI; it's hard not to be immersed in it and surrounded by it and a great deal of discussion about it. And of course, I hope that all of you are using it and playing with it, with surprising results. Some magical, some disappointing, and some surprising, but you have to enjoy it, you have to engage it because it's advancing so quickly. It is the only technology that I've known that is advancing on multiple exponentials at the same time, and so the technology is changing very, very quickly. So I advised the students at the Taiwan University to run, don't walk, and engage the AI revolution. And yet, one year later, it's incredible how much has changed.

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. 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, and hopefully, you take advantage of the opportunity ahead of you. The computer industry is transforming from its foundations, literally from studs. Everything is changing from studs on up and across every layer, and soon every industry will also be transformed. The reason for that is quite obvious because computers today are the single most important instrument of knowledge and foundational to every single industry in every field of science. If we are transforming the computer so profoundly, it will of course have implications in every industry, and I'll talk about that in just a little bit.

As you enter industry, it's important you know what's happening. Modern computing traces back to the IBM System/360. That was the architecture manual that I learned from. It's an architecture manual that you don't need to learn from. A lot has a lot better documentation and better descriptions of computers and architecture have been presented since. But the System/360 was incredibly important of its time, and in fact, the basic ideas of the System/360, the architecture of it, the principled ideas and architecture and strategy of the System/360 are still governing the computer industry today. And it was introduced a year after my birth.

In the 80s, I was among the first generation of VLSI Engineers who learned to design chips from Mead and Conway's landmark textbook, and I'm not sure if it's still being taught here. It should be. The introduction of VLSI systems based on Carver Mead's pioneering work here at Caltech on chip design methodologies and textbook revolutionized IC design and enabled our generation to design super-giant chips and ultimately the CPU. The CPU led to exponential growth in computing performance, the incredible technology advances that's called Moore's Law fueled the information technology revolution. 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: the mass production of something that was invisible, easy to copy, the mass production of software, and it led to a three trillion dollar industry.

When I sat where you sat, the IT industry was minuscule and the concept that you could make money selling software was a fantasy. And yet today it's one of the most important commodities, most important technologies, and product creations that our industry produces. However, the limits of scaling of transistor scaling and instruction-level parallelism have slowed CPU performance. The slowed CPU performance gains are happening at a time when computing demand continues to grow exponentially. 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. We see very clear signs of computing inflation as we speak.

After two decades of advancing Nvidia's CUDA, Nvidia's accelerated computing offers a path forward. That's the reason why I'm here, because finally the industry realized the incredible effectiveness of accelerated computing at precisely the time that we're witnessing computing inflation. After several decades, by offloading time-consuming algorithms to a GPU that specializes in parallel processing, we routinely achieve 10, 100, sometimes a thousandfold speedups, saving money, cost, and energy. 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. Accelerated computing has reached a tipping point. That is our first great contribution to the computer industry, our first great contribution to society: accelerated computing. It now gives us a path forward for sustainable computing where cost will continue to decline as computing requirements continue to grow.

A hundredfold of anything in time or cost or energy savings that accelerated computing opened surely would trigger a new development somewhere else. We just didn't know what it was until deep learning came to our consciousness. A whole new world of computing emerged. Jeff Hinton, Alex Krizhevsky, and Ilya Sutskever used Nvidia CUDA GPUs to train AlexNet and shocked the computer vision community by winning the 2012 ImageNet Challenge. This was the big moment, the big bang of deep learning, a pivotal moment that marked the beginning of the AI revolution.

Our decisions after AlexNet transformed our company and likely everything else. We saw the potential of deep learning and believed, just believed through principle thinking and through our own analysis of the scalability of deep learning, that this approach could learn other valuable functions. We thought maybe deep learning is a universal function learner, and how many problems are difficult or impossible to express using fundamental first principles? 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."

However, there were challenges at the time. This is 2012, shortly after AlexNet. How could we explore the limits of deep learning without having to build these massive GPU clusters? At the time, we were a rather small company. Building these massive GPU clusters could cost hundreds and hundreds of millions of dollars, and if we didn't, there was no assurance that it would be effective if we scaled. However, no one knew how far deep learning could scale, and if we didn't build it, we'd never know. This was one of those "If you build it, will they come?" Our logic was, "If we don't build it, they can't come." So, we dedicated ourselves based on our first principled beliefs and our analysis. We got ourselves to the point where we believed this was going to be so effective. When the company believes something, we should go act on it.

So, we dove deep into deep learning and over the next decade, systematically reinvented everything. We reinvented every computing layer, starting with the GPU itself, the invention of the modern GPU, which is very different than the GPU of the past that we invented in the first place. We went on to invent just about every other aspect of computing: the interconnects, the systems, the networking, and, of course, software. We invested billions into the unknown. Thousands of engineers for a decade worked on deep learning, advancing and scaling deep learning without really knowing how far we could take the technology. We invested billions and designed and built supercomputers to explore the limits of learning in AI.

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. In 2022, ten years after AlexNet and about a millionfold increase in computing later, OpenAI launched ChatGPT and AI went mainstream. During this decade, NVIDIA transformed itself 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.

We transformed our company completely. We also transformed computing completely. The fundamental way of doing computing today has been radically changed. The computing stack now uses GPUs to process large language models that are trained on supercomputers rather than CPUs that process instructions written by programmers. We are now creating software that no humans can write. We are now creating software that does things that no humans could imagine even just ten years ago. Computers are now intention-driven rather than instruction-driven. Tell a computer what you want, and it will figure it out. It will figure out how. Like humans, AI applications will understand the mission, reason, plan, and orchestrate a team of large language models to perform tasks. Future applications will do and perform very similarly to how we do things: assemble teams of experts, use tools, reason and plan, and execute our mission.

Software and what software can do has been completely changed. Even our industry, as it's being changed and transformed, created yet another industry---an industry the world's never seen before, an industry forming right in front of our eyes. AI's input and output are tokens. For all the engineers in the room, you know what I mean. These are floating-point numbers that embed intelligence. Companies are now building a new type of data center that didn't exist before, specialized in producing intelligence tokens---essentially AI factories.

Like AC generators that Nikola Tesla invented during the past Industrial Revolution, we now have AI token generators, and they will be the factories of a new Industrial Revolution. There are large industries producing energy, electricity. We now have a large industry producing something invisible called software. In the future, in the very near future, we will have industries that are producing and manufacturing intelligence tokens---AI generators. 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 acted on them.

The next wave of AI is robotics, where AI, in addition to a language model, also has a physical world model. We work with hundreds of companies building robots: robotic vehicles, pick-and-place arms, humanoid robots, and even entire gigantic warehouses that are robotic. 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. As you know, NVIDIA invented the GPU before we invented AI factories. Our first great contribution to the computer industry was reinventing computer graphics through programmable shaders. We invented the GPU and programmable shading in 2000. We wanted to integrate GPUs into every computer, so we started to combine our GPUs with motherboard chips and launched a fabulous integrated graphics chip for AMD CPUs.

Our chipset business was an instant success. I think it went from zero to a billion dollars practically overnight. But then, all of a sudden, AMD wanted to control all of the technology in the PC, and we wanted to stay independent. So, they purchased ATI and no longer needed us. We turned to Intel, which probably wasn't a great idea, but we turned to Intel and negotiated a license to connect to Intel CPUs. Apple was excited about what we were building and asked us to work on a new computer with them, which became the first MacBook Air. Well, Intel saw what happened and decided they didn't want us to do that anymore, so they terminated our agreement.

We pivoted again, and this time we licensed ARM and built a low-power SOC, a mobile SOC---the world's first SOC that was essentially a full operating computer. It was incredible. Our chip excited Google, and they asked us to work on a new device, which turned out to be the Android mobile device. Well, Qualcomm decided they didn't want us to do that, so they didn't want us to connect to their modems. It's hard to build a mobile device without being connected to a modem, and there were no other LTE modem companies, so we had to exit the mobile device market.

This happened practically on a yearly rhythm. We would build something, it would be incredibly successful, generate enormous amounts of excitement, and then one year later, we were kicked out of those markets. With no more markets to turn to, we decided to build something where we were sure there were no customers. One of the things you can guarantee is where there are no customers, there are also no competitors and nobody cares about you. So, we chose a market with no customers---a zero billion-dollar market---and it was robotics.

We built the world's first robotics computer, processing an algorithm nobody understood at the time called deep learning. This was over ten years ago. Now, ten years later, I can't be happier with what we've built and the opportunities to continue to create the next wave of AI. More importantly, we developed agility and a culture of resilience. One setback after another, we shook it off and skated to the next opportunity. Each time we gained skills and strengthened our character. We strengthened our corporate character. Our company is really hard to distract and really hard to discourage. No setback that comes our way doesn't look like an opportunity these days. Ironically, the robotics computer that we build today doesn't even need graphics, which is why our journey started in the first place.

Where we are today teaches us something. The world is uncertain, as Richard Feynman would say, and the world can be unfair and deal you tough cards. Swiftly shake it off. There's another opportunity out there or create one.

Let me tell you one more story. I used to work from one of our international sites for one month each summer. When our kids were in their teens, we spent a summer in Japan. Over a weekend, we visited Kyoto and the Silver Temple. If you haven't had a chance to go, you must. It's renowned for its exquisite moss garden. The day we visited was a quintessential Kyoto summer day---suffocatingly hot and humid, sticky heat radiating from the ground, the air thick and still. Along with the other tourists, we wandered through the meticulously groomed moss garden, and I noticed a lone gardener.

Remember, the moss garden at the Silver Temple is gigantic---about the size of this courtyard---and it has the largest collection of just about every species of moss. It's exquisitely maintained. I noticed the lone gardener squatting, carefully picking at the moss with a bamboo tweezer and putting it in a bamboo basket. The basket looked empty. For a moment, I thought he was picking imaginary moss into a pile of imaginary dead moss. So, I walked up to him and asked, "What are you doing?" In his English, he said, "I'm picking dead moss. I'm taking care of my garden." I said, "But your garden is so big." He responded, "I have cared for my garden for 25 years. I have plenty of time."

That was one of the most profound learnings in my life. This gardener had dedicated himself to his craft and his life's work. When you do that, you have plenty of time. I begin each morning the same way. I begin each morning by doing my highest priority work first. I have a very clear priority list, and I start from the highest priority work first. Before I even get to work, my day is already a success. I've already completed my most important work and can dedicate my day to helping others. When people apologize for interrupting me, I always say, "I have plenty of time," and I do.

Graduates of the class of 2024, I can hardly imagine anyone more prepared for the future than you. You dedicated yourself, you worked hard, and you earned a world-class education from one of the most prestigious schools in the world. As you commence into the next stage, take my learnings and hopefully they'll help you along the way. I hope you believe in something---something unconventional, something unexplored---but let it be informed and let it be reasoned. Then dedicate yourself to making it happen. You may find your GPU, you may find your CUDA, you may find your generative AI, you may find your NVIDIA. I hope you will see setbacks as new opportunities. Your pain and suffering will strengthen your character, your resilience, and your agility. These are the ultimate superpowers. Of all the things I value most about my abilities, intelligence is not top of that list. My ability to endure pain and suffering, my ability to work on something for a very long period of time, my ability to handle setbacks and see the opportunity just around the corner---I consider these to be my superpowers, and I hope they're yours. I hope you find a craft.

I hope you find a craft. It's not important to decide on day one; it's not even important to decide anytime soon. But I hope you do find a craft that you want to dedicate your lifetime to perfecting, to honing the skills of, and let it be your life's work. Lastly, prioritize your life. There are so many things going on, so many things to do, but prioritize your life, and you will have plenty of time to do the important things. Congratulations, class of 2024. Go get them.

Afterword

2024年6月22日13点39分于上海。