Jensen Huang, Founder and CEO of NVIDIA
发布时间 2024-03-06 00:13:14 来源
摘要
In this View From The Top interview, Shantam Jain, MBA '24, speaks with Jensen Huang, Founder and CEO of NVIDIA.
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中英文字稿 
Jensen, this is such an honor. Thank you for being here. I'm delighted to be here. Thank you. In honor of your return to Stanford, I decided we'd start talking about the time when you first left. You joined LSI Logic, and that was one of the most exciting companies at the time. You're building a phenomenal reputation with some of the biggest names in tech. And yet, you decide to leave to become a founder. What motivated you? Chris and Curtis. Chris and Curtis, I was an engineer at LSI Logic, and Chris and Curtis were at Sun. And I was working with some of the brightest minds in computer science at the time, of all time, including and de-bectoshime and others building building war stations and graphics work stations, and so on and so forth.
詹森，这真是一份荣耀。感谢你在这里。我很高兴能在这里。谢谢你。为了纪念你回到斯坦福大学，我决定我们开始谈论你第一次离开时的事情。你加入了LSI Logic，那时是最令人兴奋的公司之一。你与一些科技界最大的名字建立了出色的声誉。然而，你决定离开成为一名创始人。是什么激励了你？克里斯和柯蒂斯。克里斯和柯蒂斯，在LSI Logic我是一名工程师，而克里斯和柯蒂斯在太阳公司。那时，我与一些当时和历史上最杰出的计算机科学家一起工作，包括和德贝克托辛等人一起建造工作站和图形工作站等等。
And Chris and Curtis said one day that they like to leave Sun, and they like me to go figure out what they're going to go leave for. And I had a great job, but they insisted that I figure out with them how to build a company. So we hung out at Denny's whenever they dropped by, which is, by the way, my alma mater, my first company, my first job before CEO was a dishwasher. And I did that very well. And so anyways, we got together, and it was during the microprocessor revolution. This is 1993 and 1992 when we were getting together. The PC revolution was just getting going.
克里斯和柯蒂斯有一天说他们想离开Sun，他们希望我去弄清楚他们要离开的原因。我有一份很好的工作，但他们坚持要和我一起想办法创办一家公司。所以每当他们过来时，我们就会在丹尼斯餐厅里待着，顺便说一下，那是我的母校，我的第一家公司，我担任CEO之前的第一份工作是洗碗工。我做得很出色。总之，我们聚在一起，那时正值微处理器革命时期。那是1993年和1992年我们一起的时候。PC革命刚刚开始。
You know that Windows 95, obviously, which is the revolutionary version of Windows, didn't even come to the market yet. And Pentium wasn't even announced yet. And so this is all right before the PC revolution. And it was pretty clear that the microprocessor was going to be very important. And we thought, why don't we build a company to go solve problems that a normal computer that is powered by a general purpose computing can't. And so that became the company's mission, to go build a computer, the type of computers, and solve problems that normal computers can't.
你知道Windows 95，显然是Windows的革命性版本，甚至还没有进入市场。而且奔腾处理器甚至还没有宣布。所以这就是PC革命之前的时期。很明显微处理器将会非常重要。我们想，为什么不建立一家公司来解决普通计算机无法解决的问题呢。因此，这就成了公司的使命，建立一种计算机，解决普通计算机无法解决的问题。
And to this day, we're focused on that. And if you look at all the problems that and the markets that we opened up as a result, it's things like computational drug design, weather simulation, materials design. These are all things that we're really, really proud of, robotics, self-driving cars, autonomous software, we call artificial intelligence. And then, of course, we drove the technology so hard that eventually the computational cost went to approximately 0. And it enabled the whole new way of developing software, where the computer wrote the software itself, artificial intelligence, as we know it today. So that was it. That was the journey. Yeah. Thank you all for coming. Yeah.
直至今天，我们依然专注于这一点。如果你看看我们因此打开的市场和解决的问题，其中包括计算药物设计、天气模拟、材料设计等。这些都是我们引以为傲的成就，机器人技术、自动驾驶汽车、自主软件，我们称之为人工智能。然后，当然，我们不断推动技术发展，最终计算成本降至接近0。这使得开发软件的方式彻底改变，计算机自己编写软件，也就是我们今天所熟知的人工智能。这就是我们的旅程。谢谢大家的参与。
Well, these applications are on all of our minds today. But back then, the CEO of LSI Logic convinced his biggest investor, Don Valentine, to meet with you. He is obviously the founder of Sequoia. Now I can see a lot of founders here, edging forward in anticipation. But how did you convince the most sought-after investor in Silicon Valley to invest in a team of first-time founders building a new product for a market that doesn't even exist? I didn't know how to write a business plan. And so I went to a bookstore. And back then, there were bookstores. And in the business book section, there was this book. And it was written by somebody I knew, Gordon Bell. And this book, I should go find it again. But it's a very large book. And the book says, how to write a business plan. And that was a highly specific title for a very niche market.
嗯，如今这些应用程序正挂在我们每个人的嘴边。但是那时，LSI Logic的首席执行官说服了他最大的投资者唐·瓦伦丁去见你。显然，他是Sequoia的创始人。我现在看到这里有很多创始人，满怀期待地向前走。但是你是如何说服硅谷最受追捧的投资者投资一个由首次创始人组成、为一个甚至不存在的市场开发新产品的团队？我当时不知道如何写商业计划。于是我去了一家书店。而那时，还有书店。在商业书籍区，有一本书。而这本书，我应该再去找找。但这是一本非常厚的书。这本书写着如何写商业计划。对于一个非常特定领域的市场而言，这是一个非常具体的标题。
And it seems like he wrote it for 14 people, and I was one of them. And so I bought the book. I should have known right away that it was a bad idea. Because Gordon is super smart. And super smart people have a lot to say. And I'm pretty sure Gordon wants to teach me how to write a business plan completely. And so I picked up this book. It's like 450 pages long. Well, I never got through it. Not even close. I flipped through it a few pages. And I go, you know what? By the time I'm done reading this thing, I'll be out of business. I'll be out of money. And Lori and I only had about six months in the bank. We had already spent some medicine and a dog.
看起来他是为14个人写的，而我是其中之一。所以我买了这本书。我应该当即意识到这是一个坏主意。因为戈登非常聪明。超级聪明的人有很多东西要说。我很确定戈登想教我如何完全写一份商业计划。所以我拿起了这本书。它有大约450页长。嗯，我都没有看完。甚至一点都不接近。我随便翻了几页。我觉得，你知道吗？等我读完这本书，我可能就会破产。我可能就会破产。而且劳里和我账户里只有大约六个月的钱。我们已经花了一些钱买了些药和一只狗。
And so the five of us had to live off of whatever money we had in the bank. And so I didn't have much time. And so instead of writing the business plan, I just went to talk to Will of Corrigan. He called me one day and said, hey, you left the company. You didn't even tell me what you were doing. I want you to come back and explain it to me. And so I went back and I explained to the Will. And Will, at the end of it, he said, I have no idea what you said. And that's one of the worst elevator pitches I've ever heard. And then he picked up the phone and he called Don Valentine.
因此，我们五个人只能靠银行里的钱生活。因此，我没有太多时间。因此，我没有写商业计划，而是去找科里根的威尔谈话。他有一天打电话给我，说，嘿，你离开了公司。你甚至没有告诉我你在做什么。我希望你回来并向我解释。因此，我回去并向威尔解释。最后，威尔说，我完全不明白你在说什么。这是我听过的最糟糕的电梯推销。然后他拿起电话打给了唐·瓦伦泰因。
And he called Don and he says, Don, I want you to give, I'm going to send a kid over. I want you to give him money. He's one of the best employees at LSI Logic ever had. And so the thing I learned is you can make up a great interview. You could even have a bad interview. But you can't run away from your past. And so have a good past. Try to have a good past. And in a lot of ways, I was serious when I said, I was a good dishwasher. I was probably Denny's best dishwasher. I planned my work. I was organized. I was mison plus. And then I watched the living daylights out of the dishes. And then they promoted me to bus. I was certain I'm the best busboy Denny's ever had. I never left the station with empty handed. I never came back empty handed. I was very efficient. And so anyways, eventually I became a CEO. I'm still working on being a good CEO.
他打电话给唐恩，说：“唐恩，我想让你给一个孩子钱。他是LSI Logic有史以来最优秀的员工之一。”我学到的事情是，你可以编造一个出色的面试。你甚至可以有一个糟糕的面试。但你无法逃避你的过去。所以要有一个美好的过去。尽量拥有一个美好的过去。在很多方面，我说我是一个优秀的洗碗工时是认真的。我可能是Denny's最棒的洗碗工。我计划我的工作。我很有条理。我很注重细节。然后我把碗洗得干干净净。然后他们提拔我做餐馆清洁工。我确信我是Denny's有史以来最棒的清洁工。我从不空手而回。我非常高效。总之，最终我成为了一名CEO。我仍在努力成为一名优秀的CEO。
You talk about being the best. You needed to be the best among 89 other companies that were funded after you to build the same thing. And then with 69 months of runway left, you realized that the initial vision was just not going to work. How did you decide what to do next to save the company when the cards were so stacked against you? Well, we started this company called Forex already Computing. And the question is, what's it for? What's the killer app? And that came our first great decision. And this is what Sequoia funded. The first great decision was the first killer app was going to be 3D graphics. And the technology was going to be 3D graphics. And the application was going to be video games. At the time, 3D graphics wasn't possible to make cheap. It was $1 million image generators from Silicon graphics. And so it was $1 million, and it's hard to make cheap. And the video game market was $0 billion. So you have this incredible technology that's hard to commoditize and commercialize.
你谈论要成为最好的。你需要在其他89家公司中脱颖而出，这些公司在你之后获得资金来构建同样的东西。然后在还有69个月的资金储备时，你意识到最初的愿景根本行不通。当面对重重困难时，你如何决定下一步该做什么来挽救公司呢？嗯，我们创立了这家名为Forex already Computing的公司。问题是，它是用来做什么的？什么是杀手级应用程序？这就是我们第一个伟大的决定所在。这也是Sequoia资助的原因。第一个伟大的决定是第一个杀手级应用程序将是3D图形。而技术将会是3D图形。应用程序将会是视频游戏。当时，要制作成本低廉的3D图形是不可能的。硅图形的图像生成器价格高达100万美元。因此它的制作成本很高。而视频游戏市场规模为0亿美元。因此你有这种难以商业化和商品化的令人难以置信的技术。
And then you have this market that doesn't exist. That intersection was the founding of our company. And I still remember when Don, at the end of my presentation, Don was still kind of, he said, one of the things he said to me, which made a lot of sense back then, it makes a lot of sense today. He says, startups don't invest in startups. Startups don't partner with startups. And his point is that in order for NVIDIA to succeed, we needed another startup to succeed. And the other startup was electronic arts. And then on the way out, he reminded me that electronic arts's CTO is 14 years old and had to be driven to work by his mom. And he just wanted to remind me that that's who I'm relying on. And then after that, he said, if you lose my money, I'll kill you. And that was kind of my memories of that first meeting. But nonetheless, we created something. We went on the next several years to go create the market, to create the gaming market for PCs. And it took a long time to do so. We're still doing it today.
然后你有这个并不存在的市场。那个交集是我们公司的起源。我仍然记得当Don在我的演讲结束时，他仍然有些犹豫，他对我说的一些事情，那时很有道理，今天仍然很有道理。他说，创业公司不会投资创业公司。创业公司不会与创业公司合作。他的观点是，为了让NVIDIA成功，我们需要另一个创业公司成功。另一个创业公司就是电子艺术。然后在离开的时候，他提醒我说，电子艺术的首席技术官只有14岁，还需要他妈妈开车送他去上班。他只是想提醒我，这就是我所依靠的人。然后在那之后，他说，如果你亏了我的钱，我会杀了你。这就是我对那次第一次会议的记忆。但尽管如此，我们创造了一些东西。在接下来的几年里，我们继续创造市场，为个人电脑创造了游戏市场。这花了很长时间才做到。我们今天仍在努力建设这个市场。
We realized that not only do you have to create the technology and invent a new way of doing computer graphics so that what was a million dollars is now $300, $400, $500. That fits in the computer. And you have to go create this new market. So we have to create technology, create markets. The idea that a company would create technology, create markets, defines NVIDIA today. Almost everything we do, we create technology, we create markets. That's the reason why people say we have a, you know, people would call it a stack, an ecosystem, words like that. But that's basically it. At the core, for 30 years, what NVIDIA realized we had to do is in order to create the conditions by which somebody could buy our products, we had to go invent this new market. And it's the reason why we were early in autonomous driving. It was the reason why we're early in deep learning. It was the reason why we're early in just about all these things, including computational drug design and discovery. All the different areas were trying to create the market while we're creating the technology.
我们意识到，不仅必须创造技术并发明一种新的计算机图形处理方式，使之从一百万美元降低到300、400、500美元。并且要开辟这个新市场。因此，我们必须创造技术，创造市场。一个公司创造技术、创造市场的理念，今天定义了英伟达。我们几乎做的每一件事都是创造技术，创造市场。这就是人们说我们有一种“堆栈”、生态系统等说法的原因。但基本上就是这样。在过去的30年里，英伟达意识到我们必须创造条件，使某人能够购买我们的产品，这就是为什么我们不仅早早涉足自动驾驶领域，还早早涉足深度学习，甚至是计算药物设计和发现等领域。在我们创造技术的同时，我们努力开拓市场。
And so that's, okay, and then we got going. And then Microsoft introduced a standard called Direct3D. And that spawned off hundreds of companies. And we found ourselves a couple of years later, competing with just about everybody. And the thing that we invented the technology we invented, 3D graphics with, the consumerized 3D with, turns out to be incompatible with Direct3D. So we started this company, we had this 3D graphics thing, a million dollar thing, we're trying to make it consumerized.
所以就这样，好的，然后我们就开始了。然后微软推出了一个叫做Direct3D的标准。这就催生了数百家公司。几年后，我们发现自己与几乎所有人竞争。我们发明的技术，用来进行3D图形处理，消费者化的3D图形技术，结果是与Direct3D不兼容的。因此，我们创办了这家公司，我们有这个3D图形技术，价值成百万美元的东西，我们正在努力将其推向消费市场。
And so we invented all this technology. And then shortly after, it became incompatible. And so we had to reset the company or go out of business. But we didn't know how to build it the way that Microsoft had defined it. And I remember a meeting on a weekend, and the conversation was, we now have 89 competitors. I understand that the way we do it is not right, but we don't know how to do it the right way. And thankfully, there was another bookstore. And the bookstore is called Fries, Fries Electronics. I don't know if it's still here.
因此我们发明了所有这些技术。然后不久之后，技术就变得不兼容了。因此我们不得不重新设定公司或者破产。但是我们不知道如何按照微软定义的方式构建它。我记得一个周末的会议，谈话内容是，我们现在有89个竞争对手。我知道我们的做法不对，但是我们不知道如何正确地做。幸运的是，还有另一家书店。这家书店叫做Fries，Fries电子。我不知道它现在是否还存在。
And so I had, I think I drove Madison, my daughter on a weekend to Fries. And it was sitting right there, the OpenGL manual, which would define how Silicon Graphics did computer graphics. And so it was right there, it was like $68 a book. And so I had a couple hundred dollars, I bought three books. I took it back to the office and I said, guys, I found it, our future. And I handed out, I had three versions of it, I handed out, had a big nice center fold. The center fold is the OpenGL pipeline, which is the computer graphics pipeline. And I handed it to the same geniuses that I founded the company with.
因此，我想我在一个周末开车带着我的女儿麦迪逊去了弗里斯。它就坐在那里，OpenGL手册，它定义了硅图形如何进行计算机图形。它就在那里，每本书大约68美元。我带了几百美元，买了三本书。我把它带回办公室，告诉大家，我找到了我们的未来。我分发了三种版本的书，有一个漂亮的中央折页。中央折页是OpenGL流水线，也就是计算机图形流水线。我把它交给了和我一起创立公司的那些天才。
And we implemented the OpenGL pipeline like nobody had ever implemented the OpenGL pipeline, and we built something the world never seen. And so a lot of lessons are right there. That moment in time for our company gave us so much confidence. And the reason for that is you can succeed in doing something, inventing a future, even if you were not informed about it at all. And it's kind of my attitude about everything now.
我们实现了OpenGL管线，就像没有人曾经实现过的那样，我们建造了世界从未见过的东西。因此，我们从中学到了很多经验教训。那个时刻对我们公司来说带来了很多信心。原因在于，即使你对某件事一无所知，也可以成功地创造未来。这已经成为了我的一种态度。
When somebody tells me about something and I've never heard of it before, or if I've heard of it, never don't understand how it works at all, my first thought is always, how hard can it be? And it's probably just a textbook away. You know, you're probably one archived paper away from figuring this out. And so I spent a lot of time reading archived papers. And it's true, it's true.
当有人告诉我某件事，而我之前从未听说过，或者虽然听说过但完全不明白它是如何运作的，我的第一个想法总是，这有多难呢？也许只需要一本教科书就能弄清楚。你知道的，也许你离弄清楚这个问题只差一篇归档论文。因此，我花了很多时间阅读存档论文。事实如此，真的。
You can, now of course, you can't learn how somebody else does something and do it exactly the same way in hope to have a different outcome. But you could learn how something can be done and then go back to first principles and ask yourself, giving the conditions today, given my motivation, given the instruments, the tools, given how things have changed, how would I redo this? How would I reinvent this whole thing?
现在你当然可以学习别人是如何做某件事，并且以希望获得不同结果的方式来复制。但你可以学习如何做某事，然后回到基本原则，问问自己，在当今条件下，考虑到我的动机、工具与变化，我该如何重新做这件事？我应该如何重新发明整个事情？
How would I design it? How would I build a car today? Would I build it incrementally from 1950s and 1900s? How would I build a computer today? How would I write software today? Doesn't make sense. And so I go back to first principles all the time, even in the company today and just reset ourselves. Because the world has changed. And the way we wrote software in the past was mallethic and it's designed for supercomputers, but now it's disaggregated, so on and so forth. And how we think about software today, how we think about computers today, how we think just always cause your company, always cause yourself to go back to first principles and it creates lots and lots of opportunities.
我会如何设计它？我今天会如何建造一辆汽车？我会逐步从1950年代和1900年代建造它吗？我今天会如何建造一台电脑？我今天会如何编写软件？这都没有意义。所以我经常回到第一原则，即使在公司内部，我们也总是以重置自己为目标。因为世界已经改变。过去我们编写软件的方式是铸造型的，设计用于超级计算机，但现在它已经分散化等等。我们今天如何思考软件，如何思考计算机，如何思考总是引导您的公司，总是引导您自己回到第一原则，并创造大量机会。
Yeah, the way you applied this technology turns to be revolutionary, you get all the momentum that you need to IPO and then some more, because you grow your revenue nine times in the next four years. But in the middle of all of this success, you decide to pivot a little bit, the focus of innovation happening in Vidya based on a phone call you have with this chemistry professor.
是的，你应用这项技术的方式变得革命性，你已经获得了上市所需的所有动力，甚至更多，因为在接下来的四年里，你的收入增长了九倍。但就在所有这些成功的中间，你决定稍微改变方向，创新的重点转移到了一通电话中你和这位化学教授之间关于Vidya的讨论上。
Can you tell us about that phone call and how you connected the dots from what you heard to where you went? Remember at the core, the company was pioneering a new way of doing computing. Computer graphics was the first application, but we already always knew that there would be other applications and so image processing came, particle physics came, fluids came, so on and so forth. All kinds of interesting things that we wanted to do.
你能告诉我们关于那通电话的情况，以及你是如何从所听到的信息中联系到你接下来的行动的吗？记住，在核心要点是，这家公司正在开创一种新的计算方式。计算机图形学是第一个应用领域，但我们早就知道会有其他应用领域，比如图像处理、粒子物理、流体力学等等。我们想做各种有趣的事情。
We made the processor more programmable so that we could express more algorithms, if you will. And then one day we invented programmable shaders, which made all forms of imaging and computer graphics programmable. That was a great breakthrough, so we invented that. On top of that, we invented, we tried to look for ways to express more sophisticated algorithms that could be computed on our processor, which is very different than a CPU.
我们让处理器更加可编程，这样我们就能表达更多的算法，你可以这样理解。然后，有一天我们发明了可编程着色器，使所有形式的成像和计算机图形都可编程。这是一个巨大的突破，所以我们发明了这个。除此之外，我们尝试寻找能在我们处理器上计算更复杂算法的方法，这与 CPU 非常不同。
And so we created this thing called CG, I think it was 2003 or so, C for GPUs. It predated CUDA by about three years. The same person who wrote the textbook that saved the company, Mark Kilgard, wrote that textbook. And so CG was super cool, we wrote textbooks about it, we started teaching people how to use it, we developed tools and such. And then several researchers discovered it. Many of the researchers here, students here at Stanford, was using it. Many of the engineers that then became engineers in NVIDIA were playing with it. A couple of doctors at Mass General picked it up and used it for CT reconstruction, so I flew out and saw them and said, you know, what are you guys doing with this thing? And they told me about that. And then a computational quantum chemist used it to express his algorithms. And so I realized that there's some evidence that people might wanna use this. And it gave us incrementally more confidence that we had to go do this, that this field, this form of computing could solve problems that normal computers really can't. And reinforced our belief and kept us going. Every time you heard something new, you really savored that surprise. And that seems to be a theme throughout your leadership at NVIDIA. It feels like you make these bets so far in advance of technology inflections that when the apple finally falls from the tree, you're standing right there in your black leather jacket waiting to catch it. How do you find the can always seems like a diving catch? It does seem like a diving catch. You do things based on core beliefs. You know, we deeply believe that we could create a computer that solves problems and normal processing can't do.
因此我们创造了一个叫做CG的东西，我想大约是在2003年，C代表GPU。它比CUDA早大约三年出现。那本拯救了公司的教科书的作者马克·基尔加德也写了那本教科书。所以CG非常酷，我们写了关于它的教科书，开始教人们如何使用它，开发工具等。然后一些研究人员发现了它。斯坦福大学的许多研究生在使用它。后来成为NVIDIA工程师的许多工程师也在使用它。麻省总医院的几位医生开始使用它进行CT重建，所以我飞过去看了一下他们，问他们在用这个东西做什么。然后一位计算量子化学家使用它来表达他的算法。所以我意识到有一些证据表明人们可能想要使用这个东西。这给了我们更多信心，我们必须去做这件事，这种形式的计算可以解决正常计算机无法解决的问题。这加强了我们的信念，并让我们继续前行。每当你听到一些新的东西时，你真的会好好体味这种惊喜。这似乎是你在NVIDIA领导下的主题。感觉你总是在科技转折点前做出这些赌注，当苹果最终从树上掉下来时，你就站在那里，穿着黑色皮夹克，等着接住它。你是如何做到看起来总是像一个扑救呢？的确看起来像一个扑救。你做事情是基于核心信念的。我们深信我们能够创造一台解决正常处理无法做到的问题的计算机。
That there are limits to what a CPU can do. There are limits to what general purpose computing can do. And then there are interesting problems that we can go solve. The question is always, are those interesting problems only? Or can they also be interesting markets? Because if they're not interesting markets, it's not sustainable. And NVIDIA went through about a decade where we were investing in this future and the markets didn't exist. There was only one market at the time, it was computer graphics. For 10, 15 years, the markets that fuels NVIDIA today just didn't exist. And so how do you continue with all of the people around you, our company and NVIDIA's management team and all of the amazing engineers that are creating this future with me? All of your shareholders, your board of directors, all your partners, you're taking everybody with you and there's no evidence of a market. That is really, really challenging. The fact that the technology can solve problems and the fact that you have research papers that are used that are made possible because of it are interesting, but you're always looking for that market. But nonetheless, before a market exists, you still need early indicators of future success.
CPU的能力是有限的。通用计算也有其局限性。然后就有了我们可以解决的有趣问题。关键问题始终是，这些有趣的问题只是有趣吗？还是它们也可能是有趣的市场？因为如果它们并不具有市场吸引力，那是不可持续的。NVIDIA经历了大约十年的投资未来发展，市场并不存在。当时只有一个市场，即计算机图形。在过去的十到十五年里，如今推动NVIDIA发展的市场根本不存在。那么，你要如何继续发展，围绕身边的所有人——我们公司、NVIDIA的管理团队以及一切为创造未来而努力的优秀工程师们？所有的股东、董事会成员和合作伙伴，你要带着每个人一起前行，而却找不到市场的证据。这是非常具有挑战性的。技术可以解决问题，而且因此产生了许多研究论文，这是令人感兴趣的，但你始终在寻找那个市场。尽管如此，即使市场尚不存在，你还是需要未来成功的早期指标。
We have this phrase in the company, there's a phrase called key performance indicators. Unfortunately, KPIs are hard to understand. I find KPIs hard to understand. What's a good KPI? A lot of people, when we look for KPIs, you go gross margins. That's not a KPI, that's a result. You're looking for something that's an early indicators of future positive results. And as early as possible. And the reason for that is because you want early, that early sign that you're going in the right direction. And so we have this phrase that's called EOIFS, early indicators, EIO of this. Early indicators are future success. And it helps people, because I was using it all the time to give the company hope that, hey look, we solved this problem, we solved that problem, we solved this problem. The markets didn't exist, but there were important problems. And that's what the company's about, to solve these problems. We want to be sustainable, and therefore the markets have to exist at some point. But you want to decouple the result from evidence that you're doing the right thing.
在公司中，我们有一个短语，叫做关键绩效指标。不幸的是，KPIs很难理解。我觉得KPIs很难理解。什么是一个好的KPI？很多人在寻找KPIs时会选择毛利率。那不是一个KPI，那是一个结果。你要找的是未来积极结果的早期指标。尽可能早地找到。这是因为你想要早点知道自己朝着正确的方向前进。因此，我们有一个短语叫做EOIFS，早期指标，对未来成功的早期指标。这有助于人们，因为我经常使用它来给公司带来希望，告诉大家，嘿，看，我们解决了这个问题，解决了那个问题，解决了这个问题。市场可能还不存在，但这些问题很重要。这就是公司的目标，解决这些问题。我们想要持续发展，因此市场在某个时候必须存在。但你要将结果与证据表明你正在做正确的事情分开。
And so that's how you kind of solve this problem of investing into something that's very, very far away. And having the conviction to stay on the road is defined as early as possible to indicators that you're doing the right things. And so start with a core belief, unless something changes your mind, you can continue to believe in it. And look for early indicators of future success. What are some of those early indicators that have been used by product teams at NVIDIA? All kinds. I saw a paper. Along before I saw the paper, I met some people that needed my help on this thing called deep learning. At a time, I didn't know what deep learning was. And they needed us to create a domain-specific language so that all of their algorithms could be expressed easily on our processors. And we created this thing called KuDNN.
这就是你如何解决投资于遥远事物的难题。早早确定在正确道路上的信念，并保持这种信念尽早到达。从核心信念开始，除非有什么事情改变了你的想法，你可以继续相信它。寻找未来成功的早期指标。在NVIDIA的产品团队中，有哪些早期指标被使用呢？各种各样。我看过一篇论文。在看到这篇论文之前，我遇到了一些需要帮助的人，他们要做的是深度学习。当时我不知道什么是深度学习。他们需要我们创建一个特定领域的语言，以便所有的算法都可以轻松表达在我们的处理器上。我们创造了这个叫做KuDNN的东西。
And it's essentially the sequel is in storage computing. This is neural network computing. And we created a language, if you will, domain-specific language for them. And it kind of like the OpenGL of deep learning. And so they needed us to do that so that they could express their mathematics. And they didn't understand CUDA, but they understood their deep learning. And so we created this thing in the middle for them. And the reason why we did it was because even though there were zero, I mean, these researchers had no money. And this is kind of one of the great skills of our company that you're willing to do something even though the financial returns are completely nonexistent. Or maybe very, very far out, even if you believed in it.
这实质上是存储计算的续集。这是神经网络计算。我们为他们创造了一种语言，也就是领域特定语言。它有点像深度学习的OpenGL。所以他们需要我们这样做，这样他们可以表达他们的数学。他们不理解CUDA，但他们懂得他们的深度学习。所以我们为他们创建了这个中间环节。我们这样做的原因是因为，尽管这些研究人员没有钱。这是我们公司的一个伟大技能，即使财务回报完全不存在，或者可能非常遥远，即使你相信它，你仍然愿意做某些事情。
We ask ourselves, is this worthy work to do? Does this advance a field of science somewhere that matters? Notice, this is something that I've been talking about since the very beginning of time, we find inspiration not from the size of a market, but from the importance of the work. Because the importance of the work is the early indicators of a future market. And nobody has to write a business case on it. Nobody has to show me a P&L. Nobody has to show me a financial forecast. The only question is, is this important work? And if we didn't do it, would it happen without us? Now, if we didn't do something and something could happen without us, it gives me tremendous joy, actually.
我们问自己，这是值得做的工作吗？这是否推动了某个重要的科学领域？请注意，这是我从一开始就在谈论的事情，我们从工作的重要性中获得灵感，而不是市场规模。因为工作的重要性是未来市场的早期指标。没人需要为此编写商业案例，也没人需要向我展示损益表或财务预测。唯一的问题是，这项工作重要吗？如果我们不做，会不会没有人做？现在，如果我们不做某事，而且事情也可以在没有我们的情况下发生，这实际上会给我带来巨大的喜悦。
And the reason for that is, could you imagine, the world got better, you didn't have to lift a finger? That's the definition of ultimate laziness. And in a lot of ways, you want that habit. And the reason for that is this. You want the company to be lazy about doing things that other people always do, can do. If somebody else can do it, let them do it. We should go select the things that, if we didn't do it, the world would fall apart. You have to convince yourself of that. That if I don't do this, it won't get done. That is, and if that work is hard, and that work is impactful and important, then it gives you a sense of purpose. Does that make sense? And so our company has been selecting these projects, Deep Learning was just one of them. And the first indicator of the success of that was this, fuzzy cat that Andrew Ann came up with. And then Alex Kershefsky detected cats, not all the time, but successfully enough that it was, this might take us somewhere. And we reasoned about the structure of deep learning and work computer scientists, and we understand how things work.
这样做的原因是，你能想象一下吗，世界变得更好了，你甚至不用动手吗？这就是终极懒惰的定义。在很多方面，你想要这种习惯。原因在于，你希望公司在做那些其他人总是做、能够做的事情时变得懒惰。如果别人能做到，就让他们去做。我们应该选择那些如果我们不去做，世界就会崩溃的事情。你必须让自己相信这一点。如果我不做这件事，它就不会完成。如果那项工作又困难，又具有影响力和重要性，那就会给你一种目标感。明白吗？所以我们的公司已经在选择这些项目了，深度学习只是其中之一。而这种成功的第一指标是，安德鲁·安提德提出的那只模糊的猫。然后亚历克斯·科舍夫斯基成功地检测到了猫，虽然不是每次都成功，但足够成功以至于我们认为这可能会带领我们走向某个地方。我们对深度学习的结构进行了推理，并与计算机科学家一起工作，我们理解事物是如何运作的。
And so we convinced ourselves this could change everything. And anyhow, but that's an example. So these selections that you've made, they've paid huge dividends, both literally and figuratively. But you've had to steer the company through some very challenging times. Like when it lost 80% of its market cap amid the financial crisis, because Wall Street didn't believe in your bet on ML. In times like these, how do you steer the company and keep the employees motivated at the task at hand? My reaction during that time is the same reaction I had about this week. Earlier today, you asked me about this week. My pulse was exactly the same. This week is no different than last week or the week before that. And so the opposite of that, when you drop 80%.
因此，我们说服自己这可能会改变一切。总之，那只是一个例子。你所做的选择带来了巨大的回报，无论是字面意义上还是象征意义上。但你也不得不在公司遇到一些非常具有挑战性的时刻。比如在金融危机中失去了80%的市值，因为华尔街不相信你对机器学习的押注。在这种时刻，你如何引导公司，保持员工对手头任务的积极性？那段时间我的反应与此时的反应一样。你在今天早些时候问我这个星期的情况。我的心跳完全相同。这个星期与上一周或上上周没有任何不同。与之相反，当你损失80%的时候。
Don't get me wrong. When your share price drops 80%, it's a little embarrassing. Okay? And you just wanna wear a t-shirt that says, wasn't my fault. Hahaha. But even more than that, you just don't wanna, you don't wanna get out of your bed, you don't wanna leave the house. All of that is true. All of that is true. But then you go back to just doing your job and woke up at the same time, prioritize my day in the same way. I go back to what do I believe? You gotta always go check back to the core. What do you believe? What are the most important things? And just check them off. Sometimes it's helpful, family loves me. Okay, check. You know, double cheat on. And so you just gotta check it off. And you go back to your core and then go back to work.
别误会我的意思。当你的股价下跌了80%，有点尴尬是吧。好吧？你可能只想穿件写着“这不是我的错”的T恤。哈哈。但更重要的是，你可能只想躺在床上，不想出门。所有这些都是真的。但然后你会回归到做你的工作，每天早上都会醒来，按照同样的方式安排我的一天。我会回到我相信的事情是什么？你必须始终回到核心。你相信什么？什么是最重要的事情？然后逐一核对。有时候，家人爱我。好的，核对。然后你只需要把它核对下来。然后回到你的核心，然后回到工作。
And then every conversation's go back to the core. Keep the company focused back on the core. Do you believe in it? Did something change? The stock price changed, but did something else change? The physics change? The gravity change? Did all of the things that we assumed, that we believed, that led to our decision, did any of those things change? Because if those things change, you gotta change everything. But if none of those things change, you change nothing. Keep on going. Yeah. That's how you do it. In speaking with your employees, they say that you. Try to avoid the public. Ha ha ha ha. In speaking with your employees, they've said that you're a leadership student. Including the employees. I'm just kidding. Now leaders have to be seen, unfortunately. That's the hard part. You know, I was an electrical engineering student and I was quite young when I went to school.
然后每一次谈话都要回到核心问题。让公司专注于核心问题。你相信吗？有什么改变吗？股价变了，但还有其他什么改变吗？物理规律变了吗？重力变了吗？我们假设的一切，我们相信的一切，导致我们决策的一切，有哪一样发生了改变吗？因为如果那些事物发生了改变，你就得改变一切。但如果那些事物没有改变，你就不要改变什么。继续前行。是的，这就是做事的方式。与你的员工交谈时，他们说你倾向于避开公众视野。哈哈哈。与你的员工交谈时，他们说你是一个领导学生。包括员工在内。开玩笑啦。现在领导者必须被看见，遗憾的是。这是困难的一部分。你知道，我曾是一名电气工程学生，当时我还很年轻。
When I went to college, I was still 16 years old and so I was young when I did everything. And so I was a bit of an introvert. Kind of shy. I don't enjoy public speaking. I'm delighted to be here. I'm not suggesting. Ha ha ha ha. But it's not something that I do naturally. And so when things are challenging, it's not easy to be in front of precisely the people that you care most about. You know? And the reason for that is because could you imagine a company meeting with just our stock prices dropped by 80%? And the most important thing I have to do is the CEO is this. To come and face you, explain it. And partly you're not sure why. Partly you're not sure how long, how bad. You just don't know these things. But you still gotta explain it. Face all these people. And you know what they're thinking. Some of them are probably thinking we're doomed.
当我去上大学的时候，我还只有16岁，所以在做任何事情的时候我都很年轻。我有点内向，有些害羞。我不喜欢公开演讲。我很高兴能在这里。我并不是在暗示什么。哈哈哈。但这不是我天生擅长的。当事情变得困难时，在最关心的人面前站出来并不容易。你知道吗？这是因为你可以想象一下，如果有一个公司会议，我们的股价下跌了80%。而作为CEO，我最重要的就是要做的就是来面对你们，解释这个问题。在某种程度上你不确定原因。在某种程度上你也不确定情况有多糟糕，有多久。你根本不知道这些事情。但你还是必须解释清楚。面对所有这些人。而且你知道，他们在想什么。有些人可能会认为我们注定要失败了。
Some people are probably thinking you're an idiot. And some people are probably thinking something else. And so there are a lot of things that people are thinking and you know that they're thinking those things. But you still have to get in front of them and deal, do the hard work. It may be thinking of those things, but yet not a single person of your leadership team left during times like this. And in fact. Unemployable. That's what I keep reminding them. I'm just kidding. I'm surrounded by geniuses. I'm surrounded by geniuses, yeah. Other geniuses. Unbelievable. NVIDIA is well known to have singularly the best management team on the planet. This is the deepest technology management team the world's ever seen. I'm surrounded by a whole bunch of them. And they're just geniuses. Business teams. Marketing teams. Sales teams. Just incredible. Engineering teams. Research teams. Unbelievable. Yeah.
有些人可能认为你是个笨蛋。还有一些人可能会想其他的事情。所以人们会有很多想法，而你知道他们在想些什么。但你仍然要站在他们面前，努力工作。也许他们在想这些事情，但在这种时候，你的领导团队没有一个人离开。事实上,不可雇用。这就是我一直在提醒他们的。我只是开玩笑。我被一群天才包围着。是其他天才。不可思议。英伟达众所周知拥有全球最优秀的管理团队。这是世界上有史以来最深厚的技术管理团队。我被他们中的一群包围着。他们只是天才。商业团队。营销团队。销售团队。令人难以置信。工程团队。研究团队。令人难以置信。是的。
Your employees say that your leadership style is very engaged. You have 50 direct reports. You encourage people across all parts of the organization to send you the top five things on their mind. And you constantly remind people that no task is beneath you. Can you tell us why you've purposefully designed such a flat organization? And how should we be thinking about our organizations that we design in the future? No task is, to me, no task is beneath me. Because remember, I used to be a dishwasher. And I mean that. I used to clean toilets. I cleaned a lot of toilets. I've cleaned more toilets than all of you combined. And some of them just can't unsee. LAUGHTER I don't know what to tell you. That's life. And so you can't show me a task. That's beneath me.
你的员工说你的领导风格非常亲近。你有50名直接下属。你鼓励组织中各个部分的人向你发送他们心中最重要的五件事情。你经常提醒大家没有什么任务是不能由你来做的。你可以告诉我们为什么你特意设计了这样一个扁平的组织结构吗？我们应该如何思考未来设计的组织？对我来说，没有什么任务是低于我的身份的。因为请记住，我曾经是一个洗碗工。我当时的确是这样的。我曾经打扫过厕所。我打扫过很多的厕所。我打扫过的厕所比你们所有人加起来都要多。有些情景你们可能永远无法忘记。哈哈。我不知道该告诉你们什么。这就是生活。因此你们无法找到一项对我来说低于我的任务。
Now I'm not doing it only because of, whether it's beneath me or not beneath me. If you send me something and you want me input on it, and I can be of service to you, and in my review of it, share with you how I reasoned through it, I've made a contribution to you. I've made it possible for you to see how I reasoned through something. And by reasoning, as you know, how someone reasons through something empowers you. You go, oh my gosh, that's how you reasoned through something like this. It's not as complicated as it seems. This is how you reasoned through something that's super ambiguous. This is how you reasoned through something that's incalculable. This is how you reasoned through something that seems to be very scary. This is how you seem, do you understand? And so I show people how to reason through things all the time. Strategy things, you know, how to forecast something, how to break a problem down. And you're just, you're empowering people all over the place.
现在我做这件事并不仅仅因为它是否适合我，或者不适合我。如果你给我发些东西，想让我提出意见，我可以为你提供帮助，通过我的反馈告诉你我是如何思考的，这样我就为你作出了贡献。我让你看到了我是如何思考的。而你知道，看到别人如何思考某事会让你变得更有能力。你会惊叹，原来你是这样思考这种事情的。它并不像看起来那么复杂。这就是你如何思考一些超级模糊的事情。这就是你如何思考一些难以计算的事情。这就是你如何思考一些看起来令人害怕的事情。你明白了吗？所以我经常向人们展示如何思考事情。战略性的事情，你知道，如何预测某事，如何解决问题。你只是，你让人们在各个方面变得更有能力。
And so that's how I see it. If you send me something, you want me to help review it, I'll do my best. And I'll show you how I would do it. In the process of doing that, of course, I learned a lot from you. Is that right? You gave me a seat of a lot of information, I learned a lot. And so I feel rewarded by the process. It does take a lot of energy sometimes because, you know, you got in order to add value to somebody and they're incredibly smart as a starting point. And I'm surrounded by incredibly smart people. You have to at least get to their plane, you know? You have to get into their headspace. And that's really hard. That's really hard. And that takes just an enormous amount of emotional and intellectual energy. And so I feel exhausted after I work on things like that. I'm surrounded by a lot of great people.
所以这就是我的看法。如果你送给我什么东西，希望我帮你审阅，我会尽力而为。我会展示我会怎么处理。在这个过程中，当然，我从你那里学到了很多。对吧？你给了我很多信息，我学到了很多。所以我觉得这个过程给了我回报。有时候这确实需要很多精力，因为你知道，要想为别人增加价值，他们本身就非常聪明。我周围都是非常聪明的人。你至少要达到他们的水准，对吧？你要进入他们的思维空间。这真的很难。这真的很难。这需要巨大的情感和智力能量。所以我在处理这种事情后感觉很疲惫。我被很多优秀的人包围着。
A CEO should have the most direct reports by definition because the people that report to the CEO requires the least amount of management. It makes no sense to me that CEOs have so few people reporting to them. Except for one fact that I know to be true. The knowledge, the information of a CEO is supposedly so valuable, so secretive. You can only share with two other people, or three. And their information is so invaluable, so incredibly secretive that they can only share with a couple more. Well, I don't believe in a culture and environment where the information that you possess is the reason why you have power. I would like us all to contribute to the company. And our position in the company should have something to do with our ability to reason through complicated things, lead other people to achieve greatness, inspire, empower other people, support other people. Those are the reasons why the management team exists. In service of all of the other people that work in the company, to create the conditions by which all of these amazing people volunteer to come work for you instead of all the other amazing high tech companies around the world, they elect it, they volunteer to work for you.
一位首席执行官应该根据定义拥有最直接的下属，因为向首席执行官汇报的人需要最少的管理。对我来说，首席执行官只有很少的人向其汇报是毫无意义的。除了一个我认为是真实的事实。首席执行官的知识、信息据说是如此宝贵、如此神秘。只能分享给其他两个人，或三个人。而他们的信息是如此宝贵、如此难以揭示，以至于他们只能分享给几个人。嗯，我不相信知识就是获得权力的原因的文化和环境。我希望我们都为公司做出贡献。我们在公司的位置应该与我们推理复杂事物的能力、带领他人取得伟大成就、鼓舞他人、赋予他人权力、支持他人有关。这些是管理团队存在的原因。为了服务于公司内的其他所有人，创造这样一种条件，让所有这些了不起的人自愿前来为你工作，而不是为全球各地其他出色的高科技公司工作，他们选择了，他们自愿为你工作。
And so usually you create the conditions by which they could do their life's work, which is my mission. You know, you've probably heard it. I've said that pretty clearly, and I believe that. What my job is is very simply to create the conditions by which you could do your life's work. And so how do I do that? What does that condition look like? What that condition should result in great deal of empowerment, you can only be empowered if you understand the circumstance, isn't it right? You have to understand the context of the situation you're in in order for you to come up with great ideas. And so I have to create a circumstance where you understand the context, which means you have to be informed. And the best way to be informed is for there to be as little layers of information, mutilation, right, between us. And so that's the reason why it's very often that I'm reasoning through things like in a nor audience like this.
因此，通常你们创造了他们可以开展终身事业的条件，这就是我的使命。你知道的，你们可能已经听过了。我已经说得很清楚了，我相信这一点。我的工作很简单，就是创造条件让你们能够开展终身事业。那么我该如何做呢？这种条件是什么样的？这种条件应该产生一种巨大的赋权感，只有了解情况才能获得赋权，对吧？你必须了解自己所处的情境，才能提出好的主意。所以我必须创造一个让你们了解情境的条件，这意味着你们必须得到通报。而获得通报的最佳方式就是我们之间的信息层次尽可能少、不被扭曲。这就是为什么我经常会像在这样一个普通的听众面前思考事情的原因。
I say first of all, this is the beginning facts. These are the data that we have. This is how I would reason through it. These are some of the assumptions. These are some of the unknowns. These are some of the knowns. And so you reason through it. And now you've created an organization that's highly empowered. In video's 30,000 people, we're the smallest large company in the world. We're tiny little company. But every employee is so empowered and they're making smart decisions on my behalf every single day. And the reason for that is because, you know, they understand my condition. They understand my condition. I'm very transparent with people. And I believe that I can trust you with the information.
首先，我要说的是，这是事实的开端。这是我们拥有的数据。这是我推理的方式。这是一些假设。这是一些未知的因素。这是一些已知的因素。所以你要通过推理来解决问题。现在，你已经建立了一个高度赋权的组织。在视频中的3万人中，我们是世界上最小的大公司。我们是一个微小的公司。但是每位员工都被赋予了巨大的权力，他们每天都在为我做着明智的决定。原因是因为，你知道，他们了解我的情况。他们了解我的情况。我对人们非常坦诚，并且相信我可以信任你们这些信息。
Oftentimes the information is hard to hear and the situations are complicated, but I trust that you can handle it. You know, a lot of people hear me say, you know, your adults here, you can handle this. Sometimes they're not really adults. They just graduated. I'm just kidding. I know that when I first graduated, I was barely an adult. And I was fortunate that I was trusted with important information. So I want to do that. I want to create the conditions for people to do that. I do want to now address the topic that is on everybody's mind, AI.
很多时候，信息很难听懂，情况也很复杂，但我相信你能处理好。你知道，很多人听我说，你们是成年人了，能应对这些情况。有时候他们并不是真正的成年人。他们只是刚刚毕业。我只是开玩笑。我知道当我刚毕业的时候，我几乎还不算是一个成年人。我很幸运被信任处理重要信息。所以我想要这样做。我想创造条件让人们做到这一点。我现在想要谈一下大家都在关注的话题，人工智能。
Last week, you said that generative AI and accelerated computing have hit the tipping point. So as this technology becomes more mainstream, what are the applications that you personally are most excited about? Well, you have to go back to first principles and ask yourself, what is generative AI? What happened? What happened was we now have the ability to have software that can understand something. They can understand why, you know, what is, first of all, we digitized everything. That was, you know, like for example, gene sequencing. You digitize genes. But what does it mean? That sequence of genes, what does it mean? We digitize amino acids. But what does it mean?
上周，你说生成式人工智能和加速计算已经达到了临界点。因此，随着这项技术变得更加普及，你个人最感兴趣的应用是什么？嗯，你必须回到第一原则，问问自己，什么是生成式人工智能？发生了什么？发生的是我们现在有能力拥有可以理解事物的软件。它们能够理解为什么，你知道，首先，我们数字化了一切。就像基因测序。我们数字化了基因。但是这意味着什么？那串基因序列，意味着什么？我们数字化了氨基酸。但是这意味着什么？
And so we now have the ability, we digitize words, we digitize sounds. We digitize the images, videos. We digitize a lot of things. But what does it mean? We now have the ability through a lot of studying, a lot of data and for patterns and relationships. We now understand what they mean. Not only do we understand what they mean, we can translate between them. Because we learned about the meaning of these things in the same world. We didn't learn about them separately. So we learned about speech and words and paragraphs and vocabulary in the same context. So we found correlations between them and they're all registered, if you will. Registered to each other. And so now we, not only do we understand the modality, the meaning of each modality, we can understand how to translate between them.
因此，我们现在拥有了数字化文字、声音、图像和视频的能力。我们数字化了很多东西。但这意味着什么呢？现在我们通过大量学习、大量数据以及模式和关系的研究，我们现在理解它们的含义。我们不仅理解它们的含义，还可以在它们之间进行翻译。因为我们在同一个世界中学习了这些事物的意义。我们并没有分开学习它们。因此，我们在同一个语境中学习了语音、词语、段落和词汇。因此我们找到了它们之间的关联，并且它们都登记在册，可以彼此联系。因此，现在我们不仅理解每种形式的含义，还可以理解如何在它们之间进行翻译。
And so for obvious things, you could caption video to text that's captioning. Text to images, mid-journey. Text to text, chat GPT, amazing things. And so we now know that we understand meaning and we can translate. The translation of something is generation of information. And all of a sudden you have to take a step back and ask yourself, what is the implication in every single layer of everything that we do? And so I'm exercising in front of you. I'm reasoning in front of you. The same thing I did a quarter 15 years ago. When I first saw Alex Net some 13, 14 years ago, I guess.
因此，对于明显的事情，你可以给视频加文字标题进行字幕。文字到图像，中途的旅程。文字到文字，聊天 GPT，令人惊奇的事情。现在我们知道我们理解意义，我们可以翻译。翻译某物就是信息的生成。突然间，你不得不退后一步，问自己，我们所做的每一个层面都意味着什么？所以我在你面前锻炼，我在你面前推理。我在15年前的一个季度做的同样的事情。当我大约13、14年前第一次看到Alex Net的时候，我想。
How I reasoned through it. What did I see? How interesting? What can it do? Very cool. But then most importantly, what does it mean? What does it mean? What does it mean to every single layer of computing? Because we're in the world of computing. And so what it means is that the way that we process information fundamentally will be different in the future. That's when NVIDIA builds, chips and systems. The way we write software will be fundamentally different in the future.
我是如何通过推理来解决的。我看到了什么？多有趣啊！它能做什么？非常酷。但更重要的是，它意味着什么？这意味着什么？这对每一层计算都意味着什么？因为我们处在计算的世界中。所以它的意义在于，我们未来处理信息的方式将会有根本的不同。这就是当NVIDIA构建芯片和系统时所做的。我们未来编写软件的方式将会有根本的不同。
The type of software will be able to write in the future will be different to new applications. And then also the processing of those applications will be different. What was historically a retrieval based model where information was pre-recorded, if you will, almost. You know, we wrote the text pre-recorded. And we retrieved that based on some recommender system algorithm. In the future, some seed of information will be the starting point. We call them prompts, as you guys know. And then we generate the rest of it.
将来我们能够编写的软件类型将与新应用程序不同。同时，这些应用程序的处理方式也会有所不同。在历史上，信息检索模型是基于预先录制的，你可以这么说。你知道，我们把文本预先录制下来。然后我们根据某种推荐系统算法来检索它。在将来，一些信息种子将成为起点。我们称之为提示，你们都知道的。然后我们生成其余的部分。
And so the future of computing will be highly generated. Well, let me give you an example of what's happening. For example, we're having a conversation right now. Very little of the information I'm conveying to you is retrieved. Most of it is generated. It's called intelligence. And so in the future, we're gonna have a lot more generative. Our computers will perform in that way. It's gonna be highly generative instead of highly retrieval based.
因此，计算的未来将会是高度生成的。让我举个例子来说明正在发生的事情。例如，我们正在进行一场对话。我传达给你的信息很少是检索来的，大部分是生成的。这被称为智能。因此，在未来，我们将会有更多的生成性。我们的计算机将以这种方式运作。它们将是高度生成性的，而不是基于高度检索的。
Then you go back and you're gonna ask yourself, you know, now for entrepreneurs, you're gonna ask yourself, what industries will be disrupted there for? Will we think about networking the same way? Will we think about storage the same way? Will we think about, would we be as abusive of internet traffic as we are today? Probably not. Notice we're having a conversation right now. And I want to get in my car every question.
然后你回去，你会问自己，你知道，现在对于企业家来说，你会问自己，哪些行业会受到颠覆？我们会以同样的方式思考网络吗？我们会以同样的方式考虑存储吗？我们会像今天这样滥用互联网流量吗？很可能不会。注意我们现在正在进行对话。而我想把每一个问题都放在我的车里。
So we don't have to be as abusive of transformation, information transporting as we used to. What's gonna be more? What's gonna be less? What kind of applications? You know, et cetera, et cetera. So you can go through the entire industrial spread and ask yourself what's gonna get disrupted, what's gonna get big different, what's gonna get nude, you know, so on and so forth. And that reasoning starts from what is happening.
因此，我们不再像过去那样对转型和信息传输进行滥用。未来会有什么？会有什么减少？有什么样的应用？你知道的，等等。因此，你可以遍历整个工业领域，问自己什么会被打破，什么会有重大改变，什么会变得新颖，你知道的，等等。并且这种推理始于正在发生的事情。
What is generative AI? Foundationally, what is happening? Go back to first principles with all things. There was something I was gonna tell you about organization. You asked the question and I forgot to answer it. The way you create an organization, by the way, someday, don't worry about how other companies or charts look. You start from first principles. Remember what an organization is designed to do?
什么是生成式人工智能？基本上，发生了什么事情？在所有事情上回到最基本的原则。我有件事要告诉你关于组织。你问了这个问题，我忘记回答了。创造一个组织的方式，顺便说一句，有一天，不要担心其他公司或图表是什么样子。你要从最基本的原则开始。记住组织的设计是为了做什么？
The organizations of the past where there's a king, you know, CEO, and then you have all these, you know, the royal subjects, you know, the royal court, and then east out, and then you keep working your way down. Eventually, they're employees. But the reason why it was designed that way is because they wanted the employees to have as little information as possible because their fundamental purpose of the soldiers is to die in the field of battle. To die without asking questions, you guys know this. I don't, I only have 30,000 employees.
过去的组织结构中有国王，CEO，然后是一堆皇家臣民，皇家法庭等等，一层一层往下。最终他们只是雇员而已。但是这么设计的原因是因为他们希望雇员知之甚少，因为士兵的基本任务是在战场上牺牲。不问为什么，你们都知道这一点。我只有3万名员工。
I would like them, none of them to die. I would like them to question everything. Does that make sense? And so the way you organize in the past and the way you organize today is very different. Second, the question is what is NVIDIA build? An organization is designed so that we could build whatever it is we build better. And so if we all build different things, why are we organized the same way? Why would this organizational machinery be exactly the same irrespective of what you build? It doesn't make any sense.
我希望他们中没有人死去。我希望他们质疑一切。这有道理吗？所以你过去的组织方式和现在的方式是非常不同的。其次，问题是英伟达建立了什么？一个组织被设计成我们可以更好地构建我们所构建的任何东西。所以如果我们都在构建不同的东西，为什么我们以相同的方式组织？为什么无论你构建什么，这个组织机构都是一模一样的？这没有任何意义。
You build computers, you organize this way. You build healthcare services, you build exactly the same way. It makes no sense whatsoever. And so you had to go back to first principles, just ask yourself what kind of machinery, what is the input, what is the output, what are the properties of this environment, you know, what is the forest that this environment is? The forest that this animal has to live in, what is its characteristics? Is it stable most of the time? You're trying to squeeze out the last drop of water? Or is it changing all the time? Being attacked by everybody?
你制造电脑时是这样组织的。你建立医疗服务也是完全相同的方式。这根本毫无意义。所以你必须回到最基本的原则，问问自己这种机器的特性是什么，输入是什么，输出是什么，这个环境的属性是什么，你知道，这个环境是什么样子的？这个动物必须生活在什么样的森林里，它的特点是什么？它大部分时间是稳定的吗？你是在努力挤出最后一滴水吗？还是一直在被所有人攻击？
And so you got to understand, you're the CEO, your job is to architect this company. That's my first job, to create the conditions by which you can do your life's work. And the architecture has to be right. And so you have to go back to first principles and think about those things. And I was fortunate that when I was 29 years old, you know, I had the benefit of taking a step back and asking myself, you know, how would I build this company for the future and what would it look like? And, you know, what's the operational system, which is called culture? What kind of behavior do we encourage, enhance, and what do we discourage and not enhance? You know, so on and so forth. And anyways. I want to save time for audience questions, but this year's theme for View from the Top is Redefining Tomorrow. And one question we've asked, all of our guests is, Jensen, as the co-founder and CEO of NVIDIA. If you were to close your eyes and magically change one thing about tomorrow, what would it be? Were we supposed to think about this in advance? LAUGHTER I'm going to give you a horrible answer. I don't know that it's one thing. Look, there are a lot of things we don't control. You know, there are a lot of things we don't control. Your job is to make a unique contribution, live a life of purpose, to do something that nobody else in the world would do or can do, to make a unique contribution, so that in the event that after you were done, everybody says, you know, the world was better because you were here. And so I think that to me, I live my life kind of like this. I go forward in time and I look backwards. So you asked me a question that's exactly from a computer vision pose perspective, exactly the opposite of how I think.
因此，你必须理解，你是这家公司的首席执行官，你的工作是设计这家公司。这是我的第一项工作，为你创造能够完成你毕生事业的条件。而这种架构必须正确。因此，你必须回到第一原则，并考虑这些事情。幸运的是，当我29岁时，我有幸能够退一步，问自己，我将如何为未来建立这家公司，它会是什么样子？以及，运作系统是什么，也就是文化？我们鼓励怎样的行为，增强哪些方面，我们又会阻止什么，不会增强什么？等等。总之，我想为观众的问题留时间，但今年“鸟瞰高处”（View from the Top）的主题是重新定义未来。我们问过所有嘉宾的一个问题，Jensen，作为NVIDIA的联合创始人和首席执行官。如果你闭上眼睛，魔法般地改变明天的一件事，那会是什么？我们应该提前考虑这个问题吗？（笑声）我的回答可能很糟糕。我不确定是一件事。你知道，有很多事情我们无法控制。你的工作是做出独特的贡献，过有意义的生活，做一些世界上没有其他人会做或能做到的事情，做出独特的贡献，以至于在你完成后，每个人都会说，你在这里让世界变得更好。因此，对我来说，我过着这样的生活。我往前走，然后回头看。所以你问我的这个问题正好从计算机视觉的角度来看，与我的思考方式完全相反。
Translation into Chinese: 尽量易读
对不起，您输入的内容无法翻译。请您提供一个可以翻译的句子或问题。
I never look forward from where I am. I go forward in time and look backwards. And the reason for that is it's easier. I would look backwards and kind of read my history. We did this and we did that way and we broke that problem down. Doesn't make sense. And so it's a little bit like how you guys solve problems. You figure out what is the end result that you're looking for and you work backwards to achieve it. And so I imagine NVIDIA making a unique contribution to advancing the future of computing, which is the single most important instrument of all humanity. Now it's not about our self-importance, but this is just what we're good at. And it's incredibly hard to do. And we believe we can make an absolute unique contribution that's taken us 31 years to be here and we're still just beginning our journey. And so this is insanely hard to do.
我从不往前看，我往前走，然后往回看。原因很简单，这样更容易。我往回看，看着自己的历史。我们这样做，我们那样做，我们解决了那个问题。看着一切并不合理。就像你们解决问题的方式。你们确定要达到的最终结果，然后向后推进以实现它。所以我想象着NVIDIA对推进计算机未来做出独特贡献，这是人类最重要的工具。这并不是为了凸显我们的重要性，而是因为我们擅长这个领域。这是极其困难的。我们相信我们可以做出绝对独特的贡献，花了我们31年才走到今天，但我们的旅程还远未结束。所以这是极其困难的。
Please provide the English text that you'd like me to translate to Chinese.
And when I look backwards, I believe that we made, I believe that that we're going to be remembered as a company that kind of changed everything. Not because we went out and changed everything through all the things that we said, but because we did this one thing that was insanely hard to do that we're incredibly good at doing, that we love doing we did for a long time. I'm part of the GSB lead. I graduated in 2023. So my question is, how do you see your company in the next decade as what challenges do you see your company would face and how you are positioned for that? First of all, can I just tell you what was going on through my head? As you say, what challenges? The list that flew by my head was so, so large that I was trying to figure out what to select. Now, the honest truth is that when you ask that question, most of the challenges that showed up for me were technical challenges.
当我回顾过去的时候，我相信我们做到了，我相信我们将作为一家改变一切的公司而被记住。不是因为我们通过所说的一切改变了一切，而是因为我们做了一件非常艰难的事情，我们非常擅长做这件事，我们热爱做这件事，而且我们长时间以来一直在做这件事。我是GSB的一员，我于2023年毕业。所以我的问题是，您如何看待您的公司在未来十年内将面临的挑战，以及您如何为此做好准备？首先，我可以告诉您我脑海中正在想什么吗？当您问及挑战时，我脑海中飞快闪过的名单是如此之长，以至于我在尝试选择。现在，诚实地说，当您提出这个问题时，我脑海中出现的大多数挑战都是技术挑战。
炒鸡蛋是我最喜欢的早餐食物。
And the reason for that is because that was my morning. If you were to, you know, chosen yesterday, it might have been market creation challenges. There are some markets that I, gosh, I just desperately would love to create. I just, can we just do it already? You know? But we can't do it alone. NVIDIA is a technology platform company. We're here in service of a whole bunch of other companies so that they could realize, if you will, our hopes and dreams through them. And so some of the things that I would love, I would love for the world of biology to be at a point where it's kind of like the world of chip design 40 years ago, computer aided in design, EDA, that entire industry, really made possible for us today.
原因是因为这是我的早晨。如果你选择昨天的话，可能会遇到市场创造的挑战。有一些市场，我真的很想创建。我只是很迫切想要做到。但我们不能独自做到。英伟达是一家技术平台公司。我们在为其他公司提供服务，让他们能够通过我们实现他们的愿望和梦想。我希望生物学的世界能够像40年前的芯片设计领域一样，有计算机辅助设计等技术，整个行业为我们今天所实现的一切提供了可能性。
Wellness is not just about physical health, but also about mental and emotional well-being. It is important to take care of all aspects of ourselves in order to lead a balanced and fulfilling life.
And I believe we're going to make possible for them tomorrow. Computer aided drug design, because we're able to now represent genes and proteins and even cells now. Very, very close to be able to represent and understand the meaning of a cell, a combination of a whole bunch of genes. What does a cell mean? It's kind of like, what does that paragraph mean? Well, if we could understand a cell like we can understand a paragraph, imagine what we could do. And so I'm anxious for that to happen. I'm kind of excited about that. There's some that I'm just excited about that I know we're around the corner on.
我相信我们将有可能在明天为他们做到这一点。计算机辅助药物设计，因为现在我们能够表示基因、蛋白质甚至细胞。非常非常接近能够表示和理解细胞的意义，一大堆基因的组合。细胞意味着什么？有点像，那个段落的意思是什么？嗯，如果我们能够像理解段落一样理解细胞，想象一下我们可以做什么。所以我迫切希望这种情况发生。我对此感到兴奋。有些事情我只是感到兴奋，我知道我们马上就能做到。
For example, humanoid robotics. The very, very close around the corner. And the reason for that is because if you can tokenize and understand speech, why can't you tokenize and understand manipulation? And so these kind of computer science techniques, once you figure something out, you ask yourself, I forgot to do that, why can't I do that? And so I'm excited about those kind of things. And so that challenge is kind of a happy challenge.
例如，人形机器人。就在不久的将来。这样的原因是因为如果你能对语音进行标记和理解，为什么不能对操作进行标记和理解呢？因此，一旦掌握了这些计算机科学技术，你会问自己，我忘记了做那件事，为什么不能做呢？我对这些事情感到兴奋。因此，这种挑战是一种快乐的挑战。
Some of the other challenges, some of the other challenges, of course, are industrial and geopolitical and they're social. But you've heard all that stuff before. These are all true. The social issues in the world, the geopolitical issues in the world, why can't we just get along at things in the world? Why do I have to say those kind of things in the world? Why do I have to say those things and amplify them in the world? Why do we have to judge people so much in the world? You know, all those things, you guys all know that. I don't have to say those things over again.
一些其他挑战，当然还有工业、地缘政治和社会方面的挑战。但这些你们之前都听过了。这些都是事实。世界上的社会问题，世界上的地缘政治问题，为什么我们不能和平共处呢？为什么我必须在世界上说出那些话？为什么我必须在世界上说出那些话并放大它们？为什么我们在世界上要如此苛刻地评判他人？你们都知道那些事情，我不必再重复。
My name's Jose. I'm a class of the 2023 from the GSB. My question is, are you worried at all about the pace at which we're developing AI? And do you believe that any sort of regulation might be needed? Thank you. Yeah, the answer is yes and no. We need, you know, the greatest breakthrough in modern AI, of course, deep learning and it enabled great progress. But another incredible breakthrough is something that humans know and we practice all the time and we just invented it for language models called grounding, reinforcement learning, human feedback.
我的名字是何塞。我是来自GSB的2023年级学生。我的问题是，你是否担心我们正在发展人工智能的速度？你是否认为需要任何形式的监管？谢谢。是的，答案是肯定的，也有否定的部分。当然，现代人工智能领域最伟大的突破是深度学习，它取得了很大的进展。但另一个令人难以置信的突破是人类知道并且我们也一直在实践的，我们刚刚为语言模型发明的东西，叫做基础、强化学习和人类反馈。
I provide reinforcement learning human feedback every day. That's my job. And for their parents in the room, you're providing reinforcement learning human feedback all the time. Okay? Now we just figured out how to do that at a systematic level for artificial intelligence. There are a whole bunch of other technologies necessary to guardrail, fine-tune, ground, for example. How do I generate tokens that obey the laws of physics? You know, right now things are floating in space and doing things and they don't obey the laws of physics. How do, that requires technology. Guardrail only requires technology. Fine-tuning requires technology. Alignment requires technology. Safety requires technology.
每天我都提供强化学习的人类反馈，这是我的工作。而且对于在场的父母来说，你们一直在为他们提供强化学习的人类反馈。好吗？现在我们刚刚找到了如何在人工智能的系统水平上做到这一点。还有许多其他技术需要来保护、微调、基础，比如如何生成遵守物理法则的令牌。你知道，现在有些东西在太空中漂浮并进行操作，但它们并不遵守物理法则。这需要技术。保护只需要技术。微调需要技术。对齐需要技术。安全需要技术。
The reason why planes are so safe is because, you know, all of the autopilot systems are surrounded by diversity and redundancy and all kinds of different functional safety and active safety systems that were invented. I need all of that to be invented much, much faster. You also know that the border between security and artificial intelligence, cyber security and artificial intelligence is going to become blurry and blurry and we need technology to advance very, very quickly in the area of cyber security in order to protect us from artificial intelligence.
飞机如此安全的原因是因为，你知道，所有自动驾驶系统都被多样性和冗余以及各种不同的功能安全和主动安全系统所包围，这些系统是被发明出来的。我需要所有这些都被更加快速地发明出来。你也知道，在安全和人工智能、网络安全和人工智能之间的边界将变得越来越模糊，我们需要技术在网络安全领域迅速发展，以保护我们免受人工智能的威胁。
And so, in a lot of ways we need technology to go faster. A lot faster. Okay, regulation. There's two types of regulation. There's social regulation. I don't know what to do about that. But there's product and services regulation. You know exactly what to do about that. Okay, so the FAA, the FDA, the NHTSA, you name it, all the F's and all the N's and all the, you know, FCC's, they all have regulations for products and services that have particular use cases, bar exams and doctors and so on and so forth. You all have qualification exams. You all have standards that you have to read. You all have to continuously be certified, accountants and so on and so forth. Whether it's a product or a service, there are lots and lots of regulations. Please do not add a super regulation that cuts across a bit. The regulator who is regulating accounting should not be the regulator that regulates a doctor. You know, I love accountants, but I just, you know, if I ever need an open heart surgery, the fact that they can close books is interesting but not sufficient. And so, I would like all of those fields that already have products and services to also enhance their regulations in context of, in the context of AI.
因此，在很多方面，我们需要技术发展得更快。快很多。好吧，有监管。有两种类型的监管。有社会监管。我不知道该怎么办。但有产品和服务监管。你们都知道该怎么办。所以， FAA、FDA、NHTSA，你们可以列举出来，看看所有有关产品和服务的规定，涉及特定用例、律师考试、医生等等。你们都有资格考试。你们都有必须遵守的标准。你们必须持续获得认证，会计师等等。无论是产品还是服务，都有许许多多的规定。请不要增加一个全面涵盖的超级规范。监管会计的监管者不应该是监管医生的监管者。你们知道，我喜欢会计师，但是如果我需要开刀，他们擅长关账是有趣的，但并不足够。因此，我希望所有那些已经有产品和服务的领域也在人工智能的背景下加强他们的监管。
But I left out this one very big one, which is the social implication of AI. And how do you, how do you deal with that? I don't have great answers for that. But, you know, enough people are talking about it. But it's important to subdivide all of this into chunks. Doesn't make sense so that we don't, we don't become super hyper focused on this one thing. At the expense of a whole bunch of routine things that we could have done. And as a result, people are getting killed by cars and planes and, you know, those make any sense. We should make sure that we do the right things there.
但我遗漏了一个非常重要的方面，即人工智能的社会影响。你如何处理这个问题？这个问题我没什么好答案。但是，足够多的人都在讨论这个问题。重要的是将这一切进行细分。这样我们不会过度关注某一件事，而忽视了我们本可以做的一大堆事情。结果可能导致人们死于车辆和飞机事故，这有意义吗？我们应该确保在这方面做出正确的决策。
Okay, very practical things. May I take one more question? Well, we have some rapid fire questions for you as view from the observation. Okay. Which was trying to avoid that. Okay, all right, far away, far away. Okay, well, your first job was at Denny's. They now have a boot dedicated to you. What was your fondest memory of working? My second job was AMD, by the way. Is there a boot dedicated to me there? I'm just kidding. I'm going to love my job there. I did. I loved it. It was a great company. Yeah.
好的，非常实用的事情。我能再问一个问题吗？嗯，我们有一些针对你的快速提问。好的，试着避开那个吧。好，来吧，来吧。好的，你第一份工作是在丹尼斯餐厅。现在他们有一个专门为你制作的靴子。在那里工作，你最珍贵的回忆是什么？顺便说一下，我的第二份工作是在AMD。那里有专门为我制作的靴子吗？开玩笑的。我很喜欢那份工作。我喜欢它。那是一个很棒的公司。是的。
And if they were a worldwide shortage of black leather jackets, what would we be seeing wearing? Oh, no, I've got a large reservoir of black jackets. I'll be the only person who is not concerned. You spoke a lot about textbooks. If you had to write one, what would it be called? I went right one. You're asking me a hypothetical question that has no possibility of. That's fair. And finally, if you could share one parting piece of advice to broadcast across Stanford, what would it be? It's not a word, but have a core belief. Go check it every day. Pursuit with all your might. Pursuit for a very long time. Surround yourself with people you love and take them on that right. That's the story of NVIDIA. And since this last hour has been a treat, thank you for spending time. Thank you very much. APPLAUSE.
如果全球黑色皮夹克供不应求，我们会看到什么样的穿着呢？哦，不，我有很多黑色夹克。我将是唯一一个不担心的人。你说了很多关于教科书的事情。如果你不得不写一本，它会叫什么名字？我已经写了一本。你问我一个没有可能性的假设性问题。那很公平。最后，如果你能分享一条告别的建议，传播到整个斯坦福大学，那会是什么？这不是一个词，但要有一个核心信念。每天检查一下。全力以赴追求。长时间追求。让自己周围充满你爱的人，一起走对的路。这就是英伟达的故事。在这最后一个小时里是一次愉快的会面，谢谢你花时间。非常感谢。掌声。