칼텍 2024년 개교 연설:  AI, 가속화된 컴퓨팅, 그리고 인생의 일거리 찾기에 대한 NVIDIA CEO 젠슨 황의 이야기

### 젠슨 황의 칼텍 입학 연설:  AI 혁명을 받아들여야 하는 이유
* 🎓 엔비디아의 CEO인 젠슨 황은 2024년 칼텍 수업에서 연설을 통해 AI와 가속 컴퓨팅의 혁신적 힘을 강조했습니다.
* 👨 그는 NVIDIA에서의 자신의 경험을 바탕으로 인내와 회복력, 좌절을 극복하는 능력의 중요성을 강조했습니다.
* 💡 황은 초창기부터 2012년 알렉스넷의 혁신으로 촉발된 AI 혁명에서 중추적인 역할을 담당하기까지 엔비디아의 여정에 대해 이야기했습니다.
* 🤖 그는 명령어 중심 컴퓨팅에서 AI가 인간처럼 작업을 이해하고 실행하는 의도 중심 컴퓨팅으로의 전환에 대해 설명했습니다.
* 🏭 황은 AI로 구동되는 새로운 산업인 '인텔리전스 토큰'을 생산하는 새로운 데이터 센터인 'AI 팩토리'의 개념을 소개했습니다.
* 🧑 그는 교토의 한 꼼꼼한 정원사의 이야기를 공유하며 자신의 일에 대한 헌신과 삶의 우선순위를 정하는 것의 중요성을 강조했습니다.
* 🚀 황은 졸업생들에게 미래의 불확실성을 포용하고 좌절을 성장과 혁신의 기회로 삼으라고 격려했습니다.
* 🏆 그는 졸업생들에게 "고통과 고통은... 최고의 초능력"이라고 강조하며 자신이 열정을 가진 기술을 찾고 그것을 완성하는 데 평생을 바칠 것을 촉구하며 마무리했습니다.

NVIDIA의 CEO가 회복탄력성과 자신의 일에 대한 헌신을 강조하며 AI 혁명을 헤쳐나가는 데 대한 인사이트를 공유합니다.

It really makes me cringe listening to all of that. Thank you for that kind introduction, but I hate hearing about myself.

And just as a show, well, maybe if 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 know you're graduating from Caltech. This is the school of the great Richard Feynman, Linus Pauling,

and someone who's very influential to me and our industry, Carver Mead. Yeah, 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'll 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 to your character,

determination, 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 at NVIDIA were from Caltech.

And one of the reasons why I'm giving this 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 have 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 climb. I just hope that today is not my peak.

Not the 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.

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

And so I spoke about the long road of creating CUDA, a programming model. The programming model that we dedicated over 20 years to inventing,

and that is revolutionizing computing today. I spoke about a very, quite public, canceled Sega game console project we worked on,

and where intellectual honesty, something that I know Richard Feynman cared 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. But 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 with it,

because it's advancing so quickly. It is the only technology that I've known that is advancing on multiple exponentials simultaneously.

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 it's changed.

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

that you're graduating into. And these are extraordinary things that are happening that you should have an intuitive understanding of,

because it's going to matter to you, it's going to matter to the industry. And hopefully, you take advantage of the opportunities ahead of you.

The computer industry is transforming from its foundations, literally from the ground up. Everything is changing from the ground up.

And across every layer, and soon, every industry will also be transformed. And the reason for that is quite obvious,

because computers today are the single most important instrument of knowledge. And it's foundational to every single industry and 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. And 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.  Much better documentation and better descriptions of computers and architecture

have been presented since. But the System/360 was incredibly important at its time, 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.

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 in the introduction to VLSI systems. Based on Carver Mead's pioneering work here at Caltech

on chip design methodologies and a textbook that revolutionized IC design. And it enabled our generation to design supergiant chips and ultimately the CPU.

The CPU led to exponential growth in computing. The 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 $3 trillion 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 Moore's Law scaling, of transistor scaling,

and instruction-level parallelism have slowed CPU performance. And the slowed CPU performance gains are happening at a time when computing demand continues to grow exponentially.

This exponentially growing gap between the demand for computing and the capabilities of computers, if not addressed, will cause computing energy consumption and cost inflation,

which would eventually stifle every industry. We see very clear signs of computing inflation as we speak. And 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 1,000-fold 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 hundred-fold improvement in time, cost, or energy savings, which accelerated computing enabled, 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

Using NVIDIA CUDA GPUs to train AlexNet, we shocked the computer vision community by winning the 2012 ImageNet Challenge.

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

Our decisions after AlexNet transformed our company—and likely everything else—in a way worth noting.

We saw deep learning's potential and believed, based on principled thinking and our analysis of its scalability,

that it could learn other valuable functions. We thought deep learning might be a universal function learner, solving problems difficult

or impossible to express using fundamental first principles.  So, we thought,

this is a technology we must prioritize, as its limits are potentially constrained only by model and data scale.

However, challenges existed in 2012. How could we explore deep learning's limits without building massive GPU clusters?

We were a small company; building such clusters could cost hundreds of millions of dollars.  And if we didn't,

there was no guarantee of effectiveness at scale.  No one knew how far deep learning could scale.

And if we didn't build it, we'd never know.  It was a 'build it and they will come' situation,

but our logic was: if we don't build it, they can't come. So, based on our principled beliefs and analysis,

we dedicated ourselves to it. We reached a point where we believed it would be so effective,

and when the company believes something, we act on it. So we dove deep into deep learning.

And over the next decade, we 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 GPUs of the past that we invented,

and we went on to invent just about every other aspect of computing: the interconnects, the systems,

the networking, and of course, the software. We invested billions. We invested billions into the unknown.

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

We invested billions. And we designed and built supercomputers to explore the limits of deep learning and AI.

And 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, 10 years after AlexNet, and about a million-fold increase in computing power later,

a million-fold. Imagine what it would be like if your laptop were a million times more capable?

Jensen Huang’s Speech At CalTech｜黃仁勳加州理工演講

New SciTech 新科技

황은 자기 축하 소개에 불편함을 표합니다.

그는 청중의 NVIDIA 및 GPU에 대한 친숙도를 측정합니다.

그는 칼텍 졸업생과 내빈에게 공식적으로 연설합니다.

그는 영향력 있는 칼텍 동문들을 언급하며 학교의 명성을 강조합니다.

그는 부모와 가족의 희생을 인정합니다.

황은 인내와 희생의 중요성을 강조합니다.

그는 칼텍과 엔비디아의 관계를 강조하며 졸업생들을 은근히 모집합니다.

그는 자신의 이전 취임 연설과 조언을 제공하는 방식에 대해 언급합니다.

그는 오랜 기간 기술 분야의 CEO로 재직하며 얻은 교훈을 되돌아봅니다.

그는 CUDA와 취소된 세가 프로젝트에 대한 NVIDIA의 작업에 대해 설명합니다.

황은 컴퓨터 산업에서 일어나고 있는 근본적인 변화에 대해 이야기합니다.

그는 컴퓨터가 가장 중요한 지식의 도구라고 설명합니다.

그는 IBM 시스템/360을 언급하며 현대 컴퓨팅의 역사를 추적합니다.

무어의 법칙의 영향과 CPU 성능의 한계에 대해 설명합니다.

그는 증가하는 컴퓨팅 수요에 대한 해결책으로 가속화된 컴퓨팅을 소개합니다.

그는 AlexNet의 영향과 AI 혁명의 시작에 대해 설명합니다.

그는 딥러닝에 투자할 때의 어려움과 위험에 대해 설명합니다.

그는 딥 러닝과 AI에 대한 엔비디아의 막대한 투자에 대해 설명합니다.

그는 ChatGPT의 출시와 AI의 주류화에 대해 강조합니다.

그는 명령어 중심 컴퓨팅에서 의도 중심 컴퓨팅으로의 전환에 대해 설명합니다.

그는 로봇 공학을 중심으로 차세대 AI의 물결에 대해 이야기합니다.

그는 다양한 시장에서 좌절과 전환을 겪은 NVIDIA의 경험에 대해 이야기합니다.

그는 회복탄력성과 도전에 대한 적응의 중요성을 강조합니다.

그는 교토의 한 정원사에 대한 이야기를 들려주며 헌신과 인내를 강조합니다.

그는 영향력이 큰 작업에 우선순위를 두는 자신의 일상을 설명합니다.

마무리 발언 및 졸업생에게 드리는 조언

그는 졸업반을 축하하고 마지막 지혜의 말씀을 전합니다.

그는 졸업생들에게 틀에 얽매이지 않는 아이디어를 믿고 좌절을 인내하라고 격려합니다.

그는 회복탄력성과 도전을 극복하는 능력을 주요 강점으로 꼽습니다.

그는 졸업생들에게 평생을 바칠 수 있는 직업을 찾으라고 조언합니다.

그는 삶의 우선순위를 정하고 충분한 시간을 갖는 것이 중요하다고 강조합니다.