黃仁勳AI風潮
五月底，整個台灣壟罩在黃仁勳AI(人工智慧)的風潮中，首先是Nvidia公布財報，股票大漲25%，全球驚動。Nvidia 預告AI伺服器大賣，ChatGPT推動的AI世代已經來臨，這些呼應黃仁勳先前宣告的「全球AI市場iPhone時刻」悄然降臨。（5/27）黃仁勳受邀在台大畢業典禮致詞，揭示每個公司和個人都必須學會善用人工智慧，以最快速度(Run, don't walk)在各行各業做出傑出表現。(5/29)黃仁勳在Computex發表演講，將Nvidia經過5年來的研究成果展現給世人，讓我們瞭解圖像顯卡(GPU)與AI晶片的關係，清楚建構出AI革命風潮下的產業變革。
5/29在網路同步聆聽黃仁勳的現場演講，我立刻在FB上傳述內心對這場演講的感觸：
坦白講，我對5/29黃仁勳的演講內容多數一知半解，尤其很多GPU與CPU相互依存關係、資料中心運算效能、AI在【文字-圖檔-動畫-影像】轉換的運算邏輯、Digital Twin的應用發展、.......，但是，我知道AI產業最精華的知識就在今天的演講內容裡，在演講時所展示的PPT上。我的一知半解驅使我想將黃仁勳全部演講內容徹底搞清楚。
為此，我準備整理他演講的內容，以影片截圖為主，放在我的部落格。這是一件艱鉅而漫長的工作，我每天做一段內容，更新在這篇文章中。
影片來源以發佈於Nvidia官方網站為版本：Nvidia官網....COMPUTEX 2023 Keynote NVIDIA Keynote，Nvidia官方網站也發佈另一個正式版本I am AI.，兩段影片可以相互引照。
影片全長2：16：56，演講從19：30開始，司儀先報幕，黃仁勳出場：「我們回來了，這是我在大約4年之後，首度與滿滿的聽眾面對面演說，大家祝我好運吧！」黃仁勳幽默的開場，引發聽眾掌聲。
接著黃仁勳開始展示RTX圖形顯示卡的威力，說明顯卡如何利用AI模擬光線與物件的運算來顯示以下圖片：
上述畫面如果運用6年前的Cuda GPU來運算需要耗費很長時間才能逐漸生成畫面，生成過程中的畫面是：
若用CPU來跑這畫面則需要好幾個小時。
黃仁勳說：RTX圖形顯卡的三大底層技術是：(1) 硬體加速光影追蹤(hardware accelerated ray tracing)、(2) Nvidia Tensor core GPUs 人工智慧處理器、(3)全新演算法( algorithms )。RTX顯卡是圖像顯示的一大突破，上述圖像只要幾秒鐘就完成。現在，Nvidia推出第三代ADA架構的RTX圖形顯卡，來挑戰繪圖終極技術「光影追蹤」(Ray Tracing)技術。下圖是他展示搭載RTX圖形顯卡(4060)的電腦，它們比PlayStation更強大，已經進入產線生產，將成為GPU的主流產品。
接下來，黃仁勳用以下PPT說明AI技術如何將圖片、聲音、影像、動畫生成數位化身(digital avatar)，如此，每個人都可以編寫電動遊戲腳本，製作動畫遊戲，Nvidia發展深度學習的AI技術：TensorRT，功能強大。
PPT展示Nvidia ACE協助遊戲公司設計遊戲的系統流程，Nvidia ACE：遊戲角色生成的AI模型，它包括：(1)解說、對話與動畫底層模型、(2)利用Nvidia NEMo加以客製化、(3)連結雲端。
以下是黃仁勳使用英文的介紹：Today we're announcing Nvidia ACE, Avatar Cloud Engine, that is designed for animating to bringing a digital avatar to life. It has several characteristics, several capabilities, speech recognition, text-to-speech, natural language understanding, basically a large language model, and using the sound that you will be generating with your voice, animate the face and using the sound and the expression that you're saying animate your gestures. All of this is completely trained by AI. We have a service that includes pre-trained models that you can come, developers can come, and modify and enhance for your own application, for your own story because every game has a different story. And then you can deploy it in the cloud or deploy it on your device. Has a great backend, has a TensorRT. TensorRT is Nvidia's deep learning, optimizing compiler, and you could deploy it on Nvidia GPUs as well as output Onyx, an industry standard backend, so that you can run it on any device.(TensorRT是 Nvidia的深度學習編譯器)
接下來黃仁勳使用Unreal Engine 5來生成文以下影片的佈景介紹，注意，影片中所有場景都是AI生成，人物是由化身軟體生成，場景中從各不同角度投射的燈光，都是AI生成的，這些就是Ray tracing的技術。
黃仁勳介紹從1964年開始到2023年的電腦產業的大變化，如以下截圖所示，我們都歷經1964年IBM System/360的年代，他說：This computer revolutionized several things. The first computer in history to introduce the concept of a central processing unit, the CPU, virtual memory, expandable I/O, multitasking, the ability to scale this computer for different applications across different computing ranges. And one of the most important contributions and one of its greatest insights is the importance of preserving software investment. The software ran across the entire range of computers and it ran across multiple generations. So that the software you develop, IBM recognized the importance of software, recognized the importance of preserving your investment, and very importantly recognized the importance of installed base. This computer revolutionized not only computing and many of us grew up reading the manuals of this computer to understand how computer architecture worked, to even learn about DMA for the very first time, this computer not only revolutionized computing, it revolutionized the thinking of the computer industry. System/360 and the programming model of the System/360 has largely retained until today. In 60 years a trillion dollars worth of the world's data center all basically used a computing model that was innovated all the way 60 years ago. Until now, there are two fundamental transitions happening in the computer industry today. All of you are deep within it and you feel it. There are two fundamental trends.
(該兩大趨勢是：(1)CPU高速運算力已經達到極限、(2)資料中心規模日趨龐大。不過黃仁勳在以下演講提到第3大趨勢：(3)快速運算是跨領域的。)
電腦產業有3大發展趨勢：(1)CPU高速運算力已經達到極限。The first trend is because CPU scaling has ended, the ability to get 10 times more performance every five years has ended. The ability to get 10 times more performance every five years at the same cost is the reason why computers are so fast today. The ability to sustain 10 times more computing every five years without increase in power is the reason why the world's data center hasn't consumed so much more power on Earth. That trend has ended and we need a new computing approach and accelerated computing is the path forward. It happened at exactly the time when a new way of doing software was discovered, deep learning, these two events came together and it's driving computing today. Accelerated computing and generative AI. This way of doing software, this way of doing computation is a reinvention from the ground up and it's not easy. Accelerated computing is a full stack problem. It's not as easy as general purpose computing. The CPU is a miracle. High level programming languages, great compilers, almost anybody could write reasonably good programs, because the CPU is so flexible. However, its ability to continue to scale and performance has ended and we need a new approach.(以克服 Accelerate computing full stack的難題). You have to re-engineer everything from the top down and from the bottom up, from the chip to the systems, to the systems' software, new algorithms and of course optimizing the new, the applications.
(2)資料中心規模日趨龐大。The second is that it's a data center scale problem. And the reason why it's a data center scale problem is today the data center is the computer. Unlike the past, when your PC was a computer or the phone was a computer, today your data center is the computer. The application runs across the entire data center and therefore it's vital that you have to understand how to optimize the chips, the compute, the software across the NIC, the switch, all the way to the other end in a distributor computing way.
(3)快速運算是跨領域的。And the third accelerated computing is multi-domain. It's domain specific. The algorithms and the software stacks that you create for computational biology and the software stack you create for computational fluid dynamics are fundamentally different. Each one of these domains of science need their own stack, which is the reason why accelerated computing has taken us nearly three decades to accomplish. This entire stack has taken us nearly three decades. However, the performance is incredible, and I'll show you.
黃仁勳說：30年過後，我們正處引爆點(tipping point)，遊戲產業的困局已經被Nvidia加速運算力循環圈克服了。
After three decades, we realize now that we're at the tipping point. A new computing model is extremely hard to come by. And the reason for that is this. In order for there to be a new computing model, you need developers. But a developer would only come if they're, and developers have to create applications that end users would buy. And without end users, there would be no customers, no computer companies to build computers, without computer companies, like yourself building computers, there would be no install base. Without install base, there would be no developers. Without developers, there'll be no applications. This loop (如上圖) has been suffered by so many computing companies in the 40 years that I've been in this industry, this is really one of the first major times in history a new computing model has been developed and created. We now have 4 million developers, 3000 plus applications, 40 million CUDA downloads in history, 25 million just last year. 40 million downloaded in history, 25 million just last year. 15,000 startup companies in the world built on Nvidia today, building on Nvidia today, and 40,000 large companies, enterprises around the world, are using accelerated computing.
黃仁勳說：我們來到一個全新運算年代(a new computing era)：運算能無休止地加速，成本也快速下降!
We have now reached the tipping point of a new computing era. This new computing model is now enjoyed and embraced by just about every computer company and every cloud company in the world. There's a reason for that. It turns out that every single computing approach its benefit in the final analysis is lower cost. The PC revolution that started and that Taiwan enjoyed in 1984, the year I graduated, that decade in the eighties was the PC revolution. PC brought computing to a price point nobody's ever seen before. And then of course, mobile devices was convenient and it also saved enormous amounts of money. We aggregated and combined the camera, the music player, your PC, a phone. So many different devices were all integrated into one. And as a result, not only are you able to enjoy your life better, it also saves a lot of money and great convenience. Every single generation provided something new and saved money. Well, this is how accelerated computing works. This is accelerated computing used for large language models.
黃仁勳說：生成式AI是一台1千萬美金的伺服器，它相當於960台CPU伺服器，用電11GWh。若採GPU伺服器，只需48台，用電3.2GWh，效能是CPU架構的44倍。這48台GPU伺服器的Data Center，大幅降低生成式AI的使用成本(例如耗電)，同時提高效能。
For large language models, basically the core of generative AI is a $10 million server and we costed everything. We costed the process, we costed all the chips, we costed all the network, we costed literally everything. And so $10 million gets you nearly a thousand CPU servers. And to train to process this large language model takes 11 gigawatt hours. And this is what happens when you accelerate this workload with accelerated computing. For a $10 million server, you buy 48 GPU servers. It's the reason why people say that GPU servers are so expensive. Remember people say GPU servers are so expensive. However, the GPU server is no longer the computer. The computer is the data center. Your goal is to build the most cost effective data center, not build the most cost effective server. Back in the old days when the computer was the server, that would be a reasonable thing to do, but today the computer is the data center. And so what you want to do and create is the most effective data center with the best TCO. So for $10 million, you buy 48 GPU servers. It only consumes 3.2 gigawatt hours and 44 times the performance.
Let me just show it to you one more time. This is before and this is after. And this is, we want dense computers, not big ones. We want dense computers, fast computers, not big ones. And so that's ISO budget. Let me show you something else. 接下來，黃仁勳展示各種data center的建造規模與成本。他提出一個最大效能的資料中心：維持11GWh耗電水準，裝置172台GPU伺服器，總投資是3400萬美金，效能提高到150倍。
接下來，黃仁勳提出他建議的理想Data center，他一再強調： if your goal is to get the work done, you don't care how.。他將效能鎖在1X LLM，從事國際標準(ISO)工作，建議使用以下的資料中心架構：2台GPU伺服器，總投資是40萬美金，0.13GWh耗電水準，The more you buy, the more you save. 這時黃仁勳用台語講一句【省很多錢】引發哄堂大笑。
That's Nvidia. You don't have to understand the strategy, you don't have to understand the technology. The more you buy, the more you save. That's the only thing you have to understand. .......You have heard me talk about this for so many years. In fact, every single time you saw me, I've been talking to you about accelerated computing. I've been talking about accelerated computing, well, for a long time, well over two decades.接下來，PPT打出一排數學公式，我稱它【黃仁勳方程式】：
41:00以下內容都在解說黃仁勳方程式：This equation is very complicated. This is the cost of building a data center. The data center TCO is a function of, and this is the part where everybody mess up. It's a function of the chips, of course, no question. It's a function of the systems, of course, no question. But it's also because there's so many different use cases. It's a function of the diversity of systems that can be created. It is the reason why Taiwan is at the bedrock at the foundation of the computer industry. Without Taiwan, why would there be so many different configurations of computers? Big, small, powerful, cheap, enterprise, hyperscale, super computing, so many different types of configurations. And all completely compatible. The ability for the hardware ecosystem of Taiwan to have created so many different versions that are software compatible. Incredible. The throughput of the computer of course is very important. It depends on the chip, but it also depends on the algorithm. Because without the algorithm libraries accelerated computing does nothing. And so you need to algorithm software libraries. It's a data center scale problem. So networking matters. And networking matters, distributed computing is all about software. Again, system software matters. And before, before long, in order for you to present your system to your customers, you have to ultimately have a lot of applications that run on top of it. The software ecosystem matter.
演講內容中黃仁勳將Data center的營運內涵與電腦產業發展的關鍵要素陳述一遍，他特別提及台灣廠商的貢獻，這句話用藍色字標示出來。
Well, the utilization of a data center is one of the most important criteria of its TCO. Just like a hotel. If the hotel is wonderful, but it's mostly empty, the cost is incredible. And so you need the utilization to be high. In order for the utilization to be high, you have to have many different, many different applications. So the richness of the applications matter. Again, the algorithm in libraries and now the software ecosystem. You purchase a computer, but these computers are incredibly hard to deploy from the moment that you buy the computer to the time that you put that computer to work to start making money, that difference can be weeks, if you're very good at it, incredibly good at it. We can stand up a super computer in a matter of a couple of weeks, because we build so many all around the world. But if you're not very good at it, it could take a year. That difference, depriving yourself the year of making money and the year of depreciation, incredible cost.
Lifecycle optimization.
Because the data center is software defined, there are so many engineers that will continue to refine and continue to optimize the software stack. Because NVIDIA's software stack is architecturally compatible across all of our generations, across all of our GPUs. Every time we optimize something, it benefits everybody. So lifecycle optimization, and of course finally the energy that you use, power. But this equation is incredibly complicated. Well, because we have now addressed so many different domains of science, so many industries, and in data processing, in deep learning, classical machine learning, so many different ways for us to deploy software from the cloud to enterprise to supercomputing to the Edge, so many different configurations of GPUs, from our HGX versions to our Omniverse versions, to our cloud GPU and graphics version, so many different versions. Now, the utilization is incredibly high. The utilization of Nvidia GPU is so high, almost every single cloud is over extended. Almost every single data center is over extended. There are so many different applications using it. So we have now reached the tipping point of accelerated computing. We have now reached the tipping point of generative AI. And I want to thank all of you for your support and all of your assistance and partnership in making this dream happen. Thank you.
黃仁勳說：Data center的營運需要不斷進行lifecycle optimization，植入NVIDIA的功能與貢獻，揭示NVIDIA已經來到加速運散的引爆點，巧妙進行置入性行銷，他為此向觀眾致謝，並贏得掌聲。(果然是IT產品最偉大推銷員)
接下來，黃仁勳又開啟另一演講重頭戲：H100伺服器，並宣告H100已經在台灣量產，PPT打出客戶名單：
Every single time we announce the new product, the demand for every single generation increased and increased and increased. And then one generation, it hockey steps, we stick with it, we stick with it, we stick with it, Kepler, and then Volta, and then Pascal, and then Volta, and then Ampere. And now this generation of accelerated computing, the demand is literally from every corner of the world. And we are so, so, so excited to be in full volume production of the H100. This is incredible. H100 is in full production, manufactured by companies all over Taiwan, used in clouds everywhere, enterprises everywhere. And let's take a look at a short video of how H100 is produced. This computer, 35,000 components on that system board, eight Hopper GPUs.
(展示H100的生產影片)
接下來，黃仁勳走到蓋著布幔的產品架，show出H100，他拿出一片板卡，並對產品架上的H100伺服器進行解說：This is 65 pounds. It takes robots to lift it, of course, and it takes robots to insert it, because the insertion pressure is so high and it has to be so perfect. This computer is $200,000, and it replaces an entire room of other computers. It's a very, very expensive computer. It's the world's single most expensive computer that you can say, "The more you buy, the more you save." This is what a compute tray looks like. Even this is incredibly heavy.
This is the brand new H100 with the world's first computer that has a transformer engine in it. The performance is utterly incredible. Hopper is in full production. We've been driving computing, this new form of computing for 12 years. When we first met the deep learning researchers, we were fortunate to realize that not only was deep learning going to be a fantastic algorithm for many applications initially, computer vision and speech, but it would also be a whole new way of doing software.
(以下在說明NVIDIA如何重創GPU的運力)This fundamental new way of doing software that can use data to develop, to train a universal function approximator of incredible dimensionality. It can basically predict almost anything that you have data for, so long as the data has structure that it can learn from. And so we realized the importance of this new method of developing software, and then it has the potential of completely reinventing computing. And we were right. 12 years later, we have reinvented literally everything. We reinvented, of course, we started by creating a new type of library. It's essentially like a SQL, except for deep learning for neural network processing. It's like a rendering engine, a solver for neural network processing called (indistinct). We reinvented the GPU. People thought that GPUs would just be GPUs. They were completely wrong. We dedicated ourselves to reinventing the GPUs, so that it's incredibly good at Tensor processing. We created a new type of packaging called SXM and worked with TSMC on CoWos, so that we could stack multiple chips on the same dye. NVLink, so that we can connect these SXM modules together with high speed chip to chip interconnect. Almost a decade ago, we built the world's first chip to chip (indistinct), so that we can expand the memory size of GPUs using SXMs and NVLink. And we create a new type of motherboard, we call it HGX that I just showed you.
(52：00)No computers has ever been this heavy before or consumed this much current. Every aspect of a data center had to be reinvented. We also invented a new type of computer appliance so that we could develop software on it so that third party developers could develop software on it with a simple appliance we call DGX, basically a giant GPU computer. DGX. We also purchased Mellanox, which is one of the great strategic decisions of our company because we realized that in the future, if the data center is the computer, then the networking is the nervous system. If the data center is the computer, then the networking defines the data center. That was an incredibly good acquisition and since then we've done so many things together and I'm gonna show you some really, really amazing work today. And then of course, an operating system.
If you have a nervous system, a distributed computer, it needs to have an operating system. And the operating system of this distributed computering we call Magnum IO. Some of our most important work. And then all of the algorithms and engines that sit on top of these computers, we call Nvidia AI. The only AI operating system in the world that takes data processing from data processing
to training, to optimization, to deployment and inference. End-to-end deep learning processing. It is the engine of AI today. Well, every single generation since Kepler, which is K80, to Pascal Volta, Ampere, Hopper, every two years, every two years, we took a giant leap forward. But we realized we needed more than even that, and which is the reason why we connected GPUs to other GPUs called NVLink, built one giant GPU, and we connected those GPUs together using InfiniBand into larger scale computers. That ability for us to drive the processor and extend the scale of computing made it possible for the AI research organization, the community, to advance AI at an incredible rate.
We just kept pushing and pushing and pushing. Hopper went into production August of last year. August, 2022. 2024, which is next year, we'll have Hopper-Next. Last year we had Quantum. Two years from now or next year, we'll have Quantum-Next. So every two years we take giant leaps forward (NVIDIA每兩年作一次大跳升)and I'm expecting the next leap to be giant as well.This is the new computer industry.
55：25
Software is no longer programmed just by computer engineers. Software is programmed by computer engineers working with AI supercomputers. These AI supercomputers are a new type of factory. It is very logical that a car industry has factories. They build things that you can see, cars. It is very logical that computer industry has computer factories. You build things that you can see, computers. (黃仁勳大膽預言)In the future, every single major company will also have AI factories and you will build and produce your company's intelligence.
And it's a very sensible thing. We cultivate and develop and nourish our employees and continue to create the conditions by which they can do their best work. (黃仁勳這樣讚許NVIDIA)We are intelligence producers already. It's just that the intelligence producers, the intelligence are people. In the future, we will be intelligence producers, artificial intelligence producers. And every single company will have factories and the factories will be built this way. This translates to your throughput. This translates to your scale and you will build it in a way that is very, very good TCO. Well, our dedication to pursuing this path and relentlessly increasing the performance. just think in 10 years time, we increased the throughput, we increased the scale, the overall throughput across all of that stack by 1 million x(一百萬倍) in 10 years. Well just now, in the beginning, I showed you computer graphics. In five years, we improved the computer graphics by 1000 times.
57：00
In five years, using artificial intelligence and accelerated computing. Using accelerated computing and artificial intelligence, we accelerated computer graphics by 1000 times in five years. Moore's law is probably currently running at about two times. A thousand times in five years. A thousand times in five years is 1 million times in 10 years. We're doing the same thing in artificial intelligence. Now, question is, what can you do when your computer is 1 million times faster? (黃仁勳提出一個尖銳問題)What would you do if your computer was 1 million times faster?
57：30
Well, it turns out that the friends we met at University of Toronto, Ilya Sutskever, Alex Krizhevsky, and Geoffrey Hinton. Ilia Sutskever was the founder of OpenAI, who discovered the continuous scaling of artificial intelligence and deep learning networks and came up with the ChatGPT breakthrough. Well, in this general form, this is what has happened. The transformer engine and the ability to use unsupervised learning, unsupervised learning, be able to learn from a giant amount of data and recognize patterns and relationships across a large sequence. And using transformers to predict the next word, large language models were created, and the breakthrough, of course, is very clear. But the important thing is this.
58：30
We now have a software capability to learn the structure of almost any information. We can learn the structure of text, sound, images, there is structure in all of us, physics, proteins, DNA, chemicals, anything that has structure. Of course you can learn English and Chinese and Japanese and so on and so forth, but you can also learn the language of many other things. And then the next breakthrough came, generative AI. Once you can learn the language, once you can learn the language of certain information, then with control and guidance from another source of information, that we call prompts, we can now guide the AI to generate information of all kinds. We can generate text-to-text, text-to-image. But the important thing is this, information transformed to other information is now possible. Text to proteins, text to chemicals, images to 3D, images to 2D, images to text, captioning, video to video. So many different types of information can now be transformed.
For the very first time in history we have a software technology that is able to understand the representation of information of many modalities. We can now apply computer science, we can now apply the instrument of our industry. We can now apply the instrument of our industry to so many different fields that were impossible before. This is the reason why everybody is so excited. Now let's take a look at some of these.
Nvidia高速運算伺服器：Grace Hopper：
黃仁勳用各種圖示展現GPU的運算進化：
以上是我正在整理的5/29黃仁勳現場演講的內容範例，目前先分享這部份，下次若有補充更新，我將在FB宣佈，各位再來點閱。
科技達人重新剪輯黃仁勳的演講
（5/27）黃仁勳受邀在台大畢業典禮致詞
5月27日黃仁勳受邀在台大畢業典禮致詞，我在線上同步聆聽，也立即在FB發抒我的感想：
這場演講全球注目，全程演講20分鐘，這是國際產業明星以自己的經歷知識對年輕人提出忠告的演講，如賈伯斯在史丹福大學畢業典禮演講一樣，稀世國寶。我真希望年輕人能逐句細嚼慢嚥，最好直接從英文原句聽出其中的哲理。
以下是我直接轉載商業周刊的中文譯稿(陳瑋鴻整理)，中間穿插黃仁勳在Computex演講的截圖，有空我會將黃仁勳口述的精彩英文語句補入，或者對照英文講稿用我的中文重新翻譯，以呈現英文原句言詞之美。
（台語）大家好，我今天本來想跟你們說台語，但是⋯⋯我越想越緊張。我在美國長大的，所以我的台語不是很標準，所以我今天跟你們說英文好不好？(台下掌聲)
Okay，那我們開始吧！
（英語）各位貴賓、各位家長，2023年的台大畢業生，大家好！今天是你們的特別日子，也是夢想成真的日子——屬於你們父母的，你們要趕快離家。今天是榮耀的日子，父母親犧牲了他們自己，成就了你們。我爸爸媽媽在這，我哥哥也在這，讓我們向養育我們的父母親展現我們的感謝。
十年前我第一次來台大，陳教授邀請我來看他的物理實驗室。我記得他的兒子在矽谷得知了Nvidia發明的CUDA（Compute Unified Device Architecture，統一計算架構）技術，建議陳教授在量子物理模擬使用它。當我抵達的時候，他秀給我看他創造的東西：整個房間的Nvidia遊戲顯卡，插在開放式電腦的主機板上，金屬架上都是散熱用的大同電扇。他以台灣人的方式，用遊戲顯卡做了一個超級電腦。他在這裡，做了早期Nvidia JOURNEY示範。他很驕傲，他說：「黃先生，因為你的關係，我可以完成我的事業。」他說的那些話至今仍感動我，完美詮釋了我們公司的價值：幫助這個時代的愛因斯坦與達文西完成他們的事業。
我很高興能再次回到台大，擔任你們的致詞嘉賓。
當我從俄勒岡州立大學畢業時，世界還比較簡單。電視還很大一台，沒有無線電視跟MTV、沒有手機和行動電話。那是1994年，IBM個人電腦跟MAC麥金塔開始了個人電腦革命。開始日後晶片與運算程式的發展。
你們正處在的世界更複雜，面臨著地緣政治、社會和環境上的變化和挑戰，被科技包圍著。我們處於一個永遠連接和沉浸的數據世界，與現實世界平行存在。在40年前，當電腦產業創造了家用PC，持續研究AI技術，我們的運算程式駕駛著汽車、或研讀X光片影像。AI為電腦自動化開啟了大門，其服務涵蓋了世界最大的兆級產業：健康照護、金融服務、運輸與製造產業。
AI為我們帶來了巨大的機遇，反應敏捷的企業將利用AI技術提升競爭力，而未能善用AI的企業將面臨衰退。很多企業家，包含今天在場的許多人，未來將會開創新公司。如同過去的每個計算機時代能創造新的產業，AI也創造了以前不存在的新工作機會，像是：數據工程師、詠唱工程師、AI工廠操作員和AI安全工程師等，這些工作以前從未存在過。
自動化工作將淘汰一些工作，並且毫無疑問的，AI會改變每一個工作，大幅加強程式設計師、設計師、藝術家、行銷人員和製造計劃者的工作表現。就像在你們之前的每個世代，擁抱科技以獲得成功。每個公司與你必須學會利用AI的優勢，在AI的幫助下做出驚人成就。
有些人擔心AI可能會搶走你的工作，有些人可能會讓AI發展出自我意志。我們正處於一個新領域的開始，就像個人電腦、網路、移動設備與雲端技術一樣。但是AI的影響更為根本，每個運算層面都會被重新改寫。它改變了我們撰寫軟體的方式、執行軟體的方式。
從各方面來看，這是電腦產業的再生契機，對於台灣企業而言更是一個黃金機遇。你們正是這個產業的重要基石。在下個十年，我們的產業將使用新型AI電腦取代價值上兆美元的傳統電腦。
我的旅程始於你們40年之前，1984年是一個完美的畢業年份，我預測2023年也將如此。我能告訴你什麼呢？今天是迄今為止你們最成功的一天，你們從台大畢業了，我也曾經成功過。在我創辦了Nvidia前，我經歷過失敗，而且是大失敗，說起來令人恥辱和尷尬，甚至幾乎讓我們走向毀滅。讓我給你們講3個故事，這些故事定義了Nvidia今天的樣貌。
我們創辦Nvidia是為了創造加速運算技術。我們的第一個應用是用於個人電腦遊戲的3D圖形，我們發明了一種非傳統的前向紋理處理技術(?)，而且成本相對低廉。我們贏得了與SEGA建造遊戲主機的合約。這吸引了遊戲開發商用我們的平台開發遊戲，並提供我們公司資金。
但經過了一年的開發期程，我們意識到我們設計的架構是錯誤策略，從技術端來看是不合格的。而與此同時，微軟即將宣布基於反向紋理映射和三角形的Windows 95 Direct3D。這代表如果我們完成了SEGA的遊戲機，我們將會創造出與Windows不相容的產品；但如果我們不完成這個合約，我們就會破產。無論如何，我們都會面臨倒閉的命運。
我聯絡了SEGA執行長，向他解釋我們的發明是錯誤的，我們無法完成合約以及遊戲主機，並建議SEGA尋找其他合作夥伴。我對他說：「我們必須停下來。」但我需要SEGA全額支付我們的費用，否則Nvidia將無法繼續經營。我很難為情的向SEGA執行長生提出這個要求，但令我驚訝的是，他同意了。他的理解和慷慨讓我們多活了3個月，在那段時間，我們建造了Riva 128，就在我們差點沒錢時，Riva 128震撼了新興的3D市場，讓我們開始受到關注，也拯救了公司營運。
市場對我們的晶片需求旺盛，讓我從4歲離開台灣後又回到了台灣。我與台積電的張忠謀先生會面，並開始一段持續25年的合作關係。我們坦誠面對錯誤、謙卑的尋求幫助，拯救Nvidia的存續。這些特質對於像你們這樣最聰明、最成功的人而言，是最難養成的。
在2007年，我們宣布了CUDA GPU加速運算技術，我們的期望是讓CUDA成為一個程式設計模型，在科學運算、物理模擬到圖像處理方面，都能提升應用程式的效能。
one giant GPU
創建一個全新的運算模型非常困難，且在歷史上實屬罕見。自從IBM System 360以來，CPU的運算模型已經成為標準已有60年的時間。CUDA需要開發人員撰寫應用程式，並展示GPU的優勢；開發人員需要一個大型的使用者基礎；大型的CUDA使用者基礎，需要市場上有人購買新的應用程式。因此，為了解決先有雞還是先有蛋的問題。
我們利用我們的遊戲顯卡GPU GeForce，它已經擁有龐大的遊戲市場，以建立使用者基礎。但CUDA的成本非常高，Nvidia的利潤在多年來遭受巨大的打擊，我們的市值僅僅維持在10億美元上下。我們多年的低迷表現，讓股東們對CUDA持懷疑態度，並希望我們專注於提高盈利能力。
但我們堅持下來，我們相信加速運算的時代將會到來，我們創建了一個名為GTC的會議，並在全球不辭辛勞的推廣CUDA技術。
然後CT重建、分子動力學、粒子物理學、流體動力學和圖像處理等應用程式開始大量出現，我們的開發人員撰寫算法，並加快了晶片運算速度。
2014年，Alex在我們的GPU上進行了訓練，開啟AI的大爆炸，幸運的是，我們意識到了深度學習的潛力，我們冒著一切風險去追求深度學習。多年後，AI革命開始了，Nvidia成為了推動引擎。我們為AI發明了CUDA，這個旅程鍛造了我們的品格，承受痛苦和苦難，是在追求願景的路上必經之痛。
再講一個故事，在2010年，Google將Android系統打造成出色圖形作業平台，而手機行業也有調制解調器的晶片公司。Nvidia優秀的運算能力，讓Nvidia成為Android系統良好的合作夥伴。我們取得成功、股價飆升，但競爭對手也很快就湧入，調制解調器製造商們也在學習如何生產運算晶片，而我們卻在學習調制解調器。
因為手機市場龐大，我們能搶佔市占率。然而，我們卻做出艱難的決定，放棄這塊市場。因為Nvidia的使命，是創造出能解決「普通電腦解決的問題」的電腦，我們應該專注在願景上，發揮我們的獨特貢獻。我們的放棄獲得了回報，我們創造了一個新的市場——機器人技術，擁有神經網路處理器和運行AI算法的安全架構。
當時，這還是個看不見規模的市場。從巨大的從手機市場撤退，再創造一個不知道市場規模的機器人市場。然而，現在的我們擁有數十億美元的自動駕駛、機器人技術的事業，也開創一個新的產業。「撤退」對像你們如此聰明且成功的人來說並不容易。然而，戰略性的撤退、犧牲、決定放棄什麼是成功的核心，非常關鍵的核心。
2023年畢業的同學們，你們即將進入一個正在經歷巨大變革的世界，就像我畢業時遇到個人電腦和晶片革命時一樣，你們正處於AI的起跑線上。每個行業都將被革命、重生，為新思想做好準備 - 你們的思想。在40年的時間裡，我們創造了個人電腦、網路、移動設備、雲端技術。現在的AI時代，你們將創造什麼？
無論是什麼，全力以赴去追求它，跑，不要用走的。Run. Don't walk, either running for food or running from being food.跑去搶食物，或者跑而免於成為食物。 你往往無法知道自己正處在哪一種情況，但無論如何，都要保持奔跑。
在你的旅程中，帶上一些我犯過的錯、有過的經驗。希望你們能謙卑的面對失敗，承認錯誤並尋求幫助。你們將承受實現夢想所需的痛苦和苦難，並做出犧牲，致力於有意義的生活，衝刺你們人生的事業。2023年畢業的同學們，我致以衷心向你們每一位祝賀。加油！
(14年前)黃仁勳在大陸電視台的專訪
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波士堂02-NVIDIA公司的创始人及总裁，黄仁勋
波士堂03-NVIDIA公司的创始人及总裁，黄仁勋
波士堂04-NVIDIA公司的创始人及总裁，黄仁勋
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[中文字幕翻譯] 黃仁勳在台大2023年畢業典禮致辭
必備的IT技能
當代員工必備的IT技能
許多僱主都希望員工能熟練運用一系列技術工具，涵蓋數據分析、在線協作、項目管理等。
本文轉載自紐約時報
現如今的職場，僅僅掌握Word和Excel的基本功能，已經不夠了。
分析、組織和溝通技術的大爆發正在重塑辦公室工作的方方面面。無論是從事銷售、營銷、項目管理、設計或是其他諸多領域的工作，僱主都希望員工可以處理和分析數據，並將其製作成漂亮的演示文稿。而隨著遠程辦公已成常態，人們還必須掌握在線協作工具的高級功能。
「在組織內部，技術不單隻是起到賦能作用，它的確還能產生顛覆效應，能夠帶來變革與價值，這一點毫無爭議。」哥倫比亞大學商學院(Columbia Business School)院長科斯蒂斯·馬格拉斯(Costis Maglaras)說，「因此，人們需要掌握一些核心知識，即便他們自己不用成為技術專家。」
技術研究與諮詢公司Gartner的《2022年數字員工調查》(2022 Digital Worker Survey)發現，普通的辦公室上班族在工作中會用到11種應用程式，有17%的人會用到16種甚至更多。其中有些技能可以在工作中學會。但網絡招聘平台ZipRecruiter的首席經濟學家茱莉亞·波拉克(Julia Pollak)指出，許多僱主都希望員工入職時就能技術嫻熟，尤其是對於一些特定的熱門應用程式。
那麼，如今的辦公室上班族需要掌握哪些技術方面的技能？——要完成工作，他們需要了解哪些應用程式？對此，我們詢問了僱主、人才中介、諮詢師以及教育學者等人。
解讀數據
如今，企業在銷售、生產力及其他業務領域產生的數據變得越來越多，於是便希望員工能夠及時掌握可以解讀這些數據的軟體。
Gartner分析師喬·馬裡亞諾(Joe Mariano)談到，微軟(Microsoft Corp.)的Power BI和Salesforce旗下的Tableau已成為數字可視化領域的頭部軟體。例如，在線教育平台領英學習(LinkedIn Learning) 2022年熱門課程排行榜上，「Power BI基礎培訓」(Power BI Essential Training)就位列第13名。
只需移動光標輕輕一點，再輔以自動化的人工智能(AI)建議，員工便可以將多個數據來源——如電子表格、數據庫或客戶關係管理軟體——結合起來，並將其轉化成圖表或其他圖形。管理者可以按地區、月份、年份或是銷售人員來查看具體的銷售情況。城市規劃者則能繪製出顯示城市一天當中人流量的地圖。
不過，常駐領英的職場專家凱瑟琳·費舍爾(Catherine Fisher)指出，熟練掌握傳統軟體依然重要，如微軟Excel和Alphabet Inc.旗下Google(Google)的Google Sheets。ZipRecruiter的波拉克說，除常規任務外，員工還要了解數據透視表等高級功能，它可以讓傳統電子錶格實現Power BI或是Tableau的部分功能，例如對比電子錶格中提取的數據子集，然後自動生成報告或圖表。
事實上，領英學習上排名第二的課程就是關於Excel的，網絡學習平台Udemy的用戶目前正在學習的提高辦公效率的十大技能中，Excel也佔據了六席。費舍爾說，領英用戶去年在個人資料中添加的前幾大技能中，就有微軟或Google的電子製表軟體這一項。
無論是從事銷售、設計或其他諸多領域的工作，僱主都希望員工可以處理和分析數據。圖片來源：JON KRAUSE
製作生動的演示文稿
得克薩斯大學奧斯汀分校(University of Texas at Austin)負責繼續教育與職業教育的副教務長阿特·馬克曼(Art Markman)談到，對員工來說，除Excel和Google Sheets外，掌握其他傳統辦公軟體也很重要，如Word或Google Docs，以及PowerPoint或Google Slides。但同樣，僱主不僅希望員工知曉如何創建文件或是幻燈片這些基本功能，還要會運用其中的高級功能。
「現在當人們拿到一份文檔或是幻燈片時，他們的預期比以前更高。」波拉克說，「人們希望它能講述一個生動的故事，希望它能給人一種專業的感覺。」這或許意味著，在陳述觀點時，演示文稿中不能只有靜態圖像，還要有動態畫面。她說，就文字處理而言，僱主可能想看到更高級的版式，例如文檔各部分之間可以相互連結，此外還要熟悉分享、評論等協作功能。
「我知道自己需要加強的一個重點是我的呈現和展示能力。」梅麗莎·巴倫(Melissa Barron)說，這位得克薩斯大學奧斯汀分校2022屆的MBA畢業生目前供職於超市巨頭H-E-B的戰略部門。其工作涉及用微軟PowerPoint製作大量精美的幻燈片，並且要用到影片、自定義顏色等高級功能。有時，她會藉助在線設計軟體Canva來製作圖表。
熟諳溝通工具
自新冠疫情以來，遠程會議工具——如Zoom、Google Meet、Microsoft Teams和Salesforce Inc.的Slack——發展勢頭強勁。但許多辦公室上班族只了解它們的基本功能。
「拿Microsoft Teams來說，人們會使用它的聊天和會議功能，這些都沒問題。」馬裡亞諾說，「但他們不曾用到最高級的功能」，例如可以在團隊內部討論特定主題或項目的方法。他說，員工還必須知道如何將其他應用程式接入這些會議軟體，這樣他們就能分享文檔、管理項目。
除此之外，僱主還希望員工能更深入地了解最莊重的通訊軟體之一——電子郵件，掌握它的高級功能。管理電子郵件——如整理郵件列表、群發郵件和撰寫有效的郵件主題——是領英用戶在個人資料中列出的另一項排名靠前的技能。在ZipRecruiter的技能指數(Skills Index)中，排名第一的便是溝通能力，相比之下，掌握Microsoft Outlook僅排在第20位。
專案管理
通過任務管理、時間追蹤、文檔共享等功能，組織和規劃類軟體讓協調工作變得更容易。
例如，一家營銷公司可以將其工作流程自動化，這樣當一個人完成手頭任務後，流程中的下一個人便能自動收到提醒。這些軟體可以接入其他應用程式或服務，如Google Drive，如此一來，員工就可以直接從專案管理軟體中訪問相關文件。他們還可以追蹤每項任務所花的時間，以此評估工作效率或是確定應向客戶收取的費用。
例如，巴倫就在使用專案管理軟體Asana；這是2022年領英用戶加入個人資料中最多的科技技能之一。Atlassian旗下規劃軟體Trello也位列其中。據馬裡亞諾，其他的頭部應用程式還包括ClickUp和Smartsheet。根據培生集團(Pearson PLC)旗下Credly，專案管理在最受歡迎的技術認證排行榜中位列第五。在線教育提供商會利用Credly來管理數字認證項目。
引入自動化
不管一種軟體功能多強大，使用過程中還是需要不停移動光標並點擊，而且通常要反覆點擊相同的按鍵和圖標。有了機器人流程自動化，人們可以創建自動操作指令，讓它們來完成那些重複性的電腦任務。例如可以通過編程，讓自動指令複製人類在發送文件、查閱表格，然後將資訊輸入數據庫時通常會進行的操作。
「如今拼的就是效率。」人力資源及諮詢公司羅致恆富(Robert Half)高級執行董事麥克·斯泰尼茨(Mike Steinitz)說，「我們要如何提高效率？就是很多事情都要實現自動化。」
編寫自動指令不一定需要複雜的技能。許多人會利用一種名為「無代碼」的方法——你要做的就是點選數據源、應用程式和操作類型，然後將它們合併起來。微軟的Power Automate就具備「無代碼」功能，它也是2022年Udemy上學習人數增長最快的提高辦公效率的技能。
學點編程
儘管如此，哥倫比亞大學商學院教授西馬克·莫拉米(Ciamac Moallemi)指出，有時候員工學一點編程還是有用的。莫拉米博士說，就比如Excel支持多種類型的分析功能，但若是達到一定的複雜程度，或者數據量過大時，選擇Python這樣的編程語言來計算會更容易。
員工也無需成為編程達人，因為他們可以下載開源代碼模塊，這些被稱為「軟體包」的模塊可以執行他們所需要的分析指令。而今，Python在領英學習最受歡迎的課程排名中位列第九，費舍爾說，哥倫比亞商學院也表示，目前該校超過一半的在校生會在課堂上使用Python。
莫拉米還推薦了數據庫查詢語言SQL，它在領英學習的榜單上位列第15。藉助SQL，你可以檢索到你想要的數據子集，如特定地區、特定年齡段的客戶會如何購買特定產品。
馬格拉斯博士說，學習編程還有一個好處：增強團隊協作。掌握基本的編程技能有助於業務人員更好地與團隊中的程式員、數據科學家以及用戶體驗設計師展開合作。
巧用AI
隨著AI工具的大量出現，無論是生成文本和代碼的ChatGPT，還是生成圖像的Stable Diffusion和 Midjourney，如今，某種程度的AI可以說是人人觸手可得。例如，微軟剛剛推出了名為Copilot的服務，將包括OpenAI旗下GPT-4在內的AI模型整合到自身的一系列辦公軟體中（GPT-4是ChatGPT背後技術的新版本）。舉例來說，Word和Outlook將可以根據其他文件中的資訊自動生成文本，PowerPoint也可以根據用戶的自然語言指令以及其他文件中的素材來自動創建演示文稿。與此同時，Salesforce的Slack將藉助OpenAI的技術對談話進行總結，並輔助內容起草。
自從去年11月30日ChatGPT問世以來，Udemy上的教學者已推出了逾300門有關該工具的課程，包括如何利用它來總結文件、調試代碼。AI投資人兼顧問Allie K. Miller曾在IBM和亞馬遜雲業務Amazon Web Services負責AI業務，她說，大多數人都應通過一些課程來學習AI的高級原理，例如在線課程平台Coursera聯合創始人吳恩達(Andrew Ng)開設的「AI for Everyone」，同時，對於任何有望節省工作量的新工具，都應該去嘗試。
「想想你經常要完成的任務，然後把AI運用進來，看看你能否在自己不想成為專家的事情上，把效率提高80%。」她說，「就比如，我並不想成為寫電子郵件的專家。」
（本文作者是北卡羅來納州阿什維爾的一名作家，他的聯繫方式為：reports@wsj.com）
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