This is what hundreds of millions of gamers in the world plays on. It's a G-Force.
這是全球數億玩家所使用的設備。它是一塊 G-Force 顯示卡。
00:06
This is the chip that's inside.
這是它內部的晶片。
00:08
For nearly 30 years, Nvidia's chips have been coveted by gamers, shaping what's possible in graphics and dominating the entire market since it first popularized the term graphics processing unit with the G-Force 256.
The biggest risk is really kind of US-China relations and the potential impact to TSMC.
最大的風險其實是美中關係以及對台積電的潛在影響。
01:11
If I'm a shareholder in Nvidia, that's really the only thing that keeps me up in life.
如果我是 Nvidia 的股東，這真的是唯一讓我寢食難安的事情。
01:16
This isn't the first time Nvidia has found itself teetering on the leading edge of an uncertain emerging market.
這不是 Nvidia 第一次處於不確定的新興市場的最前沿。
01:23
It's neared bankruptcy a handful of times in its history when founder and CEO Jensen Huang bet the company on impossible-seeming ventures.
在其歷史中，當創辦人兼執行長黃仁勳將公司押注在看似不可能的冒險上時，公司曾數次瀕臨破產。
01:30
Every company makes mistakes and I make a lot of them and some of them puts the company in peril, especially in the beginning.
每家公司都會犯錯，我也犯了很多錯，其中一些錯誤讓公司陷入危機，尤其是在初期。
01:40
Because we're small and we're up against very, very large companies and we're trying to invent this brand new technology.
因為我們規模很小，面對的是非常、非常大的公司，而我們試圖發明這項全新的技術。
01:47
We sat down with Huang at Nvidia's Silicon Valley headquarters to find out how he pulled off this latest reinvention and got a behind-the-scenes look at all the ways it powers far more than just gaming.
Now one of the world's top 10 most valuable companies, Nvidia is one of the rare Silicon Valley giants that 30 years in still has its founder at the helm.
I delivered the first one of these inside an AI supercomputer to open AI when it was first created.
我在 AI 超級電腦首次問世時，就將第一批這類產品交付給 OpenAI。
02:22
60-year-old Jensen Huang, a fortune business person of the year and one of time's most influential people in 2021, immigrated to the US from Taiwan as a kid and studied engineering at Oregon State and Stanford.
In the early 90s, Huang met fellow engineers Chris Malakowski and Curtis Priem at Denny's, where they talked about dreams of enabling PCs with 3D graphics, the kind made popular by movies like Jurassic Park at the time.
在 90 年代初期，黃仁勳在丹尼餐廳（Denny's）結識了工程師同好 Chris Malakowski 和 Curtis Priem，他們在那裡談論了為 PC 資賦 3D 繪圖功能的夢想，也就是當時透過《侏羅紀公園》等電影而廣受歡迎的那種畫面。
02:47
If you go back 30 years, at the time the PC revolution was just starting, and there was quite a bit of debate about what is the future of computing and how should software be run.
如果回到 30 年前，當時 PC 革命才剛開始，對於運算的未來以及軟體該如何運作，存在著相當多的爭論。
02:58
And there was a large camp, and rightfully so, that believed that CPU or general purpose software was the best way to go, and it was the best way to go for a long time.
當時有一大派陣營，而且有其道理，認為 CPU 或通用軟體是最佳路徑，而且在很長一段時間內確實如此。
03:10
We felt, however, that there was a class of applications that would be possible without acceleration.
然而，我們覺得有一類應用程式若沒有加速技術是無法實現的。
03:18
The friends launched NVIDIA out of a condo in Fremont, California in 1993.
這幾位好友於 1993 年在加州佛利蒙市的一間公寓裡創立了 NVIDIA。
03:22
The name was inspired by envy for next version and NVIDIA, the Latin word for envy.
They hoped to speed up computing so much, everyone would be green with envy.
他們希望能大幅加速運算，讓所有人都「嫉妒得眼紅」（green with envy）。
03:31
At more than 80% of revenue, its primary business remains GPUs.
GPU 佔其營收超過 80%，至今仍是其核心業務。
03:35
Typically sold as cards that plug into a PC's motherboard, they accelerate, add computing power to central processing units, CPUs from companies like AMD and Intel.
這些產品通常以擴充卡的形式插入 PC 主機板，為 AMD 和 Intel 等公司的中央處理器（CPU）進行加速並增加運算能力。
03:45
You know, there were one among tens of GPU makers at that time.
要知道，當年他們只是數十家 GPU 製造商之一。
03:49
They are the only ones, and them and AMD actually, who really survived.
他們是唯一存活下來的廠商，或者說，真正存活下來的只有他們和 AMD。
03:53
Because NVIDIA worked very well with the software community, this is not a chip business.
因為 NVIDIA 與軟體社群合作無間，這不是單純的晶片生意。
04:00
This is a business of figuring out things end to end.
這是一門從頭到尾解決問題的事業。
04:03
But at the start, its future was far from guaranteed.
但在起步階段，它的未來遠非穩操勝券。
04:07
In the beginning, there weren't that many applications for it, frankly.
老實說，一開始並沒有那麼多應用場景。
04:10
And we smartly chose one particular combination that was a home run.
我們很聰明地選擇了一個特定的組合，結果大獲成功。
04:15
It was computer graphics, and we applied it to video games.
那就是電腦圖形技術，我們將其應用於電玩遊戲。
04:20
Now, NVIDIA is known for revolutionizing gaming and Hollywood, with rapid rendering of visual effects.
如今，NVIDIA 以透過快速渲染視覺特效來革新遊戲和好萊塢電影而聞名。
04:26
NVIDIA designed its first high-performance graphics chip in 1997, designed, not manufactured, because Wong was committed to making NVIDIA a fabulous chip company, keeping capital expenditure way down by outsourcing the extraordinary expense of making the chips to TSMC.
NVIDIA today wouldn't be here, and nor do the other thousand fabulous semiconductor companies wouldn't be here, if not for the pioneering work that TSMC did.
如果沒有台積電的開創性工作，今天就不會有 NVIDIA，其他上千家優秀的半導體公司也不會存在。
05:03
In 1999, after laying off the majority of workers and nearly going bankrupt to do it, NVIDIA released what it claims was the world's first official GPU, the GeForce 256.
It was the first programmable graphics card that allowed custom shading and lighting effects.
這是第一張可程式化繪圖卡，允許自訂著色與光影效果。
05:17
By 2000, NVIDIA was the exclusive graphics provider for Microsoft's first Xbox.
到 2000 年，NVIDIA 成為微軟第一代 Xbox 的獨家繪圖供應商。
05:22
Microsoft in the Xbox happened at exactly the time that we invented this thing called a programmable share, and it defines how computer graphics is done today.
微軟的 Xbox 正好在我們發明了這個叫做可程式化著色器（programmable shader）的東西時出現，它定義了今日電腦繪圖的運作方式。
05:32
NVIDIA went public in 1999, and its stock stayed largely flat until demand went through the roof during the pandemic.
NVIDIA 在 1999 年上市，其股價在疫情期間需求暴增之前，大體上維持平盤。
05:38
In 2006, it released a software toolkit called CUDA that would eventually propel it to the center of the AI boom.
2006 年，它發布了一套名為 CUDA 的軟體工具包，最終將其推向人工智慧熱潮的中心。
05:45
It's essentially a computing platform and programming model that changes how NVIDIA GPUs work, from serial to parallel compute.
Parallel computing is, let me take a task and attack it all at the same time using much smaller machines.
平行運算就是，讓我拿一個任務，使用許多較小的機器同時處理。
06:02
It's the difference between having an army where you have one giant soldier who's able to do things very well, but one at a time, versus an army of thousands of soldiers who are able to take that problem and do it in parallel.
But just last year, NVIDIA had to abandon a $40 billion bid to acquire ARM, citing significant regulatory challenges.
但就在去年，NVIDIA 不得不放棄以 400 億美元收購 ARM 的出價，理由是面臨重大的監管挑戰。
06:51
ARM is a major CPU company known for licensing its signature ARM architecture to Apple for iPhones and iPads, Amazon for Kindles, and many major car makers.
ARM 是一家主要的 CPU 公司，以其將其標誌性的 ARM 架構授權給 Apple 用於 iPhone 和 iPad、Amazon 用於 Kindle，以及許多主要汽車製造商而聞名。
07:05
Despite some setbacks, today, NVIDIA has 26,000 employees, a newly-built polygon-themed headquarters in Santa Clara, California, and billions of chips used for far more than just graphics.
Turns out, the same parallel processing needed to create lifelike graphics is also ideal for deep learning, where a computer learns by itself rather than relying on a programmer's code.
It had the good wisdom to go put the whole company behind it.
NVIDIA 很明智地傾全公司之力投入其中。
07:55
We saw early on, about a decade or so ago, that this way of doing software could change everything.
我們在大約十年前就看出，這種軟體開發方式能改變一切。
08:02
We changed the company from the bottom all the way to the top and sideways.
我們從上到下、從裡到外改變了這家公司。
08:06
Every chip that we made was focused on artificial intelligence.
我們製造的每一顆晶片都專注於人工智慧。
08:10
Brian Katanzara was the first and only employee on NVIDIA's deep learning team six years ago.
六年前，Brian Katanzara 是 NVIDIA 深度學習團隊的第一位，也是唯一的員工。
08:15
Now it's 50 people and growing.
現在團隊已有 50 人，而且還在持續成長。
08:17
For 10 years, Wall Street asked NVIDIA, why are you making this investment no one's using it?
十年來，華爾街一直問 NVIDIA，為什麼要做這項沒人使用的投資？
08:23
And they valued it at $0 in our market cap.
他們認為這在我們的市值中價值為零。
08:25
And it wasn't until around 2016 that 10 years after CUDA came out, that all of a sudden people understood this is a dramatically different way of writing computer programs and it has transformational speedups that then yield breakthrough results in artificial intelligence.
直到 2016 年左右，也就是 CUDA 推出十年後，大家才突然明白，這是一種截然不同的電腦程式編寫方式，它能帶來變革性的加速效果，進而在人工智慧領域取得突破性成果。
08:42
So what are some real world applications for NVIDIA's AI?
那麼，NVIDIA 的人工智慧有哪些實際應用呢？
08:45
Healthcare is one big area.
醫療保健是一個重要領域。
08:47
Think far faster drug discovery and DNA sequencing that takes hours instead of weeks.
想想看更快的藥物研發，以及只需數小時而非數週的 DNA 定序。
08:51
We were able to achieve the Guinness World Record in a genomic sequencing technique to actually diagnose these patients and administer one of the patients in the trial to have a heart transplant, a 13 year old boy who's thriving today as a result, and then also a three month old baby that was having epileptic seizures
and to be able to prescribe an anti-seizure medication.
我們得以為其開立抗癲癇藥物。
09:14
And then there's art powered by NVIDIA AI, like Rafique Anadol's creations that cover entire buildings.
此外還有由 NVIDIA AI 驅動的藝術，像是 Rafique Anadol 覆蓋整棟建築物的創作。
09:19
And when crypto started to boom, NVIDIA's GPUs became the coveted tool for mining the digital currency.
當加密貨幣開始蓬勃發展時，NVIDIA 的 GPU 成為挖掘數位貨幣的搶手工具。
09:24
Which is not really a recommended usage, but that has created problems because crypto mining has been a boom or bust cycle.
這雖非推薦的用法，卻也引發了一些問題，因為加密貨幣挖礦經歷了繁榮與蕭條的循環。
09:34
So gaming cards go out of stock, prices get paid up, and then when the crypto mining boom collapses, then there's a big crash on the gaming side.
因此，遊戲顯卡缺貨，價格飆升；而當加密貨幣挖礦熱潮崩盤時，遊戲市場便會遭遇重挫。
09:43
Although NVIDIA did create a simplified GPU made just for mining, it didn't stop crypto miners from buying up gaming GPUs, sending prices through the roof.
And although that shortage is over, NVIDIA caused major sticker shock among some gamers last year by pricing its new 40 series GPUs far higher than the previous generation.
Now there's too much supply, and the most recently reported quarterly gaming revenue was down 46% from the year before.
現在供過於求，最近一季的遊戲營收較去年同期下降了 46%。
10:10
But NVIDIA still beat expectations in its most recent earnings report, thanks to the AI boom.
但拜 AI 熱潮所賜，NVIDIA 在最新的財報中仍超出了市場預期。
10:15
As tech giants like Microsoft and Google fill their data centers with thousands of NVIDIA A100s, the engines used to train large language models like chat GPT.
When we ship them, we don't ship them in packs of one, we ship them in packs of eight.
我們出貨時，不是單顆出貨，而是一次出貨八顆。
10:31
With a suggested price of nearly $200,000, NVIDIA's DGX A100 server board has eight AMPR GPUs that work together to enable things like the insanely fast and uncannily human-like responses of chat GPT.
Are there any ways that NVIDIA is sort of protecting against some of these bigger fears that people have or building in safeguards?
NVIDIA 是否有採取任何措施來防範人們對此的擔憂，或正在建立相關的防護機制？
11:33
Yes, I think the safeguards that we're building as an industry about how AI is going to be used are extraordinarily important.
是的，我認為我們作為一個產業，針對 AI 應用方式所建立的防護機制極其重要。
11:42
We're trying to find ways of authenticating content so that we can know if a video was actually created in the real world or virtually similarly for text and audio.
But being at the center of the generative AI boom doesn't make NVIDIA immune to wider market concerns.
但身處生成式 AI 熱潮的中心，並不意味輝達能對更廣泛的市場擔憂免疫。
11:58
In October, the U.S. introduced sweeping new rules that banned exports of leading edge AI chips to China, including NVIDIA's A100.
10 月，美國推出了全面的新規定，禁止向中國出口先進的 AI 晶片，包括輝達的 A100。
12:06
About a quarter of your revenue comes from mainland China.
貴公司約四分之一的營收來自中國大陸。
12:08
How do you call them investor fears over the new export controls?
您如何看待投資人對新出口管制的擔憂？
12:12
Well, NVIDIA's technology is export control.
嗯，輝達的技術本就受到出口管制。
12:16
It's a reflection of the importance of the technology that we make.
這反映了我們所製造技術的重要性。
12:18
The first thing that we have to do is comply with the regulations.
我們首先必須做的是遵守法規。
12:20
And it was a turbulent month or so as the company went upside down to re-engineer all of our products so that it's compliant with the regulation and yet still be able to serve the commercial customers that we have in China.
We're able to serve our customers in China with the regulated parts and delightfully support them.
我們能夠使用合規的零件服務我們在中國的客戶，並給予他們極大的支持。
12:43
But perhaps an even bigger geopolitical risk for NVIDIA is its dependence on TSMC in Taiwan.
但對輝達來說，也許更大的地緣政治風險是其對台灣台積電的依賴。
12:48
There's two issues. One, will China take over the island of Taiwan at some point?
這有兩個問題。第一，中國是否會在某個時間點接管台灣？
12:54
And two, is there a viable competitor to TSMC?
第二，台積電是否有可行的競爭對手？
12:59
And as of right now, Intel is trying aggressively to get there and their goal is by 2025 and we will see.
目前，英特爾正積極試圖趕上，他們的目標是在 2025 年實現，我們拭目以待。
13:09
And this is not just an NVIDIA risk.
這不僅是輝達的風險。
13:10
This is a risk for AMD, for Qualcomm, even for Intel.
這是超微、高通，甚至是英特爾的風險。
13:14
This is a big reason why the US passed the Chips Act last summer, which sets aside $52 billion to incentivize chip companies to manufacture on US soil.
Now TSMC is spending $40 billion to build two chip fabrication plants, fabs in Arizona.
現在，台積電正斥資 400 億美元在亞利桑那州建造兩座晶圓廠。
13:29
The fact that it matters is TSMC is a really important company and the world doesn't have more than one of them.
這件事之所以重要，是因為台積電是一家非常重要的公司，而世界上沒有第二家像它這樣的公司。
13:35
It is imperative upon ourselves and them, for them to also invest in diversity and redundancy.
我們和他們都有責任，他們也必須投資於多元化和備援機制。
13:42
Will you be moving any of your manufacturing to Arizona?
您是否會將部分製造業務遷至亞利桑那州？
13:45
Oh, absolutely. We'll use Arizona.
哦，絕對會。我們將使用亞利桑那州。
13:46
Yeah.
是的。
13:47
And then there's the chip shortage.
此外還有晶片短缺的問題。
13:49
As it largely comes to a close and supply catches up with demand, some types of chips are experiencing a price slump.
隨著短缺狀況基本結束，供給逐漸追上需求，某些類型的晶片正經歷價格下跌。
13:55
But for NVIDIA, the chatbot boom means demand for its AI chips continues to grow, at least for now.
但對 NVIDIA 而言，聊天機器人熱潮意味著對其 AI 晶片的需求持續增長，至少目前是如此。
14:00
See, the biggest question for them is how do they stay ahead?
請看，對他們來說最大的問題是如何保持領先？
14:04
Because their customers can be their competitors also.
因為他們的客戶也可能成為他們的競爭對手。
14:08
You know, Microsoft can try and design these things internally.
您知道，微軟可能會嘗試在內部設計這些東西。
14:12
Amazon and Google are already designing these things internally.
亞馬遜和谷歌已經在內部設計這些東西了。
14:15
Tesla and Apple are designing their own custom chips too.
特斯拉和蘋果也在設計他們自己的客製化晶片。
14:19
But Jensen says competition is a net good.
但黃仁勳表示，競爭是件好事。
14:21
The amount of power that the world needs in the data center will grow.
全球對資料中心算力的需求將持續增長。
14:24
And you can see in the recent trends, it's growing very quickly.
從近期趨勢可以看出，其增長非常迅速。
14:28
And that's a real issue for the world.
這對全球來說是個真正的挑戰。
14:34
While AI and chat GPT have been generating lots of buzz for NVIDIA, it's far from Huang's only focus.
雖然 AI 和 Chat GPT 為 NVIDIA 帶來了極高的熱度，但這遠非黃仁勳唯一的關注點。
14:41
And we take that model and we put it into this computer, and that's a self-driving car.
我們將該模型放入這台電腦中，就變成了自動駕駛汽車。
14:47
And we take that computer and we put it into here, and that's a little robot computer.
我們將那台電腦放入這裡，就變成了一台小型機器人電腦。
14:52
Like the kind that's used with Amazon.
就像亞馬遜使用的那種。
14:54
That's right.
沒錯。
14:55
Amazon and others use NVIDIA to power robots in their warehouses, and to create digital twins of the massive spaces and run simulation to optimize the flow of millions of packages each day.
Now they're used to power the world's biggest e-commerce operations.
如今，它們被用來驅動全球最大的電子商務營運。
15:15
NVIDIA's Tegra chips were also used in Tesla Model 3's from 2016 to 2019.
NVIDIA 的 Tegra 晶片也曾於 2016 年至 2019 年間用於 Tesla Model 3。
15:19
Now Tesla uses its own chips, but NVIDIA is making autonomous driving tech for other car makers, like Mercedes-Benz.
現在 Tesla 使用自家晶片，但 NVIDIA 正為其他汽車製造商（如 Mercedes-Benz）開發自動駕駛技術。
15:26
So we call it NVIDIA Drive, and it basically NVIDIA drives a scalable platform, whether you want to use it for simple ADAS, assisted driving for your emergency braking warning, pre-collision warning, or just holding the lane for cruise control, all the way up to a rubber taxi
where it is doing everything, driving anywhere in any condition, any type of weather.
在全自動模式下，它能處理所有事務，在任何天氣狀況下，於任何地方行駛。
15:47
NVIDIA is also trying to compete in a totally different arena, releasing its own data center CPU, grace.
NVIDIA 也正試圖在一個完全不同的領域競爭，發布其自家的資料中心 CPU——Grace。
15:53
What do you say to gamers who wish you had kept focus entirely on the core business of gaming?
對於那些希望你們能完全專注於遊戲核心業務的玩家，你有什麼話想說？
16:01
Well, if not for all of our work in physics simulation, if not for all of our research and artificial intelligence, what we did recently with GeForce RTX would not have been possible.
For us to take computer graphics and video games to the next level, we had to reinvent and disrupt ourselves, basically simulating the pathways of light and simulate everything with generative AI, and so we compute one pixel and we imagine with AI the other seven.
為了將電腦圖形和電玩遊戲提升到新的層次，我們必須自我革新與顛覆，基本上是模擬光線的路徑，並透過生成式 AI 模擬一切，因此我們運算一個像素，並透過 AI 推算出其餘七個。
16:42
It's really quite amazing. Imagine a jigsaw puzzle and we gave you one out of eight pieces, and somehow the AI filled in the rest.
這真的很驚人。想像一個拼圖，我們給你八片中的一片，然後 AI 以某種方式填補了剩下的部分。
16:51
Ray Tracing is used in nearly 300 games now, like Cyberpunk 2077, Fortnite, and Minecraft.
