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1
Jensen Long, thank you.
詹森·朗，謝謝你。
2
Thank you for having me here at this beautiful NVIDIA campus.
感謝大家邀請我來到這座美麗的NVIDIA園區。
3
You and I have known each other a very long time and have a lot of ties, Stanford ties and the like.
你我相識已久，而且有很多交集，比如斯坦福的校友關係之類的。
4
So I'm really looking forward to our conversation.
我非常期待我們的對話。
5
But let me start with this young kid.
但讓我先從這個小男孩說起。
6
What, Nanai comes to the United States of America?
什麼，Nanai要來美國了？
7
America is in many ways one big immigrant story.
在很多方面，美國就是一個宏大的移民故事。
8
Very different stories, but one big story of people who come seeking a better life, people who believe that their children are going to do better.
這些故事各不相同，但都圍繞著同一個宏大的主題：人們來到這裡追尋更好的生活，他們相信自己的孩子會過得更好。
9
So talk about arriving here.
那我們就聊聊來到這裡的經歷吧。
10
It couldn't have been easy.
這肯定不容易。
11
I was born in Taiwan.
我出生在臺灣。
12
When I was five, my father got a job in Thailand to help start an oil refinery.
我五歲那年，父親在泰國找到了一份工作，幫忙籌建一座煉油廠。
13
And so we moved to Thailand.
於是我們搬到了泰國。
14
We were there for about four years.
我們在那裡待了大約四年。
15
In 1973, there was a coup, as it happens every now and then.
1973年發生了一場政變，這種事時有發生。
16
And my parents thought it was unsafe for us to be there.
而且我父母覺得我們待在那裡不安全。
17
My older brother was one year older than me.
我哥哥比我大一歲。
18
He was 10.
他當時十歲。
19
I was nine.
我當時九歲。
20
my parents wanted to send us out as soon as possible and so they got our uncle who lives in Washington,
我的父母想盡快把我們送出去，所以他們聯絡了住在華盛頓的叔叔。
21
Tacoma, Washington to take us for a little while.
帶我們去華盛頓州的塔科馬待上一陣子。
22
My first impression was was I'd never stood in a house with carpets before and it was the strangest feeling.
我的第一印象是，我以前從來沒有站過鋪著地毯的房子裡，那感覺真是太奇怪了。
23
I felt like I was walking on my bed with my shoes on.
我感覺就像穿著鞋子踩在自己的床上一樣。
24
And just everything from cereal and the morning television, speed racer, and in the afternoon, Partridge family, and all the candy,
還有那些麥片、早上的電視節目《Speed Racer》，下午的《Partridge Family》，還有各種各樣的糖果，所有這些回憶都湧上心頭。
25
the Snickers bars, everything was like, I couldn't imagine what this amazing country was.
那些士力架巧克力，所有的一切都讓我難以想像這個神奇的國家究竟是什麼樣子。
26
And everything was so beautiful.
一切都那麼美好。
27
The cars, everything was just incredible.
那些車，所有的一切都簡直太棒了。
28
And we were in Tacoma, Washington for about three months
我們當時在華盛頓州的塔科馬待了大約三個月。
29
and then they sent us to the most affordable accessible boarding school in America because my parents couldn't afford much.
然後他們把我們送到了美國收費最低、條件最便利的寄宿學校，因為我父母負擔不起太貴的費用。
30
A wonderful school.
一所非常棒的學校。
31
They welcome students of all backgrounds, many from difficult homes, some international kids and that's how we were able to come.
他們歡迎各種背景的學生，其中許多來自困難家庭，還有一些是國際學生，我們也是這樣才得以入學的。
32
They sponsored us and allowed us to come to United States
他們為我們提供了擔保，讓我們能夠來到美國。
33
and stay in that boarding school and I was there for about two years in Oneida, Kentucky.
我留在那所寄宿學校，在肯塔基州的奧奈達待了大約兩年。
34
I have never heard of Oneida, Kentucky till this particular moment.
在這一刻之前，我從來沒聽說過肯塔基州的奧奈達這個地方。
35
If you look at Onida, Kentucky in Google Maps, the beautiful thing is there's nothing around it.
如果你在谷歌地圖上檢視肯塔基州的奧尼達，會發現它周圍什麼都沒有，這正是它的美妙之處。
36
It's just one little dot.
這只是一個小點而已。
37
I think when I was there was probably population 600.
我記得我在那兒的時候，人口大概是600人。
38
It's probably population 600 now.
現在這裡的人口大概有600了。
39
Beautiful, incredible.
太美了，太不可思議了。
40
I had a wonderful, wonderful childhood there.
我在那裡度過了非常非常美好的童年。
41
It was very difficult and very scary coming to United States by yourself.
獨自一人來到美國非常艱難，也非常令人害怕。
42
But it's amazing what my older brother did, frankly.
但說實話，我哥哥做的事真的很了不起。
43
I just followed him around.
我就只是一直跟在他後面。
44
He was 10 years old.
他當時十歲。
45
Could you imagine a 10-year-old bringing along a 9-year-old?
你能想像一個10歲的孩子帶著一個9歲的孩子嗎？
46
Traveled all the way from Thailand, never been to the United States.
我從泰國遠道而來，之前從未去過美國。
47
Landed in Tacoma, Washington, and went to Kentucky, but laid over in Chicago.
我降落在華盛頓州塔科馬，之後要去肯塔基，但中途在芝加哥轉機停留。
48
Oh, my goodness.
我的天哪。
49
The airport, Chicago airport is gigantic.
芝加哥機場非常大。
50
And so we had to go find a connecting flight.
所以我們不得不去尋找一個銜接航班。
51
And a 10-year-old did all that by himself.
而這一切都是一個10歲的孩子獨自完成的。
52
My older brother is incredible.
我的哥哥非常了不起。
53
That's amazing.
這太令人驚歎了。
54
So this was risky in a lot of ways
所以這在很多方面都存在風險。
55
but your parents felt that the greater risk would be to stay where you were.
但你的父母認為，留在原地的風險會更大。
56
How were you treated?
你受到了怎樣的對待？
57
Were people friendly?
那裡的人友好嗎？
58
Were they surprised?
他們感到驚訝嗎？
59
How did that part of it go?
那部分進行得怎麼樣了？
60
You know, 1973, United States in Kentucky,
你知道，1973年，美國肯塔基州，
61
there were still biases, and nobody had the school had never seen this Chinese kid before.
偏見依然存在，而且學校裡以前從來沒有人見過這個中國孩子。
62
Wonderful kids there, but none of them ever seen the Chinese before.
那裡的孩子們都很可愛，但他們以前從來沒見過中國人。
63
And so there were all of the things
於是所有那些事情都發生了。
64
that comes along with being a stranger in a town where nobody's ever seen someone like you before.
這是在一個從未有人見過像你這樣的人的小鎮上，作為一個陌生人所伴隨而來的。
65
And so there's a lot of biases and, of course, things like that.
所以存在很多偏見，當然還有諸如此類的問題。
66
But kids are kids.
但孩子畢竟是孩子。
67
And so for us at the time, you know, the United States was amazing.
所以在那個時候，對我們來說，美國真的很棒。
68
And I was part of the swim team.
而且我當時是游泳隊的一員。
69
And I was part of the soccer team.
而且我當時是足球隊的一員。
70
And the food was interesting and, you know, sausage and gravy.
食物也很有意思，比如香腸配肉汁。
71
Who wouldn't like that?
誰會不喜歡呢？
72
You know, hamburgers.
你知道的，就是漢堡包。
73
And after the swim meet, the coach just took us to the most amazing restaurant in the world.
游泳比賽結束後，教練直接帶我們去了世界上最棒的餐廳。
74
And the food came in boxes.
而且食物都是裝在盒子裡送來的。
75
And the menu was all lit up.
而且選單全都亮著燈。
76
And we sat in a restaurant that seemed like a spaceship.
我們坐在一家看起來像宇宙飛船的餐廳裡。
77
And it was McDonald's.
而且那是麥當勞。
78
McDonald's, right.
麥當勞，對的。
79
You know?
你知道嗎？
80
And so I think it's really all about expectations in life.
所以我覺得這其實都和人生中的期望有關。
81
You know when you came from an even more difficult circumstance
你知道當你是從更加艱難的環境中走過來的時候
82
That's the thing about immigrants is that that when you come into
移民的情況就是這樣，當你來到這裡時
83
when you come to America you came because of choice You wanted to be here.
當你來到美國時，你是出於選擇而來的，你是自己想要來到這裡的。
84
My dad sent us here
我爸爸送我們來這裡的
85
But in fact when I was four years old
但實際上，在我四歲的時候
86
He had the opportunity to come to be trained in the United States
他曾有機會前往美國接受培訓。
87
and turned out the place he came to was New York Could you imagine to come to the United States and
結果他來到的地方是紐約。你能想像來到美國卻
88
It's New York City from Taiwan in the 60s.
這是60年代從臺灣視角看到的紐約市。
89
And so he always said that he wanted his family to eventually come to America, to come to this incredible place.
所以他總是說，希望自己的家人最終能來到美國，來到這個令人嚮往的地方。
90
So the thing that's really great about immigrants is you came with very little expectation.
移民群體最可貴的一點在於，他們初來乍到時幾乎沒有什麼過高的期望。
91
You came with great hopes and dreams.
你當初是懷著滿滿的希望與夢想而來的。
92
You appreciate everything.
你會欣賞一切。
93
And with a sense of gratitude for where you are and the new opportunities.
同時心懷感恩，珍惜當下所處的位置和眼前的新機遇。
94
Exactly.
完全正確。
95
And this miracle that you're witnessing, I remember very clearly what it's like to see America for the first time.
而你們現在所見證的這個奇蹟，我非常清楚地記得第一次看到美國時的感受。
96
This miracle and all these people and how welcoming they are.
這真是個奇蹟，還有這裡所有的人，他們是如此熱情好客。
97
To see it through those eyes.
透過那些眼睛去看。
98
But then, of course, you go from this little kid in Kentucky, you're on the swim team, sounds like you were doing okay.
不過話說回來，你從肯塔基州的一個小孩子開始，加入了游泳隊，聽起來當時你表現得還不錯。
99
I was a good athlete.
我曾經是個不錯的運動員。
100
Yeah, I was a good athlete.
對，我以前是個不錯的運動員。
101
And so we met our parents back in Tacoma, Washington.
於是我們在華盛頓州的塔科馬與父母會合了。
102
they had nothing and literally they put all their belongings on the plane
他們當時一無所有，真的是把所有家當都搬上了飛機。
103
so they had nothing and they came with the you know literally the suitcase I mean
所以他們當時一無所有，來的時候真的就只帶著一個行李箱。
104
that story the immigrant story
那個故事，移民的故事
105
and they saved their way to build a life for their kids my mom was a maid at a Catholic school
他們省吃儉用，為子女打拼出一片生活。我母親曾在一所天主教學校當女傭。
106
and my father was an engineer.
而我的父親是一名工程師。
107
They saved everything they had.
他們把自己所有的一切都存了下來。
108
My dad bought a green truck, a van, and there were no seats in it.
我爸爸買了一輛綠色的卡車，還有一輛廂式貨車，那輛貨車裡面沒有座位。
109
So he put a carpet in the back and put some milk crates.
於是他在後車廂鋪了塊地毯，還放了些裝牛奶的塑膠箱。
110
We got into that green van
我們上了那輛綠色的麵包車。
111
and he drove us all the way from Oregon down to LA so that we could go see Disneyland.
他一路開車從奧勒岡州帶我們到洛杉磯，就為了讓我們能去迪士尼樂園玩。
112
And that was the one vacation of our family.
那是我們家唯一的一次度假。
113
How did you end up going on to college and then Stanford?
你後來是怎麼考上大學，又進入斯坦福大學的？
114
And how does, and working at Denny's, which we have to cover at some point.
還有在Denny's餐廳工作是怎樣的體驗，我們總得找個時候聊聊這個話題。
115
I know, I must be Denny's best ambassador.
我知道，我肯定是Denny's最棒的代言人。
116
Yeah, I think you are definitely.
對，我覺得你說得完全正確。
117
I love Denny's so much.
我超愛Denny's。
118
It never occurred to me to be able to afford or, you know, go anywhere for college.
我從來沒想過自己能負擔得起大學的費用，或者說，能去任何地方上大學。
119
And so it was always just my plan that I would grow up in Oregon.
所以我一直以來的計劃就是在奧勒岡州長大。
120
And I loved math, I loved science.
而且我熱愛數學，熱愛科學。
121
And when you're in high school, and you love math and science, you're only going to have three other friends who also love math and science.
當你上高中的時候，如果你熱愛數學和科學，你可能只會交到另外三個同樣熱愛數理學科的朋友。
122
And so the two or three friends of ours, we were all in a club, so that we were in a math club.
所以我們那兩三個朋友，大家都在同一個社團裡，那就是數學社。
123
We were also in a science club.
我們當時也參加了一個科學俱樂部。
124
Right.
對。
125
And then we were in a computer club.
後來我們加入了一個計算機俱樂部。
126
All together.
大家一起。
127
All together.
大家一起來。
128
And so, and then afterwards, after, you know, clubbing, we would go play, played arcade games, you know,
之後，泡完夜店我們就會去玩街機遊戲。
129
play pinball or go play arcade games.
玩彈珠檯或者去玩街機遊戲。
130
And that was growing up.
而那就是成長的過程。
131
And my best friend, Dean Verheyen, he said, you know, I'm just going to go to Oregon State University.
而我最好的朋友迪恩·韋海恩，他說，你知道的，我就是要去奧勒岡州立大學。
132
My brother went there, my parents went there.
我哥哥去了那裡，我的父母也去了那裡。
133
And I said, that sounds great.
然後我說，這聽起來很棒。
134
Oregon State had a good engineering program.
奧勒岡州立大學的工程學專案很不錯。
135
That was just good happenstance.
那純粹是巧合。
136
I loved the school and it turned out that there was another kid there, Lori.
我很喜歡那所學校，而且後來發現那裡還有另一個孩子，叫Lori。
137
She was one of three girls in a school of, in a class of 250 other boys.
她所在的班級有250名男生，而她是班上僅有的三名女生之一。
138
And she was, you know, she was, I was the youngest kid in school.
而且她那時候，你知道的，她那時候，我是學校裡年紀最小的孩子。
139
I was only 16.
我當時才16歲。
140
And she was a year and a half older than me.
而且她比我大一歲半。
141
And I was determined to go, you know, meet her. And so
我當時下定決心要去，你知道的，去見她，所以
142
I you know statistically weeded out everybody by getting myself maneuvered into her lab class
你也知道，我是通過想辦法進入她的實驗課，從統計層面上把其他人都排除掉了。
143
so now I've reduced the population from 250 competition from 250 to four
所以現在我已經把參賽人數從250人縮減到了4人。
144
it's quite clever that's very clever that was very strategic given
這相當聰明，非常聰明，從當時的情況來看很有策略性。
145
when I was a kid and well I knew I knew odds
當我還是個孩子的時候，我就很清楚那些機率了。
146
and and uh and then
然後，呃，然後
147
and then you know I gave her the ultimate pickup I asked her if she wanted to see my homework. That was so smooth.
然後你知道的，我用了最絕的搭訕話術，我問她想不想看我的作業。那真是太自然了。
148
That was really smooth of you to do.
你剛才做得真利落。
149
I cinched it.
我搞定了。
150
And so we've been together ever since.
從那以後我們就一直在一起了。
151
That's great.
太好了。
152
So that's how you met.
原來你們是這麼認識的。
153
Yeah, we met.
是的，我們見過。
154
We've been in college.
我們已經上大學了。
155
So you're in Corvallis, Oregon State.
所以你現在在奧勒岡州立大學的科瓦利斯校區。
156
You are doing well in school.
你在學校表現很好。
157
How does Stanford come onto your radar screen?
斯坦福大學是怎麼引起你注意的？
158
I always wanted to go to a great school.
我一直都想上一所好學校。
159
You know, I always imagined, you know, getting a master's.
你知道，我一直都在想，你知道，要去讀個碩士學位。
160
But I never imagined that I could afford it.
但我從未想過自己能買得起它。
161
And so during Oregon State, the recruiters came from Silicon Valley.
所以在奧勒岡州立大學就讀期間，來自矽谷的招聘人員來到了學校。
162
And so I took a job at AMD.
於是我就去AMD上班了。
163
And that was a really good choice because the work was really interesting.
那真是個非常好的選擇，因為那份工作特別有意思。
164
I had really great colleagues.
我以前的同事都特別好。
165
and and they had this this um a program where you could go to stanford
他們有一個專案，你可以去斯坦福大學。
166
and work at amd and they pay everything and so see
我在AMD工作，公司承擔所有費用，你看。
167
and so it was an incredible thing so when i was being that
所以那真是一件不可思議的事，當我身處那種狀態時
168
when they were pitching the offer to me they said i
當他們向我推銷這個方案時，他們說我
169
said hang on a second i can go to i can
說等一下，我能去，我可以
170
work at amd you could pay me a great salary
在AMD工作的話，你可以給我一份很高的薪水。
171
and you're going to pay me to go to stanford at the same time
同時你還要出錢供我去斯坦福大學讀書。
172
and they said yep you said that sounds good that sounds like like a dream come true.
他們說沒錯，你也說那聽起來不錯，簡直就像夢想成真。
173
So I took that job.
所以我接受了那份工作。
174
And then of course, a year later, Lori graduated, we got married.
然後當然，一年後洛莉畢業了，我們就結婚了。
175
And then life started.
然後，生命開始了。
176
So I took some classes, took some time off, took some classes, took some time off.
於是我上了一些課，休息了一段時間，又上了一些課，再休息了一段時間。
177
And altogether, it must have taken me some eight years to graduate.
我前前後後花了大概八年時間才畢業。
178
I'm probably the longest running student at Stanford.
我可能是斯坦福大學在讀時間最長的學生了。
179
Nobody paid Stanford more for the education.
沒有人比斯坦福大學本身為教育付出的代價更高。
180
And along the way, Spencer Madison came and along the way I founded NVIDIA and literally during my entire tenure
一路走來，斯賓塞·麥迪遜加入了，而我創立了NVIDIA，而且在我整個任職期間，情況一直都是如此。
181
you know during that time of of being at Stanford my life happened
你知道，在斯坦福大學的那段時間裡，我的人生髮生了很多事。
182
when you're when you're going to school you think
當你去上學的時候，你會思考
183
that the school work is awfully academic and the reason for
學業內容過於偏重理論，而背後的原因是
184
that is because you're not sure whether there's any purpose and any benefit in learning this but the benefit of
那是因為你不確定學習這個是否有任何目的和好處，但它的好處在於
185
working and going to school at the same time, especially Stanford, I could see so much of the principles being taught
一邊工作一邊上學，尤其是在斯坦福大學，我能真切地看到很多課堂上講授的原理在實際中得到體現。
186
and how important it is in everything that I do today.
以及它在我如今所做的一切事情中是多麼重要。
187
And so it was a great privilege to have all of that happening to me at the same time.
能同時經歷這一切，真是莫大的榮幸。
188
My family, my kids, my company, Stanford, you know, working, all of it kind of in one giant soup.
我的家庭、我的孩子、我的公司、斯坦福大學，還有工作，所有這些都攪成了一鍋大雜燴。
189
People talk about Silicon Valley and the ecosystem and so forth, but you just used a really great term.
人們總是談論矽谷和生態系統之類的話題，但你剛才用了一個非常棒的說法。
190
It is really kind of a big stew, isn't it?
這簡直就像一鍋大雜燴，不是嗎？
191
and you meet very interesting people and they change your thinking about something and that leads to something.
你會遇到非常有趣的人，他們會改變你對某些事情的看法，而這種改變往往會帶來新的機遇。
192
It sounds like Stanford was a place like that for you.
聽起來斯坦福大學對你來說就是這樣一個地方。
193
It completely shaped my attitude and my perspective about computer science and its impact to industry and industrial strategies.
它徹底改變了我對電腦科學及其對產業和產業戰略影響的態度和看法。
194
that that entire intersection between technology applications um the fundamental science
那是技術應用與基礎科學之間的整個交叉領域。
195
and the strategies of computer science really for me formulated in
而電腦科學的各種策略，對我來說，真正形成了
196
that during that time right
那是在那段時間，對吧
197
and it was really cool now talk about the founding of
而且那真的很酷，現在我們來談談它的創立過程。
198
nvidia you know the early day founder stories are are really fascinating
輝達，你知道的，早期那些創始人的故事真的非常引人入勝。
199
because you have to take a you have to take a
因為你得去拿一個，你得去拿一個
200
chance you take a risk uh you You don't know if it's going to work out, but what was the kernel that made you and your co-founders think, maybe we have something here?
機遇意味著承擔風險，你不知道它是否會成功，但當初是什麼核心因素讓你和聯合創始人覺得，或許我們找到了可行的方向？
201
During that time, it was at the beginning of the PC revolution.
那段時間，個人電腦革命剛剛拉開序幕。
202
It was the beginning of really Moore's Law, the beginning of the CPU era, this incredible technology engine that really changed everything.
那正是摩爾定律真正開始生效的時代，也是CPU時代的開端，這臺不可思議的技術引擎徹底改變了一切。
203
All of that in Silicon Valley was all about CPUs.
204
Everything was about general-focused computing.
205
Everything was about CPUs.
206
Everything was about Moore's Law.
207
Everything was about the PC.
208
And there were two simultaneous ideas that Chris Curtis and I were considering.
209
And one idea, of course, is every application that's interesting and meaningful, can it be run on a CPU?
210
And we believe that there were so many interesting problems to solve, whether it's real-time computer graphics,
211
which is at the time one of the hardest problems to do in computer science, or simulation or other things beyond that,
212
a normal computer like a CPU can't possibly be the right format.
213
And so we imagine that there would be a way to accelerate the CPU,
214
offload the work that is not suitable for general purpose things.
215
It's kind of like in your house you only have one tool, one tool in the kitchen.
216
Or you go into your garage, there's only one tool in your garage.
217
You come to your company, there's only one tool.
218
You know, there's the right tool for the right job.
219
And we believe that there's another tool that could augment the CPU and make that computer essentially a supercomputer.
220
Right.
221
And so that's one idea.
222
The second idea was how do we even get this company started?
223
The problem is the CPU at this point, general purpose computing as of today, is probably about 64 years old.
224
And so the nature of the computer industry is that if you create an architecture that produces benefits to applications,
225
the install base goes up,
226
the sales goes up, applications use more of it this positive virtual cycle would cause the two to reinforce itself
227
and so in fact over the course of last 64 years there's
228
so many applications are built on top of the cpu how
229
do you even cause the application developers to consider another architecture architecture right
230
that chicken or egg problem is incredibly hard to solve and has never been solved in fact aside from nvidia today,
231
really everything else runs on CPUs.
232
And so the idea that we would get this new architecture to be adopted by developers was incredibly hard.
233
And so the question is, what's the first application?
234
And so which brought NVIDIA to its second idea, which was maybe the first application is computer graphics.
235
But the problem is computer graphics was such a small market at the time.
236
It was just Silicon Graphics.
237
And Silicon Graphics was such a, it was a large company, but by the standards of computer architectures, very, very small.
238
And so the question is, how do we find an application that would on the one hand need this architecture,
239
on the other hand be sufficiently high volume to cause this architecture
240
to proliferate and so we thought maybe 3d graphics for video games computer games are not only fun
241
but they perhaps more than any other application push the edge
242
of computing power both pc upgrades for consumers are driven not by the grown-ups in the house
243
but by the kids who want a more powerful gaming machine all right
244
so you've got the brand new geforce 3 graphics card here
245
that's exactly right the geforce 3 is the the latest generation graphics processor from nvidia
246
so this product actually includes a 57 million transistor graphics processor
247
to put it in perspective that's the that's more transistors than a penny
248
and four plus a penny put together i just described a business plan yeah
249
that is impossible to fund and it was i still remember explaining it everybody's going yeah
250
but you know this and that this requires this be solved
251
and have multiple chicken and eggs
252
and yet silicon valley right here sand hill road would finance me sequoia capital and sutter hill right,
253
would be investors in this company and that
254
that we would be able to attract the brightest minds the
255
brightest computer scientists in the world to come work here
256
but we were just determined
257
that on first principle basis the general-purpose computer cannot possibly be the only computing platform right
258
and that there were too many interesting problems that we could solve if we were to introduce this new idea.
259
What's key about this is it's programmable.
260
That's exactly right.
261
For the first time, the graphics processor is as programmable as the CPU.
262
We have an instruction set, just like the Intel processors have an instruction set that you can program Microsoft Word or Windows with.
263
We now have an instruction set that game programmers can program to create special effects on our processor.
264
So you literally were swimming upstream on this.
265
People you were trying to convince.
266
For 30 years.
267
For 30 years.
268
Yeah.
269
But then something happens and the problem that you've solved, it turns out has many other applications.
270
NVIDIA!
271
The best way to play the netbook explosion.
272
That's right, NVDA.
273
First of all, the company understands the netbook opportunity.
274
Quote, there's more and more people proving that they want to be able to take the computer with them.
275
NVIDIA came to us many months ago and talked to us about an amazing graphics part that they wanted to build.
276
And we said, this is fantastic, but we'd like to use it in a notebook.
277
Can we work together on this?
278
And we've been working together with NVIDIA for many, many months, and they've created something really great.
279
I remember listening to you at Stanford at a talk, and you said, we just kept trying to solve hard problems.
280
Computer graphics is basically a simulation of the world.
281
It's a simulation problem.
282
In a lot of ways, artificial intelligence is a simulation of the mind, simulation of the brain.
283
And so the computation of simulation can be done not completely, but largely in parallel.
284
and so the the architecture the processor
285
that you want to use for simulation versus the architecture used
286
for task execution a recipe is step one step two step
287
three in the case of simulation the world is happening concurrently
288
and in parallel all the same time right now and
289
so on that first principle you would think that simulation of the world whether it's quantum Newtonian or otherwise,
290
should be something that is a different architecture than recipe execution and instruction execution.
291
And so that's the big, big idea.
292
Now the question then is, how do you manifest that idea in technology?
293
Number one.
294
Number two, how do you go find application for it?
295
And so we found, of course, the first application, computer graphics.
296
The second application was seismic processing or inverse physics, CT reconstruction, ultrasound, seismic, very similar problems.
297
The next problem we found was molecular dynamics, Newtonian physics.
298
And, you know, on and on and on and on.
299
And we just kept finding one problem after another.
300
And then one day, some researchers, one at Stanford, Andrew Yang, Jeff Hinton at the University of Toronto, Yann Le Kun over at New York University,
301
they were all trying to solve for a similar problem, which is deep learning.
302
And they reached out to us and I, you know, being alert, I realized that this is a problem
303
that we could really make a contribution to and I was happy to help.
304
And because of the work that we did together, it achieved a level of computer vision capability that no one had ever imagined.
305
because of its success, it triggered even further introspection.
306
Why does it work?
307
What else can it do?
308
How far can it go?
309
What is the implication to computer science?
310
What is the implication to the whole industry that we're built on?
311
And so step by step by step, we broke everything down to its first principles.
312
And then we rebuilt back where NVIDIA is going to be in that world?
313
How do we apply all of the techniques that we know?
314
How do we navigate step by step so that on the one hand, we can pursue this unknown future?
315
And so all of this is about reasoning, vision, strategy, discipline, patience, belief,
316
belief at the core of grit.
317
We suffered our way here.
318
We suffered every single step of the way we suffered our way here because nobody believed in it.
319
So we had the benefit of building all of this for a decade before anybody even paid attention.
320
The hard part, of course, is that you're endeavoring something that has no positive feedback, no external motivation.
321
How do you stay motivated?
322
And how do your employees stay motivated?
323
Because there was this period of time where NVIDIA was not on the tip of the tongue of anybody
324
here in the Valley.
325
All kinds of other things were going on.
326
How did you stay, not just you stay focused, but how did you keep a workforce believing and people believing,
327
engineers believing that this was, there must have been a tough inspirational speech at some time.
328
How do you do
329
that first of all we have to believe in what we're
330
doing we have to go back to our core values as
331
you know in almost everything great that's done you have to
332
go back to your core foundations you have to demonstrate that
333
that you are determined to go pursue that that you see
334
that future in your mind's eye even though nobody else can you see
335
that future in your mind's eye you have to tell the story
336
so that everybody else could see it in their mind's eye
337
and you have to believe it yourself welcome Welcome to the world's first conference dedicated to our industry,
338
dedicated to the visual computing industry.
339
Welcome to GTC 2010.
340
GTC is all about the celebration of the wonderful discoveries
341
and amazing inventions that are made possible because of the GPU computing revolution.
342
GTC is a celebration of your work.
343
has to first start from what are your core beliefs.
344
For what reason do you believe this future will happen?
345
You could say that about America.
346
You could say that about Stanford.
347
You could say that about NVIDIA.
348
America has core foundations.
349
We spoke a little bit about it.
350
The American dream has a core foundation.
351
There's a reason why it exists.
352
There's a purpose that drives it.
353
There's a promise of it.
354
There are pillars that keeps it up.
355
When you see the world from my from my lens
356
when you see america from my lens and
357
when you see america now today as i travel around the world
358
and see america from the lens of everywhere i've been
359
and the countries that aspire to be us the industries
360
that would aspire to want to be part of our industry
361
you know it it is just genuinely a miracle it is a miracle
362
and i very often i teach young people of course and sometimes I think that some of this has gotten lost.
363
There is something about this risk-taking,
364
this willing to fail, the fact that you are in a place where you'll get a second chance,
365
the role of free speech, the role of laws that are reliable, that is really quite extraordinary.
366
Exactly.
367
We have a word for that.
368
We call it freedom.
369
We call it freedom, and that freedom has enabled so much.
370
That's right.
371
What would you say to these young people?
372
Because there is unfortunately right now among my students, there's a kind of fearfulness.
373
There's a kind of sense of, I'm not going to be able to achieve those things.
374
Maybe this world, whether it's I won't have a job because of AI, or is that American dream still really there?
375
What would you say to them?
376
I would always retreat back to first principles.
377
I would always reason from first principles.
378
When I'm uncertain about the future, when things are moving too fast, you always go back to first principles.
379
What are my core values?
380
What makes me great today?
381
What do I aspire for?
382
And to go back to that, I would say to young people that it's possible to have multiple feelings at the same time.
383
It's possible to be grateful for everything that you have, to be unsatisfied with where we are at,
384
and to have aspirations for greatness.
385
You're allowed to have all of those feelings at exactly the same time.
386
Doing technology change, doing world change, is the only opportunity for greatness.
387
Status quo, you know, it's really hard to make a difference.
388
And so I would deeply dive into the capabilities of artificial intelligence and use it in every possible way.
389
And it's not just about master of science, mastery of science, but it's about mastery of using artificial intelligence in my field of science.
390
Your purpose, if you decide to go into medicine, is to care for people.
391
It's different than studying a radiology scan.
392
Your purpose as an engineer is to either solve a known problem or discover problems that have never been solved, that are worthy to be solved.
393
There's a fundamental difference between the tasks that we do in our job versus the purpose of our job.
394
Everybody's purpose, every job's purpose, surprisingly, consists of tasks, but not defined by the tasks.
395
Let's talk about AI, actually, because you are foundational now to that entire revolution.
396
But you're an optimist about the technology.
397
Cautious optimist.
398
Cautious optimism.
399
Describe for me cautious optimism, because there's some people who are kind of, we've lived in the valley a long time.
400
There are people who are kind of wild-eyed optimists as well.
401
But talk to me about what cautious optimism means and how you think about that.
402
Intelligence is foundational to every aspect of society, every aspect of industry, everything we endeavor.
403
In almost everything that we do, the fundamental ingredient is intelligence.
404
Now, of course, we have to be cautious.
405
We have to be cautious so
406
that we advance the technology as quickly as we can so that it works as we promised,
407
so that it's functional as we expect, so that it doesn't produce intelligence.
408
That sounds like it's intelligence, but it's not.
409
It's flawed.
410
Functional things are safer.
411
I want my car to function as promised.
412
AI needs to function as promised.
413
What, when you think about this whole question internationally and the like, what concerns you about where we are now?
414
I think that one of the challenges is to define what is AI.
415
That is probably one of the most, there's a lot of different ways to think about the technology.
416
But one way to think about it is it's like a five-layer cake and we have to win every single layer.
417
The first layer is just energy, energy, land, power, and shell.
418
First layer.
419
Second layer is the chips in layer where I'm at.
420
The third layer is the infrastructure layer, which is kind of like cloud services.
421
The next layer above that is the AI model layer.
422
This is where everybody talks.
423
Yes.
424
Right.
425
But it's not the only layer.
426
Everything, everything about that layer is very important.
427
But ultimately, the most important layer to our nation is the layer above that, which is the application layer.
428
Right.
429
using AI for healthcare, using AI for military applications, using for defense, for cybersecurity, using AI for transportation, for manufacturing,
430
that is what's going to drive our industry forward.
431
But I do think that our nation also has to be very alert that this is a very important time.
432
And although we are ahead, although we are the world leader, during an inflection in technology is exactly when leadership can change.
433
And that we have to make sure that as we come up with policies, we don't hinder the most important layer, which is the highest layer, the application rate.
434
That is the layer that whoever advances that layer most will exploit this industrial revolution the most.
435
Somebody comes here from Taiwan.
436
They're nine.
437
They end up in Kentucky of all places.
438
They somehow go to Oregon State because their friend goes to Oregon State and the family's there.
439
And then they go to Stanford because somebody will pay for it.
440
You meet a collection of people who are kind of intellectual geniuses, but they're a push on your own.
441
That's right.
442
I'm the busboy, Denny's, that met up with Chris and Curtis, the two geniuses.
443
I mean, that's how the story goes.
444
That's the story, right?
445
Yeah, yeah.
446
And you found this company on an idea.
447
So a while before this idea actually shows that you were right.
448
Does that happen only in America because of some of those foundational institutions, ideas that you talked about?
449
I think that it's a chain of extremely low probability events that leads to India.
450
and without the tailwind that America provides.
451
America provides tailwind, not headwind.
452
It provides tailwind, laws and rules that are understandable and that you can count on,
453
a business environment, an industrial environment with people playing by the rules that you can understand and count on.
454
When everybody's playing by the rules, because the rules and laws are for good reason, then at least you could find where there are segments in the market
455
that are underserved and that you can rely on that.
456
You can rely on the fact that you can create something great
457
and take it to market in a way that serves a market
458
that has a demand and that it wouldn't be foreclosed on you randomly, arbitrarily, unknowingly, you know, unpredictably.
459
Those those things that entrepreneurs rely on, you know, are alive and well here.
460
You know, as an immigrant, you come here by choice.
461
You witness a miracle because compared to where you came from, the circumstances you came from, it is a miracle.
462
The resources are incredible, are abundant.
463
You want to work hard because you're desperate to succeed.
464
An entrepreneur, you're desperate to succeed.
465
If you don't work hard every single day, you will perish.
466
The entrepreneurial spirit, the immigrant spirit is rather similar, actually.
467
Very similar.
468
Yeah.
469
Right.
470
You have the same feelings.
471
I am certain my feelings about NVIDIA and my constant desperation to do better
472
is exactly the same feelings my parents have to secure a living for their family.
473
They have nothing else to rely on, nothing to fall back to.
474
I can't imagine another place where this is possible.
475
You know, this is genuinely, NVIDIA genuinely is a only in America story.
476
In a lot of ways, I'm a only in America story.
477
This is in one lifetime.
478
I'm not talking about the fifth generation thing.
479
I'm not talking about third generation thing.
480
This all in one body in one generation with parents
481
that gave up everything to be here with no way to fall back and they
482
sacrificed their whole life to build a life for their children
483
so that we could have more opportunities than they did
484
and for a country to create the opportunities for me and all the the resources,
485
the systems, the institution, the foundation that makes a company like NVIDIA to be possible.
486
I am the embodiment of the American dream.
487
We will be able to share new courses.
488
Remember, learning English is a journey.
489
You are doing well, keep going!
490
We'll see you next time!
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本課的詞彙與口語要點
這節 B2 級別的口語課以影片“【🎧News English - 黃仁勳談年輕人如何面對未來】黃仁勳： AI 時代不要被恐懼限制，真正的成功來自冒險｜人生目的比工作任務更重要｜輝達背後的移民故事與美國夢｜英語｜英語新聞練習”為素材。 這段影片共有 490 個句子、5365 個單詞可供跟讀。 講話部分時長為 34:44。 說話人語速自然，每分鐘約 154 個詞，接近日常對話。 85% 的單詞屬於英語最常用的 3,000 詞，其餘的詞建議在練習前先學一下。
影片中的重點詞彙
影片中最難的 15 個單詞，附發音和釋義：
| 單詞 | 發音 | 釋義 |
|---|---|---|
| graphics 名詞 | /ˈɡɹæfɪks/ | 圖學 /圖學 |
| simulation 名詞 | /ˌsɪm.jəˈleɪ.ʃn̩/ | 模擬 /模擬 |
| compute 動詞 | /kəmˈpjuːt/ | 計算 /計算 |
| cautious 形容詞 | /ˈkɔːʃəs/ | 謹慎 /謹慎, 慎重 |
| silicon 名詞 | /ˈsɪləˌkɑn/ | 矽, 矽 |
| immigrant 名詞 | /ˈɪmɪɡɹənt/ | 移民, 移民者 |
| miracle 名詞 | /ˈmɪɹ.ə.kəl/ | 奇跡 /奇蹟 |
| instruction 名詞 | /ɪnˈstɹʌkʃən/ | 教導 /教導 |
| mathematics 名詞 | /mæθ(.ə)ˈmæt.ɪks/ | 數學 /數學, 算學 /算學 |
| aspire 動詞 | /əˈspaɪɚ/ | 熱望 /熱望, 渴望 |
| tailwind 名詞 | /ˈteɪlwɪnd/ | 順風 /順風 |
| optimism 名詞 | /ˈɑptɪmɪzəm/ | 樂觀主義 /樂觀主義 |
| lens 名詞 | /lɛnz/ | 透鏡 /透鏡, 鏡片 /鏡片 |
| netbook 名詞 | 上網本 /上網本 | |
| optimist 名詞 | /ˈɑptɪmɪst/ | 樂天派 /樂天派 |
影片中出現的短語動詞
| 單詞 | 釋義 |
|---|---|
| turn out 動詞 | 原來 /原來 |
| go up 動詞 | 上昇 /上升, 上去 |
| come up with 動詞 | 想出, 提出 |
| end up 動詞 | 到達 |
| give up 動詞 | 投案, 自首 |
| grow up 動詞 | 長大 /長大 |
| look forward to 動詞 | 期待, 嚮往 |
| put together 動詞 | 拼合, 組合 /組合 |
值得反覆跟讀的句子
影片中簡短而完整的句子，可以直接用在日常對話裡：
- Can we work together on this?
- What is the implication to the whole industry that we're built on?
- How do you stay motivated?
- What would you say to these young people?
- What would you say to them?
影片中的語法
說話人最常用的結構，並附上影片中的原話：
| 結構 | 影片中的用法 |
|---|---|
| 現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事 | have known · have never heard · I've reduced |
| 被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的 | was born · be trained · being taught |
| 定語從句 who / which + 從句 — 補充說明人或事物 | people who come · friends who also · idea, which was |
需要注意的發音
說話人用了 54 次縮略和弱讀形式，例如 you're, I'm, we've。請按聽到的簡短形式來說。
- “th” 音: mathematics /mæθ(.ə)ˈmæt.ɪks/, athlete /ˈæθ.liːt/, altogether /ɔl.tuˈɡɛð.ɚ/
- “sh” 和 “zh” 音: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, cautious /ˈkɔːʃəs/, instruction /ɪnˈstɹʌkʃən/, foundational /faʊnˈdeɪ.ʃə.nəl/, implication /ˌɪmpləˈkeɪʃən/
- 長單詞——注意重音位置: mathematics /mæθ(.ə)ˈmæt.ɪks/, foundational /faʊnˈdeɪ.ʃə.nəl/, optimism /ˈɑptɪmɪzəm/, genuinely /ˈd͡ʒɛn.ju.ɪn.li/, programmable /pɹəʊˈɡɹæməbəl/
如何用這段影片練習
- 先完整聽一遍影片，不要開口，記下不認識的單詞。
- 先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
- 錄下自己的聲音並與原聲對比，特別注意 graphics, simulation, compute 這類單詞。
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。