ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: Jensen Huang on Vision, Risk, and the GPU | Only In America
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My parents had nothing, sold everything, came to America.
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That was the beginning.
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Along the way, the jobs I had, the schools I went to, the opportunities I had, and here we are, NVIDIA being the most consequential technology company in the world.
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This can't possibly happen anywhere else.
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America leads the world in technological innovation.
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This innovation, taking place all around us in Silicon Valley, is possible here because of our American freedoms.
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But to take advantage of the opportunities that freedom affords us, it requires taking risk and facing the unknown with courage and determination.
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Jensen Wang, the founding CEO of NVIDIA, had a vision, took significant risk, and now leads an iconic American business,
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driving change at the heart of global technology.
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His journey as an immigrant is fascinating, and it has so much to tell us about our country and what makes it exceptional.
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America presented the opportunities that enabled Jensen's risk -taking to change the world.
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Now, for Only in America, my conversation with Jensen Wong.
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the amazing condi rice i'm in uniform how are you good to see you nice to see you see you too Welcome.
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Thank you.
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What do you think about our new building?
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I think this is amazing.
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This is a building that is only possible with science and a supercomputer.
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Right?
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It's fantastic.
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This was completely built in a simulation first.
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You're kidding.
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This building is designed so that we make maximum use of this incredible space called California.
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Yeah.
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And so we decided let's use the space for the beautiful sunlight
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and the weather and this entire space is very energy efficient
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so we use the maximum amount of sunlight and that's why there are light wells on the roof
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but when you have light wells in the roof it is extremely difficult to remove the heat
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and so notice where the triangles are the light wells are only where it needs to be I see.
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And the roof looks flat to us from here, but is undulated to track the sun.
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So it lets sun in just enough, but doesn't let any more sunlight in than is needed.
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Otherwise, we'll have to remove the heat.
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It's so hot.
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So it's completely efficient then.
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Completely efficient.
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We simulated this building every single hour today through a simulation of the entire year.
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and we moved everything until it was perfect perfectly harmonious mathematically well i would expect
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that of you that's an engineer trying to be an architect i like it i like it yeah
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and the undulation this one creates a baseball cap
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and the reason for that is
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because you could see during in the in the afternoon
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when the sun's about to set it comes right into the building
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And so I sloped the walls to move the offices further back in.
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And so this is the baseball cap of the building.
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And it fits right into the beautiful design.
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If you make me look too put together, everybody knows it's fake.
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I've got to be a little wicky wonky.
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Something's got to be a little off.
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There it is.
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Do we still call you Madam Secretary?
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No, you're not. Would you?
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I tell people, I'm good for now.
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I tell people, Madam Secretary was a while ago, Dr. Rice was my father, and Professor Rice is with my students,
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so I think that leaves us with Connie.
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My goodness.
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That's a lot of good titles.
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Jensen Wang, thank you.
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Thank you for having me here at this beautiful NVIDIA campus.
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You and I have known each other a very long time.
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And I have a lot of ties, Stanford ties and the like.
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So I'm really looking forward to our conversation.
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But let me start with this.
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young kid, nine, comes to the United States of America.
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America is in many ways one big immigrant story.
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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.
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So talk about arriving here.
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It couldn't have been easy.
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I was born in Taiwan.
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When I was five, My father got a job in Thailand to help start an oil refinery.
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And so we moved to Thailand.
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We were there for about four years.
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In 1973, there was a coup, as it happens every now and then.
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And my parents thought it was unsafe for us to be there.
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My older brother was one year older than me.
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He was 10.
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I was 9.
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My parents wanted to send us out as soon as possible.
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And so they got our uncle, who lives in Washington, in Tacoma, Washington to take us for a little while.
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My first impression was I'd never stood in a house with carpets before.
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And it was the strangest feeling.
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I felt like I was walking on my bed with my shoes on.
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And just everything from cereal and the morning television, the speed racer, and in the afternoon,
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Partridge Family, and all the candy, the Snickers bars, Everything was like, I couldn't imagine what this amazing country was.
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And everything was so beautiful.
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The cars, everything was just incredible.
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And we were in Tacoma, Washington for about three months.
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And then they sent us to the most affordable, accessible boarding school in America because my parents couldn't afford much.
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A wonderful school.
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They welcomed students of all backgrounds, many from difficult homes, some international kids.
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And that's how we were able to come.
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They sponsored us and allowed us to come to the United States and stay in that boarding school.
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And I was there for about two years in Oneida, Kentucky.
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I have never heard of Oneida, Kentucky until this particular moment.
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If you look at Oneida, Kentucky in Google Maps, the beautiful thing is there's nothing around it.
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It's just one little dot.
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I think when I was there, it was probably population 600.
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It's probably population 600 now.
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Beautiful, incredible.
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I had a wonderful, wonderful childhood there.
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It was very difficult and very scary coming to the United States by yourself.
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But it's amazing what my older brother did, frankly.
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I just followed him around.
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He was 10 years old.
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Could you imagine a 10 -year -old bringing along a 9 -year -old?
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Traveled all the way from Thailand, never been to the United States.
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Landed in Tacoma, Washington, and went to Kentucky, but laid over in Chicago.
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Oh, my goodness.
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The airport, Chicago airport is gigantic.
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And so we had to go find a connecting flight, and he, you know, a 10 -year -old did all that by himself.
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My older brother's incredible.
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That's amazing.
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So this was risky in a lot of ways, but your parents felt that the greater risk would be to stay where you were.
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How were you treated?
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Were people friendly?
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Were they surprised?
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How did that part of it go?
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You know, 1973, United States in Kentucky, there were still biases and nobody had,
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the school had never seen this Chinese kid before.
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Wonderful kids there, but none of them ever seen a Chinese before.
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And so there were all of the things that comes along with, you know, being a stranger in a town where nobody's ever seen someone like you before.
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And so there's a lot of biases and, of course, things like that.
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But kids are kids.
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And so for us at the time, you know, the United States was amazing.
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And I was part of the swim team and I was part of the soccer team.
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And the food was interesting.
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You know, sausage and gravy.
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Who wouldn't like that?
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You know, hamburgers.
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And after the swim meet, the coach just took us to the most amazing restaurant in the world, and the food came in boxes,
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and the menu was all lit up.
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And we sat in a restaurant that seemed like a spaceship, and it was McDonald's.
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McDonald's, right.
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You know, and so I think it's really all about expectations in life.
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You know, when you came from an even more difficult circumstance, that's the thing about immigrants is that when you come to America, you came because of choice.
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You wanted to be here.
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My dad sent us here, but in fact, when I was four years old, he had the opportunity to come to be trained in the United States,
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and it turned out the place he came to was New York.
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Oh, boy.
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Could you imagine to come to the United States?
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And it's New York City from Taiwan in the 60s.
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And so he always said that he wanted his family to eventually come to America, to come to this incredible place.
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So the thing that's really great about immigrants is you came with very little expectation.
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You came with great hopes and dreams.
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You appreciate everything.
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And with a sense of gratitude for where you are and the new opportunities.
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Exactly.
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And this miracle that you're witnessing, I remember very clearly what it's like to see America for the first time.
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This miracle and all these people and how welcoming they are.
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To see it through those eyes.
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But then, of course, you go from this little kid in Kentucky, you're on the swim team.
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It sounds like you were doing okay i was a good athlete yeah i was a good athlete
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and so we met our parents back in tacoma washington
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um they they they had nothing
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and literally they put all their belongings on the plane
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so they had nothing and they came with the you know literally the suitcase i mean
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that story the immigrant story and
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and they saved their way to to uh build a life
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for their kids my mom was a a maid at a catholic school.
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Uh, and, uh, my father was an engineer.
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Uh, they saved everything they had.
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My dad, uh, bought a green truck, uh, a, um, a van and, um, there were, there were no seats in it.
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So he put a carpet in the back and put some milk crates.
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Uh, we got into the, to that green van and he drove us all the way from Oregon down to LA
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so that we could go see disneyland and um
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that was the one vacation of our family how did you end up going on to college
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and then stanford and how does and working at denny's
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which we have to cover at some point i know i
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i i must be denny's best yeah i think you are definitely i i love denny
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so much it never occurred to me to be able to afford
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or or you know go anywhere for college and and so
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so it was it was always just my plan
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that I would grow up in Oregon and um I loved math I love science
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and when you're in high school and you love math
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and science you're you're only going to have three other friends who also love math
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and science right and so the the two
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or three friends of ours we were all in a club
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so that we were in a math club we were also in the science club right
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and then we were in the computer club all together all together and so and then afterwards after you know, clubbing.
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We went, we would go play, played arcade games, you know, play pinball or go played arcade games.
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And, and that, that was, that was growing up.
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And, uh, my best friend, uh, Dean Verheyen, he, uh, he said, you know, I'm just going to go to Oregon State University.
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My, my, uh, my brother went there and my parents went there.
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And I said, that sounds great.
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Oregon State had a good engineering program.
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That was just good happenstance.
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Um, I loved the school and it turned out that, um, uh, there was another kid there, Lori, uh, she, she was one of three girls in a,
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in a school of, in a class of 250 other boys.
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And, um, uh, and, and she was, you know, she was, I was the youngest kid in school.
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I was only 16 and, uh, she was a year and a half older than me.
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And I was determined to go, you know, meet her.
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And so I, you know, statistically weeded out everybody by getting myself maneuvered into her lab class.
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So now I've reduced the population from 250, competition from 250 to four.
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That's quite clever.
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That's very clever.
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That was very strategic, even when I was a kid.
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And, well, I knew odds.
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And then, you know, I gave her the ultimate pickup light.
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I asked her if she wanted to see my homework. That was so smooth.
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That was really smooth of you to do.
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I cinched it.
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And so we've been together ever since.
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That's great.
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So that's how you met.
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Yeah, we met.
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We've been in college.
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So you're in Corvallis, Oregon State.
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You are doing well in school.
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How does Stanford come onto your radar screen?
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I always wanted to go to a great school.
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You know, I always imagined, you know, getting a master's, but I never imagined that I could afford it.
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And so during Oregon State, the recruiters came from Silicon Valley, and so I took a job at AMD.
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And that was a really good choice because the work was really interesting.
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I had really great colleagues
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and they had this this um a program where you could go to stanford
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and work at amd and they pay everything and so see
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and so it was an incredible thing so when i was being
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when they were pitching the offer to me i said i
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said hang on a second i can go to work at amd you can pay me a great salary
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and you're going to pay me to go to stanford at the same time
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and they said yep you said that sounds good
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that sounds like like a dream come true so i took
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that job and then of course a year later laurie graduated we got married
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and um i and then life started
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so i uh i took some classes took some time off took some classes took some time off
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and altogether it must have taken me some eight years to
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grab i'm probably the longest running student at stanford nobody paid stanford more for the education
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and uh along the way spencer madison came
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and along the way i i found an nvidia
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and literally during my entire tenure you know during
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that time of of uh being at stanford my life happened
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when you're when you're going to school you think
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that the school work is awfully academic and the reason for
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that is because you're not sure whether there's any purpose any benefit in learning this.
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But the benefit of working and going to school at the same time, especially Stanford,
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I could see so much of the principles being taught and how important it is in everything that I do today.
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And so it was a great privilege to have all of that happening to me at the same time.
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My family, my kids, my company, Stanford, you know, working all of it kind of in one giant soup.
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People talk about Silicon Valley and the ecosystem, but you just used a really great term.
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It is really kind of a big stew, isn't it?
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And you meet very interesting people, and they change your thinking about something, and that leads to something.
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It sounds like Stanford was a place like that for you.
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It completely shaped my attitude and my perspective about computer science and its impact to industry and industrial strategies.
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that that entire intersection between technology applications um the fundamental science
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and the strategies of computer science really for me formulated in
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that during that time right
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and it was really cool now talk about the founding of
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nvidia you know the early day founder stories are really fascinating
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because you have to take a you have to take a
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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?
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During that time, it was at the beginning of the PC revolution.
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It was the beginning of really Moore's Law, the beginning of the CPU era, this incredible technology engine that really changed everything.
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All of that in Silicon Valley was all about CPUs.
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Everything was about general purpose computing.
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Everything was about CPUs everything was about Moore's Law, everything was about the PC.
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And there were two simultaneous ideas that Chris Curtis and I were considering.
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And one idea, of course, is every application that's interesting and meaningful, can it be run on a CPU?
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And we believe that there were so many interesting problems to solve, whether it's real -time computer graphics,
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which is at the time one of the hardest problems to do in computer science or simulation or other things beyond that.
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A normal computer like a CPU can't possibly be the right format.
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And so we imagine that there would be a way to accelerate the CPU,
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offload the work that is not suitable for general purpose things.
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It's kind of like in your house you only have one tool, one tool in the kitchen.
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Or you go into your garage, there's only one tool in your garage.
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You come to your company, there's only one tool you know there's there's a there's the right tool for the right job
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and we we believe that there's there's a another tool
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that could augment the cpu and make that computer essentially a super computer
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and right and and um so
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that was that's one idea the second idea was was how
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do we even get this company started the problem is the
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cpu at this point general purpose computing as of today is probably about 64 years old
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and so the nature of the computer industry is that
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if you create an if you create an architecture
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that produces benefits to applications the install base goes up the sales goes up
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applications use more of it this positive virtual cycle would cause the two to reinforce itself.
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And so in fact, over the course of the last 64 years, there's so many applications are built on top of the CPU.
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How do you even cause the application developers to consider another architecture?
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Another architecture, right.
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That chicken or egg problem is incredibly hard to solve and has never been solved.
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In fact, aside from NVIDIA today, really everything else runs on CPUs.
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And so the idea that we would get this new architecture to be adopted by developers was incredibly hard.
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And so the question is, what's the first application?
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And so which brought NVIDIA to its second idea, which was maybe the first application is computer graphics.
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But the problem is computer graphics was such a small market at the time it was just silicon graphics right
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and silicon graphics was such a it was a large company
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but by by the the standards of computer architectures very very small
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and so the question is how do we find an application
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that would on the one hand need this architecture on the
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other hand be sufficiently high volume to cause this architecture to proliferate
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and so we thought maybe 3d graphics for video games computer games are not only fun
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but they perhaps more than any other application push the edge
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of computing power most pc upgrades for consumers are driven not by the grown -ups in the house
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but by the kids who want a more powerful gaming machine all right
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so you've got the brand new geforce 3 graphics card here
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that's exactly right the geforce 3 is the the latest generation graphics processor from nvidia
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so this product actually includes a 57 million transistor graphics processor
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to put in perspective that's that's more transistors than a penium
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four plus a penium three put together i just described the business plan yeah
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that is impossible to fund and it was i still remember explaining it everybody's going yeah
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but you know this and that this requires this be solved
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and have multiple chicken and eggs
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and yet silicon valley right here sand hill road would finance me sequoia capital
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and sutter hill right would be investors in this company and that
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that we would be able to attract the brightest minds the
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brightest computer scientists in the world to come work here
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but we were just determined
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that on first principle basis the general purpose computer cannot possibly be the only computing platform right
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and that there were too many interesting problems that we could solve
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if we were to introduce this new idea what's key about
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this is it's programmed that's exactly right for the first time the graphics processor is as programmable as the CPU.
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We have an instruction set, just like the Intel processors have an instruction set that you can program Microsoft Word or Windows with, we now have an instruction set that game programmers can program to create special effects on our processor.
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So you literally were swimming upstream on this, people you were trying to convince.
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For 30 years.
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For 30 years.
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Yeah.
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But then something happens and the problem
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that you've solved it turns out has many other applications nvidia
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the best way to play the netbook explosion that's right n
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-v -d -a first of all the company understands the netbook opportunity quote there's more
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and more people proving
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that they want to be able to take the computer with them nvidia came to us many months ago
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and talked to us about an amazing graphics part that they wanted to build.
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And we said, this is fantastic, but we'd like to use it in a notebook.
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Can we work together on this?
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And we've been working together with NVIDIA for many, many months, and they've created something really great.
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I remember listening to you at Stanford at a talk and you said, we just kept trying to solve hard problems.
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Computer graphics is basically a simulation of the world.
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It's a simulation problem.
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In a lot of ways, artificial intelligence is a simulation of the mind, simulation of the brain.
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And so the computation of simulation can be done not completely, but largely in parallel.
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And so the architecture, the processor that you want to used for simulation versus the architecture used for task execution.
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A recipe is step one, step two, step three.
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In the case of simulation, the world is happening concurrently and in parallel all the same time right now.
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And so on that first principle, you would think that simulation of the world, whether it's quantum, Newtonian, or otherwise,
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should be something that is a different architecture than recipe execution and instruction execution.
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And so that's the big, big idea.
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Now the question then is, how do you manifest that idea in technology, number one?
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Number two, how do you go find application for it?
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And so we found, of course, the first application, computer graphics.
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The second application was seismic processing or inverse physics, CT reconstruction, ultrasound, seismic, very similar problems.
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The next problem we found was molecular dynamics, Newtonian physics.
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And, you know, on and on and on and on, and we just kept finding one problem after another.
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And then one day, some researchers, one at Stanford, Andrew Yang, Jeff Hinton at the University of Toronto, Jan LeCun over at New York University,
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they were all trying to solve for a similar problem, which is deep learning.
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And they reached out to us, and I, you know, being alert, I realized that this is a problem that we could really make a contribution to,
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and I was happy to help.
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And because of the work that we did together, it achieved a level of computer vision capability that no one had ever imagined.
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And because of its success, it triggered even further introspection.
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Why does it work?
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What else can it do?
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How far can it go?
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What is the implication to computer science?
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What is the implication to the whole industry that we're built on?
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And so step by step by step, we broke everything down to its first principles.
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And then we rebuilt back where NVIDIA is going to be in that world.
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How do we apply all of the techniques that we know?
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How do we navigate step by step so that on the one hand, we can pursue this unknown future?
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And so all of this is about reasoning, vision, strategy, discipline, patience.
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Belief.
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Belief.
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At the core of grit, we suffered our way here.
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You know, we suffered every single step of the way we suffered our way here because nobody believed in it.
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Right.
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So we had the benefit of building all of this for a decade before anybody even paid attention.
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The hard part, of course, is that you're endeavoring something that has no positive feedback, no external motivation.
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How do you stay motivated?
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And how do your employees stay motivated?
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Because there was this period of time where NVIDIA was not
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on the tip of the tongue of anybody here in the Valley.
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All kinds of other things were going on.
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And how did you stay, not just you stay focused, but how did you keep a workforce believing and people believing,
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engineers believing that this was, that must have been a tough inspirational speech at some time?
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How do you do that?
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First of all, we have to believe in what we're doing.
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We have to go back to our core values.
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As you know, in almost everything great that's done, you have to go back to your core foundations you have to demonstrate
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that that you are determined to go pursue that that you see
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that future in your mind's eye even though nobody else can you see
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that future in your mind's eye you have to tell the story
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so that everybody else could see it in their mind's eye
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and you have to believe it yourself welcome to the world's
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first conference dedicated to our industry dedicated to the visual computing industry.
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Welcome to GTC 2010.
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GTC is all about the celebration of the wonderful discoveries and amazing inventions that are made possible because
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of the GPU computing revolution.
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GTC is a celebration of your work.
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It has to first start from what do you what are your core beliefs?
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For what reason do you believe this future will happen.
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You could say that about America.
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You could say that about Stanford.
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You could say that about NVIDIA.
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America has core foundations.
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We spoke a little bit about it.
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The American dream has a core foundation.
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There's a reason why it exists.
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There's a purpose that drives it.
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There's a promise of it.
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There are pillars that keeps it up.
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When you see the world from my lens, when you see America from my lens, and when you see America now today as I travel around the world,
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and see America from the lens of everywhere I've been and the countries that aspire to be us,
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the industries that would aspire to want to be part of our industry.
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You know, it is just genuinely a miracle.
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It is a miracle.
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And I very often, I teach young people, of course.
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And sometimes I think that some of this has gotten lost.
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There is something about this risk -taking, this willing to fail.
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The fact that you are in a place where you'll get a second chance, the role of free speech,
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the role of laws that are reliable, that is really quite extraordinary.
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Exactly.
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We have a word for that.
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We call it freedom.
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We call it freedom.
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And that freedom has enabled so much.
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That's right.
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What would you say to these young people?
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Because there is, unfortunately right now, among my students, there's a kind of fearfulness.
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There's a kind of sense of, I'm not going to be able to achieve those things.
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Maybe this world, whether it's I won't have a job because of AI, or is that American dream still really there?
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What would you say to them?
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I would always retreat back to first principles.
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I would always reason from first principles when I'm uncertain about the future
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when things are moving too fast you always go back to
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first principles what are my core values what makes me great today what do I aspire for
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and to go back to that I would say to to young people
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that it's possible to have multiple feelings at the same time it's possible to be grateful for everything that you have,
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to be unsatisfied with where we are at, and to have aspirations for greatness.
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You're allowed to have all of those feelings at exactly the same time.
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Doing technology change, doing world change, is the only opportunity for greatness.
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Status quo, you know, it's really hard to make a difference.
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And, and so I would, I would deeply dive into the capabilities of artificial intelligence and use it in every possible way.
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And, and, 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.
430
Your purpose, if you decide to go into medicine is to care for
431
people it's different than studying a radiology scan your purpose as an engineer is to either solve a known problem
432
or discover problems that have never been solved
433
that are worthy to be solved there's a fundamental difference between the tasks
434
that we do in our job versus the purpose of our
435
job everybody's purpose every job's purpose surprisingly consists of tasks
436
but not defined by the tasks let's talk about um ai actually yeah
437
because um you are foundational now to that entire revolution yeah
438
but you're you're an optimist about you know the technology cautious optimist Cautious, describe for me cautious optimism,
439
because there's some people who are kind of, we've lived in the valley a long time, there are people who are kind of wild -eyed optimists as well.
440
But talk to me about what cautious optimism means and how you think about that.
441
Yeah, intelligence is foundational to every aspect of society, every aspect of industry, everything we endeavor.
442
In almost everything that we do, the fundamental ingredient is intelligence.
443
Now, of course, we have to be cautious.
444
We have to be cautious so
445
that we advance the technology as quickly as we can so that it works as we promised,
446
so that it's functional as we expect, so that it doesn't produce intelligence that it sounds like it's intelligence, but it's not.
447
It's flawed.
448
Functional things are safer.
449
I want my car to function as promised.
450
AI needs to function as promised.
451
What, when you think about this whole question, internationally and the like, what concerns you about where we are now?
452
I think that one of the challenges is to define what is AI.
453
That is probably one of the most, there's a lot of different ways to think about the technology, but one way to think about it is it's like a five -layer cake, and we have to win every single layer.
454
The first layer is just energy, energy, land, power, and shell, first layer.
455
Second layer is the chips in layer where I'm at.
456
The third layer is the infrastructure layer, which is kind of like cloud services.
457
The next layer above that is the AI model layer.
458
This is where everybody talks.
459
Yes.
460
Right.
461
But it's not the only layer.
462
Everything, everything about that layer is very important.
463
But ultimately, the most important layer to our nation is the layer above that, which is the application layer.
464
But using AI for health care, using AI for military applications, using for defense, for cybersecurity, using AI for transportation, for manufacturing,
465
that is what's going to drive our industry forward.
466
But I do think that our nation also has to be very alert that this is a very important time.
467
And although we are ahead, although we are the world leader.
468
During an inflection in technology is exactly when leadership can change
469
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.
470
That is the layer that whoever advances that layer most will exploit this industrial revolution the most.
471
Somebody comes here from Taiwan.
472
They're nine.
473
They end up in Kentucky of all places.
474
Yeah.
475
And they somehow go to Oregon State because their friend goes to Oregon State and the family's there.
476
And then they go to Stanford because somebody will pay for it.
477
You meet a collection of people who are kind of intellectual geniuses, but they're a push on your own.
478
That's right.
479
I'm the busboy, Denny's, that met up with Chris and Curtis, the two geniuses.
480
I mean, that's how the story goes.
481
That's the story, right?
482
Yeah, yeah.
483
And you found this company on an idea.
484
So a while before this idea actually shows that you were right,
485
does that happen only in America because of some of those foundational institutions, ideas that you talked about?
486
I think that it's a chain of extremely low probability events that leads to India.
487
And without the tailwind that America provides, America provides tailwind, not headwind.
488
You know, it provides tailwind, laws and rules that are understandable and that you can count on.
489
A business environment, an industrial environment with people playing by the rules that you can understand and count on.
490
You know, when everybody's playing by the rules, because the rules and laws are for good reason,
491
then at least you could find where there are segments in the market that are underserved
492
and that you can rely on that.
493
You can rely on the fact that you can create something great
494
and take it to market in a way that serves a market
495
that has a demand and that it wouldn't be foreclosed on you randomly, arbitrarily, unknowingly, you know, unpredictably.
496
Those those things that entrepreneurs rely on, you know, are alive and well here.
497
You know, as an immigrant, you come here by choice.
498
You witness a miracle because compared to where you came from, the circumstances you came from, it is a miracle.
499
The resources are incredible, are abundant.
500
You want to work hard because you're desperate to succeed.
501
An entrepreneur, you're desperate to succeed.
502
If you don't work hard every single day, you will perish.
503
The entrepreneurial spirit, the immigrant spirit is rather similar, actually.
504
Very similar.
505
Yeah.
506
Right.
507
You have the same feelings.
508
I am certain my feelings about NVIDIA
509
and my constant desperation to do better is exactly the same
510
feelings my parents have to secure a living for their family
511
they have nothing else to rely on nothing to fall back
512
to i can't imagine a another place where this is possible
513
yeah you know this is genuinely nvidia genuinely is a only
514
in america story in a lot of ways i'm a only
515
in america story this is in one lifetime i'm not talking
516
about the fifth generation thing i'm not talking about third generation
517
thing this all in one body in one generation with parents
518
that gave up everything to be here with no way to fall back
519
and they sacrificed their whole life to build a life for their children
520
so that we could have more opportunities than they did
521
and for a country to create the opportunities for me and all the resources,
522
the systems, the institution, the foundation that makes a company like NVIDIA to be possible.
523
I am the embodiment of the American dream.
524
Thank you.
ดาวน์โหลดแอป
ทุกอย่างที่ต้องใช้เพื่อพูดได้คล่อง
AI ให้คะแนนการออกเสียงให้คะแนนทุกประโยค
ฝึก IPAเชี่ยวชาญทุกเสียง
คำศัพท์สร้างคลังคำของคุณ
เกมคำศัพท์เล่นไปเรียนไป
ช่องเดียวกัน
FOR YOU
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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
บทเรียนฝึกพูดนี้ใช้วิดีโอ “Jensen Huang on Vision, Risk, and the GPU” เป็นสื่อ วิดีโอนี้มี 524 ประโยค และ 6027 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 39:18 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 153 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน 85% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก
คำศัพท์สำคัญในวิดีโอนี้
คำที่ควรเรียนรู้ 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:
| คำศัพท์ | คำอ่าน | ความหมาย |
|---|---|---|
| layer คำนาม | /ˈleɪ̯ɚ/ | ชั้น |
| graphics คำนาม | /ˈɡɹæfɪks/ | กราฟิกส์, กราฟิก |
| solve คำกริยา | /sɒlv/ | แก้, แก้ไข |
| architecture คำนาม | /ˈɑː.kɪˌtɛk.t͡ʃə/ | สถาปัตยกรรม |
| principle คำนาม | /ˈpɹɪn.sɪ.pəl/ | หลัก, คติ |
| immigrant คำนาม | /ˈɪmɪɡɹənt/ | ผู้อพยพ |
| silicon คำนาม | /ˈsɪləˌkɑn/ | ซิลิคอน |
| miracle คำนาม | /ˈmɪɹ.ə.kəl/ | ปาฏิหาริย์ |
| cautious คำคุณศัพท์ | /ˈkɔːʃəs/ | ระวัง, รอบคอบ |
| swim คำกริยา | /swɪm/ | ว่ายน้ำ, ว่าย |
| mathematics คำนาม | /mæθ(.ə)ˈmæt.ɪks/ | คณิตศาสตร์ |
| fundamental คำคุณศัพท์ | /ˌfʌn.dəˈmɛn.təl/ | พื้นฐาน, เบื้องต้น |
| artificial คำคุณศัพท์ | /ˌɑː.tɪˈfɪʃ.əl/ | เทียม |
| lens คำนาม | /lɛnz/ | เลนส์, แว่น |
| sunlight คำนาม | /ˈsʌnˌlaɪt/ | แดด, แสงอาทิตย์ |
กริยาวลีที่คุณจะได้ยิน
| คำศัพท์ | ความหมาย |
|---|---|
| give up คำกริยา | ยอมแพ้ |
ประโยคที่ควรฝึกพูดซ้ำ
ประโยคสั้น ๆ ที่สมบูรณ์จากวิดีโอ ซึ่งนำไปใช้ในบทสนทนาประจำวันได้:
- What do you think about our new building?
- Can we work together on this?
- What is the implication to the whole industry that we're built on?
- How do you stay motivated?
- How do you do that?
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | have known · have never heard · I've reduced |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | was completely built · is designed · is undulated |
| Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ | people who come · friends who also · idea, which was |
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 63 ครั้ง เช่น you're, I'm, we've ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, smooth /smuːð/, athlete /ˈæθ.liːt/, worthy /ˈwɝ.ði/, altogether /ɔl.tuˈɡɛð.ɚ/
- เสียง “sh” และ “zh”: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, cautious /ˈkɔːʃəs/, instruction /ɪnˈstɹʌkʃən/, efficient /ɪˈfɪʃənt/, execution /ˌɛk.sɪˈkjuː.ʃən/
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, mathematics /mæθ(.ə)ˈmæt.ɪks/, fundamental /ˌfʌn.dəˈmɛn.təl/, execution /ˌɛk.sɪˈkjuː.ʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/
เสียงที่คนไทยมักออกเสียงยาก:
- เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: graphics /ˈɡɹæfɪks/, solve /sɒlv/, principle /ˈpɹɪn.sɪ.pəl/, miracle /ˈmɪɹ.ə.kəl/, cautious /ˈkɔːʃəs/
- /v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: solve /sɒlv/, innovation /ˌɪn.əˈveɪ.ʃən/, versus /ˈvɝsəs/, clever /ˈklɛv.ɚ/, developer /dɪˈvɛləpɚ/
- /z/ — เสียงก้อง ไม่ใช่ /s/: lens /lɛnz/, physics /ˈfɪz.ɪks/, optimism /ˈɑptɪmɪzəm/, seismic /ˈsaɪzmɪk/, afterwards /ˈæf.tɚ.wɚdz/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง layer, graphics, solve เป็นพิเศษ
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ