Практика Shadowing: 【🎧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｜英偉達背後的移民故事與美國夢｜英語｜英語新聞練習 - Изучайте разговорный английский по видео
Создание урока...
1
Jensen Long, thank you.
2
Thank you for having me here at this beautiful NVIDIA campus.
3
You and I have known each other a very long time and have a lot of ties, Stanford ties and the like.
4
So I'm really looking forward to our conversation.
5
But let me start with this young kid.
6
What, Nanai comes to the United States of America?
7
America is in many ways one big immigrant story.
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.
9
So talk about arriving here.
10
It couldn't have been easy.
11
I was born in Taiwan.
12
When I was five, my father got a job in Thailand to help start an oil refinery.
13
And so we moved to Thailand.
14
We were there for about four years.
15
In 1973, there was a coup, as it happens every now and then.
16
And my parents thought it was unsafe for us to be there.
17
My older brother was one year older than me.
18
He was 10.
19
I was nine.
20
my parents wanted to send us out as soon as possible and so they got our uncle who lives in Washington,
21
Tacoma, Washington to take us for a little while.
22
My first impression was was I'd never stood in a house with carpets before and it was the strangest feeling.
23
I felt like I was walking on my bed with my shoes on.
24
And just everything from cereal and the morning television, speed racer, and in the afternoon, Partridge family, and all the candy,
25
the Snickers bars, everything was like, I couldn't imagine what this amazing country was.
26
And everything was so beautiful.
27
The cars, everything was just incredible.
28
And we were in Tacoma, Washington for about three months
29
and then they sent us to the most affordable accessible boarding school in America because my parents couldn't afford much.
30
A wonderful school.
31
They welcome students of all backgrounds, many from difficult homes, some international kids and that's how we were able to come.
32
They sponsored us and allowed us to come to United States
33
and stay in that boarding school and I was there for about two years in Oneida, Kentucky.
34
I have never heard of Oneida, Kentucky till this particular moment.
35
If you look at Onida, Kentucky in Google Maps, the beautiful thing is there's nothing around it.
36
It's just one little dot.
37
I think when I was there was probably population 600.
38
It's probably population 600 now.
39
Beautiful, incredible.
40
I had a wonderful, wonderful childhood there.
41
It was very difficult and very scary coming to United States by yourself.
42
But it's amazing what my older brother did, frankly.
43
I just followed him around.
44
He was 10 years old.
45
Could you imagine a 10-year-old bringing along a 9-year-old?
46
Traveled all the way from Thailand, never been to the United States.
47
Landed in Tacoma, Washington, and went to Kentucky, but laid over in Chicago.
48
Oh, my goodness.
49
The airport, Chicago airport is gigantic.
50
And so we had to go find a connecting flight.
51
And a 10-year-old did all that by himself.
52
My older brother is incredible.
53
That's amazing.
54
So this was risky in a lot of ways
55
but your parents felt that the greater risk would be to stay where you were.
56
How were you treated?
57
Were people friendly?
58
Were they surprised?
59
How did that part of it go?
60
You know, 1973, United States in Kentucky,
61
there were still biases, and nobody had the school had never seen this Chinese kid before.
62
Wonderful kids there, but none of them ever seen the Chinese before.
63
And so there were all of the things
64
that comes along with being a stranger in a town where nobody's ever seen someone like you before.
65
And so there's a lot of biases and, of course, things like that.
66
But kids are kids.
67
And so for us at the time, you know, the United States was amazing.
68
And I was part of the swim team.
69
And I was part of the soccer team.
70
And the food was interesting and, you know, sausage and gravy.
71
Who wouldn't like that?
72
You know, hamburgers.
73
And after the swim meet, the coach just took us to the most amazing restaurant in the world.
74
And the food came in boxes.
75
And the menu was all lit up.
76
And we sat in a restaurant that seemed like a spaceship.
77
And it was McDonald's.
78
McDonald's, right.
79
You know?
80
And so I think it's really all about expectations in life.
81
You know when you came from an even more difficult circumstance
82
That's the thing about immigrants is that that when you come into
83
when you come to America you came because of choice You wanted to be here.
84
My dad sent us here
85
But in fact when I was four years old
86
He had the opportunity to come to be trained in the United States
87
and turned out the place he came to was New York Could you imagine to come to the United States and
88
It's New York City from Taiwan in the 60s.
89
And so he always said that he wanted his family to eventually come to America, to come to this incredible place.
90
So the thing that's really great about immigrants is you came with very little expectation.
91
You came with great hopes and dreams.
92
You appreciate everything.
93
And with a sense of gratitude for where you are and the new opportunities.
94
Exactly.
95
And this miracle that you're witnessing, I remember very clearly what it's like to see America for the first time.
96
This miracle and all these people and how welcoming they are.
97
To see it through those eyes.
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.
99
I was a good athlete.
100
Yeah, I was a good athlete.
101
And so we met our parents back in Tacoma, Washington.
102
they had nothing and literally they put all their belongings on the plane
103
so they had nothing and they came with the you know literally the suitcase I mean
104
that story the immigrant story
105
and they saved their way to build a life for their kids my mom was a maid at a Catholic school
106
and my father was an engineer.
107
They saved everything they had.
108
My dad bought a green truck, a van, and there were no seats in it.
109
So he put a carpet in the back and put some milk crates.
110
We got into that green van
111
and he drove us all the way from Oregon down to LA so that we could go see Disneyland.
112
And that was the one vacation of our family.
113
How did you end up going on to college and then Stanford?
114
And how does, and working at Denny's, which we have to cover at some point.
115
I know, I must be Denny's best ambassador.
116
Yeah, I think you are definitely.
117
I love Denny's so much.
118
It never occurred to me to be able to afford or, you know, go anywhere for college.
119
And so it was always just my plan that I would grow up in Oregon.
120
And I loved math, I loved science.
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.
122
And so the two or three friends of ours, we were all in a club, so that we were in a math club.
123
We were also in a science club.
124
Right.
125
And then we were in a computer club.
126
All together.
127
All together.
128
And so, and then afterwards, after, you know, clubbing, we would go play, played arcade games, you know,
129
play pinball or go play arcade games.
130
And that was growing up.
131
And my best friend, Dean Verheyen, he said, you know, I'm just going to go to Oregon State University.
132
My brother went there, my parents went there.
133
And I said, that sounds great.
134
Oregon State had a good engineering program.
135
That was just good happenstance.
136
I loved the school and it turned out that there was another kid there, Lori.
137
She was one of three girls in a school of, in a class of 250 other boys.
138
And she was, you know, she was, I was the youngest kid in school.
139
I was only 16.
140
And she was a year and a half older than me.
141
And I was determined to go, you know, meet her. And so
142
I you know statistically weeded out everybody by getting myself maneuvered into her lab class
143
so now I've reduced the population from 250 competition from 250 to four
144
it's quite clever that's very clever that was very strategic given
145
when I was a kid and well I knew I knew odds
146
and and uh and then
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.
148
That was really smooth of you to do.
149
I cinched it.
150
And so we've been together ever since.
151
That's great.
152
So that's how you met.
153
Yeah, we met.
154
We've been in college.
155
So you're in Corvallis, Oregon State.
156
You are doing well in school.
157
How does Stanford come onto your radar screen?
158
I always wanted to go to a great school.
159
You know, I always imagined, you know, getting a master's.
160
But I never imagined that I could afford it.
161
And so during Oregon State, the recruiters came from Silicon Valley.
162
And so I took a job at AMD.
163
And that was a really good choice because the work was really interesting.
164
I had really great colleagues.
165
and and they had this this um a program where you could go to stanford
166
and work at amd and they pay everything and so see
167
and so it was an incredible thing so when i was being that
168
when they were pitching the offer to me they said i
169
said hang on a second i can go to i can
170
work at amd you could pay me a great salary
171
and you're going to pay me to go to stanford at the same time
172
and they said yep you said that sounds good that sounds like like a dream come true.
173
So I took that job.
174
And then of course, a year later, Lori graduated, we got married.
175
And then life started.
176
So I took some classes, took some time off, took some classes, took some time off.
177
And altogether, it must have taken me some eight years to graduate.
178
I'm probably the longest running student at Stanford.
179
Nobody paid Stanford more for the education.
180
And along the way, Spencer Madison came and along the way I founded NVIDIA and literally during my entire tenure
181
you know during that time of of being at Stanford my life happened
182
when you're when you're going to school you think
183
that the school work is awfully academic and the reason for
184
that is because you're not sure whether there's any purpose and any benefit in learning this but the benefit of
185
working and going to school at the same time, especially Stanford, I could see so much of the principles being taught
186
and how important it is in everything that I do today.
187
And so it was a great privilege to have all of that happening to me at the same time.
188
My family, my kids, my company, Stanford, you know, working, all of it kind of in one giant soup.
189
People talk about Silicon Valley and the ecosystem and so forth, but you just used a really great term.
190
It is really kind of a big stew, isn't it?
191
and you meet very interesting people and they change your thinking about something and that leads to something.
192
It sounds like Stanford was a place like that for you.
193
It completely shaped my attitude and my perspective about computer science and its impact to industry and industrial strategies.
194
that that entire intersection between technology applications um the fundamental science
195
and the strategies of computer science really for me formulated in
196
that during that time right
197
and it was really cool now talk about the founding of
198
nvidia you know the early day founder stories are are really fascinating
199
because you have to take a you have to take a
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?
201
During that time, it was at the beginning of the PC revolution.
202
It was the beginning of really Moore's Law, the beginning of the CPU era, this incredible technology engine that really changed everything.
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!
✨ Рекомендуемое видео
B21
170 предложений
( Português com legendas em inglês)English Speeches Steve Jobs Stanford Commencement Speech 2005葡萄牙文
Business EnglishDaily ConversationUniversity Students
B22
649 предложений
Nvidia’s Jensen Huang on the AI revolution, job losses and what drives him | Full interview
Business EnglishDaily ConversationUniversity Students
B22
198 предложений
ENGLISH SPEECH | RADHIKA AMBANI: Learn How To Learn (English Subtitles)
Business EnglishDaily ConversationUniversity Students
B11
445 предложений
Slow English Listening | Jensen Huang: From Dishwasher to AI Pioneer (A2-B1)
Business EnglishDaily ConversationHigh School Students
B21
167 предложений
Why I left America for good
American AccentBusiness EnglishDaily ConversationUniversity Students
B21,109
32 предложений
🎧Listening English Podcast - IELTS Speaking PART 2-3 Band 6.5+ & Topic: COMMUNICATION
IELTS PrepBusiness EnglishDaily Conversation
B22
380 предложений
1955 vs 2025, who actually had it better?
American AccentBusiness EnglishDaily ConversationUniversity Students
B23
267 предложений
2026年ハーバード大学ロニーチェンの卒業式スピーチ『AIなんてクソくらえ。』【世界情勢理解】【英語スピーチ】 リスニング 日本語字幕
Business EnglishDaily ConversationUniversity Students
B11
41 предложений
Lesson 5+6(1)
Business EnglishDaily ConversationHigh School Students
B22
224 предложений
資工科系真的不能念了嗎？為什麼新鮮人突然找不到工作？工程師飯碗不保？！｜Kelly Tsai
Business EnglishUniversity StudentsWorking Adults
B12
136 предложений
Learn English with Stories ⭐ I Ran Away | Level 3 English Listening & Speaking Practice
Business EnglishDaily ConversationUniversity Students
B23
48 предложений
Obama video truyền cảm hứng || Tiếng anh phụ đề song ngữ, phát triển bản thân
Business EnglishDaily ConversationUniversity Students
Лексика и советы по произношению к этому уроку
Этот урок разговорной практики уровня B2 построен на видео «【🎧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｜英偉達背後的移民故事與美國夢｜英語｜英語新聞練習». В этом видео 490 предложений и 5365 слов для шедоуинга. Речь длится 34:44. Говорящий говорит в естественном темпе, около 154 слов в минуту, близком к повседневной речи. 85% слов входят в 3000 самых частых слов английского языка; остальные стоит разобрать перед занятием.
Ключевая лексика этого видео
Самые сложные слова из видео (15), с произношением и значением:
| Слово | Произношение | Значение |
|---|---|---|
| graphics существительное | /ˈɡɹæfɪks/ | проекти́рование, черче́ние |
| simulation существительное | /ˌsɪm.jəˈleɪ.ʃn̩/ | симуля́ция |
| compute глагол | /kəmˈpjuːt/ | вычисля́ть, вы́числить |
| cautious прилагательное | /ˈkɔːʃəs/ | осторо́жный, внима́тельный |
| silicon существительное | /ˈsɪləˌkɑn/ | кре́мний, сили́ций |
| immigrant существительное | /ˈɪmɪɡɹənt/ | иммигра́нт, иммигра́нтка |
| miracle существительное | /ˈmɪɹ.ə.kəl/ | чу́до, ди́во |
| processor существительное | /ˈpɹɑˌsɛsɚ/ | проце́ссор |
| instruction существительное | /ɪnˈstɹʌkʃən/ | инстру́кция, инструкта́ж |
| mathematics существительное | /mæθ(.ə)ˈmæt.ɪks/ | матема́тика |
| aspire глагол | /əˈspaɪɚ/ | стреми́ться |
| tailwind существительное | /ˈteɪlwɪnd/ | попу́тный ве́тер |
| optimism существительное | /ˈɑptɪmɪzəm/ | оптими́зм |
| lens существительное | /lɛnz/ | ли́нза, объекти́в |
| netbook существительное | нетбу́к |
Фразовые глаголы, которые вы услышите
| Слово | Значение |
|---|---|
| turn out глагол | ока́зываться, оказа́ться |
| go up глагол | поднима́ться, подня́ться |
| come up with глагол | придумывать, приходить в голову |
| end up глагол | зака́нчиваться, зако́нчиться |
| give up глагол | сдава́ться, сда́ться |
| grow up глагол | расти́, выраста́ть |
| look forward to глагол | ждать с нетерпе́нием, не мочь дожда́ться |
| put together глагол | собрать, составить |
Фразы, которые стоит повторять
Короткие законченные фразы из видео, которые пригодятся в повседневном разговоре:
- 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?
Грамматика в этом видео
Конструкции, которые говорящий использует чаще всего, с точными словами из видео:
| Конструкция | В видео |
|---|---|
| Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас | have known · have never heard · I've reduced |
| Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает | was born · be trained · was determined |
| Придаточные определительные who / which + предложение — уточнение о человеке или предмете | people who come · friends who also · idea, which was |
Произношение, на которое стоит обратить внимание
Говорящий использует 54 сокращённых и редуцированных форм, например you're, I'm, we've. Произносите их коротко, так, как слышите.
- Звуки «th»: mathematics /mæθ(.ə)ˈmæt.ɪks/, athlete /ˈæθ.liːt/, altogether /ɔl.tuˈɡɛð.ɚ/
- Звуки «sh» и «zh»: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, cautious /ˈkɔːʃəs/, instruction /ɪnˈstɹʌkʃən/, foundational /faʊnˈdeɪ.ʃə.nəl/, implication /ˌɪmpləˈkeɪʃən/
- Длинные слова — следите за ударением: mathematics /mæθ(.ə)ˈmæt.ɪks/, foundational /faʊnˈdeɪ.ʃə.nəl/, optimism /ˈɑptɪmɪzəm/, genuinely /ˈd͡ʒɛn.ju.ɪn.li/, programmable /pɹəʊˈɡɹæməbəl/
Как заниматься с этим видео
- Прослушайте всё видео один раз молча и выпишите незнакомые слова.
- Начните со скорости 0,75×, повторяйте предложение за предложением и вернитесь к обычной скорости, когда станет легко.
- Запишите себя и сравните с оригиналом, обращая внимание на такие слова, как graphics, simulation, compute.
Что такое техника Shadowing?
Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.