쉐도잉 연습: 半导体女王 AMD CEO苏姿丰Lisa Su:AI未来10年新发现将超过去30年总和！如何创造属于自己的运气？ - 영상으로 영어 말하기 배우기
레슨 만드는 중...
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may discover more in the next 10 years than we have in the last 30.
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But let me be clear about something.
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Technology itself does not decide what the future looks like.
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The best people do.
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For everything that AI can do, AI can't decide which problems are worth solving.
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All right.
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Good afternoon, everyone.
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President Hornbluth, Chairman Gorenberg, trustees, faculty, family, friends, and most importantly,
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the MIT class of 2026, congratulations.
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You've earned this, and I can tell you that standing here feels very different than I expected.
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I've given a lot of talks over the years, but this one is quite personal.
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And as you can imagine, with Murphy's Law, I somehow managed to lose my voice this week.
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So please bear with me if I sound a little rough.
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But I couldn't be happier to be here with you, And if I give you a little bit of my story, I came to MIT in the fall of 1986.
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My parents dropped me off at Next House.
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I was 17 years old, born in Taiwan, raised in Queens,
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and I was pretty sure I was good at math.
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Then of course I walked into 6001 and 6002.
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And within about two weeks I realized that there were a lot of people at MIT who were very, very good at math.
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And I remember staring at those first problem sets thinking, my goodness, these are super hard.
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And I never really pulled an all-nighter all-nighter until freshman year.
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It was a new experience, but it was a lot of fun doing it together with your classmates.
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Now, MIT has this incredible way of pushing you further than you thought you could go.
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You wrestled with the problem, you blew up a circuit or two.
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Yes, some of you may have.
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And then somehow the thing worked.
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And suddenly you realize that you could build something real.
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And that's when I started feeling like an engineer.
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One of the best parts of MIT is actually Europe.
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The opportunity as an undergraduate to work on real research.
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And that actually truly changed my life.
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My first Europe was in Professor Hank Smith's lab in building research was in Professor Hank Smith's lab in building 39,
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which Anantha tells me we're decommissioning and moving, making x-ray lithography mask blanks for a grad student.
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To be absolutely clear, at the time, I had no idea what that actually meant.
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But I got to put on my first bunny suit and walk into a clean room
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and start building devices on little two-inch wafers, which at the time was pretty state of the art.
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And I learned very quickly to be careful because those wafers were actually really delicate
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and I definitely didn't want to be the one who broke them.
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But I ran a bunch of experiments.
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Most of them didn't work the way we expected.
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And so we adjusted and we tried again and It was the coolest thing ever
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For the first time I wasn't just learning about technology in a classroom.
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I was part of a team trying to discover something new
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And I remember thinking wow We can build things this small
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things tiny enough to fit on a die the size of a coin, but powerful enough to change the world.
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And that's when I fell in love with semiconductors.
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Later, I had the privilege of working with Professor Dimitri Antoniatis, who became my PhD advisor.
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And that was where I really learned how to solve problems.
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I remember spending weeks in the clean room fabricating devices
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and then bringing my wafers up to the test lab only to discover they didn't behave the way I expected at all.
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And so I'd go back to Dimitri's office and we'd figure out what we should do next.
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And looking back that was probably where I grew the most at MIT
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because little by little I went from a new grad student learning about the field
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to someone doing original research and actually contributing something new to the field.
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And along the way I started believing in myself, not the confidence that I would always know the answer,
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but the confidence that even when I didn't know the answer, I could figure it out.
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What I realize now is MIT was teaching me something much bigger than semiconductor device physics.
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Mens et manus, mind in hand.
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When I was a student, I thought it was just a motto.
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Now I think it captures exactly what makes MIT so special.
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MIT teaches you to think deeply, but it also teaches you to build,
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to test ideas, to keep going when the first experiment or even the fifth experiment doesn't work.
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And over time, you start believing that you can solve problems that once felt impossible.
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I carried that feeling with me long after I left campus.
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When I joined IBM, I found myself starting all over again.
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IBM had hundreds of thousands of employees.
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I was 25 years old, wondering how I could possibly make a difference in a company that big.
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But I learned something important very quickly.
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Engineering actually doesn't care how old you are.
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It actually cares whether you have good ideas.
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And one of my mentors told me something that I've never forgotten.
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Run towards the hardest problems.
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At the time, I'm not sure I really knew what that meant.
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But I now realize this was the best advice I've ever received.
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Hard problems really teach you what you're capable of.
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So fast forward a bit, 12 years ago I got a chance to put that lesson to the test.
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I had the opportunity to become CEO of AMD.
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AMD had a lot of potential, but the company had been through a few tough years.
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And some of my mentors thought taking that job was actually kind of risky.
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But for me, this was my dream job.
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This is what I'd been training for all those years,
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the opportunity to work at the bleeding edge of technology on problems that really mattered.
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And the first thing we had to do was figure out what we wanted to be when we grew up.
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This is a big company that had to figure this out.
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We made a long-term bet that high-performance computing would be the most important technology of the future.
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And we gave our talented team the room to think big.
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And over the next several years, we built technology to enable the most powerful computers in the world.
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And I can tell you through all of it, I used every skill that MIT ever taught me, and then some.
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I was trying to put it in words, and I decided that calling it the engineer's instinct was kind of the right thing.
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It's the ability to face what seemed like an unsolvable problem, break it down, and methodically work through it step by step.
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But I also learned something else.
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The engineer's instinct is even more powerful when it becomes shared by a team.
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And the greatest satisfaction of my career has been bringing people
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together to do something more than any of us thought was possible.
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And that brings me to today and where you guys are.
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Over the last few decades, we've experienced several major technology shifts.
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The internet changed how we communicate.
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Mobile computing changed how we live.
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Cloud computing changed how we work.
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And now we're at the beginning of the AI wave.
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And to me, AI is really different from all those other waves.
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The way I think about it is, it's not just a tool.
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that can help us do things faster, because we have lots of tools.
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It's actually deeper than that.
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It has the potential to accelerate discovery in every field
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and help us solve problems that we've never been able to solve before.
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And to make it personal, the area that excites me the most is actually what we can do in medicine and healthcare.
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I think we've all experienced firsthand what it feels like when someone you love is sick.
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And even with incredible doctors and the best care, you realize how hard it is for any one person
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or any one team to bring together all of the knowledge
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that has been gathered to help in that critical time of need.
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AI can help us change that.
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It can help doctors and nurses and nurses
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and researchers bring the world's best expertise to each patient
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and each loved one and deliver the care that we want for the best chance of a successful outcome.
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And this, I think, is the promise of AI at its best.
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Now the way to think about it is it makes each of us more capable, whether you're talking about medicine,
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science, energy, climate,
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I think you can say we may discover more in the next 10 years than we have in the last 30.
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But let me be clear about something.
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Technology itself does not decide what the future looks like.
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The best people do.
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For everything that AI can do, AI can't decide which problems are worth solving.
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It can't make the hard judgments when the data is not there.
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It can't take responsibility for the outcomes.
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These are actually our responsibilities, and they matter now more than ever.
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This is why I feel this is such an extraordinary moment to graduate from MIT,
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because the world does not just need people who know how to use powerful tools.
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It needs people who know what to use them for, people with a sense of purpose,
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judgment, courage, people who look at a hard problem and say, I know this is really,
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really important and we can figure this out.
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And that is exactly who you have become here at MIT.
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So here's what I want to leave you with.
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I've been very fortunate in many ways.
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I have great parents.
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I received an extraordinary education.
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I've had the chance to work with great people, but I also believe I've been very lucky in my career.
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When people ask me for career advice, I often tell them, yes, you need to work really hard, but also understand that luck matters.
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And over time, I've come to believe that the best people find ways to make their luck.
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Luck is not just being in the right place at the right time.
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It is taking the risk to work on something really hard.
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It's challenging yourself.
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It's choosing problems where you may not know the answer.
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It's surrounding yourself with people who make you better.
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And yes, it's believing that you, the class of 2026, can change the world.
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So be incredibly ambitious about what problems you choose to solve.
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Run toward the hardest ones.
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And trust what MIT has taught you, that engineer's instinct.
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That's how you make your own luck.
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I want to take a moment to acknowledge all the families and loved ones who are here in the audience today.
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None of these graduates got here without you.
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Thank you for believing them, supporting them, and helping them reach this moment.
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This achievement belongs to you too.
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And to the class of 2026, remember, somewhere in the years ahead,
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you're going to walk into another room where you have absolutely no idea what you're doing.
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You've done this before.
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Go figure it out.
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And as one MIT-er to another, I am incredibly honored to be here with you today.
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Congratulations, Class of 2026.
맥락 및 배경
이 비디오에서는 AMD CEO 리사 수(Lisa Su)가 과거 30년간의 발견보다 앞으로 10년간 더 많은 발견이 이루어질 것이라고 언급하며, 기술의 발전과 문제 해결에 대한 사람들의 중요성을 강조합니다. 리사 수는 짧은 인생 이야기를 통해 MIT에서의 경험과 기술에 대한 열정을 나누고, 후배들에게 긍정적인 메시지를 전달합니다. 이런 맥락은 영어 회화를 연습하는 데 있어 중요한 배경 지식을 제공합니다.
일상 커뮤니케이션을 위한 5가지 주요 문구
- “Technology itself does not decide what the future looks like.” - 기술이 미래를 결정하지는 않습니다.
- “It was a lot of fun doing it together with your classmates.” - 동급생들과 함께하는 것은 정말 재미있었습니다.
- “You could build something real.” - 당신은 현실적인 것을 만들 수 있습니다.
- “For the first time, I wasn't just learning about technology in a classroom.” - 처음으로 저는 교실에서 기술에 대해 배우지 않고 있었습니다.
- “Wow, we can build things this small.” - 와우, 우리는 이렇게 작은 것을 만들 수 있습니다.
단계별 쉐도잉 가이드
이 비디오의 내용을 따라 하며 영어 말하기를 연습하는 데 있어 효과적인 접근 방법은 다음과 같습니다.
- 비디오 시청: 먼저 전체 비디오를 시청하여 리사 수의 발음과 억양에 익숙해지세요. 유튜브 영어 공부에 적합한 자료입니다.
- 문구 선정: 위에서 언급한 5가지 문구를 선택하여 반복적으로 듣고 따라 말해보세요. 영어 회화 연습에 도움이 됩니다.
- 쉐도잉 시작: 문구를 들으면서 동시에 따라 말해보세요. 처음 몇 번은 속도를 줄여 이해한 후 조금씩 자연스러운 속도로 발전하세요.
- 녹음하기: 자신의 목소리를 녹음하여 발음과 억양을 비교해보세요. 영어 쉐도잉의 효능을 극대화할 수 있습니다.
- 태도 점검: 리사 수처럼 자신감 있게 이야기하고, 처음에는 쉽지 않지만 점차 편안함을 느끼게 될 것입니다.
이 과정을 통해 영어 실력을 향상시킬 수 있으며, 특히 IELTS 스피킹 준비에 많은 도움이 될 것입니다.
쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법
쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.