シャドーイング練習: 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？ - 動画で英語スピーキングを学ぶ
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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.
この動画で話す練習をする理由は？
この動画は、AIやテクノロジーの未来についての重要なスピーチです。スピーカーであるLisa Su氏は、自身の経験や思考を通じて、問題解決の重要性を語っています。このような実践的なコンテキストで話す練習をすることは、英語のコミュニケーション能力を向上させるためにとても有益です。特に、@YouTubeで英語学習を通じて、興味を持ちながらスピーキングスキルを鍛えることができます。実際のスピーチやプレゼンテーションスタイルに触れることで、リスニング力も同時に向上させることができるでしょう。
文法と表現の文脈
- 未来形の使用: "may discover more in the next 10 years"のように、未来の予測を表現することで、ダイナミックな内容を伝えています。
- 過去形の反省: "we have discovered"という表現は、過去の経験に基づいた信頼性を持たせ、聴衆への影響を強めています。
- 強調表現: "let me be clear about something"のフレーズは、スピーチの重要なポイントを強調する一つの方法です。相手に明確にメッセージを伝えることができます。
一般的な発音の罠
この動画では、特定の単語やフレーズが発音の課題となる場合があります。例えば、"technology"や"problems"などの言葉は、非ネイティブには発音が難しいことが多いです。正しいアクセントやリズムを身につけるためには、スピーチを繰り返し聴き、英語の発音を良くすることが重要です。また、"discover"の発音も注意が必要です。このような発音練習は、shadowspeakの技術を用いることで効果的に行えます。もし周囲の人と一緒に練習できるなら、さらに良いでしょう。皆で一緒に努力することで、英語力がきっと向上します。shadowspeaksを通じて、あなたもスピーキングスキルを磨いていきましょう！
シャドーイングとは？英語上達に効果的な理由
シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。