शैडोइंग अभ्यास: 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？ - वीडियो के साथ अंग्रेजी बोलना सीखें

may discover more in the next 10 years than we have in the last 30.

But let me be clear about something.

Technology itself does not decide what the future looks like.

The best people do.

For everything that AI can do, AI can't decide which problems are worth solving.

All right.

Good afternoon, everyone.

President Hornbluth, Chairman Gorenberg, trustees, faculty, family, friends, and most importantly,

the MIT class of 2026, congratulations.

You've earned this, and I can tell you that standing here feels very different than I expected.

I've given a lot of talks over the years, but this one is quite personal.

And as you can imagine, with Murphy's Law, I somehow managed to lose my voice this week.

So please bear with me if I sound a little rough.

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.

My parents dropped me off at Next House.

I was 17 years old, born in Taiwan, raised in Queens,

and I was pretty sure I was good at math.

Then of course I walked into 6001 and 6002.

And within about two weeks I realized that there were a lot of people at MIT who were very, very good at math.

And I remember staring at those first problem sets thinking, my goodness, these are super hard.

And I never really pulled an all-nighter all-nighter until freshman year.

It was a new experience, but it was a lot of fun doing it together with your classmates.

Now, MIT has this incredible way of pushing you further than you thought you could go.

You wrestled with the problem, you blew up a circuit or two.

Yes, some of you may have.

And then somehow the thing worked.

And suddenly you realize that you could build something real.

And that's when I started feeling like an engineer.

One of the best parts of MIT is actually Europe.

The opportunity as an undergraduate to work on real research.

And that actually truly changed my life.

My first Europe was in Professor Hank Smith's lab in building research was in Professor Hank Smith's lab in building 39,

which Anantha tells me we're decommissioning and moving, making x-ray lithography mask blanks for a grad student.

To be absolutely clear, at the time, I had no idea what that actually meant.

But I got to put on my first bunny suit and walk into a clean room

and start building devices on little two-inch wafers, which at the time was pretty state of the art.

And I learned very quickly to be careful because those wafers were actually really delicate

and I definitely didn't want to be the one who broke them.

But I ran a bunch of experiments.

Most of them didn't work the way we expected.

And so we adjusted and we tried again and It was the coolest thing ever

For the first time I wasn't just learning about technology in a classroom.

I was part of a team trying to discover something new

And I remember thinking wow We can build things this small

things tiny enough to fit on a die the size of a coin, but powerful enough to change the world.

And that's when I fell in love with semiconductors.

Later, I had the privilege of working with Professor Dimitri Antoniatis, who became my PhD advisor.

And that was where I really learned how to solve problems.

I remember spending weeks in the clean room fabricating devices

and then bringing my wafers up to the test lab only to discover they didn't behave the way I expected at all.

And so I'd go back to Dimitri's office and we'd figure out what we should do next.

And looking back that was probably where I grew the most at MIT

because little by little I went from a new grad student learning about the field

to someone doing original research and actually contributing something new to the field.

And along the way I started believing in myself, not the confidence that I would always know the answer,

but the confidence that even when I didn't know the answer, I could figure it out.

What I realize now is MIT was teaching me something much bigger than semiconductor device physics.

Mens et manus, mind in hand.

When I was a student, I thought it was just a motto.

Now I think it captures exactly what makes MIT so special.

MIT teaches you to think deeply, but it also teaches you to build,

to test ideas, to keep going when the first experiment or even the fifth experiment doesn't work.

And over time, you start believing that you can solve problems that once felt impossible.

I carried that feeling with me long after I left campus.

When I joined IBM, I found myself starting all over again.

IBM had hundreds of thousands of employees.

I was 25 years old, wondering how I could possibly make a difference in a company that big.

But I learned something important very quickly.

Engineering actually doesn't care how old you are.

It actually cares whether you have good ideas.

And one of my mentors told me something that I've never forgotten.

Run towards the hardest problems.

At the time, I'm not sure I really knew what that meant.

But I now realize this was the best advice I've ever received.

Hard problems really teach you what you're capable of.

So fast forward a bit, 12 years ago I got a chance to put that lesson to the test.

I had the opportunity to become CEO of AMD.

AMD had a lot of potential, but the company had been through a few tough years.

And some of my mentors thought taking that job was actually kind of risky.

But for me, this was my dream job.

This is what I'd been training for all those years,

the opportunity to work at the bleeding edge of technology on problems that really mattered.

And the first thing we had to do was figure out what we wanted to be when we grew up.

This is a big company that had to figure this out.

We made a long-term bet that high-performance computing would be the most important technology of the future.

And we gave our talented team the room to think big.

And over the next several years, we built technology to enable the most powerful computers in the world.

And I can tell you through all of it, I used every skill that MIT ever taught me, and then some.

I was trying to put it in words, and I decided that calling it the engineer's instinct was kind of the right thing.

It's the ability to face what seemed like an unsolvable problem, break it down, and methodically work through it step by step.

But I also learned something else.

The engineer's instinct is even more powerful when it becomes shared by a team.

And the greatest satisfaction of my career has been bringing people

together to do something more than any of us thought was possible.

And that brings me to today and where you guys are.

Over the last few decades, we've experienced several major technology shifts.

The internet changed how we communicate.

Mobile computing changed how we live.

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.

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग अभ्यास: 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？ - वीडियो के साथ अंग्रेजी बोलना सीखें

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