跟读练习: 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？ - 通过视频学习英语口语
正在创建课程...
1
may discover more in the next 10 years than we have in the last 30.
2
But let me be clear about something.
3
Technology itself does not decide what the future looks like.
4
The best people do.
5
For everything that AI can do, AI can't decide which problems are worth solving.
6
All right.
7
Good afternoon, everyone.
8
President Hornbluth, Chairman Gorenberg, trustees, faculty, family, friends, and most importantly,
9
the MIT class of 2026, congratulations.
10
You've earned this, and I can tell you that standing here feels very different than I expected.
11
I've given a lot of talks over the years, but this one is quite personal.
12
And as you can imagine, with Murphy's Law, I somehow managed to lose my voice this week.
13
So please bear with me if I sound a little rough.
14
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.
15
My parents dropped me off at Next House.
16
I was 17 years old, born in Taiwan, raised in Queens,
17
and I was pretty sure I was good at math.
18
Then of course I walked into 6001 and 6002.
19
And within about two weeks I realized that there were a lot of people at MIT who were very, very good at math.
20
And I remember staring at those first problem sets thinking, my goodness, these are super hard.
21
And I never really pulled an all-nighter all-nighter until freshman year.
22
It was a new experience, but it was a lot of fun doing it together with your classmates.
23
Now, MIT has this incredible way of pushing you further than you thought you could go.
24
You wrestled with the problem, you blew up a circuit or two.
25
Yes, some of you may have.
26
And then somehow the thing worked.
27
And suddenly you realize that you could build something real.
28
And that's when I started feeling like an engineer.
29
One of the best parts of MIT is actually Europe.
30
The opportunity as an undergraduate to work on real research.
31
And that actually truly changed my life.
32
My first Europe was in Professor Hank Smith's lab in building research was in Professor Hank Smith's lab in building 39,
33
which Anantha tells me we're decommissioning and moving, making x-ray lithography mask blanks for a grad student.
34
To be absolutely clear, at the time, I had no idea what that actually meant.
35
But I got to put on my first bunny suit and walk into a clean room
36
and start building devices on little two-inch wafers, which at the time was pretty state of the art.
37
And I learned very quickly to be careful because those wafers were actually really delicate
38
and I definitely didn't want to be the one who broke them.
39
But I ran a bunch of experiments.
40
Most of them didn't work the way we expected.
41
And so we adjusted and we tried again and It was the coolest thing ever
42
For the first time I wasn't just learning about technology in a classroom.
43
I was part of a team trying to discover something new
44
And I remember thinking wow We can build things this small
45
things tiny enough to fit on a die the size of a coin, but powerful enough to change the world.
46
And that's when I fell in love with semiconductors.
47
Later, I had the privilege of working with Professor Dimitri Antoniatis, who became my PhD advisor.
48
And that was where I really learned how to solve problems.
49
I remember spending weeks in the clean room fabricating devices
50
and then bringing my wafers up to the test lab only to discover they didn't behave the way I expected at all.
51
And so I'd go back to Dimitri's office and we'd figure out what we should do next.
52
And looking back that was probably where I grew the most at MIT
53
because little by little I went from a new grad student learning about the field
54
to someone doing original research and actually contributing something new to the field.
55
And along the way I started believing in myself, not the confidence that I would always know the answer,
56
but the confidence that even when I didn't know the answer, I could figure it out.
57
What I realize now is MIT was teaching me something much bigger than semiconductor device physics.
58
Mens et manus, mind in hand.
59
When I was a student, I thought it was just a motto.
60
Now I think it captures exactly what makes MIT so special.
61
MIT teaches you to think deeply, but it also teaches you to build,
62
to test ideas, to keep going when the first experiment or even the fifth experiment doesn't work.
63
And over time, you start believing that you can solve problems that once felt impossible.
64
I carried that feeling with me long after I left campus.
65
When I joined IBM, I found myself starting all over again.
66
IBM had hundreds of thousands of employees.
67
I was 25 years old, wondering how I could possibly make a difference in a company that big.
68
But I learned something important very quickly.
69
Engineering actually doesn't care how old you are.
70
It actually cares whether you have good ideas.
71
And one of my mentors told me something that I've never forgotten.
72
Run towards the hardest problems.
73
At the time, I'm not sure I really knew what that meant.
74
But I now realize this was the best advice I've ever received.
75
Hard problems really teach you what you're capable of.
76
So fast forward a bit, 12 years ago I got a chance to put that lesson to the test.
77
I had the opportunity to become CEO of AMD.
78
AMD had a lot of potential, but the company had been through a few tough years.
79
And some of my mentors thought taking that job was actually kind of risky.
80
But for me, this was my dream job.
81
This is what I'd been training for all those years,
82
the opportunity to work at the bleeding edge of technology on problems that really mattered.
83
And the first thing we had to do was figure out what we wanted to be when we grew up.
84
This is a big company that had to figure this out.
85
We made a long-term bet that high-performance computing would be the most important technology of the future.
86
And we gave our talented team the room to think big.
87
And over the next several years, we built technology to enable the most powerful computers in the world.
88
And I can tell you through all of it, I used every skill that MIT ever taught me, and then some.
89
I was trying to put it in words, and I decided that calling it the engineer's instinct was kind of the right thing.
90
It's the ability to face what seemed like an unsolvable problem, break it down, and methodically work through it step by step.
91
But I also learned something else.
92
The engineer's instinct is even more powerful when it becomes shared by a team.
93
And the greatest satisfaction of my career has been bringing people
94
together to do something more than any of us thought was possible.
95
And that brings me to today and where you guys are.
96
Over the last few decades, we've experienced several major technology shifts.
97
The internet changed how we communicate.
98
Mobile computing changed how we live.
99
Cloud computing changed how we work.
100
And now we're at the beginning of the AI wave.
101
And to me, AI is really different from all those other waves.
102
The way I think about it is, it's not just a tool.
103
that can help us do things faster, because we have lots of tools.
104
It's actually deeper than that.
105
It has the potential to accelerate discovery in every field
106
and help us solve problems that we've never been able to solve before.
107
And to make it personal, the area that excites me the most is actually what we can do in medicine and healthcare.
108
I think we've all experienced firsthand what it feels like when someone you love is sick.
109
And even with incredible doctors and the best care, you realize how hard it is for any one person
110
or any one team to bring together all of the knowledge
111
that has been gathered to help in that critical time of need.
112
AI can help us change that.
113
It can help doctors and nurses and nurses
114
and researchers bring the world's best expertise to each patient
115
and each loved one and deliver the care that we want for the best chance of a successful outcome.
116
And this, I think, is the promise of AI at its best.
117
Now the way to think about it is it makes each of us more capable, whether you're talking about medicine,
118
science, energy, climate,
119
I think you can say we may discover more in the next 10 years than we have in the last 30.
120
But let me be clear about something.
121
Technology itself does not decide what the future looks like.
122
The best people do.
123
For everything that AI can do, AI can't decide which problems are worth solving.
124
It can't make the hard judgments when the data is not there.
125
It can't take responsibility for the outcomes.
126
These are actually our responsibilities, and they matter now more than ever.
127
This is why I feel this is such an extraordinary moment to graduate from MIT,
128
because the world does not just need people who know how to use powerful tools.
129
It needs people who know what to use them for, people with a sense of purpose,
130
judgment, courage, people who look at a hard problem and say, I know this is really,
131
really important and we can figure this out.
132
And that is exactly who you have become here at MIT.
133
So here's what I want to leave you with.
134
I've been very fortunate in many ways.
135
I have great parents.
136
I received an extraordinary education.
137
I've had the chance to work with great people, but I also believe I've been very lucky in my career.
138
When people ask me for career advice, I often tell them, yes, you need to work really hard, but also understand that luck matters.
139
And over time, I've come to believe that the best people find ways to make their luck.
140
Luck is not just being in the right place at the right time.
141
It is taking the risk to work on something really hard.
142
It's challenging yourself.
143
It's choosing problems where you may not know the answer.
144
It's surrounding yourself with people who make you better.
145
And yes, it's believing that you, the class of 2026, can change the world.
146
So be incredibly ambitious about what problems you choose to solve.
147
Run toward the hardest ones.
148
And trust what MIT has taught you, that engineer's instinct.
149
That's how you make your own luck.
150
I want to take a moment to acknowledge all the families and loved ones who are here in the audience today.
151
None of these graduates got here without you.
152
Thank you for believing them, supporting them, and helping them reach this moment.
153
This achievement belongs to you too.
154
And to the class of 2026, remember, somewhere in the years ahead,
155
you're going to walk into another room where you have absolutely no idea what you're doing.
156
You've done this before.
157
Go figure it out.
158
And as one MIT-er to another, I am incredibly honored to be here with you today.
159
Congratulations, Class of 2026.
关于本课
在这一课中，学习者将通过观看和模仿AMD CEO苏姿丰在MIT演讲中的表达来提高英语口语能力。视频中涵盖了科技的未来、个人成长和研究经历等主题，强调了在面对挑战时的应对方法和创造机会的重要性。通过本课的练习，学习者将更好地理解科技领域的关键术语，提升英语听说能力，同时增强自信心，以备将来在相关领域的交流。
关键词汇与短语
- 半导体 (semiconductors)
- 人工智能 (AI)
- 研究 (research)
- 挑战 (challenges)
- 创造运气 (create your luck)
- 团队合作 (teamwork)
- 经验 (experience)
- 真实世界 (real world)
练习技巧
在观看苏姿丰的演讲时，建议使用影子跟读的方式进行练习。由于她的演讲速度适中且语调清晰，学习者可以轻松跟随。以下是一些具体建议：
- 首先，专注于听她的每一句话，理解其主要意思，而不是急于模仿。
- 回放短的片段，逐句进行英语影子跟读，模仿她的语音语调和表达方式。
- 注意声音的起伏，调整自己的语速，使发音更加自然流畅。
- 可以使用视频的字幕进行辅助，帮助识别和理解生词及短语，从而提高理解深度。
- 进行雅思口语练习时，可以尝试将视频中的某些观点整理成自己的见解，并以自己的话复述出来。
- 通过持之以恒的练习，学习者将逐渐提高英语发音，增强口语表达能力，更自信地参与到国际交流中。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。