Prática de Shadowing: 實戰練英文 | NVIDIA 黃仁勳榮獲2025霍金獎，分享AI革命與創業心路；AI將改變所有的工作，讓你重回工作重點；想成功，一定要告訴自已這句話 | 劍橋大學演講 | 長文、註解、大字版、中英對照 - Aprenda a falar inglês com vídeo
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Good evening everybody, thank you so much for joining us for what is about to be an exciting evening.
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Just before we get into tonight's proceedings, just a tiny in -house rule from me,
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please do resist the urge to take photographs or videos on your personal mobile devices.
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We have professional photographers in -house and I would trust you to leave it to the professionals to take care of that.
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Thank you.
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Okay, so created in 2017,
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the Professor Stephen Hawking Fellowship was established to recognize individuals who have made distinguished contributions to science and technology.
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Administered every year by the Hawking Fellowship Committee, the fellowship acts to memorialize
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Professor Hawking's legacy and to celebrate innovation in the field.
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Fellows from past years have included
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Bill Gates in 2019, Jane Goodall in 2020, and the Open AI team in 2023.
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Tonight's honoree makes a stunning addition to that list of inducted fellows.
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Jensen Huang is the founder, CEO, and president of NVIDIA.
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Founded in 1993, NVIDIA has pioneered accelerated computing.
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From the invention of the GPU in 1999 to major developments like the Grace Hopper Superchips,
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this Stanford graduate and alumni has been a trailblazer in innovation
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and tech pioneering since the very beginning please give me a warm Cambridge Union welcome in inviting the 2025
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Professor Stephen Hawking Fellow Mr Jensen Huang.
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Thank you.
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Thank you.
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Thank you very much.
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Thank you.
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Thank you.
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Thank you.
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And now I would like to take the opportunity to invite
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Lucy Hawking, Professor Hawking's daughter, to present this year's fellowship.
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Thank you so much.
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Maybe I'll have my wife holding it.
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Oh, perfect, please.
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I feel safer with her holding it.
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Thank you.
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Wow.
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This is quite a moment.
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You know, today when I arrived, I was so overwhelmed by being on Cambridge
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that I decided to bask in the moment and write a proper speech and and
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so I was in is it the st. James Lodge is
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it where it was a beautiful place the fireplace was was going
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and and I sat in a must have been a 3 ,000 year old chair chair
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and it was it was incredible and and here it is I wrote it.
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So Lucy, Ivan, members of the Hawking family and everyone, everyone here, incredible, everyone here.
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I'm deeply honored to receive the Stephen Hawking Fellowship.
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And to receive it here at Cambridge is profoundly humbling.
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Cambridge is a cathedral, a cathedral of world -changing ideas.
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Newton redefining motion and gravity,
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Darwin questioning creation, Maxwell one of my favorites uniting light and magnetism,
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Turing another favorite imagining a machine that could think,
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and Stephen Hawking expanding our understanding of time and universe.
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Professor Hawking's life showed that curiosity has no boundaries.
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By the way, that was one of his theories, as you know, no boundaries.
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Even when his body was confined, his mind traveled beyond the stars.
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He reminded us that discovery does not come only from intellect, but from conviction and optimism.
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And through his life's work and through the way he lived his life, he inspired us to look beyond our limitations,
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to meet challenge with curiosity and humor.
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I can think of no higher compliment than to be associated with that spirit.
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NVIDIA is a story of nearly impossible odds.
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The three of us, three friends, in a townhouse in 1993,
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set out with an idea that we invent a new way of doing computing to solve problems that normal computers cannot.
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Along the way we invented a new product category, the GPU.
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We invented in fact a new form of computing called CUDA, accelerated computing.
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We invented new strategies to take that technology and that architecture and proliferate it literally all over the world.
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And along the way we created the instrument of scientists,
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artists, designers, dreamers, and most importantly we sparked a new industrial revolution,
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the AI industrial revolution.
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We've come a long ways and
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our discoveries has led to the most impactful technology of our time and and probably of all time,
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the ability to manufacture intelligence.
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We've now seen the technology advance incredibly in the last decade.
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It is now transforming every application, every field of science, every industry.
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Everyone will be impacted.
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Every company will use it.
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Every nation will build it.
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It will now be part of infrastructure, the intelligence infrastructure.
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And to realize that like energy, like the internet, we will now be building AI infrastructure all over the world.
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That observation, that realization led to the company that you see today.
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the company helping every company, every industry, every country build out artificial intelligence as part of their social fabric.
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And so here we are, 33 years later, I'm the longest serving tech CEO the world's ever known.
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The way you achieve that, by the way, is don't get bored and don't get fired.
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and in a lot of ways I feel NVIDIA
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and I have been reborn that the company is renewed
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that the entire technology industry is being completely reinvented every single
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layer of the single most important instrument of humanity computers is being reinvented from chips to systems,
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the software, algorithms, applications, and the potential impact.
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In no time in history has this happened, surely not in the last hundred years.
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And now the entire tech stack is being reinvented, the entire tech industry is being reinvented,
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and in fact we do feel completely renewed.
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And so in a lot of ways, I'm starting where you're starting.
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We're all newbies now.
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We're all looking at a future, beaming with opportunity and equal amounts of concern.
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No technology of this capability can be advanced without thoughtfulness, without care, without scrutiny.
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And yet the opportunities ahead is incredible.
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And so I feel like a startup again.
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33 years later, NVIDIA is now the world's largest startup.
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And so I'm incredibly, incredibly proud to receive this.
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It's a great honor.
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And I'm looking forward to spending time with all of you.
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Thank you.
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It might be me.
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It might be that.
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One of us has to go.
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Perfect, I think we're fine.
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Wonderful.
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Well, thank you very much for joining us today,
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and I think that it would be a miss if I didn't begin by saying congratulations on receiving the 2025
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Professor Stephen Hawking Fellowship.
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Thank you.
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With such a rich career and legacy and sense of achievement in all that you've been able to do so far.
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You're making me nervous.
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I promise you I'm on my best behavior today.
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That setup is too much.
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You know taking all of that into consideration it's tricky to figure out where to start but to quote
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The Sound of Music let's start at the very beginning it's a very good place to start.
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So being born in Taipei and moving to America when you were nine,
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what comes really apparent in your early years is a great sense of determination, drive and personal discipline.
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How are you able to cultivate that through your education, through your time at Oregon State,
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through your time at Stanford to keep going and to continue being relentless even in your early education?
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My mom taught me English and she doesn't speak English and that kind of tells you all.
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That tells you something about the impression that parents could leave with their children.
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She told me when I was young that I was special,
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that somehow I took tests and I did well on tests.
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And she encouraged me.
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Oftentimes, if people tell you that you're better, greater, more capable than you are, you might live up to that expectation.
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It reminds us to do the same with our companies and it reminds us to do the same with each other.
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And she left with me an impression that nothing could be that hard.
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You know, to this day, people have seen me adapt,
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and I think it was Professor Hawken that said adaptive intellect is the ability to adapt.
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Didn't he say that?
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True intellect is the ability to adapt.
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In a lot of ways, that kind of defines NVIDIA, kind of defines me.
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I approach almost everything from the perspective you know how hard can it be
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now oftentimes turns out to be really hard
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but you approach it with the attitude how hard could it be
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and if you look at all the things
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that we've done as a company
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and what I've done I've never been CEO before this is my first CEO gig okay
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and so I think I'm I'm gonna get it right someday
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but when we first started the company
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and I never raised money I never wrote a business plan I still haven't written a business plan and
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and I've never been a CEO never even been a manager in all cases
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I think I think the the the the fact that she was able to teach me English and she doesn't speak English,
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she can't read English, so you got to ask yourself how did she do it?
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Turns out a piece of paper in a dictionary.
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And I approached almost everything like that, you know, how hard could it be and break it down to first principles
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and you learn along the way and I meant it earlier, so long as you could stay in the game long enough to learn the sport
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and staying in the game is in fact most of it.
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I was able to do what I'm doing today because I didn't get bored and I didn't get fired.
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That I think was the magic all of it.
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It's 100 % of it.
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I think staying on that topic of NVIDIA being the beginning of a lot of firsts for you,
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it's as if you've read my mind this is flowing so beautifully.
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Let's talk about the decision that led to you becoming CEO of the company.
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So the dream and plan for NVIDIA came together over many coffee meetings at the chain restaurant Denny's.
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Those meetings revealed to your co -business partners Priam and Malachowski that you were to be the CEO of NVIDIA.
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What do you think made them unanimously decide
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that you were the best fit amongst the group to lead NVIDIA as its CEO?
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I think because they didn't want the job
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and they were right they didn't want the job and
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and all three of us were engineers and
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and I think that's the answer they didn't want the job in retrospect I could have been smarter myself
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and and to be CEO is a is a lifetime of sacrifice
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Most people think that it's about leading and being in command and being on top.
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None of that is true.
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You're in service of the company.
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You're creating conditions for other people to do their life's work.
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You're inspiring through example.
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Most of the examples are making difficult decisions during very difficult times.
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It's mostly about sacrifice it's about strategy
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and strategy as you know is not choose not just about
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choosing what to do it's about choosing what not to do which is sacrifice and
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and the death and the determination the conviction the pain and suffering
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that goes along with with overcoming obstacles that's all sacrifice
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it's a you know being the CEO I'm not trying to talk anybody out of the profession
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if If you were to do it, it is the greatest honor, but you have to realize that it's not about fame and glory,
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it's mostly about pain and suffering.
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A lot of that I attribute to me growing up.
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It wasn't an easy journey.
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We came to America, went to America.
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my parents wanted us to pursue the American dream they didn't have very much they were quite modest
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and moving to the United States was quite difficult for us in 1973
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but somehow we made our way through it and I think the life of struggle endeavor,
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nothing for granted having to earn everything, that I think was good CEO training.
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And so I think the answer is that they were smart, they didn't want the job.
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And they live very high quality lives now.
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And building upon what you said about struggle and strategy and personal development, at a recent visit to Stanford you told an audience of students,
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greatness comes from character, character comes out from people who have suffered.
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In the beginning years of building Nvidia, amongst many setbacks, how did you keep going on?
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Because you said that you had never fundraised before at such a high level, you'd never pitched such a brilliant idea,
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you'd never had a business plan, but there was still this idea of strategy and duty and personal sacrifice.
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It's one thing to say it, but to live it is often really difficult.
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How did you continue to keep the faith in those really beginning stages?
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I say to this day, we believe what we believe.
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And so we reason about our perspective of the future,
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and you reason about it from first principles that you learn through education and fundamental first principles.
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And you have to reason all the way back to either the first principles of computer science
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or the first principles of physics or whatever first principles you could hang on to,
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and you try to reason as far back as you can now once you do
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that and you come to the conclusion
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that all of the all of the environmental conditions
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and all the information that you have causes you to believe in what you believe then at
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that point you believe what you believe
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and you got to decide am I going to be somebody who does something with it do something about it
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or do I just become one of those people that you know
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that say something like oh yeah I knew that
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or I also knew that or I've said that before
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but you did nothing about it and so I tend to be somebody
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that that would would reason about these things and believe it
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so deeply that frankly I could see it in my head
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and once I could see it in my head as far
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as I'm concerned is might as well be real everything else is just details
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and so you manifest your belief as deeply as you can and after that is you're kind of hard to dissuade
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Now of course every single day I gut check all of the assumptions that I made.
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All the assumptions that I used to reason about the strategy.
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I'm constantly re -evaluating.
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If any of that changes, if any of the assumptions or any of the principles that I used were flawed,
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I'm quick to adapt you know so I'm constantly learning through failure
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and I'm quick to adapt and by adapting you get to stay in the game and so
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so I think it's not a complicated equation oftentimes oftentimes I think I think people have a hard time pivoting
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because they feel that their ego is somehow tied to some decision they had made
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or something that they said and that's really hard for CEOs.
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You know, for CEOs, I stand up and, you know, I have a video today.
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We have 50 ,000 people and I declare something about the future.
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You know, I describe what I think is a direction the company ought to go.
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And you reason about why you do so.
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And I do that in front of 50 ,000 people.
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And I'm doing it constantly.
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I'm saying it constantly because you can't just say something once.
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You've got to say it a thousand times.
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After you say something a thousand times, when you discover that, in fact,
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you were wrong, it's fairly difficult to pivot.
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But over time, I've earned the right to change my mind.
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And as soon as something is wrong, and I feel that it's...
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And the way we describe it is we have a phrase in the company, and I remind ourselves of that all the time.
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We have to be intellectually honest.
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You know, if I know that we have to change our mind and I don't, that's a character question.
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You know, it's an ego question.
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You know, somehow I'm preventing myself from allowing everybody else to do the right things.
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And so I'm quick to get out of that.
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And over time I discovered that leaders are not meant to be right.
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That's not our job.
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You know, we're not our job isn't to be right.
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Our job is to help other people succeed
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It's related not the same and so to the extent that they always believe that I want to help people succeed
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Then they want to help me succeed when I change my mind
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Nobody thinks about it twice.
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It's almost like they never forgot what I said before
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You know this new thing that I say was yeah, he's right And I change it a little bit.
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Yeah, he's still right
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And so, you know, people just want you to succeed.
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And you want to create the conditions where you allow yourself to be vulnerable, to change your mind, to be wrong.
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But because people know that I always have their best interest
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and I want the company to succeed and I want us to realize the future, you know, they'll go along with it.
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Wonderful.
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Let's talk about the GPU.
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So in 1999, NVIDIA creates the light bulb moment in the tech industry
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when the company manages to offload graphic rendering from the CPU to the GPU.
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In 2006, you followed up with the CUDA microservices with over 400 libraries.
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When you're creating and innovating at NVIDIA, how do you strike the balance between building and developing on pre -existing technologies and deciding,
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like you said, when to pivot into a new lane?
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How do you keep that balance?
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It's really hard.
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And the reason for that is this when you when you reinvent some for example let's use a phone for example
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when the iPhone came along it's still a phone
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and initially it didn't do much more than a phone it had a browser
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and most people didn't need a browser all the time
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and it had a map that was nice and a place of music
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that was nice but it cost five times more and
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so the challenge is at the time of a new product
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category the cost of the technology is much higher than the value
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that it provides and the same thing with the GPU we invented the GPU
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because we wanted computer graphics to be a medium
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that was expressible through software prior to us prior to the GPU graphics accelerators were fixed function
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garot shading with the caro shading fun shading was fun shading the way
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it you know whatever specular highlight you decide that's exactly the way you would render it well we decided
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that that computer graphics should be a artistic storytelling medium
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and the software it should be programmable by software using what is called a programmable shader
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so we invented this idea called it a real time programmable shader
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and so that that led to a bunch of future opportunities
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but on the day we announced that there were no applications
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but it cost twice as much and so here's something
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that you have no need for
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and as the current customer would prefer to have something
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that is quite frankly half the price than to have something that has future promise.
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And so there's no easy answer for that aside from you've got to believe what you believe and when you create it,
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you know, the rest of it is about ecosystem development and inspiring the developers to create applications that realize the potential,
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a whole bunch of software you know that kind of stuff
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and that's all that's all kind of mechanical work
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but in order to go do that you have to believe in a future
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and then crossing that crossing
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that chasm is extremely painful it's life -threatening most companies don't make
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it most companies don't make it from classical phone to become a smartphone notice none of them did
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No feature, back then we call our phones feature phones, I don't know, phones.
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And so they were called phones, and then now they're called smartphones.
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No phone company made it through to the smartphone.
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Nvidia is the only company that made it from one generation to another generation to another generation to another generation, and we constantly reinvented ourselves,
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we've now reinvented ourselves through six computing eras.
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and the mechanics of it is fairly mundane it's easy to explain
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you know I'll teach a course here one of these days
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it's a it's a five it's a five -step program
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and it's not it's not hard but the part
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that is hard it's the courage to do it because
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when you leap across to that next thing at the moment
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when you're in the middle of
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that canyon your your cost is incredibly high your Their value is incredibly non -existent and very, very rarely do people cross to the other side.
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That's 100 % courage, the ability to endure pain and suffering, the rest of it is skills.
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One of the beautiful things about NVIDIA's work is the way in
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which its discoveries have reverberated across the world and have spurred on lots of different technologies.
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This year Bristol University unveiled the Isambard AI, the 11th fastest supercomputer in the world,
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powered by 5 ,448 GH200 Grace Hopper superchips.
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Isambard AI is being lauded for being able to power cutting -edge medical and sustainable research.
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How does it feel to see the work of the Grace
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Hopper superchips be extrapolated upon to build what is essentially really crucial technology
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that is shifting medical research at the highest levels? you know before is a bar
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so that we get the record straight the fastest supercomputer in the UK built I built
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and it was called Cambridge one did you guys know
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that okay I never got I was I did it
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because I thought Nvidia was going to have a headquarters here in the UK
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we did you guys did you guys ever hear about the story
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I almost bought a a UK company it was blocked by the UK just to I
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I know breaks my heart to this day
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so anyways we almost bought arm you guys know
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that right we almost bought arm
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and I worked on it for a long time I thought
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it would have been a great idea I still think it would have been a great idea I
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I don't think it's too late.
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No, I'm just kidding.
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Anyways, Arm's a great company, turned out.
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So anyhow, what we build is the most crucial instrument of knowledge discovery.
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And for the very first time,
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we built a computer that can understand the meaning of the information that it is processing.
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It's not just processing data, it understands the meaning of the data it's processing.
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So for example it's not processing letters, it's processing words and understands the words.
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It's not just processing a whole bunch of numbers,
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it understands that these numbers represent fluid flow or that it understands
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that this sequence of numbers actually represents a protein
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or a small molecule chemical
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and it understands the meaning of it its vocabulary its meaning
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it understand its functionality for example you know understand the the semantics it understands the context
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that it's in and therefore it understands how we would react in that context.
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I've just used a bunch of words that when you apply to Chachapulte is very, very obvious.
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But remember the protein and those English words to the computer is the same.
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To the extent that we can help the computer understand the nature of proteins, the meaning of proteins, the structure of it,
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its dynamics, it should be able to understand how it interacts with other chemicals and other proteins in various contexts.
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We should be able to talk to our protein in the future.
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What are you?
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How would you behave?
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Are you soluble?
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How do you behave in temperature, high temperature?
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How do you behave in different types of liquids in different contexts?
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How would you react to this particular chemical?
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How would you bind to it?
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You could just literally talk to a protein in the future.
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Now what I just described sounds a little ridiculous right now, but As you know, you could talk to an image today.
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Just go up to an image and, you know, what are you?
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I'm a picture of a cat.
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What kind of cat are you?
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Can you move?
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And all of a sudden the image turns into a video.
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And so notice this is the world that we're in now.
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Not only were you processing data, we understand the data that we're processing.
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And the implications of that in the field of, of course, drug discovery or,
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you know, material sciences or any other any form of science is really quite profound.
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So, you know, we've created what Turing, you know, imagined, right, artificial intelligence.
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One of the key visions at NVIDIA is about being able to turn biology into engineering, and NVIDIA is alongside other AI companies doing so.
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How hard could it be?
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Turns out it's super hard.
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We've been at it for 10 years, I'm still working on it.
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NVIDIA alongside other AI companies are doing a lot of really
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good work at studying molecular biology particularly as it pertains to things like amino acids
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and trying to not only keep the work that you do limited to computers but also to do cutting -edge medical research.
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At what point did you realize
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that NVIDIA possessed the capability to switch into helping with the healthcare industry
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and what are your views on the progress that's being made in molecular biology research using AI?
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40 years ago, something really amazing happened.
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43 years ago.
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I was the first generation of engineers that designed a computer inside a computer.
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Before then, all of the engineers did it practically by hand.
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Prototyped our systems into existence.
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My generation was the generation of what is called computer aided design.
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Well, 40 years later,
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100 % of what we build exists completely as a digital twin inside a computer before we make it.
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To the point where in the old days, we would tape out a chip, we'd go fab a chip,
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and we hope it works today these chips are a billion
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times more complicated tens of thousands of engineers work on it at one time we send it to the fab
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and when it comes back I know it works
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and the reason why I know it works
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because it's been living inside another computer for a long time well
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that's 40 years later
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I believe the time is about now where we have the ability to represent the various hierarchies of biology so
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that we can have computer aided drug design the idea of drug discovery
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I the even the word is wrong drug discovery it's like hey honey I'm gonna go look for mushrooms now
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and some days you come back empty -handed most of the time some some days
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you come back with some amazing truffles look what I discovered today
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I found it today now of course drug discovery is a little bit like
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that it's much more science and less engineering
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if you look at the way we do computers design design today it's a hundred percent engineering
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and so we have every single year we get better and better
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and better at it drug discovery is very very hard every
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single drug for every single disease almost feels like a brand new discovery like a brand new journey
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we we have to create the computer tools and the information representation,
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transistors and logical gates and large functions and large chips,
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we have different languages and different tools to represent the different hierarchies of electronic design.
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We need to discover the hierarchy of information representation for biological design.
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Does that make sense?
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And so once we have that, once we have tools that understand that representation
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and we can manipulate it then the world becomes drug design
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and every single year that goes by we can become better
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and better and better at it we stand on the top you know on the shoulders of giants
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and one of these days you know who knows and so that was
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that was I just reason I literally just did just now what I did ten years ago at NVIDIA
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and I explained this to a bunch of computer scientists and
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and then everybody goes okay well let's go give it a try how hard can it be
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and so it's been 10 years and I gotta tell you it's super hard
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but it's okay but I still believe it I have a hundred percent certainty
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that we will discover the representation of biology and the tools necessary to go design and the reason for that is this
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Although we're all a little different, we're largely the same.
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So there's obviously structure in biology, and obviously it's a repeatable thing.
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Not only is NVIDIA crossing over in terms of its legacies into different fields, it's...
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By the way, I just wrote the entire business plan of NVIDIA going into drug discovery, and this business is now, for us, billions of dollars,
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and I just wrote the entire business plan just now.
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No number, no spreadsheets were used.
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No numbers were applied.
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No calculus was necessary.
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Just like that.
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All simple reasoning.
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I did the same with autonomous vehicles.
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I did the same with robotics.
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I did exactly the same for artificial intelligence.
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Just like that.
428
We sat there and reasoned about it step by step by step.
429
Usually we have the benefit of whiteboard it makes it easier to communicate after
430
that somebody takes a picture of it the company goes off
431
and ten years later in the case of artificial intelligence 15 years later here
432
we are I mean let's talk a little bit more about artificial intelligence
433
so not only is the work
434
that Nvidia is doing crossing over different fields of science
435
but it's crossing across borders in September 2015 Nvidia announced a
436
two billion pound investment in the UK AI ecosystem alongside firms like Balterton, Phoenix, Port, Excel.
437
I did it right in front of the Prime Minister.
438
Keir Starmer.
439
Yes.
440
I mean Keir Starmer champions.
441
I'll invest in your company.
442
I'll invest in your company.
443
Their two billion dollars went really fast.
444
Not only did Keir Starmer champion it, it was also recognized by a bunch of other business leaders who thanked NVIDIA for its bold leadership.
445
How does it feel to be able to support burgeoning leadership in AI and creativity and innovation, particularly in the UK and also across the world?
446
NVIDIA succeeds when other companies succeed.
447
Remember we're a platform company, we're a tools company, we're a platform computing platform company.
448
Nobody wakes up in the morning, okay, nobody in your family wakes up in the morning and says this morning,
449
guess what we need? we need to buy a computing platform.
450
Nobody says that.
451
And so the only reason why we succeed is because companies and developers who use our platform create something incredible.
452
That's how we succeed.
453
And so in a lot of ways I've got one of the really the one of the greatest jobs ever
454
which is to have a company whose mission is to desire other people to succeed
455
and this is a phrase
456
that is always inside the company I'm always reminding everybody in the company
457
that that we want and we need and we desire other people to succeed
458
and it's through their success we get to ride their their coattails
459
and so that's that's it you know and so when I
460
when I saw that the UK really is a has a Goldilocks moment.
461
You have incredible researchers here, this is the home of computer science.
462
If there's a country that represents computer science, that's got to be UK.
463
You've got rich ecosystem of entrepreneurs and the only thing that you don't have,
464
as it turns out, is the instrument of knowledge, the instrument of science, the instrument necessary to do that.
465
And I I felt that with our involvement, we might be able to create that spark,
466
and then maybe UK becomes one of the video's headquarters after all.
467
You really have an extraordinary moment.
468
But for some reason, I do feel that the culture of the UK,
469
you're
470
so modest you know you know in Silicon Valley whatever however great
471
we are we describe it as a hundred times more than
472
that and however but here in the UK however great you are you're one -tenth
473
that and so so I'm here to tell you you're pretty extraordinary
474
and and look at the list of the inventors and the scientists
475
and discoverers that have come before you and that have
476
that have inspired us and has has made it possible for us to do what we do
477
and then just remember the Industrial Revolution was invented here was discovered it was created here and there's a new
478
Industrial Revolution now so take advantage of it it's got all the it's got everything
479
that that you're you're part of and everything that you're great at
480
and so it this is your moment you've got to you've got to capitalize on it
481
And you've spoken earlier on in this interview about what makes the UK such a promising field for AI development.
482
Let's talk a little bit about South Korea.
483
So NVIDIA has just committed over 260 ,000 of its most
484
advanced chips to South Korea in what is being called a $10 billion mega deal in AI.
485
What would you say is what makes
486
South Korea an ideal partner for this level of investment?
487
And what are some of the promising projects that you hope that NVIDIA might be able to achieve in the area region?
488
South Korea is one of the major regions, major countries in the world that has industrialized deeply.
489
South Korea, as you know, manufactures chips.
490
They manufacture ships, cars, electronics.
491
This is a country that is incredibly good at industrialization.
492
In just a few short decades, they reinvented the whole nation and became a global industrial giant.
493
South Korea also has a unique capability of doing software.
494
And so the technology ecosystem is able to deal with hardware and manufacturing on the one hand,
495
but software and the artistry of developing software on the other hand.
496
And so I think it's a real opportunity to see them take advantage of artificial intelligence and reinvent how industrialization works.
497
You know, this is the next era of AI.
498
Currently, AI understands language and numbers and images and videos.
499
But in the future, in the near future, we need AI to understand the laws of physics,
500
understand causality and object permanence and, you know, inertia and that needs to understand gravity and things like that, friction and things like that.
501
So I think when that happens and when we create embodied AI,
502
AI that could operate, manipulate and operate in the world, then we're going to reinvent how industry works.
503
And as you know, the world has quite a severe shortage of labor.
504
The world's GDP would be much higher today if not for the fact that we had a shortage of labor.
505
We have shortage of labor in just about every single company, every single field.
506
And so although we talk about jobs being lost, it is very likely that all jobs will be transformed.
507
And it's very likely that AI is going to drive productivity gains and GDP growth like we've never seen before.
508
And that's certainly the hope.
509
And I believe in it.
510
Just the last question from you before we hand over to the floor in the live audience Q &A.
511
Many of the young people sat here today, like you, have a passion for science.
512
Cambridge is a university much like your alma mater
513
Stanford that has produced 126
514
Nobel Prize laureates with an exciting AI development scheme.
515
The city has produced a culture of many eager to turn their ideas into businesses
516
and to top firms as you have successfully done.
517
What is one piece of advice
518
you would share to many of the young people who've got
519
an idea who've got a bullet point who've got a bit of code
520
that they're excited about how would you encourage them today
521
there are some skills there are some skills in determining whether an idea is a good one
522
or all the if you want to do something just go do it and so long as you're you're you're intellectually honest,
523
you're contextually alert, you're environmentally alert,
524
understanding what's going on around the world and your industry and you're willing to pivot, then that's all fine.
525
Okay, but nonetheless there are skills in determining upfront whether an idea is worth pursuing or not.
526
But I would say that the singular best piece of advice for entrepreneurs,
527
and it served me incredibly is to have this childlike view of the future
528
which is optimistic you're curious and you ask yourself you know how hard could it be
529
and don't let anybody tell you
530
that in fact it's really hard you're gonna find out for yourself
531
that in fact it's really hard but you have plenty of time to do
532
that you have plenty time plenty of time to do that
533
and so people have asked me would I start the company again
534
if I knew everything today back then the answer of course not
535
it's too scary it's too painful too much sacrifice how could
536
you take all of the you know all of the feelings and everything
537
that I've learned all bottled up at this moment transport it back into a 29 year old body
538
and go hey with that get at it you're never gonna do it
539
You're never gonna do it.
540
And so don't don't be don't be afraid of ignorance
541
and don't be afraid of being naive
542
and don't be afraid of You know all of those things you're gonna learn everything you need to learn along the way
543
And just you know if you if you're really passionate about something go do it, you know, just tell yourself how hard can it be?
544
Thank you so much Jensen and on that note
545
We are going to open the questions up to the floor
546
So if you do have a question
547
Please stick your hand up nice and high and then
548
when called upon to my word and then when called upon to do
549
that we will get an EM to bring you a microphone.
550
Let's start right here at the front with the gentleman who's got his hand up
551
and then afterwards we will go upstairs to the man in the dark blue jumper.
552
So we'll start here and then go upstairs.
553
Jensen your point about cooperating with each other and not competing really resonated
554
but here at the University we spent a lot of our time training students for exams, marking them, ranking them and so on.
555
What would your advice be to Cambridge University?
556
Should this 800 and something year old institution abolish exams
557
and have a more cooperative way of building up our student base
558
so that we can adapt to the future which is coming with AI?
559
I guess if there's no ranking and rating,
560
it would be hard for you to know whether the problems
561
and the curriculum is even harder not
562
that is sufficiently challenging to push the students I get
563
that a long time ago
564
when I first started the company there was a concept in management called ranking and rating it was created by another
565
Silicon Valley company and and because because
566
that company was was famed for being incredibly good at management I took a lot of the the learnings
567
that were being being talked about
568
and I applied it in video I can tell you today I've dropped 100 % of them.
569
There's no benefit in ranking people.
570
Their ideas called even back then 360 peer review.
571
There's no benefit in that.
572
There's no benefit in asking people to rate and review you.
573
I found that most of those techniques don't really work
574
but in the context of academia frankly with AI
575
I think the idea of information being somehow unaccessible so
576
that it's even worthwhile to compete anymore to get the right answer
577
I think those days are kind of gone I do wonder I do wonder how I wonder
578
if the way you teach courses are going to be very similar to the way
579
we renew ourselves at a corporate level continuously learning continuously renewing ourselves
580
I'm quite famed for saying I don't fire anybody and
581
and the reason for
582
that is very is is grounded in in in wisdom we want to encourage our employees to innovate
583
which which requires them to take risk
584
which requires them to be vulnerable they're gonna do something
585
that puts themselves out and they'll likely fail and
586
if they're taking sufficient risk that is proportional to the type of company
587
that we want to be then they should fail often but
588
if if we let go of people
589
that we rank to the bottom the bottom 5 % rule you might have heard of
590
that management technique as well every single year remove the bottom 5 %
591
because if you do that then what's left in the soup you know is consummate okay
592
but that's complete nonsense
593
because we love stew we want we want the mess of a stew not the purity of a consummate
594
and so in fact we don't want that five percent to be lost
595
because they they happen to be precisely the people who had just taken a risk failed
596
and they learned something from it and they might tomorrow be the outlier
597
that reinvented something to save your company and
598
so I think the the old cultures the old cultures the old systems deserve some reevaluation
599
and certainly because intelligence is now a commodity we got to say
600
that kind of out loud intelligence is about to be a commodity than what's left
601
and what's left is a lot of the things
602
that we were just talking about it's courage it's intellectual honesty it's the absence of ego,
603
the ability to be vulnerable in public so that you can create.
604
You know, as you know, artists and inventors and creators, they're humiliated, laughed at often because what they do isn't always perfect.
605
And so you need to have that humility, the vulnerability, and yet the courage to put yourself out there and be laughed at.
606
And so some of those things I think are going to become more important.
607
Can we take a question in the greenish jumper right there?
608
Yeah, indeed.
609
Hi, Jensen.
610
Hi.
611
Thank you for coming to Cambridge, and congratulations again on your fellowship.
612
Thank you.
613
My name is Josh.
614
I'm in Trinity College.
615
You mentioned earlier that you think that the future of jobs are going to change as a result of AI.
616
And a lot of young people, aspiring lawyers, accountants, consultants, bankers, they don't really share that optimism.
617
I think they're more pessimistic about it.
618
So I was hoping you could just maybe shed some more light onto what you think
619
that job transformation will look like and what the benefits will be
620
that will come from it okay one is is optimism in
621
humanity one part of the answer one part of the answer is pragmatism
622
and then one part of the answer is evidence okay
623
I'll start with evidence I radiology was going to be the first industry to be completely destroyed by artificial intelligence
624
and in fact almost every radiologist today uses AI and yet the number of radiologists being hired has increased
625
why is that because they can now do more things there are
626
so many cases that went undiagnosed using radiology and there are
627
so many cases that were not deeply diagnosed
628
because there's just a bottleneck of how many radiologists could study all those all those images
629
and so now the basic stuff is done very very quickly now the number of cases
630
that they're getting the depth of the cases
631
that they're getting it much much more much more interesting as
632
a result more more people getting hired the pragmatic part of it
633
let's say that you have a job
634
and within your job part of it is doing some task
635
that however you describe that task that task became infinitely fast okay and
636
and it used to take you a week to do something
637
now it takes you a second to do it well my question to you is based on
638
that what is more likely to happen that you now have more time to enjoy coffee or you just became busier.
639
It is likely you just became busier.
640
It's just another example of the radiology.
641
The reason for that is because that thing
642
that you were doing is causing you to not be able to do all the other stuff later,
643
past that pipeline, past that workflow, past that task.
644
Now that task is done infinitely fast,
645
all of a sudden the answer came back to you you're not a critical path again it's no different than
646
if I were to issue an instruction to my my my group of people
647
that I'm working with and they're supposed to go off
648
and do a research or do some analysis or simulation
649
and bring that answer back to me so
650
that I can make the next decision today it'll take a day
651
or a week for them to do that meanwhile I'm doing something else but
652
if it takes a second for them to come back
653
I'm back in the critical path again I'm busier than ever now the reason for
654
that is because I have so many ideas we have
655
so many ideas to go pursue that
656
when the tasks become super fast it turns out we could do more things we become busy there's some evidence
657
that smartphones made us busier not less busy there's some evidence
658
that computers made us busy or not less busy and
659
so it's because we're we are now increasingly the critical path
660
and because we have so many ideas we can pursue more things.
661
Now just hope in humanity.
662
We always find a way to discover new things to do, new things to be busy and I'm hoping that
663
as a result we'll go off and work on the problems that are the most meaningful,
664
the most valuable in all of our work which is the ones that are poorly defined.
665
The poorly defined work is the most valuable of all work.
666
discovery work, creativity work, the original creativity work.
667
I'm not talking about change the cat, you know, to a cat with shag rag, you know, shag rugs, you know,
668
I'm talking about really creating something out of the box.
669
And so the ability for us to use AI to solve problems that are fairly easy to, it doesn't have to be even easy,
670
that is describable, so that we could go and work on things that are very difficult to describe, I think is really quite powerful.
671
Thank you.
672
Shall we take the question from the member in the gallery?
673
Hi, hi Jason, my name is Liz.
674
Every job will change.
675
You will not lose your job to AI, you will lose your job to someone who uses AI.
676
Thanks for that.
677
Hi Jason, my name is Lily.
678
I'm an MBA student at the Judge Business School.
679
I also run my own venture builder called Funnel X.
680
It's very interesting that you just mentioned the UK's position almost having this Goldilocks moment,
681
because we commonly believe the AI game is actually happening in China or happening in the US.
682
So what do you think the UK
683
or the UK startup community can do more to leverage its current position and benefiting more from the AI game?
684
Regulate less.
685
And I mean that deeply and genuinely for the best interest in the UK.
686
I know that regulators, and because so much of regulators are lawyers,
687
which is good, but because they want to protect us, they want to protect society, they could regulate too early.
688
Especially on technology like this, where it's hard to predict the future.
689
You could watch sci -fi movies, but that's not the future.
690
That's called a sci -fi movie
691
and to use the science fiction movies projected into reality
692
and through some words of some people cause social panic to the point that you cause,
693
you overregulate, you're stifling the UK's ability to innovate.
694
The fact of the matter is China, as you know, technology is underregulated
695
And the reason for that is because the leaders in China mostly are engineers.
696
The leaders in the United States are mostly lawyers.
697
And so you can kind of tell what's going on
698
and the rate of technology evolution and industry evolution in China is just running incredibly fast because they regulate late.
699
They wait until the problems show up and they regulate the problems.
700
they solve they create regulations the way engineers solve problems
701
you know let's not dream it up let's let's observe the problem let's try understand the root cause the problem
702
and then solve the problem and and
703
so anyhow I would say under regulate less Jensen thank you very much for this evening I know
704
that we have so many questions
705
but in the interest of time I think it's fitting for us to conclude there
706
but I want to say a huge thank you from us from all of the members gathered here today
707
and on behalf of the selection committee congratulations again on being the Twin Strengths of Five.
708
Thank you very very much.
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Vocabulário e dicas de fala para esta lição
Este vídeo tem 708 frases e 8372 palavras para praticar shadowing. A fala dura 57:59. O falante mantém um ritmo estável de cerca de 144 palavras por minuto, confortável para o shadowing. 84% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| discovery substantivo | /dɪˈskʌv.ɹi/ | descoberta |
| succeed verbo | /səkˈsiːd/ | seguir |
| incredibly advérbio | /ɪŋˈkɹɛdɪbli/ | incrivelmente |
| principle substantivo | /ˈpɹɪn.sɪ.pəl/ | princípio |
| chip substantivo | /t͡ʃɪp/ | lasca, fragmento |
| artificial adjetivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificial |
| fellowship substantivo | /ˈfɛl.ə.ʃɪp/ | irmandade |
| deeply advérbio | /ˈdi(ː)pli/ | profundamente |
| sacrifice substantivo | /ˈsæk.ɹɪ.faɪs/ | sacrifício |
| instrument substantivo | /ˈɪnstɹəmənt/ | instrumento, aparelho |
| biology substantivo | /baɪˈɑ.lə.d͡ʒi/ | biologia |
| adapt verbo | /əˈdæpt/ | adaptar |
| invent verbo | /ɪnˈvɛnt/ | inventar |
| rank substantivo | /ˈɹæŋk/ | fileira, coluna |
| solve verbo | /sɒlv/ | resolver, solucionar |
Phrasal verbs que você vai ouvir
| Palavra | Significado |
|---|---|
| turn out verbo | terminar |
| come back verbo | voltar |
| go off verbo | explodir |
| figure out verbo | descobrir, deduzir |
| grow up verbo | crescer |
| look forward to verbo | ansiar por, estar ansioso para |
| stand up verbo | levantar |
Frases que vale a pena repetir
Frases curtas e completas do vídeo que você pode usar na conversa do dia a dia:
- How do you keep that balance?
- How do you behave in temperature, high temperature?
- How would you react to this particular chemical?
- How would you bind to it?
- What kind of cat are you?
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | have made · have included · has pioneered |
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | was established · was confined · be associated |
| Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa | people who have · somebody who does · future which is |
Pronúncia para ficar de olho
O falante usa 172 contrações e formas reduzidas, como I'm, you're, don't. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “sh” e “zh”: artificial /ˌɑː.tɪˈfɪʃ.əl/, fellowship /ˈfɛl.ə.ʃɪp/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, shade /ʃeɪd/, congratulations /kənˌɡɹæt͡ʃəˈleɪʃ(ə)nz/
- Palavras longas — acerte a sílaba tônica: incredibly /ɪŋˈkɹɛdɪbli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, biology /baɪˈɑ.lə.d͡ʒi/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, ecosystem /ˈikoʊˌsɪstəm/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: succeed /səkˈsiːd/, chip /t͡ʃɪp/, fellowship /ˈfɛl.ə.ʃɪp/, instrument /ˈɪnstɹəmənt/, adapt /əˈdæpt/
- /l/ final — a língua toca o céu da boca, não vira “u”: principle /ˈpɹɪn.sɪ.pəl/, artificial /ˌɑː.tɪˈfɪʃ.əl/, crucial /ˈkɹuː.ʃəl/, cathedral /kəˈθiːdɹəl/, meanwhile /ˈmiːnwaɪl/
- /h/ — um sopro suave, diferente do “r”: humanity /hjuˈmænɪti/, behave /bɪˈheɪv/, hire /ˈhaɪɹ/, headquarters /ˈhɛdˌk(w)ɔɹ.tɚz/, hierarchy /ˈhaɪ.ə.ɹɑː(ɹ).ki/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
- Grave a sua voz e compare com o original, prestando atenção a palavras como discovery, succeed, incredibly.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.