Name: AI教父黃仁勳：Nvidia最大貢獻係將CPU、GPU成本降低100萬倍｜人工智能雖不是由第一原理推演答案 但可模擬人類生物學 香港科技大學可建設世界第一間AI醫院｜HKUST｜美股投資｜CC字幕 (AI教父黃仁勳：Nvidia最大貢獻係將CPU、GPU成本降低100萬倍｜人工智能雖不是由第一原理推演答案 但可模擬人類生物學 香港科技大學可建設世界第一間AI醫院｜HKUST｜美股投資｜CC字幕)
Uploaded: 2024-12-01T04:54:31.000Z
Duration: 27 min 19 s

Imagine if there's something in the world that you rely on.

想象一下，如果世界上有什麼東西是你所依賴的。

We reduced it in the last 10 years by 1 million times.

我們在過去 10 年中將其減少了 100 萬倍。

Well, when something happens, when the cost of something reduces by a million times, your habits fundamentally change.

好吧，當事情發生時，當某樣東西的成本降低了一百萬倍時，你的習慣就會發生根本性的改變。

How you think about computing fundamentally changed.

你對計算的思考方式發生了根本性的改變。

That is the single greatest contribution NVIDIA ever made.

這是英偉達有史以來做出的最大貢獻。

Some of our professors here may slightly disagree because you still need a lot of money to buy your GPUs.

我們在座的一些教授可能會略有異議，因為你仍然需要很多錢來購買 GPU。

I gave you a million times discount in the last 10 years.

過去 10 年裡，我給你打了無數次折扣。

Ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha, ha.

哈，哈，哈，哈，哈，哈，哈，哈，哈，哈，哈，哈，哈，哈，哈，哈，哈。

Jensen, it's just so nice to have you here at HKUST and in Hong Kong.

詹森，很高興你能來到科大和香港。

Let me just say, can we just please show how much we love Jensen?

我只想說，我們能不能表示一下我們有多愛詹森？

Jensen, you know, I have been preparing for this moment.

詹森，你知道，我一直在為這一刻做準備。

I thought it's definitely a highlight of my HKUST career, for sure.

我想這絕對是我科大生涯中的一個亮點。

I just couldn't sleep last night for one very important reason.

我昨晚睡不著，有一個非常重要的原因。

Because I am going to introduce you as the number one CEO in this universe.

因為我要把你介紹成這個宇宙的頭號首席執行官。

Maybe entire universe, at least in this universe.

也許是整個宇宙，至少是這個宇宙。

This company, Apple's stock was going up last night.

這家公司，蘋果公司的股票昨晚上漲了。

And yours wasn't doing that particularly well.

And I couldn't wait until the market was closed.

I asked my wife, I said, did Nvidia hang on there?

我問我的妻子，我說，Nvidia 堅持住了嗎？

So we have less than an hour this afternoon.

今天下午我們只有不到一個小時的時間。

I'm just diving, Jensen, with some tough questions for you.

詹森，我正在潛水，有些棘手的問題要問你。

So you have been leading this technology field, AI field, for a long period of time.

所以，你在這個技術領域，即人工智能領域，已經領跑了很長時間。

Just for the sake of our audience, just tell us how you think about AI, more specifically, recently, artificial general intelligence, the impact AI is having and will have for all the society and the industry.

為了聽眾著想，請談談您對人工智能的看法，更具體地說，最近的人工通用智能，以及人工智能正在和將要對整個社會和行業產生的影響。

Thank you for the opportunity to spend time with you.

感謝您給我這個機會與您共度時光。

Harry is one of the most consequential computer scientists of our time.

哈里是當代最具影響力的計算機科學家之一。

And he's been a hero of mine and many others around the world for a very long time.

長期以來，他一直是我和世界各地許多人心目中的英雄。

Harry, as you know, the transformative, the groundbreaking capability happened when an artificial intelligence network can learn to understand data of all kinds.

哈里，正如你所知道的，當人工智能網絡能夠學會理解各種數據時，就會產生變革性的、開創性的能力。

Of course, language, and images, and sequences of proteins, and sequences of amino acids, and sequences of chemicals.

當然，還有語言、影像、蛋白質序列、氨基酸序列和化學物質序列。

And all of a sudden, we now have computers that can understand the meaning of words and such.

突然之間，我們有了能理解文字含義的電腦。

And because of generative AI, we can translate from one modality of information to another modality of information.

由於人工智能的生成性，我們可以將一種資訊模式轉化為另一種資訊模式。

For example, from text to images, from text to text, from protein to text, from text to protein, from text to chemicals.

例如，從文本到影像、從文本到文本、從蛋白質到文本、從文本到蛋白質、從文本到化學物質。

This universal, initially a universal function approximator, evolved into a universal language translator of every kind.

這種通用性最初是一種通用函數近似器，後來發展成為各種通用語言翻譯器。

And so the question then is, what can we do with that?

那麼問題來了，我們該怎麼做呢？

And you see the number of startups around the world and a number of capabilities with the combination of all these different modalities and capabilities.

你可以看到全球初創企業的數量，以及將所有這些不同模式和能力結合在一起的能力。

And so I think the really amazing breakthrough is that we can now understand the meaning of information.

是以，我認為真正令人驚歎的突破在於，我們現在可以理解資訊的意義了。

And so what does that mean to you if you are a digital biologist, so that you can understand the meaning of the data you're looking at, so that you could find a needle in a haystack?

那麼，如果你是一名數字生物學家，這對你意味著什麼呢？這樣你就能理解你所查看的數據的意義，從而大海撈針？

What does that mean if you are, in the case of NVIDIA, a chip designer, system designer?

如果你是英偉達公司的芯片設計師、系統設計師，這意味著什麼？

What does that mean to you if you're an ag tech, or climate science, or climate tech, or energy looking for a new material?

如果您是農業技術、氣候科學、氣候技術或能源方面的專家，這對正在尋找新材料的您來說意味著什麼？

So this is really the groundbreaking thing, is that we now have the concept of a universal translator.

是以，我們現在有了通用翻譯機的概念，這才是真正的突破。

你可以理解任何你喜歡的東西。

Yeah, Jensen, in May, we were at a Microsoft CEO summit.

是的，詹森，五月份，我們參加了微軟 CEO 峰會。

So many of us listened to your really description about the amazing impacts of AI on the society.

我們很多人都聽了您關於人工智能對社會的巨大影響的描述。

I thought that what you said really resonated with me.

我覺得你說的話讓我很有共鳴。

在某種程度上，甚至讓我感到震驚。

So you kind of took us back to the entire history of a human being.

所以，你算是把我們帶回了人類的整個歷史。

You said, well, there's this agricultural revolution, so we actually manufacture more food.

你說，有了這場農業革命，我們實際上製造了更多的食物。

Then we have industrial revolution, we actually manufacture more iron steels.

工業革命之後，我們生產了更多的鋼鐵。

Then we have information technology, we actually have more information.

有了信息技術，我們實際上掌握了更多的資訊。

Now, in this intelligence, this era, so what you are doing at NVIDIA, what AI is doing is actually manufacturing intelligence.

現在，在這個智能時代，你們在英偉達所做的事情，人工智能所做的事情實際上就是製造智能。

Can you elaborate a little bit more on why this thing is just so enormously important?

你能再詳細解釋一下為什麼這件事如此重要嗎？

Yeah, when you look at what we've done together, Harry, and you were in the middle of all of this, from the perspective of computer science, we've reinvented the whole stack.

是的，當你看看我們一起做的事情，哈里，從計算機科學的角度來看，我們重塑了整個堆棧，而你就在這中間。

Meaning, the way we used to develop software, and when you think about computer science, you have to think about software development, how software is done.

也就是說，我們過去開發軟件的方式，當你考慮計算機科學時，你必須考慮軟件開發，考慮軟件是如何完成的。

And so we used to code software with our hands.

是以，我們習慣用雙手編寫軟件。

We imagined what the function it is that we would like to implement, whatever algorithm we'd like to implement, and we use our own creativity, we type it into the computer.

我們想象我們想要實現的功能是什麼，我們想要實現的算法是什麼，然後我們發揮自己的創造力，把它輸入電腦。

I started with Fortran, and I learned Pascal, and then C and C++, and of course, each one of these languages allow us to express our thoughts into code.

我從 Fortran 開始，然後學習 Pascal、C 和 C++，當然，每一種語言都能讓我們用代碼表達自己的想法。

這些代碼在 CPU 上運行得非常好。

All of a sudden, now we use observed data, and we give this observed data to a computer, and we say, what is the function that you see inside this code?

突然間，我們使用了觀察到的數據，並將這些觀察到的數據交給計算機，然後我們說，你在這段代碼中看到的函數是什麼？

What are the patterns and relationships that you observe by studying all of the data that we presented to you?

通過研究我們向您提供的所有數據，您觀察到了哪些模式和關係？

And instead of using code, coding, we now use machine learning.

現在，我們不再使用代碼、編碼，而是使用機器學習。

And the machine generates not software, it generates neural networks that are processed on GPUs.

機器生成的不是軟件，而是在 GPU 上處理的神經網絡。

And so from coding to machine learning, from CPUs to GPUs, and because GPUs are so much more powerful, the type of software we can now develop is extraordinary.

是以，從編碼到機器學習，從 CPU 到 GPU，由於 GPU 的功能更加強大，我們現在可以開發的軟件類型非比尋常。

And what sits on top of it is artificial intelligence.

And so computer science has really been transformed pretty much at the description I just said.

是以，計算機科學確實發生了很大的變化，就像我剛才所說的那樣。

Now the question is, what happens to our industry?

現在的問題是，我們的產業會發生什麼變化？

Of course, we're all racing to use machine learning to go discover new AIs.

當然，我們都在爭先恐後地利用機器學習去發現新的人工智能。

And maybe that's, of course, one of the things about AI that you know very well is the automation of cognition.

當然，也許這也是你所熟知的人工智能的一個方面，那就是認知的自動化。

And problem solving could be distilled down to, if I could, three basic ideas that you observe and perceive the environment, understand it, reason about it, and then come up with a plan to interact with it, whatever you decide your goals are.

如果可以的話，解決問題可以提煉為三個基本理念，即觀察和感知環境、理解環境、推理環境，然後制定與環境互動的計劃，無論你的目標是什麼。

And so perception, reasoning, and planning.

The three fundamental steps of problem solving.

AI教父黃仁勳：Nvidia最大貢獻係將CPU、GPU成本降低100萬倍｜人工智能雖不是由第一原理推演答案 但可模擬人類生物學 香港科技大學可建設世界第一間AI醫院｜HKUST｜美股投資｜CC字幕 (AI教父黃仁勳：Nvidia最大貢獻係將CPU、GPU成本降低100萬倍｜人工智能雖不是由第一原理推演答案 但可模擬人類生物學 香港科技大學可建設世界第一間AI醫院｜HKUST｜美股投資｜CC字幕)

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