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734 句
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1
Oh, no. This is not planned, but we know who it is.
哦，糟了。這不在計劃之內，但我們知道是誰幹的。
2
Oh, no. No. Mr. President?
哦，不。不。總統先生？
3
Oh.
哦。
4
Yes, sir.
是的，先生。
5
I got to tell you something.
我有件事得告訴你。
6
Ladies and gentlemen, GPU Jesus.
女士們先生們，GPU之神來了。
7
They love you.
他們愛你。
8
They love you.
他們愛你。
9
Thank you.
謝謝。
10
I love you back.
我也愛你。
11
Number one podcast in the world.
全球排名第一的播客。
12
In the world.
在這個世界上。
13
Wow.
哇。
14
We like the new jacket.
我們喜歡這件新夾克。
15
Well, you know.
你懂的。
16
You auctioned the open?
你是通過拍賣獲得的開盤資格嗎？
17
I just, I felt you guys needed some energy.
我只是覺得你們需要提提神。
18
Yes.
是的。
19
I know we're talking about serious stuff here, but we need to talk about it with energy.
我知道我們現在討論的是很嚴肅的事情，但我們需要帶著幹勁來談。
20
Yes.
是的。
21
Let's start with this essay from this weekend. Which one?
我們先從這週末的這篇文章開始吧。哪一篇？
22
Let's start with Dario's essay.
我們先從達里奧的文章開始。
23
Was Hemingway involved?
海明威有參與嗎？
24
Actually, did anybody run it through Pangram?
話說，有沒有人用 Pangram 跑過一遍？
25
I don't even know how much of it was AI helped, but that was a pretty incredible thing.
我甚至不知道其中有多少是藉助人工智慧完成的，但那真的是相當了不起的事。
26
And then I think what a lot of people were surprised
然後我覺得很多人都感到很意外。
27
by was the coalescing of the Frontier Labs around the essay itself.
其核心在於前沿實驗室圍繞這篇論文本身所形成的凝聚合力。
28
Just, Jensen, unpack what happened, how you read it, how you interpreted it, and then we'll get into some details that were inside of it.
詹森，你先梳理一下事情的經過，說說你是怎麼看待這件事、怎麼理解的，之後我們再深入探討其中的細節。
29
But maybe just high -level thoughts to kick it off.
不過或許先從一些高層次的思路入手來開啟討論就好。
30
Well, first of all, there were a lot of stuff in there.
嗯，首先，裡面有很多東西。
31
Yeah.
是的。
32
And first, there's a part about safety which we have to take very seriously.
首先，有一部分涉及安全問題，我們必須高度重視。
33
Safety is paramount, obviously.
顯然，安全是重中之重。
34
Safety and leadership are not false.
安全與領導力絕非空談。
35
They're false choices.
這些都是虛假的兩難選擇。
36
You're able to innovate quickly.
你能夠快速創新。
37
You're able to execute quickly.
你能夠快速執行。
38
And America's able to lead and to do it safely.
而且美國有能力發揮領導作用，並以安全的方式開展相關行動。
39
I think those are false choices, but safety is obviously important.
我認為這些都是虛假的選擇，但安全顯然是很重要的。
40
There's a matter of of internal control that I think he was speaking to.
我認為他當時談到的是一個內部控制方面的問題。
41
Obviously, the Coxson whistleblower is very serious matter.
顯然，考克森舉報事件是一件非常嚴肅的事情。
42
Whenever you have a whistleblower, you gotta take it very seriously.
只要出現舉報人，就必須高度重視。
43
I thought Coxson had great courage to put out what his concerns were.
我認為考克森敢於說出自己的擔憂，非常有勇氣。
44
And even then, there were some issues that were kind of conflated within that.
即便如此，其中還是有一些問題被混為一談了。
45
I think the whistleblowing is fine.
我認為這次舉報是合理的。
46
I think the scientific prediction about the future is less fine because it's not grounded on science, obviously.
我認為關於未來的科學預測不夠準確，因為它顯然沒有建立在科學的基礎上。
47
And it was expressed by a scientist, but it was obviously not grounded on science.
這話是一位科學家說的，但顯然並沒有科學依據。
48
And so I take issue with that.
所以我對此持有異議。
49
But obviously the whistleblower part of it, you know, I think there's just a whole bunch of stuff.
但很明顯，這裡涉及到舉報人的部分，你知道的，我覺得裡面牽扯的事情可多了。
50
Pausing, pacing, those are all the voluntary things that they could do if they feel that their company is out of control.
暫停、調整節奏，這些都是他們在覺得公司失控時可以主動採取的做法。
51
If Coxon saw something, obviously we don't know what Coxon saw,
如果考克森當時看到了什麼，顯然我們並不知道他到底看到了什麼。
52
but if he saw that the company was out of control and maybe it's a transition from research to engineering.
但如果他發現公司已經失控，或許這就是從研究階段向工程化階段的轉型期。
53
As you know, these labs are transitioning from research to engineering.
如您所知，這些實驗室正從研究階段轉向工程化階段。
54
Extraordinary talent, extraordinary engineering, but obviously engineering is different than research maybe
非凡的才華，卓越的工程技術，但顯然工程工作和研究可能有所不同。
55
that transition is clumsy you know I don't we don't know what what he saw
那個轉折太生硬了，你知道的，我們根本不清楚他到底看到了什麼。
56
and ultimately only he knows but
說到底只有他自己知道，但是
57
if there was a matter of lack of control that's a
如果存在失控的問題，那就是
58
different topic how should the government deal with it now all of a sudden regulation
換個話題，政府現在突然出臺監管措施，該如何應對？
59
and rate I mean it just covers everything in one block
而且這個費率，我的意思是它一次性包含了所有費用。
60
can you just help us sort of unpack we played we
你能不能幫我們稍微理一理我們剛才玩的那些內容啊
61
tried to play this game actually this week on the pod
我這週在播客節目裡還試著玩了這款遊戲
62
and it was difficult which is how do you describe like you know my mom calls me
而且這真的很難，你要怎麼形容那種感覺呢，就像，你懂的，我媽媽給我打電話的時候
63
and she's like Chmoth what is this whole civilizational death thing.
然後她就問，查莫斯，這整個文明滅亡的事到底是怎麼回事。
64
I don't know how to explain it to her.
我不知道該怎麼跟她解釋這件事。
65
So when you have very smart people like that quantize it and quantify it, I think that's probably what's perturbing to some people.
所以當有那樣非常聰明的人把它量化、用資料來衡量時，我覺得這可能就是讓一些人感到不安的原因。
66
They're like, what does that mean, 10 % of extinction?
他們就會問，這是什麼意思，10%的物種滅絕？
67
Nobody knows how to explain that to the average person, how that's even possible.
沒人知道該怎麼向普通人解釋這件事，解釋這怎麼可能發生。
68
Well, first of all, we shouldn't because it's made up.
首先，我們不應該這麼做，因為這是虛構的。
69
First of all, I think that we shouldn't because it's made up.
首先，我認為我們不應該這麼做，因為這是編造的。
70
And these are well -educated.
而且這些人都受過良好教育。
71
They're called researchers.
他們被稱為研究人員。
72
Obviously, they're working in a lab.
很明顯，他們正在實驗室裡工作。
73
And so the confluence of these words and then the prediction is alarming and troubling, and it shouldn't be done.
因此，這些言論與相關預測交織在一起，令人警覺和不安，這種做法是不可取的。
74
It's irresponsible.
這是不負責任的。
75
Now the fact of the matter is, let's go back and look at the real facts.
現在事實是，讓我們回過頭來看看真實情況。
76
The facts are there was a prediction that in five years time
事實是，曾有一項預測認為五年後
77
radiology will be completely taken over by artificial intelligence and there'll be no radiologists in the world.
放射學將完全被人工智慧取代，屆時世界上將不再有放射科醫生。
78
That has proven to be exactly the opposite.
事實證明情況恰恰相反。
79
We need more radiologists than ever in the world.
全世界對放射科醫生的需求正達到歷史新高。
80
However, AI has taken over radiology completely, which is great.
然而，人工智慧已經完全接管了放射學領域，這是一件好事。
81
It is automated scan reading, which is great.
這是自動掃讀功能，非常好用。
82
There was a prediction that within six to twelve months, wasn't it just last year, within six to 12 months.
曾有一個預測說在六到十二個月內，不就是去年的時候嗎，說在六到十二個月內。
83
90 % of code would already be generated by AI.
90%的程式碼已經由人工智慧生成。
84
That has turned out to be wrong.
事實證明那是錯誤的。
85
Within six to nine months, that was predicted last year, 50 % of entry jobs will be wiped out.
去年曾有人預測，在六到九個月內，50%的入門級崗位將會消失。
86
That has proven to be wrong.
事實證明這是錯誤的。
87
Let's see, what else has proven to be wrong?
讓我想想，還有哪些事後來被證明是錯的？
88
I mean, all of these predictions have been wrong.
我的意思是，所有這些預測都錯了。
89
Right.
對。
90
And if you're - That GPT -2 would be too unsafe to release, that Lama -3 would be too unsafe to release.
而且你也知道，GPT-2 當時被認為太不安全而不能釋出，Llama 3 也曾被認為太不安全而不能釋出。
91
Oh, Juan.
哦，胡安。
92
Yeah, we've heard these.
是的，這些我們已經聽說過了。
93
Half of white collar jobs would be gone next year.
明年將有一半的白領工作崗位消失。
94
The jobs apocalypse.
就業末日論
95
We have to take accountability.
我們必須承擔責任。
96
We have to take account for all of the stupid predictions that were made.
我們必須考慮到所有那些曾經做出的愚蠢預測。
97
Right.
對。
98
Somebody has to take...
總得有人來承擔……
99
And so we have to just keep track of all that.
所以我們得把所有這些情況都記錄下來。
100
And of course people do.
當然，人們確實會這麼做。
101
And remind us that those predictions are inconsistent with ultimately America winning the AI race.
這也提醒我們，這些預測與美國最終贏得人工智慧競賽的結果並不相符。
102
The short form for that is some people are saying, you know, they say trust the experts and they use the analog of COVID, which again started with people that were researchers,
簡而言之，有些人說要相信專家，還拿新冠疫情打比方，要知道新冠疫情最初也是由研究人員最先發現的。
103
educated people that had an asymmetric awareness of the thing that the rest of us did not, saying things that ultimately turned out, we find out in facts, not to be true.
那些受過教育的人掌握著我們其他人所沒有的不對稱資訊，他們發表的言論最終被事實證明是不實的。
104
And so there's this war that's happening right now between the trust the experts movement and the you know Well,
所以現在正有一場爭論，一邊是主張信任專家的群體，另一邊則是那些認為……
105
let's just look at the actual history of these predictions and let's just think more methodically
我們不妨看看這些預測的實際歷史，然後更有條理地思考一下。
106
What where is this coming from
這到底是從哪冒出來的
107
because it's coming from inside the places that's actually making it like what do you think is the Psychological makeup
因為這些想法恰恰來自那些真正在製造問題的內部環節，你覺得這背後的心理動因是什麼？
108
or what is the real incentive?
或者說，真正的動機是什麼？
109
Maybe it's a business incentive.
說不定這是一項商業激勵措施。
110
Maybe it's a political incentive Can you just maybe guess
說不定這是出於政治動機，你能不能大概猜一下？
111
or how do you how do you think about what why they're doing this?
或者說，你如何看待他們這麼做的原因？
112
Well, first of all, I've got to tell you, these are some of the most consequential companies in history.
首先我得告訴你，這些都是歷史上最具影響力的公司之一。
113
Extraordinary engineers, extraordinary researchers, really fantastic work.
傑出的工程師們，傑出的研究人員們，工作真的非常出色。
114
On the one hand, I work very closely with them as companies to companies.
一方面，我與他們作為公司之間保持著非常緊密的合作關係。
115
On the other hand, we have to have conversations like this in public.
另一方面，我們必須在公開場合進行這樣的對話。
116
And it's really unfortunate.
這實在太不幸了。
117
And I think that these companies really ought to be built the way that we used to build companies, which is in silence.
我認為這些公司真的應該像我們過去創辦公司那樣，在低調中發展。
118
Right.
對。
119
So wait, Jensen, you don't allow anybody in your organization to speak for the entire organization, especially when they're having a bad weekend or they rage quit?
所以等等，詹森，你不允許你團隊裡的任何人代表整個組織發言，尤其是當他們剛度過一個糟糕的週末或者是一怒之下辭職的時候？
120
They're not allowed to tweet on your behalf and the organization's behalf?
他們不被允許代表你以及該組織釋出推文嗎？
121
Because, well, that's what they decided when they came to work for us.
畢竟，他們當初來我們這兒工作時就已經做出了這個決定。
122
And we told them, this is the way you behave when you work in our company.
我們告訴他們，在我們公司工作就該遵守這樣的行為準則。
123
And if you like the culture of our company, which, as you know, the NVIDIA culture and the NVIDIA employee base, incredibly happy.
如果您認同我們公司的文化，眾所周知，輝達的文化和員工團隊都非常積極融洽。
124
They like the fact that the company is consistent, that we're stable, that our core values are consistent with taking care of the families
他們欣賞公司始終如一的經營理念，認可我們的穩定發展，也認同我們的核心價值觀始終以關懷員工家庭為出發點。
125
and creating the conditions by which they can do their life's work.
併為他們創造條件，讓他們能夠投身於自己畢生的事業。
126
That we do meaningful work.
我們從事有意義的工作。
127
We do it as quietly as we can and we contribute to everybody else's success, which we're very proud of.
我們儘可能低調地去做這件事，並且為所有人的成功貢獻力量，這讓我們深感自豪。
128
And so those kind of core values people are attracted to.
所以人們會被這類核心價值觀所吸引。
129
But when you come and work in our company, there are also some things that we don't appreciate that you do.
但當你來到我們公司工作時，也有一些我們不認可的行為。
130
Like, for example, we don't welcome political discourse inside our company.
比如說，我們不歡迎在公司內部進行政治討論。
131
Take it home.
把它帶回家。
132
You guys talk about politics outside the company.
你們在公司外面談論政治。
133
Yeah.
是的。
134
We are, the company is an apolitical company.
我們公司是一家不涉及政治的企業。
135
You know, we're bipartisan.
你知道，我們是跨黨派合作的。
136
We want America to succeed.
我們希望美國取得成功。
137
And we want whatever government is in place, we'll do everything in our power to help America succeed.
無論哪一方執政，我們都將盡一切努力助力美國取得成功。
138
And so the discourse about race and religion and politics and all of that stuff,
因此，關於種族、宗教、政治以及所有這類話題的討論，
139
we tell people, do it outside the company.
我們告訴員工，要在公司外做這件事。
140
It's not for us.
這不是給我們的。
141
In terms of maybe AI regulation, then more narrowly, Satya was here this morning and what he said is, you know, before we talk about regulation that could really stymie things,
就人工智慧監管而言，具體一點說，薩提亞今天早上來過這裡，他表示，在我們討論可能會嚴重阻礙發展的監管措施之前，
142
why don't we just get some basics right?
我們為什麼不先把一些基礎的事情做好呢？
143
Why don't we get measurement right?
我們為什麼不能把測量工作做好呢？
144
Why don't we get standardization right? right um where do you
我們為什麼沒法把標準化做好？對了，那我們該從哪裡……
145
land on get engineering right get the engineering right right translate the research in a more predictable way so
把工程技術做對，以更可預測的方式轉化研究成果。
146
that we're not fear -mongering keep it inside until we're ready
我們這不是在製造恐慌，在準備就緒之前先保密。
147
to expose it um what do you think the right response is you know demis had a proposal
要揭露這件事，你覺得正確的應對方式應該是什麼？你知道的，德米斯提出過一個方案。
148
which was sort of this more finra -like organization it's not clear what daria wants this transnational mutated thing
這有點像美國金融業監管局（FINRA）那樣的組織，目前還不清楚達裡婭想要這個跨國變異的機構做什麼。
149
that has some sort of control where do you land on
這其中存在某種程度的管控，你對此持什麼立場？
150
this the sort of perspective of What do we need right now?
這正是我們當下需要的那種視角。
151
You know, regulations should solve actual problems.
要知道，制定規定是為了解決實際問題。
152
And so the question is, what actual problems have we enjoyed?
所以問題來了，我們真正遇到過哪些實際問題呢？
153
And if you look at the actual problems, all of the actual problems so far have come from the labs.
如果你看看實際出現的問題，到目前為止所有實際問題都來自實驗室。
154
And the reason for that, the reason for that, and just in their defense, the reason for that is because they have the most compute.
究其原因，客觀來說，是因為他們擁有最強大的計算資源。
155
Right.
對。
156
And the reason for that is because they're trying to solve the frontier problems.
原因就在於他們正試圖解決前沿領域的問題。
157
And so in their defense.
所以為他們說句公道話。
158
And so it's sensible that the labs, the frontier labs, will be where the most danger come from.
因此，最前沿的實驗室會是危險的主要來源，這一點是合乎情理的。
159
It is unlikely that a high school student did something.
一個高中生不太可能做出這種事。
160
Because they just simply won't have enough compute.
因為它們根本就沒有足夠的計算能力。
161
Right.
162
And so it's unlikely that a startup will be the reason because they won't have enough compute.
因此，初創企業不太可能成為實現這一目標的主體，因為它們沒有足夠的計算資源。
163
In fact, you could look across the planet and everybody won't have enough compute with the exception of the frontier labs.
事實上，放眼全球，除了頂尖實驗室之外，所有機構的計算資源都不夠用。
164
And so now the question is, if you look at what actually happened, and they're doing pioneering work, it's really very hard.
所以現在問題是，如果你看看實際發生的情況，他們正在做開創性的工作，這其實非常困難。
165
They're transitioning from research to engineering engineering,
他們正從研究階段轉向工程開發階段。
166
I could imagine, and they're obviously building some of the most consequential technology and companies in the world.
我完全可以想像，他們顯然正在打造一些全球最具影響力的技術和企業。
167
They're building their company, they're building their culture, they're building the technology, they're building engineering, they're building products all at the same time.
他們同時在建設公司、塑造企業文化、開發技術、搭建工程團隊、打造產品。
168
And so I can understand it's a little bit hair on fire.
所以我能理解現在情況有點緊急。
169
But nonetheless, the four incidents from one lab, the one giant incident from the other lab,
但即便如此，其中一個實驗室發生了四起事故，另一個實驗室則發生了一起重大事故。
170
the first thing that you have to do is just root cause the problem from an engineering perspective.
你首先要做的是從工程角度找出問題的根本原因。
171
What happened?
發生什麼事了？
172
What could we have done differently?
我們本可以採取哪些不同的做法？
173
And what are we going to implement and institutionalize, whether it's technology or methods or processes, and make sure that we don't let it happen again?
我們將落實並制度化哪些措施，無論是技術、方法還是流程，來確保此類事件不再發生？
174
Now, I would bet you money that in every single one of those cases is within their control
我敢跟你打賭，這些情況裡每一件事其實都在他們的掌控範圍之內。
175
in the future to prevent it.
未來將採取措施防止此類情況發生。
176
because the alternative, if it's not in their control, and I'm sure that they are, I'm sure those four incidents won't happen again.
因為如果情況不在他們的掌控之中，那後果會很嚴重，而我相信他們有能力控制局面，我確信那四起事件不會再發生。
177
I'm sure they root -caused it and fixed it.
我敢肯定他們已經找到了根本原因並修復了問題。
178
I'm sure they have now much better technology for sandboxes and runtimes and monitors and continuous monitors.
我敢肯定，他們現在在沙箱、執行時環境、監控工具以及持續監控方面的技術要好得多。
179
And so I'm certain they have much, much better technology now.
所以我確信他們現在擁有的技術要先進得多。
180
The alternative is also unlikely which is for them to say, look, we had these incidents.
另一種情況也不太可能發生，那就是他們站出來說：你看，我們確實發生過這些事件。
181
After we're done analyzing it, we came to the conclusion we don't know anything that happened, and we have no idea how to control it,
經過分析，我們得出的結論是，我們對發生的事情一無所知，也完全不知道該如何控制它。
182
and we're asking society for help.
我們正在向社會尋求幫助。
183
Now, if that's the case, then we ought to, you know, a bunch of companies with engineers ought to send engineers in, and we should advise them if we can.
既然如此，我們應該讓那些擁有工程師的公司派遣工程師前往現場，並且在能力範圍內為他們提供建議。
184
But I doubt it.
但我對此表示懷疑。
185
I think they have extraordinary people that got this handled.
我覺得他們有非常出色的人來處理這件事。
186
We're not operating in a vacuum.
我們並非在真空中開展工作。
187
David, last night you informed me that there is a Chinese lab, the makers of GLM, who are going to put $3 billion towards a recursive self -improvement run.
188
So maybe you could tee that up for Jeff.
189
Well, that's what was announced.
190
Zipu .com, the founder, just raised $5 billion and said
191
that one of their priorities is going to be trying to get AI
192
that trains the next AI and to try and automate as much of that as possible.
193
Yeah, I think that...
194
Well, this is the new sexy phrase.
195
As you guys know, RSI is a combination of a system of ideas.
196
It starts everything with in -context stuff.
197
It starts with skills.
198
It starts with reflection.
199
It starts with reinforcement learning and synthetic data generation.
200
And these are all very sensible ideas that causes AI to get better at solving a problem over time.
201
And you could also have low rank.
202
All of that stuff doesn't include the weights.
203
You could actually improve the weights, and it's called LoRa.
204
LoRa could be improved in synthetic data generation, reinforcement learning, enhance it without training the base model itself.
205
And then over time, you could train the base model again with all of that experience.
206
And so I think it's a sensible thing
207
that you're going to use the technology to enhance productivity of all kinds of tasks, including building AI.
208
I think that's a very logical idea.
209
And I'm certain that everybody is using it in some degree.
210
It's just this phrase is now being used to weaponize the technology in some way and maybe to turn the technology.
211
It's going to spiral out of control is the impression they're trying to give, but you don't believe that's real.
212
No, no, of course not.
213
And the reason for that is because you could RSI all day long inside your company, but when you release a product, you've got to evaluate it, don't you?
214
You have to test it again, don't you?
215
You have to make sure that there's no regression, right?
216
And so the basic process of control, these labs are going to, as they move from labs to engineering, they will have much, much better control.
217
Right and when they have much better control that control comes from methods
218
and knowledge and practice and tools and technology All of those things
219
that lead to better control verification
220
and evals It's going to enable RSI to be done inside the company
221
and for good price to be released outside about open source for a second.
222
I mean this hugging face
223
We were communicating about this and I said it's going to be one of the most consequential acquisitions.
224
I don't even want to call it a transaction because I think it's more important than that.
225
Give us your first principles explanation of open source versus closed source versus open weights
226
and how the ecosystem should fit together over time.
227
The world needs both closed models and open models.
228
You want to use, I use as much closed models as I can.
229
This weekend I used four of them.
230
And they work terrifically.
231
They're frontier.
232
They're great experience.
233
They work incredibly well.
234
They're getting better all the time.
235
And the way I think about closed models is kind of like bottled water.
236
Water is free, you guys.
237
I don't know if I've told you guys, but water is free.
238
I don't want to burst everybody's bubble.
239
But water is free.
240
And this morning, I used a lot of free water taking a shower.
241
And so you use the right water in the right places.
242
And this is no different than electricity.
243
This is you know, there's no different than all kinds of commodities that we use in the world.
244
You need both Now in the case of open the reason why you need it is
245
because it could be for sovereignty reasons privacy reasons
246
Proprietary technology reasons look at the facts the facts are in the last six months
247
400 billion dollars of venture funding went into AI native companies 80 % of them use open models.
248
If not for open models, how could they build their dream?
249
Because their dream could be different.
250
Obviously, it would be different than the labs, the Frontier Labs dreams.
251
And America has so many different ways to innovate.
252
That's one of our core strengths.
253
Great ideas just coming out of the fountain.
254
And so open models enables that.
255
Open models enables every single, if we want to win the AI race, It's not about a few technology companies winning the AI race.
256
It's about every company in America.
257
Every company, every industry, every researcher, every teacher, every student, every startup.
258
Everybody wins.
259
Some of them will use closed models.
260
A lot of them will use open models.
261
There's 10 million.
262
Does it matter if the open models come from China or the U .S.?
263
Well, we're doing everything we can to make a contribution in open models.
264
However, the moment you download,
265
like, for example, probably the vast majority of the world's contribution to open source today is coming from China.
266
They just have a lot more engineers.
267
They produce everything in large scale because it's a larger country.
268
And so they produce science and math students in volume.
269
That's one of our disadvantages.
270
They're manufacturing them through amazing universities like Tsinghua University in high volume.
271
Well, they contribute open source today.
272
We download Linux, we download Kubernetes, we download all the software.
273
A lot of it has been touched by Chinese.
274
Once you download it, it's yours.
275
We fork it, we improve it, we make it ours.
276
When you download one of these Chinese models, it just happens to be made by some really great researchers in China, but it's now yours.
277
So what exactly is the race?
278
The race?
279
Yeah.
280
I think that's a really good point.
281
My point is the race is really about who exploits the technology best.
282
You know, the last industrial revolution, all of the inventors were Maxwell, Volta, Ampere.
283
None of them were American.
284
They were right the last industrial revolution came from Europe, but we exploited it We took advantage of it socially better than anybody else in the world
285
and look how it turned out for us I want to make sure that this next generation doors just like this.
286
Yeah So why are the communists getting their message out so successfully here right now?
287
You know, I think first of all, the narrative is much more practical.
288
The narrative is much more practical.
289
Nobody in China is saying that there's end of this and end of that and cataclysmic this and you know.
290
Doom or that.
291
Doom or that.
292
They're much more pragmatic about it.
293
They see AI as a technology that's going to advance their economy, advance their society.
294
And they don't have these groups who are basically saying it's going to end civilization.
295
And we're making it up.
296
The part that is frustrating is if it's true...
297
If it was true, then we ought to talk about it and go do something about it.
298
Even
299
if it's true, we ought to spend more time doing something about it than worrying a bunch of people who can't do anything about it.
300
It's our job to build it right.
301
Has there ever been a point in history where so many people have so vehemently said something that is so untrue?
302
And they're actually demonstrably untrue.
303
And it actually makes sense is untrue.
304
It's not based on science.
305
It's not based on research.
306
Everything that's based on science and research proves otherwise.
307
The fear of the frontier.
308
Humans have never been there.
309
We've never seen it.
310
Therefore, we're scared of it.
311
And therefore, it's easy to tell everyone to be scared of it.
312
It could be life experience as well, David.
313
So let me give you an example.
314
When I first graduated from school, I was an engineer and I didn't do that much typing.
315
And the reason for that is because I was the first generation before software became popular.
316
We had to go build the computers to make software pop up possible.
317
Could you imagine?
318
In this generation every single engineer who came into the world of engineering you spend all your time typing Literally, that's what you do
319
When you get a job they give you a laptop they give you a chair
320
and you start typing just you type all day long You type from the moment you wake up to the month.
321
Well, there was engineering before typing, right?
322
And so can you imagine
323
that the world has a mountain of engineering work to do where most of it is not typing anymore?
324
Sure.
325
We had busy engineers before typing.
326
I think we're going to do a lot of great engineering after typing.
327
When I say typing, I mean coding.
328
So even at NVIDIA, when software engineers talk to me, I tell them, you're just typing.
329
I've been saying that forever, but obviously for fun.
330
And I tell them, my favorite key is backspace.
331
And the reason for that is because the best software is the smallest software.
332
So I want you to use backspace as often.
333
Let's actually talk about NVIDIA.
334
Let's do a little teardown of NVIDIA.
335
So, teardown meaning just explain the pieces, because there's a lot of strategy at play.
336
Let's start at the absolute bottom.
337
Oh, no.
338
this is not planned
339
but we know who it is oh no no mr. president
340
oh yes sir I got to tell you something it I I
341
if it wasn't because of you calling I would I'm on stage with the besties I'm on stage with the besties.
342
I'm on stage with the besties.
343
I'm on stage with Sax.
344
Yeah.
345
You know, the whole group.
346
Yeah, Jason's here.
347
Chamat's here.
348
Dave and David's here.
349
Yeah, I'm sitting in front of a few thousand people.
350
And we're talking, as it turns out, we were talking about you.
351
Good job, sir.
352
Good job.
353
The fact that you saw through all of that, I mean, there's a lot of complexity and the fact
354
that a matter you saw through all of that and and I we're all just really grateful
355
tell him I said hi
356
do you want to say hi to the crowd Jason would
357
like Jason would like to put you on the even Jason
358
speaker mode how do we put how do we put on POTUS
359
and spit put them on speaker speaker yeah right into the microphone hold on sir We're getting a microphone.
360
Mr. President, you're now talking to the planet.
361
You see, the great thing about life is
362
that Jensen can develop the most complex computer chip in the world that nobody can copy for 10 years, but he can't figure out how to put me on speaker.
363
We have to remember this one.
364
So, the AI, it's almost this conspiracy, and the happiest group is China, and China is very happy.
365
And I could even say in the country, a lot of states are happy that weren't going to get anything
366
because they're being inundated by people that want to be there.
367
But now all of a sudden, you see they're building in Finland.
368
They want to build one.
369
Google wants to build a big one in Finland, which I'm not happy about because they weren't able to get permitting.
370
And I'm telling you, it's all a hoax.
371
The data centers are great and they make people wealthy
372
and they make states wealthy and it's the oil of the next 20, 25 years.
373
It's bigger than the Internet and the AI, you know, much more so.
374
And they're just playing right into the hands of a lot of people that don't want to see it happen, and that could be political people, it could also be China.
375
And we're not going to let that happen.
376
It's a hoax.
377
You're right, we're not going to let that happen, sir.
378
No, we're not going to let it happen.
379
The robots are not going to be taking over the world.
380
and that's not going to happen.
381
You know, my uncle was...
382
Hey, the top, probably maybe the best of all time, frankly, but professors at MIT for 41,
383
42 years and known as being one of the most brilliant men.
384
And he was he was there for 41 years as the top.
385
He was like at the top, top of the ladder, top of did many things.
386
Jensen knows all about it, but did many things.
387
So I have a little genetic, a little genetic strength, if you believe in the race theory
388
but i do i have genetics that explains why you know
389
so much about ai yeah well i know about ai i
390
know i also have common sense about ai uh the robots
391
will not be taken over uh the ai will not be
392
taken over the rest of the world the whole thing is a hoax now with
393
that we have to be a little bit careful we have
394
to very be you know we have to do things
395
and we have to do them prudently but
396
that doesn't mean we're going to stop an industry
397
because you know as we work on the next 10 years about how to destroy it
398
so i'm with you all the way i didn't even know
399
how you felt about it i assumed you felt the same way as me yes
400
and we if we're going to lead
401
and i have an expression it's whoever wins ai wins that's how big it is it's bigger than the internet
402
and whoever wins ai wins and we can't let this kind of stuff happen
403
and that includes very much includes data centers there are communities
404
that were dying that have data centers right now and now they're wealthy communities, really wealthy communities.
405
We're going to make sure that everybody wins in the AI race in America.
406
Every industry, every company, every state, every people.
407
Good.
408
Well, I feel strongly about it, and I have the position that can do something about it.
409
We're not going to let that stuff happen.
410
So I have no idea who's at the meeting.
411
I have no idea who the hell I'm talking to, but I'll see you. Did you hear that?
412
Did you hear that? thousands of people are clapping for you sir
413
but uh he's done an amazing job and david has done an amazing job
414
and good luck to everybody
415
and uh we're going to stay with the future the country
416
has never done better we have 20 trillion dollars of investment coming into the country
417
and that's as opposed to much less than one trillion under sleepy joe biden and that was for four years.
418
This is in one year.
419
So, you know, it's really the country has never seen anything like it, and we're going to keep it going.
420
And so thank you all very much.
421
Thank you, Mr. President.
422
I'll call you back later.
423
Thank you, Mr. President.
424
Thank you.
425
Well, that was unique.
426
I thought it was a bit.
427
Did you know that was happening?
428
I thought it was a bit.
429
You thought it was a high?
430
No, it was real.
431
I thought it was a bit at first, and I was like, put him on speakerphone.
432
Wow. And he calls you.
433
How do you think?
434
He calls you any hour at the night, right?
435
Well, we were in the Oval that time when he called you.
436
I was sleeping.
437
I was in Europe.
438
You were asleep, and he said, wake him up.
439
I felt so bad because he's like, who's coming to this dinner?
440
And we go through the list, and he's like, well, what about Jensen?
441
I said, no, sir, he's on vacation because he had to postpone this vacation for five years.
442
And he's like, get him on the phone.
443
What's vacation?
444
But why do you think he sees through the hoax?
445
It's this is the thing that is really quite an extraordinary thing.
446
It was it's polling minus 80.
447
Yeah.
448
So for anyone else that's sitting in the Oval Office, you're going to do what's popular.
449
You're representing the people.
450
This is what everyone wants.
451
They want to shut down the data centers, end AI.
452
It seems to be the popular thing in the moment.
453
But he says it's a hoax and he calls it.
454
How does he do that?
455
I got to tell you, I'm not sure.
456
And the reason for that is because a lot of people are falling for it.
457
And so the fact of the matter is it's complicated.
458
You know at first, I mean if you look at the story, if you look at the stories, it's all anchored on two things.
459
The first thing that it was anchored on was national security.
460
And recently that was all blown to bits.
461
And so that story is no longer anchored on national security.
462
Now it's anchored on safety.
463
Now if you want AI to be safe, the first thing is we need to make sure that the labs that are building it are in control.
464
That they're good tests for them.
465
if we would like to have third parties to to make sure
466
that a third party evaluate
467
if their party evaluators are available that's no different than financial control you guys know we have auditors
468
and the auditors are quite quite they don't have to be as expert as we are in our business
469
but they just have to ask the right questions
470
and I think I heard somebody say that it's good to have independent auditors
471
or evaluators but they just have to have multiple.
472
I agree with that too, just as there's multiple evaluated and auditors.
473
It makes sure that one company doesn't become you know pilled or somehow influenced for whatever reason.
474
And so you know there's a lot of different ways
475
that you could solve this and so that I think the number one thing is let's build the technology safely.
476
Let's make sure that the testing of it is safe.
477
And I recognize completely that what is being built is extraordinary.
478
But these are extraordinary companies, and we ought to hold them to extraordinary standards.
479
And they want to be.
480
I wanted to go back to open source for a second.
481
A year ago, we weren't taking it very seriously.
482
It was two years, 18 months behind.
483
The one thing that, you know, one of the, as you guys know, one of the challenges when you're on the call with President Trump is hard to say something.
484
I'm going to get in trouble for that.
485
I'm sure he's going to call me up on that. But anyhow, what I was going to tell him and all of you is that AI is creating an enormous number of jobs.
486
The thing that he wanted more than anything at the beginning of the administration and my first phone call with him, my first time I met him, is that he wants to create jobs in America,
487
he wants to re -industrialize the United States, he wants to make sure that the United States has the energy to support the next industrial revolution.
488
Without energy, there's no industrial growth.
489
And so he wants to make sure that there's energy growth, that there's job growth, that we're re -industrializing the supply chain.
490
Look at everything that we're doing right now.
491
All of it is happening right now as we speak.
492
We're creating more jobs than ever.
493
We're creating software jobs.
494
We were just talking about earlier 400 billion dollars of venture
495
financing went into the AI industry just recently yeah six months
496
well that's created a ton of jobs that's created a ton
497
of jobs it's created you know obviously enormous amount of demand for compute
498
which I'm happy about which is also
499
which is also creating a lot of demand for data centers
500
and we ought to talk about that I I think I was just, I was talking to Governor Abbott of Texas,
501
and he was, he wants to appeal to the industry to make sure
502
that we are empathetic to the small communities as we're building data centers all across America, just to be better listeners.
503
Let's actually talk about that for a second.
504
What's incredible about NVIDIA, if you break down the component parts,
505
is you've effectively had to become the bank of AI to get the ecosystem going.
506
And you've had to do it at all the levels.
507
You know, you just did this thing with Cloverleaf where you're doing Land Power Shell.
508
You did this great thing with BlackRock and Goldman and all these folks to essentially create the financing capability.
509
Walk us through your capital allocation strategy.
510
What has to happen to get a broader ecosystem of folks to be able to come in and underwrite this next phase?
511
Well, we're creating, as you guys know, this is a new industrial revolution and every aspect of it is true.
512
This new industry requires manufacturing.
513
Just as the electricity, internet,
514
and now AI, we power anything, we can find anything.
515
Now with AI we can ask and know anything.
516
Isn't that right?
517
And so that's our future.
518
We tap into the ether and we can ask it of anything we want and it could explain it to us.
519
Now, in order for that to happen, it's got to produce the intelligence.
520
And so that's a production process, which is the reason why this infrastructure has to get built.
521
But once you get the infrastructure built, the question is, what about all of the other layers across the United States?
522
This industry isn't just about the model.
523
It's not just about the chips.
524
It's mostly about the applications on top.
525
It's mostly about the infrastructure layer, or the data centers and all the infrastructure, the construction, the electricity, the power generation,
526
all of that is involved.
527
And so I look across the entire ecosystem and look for bottlenecks.
528
And if there are places where extraordinary companies are being built.
529
Constraints.
530
Constraints, extraordinary companies being built.
531
Maybe it's a supply chain that has to get scaled up so that when we're ready to deploy compute,
532
that they'll be ready for us land power shell.
533
And so this is no different than looking at the supply chain upstream.
534
You know, I probably think about the long -term supply chain more than most because our company is really large.
535
And in order for us to succeed, a whole bunch of companies has to support me.
536
You know, it's got a Corning has this, you know, Wendell Corning has to support me, Lumentum and, you know, TSMC, of course, and memory companies.
537
And so we started working with all of these companies long before the revolution
538
that the growth came so
539
that the growth could happen now i'm going now i'm doing
540
a downstream i have one cycle tends to be though
541
that the earnings over time over long stretches of time tends
542
to move up the stack right towards the application layer where
543
you can over earn for larger periods of time um i
544
mean you bought hugging face now you're sort of in the actively in the serving business i mean it seems pretty
545
natural that products like open router make a lot of sense it seems pretty
546
obvious that you know there are better versions of ways to build things like bedrock I'm sure you think about it
547
What's the natural conclusion?
548
Because it seems like the folks up here have no issue trying to move down.
549
And you have the best balance sheet, these incredible engineers, and you have the proven experience to make it right and engineer the product and get it out.
550
So how do you think about looking up and saying, I could probably do that.
551
The reason why Nvidia runs every single model in the world, we were the only, it's incredible.
552
Last year, about a year and a half ago, the only thing we ran was OpenAI.
553
Yeah, and now look at amazing models are available the metamuse is available You got grok is available grok bots incredible
554
We now run Gemini and
555
Anthropica is scaling up on our platform as well
556
Since a year and a half ago you got all these frontier AI models
557
that are now open that are available So the number of models that are growing
558
There's a whole bunch of companies that I won't mention
559
that are building frontier models as well and the number of AI labs are growing.
560
The ineffables, the reflections, the list goes on.
561
The physical intelligence, the list goes on.
562
And so all of these labs are building on NVIDIA.
563
And the reason for that is because as a company, I'd rather for us to help everybody succeed instead of taking a slice out.
564
And so we would go up as far as we need to, but as low as possible.
565
Our strategy is go up as far as we need to and as low as possible.
566
And the reason for that is because if I do that, if I solve the, if not for NVIDIA creating QDNN, all of the frameworks wouldn't exist.
567
If not for us creating Megatron core, then all of the large -scale training wouldn't have happened.
568
So we go and we invent all the technology necessary as far as we need to, and then we let a thousand flowers bloom.
569
And so that posture allows us to be, quite frankly, the only...
570
Well, look, let's be honest.
571
I agree with you.
572
The pushback would be that it really would be great to have more competition at the hyperscale layer.
573
And I think you've done a great job supporting the neoclouds.
574
There are some, and by the way, I think you introduced me to Nambias.
575
Superb.
576
Great.
577
Everything, they're amazing.
578
But we need like 50 of these guys.
579
We need 100 of them.
580
We need 1 ,000 of them.
581
And it just may take some...
582
Yeah.
583
You know, Shemar, it's just I'm surprisingly uncompetitive.
584
Really?
585
Yeah.
586
That's not my thing.
587
You know, my thing is kind of like, for example, I'd be more than happy with five hyperscalers.
588
However, the reason I noticed the early customers of all the neoclouds, all the what we call NCPs, all the early customers were the hyperscalers.
589
Exactly.
590
And the reason for that is because the hyperscalers plan once a year, but the market dynamics is so volatile right now that they're always almost wrong.
591
And so with all these regional clouds who are agile, they can move fast.
592
They know their state or they know their country.
593
They know their region.
594
They're securing land, power, and shell in a way that's hard for somebody who sits in Seattle
595
or sits in Palo Alto to be able to see the planet.
596
And so we now have basically a large -scale distributed network of companies that are building, securing land power shelf for us.
597
And now countries realize it's strategic.
598
So many countries are saying, I'm going to take my power and only give it to my own companies.
599
Well, NVIDIA is in that country as well, and we could help the NeoClouds in that country grow.
600
And so whether it's Firmus in Australia, we just did a whole bunch of stuff in Australia, brought on two more gigabytes.
601
Southeast Asia, of course, IOH and others bring on a few gigabytes.
602
And so we're building, gigawatts.
603
So we're building, you know, we're scaling up, you know.
604
It's pretty clear though.
605
I just want to get this one thing in.
606
It's pretty clear that you're going pretty high up and getting very focused on open source.
607
Obviously, you have your Nemotrons doing exceptionally well.
608
I use them often.
609
Hugging Face, Poolside and Laguna.
610
Very, very solid product that you're now acqui -hiring, hiring, whatever it is.
611
And then you have your open source stack for self -driving, also very disruptive.
612
We are the frontier model in five domains.
613
Yeah.
614
And so you don't seem to build products to get the silver medal.
615
You seem to go for the gold.
616
So are you going for the gold, and will you have the best hands -down open source model?
617
And then part B to that is, can open source catch up to Frontier Models, and are you the person to do it?
618
So the logic, Jason, is that we will build it because, one, we can, we have the skills to do it,
619
and because our customers need us to do it.
620
Right.
621
So for example, Alpamayo is the world's first thinking self -driving car.
622
And by thinking, by reasoning, you don't need as much data as, you know, you don't have to train on a few billion hours of road data.
623
Because you can reason about it.
624
Break down the problem into, oh, I've seen this before.
625
It's not exactly the same, but it's largely the same as that.
626
Okay.
627
And so Alpamayo, why is it necessary?
628
Well, there's a whole bunch of car companies.
629
Every car in the world is going to be autonomous.
630
But beyond that, every ag tech, every truck, every van, and most of them aren't big enough in scale to be able to build that whole stack.
631
So I'll build an extraordinary stack for them.
632
They do last mile adapting for their application.
633
Now everything that moves in the future could be autonomous.
634
If not for us building some of the biology models, the world wouldn't have it the ESM2 protein found language model, we created that.
635
ESM fold, open fold, alpha fold 2, all the stuff with QEquivariant, all of that stuff, technology, wouldn't have existed if we didn't build it.
636
One of my favorites, Proteina Complexa, is synthesizing next generation proteins and it's binding.
637
It's groundbreaking stuff.
638
We built that.
639
And so we'll build that because Lily needs it
640
and Mark needs it and others need it and they don't have the capability to do it or they're not yet there.
641
And so we can make a real contribution.
642
So I do everything out of need.
643
I'm not trying to disrupt.
644
I mean, we don't wake up in the morning trying to disrupt anybody.
645
We just wake up in the morning, try to help everybody.
646
Jensen, what about competitive threats that might be emerging to your core business?
647
Can you just comment?
648
We're just so nice.
649
Yes.
650
Well, I know this is...
651
Well, I actually want to just get your...
652
Let's just call it a take.
653
What's your take on TerraFab?
654
100 million square foot facility Elon's announced.
655
If anybody could do it, he can.
656
And the two of us were on a flight together to a country.
657
With a person who sometimes calls you on the phone.
658
It was a nice plane.
659
And we had like, you know, Elon likes to talk about these things.
660
And so we spent a lot of time talking about it. Is that even competitive?
661
Anybody could do it, he could do it.
662
Because you design chips, you don't fab them.
663
Could your chips be fab there or is it?
664
Well, we know a lot about process technology because we're pushing the limits of everything.
665
And because we scale at such large scale, we have incredible memory technology inside the company.
666
We're the world's best 30s company.
667
We got lots of amazing tech.
668
So your take is you've talked a lot about it?
669
So we could just, yeah, we could talk about it.
670
And you can't discourage Elon from doing it, which is one of his super power.
671
And once he decides to go do something, it's hard to stop him.
672
And so I - Can you give us your take on where China is with advanced lithography systems, native grown?
673
They're gonna get there by 2030. By 2030.
674
Yeah, and 2030 is just around the corner.
675
Yeah, also that's - Sorry, how long will all be dead at that time, so.
676
And for China, does that mean the switch is flipped and then that's all gonna go into mainland fabs almost immediately?
677
You know, the way to think about China is really good at high volume production.
678
And this is just a matter of time.
679
And, you know, I think in decades as well.
680
You know, I've been around a long time.
681
And, you know, for NVIDIA, I've got to think about what happens next decade and decade after that.
682
So two or three years, it's just a click.
683
It's nothing.
684
And so as far as they're concerned, they're already there.
685
They're already there.
686
Yeah.
687
Jensen, Elon, and Gwen.
688
We've got to run.
689
America, we've got to run.
690
Speed run.
691
We've got to.
692
Speed run.
693
Slowing down is definitely the wrong strategy.
694
I mean, it feels apparent, I think, to most of us in the industry that we're kind of in the AGI moment, and it's a definition, obviously, just as smart as any other human.
695
I think we're already there.
696
We're there, right?
697
Yeah, yeah.
698
And so then super intelligence is the next waypoint.
699
Based on what you see, based on your customer base, based on your history here.
700
And Jason, I think we're there too.
701
You think we're at super intelligent?
702
Yeah, yeah.
703
When you take a narrow segment, a narrow segment, I mean, my self -driving car, I don't want you to make me an omelet.
704
I just want you to drive the car.
705
Right.
706
That is super intelligent.
707
Super intelligent.
708
And it's better than a human.
709
Yeah, yeah.
710
Like one -tenth of the accident rate.
711
Exactly.
712
Synthesizing proteins, doing virtual screening of proteins.
713
We're already there.
714
Yeah.
715
Yeah, yeah.
716
Are you having fun being on the frontier of humanity?
717
I like it.
718
Yeah.
719
Ladies and gentlemen.
720
I like it.
721
I like it.
722
And guys, it's great there.
723
The future is great, and we want to get there.
724
Listen, a lot of us don't have to work, but I got to tell you, it's too good not to be. So fun.
725
Right?
726
And so I want to be there.
727
I want all of you guys there with me.
728
We're all going to be there.
729
We're going to be enormously successful together as a humanity.
730
And in the meantime, we got to encourage them, urge them on.
731
They're doing really, really important work, as you guys know, and I want them to succeed.
732
I also would love for us to tone down the the the drama and
733
Make and most importantly we need all of America to come with us.
734
That's how we make it Yeah, ladies and gentlemen, Jensen long
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背景與情境
這段影片記錄了一場關於AI發展的討論，其間還意外插入了川普的來電。對話中涉及科技趨勢、安全議題等，語速中等且夾雜口語化表達，非常適合練習「shadow speak」。透過這類真實場景，學習者能熟悉英語在非正式討論中的運用，同時瞭解時事話題的表達方式。
日常溝通必學5句
- Safety is paramount ：安全至關重要。可用於強調某件事的重要性，如「在團隊合作中，信任是paramount」。
- take it very seriously ：認真對待。影片中多次出現，例如「面對檢舉，我們必須take it very seriously」。
- false choices ：虛假選擇。指看似矛盾卻可共存的選項，如「創新與安全並非false choices」。
- take issue with ：對……有異議。可用於表達不同觀點，如「我對這個結論take issue with，因為資料不足」。
- out of control ：失控。描述事物超出預期，如「這項計畫如果不調整，可能會out of control」。
影子跟讀（shadow speak）分步指南
步驟1：熟悉內容
先靜聽影片1-2遍，瞭解對話主題與邏輯。注意「Oh, no. This is not planned」這類口語化開頭，感受自然的語氣轉折。
步驟2：逐句跟讀
選擇3-5句話，按下暫停鍵，模仿發音、語調與節奏。例如「Safety and leadership are not false choices」，注意「false」的重音與「choices」的尾音。透過「shadowspeak」練習，提升口語流暢度。
步驟3：重現情境
關閉聲音，看著畫面嘗試復述對話。重點關注「I take issue with that」這類表達異議的句子，學習如何自然切入話題。此步驟可有效鍛煉英語思維與即興表達能力。
步驟4：反思調整
錄下自己的跟讀內容，對比原版。檢查是否有發音不準（如「paramount」的「a」音）或語速不匹配的問題，針對性改進，逐步提高英語發音與口語水平。
透過這段影片的「shadow speak」練習，不僅能學會實用短語，還能掌握英語在真實對話中的運用技巧。持之以恆，口語能力必將顯著提升。
影片中的語法
說話人最常用的結構，並附上影片中的原話：
| 結構 | 影片中的用法 |
|---|---|
| 現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事 | has proven · has taken · has turned |
| 被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的 | is not planned · was expressed · be done |
| 定語從句 who / which + 從句 — 補充說明人或事物 | difficult which is · reading, which is · companies, which is |
什麼是跟讀法？
跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。