Luyện nói tiếng Anh bằng Shadowing qua video: Andrew Ng on Coding Agents and Deciding What to Build
Chọn chế độ luyện tập
Mỗi chế độ mở một phòng luyện tập toàn màn hình.
345 câu
Nếu các câu quá ngắn hoặc quá dài, hãy bấm Edit để chỉnh sửa.
1
You know, I'm actually very excited about the future of databases.
Bạn biết không, tôi thực sự rất hào hứng về tương lai của các cơ sở dữ liệu.
2
I know I'm going to ask you another question, I'll just share one thing.
3
I feel like right now,
4
the world is going through a massive data center build-out with a lot of AI training and inference data centers being built.
5
I suspect something that a lot of the world does not yet appreciate is that in the future, a lot of core infrastructure,
Tôi nghi ngờ một điều mà phần lớn thế giới chưa nhận thức được là trong tương lai, rất nhiều cơ sở hạ tầng cốt lõi,
6
be it computed storage, will be used not just by humans, but by agents.
dù là lưu trữ tính toán, nó sẽ không chỉ được con người sử dụng mà còn được các tác nhân sử dụng.
7
and that agentic usage of all this infrastructure will drive massive demand.
và việc sử dụng có tính chủ động của toàn bộ cơ sở hạ tầng này sẽ tạo ra nhu cầu khổng lồ.
8
And so in addition to the AI data center buildup for training and inference,
Và do đó, ngoài việc xây dựng các trung tâm dữ liệu AI phục vụ cho việc huấn luyện và suy luận,
9
I think a lot of databases that are built for human usage today, and we can only look at or query so much data,
Tôi nghĩ rằng rất nhiều cơ sở dữ liệu ngày nay được xây dựng cho mục đích sử dụng của con người, và chúng ta chỉ có thể xem hoặc truy vấn một lượng dữ liệu nhất định.
10
but with agentic usage, I think the demand for core infrastructure for compute will continue to skyrocket.
nhưng với việc sử dụng các tác tử tự chủ, tôi nghĩ nhu cầu về hạ tầng tính toán cốt lõi sẽ tiếp tục tăng vọt.
11
So you're very bullish on this category at home.
Vậy là bạn rất lạc quan về ngành hàng này ở thị trường trong nước.
12
Yeah, it's an amazing space.
Vâng, đó là một không gian thật tuyệt vời.
13
I mean, we feel very fortunate to be in the thick of it.
Ý tôi là, chúng tôi cảm thấy rất may mắn khi được tham gia trực tiếp vào những công việc cốt lõi.
14
And actually, we had a partner day yesterday where we invited a lot of people who work with builders or developers.
Và thực ra, hôm qua chúng tôi đã tổ chức một ngày đối tác, nơi chúng tôi mời rất nhiều người làm việc với các nhà thầu hoặc nhà phát triển.
15
And basically, everyone's saying, yeah, we're running out of compute.
Và về cơ bản, mọi người đều đang nói rằng, đúng vậy, chúng ta đang dần cạn kiệt tài nguyên tính toán.
16
We need, like, the amount of builders pouring into the world is just going crazy right now.
Chúng ta đang thấy số lượng các nhà phát triển đổ vào thị trường hiện tại đang tăng một cách chóng mặt.
17
Yeah.
Vâng.
18
So, actually, I'm going to start because you have one of the most impressive backgrounds that, and I couldn't memorize it all.
Thực ra thì tôi sẽ bắt đầu trước, bởi vì hồ sơ của bạn là một trong những hồ sơ ấn tượng nhất mà tôi từng thấy, và tôi không thể nhớ hết tất cả những thông tin trong đó.
19
So, I'm just going to, I know you're very humble.
Vậy nên, tôi chỉ muốn nói rằng, tôi biết bạn rất khiêm tốn.
20
I'm going to read out for anyone who doesn't know, you're the founder of Deep Learning AI.
Tôi sẽ đọc lên để những ai chưa biết thì rõ, bạn là người sáng lập Deep Learning AI.
21
You're a general partner of an AI fund, managing partner of AI Aspire, chairman and co-founder of Coursera, professor at Stanford University.
Bạn là đối tác chung của một quỹ đầu tư AI, đối tác quản lý của AI Aspire, chủ tịch và đồng sáng lập của Coursera, đồng thời là giáo sư tại Đại học Stanford.
22
Previously, you were the founding lead of Google Brain, VP and chief scientist at Baidu,
Trước đây, ông là người lãnh đạo sáng lập của Google Brain, phó chủ tịch và nhà khoa học trưởng tại Baidu.
23
director, formerly director of the Stanford AI Lab.
giám đốc, từng là giám đốc Phòng thí nghiệm Trí tuệ Nhân tạo của Stanford.
24
and you are part of the Time 100 AI list for the most influential AI persons in the world.
và bạn là một phần của danh sách Time 100 AI, danh sách những nhân vật có ảnh hưởng lớn nhất trong lĩnh vực AI trên thế giới.
25
So first of all, that's crazy.
Trước hết, điều đó thật điên rồ.
26
Thank you.
Cảm ơn bạn.
27
Thank you.
28
What a life.
Thật là một cuộc đời.
29
Actually, how do you have the energy to kind of keep reinventing yourself and keep doing or do all these things?
Thực ra, làm sao mà bạn có đủ năng lượng để liên tục tái tạo bản thân và tiếp tục thực hiện tất cả những việc này vậy?
30
I feel like the answer I have is kind of the obvious one is I've been really
Tôi cảm thấy câu trả lời mà tôi có là một câu trả lời khá hiển nhiên, đó là tôi đã thực sự...
31
so blessed to work with so many great people consistently across so many organizations.
Thật may mắn khi được làm việc liên tục với rất nhiều người tuyệt vời ở rất nhiều tổ chức khác nhau.
32
I feel like for the people I get to work with, I would hardly manage to accomplish anything myself.
Tôi cảm thấy rằng với những người mà tôi phải làm việc cùng, tôi hầu như không thể tự mình hoàn thành được bất cứ việc gì.
33
So I've just been really blessed.
Vậy nên tôi thật sự đã được ban cho rất nhiều phước lành.
34
That's cool.
Hay đấy.
35
That's cool.
36
Okay, so I was going to talk about SkillsMap.
Được rồi, tôi định nói về SkillsMap.
37
I was going to try to explain it, but you are first and foremost a teacher.
Tôi định giải thích điều đó, nhưng trước hết bạn là một giáo viên.
38
So maybe you can tell us a little bit about SkillsMap.
Vậy thì có lẽ bạn có thể giới thiệu một chút về SkillsMap cho chúng tôi được không.
39
what you're doing at the moment, and then we can dive deep.
40
Yeah.
41
I think the most exciting profession to be that's very rapidly at this moment in time is AI engineering.
42
And by that, I don't mean people that'll take on an AI engineering job title, but rather people in all sorts of job roles,
43
including software engineering, front-end, back-end, DevOps, mobile, that will benefit from having AI engineering skills.
44
So my team at D1Ai did a lot of work over
45
several months to try to map out what are the skills that are needed to do AI engineering well.
46
And so we wound up categorizing.
47
So we looked at over 10,000 job descriptions, job postings, did dozens of structured interviews, ran a bunch of surveys,
48
and synthesized views, talking to partners as well.
49
And we wound up figuring out that there are four key skills needed to do AI engineering well.
50
One is the ability to build and deploy AI applications.
51
And the difference between AI and non-AI applications is they're uncertain.
52
And so how do you manage that?
53
The second is software engineering fundamentals.
54
It turns out that even if AI writing a lot of code for us, people that understand software engineering fundamentals,
55
understand what's the rewrite ratio, what's the transaction.
56
You know, people can steer coding agents much better that way.
57
Third is the ability to use coding agents.
58
And then lastly is the ability to, is a skill of shaping the build
59
because developers are no longer playing a role where someone tells them what to do and they just implement it,
60
but instead are participating in deciding what to build and also influencing the process by which something gets built built.
61
So what I'm seeing in the job market today is developers,
62
either people with an AI engineering title or people with other titles that gain some AI engineering skills,
63
are in great demand and we just can't find enough people with these skills.
64
So DeepLand.ai is excited to work upon this to to help more people become skilled at AI engineering.
65
Cool.
66
And the skills map itself, actually, first of all, starting with skills, why do you focus on skills in particular?
67
I think you have a view on skills that they're one of the more important parts.
68
Do you think that they're going to be critical as we go along,
69
or can the agents just become so good that we don't need to focus so much on providing our own custom skills?
70
How do you see this part of Wolverine?
71
Yeah, so I think about there's some things we do today that I hope an agent will take over for me.
72
For example, you may remember three-ish years ago, there was a lot of hype about the prompt engineering job
73
and how much money someone could learn by being a prompt engineer.
74
And I think at that time, it was already clear to...
75
Actually, you're Chris Manning credit.
76
He was saying this very clearly even back then,
77
which was this idea that we need humans of arcane incantations to prompt an LM to do something.
78
That's going to go away because an LM was going to learn how to make those same arcane incantations.
79
And so even today, I see some things that, like, you know, how do you manage a swarm of agents?
80
Some things humans would be needed to do.
81
Some things is more of a one-size-fits-all
82
that I think AI agents will manage other AI agents better and better in the coming years.
83
And one of the key things that distinguishes whether AI will take this over or not,
84
is whether or not humans have a fundamental context advantage.
85
It turns out when all of us are building applications, we know a ton of stuff that AI agents do not know.
86
You may remember a conversation with a customer, you know, where you said something and there was a funny expression on their face.
87
Or you may remember something that you learned, you know, five years ago, reading some market report saying people want this and not that.
88
So I find that humans have a bewildering, vast range of contacts and remarkably good at pulling out this context and make key decisions.
89
And so for that set of decisions where humans have a fundamental context advantage, until someday where, I don't know,
90
like slap a camera on us and monitor the facial expressions of everyone we talk to and have a good view.
91
That's a very difficult part.
92
So I think there's a lot to the task with humans with a fundamental context advantage
93
that we want humans to do for a long time.
94
And then giving people the skills to do that valuable work to complement the wonderful work the coding agent is doing,
95
that seems to be the path that will be on for a long time to come.
96
And this is why, I think you've probably seen, with coding agents being the wonderful things they are,
97
the number of job postings for software engineers is up, because as software engineers and AI engineers become more productive,
98
we want a lot more of them.
99
And businesses I know, even my teams, we just can't seem to find enough skilled AI engineers.
100
And I think this will continue.
101
Yeah, so let's dig into that.
102
Let's imagine two developers who are too strong developers, but one gets better results than the other.
103
What would the stronger developer be doing that the weak one isn't doing today?
104
You know, this is a great question.
105
And when we're mapping out the AI engineering skills map, that was one of the questions we repeatedly asked ourselves,
106
which is we actually interviewed a lot of people.
107
And one of the standard questions you asked them was think of a great developer, think of a less good developer.
108
What is it that a really great developer does compared to the less good ones?
109
And a lot of the answers were what we wound up distilling into the AI engineering skills map.
110
And it's actually more than one thing.
111
It's actually a lot of very different things.
112
But just to give another example, the first of four key skills we highlighted was the ability to build and deploy AI applications.
113
And it turns out that the key differentiator for AI applications compared to traditional software applications is they are uncertain.
114
Whether you prompt an LOM or trade a supervised learning algorithm, you don't know in advance what outcome you get.
115
And so, school AI developers have come up with
116
or are familiar with a set of techniques to monitor the performance of these systems, often in a Cisco way,
117
and to drive a distant error analysis and evals loop to make progress in a systematic way.
118
And so it was by asking questions like that
119
and reflecting on our own experiences that we then tried to systematically map out the not one or two,
120
but frankly, the very large number of gaps between skilled developers and novice level developers.
121
And we tried to distill
122
that into what we call the AI engineering skills map to
123
then try to work systematically to help people go from your less skilled expert level and all of these really valuable skills.
124
Is the skills map itself for anyone to use, mostly for software engineers, people with background?
125
How should people think about the skills map itself?
126
Yeah, so we published, deep into that, we published a weekly newsletter called The Batch.
127
So for five weeks in a row, I wrote about nothing but the AI engineering skills map.
128
And actually no other companies that are now looking to structure curricula around our AI engineering skills, which is actually pretty cool.
129
But I feel like for individuals, there's a way to understand what being excellent, which many of you already are,
130
but where you're already in other places where you want to become more excellent.
131
And then one thing deep learning.ai is working hard on is
132
to then create those paths for people to become skilled at AI engineering.
133
But I think the educational space, both DeepLander AI
134
and other companies still have work to do to help people
135
develop clearer paths to become skilled broadly at all of these AI engineering schools.
136
I'm curious, what are you seeing in terms of people who are pouring in?
137
Is it experienced developers?
138
Basically, there probably is some sort of curve where there's these experienced developers who don't want to let go.
139
I mean, myself, I loved the getting in the zone of coding, and it took me a long time before I was like,
140
okay, now I just let the agent do it.
141
Are you seeing this shift slowly happen where people are like, okay, I'm giving up and I'm shifting all of it to skills first?
142
Yeah, it's interesting, actually.
143
You know, I don't know, it's funny.
144
I actually don't miss coding by hand.
145
Yeah.
146
Yeah.
147
But actually, do you, Do you miss the, I don't know, do you get like in the zone,
148
you know, when you just kind of lock in on this problem, you had to go to Stack Overflow and copy and paste 50,000 things.
149
Maybe you didn't.
150
I did.
151
And then, you know, you spend three hours on the problem and finally it clicks.
152
I don't kind of get that vibe, I guess, anymore.
153
But yeah, I also.
154
Yeah.
155
Yeah.
156
Yeah.
157
I think, I bet you, I bet a lot of you have this problem too.
158
Remember before AI, we used to have a list of priorities we wanted to work on, but couldn't finish.
159
Now, my list, and I'm going to guess yours too, has gotten way longer.
160
Now that AI has supposedly made us more productive.
161
So I find that for myself, there's so many projects that we work on and don't have time to work on.
162
And even as our capabilities grew, certainly my personal ambitions grew maybe even faster than our capabilities.
163
And so, yeah, it's a different type of vibe, right?
164
I think starting a coding agent before you go to bed to see what it comes up with the next morning
165
is a different way of getting into the zone, I guess, than debugging some weird JavaScript syntax.
166
But I have to admit, I seem to like the new vibe more than the old one.
167
Yeah.
168
Yeah, we're definitely getting more dumb, that's for sure.
169
Actually, I'm curious how you design a curriculum in such a rapidly changing market.
170
Do you foresee that you're going to have to change this curriculum every single year, every month?
171
How are you iterating on it?
172
You know, it's interesting.
173
I feel like the hype is that AI advances so fast.
174
Everything changes every three months.
175
The AI engineering skills map, the one piece that does change the fastest is coding agents.
176
And the way we code now is very different than three months ago, very different than six months ago.
177
So the use of coding agents is changing very rapidly.
178
But when you look at the other parts of the schools that I find really valuable, the way we build and deploy AI applications,
179
there's a lot of fundamental knowledge about how to steer these systems, how to deploy them, how to monitor them, how to update them.
180
That is actually changing much slower than the PR hype says.
181
One of the funny thing, at least before Astra, I think Astra may have changed, I'm not sure.
182
It turns out most AI systems, they're not very good data scientists, they're not very good AI engineers.
183
You kind of ask it, what should I do in a project?
184
And for the most part, and frankly, data science, right?
185
I now don't do my data science work by hand, you know, no more coding up handers, data frames, whatever.
186
I'm still using it on the backend, but I now use AI agents to do a lot of my data science work for me.
187
And then frankly, it just makes really weird decisions.
188
It comes to really weird conclusions.
189
I hope that frontier models will get better, but somehow there's a lot of understanding the fundamentals to make trade-offs.
190
And I see that a lot of engineering is making trade-offs, right?
191
You know, cost latency and speed, reliability.
192
And the challenge is until you understand the fundamentals, you often don't even know what trade-offs exist to be made,
193
which is why when someone vipe codes without understanding, very often the coding agent will make a bunch of trade-offs
194
that you didn't even know it made a trade-off and just bites you later.
195
So I find that people that are good engineers understand the fundamentals in many, many parts of the skills needed just make much better decisions.
196
But I'm also encouraged at what's now possible to do when you vipe code without understanding.
197
But I think really good engineers just do such vastly better work than, you know, any serious engineers.
198
Yeah.
199
Yeah.
200
And actually on that one, like, as part of the skills map, you talk about shaping the build.
201
I found this kind of an interesting concept.
202
Maybe you can explain what you mean by shaping the build?
203
Yeah.
204
So as building has become easier, the bottleneck is less building, but deciding what to build.
205
And so previously, there used to be a lot of, let's say front-end engineers.
206
But now front-end engineers with AI systems can also do a lot more backend than was possible.
207
I'm personally much stronger on backend and front-end,
208
but now, frankly, I'm maybe like a bad front-end developer with AI systems.
209
But so a lot of developers have gone from a more narrow role to being a full stack developer.
210
But then even beyond that, it turns out that if a developer builds something and needs to wait for a product manager to get customer feedback,
211
and steer what's built next, that's much slower than the developer themselves has a little bit of product instinct, a little bit of product gut,
212
or can talk to a couple customers or users and get feedback and keep driving the process forward.
213
So in development, what I'm seeing is a lot of people
214
are expanding their roles from just implementation to deciding what to build.
215
And it does not mean engineers should become product managers, but it does mean that engineers that can make some product decisions
216
or more architecture decisions are much more productive than engineers that play the more narrow-sider role from a few years ago.
217
And so we call this shaping the bill where engineers that can influence product decisions,
218
have some product guts, also hope to think through the bill process.
219
Like, is this a good time to build an MVP?
220
Or is it a good time to invest in building really robust architecture
221
and thinking about scale or is this a good time to go to customers to get feedback
222
or is this a good time to do some research to better understand what back-end technology stack you use so developers
223
that can steer the building process end up contributing much more to the overall um comfier project how much
224
then should they spend this time kind of designing the system one of my favorite skills i don't know
225
if you know is this like a grill with docs creates these like architectural decisions
226
and it spends an hour grilling you exactly what you want to build.
227
It's like how much time do you think people should be focused on upfront what they want to build,
228
how they want to build it versus like just go build your MVP and then fix it later on iterating?
229
Yeah, I think it's a real question.
230
The challenge to answer that question is that the answer is stage dependent and it depends a lot on what
231
Jeff Bezos calls one-way door versus two-way doors.
232
directionally in the early stages of a project, frankly, whatever idea you have, type it into coding agent, build it, see what you get.
233
Solace can do it in a safe way and don't leak sensitive data, or just don't, so that's their protection for bad outcomes.
234
But I find that a lot of projects, one of the challenges about AI engineering is, the right decision is very stage dependent.
235
I find that for early stage projects, Solace can do it in a responsible, safe way, build a quick and dirty prototype,
236
see what happens, throw it away, build a quick and enterprise, see what happens and drive rapid learnings.
237
Then there's also a time and place, thinking carefully about architecture, figure out what's the rewrite ratio, what are the customer requirements,
238
what are sensitive data, thinking through your security infrastructure, and then doing that.
239
One thing I've seen is, I've known a lot of engineers that are really good at the zero to one startup phase.
240
I've also known a lot of good engineers, good at the mature systematic phase
241
but sometimes depending on the stage of project you're on the skill
242
and the speed and the how to think about velocity versus protecting from downside risk shifts
243
and even the use of coding agents
244
you see a lot of people talk about how oh you should always plan it out write a long spec
245
and there is a place for that and then sometimes there's a place
246
and you know you're going to go to bed in 20 minutes, just bang out some stuff, have a coding agent work for a few hours
247
and see what you get the next morning and and i think
248
that stage dependent answer has been very tricky for people to learn okay i need to switch gears
249
and i need to ask you a question
250
because i was uh chatting to swix yesterday in from latent space i don't know
251
if you know him but he's here covering uh this week
252
and he said something spicy i might misquote him but he said something like, oh, schools are dead, or the way that we're changing,
253
the way that we have to learn has to completely change.
254
I'm very, I'm sure you get this question a lot.
255
I'm curious on your take on like, how will learning change?
256
Put us forward 10 years.
257
How are we learning?
258
Surely does it look the same as what we're doing now in schools?
259
Completely different?
260
So it will change.
261
So we're having two observations.
262
So first, I'm going to say something obvious when we sound controversial, but chatbots are terrible for learning, right?
263
I love LMS, use them a lot, currently pro AI.
264
At the same time, on university campuses and then, you know, K-12, we know that students that use LMS do better on homeworks,
265
but worse than final exam.
266
And educators have known this intuitively, students have known this intuitively,
267
but the rigorous studies are now increasingly coming out that show that cognitive offloading to an LOM, meaning if a student doesn't know anything for you,
268
you get more work done, but you learn less.
269
And I think even though the rigorous studies on this are coming out in K-12
270
and in higher education universities in formal educational contexts, I think people still underestimate the degree to which this is happening among working adults.
271
I find that, you know, I love my coding agents, helps me do a ton of work
272
but the reduced thinking I have to do I think there are certain things
273
that and then to be clear there are ways to use OMS
274
that do improve learning just like I think there are ways to use TikTok that's great for learning
275
but the vast majority of ways
276
that the vast majority people use social media does not result in learning
277
and the vast majority of ways the vast majority people use OMS does not result in learning
278
so now the flip side of this though is I think we can do better.
279
So the teams I work with including Coursera, Udemy, DevEarned AI, LearnVector are working hard to create new learning experiences
280
that are not focused on getting stuff done but rather on developing rather on focusing on accelerating human development.
281
Here in Silicon Valley we're very excited about our front-end AI labs
282
and accelerating AI development and that's great very much in favor of
283
that in LearnVector
284
which was a recent announced company funded by with a Coursera's initial investor
285
I'm actually really excited about not accelerating not just accelerating AI development
286
but accelerating human development and the way
287
that we're operating is building LearnVector into a frontier AI into a frontier human development lab and as one example
288
I feel fortunate that Coursera participated in changing education 15 years or so ago,
289
but today looking back on that experience which still works, it feels very much one-to-many where we create one course
290
and everyone kind of takes the same or very similar course
291
and it works nothing's wrong with that but the technology now exists for us to create much more one-to-one experiences
292
and it will be much better.
293
So LearnVector hasn't yet announced exactly what products we have,
294
but I'm actually very optimistic from the internal quality types we have and we will have more to share in months.
295
That's super exciting.
296
Okay, I want to leave the crowd with something tangible.
297
So let's say you could design a way that everyone here could spend the next four weeks
298
upskilling to become the best AI developers, how should they spend their time for the next four weeks?
299
Where should they go?
300
What should they learn?
301
What tools should they adopt?
302
Anything that comes to mind?
303
In the case of AI engineering, DeepLandEye's focus is to help developers become skilled at AI engineering,
304
or if you're already skilled, to become even more skilled.
305
working hard on even improving what
306
Deep Learning AI offers.
307
But I actually find myself taking a lot of Deep Learning AI courses myself to learn what are the frontier tools,
308
kind of what's the latest in a voice stack or in generative UI or, you know, building local memory for agents.
309
So I find that maybe as an insider, I see a lot of ways I wish deep learning were even better.
310
But I think that my own learning, actually a lot of it has been with Deep Learning AI.
311
I hope you all will find that whole form.
312
Cool.
313
Keep building.
314
I think all of you are builders.
315
I still find myself waking up in the middle of the night too often to check on the coding agents up to.
316
Do you fire off on your phone as you wake up in the middle of the night?
317
Yeah.
318
I don't know.
319
I think coding on the phone has been so wonderful.
320
Do you text or do you use voice to chat to your coding agents?
321
I use text a lot more than voice coding, with the usual Mac Mini.
322
Yeah, OK.
323
Yeah.
324
OK, that's cool.
325
There's a bit of inside baseball.
326
I'm very curious about your setup, actually.
327
OK, first of all, I know that you had a very strict schedule.
328
And so we're incredibly grateful that we could fit you in, and you could fit us into your timing.
329
So thank you so much for coming.
330
Coming to Select is really a pleasure and an honor to have you here.
331
Thank you so much.
332
Just saying, you know, of all the credentials you rattled off of me just now, the thing that's going through my mind,
333
my one credential you didn't mention was I'm also a proud father of two choy-yong.
334
So, thank you.
335
And so the reason I need to dash is because I actually need to go pick them up from school.
336
That's super important.
337
Okay, we'll let you go then.
338
Thanks so much, Harvey.
339
Thank you, Andrew.
340
That's it, that's it.
341
That's it.
342
That's it.
343
It's a bird.
344
Right in the game, then do you want to let him never do not dance?
345
Oh, hit the noise of opera and a bird to repeat.
Tải Ứng Dụng
Mọi thứ bạn cần để nói trôi chảy
Chấm điểm AIChấm từng câu nói
Luyện IPAThành thạo mọi âm
Từ vựngMở rộng vốn từ
Game Từ vựngHọc qua chơi
FOR YOU
Video gợi ý
C14
20 câu
Alexandr Wang on what matters in an age of abundant intelligence
Daily ConversationUniversity StudentsWorking Adults
C11
719 câu
View From The Top with Roelof Botha, Managing Partner of Sequoia Capital
Daily ConversationUniversity StudentsWorking Adults
C11
747 câu
MIT Introduction to Deep Learning | 6.S191
Daily ConversationUniversity Students
C11
671 câu
GS.TS. Po-Shen Loh, ĐH Carnegie Mellon | Rèn tư duy ngược để không bị AI bào mòn | TQKS #129
Daily ConversationUniversity StudentsWorking Adults
C11
215 câu
Creator of C# and TypeScript: "AI will NEVER Replace Coders, Here's Why" | Anders Hejlsberg
Daily ConversationUniversity StudentsWorking Adults
C12
762 câu
Building Anthropic | A conversation with our co-founders
Daily ConversationUniversity StudentsWorking Adults
C11
623 câu
The Only SaaS Moats Left in 2026
Daily ConversationUniversity StudentsWorking Adults
B21
546 câu
Do Elite Degrees Still Matter In The AI Era? | Jamie Beaton from Crimson Education | EP 410
Daily ConversationUniversity StudentsWorking Adults
C17
75 câu
What AI can (and can't) currently do in a real lab | Jennifer Doudna
Daily ConversationUniversity StudentsWorking Adults
C13
188 câu
NVIDIA GTC 2025 - Keynote Highlights by NVIDIA CEO Jensen Huang
Daily ConversationUniversity StudentsWorking Adults
C15
182 câu
How to Become an AI Product Manager in 2026 | Ex-Google, Microsoft
Daily ConversationUniversity StudentsWorking Adults
C12
202 câu
He Realized his Mistake
Daily ConversationUniversity Students
Từ vựng và ghi chú luyện nói cho bài học này
Bài luyện nói này dựa trên video “Andrew Ng on Coding Agents and Deciding What to Build”. Những từ được nhắc lại nhiều nhất trong bài: skill, engineering, agent, developer, coding. Video này có 345 câu và 4552 từ để luyện shadowing. Phần lời nói dài 26:17. Người nói nói với tốc độ tự nhiên, khoảng 173 từ mỗi phút, gần với hội thoại hằng ngày. 83% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.
Từ vựng quan trọng trong video
15 từ đáng học trong video, kèm phiên âm và nghĩa:
| Từ | Phiên âm | Nghĩa |
|---|---|---|
| developer danh từ | /dɪˈvɛləpɚ/ | nhà phát triển |
| accelerate động từ | /ɪkˈsɛl.əˌɹeɪt/ | tăng tốc |
| infrastructure danh từ | /ˈɪnfɹəˌstɹʌkt͡ʃə/ | cơ sở hạ tầng |
| curious tính từ | /ˈkjʊɹ.i.əs/ | tò mò |
| stack danh từ | /stæk/ | chồng |
| architecture danh từ | /ˈɑː.kɪˌtɛk.t͡ʃə/ | kiến trúc |
| monitor danh từ | /ˈmɑ.nɪ.tɚ/ | pháo thuyền, pháo hạm |
| feedback danh từ | /ˈfiːdˌbæk/ | sự phản hồi |
| versus | /ˈvɝsəs/ | đối, đối đầu |
| vibe danh từ | /vaɪb/ | cảm giác |
| builder danh từ | /ˈbɪl.dəɹ/ | chủ thầu |
| founder danh từ | /ˈfaʊ̯n.dəː/ | người sáng lập |
| sensitive tính từ | /ˈsɛn.sɪt.ɪv/ | nhạy cảm |
| participate động từ | /pɑːˈtɪs.ɪ.peɪt/ | tham gia |
| narrow tính từ | /ˈnæɹəʊ/ | hẹp, chật hẹp |
Cụm động từ bạn sẽ nghe
| Từ | Nghĩa |
|---|---|
| give up động từ | đầu hàng |
Những câu đáng luyện lại
Các câu ngắn, trọn ý trong video mà bạn có thể dùng lại khi giao tiếp hằng ngày:
- You know, I don't know, it's funny.
- I actually don't miss coding by hand.
- How are you iterating on it?
- You kind of ask it, what should I do in a project?
- I'm curious on your take on like, how will learning change?
Ngữ pháp trong video
Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:
| Cấu trúc | Trong video |
|---|---|
| “Used to” used to + động từ — thói quen hoặc tình trạng trong quá khứ nay không còn | used to have · used to be |
| Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại | I've been · I've just been · you've probably seen |
| Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm | being built · are built · been really blessed |
| Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật | people who work · anyone who doesn · skills, which is |
Phát âm cần chú ý
Người nói dùng 87 dạng rút gọn, ví dụ I'm, don't, you're. Hãy nói theo dạng ngắn đúng như bạn nghe.
- Âm “th”: thick /θɪk/, algorithm /ˈælɡəɹɪðm̩/
- Âm “sh” và “zh”: ratio /ˈɹeɪ.ʃoʊ/, conclusion /kənˈkluːʒən/, implementation /ˌɪmplɪmənˈteɪʃən/, examination /ɪɡˌzæm.ɪˈneɪ.ʃən/, controversial /ˌkɑn.tɹəˈvɝ.ʃəl/
- Từ dài — đặt trọng âm cho đúng: developer /dɪˈvɛləpɚ/, fundamental /ˌfʌn.dəˈmɛn.təl/, accelerate /ɪkˈsɛl.əˌɹeɪt/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/
Những âm người Việt thường gặp khó:
- Cụm phụ âm cuối — đọc đủ từng âm, đừng nuốt âm cuối: vast /væst/, prompt /pɹɑmpt/, dependent /dɪˈpɛndənt/, reflect /ɹɪˈflɛkt/, expand /ɪkˈspænd/
- Âm /tʃ/, /dʒ/ và /ʒ/ — tiếng Việt không có, đừng đọc thành “ch” hay “gi”: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/, usage /ˈjuːsɪd͡ʒ/, fortunate /ˈfɔɹ.t͡ʃə.nɪt/, encourage /ɪnˈkʌɹ.ɪd͡ʒ/
- Âm /æ/ — mở miệng rộng hơn âm “e”: vast /væst/, frankly /fɹæŋkli/, stack /stæk/, rapidly /ˈɹæpɪdli/, feedback /ˈfiːdˌbæk/
Cách luyện với video này
- Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
- Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
- Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như developer, accelerate, infrastructure.
Phương Pháp Shadowing Là Gì?
Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.