ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: Andrew Ng on Coding Agents and Deciding What to Build
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You know, I'm actually very excited about the future of databases.
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I know I'm going to ask you another question, I'll just share one thing.
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I feel like right now,
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the world is going through a massive data center build-out with a lot of AI training and inference data centers being built.
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I suspect something that a lot of the world does not yet appreciate is that in the future, a lot of core infrastructure,
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be it computed storage, will be used not just by humans, but by agents.
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and that agentic usage of all this infrastructure will drive massive demand.
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And so in addition to the AI data center buildup for training and inference,
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I think a lot of databases that are built for human usage today, and we can only look at or query so much data,
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but with agentic usage, I think the demand for core infrastructure for compute will continue to skyrocket.
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So you're very bullish on this category at home.
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Yeah, it's an amazing space.
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I mean, we feel very fortunate to be in the thick of it.
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And actually, we had a partner day yesterday where we invited a lot of people who work with builders or developers.
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And basically, everyone's saying, yeah, we're running out of compute.
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We need, like, the amount of builders pouring into the world is just going crazy right now.
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Yeah.
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So, actually, I'm going to start because you have one of the most impressive backgrounds that, and I couldn't memorize it all.
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So, I'm just going to, I know you're very humble.
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I'm going to read out for anyone who doesn't know, you're the founder of Deep Learning AI.
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You're a general partner of an AI fund, managing partner of AI Aspire, chairman and co-founder of Coursera, professor at Stanford University.
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Previously, you were the founding lead of Google Brain, VP and chief scientist at Baidu,
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director, formerly director of the Stanford AI Lab.
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and you are part of the Time 100 AI list for the most influential AI persons in the world.
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So first of all, that's crazy.
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Thank you.
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Thank you.
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What a life.
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Actually, how do you have the energy to kind of keep reinventing yourself and keep doing or do all these things?
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I feel like the answer I have is kind of the obvious one is I've been really
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so blessed to work with so many great people consistently across so many organizations.
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I feel like for the people I get to work with, I would hardly manage to accomplish anything myself.
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So I've just been really blessed.
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That's cool.
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That's cool.
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Okay, so I was going to talk about SkillsMap.
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I was going to try to explain it, but you are first and foremost a teacher.
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So maybe you can tell us a little bit about SkillsMap.
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what you're doing at the moment, and then we can dive deep.
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Yeah.
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I think the most exciting profession to be that's very rapidly at this moment in time is AI engineering.
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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,
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including software engineering, front-end, back-end, DevOps, mobile, that will benefit from having AI engineering skills.
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So my team at D1Ai did a lot of work over
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several months to try to map out what are the skills that are needed to do AI engineering well.
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And so we wound up categorizing.
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So we looked at over 10,000 job descriptions, job postings, did dozens of structured interviews, ran a bunch of surveys,
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and synthesized views, talking to partners as well.
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And we wound up figuring out that there are four key skills needed to do AI engineering well.
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One is the ability to build and deploy AI applications.
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And the difference between AI and non-AI applications is they're uncertain.
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And so how do you manage that?
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The second is software engineering fundamentals.
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It turns out that even if AI writing a lot of code for us, people that understand software engineering fundamentals,
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understand what's the rewrite ratio, what's the transaction.
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You know, people can steer coding agents much better that way.
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Third is the ability to use coding agents.
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And then lastly is the ability to, is a skill of shaping the build
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because developers are no longer playing a role where someone tells them what to do and they just implement it,
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but instead are participating in deciding what to build and also influencing the process by which something gets built built.
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So what I'm seeing in the job market today is developers,
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either people with an AI engineering title or people with other titles that gain some AI engineering skills,
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are in great demand and we just can't find enough people with these skills.
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So DeepLand.ai is excited to work upon this to to help more people become skilled at AI engineering.
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Cool.
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And the skills map itself, actually, first of all, starting with skills, why do you focus on skills in particular?
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I think you have a view on skills that they're one of the more important parts.
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Do you think that they're going to be critical as we go along,
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or can the agents just become so good that we don't need to focus so much on providing our own custom skills?
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How do you see this part of Wolverine?
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Yeah, so I think about there's some things we do today that I hope an agent will take over for me.
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For example, you may remember three-ish years ago, there was a lot of hype about the prompt engineering job
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and how much money someone could learn by being a prompt engineer.
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And I think at that time, it was already clear to...
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Actually, you're Chris Manning credit.
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He was saying this very clearly even back then,
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which was this idea that we need humans of arcane incantations to prompt an LM to do something.
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That's going to go away because an LM was going to learn how to make those same arcane incantations.
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And so even today, I see some things that, like, you know, how do you manage a swarm of agents?
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Some things humans would be needed to do.
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Some things is more of a one-size-fits-all
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that I think AI agents will manage other AI agents better and better in the coming years.
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And one of the key things that distinguishes whether AI will take this over or not,
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is whether or not humans have a fundamental context advantage.
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It turns out when all of us are building applications, we know a ton of stuff that AI agents do not know.
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You may remember a conversation with a customer, you know, where you said something and there was a funny expression on their face.
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Or you may remember something that you learned, you know, five years ago, reading some market report saying people want this and not that.
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So I find that humans have a bewildering, vast range of contacts and remarkably good at pulling out this context and make key decisions.
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And so for that set of decisions where humans have a fundamental context advantage, until someday where, I don't know,
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like slap a camera on us and monitor the facial expressions of everyone we talk to and have a good view.
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That's a very difficult part.
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So I think there's a lot to the task with humans with a fundamental context advantage
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that we want humans to do for a long time.
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And then giving people the skills to do that valuable work to complement the wonderful work the coding agent is doing,
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that seems to be the path that will be on for a long time to come.
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And this is why, I think you've probably seen, with coding agents being the wonderful things they are,
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the number of job postings for software engineers is up, because as software engineers and AI engineers become more productive,
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we want a lot more of them.
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And businesses I know, even my teams, we just can't seem to find enough skilled AI engineers.
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And I think this will continue.
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Yeah, so let's dig into that.
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Let's imagine two developers who are too strong developers, but one gets better results than the other.
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What would the stronger developer be doing that the weak one isn't doing today?
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You know, this is a great question.
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And when we're mapping out the AI engineering skills map, that was one of the questions we repeatedly asked ourselves,
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which is we actually interviewed a lot of people.
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And one of the standard questions you asked them was think of a great developer, think of a less good developer.
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What is it that a really great developer does compared to the less good ones?
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And a lot of the answers were what we wound up distilling into the AI engineering skills map.
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And it's actually more than one thing.
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It's actually a lot of very different things.
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But just to give another example, the first of four key skills we highlighted was the ability to build and deploy AI applications.
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And it turns out that the key differentiator for AI applications compared to traditional software applications is they are uncertain.
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Whether you prompt an LOM or trade a supervised learning algorithm, you don't know in advance what outcome you get.
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And so, school AI developers have come up with
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or are familiar with a set of techniques to monitor the performance of these systems, often in a Cisco way,
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and to drive a distant error analysis and evals loop to make progress in a systematic way.
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And so it was by asking questions like that
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and reflecting on our own experiences that we then tried to systematically map out the not one or two,
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but frankly, the very large number of gaps between skilled developers and novice level developers.
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And we tried to distill
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that into what we call the AI engineering skills map to
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then try to work systematically to help people go from your less skilled expert level and all of these really valuable skills.
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Is the skills map itself for anyone to use, mostly for software engineers, people with background?
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How should people think about the skills map itself?
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Yeah, so we published, deep into that, we published a weekly newsletter called The Batch.
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So for five weeks in a row, I wrote about nothing but the AI engineering skills map.
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And actually no other companies that are now looking to structure curricula around our AI engineering skills, which is actually pretty cool.
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But I feel like for individuals, there's a way to understand what being excellent, which many of you already are,
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but where you're already in other places where you want to become more excellent.
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And then one thing deep learning.ai is working hard on is
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to then create those paths for people to become skilled at AI engineering.
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But I think the educational space, both DeepLander AI
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and other companies still have work to do to help people
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develop clearer paths to become skilled broadly at all of these AI engineering schools.
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I'm curious, what are you seeing in terms of people who are pouring in?
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Is it experienced developers?
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Basically, there probably is some sort of curve where there's these experienced developers who don't want to let go.
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I mean, myself, I loved the getting in the zone of coding, and it took me a long time before I was like,
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okay, now I just let the agent do it.
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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?
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Yeah, it's interesting, actually.
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You know, I don't know, it's funny.
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I actually don't miss coding by hand.
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Yeah.
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Yeah.
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But actually, do you, Do you miss the, I don't know, do you get like in the zone,
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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.
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Maybe you didn't.
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I did.
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And then, you know, you spend three hours on the problem and finally it clicks.
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I don't kind of get that vibe, I guess, anymore.
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But yeah, I also.
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Yeah.
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Yeah.
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Yeah.
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I think, I bet you, I bet a lot of you have this problem too.
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Remember before AI, we used to have a list of priorities we wanted to work on, but couldn't finish.
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Now, my list, and I'm going to guess yours too, has gotten way longer.
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Now that AI has supposedly made us more productive.
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So I find that for myself, there's so many projects that we work on and don't have time to work on.
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And even as our capabilities grew, certainly my personal ambitions grew maybe even faster than our capabilities.
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And so, yeah, it's a different type of vibe, right?
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I think starting a coding agent before you go to bed to see what it comes up with the next morning
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is a different way of getting into the zone, I guess, than debugging some weird JavaScript syntax.
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But I have to admit, I seem to like the new vibe more than the old one.
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Yeah.
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Yeah, we're definitely getting more dumb, that's for sure.
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Actually, I'm curious how you design a curriculum in such a rapidly changing market.
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Do you foresee that you're going to have to change this curriculum every single year, every month?
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How are you iterating on it?
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You know, it's interesting.
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I feel like the hype is that AI advances so fast.
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Everything changes every three months.
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The AI engineering skills map, the one piece that does change the fastest is coding agents.
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And the way we code now is very different than three months ago, very different than six months ago.
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So the use of coding agents is changing very rapidly.
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But when you look at the other parts of the schools that I find really valuable, the way we build and deploy AI applications,
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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.
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That is actually changing much slower than the PR hype says.
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One of the funny thing, at least before Astra, I think Astra may have changed, I'm not sure.
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It turns out most AI systems, they're not very good data scientists, they're not very good AI engineers.
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You kind of ask it, what should I do in a project?
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And for the most part, and frankly, data science, right?
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I now don't do my data science work by hand, you know, no more coding up handers, data frames, whatever.
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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.
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And then frankly, it just makes really weird decisions.
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It comes to really weird conclusions.
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I hope that frontier models will get better, but somehow there's a lot of understanding the fundamentals to make trade-offs.
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And I see that a lot of engineering is making trade-offs, right?
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You know, cost latency and speed, reliability.
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And the challenge is until you understand the fundamentals, you often don't even know what trade-offs exist to be made,
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which is why when someone vipe codes without understanding, very often the coding agent will make a bunch of trade-offs
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that you didn't even know it made a trade-off and just bites you later.
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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.
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But I'm also encouraged at what's now possible to do when you vipe code without understanding.
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But I think really good engineers just do such vastly better work than, you know, any serious engineers.
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Yeah.
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Yeah.
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And actually on that one, like, as part of the skills map, you talk about shaping the build.
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I found this kind of an interesting concept.
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Maybe you can explain what you mean by shaping the build?
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Yeah.
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So as building has become easier, the bottleneck is less building, but deciding what to build.
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And so previously, there used to be a lot of, let's say front-end engineers.
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But now front-end engineers with AI systems can also do a lot more backend than was possible.
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I'm personally much stronger on backend and front-end,
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but now, frankly, I'm maybe like a bad front-end developer with AI systems.
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But so a lot of developers have gone from a more narrow role to being a full stack developer.
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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,
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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,
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or can talk to a couple customers or users and get feedback and keep driving the process forward.
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So in development, what I'm seeing is a lot of people
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are expanding their roles from just implementation to deciding what to build.
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And it does not mean engineers should become product managers, but it does mean that engineers that can make some product decisions
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or more architecture decisions are much more productive than engineers that play the more narrow-sider role from a few years ago.
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And so we call this shaping the bill where engineers that can influence product decisions,
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have some product guts, also hope to think through the bill process.
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Like, is this a good time to build an MVP?
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Or is it a good time to invest in building really robust architecture
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and thinking about scale or is this a good time to go to customers to get feedback
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or is this a good time to do some research to better understand what back-end technology stack you use so developers
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that can steer the building process end up contributing much more to the overall um comfier project how much
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then should they spend this time kind of designing the system one of my favorite skills i don't know
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if you know is this like a grill with docs creates these like architectural decisions
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and it spends an hour grilling you exactly what you want to build.
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It's like how much time do you think people should be focused on upfront what they want to build,
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how they want to build it versus like just go build your MVP and then fix it later on iterating?
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Yeah, I think it's a real question.
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The challenge to answer that question is that the answer is stage dependent and it depends a lot on what
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Jeff Bezos calls one-way door versus two-way doors.
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directionally in the early stages of a project, frankly, whatever idea you have, type it into coding agent, build it, see what you get.
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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.
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But I find that a lot of projects, one of the challenges about AI engineering is, the right decision is very stage dependent.
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I find that for early stage projects, Solace can do it in a responsible, safe way, build a quick and dirty prototype,
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see what happens, throw it away, build a quick and enterprise, see what happens and drive rapid learnings.
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Then there's also a time and place, thinking carefully about architecture, figure out what's the rewrite ratio, what are the customer requirements,
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what are sensitive data, thinking through your security infrastructure, and then doing that.
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One thing I've seen is, I've known a lot of engineers that are really good at the zero to one startup phase.
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I've also known a lot of good engineers, good at the mature systematic phase
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but sometimes depending on the stage of project you're on the skill
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and the speed and the how to think about velocity versus protecting from downside risk shifts
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and even the use of coding agents
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you see a lot of people talk about how oh you should always plan it out write a long spec
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and there is a place for that and then sometimes there's a place
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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
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and see what you get the next morning and and i think
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that stage dependent answer has been very tricky for people to learn okay i need to switch gears
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and i need to ask you a question
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because i was uh chatting to swix yesterday in from latent space i don't know
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if you know him but he's here covering uh this week
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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,
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the way that we have to learn has to completely change.
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I'm very, I'm sure you get this question a lot.
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I'm curious on your take on like, how will learning change?
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Put us forward 10 years.
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How are we learning?
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Surely does it look the same as what we're doing now in schools?
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Completely different?
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So it will change.
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So we're having two observations.
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So first, I'm going to say something obvious when we sound controversial, but chatbots are terrible for learning, right?
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I love LMS, use them a lot, currently pro AI.
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At the same time, on university campuses and then, you know, K-12, we know that students that use LMS do better on homeworks,
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but worse than final exam.
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And educators have known this intuitively, students have known this intuitively,
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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,
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you get more work done, but you learn less.
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And I think even though the rigorous studies on this are coming out in K-12
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and in higher education universities in formal educational contexts, I think people still underestimate the degree to which this is happening among working adults.
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I find that, you know, I love my coding agents, helps me do a ton of work
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but the reduced thinking I have to do I think there are certain things
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that and then to be clear there are ways to use OMS
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that do improve learning just like I think there are ways to use TikTok that's great for learning
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but the vast majority of ways
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that the vast majority people use social media does not result in learning
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and the vast majority of ways the vast majority people use OMS does not result in learning
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so now the flip side of this though is I think we can do better.
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So the teams I work with including Coursera, Udemy, DevEarned AI, LearnVector are working hard to create new learning experiences
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that are not focused on getting stuff done but rather on developing rather on focusing on accelerating human development.
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Here in Silicon Valley we're very excited about our front-end AI labs
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and accelerating AI development and that's great very much in favor of
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that in LearnVector
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which was a recent announced company funded by with a Coursera's initial investor
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I'm actually really excited about not accelerating not just accelerating AI development
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but accelerating human development and the way
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that we're operating is building LearnVector into a frontier AI into a frontier human development lab and as one example
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I feel fortunate that Coursera participated in changing education 15 years or so ago,
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but today looking back on that experience which still works, it feels very much one-to-many where we create one course
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and everyone kind of takes the same or very similar course
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and it works nothing's wrong with that but the technology now exists for us to create much more one-to-one experiences
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and it will be much better.
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So LearnVector hasn't yet announced exactly what products we have,
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but I'm actually very optimistic from the internal quality types we have and we will have more to share in months.
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That's super exciting.
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Okay, I want to leave the crowd with something tangible.
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So let's say you could design a way that everyone here could spend the next four weeks
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upskilling to become the best AI developers, how should they spend their time for the next four weeks?
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Where should they go?
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What should they learn?
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What tools should they adopt?
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Anything that comes to mind?
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In the case of AI engineering, DeepLandEye's focus is to help developers become skilled at AI engineering,
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or if you're already skilled, to become even more skilled.
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working hard on even improving what
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Deep Learning AI offers.
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But I actually find myself taking a lot of Deep Learning AI courses myself to learn what are the frontier tools,
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kind of what's the latest in a voice stack or in generative UI or, you know, building local memory for agents.
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So I find that maybe as an insider, I see a lot of ways I wish deep learning were even better.
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But I think that my own learning, actually a lot of it has been with Deep Learning AI.
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I hope you all will find that whole form.
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Cool.
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Keep building.
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I think all of you are builders.
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I still find myself waking up in the middle of the night too often to check on the coding agents up to.
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Do you fire off on your phone as you wake up in the middle of the night?
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Yeah.
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I don't know.
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I think coding on the phone has been so wonderful.
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Do you text or do you use voice to chat to your coding agents?
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I use text a lot more than voice coding, with the usual Mac Mini.
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Yeah, OK.
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Yeah.
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OK, that's cool.
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There's a bit of inside baseball.
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I'm very curious about your setup, actually.
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OK, first of all, I know that you had a very strict schedule.
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And so we're incredibly grateful that we could fit you in, and you could fit us into your timing.
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So thank you so much for coming.
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Coming to Select is really a pleasure and an honor to have you here.
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Thank you so much.
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Just saying, you know, of all the credentials you rattled off of me just now, the thing that's going through my mind,
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my one credential you didn't mention was I'm also a proud father of two choy-yong.
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So, thank you.
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And so the reason I need to dash is because I actually need to go pick them up from school.
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That's super important.
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Okay, we'll let you go then.
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Thanks so much, Harvey.
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Thank you, Andrew.
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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.
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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
บทเรียนฝึกพูดนี้ใช้วิดีโอ “Andrew Ng on Coding Agents and Deciding What to Build” เป็นสื่อ คำที่ถูกพูดซ้ำบ่อยที่สุด: skill, engineering, agent, developer, coding วิดีโอนี้มี 345 ประโยค และ 4552 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 26:17 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 173 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน 83% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก
คำศัพท์สำคัญในวิดีโอนี้
คำที่ควรเรียนรู้ 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:
| คำศัพท์ | คำอ่าน | ความหมาย |
|---|---|---|
| fundamental คำคุณศัพท์ | /ˌfʌn.dəˈmɛn.təl/ | พื้นฐาน, เบื้องต้น |
| vast คำคุณศัพท์ | /væst/ | ว้างเวิ้ง, ไพศาล |
| accelerate คำกริยา | /ɪkˈsɛl.əˌɹeɪt/ | เร่ง |
| infrastructure คำนาม | /ˈɪnfɹəˌstɹʌkt͡ʃə/ | โครงสร้างพื้นฐาน |
| steer คำกริยา | /stɪɹ/ | ถือพวงมาลัย |
| architecture คำนาม | /ˈɑː.kɪˌtɛk.t͡ʃə/ | สถาปัตยกรรม |
| monitor คำนาม | /ˈmɑ.nɪ.tɚ/ | จอภาพ, หน้าจอ |
| builder คำนาม | /ˈbɪl.dəɹ/ | ผู้สร้าง, ผู้ก่อสร้าง |
| founder คำนาม | /ˈfaʊ̯n.dəː/ | ผู้สร้าง, ผู้ก่อตั้ง |
| participate คำกริยา | /pɑːˈtɪs.ɪ.peɪt/ | เข้าร่วม, ร่วมมือ |
| narrow คำคุณศัพท์ | /ˈnæɹəʊ/ | แคบ |
| ratio คำนาม | /ˈɹeɪ.ʃoʊ/ | สัดส่วน, อัตราส่วน |
| database คำนาม | /ˈdeɪtəˌbeɪs/ | ฐานข้อมูล |
| curriculum คำนาม | /kəˈɹɪk.jə.ləm/ | หลักสูตร |
| pour คำกริยา | /poɹ/ | เท |
กริยาวลีที่คุณจะได้ยิน
| คำศัพท์ | ความหมาย |
|---|---|
| give up คำกริยา | ยอมแพ้ |
ประโยคที่ควรฝึกพูดซ้ำ
ประโยคสั้น ๆ ที่สมบูรณ์จากวิดีโอ ซึ่งนำไปใช้ในบทสนทนาประจำวันได้:
- 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?
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| “used to” used to + กริยา — นิสัยหรือสภาพในอดีตที่ตอนนี้ไม่เป็นแล้ว | used to have · used to be |
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | I've been · I've just been · you've probably seen |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | being built · are built · been really blessed |
| Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ | people who work · anyone who doesn · skills, which is |
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 87 ครั้ง เช่น I'm, don't, you're ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “th”: thick /θɪk/, algorithm /ˈælɡəɹɪðm̩/
- เสียง “sh” และ “zh”: ratio /ˈɹeɪ.ʃoʊ/, conclusion /kənˈkluːʒən/, implementation /ˌɪmplɪmənˈteɪʃən/, examination /ɪɡˌzæm.ɪˈneɪ.ʃən/, controversial /ˌkɑn.tɹəˈvɝ.ʃəl/
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: developer /dɪˈvɛləpɚ/, fundamental /ˌfʌn.dəˈmɛn.təl/, accelerate /ɪkˈsɛl.əˌɹeɪt/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, architecture /ˈɑː.kɪˌtɛk.t͡ʃə/
เสียงที่คนไทยมักออกเสียงยาก:
- เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: skill /skɪl/, fundamental /ˌfʌn.dəˈmɛn.təl/, curious /ˈkjʊɹ.i.əs/, versus /ˈvɝsəs/, usage /ˈjuːsɪd͡ʒ/
- /v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: developer /dɪˈvɛləpɚ/, vast /væst/, versus /ˈvɝsəs/, productive /pɹəˈdʌk.tɪv/, vibe /vaɪb/
- /z/ — เสียงก้อง ไม่ใช่ /s/: enterprise /ˈɛntəˌpɹaɪz/, examination /ɪɡˌzæm.ɪˈneɪ.ʃən/, transaction /tɹænˈzækʃən/, observation /ˌɑbzɚˈveɪʃn̩/, supposedly /səˈpoʊ̯.zɪd.li/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง fundamental, vast, accelerate เป็นพิเศษ
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ