ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: Alexandr Wang: “This is a Once-in-a-Civilization Opportunity”
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All right.
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Full Rockstar treatment for Alexander Wang, everyone.
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All right.
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So, why don't we start out backstage.
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We were saying, you know, one of the cool ways to think about this event is,
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like, you know, this room is actually full of people who are just like us, But when we were 18 or 20,
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you know, there's some 16 year olds in this audience, you know.
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Let's jump to your story.
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I mean, you came up always really smart, like math Olympiad.
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Like, jump us to, you know, the Alex of that time.
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Like, what were you feeling?
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What were you thinking?
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And what drove you down this road?
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Yeah, well, I grew up in New Mexico, well, Salamis, New Mexico, which now Oppenheimer famous, but it really was the middle of nowhere.
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And I remember I did all these math competitions, all these computer science competitions,
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but then I knew I wanted to do really big things.
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And it was like not exactly clear how or what the exact path to do that would be.
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and I had a friend who was really into programming and after high school got an internship in the Valley.
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I think his first internship was at Palantir.
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And he was kind of this influence for me and so after I finished high school, I ended up working at Quora here in Silicon Valley.
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And then I worked there for a year.
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I took a gap year to work there and then I went to MIT.
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I was 19 when I worked at Quora, I was 18 when I went to MIT, and I was 19 when I started Scale.
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And I remember this period from like 17 to 19.
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It was, I felt like I was constantly changing, like, you know, exactly what I wanted to do was constantly changing.
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You know, I was learning so much just from the people around me, and it was just like,
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I felt like I was drinking from the fire hose pretty constantly during that time.
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and I would definitely recommend, you know, the two things that were really important.
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One is I think working at a company was really valuable
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because like I think from the outside in you have no idea how companies work.
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You have no idea what it looks like to actually build something.
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You have no idea what it looks like to iterate on something.
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You have no idea what it looks like for groups of people to make decisions
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and so I thought that was really important
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and then going to school at MIT was actually really important
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because it just gave me a lot of opportunity to explore what was interesting.
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And so it was at MIT that I started training my first models and that I played around with TensorFlow,
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which had just come out that year at MIT, and where I ultimately came up with the idea of scale.
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And then after one year of MIT, I applied to YC.
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It felt like a miracle to get in at that time.
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And YC was really critical to my entrepreneurial journey.
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Like, I don't think, like, YC is this amazing blend of, you know, they're very supportive and they obviously want you to succeed,
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but they also give it to you very real and they tell you when you're being a dumbass, which I think is, you know,
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that's what we all need in life.
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So, yeah, that was, I think, the story till then I was 19, started Scale, and the rest is history.
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I guess with, you worked with Jared Friedman at the time.
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Yes.
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And you came in with actually a a very different idea than what ended up becoming scale.
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Yeah, so we wanted to build an AI agent, funnily enough, for Doc, to help people get medical care.
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And it was a great example of an idea that I think will ultimately exist.
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Like, I think we're even seeing it now.
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Like, AI agents to help people get medical care are very real, but it was the wrong timing.
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And we worked on it for about a month or two before Jared pulled us aside and we're like,
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guys, this is, I don't know if this is going to go anywhere.
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And that's exactly what we needed to hear.
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And it was at that time when, like, you know, I had studied AI at MIT, I had, like, trained models,
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and we thought, we sort of went back to the drawing board, thought deeply about where the opportunity was and came up with scale.
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I guess selling data at the time, you know, large language models were not even, had not really come to the fore yet.
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But self -driving cars were sort of coming up and computer vision suddenly became...
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So that was sort of the first market, is that right?
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Yeah, so the story here is that like I was...
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When I was at MIT, I did a bunch of projects to train models of various forms.
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And these were like, you know, by comparison today, they're like little toy models.
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And I remember to train a model, I needed three things.
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I needed a GCP account, like I need an account on some cloud service to get compute.
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I needed the code to run, to actually train the model.
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And I needed data.
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I needed a data set.
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And for two out of these three things, you could just press a button online and get them.
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And then for the last one, for data, there was no effective way to get data for training these models.
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And so it felt incredibly obvious that this was going to be the future, that there was going to be a way to,
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you know, press a button, so to speak, and get data.
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And it was very funny because in the years that followed, like in the first many years of scale, data was very unsexy still.
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Every time we would go out to fundraise, even though our numbers were great and we had great revenue,
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you know, VCs and investors would always be very skeptical.
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They'd be like, oh, I don't know if this is a good business.
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Does this have longevity?
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Is this durable?
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And it was really weird to me, but, you know, none of the investors had ever trained a model.
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So I guess they didn't really get it.
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And fast forward to today, you know, we managed to raise money.
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We managed to keep going, managed to keep growing the business.
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But the very same investors who passed on us
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and were very dour on the potential of AI are writing think pieces today about how data is
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so critical and is one of the biggest business opportunities in AI.
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So it's very funny to see that whole thing come full circle.
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It seems like that's actually a real good case study in first principles thinking, right?
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Like you can't start a company by opening the pages of the Wall Street Journal and saying,
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well, this data is hot like we're going to go work on that.
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You literally couldn't have started scale that way.
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You had to start from simple statements that are about the world that you know to be true,
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and then sort of building something for that.
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Yeah, I think the key thing is you need to develop conviction in a set of beliefs that nobody else agrees with.
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I think if you look at all the most successful companies in the world,
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they were started at a time long before the sort of like core idea was popular.
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And they work on that.
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They toil in obscurity for years and years before, you know, the idea or the space or the concept of the business, you know, becomes consensus.
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And the only way you're going to be successful is if you're able to identify these truths about the world early, long before everyone else.
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And I think that, like, I mean, one of the most surprising things, like, you know, scale, we've been working on AI for a decade,
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you know, you just, you can't base your business decisions based on what everyone else is saying around you.
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Like, if you go too much with the herd, you will get immensely confused, and you will end up nowhere.
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And so you have to develop your own compass of what you think the future is going to look like,
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because everyone else will just confuse you.
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It seems like one of the things
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you got incredibly great at was you start with this kernel of we believe X and nobody else believes it.
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But then the mechanics of building the business are talking to investors and convincing them and not letting them demoralize you,
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talking to customers who should just get it, and then especially convincing people to come work for you.
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Yeah, I think that these early mechanics of building a company, these are things that I think you might have some predisposition to be good at,
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but nobody is good at starting a company when they start a company.
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And I remember talking to a lot of the investors who I met very early on, and a lot of them would say,
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oh, you just grew so quickly and you changed so quickly, and I didn't see it at the time.
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And I think that's probably true for literally everyone who starts a company.
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Like, nobody is good at something they've never done before, right?
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And so I think for all entrepreneurs, you start out pretty shitty at everything.
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And the whole game is how do you develop yourself to continuously improve, to get better, and learn quickly?
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Backstage, we were talking about this is actually a really lucky time to start a company because, you know, obviously you can come do YC.
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You know, the people in this room have each other, which is kind of wild.
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But not only that, now you have an ideal personal AI that's going to tell you, you know, hey, these are some ways to do it.
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Do you think that would have helped you accelerate even faster?
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What do you think it's like to start a company today with AI, in the age of AI?
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Yeah.
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Yeah.
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I mean, I really think,
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I think we're at this like amazing moment in the world where the bottleneck is not the progress of the AI models.
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The bottleneck is diffusing that through the rest of the world.
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And and helping the world adapt to this amazing technology that already exists.
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Like, I think if the models didn't improve at all from today, there would still be, like, decades and decades of, like,
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total upheaval and change in the economy and how the world operates and everything around us.
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And so I think it's, as a result, it's, like, one of the most incredible,
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it's probably a, like, once -in -a -civilization opportunity to be a dreamer
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and to have a vision and to have ambition
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and to impose a view of how the future world should look by building something amazing.
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One of the things that we were chatting about backstage is when I started scale or 10 years ago,
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if you start a company, you had to be you know, it was like David versus Goliath and you had to be clever
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and you had to find like an angle into the market
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and you had to figure out a way to compete even though you had much fewer resources.
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And now I actually think with the power of agents and AI broadly speaking, it's much closer to Goliath versus Goliath.
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But maybe the startup is like a mecha Goliath
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that is vastly enhanced by the power of agents and AI and the large companies are the more traditional Goliath, so to speak.
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But I think that startups now,
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if you properly embrace AI agents and figure out the way to leverage their strengths in the most ambitious ways,
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you can easily outcompete incumbents.
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So let's talk about superintelligence, because clearly that's even in the name of your lab.
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What does superintelligence mean operationally inside
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Meta right now?
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Yeah, I think that we, a year ago, Mark wrote this memo about personal superintelligence,
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which I think actually is very similar to your concept of personal AGI.
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But, you know, we believe that everybody in the world, you know, all the billions of people in the world are going to have a superintelligence
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that is adapted and tailored to them, that enables them to accomplish their goals, knows their context, and ultimately is an expander of their own agency.
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I think the thing that we think a lot about is agency expansion.
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How do we help people accomplish things that they couldn't have ever dreamed of before?
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And what would everyone in the world do if everything was just easy?
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And we think about this in an ecosystem way as well.
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I think Patrick mentioned it, but we don't believe in this totalizing, totalitarian view of AIs that control the world.
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We believe that these are going to enhance this very broad ecosystem.
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So, you know, we believe in billions of people all around the world all having their own personal super intelligence.
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And we also believe in, you know, an explosion of entrepreneurship.
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There's 200 million businesses that are on Metis platforms today.
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We think that number should go to billions with this explosion of creativity and using AI tools.
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And ultimately, we think that, you know, it's going to be this like dynamic ecosystem of business agents working with,
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you know, personal agents and developing this sort of like, complex ecosystem that is fully AI supercharged.
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So I was really psyched to see MetaSpark 1 .1.
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My open claw absolutely loved it.
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How has running a frontier lab been?
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The MetaSpark level is sort of the opus level.
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What's coming down the pipe?
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And also I think that you're increasingly looking at open source, which I think this audience really loves.
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Yeah, yeah.
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Yeah.
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So I think it was, it's been, you know, I've been at Meta for about a year now, and it's been quite a year.
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I think, you know, getting in and, you know, Meta, we've talked about it publicly,
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like long before, wasn't on the trajectory that was needed for Meta.
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And so I got in there and we kind of did a zero -based build of how do you, you know, build an entire frontier lab,
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you know, in some ways kind of from scratch, obviously using a lot of what we had.
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and move as quickly as possible.
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And so within nine months of that moment, we launched MuseSpark 1, and then two months later, we launched MuseImage and MuseSpark 1 .1.
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And, you know, there's a few things that I think have really struck me about this.
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You know, the first is talent density was incredibly important.
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That was the core thing to bet on.
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And, like, talent density is something that compounds naturally.
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Like, the more talented people you have, the more of the most talented people want to join you.
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You know, and I think it's kind of amazing to see on the inside, but, you know, frontier AI work is research.
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Like, we are, it is scientific work.
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We are exploring what can you do with these models?
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How can you push these models?
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What are the reaches of what can be accomplished with these models?
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Which requires a totally different mindset and operating model than.. existed for internet companies or internet products and whatnot.
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There's a lot more about experimentation, about science, about scaling.
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And everything ultimately is about how do you develop a lab, an operating model, a system
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that will just be able to compound with all of the exponential growth that will happen in the ecosystem.
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Both the exponential growth in capabilities, the exponential growth in compute, the exponential growth in adoption and usage.
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We are on this very steep exponent across maybe every dimension of the ecosystem.
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It's important to develop an organism, that's how you think about the lab, that is able to grow with that.
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It's been very exciting and we're going to be shipping a lot more.
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I think we just launched MuseSpark 1 .1, which was a great model.
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We're going to continue to have updates on the MuseSpark line.
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We also have bigger models on the way
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that I think will be much more competitive with even the very best models that are out there today.
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We're going to be launching a harness soon
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and have been working on a harness to help empower all the developers and agentic developers out there.
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And then we're also, you know, as you mentioned, we're working on open source models.
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And we want to kind of, as I described before, like we believe in a decentralized world of AI capability and progress and development.
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We want to empower the broader ecosystem and everyone in the world to be able to build and develop using this technology.
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And so we have a lot of exciting things on the way, and I think we want to be,
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we want to empower the ecosystem and developers as much as humanly possible.
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And it sounds like one of the ways, I mean, certainly when I was using MuseSpark with my OpenClaw,
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like it became clear that it was as good as Opus, especially for that sort of agentic flow with skill files,
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but it was like 8x cheaper, actually.
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Yes.
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Well, I think this goes to it.
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We don't believe in a world where these models are
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so expensive that they get rationed only for the most wealthy of developers and companies.
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It's important for everyone to be able to use the technology and to build whatever they want to build with it.
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And I think that, you know, we take a view, I think the best AI products haven't even been developed yet.
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You know, if you look at the AI ecosystem and everything that's happened, like every wave is 10 times bigger than the past wave.
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So, you know, when I started scale, the first wave was maybe self -driving cars.
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Self -driving cars are really awesome.
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They're like really, really cool.
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But that was like pales in comparison to large language models and chatbots.
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And like, you know, chatbots became this thing that was probably 10 times bigger even than self -driven cars.
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And then there were coding agents, which came a few years later.
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And coding agents were probably 10 times bigger than chatbots.
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And I think we're just on this steep curve.
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We're going to keep seeing these new modalities and form factors
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and developments of the AI paradigm that will each be dramatically bigger than the last.
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And so our point of view is, let's unleash the ecosystem, Let's explore and let's build the future of the world together.
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What's the best way to actually take advantage of the coding model for MUSE Spark?
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It's open code, right?
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Yeah.
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Today, the easiest way is to use open code.
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We have onboarding on the website, and then soon we'll have a harness of our own.
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Ultimately, I think we want great models
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that plug into all of the available harnesses and empower as much combinatorial innovation in the ecosystem as possible.
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Yeah, I know the harness is underwrapped still, but can you tease us with, I mean, I still use
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Open Claw, I still use Hermes Agent.
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These things are, I call them Ferraris that break down on the side of the road all the time.
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Is this a Ferrari that won't break down?
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Tease us a little bit.
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Yeah, hopefully it doesn't break down.
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I mean, I think we're really focused on speed.
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I think speed is, for anyone that uses these tools, speed is probably one of the most critical things.
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I think also reliability, like you mentioned, we want to be extremely reliable.
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We want to be very extensible and to scale to as complex
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and interesting of a multi -agent setup that you want to have.
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Like, I think there's so much innovation that will occur even above the harness, frankly, in terms of, like,
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how to orchestrate and set up loops and develop, like, you know, very complex ecosystems of these agents working together.
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We want to be really extensible and ultimately we want to just empower people to harness this technology.
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Because harness, actually, pun not intended.
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But there's, like, I truly believe these models are already just incredibly powerful.
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Like, they should be so powerful to fuel, you know, many, many points of expansion of GDP growth.
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And I think it's like up to smart people with vision and ambition to make all that happen.
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Let's see.
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So one question.
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I mean, when you look back on the decade,
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what do you think they'll say was obvious in hindsight about AI that people are just missing in real time right now?
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You know, so much of the debate that happens these days is around, oh, how good are the models actually getting?
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And can the models actually bridge this issue?
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And, you know, when are we going to get super intelligence?
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Is that in like two years or five years
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and you know are we going to hit a wall and you know so much of that debate is like
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i think um in some ways uh a little bit of a waste of time
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because you know i think it's inevitable
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that we're going to have very powerful models
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and um you know rather than i think we'll look back
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and say oh all this arguing around like
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when exactly was going to happen was sort of um was short -sighted
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because the reality is we are just as a entire human civilization on this incredible exponential
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but you cannot look at the progress of ai over the past decade
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and not just be totally awestruck by how far
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it's come like a decade ago the best ai models could recognize cats in youtube videos
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and now you know we're talking to um you know a digital god
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that can you know i mean we've seen some of the hacks and some of the things these systems are capable of.
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And you just can't help but be awestruck.
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And I think this trend will just continue.
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Like these models are going to become more and more powerful.
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And so I think a decade looking back, it'll be obvious that intelligence became abundant and that agency became abundant.
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Like the current trends we're on are just going to keep continuing.
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And this will be very strange.
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I mean, I think for the history of humanity,
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you know, groups of smart people getting together towards a shared goal was the bottleneck of progress.
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You know, the United States of America, in some sense, was an example.
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It's like the United States of America is formed from a group of very smart people getting together
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and having a vision for the future that they wanted to enact.
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And that's the story of nearly every company in America and the story of every YC company.
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And that's going to change like all of a sudden the scarce resource isn't going to be intelligence
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or agency I really think it's going to be vision
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and ambition it's like do you have a clear view of
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what you want the world to look like in the future what is the like one way in
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which you want to put your finger on the scale for how the future of the world will develop
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and how the how the world will look like in five to ten years
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that it does not look like today and you have the ambition
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and drive to like go through all the crap to make that happen.
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And AI will make that easier.
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Agents and AI makes that maybe 10 times or 100 times easier than it was a decade ago.
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But the flip side of that is all of a sudden you can dream bigger.
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And the world is like, there's so many things that need to evolve for us to be able to fully embrace this technology.
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The world is really just barely even ready for this technology today.
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And I think, you know, as a builder, we have a responsibility to prepare the world, right?
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Like we have to help enterprises and governments, you know, to adapt to this new technology.
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We have to help figure out how we secure the world from a biosecurity perspective or a cybersecurity perspective.
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We have to figure out how we're going to manage all these risks that we see with this new technology.
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But on the flip side, it's also the time of like, you know, unprecedented opportunity for humans.
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Like we can develop new sciences.
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We can solve problems in health and biology that have been forever unsolved.
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We can build new businesses that you couldn't have even imagined before.
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There's like new creative opportunities that couldn't have existed before.
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So it's like it's just this incredible cradle of opportunity
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and risks that I think makes it like no better time to be someone who's a builder
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and has a strong view of how the world should change.
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Do you think the path has changed?
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I mean, one of the things I saw, I think Stanford, the amount of computer science majors actually dropped by some double -digit percentage.
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People sort of worried, which is sort of insane to me.
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You still sort of need those skills to even create agents that are that good.
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Maybe that won't be true.
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I'm not really sure.
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What would you say to people in this audience right now?
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This is sort of a real question
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that people are sort of facing like should they become more word cell
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and less shape rotator like what you know what's the move
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and you know has that changed um the kind of people you're looking to hire
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and you know how you manage your teams right now at meta i think systematic
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and rigorous thinking are still incredibly important
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because you know the abstraction layer i mean i didn't used to believe
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that this is how this is going to play out but it really has like the abstraction layer just keeps changing.
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So, you know, when I started a company, back in my day, we wrote code.
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And now, you know, I'm sure nobody here writes code anymore.
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That's ridiculous.
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But now it's about how do you orchestrate the agents together?
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And then it's like, how do you develop these organizations of agents?
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Like, how do you get, like, a million agents to work together well?
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And then it'll be, how do you get, like, a trillion agents to work together well?
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I think
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that there's going to be this continued need to figure out how you structure workflows at the abstraction layer
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that we're going to be operating at.
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And that form of rigorous, systematic thinking, I mean,
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traditionally the way this would work in my era of starting companies is you would start by writing code,
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and then you would have organizations of humans, and you'd figure out how to organize those humans.
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And that requires us some thinking.
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And now maybe it's much closer to first you orchestrate the agent, then you figure out how to orchestrate these armies of agents.
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But I think systems thinking is never going to go out of style.
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So I think it's definitely a mistake to go all in on word cell.
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I think you need to shape rotate.
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But then I think much more of what's necessary going into the future is having a deeper compass
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and philosophical view on how the world should develop.
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Because I think there are many, many lessons from human history around how we think civilization can go through this period.
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And humanity will change more in the next decade than it has in the past 100 years, probably.
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And so I think the imperative for us to have positive visions for
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that and have coherent articulations of how that should develop are more important than ever.
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Let's get a little more concrete.
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I mean, one of the things I'm curious about is, like, are there sort of applications of AI
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that you're seeing among your friends or internal to meta that you can talk about that are, you know, there's sort of obvious near -term,
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maybe people haven't figured out yet.
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I mean, give us some alpha.
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I mean, I think there's still just like astronomical opportunity in agentic looping
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and figuring out how you develop systems
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that enable you to spend like 1000x more
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or 1 millionx more on tokens to drive an outcome in a continuous feedback loop.
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Like if you think about most companies, companies are just these like large scale feedback loops where humans are operating each of the edges.
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It's like, you know, companies, they get customers and they figure out how to make those customers happier.
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And if the customers are happier, then they spend more.
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And if they spend more, they can hire more people who can then go figure out how to get more customers and make those customers happier.
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And that's like this, you know, that in some sense is the feedback loop of every startup or every business.
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And, you know, these within that, there are micro feedback loops that exist.
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And I think developing agentic systems that can operate and optimize these feedback loops, there's just huge amounts of alpha there.
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I think we've seen, internally in meta, cases where if you can develop the right agentic loop
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and you have the right eval or the right metric for the agents to optimize,
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you can have a swarm of agents accomplish more than a team of 100 engineers very,
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very handily, actually very, very easily.
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And so I think figuring out what the world looks like with lots of agentic coordination problems,
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I think that is one of the most interesting problems today.
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So mechanically speaking, Markdown files, cron jobs, and then basically pointing the agent at enough data
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so that it can figure something out that maybe isn't in distribution.
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Yeah, I think figuring out, yeah, yeah, mechanically figuring out what the metric is, and yeah, it just comes down to skills, markdown files, run jobs.
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Slash goal.
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Yeah, slash goal.
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Like, I think it's always funny how mundane everything is once you really dig into it.
377
But it's not magic.
378
You know, I mean, some people put a lot of magic.
379
There's like some LinkedIn threads out there about some magic stuff.
380
Top advice.
381
Ignore LinkedIn.
382
Hey.
383
LinkedIn is where you get customers.
384
So I'd like to end on this, which is, you know, you get a telegram to send to the 18 -year -old version of yourself.
385
You know, what do you say to that person right now, given all, you know, I mean,
386
thank you for coming back and sharing your wisdom with this audience.
387
I mean, you know, what would you send in a message in a bottle to the 18 -year -old version of yourself right now?
388
Yeah, I think it really boils down to develop your own
389
internal compass for how you think the future will develop and have strong conviction in it because, you know.
390
you will get inundated with noise and people telling you shit.
391
And, like, you'll be very confusing and it'll be very hard.
392
And especially when you're young and you don't have experiences,
393
it can feel very difficult to have true conviction in what you believe and what you want to do.
394
But I think that's the most important thing.
395
Kind of as we talked about, you know, it took a deep, deep conviction in what we were building to be able to
396
weather the sort of storms of many years of chaos in the market, in the industry, in the people around us. And so,
397
and then the other piece of advice
398
I would have is try to identify what is the exponential in the world
399
that has both the steepest curve and will go the longest.
400
And, you know, many decades ago, this curve was Moore's Law.
401
And that probably was, you know, that was at the time, like clearly the right thing to invest on.
402
I think right now it's AI progress, but there will be more of these very steep curves in the future.
403
And it's fine if these curves start, you know, the starting point is very boring or like, it doesn't even seem that interesting.
404
Like, you know, when we started, when I started working on scale, you know, we had cat detectors in YouTube videos and that felt,
405
you know, it's hard to say, explain the story that that's like the most important technology of our time,
406
but it was on just this unbelievable exponential.
407
And I think I have one last thing I got to say.
408
Yes.
409
Yes.
410
Which is, we are, Meta is proud to offer everyone in this room $1 ,000 of free credits for the NewSpark API.
411
Fantastic.
412
And we're going to keep making the models better.
413
And right now NewSpark is, I think, 8x cheaper than Opus.
414
So if you convert that to Opus dollars, it's a lot more.
415
But no, everyone here will work to get everyone the details on how to get these credits, and we're really excited to see what everyone builds.
416
Alexander Wang, everyone.
ดาวน์โหลดแอป
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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
บทเรียนฝึกพูดนี้ใช้วิดีโอ “Alexandr Wang: “This is a Once-in-a-Civilization Opportunity”” เป็นสื่อ วิดีโอนี้มี 416 ประโยค และ 5628 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 32:01 ผู้พูดพูดด้วยความเร็วเป็นธรรมชาติ ประมาณ 176 คำต่อนาที ใกล้เคียงกับบทสนทนาในชีวิตประจำวัน 85% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก
คำศัพท์สำคัญในวิดีโอนี้
คำที่ควรเรียนรู้ 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:
| คำศัพท์ | คำอ่าน | ความหมาย |
|---|---|---|
| ecosystem คำนาม | /ˈikoʊˌsɪstəm/ | ระบบนิเวศ |
| loop คำนาม | /luːp/ | บ่วง |
| investor คำนาม | /ɪnˈvɛs.tə(ɹ)/ | ผู้ลงทุน |
| layer คำนาม | /ˈleɪ̯ɚ/ | ชั้น |
| explore คำกริยา | /ɪkˈsploɹ/ | สำรวจ |
| civilization คำนาม | /ˌsɪv.ɪ.laɪˈzeɪ.ʃən/ | อารยธรรม |
| steep คำคุณศัพท์ | /stiːp/ | ชัน |
| compass คำนาม | /ˈkʌmpəs/ | เข็มทิศ |
| hire คำกริยา | /ˈhaɪɹ/ | จ้าง |
| sudden คำคุณศัพท์ | /ˈsʌd.ən/ | กะทันหัน |
| density คำนาม | /ˈdɛn.sə.ti/ | ความหนาแน่น |
| humanity คำนาม | /hjuˈmænɪti/ | มนุษยชาติ |
| innovation คำนาม | /ˌɪn.əˈveɪ.ʃən/ | นวัตกรรม |
| alpha คำนาม | /ˈæl.fə/ | แอลฟา |
| explosion คำนาม | /ɛkˈsploʊ.ʒən/ | การระเบิด, ภัยการระเบิด |
กริยาวลีที่คุณจะได้ยิน
| คำศัพท์ | ความหมาย |
|---|---|
| come back คำกริยา | กลับมา |
ประโยคที่ควรฝึกพูดซ้ำ
ประโยคสั้น ๆ ที่สมบูรณ์จากวิดีโอ ซึ่งนำไปใช้ในบทสนทนาประจำวันได้:
- Let's jump to your story.
- What were you thinking?
- You literally couldn't have started scale that way.
- How can you push these models?
- Do you think the path has changed?
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| Present perfect continuous have/has been + -ing — การกระทำที่เริ่มก่อนหน้านี้และยังดำเนินอยู่ | we've been working · have been working |
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | they've never done · it's been · we've talked |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | were started · is adapted · was really psyched |
| Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ | people who are · friend who was · everyone who starts |
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 75 ครั้ง เช่น we're, don't, you're ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, thread /ˈθɹɛd/, wealthy /ˈwɛl.θi/
- เสียง “sh” และ “zh”: conviction /kənˈvɪkʃən/, accomplish /əˈkɑm.plɪʃ/, ambition /æmˈbɪʃ.ən/, civilization /ˌsɪv.ɪ.laɪˈzeɪ.ʃən/, innovation /ˌɪn.əˈveɪ.ʃən/
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: ecosystem /ˈikoʊˌsɪstəm/, incredibly /ɪŋˈkɹɛdɪbli/, developer /dɪˈvɛləpɚ/, civilization /ˌsɪv.ɪ.laɪˈzeɪ.ʃən/, humanity /hjuˈmænɪti/
เสียงที่คนไทยมักออกเสียงยาก:
- เสียงท้ายคำ — ออกเสียงให้ครบ ไม่เปลี่ยนเป็นตัวสะกดแบบไทย: harness /ˈhɑɹ.nəs/, curve /kɚv/, accomplish /əˈkɑm.plɪʃ/, compass /ˈkʌmpəs/, backstage /bækˈsteɪd͡ʒ/
- /v/ — ไม่ใช่ /w/ ให้ฟันบนแตะริมฝีปากล่าง: curve /kɚv/, investor /ɪnˈvɛs.tə(ɹ)/, developer /dɪˈvɛləpɚ/, conviction /kənˈvɪkʃən/, civilization /ˌsɪv.ɪ.laɪˈzeɪ.ʃən/
- /z/ — เสียงก้อง ไม่ใช่ /s/: civilization /ˌsɪv.ɪ.laɪˈzeɪ.ʃən/, tease /tiːz/, confused /kənˈfjuːzd/, resource /ɹɪˈzɔːs/, enterprise /ˈɛntəˌpɹaɪz/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง ecosystem, loop, investor เป็นพิเศษ
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ