ฝึกพูดภาษาอังกฤษด้วยเทคนิค Shadowing จากวิดีโอ: How I Learn Complex Skills & Difficult Subjects So Fast with AI
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How is it that some people can learn in just a day what other people take weeks or months to learn?
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And how can we use AI to positively augment our learning as opposed to confuse us, reduce our focus, and destroy our attention spans?
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Well, in this video, I'm going to show you everything
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that you need to know about how to use AI to
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learn anything in the modern era in just a fraction of the time.
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I use these tactics in order to build a business that does over $400 ,000 a month.
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I also went to a school for behavioral neuroscience, and this is exactly what we focused on.
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It's how to use technology to augment and improve our ability to learn.
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It was by far the subject I was most interested in.
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And so I use this sort of stuff all the time to help people, yourself included, you know, learn things faster.
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So before we get into how to actually use AI to learn, it's important to understand how human beings actually learn.
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Because I feel like a lot of people, you know, they misunderstand this part.
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And if you misunderstand this part, it will lead to a bunch of downstream issues and, you know, productive and effective learning.
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You know, most people believe that you learn through consumption, which is where you will read something, okay?
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You will watch something like this video, or you'll get it explained to you, say, via an AI bot or a chat bot.
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And the issue is this feels like learning, right?
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But the way the human beings actually learn, the way the, you know, memory encoding mechanisms in our brain and the retrieval mechanisms in our mind actually work is based off of production.
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And so this involves recalling things actively, predicting things actively, explaining things actively, and even attempting to do things out there in the real world, okay?
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And this feels way harder, which is why our brains, constantly trying to preserve energy, avoid that.
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But when you understand this distinction, you also understand what leads people to being extremely effective learners versus ineffective learners.
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You know, there have been a bunch of studies done on this subject.
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One of them showed that, you know, if you were to reread some source document four times and then get quizzed on it five minutes later, you would remember 83 % of the source document.
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Okay, so you'd think, wow, I've only lost 17 % of that.
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That's pretty good, right?
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Likewise, if, you know, you read a document once and then you are tested on that three times immediately after.
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Five minutes later, you'll remember 71%.
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But the thing that we're most interested in is not how much are you going to remember in five minutes, right?
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The thing that you're most interested in is how much are you going to remember in a week
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or a month or a year?
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How deeply will the things that you've learned stick with you for the rest of your life?
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And when a person rereads some source document four times, which many of us will do, right?
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Thinking that rereading the source document will teach us things and make us learn it.
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In a week, they only remember 40%, whereas actively producing and testing gets you closer to 61%,
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meaning there was a decay of only 10 % in the read once test three times group, whereas there was a decay of,
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I mean, geez, at least 50 % in the rereading it a bunch of times group.
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Okay, so I'm sure people have probably talked all day about this sort of result to you, and it's just glossed in one year out the other.
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But the reality is active production is ultimately how you learn.
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And so the whole idea of using AI to learn faster and better is actually augmenting your ability to actively produce things, not just necessarily passively read things.
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There are many different roles that artificial intelligence can play for us.
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The issue is most people will only have ever used one.
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They use the explainer role, which is where you've tried to understand something, you're a little bit stuck, and so maybe you paste in the source document to AI and you say, hey, I'm stuck, can you help me out?
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Explain this in a different way.
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The issue with that is first, as we see already, that's a passive consumption mechanism.
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You're just reading.
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The second is that that's only one of about 10 different roles that an AI can help you play.
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And those other nine roles are primarily focused on your ability to produce information.
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So if all you've ever done with AI is you've had to explain things to you in a really simple manner, not only are you only,
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you know, scratching the surface of what is truly possible in consumption versus production, but you also have nine other fundamentally different mechanisms that you're just leaving on the table.
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And so I'm going to cover these nine mechanisms.
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The first is the interviewer.
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That's when you're starting out and you're unsure of what you need.
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You don't even know enough to know what you don't know.
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So you just have an AI who has a shallow knowledge of virtually everything in existence, ask you questions to determine what direction you want to go, what your state of learning is right now,
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and so on and so forth.
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The map maker is where, you know, you're just starting a subject.
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It feels really shapeless.
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You don't have a defined curriculum in place.
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And as we know, if you put a roadmap in front of most people, they'll be able to do a thing but if they don't have a roadmap, they don't know where to go, oftentimes they're lost.
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They'll just be swimming, you know, listlessly in the water.
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Well, the map maker can help you figure out where to go.
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It can essentially produce a curriculum for you.
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I'll show you guys how to do that in a moment.
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How about the Socratic questioner?
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This is where you think you understand a topic.
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The Socratic questioner allows you to better develop your understanding of the topic by forcing you to actively produce.
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Or an examiner, you know, you've covered a topic fully.
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You want to figure out how much you actually get of a topic.
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You want to score yourself with some percentage.
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Well, that's what AI can do for you.
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It can literally be essentially an oral or verbal or written examiner, just like you used to have back in the good old days,
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you know, 16, 1700s university where you'd be doing your thesis defense in front of a bunch of people.
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You can do that now with AI.
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How about a checker?
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You know, maybe as part of your learning, you've made something.
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But you can actually have AI verify certain domains to ensure
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that your knowledge was effective in producing the outcome that you want.
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Maybe a listener where you explain it back, but in your own words, AI then helps you determine the gap based off of its understanding of the topic more generally.
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There's diagnosticians where, you know, if you have the same mistake that keeps on coming up in your learning, you can now actually ask your AI agent and I'll show you how.
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Hey, you know, I want you to go through the last five or 10 or 20 conversations we had about the subject.
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What am I persistently getting wrong?
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What do I persistently need to understand to patch these holes in my knowledge.
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How about a sparring partner?
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A lot of the time you're learning a skill because it is a real world applicable thing.
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You want to public speak.
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Maybe you're preparing for an interview.
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Maybe you're trying to crush the leak code circuit.
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Well, you can actually have a sparring partner with which to assist you on that and basically simulate things.
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And finally, clerk.
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How many hours have you spent just coming up with notes
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and trying to keep track of all this information that you've digitized?
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You don't need to do that anymore.
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AI can do all of that for you. And
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so what I'm going to do is I'm going to show
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you guys how to apply AI in all of these different ways and ultimately improve your learning from a scientific perspective, not just a thing that sounds nice, like most other people will.
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A really simple example of this is something termed space repetition.
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For those of you guys that don't know, okay, if the y -axis here is how strong your memory of a subject is, and then the x -axis is the amount of time since you learned the thing,
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you can see that when you learn the thing initially, you have a lot of memory for that thing.
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You've really consolidated at that thing.
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But when you don't review it periodically, what happens is the strength of that memory decays almost as like this inverse exponential.
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And so spaced repetition is the process whereby you consult frequently with the thing that you learned.
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You actively produce that knowledge.
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Let's say you're trying to learn how a car engine works.
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And then based off of what's called the Ebbinghaus forgetting curve, which is this psychological idea developed,
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you know, basis every now and then using, you know, AI or another tool to ask yourself, how the hell does this engine actually work?
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Okay, the pistons, the cylinders, what are the four stages of combustion, you know, intake, and so on and so forth.
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And then in that way, your memory actually gets extremely strong.
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And as you see, every time you, you know, repeat this spaced repetition thing, which I'm going to talk a little bit about in a moment, two things occur.
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One, the memory lasts a lot longer.
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You can see the slope of this curve is actually a lot less steep.
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And two, it never dips as far down.
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And so the strength of the base memory just gets stronger and stronger.
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This also means that in just a few moments of studying a topic, you can usually remember it far more than
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if you were to like wait this whole time and have to restudy it again from scratch, right?
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So that's just one of many ways that you can use AI.
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AI can write your flashcards for you.
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It can keep a schedule or you can use an app like Anki.
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It can turn any mistakes you make during a sparring session
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or some sort of listening session into new cards for you to review.
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You know, I'm going to give you guys some prompts
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and stuff like that that you can use for that sort of thing as well.
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But the whole idea is, you know, this is one simple example of how AI can help you.
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So if you don't know enough about a topic to even begin knowing what you know, what you don't know, let's chat about the interviewer.
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The interviewer is the simple idea where you have a chat bot up here on the left, you have you on the right, and basically you're saying, hey, I really want to learn X, Y, and Z thing
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and instead of just saying help me learn xyz thing you say could you drill me on anything
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and everything under the sun to determine how much i already know about xyz thing
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and then in conjunction with the map maker in a second
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um what i need to learn in order to patch the holes in my knowledge
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and this makes sense
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that ai would be good at this right i mean it's
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a massive uh compressed form of basically the whole internet
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that is essentially the whole point of ai really just to
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point you in the right direction to act as a fast way of approximating knowledge And so, you know, if you're like, hey, I want to learn, you know, coding, AI might ask you, okay, what is this for?
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Well, it's for a job interview in three weeks.
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Now the AI knows, okay, it's not actually that you need to learn all of coding.
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You need to learn enough coding to make a job interview work.
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Okay, so what do you know already?
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Well, I know some basic Python, you know, maybe I've spent two weeks doing some bootcamp a while ago.
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Okay, how will you be tested?
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Well, live coding with some sort of timer.
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From here, AI is determining a variety of different things to help you accomplish your stated goal, not just the goal that you might think you have.
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And so in this case, the goal is not actually learn coding.
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The goal is pass the interview.
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You don't need to learn Python basics because that's a waste of time.
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You've already gotten to that point.
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In order to practice and evaluate and assess your learning, the simplest and easiest way to do
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that is going to be some sort of timed live coding because it's going to mirror the actual examination conditions.
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This is all stuff you would have figured out eventually if you were a perfectly rational agent, but it's stuff that you probably wouldn't know enough to know right up front.
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So if you are starting out and unsure of what you need, you can just ask the AI, hey, you know, before you teach me anything about coding, could you ask me questions about my goal,
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my level, everything that I need to know basically in order to make this work really effectively?
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And the AI model will go absolutely.
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A couple of things to watch for here, vague answers, vague plans, you know, ultimately want to be specific.
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So it's not about, oh, you know, my goal is to pass an interview.
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It's like, oh, my goal is to pass an interview that's going to be taking place on leak code.
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I know a little bit of basic Python.
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I spent two weeks getting all the way up to, I don't know, HTTP requests or something like that.
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The timer is five minutes per question, and we're going to be pulling from this question bank.
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You want to be more specific than just that.
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And that takes me to the idea of a map maker.
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So the reason why it's very important to have a map or a plan in place is because if you don't, the vast majority of your time
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and energy will simply be spent fighting off the almost emotional anxiety of not knowing what you are going to do next.
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And so you can eliminate that completely by just giving yourself something to do next, rather than finishing a module and then wondering, okay, what the hell am I going to learn next?
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What fits?
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You know, just build a plan, and then you can follow a plan.
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Even if it's not perfect, you're going to be a lot more productive as a result.
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So let's say you want to learn statistics.
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That's a pretty damn broad term.
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There's a lot that goes on in statistics, right?
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So if you just say, I want to learn a statistic, that's pretty shapeless.
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What AI can help you do, okay, is it can help you break that down into the core fundamental sub -modules of statistics.
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And so maybe you'll start with averages.
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Then you'll learn about spread and probability.
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Maybe after that, you'll learn about distributions, sampling.
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And then finally, loop around to some sort of A -B test.
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This is what AI can essentially allow you to do.
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If a subject feels really shapeless, what I will always do before I even try learning is, hey, just map out statistics for me.
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I want the main parts.
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I want the various things that depend on a bunch of other things.
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And I want to know where people usually get stuck on so I can plan my time ahead of time.
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And that takes me to the explainer.
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Many people here are already using the explainer day to day.
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You know, they'll have read how to do something.
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They'll even have set up how to do something.
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When it comes time to actually solving the problem that they initially wanted to solve, they'll be stuck in it.
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So the explainer just helps you illuminate just that one part for you, explains, you know, how the solution works or something that allows you to then, you know, learn it faster.
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The issue is most people don't do this next step, which is redoing the initial thing you wanted to do without help.
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Remember that learning is almost entirely a production thing it's not a passive consumption thing.
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If you don't know something and you ask, hey, can you help me figure this thing out?
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And then it explains it to you and you nod along and say, yeah, that makes sense.
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Okay, cool.
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I've now learned the thing.
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Without actually assessing from a baseline whether or not you know the thing without the AS help, you have not learned that thing.
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You have merely read about that thing.
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And there's a very big difference between the two, right?
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So after you've gone through a whole process once, it is integral that you revert back to the very first step, which in your case might be reading about how to set this thing up,
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and actually walk through it from first principles from the start
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in order to go from you know zero all the way
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up to one that's the way you demonstrate your knowledge
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and that's the way that you actually productively remember it and
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so anytime i'm trying to learn let's say a new ai tool
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or something like
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that you know i will use ai quite heavily to you
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know interview me on my current state of knowledge to create
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a map for me on the various concepts i need to know in order to learn it quickly
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and then i'll actually try and
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if i struggle i'll have ai explain things to me
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but i don't just stop there i'll actually once i've figured
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it out we've made it all the way to the end of whatever task I have, revert all the way back to the beginning, and then try doing it all myself.
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This only will ever take you a few additional moments of time, 10 or 15 % of the total length of time you spent setting a subject.
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But the end result and your ability to know, learn, and then articulate it to other people, like I'm doing to you right now, is insane.
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It's unmatched.
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So you need to redo things without help.
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You can't just, you know, try and one -shot things progressively as you go.
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You really do need to loop back.
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And that's ultimately how to use an explainer.
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Simple thing to say here is, hey, I tried doing XYZ.
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I got stuck at XYZ.
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Can you explain just that part at my knowledge level so that I understand it?
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Once you're done with that, again, circle back and then finish the rest.
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The Socratic questioner is also extremely powerful.
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You know, you a lot of the time are going to think that you understand stuff, again, because AI will have explained something to you.
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You'll have gone through it at least once and you'll say, yeah, I think I get the gist of it.
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But the Socratic questioner is a way for AI to basically act as a study buddy with you
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and ask you things like, okay, so why does this statistical principle work?
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Well, the reality is, if you don't know how to answer it, it will immediately have clarified to you where your gaps in knowledge are.
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And then if you don't know, maybe it can ask you a follow -up question like, okay, like what if the input to the statistical thing is doubled?
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Then you'll say, okay, then that would break.
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Now the AI is learning the gaps in your knowledge.
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You're also seeing the gaps in your knowledge.
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And then that can help direct you back to the explainer to learn exactly how that process works.
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And then you can circle back and forth between Socratic questioning, explanations, creating a new map to learn new things that you didn't realize you didn't know, and so on.
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This allows you to find the gap yourself.
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You now know the answer.
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It'll never just hand it over to you because it's now asking questions productively, forcing you to come up with things.
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And then as a result, you can actually understand something, not just think you understand.
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So how to try that is, you know, hey, I want to learn X, Y, and Z thing.
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Don't just give me the answer.
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Ask me questions until I find what I don't know myself.
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Next up is the examiner.
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Now, there are a lot of situations where you think you understand something, and then because you don't test yourself, you don't actually realize where you fall short.
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But with an exam that is punctuated at varying levels of knowledge, what you can do is you can actually figure out, okay, I figured out level one, I understand how that works.
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I know level two, I get level three, level four makes sense.
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Level five, didn't actually pass that test.
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Why?
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And so it's another way of you figuring out where you need to go next in order to maximally direct your learning.
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And that's all ultimately that this whole process is about.
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It's about maximally directing your learning.
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Human beings have so much potential buried inside of them.
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Our brains are so incredible.
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But simply because of like old deprecated educational institutions, formats like lectures, which were developed thousands of years ago and just do not scale into our current era,
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and then other gaps in knowledge and so on and so forth, we're just not capable of squeezing out more than 10 or 20 % of it.
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So this helps you do that.
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It's very straightforward and easy.
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A simple way to do this is saying, hey, could you quiz me on this topic?
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I just want one question at a time, make every question harder until, you know, I don't get things right.
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And then at that point, you'll know where the gap in your knowledge is.
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How about the checker?
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In this situation, there are a lot of places where, you know, you don't learn for the sake of learning.
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A lot of the time you learn for the sake of creating something.
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And so what the checker does is basically scores you on almost like a rubric similar to an examiner, but this time checking, you know, whether or not your process works to begin with.
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And so the value here is you're not assessing just the end result of your knowledge.
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What you're doing is you're assessing the process required to create the knowledge itself.
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And so, you know, I don't know if you guys have ever taken a really introductory math class.
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A lot of the time they don't just say, don't just give me the answer.
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I actually want to know the process behind your work.
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So show your work.
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Show me, you know, how you multiply the two variables together or whatever.
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What the checker does is it assesses the intermediate steps to determine
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that your logic and your reasoning was correct in order to get there.
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In this way, you know, if you have any issues, the model can figure this out.
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So if you made something like a summary, a proof, some sort of code, math problem, or something like that, actually have it evaluate or check your reasoning to see
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if there's a shorter and faster way you could have made it to the same point.
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In this result, you know, maybe we could have shortened the summary down twice, got it literally to about half the length, and as a result, the next time you encounter this problem, your process itself will be faster,
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not just the end result.
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So in this case, you might say, hey, here's my work for determining x, y, and z.
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Can you find what's wrong are missing and let me know if there's a faster or easier way to get there.
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Don't rewrite any of it.
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Next up is this idea of the listener.
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So the end result is, you know, you've now learned something to a reasonable amount of time, but you have to understand that there are different ways to demonstrate your knowledge.
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There's like a written way of doing it.
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There's like a verbal way of doing it.
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I mean, there's like five or 10 and the best learners know how to provide that knowledge in multiple different formats.
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And so it's not enough just to, you know, say something or write something, you know, with your fingers.
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My recommendation is if you really want to maximize all the different highways of your mind, you know, you should say something, you should visualize something with maybe some sort of graphic,
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you should write something, you should really try and engage yourself maximally across all senses.
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What the listener does is it compares your words across all
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of these to the source document on all of these different
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testing mechanisms on the verbal reasoning on like your ability to write things out on your ability to visualize things
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and so on and so forth, to determine whether or not you were correct.
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And so what I will typically do is when I really want to make sure I understand a topic, I will just hold down my voice transcription key and I'll just start talking into my computer.
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After I do my voice transcription, I will try and draw a quick image of the concept to make sure I understand.
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And in addition, I will write a much more short
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and succinct version of my explanation since verbal voice transcriptions
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and stuff like that tend to be a lot longer than their written counterparts.
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I'll then compare it to the source document, whatever it is I'm trying to learn.
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Maybe it's a new neural network architecture or something like that.
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And now it has three different means by which to assess whether or not I understand something, the visual, the verbal, and then ultimately the written.
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And it can compare all three of these to the source document.
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And as a result, it can listen to me and determine whether or not I've made it.
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So here, maybe you'll say, hey, I want to explain this idea in my own words.
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I want you to grade it against the source and tell me exactly what I missed.
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The diagnostician, you know, despite being kind of a tongue twister, is a very important part of understanding.
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Essentially, a lot of the mistakes that you make in learning occur at the same fundamental roots.
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Maybe you just don't really understand some simple mathematical operation or something, and as a result, you don't understand how to do a specific thing in statistics, you don't understand how to do a specific thing in biology,
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and then you don't understand how to do a specific thing in chemistry.
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Let's say you're just in a university environment trying to learn all this stuff.
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The reality is you don't actually know what's holding you up, despite the fact that these are all templated issues.
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So what the diagnostician does is all of these things are the result of a shared misunderstanding.
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What you do is you just ask the model, hey, like, what is the recurring mistaken reasoning or logic that I keep on making across all of these, you know, listenings, examinations, you know,
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checks and so on and so forth.
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Read through my last, you know, 100 conversations, let's say, and figure this out.
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And it'll actually go and it'll find the core reasons why you misunderstand stuff.
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It will literally go and tell you, like, hmm, you appear to be making the same sort of logical leap from X to Y.
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The reason for that is probably that you don't understand Z and now you, instead of trying to learn the specific outcome of each, can just learn the root thing that keeps on making you make the same mistake.
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So, you know, here are a bunch of things I keep getting wrong.
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What misunderstanding do all of these things have in common?
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Just feed that to your AI agent and you will be far better off.
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The sparring partner is extremely valuable and I personally have been using this a lot, especially as I've started doing more podcasts, interviews, and then high stakes negotiations in business.
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So, I will often actually have AI give me some sort of back and forth Q &A.
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You know, if I'm like, hey, I have an interview coming up for X, Y, and Z, I want to spar back and forth, you know, verify that I know what I'm doing.
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I'm going to do this verbally.
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And then I also am going to set a timer with every question to try and figure it out within 30 seconds, because I think that's probably how much time I'm actually going to be given.
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Let's do it.
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Hey, why should we hire you?
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Well, it's because I work hard, right?
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You send that back to an AI agent that you've pre -primed as a sparring partner, it's probably not going to be too happy.
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And it's right, right?
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So let's say something like, too vague, give me an example.
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Well, last month, don't they cut our wait times in half?
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So maybe that's what you should have ultimately led with.
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This sort of sparring in process is, it's very difficult to do correctly.
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And you have the ability to adjust that difficulty slider.
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You can reduce the amount of time you have to come up with each question.
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You can obviously, you know, force the AI model to be a higher and higher and higher level interviewer as you go across.
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But the result is you actually end up getting a couple of reps on it but actually having to do the reps.
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And I mean that in a good way.
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You know, a common thing that I'll teach people how to do is I'll teach them how to do, you know, sales calls for AI and automation services, because that is part of what I sell and what I show people how to sell on this channel.
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And so, you know, they will have never jumped on a sales call before in their entire life.
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They'll be absolutely terrified.
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You know, they don't know what to say.
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They don't know how to frame the call.
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They don't know how to like, you know, generate any perceived value for their service.
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A lot of the time I'll say like, hey, go into a conversation with an AI agent.
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Here is a brief like template of the call.
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And it's going to pretend that it's a client and
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or a prospective client and you are a prospective salesperson and you guys can just go at it.
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This won't 100 % be the exact same thing that you are going to see in real life, but this is going to allow you to get at least a rough go through the very first rep, which will make you significantly better.
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This sort of like
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simulated training environment is going to be extremely important over the coming months
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and years as AI makes us better and we make AI better in turn.
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So use this when you're practicing a skill.
351
It's speaking, it's interviews, it's sales calls.
352
And in order to do this, all you need to say is something like, hey, I want you to play a tough hiring manager for XYZ thing that I'm trying to do.
353
Or I want you to pretend that you're interviewing me.
354
Here's a big list of all the previous interviews this interviewer has done.
355
Don't go easy on me.
356
Push back hard and make sure I fully understand it.
357
I want every answer to be generous in 30 seconds.
358
Finally, highly undervalued role of AI here is the clerk.
359
That's where you have a bunch of really messy notes or messy intermediate checking steps, which you just feed that in AI and you say, hey, can you make me a clean outline of this?
360
Or hey, can you use this to make a bunch of flashcards for me?
361
As mentioned before with spaced repetition, you can get 5 to 10x as time efficient.
362
You know, per unit time you spend, you can remember 5 to 10x more simply by organizing your information and then consulting with it on a regular basis.
363
And in this case, the reason I like the clerk is because all of the thinking is your own.
364
What you're doing is you're just organizing it a little bit better because, you know, as a human being, you're constantly juggling all these different disciplines and ideas and concepts.
365
AI really can bring those together for you into higher and higher level and more simple abstractions, which can ultimately improve your ability to to organize it mentally as well.
366
Here you might just say, turn my messy notes into a clean outline.
367
Don't add anything I didn't write, just make it clear, you know, the hierarchy of information and how I intend to, I don't know, turn this into an essay or something.
368
So that covers all of the roles.
369
Next, I just want to talk about some high -level principles behind AI, including reasons not to use AI as well for certain types of learning.
370
One really valuable thing to use AI to do is to help find stuff at your level.
371
Now, in case you didn't know, you learn the most
372
when you are pushed just a little bit outside of your comfort zone not too much
373
or too little the whole idea is you need something
374
that challenges you okay it's called the zone of proximal development in psychology
375
and it's a zone that's just a little bit out of reach
376
that consistently allows you to you know lengthen your your your reach until eventually you can grasp the subject
377
so you know here we go
378
so a simple way to do this is hey find stuff
379
at my level i want you to quiz me on statistics easy all the way up to hard stop
380
when i start guessing things and i clearly don't realize them.
381
Then I want you to tell me my level.
382
From there, you know, we can go back to these other roles.
383
Map maker, we can do interviewers, we can start checking, we can start doing evaluations and eventually diagnosticians.
384
I also want you to explain this concept at three different levels for a child, for a beginner, for, you know, a teenager, for a high schooler, for an expert, and so on and so forth.
385
What you can do here is you can determine, okay, what level of understanding do I have around the vocabulary, the nuanced subjects, and so on and so forth,
386
and what do I need to know in order to be able to communicate topics like this more diligently and better.
387
Finally, you can also say, hey, you know, I want you to find me the best possible explanations for this thing.
388
You know, in my case, I know how to do A, I don't know how to do B, I'm approximately the level of a beginner, as we've assessed earlier.
389
I want you to figure out why each one is really good.
390
And I also want you to give me a bunch of links for it.
391
The reason why this is good is because human beings are still sort of the arbiter, at least for now, of taste and our ability to explain things in simple ways.
392
I've come up with so many really simple explanations
393
that I've learned in like five seconds
394
because somebody just put it in the perfect metaphorical way
395
that makes sense for me that I never would have really found had I not had AI.
396
And as a result, you know, when you find that perfect explanation that just clicks, you can usually save yourself days, weeks, or months of trying to learn it another way.
397
And human beings ultimately are just the ones that have created the most of that so far.
398
So, you know, what I'll do is I'll basically say, hey, can you find me a bunch of resources for somebody that knows how to do this, but maybe doesn't know how to do that.
399
I give them every piece of information that they need about me in order to be able to find that information better.
400
And then they'll come back with a hit list of like, yeah, you know, neural networks easily explained was written a couple years ago by this person, probably the simplest and easiest beginner tutorial.
401
And then I'll just take
402
that tutorial instead of trying to muddle you around with like crappy textbooks
403
or crappy lectures that might be a little bit advanced or a little bit too simple for me.
404
Another great use of AI is as a simulation.
405
We talked a little bit about this in the context of interviewing.
406
So you could role play.
407
You could say, hey, could you play a tough interviewer for you know a statistics job at uh i don't know anthropic
408
or something one question at a time i want you to score me at the end
409
but you could also actually give it a realistic task
410
and then have it produce the deliverables you would need to generate
411
so i don't know maybe you have some sort of customer concern
412
or q a or something like
413
that you have to respond to it maybe that's the job you want support a customer service role
414
but you can actually have it like come up with a bunch of emails
415
and then you have to go through and navigate and then answer those questions intelligently.
416
So then it can grade you based off what you do.
417
It can grade you based off an examination, so just the end result or the checked process that gets you there.
418
And then once you have all that, you'll know a lot better about where you're learning.
419
Likewise, you can actually have Claude, Codex, other AI agents build you practice apps.
420
These practice apps make it really easy to just blitz through a bunch of flashcard style things.
421
They allow you to visualize your knowledge in a variety of ways.
422
And I use this sort of thing because it's important to have, as mentioned, not just like a textual ability to understand a subject, but you have to be able to articulate it like with your words.
423
You have to be able to like visualize it on a screen.
424
You know, hey, draw a thing that corresponds to this thing.
425
Cloud and other AI agents are actually at the level where you can legitimately do this today.
426
You don't need to like have a whole examiner doing that for you.
427
And they can do it for cents on the dollar.
428
So building practice apps and stuff like that can also be very cool.
429
Ultimately, just run it, get feedback, continuously make it harder, run it again.
430
This thing's like the hyperbolic time chamber for knowledge.
431
Finally, there is a case against AI.
432
The big case is learning tends to happen during the struggle.
433
If you think about the difficulty on this graph is the y -axis
434
and the amount of time it takes sort of as this axis.
435
Well, maybe that's not entirely correct, but the difficulty is this hump here.
436
You know, normally you would have to go through a hump of really squinting at the screen and being, what the hell's going on?
437
I can't make this work in order to learn something because, again, it's active production.
438
AI allows you to shortcut that a lot of the time, and that leads to you ultimately remembering nothing.
439
You know, if it explains something to you, it'll feel like understanding, but it isn't.
440
A very interesting scientific result a few years ago was
441
that people that use GPS apps tend to have poorer representations of their environment in their brains.
442
And I think AI is quite similar insofar that, you know, if you're not doing the thinking now, you're just shortcutting it to get to the end result.
443
It will, like a GPS, weaken the brain regions that are responsible ultimately for coming up with the logic
444
that gets you places so you know when ai agrees with you
445
when you're half right when it invents random numbers
446
and sources it turns this process of learning
447
and struggling into just collecting a bunch of notes let's say
448
on a subject this of course can actually be very detrimental
449
to your learning the important thing to realize is you don't have to use ai the same way
450
that everybody else is using ai ai at the end of the day can be a thought partner
451
and it can improve multiple areas of your life you just cannot fall into
452
that default way that people tend to you know be incentivized to use it.
453
Hey, Claude, make me a billion dollars and make no mistakes.
454
I want to be the president of the United States Codex.
455
I want you to make it happen.
456
Like you're going to need to spend a little bit more time and effort being strategic about how you deploy it until, you know, we get to the point where it's just generally a lot more intelligent.
457
So it's the same thing with any technology, right?
458
It's the same thing with social media apps.
459
It's the same thing with, you know, Facebook.
460
There are a lot of useful and legitimate uses of technology like Facebook, but the default mode, the way that most people use technology like this is pretty bad.
461
And so it's important just to understand that you have the ability to make this powerful for you.
462
And hopefully this video has helped illuminate at least a little bit about how to do that.
463
Really hope you guys appreciated the video, had a lot of fun putting it together.
464
As mentioned, I use tools like this virtually every day in my own life
465
and my own business in order to achieve what I would consider to be, you know, pretty above average results.
466
You guys want to learn how to do this sort of thing yourself practically for the purposes of a business.
467
Definitely check out Maker School in the description.
468
It's my 90 day accountability program where I help people get their very first paying customer for an AI or automation service, or I give you all your money back.
469
I developed it using the similar concepts and methodologies to what I talked about today, so absolutely give it a go.
470
Aside from that, please like, subscribe, and support the channel, and I'll catch all y 'all in the next video.
471
Thank you very much.
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คำศัพท์และข้อสังเกตด้านการพูดสำหรับบทเรียนนี้
วิดีโอนี้มี 471 ประโยค และ 7267 คำ สำหรับฝึกพูดตาม ช่วงที่มีเสียงพูดยาว 29:35 ผู้พูดพูดเร็ว ประมาณ 246 คำต่อนาที จึงมีการเชื่อมเสียงและลดเสียงค่อนข้างมาก 85% ของคำอยู่ใน 3,000 คำที่ใช้บ่อยที่สุดในภาษาอังกฤษ ส่วนที่เหลือควรดูไว้ก่อนเริ่มฝึก
คำศัพท์สำคัญในวิดีโอนี้
คำที่ควรเรียนรู้ 15 คำจากวิดีโอ พร้อมคำอ่านและความหมาย:
| คำศัพท์ | คำอ่าน | ความหมาย |
|---|---|---|
| topic คำนาม | /ˈtɑpɪk/ | หัวข้อ, กระทู้ |
| mechanism คำนาม | /ˈmɛk.ə.nɪ.zəm/ | กลไก, เครื่อง |
| explanation คำนาม | /ˌɛkspləˈneɪʃən/ | การอธิบาย |
| gap คำนาม | /ɡæp/ | ช่อง |
| consumption คำนาม | /kənˈsʌmp.ʃən/ | การบริโภค |
| axis คำนาม | /ˈæksɪs/ | แกน |
| principle คำนาม | /ˈpɹɪn.sɪ.pəl/ | หลัก, คติ |
| examination คำนาม | /ɪɡˌzæm.ɪˈneɪ.ʃən/ | ไต่สวน |
| clerk คำนาม | /klɝk/ | เสมียน, พนักงาน |
| timer คำนาม | /ˈtaɪmɚ/ | เครื่องจับเวลา |
| beginner คำนาม | /bəˈɡɪnɚ/ | มือใหม่ |
| listener คำนาม | /ˈlɪs(ə)nɚ/ | ผู้ฟัง |
| occur คำกริยา | /əˈkɜː/ | เกิด |
| compare คำกริยา | /kəmˈpɛɚ/ | เปรียบเทียบ |
| solve คำกริยา | /sɒlv/ | แก้, แก้ไข |
ไวยากรณ์ในวิดีโอนี้
โครงสร้างที่ผู้พูดใช้บ่อยที่สุด พร้อมคำพูดจริงจากวิดีโอ:
| โครงสร้าง | ในวิดีโอ |
|---|---|
| ประโยคเงื่อนไข if + อนุประโยค, will/would + กริยา — เงื่อนไขและผลลัพธ์ | if you misunderstand this part, it will lead · if i struggle i'll have · if it explains something to you, it'll feel |
| Present perfect have/has + กริยาช่อง 3 — เหตุการณ์ในอดีตที่ยังเกี่ยวข้องกับปัจจุบัน | have been · I've only lost · you've learned |
| Passive voice be + กริยาช่อง 3 — เน้นสิ่งที่เกิดขึ้น ไม่ใช่ผู้กระทำ | is based · are tested · be tested |
| Relative clauses who / which + อนุประโยค — ข้อมูลเพิ่มเติมเกี่ยวกับคนหรือสิ่งของ | consumption, which is · harder, which is · role, which is |
การออกเสียงที่ควรระวัง
ผู้พูดใช้รูปย่อและรูปลดเสียง 169 ครั้ง เช่น don't, you're, I'm ให้พูดแบบสั้นตามที่ได้ยิน
- เสียง “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, thesis /ˈθisɪs/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
- เสียง “sh” และ “zh”: explanation /ˌɛkspləˈneɪʃən/, essentially /ɪˈsɛnʃəli/, consumption /kənˈsʌmp.ʃən/, examination /ɪɡˌzæm.ɪˈneɪ.ʃən/, automation /ˌɔ.təˈmeɪ.ʃən/
- คำยาว — ลงเสียงหนักให้ถูกพยางค์: mechanism /ˈmɛk.ə.nɪ.zəm/, explanation /ˌɛkspləˈneɪʃən/, essentially /ɪˈsɛnʃəli/, examiner /əɡˈzæmɪnɚ/, difficulty /ˈdɪfɪkəlti/
วิธีฝึกกับวิดีโอนี้
- ฟังวิดีโอให้จบหนึ่งรอบโดยยังไม่ต้องพูด แล้วจดคำที่ยังไม่รู้จัก
- เริ่มที่ความเร็ว 0.75× พูดตามทีละประโยค แล้วกลับไปใช้ความเร็วปกติเมื่อเริ่มคล่อง
- อัดเสียงตัวเองแล้วเทียบกับต้นฉบับ โดยสังเกตคำอย่าง topic, mechanism, explanation เป็นพิเศษ
เทคนิค Shadowing คืออะไร?
Shadowing เป็นเทคนิคการเรียนรู้ภาษาที่ได้รับการรับรองทางวิทยาศาสตร์ พัฒนาขึ้นสำหรับการฝึกนักแปลมืออาชีพ วิธีการนี้เรียบง่ายแต่ทรงพลัง: คุณฟังเสียงภาษาอังกฤษจากเจ้าของภาษาและพูดตามทันที — เหมือนเงาที่ตามผู้พูดด้วยช่วงเวลาห่าง 1-2 วินาที การวิจัยแสดงว่าเทคนิคนี้ปรับปรุงความแม่นยำในการออกเสียง ทำนองเสียง จังหวะ การเชื่อมเสียง การฟังเข้าใจ และความคล่องแคล่วในการพูดได้อย่างมีนัยสำคัญ