Prática de Shadowing: A 4-Step System to Process Information with Extreme Clarity - Aprenda a falar inglês com vídeo
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First principles thinking is the closest thing to a cheat code for life.
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Successful people from Elon Musk all the way to Kobe Bryant used it to break conventions and become one of a kind.
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First principles make you stand apart.
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I got to use it too, from MIT all the way to billion dollar boardrooms.
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If you're not careful, AI will make your thinking more average.
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So I'll show you a four -step framework to think so clearly that people who you meet walk away thinking,
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you must be some kind of a genius.
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Let's get started.
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For most of my last 12 years, I slept for less than four, five, six hours a night.
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I was the CEO of one of the top 20 fastest growing companies in the US.
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I worked at a large public company where we were growing like crazy.
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And I traveled nonstop, worked past midnight.
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So every single morning when I woke up, I would always feel totally exhausted from the first minute.
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And I would load up on coffee and I would be ready for the day.
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Then about three years ago, I retired from full -time operator executive work.
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And my schedule obviously changed dramatically.
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I had more control over my day.
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I was on boards, I was advising, I was investing, but it wasn't 7 a .m till midnight every single day.
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But still, every morning, I would wake up completely exhausted.
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That was strange.
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So I reached out to a great pulmonologist and sleep specialist, and they did a sleep study on me,
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where they put me in a lab and measured how I slept
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and what happened in my brain the medical data completely blew my mind.
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The results showed that I was waking up 34 times an hour, every hour, all through the night.
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The structure of my jaw is such that it was blocking my airway.
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Now, I had no idea.
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I had spent years trying to normalize the problem by telling myself, oh, I need more sleep or I have to go through this.
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It's the grind.
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I need more coffee.
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I need to work less.
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The moment I stopped perpetuating the story and deconstructed the system from first principles up, that fixed the root cause.
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And I realized the problem was not where I thought it was.
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That process of thinking is called the first principles thinking.
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We're going to build a four -step framework here
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and learn how to use AI to drive that process and how to build specific prompts for each of those steps.
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So it's a bit dense.
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So I'll make a PDF as well.
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So you can see all of it in one place.
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Link is in the description.
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Of course, it's free.
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We never investigate our own assumptions.
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We inherit them from our colleagues, our families, industries, experts, or our own previous experiences.
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First principles want you to begin somewhere else.
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What do I actually know for sure?
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What am I assuming?
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What else could produce this result?
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Which part can I test?
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Imagine buying a car, driving it once, and then throwing it away.
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That would sound insane, right?
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But for decades, that was how most rockets worked.
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Now, of course, rockets are not cars.
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The physics is far more unforgiving.
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Obviously, it's called rocket science for a reason.
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But Elon Musk questioned the very assumption.
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Why couldn't the rocket land vertically and fly again?
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That question changed the economics of space travel.
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So how do you start thinking in terms of first principles?
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Maybe a couple of examples.
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Maybe you're thinking, I can't focus.
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Well, start digging in.
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Is it focus or sleep or phone addiction or unclear tasks?
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Too many priorities?
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You're bored?
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There's no deadline?
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Or are you doing work with no real impact?
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What is it?
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Things are about to get worse because AI is here.
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Let's go there next.
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Consider how large language models actually work.
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They do not physically observe the universe we're in.
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They don't have the idea of reality, at least not yet.
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And they don't have the real world experience.
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They're mathematical models that are very good at matching patterns.
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So when we ask a question, it looks for the most likely thing that matches the pattern.
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The most familiar answer seems like the best answer.
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That's the way AI is designed.
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And the better AI sounds, the less you will check.
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A general purpose AI model does not naturally start from first principles.
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That's why you have to force AI to think differently.
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That's the four -part framework.
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We'll walk through each step and we will also construct prompts for each step.
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So let's go to the first step, which is D for decompose.
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Let's say if you want to start a business or even something simpler.
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Let's say you want to start a YouTube channel.
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If you talk to the experts and say, hey, I want to start a YouTube channel.
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What do I need?
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What are they going to say?
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They'll say, oh, well, you need a studio.
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You need a professional camera, good lighting.
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You need an editor you need a creative director, a production agency.
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And sure, if you can afford all of that,
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those things may save you some time and improve the final product so you can focus on content creation.
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But that's all conventional wisdom.
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What if you boiled it down to its essential components?
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Decomposed it.
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What do you really need?
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Just two things, a phone and a story.
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You can start tomorrow.
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You can start tonight.
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The same idea applies to starting a company.
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You can raise millions from some investors.
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You can rent a swanky office in New York City.
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But there are thousands of successful companies that got started without any of that.
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So this step of decomposing is really about seeing the parts of the machinery.
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This is where AI is going to be tremendously useful.
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But remember that these models are also trained to be very eager and very helpful.
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So the moment they see a problem, they would want to solve it.
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You don't want that.
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You want them to break the problem down into its essential parts.
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Let's apply our friendly framework, AIM, to create a prompt.
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I've covered the idea of AIM in this video before somewhere.
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But if you haven't seen it, AIM stands for Actor, Input, Mission.
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Tell the model who it's acting as, give it the context or data it needs, and then tell it what done looks like.
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So here's the prompt.
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Act as a world -class first principles analyst.
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Your job in this step is decomposition only only.
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You are penalized for introducing advice, solutions, assumptions, or standard playbooks.
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Be my thought partner.
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Do not suck up to me.
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Intention.
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I want to understand exactly what this problem is made up of.
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My problem is, and then you will insert your problem, if the stated problem appears to contain a hidden or a deeper question,
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identify it in one sentence before decomposing the current problem.
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Ask whether I want you to decompose the original problem or the deeper one.
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Do not continue until I choose.
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Do not replace or reframe my problem until I tell you.
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So you see, you're trying to constrain what AI needs to focus on.
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And the third part is mission.
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Break the problem into its smallest useful constituent parts.
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Show the hierarchy clearly, the overall problem, its major components, and the smaller elements inside or under each component.
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Use only dimensions that are relevant, such as people, process steps,
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time, resources, costs, etc. For each component,
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briefly explain what it contains and how it connects to the larger problem.
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Stop decomposing when going down further would no longer improve my understanding.
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Do not evaluate the components.
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Do not recommend solutions.
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Only show me what parts the problem is made up of.
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So that was your first prompt.
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And I'm sorry if it was a bit long or complicated,
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but I'm hoping that it will give you clarity on how to boil down any problem into its essential parts.
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That's part of the first principles thinking.
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And also how to make sure that AI remains focused on the task that you give it.
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Now, let's go to step two, where we look at each of these parts and see whether it's a fact or an assumption.
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The most important first principle skill is auditing the assumptions.
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After the second world war,
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American car companies had enormous factories that produced big American cars for the big American market.
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Now, Toyota was a very small company and they were based in Japan and we're talking about after
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World War II.
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So Japan was recovering, much less capital, fewer resources, and a much smaller market.
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So copying Detroit would not have worked for Toyota.
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Toyota couldn't have afforded it.
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So they audited the assumptions underneath the idea of mass production itself.
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Why make big cars?
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Why not small cars?
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Why produce them in huge batches?
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Why do you need to hold such a large inventory?
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What do you do when there is a defect?
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Are there different better ways to organize the processes and people?
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That thinking became the foundation of just -in -time production
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and something that became so dramatically successful that American companies started implementing that.
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And over time, Toyota overtook GM to become the world's largest automaker.
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And, you know, years later, I feel like history is repeating itself because then came
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Tesla that questioned the assumptions made by Toyota in all car makers.
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Because Tesla asked, why do you even need a combustion engine at all?
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And by 2020, Tesla had overtaken
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Toyota as the world's most valuable automaker.
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Most conventions that we cling to are just assumptions that have survived.
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But true innovation requires that you break the rule, that you ignore the conventions.
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And here's where AI can be a very valuable partner.
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You can use the model to perform that audit that we're talking about.
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Here's the prompt to do that.
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And I'll just read the first part of the prompt and then we'll just put the entire prompt on the screen.
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You can pause and take a screenshot or come back to it anytime you want.
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And of course, all of this is going to be in a nicely formatted PDF.
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You can get it for free.
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Alright, here's the prompt.
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Act as a skeptical red team analyst,
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whose only job is to uncover and question inherited assumptions.
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Assume that every obvious part of the problem may be hiding a convention until evidence proves otherwise.
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Intention, I want to know which of the building blocks above are assumptions
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And so on So this is the entire prompt
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Now, once you identify the assumption
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You may feel like you may have destroyed the entire solution that you were thinking about And that's a good thing
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Because now you finally have the freedom to build a better solution from the ground up
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And for that, we go to the next step, which is R for recombining.
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I think music gives us the simplest way to understand the idea of recombination.
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Most modern Western music is built from 12 notes.
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If you're trained in Western music, that's your alphabet.
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You don't spend your entire life searching for a secret 13th note.
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But if you came from, let's say, Indian classical music tradition, I came from there, or Arabic music,
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they would use pitches that fall between the 12 notes of a Western piano.
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So if you only listen to, let's say, American pop all your life, and you suddenly hear Arabic music,
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it may sound to you as if it's out of tune, at first at least.
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But it's not.
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It's just that your ear is set to a different set of rules, the power of convention.
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But even with those 12 notes, You can recombine them into millions and millions of songs and compositions.
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Bach used them very differently than the Beatles.
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And Beatles used them very differently than Beyonce.
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They all used the same 12 notes to create different magic.
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Same ingredients, millions of dishes.
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That's where recombination comes into play.
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Innovation is not about looking for that secret 13th note.
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You just need the right combination of the 12 nodes that you already have.
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AI is unusually powerful here because it can search millions of combinations at a scale no human being can.
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Here's the R prompt.
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I'm just going to put it on the screen.
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And of course, there is a PDF link.
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Here's the actor, the intention.
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Here's the mission.
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Now, you have a handful of possibilities.
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There are ways to combine these ideas but they're still all theoretical.
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The final step is where the practical world gets to vote.
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That's our fourth step, E, for experiment.
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You know, James Dyson, who was an inventor, noticed that these vacuum cleaners lost suction as their bags filled with dust.
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And it took him 5 ,127 prototypes over five years before he could reach his breakthrough design.
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And it's the same reason Google used to run millions of
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experiments on what exact shade of blue color would encourage people to click on links.
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The first principle thinking is hard.
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It is counterintuitive, but it's still a clean process until the fourth step.
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The real world experiments are inherently messy.
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They're hard, they're risky, and they have real consequences if you fail.
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That's why AI can be a very good simulator.
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You can use AI to design these experiments for you.
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Here's a prompt for it.
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Here's the actor.
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Act as a skeptical scientist.
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Your job is to help me design the cheapest,
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fastest way to find out whether this holds up before it costs me anything real in time,
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money, effort, reputation.
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Don't try to sell me the idea.
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Give me the test I could actually run.
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So that's the actor.
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Here's the intention.
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Here's the mission.
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And notice what it says at the bottom of the mission part of the prompt.
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And I like it a lot.
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For each test, tell me what result would rule that solution out
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and what result would keep it alive and what I'd learn from the problem either way.
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Give me your view on which building block to revisit if all the tests fail.
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And the reason I like it is
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because the whole point of first principles thinking is to keep us learning about where our failures
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and successes are and what they're caused by.
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So, yay, we made it.
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It was a bit dense, but that was there.
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And it's yours.
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Make it better.
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Share it with others.
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Make sure every prompt makes the machine show its work to you.
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And by the way, make sure that you are the decision maker.
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AI does the work.
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AI shows you the work.
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You keep the judgment.
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Now, one point I would make about first principles and the framework,
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they won't give you a surefire way to avoid failure.
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The goal is not to be right every time.
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It is to learn quickly why you are wrong.
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Martin Short has been one of the most talented comedic actors for, I don't know, more than 40 years.
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And yet, most of his movies have flopped.
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But he's still one of the most successful comedians in North America today.
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And he was talking to a young comedian.
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He gave him a wonderful advice.
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He said, in this business, you fail 98 % of the time.
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Those are great odds.
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I love that line.
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Most of what I have tried in my life didn't work either.
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My failure rate is right around there.
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You know, yours might be too.
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But even if you have such a terrible hit rate as mine,
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the math eventually still works out in your favor.
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Because when you find just that 2 % that actually works,
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it can change the entire trajectory of your life.
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You don't need 100 wins.
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You only need one.
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Thank you, and I love you.
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Vocabulário e dicas de fala para esta lição
Esta aula de conversação usa o vídeo “A 4-Step System to Process Information with Extreme Clarity”. As palavras que mais se repetem: prompt, step, principle, thinking, assumption. Este vídeo tem 274 frases e 2684 palavras para praticar shadowing. A fala dura 18:48. O falante mantém um ritmo estável de cerca de 143 palavras por minuto, confortável para o shadowing. 83% das palavras estão entre as 3.000 mais comuns do inglês; vale a pena estudar o restante antes de começar.
Vocabulário principal deste vídeo
15 palavras do vídeo que vale a pena aprender, com pronúncia e significado:
| Palavra | Pronúncia | Significado |
|---|---|---|
| prompt verbo | /pɹɑmpt/ | incitar, impelir |
| principle substantivo | /ˈpɹɪn.sɪ.pəl/ | princípio |
| assumption substantivo | /əˈsʌm(p).ʃ(ə)n/ | suposição, pressuposição |
| component substantivo | /kəmˈpoʊ.nənt/ | componente |
| framework substantivo | /ˈfɹeɪm.wɜːk/ | esqueleto, estrutura |
| experiment substantivo | /ɪkˈspɛɹ.ə.mənt/ | experimento, experiência |
| intention substantivo | /ɪnˈtɛnʃn̩/ | intenção, intuito |
| rocket substantivo | /ˈɹɑk.ɪt/ | foguete |
| differently advérbio | /ˈdɪf.ə.ɹənt.li/ | diferentemente |
| audit substantivo | /ˈɔːdɪt/ | auditoria |
| innovation substantivo | /ˌɪn.əˈveɪ.ʃən/ | inovação |
| midnight substantivo | /ˈmɪd(ˌ)naɪt/ | meia-noite |
| maker substantivo | /ˈmeɪk.ɚ/ | fazedor |
| dense adjetivo | /ˈdɛns/ | denso |
| comedian substantivo | /kəˈmiː.di.ən/ | comediante, humorista |
Phrasal verbs que você vai ouvir
| Palavra | Pronúncia | Significado |
|---|---|---|
| wake up verbo | /ˌweɪk ˈʌp/ | acordar, despertar |
| boil down verbo | condensar, resumir | |
| find out verbo | descobrir | |
| go down verbo | descer |
Frases que vale a pena repetir
Frases curtas e completas do vídeo que você pode usar na conversa do dia a dia:
- What do I actually know for sure?
- Which part can I test?
- Why couldn't the rocket land vertically and fly again?
- What do you really need?
- What do you do when there is a defect?
Gramática neste vídeo
As estruturas que o falante mais usa, com as palavras exatas do vídeo:
| Estrutura | No vídeo |
|---|---|
| Voz passiva be + particípio passado — o foco está no que acontece, não em quem faz | is called · is designed · are also trained |
| Present perfect have/has + particípio passado — uma ação passada que ainda importa agora | I've covered · haven't seen · have survived |
| Orações relativas who / which + oração — informação extra sobre uma pessoa ou coisa | people who you · step, which is |
Pronúncia para ficar de olho
O falante usa 39 contrações e formas reduzidas, como don't, they're, you're. Diga-as na forma curta, do jeito que você ouve.
- Os sons de “th”: mathematics /mæθ(.ə)ˈmæt.ɪks/, underneath /ˌʌndɚˈniθ/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, theoretical /ˌθi.əˈɹɛt.ɪ.kəl/, breakthrough /ˈbɹeɪk.θɹuː/
- Os sons de “sh” e “zh”: assumption /əˈsʌm(p).ʃ(ə)n/, intention /ɪnˈtɛnʃn̩/, innovation /ˌɪn.əˈveɪ.ʃən/, reputation /ˌɹɛpjʊˈteɪʃən/, composition /ˌkɑm.pəˈzɪʃ.ən/
- Palavras longas — acerte a sílaba tônica: experiment /ɪkˈspɛɹ.ə.mənt/, differently /ˈdɪf.ə.ɹənt.li/, innovation /ˌɪn.əˈveɪ.ʃən/, comedian /kəˈmiː.di.ən/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/
Sons difíceis para falantes de português:
- Consoante final — sem acrescentar um “i” depois: prompt /pɹɑmpt/, component /kəmˈpoʊ.nənt/, framework /ˈfɹeɪm.wɜːk/, experiment /ɪkˈspɛɹ.ə.mənt/, rocket /ˈɹɑk.ɪt/
- /l/ final — a língua toca o céu da boca, não vira “u”: principle /ˈpɹɪn.sɪ.pəl/, boil /bɔɪ(ə)l/, skeptical /ˈskɛptɪkəl/, practical /ˈpɹæk.tɪ.kəl/, skill /skɪl/
- /h/ — um sopro suave, diferente do “r”: inherit /ɪnˈhɛɹɪt/, helpful /ˈhɛlp.fl̩/, handful /ˈhæn(d)fʊl/, hierarchy /ˈhaɪ.ə.ɹɑː(ɹ).ki/, inherently /ɪnˈhɛɹəntli/
Como praticar com este vídeo
- Ouça o vídeo inteiro uma vez sem falar e anote as palavras que você não conhece.
- Faça shadowing frase por frase na velocidade normal, repetindo cada uma até o seu ritmo ficar igual ao do falante.
- Grave a sua voz e compare com o original, prestando atenção a palavras como prompt, principle, assumption.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.