Shadowing-Übung: This is Crazy - Englisch Sprechen Lernen mit Video
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The exponential time hypothesis is false.
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This is completely f*ing insane.
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OpenAI has done it again.
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They are now releasing even more proofs.
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When will the proof stop?
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We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory.
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That's right, you heard it here.
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They are releasing a proof about my boy, computer scientist.
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And as somewhat of a computer scientist myself, I was naturally intrigued.
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And learning that the exponential time hypothesis is false means
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that we could fundamentally be entering into an era in which all modern diseases are solved in relatively short amount of computation,
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and it also means that all encryption is broken and everything you enjoy is completely gone and society collapses.
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So we have to talk about this.
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I'll help you understand it.
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Okay.
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As again, as a computer scientist myself, be glad to walk you through what is NP complete,
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what this all means, what is the exponential time hypothesis, and what does this actually mean?
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Should you be worried?
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Is P equal NP?
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But before we do, a quick thank you to the sponsor.
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I have to say one of my favorite parts of this AI revolution is GrokBot.
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And that is because so many little tedious tasks are now me just going,
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hey, can you update my calendar with all my kids' upcoming soccer games for this tournament?
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Or, hey, put milk on my list for things to pick up.
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And with the ability to add custom MCPs, you could actually add any of your applications and be able to sync with it.
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So GrokBot allows me to effectively control my life and just take out all the tedium.
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Honestly, kind of feels like the promise of AI that we kept on hearing
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that it's gonna make our life better because this actually makes life better.
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And my wife is much happier now that I'm a little bit more organized.
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So if you'd like to support the channel, go check out GrokBot, which is available on the web, desktop applications, and on mobile, Android included.
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I feel like they're thinking of me when they made that.
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I'm very, very, very happy.
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All right, so OpenAI is back at it again.
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This time, people are freaking out for a different reason.
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It's not for the purity of math that they're losing everybody on.
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In fact, it's for something a little bit different, because if what they have proved is correct, it is a step towards P equaling NP.
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Now, with this announcement, they actually announced three big discoveries.
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The first one for local smoothing of the Kakeya conjecture.
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The third for the existence of quantum chromodynamics and completions of the standard model.
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But it's the middle one.
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It's the middle one we're going to talk about, okay?
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The exponential time hypothesis.
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Now, to understand the exponential time hypothesis, you need to understand this very small Boolean expression right here.
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A or B or C.
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This is called a 3SAT problem.
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There's three variables in a group
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and we need to see what values of all the variables need to be to be able to satisfy this.
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So if we just simply set all of them to false, it would be false.
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But if we just set any one of them to be true, then we have satisfied that Boolean expression.
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We have found something that makes it true.
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And in this case, we just set C to true.
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But let's make it a little bit harder.
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Let's add one more group.
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So now we have two sets of three.
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This is still a three -sat problem.
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We have A or B or C and we have not a
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or d or e so now we have five variables a through e so
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if we were to kind of guess well what would satisfy
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this we would actually have to start guessing okay is all
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of them false satisfied no how about one of them true d true e
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and d true how about c true yes okay c is true
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this works awesome we now have satisfied this equation but notice
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that it took quite a few more guesses to get somewhere also just a side note the fact
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that this actually is the same it's the same thing
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that made them both true that's just an anomaly just okay don't think about it too hard all right
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so let's add actually a third one in here notice
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that we have not see in the third one f
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and g so that that puts our total variables all the way up to g
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or seven so now when we're checking notice
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that this takes a lot longer we have a lot more guesses like this would take us a
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while to figure out
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if you were doing this by hand you would have to do a bit of guessing
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but lo and behold we got it we were able to get the answer after
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so many guesses but you can see something here.
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Every time we add a new group, and more specifically, every time we add a new variable, this requires a much larger amount of checking.
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Now this is kind of like the fundamental premise of the exponential time hypothesis.
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This hypothesis is super important because if it is true, then P does not equal NP.
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But right now, it hasn't been proven to be actually true.
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It's only a hypothesis.
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And the basic of the hypothesis is that the growth of the 3SAT problem is effectively bounded exponentially, like 2 to the n.
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Now, there are some improvements on the algorithms,
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and people have been able to kind of reduce that base down to 1 .3 with clever programming tricks,
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but that still is 1 .3 to the n.
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To put this into perspective, I'm going to give a very special number.
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Just go with it for a second.
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1 .3 to the 2500.
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This right here is more atoms than in the known universe.
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So you can see that even with that much smaller base, when you get to so many of these, it becomes impossible to actually compute.
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Like we don't have the computation ever to be able to solve this.
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But what OpenAI just did is they found an equation that they were able to solve that looks like this,
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two to the N divided by the squirt of the log of N.
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And this takes it from being exponential to being sub -exponential, which is exactly what ETH said, nope, that cannot happen.
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I put my foot on the ground.
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We will not have non -exponentials here.
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No, well, we actually have sub -exponentials.
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Now, this is not polynomial time.
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This is still exponential time, but it's sub -exponential because this does grow with the growth of n, which makes n smaller.
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So the growth is just not quite the same.
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If you remember my last equation, that 1 .3 to the 2 ,500, you can see right here that has greatly reduced it.
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Now, this is still completely not even computable
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that number is just gigantic we're not going to be able to solve
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that in my lifetime but as you can see it's significantly reduced
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and this means is there a path to continuing to reduce
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this are we just starting the walking of reducing this now this may sound a little bit familiar
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because if you just remember a few weeks ago on october 6th
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when open ai dropped like 372 different uh proofs
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or was it i can't remember what it was doesn't really matter it was a lot of proofs.
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One of them was about integer multiplication.
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The integer multiplication started off as being this like barely in just the tiniest improvement of all time.
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And then over the course of the next couple weeks, you started seeing improvement and improvement and improvement.
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So is this going to be another one of those cases
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where it starts off with just barely a little bit of an improvement
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that just leads to a deluge of huge improvements on this running time?
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Are we going to see something like p does not equal np
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now you're probably asking
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i don't know what p equal does not equal np even means what is the implication
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this is where all the scary stuff actually happens because
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if this starts to improve a whole bunch
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and we open up new doors
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and we eventually get this all the way down into polynomial time
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this will just upend society yes p equals np will make things kind of crazy for us okay
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so we have to talk about p equals np
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because this is the big question because
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if p equals np we are all screwed
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and we are also in a super great world where we could cure cancer
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it's the craziest world we could ever possibly live in
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and this is why there exists a set of problems called
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np problems non -deterministic polynomial problems these problems have a very special condition on them
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they can be verified in polynomial time this is really important
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so when we go back to this i can look at this one right here and go like this.
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Okay, that one's true.
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That one's true because not zero.
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And that one's true.
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Boom.
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I just verified with my eyeballs a problem that is super hard, that is exponentially difficult to solve for,
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but I can verify it in linear time.
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It's super easy to see if something's right, super hard to determine the problem.
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Inside of NP, there's a set of problems called P.
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And now this is the big question.
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Is all the problems in NPP or is just some of them?
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P means that they are solvable in polynomial time.
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See, then there's one other group of problems we'd like to call NP -hard.
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Now, this is kind of a funny one.
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It has a really funny name to it because you're like, oh, well, why aren't all the problems inside of NP?
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It's called NP -hard.
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Well, what they mean is that they're at least as hard as every other problem inside of NP.
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And so this little intersection right here is a very special class of problems we like to refer to as NP -complete.
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NP -complete problems are NP hard problems that are verifiable in polynomial time.
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This problem is NP complete.
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Now, NP complete has this really special property, which every problem that is NP complete, you can represent by any other problem that's NP complete,
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meaning that the traveling salesman can actually be represented as a three SAT problem.
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That is why I showed you this little formula over here,
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because 2500 is the size of the United States being represented as a three SAT formula solving the traveling salesman
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which means it takes forever to solve we can't solve
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this even with the best stuff the best computers in the universe can't solve
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that problem right now but here's the thing with that paper showing
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that exponential time hypothesis is in fact false
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that we are sub exponential there's a chance
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that we could keep on figuring out how to reduce it into polynomial time
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and that's where things get crazy and the reason why is
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that once one of these problems become polynomial time all of these problems become polynomial time by the sheer nature
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that they are the hardest of all the problems within np
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now where this gets really really bad is think of rsa rsa
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which of course is like how you do public key encryption there's like many versions of this
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and this is not the complete picture
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but just go with me here effectively there's two prime numbers that are multiplied together and then you get this public key.
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that anybody can look at and it's completely fine
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because they don't know how to get these private keys from this public one.
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Yes, I know there's an inverted matrix involved in all of this.
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We're not going to talk about it.
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Just don't worry about that, okay?
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Just keep it simple.
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So that means these private keys, to be able to find those, you have to do something called integer factorization.
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And that turns out to be a really hard problem.
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Not an NP hard problem, but a very hard problem inside of NP because we can validate that in polynomial time,
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but we cannot solve for it in polynomial time.
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And since exponential time hypothesis is now false and we are now sub -exponential, if this does improve and go to polynomial,
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that means we can crack RSA in polynomial time, which means powerful computers would be able to break encryption across the internet.
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This also goes for like SHA -256.
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This also goes for HMAC.
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This goes for pseudo -random number generators.
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It goes for blockchain, which awesome.
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Love to see that last one get taken down
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but nonetheless everything would kind of fall apart
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but there's also protein folding turns out protein folding is in fact going to be an NP complete problem
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so if we could change that we could potentially do research what would take decades
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or hundreds of years you could potentially do in like a year
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you would even be able to solve sudokus in polynomial time now
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that would be a crazy world right I mean solving sudokus okay that's getting a little too fanciful the name
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Is the NP a gin?
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Wortschatz und Sprechhinweise zu dieser Lektion
Diese Sprechübung basiert auf dem Video „This is Crazy“. Diese Wörter kommen am häufigsten vor: exponential, polynomial, hypothesis, solve, complete. Dieses Video enthält 190 Sätze und 2263 Wörter zum Nachsprechen. Der gesprochene Teil dauert 11:26. Der Sprecher spricht schnell, etwa 198 Wörter pro Minute – rechne mit verbundenen und verkürzten Lauten. 84 % der Wörter gehören zu den 3.000 häufigsten im Englischen; den Rest solltest du dir vor dem Üben ansehen.
Wichtiger Wortschatz in diesem Video
15 lernenswerte Wörter aus dem Video, mit Aussprache und Bedeutung:
| Wort | Aussprache | Bedeutung |
|---|---|---|
| solve Verb | /sɒlv/ | lösen |
| hypothesis Substantiv | /haɪˈpɒθɪsɪs/ | Hypothese |
| variable Adjektiv | [ˈvæɹ.i.ə.bl̩] | variabel |
| satisfy Verb | /ˈsætɪsfaɪ/ | befriedigen, zufriedenstellen |
| scientist Substantiv | /ˈsaɪəntəst/ | Wissenschaftler, Wissenschaftlerin |
| equation Substantiv | /ɪˈkweɪ.ʒən/ | Ausgleich, Gleichgewicht |
| verify Verb | /ˈvɛɹɪfaɪ/ | belegen, beweisen |
| encryption Substantiv | /ɪnˈkɹɪpʃən/ | Verschlüsselung |
| discovery Substantiv | /dɪˈskʌv.ɹi/ | Entdeckung |
| formula Substantiv | /ˈfɔː.mjʊ.lə/ | Formel |
| announce Verb | /əˈnaʊn(t)s/ | ankündigen, verkünden |
| mathematics Substantiv | /mæθ(.ə)ˈmæt.ɪks/ | Mathematik |
| fold Verb | /ˈfəʊld/ | falten, falzen |
| quantum Substantiv | /ˈkwɑntəm/ | Quantum, Menge |
| salesman Substantiv | Verkäufer |
Phrasal Verbs, die du hören wirst
| Wort | Bedeutung |
|---|---|
| figure out Verb | herausfinden |
| check out Verb | überprüfen |
| fall apart Verb | auseinanderfallen |
| open up Verb | öffnen |
| pick up Verb | aufheben, aufnehmen |
| take down Verb | herunternehmen, abnehmen |
| take out Verb | herausbringen, herausnehmen |
| turn out Verb | sich herausstellen als/dass, sich erweisen als |
Sätze zum Wiederholen
Kurze, vollständige Sätze aus dem Video, die du im Alltag wiederverwenden kannst:
- That's right, you heard it here.
- I'll help you understand it.
- Should you be worried?
- I'm very, very, very happy.
- Let's add one more group.
Grammatik in diesem Video
Die Strukturen, die der Sprecher am häufigsten verwendet, mit den genauen Worten aus dem Video:
| Struktur | Im Video |
|---|---|
| Passiv be + Partizip Perfekt – wichtig ist, was geschieht, nicht wer es tut | was naturally intrigued · are solved · is broken |
| Present Perfect have/has + Partizip Perfekt – eine vergangene Handlung, die jetzt noch wichtig ist | has done · have proved · have satisfied |
| Relativsätze who / which + Satz – eine Zusatzinformation über eine Person oder Sache | GrokBot, which is · exponential, which is · time, which means |
Aussprache, auf die du achten solltest
Der Sprecher verwendet 27 Kurzformen und abgeschwächte Formen, zum Beispiel don't, we're, can't. Sprich sie in der kurzen Form, so wie du sie hörst.
- Die „th“-Laute: hypothesis /haɪˈpɒθɪsɪs/, mathematics /mæθ(.ə)ˈmæt.ɪks/, smooth /smuːð/, nonetheless /ˌnʌnðəˈlɛs/, algorithm /ˈælɡəɹɪðm̩/
- Die Laute „sh“ und „zh“: equation /ɪˈkweɪ.ʒən/, encryption /ɪnˈkɹɪpʃən/, potentially /pəˈtɛnʃ(ə)li/, completion /kəmˈpliː.ʃən/, sheer /ʃɪɚ/
- Lange Wörter – auf die Betonung achten: hypothesis /haɪˈpɒθɪsɪs/, potentially /pəˈtɛnʃ(ə)li/, mathematics /mæθ(.ə)ˈmæt.ɪks/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, fundamental /ˌfʌn.dəˈmɛn.təl/
Laute, die Deutschsprachigen schwerfallen:
- /w/ — mit runden Lippen, nicht wie das deutsche „w“ (/v/): equation /ɪˈkweɪ.ʒən/, quantum /ˈkwɑntəm/, squirt /skwɝt/
- Stimmhafter Auslaut — /b/, /d/, /g/, /z/, /v/ am Wortende nicht verhärten: solve /sɒlv/, fold /ˈfəʊld/, bound /ˈbaʊ̯nd/, divide /dɪˈvaɪd/, organize /ˈɔɹɡənaɪz/
- /æ/ — offener als „ä“: variable [ˈvæɹ.i.ə.bl̩], satisfy /ˈsætɪsfaɪ/, mathematics /mæθ(.ə)ˈmæt.ɪks/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, crack /kɹæk/
So übst du mit diesem Video
- Höre dir das ganze Video einmal an, ohne zu sprechen, und notiere die Wörter, die du nicht kennst.
- Beginne mit 0,75-facher Geschwindigkeit, sprich Satz für Satz nach und wechsle zur normalen Geschwindigkeit, sobald es leichtfällt.
- Nimm dich auf und vergleiche mit dem Original; achte dabei auf Wörter wie solve, hypothesis, variable.
Was ist die Shadowing-Technik?
Shadowing ist eine wissenschaftlich fundierte Sprachlerntechnik, die ursprünglich für die professionelle Dolmetscherausbildung entwickelt und durch den Polyglotten Dr. Alexander Arguelles populär gemacht wurde. Die Methode ist einfach aber wirkungsvoll: Du hörst englisches Audio von Muttersprachlern und wiederholst es sofort laut — wie ein Schatten, der dem Sprecher mit nur 1–2 Sekunden Verzögerung folgt. Anders als passives Hören oder Grammatikübungen zwingt Shadowing dein Gehirn und deine Mundmuskulatur, gleichzeitig echte Sprachmuster zu verarbeiten und zu reproduzieren. Studien zeigen, dass es Aussprachegenauigkeit, Intonation, Rhythmus, verbundene Sprache, Hörverständnis und Sprechflüssigkeit signifikant verbessert — was es zu einer der effektivsten Methoden für die IELTS Speaking-Vorbereitung und reale englische Kommunikation macht.