Shadowing Practice: This is Crazy - Learn English Speaking with Video
Kies een oefenmodus
Elke modus opent een volledig scherm oefenruimte.
190 zinnen
Als zinnen te kort of te lang zijn, klik op Bewerken om ze aan te passen.
1
The exponential time hypothesis is false.
2
This is completely f*ing insane.
3
OpenAI has done it again.
4
They are now releasing even more proofs.
5
When will the proof stop?
6
We are announcing three new discoveries in mathematics, computer science, and physics about harmonic analysis, complexity theory, and quantum field theory.
7
That's right, you heard it here.
8
They are releasing a proof about my boy, computer scientist.
9
And as somewhat of a computer scientist myself, I was naturally intrigued.
10
And learning that the exponential time hypothesis is false means
11
that we could fundamentally be entering into an era in which all modern diseases are solved in relatively short amount of computation,
12
and it also means that all encryption is broken and everything you enjoy is completely gone and society collapses.
13
So we have to talk about this.
14
I'll help you understand it.
15
Okay.
16
As again, as a computer scientist myself, be glad to walk you through what is NP complete,
17
what this all means, what is the exponential time hypothesis, and what does this actually mean?
18
Should you be worried?
19
Is P equal NP?
20
But before we do, a quick thank you to the sponsor.
21
I have to say one of my favorite parts of this AI revolution is GrokBot.
22
And that is because so many little tedious tasks are now me just going,
23
hey, can you update my calendar with all my kids' upcoming soccer games for this tournament?
24
Or, hey, put milk on my list for things to pick up.
25
And with the ability to add custom MCPs, you could actually add any of your applications and be able to sync with it.
26
So GrokBot allows me to effectively control my life and just take out all the tedium.
27
Honestly, kind of feels like the promise of AI that we kept on hearing
28
that it's gonna make our life better because this actually makes life better.
29
And my wife is much happier now that I'm a little bit more organized.
30
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.
31
I feel like they're thinking of me when they made that.
32
I'm very, very, very happy.
33
All right, so OpenAI is back at it again.
34
This time, people are freaking out for a different reason.
35
It's not for the purity of math that they're losing everybody on.
36
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.
37
Now, with this announcement, they actually announced three big discoveries.
38
The first one for local smoothing of the Kakeya conjecture.
39
The third for the existence of quantum chromodynamics and completions of the standard model.
40
But it's the middle one.
41
It's the middle one we're going to talk about, okay?
42
The exponential time hypothesis.
43
Now, to understand the exponential time hypothesis, you need to understand this very small Boolean expression right here.
44
A or B or C.
45
This is called a 3SAT problem.
46
There's three variables in a group
47
and we need to see what values of all the variables need to be to be able to satisfy this.
48
So if we just simply set all of them to false, it would be false.
49
But if we just set any one of them to be true, then we have satisfied that Boolean expression.
50
We have found something that makes it true.
51
And in this case, we just set C to true.
52
But let's make it a little bit harder.
53
Let's add one more group.
54
So now we have two sets of three.
55
This is still a three -sat problem.
56
We have A or B or C and we have not a
57
or d or e so now we have five variables a through e so
58
if we were to kind of guess well what would satisfy
59
this we would actually have to start guessing okay is all
60
of them false satisfied no how about one of them true d true e
61
and d true how about c true yes okay c is true
62
this works awesome we now have satisfied this equation but notice
63
that it took quite a few more guesses to get somewhere also just a side note the fact
64
that this actually is the same it's the same thing
65
that made them both true that's just an anomaly just okay don't think about it too hard all right
66
so let's add actually a third one in here notice
67
that we have not see in the third one f
68
and g so that that puts our total variables all the way up to g
69
or seven so now when we're checking notice
70
that this takes a lot longer we have a lot more guesses like this would take us a
71
while to figure out
72
if you were doing this by hand you would have to do a bit of guessing
73
but lo and behold we got it we were able to get the answer after
74
so many guesses but you can see something here.
75
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.
76
Now this is kind of like the fundamental premise of the exponential time hypothesis.
77
This hypothesis is super important because if it is true, then P does not equal NP.
78
But right now, it hasn't been proven to be actually true.
79
It's only a hypothesis.
80
And the basic of the hypothesis is that the growth of the 3SAT problem is effectively bounded exponentially, like 2 to the n.
81
Now, there are some improvements on the algorithms,
82
and people have been able to kind of reduce that base down to 1 .3 with clever programming tricks,
83
but that still is 1 .3 to the n.
84
To put this into perspective, I'm going to give a very special number.
85
Just go with it for a second.
86
1 .3 to the 2500.
87
This right here is more atoms than in the known universe.
88
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.
89
Like we don't have the computation ever to be able to solve this.
90
But what OpenAI just did is they found an equation that they were able to solve that looks like this,
91
two to the N divided by the squirt of the log of N.
92
And this takes it from being exponential to being sub -exponential, which is exactly what ETH said, nope, that cannot happen.
93
I put my foot on the ground.
94
We will not have non -exponentials here.
95
No, well, we actually have sub -exponentials.
96
Now, this is not polynomial time.
97
This is still exponential time, but it's sub -exponential because this does grow with the growth of n, which makes n smaller.
98
So the growth is just not quite the same.
99
If you remember my last equation, that 1 .3 to the 2 ,500, you can see right here that has greatly reduced it.
100
Now, this is still completely not even computable
101
that number is just gigantic we're not going to be able to solve
102
that in my lifetime but as you can see it's significantly reduced
103
and this means is there a path to continuing to reduce
104
this are we just starting the walking of reducing this now this may sound a little bit familiar
105
because if you just remember a few weeks ago on october 6th
106
when open ai dropped like 372 different uh proofs
107
or was it i can't remember what it was doesn't really matter it was a lot of proofs.
108
One of them was about integer multiplication.
109
The integer multiplication started off as being this like barely in just the tiniest improvement of all time.
110
And then over the course of the next couple weeks, you started seeing improvement and improvement and improvement.
111
So is this going to be another one of those cases
112
where it starts off with just barely a little bit of an improvement
113
that just leads to a deluge of huge improvements on this running time?
114
Are we going to see something like p does not equal np
115
now you're probably asking
116
i don't know what p equal does not equal np even means what is the implication
117
this is where all the scary stuff actually happens because
118
if this starts to improve a whole bunch
119
and we open up new doors
120
and we eventually get this all the way down into polynomial time
121
this will just upend society yes p equals np will make things kind of crazy for us okay
122
so we have to talk about p equals np
123
because this is the big question because
124
if p equals np we are all screwed
125
and we are also in a super great world where we could cure cancer
126
it's the craziest world we could ever possibly live in
127
and this is why there exists a set of problems called
128
np problems non -deterministic polynomial problems these problems have a very special condition on them
129
they can be verified in polynomial time this is really important
130
so when we go back to this i can look at this one right here and go like this.
131
Okay, that one's true.
132
That one's true because not zero.
133
And that one's true.
134
Boom.
135
I just verified with my eyeballs a problem that is super hard, that is exponentially difficult to solve for,
136
but I can verify it in linear time.
137
It's super easy to see if something's right, super hard to determine the problem.
138
Inside of NP, there's a set of problems called P.
139
And now this is the big question.
140
Is all the problems in NPP or is just some of them?
141
P means that they are solvable in polynomial time.
142
See, then there's one other group of problems we'd like to call NP -hard.
143
Now, this is kind of a funny one.
144
It has a really funny name to it because you're like, oh, well, why aren't all the problems inside of NP?
145
It's called NP -hard.
146
Well, what they mean is that they're at least as hard as every other problem inside of NP.
147
And so this little intersection right here is a very special class of problems we like to refer to as NP -complete.
148
NP -complete problems are NP hard problems that are verifiable in polynomial time.
149
This problem is NP complete.
150
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,
151
meaning that the traveling salesman can actually be represented as a three SAT problem.
152
That is why I showed you this little formula over here,
153
because 2500 is the size of the United States being represented as a three SAT formula solving the traveling salesman
154
which means it takes forever to solve we can't solve
155
this even with the best stuff the best computers in the universe can't solve
156
that problem right now but here's the thing with that paper showing
157
that exponential time hypothesis is in fact false
158
that we are sub exponential there's a chance
159
that we could keep on figuring out how to reduce it into polynomial time
160
and that's where things get crazy and the reason why is
161
that once one of these problems become polynomial time all of these problems become polynomial time by the sheer nature
162
that they are the hardest of all the problems within np
163
now where this gets really really bad is think of rsa rsa
164
which of course is like how you do public key encryption there's like many versions of this
165
and this is not the complete picture
166
but just go with me here effectively there's two prime numbers that are multiplied together and then you get this public key.
167
that anybody can look at and it's completely fine
168
because they don't know how to get these private keys from this public one.
169
Yes, I know there's an inverted matrix involved in all of this.
170
We're not going to talk about it.
171
Just don't worry about that, okay?
172
Just keep it simple.
173
So that means these private keys, to be able to find those, you have to do something called integer factorization.
174
And that turns out to be a really hard problem.
175
Not an NP hard problem, but a very hard problem inside of NP because we can validate that in polynomial time,
176
but we cannot solve for it in polynomial time.
177
And since exponential time hypothesis is now false and we are now sub -exponential, if this does improve and go to polynomial,
178
that means we can crack RSA in polynomial time, which means powerful computers would be able to break encryption across the internet.
179
This also goes for like SHA -256.
180
This also goes for HMAC.
181
This goes for pseudo -random number generators.
182
It goes for blockchain, which awesome.
183
Love to see that last one get taken down
184
but nonetheless everything would kind of fall apart
185
but there's also protein folding turns out protein folding is in fact going to be an NP complete problem
186
so if we could change that we could potentially do research what would take decades
187
or hundreds of years you could potentially do in like a year
188
you would even be able to solve sudokus in polynomial time now
189
that would be a crazy world right I mean solving sudokus okay that's getting a little too fanciful the name
190
Is the NP a gin?
App downloaden
Alles wat je nodig hebt om vloeiend te spreken
AI UitspraakScoor elke zin
IPA OefeningBeheers elk geluid
WoordenschatBouw je woordenschat op
WoordenspelLeren terwijl je speelt
Zelfde Kanaal
C11
It’s not just you
Daily ConversationUniversity StudentsWorking Adults
C11
Go’s Big Mistake
Daily ConversationUniversity StudentsWorking Adults
C13
He Realized his Mistake
Daily ConversationUniversity Students
B23
People Are Mad They're Told to Learn
Business EnglishUniversity StudentsWorking Adults
B22
Maybe we were wrong
Business EnglishUniversity StudentsWorking Adults
B23
You won't fall behind
Daily ConversationWorking Adults
B23
He’s always wrong
Business EnglishDaily ConversationUniversity Students
C27
Github are you joking?
Business EnglishUniversity StudentsWorking Adults
B2Casey Muratori Judges Our Digging Games | TheStandup
Daily ConversationWorking Adults
FOR YOU
Aanbevolen Video
C12
182 zinnen
We Let $200 GPT-Pro Think for 7 Days on Unsolved Math
Daily ConversationUniversity StudentsWorking Adults
C11
484 zinnen
How to Build an AI Agent for Every Part of Your Life
Daily ConversationUniversity StudentsWorking Adults
C11
130 zinnen
3 Ways AI Will Completely Change Your Life by 2029
Daily ConversationUniversity Students
C13
134 zinnen
Escaping the Trap of Modern Life
Daily ConversationUniversity Students
C11
17 zinnen
Very excited to share that I now have a column in New Scientist
Daily ConversationUniversity StudentsWorking Adults
C11
97 zinnen
Explaining how AI is killing the PhD in less than 10 minutes
Daily ConversationUniversity Students
C111
39 zinnen
You Will Only Like Half of This Video
Daily ConversationUniversity StudentsWorking Adults
C12
223 zinnen
9 Insane Claude Mods I Already Can't live Without
Daily ConversationUniversity StudentsWorking Adults
C13
933 zinnen
My Joe Rogan Appearance: What I Learned (ft. Young Jamie & Viktor Hovland)
Daily ConversationUniversity StudentsWorking Adults
C141 zinnen
No, Really — Is A.I. a Bubble? | The Ezra Klein Show
Daily ConversationUniversity StudentsWorking Adults
C12
82 zinnen
China just dropped a new frontier model... and nobody can explain how
Daily ConversationUniversity StudentsWorking Adults
Woordenschat en spreektips bij deze les
Deze spreekles is gebaseerd op de video “This is Crazy”. Deze woorden komen het vaakst terug: exponential, polynomial, hypothesis, solve, complete. Deze video bevat 190 zinnen en 2263 woorden om na te spreken. Het gesproken deel duurt 11:26. De spreker praat snel, ongeveer 198 woorden per minuut, dus reken op verbonden en ingeslikte klanken. 84% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden; de rest kun je beter vooraf bekijken.
Belangrijke woorden in deze video
15 woorden uit de video die het leren waard zijn, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| solve werkwoord | /sɒlv/ | oplossen |
| hypothesis zelfstandig naamwoord | /haɪˈpɒθɪsɪs/ | hypothese, stelling |
| variable bijvoeglijk naamwoord | [ˈvæɹ.i.ə.bl̩] | veranderlijk, variabel |
| satisfy werkwoord | /ˈsætɪsfaɪ/ | voldoen, voldoende zijn |
| scientist zelfstandig naamwoord | /ˈsaɪəntəst/ | wetenschapper, wetenschapster |
| equation zelfstandig naamwoord | /ɪˈkweɪ.ʒən/ | vergelijking |
| discovery zelfstandig naamwoord | /dɪˈskʌv.ɹi/ | ontdekking, vinding |
| formula zelfstandig naamwoord | /ˈfɔː.mjʊ.lə/ | formule |
| potentially bijwoord | /pəˈtɛnʃ(ə)li/ | potentieel |
| announce werkwoord | /əˈnaʊn(t)s/ | aankondigen, declareren |
| mathematics zelfstandig naamwoord | /mæθ(.ə)ˈmæt.ɪks/ | wiskunde, mathematiek |
| fold werkwoord | /ˈfəʊld/ | vouwen |
| quantum zelfstandig naamwoord | /ˈkwɑntəm/ | kwantiteit |
| salesman zelfstandig naamwoord | verkoper | |
| naturally bijwoord | /ˈnæt͡ʃ.(ə.)ɹə.li/ | natuurlijk |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| figure out werkwoord | uitvogelen, er achter komen |
| check out werkwoord | onderzoeken |
| fall apart werkwoord | uiteenvallen, uit elkaar vallen |
| pick up werkwoord | oppakken, optillen |
| turn out werkwoord | uitvallen, resulteren |
Zinnen om te herhalen
Korte, volledige zinnen uit de video die je in alledaagse gesprekken kunt gebruiken:
- 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.
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | was naturally intrigued · are solved · is broken |
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | has done · have proved · have satisfied |
| Betrekkelijke bijzinnen who / which + zin — extra informatie over een persoon of ding | GrokBot, which is · exponential, which is · time, which means |
Uitspraak om op te letten
De spreker gebruikt 27 samentrekkingen en verkorte vormen, zoals don't, we're, can't. Spreek ze kort uit, zoals je ze hoort.
- De “th”-klanken: hypothesis /haɪˈpɒθɪsɪs/, mathematics /mæθ(.ə)ˈmæt.ɪks/, smooth /smuːð/, nonetheless /ˌnʌnðəˈlɛs/, algorithm /ˈælɡəɹɪðm̩/
- De klanken “sh” en “zh”: equation /ɪˈkweɪ.ʒən/, encryption /ɪnˈkɹɪpʃən/, potentially /pəˈtɛnʃ(ə)li/, completion /kəmˈpliː.ʃən/, sheer /ʃɪɚ/
- Lange woorden — let op de klemtoon: hypothesis /haɪˈpɒθɪsɪs/, potentially /pəˈtɛnʃ(ə)li/, mathematics /mæθ(.ə)ˈmæt.ɪks/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, fundamental /ˌfʌn.dəˈmɛn.təl/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: solve /sɒlv/, fold /ˈfəʊld/, bound /ˈbaʊ̯nd/, divide /dɪˈvaɪd/, organize /ˈɔɹɡənaɪz/
- /æ/ — opener dan de Nederlandse “e”: variable [ˈvæɹ.i.ə.bl̩], satisfy /ˈsætɪsfaɪ/, mathematics /mæθ(.ə)ˈmæt.ɪks/, naturally /ˈnæt͡ʃ.(ə.)ɹə.li/, crack /kɹæk/
- /g/ — een harde plofklank, geen Nederlandse “g”: greatly /ˈɡɹeɪ̯t.li/, algorithm /ˈælɡəɹɪðm̩/, organize /ˈɔɹɡənaɪz/, gigantic /d͡ʒaɪˈɡæntɪk/, intrigue /ˈɪntɹiːɡ/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als solve, hypothesis, variable.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.