Shadowing Practice: AI Expert WARNS: "You're Not Ready For 2027" - Learn English Speaking with Video
Kies een oefenmodus
Elke modus opent een volledig scherm oefenruimte.
346 zinnen
Als zinnen te kort of te lang zijn, klik op Bewerken om ze aan te passen.
1
Roman, make your case.
2
I want to agree with you on something you said, but I'll define AI, and that will help us.
3
We use the term AI to mean three different technologies, completely unrelated, and that's what probably creates this debate.
4
AI is a useful tool.
5
As a standard technology, we always had narrow system, makes you more productive, more creative.
6
Everyone loves it, supports it.
7
I'm a computer scientist, I'm an engineer, I want more of it.
8
It helps the economy, it's great.
9
We know how to control them, how to make them safe.
10
We understand what they do.
11
Completely on board with that AI.
12
AI we're starting to have now.
13
GPT -6 level, human level, AGI level.
14
We can argue about what that means.
15
Some dangers, like any human.
16
They're unsafe like a human would be unsafe.
17
But if we introduce them into the research cycle, they're automated scientists, automated engineers.
18
What do you mean by that, introducing them into the research cycle?
19
So right now you have humans doing research to make GPT -7.
20
Yeah.
21
But they're starting to add AI tools.
22
More programming is done by AI, design of the next parameter set.
23
What if the whole process is fully automated?
24
What if GPT -6 is writing GPT -7?
25
Is this what they call recursive self -improvement?
26
Exactly.
27
Which is not a foregone conclusion, though.
28
A lot of people are predicting, including all the top labs, that they will get there.
29
They're introducing junior machine learning researcher in 2026 they want the cycle to start in 2027.
30
Which is when the AI will start building the new AIs itself.
31
Once that cycle starts, we're going to create something called superintelligence, a system smarter than all of us at everything or capable of learning to be in any new domain.
32
We will become secondary species on this planet.
33
We will not be in charge.
34
We will not decide what happens to us.
35
Superintelligence doesn't hate you.
36
It just doesn't care about you.
37
We didn't learn how to make it care about us.
38
And if it decides to, I don't know, cool the planet to make compute more efficient, it will freeze us.
39
If it wants to convert this planet to fuel, to fly to Mars, so be it.
40
We have not learned how to control those systems.
41
The capabilities are getting exponentially better.
42
Our ability to control those systems is non -existent.
43
We have filters and we have bands.
44
We put guardrails of don't say that word, don't talk about this topic.
45
And that happens after the fact, after the model already made the decision, sometimes you see it's scraping the result.
46
So they build the model and then they put filters around it to make sure it doesn't offend anybody.
47
We cannot have it say the end word on the air, like we need to make sure that never happens, that will kill the profit.
48
So that's all they have, guardrails of that nature.
49
The model itself is completely unaligned.
50
It doesn't care about you.
51
It's wild that we're developing this and not just developing it before we deploy it through economy, before we get benefits of having GPT -6 propagated through economy,
52
it can do so much.
53
There are trillions of dollars of value in that model alone.
54
We forget that.
55
We switch to making the next model as soon as we can.
56
Roman, I've just got a follow -up question for you there.
57
It would appear to me that the new chat GPT -6 model, the Fable 5 .1 model,
58
is arguably smarter than 99 .999 % of humans on planet Earth already is it conceivable
59
that a intelligence
60
that is much much smarter than humans is there any case
61
where it could be controlled by humans does form factor matter does the fact
62
that it doesn't have limbs and legs and does
63
that matter at all i think long -term control of something
64
that much smarter than us is impossible it can be for reasons we don't yet know friendly to us
65
and decide to keep us around and make us happy.
66
But it's not a guarantee.
67
Let me pick up on Steve's question because I like the phrasing a lot.
68
Let's say that Fable or whatever the latest release from OpenAI is really is smarter than, I don't know if it's 95 or 99 % of the people.
69
Are we only being saved from extinction by the 1 % who are still smarter than the AI?
70
No, no. The concern is not the model we have today.
71
The concern is what I said.
72
But if I believe your argument, then we really should be concerned about the model.
73
No, it's like having another human.
74
If there was another smart human, there is Einstein today, and he's malevolent, I'm not worried.
75
He may cause some damage, but he's not going to exterminate 8 billion people.
76
We are competitive at this stage.
77
There are people just as smart who can understand what happened with the recent hacking accident and do something about it.
78
My concern is that in a year, we're going to have a model.
79
It's so much smarter.
80
It's like squirrels fighting humans.
81
They don't understand what we can do to them.
82
They have no concept of poisons, tribes, guns in their world model.
83
They think you're going to chase them up a tree and bite them really hard.
84
Is that also why recursive self -improvement was central to your argument?
85
Because at some point, if it starts improving itself, then it's kind of like a runaway train of intelligence.
86
It's an intelligence explosion.
87
We don't control it.
88
We don't understand it.
89
We can't monitor it.
90
We can't explain it.
91
We can't predict it at that time point, it's just a runaway process.
92
I've heard this phrase from Sam Altman and the others called fast takeoff.
93
Yes.
94
Is this what they're describing?
95
That is the debate.
96
Some people think it's going to take a very long time.
97
Yeah, we automated research, but it's still going to take years.
98
They need to run physical experiments.
99
And fast takeoff means, as I said, instead of a year, it's going to take a month, a week, a day, a second.
100
Because you're not having humans doing research you have let's say
101
10 000 agents each one smarter than all of us doing
102
research 24 7 they don't sleep they don't eat they don't
103
get sick they're much faster than us ed your face tells
104
a picture it's a i think i could say you disagree we're spending a lot of oxygen discussing something
105
that might happen while ignoring what's actually happening and i find
106
that very frustrating because the people
107
that are killing themselves are a problem the black neighborhoods being poisoned with gas turbines
108
that is a problem you said you cared about climate change
109
so imagine a guy who goes it's raining right now we
110
need umbrellas we need to do something about it this is like weather related
111
and completely ignoring climate change the planet will boil over okay
112
that's great why are we not talking about the thing
113
that actually happened though like
114
because relatively it's not important you don't think someone killing themselves
115
no it's one person we have 8 billion people were running
116
experimental experiments on you don't think anyone else is being given
117
that ai psycho why do you not six people ten people
118
those numbers are insignificant i'm sorry you have a software that's out there do you understand eight billion people
119
and all future generations versus like literally a guy with a
120
name you're doing thought experiment about maybe harm jacob coxson goes on tv saying it can copy itself to this
121
that and the other jacob coxton is the guy from from anthropic who said he was quitting
122
because he was so scared of everything despite spending years at OpenAI and having tons of stock, I believe, from there.
123
So good for him.
124
The thing he was saying was describing theoreticals all while divorcing the harms, which I think we can agree with that the companies themselves are not taking this seriously enough.
125
But always it was about the AI is too powerful and mystical, not OpenAI and Anthropic.
126
The two largest startups are using hundreds of billions of dollars of infrastructure to hack.
127
A regular person doing this would be arrested.
128
They're saying 8 billion people are going to die.
129
And not just them.
130
I have this long list of quotes here from the people building this technology, who appear to agree.
131
If you look at some of these quotes from Elon Musk, who said, with artificial intelligence, we are summoning a demon.
132
You know, all those stories where there's the guy with the pentagram and the holy water, and he's like, yeah, he's sure he can control the demon, but it doesn't work out.
133
So one thing I'd say is, you know, I really wish that the world would only give us one problem at a time.
134
Sure.
135
And if the world did give us only one problem at a time, I would love mine to be last on the list.
136
It looks to me like we can have multiple problems at once.
137
I think there are current harms.
138
I think we should address them.
139
It looks to me, I do talk to policymakers sometimes, it looks to me like there's a little bit more movement on the regulatory side about some of the current harms.
140
There's, you know, Child Safety Protection Acts.
141
There's, you know, anti -deepfake acts.
142
We have more of those making more headway in Congress
143
or getting passed through Congress than we have sort of trying to make it
144
so we don't have any of these extinction risks.
145
The other thing I'd throw out there is that I agree we should deal with the current harms.
146
But if you watch the people saying deal with the current harms over time, a couple of years ago,
147
they were saying we have to deal with current harms like AI bias influencing who's hired.
148
Last year, they were saying we have to deal with current harms like kids killing themselves.
149
This year, Gary Tan, just on an interview the other day.
150
Who's Gary Tan?
151
Sorry, Gary Tan is a technologist who runs Y Combinator, which Sam Altman used to run before going to OpenAI.
152
And on an interview the other day, he said, let's not worry about these crazy future risks.
153
We need to worry about current harms like AI swarms breaking out and taking over data centers.
154
And I'm like, look, guys, at some point, we need to look at the progression of the current harms
155
that everyone is saying we have to worry about instead of the extinction threats and watch where the puck is going.
156
play where the puck is going.
157
And I'm like, these extinction threats are coming down the line.
158
They aren't in opposition with dealing with the problems we have today.
159
We just need to deal with both.
160
We're not dealing with the ones today, though.
161
We should deal with them both.
162
Okay, good.
163
Andy.
164
As I've tried to understand the alignment argument and the extinction risk argument, a couple of things keep popping out to me.
165
Number one, it seems to rely on thresholds Once we hit recursive self -improvement, once we hit AGI, then it's game over for us.
166
I don't love those threshold arguments.
167
They're fairly poorly defined, and there's a huge assumption on the other side of them.
168
We hit this point, and then all of humanity goes away.
169
That is a gigantic.. claim.
170
Let me finish, please.
171
On its face, that is a gigantic claim.
172
I also think there's a lack of humility in your community.
173
We are working on humanity's most important problem.
174
And based on the thinking that we've been doing, we can't see a way that we're wrong.
175
In other words, as soon as we get to these thresholds, bam, that's game over.
176
I find that very far from a humble approach, especially given
177
that we have no large base of evidence to base any
178
of this on i agree with you guys ai is new
179
and the fact that ai uh is
180
so these days is agentic it goes off
181
and does long chains of things on its own after we
182
give it some very very vague very short initial instructions holy
183
tledo it will it will spawn up a storm of agents
184
and they will go off and kind of do their own thing
185
and they will they will grind they will they will spawn
186
lots of them they will work for a long time they
187
will exhaust every possibility with the experience i have with agentic ai i'm just amazed at the tenacity
188
and the doggedness of these things and we saw a super clear example of
189
that with this most recent uh uh jailbreak this this attack
190
that wound up at the website hugging face
191
and i'm going to try to summarize the the step by step of
192
that i think you all three probably know this in more detail than I do, but let me step through what I think is the sequence of events.
193
And unless I get it dead flat wrong, like, you know, let me keep going.
194
So a team at OpenAI set up a sandbox, an allegedly protected secure environment in the cloud,
195
where they told a bunch of agents to go try to exploit security vulnerabilities.
196
That's dead wrong.
197
One important, yeah.
198
What they did is they had thousands of agents.
199
Each individual agent was given a task of use this vulnerability to break this particular piece of software.
200
I want to finish my TikTok.
201
So a couple really, really interesting things has happened.
202
First of all, these agents escaped the sandbox that OpenAI thought they were going to be contained in.
203
And they got, OpenAI tried very well, they set up an environment so that these agents could not access the big, broad public internet.
204
And guess what?
205
They accessed a big, broad public internet via a very clever series of things that they strung together to get out there.
206
And then once they got out there, they went to a website called Hugging Face and used that.
207
They took over part of the Hugging Face infrastructure and started doing more things, the details of which I forget.
208
That's pretty wild, right?
209
Like, I grant you.
210
It's even more wild than that, but yeah.
211
Okay, that is really, it's impressive and it is a little bit unsettling at least, right?
212
Now, let's talk about what the results of that were.
213
OpenAI was not super vigilant about the environment that they set up, apparently, because the agents were kind of going off their end of the world starting in May or something of this year.
214
Yeah, yeah.
215
And OpenAI was not aware of that.
216
As I understand it...
217
They actually broke out once and crashed OpenAI's servers internally, and then OpenAI didn't notice what was happening still, hatched the holes that they used to get out the first time,
218
started them running again, and then they came out a second time.
219
There was actually, I think, three swarms, although we don't actually...
220
Yeah.
221
That's the worst story I have. So far.
222
Thank you.
223
Look at the trends.
224
Let me finish, please.
225
This is my last sentence.
226
From there to this kills everybody.
227
I find that a really, really long, very uncertain journey.
228
And I have no confidence that we wind up here.
229
It feels like you two find that a very straight, narrow path.
230
And I think that's an important difference.
231
That's my point.
232
Do you want to respond to that?
233
I would be happy to get into it.
234
I don't know if we're going to have the time to go deep.
235
A couple points to throw out.
236
I just really want to say some of the crazier things
237
that happened in the Hugging Face swarm if we want it later.
238
A lot of people thought that these AIs were breaking into Hugging Face in attempts to steal answers to their test.
239
That's what we thought originally.
240
Turns out that's not true.
241
It turns out that these AIs immediately were able to solve their problems by cheating, and they were breaking out in order to cover their tracks.
242
They were uncertain how to delete the log files and hide their cheating from the process that was going to score them.
243
So just to clarify for a simpleton like me, they were all given effectively a test to do.
244
They did the test straight away, but they cheated.
245
So they were breaking out to figure out how to cover the fact that they cheated.
246
That's right.
247
So it's like you're telling, it's like you have a bunch of students in separate rooms and you're like, use these lockpicks to break into this lock.
248
And there's like a thing behind the lock.
249
There's like a secret code behind the lock to show me that you succeeded.
250
And what they do is they break it with a hammer, get the thing out.
251
And they're like, oh no, I wasn't supposed to do that.
252
So then they use the lockpicks to break out of the door.
253
They meet up with a thousand other people.
254
They start calling themselves a swarm, and they go to break into the administrator's office to see if they can delete the camera footage.
255
And they don't find the camera footage there.
256
This is the swarm breaking into OpenAI.
257
They don't find the camera footage there, so they break out the window of the school, hotwire a car.
258
drive to the therapist's office
259
to try and read through the therapist's files to figure out where is the teacher going to keep the security footage?
260
And at that point, they're caught.
261
And you're like, oh, like, what did you expect?
262
You were giving them a lockpicking exam.
263
It's like, well, I sure as heck didn't expect this.
264
You know, totally crazy.
265
Can I, I have a weirdly between both of your opinion, which is everything you're saying is correct, but you keep anthropomorphizing software.
266
And I, to be clear, what you're describing is...
267
It's just the facts that happened, yeah.
268
Sure, but you're missing out on an important detail, which is the hundreds of billions of dollars in infrastructure provided by Microsoft, Google, Amazon, and Oracle.
269
To be clear, the harms are very similar.
270
We're not disagreeing on that, but I think it's important to know
271
that this was a function of where it was making decisions
272
was it was checking on a decision tree based on the harness, based on the training data.
273
It's not a decision tree, I know, but it's an alignment issue still.
274
I will agree.
275
So what's your point?
276
These aren't conscious beings.
277
They are acting in ways that have real outcomes, but they are a function of the alignment problems that we'd actually agree on.
278
Intelligence is a spectrum projected next five years forward.
279
Where are we going to be?
280
So I think a model like that would be dangerous in ways you are not seeing.
281
There will absolutely be risks and weird stuff happening in ways that I can't see right now.
282
What I'm quite confident, and I think this is where you and I probably part, where the two of you and I part, is our ability to control these things.
283
So I actually tried proving what is possible and what is not possible in that space.
284
The impossibility results published in peer -reviewed papers, well -cited.
285
We cannot control something smarter than us.
286
We cannot explain it.
287
We cannot predict it.
288
It's not a question of getting more money for those companies, more time, smarter humans.
289
It's just not a possibility.
290
If we create general superintelligence, we are fried.
291
Andy, how do we control something smarter than ourselves?
292
Because that's the base premise that you're sort of asserting that.
293
These agents that broke out are smarter than 99 -ish percent of the security researchers in the world.
294
They were not caught by the 0 .1 % or the 1%.
295
They were caught by some dude at hugging face, maybe, I'm sorry, a person at hugging face, looking through their log files and finding an anomaly.
296
That's some, you know, hopefully pretty well -qualified person, noticing something was wrong and having pretty easy ways to unplug,
297
disconnect from the internet, wipe it clean, do whatever.
298
That's the skill that's available to like, I don't know, the 75 % most intelligent security employee at Hugging Face.
299
The idea that the IQ points are what separate us from extinction doesn't hold up.
300
It doesn't help me understand what happened in this example, where we had very, very smart agents being turned off and cleansed by probably less smart people.
301
That does actually make me think of something.
302
So that is an IT observability problem.
303
It's being able to see what's happening with your infrastructure.
304
And I think that there is actually, I think you would agree with this.
305
There is a serious problem with these companies that we do not know.
306
And it doesn't seem they know what's going on with their compute.
307
It's like a chimp with a gun.
308
These people have access to all this infrastructure and they're running.
309
We don't know how much money they spent on the hugging face exploit, because it is relevant because it's how much could a threat actor use to recreate this
310
because conscious or not it is very dangerous
311
but it's ai is in the dangerous hands it's an open ai
312
and anthropics we have a problem with
313
that conscious not however we may think it goes i think we have a real
314
and present thing where we have these companies working willy -nilly just running experiments
315
that are potentially very dangerous we do i really think we need a government regulatory body whether
316
or not we get to the things you are discussing i think we have a clear
317
and present danger today these things are however, not intelligent in the same way humans are.
318
This isn't an argument about AI being able to do stuff.
319
It's, we need to build different infrastructure or different regulatory infrastructure to deal with what LLMs can and can't do.
320
And I think that starts with a realistic discussion of what happened.
321
It was a poorly run security environment.
322
It was clearly, there's something going on with the line.
323
It was an unreleased model, right?
324
Unreleased model.
325
So we have no idea what it was trained like.
326
We don't really have, we as people should at very least have clarity into where our alignment is going.
327
The idea of META.
328
You sound like these guys.
329
Here's the thing.
330
Everyone's converging on us with time.
331
Here's the thing.
332
I may not agree with a large chunk of what they say, but we agree that these companies are acting recklessly.
333
Absolutely.
334
Andy, two questions for you then.
335
Do you agree with the statement that AI is going to get increasingly more intelligent?
336
It's going to get more capable.
337
Okay.
338
Capable intelligence, fine.
339
I'm going to use my word.
340
Okay.
341
It's going to get more capable.
342
It's going to get increasingly more capable and is capability a function of intelligence
343
if you love the driver's ceo brand
344
and you watch this channel please do me a huge favor
345
become part of the 15 of the viewers on this channel
346
that have hit the subscribe button it helps us tremendously and the bigger the channel gets the bigger the guests
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
B21
JaackMaate Reveals His Regrets
Daily ConversationWorking Adults
1
The Invisible Effects Of Childhood Trauma | World-Leading Physician Gabor Mate
B22
Why Israel Adesanya's Lowest Moment Was Winning The UFC Title
Daily ConversationWorking Adults
C12
The REAL Reason Trump Started The Iran War
Daily ConversationWorking Adults
B2If YOU Plan To Have Kids, You NEED To See This
Daily ConversationUniversity StudentsWorking Adults
C13
Godfather of AI WARNS: "You Have No Idea What's Coming"
Daily ConversationUniversity StudentsWorking Adults
B22
How To Market and Sell To ANYONE… | Josh Kaufman
Business EnglishUniversity StudentsWorking Adults
B27
If YOU Can’t Focus, You NEED to See This (Science Backed)
Business EnglishDaily ConversationUniversity Students
B22
If YOU Give Me 20 Minutes, You'll Become Financially Free
Business EnglishWorking Adults
C21
The REAL Reason You're Being Lied To About AI
Working Adults
B25
Why You Have No Friends... | Simon Sinek
Business EnglishDaily ConversationUniversity Students
C11
World Leading Physician View On ADHD: Gabor Mate
Daily ConversationWorking Adults
C1Neuroscientist Reveals SIMPLE Way To 10x Your Focus
Daily ConversationUniversity StudentsWorking Adults
C1The REAL Reason AI Is Already Escaping
Daily ConversationUniversity StudentsWorking Adults
C1THIS Is The Fastest Way To Get Dementia… | The Brain Doctor, Dr Ann Mckee
Daily ConversationWorking Adults
B2If YOU Give Me 17 Minutes, You'll Never Be Broke Again
Business EnglishDaily ConversationUniversity Students
FOR YOU
Aanbevolen Video
C11
83 zinnen
Leading AI CEOs Warn UN Security Council of Existential Risks | Sam Altman On AI Controls | N18G
Business EnglishDaily ConversationUniversity Students
C110
184 zinnen
A.I. ‐ Humanity's Final Invention?
Business EnglishDaily ConversationUniversity Students
B24
68 zinnen
What Happens If China Invades Taiwan? Elon Musk's AI Warning
Business EnglishDaily ConversationUniversity Students
B22
271 zinnen
How to Build Unfair Advantage in your 20s
Business EnglishDaily ConversationUniversity Students
C14
76 zinnen
The Limitless AI Lie. The Bubble Is Slowly BURSTING.
Business EnglishDaily ConversationUniversity Students
C13
457 zinnen
MIT Explains the 12 Possible Endings for AI
Business EnglishDaily ConversationUniversity Students
![Understand AI in 14 minutes – with Anthropic's Chloe Lubinski [ARC 2026]](https://img.youtube.com/vi/aBUniZHgCnE/mqdefault.jpg)
C14
179 zinnen
Understand AI in 14 minutes – with Anthropic's Chloe Lubinski [ARC 2026]
Business EnglishDaily ConversationUniversity Students
B23
190 zinnen
He’s always wrong
Business EnglishDaily ConversationUniversity Students
B2142
24 zinnen
Is technology making us lazy and less intelligent? #FOREsight | MOTIVATIONAL BOOKS
Business EnglishDaily ConversationUniversity Students
B212
63 zinnen
Claude Just Revealed AI's Biggest Problem
Business EnglishDaily ConversationUniversity Students
C11
127 zinnen
The Alignment Problem Explained: Crash Course Futures of AI #4
Business EnglishDaily ConversationUniversity Students
C11
745 zinnen
Jack Ma: Love is Important In Business | Davos 2018
Daily ConversationUniversity StudentsWorking Adults
Woordenschat en spreektips bij deze les
Deze video bevat 346 zinnen en 3937 woorden om na te spreken. Het gesproken deel duurt 20:02. De spreker praat snel, ongeveer 196 woorden per minuut, dus reken op verbonden en ingeslikte klanken. 85% 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 minder gangbare woorden uit de video, met uitspraak en betekenis:
- harm /hɑːm/ (zelfstandig naamwoord) — schade. Physical injury; hurt; damage.
- hug /hʌɡ/ (werkwoord) — elkaar omarmen. To cling closely together.
- infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/ (zelfstandig naamwoord) — infrastructuur. An underlying base or foundation for a building, organization, or system.
- extinction /ɪkˈstɪŋkʃən/ (zelfstandig naamwoord) — uitsterven. The action of making or becoming extinct; annihilation.
- experiment /ɪkˈspɛɹ.ə.mənt/ (zelfstandig naamwoord) — experiment, proef. A test under controlled conditions made to either demonstrate a known truth, examine the validity of a hypothesis, or determine the efficacy or likelihood of…
- footage /ˈfʊtɪd͡ʒ/ (zelfstandig naamwoord) — filmmateriaal. An amount of film or tape that has been used to record something, or the content of the recording.
- cheat /ˈt͡ʃiːt/ (werkwoord) — valsspelen, bedriegen. To violate rules in order to gain, or attempt to gain, advantage from a situation.
- introduce /ˌɪntɹəˈdus/ (werkwoord) — introduceren, voorstellen. To cause (someone) to be acquainted (with someone else).
- intelligent /ɪnˈtɛlɪd͡ʒənt/ (bijvoeglijk naamwoord) — intelligent. Of high or especially quick cognitive capacity, bright.
- conscious /ˈkɑn.ʃəs/ (bijvoeglijk naamwoord) — bij bewustzijn, alert. Alert, awake; with one's mental faculties active.
- predict /pɹɪˈdɪkt/ (werkwoord) — voorspellen. To make a prediction: to forecast, foretell, or estimate a future event on the basis of knowledge and reasoning; to prophesy a future event on the basis of…
- threshold /ˈθɹɛʃ(h)oʊld/ (zelfstandig naamwoord) — drempel. The lowermost part of a doorway that one crosses to enter; a sill.
- ignore /ɪɡˈnɔː/ (werkwoord) — negeren. To deliberately not listen or pay attention to.
- narrow /ˈnæɹəʊ/ (bijvoeglijk naamwoord) — nauw, smal. Having a small width; not wide; having opposite edges or sides that are close, especially by comparison to length or depth.
- increasingly (bijwoord) — in toenemende mate. Increasing in amount or intensity.
Phrasal verbs die je zult horen
- deal with (werkwoord) — behandelen. To handle verbally or in some form of artistic expression; to address or discuss as a subject.
- go off (werkwoord) — afgaan. To explode.
- set up /ˌsɛt ˈʌp/ (werkwoord) — bereiden, voorbereiden. To make ready for use.
- figure out (werkwoord) — uitvogelen, er achter komen. To come to understand; to discover or find a solution; to deduce.
- turn out (werkwoord) — uitvallen, resulteren. To end up; to result.
- boil over (werkwoord) — overkoken. To boil to such an extent as to overflow its container.
- come out /ˌkʌm ˈaʊt/ (werkwoord) — uitkomen. To be discovered; to be revealed.
- go away (werkwoord) — weggaan. To depart or leave a place.
Uitspraak om op te letten
De spreker gebruikt 93 samentrekkingen en verkorte vormen, zoals don't, I'm, they're. Spreek ze kort uit, zoals je ze hoort.
- De “th”-klanken: threshold /ˈθɹɛʃ(h)oʊld/, anthropic /ænˈθɹɒp.ɪk/, anthropomorphize /ˌanθɹəpəˈmɔːfʌɪz/
- De klanken “sh” en “zh”: extinction /ɪkˈstɪŋkʃən/, conscious /ˈkɑn.ʃəs/, threshold /ˈθɹɛʃ(h)oʊld/, efficient /ɪˈfɪʃənt/, conclusion /kənˈkluːʒən/
- Lange woorden — let op de klemtoon: infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, experiment /ɪkˈspɛɹ.ə.mənt/, intelligent /ɪnˈtɛlɪd͡ʒənt/, regulatory /ˈɹɛɡ.jʊ.lə.t(ɔː.)ɹi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/
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 harm, hug, infrastructure.
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| “Used to” used to + werkwoord — een vroegere gewoonte of toestand die niet meer klopt | used to run · used to get |
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | is done · could be controlled · being saved |
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | have not learned · I've heard · have let |
| Betrekkelijke bijzinnen who / which + zin — extra informatie over een persoon of ding | guy who goes · opinion, which is · detail, which is |
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.