Shadowing Practice: When A.I. Comes for Your Purpose in Life - Learn English Speaking with Video
בחר מצב תרגול
כל מצב פותח חדר תרגול במסך מלא.
537 משפטים
אם המשפטים קצרים או ארוכים מדי, לחץ על ערוך כדי להתאים אותם.
1
From the New York Times, I'm Rachel Abrams, and this is The Daily.
2
This morning, one of the hardest problems in mathematics may have just been solved, not by a human, but by artificial intelligence.
3
So many people are freaked out about AI taking their jobs, and now you can add mathematicians to that list.
4
In the span of just a few months, AI systems have tackled some of the biggest problems in math.
5
And they've solved them.
6
For decades, mathematicians have been working to solve this problem.
7
They haven't been able to crack it.
8
But AI, it just did it easily.
9
Last month, it was one of the field's so -called millennium problems.
10
And then last week, OpenAI released a torrent of over 350 other results, covering virtually every corner of mathematics.
11
These are problems that literally the smartest humans on Earth have been working on for decades and sometimes even centuries.
12
This AI takeover of the field has raised some uncomfortable questions and unleashed a range of emotions.
13
And so today, we speak with a mathematician, Cornell math professor Stephen Strogatz, about what happens when AI doesn't just come for your job,
14
but for your purpose.
15
It's Tuesday, October 13th.
16
Steve Strogatz, thank you so much for joining us.
17
Hi, Rachel.
18
I'm really happy to see you and be here with you.
19
So, Steve, one of the reasons we're talking to you is, of course, because you are an esteemed professor at Cornell.
20
But we are really talking to you
21
because you are known for translating math into English for those of us who do not speak math.
22
And you actually have a book that gets at this very thing.
23
It is called Big Math.
24
And so we are turning to you to help sort of translate this destabilizing moment in math
25
that you and your colleagues are living through.
26
Yeah.
27
Can you just start by explaining to us, how would you sum up the mood in math right now?
28
Ooh, the mood.
29
Yeah, the mood.
30
The mood is discombobulated.
31
We're all a swirl.
32
We're spinning.
33
Some people are exhilarated.
34
They see a great golden age upon us with the help of our computer friends.
35
Most people, I think, are feeling grief, and we're in different stages.
36
Some people are bargaining, some are accepting, some are still denying.
37
What stage of grief are you in?
38
Today, I'm feeling pretty good.
39
I have been dark.
40
There have been days when I actually cried during interviews.
41
You know what's actually being lost is we like to think of ourselves as heroes, and maybe that's over.
42
That's the big news.
43
it's hard to be a hero
44
if you can't be the first to solve a problem
45
or to understand something or to explain it to the world
46
and for the thousands of years that our culture of math has existed
47
we some of us did fantasize about being heroes in our own story
48
and now that's over at least i mean
49
this is the grief version of myself talking i can of course like any professor argue against myself
50
But in the sad version, we won't get to be heroes anymore.
51
I want to get into this, Steve, because I feel like a lot of people listening aren't going to quite understand what you mean by heroes
52
and what you mean by solving problems that nobody's ever solved before.
53
Sure.
54
Like when I think of, for example, a math college professor teaching students, I'm thinking of that scene in Good Will Hunting.
55
I also put an advanced Fourier system on the main hallway chalkboard.
56
And I'm hoping that one of you might prove it by the end of the semester.
57
Where Matt Damon's character is solving a very long, complicated math problem on a chalkboard.
58
Sorry.
59
What do you do?
60
Sorry.
61
That's people's work.
62
You can't graffiti here.
63
Am I Matt Damon in this version of the story?
64
You're either Matt Damon or you're the guy that finds Matt Damon's proof and thinks,
65
my God, this guy's a genius and he just needs a little bit of support.
66
Oh, my God.
67
Looks right.
68
I'm probably that guy.
69
Okay, so I'm sure that your days are more complicated than this movie scene.
70
I'm just trying to get a sense of, like, are you helping people solve what lay people might think of as complicated puzzles?
71
That's a good word for it, yes.
72
Our world is about puzzles.
73
It's puzzles that we create, depending on which type of math we're doing.
74
Sometimes it's puzzles that nature hands to us.
75
That's the kind of math I do, actually.
76
Wait, tell us what you mean by that.
77
That's really interesting.
78
Well, here's something I've personally been fascinated with, which is how do groups get organized?
79
Could be groups of people, could be groups of crickets.
80
I mean, that's one thing I might be known for.
81
We have crickets here in Ithaca, a species called the snowy tree cricket.
82
They chirp in enormous choruses, all chirping in unison, in sync.
83
And normally, like if you see people, say in a symphony, all the musicians playing in perfect time, they're very highly trained expert musicians.
84
But yet these crickets can do it too.
85
How do they chirp in unison?
86
And we now know the answer that when one cricket hears the chirp of another,
87
it actually makes a little adjustment into its nervous system that changes the timing of its next chirp.
88
And we can measure that.
89
So now the math problem is, suppose I have a thousand of them in a field.
90
How does that collective get itself so that all thousand are chirping in unison?
91
And you might say, I don't care about crickets, but your heart also has things that go in unison.
92
You have special cells that trigger the rest of your heart to beat
93
and pump blood to your body and to your brain.
94
And if those 10 ,000 cells are not organized to all zap in unison, telling your ventricles to pump, you're going to be very sick.
95
So what I'm saying is to a mathematician, these are the same problem.
96
This is variations on a theme.
97
And what we try to do is abstract the messy real world into a very beautiful,
98
pristine mathematical world where we can figure out how can we encourage synchrony?
99
How can we discourage it?
100
There are cases where it's bad, like people who have Parkinson's disease.
101
or epilepsy, their brain cells are firing in sync when they shouldn't be.
102
This is a math problem.
103
So just to summarize, Steve, what you're saying is that understanding a thousand crickets and why they move and make the noise that they do,
104
that might not be of great interest or importance to many people.
105
However, understanding that can help you understand how the heart works, for example.
106
And so it might not matter to me how a cricket makes its noise, although I am actually interested in that listening to you explain it.
107
But it should matter to me because the downstream effects of that, the medical implications, that matters to me.
108
That's the argument.
109
This is a spooky aspect of history that if you let people follow their curiosity, great things can happen, but it might take centuries.
110
I feel like what I'm getting from what you're describing is the sense
111
that you are a person who doesn't just care about math
112
because it's good for your job or to get to an end result.
113
I'm getting the sense from you that you care about understanding math because you are curious about the world around you.
114
And I wonder
115
if you could just talk a little bit about how people feel about themselves in the math field
116
and the feelings they get from actually solving some of these puzzles.
117
Yeah, it's good to go here.
118
This question of feeling is what's at stake, among other things.
119
You mentioned puzzles.
120
Think about what it is to do whatever your favorite puzzle is, jigsaw, sudoku, crossword puzzle.
121
It sort of feels good to be stumped, and then it's satisfying when you have a breakthrough.
122
When you finally get the word of the crossword, yes.
123
Yeah, isn't that good?
124
Everything clicks into place.
125
People like that feeling, and a lot of us live for that.
126
We want that.
127
It's like an addiction.
128
We want that feeling over and over.
129
So we're in the business of pleasuring ourselves that way.
130
Now, can I ask you to describe that slightly differently?
131
I wanted to say it that way because I actually think it is sexual slightly.
132
Because think of it abstractly if I don't want to be too risque here.
133
But what's going on is there's a quest.
134
In our case, we want answers.
135
We want solutions and we want understanding.
136
We want aha moments.
137
And so the fact that the problems resist and have to be seduced into yielding their secrets, it's kind of sexy.
138
I'm sorry.
139
Why are you resistant to that?
140
I'm not.
141
I'm just thinking about how many 17, 18 -year -olds are hearing this and are suddenly going to sign up to be math makers.
142
They should.
143
You've recruited an entire generation of mathematicians.
144
No, this interview took a bit of a turn, but I think a lot of people can appreciate and relate to the idea of the satisfaction.
145
I hope that you will keep this part of it because this is real.
146
You know, I'm not the mathematician, Steve, so you are the expert.
147
I'm going to defer to you about the pleasure you people get from math.
148
But I just I just want to really underscore that to a lot of people, this is not just a job,
149
that for some people, this is a passion that goes beyond a passion.
150
And so this kind of gets me to my next point, which is I want to talk about this kind of dawning realization
151
that you and other people in your field have had about how disruptive AI could be.
152
And I want to go back and have you explain to me
153
and to our audience what your initial perception of AI was.
154
Yeah.
155
You know, at first we were laughing at AI.
156
You may remember when it first came out, it was hallucinating all the time, couldn't be trusted.
157
There was a joke going around and it was real.
158
It couldn't count the number of ours in strawberry.
159
Sure.
160
But that was like 2022.
161
As the years went by, AI Bye got better at math very fast.
162
It soon was as good as a good high school student.
163
Then it was a good college student.
164
Then in March of this year, it started to be at the level of a very good graduate student,
165
which is a measure that means something to us as math professors because we deal with grad students all day long.
166
Can you just explain what you started seeing that made your perception change?
167
Sure.
168
So this is the gets into the word that's everywhere nowadays, benchmarks.
169
We give them tasks and we see, can they solve a problem?
170
And how do they solve it?
171
Do they solve it with style?
172
I guess I keep coming up with adjectives that will surprise you besides our sexy, passionate nature.
173
We're also very interested in style.
174
We want to be elegant.
175
The highest compliment you could give to another mathematician is, oh, what an elegant proof.
176
What does elegant mean for us?
177
Elegant for us is something that has a surprise value, but is also inevitable once you see it.
178
You know, it's a head slapping thing like that is so perfect.
179
That's such a great argument.
180
Why didn't I think of that?
181
So for a long time, the AIs, the computers were not producing elegant math.
182
They behaved like machines.
183
They would get the job done, but with no panache, no flair.
184
But by May of this year,
185
everybody sat up straight when an AI solved a problem that the world's best mathematicians were interested in.
186
And it did it with flair.
187
Okay, so take us to May, Steve.
188
What did you see exactly?
189
And how did it start changing your perception about what the models could do?
190
Well, first of all, it solved a problem that a very charming old mathematician had posed, because it's part of the culture.
191
I have to tell you why this problem is a beloved problem.
192
Tell me.
193
This is a puzzle associated with a mathematician named Paul Erdős, a Hungarian -American mathematician who was completely,
194
ridiculously unable to function in the real world.
195
What do you mean?
196
He didn't really have a home.
197
He would show up at your door, meaning you're any mathematician around the world,
198
he had a beaten up old suitcase and he would show up and say,
199
my brain is open, which means I'm ready to work with you on a math problem.
200
And that meant you had to take him into your house and take care of him because he couldn't make toast.
201
Wow.
202
I'm really starting to get the sense that mathematicians are real characters, Steve.
203
He was also a methamphetamine junkie.
204
Wow.
205
Okay.
206
This guy would take so many drugs because he was hyped up all the time wanting to do math.
207
Wow.
208
Now, why would anyone put up with him?
209
He was extremely brilliant.
210
And he would come up with these really good questions, which it turns out is not so easy to do.
211
So he was a really good puzzle poser.
212
And he also left the legacy on the order of a thousand great puzzles that we still don't know how to solve.
213
I want to say one last thing about him.
214
So he was constantly winning money because he was
215
so good at math that people would give him prize money for various things.
216
And he would always give the money away to young people, including a person who today is considered maybe the greatest living mathematician, Terry Tao.
217
And so there's a picture of little
218
Terry Tao, who is often called the Mozart of math because he was a child prodigy.
219
Sorry, I'm getting a little verklempt here, like they say on Saturday Night Live.
220
No, it's something you care about.
221
Because it's so sweet, this generational passing.
222
You see the picture of old man Erdős with his gray hair.
223
The two of them are sitting together with their paper and pencil.
224
Such nerds with very serious faces.
225
Terry Tao is like eight years old.
226
Two great mathematicians working together,
227
happy as clams on some real world inconsequential problem for the pleasure of being together.
228
So my point was that Erdős was a sweet man, in addition to being a junkie and a genius.
229
Not mutually exclusive.
230
Not all exclusive.
231
Okay, so the fact that Erdős left us these beautiful problems and a f*ing machine, you could bleep me.
232
solved the unit distance conjecture of Erdős.
233
Okay, we have to translate this into English.
234
So in May, one of the puzzles created by this man Erdős
235
that people have spent a lot of time trying to solve and is a passion, it sounds like.
236
Oh, yeah.
237
What happens to it?
238
It's solved by AI?
239
Is that what you're telling us?
240
It's solved by AI.
241
Okay.
242
OpenAI, somebody in there sets their machine to working on the one of these great Erdős problems
243
and it solves it in a way...
244
that is very beautiful.
245
Elegant.
246
Absolutely elegant.
247
Very imaginative.
248
An aesthetic high point.
249
All of us are agog because this is so clever and we can learn from it.
250
This is important.
251
It's not opaque.
252
We can follow what it did.
253
It took a certain part of math.
254
You don't need to know what I mean by this, but it's called discrete geometry.
255
Okay.
256
And then it took a different part of math called number theory, two parts that hadn't talked to each other much in the past
257
and found that if you put them in conversation with each other, that was like the magic key.
258
No one thought of making that connection before.
259
It was the key to the problem.
260
So objectively, what it did was really beautiful and brilliant, but it wasn't human.
261
Now, it stole.
262
Some people would say it stole from us because it learned from thousands of years of human ingenuity.
263
It built from other people's work.
264
It read every book.
265
It read every math paper.
266
It has knowledge like no human mathematician has ever had.
267
So it's a totally alien kind of intelligence that we have created that's now teaching us.
268
That's what happened in May.
269
That was just May.
270
This is October.
271
We'll be right back.
272
I want to get back to the story of this dawning realization in your world, Steve, about AI.
273
So you have this big aha moment in May.
274
We are now, as we speak, in the month of October.
275
Last month, OpenAI announced that it had solved this huge math problem, this millennium problem.
276
And it made all kinds of news that had broken outside of the math community.
277
It got to lay people like me, for instance.
278
And I want to just explain, first of all, to our listeners, what is the Millennium Problem and why did this feel like a watershed moment?
279
So there are seven of these problems.
280
They were all formalized to commemorate the new century.
281
That's why they're the Millennium Problems.
282
In the year 2000, some of the world's finest mathematicians got together to take stock.
283
Like, where are we in our field? and what are seven great mountains that we would like to climb.
284
These are problems created for the sake of solving them, like what you were talking about earlier.
285
These are problems made for the satisfaction of solving them.
286
That's right.
287
They're aspirational puzzles.
288
So these were our big dreams, seven of them, in seven different parts of math.
289
Okay.
290
So one of the seven millennium problems was already solved a
291
few years back by a really crazy Russian mathematician who didn't like to cut his fingernails so that they got very long.
292
Like, imagine if you never cut your fingernails ever.
293
I don't want to make all mathematicians sound crazy or drug addicts or whatever.
294
I'm just giving you the truth here.
295
You're going to have to list some other ones then.
296
But, okay, I'm just, okay.
297
One of them was solved already.
298
There were six remaining. By September, rumor started to leak out
299
that one of the AI companies was on the verge of solving one of the last six millennium problems.
300
And the rumor was that it was the one called the Navier -Stokes millennium problem.
301
And what was that?
302
Well, it's about water and air and how they flow.
303
Okay.
304
And the equations we use to describe the flow of anything that can flow,
305
honey, lava, blood, that's governed by these equations, the Navier -Stokes equations.
306
And it was to show that the Navier -Stokes equations could break down is what the problem was.
307
Show that the Navier -Stokes equations have a fatal flaw.
308
And so, Steve, when you start hearing rumors that the AI had solved this problem, your reaction is what?
309
There was a feeling of like, this is it.
310
This day has come.
311
Wait, that sounds, I can't tell if you're Doomer -esque about this.
312
That's the problem.
313
No one knows how to feel.
314
You might have an instinctive Doomer feeling or techno -optimist feeling, and then you felt happy or you felt terrified.
315
My first feeling, I guess, was terrified.
316
are we as a species going to be able to control this thing we created
317
we knew that it would get to be superhuman in intelligence we already saw it in chess
318
but chess doesn't matter math matters and now
319
that it's superhuman in at least one aspect of math the problem solving aspect
320
it's going to be a short hop and skip and a jump till it's superhuman at all aspects of math, I think.
321
And where does that leave us in math?
322
But also, are we the canary in the coal mine for all of human?
323
creative work, whether it's poetry or journalism or medicine or you name it.
324
Now, maybe it's a great thing.
325
Maybe it cures cancer for us.
326
Maybe it develops new kinds of batteries.
327
I mean, this is not just idle speculation.
328
It's quite possible if we're smart enough to harness this well, we might be in the best time ever to be alive.
329
But why I tend to be a doomer is because we don't understand this thing we built.
330
If you ask it, how did you solve the Navier Stokes Millennium problem?
331
It gives a crappy explanation.
332
It can't explain itself very well.
333
Maybe it will get better at explaining itself.
334
Even if it does, I think it will become so advanced so soon that even the best,
335
most patient pedagogical teacher will not be able to explain itself to us
336
because it'd be like me teaching calculus to my dog.
337
My dog can't understand it.
338
I'm too advanced for it.
339
And I think we are going to be in the relation to AI that my dog is to me soon.
340
And therefore, how do I know it's going to have my, as a species, my human species best interest at heart?
341
I want to just zoom out here for a second, Steve, because I'm hearing two things from you.
342
I'm hearing that you are both scared of the actual tool itself and it getting out of the control of humans.
343
But I am also hearing from you
344
that you're worried about this tool taking away the satisfaction that people get and the passion people have of just solving problems,
345
sort of this fundamental thing that we can all relate to.
346
Yes, both.
347
It's just so easy to dismiss the second one.
348
The satisfaction that we get from solving problems and the joy we get as a community to work on things together,
349
all that, all the human stuff.
350
there's a certain hard -boiled kind of person who says that's not that valuable.
351
Like, so what?
352
Get over it.
353
Suck it up.
354
Really?
355
But everybody understands what it means to have something that gives you fulfillment.
356
They should.
357
A sensitive person acting in good faith gets that.
358
But I can tell you, if you look at the internet, especially GoOnX, where there's a lot of people talking about AI and math,
359
there's a very popular joke that I actually think is pretty funny going around that says, put the fries in the bag, Terrence.
360
Terrence Tao, who we spoke about earlier.
361
Terrence Tao, yeah.
362
That same Terry Tao.
363
You know, there's certain people who are gloating,
364
who have a feeling of satisfaction that the smartest kid in the class is now going to be out of a job,
365
just like the rest of us.
366
That when automation comes, it's bad for everybody.
367
Now it's your turn.
368
But just for a moment, if you'll indulge me, I want to talk about the optimistic case for a second and just pose that to you?
369
Yeah, yeah.
370
Because the good faith optimistic case for AI has always been that AI could do the grunt work and then free you,
371
Steve, up to do more meaningful work, right?
372
Like the AI companies will say,
373
this is a tool and it's only going to allow you to continue doing what you're trying to do, but better.
374
And I just sort of wonder if you think that that argument could be correct.
375
I do think it could be correct.
376
I really do.
377
I'm going to put on my happy face now and I'm not faking it.
378
I actually do find it very exciting and uplifting.
379
And the teacher in me who wants young people to have hope, I don't want to lie to them.
380
I want them to have hope and I want to mean it.
381
At Cornell, I'm teaching kids, juniors,
382
seniors in college, and they are in the best position to make use of this new tool because they're young and adaptable.
383
I mean, the other day at a town hall meeting, the math department, of course, we're all freaking out.
384
So all the students are gathered, all the faculty at this meeting, one of my colleagues said, it's like a new telescope.
385
Where are you going to point your telescope?
386
So that's the question.
387
Where are you going to point this thing?
388
That's going to determine your success in the future.
389
I guess the question though, is in this optimistic scenario, are we sure that the AI won't take all of it.
390
Like who says it won't start to have great taste or it won't come up with good questions.
391
So far, it has no aptitude for asking good questions.
392
There's no Paul Erdős AI yet.
393
So you're saying podcast host is still a pretty safe profession.
394
I don't know.
395
Maybe.
396
Okay.
397
That's the question.
398
Are you using AI in your own work, Steve, yet at all?
399
Yes, I do use it and I really enjoy it. For what?
400
I use it for all kinds of things.
401
I use it to teach myself.
402
I can ask it questions like if I wanted to learn about the Millennium problem, the Navier Stokes problem.
403
I'm not an expert in that.
404
I ask it to tutor me, and it's so good.
405
But not just math.
406
I mean, I'm really curious about all of school.
407
I always have been.
408
I loved all the subjects.
409
So this will really get us to a strange place, but it's true.
410
I've been thinking a lot about the meaning of life.
411
Like, what's the point?
412
Which is a question a lot of us are asking in my profession.
413
What is the point?
414
What have you come up with?
415
Oh, you want to know the answer to that one, too?
416
I would love the answer.
417
Yeah.
418
I'll just tell you about my journey, not the answer.
419
I know that people have thought about this for millennia.
420
And I was reminded of this old bird song, Turn, Turn, Turn.
421
Turn everything, turn, turn, turn. So that's biblical, right?
422
That's from Ecclesiastes, which I had never read.
423
So I wanted to learn what's Ecclesiastes about, because there's a famous passage in there where he says,
424
basically, he's looking around and trying to understand what's the point of it all.
425
And his really very modern and very dark, initially stark -seeming conclusion is there is no point.
426
It's all vanity, he says.
427
It's vanity of vanities.
428
Everything I do is in vain.
429
It accomplishes nothing.
430
I got nowhere by the people I loved, the work I did.
431
It was all for nothing.
432
and he says in there he the writer says you know that in one famous passage like i saw
433
um that the the race i'm sorry it's
434
so beautiful no he says the the race is not always to the swift
435
like the the fastest person doesn't necessarily win um or the battle is not to the strong
436
In other words, people don't get what they deserve.
437
It doesn't work like that.
438
Life is unfair.
439
The race is not to the swift.
440
The battle is not for the strong.
441
Time and chance happeneth to them all.
442
Like there's a lot of randomness.
443
We get old.
444
A lot of random stuff happens.
445
No matter how strong or fast we are, it's true for all of us.
446
Okay.
447
So then you think, Jesus, who wants to live in a world like that?
448
Well, you know what you're making me think of is
449
that Isn't math the way you've described in an attempt to understand and organize and make things a little bit random?
450
We're pushing back.
451
Sure, we would like to find meaning.
452
But if you really go to this place in the book, then, you know, you're a blip.
453
You only live a short time, certainly in cosmic sense.
454
So what are you going to do with yourself while you're here?
455
Okay, why am I going to this insane place?
456
No, I love it.
457
I think this is where we are in math because this is
458
when the student in the back of the room at our town hall says, what's the point?
459
If the AI can do math better than any human being, sure, I could sit there and learn from it.
460
But I want to discover things, too.
461
I want that thrill.
462
I don't want my life to be in vain.
463
Yeah, that's exactly right.
464
I don't want my life to be in vain.
465
So if you take that away from me, what am I doing?
466
So you could see why I'm looking to this ancient wisdom.
467
And as I understand it, I'm no biblical scholar, but as I understand it, the lesson is, well, that your life is precious because it's short.
468
That you have to make the most of it while you're here.
469
So, um, there's another line in there that says everything is beautiful in its time.
470
Like, God, I'm sorry, I'm a wreck.
471
But if everything is beautiful in its time, we get to have a turn where we're in love, we're creative, we do what's meaningful,
472
we get to walk our dog, whatever.
473
We do have a time.
474
Otherwise, we're dust, right?
475
Right.
476
We're ashes to ashes, dust to dust.
477
The beautiful part is the in between.
478
Am I getting to you?
479
No, I'm not crying.
480
You're crying.
481
I think I did get to you.
482
I'm the only one that gets to make people cry on this show, Steve.
483
Whoa.
484
What did you say to the student?
485
What did you say to the student who wanted to know?
486
I didn't answer.
487
It was a town hall.
488
So there were like, there were a hundred people there, but I want to talk to her later.
489
What would you say?
490
So let's read this book together.
491
Are you saying that AI is going to make you more religious?
492
I think it sure seems like it, doesn't it?
493
Yeah, maybe.
494
But I don't think it's about God.
495
I think it's about people.
496
Because that I do believe in.
497
Maybe you are also appreciating what makes us human more and what makes our time here special.
498
And I wonder if that is going to maybe in your mind be one result of all of this.
499
I think it might.
500
It might.
501
I hope so.
502
Man, you're killing me, Rachel.
503
This was supposed to be a math show, but you took me to a really interesting place.
504
Thank you for coming with me.
505
I hope that our listeners get as much out of this conversation as I did.
506
So, Steve Strogath, thank you so much for joining us.
507
Wow.
508
Thank you, Rachel.
509
I'm going to have to go home and lie down.
510
We'll be right back.
511
Here's what else you need to know today.
512
In an effort to boost
513
Republicans in the midterms, Elon Musk has funneled more than $276 million to Republicans
514
and his super PAC since the beginning of July.
515
That makes Musk by far the largest known donor this election cycle.
516
To help Republicans maintain their Senate majority, Musk's team says he gave $158 million to the party's
517
Senate leadership fund.
518
That's one of the largest political donations in American history.
519
And Homeland Security agents searched the D .C home of Representative Ilhan Omar on Monday.
520
The search comes after
521
Homeland Security Secretary Mark Wayne Mullen said last month
522
that his department was investigating Omar and her family's immigration to the U .S.
523
He suggested that one outcome of that investigation could be deportation.
524
Omar has long said that such allegations of immigration fraud are false and politically motivated.
525
The search took place less than 60 days before the midterms,
526
in spite of a Justice Department policy that discourages unnecessary investigative steps in that window out of fear of influencing elections.
527
Omar is running for re -election in Minnesota.
528
Today's episode was produced by Jack D 'Isidoro, Adrian Hurst, and Michael Simon -Johnson, with help from Eric Krupke.
529
It was edited by Annie Minoff, with help from Paige Cowett.
530
Contains music by Pat McCusker and Marion Lozano.
531
Original music by Martin Courtney.
532
It was engineered by Alyssa Moxley.
533
Our theme music is by Wonderly.
534
And special thanks to Dan Powell.
535
That's it for The Daily.
536
I'm Rachel Abrams.
537
See you tomorrow.
הורד אפליקציה
כל מה שאתה צריך כדי לדבר בשטף
הגיית AIציון לכל משפט
תרגול IPAשלוט בכל צליל
אוצר מיליםבנה את מאגר המילים שלך
משחק אוצר מיליםלמד תוך כדי משחק
אותו ערוץ
B21
Canada Is Done With Trump
Daily ConversationWorking Adults
B21
Tell Us About Your Household Debates
Daily ConversationWorking Adults
B2‘Buy Now, Pay Later’: A New Wave of Consumer Debt
Daily ConversationWorking Adults
B21
She Fell in Love With ChatGPT. Like, Actual Love. With Sex.
Daily ConversationWorking Adults
B2Vegetables Don't Have to Be Boring
Business EnglishDaily ConversationUniversity Students
B2Why Gas Prices Just Keep Going Up
Business EnglishWorking Adults
FOR YOU
המלץ על סרטון
C11
86 משפטים
The End of Human Mathematics? AI's Recent Breakthrough -- And What's Next
Daily ConversationUniversity StudentsWorking Adults
C11
259 משפטים
Talk Back: Why Arguing With Yourself Can Help You Flourish
Daily ConversationUniversity StudentsWorking Adults
C11
510 משפטים
Inside the secret race to build artificial intelligence | The Gray Area
Daily ConversationUniversity StudentsWorking Adults
C185
66 משפטים
What science can’t answer, according to physicist Jim Al-Khalili | Big Think
Daily ConversationUniversity StudentsWorking Adults
C12
174 משפטים
This 1 Book Has Produced More Geniuses Than Any University in History
Daily ConversationUniversity StudentsWorking Adults
C11
358 משפטים
It’s not just you
Daily ConversationUniversity StudentsWorking Adults
C11
17 משפטים
Very excited to share that I now have a column in New Scientist
Daily ConversationUniversity StudentsWorking Adults
C11
74 משפטים
AI & Inequality | Daron Acemoglu: Big Tech poses risks as AI reshapes society
Daily ConversationUniversity StudentsWorking Adults
C11
526 משפטים
What AI Can Never Replace w/ Megan Kobzej | Cloud English Podcast Episode 91
Daily ConversationUniversity StudentsWorking Adults
C11
251 משפטים
Things That Are Not What They Seem and the Truth About Life | Advanced Podcast for Conversations
Daily ConversationUniversity StudentsWorking Adults
B22
351 משפטים
This summer's hottest trend? Monoculture. | It's Been a Minute
Daily ConversationUniversity StudentsWorking Adults
C18
55 משפטים
When AIs act emotional
Daily ConversationUniversity StudentsWorking Adults
אוצר מילים והערות הגייה לשיעור הזה
שיעור הדיבור הזה ברמה C1 מבוסס על הסרטון “When A.I. Comes for Your Purpose in Life”. בסרטון הזה יש 537 משפטים ו-5455 מילים לתרגול Shadowing. הדיבור נמשך 33:10. הדובר מדבר בקצב טבעי של כ-164 מילים בדקה, קרוב לשיחה יומיומית. 83% מהמילים נמצאות בין 3,000 המילים הנפוצות ביותר באנגלית; כדאי ללמוד את השאר לפני שמתחילים.
אוצר מילים מרכזי בסרטון הזה
15 המילים המתקדמות ביותר בסרטון, עם ההגייה והמשמעות שלהן:
| מילה | הגייה | משמעות |
|---|---|---|
| mathematician שם עצם | /ˌmæθ.(ə.)məˈtɪʃ.ən/ | מָתֶמָטִיקַאי |
| puzzle שם עצם | /ˈpʌzəl/ | חִידָה |
| millennium שם עצם | /mɪˈlɛnɪəm/ | מִילֶניוּם |
| elegant שם תואר | /ˈɛləɡənt/ | אֶלֶגַנְטִי, מְהֻדָּר |
| chirp פועל | /t͡ʃɚp/ | צִפְצֵף |
| equation שם עצם | /ɪˈkweɪ.ʒən/ | משוואה \ מִשְׁוָאָה |
| rumor שם עצם | /ˈɹumɚ/ | שְׁמוּעָה |
| vain שם תואר | /veɪn/ | גאוותן |
| optimistic שם תואר | /ˌɒptɪˈmɪstɪk/ | אוֹפְּטִימִי |
| grief שם עצם | /ɡɹiːf/ | אֵבֶל |
| crossword שם עצם | /ˈkɹɒswɜː(ɹ)d/ | תַּשְׁבֵּץ, תַּשְׁחֵץ |
| fingernail שם עצם | /ˈfɪŋɡɚˌneɪl/ | ציפורן \ צִפֹּרֶן |
| listener שם עצם | /ˈlɪs(ə)nɚ/ | מַאֲזִין |
| vanity שם עצם | /ˈvænəti/ | הֶבֶל |
| telescope שם עצם | /ˈtɛl.əˌskoʊp/ | טֶלֶסְקוֹפּ |
Phrasal verbs שתשמעו
| מילה | הגייה | משמעות |
|---|---|---|
| come out פועל | /ˌkʌm ˈaʊt/ | יָצָא |
| figure out פועל | הֵבִין, מצא תשובה | |
| lie down פועל | שָׁכַב | |
| put up with פועל | הִשְׁלִים עִם |
משפטים שכדאי לחזור עליהם
משפטים קצרים ושלמים מהסרטון שאפשר להשתמש בהם בשיחה יומיומית:
- What stage of grief are you in?
- How can we discourage it?
- Why didn't I think of that?
- Is that what you're telling us?
- I think it sure seems like it, doesn't it?
דקדוק בסרטון הזה
המבנים שהדובר משתמש בהם הכי הרבה, עם המילים המדויקות מהסרטון:
| מבנה | בסרטון |
|---|---|
| הווה מושלם ממושך (Present perfect continuous) have/has been + -ing — פעולה שהתחילה בעבר ועדיין נמשכת עכשיו | have been working · I've been thinking |
| הווה מושלם (Present perfect) have/has + past participle — פעולה שקרתה בעבר ועדיין יש לה חשיבות או השפעה עכשיו | have tackled · they've solved · haven't been |
| סביל (Passive voice) be + past participle — הדגש הוא על מה שקורה, לא על מי שעושה את הפעולה | been solved · are freaked · are known |
| משפטי זיקה (Relative clauses) who / which + clause — מידע נוסף על אדם או דבר | with, which is · people who have · person who doesn |
הגייה ששווה לשים לב אליה
הדובר משתמש ב-125 קיצורים וצורות מקוצרות, כמו I'm, you're, we're. אמרו אותם בצורה הקצרה, כפי שאתם שומעים אותם.
- הצלילים של “th”: mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/, breakthrough /ˈbɹeɪk.θɹuː/, aesthetic /ɛsˈθɛ.tɪk/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/
- הצלילים של “sh” ו-“zh”: mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/, equation /ɪˈkweɪ.ʒən/, realization /ˌɹɪə.laɪˈzeɪ.ʃən/, musician /mjuˈzɪʃən/, allegation /ˌæl.ɪˈɡeɪ.ʃən/
- מילים ארוכות — שימו לב להטעמה: mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/, optimistic /ˌɒptɪˈmɪstɪk/, realization /ˌɹɪə.laɪˈzeɪ.ʃən/, allegation /ˌæl.ɪˈɡeɪ.ʃən/, implication /ˌɪmpləˈkeɪʃən/
צלילים שדוברי עברית מתקשים בהם:
- /w/ — שפתיים מעוגלות, לא /v/: equation /ɪˈkweɪ.ʒən/, crossword /ˈkɹɒswɜː(ɹ)d/, whoa /woʊ/
- /æ/ — פתוח יותר מה-“אֶ” העברית, לא כמו “a” ב-“cup”: mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/, vanity /ˈvænəti/, translate /tɹænzˈleɪt/, allegation /ˌæl.ɪˈɡeɪ.ʃən/, satisfy /ˈsætɪsfaɪ/
- /r/ — הלשון לא נוגעת בשום מקום, לא ה-“ר” הגרונית: crickets /ˈkɹɪk.ɪts/, doomer /ˈduːmə(ɹ)/, rumor /ˈɹumɚ/, grief /ɡɹiːf/, chalkboard /ˈt͡ʃɔːk.bɔː(ɹ)d/
איך לתרגל עם הסרטון הזה
- הקשיבו לכל הסרטון פעם אחת בלי לדבר, ורשמו את המילים שאתם לא מכירים.
- התחילו במהירות 0.75×, תרגלו Shadowing משפט אחר משפט, וחזרו למהירות רגילה כשזה מרגיש קל.
- הקליטו את עצמכם והשוו למקור, תוך תשומת לב למילים כמו mathematician, puzzle, millennium.
מהי טכניקת השאדואינג?
שאדואינג היא טכניקת למידת שפה מבוססת מדע שפותחה במקור להכשרת מתורגמנים מקצועיים ופופולרית על ידי הפוליגלוט ד"ר אלכסנדר ארגואלס. השיטה פשוטה אך עוצמתית: אתה מקשיב לאודיו באנגלית ילידית וחוזר עליו בקול רם מיד — כמו צל העוקב אחרי הדובר עם עיכוב של 1–2 שניות בלבד. בניגוד להאזנה פסיבית או תרגילי דקדוק, שאדואינג מכריח את המוח והשרירים בפה לעבד ולשחזר דפוסי דיבור אמיתיים בו זמנית. מחקרים מראים שזה משפר משמעותית את דיוק ההגייה, האינטונציה, הקצב, הדיבור המחובר, הבנת השמיעה ושטף הדיבור — מה שהופך אותו לאחת השיטות היעילות ביותר להכנה לIELTS Speaking ולתקשורת באנגלית בעולם האמיתי.