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From the New York Times, I'm Rachel Abrams, and this is The Daily.

This morning, one of the hardest problems in mathematics may have just been solved, not by a human, but by artificial intelligence.

So many people are freaked out about AI taking their jobs, and now you can add mathematicians to that list.

In the span of just a few months, AI systems have tackled some of the biggest problems in math.

And they've solved them.

For decades, mathematicians have been working to solve this problem.

They haven't been able to crack it.

But AI, it just did it easily.

Last month, it was one of the field's so -called millennium problems.

And then last week, OpenAI released a torrent of over 350 other results, covering virtually every corner of mathematics.

These are problems that literally the smartest humans on Earth have been working on for decades and sometimes even centuries.

This AI takeover of the field has raised some uncomfortable questions and unleashed a range of emotions.

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,

but for your purpose.

It's Tuesday, October 13th.

Steve Strogatz, thank you so much for joining us.

Hi, Rachel.

I'm really happy to see you and be here with you.

So, Steve, one of the reasons we're talking to you is, of course, because you are an esteemed professor at Cornell.

But we are really talking to you

because you are known for translating math into English for those of us who do not speak math.

And you actually have a book that gets at this very thing.

It is called Big Math.

And so we are turning to you to help sort of translate this destabilizing moment in math

that you and your colleagues are living through.

Yeah.

Can you just start by explaining to us, how would you sum up the mood in math right now?

Ooh, the mood.

Yeah, the mood.

The mood is discombobulated.

We're all a swirl.

We're spinning.

Some people are exhilarated.

They see a great golden age upon us with the help of our computer friends.

Most people, I think, are feeling grief, and we're in different stages.

Some people are bargaining, some are accepting, some are still denying.

What stage of grief are you in?

Today, I'm feeling pretty good.

I have been dark.

There have been days when I actually cried during interviews.

You know what's actually being lost is we like to think of ourselves as heroes, and maybe that's over.

That's the big news.

it's hard to be a hero

if you can't be the first to solve a problem

or to understand something or to explain it to the world

and for the thousands of years that our culture of math has existed

we some of us did fantasize about being heroes in our own story

and now that's over at least i mean

this is the grief version of myself talking i can of course like any professor argue against myself

But in the sad version, we won't get to be heroes anymore.

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

and what you mean by solving problems that nobody's ever solved before.

Sure.

Like when I think of, for example, a math college professor teaching students, I'm thinking of that scene in Good Will Hunting.

I also put an advanced Fourier system on the main hallway chalkboard.

And I'm hoping that one of you might prove it by the end of the semester.

Where Matt Damon's character is solving a very long, complicated math problem on a chalkboard.

Sorry.

What do you do?

Sorry.

That's people's work.

You can't graffiti here.

Am I Matt Damon in this version of the story?

You're either Matt Damon or you're the guy that finds Matt Damon's proof and thinks,

my God, this guy's a genius and he just needs a little bit of support.

Oh, my God.

Looks right.

I'm probably that guy.

Okay, so I'm sure that your days are more complicated than this movie scene.

I'm just trying to get a sense of, like, are you helping people solve what lay people might think of as complicated puzzles?

That's a good word for it, yes.

Our world is about puzzles.

It's puzzles that we create, depending on which type of math we're doing.

Sometimes it's puzzles that nature hands to us.

That's the kind of math I do, actually.

Wait, tell us what you mean by that.

That's really interesting.

Well, here's something I've personally been fascinated with, which is how do groups get organized?

Could be groups of people, could be groups of crickets.

I mean, that's one thing I might be known for.

We have crickets here in Ithaca, a species called the snowy tree cricket.

They chirp in enormous choruses, all chirping in unison, in sync.

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.

But yet these crickets can do it too.

How do they chirp in unison?

And we now know the answer that when one cricket hears the chirp of another,

it actually makes a little adjustment into its nervous system that changes the timing of its next chirp.

And we can measure that.

So now the math problem is, suppose I have a thousand of them in a field.

How does that collective get itself so that all thousand are chirping in unison?

And you might say, I don't care about crickets, but your heart also has things that go in unison.

You have special cells that trigger the rest of your heart to beat

and pump blood to your body and to your brain.

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.

So what I'm saying is to a mathematician, these are the same problem.

This is variations on a theme.

And what we try to do is abstract the messy real world into a very beautiful,

pristine mathematical world where we can figure out how can we encourage synchrony?

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.

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This is a math problem.

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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,

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that might not be of great interest or importance to many people.

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However, understanding that can help you understand how the heart works, for example.

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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.

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But it should matter to me because the downstream effects of that, the medical implications, that matters to me.

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That's the argument.

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This is a spooky aspect of history that if you let people follow their curiosity, great things can happen, but it might take centuries.

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I feel like what I'm getting from what you're describing is the sense

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that you are a person who doesn't just care about math

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because it's good for your job or to get to an end result.

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I'm getting the sense from you that you care about understanding math because you are curious about the world around you.

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And I wonder

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if you could just talk a little bit about how people feel about themselves in the math field

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and the feelings they get from actually solving some of these puzzles.

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Yeah, it's good to go here.

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This question of feeling is what's at stake, among other things.

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You mentioned puzzles.

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Think about what it is to do whatever your favorite puzzle is, jigsaw, sudoku, crossword puzzle.

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It sort of feels good to be stumped, and then it's satisfying when you have a breakthrough.

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When you finally get the word of the crossword, yes.

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Yeah, isn't that good?

124

Everything clicks into place.

125

People like that feeling, and a lot of us live for that.

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We want that.

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It's like an addiction.

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We want that feeling over and over.

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So we're in the business of pleasuring ourselves that way.

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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.

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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.

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In our case, we want answers.

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We want solutions and we want understanding.

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We want aha moments.

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And so the fact that the problems resist and have to be seduced into yielding their secrets, it's kind of sexy.

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I'm sorry.

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Why are you resistant to that?

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I'm not.

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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.

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They should.

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You've recruited an entire generation of mathematicians.

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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.

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I hope that you will keep this part of it because this is real.

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You know, I'm not the mathematician, Steve, so you are the expert.

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I'm going to defer to you about the pleasure you people get from math.

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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.

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And I want to go back and have you explain to me

153

and to our audience what your initial perception of AI was.

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Yeah.

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You know, at first we were laughing at AI.

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You may remember when it first came out, it was hallucinating all the time, couldn't be trusted.

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There was a joke going around and it was real.

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It couldn't count the number of ours in strawberry.

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Sure.

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But that was like 2022.

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As the years went by, AI Bye got better at math very fast.

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It soon was as good as a good high school student.

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Then it was a good college student.

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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.

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Can you just explain what you started seeing that made your perception change?

167

Sure.

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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?

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And how do they solve it?

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Do they solve it with style?

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I guess I keep coming up with adjectives that will surprise you besides our sexy, passionate nature.

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We're also very interested in style.

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We want to be elegant.

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The highest compliment you could give to another mathematician is, oh, what an elegant proof.

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What does elegant mean for us?

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Elegant for us is something that has a surprise value, but is also inevitable once you see it.

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You know, it's a head slapping thing like that is so perfect.

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That's such a great argument.

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Why didn't I think of that?

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So for a long time, the AIs, the computers were not producing elegant math.

182

They behaved like machines.

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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.

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And it did it with flair.

187

Okay, so take us to May, Steve.

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What did you see exactly?

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And how did it start changing your perception about what the models could do?

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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.

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Yes, both.

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It's just so easy to dismiss the second one.

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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.

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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.

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They should.

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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.

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Terrence Tao, who we spoke about earlier.

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Terrence Tao, yeah.

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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.

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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?

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Like the AI companies will say,

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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.

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I do think it could be correct.

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I really do.

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I'm going to put on my happy face now and I'm not faking it.

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I actually do find it very exciting and uplifting.

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And the teacher in me who wants young people to have hope, I don't want to lie to them.

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I want them to have hope and I want to mean it.

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At Cornell, I'm teaching kids, juniors,

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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.

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So all the students are gathered, all the faculty at this meeting, one of my colleagues said, it's like a new telescope.

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Where are you going to point your telescope?

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So that's the question.

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Where are you going to point this thing?

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That's going to determine your success in the future.

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I guess the question though, is in this optimistic scenario, are we sure that the AI won't take all of it.

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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.

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There's no Paul Erdős AI yet.

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So you're saying podcast host is still a pretty safe profession.

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I don't know.

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Maybe.

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Okay.

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That's the question.

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Are you using AI in your own work, Steve, yet at all?

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Yes, I do use it and I really enjoy it. For what?

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I use it for all kinds of things.

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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.

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I ask it to tutor me, and it's so good.

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But not just math.

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I mean, I'm really curious about all of school.

407

I always have been.

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I loved all the subjects.

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So this will really get us to a strange place, but it's true.

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I've been thinking a lot about the meaning of life.

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Like, what's the point?

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Which is a question a lot of us are asking in my profession.

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What is the point?

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What have you come up with?

415

Oh, you want to know the answer to that one, too?

416

I would love the answer.

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Yeah.

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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.

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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.

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It's all vanity, he says.

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It's vanity of vanities.

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Everything I do is in vain.

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It accomplishes nothing.

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I got nowhere by the people I loved, the work I did.

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It was all for nothing.

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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

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like the the fastest person doesn't necessarily win um or the battle is not to the strong

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In other words, people don't get what they deserve.

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It doesn't work like that.

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Life is unfair.

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The race is not to the swift.

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The battle is not for the strong.

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Time and chance happeneth to them all.

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Like there's a lot of randomness.

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We get old.

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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.

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But if you really go to this place in the book, then, you know, you're a blip.

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You only live a short time, certainly in cosmic sense.

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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.

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So if you take that away from me, what am I doing?

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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.

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Лексика и советы по произношению к этому уроку

Этот урок разговорной практики построен на видео «When A.I. Comes for Your Purpose in Life». В этом видео 537 предложений и 5455 слов для шедоуинга. Речь длится 33:10. Говорящий говорит в естественном темпе, около 164 слов в минуту, близком к повседневной речи. 83% слов входят в 3000 самых частых слов английского языка; остальные стоит разобрать перед занятием.

Ключевая лексика этого видео

15 слов из видео, которые стоит выучить, с произношением и значением:

Слово	Произношение	Значение
mathematics существительное	/mæθ(.ə)ˈmæt.ɪks/	матема́тика
solve глагол	/sɒlv/	реша́ть, реши́ть
puzzle существительное	/ˈpʌzəl/	зага́дка, затрудне́ние
millennium существительное	/mɪˈlɛnɪəm/	тысячеле́тие
elegant прилагательное	/ˈɛləɡənt/	элега́нтный, шика́рный
satisfaction существительное	/sætɪsˈfækʃən/	удовлетворе́ние
equation существительное	/ɪˈkweɪ.ʒən/	уравне́ние
mood существительное	/muːd/	настрое́ние, расположе́ние
passion существительное	/ˈpæʃən/	страсть, аффе́кт
complicated прилагательное	/ˈkɑm.plɪˌkeɪ.tɪd/	сло́жный, запу́танный
aspect существительное	/ˈæspɛkt/	аспе́кт, вид
dust существительное	/dʌst/	пыль, прах
cricket существительное	/ˈkɹɪkɪt/	сверчо́к
perception существительное	/pɚˈsɛpʃ(ə)n/	восприя́тие, перце́пция
grief существительное	/ɡɹiːf/	го́ре, печа́ль

Фразовые глаголы, которые вы услышите

Слово	Произношение	Значение
come up with глагол		придумывать, приходить в голову
break down глагол		ломаться, сломаться
come out глагол	/ˌkʌm ˈaʊt/	выходи́ть, вы́йти
figure out глагол		разбира́ться, разобра́ться
get over глагол		преодолева́ть, преодоле́ть
go back глагол		возвраща́ться, возврати́ться
lie down глагол		ложи́ться, лечь
look around глагол		осма́триваться, осмотре́ться

Фразы, которые стоит повторять

Короткие законченные фразы из видео, которые пригодятся в повседневном разговоре:

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?

Грамматика в этом видео

Конструкции, которые говорящий использует чаще всего, с точными словами из видео:

Конструкция	В видео
Present Perfect Continuous have/has been + -ing — действие началось раньше и продолжается	have been working · I've been thinking
Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас	have tackled · they've solved · haven't been
Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает	been solved · are freaked · are known
Придаточные определительные who / which + предложение — уточнение о человеке или предмете	with, which is · people who have · person who doesn

Произношение, на которое стоит обратить внимание

Говорящий использует 125 сокращённых и редуцированных форм, например I'm, you're, we're. Произносите их коротко, так, как слышите.

Звуки «th»: mathematics /mæθ(.ə)ˈmæt.ɪks/, gather /ˈɡæðɚ/, mathematical /ˌmæθ(.ə)ˈmæt.ɪ.kəl/, aesthetic /ɛsˈθɛ.tɪk/, breakthrough /ˈbɹeɪk.θɹuː/
Звуки «sh» и «zh»: satisfaction /sætɪsˈfækʃən/, equation /ɪˈkweɪ.ʒən/, passion /ˈpæʃən/, perception /pɚˈsɛpʃ(ə)n/, immigration /ˌɪmɪˈɡɹeɪʃən/
Длинные слова — следите за ударением: mathematics /mæθ(.ə)ˈmæt.ɪks/, satisfaction /sætɪsˈfækʃən/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, optimistic /ˌɒptɪˈmɪstɪk/, immigration /ˌɪmɪˈɡɹeɪʃən/

Звуки, трудные для русскоязычных:

/w/ — губы округлены, это не /v/: equation /ɪˈkweɪ.ʒən/, swift /swɪft/, nowhere /ˈnoʊ.(h)wɛɹ/, wisdom /ˈwɪzdəm/, quest /kwɛst/
/h/ — лёгкий выдох, а не русское «х»: nowhere /ˈnoʊ.(h)wɛɹ/, honey /ˈhʌni/
Звонкие согласные в конце слова — не оглушайте их: solve /sɒlv/, mood /muːd/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, passage /ˈpæsɪd͡ʒ/, pose /poʊz/

Как заниматься с этим видео

Прослушайте всё видео один раз молча и выпишите незнакомые слова.
Начните со скорости 0,75×, повторяйте предложение за предложением и вернитесь к обычной скорости, когда станет легко.
Запишите себя и сравните с оригиналом, обращая внимание на такие слова, как mathematics, solve, puzzle.

Что такое техника Shadowing?

Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.

Техника шедоуинга: читать полное пошаговое руководство →

Практика Shadowing: When A.I. Comes for Your Purpose in Life - Изучайте разговорный английский по видео

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Лексика и советы по произношению к этому уроку

Ключевая лексика этого видео

Фразовые глаголы, которые вы услышите

Фразы, которые стоит повторять

Грамматика в этом видео

Произношение, на которое стоит обратить внимание

Как заниматься с этим видео

Что такое техника Shadowing?