Fluentide

A Chinese podcast · Same story, 4 levels

Former NFL Lineman John Urschel, Now an MIT Professor, Solves a 30-Year-Old Math Conjecture on Gaussian Elimination — episode cover art
5:52

从 NFL 到麻省理工,他解开一个三十年的数学问题

Former NFL Lineman John Urschel, Now an MIT Professor, Solves a 30-Year-Old Math Conjecture on Gaussian Elimination

About this story

Former NFL player John Urschel, now an MIT math professor, posts a preprint proving a 30-year-old conjecture about Gaussian elimination and random matrices. HSK 5+ Chinese listening practice.

This is an HSK 5+ Chinese listening episode that runs about 6 minutes. The full Mandarin script is shown with tap-for-pinyin and a line-by-line English translation, so you can listen and read at once — comprehensible input in the sense of Stephen Krashen's i+1 theory. It teaches 12 key vocabulary words such as 教授、方法、数字 and walks through 7 grammar patterns, each explained in English with examples. The same news story is retold at 4 difficulty levels — use the level selector above to find the version that is challenging but still understandable for you.

Read at your level

原文

Read the complete story in Chinese. Reveal pinyin and English only when you need them.

2026年10月5日,星期一。
一篇论文出现在网上。
论文的名字,是 On the Growth Factor of Random Matrices。
写论文的人,叫约翰·乌歇尔。
他今年35岁。
他是 MIT 的数学教授。
以前,他是 NFL 的橄榄球运动员。
这篇论文,讲一个很老的数学方法。
这个方法,叫高斯消元法。
高斯消元法,用来解方程。
方程,是一个问题,里面有一个你不知道的数字。
你要找出这个数字。
银行算钱,用它。
电脑算东西,用它。
电路里,也有它。
电路,是电脑和机器里的东西。
很多人每天用它。
但是,很多人不知道它为什么好。
这篇论文,问一个问题:
问题很大的时候,这个方法会不会错?
这个问题,人们想了三十年。
1995年,一个叫 Nick Trefethen 的数学家,说了一个猜想。
猜想,是一个问题。
很多人觉得它是对的,但是没有人能证明。
1997年,他和 David Bau 写了一本书。
书里第169页,也说了这个猜想。
三十年,没有人证明这个猜想。
2026年10月5日,约翰·乌歇尔的论文说:这个猜想是对的。
论文里有一个定理。
定理说:问题很大的时候,这个方法很少错。
很少比根号 n 高很多。
n,是问题的大小。
问题越大,n 越大。
有的问题,有非常多的数字。
差不多100%的时候,这个方法是对的。
不是100%,是差不多100%。
这个结果,说的是随机矩阵。
矩阵,是很多数字放在一起。
随机矩阵,就是这些数字没有规律。
这个结果,让很多研究数学的人高兴。
但是,数学的结果,要很多人看。
现在,这篇论文还是一篇预印本。
预印本,是还没有正式发表的文章。
它还没有经过同行评审。
同行评审,是别的数学家看这篇文章。
他们要验证这个证明。
他们要看看:这个证明对不对?
这个阶段,很重要。
如果证明不对,这个结果就没有意义。
所以,这个结果,还要等他们看。
但是,这个问题,人们已经想了三十年。
这个方法,为什么重要?
因为很多地方用它。
银行、金融、电脑、电路,都用它。
金融,就是银行和钱的事。
问题很大的时候,数据很多。
数据很多的时候,计算要很快。
这个方法,效率很高。
所以,很多人用它。
但是,它的风险在哪里?
风险,就是它会不会错。
如果它错了,结果就错了。
所以,数学家想知道:它错的可能有多大?
概率,就是可能的大小。
他的论文,回答的正是这个问题。
它说:错的可能很小。
这个方法差不多都是对的。
很多人看这篇论文,是因为写论文的人。
很多人知道约翰·乌歇尔,是因为橄榄球。
在美国,橄榄球很有名。
NFL,是美国最大的橄榄球联盟。
打橄榄球,钱很多,知道你的人也很多。
2014年,NFL 选择了他。
他在第五轮被选中。
他在宾州州立大学学习数学。
他的数学,一直很好。
他一边打球,一边在 MIT 上课。
他还在写数学论文。
他的球队不知道他在 MIT 上课。
球队,就是一起打橄榄球的人。
2015年8月,他的大脑受伤了。
那次受伤很重。
受伤以后,他继续打球。
但是他很担心。
他担心以后大脑会更不好。
他怕以后不能再想数学。
很多人担心,大脑受伤以后,记忆和思考会不好。
2017年,他决定退休。
这件事,很多人不明白。
打橄榄球,钱很多,知道你的人也很多。
这些,他都不要了。
为什么退休?
一个原因是他的大脑。
另一个原因,是他爱数学。
他回去学习数学。
后来,他拿到了数学博士学位。
2019年,他出了一本书。
书里,他说橄榄球,也说数学。
现在,他教数学,也带博士生做研究。
他有时候去开会,讲他的数学。
他也接受采访。
在采访里,他说:我很喜欢没有人知道我。
他喜欢去没有人认识他的地方。
在 MIT 的网站上,他的介绍里没有橄榄球。
他的简历,到第八页才说橄榄球。
有一次考试,学生穿着他的橄榄球衣服。
他说:如果橄榄球能让学生来学数学,那很好。
他说,他想知道为什么,也想知道怎么做。
现在,很多媒体写他。
媒体,就是报纸、电视和网站。
很多人说,这是一个突破。
突破,是一个很大的成果。
但是,数学的突破,要时间。
这个结果,还要等同行看。
这也是数学的方式。
一个结果,要很多人验证,才是真的。
验证,是数学里很重要的事。
所以,这个结果,还要等。
但是,这个问题,人们已经想了三十年。
很多人说,这是一个很大的问题。
约翰·乌歇尔,也想知道答案。
你想一想。
一个问题,很多人想了三十年,没有人能证明。
如果是你,你会不会也想试一试?
English transcript reference

October 5, 2026, Monday.

A paper appeared online.

The paper's name is On the Growth Factor of Random Matrices.

The person who wrote the paper is named John Urschel.

He is 35 years old this year.

He is a mathematics professor at MIT.

Previously, he was an NFL football player.

This paper discusses a very old mathematical method.

This method is called Gaussian elimination.

Gaussian elimination is used to solve equations.

An equation is a problem that contains a number you don't know.

You need to find this number.

Banks use it to calculate money.

Computers use it to compute things.

In electrical circuits, it is also present.

A circuit is something inside computers and machines.

Many people use it every day.

But many people don't know why it is good.

This paper asks a question:

When the problem is very large, will this method make mistakes?

People have thought about this question for thirty years.

In 1995, a mathematician named Nick Trefethen stated a conjecture.

A conjecture is a question.

Many people think it is correct, but no one can prove it.

In 1997, he and David Bau wrote a book.

On page 169 of the book, this conjecture is also stated.

For thirty years, no one proved this conjecture.

On October 5, 2026, John Urschel's paper says: this conjecture is correct.

In the paper there is a theorem.

The theorem says: when the problem is very large, this method rarely makes mistakes.

Rarely is it much higher than the square root of n.

n is the size of the problem.

The larger the problem, the larger n is.

Some problems have a very large number of numbers.

About 100% of the time, this method is correct.

Not 100%, but about 100%.

This result is about random matrices.

A matrix is many numbers put together.

A random matrix means these numbers have no pattern.

This result makes many people who study mathematics happy.

But mathematical results need many people to look at them.

Right now, this paper is still a preprint.

A preprint is an article that has not yet been formally published.

It has not yet undergone peer review.

Peer review is other mathematicians reading this article.

They need to verify this proof.

They need to see: is this proof correct?

This stage is very important.

If the proof is not correct, this result is meaningless.

So, this result still has to wait for them to look at it.

But this question, people have already thought about for thirty years.

Why is this method important?

Because many places use it.

Banks, finance, computers, circuits all use it.

Finance is the business of banks and money.

When the problem is very large, there is a lot of data.

When there is a lot of data, calculations need to be very fast.

This method is very efficient.

So, many people use it.

But where is its risk?

Risk is whether it will make mistakes.

If it makes a mistake, the result is wrong.

So, mathematicians want to know: how big is the probability that it makes a mistake?

Probability is the size of the possibility.

His paper answers exactly this question.

It says: the probability of making a mistake is very small.

This method is almost always correct.

Many people read this paper because of the person who wrote it.

Many people know John Urschel because of football.

In the United States, football is very famous.

The NFL is the largest football league in the United States.

Playing football, there is a lot of money, and many people know you.

In 2014, the NFL chose him.

He was selected in the fifth round.

He studied mathematics at Penn State University.

His mathematics was always very good.

He played football while taking classes at MIT.

He was also writing mathematics papers.

His team did not know he was taking classes at MIT.

A team is the people who play football together.

In August 2015, his brain was injured.

That injury was very serious.

After the injury, he continued to play football.

But he was very worried.

He worried that his brain would get worse in the future.

He feared he would no longer be able to think about mathematics.

Many people worry that after a brain injury, memory and thinking will be worse.

In 2017, he decided to retire.

Many people did not understand this.

Playing football, there is a lot of money, and many people know you.

He gave all of this up.

Why retire?

One reason is his brain.

Another reason is that he loves mathematics.

He went back to study mathematics.

Later, he obtained a mathematics PhD.

In 2019, he published a book.

In the book, he talks about football and also about mathematics.

Now, he teaches mathematics and also supervises doctoral students in research.

He sometimes goes to conferences and talks about his mathematics.

He also gives interviews.

In an interview, he said: I really enjoy no one knowing me.

He likes going to places where no one knows him.

On MIT's website, his introduction does not mention football.

His CV only mentions football on page eight.

Once, during an exam, a student wore his football jersey.

He said: if football can make students come to study mathematics, that is great.

He said, he wants to know why, and also wants to know how.

Now, many media outlets write about him.

Media are newspapers, television, and websites.

Many people say this is a breakthrough.

A breakthrough is a very big achievement.

But a mathematical breakthrough takes time.

This result still has to wait for peers to look at it.

This is also the way of mathematics.

A result must be verified by many people before it is true.

Verification is a very important thing in mathematics.

So, this result still has to wait.

But this question, people have already thought about for thirty years.

Many people say this is a very big question.

John Urschel also wants to know the answer.

Think about it.

A question that many people have thought about for thirty years, and no one can prove.

If it were you, would you also want to give it a try?

Listen again

Try it without the transcript and notice what sounds clearer.

What vocabulary does this episode teach?

词汇
jiàoshòuprofessor

HSK 5. Used for university faculty; 数学教授 = math professor.

fāngfǎmethod; way

HSK 4. Common noun for a way of doing something; 数学方法 = mathematical method.

shùzìnumber; digit

HSK 4. Used for numerical digits; 很多数字 = many numbers.

zhèngmíngto prove; proof

HSK 4. Both verb and noun; 证明这个猜想 = prove this conjecture.

jiéguǒresult

HSK 4. Common noun; 这个结果 = this result.

zhòngyàoimportant

HSK 4. Adjective; 很重要 = very important.

cǎifǎnginterview; to interview

HSK 5. Both noun and verb; 接受采访 = give an interview.

lùnwénpaper; thesis

HSK 5. Common academic noun; appears as 一篇论文 (a paper).

yùndòngyuánathlete; player

HSK 5. 橄榄球运动员 = football player.

fāngchéngequation

HSK 5. Mathematical term; 解方程 = solve an equation.

fēngxiǎnrisk

HSK 5. Used in finance and general contexts; 风险在哪里 = where is the risk.

gàilǜprobability

HSK 5. Mathematical term; 错的可能 = probability of error.

* beyond level超纲词

What grammar patterns appear in this episode?

语法
是……的↗

Emphasizes a fact or description, often with a noun phrase.

他是 MIT 的数学教授。

他是 NFL 的橄榄球运动员。

用来 + Verb

Indicates the purpose of something: 'used to do'.

高斯消元法,用来解方程。

越……越……↗

Expresses 'the more..., the more...'.

问题越大,n 越大。

一边……一边……

Indicates two actions happening simultaneously.

他一边打球,一边在 MIT 上课。

如果……就……↗

Conditional structure: 'if..., then...'.

如果证明不对,这个结果就没有意义。

如果橄榄球能让学生来学数学,那很好。

是……的↗

Used to emphasize a detail in a past event.

他在第五轮被选中。

让 + Noun + Verb

Causative: 'make/let someone do something'.

这个结果,让很多研究数学的人高兴。

Proper nouns

专有名词
约翰·乌歇尔Yuēhàn Wūxiē'ěrJohn UrschelMITMITMITNFLNFLNFLNick TrefethenNick TrefethenNick TrefethenDavid BauDavid BauDavid Bau宾州州立大学Bīnzhōu Zhōulì DàxuéPenn State University

Sources

来源

Free account

Keep learning from this story

Create a free account to keep saved words and your preferred level together.

  • Keep words with their story context
  • Remember your preferred level
  • Build your vocabulary over time

Keep learning