Shadowing Practice: Why This Is the Most Exciting Time to Be Human | Ken Ono, Axiom Math - Learn English Speaking with Video
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Exactly one year ago, I was a happy-go-lucky university professor, writing my papers.
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I would have described myself as someone who had enjoyed the privilege of being good at mathematics.
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And then there was a dramatic change.
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Now, I'm deeply troubled.
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For the first time, I struggled to assemble questions that ChatGPT would get wrong.
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These models know more facts than any human you would ever find.
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I was devastated.
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I was thinking, how am I going to stay ahead of AI?
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Is that I actually think that's the wrong question.
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Knowledge quickly became cheap.
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If our goal is to always stay ahead of AI, then I think we're going to lose.
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My name is Ken Ono.
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I'm a mathematician, and I also work in the space called AI for Math.
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I'm a professor at the University of Virginia on leave, and I'm the founding mathematician at Axiom Math.
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The very first time I heard the term artificial intelligence was in 1993,
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and I met a faculty member who said, I work in artificial intelligence.
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My very first words were, oh, that's interesting.
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I specialize in natural intelligence, and I was cocky.
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I would have my butt handed to me many times for using words like that, but that's where I came from.
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Exactly one year ago, I was a happy-go-lucky university professor, writing my papers, enjoying life at the University of Virginia,
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and then there was a dramatic change.
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I came face to face with large language models at work.
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The Frontier Math Program,
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where this company based in Berkeley called Epoch AI hired professional mathematicians from around the world to assemble very difficult math problems,
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and their goal was to assess the capabilities of large language models as they improve.
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For the first time, I struggled to assemble questions that ChatGPT would get wrong.
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I was devastated.
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At the time, I was one of the few scientists who had been given access to these state-of-the-art models.
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And I had to be patient for a few months to go by before the world did recognize that, yeah, these models know so much.
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These models know more facts than any human you would ever find.
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So for a few months, I was devastated.
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I was thinking, how am I going to stay ahead of AI?
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I actually think that's the wrong question.
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If our goal is to always stay ahead of AI, then I think we're going to lose.
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Nobody would be interested in watching Usain Bolt race against a motorcycle in the one-mile run.
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It's not a fair race.
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But we still watch the Olympics.
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We, as a society, now know how to accept that machines can outperform humans in every physical way.
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But we're still now coming to grips with the fact that the brain, deep inquiry, computers have caught up.
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The large language models should be thought of as the most extraordinary librarian the world has ever seen.
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If it has been written down, the large language model has probably seen it.
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If it's on YouTube, the large language model has probably been trained on it.
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If you read a newspaper article by the afternoon, the large language model has probably seen it.
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Good luck with competing with that ability to collect information.
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Information, knowledge is now cheap.
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But how you use it and how you verify it has become more expensive.
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Do you want your librarian to be your neurosurgeon?
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Do you want your librarian to be your air traffic controller,
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somehow keeping an eye on the hundreds of planes that are flying over North America or Korea?
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No way, because that human judgment is important.
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My identity has changed.
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My view on intelligence now has changed quite a bit.
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The ability to reason, make proper inferences, whether you can do it quickly or slowly, it doesn't matter.
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But can you create a new concept?
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Can you generate ideas?
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Can you string concepts together in a deep way?
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That is intelligence.
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That is not the regurgitation of facts.
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And we're not good at teaching that.
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Are you good at setting the dials to design a system from scratch that was going to produce some gadget,
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whether it's an industry or a computer program, or perhaps a whole new area of science.
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That's deep intelligence.
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And it rarely is the form that is recognized in schools at any level.
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Do you have the ability to recognize patterns in areas of thought
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that can be transferred from one discipline to another so that you can propel another area forward?
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I would have said five years ago, oh, that's just being in the right spot at the right time.
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But that's unfair.
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Being in the right spot at the right time, well, you still have to make that observation.
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So there's an element of recognizing a target of opportunity.
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That is genius.
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And I don't use genius very lightly.
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The student, the worker, who becomes an expert in their niche field
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because they plug away and learn something new about
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that field every day is so hard-nosed and is so committed
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that that is also intelligence and a kind of genius that we need to recognize.
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I have a very unique personal story.
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I'm a son of a mathematician.
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When I was a child, I was considered gifted in mathematics and my parents decided at an early age that I was gonna be a mathematician.
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My parents actually had a plan for all three of the boys.
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My oldest brother was gonna be a pianist, he did it.
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I was the youngest, was gonna be a mathematician.
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And the middle son, who they said wasn't good at math and wasn't gifted in music, well, he should just go work in a bank,
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just make a living, which inspired him to do great things.
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My brother Santa has gone on to become the president of the University of Michigan.
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He's a very distinguished scientist,
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and he was literally driven by this need to prove that his early assessment was incorrect.
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For me, it almost went in a very bad way.
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I dropped out of high school.
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The last thing I wanted to be in high school was anything my parents wanted me to be.
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I didn't want to be the one Asian kid in class
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that was expected to be good at math when all the other kids had lives.
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I couldn't play baseball, you know, the all-American pastime, and I hated it.
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In April of 1984, I was in the mindset that I'm gonna run away from home, I'm never gonna see my parents again, and I don't care about that.
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I'm gonna strike out on my own.
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In April 1984, a letter came to the house, addressed to my father.
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It was on this yellowed piece of paper that looked like it might as well have been 100 years old.
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It was a letter written by Janaki Amal, who was a widow of the Indian mathematician Ramanujan.
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And she thanked my father for making a small gift to help commission a statue in memory of Ramanujan.
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And me, seeing my dad cry, and he never cried.
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It was very, almost no emotions.
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This letter brought him to tears, and he brought this letter to me afterwards.
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I have to tell someone what this is about.
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So Ramanujan, it turned out, I learned that day, was a mystic, an autodidact.
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He had visions of mathematics that his goddess, he believed, gave him gifts of formulas that he would write down in his notebooks.
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Because of his passion for mathematics, he didn't study in any of his other courses, so he ended up flunking out of college twice.
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He had my dad talking about someone who was a two-time college dropout,
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but had left behind three notebooks filled with formulas that he was studying himself.
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And I only learned later, one of the reasons that my father was
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so in love with the story of Ramanujan is because Ramanujan had actually represented hope for him,
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his one chance in life as a Japanese mathematician post-World War II.
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My father wanted to be a mathematician, but he went to college at a time when the world was at war.
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He loved mathematics as a way of escaping from long lines waiting for food.
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In the aftermath of World War II,
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the United States sent some of their best mathematicians to Japan to rebuild the universities and train the mathematicians.
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My father first learned about Ramanujan at the conference where he was discovered by a Princeton professor
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who invited him to study with him at Princeton, launching his career.
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Ramanujan died a very early age at 32.
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He was nearly forgotten, and the mathematicians of the world contributed small gifts to give Mrs. Ramanujan the statue
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that the government had promised her in 1920.
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That letter and a photograph that she shared with us of the statue,
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it represented him remembering what it was like for him to struggle and the moment where he got his chance.
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So what did Ramanujan mean to me that day?
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It gave me hope in the following.
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It was the first time I heard my parents say and look up to, like a hero, someone who hadn't gone to Harvard or Princeton and was a perfect student.
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On the contrary, it turned out my father's hero was a two-time college dropout.
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And I needed that.
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Later at the University of Chicago, I was a horrible student.
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But right before my senior year, flipping through the channels on the television, I saw a video, public broadcasting service,
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documentary about Ramanujan, who I hadn't thought about in years.
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And I was fascinated
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because here on In Color on TV was more than the vaguest of outlines about Ramanujan that my dad told me.
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There was the whole story.
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And it kind of jump-started me.
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I had a lot of catching up to do.
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There I became a good student.
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And it was then when the biography of Ramanujan came out called The Man Who Knew Infinity.
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Maybe it was a sign.
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I was meant to follow Ramanujan.
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And so I started to work on a thesis based on his work.
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And I'm so glad I made that choice because by the end of my PhD, what I worked on was called the theory of Galois representations,
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which was meant to study Ramanujan's backwater mathematics.
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But by 1993, the bombshell news and mathematics for the end of the 20th century was a proof of Fermat's last theorem.
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And the proof of Fermat's last theorem depend on these Galois representations.
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I don't know what it is.
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Following Ramanujan every time he appeared has been like the best decision I've ever made in my life
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and every one of those could have gone a different way.
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So one important theme about Ramanujan for me is where would we be?
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And I don't mean just me as a mathematician.
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Where would we all collectively be had he not been discovered?
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That is a world I cannot fathom and because of that you're left wondering there must be other Ramanujans walking planet Earth.
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Maybe they don't come from privilege.
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How do we find them and how do we nurture them when we find them?
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I was lucky enough for a number of years to run
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a program called the Spirit of Ramanujan where we looked for undiscovered talent.
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And what's interesting about this, my boss, my former student Karina Hong, studied with me in a a research program.
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She was one of our first recipients.
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We discovered her.
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It makes me wonder where she would be today had she not received this spirit of Ramanujan Fellowship.
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There are, I am sure, many, many undiscovered folks that we need to find.
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I think the idea, the ability, and the potential to be someone like Ramanujan, or at least creative in a productive way,
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I think it resides in us all.
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You just have to give students of all ages the opportunity to A,
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be brave enough to act on their curiosity and then offer them a system that embraces it.
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Some of your best students in Korea, the best students in the United States, the best students worldwide, they're stressed out in high school.
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They're probably even stressed out in middle school worrying about how do I get into the right high school?
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How do I get into the right college?
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Will I get the right test scores?
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If you're motivated to participate in those just because they are check boxes, that's messed up.
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And I'm not saying that you shouldn't do that
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because I don't want to be ignorant and say don't participate in the system
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that will ultimately decide your fate with regard to college, but pause and recognize you're participating in that system.
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Education starts with inspiring people to want to know more about the world in which they live,
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wanting to know more about the cultures of the world
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because we share the world together
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and appreciate what is different in other parts of the world
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and in other cultures because that's why I went to college, right?
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I wanted to learn about those things.
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That's why I travel the world.
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If you have children that are infants, how wonderful is it to play with them, say with like a stack of boxes or building blocks.
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Play for children is science.
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They're not really learning about gravity, but they're really learning about gravity.
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They may pile blocks on top of the udder and knock them over and giggle and they'll do it again.
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Think about how wonderful the world is
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when you get to learn about it without worrying about what your future and what your reputation will be.
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I want when my students are in my class to say this is a wonderful class Professor Ono
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because the subject is beautiful and I do my very best to try to get that across.
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But I'm not a fool.
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I know
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that I'm participating in a system where at the end of
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the day the students are worried am I going to get an A
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or not and how will that impact my ability to go to graduate school in math
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or medical school or law school because that gpa is
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so important and i hate that i utterly hate
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that why it's an opportunity lost what i like about ai
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and this is actually how i transitioned from being devastated by
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ai has already read my papers it understands my papers better
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than i remember them think about all the subjects in adjacent areas of mathematics
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that I could just ask AI about
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and AI is not going to laugh at me
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and it as a great librarian will dutifully answer any question I would ask.
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The access to knowledge, if you are privileged enough to have access to the internet
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and you are privileged enough to be able to afford access to a large language model, knowledge quickly became cheap.
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In the United States it could cost $80,000 to spend one year attending a university.
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And here's the dirty secret.
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I could learn everything that you could learn book-wise, academically from a large language model at my own pace,
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probably accelerated with a large language model.
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What I would not get would be the human access, how the right questions were derived, what the next questions in the field might be.
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That's why we still go to college and that's why we still need professors.
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But all of the other stuff, the tutoring, the precision learning, that AI can help with.
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I actually believe that we in this world aren't doing the best we can at educating our children.
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And I don't say that to be critical of educators.
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I am an educator.
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But it's always a treat to visit a kindergarten class, a first grade class, when it's bring your parent to school day so they can talk about what they do.
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Oh, I know all the prime numbers, or I'm really good at adding that wonder, and I want to just bottle up this energy.
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because if we could maintain that wonder in the world and the energy that children have when everything around them is new,
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think about where we would be today.
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So go find your passion.
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The best scientists in the world need to still view the world as a wondrous thing.
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The best doctors in the world still need to recognize that what they practice
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is supposed to come from a place of benevolence, not, I have this practice, but I'm a university professor that happens to be, has a clinical practice, and I'm gonna write articles about my patients.
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I think that's messed up.
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If we pay so much attention on and value so much perfection and speed in ordinary test taking,
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then how are we training someone to be the next Einstein, or the next, you name,
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famous professor, was just wondering out loud in their lab, I wonder if such and such is true.
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For my children, I want them to be passionate about the world that they live in.
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And if you're passionate about the world that you live in, then you're deeply worried about the climate.
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You're deeply worried about the conflict that exists between different cultures, which there's wars all over the place.
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How can that happen?
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Who are the people that you really look up to the most?
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Maybe they are the oddballs.
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In my country, in the United States, where education is so expensive, you could come out of college with $150,000 in debt.
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And then you may go on to a professional school accruing another $200,000 in debt.
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And then three years later discovering, I can't stand the sight of blood.
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But now I can't leave my profession because I have all of these loans.
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That is purgatory.
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You then are stuck.
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And that's when your life is, well, I go to work because it pays the bills.
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Who owns your identity?
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You do.
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Woordenschat en spreektips bij deze les
Deze video bevat 240 zinnen en 3037 woorden om na te spreken. Het gesproken deel duurt 19:29. De spreker praat in een natuurlijk tempo van ongeveer 156 woorden per minuut, dicht bij een alledaags gesprek. 83% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden; de rest kun je beter vooraf bekijken.
Belangrijke woorden in deze video
15 woorden uit de video die het leren waard zijn, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| mathematics zelfstandig naamwoord | /mæθ(.ə)ˈmæt.ɪks/ | wiskunde, mathematiek |
| participate werkwoord | /pɑːˈtɪs.ɪ.peɪt/ | meedoen, deelnemen |
| privilege zelfstandig naamwoord | /ˈpɹɪv.(ɪ.)lɪd͡ʒ/ | voorrecht, privilege |
| devastated bijvoeglijk naamwoord | /ˈdɛv.əˌsteɪ.təd/ | kapot |
| librarian zelfstandig naamwoord | /laɪˈbɹɛə̯ɹi.ən/ | bibliothecaris, bibliothecaresse |
| genius zelfstandig naamwoord | /ˈd͡ʒin.jəs/ | genie |
| scientist zelfstandig naamwoord | /ˈsaɪəntəst/ | wetenschapper, wetenschapster |
| statue zelfstandig naamwoord | /ˈstæt͡ʃu/ | standbeeld |
| assemble werkwoord | /əˈsɛm.bəl/ | in elkaar zetten |
| formula zelfstandig naamwoord | /ˈfɔː.mjʊ.lə/ | formule |
| passion zelfstandig naamwoord | /ˈpæʃən/ | passie, hartstocht |
| dramatic bijvoeglijk naamwoord | /dɹəˈmætɪk/ | dramatisch |
| representation zelfstandig naamwoord | /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/ | weergave, representatie |
| artificial bijvoeglijk naamwoord | /ˌɑː.tɪˈfɪʃ.əl/ | artificieel, kunstmatig |
| passionate bijvoeglijk naamwoord | /ˈpæʃənɪt/ | hartstochtelijk, gepassioneerd |
Phrasal verbs die je zult horen
| Woord | Uitspraak | Betekenis |
|---|---|---|
| mess up werkwoord | verrommelen, in de war brengen | |
| turn out werkwoord | uitvallen, resulteren | |
| come out werkwoord | /ˌkʌm ˈaʊt/ | uitkomen |
| end up werkwoord | belanden, terechtkomen | |
| leave behind werkwoord | achterlaten | |
| write down werkwoord | opschrijven, neerschrijven |
Uitspraak om op te letten
De spreker gebruikt 58 samentrekkingen en verkorte vormen, zoals I'm, don't, gonna. Spreek ze kort uit, zoals je ze hoort.
- De “th”-klanken: mathematics /mæθ(.ə)ˈmæt.ɪks/, theorem /ˈθiərəm/, thesis /ˈθisɪs/, aftermath /ˈæf.tɚˌmæθ/, mathematician /ˌmæθ.(ə.)məˈtɪʃ.ən/
- De klanken “sh” en “zh”: passion /ˈpæʃən/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/, artificial /ˌɑː.tɪˈfɪʃ.əl/, passionate /ˈpæʃənɪt/, reputation /ˌɹɛpjʊˈteɪʃən/
- Lange woorden — let op de klemtoon: mathematics /mæθ(.ə)ˈmæt.ɪks/, participate /pɑːˈtɪs.ɪ.peɪt/, devastated /ˈdɛv.əˌsteɪ.təd/, librarian /laɪˈbɹɛə̯ɹi.ən/, representation /ˌɹɛp.ɹə.zɛnˈteɪ.ʃən/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als mathematics, participate, privilege.
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | have caught · has ever seen · has been written |
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | had been given · should be thought · has been written |
| Betrekkelijke bijzinnen who / which + zin — extra informatie over een persoon of ding | someone who had · someone who was · someone who hadn |
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.