Prática de Shadowing: Khi kiến thức "bão hòa", đâu là năng lực đắt giá nhất? | đi-code ep 30 - Aprenda a falar inglês com o YouTube
C1
⏸ Pausado
Exactly one year ago, I was a happy-go-lucky university professor, writing my papers.
237 frases
Se as frases estiverem muito curtas ou longas, clique em Edit para ajustá-las.
1
Exactly one year ago, I was a happy-go-lucky university professor, writing my papers.
2
I would have described myself as someone who had enjoyed the privilege of being good at mathematics.
3
And then there was a dramatic change.
4
Now, I'm deeply troubled.
5
For the first time, I struggled to assemble questions that ChatGPT would get wrong.
6
These models know more facts than any human you would ever find.
7
I was devastated.
8
I was thinking, how am I going to stay ahead of AI?
9
Is that I actually think that's the wrong question.
10
Knowledge quickly became cheap.
11
If our goal is to always stay ahead of AI, then I think we're going to lose.
12
My name is Ken Ono.
13
I'm a mathematician, and I also work in the space called AI for Math.
14
I'm a professor at the University of Virginia on leave, and I'm the founding mathematician at Axiom Math.
15
The very first time I heard the term artificial intelligence was in 1993.
16
And I met a faculty member who said, I work in artificial intelligence.
17
My very first words were, oh, that's interesting.
18
I specialize in natural intelligence, and I was cocky.
19
I would have my butt handed to me many times for using words like that.
20
But that's where I came from.
21
Exactly one year ago, I was a happy-go-lucky university professor, writing my papers, enjoying life at the University of Virginia,
22
and then there was a dramatic change.
23
I came face to face with large language models at work.
24
The Frontier Math Program, where this company based in Berkeley called Epoch AI hired professional
25
mathematicians from around the world to assemble very difficult math problems
26
and their goal was to assess the capabilities of large language models as they improve.
27
For the first time, I struggled to assemble questions that ChatGPT would get wrong.
28
I was devastated.
29
At the time, I was one of the few scientists who had been given access to these state-of-the-art models.
30
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.
31
These models know more facts than any human you would ever find.
32
So for a few months, I was devastated.
33
I was thinking, how am I going to stay ahead of AI?
34
I actually think that's the wrong question.
35
If our goal is to always stay ahead of AI, then I think we're going to lose.
36
Nobody would be interested in watching Usain Bolt race against a motorcycle in the one-mile run.
37
It's not a fair race.
38
But we still watch the Olympics.
39
We, as a society, now know how to accept that machines can outperform humans in every physical way.
40
But we're still now coming to grips with the fact that the brain, deep inquiry, computers have caught up.
41
The large language models should be thought of as the most extraordinary librarian the world has ever seen.
42
If it has been written down, the large language model has probably seen it.
43
If it's on YouTube, the large language model has probably been trained on it.
44
If you read a newspaper article by the afternoon, the large language model has probably seen it.
45
Good luck with competing with that ability to collect information.
46
Information, knowledge is now cheap.
47
But how you use it and how you verify it has become more expensive.
48
Do you want your librarian to be your neurosurgeon?
49
Do you want your librarian to be your air traffic controller,
50
somehow keeping an eye on the hundreds of planes that are flying over North America or Korea?
51
No way, because that human judgment is important.
52
My identity has changed.
53
My view on intelligence now has changed quite a bit.
54
The ability to reason, make proper inferences, whether you can do it quickly or slowly, it doesn't matter.
55
But can you create a new concept?
56
Can you generate ideas?
57
Can you string concepts together in a deep way?
58
That is intelligence.
59
That is not the regurgitation of facts.
60
And we're not good at teaching that.
61
Are you good at setting the dials to design a system from scratch that was going to produce some gadget,
62
whether it's an industry or a computer program or perhaps a whole new area of science.
63
That's deep intelligence.
64
And it rarely is the form that is recognized in schools at any level.
65
Do you have the ability to recognize patterns in areas of thought
66
that can be transferred from one discipline to another so that you can propel another area forward?
67
I would have said five years ago, oh, that's just being in the right spot at the right time.
68
But that's unfair.
69
Being in the right spot at the right time, well, you still have to make that observation.
70
So there's an element of recognizing a target of opportunity.
71
That is genius.
72
And I don't use genius very lightly.
73
The student, the worker, who becomes an expert in their niche field
74
because they plug away and learn something new about
75
that field every day is so hard-nosed and is so committed
76
that that is also intelligence and a kind of genius that we need to recognize.
77
I have a very unique personal story.
78
I'm a son of a mathematician.
79
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.
80
My parents actually had a plan for all three of the boys.
81
My oldest brother was gonna be a pianist, he did it.
82
I was the youngest, was gonna be a mathematician.
83
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,
84
just make a living, which inspired him to do great things.
85
My brother Santa has gone on to become the president of the University of Michigan.
86
He's a very distinguished scientist,
87
and he was literally driven by this need to prove that his early assessment was incorrect.
88
For me, it almost went in a very bad way.
89
I dropped out of high school.
90
The last thing I wanted to be in high school was anything my parents wanted me to be.
91
I didn't want to be the one Asian kid in class
92
that was expected to be good at math when all the other kids had lives.
93
I couldn't play baseball, you know, the all-American pastime, and I hated it.
94
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.
95
I'm gonna strike out on my own.
96
In April 1984, a letter came to the house, addressed to my father.
97
It was on this yellowed piece of paper that looked like it might as well have been 100 years old.
98
There was a letter written by Janaki Amal, who was a widow of the Indian mathematician Ramanujan.
99
And she thanked my father for making a small gift to help commission a statue in memory of Ramanujan.
100
And me, seeing my dad cry, and he never cried.
101
He was very, almost no emotions.
102
This letter brought him to tears, and he brought this letter to me afterwards.
103
I have to tell someone what this is about.
104
So Ramanujan, it turned out, I learned that day, was a mystic, an autodidact.
105
He had visions of mathematics that his goddess, he believed, gave him gifts of formulas that he would write down in his notebooks.
106
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.
107
He had my dad talking about someone who was a two-time college dropout,
108
but had left behind three notebooks filled with formulas that he was studying himself.
109
And I only learned later, one of the reasons that my father was
110
so in love with the story of Ramanujan is because Ramanujan had actually represented hope for him,
111
his one chance in life as a Japanese mathematician post-World War II.
112
My father wanted to be a mathematician, but he went to college at a time when the world was at war.
113
He loved mathematics as a way of escaping from long lines waiting for food.
114
In the aftermath of World War II,
115
the United States sent some of their best mathematicians to Japan to rebuild the universities and train the mathematicians.
116
My father first learned about Ramanujan at the conference where he was discovered by a Princeton professor
117
who invited him to study with him at Princeton, launching his career.
118
Ramanujan died a very early age at 32.
119
He was nearly forgotten, and the mathematicians of the world contributed small gifts to give Mrs. Romanudgen the statue
120
that the government had promised her in 1920.
121
That letter and a photograph that she shared with us of the statue,
122
it represented him remembering what it was like for him to struggle and the moment where he got his chance.
123
So what did Romanudgen mean to me that day?
124
It gave me hope in the following.
125
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.
126
On the contrary, it turned out my father's hero was a two-time college dropout.
127
And I needed that.
128
Later at the University of Chicago, I was a horrible student.
129
But right before my senior year, flipping through the channels on the television,
130
I saw a video, public broadcasting service documentary about Ramanujan, who I hadn't thought about in years.
131
And I was fascinated
132
because here on In Color on TV was more than the vaguest of outlines about Ramanujan that my dad told me.
133
There was the whole story.
134
And it kind of jump-started me.
135
I had a lot of catching up to do.
136
There I became a good student.
137
and it was then when the biography of Ramanujan came out called The Man Who Knew Infinity.
138
Maybe it was a sign.
139
Maybe I was meant to follow Ramanujan.
140
And so I started to work on a thesis based on his work
141
and I'm so glad I made that choice
142
because by the end of my PhD what I worked on was called the theory of Galois representations
143
which was meant to study Ramanujan's backwater mathematics.
144
But by 1993, the bombshell news in mathematics for the end of the 20th century was a proof of Frommas' last theorem.
145
And the proof of Frommas' last theorem depend on these Galois representations.
146
I don't know what it is.
147
Following Ramanujan every time he appeared has been like the best decision I've ever made in my life.
148
And every one of those could could have gone a different way.
149
So one important theme about Ramanujan for me is where would we be?
150
And I don't mean just me as a mathematician.
151
Where would we all collectively be had he not been discovered?
152
That is a world I cannot fathom.
153
And because of that, you're left wondering, there must be other Ramanujans walking planet Earth.
154
Maybe they don't come from privilege.
155
How do we find them?
156
and how do we nurture them when we find them.
157
I was lucky enough for a number of years to run a program called the Spirit of Ramanujan, where we looked for undiscovered talent.
158
And what's interesting about this, my boss, my former student, Karina Hong, studied with me in a research program.
159
She was one of our first recipients.
160
We discovered her.
161
Makes me wonder where she would be today had she not received this Spirit of Ramanujan Fellowship.
162
There are, I am sure, many, many undiscovered folks that we need to find.
163
I think the idea, the ability and the potential to be someone like Romanogen, or at least creative in a productive way, I think it resides in us all.
164
You just have to give students of all ages the opportunity to A, be brave enough to act on their curiosity,
165
and then offer them a system that embraces it.
166
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.
167
They're probably even stressed out in middle school, worrying about how do I get into the right high school?
168
How do I get into the right college?
169
Will I get the right test scores?
170
If you're motivated to participate in those just because they are checkboxes, that's messed up.
171
And I'm not saying that you shouldn't do that because I don't want to be ignorant and say,
172
don't participate in the system that will ultimately decide your fate with regard to college
173
but pause and recognize you're participating in that system.
174
Education starts with inspiring people to want to know more about the world in which they live,
175
wanting to know more about the cultures of the world
176
because we share the world together
177
and appreciate what is different in other parts of the world and in other cultures because that's why I went to college.
178
I wanted to learn about those things.
179
That's why I travel the world.
180
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.
181
Play for children is science.
182
They're not really learning about gravity, but they're really learning about gravity.
183
They may pile blocks on top of the udder and knock them over and giggle and they'll do it again.
184
Think about how wonderful the world is
185
when you get to learn about it without worrying about what your future and what your reputation will be.
186
I want when my students are in my class to say this is a wonderful class Professor Ono
187
because the subject is beautiful and I do my very best to try to get that across.
188
But I'm not a fool.
189
I know
190
that I'm participating in a system where at the end of
191
the day the students are worried am I going to get an A
192
or not and how will that impact my ability to go to graduate school in math
193
or medical school or law school because that gpa is
194
so important and i hate that i utterly hate
195
that why it's an opportunity lost what i like about ai
196
and this is actually how i transitioned from being devastated by
197
ai has already read my papers it understands my papers better
198
than i remember them think of all the subjects in adjacent areas of mathematics that I could just ask AI about.
199
And AI is not going to laugh at me and it, as a great librarian, will dutifully answer any question I would ask.
200
The access to knowledge, if you are privileged enough to have access to the internet
201
and you are privileged enough to be able to afford access to a large language model, knowledge quickly became cheap.
202
In the United States it could cost $80,000 to spend one year attending a university.
203
And here's the dirty secret.
204
I could learn everything that you could learn book-wise, academically from a large language model at my own pace,
205
probably accelerated with a large language model.
206
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.
207
That's why we still go to college and that's why we still need professors.
208
But all of the other stuff, the tutoring, the precision learning that AI can help with.
209
I actually believe that we in this world aren't doing the best we can at educating our children.
210
And I don't say that to be critical of educators.
211
I am an educator, but it's always a treat to visit a kindergarten class,
212
a first grade class, when it's bring your parent to school day so they can talk about what they do.
213
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.
214
Because if we could maintain that wonder in the world, and the energy that children have when everything around them is new,
215
think about where we would be today.
216
So go find your passion.
217
The best scientists in the world need to still view the world as a wondrous thing.
218
The best doctors in the world still need to recognize
219
that what they practice is supposed to come from a place of benevolence, not, I have this practice, but I'm a university professor that happens to be,
220
has a clinical practice, and I'm going to write articles about my patients.
221
I think that's messed up.
222
If we pay so much attention on and value so much perfection and speed in ordinary test taking,
223
then how are we training someone to be the next Einstein?
224
or the next you-name famous professor was just wondering out loud in their lab, I wonder if such and such is true.
225
For my children, I want them to be passionate about the world that they live in.
226
And if you're passionate about the world that you live in, then you're deeply worried about the climate.
227
You're deeply worried about the conflict that exists between different cultures, which there's wars all over the place.
228
How can that happen? who are the people that you really look up to the most?
229
Maybe they are the oddballs.
230
In my country, in the United States, where education is so expensive, you could come out of college with $150,000 in debt,
231
and then you may go on to a professional school accruing another $200,000 in debt, and then three years later discovering, I can't stand the sight of blood,
232
but now I can't leave my profession because I have all of these loans.
233
that is purgatory.
234
You then are stuck.
235
And that's when your life is, well, I go to work because it pays the bills.
236
Who owns your identity?
237
You do.
Baixar aplicativo
Everything you need to speak fluently
AI PronunciationScore every sentence
IPA PracticeMaster every sound
VocabularyBuild your word bank
Vocab GameLearn while playing
O que você vai aprender
Nesse vídeo, você desenvolve três habilidades essenciais para a prática de conversação em inglês: a capacidade de entender falas rápidas e naturais (mesmo com mudanças de tônica), a arte de articular ideias complexas com clareza e a habilidade de identificar nuances no discurso, como ironia e reflexão. Essas são ferramentas chave para se comunicar de forma eficaz, especialmente em contextos profissionais ou acadêmicos.
Ouça esses sons
A fala do professor Ken Ono é repleta de conexões e reduções típicas do inglês coloquial. Observe como ele liga palavras como "happy-go-lucky" (com uma ênfase suave no "go") e "state-of-the-art" (onde o "of" é reduzido a /əv/). Outro exemplo é a frase "I would have described", na qual "would have" se torna "would've" (/wʊd əv/), uma redução comum em falas rápidas. Esses detalhes ajudam a tornar sua pronúncia mais natural e sua compreensão mais precisa.
Fale como um nativo
Para imitar o ritmo e a ênfase do orador, use a técnica de shadowing em inglês: reproduza suas frases imediatamente após ouvi-las, focando na entonação e na pausa. Perceba que ele enfatiza palavras como "devastated", "extraordinary" e "expensive" para transmitir emoção e importância. Além disso, siga sua estrutura de pensamento: comece com uma história pessoal, depois uma reflexão e, por fim, uma pergunta provocativa. Essa abordagem não só melhora sua fluência, mas também sua capacidade de organizar ideias. Pratique diariamente com trechos curtos — você verá progressos rápidos! E lembre: plataformas de shadowing site ou ferramentas de shadow speech podem ajudar a treinar essas habilidades de forma mais estruturada.
O que é a Técnica de Shadowing?
Shadowing é uma técnica de aprendizado de idiomas com base científica, originalmente desenvolvida para o treinamento de intérpretes profissionais. O método é simples, mas poderoso: você ouve áudio em inglês nativo e repete imediatamente em voz alta — como uma sombra seguindo o falante com 1-2 segundos de atraso. Pesquisas mostram melhora significativa na precisão da pronúncia, entonação, ritmo, sons conectados, compreensão auditiva e fluência na fala.