쉐도잉 연습: AI Designed Viruses That Never Existed Before - 영상으로 영어 말하기 배우기

연습 모드 선택

각 모드는 전체 화면 연습실로 열립니다.

141 문장

문장이 너무 짧거나 길면 Edit를 눌러 조정하세요.

Antibiotic resistant bacteria killed an estimated 1 .27 million people in 2019.

More than HIV slash AIDS or malaria.

And what I find really fascinating is that number is projected to keep climbing.

In fact, 40 million more deaths by 2050 if nothing changes.

Now there are some interesting things happening to hopefully change this.

I mean phage therapy it's using viruses to kill specific bacteria instead of broad -spectrum antibiotics.

People have been very hopeful about it, but to be frank, it's been slow for decades.

And the reason for this is because designing effective phages takes years.

But essentially, if a model can generate working candidates in days, that's a real medical breakthrough.

But it comes with kind of an unexpected security problem.

So let's take a step back here.

Scientists have used artificial intelligence to create brand new viruses, 16 of them.

Researchers at Stanford University and the Arc Institute say they trained an AI model called Evo on huge amounts of DNA data.

Then that information was used to create the new synthetic viruses.

On August 5th, a peer -reviewed study by Stanford was released

that showed how they trained a model to write DNA the same way, at its core, that GPT writes text.

The Stanford -based team is led by Brian He, who's working with the ARC Institute and bioengineering grad student Samuel King.

And it was he who built EVO2, which is a genome language model.

You can predict the next token, except the tokens are base pairs and the training data is the genetic code of viruses,

bacteria, plants and humans.

So the team used EVO2 to design candidate genomes for 5X1 74,

which is a virus that infects E coli, and the first DNA genome ever fully sequenced.

They essentially synthesized close to 300 of the model's designs.

Now out of those 300, 16 worked which meant new viruses that don't exist anywhere in nature.

I mean, some killing E coli more effectively than the original.

Now, the virus itself that was created is harmless.

It really only infects E coli.

But the paper, the Stanford paper that came out of it,

proving this came out to what I found very fascinating and a lot of others did.

It was from biosecurity researchers at John Hopkins and they basically call the findings a source of urgent biosafety and biosecurity questions.

Yeah, here is the actual technical problem.

DNA screening systems catch sequences by matching them against a database of known threats, things that already exist.

But this sequence has no match because nothing like it existed before it was created.

And to be fair, there's an industry -led screening system already.

I mean, the International Gene Synthesis Consortium has more than 40 gene synthesis companies that agreed to a shared screening protocol.

And even more than 175 developers of AI biodesign tools have

even signed commitments to only use the synthesis providers who follow those standards, which I think is pretty exciting.

People are seeing that this could be a real threat and taking action, hopefully ahead of time.

But all of that infrastructure really answers one question.

Does this sequence look like something we already know is dangerous?

Adoption is essentially voluntary and uneven, especially among smaller providers.

And none of it was built to catch a sequence that resembles nothing in any database.

I mean it's making something that doesn't exist yet.

And that's the real hole of Evo 2 exposed.

It's a mismatch between the rules that were set in place to catch these things and what something can do,

what a generative model can do and now produce something that hasn't existed before.

How do we put guardrails for that?

Running Evo 2, which by the way is open source right now, at full scale takes some serious hardware easily.

It's open source but it probably takes a quarter million dollars or more in compute.

It's really out of reach for someone working alone

but well within reach for a university lab or a startup

or a state program and

that barrier tends to drop over time

now one caveat worth knowing evo2 in its raw form couldn't reliably design a specific phage like phi x1 74.

it really doesn't roll off the tongue easy

it couldn't design one of those on its own

it has to take an extra step supervised to narrow the model's focus on that phage family.

So this was a deliberate targeted process by people who really understood what exactly the model needed to be doing.

Not a case of typing a prompt in and a virus comes out.

Much more complicated than that.

But it is still a real shift.

I mean, a predictive model tells you about biology that already exists, whereas a generative model invents biology that never did.

How does something like EVO2 actually work?

And why is this different from a normal biology AI tool?

I mean, they've been around for a while now.

Most AI biology tools are narrow.

AlphaFold predicts how an existing protein folds, which, yes, is extremely useful, but it's answering a question about something that already exists,

whereas EVO2 is generative.

For scale, to put this in perspective for you,

9 trillion nucleotides is enough genetic material to cover the entire human genome about 3 ,000 times over.

And EVO2 gets there with just 4 billion parameters, which is smaller than GPT -3s, going back a while now,

$170 billion, which should really tell you the power here came from what it was trained on, not from the size of the model itself.

Three years ago, four years ago, an LLM writing usable code was cool.

It's a cool party trick.

Here's how I can develop some code.

But now, I mean, it produces full production ready software.

Capability curves in AI don't move straight.

They move in jumps, in leaps.

And we've watched this happen again and again, especially when it comes to language and code.

And we're watching the opening move of the same jump when it comes to genome design in biotech.

Okay, I want to go back to the hit rate because I feel like we skimmed over that earlier.

16 out of 300.

It's not high for the 16 out of 300 viruses that actually took.

I mean, that puts this closer to the GPT -2 era than the GPT -4 era of the technology.

It's on the same curve though.

Every generative model improves on more data, better outputs, better architecture.

But let's zoom out to say two to five years.

What's going to happen with this?

I really don't think anyone's saying something like EVO2 is designing a human genome next year

but every capability curve

we've watched has moved toward the harder version of the problem

not away from it now bacteriophage run a few thousand base

pairs bacteria runs into the millions humans into the billions the gap between 16 working phage gmones

and a model that reliably designs genomes for more complex organisms is an engineering gap

and i really think it's not a a fundamental one

that will come with time all right

but this isn't the first time biology has hit a moment

like this back in february 1975 around 140 scientists actually gathered

to figure out what to do about a brand new capability

100

which at the time was recombinant dna

101

which is engineering genes across the species for a first time

102

now paul berg who was the organizer of it looked for an existing framework to follow and found nothing.

103

So instead the scientists wrote their own voluntary safety guidelines before any regulations forced them to.

104

And those guidelines really shaped biotech policy for the next 50 years.

105

How was 70s by the way 50 years ago?

106

It's wild.

107

But 50 years later scientists reconvened in the same convention center for a 50th anniversary meeting

108

and stated the reason was for the same kind of moment was currently happening

109

that happened in the 70s but this time with AI driven biotech.

110

Basically a call for the people building this to set guardrails before anyone makes them.

111

Evo2's own paper does exactly that.

112

It argues for oversight.

113

The open question I think on everyone's minds is whether

114

that instinct spreads to the next lab

115

that picks up an open source genome model

116

or what guidelines will they follow whether it will stay limited

117

to the one team careful enough in this case thank goodness to publish

118

it now i want to be clear here none of this necessarily makes ai designed medicine dangerous by default

119

i think there are so many incredible breakthroughs that we are seeing

120

because of this phage therapy against resistant infections could save lives

121

and that's the outcome his team is actually working towards

122

but it also the flip side you have to talk about

123

that because a technology

124

that can do enormous good can also do enormous harm from the same capability

125

and that is something

126

that we need to really have conversations around how to put guardrails for how to govern it before it scales not after.

127

A lot of coverage jumped straight to the scariest version of this story.

128

Researchers in the US have for the first time used artificial intelligence to design new viruses.

129

What could possibly go wrong?

130

There's also concern that the technology could create new and yet unheard of bioweapons.

131

Let's be honest here.

132

If somebody actually wanted to cause harm with a pathogen today, training a genome model from scratch is definitely not the easy path.

133

The big deal here isn't that phage is dangerous.

134

It's that the thing standing between a generative model

135

and a dangerous sequence just got proven not to work while the regulatory conversation is still stuck on chatbots.

136

Now, I'm not asking if this specific phage we spoke about is scary.

137

I'm asking whether you're comfortable with what it proves is now possible.

138

Leave in the comments your thoughts.

139

Do you like topics like this around biotech?

140

I'm kind of obsessed lately with biotech, so let me know.

141

All right, I'll see you in the next video.

앱 다운로드

유창하게 말하는 데 필요한 모든 것

AI 발음 평가문장마다 점수 확인

IPA 발음 연습모든 소리 마스터

단어 학습나만의 단어장 만들기

단어 게임놀면서 외우기

QR 스캔

Download on theApp Store Get it onGoogle Play

FOR YOU

이 레슨의 어휘와 말하기 포인트

이 말하기 레슨은 영상 “AI Designed Viruses That Never Existed Before”을(를) 바탕으로 합니다. 가장 자주 반복되는 단어는 다음과 같습니다: model, Evo, genome, viruses, design. 이 영상에는 섀도잉할 문장 6개와 단어 1694개가 있습니다. 말하는 구간의 길이는 10:54입니다. 화자는 분당 약 155단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 영어에서 가장 많이 쓰이는 3,000단어에 속하는 단어가 78%뿐이라 어휘가 어려운 편입니다.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
virus 명사	/ˈvaɪ.ɹəs/	바이러스, 병독
genome 명사	/ˈd͡ʒiː.noʊm/	게놈
sequence 명사	/ˈsiː.kwəns/	순서
biology 명사	/baɪˈɑ.lə.d͡ʒi/	생물학
gene 명사	/d͡ʒiːn/	유전자
scientist 명사	/ˈsaɪəntəst/	과학자
synthesis 명사	/ˈsɪnθəsɪs/	통합, 종합
rail 명사	/ɹeɪ(ə)l/	선로, 레일
curve 명사	/kɚv/	곡선
researcher 명사	/ˈɹiˌsɝ.t͡ʃɚ/	연구원, 연구자
harm 명사	/hɑːm/	해
narrow 형용사	/ˈnæɹəʊ/	좁은, 좁다
scary 형용사	/ˈskɛə.ɹi/	공포의, 무섭다
database 명사	/ˈdeɪtəˌbeɪs/	데이터베이스
enormous 형용사	/ɪˈnɔː.məs/	거대하다

영상에 나오는 구동사

단어	발음	뜻
come out 동사	/ˌkʌm ˈaʊt/	나오다
go back 동사		돌아가다
pick up 동사		줍다

따라 말해 볼 만한 문장

영상에 나오는 짧고 완결된 문장으로, 일상 대화에서 그대로 쓸 수 있습니다:

Does this sequence look like something we already know is dangerous?
I mean, it's making something that doesn't exist yet.
How do we put guard rails for that?
I mean, they've been around for a while now.
Do you like topics like this around biotech?

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	is projected · was released · is led
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	have been · have used · hasn't existed
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	people who really · parameters, which is

주의할 발음

화자는 don't, I'm, couldn't 같은 축약형과 약화된 형태를 19번 사용합니다. 들리는 대로 짧게 발음하세요.

“th” 소리: synthesis /ˈsɪnθəsɪs/, breakthrough /ˈbɹeɪk.θɹuː/, gather /ˈɡæðɚ/, synthetic /sɪnˈθɛtɪk/, pathogen /ˈpæθəd͡ʒn̩/
“sh”와 “zh” 소리: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, regulation /ˌɹɛɡjʊˈleɪʃən/, infection /ɪnˈfɛkʃən/, publish /ˈpʌblɪʃ/
긴 단어 — 강세 위치에 주의: biology /baɪˈɑ.lə.d͡ʒi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/

한국어 화자가 어려워하는 소리:

/f/ — ㅍ(/p/)으로 바꾸지 말고 윗니를 아랫입술에 대기: artificial /ˌɑː.tɪˈfɪʃ.əl/, fold /ˈfəʊld/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, fundamental /ˌfʌn.dəˈmɛn.təl/, infection /ɪnˈfɛkʃən/
/v/ — ㅂ(/b/)과 구별하기: virus /ˈvaɪ.ɹəs/, curve /kɚv/, provider /pɹəˈvaɪ.də(ɹ)/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/, developer /dɪˈvɛləpɚ/
/z/ — ㅈ이 아니라 성대를 울리는 /s/: whereas /(h)wɛɹˈæz/, resistant /ɹəˈzɪstənt/, expose /ɪkˈspoʊz/, zoom /zuːm/, resemble /ɹɪˈzɛmbəl/

이 영상으로 연습하는 방법

먼저 말하지 않고 영상을 끝까지 듣고 모르는 단어를 적어 둡니다.
0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, virus, genome, sequence 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

섀도잉 방법: 단계별 전체 가이드 읽기 →

쉐도잉 연습: AI Designed Viruses That Never Existed Before - 영상으로 영어 말하기 배우기

연습 모드 선택

앱 다운로드

추천 영상

Viruses: Components & Shapes - Microbiology Acellular Pre-Nursing, Pre-Med | @LevelUpRN

I read Anthropic's 92-page risk factor document, so you don't have to | Explainer

What if quantum physics could eradicate illness? | Jim Al-Khalili for Big Think

Why Ebola Keeps Coming Back

Explaining how AI is killing the PhD in less than 10 minutes

Session #1: Life Sciences in the 21st Century – A Pathway to Scientific Discovery - Rotem Sorek

This Woman Cured Her Cancer with an Insane Method

3 Ways AI Will Completely Change Your Life by 2029

We Let $200 GPT-Pro Think for 7 Days on Unsolved Math

Editing Your DNA, with Nobel Prize Winner Dr. Jennifer Doudna

Explanation of Vaccine Adjuvants

What AI can (and can't) currently do in a real lab | Jennifer Doudna