跟讀練習: AI Designed Viruses That Never Existed Before - 通過影片學習英語口語

選擇練習模式

每種模式都會開啟全屏練習室。

141 句

如果句子過短或過長，請點選 Edit 進行調整。

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.

下載應用

流利口語所需的一切

AI 發音評分逐句打分

音標練習掌握每個音

詞彙學習建立你的詞庫

詞彙遊戲邊玩邊學

掃碼

Download on theApp Store Get it onGoogle Play

FOR YOU

本課的詞彙與口語要點

這節口語課以影片“AI Designed Viruses That Never Existed Before”為素材。影片中反覆出現的詞有：model, Evo, genome, viruses, design。這段影片共有 6 個句子、1694 個單詞可供跟讀。講話部分時長為 10:54。說話人語速自然，每分鐘約 155 個詞，接近日常對話。只有 78% 的單詞屬於英語最常用的 3,000 詞，詞彙難度較高。

影片中的重點詞彙

影片中 15 個值得學習的單詞，附發音和釋義：

單詞	發音	釋義
virus 名詞	/ˈvaɪ.ɹəs/	病毒
genome 名詞	/ˈd͡ʒiː.noʊm/	基因組 /基因組, 基因體 /基因體
sequence 名詞	/ˈsiː.kwəns/	序列, 順序 /順序
biology 名詞	/baɪˈɑ.lə.d͡ʒi/	生物學 /生物學
gene 名詞	/d͡ʒiːn/	基因
scientist 名詞	/ˈsaɪəntəst/	科學家 /科學家
synthesis 名詞	/ˈsɪnθəsɪs/	綜合 /綜合
essentially 副詞	/ɪˈsɛnʃəli/	本質上 /本質上, 基本上
rail 名詞	/ɹeɪ(ə)l/	軌 /軌, 鐵軌 /鐵軌
curve 名詞	/kɚv/	彎子 /彎子
researcher 名詞	/ˈɹiˌsɝ.t͡ʃɚ/	研究員 /研究員, 研究者
harm 名詞	/hɑːm/	損害 /損害
whereas	/(h)wɛɹˈæz/	然而
narrow 形容詞	/ˈnæɹəʊ/	窄
scary 形容詞	/ˈskɛə.ɹi/	恐怖

影片中出現的短語動詞

單詞	發音	釋義
come out 動詞	/ˌkʌm ˈaʊt/	出, 出來 /出來
figure out 動詞		弄清楚
go back 動詞		回去, 返回
pick up 動詞		拾取, 撿 /撿

值得反覆跟讀的句子

影片中簡短而完整的句子，可以直接用在日常對話裡：

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?

影片中的語法

說話人最常用的結構，並附上影片中的原話：

結構	影片中的用法
被動語態 be + 過去分詞 — 強調發生了什麼，而不是誰做的	is projected · was released · is led
現在完成時 have/has + 過去分詞 — 過去發生但與現在仍有關聯的事	have been · have used · hasn't existed
定語從句 who / which + 從句 — 補充說明人或事物	people who really · parameters, which is

需要注意的發音

說話人用了 19 次縮略和弱讀形式，例如 don't, I'm, couldn't。請按聽到的簡短形式來說。

“th” 音: synthesis /ˈsɪnθəsɪs/, breakthrough /ˈbɹeɪk.θɹuː/, gather /ˈɡæðɚ/, synthetic /sɪnˈθɛtɪk/, pathogen /ˈpæθəd͡ʒn̩/
“sh” 和 “zh” 音: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, regulation /ˌɹɛɡjʊˈleɪʃən/, infection /ɪnˈfɛkʃən/, publish /ˈpʌblɪʃ/
長單詞——注意重音位置: biology /baɪˈɑ.lə.d͡ʒi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/

中文母語者容易讀錯的音：

/v/ — 不要讀成 /w/，上齒輕觸下唇: virus /ˈvaɪ.ɹəs/, curve /kɚv/, provider /pɹəˈvaɪ.də(ɹ)/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/, developer /dɪˈvɛləpɚ/
詞尾子音 — 要讀清楚，後面不要加母音: virus /ˈvaɪ.ɹəs/, sequence /ˈsiː.kwəns/, scientist /ˈsaɪəntəst/, synthesis /ˈsɪnθəsɪs/, curve /kɚv/
/r/ 和 /l/ 的區別: rail /ɹeɪ(ə)l/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, regulation /ˌɹɛɡjʊˈleɪʃən/, regulatory /ˈɹɛɡ.jʊ.lə.t(ɔː.)ɹi/

如何用這段影片練習

先完整聽一遍影片，不要開口，記下不認識的單詞。
先用 0.75 倍速逐句跟讀，熟練之後再回到正常速度。
錄下自己的聲音並與原聲對比，特別注意 virus, genome, sequence 這類單詞。

什麼是跟讀法？

跟讀法 (Shadowing) 是一種有科學依據的語言學習技巧，最初開發用於專業口譯員的培訓，並由多語言者Alexander Arguelles博士普及。這個方法簡單而強大：您在聽英語母語原聲的同時立即大聲重複——就像是一個延遲1-2秒緊跟說話者的影子。與被動聽力或語法練習不同，跟讀法強迫您的大腦和口腔肌肉同時處理並模模擬實的講話模式。研究表明它能顯著提高發音準確性，語調，節奏，連讀，聽力理解和口語流利度——使其成為雅思口語備考和真實英語交流最有效的方法之一。

影子跟讀法: 閱讀完整分步指南 →

跟讀練習: AI Designed Viruses That Never Existed Before - 通過影片學習英語口語

選擇練習模式

下載應用

推薦影片

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

I Tested The Viral Reverse Engineering Repo

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

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

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

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