シャドーイング練習: AI Designed Viruses That Never Existed Before - 動画で英語スピーキングを学ぶ

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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.

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How was 70s by the way 50 years ago?

106

It's wild.

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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

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

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

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Evo2's own paper does exactly that.

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It argues for oversight.

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

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that instinct spreads to the next lab

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that picks up an open source genome model

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or what guidelines will they follow whether it will stay limited

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

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

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

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because of this phage therapy against resistant infections could save lives

121

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

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but it also the flip side you have to talk about

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that because a technology

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

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and that is something

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

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

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

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What could possibly go wrong?

130

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

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Let's be honest here.

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

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

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It's that the thing standing between a generative model

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

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

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

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Leave in the comments your thoughts.

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Do you like topics like this around biotech?

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

141

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

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このレッスンの語彙とスピーキングのポイント

このスピーキングレッスンは、動画「AI Designed Viruses That Never Existed Before」を教材にしています。繰り返し出てくる語は次のとおりです：model, Evo, genome, viruses, design。この動画には、シャドーイング用の文が6文、単語が1694語あります。音声の長さは10:54です。話す速さは1分あたり約155語で、日常会話に近い自然なペースです。英語の頻出3,000語に含まれる単語は78%だけなので、語彙は難しめです。

この動画の重要語彙

動画に出てくる覚えておきたい単語15語を、発音と意味つきで紹介します。

単語	発音	意味
virus 名詞	/ˈvaɪ.ɹəs/	ウイルス, ウィルス
genome 名詞	/ˈd͡ʒiː.noʊm/	ゲノム
sequence 名詞	/ˈsiː.kwəns/	順序
biology 名詞	/baɪˈɑ.lə.d͡ʒi/	生物学
gene 名詞	/d͡ʒiːn/	遺伝子
scientist 名詞	/ˈsaɪəntəst/	科学者
capability 名詞	/ˌkeɪ.pəˈbɪl.ɪ.ti/	能力
synthesis 名詞	/ˈsɪnθəsɪs/	統合, 総合
rail 名詞	/ɹeɪ(ə)l/	手摺, 手すり
curve 名詞	/kɚv/	曲線
researcher 名詞	/ˈɹiˌsɝ.t͡ʃɚ/	研究員, 研究者
narrow 形容詞	/ˈnæɹəʊ/	狭い
scary 形容詞	/ˈskɛə.ɹi/	怖い
genetic 形容詞	/d͡ʒəˈnɛt.ɪk/	遺伝的
database 名詞	/ˈdeɪtəˌbeɪs/	データベース

動画に出てくる句動詞

単語	発音	意味
come out 動詞	/ˌkʌm ˈaʊt/	出る
go back 動詞		戻る, 帰る
pick up 動詞		拾う, 拾い上げる

繰り返し練習したい文

動画に出てくる短く完結した文です。日常会話でそのまま使えます。

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?

この動画の文法

話し手がよく使っている文型を、動画の実際の表現とともに紹介します。

文型	動画での表現
受動態 be + 過去分詞 — 誰がするかより、何が起きるかに焦点を当てる	is projected · was released · is led
現在完了形 have/has + 過去分詞 — 過去の出来事が今も関係している	have been · have used · hasn't existed
関係詞節 who / which + 節 — 人や物について情報を加える	people who really · parameters, which is

注意したい発音

話し手はdon't, I'm, couldn'tなど、短縮形や弱形を19回使っています。聞こえたとおりの短い形で発音しましょう。

「th」の音: synthesis /ˈsɪnθəsɪs/, breakthrough /ˈbɹeɪk.θɹuː/, gather /ˈɡæðɚ/, synthetic /sɪnˈθɛtɪk/, pathogen /ˈpæθəd͡ʒn̩/
「sh」と「zh」の音: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, regulation /ˌɹɛɡjʊˈleɪʃən/, infection /ɪnˈfɛkʃən/, publish /ˈpʌblɪʃ/
長い単語（アクセントの位置に注意）: biology /baɪˈɑ.lə.d͡ʒi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/

日本語話者が苦手な音：

/r/ と /l/ の区別 — /l/ は舌先を歯茎につけ、/r/ はどこにもつけない: rail /ɹeɪ(ə)l/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/, necessarily /ˌnɛs.əˈsɛɹ.ə.li/, regulation /ˌɹɛɡjʊˈleɪʃən/, regulatory /ˈɹɛɡ.jʊ.lə.t(ɔː.)ɹi/
/v/ — /b/ にならないように、上の歯を下唇に当てる: virus /ˈvaɪ.ɹəs/, curve /kɚv/, provider /pɹəˈvaɪ.də(ɹ)/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/, developer /dɪˈvɛləpɚ/
/f/ — 「フ」ではなく、上の歯と下唇で出す: artificial /ˌɑː.tɪˈfɪʃ.əl/, fold /ˈfəʊld/, infrastructure /ˈɪnfɹəˌstɹʌkt͡ʃə/, fundamental /ˌfʌn.dəˈmɛn.təl/, infection /ɪnˈfɛkʃən/

この動画での練習方法

まず声を出さずに動画を最後まで聞き、知らない単語をメモします。
まず0.75倍速で一文ずつシャドーイングし、慣れてきたら通常の速度に戻します。
自分の声を録音して元の音声と比べます。virus, genome, sequenceなどの単語に特に注意しましょう。

シャドーイングとは？英語上達に効果的な理由

シャドーイング（Shadowing）は、もともとプロの通訳者養成プログラムで開発された言語学習法で、多言語習得者として知られるDr. Alexander Arguelles によって広く普及されました。方法はシンプルですが非常に効果的：ネイティブスピーカーの英語を聞きながら、1〜2秒の遅延で声に出してすぐに繰り返す——まるで「影（shadow）」のように話者を追いかけます。文法ドリルや受動的なリスニングと異なり、シャドーイングは脳と口の筋肉が同時にリアルタイムで英語を処理・再現することを強制します。研究により、発音精度、抑揚、リズム、連音、リスニング力、そして会話の流暢さが大幅に向上することが確認されています。IELTSスピーキング対策や自然な英語コミュニケーションを目指す方に特におすすめです。

シャドーイングのやり方: ステップ別の完全ガイドを読む →

シャドーイング練習: AI Designed Viruses That Never Existed Before - 動画で英語スピーキングを学ぶ

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