Pratique du Shadowing: AI Designed Viruses That Never Existed Before - Apprendre l'anglais à l'oral avec la vidéo
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Antibiotic resistant bacteria killed an estimated 1 .27 million people in 2019.
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More than HIV slash AIDS or malaria.
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And what I find really fascinating is that number is projected to keep climbing.
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In fact, 40 million more deaths by 2050 if nothing changes.
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Now there are some interesting things happening to hopefully change this.
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I mean phage therapy it's using viruses to kill specific bacteria instead of broad -spectrum antibiotics.
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People have been very hopeful about it, but to be frank, it's been slow for decades.
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And the reason for this is because designing effective phages takes years.
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But essentially, if a model can generate working candidates in days, that's a real medical breakthrough.
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But it comes with kind of an unexpected security problem.
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So let's take a step back here.
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Scientists have used artificial intelligence to create brand new viruses, 16 of them.
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Researchers at Stanford University and the Arc Institute say they trained an AI model called Evo on huge amounts of DNA data.
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Then that information was used to create the new synthetic viruses.
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On August 5th, a peer -reviewed study by Stanford was released
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that showed how they trained a model to write DNA the same way, at its core, that GPT writes text.
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The Stanford -based team is led by Brian He, who's working with the ARC Institute and bioengineering grad student Samuel King.
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And it was he who built EVO2, which is a genome language model.
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You can predict the next token, except the tokens are base pairs and the training data is the genetic code of viruses,
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bacteria, plants and humans.
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So the team used EVO2 to design candidate genomes for 5X1 74,
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which is a virus that infects E coli, and the first DNA genome ever fully sequenced.
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They essentially synthesized close to 300 of the model's designs.
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Now out of those 300, 16 worked which meant new viruses that don't exist anywhere in nature.
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I mean, some killing E coli more effectively than the original.
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Now, the virus itself that was created is harmless.
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It really only infects E coli.
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But the paper, the Stanford paper that came out of it,
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proving this came out to what I found very fascinating and a lot of others did.
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It was from biosecurity researchers at John Hopkins and they basically call the findings a source of urgent biosafety and biosecurity questions.
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Yeah, here is the actual technical problem.
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DNA screening systems catch sequences by matching them against a database of known threats, things that already exist.
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But this sequence has no match because nothing like it existed before it was created.
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And to be fair, there's an industry -led screening system already.
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I mean, the International Gene Synthesis Consortium has more than 40 gene synthesis companies that agreed to a shared screening protocol.
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And even more than 175 developers of AI biodesign tools have
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even signed commitments to only use the synthesis providers who follow those standards, which I think is pretty exciting.
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People are seeing that this could be a real threat and taking action, hopefully ahead of time.
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But all of that infrastructure really answers one question.
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Does this sequence look like something we already know is dangerous?
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Adoption is essentially voluntary and uneven, especially among smaller providers.
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And none of it was built to catch a sequence that resembles nothing in any database.
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I mean it's making something that doesn't exist yet.
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And that's the real hole of Evo 2 exposed.
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It's a mismatch between the rules that were set in place to catch these things and what something can do,
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what a generative model can do and now produce something that hasn't existed before.
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How do we put guardrails for that?
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Running Evo 2, which by the way is open source right now, at full scale takes some serious hardware easily.
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It's open source but it probably takes a quarter million dollars or more in compute.
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It's really out of reach for someone working alone
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but well within reach for a university lab or a startup
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or a state program and
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that barrier tends to drop over time
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now one caveat worth knowing evo2 in its raw form couldn't reliably design a specific phage like phi x1 74.
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it really doesn't roll off the tongue easy
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it couldn't design one of those on its own
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it has to take an extra step supervised to narrow the model's focus on that phage family.
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So this was a deliberate targeted process by people who really understood what exactly the model needed to be doing.
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Not a case of typing a prompt in and a virus comes out.
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Much more complicated than that.
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But it is still a real shift.
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I mean, a predictive model tells you about biology that already exists, whereas a generative model invents biology that never did.
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How does something like EVO2 actually work?
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And why is this different from a normal biology AI tool?
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I mean, they've been around for a while now.
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Most AI biology tools are narrow.
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AlphaFold predicts how an existing protein folds, which, yes, is extremely useful, but it's answering a question about something that already exists,
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whereas EVO2 is generative.
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For scale, to put this in perspective for you,
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9 trillion nucleotides is enough genetic material to cover the entire human genome about 3 ,000 times over.
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And EVO2 gets there with just 4 billion parameters, which is smaller than GPT -3s, going back a while now,
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$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.
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Three years ago, four years ago, an LLM writing usable code was cool.
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It's a cool party trick.
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Here's how I can develop some code.
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But now, I mean, it produces full production ready software.
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Capability curves in AI don't move straight.
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They move in jumps, in leaps.
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And we've watched this happen again and again, especially when it comes to language and code.
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And we're watching the opening move of the same jump when it comes to genome design in biotech.
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Okay, I want to go back to the hit rate because I feel like we skimmed over that earlier.
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16 out of 300.
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It's not high for the 16 out of 300 viruses that actually took.
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I mean, that puts this closer to the GPT -2 era than the GPT -4 era of the technology.
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It's on the same curve though.
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Every generative model improves on more data, better outputs, better architecture.
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But let's zoom out to say two to five years.
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What's going to happen with this?
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I really don't think anyone's saying something like EVO2 is designing a human genome next year
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but every capability curve
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we've watched has moved toward the harder version of the problem
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not away from it now bacteriophage run a few thousand base
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pairs bacteria runs into the millions humans into the billions the gap between 16 working phage gmones
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and a model that reliably designs genomes for more complex organisms is an engineering gap
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and i really think it's not a a fundamental one
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that will come with time all right
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but this isn't the first time biology has hit a moment
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like this back in february 1975 around 140 scientists actually gathered
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to figure out what to do about a brand new capability
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which at the time was recombinant dna
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which is engineering genes across the species for a first time
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now paul berg who was the organizer of it looked for an existing framework to follow and found nothing.
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So instead the scientists wrote their own voluntary safety guidelines before any regulations forced them to.
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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?
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It's wild.
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But 50 years later scientists reconvened in the same convention center for a 50th anniversary meeting
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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
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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?
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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.
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All right, I'll see you in the next video.
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Vocabulaire et conseils d’expression pour cette leçon
Cette leçon d’expression orale s’appuie sur la vidéo « AI Designed Viruses That Never Existed Before ». Les mots qui reviennent le plus souvent : model, Evo, genome, viruses, design. Cette vidéo contient 6 phrases et 1694 mots à répéter en shadowing. La partie parlée dure 10:54. Le locuteur parle à un rythme naturel d’environ 155 mots par minute, proche d’une conversation courante. Seuls 78 % des mots font partie des 3 000 mots les plus courants en anglais, le vocabulaire est donc exigeant.
Vocabulaire clé de cette vidéo
15 mots de la vidéo à retenir, avec leur prononciation et leur sens :
| Mot | Prononciation | Sens |
|---|---|---|
| virus nom | /ˈvaɪ.ɹəs/ | virus |
| genome nom | /ˈd͡ʒiː.noʊm/ | génome |
| sequence nom | /ˈsiː.kwəns/ | suite, séquence |
| biology nom | /baɪˈɑ.lə.d͡ʒi/ | biologie |
| gene nom | /d͡ʒiːn/ | gène |
| scientist nom | /ˈsaɪəntəst/ | scientifique, savant |
| capability nom | /ˌkeɪ.pəˈbɪl.ɪ.ti/ | capacité |
| synthesis nom | /ˈsɪnθəsɪs/ | synthèse |
| essentially adverbe | /ɪˈsɛnʃəli/ | essentiellement, en essence |
| rail nom | /ɹeɪ(ə)l/ | rail |
| curve nom | /kɚv/ | courbe |
| researcher nom | /ˈɹiˌsɝ.t͡ʃɚ/ | chercheur, chercheuse |
| harm nom | /hɑːm/ | mal, tort |
| whereas | /(h)wɛɹˈæz/ | tandis que, alors que |
| narrow adjectif | /ˈnæɹəʊ/ | étroit |
Les verbes à particule que vous entendrez
| Mot | Prononciation | Sens |
|---|---|---|
| come out verbe | /ˌkʌm ˈaʊt/ | sortir |
| figure out verbe | cerner | |
| go back verbe | rentrer, retourner | |
| pick up verbe | soulever |
Des phrases à répéter
Des phrases courtes et complètes de la vidéo, à réutiliser dans la conversation de tous les jours :
- 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?
La grammaire de cette vidéo
Les structures que le locuteur utilise le plus, avec les mots exacts de la vidéo :
| Structure | Dans la vidéo |
|---|---|
| Voix passive be + participe passé — l’accent est mis sur ce qui arrive, pas sur qui le fait | is projected · was released · is led |
| Present perfect have/has + participe passé — une action passée qui compte encore maintenant | have been · have used · hasn't existed |
| Propositions relatives who / which + proposition — une précision sur une personne ou une chose | people who really · parameters, which is |
Prononciation à surveiller
Le locuteur utilise 19 contractions et formes réduites, comme don't, I'm, couldn't. Prononcez-les sous leur forme courte, telles que vous les entendez.
- Les sons « th »: synthesis /ˈsɪnθəsɪs/, breakthrough /ˈbɹeɪk.θɹuː/, gather /ˈɡæðɚ/, synthetic /sɪnˈθɛtɪk/, pathogen /ˈpæθəd͡ʒn̩/
- Les sons « sh » et « zh »: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, regulation /ˌɹɛɡjʊˈleɪʃən/, infection /ɪnˈfɛkʃən/, publish /ˈpʌblɪʃ/
- Mots longs — placez bien l’accent: biology /baɪˈɑ.lə.d͡ʒi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/
Les sons difficiles pour les francophones :
- /h/ — il se souffle, il n’est pas muet: harm /hɑːm/, whereas /(h)wɛɹˈæz/, hardware /ˈhɑɹdˌwɛɹ/, harmless /ˈhɑɹmləs/, hopeful /ˈhoʊpfl̩/
- /tʃ/ et /dʒ/ — à ne pas adoucir en « ch » et « j »: genome /ˈd͡ʒiː.noʊm/, biology /baɪˈɑ.lə.d͡ʒi/, gene /d͡ʒiːn/, researcher /ˈɹiˌsɝ.t͡ʃɚ/, genetic /d͡ʒəˈnɛt.ɪk/
- /r/ anglais — langue recourbée, sans frotter la gorge: virus /ˈvaɪ.ɹəs/, rail /ɹeɪ(ə)l/, researcher /ˈɹiˌsɝ.t͡ʃɚ/, whereas /(h)wɛɹˈæz/, narrow /ˈnæɹəʊ/
Comment s’entraîner avec cette vidéo
- Écoutez la vidéo en entier une fois sans parler et notez les mots que vous ne connaissez pas.
- Commencez à la vitesse 0,75×, répétez phrase par phrase, puis revenez à la vitesse normale quand cela devient facile.
- Enregistrez-vous et comparez avec l’original, en faisant attention à des mots comme virus, genome, sequence.
Qu'est-ce que la technique du Shadowing ?
Le Shadowing est une technique d'apprentissage des langues fondée sur la science, développée à l'origine pour la formation des interprètes professionnels. Le principe est simple mais puissant : vous écoutez de l'anglais natif et le répétez immédiatement à voix haute — comme une ombre suivant le locuteur avec un décalage de 1 à 2 secondes. Les recherches montrent une amélioration significative de la précision de la prononciation, de l'intonation, du rythme, des liaisons, de la compréhension orale et de la fluidité.