Pratica di Shadowing: AI Designed Viruses That Never Existed Before - Impara a parlare inglese con i video
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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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Vocabolario e note di pronuncia per questa lezione
Questa lezione di conversazione si basa sul video “AI Designed Viruses That Never Existed Before”. Le parole che ritornano più spesso: model, Evo, genome, viruses, design. Questo video contiene 6 frasi e 1694 parole da ripetere con lo shadowing. Il parlato dura 10:54. Chi parla ha un ritmo naturale di circa 155 parole al minuto, vicino a una conversazione quotidiana. Solo il 78% delle parole rientra nelle 3.000 più comuni dell’inglese, quindi il lessico è impegnativo.
Vocaboli chiave di questo video
15 parole del video che vale la pena imparare, con pronuncia e significato:
| Parola | Pronuncia | Significato |
|---|---|---|
| virus sostantivo | /ˈvaɪ.ɹəs/ | virus |
| genome sostantivo | /ˈd͡ʒiː.noʊm/ | genoma |
| sequence sostantivo | /ˈsiː.kwəns/ | sequenza |
| biology sostantivo | /baɪˈɑ.lə.d͡ʒi/ | biologia |
| gene sostantivo | /d͡ʒiːn/ | gene |
| scientist sostantivo | /ˈsaɪəntəst/ | scienziato, scienziata |
| capability sostantivo | /ˌkeɪ.pəˈbɪl.ɪ.ti/ | capacità, possibilità |
| synthesis sostantivo | /ˈsɪnθəsɪs/ | sintesi |
| essentially avverbio | /ɪˈsɛnʃəli/ | in essenza |
| rail sostantivo | /ɹeɪ(ə)l/ | parapetto, balaustra |
| curve sostantivo | /kɚv/ | curva |
| researcher sostantivo | /ˈɹiˌsɝ.t͡ʃɚ/ | ricercatore, ricercatrice |
| harm sostantivo | /hɑːm/ | danno, male |
| whereas | /(h)wɛɹˈæz/ | laddove, mentre |
| narrow aggettivo | /ˈnæɹəʊ/ | stretto, angusto |
I phrasal verb che sentirai
| Parola | Significato |
|---|---|
| figure out verbo | scoprire, rendersi conto |
| go back verbo | tornare |
| pick up verbo | tirare su, sollevare |
Frasi da ripetere
Frasi brevi e complete del video che puoi riutilizzare nella conversazione di tutti i giorni:
- 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 grammatica di questo video
Le strutture che chi parla usa di più, con le parole esatte del video:
| Struttura | Nel video |
|---|---|
| Forma passiva be + participio passato — conta ciò che accade, non chi lo fa | is projected · was released · is led |
| Present perfect have/has + participio passato — un’azione passata che conta ancora adesso | have been · have used · hasn't existed |
| Frasi relative who / which + frase — un’informazione in più su una persona o una cosa | people who really · parameters, which is |
Pronuncia a cui fare attenzione
Chi parla usa 19 contrazioni e forme ridotte, come don't, I'm, couldn't. Pronunciale nella forma breve, così come le senti.
- I suoni “th”: synthesis /ˈsɪnθəsɪs/, breakthrough /ˈbɹeɪk.θɹuː/, gather /ˈɡæðɚ/, synthetic /sɪnˈθɛtɪk/, pathogen /ˈpæθəd͡ʒn̩/
- I suoni “sh” e “zh”: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, regulation /ˌɹɛɡjʊˈleɪʃən/, infection /ɪnˈfɛkʃən/, publish /ˈpʌblɪʃ/
- Parole lunghe — attenzione all’accento: biology /baɪˈɑ.lə.d͡ʒi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/
I suoni difficili per chi parla italiano:
- /h/ — va soffiata, non è muta: harm /hɑːm/, whereas /(h)wɛɹˈæz/, hardware /ˈhɑɹdˌwɛɹ/, harmless /ˈhɑɹmləs/, hopeful /ˈhoʊpfl̩/
- Consonante finale — senza aggiungere una vocale dopo: virus /ˈvaɪ.ɹəs/, sequence /ˈsiː.kwəns/, scientist /ˈsaɪəntəst/, synthesis /ˈsɪnθəsɪs/, curve /kɚv/
- /æ/ — più aperta della “e”: whereas /(h)wɛɹˈæz/, narrow /ˈnæɹəʊ/, gather /ˈɡæðɚ/, scratch /skɹæt͡ʃ/, barrier /ˈbæɹi.ə/
Come esercitarsi con questo video
- Ascolta tutto il video una volta senza parlare e annota le parole che non conosci.
- Inizia a velocità 0,75×, fai shadowing frase per frase e torna alla velocità normale quando diventa facile.
- Registrati e confronta con l’originale, facendo attenzione a parole come virus, genome, sequence.
Cos'è la tecnica dello Shadowing?
Shadowing è una tecnica di apprendimento delle lingue supportata da studi scientifici, originariamente sviluppata per la formazione dei traduttori professionisti e resa popolare dal poliglotta Dr. Alexander Arguelles. Il metodo è semplice ma potente: ascolti un audio in inglese di madrelingua e lo ripeti immediatamente ad alta voce — come un'ombra che segue il parlante con un ritardo di solo 1–2 secondi. A differenza dell'ascolto passivo o degli esercizi di grammatica, lo shadowing costringe il tuo cervello e i muscoli della bocca a elaborare e riprodurre simultaneamente i modelli di discorso reale. La ricerca dimostra che migliora significativamente la precisione della pronuncia, l'intonazione, il ritmo, il discorso connesso, la comprensione dell'ascolto e la fluidità del parlato — rendendolo uno dei metodi più efficaci per la preparazione alla prova di speaking dell'IELTS e per la comunicazione reale in inglese.