Shadowing Practice: AI Designed Viruses That Never Existed Before - Learn English Speaking with 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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Vocabulario y notas de pronunciación para esta lección
Esta lección de conversación se basa en el vídeo “AI Designed Viruses That Never Existed Before”. Las palabras que más se repiten: model, Evo, genome, viruses, design. Este vídeo tiene 6 frases y 1694 palabras para practicar shadowing. La parte hablada dura 10:54. El hablante habla a un ritmo natural de unas 155 palabras por minuto, cercano a una conversación cotidiana. Solo el 78 % de las palabras está entre las 3.000 más comunes del inglés, por lo que el vocabulario es exigente.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| virus sustantivo | /ˈvaɪ.ɹəs/ | virus |
| genome sustantivo | /ˈd͡ʒiː.noʊm/ | genoma |
| sequence sustantivo | /ˈsiː.kwəns/ | secuencia |
| biology sustantivo | /baɪˈɑ.lə.d͡ʒi/ | biología |
| gene sustantivo | /d͡ʒiːn/ | gen, gene |
| scientist sustantivo | /ˈsaɪəntəst/ | científico, científica |
| synthesis sustantivo | /ˈsɪnθəsɪs/ | síntesis |
| essentially adverbio | /ɪˈsɛnʃəli/ | esencialmente, en esencia |
| rail sustantivo | /ɹeɪ(ə)l/ | barra, barandilla |
| curve sustantivo | /kɚv/ | curva |
| researcher sustantivo | /ˈɹiˌsɝ.t͡ʃɚ/ | investigador, investigadora |
| whereas | /(h)wɛɹˈæz/ | mientras que (locution) |
| narrow adjetivo | /ˈnæɹəʊ/ | estrecho, angosto |
| scary adjetivo | /ˈskɛə.ɹi/ | temible, espantoso |
| genetic adjetivo | /d͡ʒəˈnɛt.ɪk/ | genético |
Phrasal verbs que vas a escuchar
| Palabra | Pronunciación | Significado |
|---|---|---|
| come out verbo | /ˌkʌm ˈaʊt/ | revelarse, salir a la luz |
| figure out verbo | averiguar, captar | |
| pick up verbo | coger, asir | |
| roll off verbo | /ˈɹol ˈɔf/ | rodar de/desde |
Frases que vale la pena repetir
Frases cortas y completas del vídeo que puedes reutilizar en la conversación diaria:
- 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?
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | is projected · was released · is led |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | have been · have used · hasn't existed |
| Oraciones de relativo who / which + oración — información extra sobre una persona o cosa | people who really · parameters, which is |
Pronunciación a tener en cuenta
El hablante usa 19 contracciones y formas reducidas, como don't, I'm, couldn't. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “th”: synthesis /ˈsɪnθəsɪs/, breakthrough /ˈbɹeɪk.θɹuː/, gather /ˈɡæðɚ/, synthetic /sɪnˈθɛtɪk/, pathogen /ˈpæθəd͡ʒn̩/
- Los sonidos de “sh” y “zh”: essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, regulation /ˌɹɛɡjʊˈleɪʃən/, infection /ɪnˈfɛkʃən/, publish /ˈpʌblɪʃ/
- Palabras largas — cuida el acento: biology /baɪˈɑ.lə.d͡ʒi/, capability /ˌkeɪ.pəˈbɪl.ɪ.ti/, essentially /ɪˈsɛnʃəli/, artificial /ˌɑː.tɪˈfɪʃ.əl/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/
Sonidos difíciles para hispanohablantes:
- s + consonante al inicio — sin añadir una “e” delante: scary /ˈskɛə.ɹi/, scratch /skɹæt͡ʃ/, startup /ˈstɑɹtˌʌp/, skim /skɪm/
- /v/ — no es /b/: los dientes tocan el labio inferior: virus /ˈvaɪ.ɹəs/, curve /kɚv/, provider /pɹəˈvaɪ.də(ɹ)/, voluntary /ˈvɑ.lənˌt(ɛ)ɹi/, developer /dɪˈvɛləpɚ/
- /z/ — sonora, no /s/: whereas /(h)wɛɹˈæz/, resistant /ɹəˈzɪstənt/, expose /ɪkˈspoʊz/, zoom /zuːm/, resemble /ɹɪˈzɛmbəl/
Cómo practicar con este vídeo
- Escucha el vídeo entero una vez sin hablar y anota las palabras que no conoces.
- Empieza a velocidad 0,75×, haz shadowing frase por frase y vuelve a la velocidad normal cuando te resulte fácil.
- Grábate y compara con el original, prestando atención a palabras como virus, genome, sequence.
¿Qué es la Técnica de Shadowing?
Shadowing es una técnica de aprendizaje de idiomas respaldada por la ciencia, desarrollada originalmente para la formación de intérpretes profesionales y popularizada por el políglota Dr. Alexander Arguelles. El método es simple pero poderoso: escuchas audio en inglés nativo y lo repites en voz alta de inmediato, como una sombra que sigue al hablante con solo 1-2 segundos de retraso. A diferencia de la escucha pasiva o los ejercicios de gramática, el shadowing obliga a tu cerebro y músculos de la boca a procesar y reproducir simultáneamente patrones de habla reales. Las investigaciones muestran que mejora significativamente la precisión de la pronunciación, la entonación, el ritmo, el habla conectada, la comprensión auditiva y la fluidez al hablar, convirtiéndola en una de las metodologías más efectivas para la preparación del IELTS Speaking y la comunicación en inglés en el mundo real.