Shadowing Practice: What AI can (and can't) currently do in a real lab | Jennifer Doudna - Learn English Speaking with Video
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There's a lot of discussion about AI and biology, a lot, right?
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And people are always asking me about this too.
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You know, how is AI going to change biology?
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How is it going to change the genome editing?
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And my immediate answer is, well, it's certainly going to accelerate everything.
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It's making our work go a lot faster
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because we can increasingly speed up things that used to take a lot more time.
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And I think in the future, that'll happen even more.
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You know, I do imagine a day
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when we can use AI to help us predict which experiments are going to be worth doing.
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And that means that we don't have to do as many, we hope, you know.
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But I think artificial intelligence absolutely needs human intelligence in biology to be useful.
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scientists are increasingly using ai in biology to accelerate the rate of experimentation
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and the way they're doing this is in a few ways
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they are using it to do very you know kind of mundane types of things
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that we used to have to spend a lot of time on
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and i'm referring here to things like writing summaries of projects that we need to submit, you know, taking large data sets and culling them,
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organizing data into different domains, combining different types of sets of data, converting data from one format into another,
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all those types of things used to take huge amounts of time.
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Sometimes they still do.
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But, you know, increasingly AI is a real assist there.
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So that's very interesting.
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And lots of people are using it now for that kind of thing.
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What I think is potentially more interesting, but it's going to take longer, but we're starting to see this happening,
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is that scientists are using AI to make predictions about data,
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to discover things in sets of data that might be very hard to discern manually, but where, you know, a machine learning system can go through data
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and find trends that would be hard to see by inspecting data yourself,
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but a machine system that has the ability to look across large sets of data for small
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but significant trends can pick these out.
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I think that's very exciting.
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And we're seeing this happening more and more.
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And I do think in the future, as scientists become better at applying AI to particular questions in biology,
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that we're going to see an acceleration of even things like hypothesis generation
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and the ability to answer questions that might have taken, you know, a long time previously, but can go fast now because you can use coding agents,
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for example, to write code quickly.
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So those are where I'm seeing really interesting opportunities even now, and it's going to accelerate in the future.
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In experimental biology, it's interesting to think about the role of computation.
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I think, you know, when I started out in biology, we used computers to do things that were,
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you know, annoying, but, you know, needed to get done and were very hard to do manually.
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And I'm thinking about things like converting data from one format to another, combining sets of data together.
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You know, I used to be focusing on molecular structure determination using X -ray crystallography, for example.
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And that was a field where, at the time, we did lots of,
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you know, collecting of X -ray diffraction data from crystals and then combining data from different crystals,
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having to convert it into different formats, things of that type.
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Even just collating data, you know, things that sound very boring, and they were, but they had to be done and they had to be done right.
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So we used computation that way back in those days.
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But, you know, increasingly, you know, computers and the models that we're training on data sets are becoming a lot more sophisticated.
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So increasingly, we're able to use these for predictions and for the kinds of,
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you know, accelerations of experimental science that weren't possible in the past.
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Do I think scientists will become, you know, desk jockeys in the future?
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No. And the reason is that I don't see robots, you know, this would require really very sophisticated robots.
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And I think that robotics right now, even with some of the exciting things that have happened, just are not at the point and frankly,
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probably not even close to being capable the way a person is to make changes to experiments on the fly.
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We do this all the time in the lab.
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It's kind of a natural thing for a graduate student
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or a postdoc in the lab to be working on a protocol and realize that, oh, I need to double the volume of this thing
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or I need to change the way I'm doing some manipulation of a sample.
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That's something that a person can do easily on the fly, whereas for a machine, it would take a lot of effort.
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And believe me, I've seen this myself in my lab,
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how much work it is to change a robotic format that we have for a robot versus just telling a student, hey, you know, instead of,
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you know, manipulating the sample this way, do it that way.
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They can change like that.
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What I think is going to happen in the future with experimental science is actually
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that I think each person who is an experimentalist, like let's say a graduate student,
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will effectively have a set of technical staff that are computers or AI agents assisting them.
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And those AI agents will be brilliant, but somewhat naive about the actual details of the project.
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And so we find this now, you know, that students are using particularly coding agents this way,
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where they have coding agents that are set up to help with data manipulation, help them with collecting data in an experiment, even designing how to collect the data
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but they don't have the ability to tell the scientist this is the experiment you should be doing
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or you should be doing it this way and not that way, again, sort of depending on the details.
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But this is really what we're seeing.
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And so I imagine that will probably be true for a while, again, because it gets back to this challenge of how do we
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ever get enough data to train models to be able to not only kind of regurgitate things that they've been trained on,
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but to really be innovative and come up with new ideas.
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I don't see that in the near future.
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Woordenschat en spreektips bij deze les
Deze spreekles op niveau C1 is gebaseerd op de video “What AI can (and can't) currently do in a real lab”. Deze woorden komen het vaakst terug: biology, scientist, increasingly, agents, experiment. Deze video bevat 75 zinnen en 1102 woorden om na te spreken. Het gesproken deel duurt 6:39. De spreker praat in een natuurlijk tempo van ongeveer 166 woorden per minuut, dicht bij een alledaags gesprek. 84% van de woorden hoort bij de 3.000 meest gebruikte Engelse woorden; de rest kun je beter vooraf bekijken.
Belangrijke woorden in deze video
De 15 moeilijkste woorden uit de video, met uitspraak en betekenis:
| Woord | Uitspraak | Betekenis |
|---|---|---|
| accelerate werkwoord | /ɪkˈsɛl.əˌɹeɪt/ | versnellen |
| computation zelfstandig naamwoord | /ˌkɑmpjʊˈteɪʃn̩/ | berekening |
| excite werkwoord | /ɪkˈsaɪt/ | prikkelen, opwinden |
| manually bijwoord | /ˈmænj(u)əliː/ | manueel, met de hand |
| acceleration zelfstandig naamwoord | /əkˌsɛl.əˈɹeɪ.ʃən/ | versnelling, acceleratie |
| manipulation zelfstandig naamwoord | /məˌnɪp.juˈleɪ.ʃən/ | manipulatie, gebruik |
| prediction zelfstandig naamwoord | /pɹɪˈdɪkʃən/ | voorspelling |
| sophisticated bijvoeglijk naamwoord | wereldwijs | |
| collate werkwoord | /kəˈleɪt/ | sorteren |
| crystallography zelfstandig naamwoord | /ˌkɹɪstəˈlɑɡɹəfi/ | kristallografie |
| discern werkwoord | /dɪˈzɝn/ | onderscheiden |
| robotics zelfstandig naamwoord | /ɹoʊˈbɑ.tɪks/ | robotica |
| inspect werkwoord | /ɪnˈspɛkt/ | inspecteren, onderzoeken |
| manipulate werkwoord | /məˈnɪp.jʊ.leɪt/ | manipuleren |
| genome zelfstandig naamwoord | /ˈd͡ʒiː.noʊm/ | genoom |
Phrasal verbs die je zult horen
| Woord | Betekenis |
|---|---|
| come up with werkwoord | bedenken, verzinnen |
Grammatica in deze video
De structuren die de spreker het meest gebruikt, met de exacte woorden uit de video:
| Structuur | In de video |
|---|---|
| “Used to” used to + werkwoord — een vroegere gewoonte of toestand die niet meer klopt | used to take · used to have · used to be |
| Present perfect have/has + voltooid deelwoord — iets uit het verleden dat nu nog telt | have happened · I've seen · they've been trained |
| Lijdende vorm be + voltooid deelwoord — het gaat om wat er gebeurt, niet om wie het doet | be done · are set · they've been trained |
Uitspraak om op te letten
De spreker gebruikt 19 samentrekkingen en verkorte vormen, zoals we're, don't, I'm. Spreek ze kort uit, zoals je ze hoort.
- De klanken “sh” en “zh”: computation /ˌkɑmpjʊˈteɪʃn̩/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, manipulation /məˌnɪp.juˈleɪ.ʃən/, prediction /pɹɪˈdɪkʃən/, diffraction /dɪˈfɹækʃən/
- Lange woorden — let op de klemtoon: accelerate /ɪkˈsɛl.əˌɹeɪt/, manually /ˈmænj(u)əliː/, acceleration /əkˌsɛl.əˈɹeɪ.ʃən/, manipulation /məˌnɪp.juˈleɪ.ʃən/, crystallography /ˌkɹɪstəˈlɑɡɹəfi/
Klanken die Nederlandstaligen lastig vinden:
- Stemhebbende eindmedeklinker — /b/, /d/, /g/, /z/, /v/ niet verscherpen: naive /naɪˈiv/, organize /ˈɔɹɡənaɪz/
- /æ/ — opener dan de Nederlandse “e”: manually /ˈmænj(u)əliː/, diffraction /dɪˈfɹækʃən/, frankly /fɹæŋkli/
- /g/ — een harde plofklank, geen Nederlandse “g”: crystallography /ˌkɹɪstəˈlɑɡɹəfi/, organize /ˈɔɹɡənaɪz/
Zo oefen je met deze video
- Luister de hele video één keer zonder te spreken en noteer de woorden die je niet kent.
- Begin op 0,75× snelheid, spreek zin voor zin na en ga terug naar normale snelheid zodra het makkelijk gaat.
- Neem jezelf op en vergelijk met het origineel; let daarbij op woorden als accelerate, computation, excite.
Wat is de Shadowing-techniek?
Shadowing is een wetenschappelijk onderbouwde taalleermethode die oorspronkelijk is ontwikkeld voor professionele tolkentraining en gepopulariseerd door polyglot Dr. Alexander Arguelles. De methode is eenvoudig maar krachtig: je luistert naar native Engelse audio en herhaalt het onmiddellijk hardop — als een schaduw die de spreker volgt met slechts 1–2 seconden vertraging. In tegenstelling tot passief luisteren of grammaticadrills, dwingt shadowing je hersenen en mondspieren om echte spraakpatronen tegelijkertijd te verwerken en te reproduceren. Onderzoek toont aan dat het de uitspraaknauwkeurigheid, intonatie, ritme, verbonden spraak, luisterbegrip en spreekvaardigheid aanzienlijk verbetert — waardoor het een van de meest effectieve methoden is voor IELTS Speaking-voorbereiding en echte Engelse communicatie.