Практика Shadowing: We Can Now Simulate a Human Brain, Scientists Show - Изучайте разговорный английский по видео
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Supercomputers are now good enough to simulate a full human brain.
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That sounds like science fiction, but that's what scientists showed in a recent paper.
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Haven't we tried this before and it didn't work?
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Kind of, but this time it's different.
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Computers have much advanced and the scientists have come up with better ways to calculate what human brains do.
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How?
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And even if we could simulate a human brain, should we?
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Let's have a look.
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The current models for artificial intelligence use computer code to simulate neural networks,
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which are motivated by the functions of neurons in real brains.
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But these AIs are far from the real thing.
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The biggest difference may be that real brains are extremely differentiated in function.
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They have areas for different tasks.
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Scientists have tried to reproduce the function of real neural nets on a computer for decades.
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They started with a small worm whose brain has just about 300 neurons.
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The first simulations could reproduce simple movements of the worm.
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By 2023, science and technology had advanced enough to fully simulate the brain of a fruit fly with about 140,000 neurons.
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The Allen Institute for Brain Science, meanwhile, has been working on putting a mouse brain on a supercomputer.
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The entire mouse brain has something like 70 million neurons.
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They are now at a few million, and that runs on a supercomputer in Japan.
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The new paper now outlines a new method that'd be a big and sudden jump forward, almost reaching the capacity to simulate the human brain.
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The human brain has about 80 billion neurons.
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In the paper, they write that with their new method,
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network sizes of 2 times 10 to the 10 neurons can be reached with our approach on the upcoming exascale supercomputer Jupiter.
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So that'd be 20 billion neurons with the next upcoming supercomputer.
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It's fair to expect then that the next generation of supercomputers will be able to simulate the entire human brain.
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What is this new method?
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Basically, they have removed a bottleneck in the way that big neural networks are currently simulated.
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With the previous method, one would instantiate the entire network on the entire computing cluster.
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With the new method, they say we give some 100,000 neurons to each GPU, let each GPU deal with those, and then connect the GPUs.
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That is, this is a massively parallel and local approach that avoids a lot of shoveling around of information, so it's computationally much leaner.
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The maths is then pretty straightforward.
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In detail, they estimate that a single NVIDIA A100 GPU could deal with about 225,000 neurons.
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The Leonardo supercomputer in Italy has about 14,000 GPUs, so that'd make about a few billion neurons.
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Jupiter is a new supercomputer cluster currently under development in Eulich in Germany.
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Each GPU could manage 800,000 or so neurons and a total of 20 billion.
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That seems big, but wait, wasn't there a similar project a decade ago?
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Yes, there was the Human Brain Project, an international effort but centered in the EU.
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It ran from 2013 to 2023, had over a billion euros in funding and aimed to build a digital human brain too.
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They wanted to simulate a few million neurons, but it was a disaster.
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The project leadership had this great ambition of simulating parts of the brain, but it was somewhat unclear what this would be good for.
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Neuroscientists wanted to study more specific details of processes in certain parts of the brain.
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That led to a quite big rift in the community and in the end, to make a long story short, Not much came out of it, besides a lot of reports.
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Some of the authors of the new paper also worked on software for the previous human brain project.
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So what's different this time?
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In a nutshell, more neurons, fewer neuroscientists.
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I think these people just want to simulate a human brain to see what it does.
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They don't have any particular plan for what to do with it.
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And you know, I can totally see the point of doing this.
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What could such a simulation do for us?
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I don't know, but we can talk about some things it wouldn't do.
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We actually don't have the exact map of any human neural network that's called the connectome.
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So we can't build an entire human brain because we don't know exactly what it looks like.
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Without doing that first, a computer simulation could at best be a guess for how the brain is connected,
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and it's somewhat unclear how much it could do.
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Another issue is that it'd be difficult to train such an artificial human brain
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because we'd first have to figure out how to convert input into neural signals.
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We know some of that, but only in rough terms.
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And then there is the moral question.
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Should we simulate a human brain?
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What if it does actually think?
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What if it suffers?
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What if the EU Commission says we're not allowed to turn it off and it blocks the only German supercomputer cluster?
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Personally, I think it'd well be worth it.
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It'd demonstrate that there's at least one functioning brain in Europe.
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I read a lot of news to keep you well informed
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and I found that the best way to do this is on Ground News, who've been sponsoring this video.
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Ground News is a news platform that collects and summarizes news which has been published all over the world.
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Not only do they collect all articles on the same story in one place and give you a quick summary,
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they also give you a lot of extra information that you don't find in the standard media.
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Take for example this recent story about scientists studying polarize.
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You can see right away that this basically wasn't covered on the political right as almost all climate change stories.
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Ground News gives you a quick overview on the most important points.
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And here you can see who owns the media outlets.
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You get a factuality check for each news item and you can see where the story has appeared.
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Ground News also has this great feature called Blindspot.
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This tells you which news has been almost exclusively covered only by one side of the political spectrum.
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And of course, I have a special offer for you.
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That's a 40% discount on the Vantage plan, which gives you access to all their features.
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All you need to do is use my link ground.news.sabina or use the QR code so they'll know I sent you.
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I really think that Ground News is onto something with their news overview.
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So go and have a look.
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Thanks for watching.
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See you tomorrow.
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Thank you.
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Лексика и советы по произношению к этому уроку
В этом видео 85 предложений и 1106 слов для шедоуинга. Речь длится 6:59. Говорящий говорит в естественном темпе, около 158 слов в минуту, близком к повседневной речи. 82% слов входят в 3000 самых частых слов английского языка; остальные стоит разобрать перед занятием.
Ключевая лексика этого видео
15 слов из видео, которые стоит выучить, с произношением и значением:
| Слово | Произношение | Значение |
|---|---|---|
| scientist существительное | /ˈsaɪəntəst/ | учёный, учёная |
| cluster существительное | /ˈklʌstɚ/ | кисть, пучо́к |
| simulation существительное | /ˌsɪm.jəˈleɪ.ʃn̩/ | симуля́ция |
| connect глагол | /kəˈnɛkt/ | свя́зывать, связа́ть |
| collect глагол | /ˈkɑlɪkt/ | собира́ть, собра́ть |
| upcoming прилагательное | предстоя́щий, наступа́ющий | |
| mouse существительное | /maʊs/ | мышь, мы́шка |
| artificial прилагательное | /ˌɑː.tɪˈfɪʃ.əl/ | иску́сственный |
| overview существительное | /ˈoʊ.vɚˌvjuː/ | обозре́ние |
| unclear прилагательное | /ʌnˈklɪɚ/ | нея́сный |
| worm существительное | /wɝm/ | червь, червя́к |
| reproduce глагол | /ˌɹi.pɹəˈdus/ | воспроизводи́ть, воспроизвести́ |
| meanwhile наречие | /ˈmiːnwaɪl/ | в э́то вре́мя, в то́ же вре́мя |
| moral прилагательное | /ˈmoɹəl/ | мора́льный, нравоучи́тельный |
| suffer глагол | /ˈsʌfɚ/ | страда́ть, му́чаться |
Фразовые глаголы, которые вы услышите
| Слово | Значение |
|---|---|
| come up with глагол | придумывать, приходить в голову |
| figure out глагол | разбира́ться, разобра́ться |
Грамматика в этом видео
Конструкции, которые говорящий использует чаще всего, с точными словами из видео:
| Конструкция | В видео |
|---|---|
| Present Perfect Continuous have/has been + -ing — действие началось раньше и продолжается | has been working · who've been sponsoring |
| Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас | have come · have tried · have removed |
| Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает | are motivated · can be reached · is connected |
Произношение, на которое стоит обратить внимание
Говорящий использует 21 сокращённых и редуцированных форм, например don't, it'd, that'd. Произносите их коротко, так, как слышите.
- Звуки «sh» и «zh»: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, artificial /ˌɑː.tɪˈfɪʃ.əl/, fiction /ˈfɪkʃən/, ambition /æmˈbɪʃ.ən/, differentiate /dɪf.əˈɹɛn.ʃi.eɪt/
- Длинные слова — следите за ударением: artificial /ˌɑː.tɪˈfɪʃ.əl/, exclusively /ɪkˈskluːsɪvli/, mathematics /mæθ(.ə)ˈmæt.ɪks/, differentiate /dɪf.əˈɹɛn.ʃi.eɪt/, computationally /ˌkɑmpjʊˈteɪʃənəli/
Как заниматься с этим видео
- Прослушайте всё видео один раз молча и выпишите незнакомые слова.
- Начните со скорости 0,75×, повторяйте предложение за предложением и вернитесь к обычной скорости, когда станет легко.
- Запишите себя и сравните с оригиналом, обращая внимание на такие слова, как scientist, cluster, simulation.
Что такое техника Shadowing?
Shadowing — это научно обоснованная техника изучения языка, изначально разработанная для подготовки профессиональных переводчиков и популяризированная полиглотом доктором Александром Аргуэльесом. Метод прост, но эффективен: вы слушаете аудио на английском от носителей языка и немедленно повторяете вслух — как тень, следующая за говорящим с задержкой в 1–2 секунды. В отличие от пассивного прослушивания или грамматических упражнений, Shadowing заставляет мозг и мышцы рта одновременно обрабатывать и воспроизводить реальные речевые паттерны. Исследования показывают, что это значительно улучшает точность произношения, интонацию, ритм, связную речь, понимание на слух и беглость речи — что делает его одним из самых эффективных методов для подготовки к IELTS Speaking и реального общения на английском.