Shadowing Practice: We Can Now Simulate a Human Brain, Scientists Show - Learn English Speaking with Video
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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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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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Vocabulario y notas de pronunciación para esta lección
Este vídeo tiene 85 frases y 1106 palabras para practicar shadowing. La parte hablada dura 6:59. El hablante habla a un ritmo natural de unas 158 palabras por minuto, cercano a una conversación cotidiana. El 82 % de las palabras está entre las 3.000 más comunes del inglés; conviene repasar el resto antes de empezar.
Vocabulario clave de este vídeo
15 palabras del vídeo que vale la pena aprender, con su pronunciación y significado:
| Palabra | Pronunciación | Significado |
|---|---|---|
| neural adjetivo | /ˈnʊɹəl/ | neural |
| scientist sustantivo | /ˈsaɪəntəst/ | científico, científica |
| cluster sustantivo | /ˈklʌstɚ/ | aglomeración, agrupamiento |
| simulation sustantivo | /ˌsɪm.jəˈleɪ.ʃn̩/ | simulación |
| connect verbo | /kəˈnɛkt/ | conectar, empalmar |
| collect verbo | /ˈkɑlɪkt/ | juntar, recoger |
| mouse sustantivo | /maʊs/ | ratón, laucha |
| artificial adjetivo | /ˌɑː.tɪˈfɪʃ.əl/ | artificial |
| overview sustantivo | /ˈoʊ.vɚˌvjuː/ | resumen, visión de conjunto |
| unclear adjetivo | /ʌnˈklɪɚ/ | ambiguo, equívoco |
| worm sustantivo | /wɝm/ | gusano, lombriz |
| reproduce verbo | /ˌɹi.pɹəˈdus/ | reproducir |
| meanwhile adverbio | /ˈmiːnwaɪl/ | mientras tanto |
| moral adjetivo | /ˈmoɹəl/ | moral |
| suffer verbo | /ˈsʌfɚ/ | sufrir, penar |
Phrasal verbs que vas a escuchar
| Palabra | Significado |
|---|---|
| come up with verbo | ingeniar, ingeniarse |
| figure out verbo | averiguar, captar |
Gramática en este vídeo
Las estructuras que más usa el hablante, con las palabras exactas del vídeo:
| Estructura | En el vídeo |
|---|---|
| Present perfect continuous have/has been + -ing — una acción que empezó antes y continúa | has been working · who've been sponsoring |
| Present perfect have/has + participio pasado — una acción pasada que sigue importando ahora | have come · have tried · have removed |
| Voz pasiva be + participio pasado — importa lo que ocurre, no quién lo hace | are motivated · can be reached · is connected |
Pronunciación a tener en cuenta
El hablante usa 21 contracciones y formas reducidas, como don't, it'd, that'd. Dilas en su forma corta, tal como las oyes.
- Los sonidos de “sh” y “zh”: simulation /ˌsɪm.jəˈleɪ.ʃn̩/, artificial /ˌɑː.tɪˈfɪʃ.əl/, fiction /ˈfɪkʃən/, ambition /æmˈbɪʃ.ən/, differentiate /dɪf.əˈɹɛn.ʃi.eɪt/
- Palabras largas — cuida el acento: artificial /ˌɑː.tɪˈfɪʃ.əl/, exclusively /ɪkˈskluːsɪvli/, mathematics /mæθ(.ə)ˈmæt.ɪks/, differentiate /dɪf.əˈɹɛn.ʃi.eɪt/, computationally /ˌkɑmpjʊˈteɪʃənəli/
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 neural, scientist, cluster.
¿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.