Практика Shadowing: Why AI art struggles with hands - Изучайте разговорный английский по видео
Создание урока...
1
You're called to create.
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A post -apocalyptic giraffe astronaut?
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Generated.
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Genghis Khan playing a guitar solo.
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Pixel art?
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Generated.
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A man holding a delicious apple?
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Ah, what's with his hands?
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Why can't AI art make hands?
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It doesn't matter what AI art model you use, if you have a man holding a delicious apple, his hands will look weird holding it.
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Why is this so hard?
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Seems easy enough, right?
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We've got this weird situation where AI art can instantly make.
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Abraham Lincoln dressed like glam David Bowie, but struggles with a woman holding a cell phone.
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This isn't just a weird glitch.
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The struggle of AI art with hands can actually teach you something bigger about how AI art works.
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I mean, what is so hard about this?
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I asked an artist who has taught thousands of people how to draw hands from imagination.
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Before someone becomes or starts training to be an artist, like officially training, it's pattern recognition.
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You just grow up seeing a whole bunch of hands and you start knowing what hands look like.
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You learn how things look by living in the world and recognizing patterns.
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An AI is similar but has key differences.
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Imagine an AI is like you but trapped in a museum from birth.
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All the machine has to learn from are the pictures and the little placards on the side.
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Apple.
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A red apple on a brown table.
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That's like the images it sees from the web and the descriptions that go with them.
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It's similar to how you learn but locked in that museum.
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If you want to understand an apple, you can rotate it in your hand.
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You can watch it whenever you want.
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If AI wants to understand an apple, it has to find another picture of an apple in the museum.
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Pattern recognition has allowed AI and people to draw decent apples.
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But the processes differ.
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You start training to become an artist and now you're like, okay now I have to learn the rules and that's where it becomes very different from how AI is learning.
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Artists in order to draw something complicated we tend to simplify things into basic forms and so
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when you look at a hand you pretty much have the
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big blocky part of the palm right you have the front you have the back
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and then you have the thickness and you can pretty much just make
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that into like a square with some thickness to it.
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Then an artist can add all the style and texture and detail they want.
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AI works differently.
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Look at this hand.
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The shapes are bizarre, but the AI has done a great job showing the light and texture here.
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Remember, the AI knows how things look, but not how they work.
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So these patterns in pixels are easy to understand.
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It never learned, however, that fingers don't really bend like this.
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It doesn't simplify to forms.
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Remember, it's trapped in the museum.
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So it is just trying to guess where hand -like pixels should be without knowing how hands work, like we do.
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But listen, I find this kind of dissatisfying.
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I mean, I'm basically just saying that AI can't draw hands because it's not a person.
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But AI also doesn't know anything about construction, and it can still make a beautiful skyscraper in New York City.
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So to understand this better, I spoke to two people who have worked with generative art models.
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Ilun Du is a grad student whose heart is in robotics, but, you know, AI art is like a big deal now, so he got pulled into it.
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Because of how popular these models have been in generative art, I've also been like reading a bit on that.
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And I talked to Roy Shilkrat, who has a super varied resume, but has been teaching about generative art since 2018.
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Good students that come in that are trying to break those models, take them to the next level.
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Talking to them helped me figure out three big reasons.
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Not every reason, but three big reasons that hands are tough for AI art models.
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The data size and quality, the way hands act, and the low margin for error.
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For the data size, let's go back to the museum idea.
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The museum the robot hangs out in, it has a ton of rooms dedicated to faces, but not so many rooms for hands.
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That means it has less to learn from.
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Just as an example, available datasets like Flickr HQ has 70 ,000 faces.
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70 ,000!
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And this popular one annotates 200 ,000 pics of celebrity faces for lots of details, like eyeglasses or pointy noses.
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There are There are a ton of great hand datasets that can really understand hands, like this one with 11 ,000 hands,
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but these may not have been used to train the AI that makes art.
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That data scarcity combines with the quality and complexity of the data.
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Hand data in the art museum isn't yet annotated to show how they work, like the celebrity's pointy noses.
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What they say is there's an image and there is a person in the image and the person is holding an umbrella.
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You don't give the machine a lot of clues saying, this is a person holding the umbrella.
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The thumb is going from one side of the handle and the fingers are curled.
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And then the thumb is covering the index finger, but not the other ones.
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All that is made worse because hands do lots of things compared to, say, faces.
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So there's a pretty common portrait photo face.
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There are a lot of these photos online.
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And I think that everything's very well -centered, right?
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Eyes are always around here.
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There's always this order.
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That's not true of hands which can do this, and this, and this.
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I swear I'm sober right now.
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Stan mentioned this too.
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How many fingers do you see right now?
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Like, two or three?
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Like, it doesn't know there's five, because sometimes there's two, sometimes there's three, sometimes four, sometimes five.
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You can see these problems with AI hands, but the jankiness is all over AI art.
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Just look at horses.
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You can often have, like, three legs, five legs, six legs.
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The model does not learn to explain this because there's too much diversity, and it doesn't have as much bias as we do.
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Okay, did you hear that last part he said?
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Good, because it's really important.
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It doesn't have as much bias as we do.
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We care a lot about hands and need them to be perfect.
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There is a low margin for error.
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But because the model doesn't understand hands, hasn't seen many, and because hands act weird,
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it makes pictures that are like hands it's seen in the museum, but not an exact hand.
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That's good enough for a ton of stuff, but not hands.
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Here, let me give you some examples.
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Come over here.
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So I typed, make me a person with exactly five freckles.
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So this one's from Dolly 2, this one is from Stable Diffusion, and this one is from Mid -Journey.
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So it's like, you know, great job.
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You've got, you know, a red -haired person, they're more likely to have freckles, but there are not exactly five freckles here.
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Here that doesn't really matter, because we see a freckly face, but hands require higher standards.
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Look at our apple holding man again.
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I made three other variations.
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The hands are all weird, but don't look at them right now.
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It changed the shirt stripes, the buttons, the apple style.
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None of that matters because it's stripe -like and button -like and apple -like, but hand -like isn't good enough.
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I came away from this thinking a couple of things.
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AI art is basically bad at art.
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We're just able to see it with hands.
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And B, it's never going to get any better.
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But both of those things are a bit wrong.
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I will say
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that the newest AI art generator to come out at the time of this video is Mid -Journey version 5.
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And they made some progress with hands, for sure.
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But it's not totally fixed yet.
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Don't tell the AI to hold an umbrella.
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I think they're, like, spending lots of time on, like, some things that you appreciate, which is why you like the images, and a lot of stuff that you don't actually even notice.
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I think, like, for a lot of natural scenery or something like that, I feel like the model might be getting at that people.
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And they are working on two things.
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First, they have the AI look at a ton more pictures, which requires more computing power.
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They're trying to solve that on a big scale, because if you want to train on more than a handful of images
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if you want to train
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or more than 100 images this would take tremendous resources from
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you to retrain the model itself the other solution might be
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to invite more people into the museum there's an interesting analog
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so like have you heard of like chat gbt the big difference was
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that it basically used human feedback so like they generated many many sentences
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and asked people to rate which ones are good and
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which ones are not good they basically fine -tune the model
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so that it would generate sentences
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that are convincing to people I guess it would require a lot of engineering to get people to label
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so much data but i think
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if we could just get like people to rank how good
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the images are generated by these models then like a lot of these issues will go away actually
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because they're just training the models to do what people like it's not just the hand teeth
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and abs anything where there's like a pattern a large amount
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of something it doesn't know the rule of there are this many because it's trained on different amounts
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Лексика и советы по произношению к этому уроку
В этом видео 140 предложений и 1669 слов для шедоуинга. Речь длится 9:47. Говорящий говорит в естественном темпе, около 171 слов в минуту, близком к повседневной речи. 85% 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 |
|---|---|---|
| generate глагол | /ˈd͡ʒɛn.ə.ɹeɪt/ | порожда́ть, производи́ть |
| finger существительное | /ˈfɪŋɡɚ/ | па́лец, перст |
| umbrella существительное | /ʌmˈbɹɛl.ə/ | зо́нтик, зонт |
| pixel существительное | /ˈpɪk.səl/ | пи́ксель |
| recognition существительное | /ˌɹɛkəɡˈnɪʃ(ə)n/ | узнава́ние, распознава́ние |
| delicious прилагательное | /dɪˈlɪ.ʃəs/ | вку́сный, ла́комый |
| trap существительное | /tɹæp/ | лову́шка, западня́ |
| margin существительное | /ˈmɑɹ.d͡ʒɪn/ | по́ле, поля́ |
| bias существительное | /ˈbaɪ.əs/ | предубежде́ние, предвзя́тость |
| thumb существительное | /ˈθʌm/ | большо́й па́лец |
| texture существительное | /ˈtɛkst͡ʃə(ɹ)/ | строе́ние, структу́ра |
| thickness существительное | /ˈθɪknəs/ | толщина́ |
| stripe существительное | /stɹaɪp/ | полоса́, поло́ска |
| guitar существительное | /ɡɪˈtɑɹ/ | гита́ра |
| complicated прилагательное | /ˈkɑm.plɪˌkeɪ.tɪd/ | сло́жный, запу́танный |
Фразовые глаголы, которые вы услышите
| Слово | Значение |
|---|---|
| come over глагол | подскочить, подъехать |
| figure out глагол | разбира́ться, разобра́ться |
| go away глагол | уходи́ть, уйти́ |
| grow up глагол | расти́, выраста́ть |
Грамматика в этом видео
Конструкции, которые говорящий использует чаще всего, с точными словами из видео:
| Конструкция | В видео |
|---|---|
| Present Perfect have/has + причастие прошедшего времени — прошлое действие, важное сейчас | has taught · has allowed · has done |
| Пассивный залог be + причастие прошедшего времени — важно, что происходит, а не кто это делает | are curled · is made · are generated |
| Придаточные определительные who / which + предложение — уточнение о человеке или предмете | people who have · hands which can · appreciate, which is |
Произношение, на которое стоит обратить внимание
Говорящий использует 32 сокращённых и редуцированных форм, например doesn't, don't, they're. Произносите их коротко, так, как слышите.
- Звуки «th»: thumb /ˈθʌm/, thickness /ˈθɪknəs/
- Звуки «sh» и «zh»: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, delicious /dɪˈlɪ.ʃəs/, imagination /ɪˌmæd͡ʒəˈneɪʃən/, variation /ˌvɛəɹiˈeɪʃn̩/
- Длинные слова — следите за ударением: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, diversity /daɪˈvɜː(ɹ)sɪti/, celebrity /səˈlɛb.ɹɪ.ɾi/, differently /ˈdɪf.ə.ɹənt.li/
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 generate, finger, umbrella.
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.