쉐도잉 연습: Why AI art struggles with hands - 영상으로 영어 말하기 배우기

You're called to create.

A post -apocalyptic giraffe astronaut?

Generated.

Genghis Khan playing a guitar solo.

Pixel art?

Generated.

A man holding a delicious apple?

Ah, what's with his hands?

Why can't AI art make hands?

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.

Why is this so hard?

Seems easy enough, right?

We've got this weird situation where AI art can instantly make.

Abraham Lincoln dressed like glam David Bowie, but struggles with a woman holding a cell phone.

This isn't just a weird glitch.

The struggle of AI art with hands can actually teach you something bigger about how AI art works.

I mean, what is so hard about this?

I asked an artist who has taught thousands of people how to draw hands from imagination.

Before someone becomes or starts training to be an artist, like officially training, it's pattern recognition.

You just grow up seeing a whole bunch of hands and you start knowing what hands look like.

You learn how things look by living in the world and recognizing patterns.

An AI is similar but has key differences.

Imagine an AI is like you but trapped in a museum from birth.

All the machine has to learn from are the pictures and the little placards on the side.

Apple.

A red apple on a brown table.

That's like the images it sees from the web and the descriptions that go with them.

It's similar to how you learn but locked in that museum.

If you want to understand an apple, you can rotate it in your hand.

You can watch it whenever you want.

If AI wants to understand an apple, it has to find another picture of an apple in the museum.

Pattern recognition has allowed AI and people to draw decent apples.

But the processes differ.

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.

Artists in order to draw something complicated we tend to simplify things into basic forms and so

when you look at a hand you pretty much have the

big blocky part of the palm right you have the front you have the back

and then you have the thickness and you can pretty much just make

that into like a square with some thickness to it.

Then an artist can add all the style and texture and detail they want.

AI works differently.

Look at this hand.

The shapes are bizarre, but the AI has done a great job showing the light and texture here.

Remember, the AI knows how things look, but not how they work.

So these patterns in pixels are easy to understand.

It never learned, however, that fingers don't really bend like this.

It doesn't simplify to forms.

Remember, it's trapped in the museum.

So it is just trying to guess where hand -like pixels should be without knowing how hands work, like we do.

But listen, I find this kind of dissatisfying.

I mean, I'm basically just saying that AI can't draw hands because it's not a person.

But AI also doesn't know anything about construction, and it can still make a beautiful skyscraper in New York City.

So to understand this better, I spoke to two people who have worked with generative art models.

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.

Because of how popular these models have been in generative art, I've also been like reading a bit on that.

And I talked to Roy Shilkrat, who has a super varied resume, but has been teaching about generative art since 2018.

Good students that come in that are trying to break those models, take them to the next level.

Talking to them helped me figure out three big reasons.

Not every reason, but three big reasons that hands are tough for AI art models.

The data size and quality, the way hands act, and the low margin for error.

For the data size, let's go back to the museum idea.

The museum the robot hangs out in, it has a ton of rooms dedicated to faces, but not so many rooms for hands.

That means it has less to learn from.

Just as an example, available datasets like Flickr HQ has 70 ,000 faces.

70 ,000!

And this popular one annotates 200 ,000 pics of celebrity faces for lots of details, like eyeglasses or pointy noses.

There are There are a ton of great hand datasets that can really understand hands, like this one with 11 ,000 hands,

but these may not have been used to train the AI that makes art.

That data scarcity combines with the quality and complexity of the data.

Hand data in the art museum isn't yet annotated to show how they work, like the celebrity's pointy noses.

What they say is there's an image and there is a person in the image and the person is holding an umbrella.

You don't give the machine a lot of clues saying, this is a person holding the umbrella.

The thumb is going from one side of the handle and the fingers are curled.

And then the thumb is covering the index finger, but not the other ones.

All that is made worse because hands do lots of things compared to, say, faces.

So there's a pretty common portrait photo face.

There are a lot of these photos online.

And I think that everything's very well -centered, right?

Eyes are always around here.

There's always this order.

That's not true of hands which can do this, and this, and this.

I swear I'm sober right now.

Stan mentioned this too.

How many fingers do you see right now?

Like, two or three?

Like, it doesn't know there's five, because sometimes there's two, sometimes there's three, sometimes four, sometimes five.

You can see these problems with AI hands, but the jankiness is all over AI art.

Just look at horses.

You can often have, like, three legs, five legs, six legs.

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.

Okay, did you hear that last part he said?

Good, because it's really important.

It doesn't have as much bias as we do.

We care a lot about hands and need them to be perfect.

There is a low margin for error.

But because the model doesn't understand hands, hasn't seen many, and because hands act weird,

it makes pictures that are like hands it's seen in the museum, but not an exact hand.

That's good enough for a ton of stuff, but not hands.

Here, let me give you some examples.

100

Come over here.

101

So I typed, make me a person with exactly five freckles.

102

So this one's from Dolly 2, this one is from Stable Diffusion, and this one is from Mid -Journey.

103

So it's like, you know, great job.

104

You've got, you know, a red -haired person, they're more likely to have freckles, but there are not exactly five freckles here.

105

Here that doesn't really matter, because we see a freckly face, but hands require higher standards.

106

Look at our apple holding man again.

107

I made three other variations.

108

The hands are all weird, but don't look at them right now.

109

It changed the shirt stripes, the buttons, the apple style.

110

None of that matters because it's stripe -like and button -like and apple -like, but hand -like isn't good enough.

111

I came away from this thinking a couple of things.

112

AI art is basically bad at art.

113

We're just able to see it with hands.

114

And B, it's never going to get any better.

115

But both of those things are a bit wrong.

116

I will say

117

that the newest AI art generator to come out at the time of this video is Mid -Journey version 5.

118

And they made some progress with hands, for sure.

119

But it's not totally fixed yet.

120

Don't tell the AI to hold an umbrella.

121

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.

122

I think, like, for a lot of natural scenery or something like that, I feel like the model might be getting at that people.

123

And they are working on two things.

124

First, they have the AI look at a ton more pictures, which requires more computing power.

125

They're trying to solve that on a big scale, because if you want to train on more than a handful of images

126

if you want to train

127

or more than 100 images this would take tremendous resources from

128

you to retrain the model itself the other solution might be

129

to invite more people into the museum there's an interesting analog

130

so like have you heard of like chat gbt the big difference was

131

that it basically used human feedback so like they generated many many sentences

132

and asked people to rate which ones are good and

133

which ones are not good they basically fine -tune the model

134

so that it would generate sentences

135

that are convincing to people I guess it would require a lot of engineering to get people to label

136

so much data but i think

137

if we could just get like people to rank how good

138

the images are generated by these models then like a lot of these issues will go away actually

139

because they're just training the models to do what people like it's not just the hand teeth

140

and abs anything where there's like a pattern a large amount

141

of something it doesn't know the rule of there are this many because it's trained on different amounts

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이 영상에는 섀도잉할 문장 140개와 단어 1669개가 있습니다. 말하는 구간의 길이는 9:47입니다. 화자는 분당 약 171단어로, 일상 대화에 가까운 자연스러운 속도로 말합니다. 단어의 85%가 영어에서 가장 많이 쓰이는 3,000단어에 속합니다. 나머지는 연습 전에 미리 확인해 두세요.

이 영상의 핵심 어휘

영상에 나오는 익혀 둘 만한 단어 15개를 발음, 뜻과 함께 정리했습니다.

단어	발음	뜻
finger 명사	/ˈfɪŋɡɚ/	손가락
umbrella 명사	/ʌmˈbɹɛl.ə/	우산
pixel 명사	/ˈpɪk.səl/	픽셀, 화소
recognition 명사	/ˌɹɛkəɡˈnɪʃ(ə)n/	인식
delicious 형용사	/dɪˈlɪ.ʃəs/	맛있다
trap 명사	/tɹæp/	덫, 올가미
margin 명사	/ˈmɑɹ.d͡ʒɪn/	여백, 마진
bias 명사	/ˈbaɪ.əs/	편견
thumb 명사	/ˈθʌm/	엄지, 엄지손가락
thickness 명사	/ˈθɪknəs/	두께
guitar 명사	/ɡɪˈtɑɹ/	기타
complicated 형용사	/ˈkɑm.plɪˌkeɪ.tɪd/	복잡하다
compare 동사	/kəmˈpɛɚ/	비교하다, 비유하다
label 명사	/ˈleɪ.bəl/	라벨, 레이블
solve 동사	/sɒlv/	해결하다

영상에 나오는 구동사

단어	뜻
go away 동사	떠나다, 떠나가다

이 영상의 문법

화자가 가장 많이 쓰는 문형을 영상 속 실제 표현과 함께 정리했습니다.

문형	영상 속 표현
현재완료 have/has + 과거분사 — 과거의 일이 지금도 관련이 있을 때	has taught · has allowed · has done
수동태 be + 과거분사 — 누가 하는지보다 무슨 일이 일어나는지에 초점	are curled · is made · are generated
관계절 who / which + 절 — 사람이나 사물에 대한 추가 정보	people who have · hands which can · appreciate, which is

주의할 발음

화자는 doesn't, don't, they're 같은 축약형과 약화된 형태를 32번 사용합니다. 들리는 대로 짧게 발음하세요.

“th” 소리: thumb /ˈθʌm/, thickness /ˈθɪknəs/
“sh”와 “zh” 소리: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, delicious /dɪˈlɪ.ʃəs/, imagination /ɪˌmæd͡ʒəˈneɪʃən/, variation /ˌvɛəɹiˈeɪʃn̩/
긴 단어 — 강세 위치에 주의: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, diversity /daɪˈvɜː(ɹ)sɪti/, celebrity /səˈlɛb.ɹɪ.ɾi/, differently /ˈdɪf.ə.ɹənt.li/

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0.75배속으로 한 문장씩 섀도잉을 시작하고, 익숙해지면 보통 속도로 돌아갑니다.
자신의 목소리를 녹음해 원본과 비교하고, finger, umbrella, pixel 같은 단어에 특히 주의합니다.

쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법

쉐도잉(Shadowing)은 원래 전문 통역사 훈련을 위해 개발된 언어 학습 기법으로, 다언어 학자인 Dr. Alexander Arguelles에 의해 대중화된 방법입니다. 핵심 원리는 간단하지만 매우 강력합니다: 원어민의 영어를 들으면서 1~2초의 짧은 지연으로 즉시 소리 내어 따라 말하는 것——마치 '그림자(shadow)'처럼 화자를 따라가는 것입니다. 문법 공부나 수동적인 청취와 달리, 쉐도잉은 뇌와 입 근육이 동시에 실시간으로 영어를 처리하고 재현하도록 훈련합니다. 연구에 따르면 이 방법은 발음 정확도, 억양, 리듬, 연음, 청취력, 말하기 유창성을 크게 향상시킵니다. IELTS 스피킹 준비와 자연스러운 영어 소통을 원하는 분들에게 특히 효과적입니다.

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쉐도잉이란? 영어 실력을 빠르게 키우는 과학적 방법