Luyện nói tiếng Anh bằng Shadowing qua video: Why AI art struggles with hands

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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Từ vựng và ghi chú luyện nói cho bài học này

Video này có 140 câu và 1669 từ để luyện shadowing. Phần lời nói dài 9:47. Người nói nói với tốc độ tự nhiên, khoảng 171 từ mỗi phút, gần với hội thoại hằng ngày. 85% số từ nằm trong 3.000 từ tiếng Anh thông dụng nhất; phần còn lại nên xem trước khi luyện.

Từ vựng quan trọng trong video

15 từ đáng học trong video, kèm phiên âm và nghĩa:

Từ	Phiên âm	Nghĩa
generate động từ	/ˈd͡ʒɛn.ə.ɹeɪt/	sinh ra, gây ra
finger danh từ	/ˈfɪŋɡɚ/	ngón tay
umbrella danh từ	/ʌmˈbɹɛl.ə/	dù, ô
pixel danh từ	/ˈpɪk.səl/	điểm ảnh
recognition danh từ	/ˌɹɛkəɡˈnɪʃ(ə)n/	công nhận
delicious tính từ	/dɪˈlɪ.ʃəs/	ngon
trap danh từ	/tɹæp/	bẫy
bias danh từ	/ˈbaɪ.əs/	thiên vị, thiên kiến
thumb danh từ	/ˈθʌm/	ngón tay cái
thickness danh từ	/ˈθɪknəs/	dày
guitar danh từ	/ɡɪˈtɑɹ/	đàn ghi-ta, ghi-ta
complicated tính từ	/ˈkɑm.plɪˌkeɪ.tɪd/	phức tạp
compare động từ	/kəmˈpɛɚ/	so sánh, so
rank danh từ	/ˈɹæŋk/	hàng
label danh từ	/ˈleɪ.bəl/	nhãn

Ngữ pháp trong video

Những cấu trúc người nói dùng nhiều nhất, kèm đúng cụm từ trong video:

Cấu trúc	Trong video
Thì hiện tại hoàn thành have/has + quá khứ phân từ — việc đã xảy ra nhưng còn liên quan đến hiện tại	has taught · has allowed · has done
Câu bị động be + quá khứ phân từ — nhấn vào việc xảy ra, không phải người làm	are curled · is made · are generated
Mệnh đề quan hệ who / which + mệnh đề — thêm thông tin về người hoặc vật	people who have · hands which can · appreciate, which is

Phát âm cần chú ý

Người nói dùng 32 dạng rút gọn, ví dụ doesn't, don't, they're. Hãy nói theo dạng ngắn đúng như bạn nghe.

Âm “th”: thumb /ˈθʌm/, thickness /ˈθɪknəs/
Âm “sh” và “zh”: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, delicious /dɪˈlɪ.ʃəs/, imagination /ɪˌmæd͡ʒəˈneɪʃən/, variation /ˌvɛəɹiˈeɪʃn̩/
Từ dài — đặt trọng âm cho đúng: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, diversity /daɪˈvɜː(ɹ)sɪti/, celebrity /səˈlɛb.ɹɪ.ɾi/, differently /ˈdɪf.ə.ɹənt.li/

Cách luyện với video này

Nghe hết video một lần, chưa cần nói, và ghi lại những từ bạn chưa biết.
Bắt đầu ở tốc độ 0,75×, nói đuổi từng câu, rồi quay lại tốc độ bình thường khi đã quen.
Ghi âm giọng mình rồi so với bản gốc, chú ý các từ như generate, finger, umbrella.

Phương Pháp Shadowing Là Gì?

Shadowing là kỹ thuật học ngôn ngữ có cơ sở khoa học, ban đầu được phát triển cho chương trình đào tạo phiên dịch viên chuyên nghiệp và được phổ biến rộng rãi bởi nhà đa ngôn ngữ học Dr. Alexander Arguelles. Nguyên lý cốt lõi đơn giản nhưng cực kỳ hiệu quả: bạn nghe tiếng Anh của người bản xứ và lặp lại to ngay lập tức — như một "cái bóng" (shadow) đuổi theo người nói với độ trễ chỉ 1–2 giây. Khác với luyện ngữ pháp hay học từ vựng bị động, Shadowing buộc não bộ và cơ miệng phải đồng thời xử lý và tái tạo ngôn ngữ thực tế. Các nghiên cứu khoa học xác nhận phương pháp này cải thiện đáng kể phát âm, ngữ điệu, nhịp điệu, nối âm, kỹ năng nghe và độ lưu loát khi nói — đặc biệt hiệu quả cho người luyện IELTS Speaking và muốn giao tiếp tiếng Anh tự nhiên như người bản ngữ.

Phương pháp shadowing: đọc hướng dẫn từng bước đầy đủ →

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Phát âm cần chú ý

Cách luyện với video này

Phương Pháp Shadowing Là Gì?