शैडोइंग अभ्यास: Why AI art struggles with hands - वीडियो के साथ अंग्रेजी बोलना सीखें

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

📺 उसी चैनल से

✨ अनुशंसित वीडियो

104 वाक्य

We Need To Stay Human

Daily ConversationUniversity StudentsWorking Adults

138 वाक्य

Starting art "late" is actually a HUGE advantage | YTartschool

Business EnglishDaily ConversationUniversity Students

32 वाक्य

The world's most frustrating work of art

Daily ConversationUniversity StudentsWorking Adults

154 वाक्य

Are We Getting Too Comfortable With Claude?

Business EnglishDaily ConversationUniversity Students

421 वाक्य

ChatGPT Religion: AI is Being WORSHIPPED now

Daily ConversationUniversity StudentsWorking Adults

32 वाक्य

AI challenges human identity | Vietnam Today

Daily ConversationUniversity StudentsWorking Adults

166 वाक्य

How to Create Viral AI Storytelling Shorts

Business EnglishUniversity StudentsWorking Adults

52 वाक्य

Robots and A1 - B2 Listening Lesson for High-Intermediate Students

Business EnglishDaily ConversationHigh School Students

6 वाक्य

am i an artist?

Business EnglishDaily ConversationUniversity Students

32 वाक्य

Why We Should Draw More (and Photograph Less)

Daily ConversationUniversity StudentsWorking Adults

492 वाक्य

what 600 days of art progress looks like

Business EnglishDaily ConversationUniversity Students

इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो में शैडोइंग के लिए 140 वाक्य और 1669 शब्द हैं। बोला गया हिस्सा 9:47 लंबा है। वक्ता लगभग 171 शब्द प्रति मिनट की स्वाभाविक गति से बोलता है, जो रोज़मर्रा की बातचीत के करीब है। 85% शब्द अंग्रेज़ी के 3,000 सबसे आम शब्दों में से हैं; बाकी शब्द अभ्यास से पहले देख लेना अच्छा रहेगा।

इस वीडियो की मुख्य शब्दावली

वीडियो के 15 सीखने लायक शब्द, उच्चारण और अर्थ के साथ:

शब्द	उच्चारण	अर्थ
generate क्रिया	/ˈd͡ʒɛn.ə.ɹeɪt/	उपजाना
finger संज्ञा	/ˈfɪŋɡɚ/	उंगली, अंगुल
umbrella संज्ञा	/ʌmˈbɹɛl.ə/	छाता, छतरी
delicious विशेषण	/dɪˈlɪ.ʃəs/	स्वादिष्ट, लज़ीज़
bias संज्ञा	/ˈbaɪ.əs/	पक्षपात, पूर्वाग्रह
thumb संज्ञा	/ˈθʌm/	अंगूठा
thickness संज्ञा	/ˈθɪknəs/	मोटाई
guitar संज्ञा	/ɡɪˈtɑɹ/	गिटार
complicated विशेषण	/ˈkɑm.plɪˌkeɪ.tɪd/	जटिल, पेचीदा
compare क्रिया	/kəmˈpɛɚ/	तौलना
rank संज्ञा	/ˈɹæŋk/	श्रेणीबद्ध
label संज्ञा	/ˈleɪ.bəl/	लेबल
solve क्रिया	/sɒlv/	सुलझाना, हल करना
decent विशेषण	/ˈdiː.sənt/	शोभनीय, उचित
resource संज्ञा	/ɹɪˈzɔːs/	संसाधन

इस वीडियो का व्याकरण

वक्ता जिन संरचनाओं का सबसे अधिक प्रयोग करता है, वीडियो के असली शब्दों के साथ:

संरचना	वीडियो में
Present perfect have/has + past participle — बीता हुआ काम जिसका असर अभी भी है	has taught · has allowed · has done
Passive voice be + past participle — ज़ोर इस पर कि क्या होता है, न कि कौन करता है	are curled · is made · are generated
Relative clauses who / which + उपवाक्य — व्यक्ति या वस्तु के बारे में अतिरिक्त जानकारी	people who have · hands which can · appreciate, which is

ध्यान देने योग्य उच्चारण

वक्ता 32 बार संक्षिप्त रूपों का प्रयोग करता है, जैसे doesn't, don't, they're। इन्हें वैसे ही छोटा बोलें जैसा आप सुनते हैं।

“th” की ध्वनियाँ: thumb /ˈθʌm/, thickness /ˈθɪknəs/
“sh” और “zh” की ध्वनियाँ: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, delicious /dɪˈlɪ.ʃəs/, imagination /ɪˌmæd͡ʒəˈneɪʃən/, variation /ˌvɛəɹiˈeɪʃn̩/
लंबे शब्द — बलाघात सही रखें: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, diversity /daɪˈvɜː(ɹ)sɪti/, celebrity /səˈlɛb.ɹɪ.ɾi/, differently /ˈdɪf.ə.ɹənt.li/

इस वीडियो से अभ्यास कैसे करें

पहले बिना बोले पूरा वीडियो एक बार सुनें और जो शब्द नहीं आते उन्हें लिख लें।
0.75× गति से शुरू करें, एक-एक वाक्य की शैडोइंग करें, और सहज लगने पर सामान्य गति पर लौटें।
अपनी आवाज़ रिकॉर्ड करें और मूल से तुलना करें, ख़ासकर generate, finger, umbrella जैसे शब्दों पर ध्यान दें।

शैडोइंग तकनीक क्या है?

शैडोइंग (Shadowing) एक विज्ञान-समर्थित भाषा सीखने की तकनीक है जो मूल रूप से पेशेवर दुभाषिया प्रशिक्षण के लिए विकसित की गई थी। विधि सरल लेकिन शक्तिशाली है: आप मूल अंग्रेज़ी ऑडियो सुनते हैं और तुरंत इसे ज़ोर से दोहराते हैं — जैसे वक्ता की छाया 1-2 सेकंड की देरी से। शोध से पता चलता है कि यह उच्चारण सटीकता, स्वर, लय, जुड़ी हुई ध्वनियाँ, सुनने की समझ और बोलने की प्रवाहशीलता में काफ़ी सुधार करता है।

शैडोइंग तकनीक: पूरी चरण-दर-चरण गाइड पढ़ें →

शैडोइंग अभ्यास: Why AI art struggles with hands - वीडियो के साथ अंग्रेजी बोलना सीखें

The biggest peach myth in America

Sen. Murphy vs. Trump's corruption | Today, Explained

Five things you need to know for the 2026 midterms | America, Actually

Why are weddings so damn expensive?

Are headphones destroying our hearing?

How to be less neurotic | The Gray Area

We’re already using AI more than we realize

How to solve problems like a designer

The origin of the '80s aesthetic

Why “Buy one, get one free” isn’t a great deal

How F1 drivers survive their own cars

Why 350°F is the magic number for baking

How did pink become a girly color?

The real reason Boeing's new plane crashed twice

Food waste is the world's dumbest problem

How the rich avoid paying taxes

How Vladimir Putin won Republicans' approval

Zohran Mamdani on his first priorities as NYC mayor (Full video interview)

Why we say “OK”

AI can do your homework. Now what?

We Need To Stay Human

Starting art "late" is actually a HUGE advantage | YTartschool

The world's most frustrating work of art

Are We Getting Too Comfortable With Claude?

ChatGPT Religion: AI is Being WORSHIPPED now

AI challenges human identity | Vietnam Today

How to Create Viral AI Storytelling Shorts

Robots and A1 - B2 Listening Lesson for High-Intermediate Students

am i an artist?

Why We Should Draw More (and Photograph Less)

what 600 days of art progress looks like

इस पाठ की शब्दावली और बोलने के सुझाव

इस वीडियो की मुख्य शब्दावली

इस वीडियो का व्याकरण

ध्यान देने योग्य उच्चारण

इस वीडियो से अभ्यास कैसे करें

शैडोइंग तकनीक क्या है?