Shadowing Practice: Why AI art struggles with hands - Learn English Speaking with Video
Ders oluşturuluyor...
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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Bu dersin kelimeleri ve konuşma notları
Bu videoda gölgeleme çalışması için 140 cümle ve 1669 kelime var. Konuşma bölümü 9:47 sürüyor. Konuşmacı dakikada yaklaşık 171 kelimeyle, günlük konuşmaya yakın doğal bir hızda konuşuyor. Kelimelerin %85’i İngilizcede en sık kullanılan 3.000 kelime arasında; geri kalanına başlamadan önce bakmakta fayda var.
Bu videodaki önemli kelimeler
Videodan öğrenmeye değer 15 kelime, telaffuzu ve anlamıyla:
| Kelime | Telaffuz | Anlam |
|---|---|---|
| finger isim | /ˈfɪŋɡɚ/ | parmak |
| umbrella isim | /ʌmˈbɹɛl.ə/ | güncek, şemsiye |
| pixel isim | /ˈpɪk.səl/ | piksel, gözek |
| recognition isim | /ˌɹɛkəɡˈnɪʃ(ə)n/ | tanınma |
| delicious sıfat | /dɪˈlɪ.ʃəs/ | lezzetli, tatlı |
| trap isim | /tɹæp/ | tuzak, kapan |
| margin isim | /ˈmɑɹ.d͡ʒɪn/ | kenar |
| bias isim | /ˈbaɪ.əs/ | yanlılık, ön yargı |
| thumb isim | /ˈθʌm/ | başparmak |
| guitar isim | /ɡɪˈtɑɹ/ | gitar |
| complicated sıfat | /ˈkɑm.plɪˌkeɪ.tɪd/ | karışık, komplike |
| compare fiil | /kəmˈpɛɚ/ | karşılaştırmak, kıyaslamak |
| rank isim | /ˈɹæŋk/ | satır |
| label isim | /ˈleɪ.bəl/ | etiket |
| solve fiil | /sɒlv/ | çözmek, halletmek |
Duyacağınız deyimsel fiiller
| Kelime | Anlam |
|---|---|
| figure out fiil | açığa çıkarmak |
| grow up fiil | büyümek, yetişmek |
Bu videodaki dil bilgisi
Konuşmacının en çok kullandığı yapılar, videodaki sözcüklerin aynısıyla:
| Yapı | Videoda |
|---|---|
| Present perfect have/has + fiilin üçüncü hâli — geçmişte olan ama şimdi de önemli olan bir eylem | has taught · has allowed · has done |
| Edilgen yapı be + fiilin üçüncü hâli — kimin yaptığı değil, ne olduğu önemli | are curled · is made · are generated |
| İlgi cümlecikleri who / which + cümle — bir kişi ya da şey hakkında ek bilgi | people who have · hands which can · appreciate, which is |
Dikkat edilecek telaffuzlar
Konuşmacı doesn't, don't, they're gibi kısaltılmış biçimleri 32 kez kullanıyor. Bunları duyduğunuz gibi kısa söyleyin.
- “th” sesleri: thumb /ˈθʌm/, thickness /ˈθɪknəs/
- “sh” ve “zh” sesleri: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, delicious /dɪˈlɪ.ʃəs/, imagination /ɪˌmæd͡ʒəˈneɪʃən/, variation /ˌvɛəɹiˈeɪʃn̩/
- Uzun kelimeler — vurguyu doğru yere koyun: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, diversity /daɪˈvɜː(ɹ)sɪti/, celebrity /səˈlɛb.ɹɪ.ɾi/, differently /ˈdɪf.ə.ɹənt.li/
Bu videoyla nasıl çalışılır
- Videonun tamamını konuşmadan bir kez dinleyin ve bilmediğiniz kelimeleri not edin.
- 0,75× hızla başlayın, cümle cümle tekrar edin ve kolaylaşınca normal hıza dönün.
- Kendinizi kaydedin ve orijinaliyle karşılaştırın; finger, umbrella, pixel gibi kelimelere özellikle dikkat edin.
Gölgeleme Tekniği Nedir?
Gölgeleme, başlangıçta profesyonel tercüman eğitimi için geliştirilen ve çok dilli Dr. Alexander Arguelles tarafından popüler hale getirilen, bilim destekli bir dil öğrenme tekniğidir. Yöntem basit ama güçlüdür: ana dili İngilizce olan bir sesi dinler ve hemen yüksek sesle tekrar edersiniz — konuşmacıyı 1-2 saniye gecikmeyle takip eden bir gölge gibi. Pasif dinleme veya dilbilgisi alıştırmalarının aksine, gölgeleme beyninizi ve ağız kaslarınızı gerçek konuşma kalıplarını eşzamanlı olarak işlemeye ve yeniden üretmeye zorlar. Araştırmalar, telaffuz doğruluğu, tonlama, ritim, bağlı konuşma, dinleme anlama ve konuşma akıcılığını önemli ölçüde geliştirdiğini göstermektedir — bu da onu IELTS Konuşma hazırlığı ve gerçek dünya İngilizce iletişimi için en etkili yöntemlerden biri yapar.