跟读练习: Why AI art struggles with hands - 通过视频学习英语口语
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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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本课的词汇与口语要点
这段视频共有 140 个句子、1669 个单词可供跟读。 讲话部分时长为 9:47。 说话人语速自然，每分钟约 171 个词，接近日常对话。 85% 的单词属于英语最常用的 3,000 词，其余的词建议在练习前先学一下。
视频中的重点词汇
视频中 15 个值得学习的单词，附发音和释义：
| 单词 | 发音 | 释义 |
|---|---|---|
| finger 名词 | /ˈfɪŋɡɚ/ | 手指, 指頭 /指头 |
| umbrella 名词 | /ʌmˈbɹɛl.ə/ | 傘 /伞, 雨傘 /雨伞 |
| pixel 名词 | /ˈpɪk.səl/ | 像素 |
| recognition 名词 | /ˌɹɛkəɡˈnɪʃ(ə)n/ | 認出 /认出 |
| delicious 形容词 | /dɪˈlɪ.ʃəs/ | 好吃, 可口 |
| trap 名词 | /tɹæp/ | 陷阱, 圈套 |
| margin 名词 | /ˈmɑɹ.d͡ʒɪn/ | 邊界 /边界 |
| bias 名词 | /ˈbaɪ.əs/ | 偏見 /偏见 |
| thumb 名词 | /ˈθʌm/ | 大拇指, 拇指 |
| texture 名词 | /ˈtɛkst͡ʃə(ɹ)/ | 質地 /质地, 材質 /材质 |
| thickness 名词 | /ˈθɪknəs/ | 厚度 |
| stripe 名词 | /stɹaɪp/ | 條紋 /条纹 |
| guitar 名词 | /ɡɪˈtɑɹ/ | 吉他, 六弦琴 |
| complicated 形容词 | /ˈkɑm.plɪˌkeɪ.tɪd/ | 複雜 /复杂 |
| compare 动词 | /kəmˈpɛɚ/ | 比, 比較 /比较 |
视频中出现的短语动词
| 单词 | 释义 |
|---|---|
| figure out 动词 | 弄清楚 |
| go away 动词 | 離開 /离开, 走 |
| grow up 动词 | 長大 /长大 |
视频中的语法
说话人最常用的结构，并附上视频中的原话：
| 结构 | 视频中的用法 |
|---|---|
| 现在完成时 have/has + 过去分词 — 过去发生但与现在仍有关联的事 | has taught · has allowed · has done |
| 被动语态 be + 过去分词 — 强调发生了什么，而不是谁做的 | are curled · is made · are generated |
| 定语从句 who / which + 从句 — 补充说明人或事物 | people who have · hands which can · appreciate, which is |
需要注意的发音
说话人用了 32 次缩略和弱读形式，例如 doesn't, don't, they're。请按听到的简短形式来说。
- “th” 音: thumb /ˈθʌm/, thickness /ˈθɪknəs/
- “sh” 和 “zh” 音: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, delicious /dɪˈlɪ.ʃəs/, imagination /ɪˌmæd͡ʒəˈneɪʃən/, variation /ˌvɛəɹiˈeɪʃn̩/
- 长单词——注意重音位置: recognition /ˌɹɛkəɡˈnɪʃ(ə)n/, complicated /ˈkɑm.plɪˌkeɪ.tɪd/, diversity /daɪˈvɜː(ɹ)sɪti/, celebrity /səˈlɛb.ɹɪ.ɾi/, differently /ˈdɪf.ə.ɹənt.li/
如何用这段视频练习
- 先完整听一遍视频，不要开口，记下不认识的单词。
- 先用 0.75 倍速逐句跟读，熟练之后再回到正常速度。
- 录下自己的声音并与原声对比，特别注意 finger, umbrella, pixel 这类单词。
什么是跟读法？
跟读法 (Shadowing) 是一种有科学依据的语言学习技巧，最初开发用于专业口译员的培训，并由多语言者Alexander Arguelles博士普及。这个方法简单而强大：您在听英语母语原声的同时立即大声重复——就像是一个延迟1-2秒紧跟说话者的影子。与被动听力或语法练习不同，跟读法强迫您的大脑和口腔肌肉同时处理并模仿真实的讲话模式。研究表明它能显着提高发音准确性，语调，节奏，连读，听力理解和口语流利度——使其成为雅思口语备考和真实英语交流最有效的方法之一。