AI 时代学习能力比学历更重要

我认为学习能力更重要。AI 确实改变了这一点。比如，我的创业公司在面试软件工程师时，说实话，我个人觉得学历现在对我们来说没那么重要了。更重要的是你学到了什么，你用什么工具，你能多快利用这些工具来提升自己。其中很多是 AI 工具。你对使用这些工具的心态更重要。李博士，很高兴见到你。谢谢你抽出时间。

I think the ability to learn is even more important. AI has really changed it. For example, my startup when we interview a software engineer, honestly how much I personally feel the degree they have matters less to us now. It's more about what have you learned, what tools do you use, how quickly can you superpower yourself in using these tools. And a lot of these are AI tools. What's your mindset towards using these tools matters more. Dr. Lee, it is nice to see you. Thanks for making the time.

嗨，Tim。很高兴来到这里。非常兴奋。

Hi, Tim. Very nice to be here. Very excited.

我们开始录音前聊了一下，这多么神奇，也有点遗憾。我们竟然在同一个校园里待了三年，却从未碰面。

And we were chatting a little bit before we started recording about how miraculous and I suppose unfortunate it is. Somehow we managed to spend three years on the same campus and didn't bump into each other.

是啊。我现在想知道你在哪个学院和哪个俱乐部。

I know. And now I'm wondering which college you were at and which clubs.

哦，我是 Forbes 学院的。不，我也是 Forbes。

Oh, yeah. I was Forbes. I was in Forbes College. No, I was Forbes, too.

好吧，这是给那些不知道我们在说什么的人解释的。普林斯顿有住宿学院，学生入学时会被分到不同的学院。Forbes 在很偏远的地方，旁边有个叫 Wawa 的便利店，还有通勤火车。普林斯顿还有所谓的“饮食俱乐部”，大家可以查一下，实际上是男女混合的兄弟会/姐妹会，你也在那里吃饭，除非你想自己做饭。我在 Terrace。

Okay, this is for people who don't know what the hell we're talking about. There are these residential colleges where students are split up when they come into the school. And Forbes was way out there in the sticks, right next to a fast food spot like 7-Eleven called Wawa and next to the commuter train. And then there's something called eating clubs at Princeton. People can look them up, but they're effectively co-ed fraternity/sororities where you also eat unless you want to make your own meals. And I was in Terrace.

我哪个都不是。但如果你想知道我们为什么没碰面，只能说我们都是非常勤奋的学生，只待在图书馆里。

I was not any of that. But for those of you wondering why we didn't meet, we should say we were very studious students who were only in the libraries.

是啊，我们都很勤奋。我实际上在客座图书馆的阁楼工作，每小时 6 美元。

Yeah, we were very studious. I actually made my whatever it was $6 an hour at guest library working up in the attic.

Tim，我也在那个图书馆工作。我不明白我们为什么没碰面。

Tim, I work in the same library. I don't understand why we did not meet.

太有趣了。好吧，现在我们见面了。

That's really hilarious. Okay. So, well, now we're meeting.

你改名字了吗？也许我们见过。

Did you change name or something? Maybe we did meet.

我没改名字，但我们还是见面了。

I didn't change my name, but here we are.

是的，我们重逢了。我们没碰面真是奇怪。我也有一段时间不在，因为我去了普林斯顿北京项目，之后去了首都经济贸易大学。所以我离开了一段时间，然后休学一年，最后和 2000 届一起毕业。所以我们还是有很多重叠的时间。

Yes, we've reunited. That's wild that we didn't bump into each other. I was also gone for a period of time because I went to Princeton in Beijing and went to the what was it? Capital University of Business and Economics after that. And so I was gone for a good period of time and then took a year off before graduating with the class of 2000. So still we had a lot of overlap.

是的。但我们开始对话吧，这也许是个很典型的开场，但对你来说，我觉得是个好的起点，就是按时间顺序讲讲基本情况。你在哪里长大？能描述一下你的成长经历吗？因为根据我的阅读，你的父母在我看来相当非典型。

Yes. But let's hop into the conversation and this is a very perhaps typical way to start but in your case I think it's a good place to start which is just with the basics chronologically. Where did you grow up? And could you describe your upbringing? Because based on my reading, your parents were pretty atypical for Chinese parents in my experience.

当然。是的。你知道，很多。

Certainly. Yes. You know, a lot.

是的。能谈谈吗？

Yeah. Could you speak to that please?

我想说，我的童年和成长岁月是一个双城记。我在中国一个叫成都的小镇长大。我出生在北京，但童年大部分时间在成都度过，那里以大熊猫闻名。15 岁时，我和妈妈搬到新泽西州的帕西帕尼，与爸爸团聚。所以，我从一个相对典型的中国中产家庭的孩子，变成了一个全新世界——新泽西的新移民，学习新语言、新文化，拥抱新国家。然后，我去了普林斯顿，主修物理，但也上了你上过的一些课，之后去加州理工学院读博士，研究 AI，剩下的就是历史了。

I would say my childhood and leading up to the formative years is a tale of two cities. I grew up in a town in China called Chengdu. I was born in Beijing but most of my childhood was spent in Chengdu where it's very famous for panda bears and at the age of 15 my mom and I joined my dad in a town called Parsippany, New Jersey. So I went from a relatively typical middle-class Chinese family, Chinese kid to become a new immigrant in a completely different world of all places, New Jersey, to learn a new language, to learn a new culture, to embrace a new country. And then from there on I went to Princeton as a physics major, but I did take some of the classes you took and then went to Caltech as a PhD student to study AI and the rest is history.

我想听听你父母的情况，但特别想听听你父亲，因为根据我的阅读，他似乎是一个非常异想天开、有创造力的灵魂，这与某些人形成鲜明对比，比如我采访过 Bo Sha，一位了不起的创业者，他的父亲大概是人们想象中不是虎妈而是虎爸的那种人。在 Bo 的成长中，他父亲非常严格，但如果他赢了数学竞赛，就会得到额外的爱，被允许吃某些零食等等。你能简单描述一下你的父母吗？

I want to hear about both your parents, but I want to hear a little bit about your dad because he seems like based on my reading a very whimsical sort of creative soul which is a sharp contrast in some ways to for instance I had Bo Sha on the podcast amazing entrepreneur and his father was I suppose what some folks might think of when they imagine not a tiger mom but like a tiger dad. So in the case of Bo's upbringing, his father was very strict, but if he, meaning Bo, won a math competition, then he would get extra love and he would be allowed to have certain treats and things like that. Could you just describe your parents a little bit?

首先，你显然读了我的书。谢谢。确实如此。小时候你意识不到，但我在写自己的科学回忆录时，越写越意识到，天哪，我父亲真的不典型。我父亲热爱自然，现在依然如此。他充满好奇心。他在不严肃的事情中寻找幽默和乐趣，比如他喜欢虫子、昆虫。在 1980 年代的中国，物质资源并不丰富。但成都正在扩张，所以我们住在城市边缘的公寓楼里。尽管父母在市中心工作，但周末父亲会带我去田野里玩，那里还有稻田和水牛。我有一只小狗。我的记忆里全是和虫子搏斗，有时父亲和我还会去附近的山上画画，我们上过美术课，我上过儿童美术班。但我对父亲的童年记忆就是，他是一个非常不严肃的家长，对我的成绩或课堂表现毫无兴趣。我有没有成就？有没有带回竞赛奖项？这些都无关紧要。即使我和父母搬到新泽西后，生活变得极其艰难，那是移民生活，我们很贫困。但即使那样，我记得他在庭院旧货出售中找到了很多乐趣。我们每个周末都去庭院旧货出售，就像寻宝一样。所以他就是这样充满好奇心和童心的人。

First of all, clearly you read my book. Thank you for that. It is true. As a child, you don't realize as I was just going through my own science memoir, I was writing it, the more I wrote about it, the more I realized, oh my god, I really did not have a typical dad. My dad loved and still loves nature. He's just a curious. He finds humor and fun in unserious things, you know, like he loves bugs, insects. Growing up in the 1980s in China, there isn't much abundance in terms of material resources. But my city Chengdu was expanding. So we lived in apartment complexes at the edge of the city. Even though my dad and my mom worked in the middle of the city. So on the weekends, my dad and I would just play in the fields where there's still rice fields. There's water buffaloes. I had a puppy. Really all my memory is just like fighting bugs really and then sometimes my dad and I will follow some I don't know we took an art class, I took a kids art class and we'll go to the mountains neighboring mountains to draw. But my entire childhood memory of my dad is just a very unserious parent who had no interest in my grades or what I'm doing in class. Did I achieve anything? Did I bring back any competition awards? Nothing to do with that. Even when I came to New Jersey with my parents, life became extremely tough, right? It was immigrant life. We were in a lot of poverty. And even that my memory is that he had so much fun in yard sales. Like I would just go to yard sales and those are our every weekend it was just yay let's go to yard sales and just use that as a treasure hunt almost. So it's just he's a very curious and childlike mind in that way.

我问你父母的部分原因是我知道你也是一位家长，最终我想问你如何看待育儿。这会在某个时候提到。但既然听众肯定会问自己这个问题，而我们不会涉及任何地缘政治，因为有很多人想讨论和争论那个，我们不会那样做。

I'm asking about your parents in part because I know you're a parent and ultimately I'm going to want to ask how you think about parenting. That will come up at some point. But since listeners will certainly be asking themselves this question and we're not going to get into any geopolitics because there are plenty of people who want to get into that and fight over that which we're not going to do.

但你父母为什么离开中国？催化剂是什么，或者他们离开熟悉的环境，来到一个完全陌生的国家，从成都这样的城市到新泽西郊区——我觉得你描述过那里感觉很空旷，对吧？然后还有语言障碍、经济障碍，这么多问题。为什么要搬？

But why did your parents leave China? Like what was the catalyst or what were the reasons behind leaving what you knew or leaving what they knew and coming to a very different foreign country where you're going from Chengdu, which is a city, to suburban New Jersey, which is as I think you've described it felt very empty, right? And then you have the language barriers and the financial barriers. There's so many things. Why the move?

我会给你两个答案。青少年时期的李飞飞会说：我不知道。因为我爸在我 12 岁时离开了，我和我妈在我 15 岁时才去和他团聚。那些年你是个青少年，对吧？脑子里有很多奇怪的东西。我只知道他们说，我们去美国吧，我完全不知道怎么回事。真的不知道发生了什么。只有一种模糊的感觉，那里有机会和自由，教育很不一样。而且我直觉自己不是个典型的孩子，因为我是个女孩，却热爱物理，尤其喜欢战斗机。我可以告诉你所有我喜欢的战斗机，从 F-17 到 F-16，各种型号。我就知道这些。现在作为成年人，我感激我的父母。他们非常勇敢，因为我不知道自己在这个年纪会不会抛下熟悉的国家，去一个完全陌生的、语言不通、毫无联系的地方。而且那是在互联网和 AI 出现之前。所以去另一个国家，简直就像去了另一个星球，完全隔绝了。是的。我觉得他们很勇敢。成年后的李飞飞意识到，他们想让我拥有一个他们认为对我的教育来说前所未有的机会。结果证明，这确实是真的。

I'll give you two answers. In the early teenage Fei-Fei would say, I have no idea. Because my dad left when I was 12, and my mom and I joined him when I was 15. And those years you're a teenager, right? Like there's so many strange things in your head. All I knew is that they said, let's go to America, and I had no idea. I really did not know what happened. There was this vague sense of opportunities and freedom, that education is very different. And I had a hunch that I was not a typical kid, in the sense that I was a girl and I loved physics, I loved fighter jets of all things. I can tell you all the fighter jets I love, from F-17 to F-16 to all the different things that I loved. So that's all I knew. In hindsight, as a grown-up Fei-Fei, I appreciated my parents. They're very brave people, because I don't know if at this age myself I would just pick up and leave a country I'm familiar with and go to a completely different country that I speak zero language and have zero connectivity to. And mind you, that's pre-internet, pre-AI age. So when you're going to a different country, you might as well go to a different planet. You're cut off. Yeah. I think they're very brave. The grown-up Fei-Fei realized that they wanted me to have an opportunity that they think will be unprecedented for my education. And it turned out that's kind of true.

是的。看你的履历，想象所有那些关键转折点和可能的不同路径，真是令人难以置信。所以我们会按时间顺序紧密地走一遍，但最终会谈到很多核心内容。不过我想先谈谈其他一些塑造你的人物。我也想听听你母亲的故事，因为结合你父亲的背景，这似乎很迷人也很不寻常，尤其是如果你在中国待过，特别是那个时期。他在这方面非常不寻常。是的，非常不寻常。所以人们可能会想，你的动力来自哪里？技术专注来自哪里？我很想听听你的回答，也想听听你解释鲍勃·萨贝拉是谁，如果我没念错的话。

Yeah. Well, certainly looking at your bio, I mean, it's mind-boggling to imagine all the different sliding door events and different paths you could have taken. So, we're going to hop pretty closely along chronologically, but we're going to ultimately get to a lot of the meat and potatoes of the conversation. But I want to touch on maybe some other formative figures. And I would like to hear about your mother as well, because just with the context of your dad, it's like, okay, that seems fascinating and very unusual, particularly if you've spent any time in China, especially during that period of time. He is very unusual that way. Yeah, very unusual. So then people might wonder, well, where does the drive come from? Where does the technical focus come from? And I'd love to hear your answer to that and also hear you explain who Bob Sabella was, if I'm pronouncing that correctly.

是的。这里主要涉及两个问题：我母亲是否给了我动力和技术热情，以及鲍勃在我生活中扮演了什么角色？首先，我母亲完全没有技术基因。我有时还笑她。她不会做数学，就这么说吧。所以我认为技术热情是天生的。我父亲更偏技术，但他肯定更喜欢虫子而不是方程式。所以我觉得，作为几十年的教育者和家长，你必须尊重自然的奇迹。这种内在的热爱、激情和好奇心是与生俱来的，对吧。但我母亲自律得多。她仍然不是虎妈那种类型。我不记得我母亲曾因成绩责备过我。她真的没有。我父母从不关心我带没带奖状回家。也许我带过，也许没有。但我可以告诉你，我们家墙上没有任何挂饰，这习惯一直延续到今天。即使是我自己，我的房子、办公室也没有任何成就或奖项的装饰。我母亲不在乎那些。但她确实在乎我是否专注。如果我想做某事，她不允许我做作业时玩耍。那种事会让她烦恼。她会说：‘做完作业。比如到下午 6 点。如果你没做完，就不许再做作业了。你得承担后果。’所以她灌输了一些纪律，但仅此而已。她比我父亲更严厉。她非常叛逆。她自己有一个未完成的梦想。她小时候学业很好，但文化大革命粉碎了她所有的梦想。她因此变得更叛逆，作为女儿，我确实观察和体验到了这一点。也许移民的部分原因也与此有关。多年后，她会说：‘我来新泽西时没有计划，但我想我会活下去。我只是相信我会活下去，并且我会确保飞飞活下去。’我认为那是她的力量、固执和叛逆。

Yes. There are two questions mostly: is my mom the one who put in the drive and the technical passion, and what role did Bob play in my life? So first of all, my mom has zero technical genes here. I sometimes still laugh at her. She cannot do math, let's put it this way. So I think the technical passion is just I was born with it, innate. My dad is more technical, but he loves bugs more than insects, more than equations for sure. So I think that, as an educator for so many decades now myself and also as a parent, you have to respect the wonders of nature. There is this inner love and fire and passion and curiosity that comes with the package, right. But my mom is much more disciplined person. She's still not a tiger mom in the sense. I don't remember my mom ever going after me on grades. She really did not. My both parents never ever cared about me bringing any awards home. Maybe I did, maybe I didn't. But I can tell you in our house there's zero wall hangings of anything, which actually carried to today. Even for myself, my own house, my own office have zero of those decorations of achievements or awards. It's just, my mom did not care about that. But she did care about me being a focused person. If I want to do something, she doesn't want me to play while doing homework. And that kind of thing would bother her. She would say, 'Just finish your homework. Say by 6:00 p.m. If you don't finish your homework, you're not allowed to do more homework. You have to deal with the consequences.' So she instilled some discipline, but that's about it. She's tougher than my dad. She is very rebellious. She had an unfinished dream herself. She was very academic when she was a kid herself, and the Cultural Revolution really crushed all her dreams. She became a more rebellious person in that sense that I think I did observe and experience as a daughter. Maybe part of immigration is even part of that. Many years later, she would say, 'I had no plan coming to New Jersey, but I think I'm going to survive. I just believe I'm going to survive and I'm going to make sure Fei-Fei survives.' I think that is her strength, her stubbornness, and her rebelliousness.

鲍勃是什么时候出现的？他是谁？

When does Bob enter the picture and who is Bob?

鲍勃·萨贝拉是帕西帕尼高中的数学老师。他教过我，也教过很多学生。他是在我在帕西帕尼的第二年进入我生活的，大概是在高二到高三之间，那时我开始上 AP 微积分。但他很快就成为我成长过程中最有影响力的人，作为一个新美国孩子、移民、青少年，因为他成了我的导师、朋友，最终他全家都成了我的美国家庭。他是在我非常孤独的 ESL（英语作为第二语言）学生时期成为我朋友的。我数学很好，但我想更多是因为我孤独，而他非常友好。他待我更像朋友，我们一起谈论喜欢的书、文化、科幻小说，也倾听我——我不会说困惑，但作为一个在独特环境中经历许多生活动荡的青少年。那种无条件的支持让我和他以及他的家人非常亲近。他为我做的一件事，我直到后来才感激：帕西帕尼高中无法提供完整的微积分 BC 课程，因为学校没有这个条件，他就牺牲了自己的午餐时间，他唯一的午餐时间，来教我微积分 BC。那是一对一的课程。我确信这帮助了我这个移民孩子最终进入普林斯顿。但后来我自己当了老师，整天教书很累。而他在此之上还用午餐时间给我额外上课，这真是一份礼物，我现在比青少年时期更感激。

Bob Sabella was a high school math teacher in Parsippany High School. He was my own math teacher as well as many many students. He entered my life in my second year in Parsippany, so it's kind of bordering sophomore to junior year in Parsippany High School, when I started taking AP calculus. But he quickly became the most influential person in my formative years as a new American kid, immigrant, as a teenager, because he became my mentor, my friend, and eventually his entire family became my American family. And he became my friend when I was a very lonely ESL (English as a second language) student. I was excelling in math, but I think it's more because I was lonely and he was very friendly. He treated me more like a friend who talks about books we love, talk about the culture, talks about science fiction, and also listened to me as a very, I wouldn't say confused, but teenager undergoing a lot of life's turmoil in my unique circumstance. And that unconditional support made me very close to him and his family. And one thing he did to me that I did not appreciate till later is that when Parsippany High School couldn't offer a full calculus BC class because it just didn't have that, he just sacrificed his lunch hour, his only lunch hour, to teach me calculus BC. So it was a one-to-one class. And I'm sure that contributed to me, an immigrant kid, getting into Princeton eventually. But later as I became a teacher myself, it's exhausting to teach all day long. And the fact that on top of that, he would use his lunch hours to do that extra class for me is just such a gift that I now appreciate more than I was as a teenager.

感谢那些尽心尽力的老师。这太不可思议了，尤其是当你年纪稍长，有了更多背景知识，可以回顾并意识到这一点时。我真的认为美国的公立教师是我们社会默默无闻的英雄，因为他们要应对各种背景的孩子，还要应对时代的变化。鲍勃和我分享的那些故事，关于他如何不仅对我，而且对许多学生付出额外努力——因为普尔布蒂是一个移民众多的城镇，他的学生来自世界各地，他如何帮助他们和他们的家庭。这些就是人们不常写的故事，我写这本书的部分原因就是为了赞美这样的老师。

Thank God for the teachers who go the extra mile. It's just incredible, especially when you get a bit older and you have more context and you can look back and realize. I really think these public teachers in America are the unsung heroes of our society because they are dealing with kids of all backgrounds. They're dealing with the changing times. The kind of stories Bob would share with me in terms of how he went extra miles not just with me but with many students in because Puberty is a heavily immigrant town. So his students are from all over the world and how he helped them and their family. It's just those are the stories that people don't write about and that's part of the reason I wrote the book was to celebrate a teacher like that.

是的。我想谈的太多了，我知道在话题用完之前时间就不够了。所以，我想多花点时间在鲍勃身上，同时我也想继续推进对话。我们会这样做，我可能先提几件事，然后深入问一些问题。但当然，在普林斯顿，你和你的整个家庭都必须生存下去。所以，你曾在新泽西经营一家干洗店作为选择之一，对吧？你经营了 7 年。通过这个，感觉你在很多不同层面上获得了视角，这些后来帮助了你专业上的成就，对吧？所以你学会了不仅思考那些被保护在象牙塔里的人，还有社会各个阶层的人。从社会的各个阶层，你的母亲虽然不懂技术，但她灌输给你这种纪律，而且似乎对文学和国际文学有非常广泛的欣赏和知识。所以你现在有了这种全球视角，大概当时是中文的，然后你最终去了普林斯顿，我知道我们会跳来跳去，但我很好奇 ImageNet 是怎么来的，你可以用任何你喜欢的方式介绍。你可以告诉人们它是什么，它变成了什么，为什么重要，然后谈谈它是如何开始的，或者你只谈它是如何开始的。但这是一个非常重要的篇章。

Yeah. I have so much I want to cover and I know we're going to run out of time before we run out of topics. So, I want to spend more time on Bob and at the same time I want to keep the conversation moving. So, we're going to do that and I'll just perhaps hit on a few things and then dig into a number of questions. But certainly at Princeton you but also your entire family had to survive. So, you were involved with operating a dry cleaning shop in New Jersey as one option, right? you ran that for 7 years. So through that, it feels like you've gained perspective on many different levels that have then helped inform what you've done professionally, right? So you learn to think about not just people who are protected in an ivory tower, but people all the way down in across in society. So from every swath of society your mother also although she was not technical she imbued in you this discipline and also seems to have had a very broad appreciation and knowledge of literature and international literature. So now you have this global perspective presumably at the time in Chinese and then you end up at Princeton and I know we're going to be hopping around quite a bit but I'm curious to know how Imagenet came about and you can introduce this any way you like. You can tell people what it is and what it became and why it's important and then talk about how it started or you can just talk about how it started. But it's such an important chapter.

那么让我解释一下什么是 ImageNet。表面上看，ImageNet 是在 2007 年到 2009 年间建立的，当时我是普林斯顿大学的助理教授，后来搬到了斯坦福。在这段过渡时期，我和我的学生构建了当时 AI 领域最大的计算机视觉或视觉智能的训练和基准数据集。今天，在 ImageNet 诞生近 20 年后，它的意义在于它是大数据的一个转折点。在 ImageNet 之前，AI 领域并没有研究大数据，由于这个原因和其他一些原因，AI 停滞不前。公众认为那是 AI 寒冬。尽管作为一名当时年轻的研究者，那对我来说是最令人兴奋的领域，但我理解。它没有展现出公众需要的突破。但是 ImageNet 与另外两个现代计算要素一起：一个叫做神经网络算法，另一个是现代芯片 GPU（图形处理单元）。这三者在 2012 年的一项开创性、里程碑式的工作中汇聚，即《基于深度卷积神经网络的 ImageNet 分类》。那篇论文由一组科学家完成，展示了通过 ImageNet 的大数据、GPU 的快速并行计算和神经网络算法的结合，能够在图像识别领域实现历史上前所未有的 AI 性能。那个里程碑被许多人称为现代 AI 的诞生。而我的工作 ImageNet，如果算上这些要素，是其中的三分之一，我认为这就是它的意义。我感到非常幸运和荣幸，我自己的工作对现代 AI 的诞生起到了关键作用。

So let me just explain what ImageNet is. ImageNet on the surface was built between 2007 and 2009 when I was an assistant professor at Princeton and then I moved to Stanford. So during this transitional time my student and I built this at that time the field of AI's largest training and benchmarking data set for computer vision or visual intelligence. The significance today after almost 20 years of ImageNet was it was the inflection point of big data. Before ImageNet AI as a field was not working on big data and because of that and couple of other reasons which I'll get into AI was stagnating. The public thinks that was the AI winter. Even though as a researcher, young researcher at that time, it was the most exciting field for me, but I get it. It wasn't showing breakthroughs that the public needs. But ImageNet together with two other modern computing ingredients. One is called neural network algorithm. The other one is modern chips called GPU graphic processing unit. These three things converged in a seminal work, milestone work in 2012 called ImageNet classification with deep convolutional neural networks. That was a paper that a group of scientists did to show that the combination of large data by ImageNet, fast parallel computing by GPUs and a neural network algorithm could achieve AI performances in the field of image recognition in a way that's historically unprecedented. And that particular milestone is many people call it the birth of modern AI. And my work ImageNet was one third of that if you count the elements and I think that was the significance. I feel really very lucky and privileged that my own work was pivotal in bringing modern AI to life.

但通往 ImageNet 的旅程比那更长。通往 ImageNet 的旅程始于我在普林斯顿读本科的时候。你在东亚研究系？我躲在贾德温楼，那是我们的物理系。我从小就喜欢物理。不知怎么的，我父亲对虫子、昆虫和自然的热爱在我脑海中转化成了对宇宙的好奇。所以，我喜欢仰望星空。我喜欢战斗机的速度和复杂的工程，这最终转化为对一门学科的热爱，这门学科提出了我们文明中最大胆的问题，比如什么是最小的物质？时空的定义是什么？宇宙有多大？宇宙的起源是什么？在青少年早期的热爱中，我喜欢爱因斯坦。我喜欢他的工作。我想为此去普林斯顿。但结果物理教给我的不仅仅是数学和物理。它真正教会我的是提出大胆问题的热情。所以到本科结束时，我想拥有自己的大胆问题。我不满足于仅仅追求别人的大胆问题。通过阅读书籍等，我意识到我的热情不在于物理物质，而更多地在于智能。我真的被“什么是智能”以及“我们如何制造智能机器”这个问题迷住了。那时我发誓我不知道它叫 AI。我只知道我想研究智能和智能机器。然后我申请了研究生院，去了加州理工学院。加州理工学院是我的博士。我在世纪之交的 2000 年开始。我认为那一刻我成为了一名崭露头角的 AI 科学家。那是我作为计算机科学家在 AI 领域的正式训练。然后我的物理训练在某种意义上继续，物理教会我提出大胆的问题，并把它们变成北极星。在科学术语中，那个北极星变成了一个假设。对我来说，定义我的北极星非常重要。我未来几年的第一个北极星是解决视觉智能的问题：我们如何让机器看到世界。不仅仅是看到 RGB 颜色或光的阴影；而是理解所看到的东西。我正在看着你，蒂姆。我看到你。我看到你身后一幅美丽的画。我不知道它是真的。我看到你坐在椅子上。这就是看。看就是理解这个世界是什么。所以这成了我的北极星问题。我的假设是我必须解决物体识别。然后我整个博士生涯就是与物体识别的斗争。我们做了很多很多数学模型，有很多问题，但我和我的领域都在挣扎。

But the journey to ImageNet was longer than that. The journey to ImageNet started in Princeton when I was an undergrad. You were in the East Asian study department? I was hiding in Jadwin Hall which is our physics department. I loved physics since I was a young kid. I don't know how somehow my dad's love of bugs and insects and nature translated in my head into just the curiosity for the universe. So, I loved looking to the stars. I loved the speed of fighter jets and the intricate engineering of that eventually translated into the love of the discipline that asks the most audacious question of our civilization such as what is the smallest matter? What is the definition of spacetime? How big is the universe? What is the beginning of the universe? And in that early teenagehood love I loved Einstein. I love his work. I wanted to go to Princeton for that. But it turned out what physics taught me was not just the math and physics. It was really this passion to ask audacious question. So by the end of my undergrad years, I wanted my own audacious question. I wasn't satisfied with just pursuing somebody else's audacious question. And through reading books and all that I realized my passion was not the physical matters. It was more about intelligence. I was really really enamored by the question of what is intelligence and how do we make intelligent machines. So at that time I swear I did not know it was called AI. I just knew that I wanted to pursue the study of intelligence and intelligent machines. And then I applied to grad school and I went to Caltech. Caltech was my PhD. I started in the turn of the century 2000. And I think I consider that moment I became a budding AI scientist. That was my formal training as a computer scientist in AI. Then my physics training continued in a sense that physics taught me to ask audacious question and turn them into a northstar. And in scientific terms that northstar became a hypothesis. And it was very important for me to define my northstar. And my first northstar for the following years to come was solving the problem of visual intelligence: how we can make machines see the world. And it's not just by seeing the RGB colors or the shades of light; it's about making sense of what's seen. I'm looking at you Tim. I see you. I see a beautiful painting behind you. I don't know it was real. I see you're sitting on a chair. Like that is seeing. Seeing is making sense of what this world is. So that became my northstar question. And that hypothesis that I had is I have to solve object recognition. And then that was in my entire PhD was the battle with object recognition. There were many many mathematical models we have done and there were many questions but me and my field was struggling.

我们写论文没问题，但没有突破。幸运的是，2007 年普林斯顿大学邀请我回去担任教职。那是我一生中最快乐的时刻之一。母校愿意给我教职，让我感到无比被认可。于是我开心地搬回普林斯顿，这次是作为教授，而且我实际上仍然是福布斯成员。在普林斯顿，我顿悟了：我意识到有一个被所有人忽略的假设，那就是大数据。

We can write papers no problem but we did not have a breakthrough. Then luckily for me, Princeton called me back as a faculty in 2007. It was one of the happiest moments of my life. I felt so validated that my alma mater would consider giving me a faculty job. So I happily moved back to Princeton as a faculty this time, and I continue to be a Forbes member actually. At Princeton there was an epiphany: I realized there was a hypothesis that everybody missed, and that hypothesis was big data.

我能打断你一下吗？因为这一点我特别好奇。我也想为那些对普林斯顿历史感兴趣的人暂停一下。这相当不可思议。他们应该查查普林斯顿高等研究院的历史。我记得我上过你提到的那些东亚研究课程，就在爱因斯坦教过书的教室里。那种氛围、那种光泽，你会相信你能感受到它弥漫在整个校园。这很有趣。但我要读一段《连线》杂志上关于你的长篇报道。正如你提到的，大数据在融入他们描述的研究类型之前和之后。文章写道，请随意核实或反驳，但《连线》说：‘问题在于，研究人员可能写一个算法来识别狗，另一个算法来识别猫。然后李开始思考，问题是不是不在于模型而在于数据。她认为，如果孩子通过体验视觉世界、在早年观察无数物体和场景来学习看东西，也许计算机也能以类似的方式学习。’我希望你详细阐述一下。我的问题是：为什么你看到了这一点？为什么没有更早发生？

Could I pause you there for a second? Because this is the point I'm so curious about. I just want to pause for a second also for people who are interested in some of the history of Princeton. It's pretty crazy. They should look up the history of the Princeton Institute for Advanced Study. I remember taking some of those East Asian Studies classes that you referred to in classrooms where Einstein taught. It's just the aura, the veneer. You want to believe that you can feel it just permeating the entire campus. It's fun in that respect. But I'm going to read something from a Wired piece that discussed you at length. As you mentioned big data before and after in terms of its integration into the type of research they were describing. As it was written, and please feel free to fact check this or push back on it, but in Wired they said: 'The problem was a researcher might write one algorithm to identify dogs and another to identify cats. And then Lee began to wonder if the problem wasn't the model but the data. She thought that if a child learns to see by experiencing the visual world, by observing countless objects and scenes in her early years, maybe a computer can learn in a similar way.' I want you to expand on that for sure. And the question for me is: why did you see it? Why didn't it happen sooner?

我们都是历史的学生。实际上，我不喜欢科学史叙述的一点是，它过于关注单个天才。

We're all students of history. One thing I actually don't like about the telling of scientific history is there is too much focus on single genius.

是的，同意。

Yes, agreed.

我们知道牛顿发现了现代物理学定律，但他是天才。这不是要否定牛顿，但科学是一个谱系，而且实际上是一个非线性的谱系。例如，为什么我会受到大数据这个假设的启发？因为许多其他科学家启发了我。在我的书中，我谈到了心理学家比德曼教授的一系列工作。他对 AI 不感兴趣，但对理解心智感兴趣。我在读他的论文，他特别谈到幼儿在早期能够学习的大量视觉对象。对吧？所以那项工作本身不是 ImageNet。但如果没有读到那项工作，我就不会形成我的假设。所以，虽然我为自己所做的事感到自豪，但我的书特别想以一种方式讲述 AI 的历史，即许多无名英雄、几代科学家、许多跨学科思想相互启发。所以我很幸运，当时我既对这个问题的充满热情，又受益于所有这些研究。所以是的，我的大脑中发生了一些事情，但我真的归功于许多人在他们一生对科学的奉献中所做的许多工作，才让我们走到了 ImageNet 这一步。

We know Newton discovered the modern laws of physics, but yes, he is a genius. Not to take away any of that from Newton, but science is a lineage, and science is actually a nonlinear lineage. For example, why was I inspired by this hypothesis of big data? Because many other scientists inspire me. In my book, I talked about this particular lineage of work by Professor Biederman, who was a psychologist. He was not interested in AI, but he was interested in understanding minds. I was reading his paper, and he particularly was talking about the massive number of visual objects that young children were able to learn in early ages. Right? So that piece of work itself is not ImageNet. But without reading that piece of work, I would not have formulated my hypothesis. So while I'm proud of what I have done, my book especially wanted to tell the history of AI in a way that so many unsung heroes, so many generations of scientists, so many cross-disciplinary ideas pollinate each other. So I was lucky at that time as someone who is passionate about the problem but also someone who benefited from all this research. So yes, something happened in my brain, but I would really attribute to many things happened across so many people's work throughout their lifetime devotion to science that we got to the point of ImageNet.

我很高兴你强调这一点，因为如果你真正深挖——我不认为自己是科学家，但我喜欢阅读科学史——有如此多的输入、影响和相互依赖。

I'm so glad that you're underscoring this because if you really dig, as I don't consider myself a scientist, but I love reading about the history of science. There's so many inputs, so many influences, so many interdependencies.

是的。

Yes.

单一英雄旅程的简单性因其简单而吸引人，但它几乎从来不是真的。

And the simplicity of the single hero's journey is appealing in its simplicity, but it's almost never true.

它可能从来不是真的。即使是我最大的英雄爱因斯坦，对吧？任何认识我的人，任何读过我书的人都知道我多么崇敬他，我热爱他所做的一切。狭义相对论方程是洛伦兹变换的延续。即使是爱因斯坦，他也建立在许多其他人的工作之上。所以我认为这非常重要，尤其是我们肯定会谈到这一点。我现在在硅谷中心给你打电话，我们正处于 AI 热潮之中。显然我为我的领域感到自豪，但我认为当媒体或其他什么讲述 AI 的故事时，几乎总是只谈论几个天才，这不是真的。是几代计算机科学家、认知科学家和工程师让这个领域成为现实。

It probably is never true. Even my biggest hero, Einstein, right? Anybody who knows me, anybody who read my book knows how much I revere him and I just love everything he's done. The special relativity equation is a continuation of Lorentz transform. Even Einstein, he builds upon so many other people's work. So I think it's really important, especially I'm sure we'll talk about it. I'm here calling you in the middle of Silicon Valley and we're in the middle of an AI hype. Obviously I'm very proud of my field, but I think that when the media or whatever tells the story of AI, it almost always just talk about a few geniuses and it's just not true. It's generations of computer scientists, cognitive scientists and engineers who made this field happen.

当然。我的意思是，每个人都知道沃森和克里克，但没有罗莎琳德·富兰克林和她的 X 射线晶体学，就不会有发现。不会发生。就是不会发生。直截了当。我们马上要跳到现代，但对于 ImageNet，我希望你能谈谈一些决定或时刻，那些对成功至关重要的时刻。对吧？例如，如果你试图让机器——我用非常简单的术语，因为我不够技术化——以更接近孩子的方式学习识别物体，你必须标记大量图像，对吧？我读到过亚马逊土耳其机器人是如何参与的，然后还有一个竞争方面，似乎推动了某些分水岭时刻。你能谈谈那些使它成功的要素或决定吗？

For sure. I mean, everyone knows Watson and Crick for instance, but without Rosalind Franklin and her X-ray crystallography, it doesn't happen. Doesn't happen. It just doesn't happen. Point blank. We're going to hop to modern day in a second, but with ImageNet, I would love for you to speak to some of the decisions or let's say decisions or moments that were just formative in making that successful. Right? Because for instance, if you're going to try to allow a machine to, and I'm using very simple terms because I'm not technical enough to do otherwise, to learn to identify objects closer to the path that a child would take, you have to label a lot of images, right? And I was reading about how Mechanical Turk came into play and then there's a competitive aspect that seems to have driven some of the watershed moments. Could you just speak to some of the elements or decisions that made it successful?

很多人问我这个问题，因为在 ImageNet 之后，很多人尝试制作数据集，但只有少数成功。那么是什么让 ImageNet 成功？我认为成功之一是时机：我们确实是第一批看到大数据影响的人。所以这种分类或质变本身就是成功的一部分。但同样，正如你问的，大数据的假设不仅仅是规模。很多人实际上误解了 ImageNet 以及其他数据集的意义。伴随数据集而来的是一个科学假设，即要问什么问题。例如，在视觉识别中，你可以制作一个区分 RGB 的数据集，但这不会像围绕物体组织的数据集那样有影响力。

A lot of people ask me this question because after ImageNet, many many people have attempted to make data sets but still only very few are successful. So what made ImageNet successful? I think one of the success was timing: we truly were the first people who see the impact of big data. So that very categorical or qualitative change itself is part of the success. But it's also, as you were asking, the hypothesis of big data is not just size. A lot of people actually misunderstands ImageNet's significance as well as other data sets' significance. Coming with the data set is a scientific hypothesis of what is the question to ask. For example, in visual recognition you could make a data set of discerning RGB and that would not be as impactful as a data set that is organized around objects.

嗯。

Mhm.

我们可以深入探讨为什么不是 RGB，因为 RGB 本身更容易。这是因为你必须以正确的方式提出科学问题。另一个例子是，与其制作物体数据集，为什么不制作城市数据集？你知道，那比物体更复杂。但那就太复杂了。所以每个科学探索，你必须有正确的假设并提出正确的问题。所以成功的一部分是：我们将视觉物体分类定义为正确的假设。

We can go down a rabbit hole of why not because RGB is easier per se. It's because you have to ask the scientific question in the right way. Another example is instead of making a data set of objects, why don't you make a data set of cities? You know, that's even more complicated than objects. But then that's dialing too complicated. So every scientific quest, you have to have the right hypothesis and asking the right question. So that's one part of the success: we defined visual object categorization as the right hypothesis.

我想那是一个正确之处。

That was one rightness I guess.

另一个正确之处是，人们只是认为哦这很容易，你只需收集大量数据。

Another rightness is that people just think oh it's easy you just collect a lot of data.

首先，这很费力。但除了费力之外，如何定义质量？你可能会说，如果质量足够大，我们就不关心质量了。但如何在“大”和“好”之间权衡，这是一个深刻的科学问题，我们需要做大量研究。另一个非常困难的决策是，在图像方面什么定义质量。是每张图像都有更高的分辨率？是照片级真实感？是因为日常图像看起来很杂乱？还是所有产品照片都很干净？这些问题如果你离得远，根本不会想到去问。但作为科学家，当我们制定物体识别的深层问题时，我们必须从这么多维度来问。然后你提到了亚马逊的 Mechanical Turk。这实际上是绝望的结果，因为当我们制定这个假设时，我们的结论是，我们需要至少数千万张高质量图像，涵盖各种可能的维度，无论是用户照片、产品照片还是库存照片，而且我们还需要高质量的标签。一旦我们做出这个决定，我们意识到这必须通过人工从数十亿张图像中筛选出来。

Well first of all it's laborious. But even aside from being laborious, how do you define the quality? You could say well if quality is big enough we don't care about quality. But how do you dial between what is big, what is good and how do you trade off that is a deeply scientific question that we have to do a lot of research on. And then another decision that is a set of decisions that is really hard is what defines quality in terms of image. Is it every image has higher resolution? Is it photorealistic? Is it because it's everyday images that look very cluttered? Is it all product shots that look clean? These are questions that if you're too far away, you wouldn't even think about asking. But as a scientist, as we were formulating the deep question of object recognition, we have to ask this in so many dimensions. And then you mentioned Amazon Mechanical Turk. That is actually a consequence of desperation because when we formulated this hypothesis, our conclusion is we need at least tens of millions of high quality images across every possible diverse dimension. Whether it's user photos or product shots or stock photography, and then we need also high quality labels. Once we make that decision we realize this has to be human filtered from billions of images.

所以，我们变得非常绝望。我们想，我们该怎么做？你知道，我确实尝试雇佣普林斯顿的本科生，但正如你所知，普林斯顿的本科生非常聪明，但他们对自己时间的价值评价很高。

So with that we became very desperate. We're like how are we going to do that? You know, I did try to hire Princeton undergrads and as you know, Princeton undergrads are very smart. But they have very high opinion of the value of their time.

是的，而且他们很贵。但即使我拥有世界上所有的钱，我们也没有，那也会花很长时间。所以，我们被困了很长很长时间。我们以为有其他捷径，但事实是人工标注是黄金标准。我们想训练机器，使其与人类能力相比较。所以当时我们不能走捷径。因此，我们不得不采用我们最终发现称为众包工程的方法，即众包，那是一项非常新的技术，亚马逊推出才大约一年。他们创建了一个在线市场，让人们做小任务赚钱，这些任务可以上传到互联网。我记得当我听说亚马逊的 Mechanical Turk 时，我登录了我的亚马逊账户。我查看的第一个任务，为了尝试，是给酒瓶贴标签或转录酒瓶标签。任务会给你一张酒瓶图片，你必须说这是 1999 年的波尔多等等。人们上传这些微任务，然后在线工作者，比如像我这样有空闲时间的人，就会注册并付费去做。我们意识到，这又是出于绝望，这是一种利用全球在线人口进行大规模并行处理的方式，我们就这样标注了数十亿张图像，并提炼出 1500 万张高质量图像。

Yes. And they're expensive. But even if I had all the money in the world, which we didn't, it would have taken so long. So, we were very, very stuck for a very, very long time. We thought we had other shortcuts, but the truth is human labeling is a gold standard. We want to train machines that are measured against human capability. So we cannot shortcut that at that time. So we had to go to what we eventually found out is called crowd engineering, crowdsourcing, and that was a very new technology, barely a year old or so by Amazon. They created an online marketplace for people to do small tasks to earn money, when these tasks can be uploaded on the internet. I remembered when I heard about Amazon Mechanical Turk, I logged into my Amazon account. I checked the first task I checked out to do just to try was labeling wine bottles or transcribing wine bottle labels. The task will give you a picture of a wine bottle and you have to say this is 1999 Bordeaux and all that. People upload these kind of micro tasks and then online workers like someone in their leisure time like me if I had leisure time I would just go sign up and get paid to do that. And we realized that was again out of desperation that was a massive parallel processing with online global population to do this for us and that's how we labeled billions of images and distilled it down to 15 million high quality images.

当你看到这些故事时，真是令人惊叹。我刚读完一本关于基因泰克的书，书中提到了许多小的技术转折点，这些转折点使得事情成为可能。所以如果早 5 年或早 3 年，没有 Mechanical Turk，那将是一个挑战。但正如你在科学中指出的，得到答案是一回事，但你需要前端有正确的假设或好问题。即使有 Mechanical Turk，如果你只关注使用它的机制，你可能会陷入麻烦。因为如果人类被激励，比如说，我认为我读到的例子是，识别照片中的熊猫，他们因识别熊猫而获得报酬，那么有什么能阻止他们在每张照片中都识别出熊猫，无论照片中是否存在熊猫？是的。所以，你也必须遵循激励措施。你是如何解决这个问题的？

It's just so wild when you look at these stories. I just finished a book on Genentech and there were all these little technical inflection points that also allowed things to happen. So if it had been 5 years earlier or maybe 3 years earlier, without Mechanical Turk, it presents a challenge. But also as you pointed out in science, it's one thing to get answers but you need the input on the front end with a proper hypothesis or a good question. And even with Mechanical Turk, if you're only focused on the mechanics of employing that, you can get yourself into trouble. Because if humans are incentivized, right, to let's just say, I think this was the example I read about, identify pandas in photographs and they're paid for identifying pandas, well, what's to stop them from identifying a panda in every photo, whether they exist in the photos or not? Yes. So, you have to follow the incentives as well. How did you solve for that?

这就是我和我的学生花了无数个小时讨论如何控制质量的地方。我们必须通过多个步骤来解决这个问题。我们首先需要筛选出认真做工作的在线工作者。例如，我们必须有一些前期测验，让他们了解什么是熊猫。他们阅读问题，然后一旦他们合格，我们要求他们标注熊猫，但有些图像我们知道正确答案，有些是真正的熊猫，有些不是，但标注者不知道，所以通过知道黄金标准答案在哪里，我们隐式地监控工作质量。这就是我们必须使用的计算策略，以确保标注质量。

This is where you know my student and I had I cannot tell you how many hours and hours of conversation we have about controlling the quality. We have to solve for that in multiple steps. We need to first filter out online workers who are serious about doing the work. So for example, we have to have some upfront quizzes so that they understand what a panda is. They read the question and then once they get into they qualify for that we ask them to label pandas but there are some images we know the correct answers some are true pandas some are not true pandas but the labelers don't know so in a way we implicitly monitor the quality of the work by knowing where the gold standard answers are. So these are the kind of computational tactics we have to use to ensure the quality of labeling.

太棒了，简直难以置信。我实际上想推荐一本书，《Pattern Breakers》，是我的朋友 Mike Maples Jr.写的。他最初教我天使投资的诀窍。但在识别转折点以及有时汇聚的技术趋势方面，这些趋势第一次使某些事情成为可能，从而为像你和你合作者以及你借鉴的人这样有准备头脑的人创造了机会，比如 ImageNet，《Pattern Breakers》是一本很好的读物。那么，让我们跳到现代，我想问你，因为你被称为 AI 教母，在我们的校友杂志和其他地方，但你不仅有技术视角，还有历史视角，意味着你在一个广泛的时间线上，按 AI 标准来说是广泛的，能够观察这项技术的发展、分支、危险和前景。人们错过了什么？你认为什么占据了所有注意力？人们错过了什么？无论是他们应该知道的事情，还是他们应该怀疑的事情，或者别的什么，尤其是我从硅谷中心给你打电话，我认为人们错过了 AI 中人的重要性，这个说法有多个方面或维度。AI 绝对是一项文明技术。我定义文明技术的意思是，由于这项技术的力量，它将或已经对社会产生深远的经济、社会、文化、政治下游影响。所以我刚听说，未经证实，但去年美国 GDP 增长的 50%归因于 AI 增长。显然这个数字是 4%，美国 GDP 增长了 4%。如果去掉 AI，只有 2%。这意味着从经济角度看，这是文明级的。

Amazing. Just incredible. I'll actually just put a recommendation out there for a book, Pattern Breakers, by a friend of mine, Mike Maples Jr. He taught me the ropes initially of angel investing. But in terms of identifying inflection points and in some cases converging technological trends that for the first time make something possible which then opens an opportunity for something with the right prepared mind in your case and those of your collaborators and the people you built upon for something like ImageNet, Pattern Breakers is a really good read for folks. So let's hop to modern day then for a moment and I would love to ask you right because you've been called the godmother of AI in our alumni magazine in fact and elsewhere but you've had such a not just technical but historical viewpoint meaning you've over a broad timeline well broad by AI standards been able to watch the development and forking and perils and promise of this technology. What are people missing? What do you think is eating up all the oxygen in the room? What are people missing? Whether it's things they should know or things they should be skeptical of or otherwise, especially I'm here calling you from the heart of Silicon Valley and I think people are missing the importance of people in AI and there's multiple facets or dimensions to this statement. AI is absolutely a civilizational technology. I define civilizational technology in the sense that because of the power of this technology it'll have or already having a profound impact in the economic, social, cultural, political downstream effects of our society. So I just heard this is unverified but I just heard that 50% of the US GDP growth last year is attributed to AI growth. Apparently this number is 4% for US GDP have grown 4%. If you take away AI it's only 2%. That's what means that's civilizational from an economic point of view.

它显然在重新定义我们的文化，对吧？想想看，这个词无处不在，从好莱坞到华尔街，从硅谷到政治竞选，再到 TikTok、YouTube 和 Instagram，它吸走了所有氧气。

It's obviously redefining our culture, right? Think about you're talking about the word sucking oxygen out of the room everywhere from Hollywood to Wall Street to Silicon Valley to political campaign to Tik Tok to YouTube to Insta.

日本的出租车。我刚去过那里，出租车座椅头枕后面播放着视频。我们都在谈论 AI。它无处不在。

Taxis in Japan. I was just there and the videos playing on the back of the headset and the taxi. We're all talking about AI. It's everywhere.

它影响文化。不仅影响，它正在改变我们的文化，也将改变教育。今天每个家长都在想，孩子该学什么才能有更好的未来。每个祖父母都说，“真庆幸我出生得早，不用面对 AI。”但依然为孙辈的未来担忧。所以 AI 是一种文明技术，但我认为目前缺失的是，硅谷急于谈论技术及其带来的增长。政客们则急于谈论任何能拉票的话题。但归根结底，人才是一切的核心。人创造了 AI，人将使用 AI，人将受到 AI 影响，人也应该在 AI 中有发言权。无论 AI 如何进步，人作为个体、社区、社会的尊严不应被剥夺。这正是我担心的，因为我认为焦虑在加剧，因为一些人的尊严感、自主感和参与未来的感觉正在流失，我们需要改变这一点。

It's culturally impactful. Not only impactful, it's shifting our culture and it's going to shift education. Every parent today is wondering what should their kids study to have a better future. Every grandparent is say, "I'm so glad I'm born early. I don't have to deal with AI." but still worry about their grandchildren's future. So AI is a civilizational technology, but what I think it's missing right now is that Silicon Valley is very eager to talk about tech and the growth that comes with the tech. Politicians are just eager to talk about whatever gets the vote, I guess. But really, at the end of the day, people at the heart of everything. People made AI, people will be using AI, people will be impacted by AI, and people should have a say in AI. And no matter how AI advances, people's self dignity as individuals, as community, as society should not be taken away. And that's what I worry about because I think there's so much more anxiety that because the sense of dignity and sense of agency, sense of being part of the future is slipping in some people and I think we need to change that.

我听过你说，你是个乐观主义者，因为你是母亲。极端的乐观和悲观都会让我们产生偏见，造成盲点。我很好奇，如果你尽量客观地看——这对任何人来说都很难——你觉得人们是过于担忧、担忧不足，还是担忧错了方向？对于非 CEO、非 AI 建设者和工程师的人来说，你当然是对的，很多人非常担忧。我只是想知道这种担忧是否放错了地方。因为如果你和一些最大的投资者 VC 们聊，他们当然有一种在我看来超越一切可能的技术乐观主义，认为 AI 能解决一切，但很难相信有免费的午餐。然后还有悲观者，突然明年就是天网，我们都成了机器人的奴隶或被消灭，变成回形针。现实可能介于两者之间。你认为人们担忧的是正确的事情吗？还是他们某种程度上搞错了重点？

I've heard you say that you're an optimist because you're a mother. And both optimism and pessimism to an extreme can bias us in ways that are unhelpful, right? Or create blind spots. And I'm curious if you try to put your most objective hat on, which is difficult for any human, but if you try to do that, do you think people are too worried, not worried enough, or worrying about the wrong things? For people who are not CEOs and builders and engineers behind AI because you're right of course I mean everybody will agree with this that a lot of people are very worried and I'm just wondering if it's ill-placed because if you talk to some of the VCs who are the biggest investors of course they have this sort of in my view beyond all possibilities techno optimist view of the future where AI solves everything right and it's hard to believe there's a free lunch. And then you have the doomers, the doom and gloom where suddenly it's Skynet next year and we're all slaves to robots or eliminated, turned into paper clips and reality is probably in between those two. Do you think people are worrying about the right things or have they lost the plot in some way?

首先，我称自己为务实的乐观主义者。我不是乌托邦主义者。所以我其实是那种无聊的人。我不相信任何一方的极端。我周游世界。就在上个月，我去了中东、欧洲、英国、加拿大，然后回到美国。我认为美国人和西欧人比中东和亚洲人更担忧 AI。我们不必争论他们为什么更担忧，但就我们而言，我希望我有一个扩音器告诉美国人：你们以创新著称，这个国家为人类和文明创新了那么多伟大的事物。我们有一个自由而充满活力的社会，我们有一个政治体系，我们仍然对如何建设国家有发言权。我确实希望我们的国家对使用 AI 的未来有更多的乐观和积极，而不是现在听到的那样。我认为像我这样住在硅谷的技术人员在正确的公共沟通方面有很大责任。有很多事情没有以有效的方式传达。但我确实希望我们能给这个国家的每个人灌输更多的希望和自主感，因为我认为以正确的方式使用 AI 有巨大的好处。我希望不仅仅是硅谷或曼哈顿的人，而是农村社区、传统产业、所有 50 个州的人都能拥抱并受益于 AI。

First of all, I call myself a pragmatic optimist. I'm not a utopian. So I'm actually the boring kind. I don't believe in the extreme on both sides. I travel around the world. Just last month I was in Middle East, I was in Europe, I was in UK and I was in Canada, I came back home in America. I think people in America and people in Western Europe are more worried about AI than say people in Middle East, in Asia. And I think we don't have to litigate why they're more worried, but just to come closer to home just in talk about us. I wish I have a megaphone to tell people in the US that you're known to be one of the most innovative people our country have innovated so many great things for humanity for civilization. We have a society that is free and vibrant and we have a political system that we still have so much say in how we want to build our country. I do wish that our country has more optimism and positivity towards the future of using AI than what is being heard now. I think people like me technologists living in Silicon Valley has a lot of responsibility in the right kind of public communication. So there's a lot of things that was not communicated in the effective way. But I do hope that we can instill more sense of hope and self agency into everybody in our country because I think there's so much upside of using AI in the right way. And I want not just people in Silicon Valley or in Manhattan, but I want people in rural communities in traditional industries in everywhere 50 states to be able to embrace and benefit from AI.

你为什么在建造你现在建造的东西？World Labs 是什么？为什么决定做这个？

Why are you building what you're building? What is World Labs? Why decide to do this?

我实际上经常回答这个问题，对我团队的每个成员。

I actually answer this question very often to every member of my team.

嗯。

Mhm.

我创立了 World Labs。这个答案有两个层面。从技术角度看，World Labs 正在构建下一代 AI，专注于空间智能，因为空间智能就像语言智能一样，是解锁机器不可思议能力的基础，从而帮助人类更好地创造、制造、设计、建造更好的机器人。所以空间智能是一项关键技术。

I built World Labs. There are two levels of this answer. From a technology point of view, World Labs is building the next generation AI focusing on spatial intelligence because spatial intelligence just like language intelligence is fundamental in unlocking incredible capabilities in machines so that it can help humans to create better, to manufacture better, to design better, to build better robots. So spatial intelligence is a lynchpin technology.

嗯。

Mhm.

但再往上一层，为什么我仍然是一名技术专家？因为我相信人类是唯一建立文明的物种。动物建立群落或群体，但我们建立文明。我们建立文明是因为我们想越来越好。我们想做好事。尽管一路上我们做了很多坏事，但有一种渴望：更好的生活、更好的社区、更好的社会、更健康、更繁荣。

But one level up, why am I still a technologist? Is because I believe humanity is the only species that builds civilizations. Animals builds colonies or herds, but we build civilizations. And we build civilizations because we want to be better and better. We want to do good. Even though along the way we do a lot of bad things but there is a desire of having better lives, having better community, having better society, live more healthily, have more prosperity.

这种渴望是文明的基础。因为我相信人类能做到，我相信科学和技术是建设文明最强大的工具之一，我想为此做出贡献。这就是为什么我仍然是一名科学家和技术专家，并为此创立了 World Labs。

That desire is where civilization is built upon. And because I believe that humanity can do that, I believe science and technology is the most powerful tool, one of the most powerful tools in building civilizations and I want to contribute to that. That's why I'm still a scientist and a technologist and I'm building world labs for that.

你能向人们解释什么是空间智能，以及你们正在构建的产品——至少目前的样子——是什么吗？

Can you explain to people what spatial intelligence is and what the product is so to speak at least as it stands right now that you're building?

空间智能是人类拥有的一种超越语言的能力：当你把三明治装进袋子，当你跑步或登山，当你粉刷卧室——所有与看到并将场景转化为对 3D 世界、环境的理解，进而与之互动、改变、享受、创造事物相关的事情。整个看与做的循环都由空间智能支持。对吧？你能打包三明治，意味着你知道面包的样子，知道如何把刀放在中间，知道如何把生菜叶放在面包上，知道如何把面包或三明治放进密封袋。每一步都是空间智能。

Spatial intelligence is a capability that humans have which goes beyond language is when you pack a sandwich in a bag when you take a run or a hike in a mountain. When you paint your bedroom, everything that has to do with seeing and turning that scene into understanding of the 3D world, understanding of the environment and then in turn you can interact with it, you can change it, you can enjoy it, you can make things out of it. That whole loop between seeing and doing is supported by the capability of spatial intelligence. Right? The fact that you can pack a sandwich means you know what the bread looks like. You know how to put the knife in between. You know how to put the lettuce leaf on the bread. You know how to put the bread or sandwich into a Ziploc bag. Every part of this is spatial intelligence.

嗯。

Mhm.

今天的 AI 有这种能力吗？它在变得更好，但与语言智能相比，AI 在视觉、推理以及在虚拟 3D 世界和真实 3D 世界中行动的能力仍然非常早期。

And does today's AI have that? It's getting better, but compared to language intelligence, AI is still very early in that ability to see, to reason, and also to do in world in both virtual 3D world as well as real 3D world.

这就是 World Labs 正在做的事情。我们正在创建一个前沿模型，使其具备智能能力，能够创造世界、推理世界，并让创作者、设计师或机器人等与世界互动。这就是空间智能。

So that's what World Labs is doing. We are creating a frontier model that can have intelligent capability in the model to create world, to reason around the world, and to enable, for example, creators or designers or robots to interact with the world. So that's spatial intelligence.

你能详细说说设计师、创作者或机器人与世界互动吗？我的团队一直在玩一些工具，所以谢谢你。这意味着什么？如果让你描绘一下一年后、两年后的情景，人们会如何使用它，或者机器人会如何使用它？

Could you expand on the designers, creatives, or robots interacting with the world? My team has been playing with some of the tools, so thank you for that. What does that mean? If you could paint a picture for a year from now, two years from now, how might someone use this or how might a robot use this?

几周前我和一个人聊天，真的很受启发。高中剧院的预算非常低，对吧？有时我去旧金山歌剧院或音乐剧，那些为剧院搭建的布景非常漂亮。

I was just talking to someone a couple of weeks ago, and it was really inspiring. High school theaters are very low budget, right? Sometimes I go to San Francisco opera or musicals, and the sets built for theater are just so beautiful.

嗯。

Mhm.

但高中或初中很难有那样的预算去做。想象一下，你可以用我们现在的 World Labs 模型，我们称之为 Marble。

But it's very hard for high school or middle school to have that budget to do that. Imagine that you can take today's World Labs model, we call it Marble.

嗯。

Mhm.

然后你创建一个中世纪法国小镇的布景。把它放在背景中，用这种数字形式帮助演员和动作进入那个世界。当然，根据辅助技术，无论你是在电脑上，还是最终人们可以使用头显或其他设备，你都能获得身临其境的感觉，仿佛置身于中世纪法国小镇。这对很多创作者来说将是一个惊人的创意工具。

And then you create a set in a medieval French town. And then you put that in the background and use that digital form to help transport the actors and action into that world. And of course, depending on the auxiliary technology, whether you're on a computer or eventually people can use a headset or whatever, you can have that immersive feeling of being in a medieval French town. That would be an amazing creative tool for a lot of creators.

这就是几周前我和一个人聊到的例子。但我们已经看到世界各地的创作者。有些是视觉特效创作者，有些是室内设计创作者，有些是游戏创作者，有些是教育工作者，他们想构建世界，让学生进入不同的体验。他们已经开始使用我们的模型，因为他们发现它非常强大，触手可及，能够创建 3D 世界，用来沉浸他们的角色或自己。

That was the example someone and I were talking about a couple of weeks ago. But we already see creators all over the world. Some of them are VFX creators, some are interior design creators, some are gaming creators, some are educators who want to build worlds that transport their students into different experiences. They are already starting to use our model because they find it very powerful at their fingertips to be able to create 3D worlds that they can use to immerse either their characters or themselves into.

就流程而言，如果有人想知道这是如何工作的，假设是一位公立学校老师，希望激励和教导学生，付出额外努力。使用这个是什么样子的？他们是输入文字描述他们想创建的世界，上传素材或照片，几乎像一个图片板吗？如果非技术人员，它是如何工作的？

And just process-wise, if someone's wondering how this works, let's say it's a public school teacher hoping to inspire and teach their students, going the extra mile. What does it look like for someone to use this? Are they typing in text, describing the world they'd like to create, uploading assets or photos, almost like an image board? How does it work if someone's nontechnical?

他们完全不需要懂技术。他们打开我们的页面，在桌面或手机上，但桌面更有趣，因为功能更多。然后他们可以输入，比如，一个法国中世纪小镇，或者他们可以去任何地方。他们可以用 Midjourney 或 Nano Banana 创建一张法国中世纪小镇的照片，或者他们可以拿一张真实的照片，然后上传。我们称之为提示。几分钟后，我们的模型会给你一个 3D 世界，比如，标签的一部分。它的范围确实有限。然后那个 3D 世界是真正的 3D，因为你可以用鼠标拖拽、转身、走动，看到那个世界。然后下游，如果你想使用它，有很多方法。你可以用我们网站上的一个工具，只需放置摄像机，就能制作一部电影。

They don't need to be technical at all. They open our page on desktop or on their phone, but desktop is more fun because it has more features. And then they can type, you know, a French medieval town, or they can actually go to anywhere. They can use Midjourney or Nano Banana to create a photo of a French medieval town, or they can get an actual photo about that and then they upload it. We call it prompt. And then after a few minutes, our model gives you a 3D world that is, say, a part of the tab. It does have a limit in its range. And then that 3D world is generally 3D because you can just use the mouse to drag and turn around and walk around and see that world. And then downstream, if you want to use it, you have many ways to use it. You can actually create a movie out of it by using one of our tools on the website to just put cameras and you can make a particular movie out of it.

如果你是游戏开发者……我正想说这听起来很像游戏引擎。

You could, if you're a game developer... I was just going to say it sounds a lot like a gaming engine.

是的，你可以在里面放很多角色。如果你是视觉特效专业人士，我们有很多视觉特效专业人士。他们实际上可以把这个放进电影拍摄的工作流程中，让真实演员拍摄电影。我们还有心理学研究人员在特定的精神病学研究中使用了那个沉浸式世界。

Yes, you can put a lot of characters in it. If you're a VFX professional, we have a lot of VFX professionals. They can actually take this and put it in the workflow of their movie shooting and have real actors shooting movies. We've also had psychology researchers using that immersive world in particular psychiatric studies.

我们也可以把它用作机器人训练的模拟，因为很多机器人训练需要大量数据，然后用它来生成大量不同的数据。所以这几乎就像机器人进入现实世界前的飞行模拟器？

We could also use that as the simulation for robotic training because a lot of robotic training needs a lot of data and then use that for generating a lot of different data. So is it almost like a flight simulator for robots before they go into the real world?

这是目标的一部分。我们还处于早期阶段。所以飞行模拟器还不完整，对吧？但这是旅程的一部分。

That's part of the goal. We are still early. So the flight simulator is not complete yet, right? But that's part of the journey.

你提到了精神病学研究。我想你刚才提到了。是的。那会是什么样子？

You mentioned psychiatric studies. I think that's what you just mentioned. Yes. What might that look like?

我们确实有一位研究人员联系我们，他们正在研究有心理障碍的人，比如强迫症，这些人会被某些环境触发，他们想研究触发因素，也研究治疗方法。但你怎么触发一个人，比如说，对草莓田有特别问题的人？我是编的。我的意思是，你可以带他们去草莓田，但如果你想知道是夏天的草莓田还是夜晚的草莓田，或者是草莓交配的草莓田？你怎么做？突然这位研究人员意识到，我们给了他们最便宜的方式来变化各种维度，他们可以测试并做研究。

We actually got this researcher who called us and they're studying people who have psychological disorders like obsessive-compulsive disorder where they're triggered by certain environments, and they want to study the trigger and also just study how the treatment works. But how do you trigger someone who, let's say, has a particular issue with, let's say, a strawberry field? I'm making it up. I mean, you can take them to a strawberry field, but what about if you want to know if it's a strawberry field in the summer or a strawberry field at night, or it's a strawberry field with mating strawberries? How do you do this? Suddenly this researcher realized we give them the cheapest possible way of varying all kinds of dimensions, and they can test this out and do their studies.

这真的很有趣。是的，我可以看到它被应用于，可能叫做暴露疗法，但既然你描述了它，我可以看到它如何被添加到，我的意思是几乎一切，对吧？我的意思是，如果你想想人类在现实世界中如何运作。

That's really interesting. Yeah, I could see it being applied to, it might be called exposure therapy, but now that you're describing it, I could see how it could be added into, I mean pretty much everything, right? I mean, if you think about how humans operate in the real world.

是的。现实世界和数字世界之间的界限越来越小，对吧？越来越薄，因为我们生活在许多屏幕中。我们生活在现实世界。我们在虚拟世界中做事。我们在现实世界中做事。我们将创造能够在现实世界和虚拟世界中做事的机器。

Yes. And the boundary between real world and digital world is less and less, right? Thinner and thinner because we live in many screens. We live in the real world. We do things in virtual world. We do things in real world. We will create machines that can do things in real world and virtual world.

所以我们在数字和物理空间做了很多事。

So there's a lot we do in digital and physical spaces.

你关注哪些科学家或研究人员，他们不一定是那些已经非常公开的大品牌和明星？有没有谁让你觉得，你知道，有一些非常了不起的人在做好工作？

Who are some scientists or researchers who you pay attention to who are not necessarily kind of the big brand names and marquee lights that are already very public in the world? Is there anybody who stands out where you're like, you know, there's some really tremendous people doing good work?

嗯，这是我写书的部分原因，尤其是在中间章节，我写了做 ImageNet 的历程，它结合了认知科学和计算机科学。我实际上谈到了心理学家、神经科学家和发展心理学家。你知道，有些还在世，有些已经不在了。例如，已故的 Anne Treisman，他们在过去几年都去世了。但他们是认知科学领域的巨人，他们的工作影响了计算机科学，最终影响了 AI。你知道，世界各地还有很多科学家。其中很多在美国，他们是发展心理学和 AI 领域的思想家。我关注他们的工作。

Well, that's part of the reason I wrote the book, especially in the middle chapters where I wrote about the journey of doing ImageNet that combines cognitive science with computer science. And I actually talk about psychologists and neuroscientists and developmental psychologists. You know, some of them are still with us, some of them are not. For example, the late Anne Treisman, they all passed away in the last few years. But they were giants in cognitive science whose work has informed computer science and eventually AI. You know, there are still lots of scientists around the world. Many of them are in the US who are thinkers in developmental psychology in AI. I follow their work.

我还想听听你的看法，关于那些可能——这个词可能有点重——但似乎不可避免的近期或中期发展。我给你举个例子。2008、2009 年我参与 Shopify 时，他们只有大约 10 名员工。当时有几件事正在发生，你可以问一些问题：未来 10 到 20 年，宽带接入会更多还是更少？更多。电子商务会更多还是更少？更多。当你有四五个这样的问题，在足够长的时间跨度内答案都是绝对肯定时，就开始勾勒出一幅图景了。未来几年里，有没有哪些事情你认为被低估了，几乎是必然发生的？

I would also love to get your perspective on what might be this is a very strong word but seemingly inevitable in terms of developments in the near intermediate future. And I'll give you an example of what I mean. In 2008-2009 I became involved with Shopify when they had like 10 employees. There were a few things happening around that time and you could ask questions: in the next 10 or 20 years, will there be more broadband access or less? More. Will there be more e-commerce or less? More. When you have four or five of those that seem over a long enough time horizon absolutely yes, it begins to paint a picture. Are there any things in the next handful of years you think are perhaps underappreciated as near inevitabilities?

你想让我谈谈被低估的事情？我的意思是，我不知道它们是否被高估了，但肯定是被重视的。对电力的需求是被重视的。AI 越来越多而不是越来越少的趋势是被重视的。机器人到来的长期趋势是被重视的。所以这些是被重视的。被低估的是空间智能——在某种意义上被低估了，因为每个人仍然在谈论大型语言模型，但实际上对像素和 3D 世界的世界建模被低估了，因为它驱动着从故事讲述、娱乐、体验到机器人模拟的许多事情。我认为 AI 和教育被低估了，因为我们将看到 AI 可以加速那些想学习的人的学习，这将对我们的学校系统以及人力资本格局产生下游影响，比如我们如何评估合格的工人？过去是你从哪所学校毕业、获得什么学位，但这将会改变。AI 触手可及，这一点被低估了。我认为 AI 对我们经济结构（包括劳动力市场）的影响被低估了。其中的细微差别被低估了。我认为这种要么完全乌托邦、后稀缺的言论是夸张的，或者说每个人的工作都会消失也是夸张的。但混乱的中间地带是从知识工作者到蓝领、到酒店业等等正在发生的所有这些变化。这被我们的政策制定者、学者以及整个社会所低估了。

You want me to talk about underappreciated? I mean, I don't know if they're overappreciated, but definitely appreciated. The need for power is appreciated. The trend of more AI, not less AI is appreciated. The long-term trend of robots coming is appreciated. So these are appreciated. What's underappreciated is spatial intelligence is underappreciated in the sense that everybody's still talking about large language models, but really world modeling of pixels of 3D worlds is underappreciated because it powers so many things from storytelling to entertainment to experiences to robotic simulation. I think AI and education is underappreciated because what we are going to see is that AI can accelerate the learning for those who want to learn, which will have downstream implications in our school system as well as in just human capital landscape like how do we assess qualified workers? Used to be which school you graduate from with which degree, but that will be changing. With AI being at the fingertip of so many people, that's underappreciated. I think AI's impact in our economic structure including labor market is underappreciated. The nuance is underappreciated. I think this whole rhetoric of either total utopia post scarcity is hyperbolic, or like everybody's job will be gone is hyperbolic. But the messy middle is how from knowledge worker to blue collar to hospitality to all these changes that's happening. It's underappreciated by our policy workers, by our scholars, by just overall society.

那么，从工作角度来看，有哪些细微差别呢？也许这与我之前答应要问你的问题有关，那就是你正在告诉或将要告诉你的孩子们什么。假设他们正处于决定该学什么、该专注于什么的年龄，你会如何回答这个问题，哪怕是暂时性的？

Well, what are some of the nuances from the job perspective? Maybe this ties into what I promised earlier I was going to ask you, which is what you are telling or will tell your children. Let's just say I don't know how old they are, but if we assume they are of the age where they're trying to decide what they should study, where they should focus, how would you think about answering that even provisionally?

我认为学习能力更加重要，因为当学习工具较少时，更容易沿着既定轨道走。你经历小学、初中、高中、大学，然后接受一些职业培训，这是一条路径，伴随着一系列来自学位的结构化凭证。但 AI 真的改变了这一点。例如，我的初创公司在面试软件工程师时，说实话，我个人觉得他们的学位对我们来说现在不那么重要了。更重要的是你学到了什么，你使用什么工具，你多快能利用这些工具增强自己，而这些工具很多是 AI 工具。你对使用这些工具的心态更重要。在 2025 年的今天，在 World Labs 招聘时，我不会雇佣任何不接受 AI 协作软件工具的软件工程师。不是因为我相信 AI 软件工具是完美的，而是因为我相信这首先显示了这个人随着快速发展的工具包成长的能力、开放的心态，而且最终结果是如果你能使用这些工具，你就能学习，你能更好地增强自己。所以这确实在转变。回到你的问题，你告诉年轻人什么，告诉孩子们什么？我认为学习如何学习这一永恒的价值，学习能力，现在更加重要。

I think the ability to learn is even more important because when there were fewer tools to learn, it's easier to just follow tracks. You go through elementary school, middle school, high school, college and then get some vocational training and that's kind of a path, and with that is a set of structured credentials from degrees. But AI has really changed it. For example, my startup when we interview a software engineer, honestly how much I personally feel the degree they have matters less to us now. It's more about what have you learned, what tools do you use, how quickly can you superpower yourself in using these tools, and a lot of these are AI tools. What's your mindset towards using these tools matters more. At this point in 2025, hiring at World Labs, I would not hire any software engineer who does not embrace AI collaborative software tools. It's not because I believe AI software tools are perfect. It's because I believe that shows first of all the ability of the person to grow with the fast growing toolkits, the open-mindedness, and also the end result is if you're able to use these tools, you're able to learn, you can superpower yourself better. So that is definitely shifting. Coming back to your question, what do you tell young people, tell children? I think the timeless value of learning to learn, the ability to learn is even more important now.

我们在谈话中让我想到，对于有抱负的人来说，成为超级自学者只会越来越容易，对吧？我们已经从 YouTube 上看到了这一点。现在你可以要么娱乐至死，避免做那些有助于自我成长和发展的事情，要么你可以加速成长。同样地，AI 也是如此，对吧？你向前看，我们甚至不需要向前看，但问题是老师如何审核学生是否在做他们应该做的工作？在很多层面上，这已经变得——有一些例外，但几乎不可能。学生要么逃避所有工作，要么加速完成自己的工作，但至少在短期内，输出可能看起来非常相似。所以学校教育将会发生很大变化。这非常有趣。

It strikes me as we're talking that it's only going to get increasingly easier for the ambitious to act as superpowered autodidacts, right? We've already seen this with YouTube. Now you can either entertain yourself to death and avoid doing things that help with self-growth and development, or you can supercharge it. And similarly with AI, right? You flash forward. We don't even need to flash forward, but it's how does a teacher audit that their students are doing the work they're supposed to be doing? On so many levels, it's getting to the point—there are some exceptions, but of near impossibility. And students can either avoid all work or they can supercharge their own work, but the output might look very similar at least for a period of time. So schooling is going to change a lot. It's very interesting.

我实际上认为，Tim，如果学校的评估结构使得 AI 给出的和学生给出的一样，那么评估结构本身就有问题。

I actually think Tim, if the school evaluation is structured in a way that whatever AI gives and whatever the student gives is the same, there's something wrong with the structure of the evaluation.

你能详细说说吗？这很有趣。

Can you say more about that? That's interesting.

例如，英语作文。这不是我的故事，是我听到的一个我非常认同的故事。我复述一下。一位高中一年级英语老师，在开学第一天，实际上对全班说：‘我想向你们展示我会如何给 AI 打分。’于是老师给了一个作文题目，向学生展示‘这是最好的 AI 给我的，我会告诉你们我认为哪里好、哪里不好、哪里不够好，我会给它 B-。现在我要告诉你们这是我的标准。如果你懒到让 AI 替你写作文，这就是你会得到的分数。但你可以使用 AI，这完全没问题。但如果你能自己完成工作、学习、思考，成为最好的人类创作者，并在此基础上改进，你可以得到 A+。’

For example, English essay. This is not me. This is me hearing a story that I so agree with. I'll retell the story. A high school freshman English class teacher, on the first day of school, actually said to the class, 'I want to show you how I would score AI.' So the teacher gave an essay topic, showed the students 'this is what the best AI gave me, and I'm going to show you how I think this is good, this is bad, how this is suboptimal, and I'll give it a B minus. Now I will tell you this is my bar. If you're so lazy that you ask AI to write your essay, this is what you're going to get. But you can use AI, that's totally fine. But if you can do the work, learn, think, be the best human creator you can and work on top of that, you can get to A+es.'

我知道我们只剩几分钟了。第五个问题，我想问你一个我经常问的问题：如果你可以把一句引语或一条信息放在广告牌上，让成千上万的人看到，假设他们都能理解。可以是一张图片，一个问题，一句引语，任何东西。一句格言，一句咒语，都可以。几乎可以是任何东西。你会或可能在那块广告牌上放什么？

I know we only have a few minutes left. Fifth, I wanted to ask you a question I ask a lot, which is if you could put a quote or a message, something on a billboard, something to get in front of millions, billions of people. Just assume they all understand it. Could be an image, could be a question, could be a quote, anything at all. A saying, a mantra, doesn't matter. Could be almost anything. What would you or what might you put on that billboard?

你的北极星是什么？

What is your northstar?

你的北极星是什么？这当然至关重要，又回到了你如何为自己定义或找到它。我的意思是，你谈到了大胆的问题，然后那会引向北极星或假设。除此之外，你还有什么方法鼓励人们思考找到自己的北极星？

What is your northstar? This is of course critically important and coming back to how you define that or find that for yourself. I mean you were talking about audacious questions and then that leading to a north star or hypothesis. Is there another way that you would encourage people on top of that to think about finding their north star?

我相信正是这一点让我们如此人性化，让我们如此充满活力：我们作为一个物种，可以超越仅仅追逐基本需求而活，而是追逐梦想、使命、目标和激情，每个人的北极星都不同，这没关系。不是每个人都必须把 AI 作为他们的北极星，但找到它再次触及了教育的核心，我不是指正式的课堂教育。这只是教育的旅程。其中很大一部分是学习认识自己、学习如何制定你的北极星以及如何追逐它的能力。

I believe that's how that makes us so human and makes us to be so fully alive is that we as a species can live beyond the chasing of just basic needs, right, but dreams and missions and goals and passion, and everybody's northstar is different, and that's fine. Not everybody has to have AI as their northstar, but finding that goes to the heart of education again, and I don't mean formal classroom education. It's just the journey of education. A lot of that is the ability to learn who you are and to learn how to formulate your northstar and how to chase after that.

最后一个问题。你父母有没有解释过为什么给你取名 Fei-Fei？

Last question. Did your parents ever explain to you why they named you Fei-Fei?

是的。因为我妈妈分娩时，我爸爸像往常一样迟到了医院，路上他抓了一只鸟。他放走了它，但他确实抓了一只鸟。我不知道，他只是分心了，那是在北京城里。我爸爸骑自行车去我妈妈的医院。这启发他给我取名 Fei-Fei。

Yes. It's because when my mom was going through labor, my dad was characteristically late to the hospital and along the way he caught a bird. He let it go, but he did catch a bird. I don't know, he was just distracted and it was in Beijing in the city of Beijing. My dad was bicycling to my mom's hospital. That inspired him to call me Fei-Fei.

Fei-Fei。

Fei-Fei.

哦，等等。对不起那些不会说中文的人。我忘了你会说中文，但对于那些不会说中文的人，Fei-Fei 的意思是飞翔。

Oh, wait. Sorry for those who don't speak Chinese. I forgot you do speak Chinese, but for those who don't speak Chinese, Fei-Fei means flying.

意思是飞翔。

Means flying.

是的。所以，被一只鸟启发。

Yeah. So, be inspired by a bird.

很快说一下，我觉得挺有趣的。我的第一个中文名字是费听冲，因为我非常直率诚实。所以是听，但费听冲，但刚开始时，我的中文声调不标准，人们以为我说我的名字是飞机场。所以我向老师请求，我们改了一个不那么容易混淆的名字。

Really quick, I'll just say it's kind of funny. My first Chinese name that I had was Fei Ting Chong, which is because I was very blunt and honest. So, Ting, but Fei Ting Chong, but when I was first starting, my tones in China were not polished and people thought I was saying that my name was Fei Ji Chang, which is airport. So, I petitioned my teachers and we changed my name to something less confusing.

你的新名字是什么？

What's your new name?

哦，好的。

Oh, okay.

它有点像，但底部没有那个。

It's like but it's without the at the bottom.

哦，哇。好花哨的名字。比我的高级多了……

Oh, wow. Fancy name. That's way more sophisticated than my...

嗯，我可以和我的中文老师一起写，所以我有不公平的优势。

Well, I get to script it with my Chinese teachers, so I have an unfair advantage.

李博士，非常感谢您的时间。我们会在 tim.blog/mpodcast 上为所有人提供节目笔记的链接。他们可以轻松找到您，每个人都应该看看 worldlabs.ai，我们会把其他所有链接、您的社交媒体等都放在节目链接中。但再次感谢您的时间。我真的很感激。

Dr. Li, thank you so much for the time. We will link to the show notes for everybody at tim.blog/mpodcast. They'll be able to find you easily and everybody should check out worldlabs.ai and we'll put every other link, your social and so on in the show links. But thank you for the time. I really appreciate it.

谢谢你，Tim。我很享受我们的对话。

Thank you, Tim. I enjoyed our conversation.

是的，我也是。

Yeah, likewise.

好的，再见。

Okay, bye.

再见。

Bye.