The Godmother of AI on jobs, robots & why world models are next | Dr. Fei-Fei Li

Lenny Rachitsky: A lot of people call you the godmother of AI.

Lenny Rachitsky：很多人称您为人工智能教母。

The work you did actually was the spark that brought us out of AI winter.

您所做的工作实际上是让我们走出人工智能冬天的火花。

Dr. Fei Fei Li: In the middle of 2015, middle of 2016, some tech companies avoid using the word AI because they were not sure if AI was a dirty word.

李飞飞博士：2015年中、2016年中，一些科技公司避免使用AI这个词，因为他们不确定AI是否是一个肮脏的词。

2017-ish was the beginning of companies calling themselves AI companies.

2017 年左右，许多公司开始自称为人工智能公司。

Lenny Rachitsky: There's this line, I think, this was when you were presenting to Congress.

Lenny Rachitsky：我想，这是你向国会做演讲时的台词。

There's nothing artificial about AI.

人工智能没有任何人造之处。

It's inspired by people.

它的灵感来自于人们。

It's created by people, and most importantly, it impacts people.

它是由人创造的，最重要的是，它影响着人。

Dr. Fei Fei Li: It's not like I think AI will have no impact on jobs or people.

李飞飞博士：我并不认为人工智能不会对工作或人产生影响。

In fact, I believe that whatever AI does, currently or in the future, is up to us.

事实上，我相信无论人工智能现在或将来做什么，都取决于我们。

It's up to the people.

这取决于人民。

I do believe technology is a net positive for humanity, but I think every technology is a double-edged sword.

我确实相信技术对人类来说是积极的，但我认为每项技术都是一把双刃剑。

If we're not doing the right thing as a society, as individuals, we can screw this up as well.

如果我们作为一个社会和个人没有做正确的事，我们也可能会把事情搞砸。

Lenny Rachitsky: You had this breakthrough insight of just, okay, we can train machines to think like humans, but it's just missing the data that humans have to learn as a child.

Lenny Rachitsky：你有一个突破性的见解，好吧，我们可以训练机器像人类一样思考，但它只是缺少人类小时候必须学习的数据。

Dr. Fei Fei Li: I chose to look at artificial intelligence through the lens of visual intelligence because humans are deeply visual animals.

李飞飞博士：我选择从视觉智能的角度来看待人工智能，因为人类是深度视觉动物。

We need to train machines with as much information as possible on images of objects, but objects are very, very difficult to learn.

我们需要用尽可能多的物体图像信息来训练机器，但物体非常非常难以学习。

A single object can have infinite possibilities that is shown on an image.

单个物体可以具有图像上显示的无限可能性。

In order to train computers with tens and thousands of object concepts, you really need to show it millions of examples.

为了用成千上万的对象概念训练计算机，您确实需要向其展示数百万个示例。

Lenny Rachitsky: Today, my guest is Dr. Fei-Fei Li, who's known as the godmother of AI.

Lenny Rachitsky：今天，我的嘉宾是被称为人工智能教母的李飞飞博士。

Fei-Fei has been responsible for and at the center of many of the biggest breakthroughs that sparked the AI revolution that we're currently living through.

飞飞负责并处于许多重大突破的中心，这些突破引发了我们目前正在经历的人工智能革命。

She spearheaded the creation of ImageNet, which was basically her realizing that AI needed a ton of clean-labeled data to get smarter, and that data set became the breakthrough that led to the current approach to building and scaling AI models.

她带头创建了 ImageNet，这基本上是她意识到人工智能需要大量干净标签的数据才能变得更加智能，而该数据集成为了导致当前构建和扩展人工智能模型方法的突破。

She was chief AI scientist at Google Cloud, which is where some of the biggest early technology breakthroughs emerged from.

她是谷歌云的首席人工智能科学家，谷歌云是一些最大的早期技术突破的发源地。

She was director at SAIL, Stanford's Artificial Intelligence Lab, where many of the biggest AI minds came out of.

她是斯坦福大学人工智能实验室 SAIL 的主任，许多最伟大的人工智能人才都出自该实验室。

She's also co-creator of Stanford's Human-Centered AI Institute, which is playing a vital role in a direction that AI is taking.

她还是斯坦福大学以人为中心的人工智能研究所的联合创始人，该研究所在人工智能发展的方向上发挥着至关重要的作用。

She's also been on the board of Twitter.

她也是 Twitter 的董事会成员。

She was named one of Time's 100 Most Influential People in AI.

她被《时代》杂志评为人工智能领域 100 名最具影响力人物之一。

She's also United Nations advisory board.

她还是联合国顾问委员会成员。

I could go on.

我还可以继续说下去。

Lenny Rachitsky: In our conversation, Fei-Fei shares a brief history of how we got to today in the world of AI, including this mind-blowing reminder that 9 to 10 years ago, calling yourself an AI company was basically a death knell for your brand because no one believed that AI was actually going to work.

Lenny Rachitsky：在我们的谈话中，飞飞分享了人工智能世界如何走到今天的简史，包括这个令人兴奋的提醒：9 到 10 年前，称自己为人工智能公司基本上是你品牌的丧钟，因为没有人相信人工智能实际上会发挥作用。

Today, it's completely different.

今天，情况完全不同了。

Every company is an AI company.

每家公司都是人工智能公司。

We also chat about her take on how she sees AI impacting humanity in the future, how far current technologies will take us, why she's so passionate about building a world model and what exactly world models are, and most exciting of all, the launch of the world's first large world model, Marble, which just came out as this podcast comes out.

我们还讨论了她如何看待人工智能在未来对人类的影响、当前的技术将带我们走多远、为什么她如此热衷于构建世界模型以及世界模型到底是什么，最令人兴奋的是世界上第一个大型世界模型 Marble 的推出，它刚刚在本播客发布时发布。

Anyone can go play with this at marble.worldlabs.ai.

任何人都可以在marble.worldlabs.ai 上玩这个。

It's insane.

太疯狂了。

Definitely check it out.

一定要检查一下。

Fei-Fei is incredible and way too under the radar for the impact that she's had on the world, so I am really excited to have her on and to spread her wisdom with more people.

菲菲是令人难以置信的，而且她对世界的影响也很低调，所以我真的很高兴能有她加入并与更多人传播她的智慧。

Lenny Rachitsky: A huge thank you to Ben Horowitz and Condoleezza Rice for suggesting topics for this conversation.

Lenny Rachitsky：非常感谢 Ben Horowitz 和 Condoleezza Rice 为本次对话提出的主题。

If you enjoy this podcast, don't forget to subscribe and follow it in your favorite podcasting app or YouTube.

如果您喜欢这个播客，请不要忘记订阅并在您最喜欢的播客应用程序或 YouTube 中关注它。

With that, I bring you Dr. Fei-Fei Li after a short word from our sponsors.

在我们的赞助商简短发言后，我为您带来了李飞飞博士。

Lenny Rachitsky: This episode is brought to you by Figma, makers of Figma Make.

Lenny Rachitsky：本集由 Figma（Figma Make 的制作者）为您带来。

When I was a PM at Airbnb, I still remember when Figma came out and how much it improved how we operated as a team.

当我在 Airbnb 担任产品经理时，我仍然记得 Figma 的问世以及它对我们团队运作方式的改善。

Suddenly, I can involve my whole team in the design process, give feedback on design concepts really quickly and it just made the whole product development process so much more fun.

突然间，我可以让我的整个团队参与到设计过程中，非常快速地提供设计概念反馈，这使得整个产品开发过程变得更加有趣。

But Figma never felt like it was for me.

但 Figma 从来不觉得它适合我。

It was great for giving feedback and designs, but as a builder, I wanted to make stuff.

它非常适合提供反馈和设计，但作为一名构建者，我想要制作东西。

That's why Figma built Figma Make.

这就是 Figma 构建 Figma Make 的原因。

With just a few prompts, you can make any idea or design into a fully functional prototype or app that anyone can iterate on and validate with customers.

只需几个提示，您就可以将任何想法或设计制作成功能齐全的原型或应用程序，任何人都可以与客户进行迭代和验证。

Lenny Rachitsky: Figma Make is a different kind of vibe coding tool.

Lenny Rachitsky：Figma Make 是一种不同类型的氛围编码工具。

Because it's all in Figma, you can use your team's existing design building blocks, making it easy to create outputs that look good and feel real and are connected to how your team builds.

因为一切都在 Figma 中，所以您可以使用团队现有的设计构建块，从而轻松创建看起来不错、感觉真实并与团队构建方式相关的输出。

Stop spending so much time telling people about your product vision and instead show it to them.

不要再花太多时间告诉人们你的产品愿景，而是向他们展示它。

Make code-back prototypes and apps fast with Figma Make.

使用 Figma Make 快速制作代码返回原型和应用程序。

Check it out at figma.com/lenny.

请访问Figma.com/lenny 进行查看。

Lenny Rachitsky: Did you know that I have a whole team that helps me with my podcast and with my newsletter?

Lenny Rachitsky：您知道我有一个完整的团队帮助我制作播客和时事通讯吗？

I want everyone on that team to be super happy and thrive in the roles.

我希望团队中的每个人都超级快乐并在角色中茁壮成长。

Justworks knows that your employees are more than just your employees; they're your people.

Justworks 知道您的员工不仅仅是您的员工；他们是你的人。

My team is spread out across Colorado, Australia, Nepal, West Africa, and San Francisco.

我的团队分布在科罗拉多州、澳大利亚、尼泊尔、西非和旧金山。

My life would be so incredibly complicated to hire people internationally, to pay people on time and in their local currencies, and to answer their HR questions 24/7.

我的生活将变得极其复杂，要在国际范围内雇用员工，按时以当地货币支付员工工资，并全天候（24/7）回答他们的人力资源问题。

But with Justworks, it's super easy.

但有了 Justworks，这一切就变得非常简单。

Whether you're setting up your own automated payroll, offering premium benefits, or hiring internationally, Justworks offer simple software and 24/7 human support from small business experts for you and your people.

无论您是要设置自己的自动工资单、提供优质福利还是进行国际招聘，Justworks 都可以为您和您的员工提供简单的软件和来自小型企业专家的 24/7 人工支持。

They do your human resources right so that you can do right by your people.

他们正确地利用您的人力资源，以便您能够正确地对待您的员工。

Justworks, for your people.

适合您的员工。

Lenny Rachitsky: Fei-Fei, thank you so much for being here and welcome to the podcast.

Lenny Rachitsky：飞飞，非常感谢您来到这里，欢迎来到播客。

Dr. Fei Fei Li: I'm excited to be here, Lenny.

李飞飞博士：我很高兴来到这里，Lenny。

Lenny Rachitsky: I'm even more excited to have you here.

Lenny Rachitsky：您的到来让我更加兴奋。

It is such a treat to get to chat with you.

能和你聊天真是一种享受。

There's so much that I want to talk about.

我有很多话想说。

You've been at the center of this AI explosion that we're seeing right now for so long.

长期以来，您一直处于我们现在看到的人工智能爆炸的中心。

We're going to talk about a bunch of the history that I think a lot of people don't even know about how this whole thing started, but let me first read a quote from Wired about you just so people get a sense, and in the intro I'll share all of the other epic things you've done.

我们将讨论一些历史，我想很多人甚至不知道这整件事是如何开始的，但让我先读一下《连线》杂志上关于你的一段话，这样人们就能有所了解，在介绍中我将分享你所做的所有其他史诗般的事情。

But I think this is a good way to just set context.

但我认为这是设置背景的好方法。

"Fei-Fei is one of a tiny group of scientists, a group perhaps small enough to fit around a kitchen table, who are responsible for AI's recent remarkable advances."

“飞飞是一小群科学家中的一员，这个群体可能小到足以围坐在厨房的桌子上，他们对人工智能最近取得的显着进步负有责任。”

Lenny Rachitsky: A lot of people call you the godmother of AI, and unlike a lot of AI leaders, you're an AI optimist.

Lenny Rachitsky：很多人称您为人工智能教母，与许多人工智能领导者不同，您是人工智能乐观主义者。

You don't think AI is going to replace us.

你不认为人工智能会取代我们。

You don't think it's going to take all our jobs.

你不认为它会抢走我们所有的工作。

You don't think it's going to kill us.

你不认为它会杀了我们。

So I thought it'd be fun to start there, just what's your perspective on how AI is going to impact humanity over time?

所以我认为从这里开始会很有趣，您对人工智能将如何随着时间的推移影响人类有何看法？

Dr. Fei Fei Li: Yeah, okay, so Lenny, let me be very clear.

李飞飞博士： 是的，好的，Lenny，让我说清楚。

I'm not a utopian, so it's not like I think AI will have no impact on jobs or people.

我不是一个乌托邦主义者，所以我并不认为人工智能不会对工作或人们产生任何影响。

In fact, I'm a humanist.

事实上，我是一个人文主义者。

I believe that whatever AI does, currently or in the future, is up to us.

我相信，无论人工智能现在或将来做什么，都取决于我们。

It's up to the people.

这取决于人民。

So I do believe technology is a net positive for humanity.

所以我确实相信技术对人类来说是积极的。

If you look at the long course of civilization, I think we are, and fundamentally, we're an innovative species that we...

如果你看看漫长的文明进程，我认为我们从根本上来说是一个创新物种，我们......

If you look at from written record thousands of years ago to now, humans just kept innovating ourselves and innovating our tools, and with that, we make lives better, we make work better, we build civilization, and I do believe AI is part of that.

如果你从几千年前到现在的书面记录来看，人类只是不断地创新自己和创新我们的工具，这样我们就能让生活变得更好，让工作变得更好，让我们建设文明，我相信人工智能是其中的一部分。

So that's where the optimism comes from.

这就是乐观情绪的来源。

But I think every technology is a double-edged sword, and if we're not doing the right thing as a species, as a society, as communities, as individuals, we can screw this up as well.

但我认为每项技术都是一把双刃剑，如果我们作为一个物种、一个社会、社区、个人没有做正确的事情，我们也可能会把事情搞砸。

Lenny Rachitsky: There's this line, I think, this was when you were presenting to Congress, "There's nothing artificial about AI.

Lenny Rachitsky：我想，当你向国会做演讲时，有这样一句话：“人工智能没有任何人为的成分。

It's inspired by people.

它的灵感来自于人们。

It's created by people, and most importantly, it impacts people." I don't have a question there, but what a great line.

它是由人创造的，最重要的是，它影响着人。”我对此没有疑问，但这是一条多么棒的路线啊。

Dr. Fei Fei Li: Yeah, I feel pretty deeply.

李飞飞医生：是的，我感触很深。

I started working AI two and a half decades ago, and I've been having students for the past two decades and almost every student who graduates, I remind them when they graduate from my lab that your field is called artificial intelligence, but there's nothing artificial about it.

我在两个半年前开始研究人工智能，在过去的二十年里我一直在培养学生，几乎每一个毕业生，当他们从我的实验室毕业时，我都会提醒他们，你的领域被称为人工智能，但它没有任何人造的东西。

Lenny Rachitsky: Coming back to the point you just made about how it's kind of up to us about where this all goes, what is it you think we need to get right?

Lenny Rachitsky：回到您刚才提出的观点，即这一切的走向如何取决于我们，您认为我们需要做对的是什么？

How do we set things on a path?

我们如何将事物设置在路径上？

I know this is a very difficult question to answer, but just what's your advice?

我知道这是一个很难回答的问题，但你的建议是什么？

What do you think we should be keeping in mind?

您认为我们应该记住什么？

Dr. Fei Fei Li: Yeah, how many hours do we have?

李飞飞医生： 是的，我们有多少个小时？

Lenny Rachitsky: How do we align AI?

Lenny Rachitsky：我们如何协调人工智能？

There we go.

我们开始吧。

Let's solve it.

让我们解决它。

Dr. Fei Fei Li: So I think people should be responsible individuals no matter what we do.

李飞飞博士：所以我认为无论我们做什么，人们都应该是负责任的个体。

This is what we teach our children, and this is what we need to do as grownups as well.

这是我们教导孩子的，也是我们作为成年人需要做的。

No matter which part of the AI development or AI deployment or AI application you are participating in, and most likely many of us, especially as technologists, we're in multiple points.

无论您参与人工智能开发、人工智能部署或人工智能应用的哪一部分，很可能我们很多人，尤其是作为技术人员，都处于多个点。

We should act like responsible individuals and care about this.

我们应该像负责任的人一样关心这一点。

Actually, care a lot about this.

其实大家很在意这个。

I think everybody today should care about AI because it is going to impact your individual life.

我认为今天每个人都应该关心人工智能，因为它会影响你的个人生活。

It is going to impact your community, it's going to impact the society and the future generation.

它将影响你的社区、社会和下一代。

And caring about it as a responsible person is the first, but also the most important step.

而作为一个负责任的人去关心它是第一步，也是最重要的一步。

Lenny Rachitsky: Okay, so let me actually take a step back and kind of go to the beginning of AI.

Lenny Rachitsky：好的，让我退后一步，谈谈人工智能的起源。

Most people started hearing and caring about AI, as what it's called today, just like, I don't know, a few years ago when ChatGPT came out.

大多数人开始听说并关心人工智能，就像它今天所说的那样，就像几年前 ChatGPT 出现时一样，我不知道。

Maybe it was like three years ago.

也许就像三年前一样。

Dr. Fei Fei Li: Three years ago, almost one more month, three years ago.

李飞飞医生：三年前，差不多一个月了，三年前了。

Lenny Rachitsky: Wow, okay.

莱尼·拉奇茨基：哇，好吧。

And that was ChatGPT coming out.

ChatGPT 就这样问世了。

Is that the milestone you have in mind?

这是您心目中的里程碑吗？

Lenny Rachitsky: Okay, cool.

莱尼·拉奇茨基：好的，酷。

That's exactly how I saw it.

我就是这么看的。

But very few people know there was a long, long history of people working on, it was called machine learning back then and there's other terms, and now it's just everything's AI and there was kind of a long period of just a lot of people working on it.

但很少有人知道，人们在这方面的工作有着很长很长的历史，当时被称为机器学习，还有其他术语，而现在一切都是人工智能，而且在很长一段时间内，有很多人在从事这方面的工作。

And then there's this what people refer to as the AI winter where people just gave up almost, most people did, and just, okay, this idea isn't going anywhere.

然后就是人们所说的人工智能冬天，人们几乎放弃了，大多数人都放弃了，只是，好吧，这个想法不会有任何进展。

And then the work you did actually was essentially the spark that brought us out of AI winter and is directly responsible for the world where now of just AI is all we talk about.

你们所做的工作实际上是让我们走出人工智能冬天的火花，并直接对现在我们谈论的只有人工智能的世界负责。

As you just said, it's going to impact everything we do.

正如您刚才所说，这将影响我们所做的一切。

So I thought it'd be really interesting to hear from you just the brief history of what the world was like before ImageNet and just the work you did to create ImageNet, why that was so important, and then just what happened after.

因此，我认为听到您简单介绍 ImageNet 之前的世界是什么样子以及您为创建 ImageNet 所做的工作、为什么这如此重要以及之后发生的事情会非常有趣。

Dr. Fei Fei Li: It is, for me, hard to keep in mind that AI is so new for everybody when I lived my entire professional life in AI.

李飞飞博士：对我来说，当我的整个职业生涯都在人工智能领域时，很难记住人工智能对于每个人来说都是新鲜的。

There's a part of me that is just, it's so satisfying to see a personal curiosity that I started barely out of teenagehood and now has become a transformative force of our civilization.

我内心的一部分是，看到我从青少年时期才开始的个人好奇心，现在已经成为我们文明的变革力量，我感到非常满足。

It generally is a civilizational level technology.

它通常是一种文明级别的技术。

So that journey is about 30 years or 20 something, 20 plus years, and it's just very satisfying.

所以这段旅程大约有30年或20年左右，20多年，而且非常令人满意。

So where did it all start?

那么这一切是从哪里开始的呢？

Well, I'm not even the first generation AI researcher.

嗯，我什至不是第一代人工智能研究员。

The first generation really date back to the '50s and '60s, and Alan Turing was ahead of his time in the '40s by asking, daring humanity with the question, "Is there thinking machines?" And of course he has a specific way of testing this concept of thinking machine, which is a conversational chatbot, which to his standard we now have a thinking machine.

第一代机器实际上可以追溯到 50 年代和 60 年代，艾伦·图灵在 40 年代领先于他的时代，大胆地向人类提出了这样的问题：“存在思考机器吗？”当然，他有一种特定的方法来测试思维机器的概念，这是一个会话聊天机器人，按照他的标准，我们现在有了一个思维机器。

Dr. Fei Fei Li: But that was just a more anecdotal inspiration.

李飞飞博士：但这只是一个更轶事的灵感。

The field really began in the '50s when computer scientists came together and look at how we can use computer programs and algorithms to build these programs that can do things that have been only capable by human cognition.

这个领域真正开始于 20 世纪 50 年代，当时计算机科学家聚集在一起，研究如何使用计算机程序和算法来构建这些程序，这些程序可以完成只有人类认知才能完成的事情。

And that was the beginning.

这就是开始。

And the founding fathers the Dartmouth workshop in the 1956, we have Professor John McCarthy who later came to Stanford who coined the term artificial intelligence.

1956 年达特茅斯研讨会的创始人是约翰·麦卡锡教授，他后来来到斯坦福大学，创造了人工智能这个术语。

And between the '50s, '60s, '70s, and '80s, it was the early days of AI exploration and we had logic systems, we had expert systems, we also had early exploration of neural network.

在 50 年代、60 年代、70 年代和 80 年代之间，这是人工智能探索的早期阶段，我们有逻辑系统，我们有专家系统，我们也有神经网络的早期探索。

And then it came to around the late '80s, the '90s, and the very beginning of the 21st century.

然后到了80年代末、90年代和21世纪初。

That stretch about 20 years is actually the beginning of machine learning, is the marriage between computer programming and statistical learning.

这大约 20 年实际上是机器学习的开始，是计算机编程和统计学习之间的结合。

Dr. Fei Fei Li: And that marriage brought a very, very critical concept into AI, which is that purely rule-based program is not going to account for the vast amount of cognitive capabilities that we imagine computers can do.

李飞飞博士：那次婚姻给人工智能带来了一个非常非常关键的概念，那就是纯粹基于规则的程序无法解释我们想象的计算机可以实现的大量认知能力。

So we have to use machines to learn the patterns.

所以我们必须使用机器来学习模式。

Once the machines can learn the patterns, it has a hope to do more things.

一旦机器能够学习这些模式，它就有希望做更多的事情。

For example, if you give it three cats, the hope is not just for the machines to recognize these three cats.

例如，如果你给它三只猫，希望的不仅仅是机器能够识别这三只猫。

The hope is the machines can recognize the fourth cat, the fifth cat, the sixth cat, and all the other cats.

希望机器能够识别第四只猫、第五只猫、第六只猫以及所有其他猫。

And that's a learning ability that is fundamental to humans and remaining animals.

这是一种对人类和其他动物至关重要的学习能力。

And we, as a field, realized, "We need machine learning." So that was up till the beginning of the 21st century.

作为一个领域，我们意识到“我们需要机器学习”。就这样一直到21世纪初。

I entered the field of AI literally in the year of 2000.

我实际上是在 2000 年进入人工智能领域的。

That's when my PhD began at Caltech.

那是我在加州理工学院开始攻读博士学位的时候。

Dr. Fei Fei Li: And so I was one of the first generation machine learning researchers and we were already studying this concept of machine learning, especially neural network.

李飞飞博士：所以我是第一代机器学习研究人员之一，我们已经在研究机器学习的概念，特别是神经网络。

I remember that was one of my first courses at Caltech is called neural network, but it was very painful.

我记得我在加州理工学院的第一门课程叫做神经网络，但这非常痛苦。

It was still smack in the middle of the so-called AI winter, meaning the public didn't look at this too much.

当时正值所谓的人工智能冬天，这意味着公众并没有过多关注这一问题。

There wasn't that much funding, but there was also a lot of ideas flowing around.

虽然资金不多，但也有很多想法。

And I think two things happened to myself that brought my own career so close to the birth of modern AI is that I chose to look at artificial intelligence through the lens of visual intelligence because humans are deeply visual animals.

我认为发生在我身上的两件事让我的职业生涯如此接近现代人工智能的诞生，那就是我选择通过视觉智能的视角来看待人工智能，因为人类是深度视觉动物。

We can talk a little more later, but so much of our intelligence is built upon visual, perceptual, spatial understanding, not just language per se.

我们可以稍后再谈，但我们的大部分智力都建立在视觉、感知、空间理解的基础上，而不仅仅是语言本身。

I think they're complementary.

我认为它们是互补的。

Dr. Fei Fei Li: So I choose to look at visual intelligence and my PhD and my early professor years, my students and I are very committed to a north star problem, which is solving the problem of object recognition because it's a building block for the perceptual world, right?

李飞飞博士：所以我选择研究视觉智能，在我的博士学位和早期教授生涯中，我和我的学生都非常致力于北极星问题，即解决物体识别问题，因为它是感知世界的基石，对吗？

We go around the world interpreting reasoning and interacting with it more or less at the object level.

我们环游世界，或多或少地在对象层面上解释推理并与之交互。

We don't interact with the world at the molecular level.

我们不会在分子水平上与世界互动。

We don't interact with the world as...

我们与世界的互动并不是...

We sometimes do, but we rarely, for example, if you want to lift a teapot, you don't say, "Okay, the teapot is made of a hundred pieces of porcelain and let me work on this a hundred pieces." You look at this as one object and interact with it.

我们有时会这样做，但很少，例如，如果你想举起一个茶壶，你不会说：“好吧，茶壶是用一百块瓷器做的，让我用一百块瓷器来做。”您将其视为一个对象并与之交互。

So object is really important.

所以对象真的很重要。

So I was among the first researchers to identify this as a north star problem, but I think what happened is that as a student of AI and a researcher of AI, I was working on all kinds of mathematical models including neural network, including Bayesian network, including many, many models.

所以我是第一批认为这是北极星问题的研究人员之一，但我认为发生的事情是，作为人工智能的学生和人工智能的研究员，我正在研究各种数学模型，包括神经网络，包括贝叶斯网络，包括很多很多模型。

Dr. Fei Fei Li: And there was one singular pain point is that these models don't have data to be trained on.

李飞飞博士：有一个独特的痛点是这些模型没有可供训练的数据。

And as a field, we were so focusing on these models, but it dawned on me that human learning as well as evolution is actually a big data learning process.

作为一个领域，我们非常关注这些模型，但我突然意识到，人类学习和进化实际上是一个大数据学习过程。

Humans learn with so much experience constantly.

人类不断地学习大量的经验。

In the evolution, if you look at time, animals evolve with just experiencing the world.

在进化过程中，如果你观察时间，你会发现动物只是在体验世界的过程中进化的。

So I think my students and I conjectured that a very critically-overlooked ingredient of bringing AI to life is big data.

因此，我和我的学生推测，将人工智能带入生活的一个被严重忽视的因素是大数据。

And then we began this ImageNet project in 2006, 2007.

然后我们在 2006、2007 年开始了这个 ImageNet 项目。

We were very ambitious.

我们非常雄心勃勃。

We want to get the entire internet's image data on objects.

我们想要获取整个互联网上物体的图像数据。

Now granted internet was a lot smaller than today, so I felt like that ambition was at least not too crazy.

现在的互联网比今天小得多，所以我觉得这个野心至少不是太疯狂。

Now, it's totally delusional to think a couple of graduate student and a professor can do this.

现在，认为几个研究生和一个教授就能做到这一点完全是妄想。

Dr. Fei Fei Li: And that's what we did.

李飞飞博士：我们就是这么做的。

We curated very carefully, 15 million images on the internet, created a taxonomy of 22,000 concepts, borrowing other researchers' work like linguists work on WordNet, and it's a particular way of dictionarying words.

我们非常仔细地策划了互联网上的 1500 万张图像，创建了 22,000 个概念的分类法，借鉴了其他研究人员的工作，就像语言学家在 WordNet 上的工作一样，这是一种特殊的词典词典方式。

And we combine that into ImageNet and we open-sourced that to the research community.

我们将其合并到 ImageNet 中，并向研究社区开源。

We held an annual ImageNet challenge to encourage everybody to participate in this.

我们举办了一年一度的 ImageNet 挑战赛，鼓励每个人都参与其中。

We continue to do our own research, but 2012 was the moment that many people think was the beginning of the deep learning or birth of modern AI because a group of Toronto researchers led by Professor Geoff Hinton, participated in ImageNet Challenge, used ImageNet big data and two GPUs from NVIDIA and created successfully the first neural network algorithm that can...

我们继续做自己的研究，但2012年被很多人认为是深度学习的开始或现代人工智能诞生的时刻，因为由Geoff Hinton教授领导的多伦多研究小组参加了ImageNet挑战赛，使用ImageNet大数据和NVIDIA的两块GPU，成功创建了第一个神经网络算法，可以...

Dr. Fei Fei Li: It didn't totally solve, but made a huge progress towards solving the problem of object recognition.

李飞飞博士：它并没有完全解决物体识别问题，但是在解决物体识别问题上取得了巨大的进步。

And that combination of the trio technology, big data, neural network, and GPU was kind of the golden recipe for modern AI.

大数据、神经网络和 GPU 这三项技术的结合堪称现代人工智能的黄金配方。

And then fast-forward, the public moment of AI, which is the ChatGPT moment, if you look at the ingredients of what brought ChatGPT to the world technically still use these three ingredients.

然后快进，人工智能的公共时刻，即 ChatGPT 时刻，如果你看看将 ChatGPT 推向世界的要素，技术上仍然使用这三个要素。

Now, it's internet-scale data mostly texts is a much more complex neural network architecture than 2012, but it's still neural network and a lot more GPUs, but it's still GPUs.

现在，互联网规模的数据（主要是文本）是比 2012 年复杂得多的神经网络架构，但它仍然是神经网络和更多的 GPU，但它仍然是 GPU。

So these three ingredients are still at the core of modern AI.

所以这三个要素仍然是现代人工智能的核心。

Lenny Rachitsky: Incredible.

莱尼·拉奇茨基：难以置信。

I have never heard that full story before.

我以前从未听过这个完整的故事。

I love that it was two GPUs was the first.

我喜欢第一个是两个 GPU。

I love that.

我喜欢那个。

And now it's, I don't know, hundreds of thousands, right, that are orders of magnitude more powerful.

现在，我不知道，有数十万，对吧，威力增强了几个数量级。

Lenny Rachitsky: And those two GPUs where they just bought, they were like gaming GPUs, they just went to the-

Lenny Rachitsky：他们刚刚购买的那两个 GPU，就像游戏 GPU，他们只是去了-

Lenny Rachitsky: ...

莱尼·拉奇茨基：……

GameStar that people use for playing games.

人们用来玩游戏的 GameStar。

As you said, this continues to be in a large way, the way models get smarter.

正如您所说，这在很大程度上仍然是模型变得更加智能的方式。

Some of the fastest growing companies in the world right now, I've had them all mostly on the podcast, Mercor and Surge and Scale.

目前世界上发展最快的一些公司，我主要在播客、Mercor 和 Surge and Scale 上了解过。

They continue to do this for labs, just give them more and more label data of the things they're most excited and interested in.

他们继续为实验室这样做，只是为他们提供越来越多他们最兴奋和最感兴趣的事物的标签数据。

Dr. Fei Fei Li: Yeah, I remember Alex Wang from Scale very early days.

李飞飞博士：是的，我记得很早的时候 Scale 的 Alex Wang。

I probably still has his emails when he was starting Scale.

当他开始 Scale 时，我可能还保留着他的电子邮件。

He was very kind.

他非常友善。

He keeps sending me emails about how image that inspired Scale.

他不断给我发电子邮件，讲述图像如何激发了 Scale 的灵感。

I was very pleased to see that.

我很高兴看到这一点。

Lenny Rachitsky: One of my other favorite takeaways from what you just shared is just such an example of high agency and just doing things that's kind of a meme on Twitter.

Lenny Rachitsky：从你刚才分享的内容中我最喜欢的另一个收获就是这样一个高度代理的例子，并且只是在 Twitter 上做一些类似于模因的事情。

Just you can just do things.

只是你可以做事。

You're just like, okay, this is probably necessary to move AI.

你会想，好吧，这可能是移动人工智能所必需的。

And it's called machine learning back then, right?

那时它被称为机器学习，对吧？

Was that the term most people used?

这是大多数人使用的术语吗？

Dr. Fei Fei Li: I think it was interchangeably.

李飞飞博士：我认为是可以互换的。

It's true.

这是真的。

I do remember the companies, the tech companies, I am not going to name names, but I was in a conversation in one of the early days, I think is in the middle of 2015, middle of 2016, some tech companies avoid using the word AI because they were not sure if AI was a dirty word.

我确实记得这些公司，科技公司，我不会点名，但我在早期的一次谈话中，我想是在 2015 年中期、2016 年中期，一些科技公司避免使用人工智能这个词，因为他们不确定人工智能是否是​​一个肮脏的词。

And I remember I was actually encouraging everybody to use the word AI because to me that is one of the most audacious question humanity has ever asked in our quest for science and technology, and I feel very proud of this term.

我记得我实际上鼓励每个人使用人工智能这个词，因为对我来说，这是人类在追求科学和技术的过程中提出的最大胆的问题之一，我对这个词感到非常自豪。

But yes, at the beginning some people were not sure.

但是，是的，一开始有些人并不确定。

Lenny Rachitsky: What year was that roughly when AI was a dirty word?

Lenny Rachitsky：大约是哪一年，人工智能还是一个肮脏的词？

Dr. Fei Fei Li: 2016, I think because that was-

李飞飞博士：2016年，我想因为那是-

Dr. Fei Fei Li: That was the changing.

李飞飞博士：这就是变化。

Some people start calling it AI, but I think if you look at the Silicon Valley tech companies, if you trace their marketing term, I think 2017-ish was the beginning of companies calling themselves AI companies.

有些人开始称之为人工智能，但我认为如果你看看硅谷的科技公司，如果你追踪他们的营销术语，我认为 2017 年左右是公司自称为人工智能公司的开始。

Lenny Rachitsky: That's incredible.

莱尼·拉奇茨基：这太不可思议了。

Just how the world has changed.

世界已经发生了怎样的变化。

Lenny Rachitsky: Now, you can't not call yourself an AI company.

Lenny Rachitsky：现在，你不能不称自己为人工智能公司。

Lenny Rachitsky: Oh, man.

莱尼·拉奇茨基：哦，伙计。

Okay.

好的。

Is there anything else around the history, that early history that you think people don't know that you think is important before we chat about where you think things are going and the work that you're doing?

在我们讨论您认为事情的发展方向和您正在做的工作之前，关于历史、早期历史还有其他您认为人们不知道的您认为重要的事情吗？

Dr. Fei Fei Li: I think as all histories, I'm keenly aware that I am recognized for being part of the history, but there are so many heroes and so many researchers.

李飞飞博士：我认为，就像所有的历史一样，我敏锐地意识到我被认为是历史的一部分，但有这么多的英雄和这么多的研究人员。

We're talking about generations of researchers.

我们谈论的是几代研究人员。

In my own world, there are so many people who have inspired me, which I talked about in my book, but I do feel our culture, especially Silicon Valley, tends to assign achievements to a single person.

在我自己的世界里，有很多人启发了我，我在书中谈到了这一点，但我确实感觉到我们的文化，尤其是硅谷，倾向于将成就分配给一个人。

While I think it has value, but it's just to be remembered.

虽然我认为它有价值，但只是为了被记住。

AI is a field of, at this point, 70 years old and we have gone through many generations.

人工智能是一个已有 70 年历史的领域，我们已经经历了很多代。

Nobody, no one could have gotten here by themselves.

没有人，没有人能够独自到达这里。

Lenny Rachitsky: Okay, so let me ask you this question.

Lenny Rachitsky：好的，让我问你这个问题。

It feels like we're always on this precipice of AGI, this kind of vague term people throw around, AGI is coming, it's going to take over everything.

感觉我们总是处于 AGI 的边缘，人们到处乱扔这种模糊的术语，AGI 即将到来，它将接管一切。

What's your take on how far you think we might be from AGI?

您认为我们距离通用人工智能还有多远，您有何看法？

Do you think we're going to get there on the current trajectory we're on?

您认为我们能按照目前的轨迹实现这一目标吗？

Do you think we need more breakthroughs?

您认为我们需要更多突破吗？

Do you think the current approach will get us there?

您认为目前的方法能让我们实现这一目标吗？

Dr. Fei Fei Li: Yeah, this is a very interesting term, Lenny.

李飞飞博士：是的，这是一个非常有趣的术语，Lenny。

I don't know if anyone has ever defined AGI.

我不知道有没有人定义过AGI。

There are many different definitions, including some kind of superpower for machines all the way to machines can become economically viable agent in the society.

有许多不同的定义，包括机器的某种超级大国，一直到机器可以成为社会中经济上可行的代理人。

In other words, making salaries to live.

换句话说，就是靠工资维持生计。

Is that the definition of AGI?

这就是AGI的定义吗？

As a scientist, I take science very seriously and I enter the field because I was inspired by this audacious question of, can machines think and do things in the way that humans can do?

作为一名科学家，我非常认真地对待科学，我进入这个领域是因为我受到这个大胆问题的启发：机器能否像人类一样思考和做事？

For me, that's always the north star of AI.

对我来说，这始终是人工智能的北极星。

And from that point of view, I don't know what's the difference between AI and AGI.

从这个角度来看，我不知道人工智能和通用人工智能有什么区别。

Dr. Fei Fei Li: I think we've done very well in achieving parts of the goal, including conversational AI, but I don't think we have completely conquered all the goals of AI.

李飞飞博士：我认为我们在实现部分目标方面做得很好，包括对话式人工智能，但我不认为我们已经完全征服了人工智能的所有目标。

And I think our founding fathers, Alan Turing, I wonder if Alan Turing is around today and you ask him to contrast AI versus AGI, he might just shrugged and said, "Well, I asked the same question back in 1940s," so I don't want to get onto a rabbit hole of defining AI versus AGI.

我想我们的开国元勋阿兰·图灵，我想知道阿兰·图灵今天是否还在，如果你让他对比人工智能和通用人工智能，他可能只是耸耸肩说，“好吧，我在 20 世纪 40 年代也问过同样的问题”，所以我不想陷入定义人工智能和通用人工智能的困境。

I feel AGI is more a marketing term than a scientific term as a scientist than technologist.

我觉得 AGI 更像是一个营销术语，而不是一个科学术语，作为科学家而不是技术专家。

AI is my north star, is my field's north star, and I'm happy people call it whatever name they want to call it.

人工智能是我的北极星，是我所在领域的北极星，我很高兴人们可以随意称呼它。

Lenny Rachitsky: So let me ask you maybe this way, like you described, there's kind of these components that from ImageNet and AlexNet took us to where we're today, GPUs essentially, data, label data, just like the algorithm of the model.

Lenny Rachitsky：所以让我这样问你，就像你所描述的那样，ImageNet 和 AlexNet 的这些组件将我们带到了今天的位置，GPU 本质上是数据、标签数据，就像模型的算法一样。

There's also just the transformer feels like an important step in that trajectory.

变压器感觉就像是这一轨迹中的重要一步。

Do you feel like those are the same components that'll get us to, I don't know, 10 times smarter model, something that's like life-changing for the entire world?

您是否觉得这些组件可以让我们实现（我不知道）智能 10 倍的模型，从而改变整个世界的生活？

Or do you think we need more breakthroughs?

或者您认为我们需要更多突破？

I know we're going to talk about world models, which I think is a component of this, but is there anything else that you think is like, oh, this will plateau, or okay, this will take us just need more data, more compute, more GPUs?

我知道我们要讨论世界模型，我认为这是其中的一个组成部分，但是您是否认为还有其他任何事情，哦，这会趋于稳定，或者好吧，这会让我们只需要更多数据、更多计算、更多 GPU？

Dr. Fei Fei Li: Oh no, I definitely think we need more innovations.

李飞飞博士：哦不，我绝对认为我们需要更多的创新。

I think scaling loss of more data, more GPUs, and bigger current model architecture is there's still a lot to be done there, but I absolutely think we need to innovate more.

我认为更多数据、更多 GPU 和更大当前模型架构的扩展损失仍然有很多工作要做，但我绝对认为我们需要进行更多创新。

There's not a single deeply scientific discipline in human history that has arrived at a place that says we're done, we're done innovating and AI is one of the, if not the youngest discipline in human civilization in terms of science and technology, we're still scratching the surface.

人类历史上还没有任何一门深入的科学学科已经达到了我们已经完成的地步，我们已经完成了创新，而人工智能即使不是人类文明中科学和技术方面最年轻的学科之一，我们仍然只触及表面。

For example, like I said, we're going to segue into world models.

例如，就像我说的，我们将继续研究世界模型。

Today, you take a model and run it through a video of a couple of office rooms and ask the model to count the number of chairs.

今天，您使用一个模型并通过几个办公室的视频运行它，并要求模型计算椅子的数量。

And this is something a toddler could do or maybe an elementary school kid could do, and AI could not do that, right?

这是一个幼儿可以做到的事情，或者也许是一个小学生可以做到的事情，而人工智能却做不到，对吧？

Dr. Fei Fei Li: So there's just so much AI today could not do, then let alone thinking about how did someone like Isaac Newton look at the movements of the celestial bodies and derive an equation or a set of equations that governs the movement of all bodies, that level of creativity, extrapolation, abstraction.

李飞飞博士：所以今天有很多人工智能无法做到的事情，更不用说思考像艾萨克·牛顿这样的人是如何观察天体的运动并推导出一个或一组方程来控制所有物体的运动的，创造力、外推法和抽象的水平。

We have no way of enabling AI to do that today.

今天我们还没有办法让人工智能做到这一点。

And then let's look at emotional intelligence.

然后我们来看看情商。

If you look at a student coming to a teacher's office and have a conversation about motivation, passion, what to learn, what's the problem that's really bothering you.

如果你看到一个学生来到老师的办公室并谈论动机、热情、要学什么，那么真正困扰你的问题是什么。

That conversation, as powerful as today's conversational bots are, you don't get that level of emotional cognitive intelligence from today's AI.

这种对话与当今的对话机器人一样强大，但你无法从当今的人工智能中获得那种水平的情感认知智能。

So there's a lot we can do better, and I do not believe we're done innovating.

因此，我们还有很多地方可以做得更好，而且我不相信我们已经完成了创新。

Lenny Rachitsky: Demis had this really interesting interview recently from DeepMind slash Google where someone asked him just like, "What do you think, how far are we from AGI?

Lenny Rachitsky：Demis 最近接受了 DeepMind Slash Google 的一次非常有趣的采访，有人问他，“你认为我们离 AGI 还有多远？

What does it look like going through there?" He had a really interesting way of approaching it is if we were to give the most cutting-edge model all the information until the end of the 20th century, see if it could come up with all the breakthroughs Einstein had and so far we're nowhere near that, but they could just-

穿过那里感觉怎么样？”他有一个非常有趣的方法来解决这个问题，那就是如果我们向最前沿的模型提供直到 20 世纪末的所有信息，看看它是否能够实现爱因斯坦所取得的所有突破，到目前为止我们还远远没有达到这一目标，但他们可以——

Dr. Fei Fei Li: No, we're not.

李飞飞博士：不，我们不是。

In fact, it's even worse.

事实上，情况甚至更糟。

Let's give AI all the data including modern instruments data of celestial bodies, which Newton did not have, and give it to that and just ask AI to create the 17th century set of equations on the laws of bodily movements.

让我们给AI所有的数据，包括牛顿没有的天体现代仪器数据，然后让AI创建17世纪的一组关于身体运动定律的方程。

Today's AI cannot do that.

今天的人工智能无法做到这一点。

Lenny Rachitsky: All right.

莱尼·拉奇茨基：好的。

We're ways away is what I'm hearing.

我听说我们还很远。

Lenny Rachitsky: Okay, so let's talk about world models.

Lenny Rachitsky：好的，我们来谈谈世界模型。

To me, this is just another really amazing example of you being ahead of where people end up.

对我来说，这只是另一个非常惊人的例子，说明你领先于人们的最终目标。

So you were way ahead on, okay, we just need a lot of clean data for AI and neural networks to learn.

所以你已经领先了，好吧，我们只需要大量干净的数据供人工智能和神经网络学习。

You've been talking about this idea of world models for a long time.

您已经谈论世界模型的想法很长时间了。

You started a company to build, essentially there's language models.

你创办了一家公司来构建，本质上就是语言模型。

This is a different thing.

这是另一回事。

This is a world model.

这是一个世界模型。

We'll talk about what that is.

我们将讨论那是什么。

And now, as I was preparing for this Elon's talking about world models, Jensen's talking about world models, I know Google's working on this stuff.

现在，当我准备埃隆谈论世界模型、詹森谈论世界模型时，我知道谷歌正在研究这个东西。

You've been at this for a long time and you actually just launched something that's going, we're going to talk about right before this podcast airs.

你已经从事这个工作很长时间了，而且你实际上刚刚推出了一些正在进行的东西，我们将在这个播客播出之前讨论。

Talk about what is a world model?

说说什么是世界模式？

Why is it so important?

为什么它如此重要？

Dr. Fei Fei Li: I'm very excited to see that more and more people are talking about world models like Elon, like Jensen.

李飞飞博士：我很高兴看到越来越多的人在谈论像埃隆、詹森这样的世界模特。

I have been thinking about really how to push AI forward all my life and the large language models that came out of the research world and then OpenAI and all this, for the past few years, were extremely inspiring even for a researcher like me.

我一生都在思考如何推动人工智能向前发展，在过去的几年里，研究领域中出现的大型语言模型以及 OpenAI 以及所有这一切，即使对于像我这样的研究人员来说也非常鼓舞人心。

I remembered when GPT2 came out, and that was in, I think, late 2020.

我记得 GPT2 是什么时候发布的，我想那是在 2020 年底。

I was co-director, I still am, but I was at that time full-time co-director of Stanford's Human-Centered AI institute, and I remember it was...

我曾经是联合主任，现在仍然是，但当时我是斯坦福大学以人为中心的人工智能研究所的全职联合主任，我记得那是……

The public was not aware of the power of the large language model yet, but as researchers, we were seeing it, we're seeing the future, and I had pretty long conversations with my natural language processing colleagues like Percy Liang and Chris Manning.

公众还没有意识到大型语言模型的力量，但作为研究人员，我们正在看到它，我们正在看到未来，并且我与 Percy Liang 和 Chris Manning 等自然语言处理同事进行了很长时间的对话。

We were talking about how critical this technology is going to be and the Stanford AI Institute, Human-Centered AI Institute, HAI, was the first one to establish a full research center foundation model.

我们讨论了这项技术的重要性，斯坦福人工智能研究所、以人为中心的人工智能研究所、HAI 是第一个建立完整研究中心基础模型的研究所。

Dr. Fei Fei Li: We were, Percy Liang, and many researchers led the first academic paper foundation model.

李飞飞博士：我们Percy Liang和许多研究人员领导了第一个学术论文基础模型。

So it was just very inspiring for me.

所以这对我来说非常鼓舞人心。

Of course, I come from the world of visual intelligence and I was just thinking there's so much we can push forward beyond language because humans, humans use our sense of spatial intelligence, a world understanding to do so many things and they are beyond language.

当然，我来自视觉智能的世界，我只是在想，我们可以在语言之外推动很多事情，因为人类利用我们的空间智能感，对世界的理解来做很多事情，而它们是超越语言的。

Think about a very chaotic first responder scene, whether it's fire or some traffic accident or some natural disaster.

想想一个非常混乱的急救人员场景，无论是火灾、交通事故还是自然灾害。

And if you immerse yourself in those scene and think about how people organize themselves to rescue people, to stop further disasters, to put down fires, a lot of that is movements is spontaneous understanding of objects, worlds, human situational awareness.

如果你沉浸在那些场景中，思考人们如何组织起来救援人员，阻止进一步的灾难，扑灭火灾，很多动作都是对物体、世界和人类态势感知的自发理解。

Language is part of that, but a lot of those situations, language cannot get you to put down the fire.

语言是其中的一部分，但在很多情况下，语言无法让你扑灭大火。

Dr. Fei Fei Li: So that is, what is that?

李飞飞博士：也就是说，那是什么？

I was thinking a lot.

我想了很多。

And in the meantime, I was doing a lot of robotics research and it dawned on me that the linchpin of connecting the additional intelligence, in addition to language embodied AI, which are robotics, connecting visual intelligence, is the sense of spatial intelligence about understanding the world.

与此同时，我做了很多机器人技术研究，我意识到，除了语言体现的人工智能之外，连接额外智能的关键是连接视觉智能的机器人技术，是理解世界的空间智能。

And that's when I think it was 2024, I gave a TED talk about spatial intelligence at world models.

就在那时，我认为是 2024 年，我在 TED 上发表了一场关于世界模型中的空间智能的演讲。

And I start formulating this idea back in 2022 based on my robotics and computer vision research.

早在 2022 年，我就根据我的机器人技术和计算机视觉研究开始提出这个想法。

And then one thing that was really clear to me is that I really want to work with the brightest technologists and move as fast as possible to bring this technology to life.

我真正清楚的一件事是，我真的很想与最聪明的技术专家合作，并尽快采取行动，将这项技术变为现实。

And that's when we founded this company called World Labs.

就在那时，我们创立了这家名为 World Labs 的公司。

And you can see the word world is in the title of our company because we believe so much in world modeling and spatial intelligence.

您可以在我们公司的名称中看到“世界”一词，因为我们非常相信世界建模和空间智能。

Lenny Rachitsky: People are so used to just chatbots and that's a large language model.

Lenny Rachitsky：人们已经习惯了聊天机器人，这是一个很大的语言模型。

A simple way to understand a world model is you basically describe a scene and it generates an infinitely explorable world.

理解世界模型的一个简单方法是基本上描述一个场景，它会生成一个无限可探索的世界。

We'll link to the thing you launched, which we'll talk about, but just is that a simple way to understand it?

我们将链接到您发布的内容，我们将讨论该内容，但这只是理解它的简单方法吗？

Dr. Fei Fei Li: That's part of it, Lenny.

李飞飞博士：这是一部分，Lenny。

I think a simple way to understand a world model is that this model can allow anyone to create any worlds in their mind's eye by prompting whether it's an image or a sentence.

我认为理解世界模型的一个简单方法是，这个模型可以允许任何人通过提示是图像还是句子来在他们的脑海中创建任何世界。

And also be able to interact in this world whether you are browsing and walking or picking objects up or changing things as well as to reason within this world, for example, if the person consuming, if the agent consuming this output of the world model is a robot, it should be able to plan its path and help to tidy the kitchen, for example.

并且还能够在这个世界中进行交互，无论你是在浏览、行走、拾起物体还是改变事物，以及在这个世界中进行推理，例如，如果消费的人，如果消费世界模型输出的代理是一个机器人，它应该能够规划其路径并帮助整理厨房，例如。

So world model is a foundation that you can use to reason, to interact, and to create worlds.

因此，世界模型是您可以用来推理、交互和创建世界的基础。

Lenny Rachitsky: Great.

莱尼·拉奇茨基：太棒了。

Yeah.

是的。

So robots feels like that's potentially the next big focus for AI researchers and just the impact on the world.

因此，机器人感觉这可能是人工智能研究人员的下一个重点关注点，也是对世界的影响。

And what you're saying here is this is a key missing piece of making robots actually work in the real world, understanding how the world works.

你在这里所说的是，这是让机器人在现实世界中真正工作、理解世界如何运作的关键缺失部分。

Dr. Fei Fei Li: Yeah.

李飞飞医生：是的。

Well, first of all, I do think there's more than robots.

嗯，首先，我确实认为不仅仅是机器人。

That's exciting.

这太令人兴奋了。

But I agree with everything you just said.

但我同意你刚才所说的一切。

I think world modeling and spatial intelligence is a key missing piece of embodied AI.

我认为世界建模和空间智能是具体人工智能缺失的一个关键部分。

I also think let's not underestimate that humans are embodied agents and humans can be augmented by AI's intelligence.

我还认为，我们不要低估人类是具体的代理，并且人类可以通过人工智能的智能来增强。

Just like today, humans are language animals, but we're very much augmented by AI helping us to do language tasks including software engineering.

就像今天一样，人类是语言动物，但人工智能帮助我们完成包括软件工程在内的语言任务，从而大大增强了我们的能力。

I think that we shouldn't underestimate or maybe we tend not to talk about how humans, as an embodied agents, can actually benefit so much from world models and spatial intelligence models as well as robots can.

我认为我们不应该低估，或者也许我们倾向于不谈论人类，作为一个具体的代理，实际上可以像机器人一样从世界模型和空间智能模型中受益匪浅。

Lenny Rachitsky: So the big unlocks here, robots, which a huge deal if this works out, imagine each of us has robots doing a bunch of stuff for us, they help us with disasters, things like that.

Lenny Rachitsky：所以这里的大问题是机器人，如果这成功的话，这将是一件大事，想象一下我们每个人都有机器人为我们做很多事情，它们帮助我们应对灾难，诸如此类的事情。

Games obviously is a really cool example, just like infinitely playable games that you just invent out of your head.

游戏显然是一个非常酷的例子，就像你凭空想象出来的无限可玩的游戏一样。

And then creativity feels like just like being fun, having fun, being creative, thinking of magic, wild new worlds, and environments.

然后，创造力就像是有趣、有乐趣、有创造力、思考魔法、狂野的新世界和环境。

Dr. Fei Fei Li: And also design, humans design from machines to buildings to homes and also scientific discovery.

李飞飞博士：还有设计，人类设计从机器到建筑再到家庭，还有科学发现。

There is so much.

有这么多。

I like to use the example of the discovery of the structure of DNA.

我喜欢用DNA结构的发现来举例。

If you look at one of the most important piece in DNA's discovery history is the x-ray diffraction photo that was captured by Rosalind Franklin, and it was a flat 2D photo of a structure that it looks like a cross with diffractions.

如果你看一下 DNA 发现史上最重要的部分之一，就是罗莎琳德·富兰克林 (Rosalind Franklin) 拍摄的 X 射线衍射照片，它是一张平面 2D 照片，结构看起来像带有衍射的十字。

You can google those photos.

你可以用谷歌搜索这些照片。

But with that 2D flat photo, the humans, especially two important humans, James Watson and Francis Crick, in addition to their other information, was able to reason in 3D space and deduce a highly three-dimensional double helix structure of the DNA.

但有了这张 2D 平面照片，人类，尤其是两个重要的人类，詹姆斯·沃森和弗朗西斯·克里克，除了他们的其他信息之外，还能够在 3D 空间中进行推理，并推断出 DNA 的高度三维双螺旋结构。

And that structure cannot possibly be 2D.

而且该结构不可能是二维的。

You cannot think in 2D and deduce that structure.

你无法以二维方式思考并推断出该结构。

You have to think in 3D spatial, use the human spatial intelligence.

你必须在 3D 空间中思考，利用人类的空间智能。

So I think even in scientific discovery, spatial intelligence or AI-assisted spatial intelligence is critical.

所以我认为即使在科学发现中，空间智能或人工智能辅助的空间智能也至关重要。

Lenny Rachitsky: This is such an example of, I think it was Chris Dixon that had this line that the next big thing is going to start off feeling like a toy.

Lenny Rachitsky：这就是一个例子，我认为克里斯·迪克森（Chris Dixon）说过这样一句话：下一件大事一开始就会感觉像个玩具。

When ChatGPT just came out, I remember Sam Altman just tweeted it as like, "Here's a cool thing we're playing with, check it out." Now, it's the fastest growing product to all of history, changed the world.

当 ChatGPT 刚刚推出时，我记得 Sam Altman 刚刚在推特上写道：“我们正在玩一个很酷的东西，看看吧。”现在，它是历史上增长最快的产品，改变了世界。

And it's oftentimes the things that just look like, okay, this is cool, that it's a fun to play with that end up changing the world most.

通常，那些看起来像是“好吧，这很酷，玩起来很有趣”的东西最终会最大程度地改变世界。

Lenny Rachitsky: This episode is brought to you by Sinch, the customer communications cloud.

Lenny Rachitsky：本集由客户通信云 Sinch 为您带来。

Here's the thing about digital customer communications.

这是关于数字客户沟通的事情。

Whether you're sending marketing campaigns, verification codes, or account alerts, you need them to reach users reliably.

无论您是发送营销活动、验证码还是帐户提醒，您都需要它们可靠地联系用户。

That's where Sinch comes in.

这就是辛奇的用武之地。

Over 150,000 businesses, including 8 of the top 10 largest tech companies globally use Sinch's API to build messaging, email, and calling into their products.

超过 150,000 家企业（包括全球十大科技公司中的 8 家）使用 Sinch 的 API 在其产品中构建消息传递、电子邮件和通话功能。

And there's something big happening in messaging that product teams need to know about, Rich Communication Services or RCS.

产品团队需要了解消息传递领域正在发生的重大变化，即丰富的通信服务或 RCS。

Think of RCS as SMS2.0.

将 RCS 视为 SMS2.0。

Instead of getting texts from a random number, your users will see your verified company name and logo without needing to download anything new.

您的用户将看到您经过验证的公司名称和徽标，而不是从随机数字中获取文本，而无需下载任何新内容。

Lenny Rachitsky: It's a more secure and branded experience.

Lenny Rachitsky：这是一种更安全、更品牌化的体验。

Plus you get features like interactive carousels and suggested replies.

此外，您还可以获得交互式轮播和建议回复等功能。

And here's why this matters, US carriers are starting to adopt RCS.

这就是为什么这很重要，美国运营商开始采用 RCS。

Sinch is already helping major brands send RCS messages around the world and they're helping Lenny's podcast listeners get registered first before the rush hits the US market.

Sinch 已经在帮助各大品牌在世界各地发送 RCS 消息，并且他们正在帮助 Lenny 的播客听众在热潮席卷美国市场之前首先注册。

Learn more and get started at sinch.com/lenny.

如需了解更多信息并开始使用，请访问sinch.com/lenny。

That's S-I-N-C-H.com/lenny.

那是 S-I-N-C-H.com/lenny。

Lenny Rachitsky: I reached out to Ben Horowitz, who loves what you're doing, a big fan of yours.

Lenny Rachitsky：我联系了 Ben Horowitz，他热爱你所做的事情，是你的忠实粉丝。

They're investors I believe in...

他们是我信任的投资者...

Dr. Fei Fei Li: Yeah, we've known each other for many years, but yes, right now they're investors of World Labs.

李飞飞博士：是的，我们认识很多年了，但是，现在他们是世界实验室的投资者。

Lenny Rachitsky: Amazing.

莱尼·拉奇茨基：太棒了。

Okay, so I asked him what I should ask you about and he suggested ask you why is the bitter lesson alone not likely to work for robots?

好吧，所以我问他我应该问你什么，他建议问你为什么仅仅这个惨痛的教训不太可能对机器人起作用？

So first of all, just explain what the bitter lesson was in the history of AI and then just why that won't get us to where we want to be with robots.

首先，解释一下人工智能历史上的惨痛教训，然后解释为什么这不能让我们达到我们想要的机器人目标。

Dr. Fei Fei Li: Well, first of all, there are many bitter lessons, but the bitter lessons everybody refers to is a paper written by Richard Sutton who won the Turing Award recently, and he does a lot of reinforcement learning.

李飞飞博士：嗯，首先有很多惨痛的教训，但是大家提到的惨痛教训是最近获得图灵奖的Richard Sutton写的一篇论文，他做了很多强化学习。

And Richard has said, if you look at the history, especially the algorithmic development of AI, it turns out simpler model with a ton of data always win at the end of the day instead of the more complex model with less data.

理查德表示，如果你回顾一下历史，尤其是人工智能的算法发展，就会发现拥有大量数据的更简单的模型最终总是获胜，而不是拥有更少数据的更复杂的模型。

I mean, that was actually...

我的意思是，那实际上是...

This paper came years after ImageNet.

这篇论文是在 ImageNet 之后几年发表的。

That to me was not bitter; it was a sweet lesson.

这对我来说并不痛苦；这是一个甜蜜的教训。

That's why I built ImageNet because I believe that big data plays that role.

这就是我构建 ImageNet 的原因，因为我相信大数据可以发挥这个作用。

So why can't bitter lesson work in robotics alone?

那么，为什么惨痛的教训不能仅在机器人领域发挥作用呢？

Well, first of all, I think we need to give credit to where we are today.

嗯，首先，我认为我们需要赞扬我们今天的处境。

Robotics is very much in the early days of experimentation.

机器人技术还处于实验的早期阶段。

Dr. Fei Fei Li: The research is not nearly as mature as say language models.

李飞飞博士：这项研究还没有语言模型那么成熟。

So many people are still experimenting with different algorithms and some of those algorithms are driven by big data.

许多人仍在尝试不同的算法，其中一些算法是由大数据驱动的。

So I do think big data will continue to play a role in robotics, but what is hard for robotics, there are a couple of things.

所以我确实认为大数据将继续在机器人技术中发挥作用，但对机器人技术来说困难的是有几件事。

One is that it's harder to get data.

一是获取数据更加困难。

It's a lot harder to get data.

获取数据要困难得多。

You can say, well, there's web data.

你可以说，好吧，有网络数据。

This is where the latest robotics research is using web videos.

这就是最新的机器人研究正在使用网络视频的地方。

And I think web videos do play a role.

我认为网络视频确实发挥了作用。

But if you think about what made language model worth a very...

但如果你想一想是什么让语言模型变得非常有价值......

As someone who does computer vision and spatial intelligence and robotics, I'm very jealous of my colleagues in language because they had this perfect setup where their training data are in words, eventually tokens, and then they produce a model that outputs words.

作为研究计算机视觉、空间智能和机器人技术的人，我非常嫉妒我的语言同事，因为他们有一个完美的设置，他们的训练数据是单词，最终是标记，然后他们生成一个输出单词的模型。

Dr. Fei Fei Li: So you have this perfect alignment between what you hope to get, which we call objective function and what your training data looks like.

Fei Fei Li博士：所以你希望得到的东西（我们称之为目标函数）和你的训练数据看起来是完美一致的。

But robotics is different.

但机器人技术不同。

Even spatial intelligence is different.

甚至空间智能也是不同的。

You hope to get actions out of robots, but your training data lacks actions in 3D worlds, and that's what robots have to do, right?

你希望从机器人那里得到动作，但是你的训练数据缺乏 3D 世界中的动作，而这正是机器人必须做的，对吗？

Actions in 3D worlds.

3D 世界中的动作。

So you have to find different ways to fit a, what do they call, a square in a round hole, that what we have is tons of web videos.

所以你必须找到不同的方法来适应他们所谓的圆孔中的正方形，我们拥有大量的网络视频。

So then we have to start talking about adding supplementing data such as teleoperation data or synthetic data so that the robots are trained with this hypothesis of bitter lesson, which is large amount of data.

因此，我们必须开始讨论添加补充数据，例如远程操作数据或合成数据，以便用大量数据这一惨痛教训的假设来训练机器人。

I think there's still hope because even what we are doing in world modeling will really unlock a lot of this information for robots.

我认为仍然有希望，因为即使我们在世界建模中所做的事情也将真正为机器人解锁很多此类信息。

Dr. Fei Fei Li: But I think we have to be careful because we're at the early days of this and bitter lesson is still to be tested because we haven't fully figured out the data for.

李飞飞博士：但我认为我们必须小心，因为我们还处于早期阶段，惨痛的教训仍有待检验，因为我们还没有完全弄清楚数据。

Another part of the bitter lesson of robotics I think we should be so realistic about is again, compared to language models or even spatial models, robots are physical systems.

我认为我们应该现实地看待机器人技术的惨痛教训的另一部分是，与语言模型甚至空间模型相比，机器人是物理系统。

So robots are closer to self-driving cars than a large language model.

因此，与大型语言模型相比，机器人更接近于自动驾驶汽车。

And that's very important to recognize.

认识到这一点非常重要。

That means that in order for robots to work, we not only need brains, we also need the physical body.

这意味着为了让机器人工作，我们不仅需要大脑，还需要身体。

We also need application scenarios.

我们还需要应用场景。

If you look at the history of self-driving car, my colleague Sebastian Thrun took Stanford's car to win the first DARPA challenge in 2006 or 2005.

如果你回顾一下自动驾驶汽车的历史，我的同事 Sebastian Thrun 就驾驶斯坦福大学的汽车在 2006 年或 2005 年赢得了第一届 DARPA 挑战赛。

It's 20 years since that prototype of a self-driving car being able to drive 130 miles in the Nevada desert to today's Waymo and on the street of San Francisco.

距离能够在内华达沙漠行驶 130 英里的自动驾驶汽车原型到今天的 Waymo 和旧金山街道已经过去了 20 年。

Dr. Fei Fei Li: And we're not even done yet.

李飞飞博士：我们还没有完成。

There's still a lot.

还有很多。

So that's a 20-year journey.

这是一个20年的旅程。

And self-driving cars are much simpler robots, they're just metal boxes running on 2D surfaces, and the goal is not to touch anything.

自动驾驶汽车是更简单的机器人，它们只是在 2D 表面上运行的金属盒子，目标是不接触任何东西。

Robot is 3D things running in 3D world, and the goal is to touch things.

机器人是运行在3D世界中的3D物体，目标是触摸物体。

So the journey is going to be, there's many aspects, elements, and of course one could say, well, the self-driving car, early algorithm were pre deep learning era.

所以这个旅程将会是，有很多方面、元素，当然可以说，自动驾驶汽车、早期算法是深度学习时代之前的。

So deep learning is accelerating the brains.

所以深度学习正在加速大脑的发展。

And I think that's true.

我认为这是真的。

That's why I'm in robotics, that's why I'm in spatial intelligence and I'm excited by it.

这就是我研究机器人技术的原因，这就是我研究空间智能的原因，我对此感到兴奋。

But in the meantime, the car industry is very mature and productizing also involves the mature use cases, supply chains, the hardware.

但与此同时，汽车行业已经非常成熟，产品化也涉及成熟的用例、供应链、硬件。

So I think it's a very interesting time to work in these problems.

所以我认为现在是解决这些问题的非常有趣的时刻。

But it's true, Ben is right.

但这是真的，本是对的。

We might still be subject to a number of bitter lessons.

我们可能仍然会遭受一些惨痛的教训。

Lenny Rachitsky: Doing this work, do you ever just feel awe for the way the brain works and is able to do all of this for us?

Lenny Rachitsky：在做这项工作时，你是否对大脑的工作方式以及能够为我们完成所有这一切感到敬畏？

Just the complexity just to get a machine to just walk around and not hit things and fall, does just give you more respect for what we've already got?

只是让机器四处走动而不撞到东西并摔倒的复杂性，是否会让您对我们已经拥有的东西更加尊重？

Dr. Fei Fei Li: Totally.

李飞飞博士：完全可以。

We operate on about 20 watts.

我们的运行功率约为 20 瓦。

That's dimmer than any light bulb in the room I'm in right now.

这比我现在所在房间里的任何灯泡都暗。

And yet we can do so much.

然而我们可以做的还有很多。

So I think actually the more I work in AI, the more I respect humans.

所以我认为实际上我在人工智能领域工作越多，我就越尊重人类。

Lenny Rachitsky: Let's talk about this product you just launched.

Lenny Rachitsky：我们来谈谈您刚刚推出的这款产品。

It's called Marble, a very cute name.

它叫Marble，一个非常可爱的名字。

Talk about what this is, why this is important.

谈论这是什么，为什么这很重要。

I've been playing with it, it's incredible.

我一直在玩它，这太不可思议了。

We'll link to it for folks to check it out.

我们将链接到它供人们查看。

What is Marble?

什么是大理石？

Dr. Fei Fei Li: Yeah, I'm very excited.

李飞飞博士：是的，我很兴奋。

So first of all, Marble is one of the first product that World Labs has rolled out.

首先，Marble 是 World Labs 推出的首批产品之一。

World Labs is a foundation frontier model company.

World Labs 是一家基础前沿模型公司。

We are founded by four co-founders who have deep technical history.

我们由四位拥有深厚技术历史的联合创始人创立。

My co-founders, Justin Johnson, Christoph Lassner, and Ben Mildenhall.

我的联合创始人贾斯汀·约翰逊、克里斯托夫·拉斯纳和本·米尔登霍尔。

We all come from the research field of AI, computer graphics, computer vision, and we believe that spatial intelligence and world modeling is as important, if not more, to language models and complementary to language models.

我们都来自人工智能、计算机图形学、计算机视觉的研究领域，我们相信空间智能和世界建模对于语言模型来说同样重要，甚至更重要，并且是语言模型的补充。

So we wanted to seize this opportunity to create deep tech research lab that can connect the dots between frontier models with products.

因此，我们想抓住这个机会创建深度技术研究实验室，将前沿模型与产品之间的点连接起来。

So Marble is an app that's built upon our frontier models.

Marble 是一款基于我们前沿模型构建的应用程序。

We've spent a year and plus building the world's first generative model that can output genuinely 3D worlds.

我们花了一年多的时间构建了世界上第一个可以输出真正 3D 世界的生成模型。

That's a very, very hard problem.

这是一个非常非常困难的问题。

Dr. Fei Fei Li: And it was a very hard process and we have a team of incredible, founding team of incredible technologists from incredible teams.

李飞飞博士：这是一个非常艰难的过程，我们拥有一支令人难以置信的创始团队，由来自令人难以置信的团队的令人难以置信的技术人员组成。

And then around just a month or two ago, we saw the first time that we can just prompt with a sentence and the image and multiple images and create worlds that we can just navigate in.

然后大约一两个月前，我们第一次看到我们可以只用一句话、图像和多个图像进行提示，并创建我们可以在其中导航的世界。

If you put it on Google, which we have an option to let you do that, you can even walk around.

如果您将其放在 Google 上（我们可以选择让您这样做），您甚至可以四处走动。

Even though we've been building this for quite a while, it was still just awe-inspiring and we wanted to get into the hands of people who need it.

尽管我们已经构建这个项目很长一段时间了，但它仍然令人惊叹，我们希望能够交到需要它的人手中。

And then we know that so many creators, designers, people who are thinking about robotic simulation, people who are thinking about different use cases of navigable interactable, immersive worlds game developers will find this useful.

然后我们知道，许多创作者、设计师、正在考虑机器人模拟的人们、正在考虑可导航、可交互、沉浸式世界的游戏开发人员的不同用例的人们都会发现这很有用。

So we developed Marble as a first step.

因此，我们第一步开发了 Marble。

It's again, still very early, but it's the world's first model doing this, and it's the world's first product that allows people to just prompt, we call it prompt to worlds.

虽然现在还很早，但这是世界上第一个这样做的模型，也是世界上第一个允许人们进行提示的产品，我们称之为“提示世界”。

Lenny Rachitsky: Well, I've been playing around with it.

Lenny Rachitsky：嗯，我一直在玩这个。

It is insane.

这太疯狂了。

You could just have a little Shire world where you just infinitely walk around middle earth basically, and there's no one there yet, but it's insane.

你可以拥有一个小小的夏尔世界，在那里你基本上可以无限地绕着中土世界行走，而且那里还没有人，但这太疯狂了。

You just go anywhere.

你就去任何地方。

There's dystopian world.

这里有一个反乌托邦的世界。

I'm just looking at all these examples and my favorite part, actually, I don't know if there's a feature or bug, you can see the dots of the world before it actually renders with all the textures.

我只是在看所有这些示例和我最喜欢的部分，实际上，我不知道是否存在功能或错误，您可以在使用所有纹理实际渲染之前看到世界的点。

And I just love like, you get a glimpse into what is going on with this model, basically-

我只是喜欢这样，你可以一睹这个模型的情况，基本上-

Dr. Fei Fei Li: That is so cool to hear because this is where, as a researcher, I am learning because the dots that lead you into the world was an intentional feature visualization, is not part of the model.

李飞飞博士：这真是太酷了，因为这就是作为一名研究人员我正在学习的地方，因为引导你进入世界的点是有意的特征可视化，而不是模型的一部分。

The model actually just generates the world.

该模型实际上只是生成了世界。

But we were trying to find a way to guide people into the world, and a number of engineers worked on different versions, but we converged on the dot, and so many people, you're not the only one, told us how delightful that experience is, and it was really satisfying for us to hear that this intentional visualization feature that's not just the big hardcore model actually has delighted our users.

但我们试图找到一种方法来引导人们进入这个世界，许多工程师开发了不同的版本，但我们集中在一点上，很多人，你不是唯一的人，告诉我们这种体验是多么令人愉快，我们真的很高兴听到这种有意的可视化功能，不仅仅是大型硬核模型，实际上让我们的用户感到高兴。

Lenny Rachitsky: Wow.

莱尼·拉奇茨基：哇。

So you add that to make it more, like to have humans understand what's going on-

所以你添加它是为了让它变得更多，比如让人们了解正在发生的事情-

Lenny Rachitsky: ...

莱尼·拉奇茨基：……

get more delightful.

变得更加令人愉快。

Wow, that is hilarious.

哇，太搞笑了。

It makes me think about LLMs and the way they, it's not the same thing, but they talk about what they're thinking and what they're doing.

这让我思考法学硕士和他们的方式，这不是同一件事，但他们谈论他们在想什么和他们在做什么。

Lenny Rachitsky: It also makes me think about just the Matrix.

Lenny Rachitsky：这也让我想到了《黑客帝国》。

It's exactly the Matrix experience.

这正是《黑客帝国》的体验。

I don't know if that was your inspiration.

不知道这是否是你的灵感来源。

Dr. Fei Fei Li: Well, like I said, a number of engineers worked on that.

李飞飞博士：嗯，就像我说的，很多工程师都在研究这个问题。

It could be their inspiration.

这可能是他们的灵感。

Lenny Rachitsky: It's in their subconscious.

Lenny Rachitsky：这是在他们的潜意识中。

Okay, so just for folks that may want to play around with this, maybe like, what are some applications today that folks can start using today?

好的，对于那些可能想尝试一下的人来说，也许人们今天可以开始使用哪些应用程序？

What's your goal with this launch?

您此次发布的目标是什么？

Dr. Fei Fei Li: Yeah, so we do believe that world modeling is very horizontal, but we're already seeing some really exciting use cases, virtual production for movies, because what they need are 3D worlds that they can align with the camera.

李飞飞博士：是的，所以我们确实相信世界建模是非常水平的，但我们已经看到了一些非常令人兴奋的用例，电影的虚拟制作，因为他们需要的是可以与相机对齐的 3D 世界。

So when the actors are acting on it, they can position the camera and shoot the segments really well.

所以当演员表演的时候，他们可以很好地定位摄像机并拍摄片段。

And we're already seeing incredible use.

我们已经看到了令人难以置信的用途。

In fact, I don't know if you have seen our launch video showing Marble.

事实上，我不知道您是否看过我们展示 Marble 的发布视频。

It was produced by a virtual production company.

它是由虚拟制作公司制作的。

We collaborated with Sony and they use Marble scenes to shoot those videos.

我们与索尼合作，他们使用 Marble 场景来拍摄这些视频。

So we were collaborating with those technical artists and directors, and they were saying, this has cut our production time by 40X.

所以我们与那些技术美术师和导演合作，他们说，这使我们的制作时间缩短了 40 倍。

In fact, it has to-

事实上，它必须——

Dr. Fei Fei Li: Yes, in fact it has to, because we only had one month to work on this project and there were so many things they were trying to shoot.

李飞飞博士：是的，事实上是必须的，因为这个项目我们只有一个月的时间，而他们要拍的东西太多了。

So using Marble really, really significantly accelerated the virtual production for VFX and movies.

因此，使用 Marble 确实非常显着地加速了视觉特效和电影的虚拟制作。

That's one use cases.

这是一个用例。

We are already seeing our users taking our Marble scene and taking the mesh export and putting games, whether it's games on VR or just fun games that they have developed.

我们已经看到我们的用户采用我们的 Marble 场景并进行网格导出并放置游戏，无论是 VR 游戏还是他们开发的有趣游戏。

We are showing an example of robotic simulation because when I was, I mean I still am a researcher doing robotic training.

我们展示了一个机器人模拟的例子，因为当我在的时候，我的意思是我仍然是一名进行机器人训练的研究人员。

One of the biggest pain point is to create synthetic data for training robots.

最大的痛点之一是为训练机器人创建合成数据。

And this synthetic data needs to be very diverse.

这些合成数据需要非常多样化。

They need to come from different environments with different objects to manipulate.

它们需要来自不同的环境，需要操作不同的对象。

And one path to it is to ask computers to simulate.

实现这一目标的途径之一是要求计算机进行模拟。

Dr. Fei Fei Li: Otherwise, humans have to build every single asset for robots.

李飞飞博士：否则，人类必须为机器人建造每一项资产。

That's just going to take a lot longer.

这只会花费更长的时间。

So we already have researchers reaching out and wanting to use Marble to create those synthetic environments.

因此，我们已经有研究人员伸出援手，希望使用大理石来创建这些合成环境。

We also have unexpected user outreach in terms of how they want to use Marble.

我们还意外地向用户询问了他们想要如何使用 Marble。

For example, a psychologist team called us to use Marble to do psychology research.

比如，一个心理学家团队叫我们用Marble来做心理学研究。

It turned out some of the psychiatric patients they study, they need to understand how their brain respond to different immersive things of different features.

事实证明，他们研究的一些精神病患者需要了解他们的大脑如何对具有不同特征的不同沉浸式事物做出反应。

For example, messy scenes or clean scenes or whatever you name it.

例如，凌乱的场景或干净的场景或任何你能想到的场景。

And it's very hard for researchers to get their hands on these kind of immersive scenes and it will take them too long and too much budget to create.

研究人员很难获得这种沉浸式场景，并且需要花费很长时间和太多的预算来创建。

And Marble is a really almost instantaneous way of getting so many of these experimental environments into their hands.

Marble 几乎是一种几乎可以瞬间将如此多的实验环境交到他们手中的方法。

So we're seeing multiple use cases at this point.

所以我们现在看到了多个用例。

But the VFX, the game developers, the simulation developers as well as designers are very excited.

但视觉特效、游戏开发人员、模拟开发人员以及设计师都非常兴奋。

Lenny Rachitsky: This is very much the way things work in AI.

Lenny Rachitsky：这就是人工智能的运作方式。

I've had other AI leaders on the podcast and it's always put things out there early as soon as you can to discover where the big use cases are.

我在播客上见过其他人工智能领导者，它总是尽早将内容放出来，让你能够发现最大的用例在哪里。

The head of ChatGPT told me how, when they first put out ChatGPT, he was just scanning TikTok to see how people were using it and all the things they were talking about, and that's what convinced them where to lean in and help them see how people actually want to use it.

ChatGPT 的负责人告诉我，当他们第一次推出 ChatGPT 时，他只是扫描 TikTok 以了解人们如何使用它以及他们正在谈论的所有内容，这就是说服他们向何处倾斜并帮助他们了解人们实际上想要如何使用它的原因。

I love this last use case for therapy.

我喜欢最后一个治疗用例。

I'm just imagining heights, people dealing with heights or snakes or spiders, which-

我只是想象高度，人们处理高度或蛇或蜘蛛，这-

Dr. Fei Fei Li: It's amazing.

李飞飞博士：太神奇了。

A friend of mine last night literally called me and talked about his height scare and asked me if Marble should be used.

昨晚我的一个朋友打电话给我，谈论了他对身高的恐惧，并问我是否应该使用 Marble。

It's amazing you went straight there.

令人惊奇的是你直接去了那里。

Lenny Rachitsky: Because imagining all the exposure therapy stuff, this could be so good for that.

Lenny Rachitsky：因为想象一下所有的暴露疗法，这可能非常有用。

That is so cool.

那太酷了。

Okay, so I should have asked you this before, but I think there's going to be a question of just, how does this differ from things like VO3 and other video generation models?

好的，所以我应该之前问过你这个问题，但我认为会有一个问题，这与 VO3 和其他视频生成模型有何不同？

It's pretty clear to me, but I think it might be helpful just to explain how this is different from all the video AI tools people have seen.

这对我来说很清楚，但我认为解释一下它与人们见过的所有视频人工智能工具有何不同可能会有所帮助。

Dr. Fei Fei Li: World Labs' thesis is that spatial intelligence is fundamentally very important, and spatial intelligence is not just about videos.

李飞飞博士：世界实验室的论点是，空间智能从根本上来说是非常重要的，而空间智能不仅仅与视频有关。

In fact, the world is not passively watching videos passing by.

事实上，世界并不是被动地观看路过的视频。

I love, Plato has the allegory of the cave analogy to describe vision.

我喜欢，柏拉图用洞穴比喻来描述视觉。

He said that imagine a prisoner tied on his chair, not very humane, but in a cave watching a full life theater in front of him, but the actual life theater that actors are acting is behind his back.

他说，想象一个囚犯被绑在椅子上，不是很人道，而是在一个山洞里观看他面前的完整的生活戏剧，但演员正在表演的实际生活戏剧却在他的背后。

It was just lit so that the projection of the action is on a wall of the cave.

它刚刚被点亮，以便动作的投影在洞穴的墙壁上。

And then the goal, the task of this prisoner is to figure out what's going on.

然后，这个囚犯的目标、任务就是弄清楚发生了什么。

It's a pretty extreme example, but it really shows, it describes what vision is about, is that to make sense of the 3D world or 4D world out of 2D.

这是一个相当极端的例子，但它确实表明、描述了视觉的含义，即从 2D 中理解 3D 世界或 4D 世界。

So spatial intelligence to me is deeper than only creating that flat 2D world.

因此，对我来说，空间智能比仅仅创建平面 2D 世界更深刻。

Dr. Fei Fei Li: Spatial intelligence to me is the ability to create, reason, interact, make sense of deeply spatial world, whether it's 2D or 3D or 4D, including dynamics and all that.

李飞飞博士：空间智能对我来说是创造、推理、交互、理解深层空间世界的能力，无论是2D、3D还是4D，包括动力学等等。

So World Lab is focusing on that, and of course the ability to create videos per se could be part of this.

因此，世界实验室正在关注这一点，当然，创建视频本身的能力可能是其中的一部分。

And in fact, just a couple of weeks ago, we rolled out the world's first real time demoable, real-time video generation on a single H100 GPU.

事实上，就在几周前，我们推出了世界上第一个在单个 H100 GPU 上实时可演示的实时视频生成功能。

So part of our technology includes that, but I think Marble is very different because we really want creators, designers, developers to have in their hands a model that can give them worlds with 3D structures so they can use it for their work.

我们的部分技术包括这一点，但我认为 Marble 非常不同，因为我们真正希望创作者、设计师、开发人员手中拥有一个模型，可以为他们提供具有 3D 结构的世界，以便他们可以在工作中使用它。

And that's why Marble is so different.

这就是大理石如此不同的原因。

Lenny Rachitsky: The way I see it is it's a platform for a ton of opportunity to do stuff.

Lenny Rachitsky：我认为这是一个提供大量做事机会的平台。

As you described, videos are just like, here's a one-off video that's very fun and cool and you could...

正如您所描述的，视频就像，这是一个非常有趣且很酷的一次性视频，您可以......

And that's it.

就是这样。

That's it.

就是这样。

And you move on.

然后你继续前进。

Dr. Fei Fei Li: By the way, we could in Marble, we could allow people to export in video forms.

李飞飞博士：顺便说一句，我们可以在Marble中，允许人们以视频形式导出。

So you could actually, like you said, you go into a world, so let's say it's a hobbit cave.

所以你实际上可以像你说的那样，进入一个世界，假设这是一个霍比特人洞穴。

You can actually, especially as a creator, you have such a specific way of moving the camera in a trajectory in the director's mind, and then you can export that from Marble into a video.

实际上，特别是作为创作者，您可以采用一种特定的方式按照导演脑海中的轨迹移动摄像机，然后您可以将其从 Marble 导出到视频中。

Lenny Rachitsky: What does it take to create something like this?

Lenny Rachitsky：创造这样的东西需要什么？

Just how big is the team, how many GPUs you work in?

团队有多大，使用多少个 GPU？

Anything you can share there.

你可以在那里分享任何东西。

I don't know how much of this is private information, but just what does it take to create something like this that you've launched here?

我不知道其中有多少是私人信息，但是创建您在这里推出的这样的东西需要什么？

Dr. Fei Fei Li: It takes a lot of brain power.

李飞飞博士：需要很大的脑力。

So we just talk about 20 watts per brain.

所以我们只讨论每个大脑 20 瓦。

So from that point of view, it's a small number, but it's actually incredible.

所以从这个角度来看，虽然这个数字很小，但实际上是令人难以置信的。

It's half billion years of evolution to give us those power.

经过五亿年的进化才赋予我们这些力量。

We have a team of 30-ish people now, and we are predominantly researchers and research engineers, but we also have designers and product.

我们现在有一个30人左右的团队，主要是研究员和研究工程师，但我们也有设计师和产品。

We actually really believe that we want to create a company that's anchored in the deep tech of spatial intelligence, but we are actually building serious products.

事实上，我们确实相信我们想要创建一家立足于空间智能深度技术的公司，但我们实际上正在构建严肃的产品。

So we have this integration of R&D and productization, and of course, we use a ton of GPUs.

所以我们将研发和产品化融为一体，当然，我们使用了大量的 GPU。

Lenny Rachitsky: Well, congrats on the launch.

Lenny Rachitsky：嗯，恭喜发布。

I know this is a huge milestone.

我知道这是一个巨大的里程碑。

I know this took a ton of work.

我知道这需要大量的工作。

Lenny Rachitsky: So I just want to say congrats to you and your team.

Lenny Rachitsky：我只想向你和你的团队表示祝贺。

Let me talk about your founder journey for a moment.

让我谈谈您的创始人历程。

So you're a founder of this company.

所以你是这家公司的创始人。

You started how many years ago?

你是几年前开始的？

A couple of years ago, two, three years ago?

几年前、两年前、三年前？

Lenny Rachitsky: Okay.

莱尼·拉奇茨基：好的。

What's something you wish you knew before you started this that you wish you could whisper into the ear of Fei-Fei of 18 months ago?

有什么事情你希望在开始之前就知道，并且希望能在 18 个月前的飞飞耳边低语？

Dr. Fei Fei Li: Well, I continue to wish I know the future of technology.

李飞飞博士：嗯，我仍然希望了解技术的未来。

I think actually that's one of our founding advantage is that we see the future earlier in general than most people.

我认为实际上我们的创始优势之一是我们比大多数人更早地看到未来。

But still, man, this is so exciting and so amazing that what's unknown and what's coming, but I know the reason you're asking me this question is not about the future of technology.

但是，伙计，这仍然是如此令人兴奋和令人惊奇，以至于未知和即将发生什么，但我知道你问我这个问题的原因不是关于技术的未来。

Furthermore, look, I did not start a company of this scale at 20-year-old.

而且你看，我也不是20岁就创办了这么规模的公司。

So I started a dry cleaner when I was 19, but that's a little smaller scale.

所以我在19岁的时候就开了一家干洗店，不过规模有点小。

Dr. Fei Fei Li: And then I founded Google Cloud AI and then I founded an institute at Stanford but those are different beasts.

李飞飞博士：然后我创立了谷歌云人工智能，然后我在斯坦福大学成立了一个研究所，但这些都是不同的野兽。

I did feel I was a little more prepared as a founder of the grinding journey compared to maybe the 20-year-old founders.

我确实觉得，与那些 20 岁的创始人相比，作为创始人，我在磨练之旅中准备得更充分。

But I still, I'm surprised, and it puts me into paranoia sometimes that how intensely competitive AI landscape is from the model, the technology itself, as well as talents.

但我仍然感到惊讶，有时这让我陷入偏执，认为人工智能领域的竞争从模型、技术本身到人才都是如此激烈。

And when I founded the company, we did not have these incredible stories of how much certain talents would cost.

当我创立公司时，我们并没有这些关于某些人才需要花费多少钱的令人难以置信的故事。

So these are things that continue to surprise me and I have to be very alert about.

所以这些事情继续让我感到惊讶，我必须非常警惕。

Lenny Rachitsky: So the competition you're talking about is the competition for talent, the speed at which just how things are moving.

Lenny Rachitsky：所以你所说的竞争是人才的竞争，即事物发展的速度。

Lenny Rachitsky: Yeah.

莱尼·拉奇茨基：是的。

You mentioned this point that I want to come back to that if you just look over the course of your career, you were at all of the major collections of humans that led to so many of the breakthroughs that are happening today.

你提到了这一点，我想回到这一点，如果你回顾一下你的职业生涯，你会发现你是导致今天发生的如此多突破的所有主要人类集合中的一员。

Obviously, we talk about ImageNet also just SAIL at Stanford is where a lot of the work happened, Google Cloud, which a lot of the breakthroughs happened.

显然，我们谈论 ImageNet 也只是斯坦福大学的 SAIL 是很多工作发生的地方，Google Cloud 是很多突破发生的地方。

What brought you to those places?

是什么让你来到这些地方？

Like for people looking for how to advance in their career, be at the center of the future, just is there a through line there of just what pulled you from place to place and pulled you into those groups that might be helpful for people to hear?

就像那些寻求如何在职业生涯中取得进步、成为未来中心的人一样，是否有一条贯穿的路线可以将你从一个地方拉到另一个地方，并将你拉入那些可能对人们有帮助的群体？

Dr. Fei Fei Li: Yeah, this is actually a great question, Lenny, because I do think about it, and obviously we talked about it's curiosity and passion that brought me to AI, that is more a scientific north star, right?

李飞飞博士：是的，这实际上是一个很好的问题，Lenny，因为我确实考虑过这个问题，显然我们谈到了正是好奇心和热情让我接触了人工智能，这更像是一颗科学北极星，对吧？

I did not care if AI was a thing or not, so that was one part.

我不在乎人工智能是否是​​一个东西，所以那只是一部分。

But how did I end up choosing in the particular places I work in, including starting World Labs, is I think I'm very grateful to myself or maybe to my parents' genes.

但我最终是如何选择我工作的特定地方，包括创办世界实验室，我认为我非常感谢自己，或者也许是我父母的基因。

I'm an intellectually very fearless person, and I have to say when I hire young people, I look for that because I think that's a very important quality if one wants to make a difference, is that when you want to make a difference, you have to accept that you're creating something new or you're diving into something new.

我是一个在智力上非常无所畏惧的人，我不得不说，当我雇用年轻人时，我会寻找这一点，因为我认为如果一个人想要有所作为，这是一种非常重要的品质，那就是当你想要有所作为时，你必须接受你正在创造新的东西或者你正在投入新的东西。

People haven't done that.

人们还没有这样做过。

And if you have that self-awareness, you almost have to allow yourself to be fearless and to be courageous.

如果你有这种自我意识，你几乎必须让自己变得无所畏惧和勇敢。

Dr. Fei Fei Li: So when I, for example, came to Stanford, in the world of academia, I was very close to this thing called tenure, which is have the job forever at Princeton.

李飞飞博士：例如，当我来到斯坦福大学时，在学术界，我非常接近所谓的终身教职，即永远在普林斯顿大学获得这份工作。

But I chose to come to Stanford because...

但我选择来斯坦福大学是因为...

I love Princeton.

我爱普林斯顿。

It's by alma mater.

是母校的。

It's just at that moment there are people who are so amazing at Stanford and the Silicon Valley ecosystem was so amazing that I was okay to take a risk of restarting my tenure clock.

就在那时，斯坦福大学有如此出色的人，硅谷的生态系统也如此出色，所以我可以冒险重新启动我的终身教职时钟。

Becoming the first female director of SAIL, I was actually relatively speaking a very young faculty at that time, and I wanted to do that because I care about that community.

作为 SAIL 的第一位女性主任，相对而言，我当时实际上是一个非常年轻的教师，我想这样做是因为我关心这个社区。

I didn't spend too much time thinking about all the failure cases.

我没有花太多时间去思考所有的失败案例。

Dr. Fei Fei Li: Obviously, I was very lucky that the more senior faculty supported me, but I just wanted to make a difference.

李飞飞博士：显然，我很幸运有更资深的老师支持我，但我只是想有所作为。

And then going to Google was similar.

然后去谷歌也是类似的。

I wanted to work with people like Jeff Dean, Jeff Hinton, and all these incredible demists, the incredible people.

我想与杰夫·迪恩（Jeff Dean）、杰夫·辛顿（Jeff Hinton）等人一起工作，以及所有这些令人难以置信的除雾论者，那些令人难以置信的人。

The same with World Labs.

世界实验室也是如此。

I have this passion.

我有这种热情。

And I also believe that people with the same mission can do incredible things.

我也相信，怀揣同样使命的人可以做出不可思议的事情。

So that's how it guided my through line.

这就是它引导我完成任务的方式。

I don't overthink of all possible things that can go wrong because that's too many.

我不会过度考虑所有可能出错的事情，因为那太多了。

Lenny Rachitsky: I feel like an important element of this is not focusing on the downside, focusing more on the people, the mission.

Lenny Rachitsky：我觉得其中一个重要因素不是关注不利因素，而是更多地关注人员和使命。

What gets you excited, what do you think, the curiosity.

什么让你兴奋，你怎么想，好奇心。

Dr. Fei Fei Li: Yeah.

李飞飞医生：是的。

I do want to say one thing to all the young talents in AI, the engineers, the researchers out there, because some of you apply to World Labs, I feel very privileged you considered World Labs.

我确实想对人工智能领域的所有年轻人才、工程师、研究人员说一件事，因为你们中的一些人申请了世界实验室，我对你们考虑加入世界实验室感到非常荣幸。

I do find many of the young people today think about every single aspect of an equation when they decide on jobs.

我确实发现今天的许多年轻人在决定工作时会考虑方程式的每一个方面。

At some point, maybe that's the way they want to do it, but sometimes I do want to encourage young people to focus on what's important because I find myself constantly in mentoring mode when I talk to job candidates.

在某些时候，也许这就是他们想要的方式，但有时我确实想鼓励年轻人专注于重要的事情，因为当我与求职者交谈时，我发现自己一直处于指导模式。

Not necessarily recruiting or not recruiting, but just in mentoring mode when I see an incredible young talent who is over-focusing on every minute dimension and aspect of considering a job, when maybe the most important thing is where's your passion?

不一定是招聘或不招聘，但只是在指导模式下，当我看到一个令人难以置信的年轻人才，他过度关注考虑工作的每一个细节和方面，而也许最重要的是你的热情在哪里？

Do you align with the mission?

你与使命相符吗？

Do you believe and have faith in this team?

你相信并且对这支球队有信心吗？

And just focus on the impact and you can make and the kind of work and team you can work with.

只需关注您可以产生的影响以及您可以合作的工作类型和团队。

Lenny Rachitsky: Yeah, it's tough.

莱尼·拉奇茨基：是的，这很难。

It's tough for people in the AI space.

对于人工智能领域的人来说这很艰难。

Now there's so much, so much at them, so much new, so much happening, so much FOMO.

现在有太多太多的事情，太多的新事物，太多的事情正在发生，太多的“害怕错过”。

Lenny Rachitsky: I could see the stress.

Lenny Rachitsky：我可以看到压力。

And so I think that advice is really important.

所以我认为这个建议非常重要。

Just like what will actually make you feel fulfilled in what you're doing, not just where's the fastest growing company, where's the...

就像真正能让你对所做的事情感到满足一样，不仅仅是增长最快的公司在哪里，...

Who's going to win?

谁会赢？

I don't know.

我不知道。

I want to make sure I ask you about the work you're doing today at Stanford, at the HCI.

我想确保我向您询问了您今天在斯坦福大学 HCI 所做的工作。

I think it's the-

我认为这是——

Lenny Rachitsky: HAI, Human-Centered AI Institute.

Lenny Rachitsky：HAI，以人为中心的人工智能研究所。

What are you doing there?

你在那儿做什么？

I know this is a thing you do on the side still.

我知道这仍然是你的副业。

Dr. Fei Fei Li: So yes, HAI, Human-Centered AI Institute was co-founded by me and a group of faculty like Professor John Etchemendy, Professor James Landay, Professor Chris Manning back in 2018.

李飞飞博士：是的，HAI（以人为中心的人工智能研究所）是我和 John Etchemendy 教授、James Landay 教授、Chris Manning 教授等教授于 2018 年共同创立的。

I was actually finishing my last sabbatical at Google and it was a very, very important decision for me because I could have stayed in industry, but my time at Google taught me one thing is AI is going to be a civilization of technology.

事实上，我刚刚结束在谷歌的最后一次休假，这对我来说是一个非常非常重要的决定，因为我本可以留在工业界，但我在谷歌的经历教会了我一件事，那就是人工智能将成为一种技术文明。

And it dawned on me how important this is to humanity to the point that I actually wrote a piece in New York Times, that year 2018, to talk about the need for a guiding framework to develop and to apply AI.

我意识到这对人类有多么重要，以至于我在 2018 年《纽约时报》上写了一篇文章，讨论开发和应用人工智能的指导框架的必要性。

And that framework has to be anchored in human benevolence, in human centeredness.

这个框架必须植根于人类的仁慈、以人类为中心。

And I felt that Stanford, one of the world's top university in the heart of Silicon Valley that gave birth to important companies from NVIDIA to Google, should be a thought leader to create this human-centered AI framework and to actually embody that in our research education and policy and ecosystem work.

我认为斯坦福大学是位于硅谷中心的世界顶尖大学之一，孕育了从 NVIDIA 到 Google 等重要公司，它应该成为创建这种以人为本的人工智能框架的思想领袖，并在我们的研究教育、政策和生态系统工作中实际体现这一框架。

Dr. Fei Fei Li: So I founded HAI.

李飞飞博士：所以我创立了HAI。

Fast-forward, after six, seven years, it has become the world's largest AI institute that does human-centered research, education, ecosystem, outreach, and policy impact.

快进到，六七年后，它已经成为世界上最大的人工智能机构，从事以人为本的研究、教育、生态系统、推广和政策影响。

It involves hundreds of faculty across all eight schools at Stanford, from medicine to education, to sustainability to business, to engineering, to humanities to law.

它涉及斯坦福大学所有八个学院的数百名教师，从医学到教育，到可持续发展到商业，到工程，从人文学科到法律。

And we support researchers, especially at the interdisciplinary area from digital economy, to legal studies, to political science, to discovery of new drugs, to new algorithms to that's beyond transformers.

我们支持研究人员，特别是跨学科领域的研究人员，从数字经济、法律研究、政治学、新药发现、新算法到变形金刚之外的领域。

We also actually put a very strong focus on policy because when we started HAI, I realized that Silicon Valley did not talk to Washington DC and or Brussels or other parts of the world.

实际上，我们也非常重视政策，因为当我们开始 HAI 时，我意识到硅谷没有与华盛顿特区和/或布鲁塞尔或世界其他地区进行对话。

Dr. Fei Fei Li: And given how important this technology is, we need to bring everybody on board.

李飞飞博士：鉴于这项技术的重要性，我们需要让每个人都参与进来。

So we created multiple programs from congressional bootcamp to AI index report to policy briefing, and we especially participated in policymaking including advocating for a national AI research cloud bill that was passed in the first Trump administration and participating in state level regulatory AI discussions.

因此，我们创建了多个项目，从国会训练营到人工智能指数报告，再到政策简报，我们特别参与了政策制定，包括倡导第一届特朗普政府通过的国家人工智能研究云法案，并参与州级监管人工智能讨论。

So there's a lot we did, and I continue to be one of the leaders even though I'm much less involved operationally because I care not only we create this technology, but we use it in the right way.

因此，我们做了很多事情，尽管我很少参与运营，但我仍然是领导者之一，因为我不仅关心我们创造这项技术，而且关心我们以正确的方式使用它。

Lenny Rachitsky: Wow.

莱尼·拉奇茨基：哇。

I was not aware of all that other work you were doing.

我不知道你正在做的所有其他工作。

As you're talking, I was reminded Charlie Munger had this quote, "Take a simple idea and take it very seriously." I feel like you've done that in so many different ways and stayed with it and it's unbelievable the impact that you've had in so many ways over the years.

当你说话的时候，我想起了查理·芒格的这句话：“接受一个简单的想法，并非常认真地对待它。”我觉得你已经通过很多不同的方式做到了这一点，并坚持了下来，多年来你在很多方面产生的影响令人难以置信。

I'm going to skip the lightning round and I'm just looking to ask you one last question.

我将跳过闪电回合，我只想问你最后一个问题。

Is there anything else that you wanted to share?

您还有什么想分享的吗？

Anything else you want to leave listeners with?

您还有什么想留给听众的吗？

Dr. Fei Fei Li: I am very excited by AI, Lenny.

李飞飞博士：我对人工智能感到非常兴奋，Lenny。

I want to answer one question that when I travel around the world, everybody asks me is that, if I'm a musician, if I'm a teacher, middle school teacher, if I'm a nurse, if I'm an accountant, if I'm a farmer, do I have a role in AI or is AI just going to take over my life or my work?

我想回答一个问题，当我环游世界时，每个人都会问我，如果我是一名音乐家，如果我是一名教师，中学老师，如果我是一名护士，如果我是一名会计师，如果我是一名农民，我在人工智能中扮演角色吗？或者人工智能会接管我的生活或工作吗？

And I think this is the most important question of AI and I find that in Silicon Valley, we tend not to speak heart-to-heart with people, with people like us and not like us in Silicon Valley, but all of us, we tend to just toss around words like infinite productivity or infinite leisure time or infinite power or whatever.

我认为这是人工智能最重要的问题，我发现在硅谷，我们倾向于不与人进行心与心的交谈，与像我们这样的人和不喜欢硅谷的我们的人交谈，但我们所有人，我们倾向于只是乱扔诸如无限生产力或无限闲暇时间或无限权力之类的词。

But at the end of the day, AI is about people.

但归根结底，人工智能是关于人的。

And when people ask me that question, it's a resounding yes, everybody has a role in AI.

当人们问我这个问题时，我的回答是肯定的，每个人都在人工智能中发挥着作用。

Dr. Fei Fei Li: It depends on what you do and what you want.

李飞飞博士：这取决于你做什么、想要什么。

But no technology should take away human dignity and the human dignity and agency should be at the heart of the development, the deployment, as well as the governance of every technology.

但任何技术都不应剥夺人的尊严，人的尊严和能动性应该成为每项技术的开发、部署和治理的核心。

So if you are a young artist and your passion is storytelling, embrace AI as a tool.

因此，如果您是一名年轻艺术家并且您热衷于讲故事，请拥抱人工智能作为一种工具。

In fact, embrace Marble.

事实上，拥抱大理石。

I hope it becomes a tool for you because the way you tell your story is unique and the world still needs it.

我希望它成为你的一个工具，因为你讲述故事的方式是独特的，世界仍然需要它。

But how you tell your story, how do you use the most incredible tool to tell your story in the most unique way is important.

但如何讲述你的故事，如何使用最令人难以置信的工具以最独特的方式讲述你的故事很重要。

And that voice needs to be heard.

这个声音需要被听到。

If you are a farmer near retirement, AI still matters because you are a citizen.

如果你是一位即将退休的农民，人工智能仍然很重要，因为你是公民。

You can participate in your community, you should have a voice in how AI is used, how AI is applied.

你可以参与你的社区，你应该对如何使用人工智能、如何应用人工智能有发言权。

Dr. Fei Fei Li: You work with people that you can encourage all of you to use AI to make life easier for you.

李飞飞博士：你们与人们一起工作，你们可以鼓励你们所有人使用人工智能来让你们的生活变得更轻松。

If you are a nurse, I hope you know that at least in my career, I have worked so much in healthcare research because I feel our healthcare workers should be greatly augmented and helped by AI technology, whether it's smart cameras to feed more information or robotic assistance because our nurses are overworked, overfatigued, and as our society ages, we need more help for people to be taken care of.

如果你是一名护士，我希望你知道，至少在我的职业生涯中，我在医疗保健研究方面做了很多工作，因为我觉得我们的医疗保健工作者应该得到人工智能技术的极大增强和帮助，无论是提供更多信息的智能摄像头还是机器人协助，因为我们的护士过度劳累、过度疲劳，而且随着我们社会的老龄化，我们需要更多的帮助来照顾人们。

So AI can play that role.

所以人工智能可以发挥这个作用。

So I just want to say that it's so important that even a technologist like me are sincere about that everybody has a role in AI.

所以我只想说，即使是像我这样的技术专家也真诚地认为每个人都可以在人工智能中发挥作用，这一点非常重要。

Lenny Rachitsky: What a beautiful way to end it.

Lenny Rachitsky：多么美妙的结束方式啊。

Such a tie back to where we started about how it's up to us and take individual responsibility for what AI will do in our lives.

这种联系回到了我们开始的地方，即我们如何决定人工智能将在我们的生活中做什么，并承担个人责任。

Final question, where can folks find Marble?

最后一个问题，人们在哪里可以找到大理石？

Where can they go, maybe try to join World Labs if they want to?

他们可以去哪里，如果他们愿意的话，也许可以尝试加入世界实验室？

What's the website?

网站是什么？

Where do people go?

人们去哪里？

Dr. Fei Fei Li: Well, World Labs website is www.worldlabs.ai and you can find our research progress there.

李飞飞博士：嗯，World Labs的网站是www.worldlabs.ai，你可以在那里找到我们的研究进展。

We have technical blogs.

我们有技术博客。

You can find Marble, the product there.

您可以在那里找到大理石产品。

You can sign in there.

您可以在那里登录。

You can find our job posts link there.

您可以在那里找到我们的职位发布链接。

We're in San Francisco.

我们在旧金山。

We love to work with the world's best talents.

我们喜欢与世界上最优秀的人才合作。

Lenny Rachitsky: Amazing.

莱尼·拉奇茨基：太棒了。

Fei-Fei, thank you so much for being here.

菲菲，非常感谢你来到这里。

Bye everyone.: Thank you so much for listening.

大家再见：非常感谢您的聆听。

If you found this valuable, you can subscribe to the show on Apple Podcasts, Spotify, or your favorite podcast app.

如果您发现这很有价值，您可以在 Apple Podcasts、Spotify 或您最喜欢的播客应用程序上订阅该节目。

Also, please consider giving us a rating or leaving a review as that really helps other listeners find the podcast.

另外，请考虑给我们评分或留下评论，因为这确实有助于其他听众找到播客。

You can find all past episodes or learn more about the show at lennyspodcast.com.

您可以在 lennyspodcast.com 上找到所有过去的剧集或了解有关该节目的更多信息。

See you in the next episode.

下一集见。