IL47：将重新定义人性含义的AI革命，嘉宾：克雷格·蒙迪

我们首次在这些机器中拥有了一位合作者，它将赋予我们前所未有的能力，去理解我们自身，包括我们的生物学乃至思维过程。

For the first time, we'll have a collaborator in these machines that will give us the ability to under understand ourselves, including our biology and perhaps even our thinking, to a degree that has never been possible before.

但它同时也为人类掌控自身命运创造了机会。

But it'll also create the opportunity for humans to take control of our own destiny.

我经常告诉人们，人类的进化，如同一直以来那样，已经结束了。

I often tell people that human evolution, as it has been for all of time, is over.

事实上，我们改变生存环境的速度，已经超出了传统进化过程所能适应的范围。

That in fact, you know, we're changing the environment in which we live at a rate that standard evolutionary processes don't adapt to.

想象一下，花上一小时与世界上最顶尖的交易员相处。

Imagine spending an hour with the world's greatest traders.

想象一下，从他们的经验、成功与失败中学习。

Imagine learning from their experiences, their successes, and their failures.

别再想象了。

Imagine no more.

欢迎来到《顶级交易员未剪辑版》，在这里，你可以向全球最优秀的对冲基金经理学习，从而将你的基金经理尽职调查或投资职业生涯提升到新高度。

Welcome to Top Traders Unplugged, the place where you can learn from the best hedge fund managers in the world so you can take your manager due diligence or investment career to the next level.

在我们开始今天的对话之前，请记住两件事。

Before we begin today's conversation, remember to keep two things in mind.

我们所有关于投资表现的讨论都仅限于过去，而过往表现并不能保证或暗示任何关于未来表现的信息。

All the discussion we'll have about investment performance is about the past, and past performance does not guarantee or even infer anything about future performance.

同时，请理解所有投资策略都存在显著的财务亏损风险，在做出投资决策之前，您需要向投资经理索取并充分了解其产品的具体风险。

Also understand that there's a significant risk of financial loss with all investment strategies, and you need to request and understand the specific risks from the investment manager about their product before you make investment decisions.

接下来有请资深对冲基金经理尼尔斯·卡普斯特-拉森。

Here's your host, veteran hedge fund manager Niels Kaastrup-Larsen.

对我来说，播客之旅最精彩的部分，就是有机会与来自世界各地的众多杰出人士对话。

For me, the best part of my podcasting journey has been the opportunity to speak to a huge range of extraordinary people from all around the world.

在本系列中，我邀请了其中一位——凯文·科迪龙，来主持一系列深入对话，帮助揭示和阐释新理念，助你成为更优秀的投资者。

In this series, I have invited one of them, namely Kevin Coldiron, to host a series of in-depth conversations to help uncover and explain new ideas to make you a better investor.

在这一系列中，凯文将与新书和研究论文的作者对话，以更好地理解全球经济及其塑造机制，从而让我们都能成功应对其中的挑战。

In the series, Kevin will be speaking to authors of new books and research papers to better understand the global economy and the dynamics that shape it so that we can all successfully navigate the challenges within it.

接下来，有请凯文·科迪龙。

And with that, please welcome Kevin Coldiron.

好的。

Okay.

谢谢尼尔斯，欢迎各位来到《Top Traders Unplugged》的Ideas Lab系列。

Thanks, Niels, and welcome everyone to the Ideas Lab series here on Top Traders Unplugged.

今天我们邀请的嘉宾是克雷格·门迪。

Our guest today is Craig Mundie.

克雷格在微软工作了二十二年，担任首席技术官。

Craig spent twenty two years at Microsoft where he was the chief technical officer.

他还曾担任该公司的首席研究与战略官，以及首席执行官的高级顾问。

He also served as chief research and strategy officer there, as well as senior adviser to the CEO.

克雷格还广泛参与公共政策领域，曾八年担任奥巴马总统科学与技术顾问委员会成员。

Craig has worked extensively in the public policy arena as well, including spending eight years on president Obama's council of advisers on science and technology.

他目前是中美人工智能二轨对话的联合主席。

And he's currently co chair of the track two dialogue with China on artificial intelligence.

他今天来这里，是为了讨论他与埃里克·施密特和已故的亨利·基辛格合著的书籍《创世：人工智能、希望与人类精神》。

He's here today to talk about the book he coauthored with Eric Schmidt and the late Henry Kissinger called Genesis, Artificial Intelligence, hope, and the human spirit.

对我来说，这是我对人工智能长期挑战与潜力最深入的分析之一，我非常兴奋能将这些观点分享给今天的听众。

For me, it's one of the most thoughtful analysis of the long term challenges and potential of AI that I've read, and I'm super excited to bring some of those ideas to everyone listening today.

所以，Craig，我们非常感谢你抽出时间。

So Craig, we really appreciate your time.

非常感谢你加入我们，欢迎来到本节目。

Thanks so much for joining us, and welcome to the show.

谢谢，凯文。

Thanks, Kevin.

我很高兴能来到这里。

I'm very happy to be here.

好的。

Alright.

这本书叫《创世纪》，所以我想我们可以聊聊这本书的由来。

Well, the book is called Genesis, so I thought perhaps we could talk about the genesis of the book.

这个与基辛格博士的合作项目是如何开始的？

How did the project with Doctor.

基辛格和埃里克·施密特，对于还不了解的人，埃里克·施密特曾是谷歌的首席执行官和董事长。

Kissinger and and Eric Schmidt and for those of you who don't know, Eric Schmidt was the CEO and chairman of of Google.

那么，写一本关于人工智能的书这个决定是怎么产生的呢？

How did that, you know, the decision to write a book about AI come about?

事实上，早在1998年，我就以一种很特别的方式认识了亨利·基辛格，后来我们成了非常好的朋友。

Well, it turned out that years ago, actually in 1998, I got to know Henry Kissinger in a in an interesting sort of way, and we became very good friends.

所以在过去二十多年里，我们一直合作共事，经常交流，深入探讨当时的各种议题。

And so for twenty six odd years, you know, we both did some work together and collaborated and spent quite a bit of time talking about whatever the issues of the day were.

随着时间推移，当亨利95岁的时候——也就是大约七年前——我和埃里克·施密特一起参加了名为‘Build A Bird’的会议，那一年会议在欧洲举行。

And as time went on, when Henry turned 95, which was basically like seven years now ago, and Eric Schmidt were all attending a meeting in called the Build A Bird meetings, and that one was in Europe that year.

这个会议亨利自1955年左右起每年都会参加，而我也已经参加了很久。

And it was something that Henry had gone to every year, you know, since 1955 I think, and I had been attending for a long time.

在那次由我和埃里克共同策划的会议议程中，有一个议题是关于人工智能的讨论。

And one of the topics at that meeting that Eric and I had collaborated on in putting the program together included a discussion on artificial intelligence.

当时亨利也在场，他认真聆听了讨论，结束后，他深深被机器能力超越人类这一前景所带来的深远影响所震撼。

And so Henry was there, and he listened to the discussion, and at the end of which, you know, he was really struck by the profound implications of the arrival of machines that would exceed human capabilities.

就在那一刻，他决定将余生——无论还剩多少年——专注于帮助人们理解人工智能可能带来的影响，以及我们该如何思考应对它。

And at that moment, in essence, he decided to spend the rest of his life, however many years remained, focused on trying to help people understand what the effects of that were likely to be and how we might think about dealing with it.

因此，他花了不少时间，超过一年，认真地学习和充实自己。

And so he spent quite a bit of time, a year or more, really getting educated.

亨利根本不是技术专家。

Henry was not a technologist at all.

他受过哲学和历史的训练。

You know, he's trained in philosophy and history.

他是一位杰出的战略家，也是个优秀的作家，但技术并不是他的专长。

He was a brilliant strategist and a good writer, but technology wasn't his thing.

几年后，他和埃里克——他也认识埃里克一段时间了——与来自麻省理工学院的丹·胡滕洛克尔联手，决定合写一本关于人工智能的书。

And after a couple of years, he and Eric, who he had known also for some period of time, got together with Dan Huttenlocker out of MIT and decided to write a book about AI.

这本书主要聚焦于他们所认为的人工智能潜在挑战，以及一些潜在的好处。

And that book focused more or less on the what they saw as the potential challenges of artificial intelligence and a little bit about the potential benefits.

但值得注意的是，它并没有深入探讨：我们究竟可以做些什么？

But it notably didn't go very far in trying to talk about, well, what could we do about it?

结果，在所有这些发展的同时，我在从微软退休后，开始与多家公司合作。

And it turned out, in parallel with all that development, I had, in in my retirement from Microsoft, started to work with a variety of companies.

其中一家公司正是萨姆·阿尔特曼和OpenAI的早期阶段。

One of which turned out to be Sam Altman and OpenAI in its early years.

由于这一参与，我本人像亨利一样，对如何应对超级智能机器的出现产生了浓厚兴趣。

And as a result of that involvement, I personally became very interested, as Henry had, in this question of how do you deal with the emergence of super intelligent machines?

我花了不少时间思考这个问题。

And I had spent quite a bit of time thinking about it.

因此，亨利请我在他们完成这本书时担任编辑，我接受了。

So Henry asked me to edit that first book as they were completing it, and I did.

在我们一次双周对话中，我对他说，这很好。

And and then in one of our biweekly conversations, I said to him, you know, this is good.

它帮助人们看到了潜在的好处和风险，但并没有告诉他们该如何应对。

It helps people see the benefits and potential risks, but it doesn't tell them what to do about it.

我一直在深入思考这些问题，于是我和亨利开始讨论其中的一些想法。

And I've been thinking a lot about that, and Henry and I started talking about some of those ideas.

有一天，他对我说：‘现在是你我合写一本书的时候了。’

And he said one day, okay, now it's time for you and I to write a book.

埃里克对补充一些第一本书里没有的内容很感兴趣。

And Eric was sort of interested in adding some things, you know, to that discussion that hadn't been in the first one.

实际上，我认识埃里克已经四十年了。

And I'd known Eric actually for forty years.

你知道，我们当年都是科技行业里的年轻人。

You know, we were both young guys in the tech industry.

我们曾因太阳微系统公司和我当时所在的公司——Alliant计算机系统公司之间的合作而结识。

We had come together in a collaboration between Sun Microsystems and my company, Alliant Computer Systems, at the time.

因此，我们彼此熟识并一直有互动，正如你提到的，我们还在奥巴马总统的科学与技术顾问委员会共事了八年。

And and so we had known each other and interacted including, as you pointed out, on president Obama's council of science and advisors on science and technology for eight years.

于是，我们开始了后来成为《创世》的这本书的写作。

And so we embarked on the book that became Genesis.

最终，我们在亨利去世后不久完成了这本书。

And in the end, we finished it just after Henry passed away.

是的

Yeah.

这本书的第一部分是对基辛格博士的感人致敬。

It's a the first section of the book is quite a moving tribute to to Doctor.

基辛格。

Kissinger.

我忍不住笑了。

And I had to chuckle.

我觉得你提到他最初接触计算机时，想买一台，但中央情报局不让他买。

I think you say that he he first learned about computers and he wanted to get one for himself and the CIA wouldn't let him

有一台。

have one.

是的

Yeah.

是的

Yeah.

我的意思是，事情已经发生了很大的变化。

I mean, things have changed a lot.

是的。

Yeah.

我想开始这个对话。

So I'd like to start the conversation.

这可能有点沉重，但我认为关于知识和理解的概念非常重要。

This is actually maybe a little bit heavy, but I think quite important with the concepts of knowledge and understanding.

我怀疑对于大多数听众，包括我自己在内，到目前为止与人工智能的主要互动就是向它提问。

And I suspect for most people listening, myself included, that their main interaction thus far with AI has been asking it questions.

事实上，就在上周，我参加了一场毕业典礼，演讲者说：嘿。

In fact, I I I went to a commencement ceremony just last week where the speaker said, hey.

我们现在正接近这样一个阶段：几乎任何我们能提出的问题都有可能得到解答。

We're now approaching a point where almost any question we can ask could potentially be answered.

因此，我们需要培养我们的学生成为善于提问的人，这让我觉得很有道理。

And so we need to train our students to be good question askers, and that that made sense to me.

但你的书把我们带得更远了。

But your book takes us all quite a bit further.

在由历史学家尼尔斯·弗格森撰写的引言中，你提到，在这个新时代，人类不再从自身提出的问题出发向前推进，而是面对人工智能对人类从未提出过的问题所提供的答案，这最终将知识与理解、人类的知识与人类的理解分离开来，而这是我们前所未有的经历。

In the intro, which was written by Niels Ferguson, the historian, you say in this new age, rather than working forward from questions posed by humans, humanity confronts answers provided by AI to questions that no human ever asked, and that ultimately this separates knowledge from understanding, human knowledge from human understanding in a way that we really haven't experienced before.

我知道这内容很多，但我认为这正是书中核心的基础所在。

So I know that's a lot, but I I think it's kind of foundational to what's going on in the book.

我想知道，你是否能先对比一下人类知识是如何进步的，与人工智能知识是如何进步的，以及这可能会让我们陷入什么样的困境——比如，我们得到了有效的解决方案，却不知道它们为何有效。

And I I was wondering if perhaps you could start by maybe contrasting how human knowledge advances with how AI knowledge advances and how that might leave us with, I don't know, solutions that work, but but we don't know why they work.

我认为在某种意义上，我们制造了一台机器，它的大脑在很大程度上是模仿我们认为的人类大脑工作方式构建的。

Well, I think in some sense, you know, we built a machine that has a brain that, to some significant degree, is modeled off after what we think is the way human brains work.

因此，从这个角度看，机器的学习方式并不一定与人类不同。

So in that sense, it it isn't necessarily the case that that the machine learns differently than humans.

但正如我们在书中也讨论过的，区别在于机器的学习能力远超人类。

The difference is, as we also talk about in the book, is that the capability of the machine to learn is wildly better than than humans.

原因有几个。

For a couple of reasons.

一个是，我们可以认为人脑的电路时钟频率大约为30赫兹。

One is that the circuits of our brain, you could think, sort of have a clock rate of about 30 hertz.

尽管大脑中的活动非常并行，且耗能极低，但仍然无法以如此快的速度循环执行任务。

And while they're very very parallel in that activity in your head and consume little power, it still can't cyclically do things that quickly.

而当我们用计算机来构建这些系统时，和其他任何计算机一样，这些系统的时钟频率是以吉赫兹为单位的。

And when we build these things out of computers, as is true with any other computer, the clock rates on these things are measured in gigahertz.

因此，你在处理速度上会看到比人类快一千万到一亿倍。

And so you're looking at 10 to the seventh or eighth times faster in terms of the rate which does things.

我认为另一个常被人们忽视的是，如果你把大脑看作一台计算机，那么我们大脑的输入和输出系统相对于计算机来说要弱得多。

I think the other thing that people don't stop and think as much about is that if you think of your brain as a computer, the input and output systems to our brain are very very weak relative to those that we have for the computers.

所以，我们的输入系统主要依赖眼睛、耳朵，以及在较小程度上的触觉。

So, you know, our our input systems are largely our eyes, you know, and our ears, and to some lesser extent, you know, perhaps touch.

这些感官系统不仅分辨率低，而且在某种程度上速度也慢。

And those things are both low resolution and to some extent, low speed.

其中带宽最高的是视觉，但即便如此，它也是一个抽象化的系统。

And the highest bandwidth of those is our vision, and even that is an abstracting system.

你知道，我们的眼睛是这样构造的：它看到东西后会进行一些预处理，然后将部分抽象信息传递给大脑进行推理。

You know, I mean, our eyes are built in a way where it sees things and it preprocesses some of it, and some abstract part of it gets passed into the brain for reasoning.

而且这些系统的分辨率是有限的。

And the and the resolution of these things is kinda limited.

另一方面，机器则拥有极其先进、高带宽的传感器优势。

On the other hand, machines enjoy the benefit of incredibly sophisticated high bandwidth sensors.

你知道，摄像头的分辨率比我们的眼睛还要高。

You know, cameras are higher resolution than our eyes are.

而人类借助普通的计算机，几十年来一直在寻找我们自身感知到的所有事物的数字化表示。

And humans, courtesy of regular old computers, have spent decades now finding digitized representations of all the things that we sense ourselves.

在输出端，我们所创造的一切——无论是图像还是文字——都已被记录并数字化。

And on our output side, you know, those things that we have produced, whether they're images or writings, you know, have all been recorded and now digitized.

因此，机器在输入系统的速度和分辨率方面，相对于人类具有巨大优势，它能够以某种方式完成人类所经历的相同学习过程。

And so this gives the machine a huge leg up over humans, and that with its speed and resolution of input systems, it can go through, in a sense, the same learning process that humans do.

但它完成这一过程的速度，快了几个数量级。

But it goes through it, you know, many decimal orders of magnitude faster.

这种影响在我们书中前几部分通过介绍或提醒人们什么是通才来体现。

The effect of this, which we talk about in the early parts of the book by introducing or reminding people what polymaths are.

你知道，历史上曾出现过一些人类通才，他们能够享受一种非凡的益处，即理解和整合信息，但通常仅限于少数几个领域。

You know, there been human polymaths who are people that enjoy some extraordinary benefit to to know and integrate information, but usually only across a small number of domains.

就连这些人都被视为特殊的存在。

And even those people are viewed as special.

这些机器本质上每一个都是具备非凡能力的通才。

What these machines are is essentially every machine is a polymath of extraordinary capabilities.

事实上，它掌握了所有它能够吸收的关于各个领域的知识。

In fact, it knows everything it's been able to ingest about every domain.

这实际上产生了一种近乎无限的通才能力。

Now this actually produces sort of an unlimited polymathic capability.

正如人类在历史上所看到的，许多突破往往源于某个通才个体获得了其他群体或特定个体从未有过的洞见。

And as humans have seen it in history, a lot of times breakthroughs come because as an individual who is polymathic has an insight that no other group or specific individual has had before.

为什么会这样？

Why is that?

这是因为它们能够同时整合自己在所有专长领域内的信息，就像在你的大脑中一样。

It's because it is able to integrate inside your brain across all the things that it's expert on at the same time.

所以每当它面对一个问题时，普通人往往会从一个非常狭窄的角度来看待它。

So whenever it looks at a problem, you know, a normal human would look at it fairly narrowly.

他们可能只在一个领域是专家，但如果你问另一个领域的问题，他们就完全无法应对。

They might be an expert in one domain, but if you ask a question in another domain, it's completely, you know, ill equipped to deal with it.

现在你有了这样的机器，它们会说：无论问题是什么，当我试图回答时，我都会整合我所学过的所有相关知识。

So now you've got machines that say no matter what the question is, when I try to answer it, I'm integrating across all the knowledge that that I learned about this.

重要的是要意识到，信息在我们大脑和这些机器中的存储方式，与在你的个人电脑或手机中的存储方式不同。

It's important to realize that, you know, the the way information is stored in our brains and in these machines is not the way it's stored on your personal computer or cell phone.

在那些情况下，我们会把这些数字表示形式以文件的形式存储，也就是以比特的形式。

You know, in those cases, we take these digital representations and we store them as files, you know, as bits.

然后我们懂得如何解读这些比特。

And then we know how to interpret those bits.

但当机器学习时，或者大脑学习时，我们会吸收这些信息，然后大脑会将其切分、重组，并分散到大脑的许多不同部分中。

But when machines learn, or the way the brain learns, you know, we take in this information and we sort of the brain slices and dices it and puts it and distributes it across your brain in a lot of different piece parts.

因此，回忆本质上是从这些片段中重新组装出它原本认为的内容。

And so recall is essentially about reassembling from those pieces what it thought the original was.

但它实际上并没有原始内容的精确副本。

But it doesn't actually have a copy of the exact original.

正因如此，你现在可以向这些机器提问，它们会自行整合所有训练所学的知识来作答。

And and so because of that, you can now ask these machines a question and it will answer it by by itself integrating across all the knowledge on which it's been trained.

所以，这些是终极的通才。

So these are the ultimate polymaths.

我认为这一点极其重要，值得深思：作为当今人类物种，我们永远无法在大脑中吸收机器所掌握的同等量信息。

I think that this is super important to ponder because humans will not, at least as we exist as a species today, ever be able to ingest in our brains the same amount of material that the machines do.

因此，机器拥有人类无法获得的洞察力将成为常态。

And as a result, it'll be commonplace for the machines to have insights that humans can't get.

你可能会说，那为什么人类群体也无法获得这些洞察呢？

You say, well, why don't groups of humans have those insights?

答案是，将多个个体的知识融合在一起非常困难。

The answer is it's very hard to meld the knowledge of multiple humans.

部分原因是，协作的事务越多，沟通成本就会呈平方增长。

In part, it's the communications cost grows as the square of the number of things that are collaborating.

这就是为什么小团队能够取得进展的原因。

So that's why small teams of people can make progress on things.

但当团队变得越来越大时，团队成员之间的沟通以及整合他们集体知识的成本会变得越来越难。

But if the teams get bigger and bigger, the cost of communicating among them and then trying to integrate what they collectively know gets harder and harder.

因此，将所有知识集中在同一个大脑中的优势在于，你不需要面对这些沟通成本。

And so the elegance of having all the knowledge in one brain is that you don't have any of that communication caused.

而你大脑以关联方式思考和整合信息的特性，如今在拥有更多信息的机器中得到了体现。

And its associative nature of the way your brain thinks and, know, integrates information is now manifest in machines that have a lot more information.

所以我认为，这些机器最大的馈赠，是它们能够理解或发展出人类永远无法触及的东西。

So I think in the end, the greatest gift of these machines is the things that that it will be able to understand or develop that humans never would.

但这也会带来一种令人困惑的感觉：即使它们试图向我们解释，我们可能也无法理解。

But it also creates this confounding feeling that says, even if they try to explain it to us, we might not understand.

因为事实上，它们涉及的领域如此广泛，即使被解释了，你可能仍然无法完全理解为什么所有这些因素会以这种方式融合在一起。

Because in fact, you know, it ranges across so many areas that even if it was explained, you might not understand exactly why it all comes together that way.

是的。

Yeah.

我想你在书中提到过，也许未来大学会成为人们聚集在一起，试图理解人工智能所产生的一些成果的地方。

You I think you say at one point in the book that maybe universities in the future will be places where people get together and try to try to figure out, try to understand some of the things that that AI is is producing.

是的。

Yeah.

我的意思是，我们在人工智能的早期阶段就已经看到过这种情况。

I mean, we've seen this, you know, in the earliest AI days.

这并不是五十年代的早期，而是指当前这个时代的早期。

It's kinda not not the early days of the fifties, but I mean of the current era.

例如，当DeepMind开发出AlphaGo和AlphaGo Zero，并击败了围棋世界冠军时。

For example, when you had AlphaGo and AlphaGo Zero, which were developed by the DeepMind people and and, you know, beat the world champions in Go.

当然，那个通过学习历史围棋选手对局训练出来的机器，最终学会了击败最顶尖的围棋选手。

And, of course, the machine that was trained on the people, the historical Go players, eventually learned how to beat the best Go players.

但随后他们实际上开发出了AlphaGo Zero，这台机器根本不去研究人类是如何下围棋的。

But then they actually, you know, developed AlphaGo Zero, which was a machine that didn't look at how humans played Go.

好吧？

Alright?

它只是以人类永远无法达到的速度与自己对弈。

It just played against itself at a rate that humans could never achieve.

因此，在短短几周内，它尝试的围棋对局数量超过了人类历史上所有对局的总和。

And so in a matter of weeks, it had tried more Go games than all of humans in history.

果然，它学会了思考如何下棋的新方法。

And lo and behold, it learned new ways to think about how to play the game.

所以现在，人类围棋选手会与机器对弈，或者研究机器之间对弈时的走法，从中获得启发，以达到以前无法企及的水平。

So now, human Go players play against the machine, or look at what the machines did when they played each other to get inspiration for ways that they can play the game at a level that they didn't before.

所以你觉得这种趋势会延伸到我们所做的一切事情中。

So you just think that is gonna get extrapolated into kinda everything we do.

因此，我认为大学不仅可能需要聚集起来，细致研究机器可能产生的洞见，而且整个大学的模式——乃至更广泛地说，教育的本质——在这样一个世界里将变成什么样子？在那里，从最年幼的孩子开始，每个人都能获得一个近乎无限的苏格拉底式导师，那么无论哪个教育阶段，上学的意义又是什么？

And so I think universities not only may have to come together to try to study, you know, in detail the insight that the machines might have had, But, you know, the the whole model of how the what what is a university and what and in fact, broadly, what is education gonna look like in a world where literally every person from the youngest age is going to be afforded the opportunity to have a sort of an unlimited Socratic teacher, then what does it mean to go to school at any level?

是的。

Yeah.

我可以从很多不同的方向来探讨这个问题，就在过去一两年里，我确实亲眼见证了伯克利的学生们在做项目时的状况。

There's so many different directions I could take this, and I have definitely experienced that just in the last year or two where I have students working on projects at Berkeley.

这些项目在技术上极其复杂，但他们能够完成，因为他们现在可以获取数据和代码。

And they are enormously complicated technically, but they can do it because they can, you know, now have access to data and code.

但他们随后还得让人工智能来解释输出结果，而这一点我还不太确定该如何应对。

But they then have to get AI to explain the output to, which is I'm not really quite sure how to handle this.

但我还想问一个问题，关于科学方法——在这种情况下，科学方法还有没有长远的作用？迄今为止，我们正是通过提出假设、设计实验并进行测试来推动人类知识的进步的。

But I I wanted to ask a question about the scientific method and, like, where that leaves the scientific method in that that's how we've progressed human knowledge to date is forming a hypothesis and coming up with the test and running the test.

从长远来看，这个过程还有存在的意义吗？

Then is there still a role for that process long term?

嗯，这个过程可能仍然是个好方法。

Well, the process may be a good process.

只是提出假设这一步，可能由机器来完成了。

It just may be that coming up with hypothesis might be done by the machine.

你知道的？

You know?

那么短期来看，你该如何进行实验呢？

And then the question is, in the short term, how do you do the test?

正如我们在许多其他领域所见，通常至少在计算机模拟中进行这些实验比在现实世界中更便宜。

You know, as we've seen in many other areas, you know, we've found that it's oftentimes cheaper at least to do these tests in silico than in the physical world.

随着计算能力的提升，尤其是针对人工智能本身的新型计算架构加速器的出现，但我认为到本十年末，我们还将看到大规模量子计算机的出现。

And with the computing capability emerging, not with these novel accelerators in terms of computer architecture for the AI itself, But I think by the end of this decade, we'll also see the emergence of utility scale quantum computers.

因此，我认为在物理科学领域，你将看到一种能力，能够超越人类甚至曾经能够推测的范围，去提出假设。

And so I think that, you know, in the physical sciences, you're gonna see, you know, a capability to go beyond what humans have even been able to to to speculate about, to create a hypothesis.

而这将让我们探索那些对人类而言一直无法突破的领域。

And but it'll allow us to explore things that that, you know, have been impenetrable for humans.

而且，同样地，我认为这是人工智能的馈赠，但问题是：在从事科学时，我们与机器的关系是什么？

And, you know, again, I think that that's the gift, but the question is, what is our role in relationship to the machines in doing the science?

随着机器获得越来越多的自主性，事实上，未来几年机器将与先进的机器人技术融合，机器将能够真正地在物理世界中生活、体验并理解世界，而这是它们目前所不具备的能力。

And as the machines get more agency, and in fact, as the machines in the next few years are blended with, you know, sophisticated robotics, the machines will then actually physically be able to to live in experience and understand the physical world to a degree that they don't currently do.

尽管我们有时会抱怨机器会犯一些简单的错误，但我认为其中一个原因是，它们对物理世界的认知仅来自于人类提供给它们的所有代表物理世界的合成数据。

You know, while we complain about the machines making simple mistakes at times, I think one of the reasons is that they only know the physical world by synthesis across all the artifacts that humans have given them to represent the physical world.

但与成长中的婴儿不同，它们实际上是在体验真实世界，触摸它、观察它运动等等。

But unlike a baby that grows up, you know, they're actually, you know, experiencing the real world, touching it, seeing it move, etcetera.

我认为，在仅仅通过从不同视角摄入图像来学习，与真正生活在那个环境中的体验之间，仍然存在保真度的问题。

And and I think that there's still a fidelity question between what you can learn from just ingesting images from various perspectives versus kinda living in that environment.

但即使如此，当机器具备了物理形态后，这一点也将得到极大提升。

But even that will essentially advance as the machines have a physical embodiment.

因此，当它们能够在真实世界中行动时，甚至‘进行实验’这一问题也可能由机器来完成。

And so when they can act in the real world, then even the the question of quote, doing the experiments may be able to be done by the machines as well.

所以它们能够掌握我们前所未见的计算建模能力。

So their ability to master both computational modeling of the likes of which we haven't seen.

它们能够以人类可能难以达到的复杂程度，快速编写出完成这些任务的代码。

The ability to write the code to do these things quickly at a level of complexity that humans might struggle to do.

所有这些都代表了一种思考如何开辟未来的新方式，但这一切实际上都需要我们从根本上转变人类对这一问题的思维方式。

All of these things represent a new way to think about forging a path into the future, but all of which really require some, ultimately, think, inversion of the way that humans think about this.

在书中，我们谈到了这些机器。

You in the book, we talk about these machines.

我们认为，我们与它们的关系会经历三个阶段。

We think going our relationship with them goes through three phases.

好的。

Okay.

是的。

Yep.

第一阶段，我们称之为工具阶段。

The first phase, we call the tool phase.

而我们现在主要就处在这个阶段。

And that's kinda what we're in now, mostly.

为什么会这样呢？

And why is that?

因为人类历史上所有的发明，都只是工具。

Well, because every invention humans have ever had, it was just a tool.

你知道的，它们要么增强了我们的机械能力，要么增强了我们的智力能力。

You know, it either facilitated our mechanical capability or it facilitated our intellectual capability.

但这项技术是历史上第一个不再仅仅停留在工具层面的技术。

But this technology is the first one in history that doesn't stop at being a tool.

因此，第二阶段是我们逐渐意识到，我们正在孕育一种新的物种，只是它并非生物意义上的。

And so the second stage is where we we kind of recognize that what we're doing is we're birthing a new species, it just isn't biological.

在这一点上，我认为对人类而言，最大的机遇在于思考：随着未来的发展，人类究竟想成为什么样的存在？

And at that point, you know, that's sort of the big opportunity in my mind for humans, is to figure out, well, what do humans wanna be as we go forward?

而且，我们第一次拥有了这些机器作为合作者，它们将赋予我们理解自身的能力，包括我们的生物学，甚至我们的思维方式，达到前所未有的深度。

And for the first time, we'll have a collaborator in these machines that will give us the ability to under understand ourselves, including our biology, and perhaps even our thinking, to a degree that has never been possible before.

但它也将为人类掌控自身命运创造机会。

But it'll also create the opportunity for humans to take control of our own destiny.

我经常告诉人们，人类的进化，如同以往所有时期一样，已经结束了。

I often tell people that human evolution, as it has been for all the time, is over.

事实上，我们改变生存环境的速度，已经超过了传统进化过程所能适应的范围。

That in fact, you know, we're changing the environment in which we live at a rate that standard evolutionary processes don't adapt to quickly enough.

这正是我们今天面临诸多疾病以及其他全球性挑战的原因。

And it's why we have many of the diseases we have today and and and other, you know, planetary scale challenges.

但在未来，我们应该能够设计出这些问题的解决方案。

And but in the future, you know, we we should be able to design these things out.

我们将设计出消除疾病的方法，而不是仅仅思考如何治疗它们。

We'll design out diseases instead of kinda think about how to treat them.

这带来了各种长期的道德和伦理问题，也就是我们该如何管理这一过渡。

And that brings all kinds of long term moral and ethical issues that, you know, is how we would manage this transition.

但我认为，这些正是我们的学术界必须开始思考的问题。

But I think those are the kind of problems that that our academic community are going to have to start to contemplate.

你在书中多次将人工智能与进化相类比。

You draw the analogy with evolution a couple times in the book.

一个是关于速度，你刚刚提到了这一点。

One with respect to speed, which you just alluded to.

而且你知道，如果从地质时间尺度来看，人类的历史只是短暂的一瞬。

And, you know, the fact that, you know, if you look at human the human age on a geological scale, it's just a blip.

而人工智能在人类时间尺度上的发展，也将是一瞬之间。

And essentially, the AI scale on the human scale is is also gonna be a blip.

事情将以闪电般的速度推进。

Things are gonna things are gonna move at lightning speed.

我想问一个问题，几个月前我们节目上请过一位风险投资家，她写了一篇关于人工智能经济影响的深刻文章。

And I guess one question I had is we we had a venture capitalist on the show a couple months ago, and she wrote a really thoughtful piece on the economic impact of AI.

这篇文章名为‘宝瓶座经济’。

It was called the Aquarius economy.

她探讨了她所认为的阻碍人工智能发展的‘瓶颈’。

And she she wrote about what she sees as the quote unquote blockers to AI.

所以，潜力是存在的。

So there's like, there's the potential.

我们理解这种潜力。

We understand the potential.

这里是我们现实中看到的、阻碍这一潜力实现的具体障碍。

Here are the practical things we're seeing on the ground that stand in the way of that.

她最终得出结论，我们可能还需要经历两三个资本周期，才能完全实现通用人工智能。

And she ended up concluding that we're probably a couple capital cycles away from fully realizing AG AGI.

所以我想知道

So I guess I'm interested in

是实现其能力，还是充分释放其效益？

Realizing the the capability or fully realizing its benefits?

不是能力，而是将其真正应用于实体经济。

Not the capability, but full but implementing it in the real economy.

这本来就是我想问你的：你是否觉得AI的能力远远领先于它在日常生活中实际产生的影响？

And that was gonna be my question to you is literally, do you see a big I don't know, do you see AI capabilities running well ahead of its actual impact on day to day life?

对吧？

Right?

我会说 yes。

I'd say yes.

我认为，即使是我们今天拥有的这些机器，在某种程度上也已经超越了我们制度层面利用它们的能力。

I think that even the machines we have today, I think, have to some significant degree, run ahead of our institutional capability to capitalize on them.

而且这部分原因因国家而异。

That and part of the reason it and it's different in different countries.

尤其是在美国，过去几年对人工智能的大量媒体报道几乎都集中在负面风险上。

A lot in The United States especially, a lot of the popular coverage of artificial intelligence over the last few years has basically focused a lot on the downside risks.

一些有思想的人指出存在负面风险，最终所谓的存在性风险，这使得它成了每个人似乎都担忧的头号问题。

Know, thoughtful people who identified that there were downside risks and ultimately, quote unquote, existential risks, you know, has resulted in that being, you know, the bugaboo that everybody seems to worry about.

我们会变成机器的宠物吗？

You know, are we gonna become the pets of the machine?

但有趣的是，因为我与中国人在这种对话以及其他方面的互动。

And but it's it's interesting, you know, because of my interaction with the Chinese in this in this dialogue and and otherwise.

你不会在中国看到对这些长期负面风险同样的关注。

You know, you don't find the same focus on these long term downside risks in China.

你知道，我不记得确切的数字了，但最近有一些民调显示，美国普通民众对人工智能的好感度大约在30%出头左右。

You know, if you just I don't remember the exact numbers, but there were some polls recently that said, you know, the favorability of artificial intelligence in the general population in The United States was in the, you know, like, the low 30% range or something.

而在中国，这一数字高达80%以上。

And in China, it was in the high eighties.

我认为，你所看到的是媒体运作方式的证据，也就是说，媒体已经逐渐脱离了呈现事实和新闻周期，转而转向贩卖耸人听闻的内容。

And, you know, I think there, what you're seeing is evidence of, you know, how how the media works, you know, how it's it's sort of shifted away from, you know, presenting facts and news cycles, and essentially, it's selling sensationalism.

这并不是什么新鲜事。

Now that's not new.

我的意思是，从19世纪开始，黄色新闻就一直是靠卖耸人听闻的东西来卖报纸。

I mean, you know, we always all the way back to the eighteen hundreds, you know, yellow journalism was the idea that, you know, you you sold things that that sold newspapers.

不幸的是，我认为在我们这个国家，尤其是西方，这种高度商业化、商品化的媒体环境中，我们正看到同样的现象。

And, unfortunately, I think we're seeing that in this highly, you know, commercialized, commoditized media environment in in this country and the West in particular.

这并不是说没有真正的问题，但你可能担心的是，中国无论是政府还是公众的关注点都在于：这太棒了。

Which isn't to say there aren't real issues, but it's a question of, you know, one thing you might worry about is whether or not China, whose both governmental focus and popular focus is, hey, this is great.

让我们尽可能快、尽可能广地应用它，从而获得好处。

Let's apply it as fast as we can, as broad as we can, you know, and we'll get the benefits.

我们不需要通用人工智能。

We don't need AGI.

我的意思是，现在我们已经获得了 plenty 的好处。

Know, I mean we're getting plenty of benefit as it is.

如果我们恰好达到了那个阶段，那就更好了。

If we happen to get to that point, that's even better.

这可能对战略竞争很重要。

And that may be important for strategic competition.

但你知道，我们不会干等着。

But you know, we're not waiting around.

所以当你看中国这样一个国家时——在我过去四十年访问中国的过程中，它已经从一个相当落后的经济，发展成为与美国或任何西方国家齐头并进的现代经济体。

And so you know, when you look at a country like China, which over the last the forty years that I've been visiting there, has gone from a pretty you know, backward like economy to one that is clearly a contemporary of The US or any of the western nations.

而他们正是借助计算、手机、网络等技术浪潮才实现了这一飞跃。

And if and they've done that on the back of riding this technology wave through computing, cell phones, networking, etcetera.

从某种奇怪的角度来看，他们似乎在心理上更适应‘人工智能是下一件大事’这一理念，并且全力投入其中。

And in a weird sense, they seem better mentally acclimated to the idea that AI is the next big thing and they're just all over it.

因此，我认为美国如果像现在这样过度担忧长期风险，而不以尽可能快的速度推动技术应用，其实是在害自己。

And so I think The US is not doing itself a service by wringing its hands to the degree that we do about the long term risks and not encouraging adoption at the maximum possible rate.

是的。

Yeah.

你提到中国很有趣，因为我刚听了另一个播客，他们正在讨论印度对人工智能的态度，情况非常相似。

It's interesting you bring up China because I was listening to another podcast and they were talking about the attitudes toward AI in India, and it was very similar.

并不是这种恐惧。

It wasn't this fear.

更多是，嘿。

It was more, hey.

这是未来。

This is the future.

这令人兴奋。

It's exciting.

这是新的。

It's new.

所以，这是最新趋势，技术让他们绕过了以往对‘好’的固有观念。

So It's the latest thing where technology allows them to bypass what was the legacy view of good.

就像手机取代固定电话一样。

And, you know, I mean, it's like cell phones versus landlines.

我记得在手机普及之前，就连联合国关于国家发展的统计数据，都会用每百人固定电话数量作为指标。

I mean, I remember in you know, before the cell phone, even the, you know, statistics at the UN on national, you know, national development, you know, had metrics like landline phones per 100 population.

好吧？

Alright?

即使在手机出现之后，这些仍然是衡量标准。

And those things were still the measure of goodness after the cell phone arrived.

而且，你知道，这些国家说，我们不需要那些固定电话。

And, you know, those countries said, we don't need those landlines.

我们直接用手机就行了。

We're just gonna have cell phones.

就这样结束了。

And and that was the end of it.

坦白说，我现在非常担心美国，因为每次我们看到这类变革出现时，政府往往要么被监管俘获，要么缺乏政策远见，尤其是在如今这个有点功能失调的国会环境中，缺乏长期规划。

And, you know, I frankly worry a lot about The United States right now because, you know, each time we've seen these things come, you know, we oftentimes either see regulatory capture or a lack of policy foresight on the part of the government in what has become, you know, I'll say, somewhat dysfunctional congressional environment and and a lack of long term planning.

结果发现，像印度和中国这样的国家，确实有长期规划。

And it turns out in these countries like India and China, you know, they have long term plans.

他们不会在每一次选举周期都把计划推翻重来。

They they don't tend to blow them up every every election cycle.

而且，你知道，他们认识到，自己在不到三十年的时间里，成功地让数十亿人摆脱了贫困，达到了西方的生活水平。

And and, you know, they recognize that their success in bringing literally bill you know, fractions of billions of people, you know, from poverty to, you know, western standards of living in less than thirty years.

这得益于对最新技术的积极采纳。

It has come from the aggressive adoption of the latest available technology.

所以这些人，他们并不。

So those people, they they don't.

他们还年轻，记得以前的样子，因此正在不断前进。

They're young enough to remember what it was before that and they're moving ahead.

在美国和欧洲，我认为我们的人口正在老龄化，政府也难以具备远见和行动力，无法制定出推动这一进程的政策。

In The US and Europe, I think we have an aging population and a government that is, I'll say, struggling to to have the foresight and the the ability to act against it to make policies that would promote this.

我认为，这在全球战略竞争中构成了一种风险，也关系到我所预期的下一个世界秩序的形成。

And I think that that represents a risk in terms of global strategic competition and what I think is likely to to be emergence of the next world order.

即使所有头条新闻都在报道私人领域前所未有的资本投资热潮，你还是这么说吗？

And do you you say that even though, you know, all the headlines are about kind of an unprecedented rate of capital investment in All private.

对。

Right.

是的。

Right.

但我的意思是，这难道不是一件好事吗？

But I mean, isn't that isn't that something's a good thing?

对吧？

Right?

私营部门行动更快，更灵活。

The the the private sector moves faster, is more nimble.

这不正是美国的优势所在吗？

Isn't that the it's kind of the source of strength of The US?

这是否就是那种...

Is that the kind of

确实是。

It is.

但不像以前那么独特了。

Less it's not as unique as it was before.

当你获得私人投资，再加上积极的政府政策，你可能就能走得更快。

And so when you get private investment, you know, coupled with aggressive government policies, you know, then, you know, you've you may be able to go even faster.

我参与了几个项目，其中一个是聚变能源，我们认为这项技术很快就能实现，但政府仍然说，我们认为这还需要二三十年。

And, you know, I'm I'm involved in a number of projects, you know, fusion energy being one, you know, which we think is pretty imminent, and the government still continues to say, well, we think it's twenty or thirty years away.

太阳能、风能等领域也发生过同样的情况——这些技术最初都是依靠私人资本，或许还有学术研究，在这个国家发明出来的。

And, you know, so the same thing happened with solar and and wind and others where the it was all invented on the back of private capital and perhaps academic research, largely in this country.

但最终，作为一个国家，我们在产品化方面并没有准备好去利用这些成果。

But in the end, we weren't prepared, you know, as a nation to capitalize on it from a product point of view.

这其中很大一部分原因可以追溯到政策和既得利益者的监管俘获。

And a lot of that does track back to policy and regulatory capture of incumbents.

这有点跑题了，但我对这个领域很感兴趣。

Does and this is going off in a bit of a tangent, but it's an area that I'm interested in.

中国就是从，是的，

I mean, China has gone from yeah.

正如你所说，太阳能产业是在美国发明的，但现在中国基本上已成为全球主导者。

Like you said, the the the solar industry was invented in The US, and now China is basically is is the dominant world leader.

这使他们能够以零边际成本获得相当多的能源。

And that gives them access to a fair amount of energy at zero marginal cost.

而能源是人工智能发展的一个主要障碍。

And one of the big blockers of AI is energy.

所以，这是否构成了中国的一项战略优势？因为他们拥有这种我们尚未允许自己发展的能源来源？

And so is that a, you know, a strategic advantage to China and that they've got access to this, you know, source of energy that that we've not allowed ourselves to develop?

还是说这种说法过于简单化了？

Or is that oversimplifying?

嗯，我不确定这是否过于简单化，但我认为中国总体上长期以来一直意识到自己在能源方面处于劣势。

Well, I don't know that it oversimplifies it, but I think China, I I would say, writ large, has known that it was in a deficit position relative to energy for a long time.

他们采取了一种‘全面尝试’的策略。

And they've taken on, you know, an approach which says, try them all.

你知道吗？

You know?

而且他们具备制造能力，能够支持那些已经可以量产的技术，比如风能和太阳能。

And they've had the manufacturing capacity to support those which were already manufacturable like wind and solar.

如果你仔细看，他们正在同时建设三种不同类型的裂变核电厂，并且有一个由政府大力资助的聚变能源项目。

If you look at it, you know, they're simultaneously building three different types of fission nuclear plants, and, you know, they've got a very aggressive government funded program around fusion.

而如果你看看美国，我认为这些领域的突破现在都是由私营部门资助的。

And, you know, if you look at The United States, again, I think that the breakthroughs in this are all being funded privately now.

但如果没有一项明确的政策，表明我们希望部署这些技术，甚至能够向全球销售它们，我们可能会发现，自己再次在这里发明了它们。

But, you know, absent a policy, you know, that says we wanna deploy these and in fact, you know, be able to sell them around the world and everything else, you know, we may find that we could invent it here again.

然后在我们广泛利用之前，它们就会从我们手中溜走。

And then before we capitalize on it broadly, it'll, you know, it'll escape us.

因此，拥有完整的基础设施、融资和推广体系至关重要。如果你看看今天的美国，私营公司为获取电力以部署数据中心所付出的努力真的很有意思。

And so having the rest of the infrastructure financing adoption you know, if you if you take today, The United States, I think it's really interesting to look at what the private companies are having to do to get the electricity to do the data center deployments.

对吧？

Right?

这个国家即使使用所有传统方法，也完全无法满足这些公司所需电力。

The country has no capacity using all of its classic methods to really give these people what they need.

而我们却拥有所谓的全球最优质电力。

And we have the, quote, best power in the world.

但电网没有能力输送电力，我们也没有足够的发电能力。

But the grid doesn't have the capacity to haul it, and we don't have the generating capacity.

今天，如果你带着一座全新的发电厂来到美国，说你想把它接入电网。

Today, if you walked up with a brand new power plant in The United States and said you wanted to connect it to the grid.

等待并网的时间长达七年。

The meantime to wait is seven years for an interconnection.

这些问题是，如果你希望留住可能正在流入的资本以及来自本地创业活动的创新想法，就无法容忍的。

And these are the things that, you know, you can't tolerate if you wanna let the capital that may be flowing and the ideas that still come from the entrepreneurial activities here.

他们可以生产这些设备，但如果无法接收，那就是个大问题。

They can produce these things, but if it if the ability to receive it isn't there, that's problematic.

现在发生的情况是，在各州——这并非全国统一，而是各州各自为政——那些允许在电表后为数据中心供电的州，人们正大量购买全球的燃气轮机和其他设备，安装在数据中心后面，说：好吧，我们自己发电。

Now what's happening is in the states, which is not uniform because this is all state by state, in the states that allow behind the meter powering of your data center, you're looking at these guys now buying up the world's, you know, gas fired turbines and other things and slapping them on the back of their data centers and saying, fine, we'll bring our own power.

而且，我真的认为这是正确的解决方案。

But and and I and I actually think that is the right solution.

但这并不是政策远见所导致的结果。

But it has not been a matter of policy foresight that led to that.

事实上，当前的能源基础设施已经无法满足这些公司的需求。

It's in fact the collapse of the current energy infrastructure to be able to meet the needs of of the companies.

当然，当你走出美国时，你可能会说，那我们就得在每个地方都自备电力。

And of course, when you move outside The United States, you you could say, well, then we're gonna have to bring our own power everywhere.

因为其他国家都无法提供足够的电力，也许中国除外。

Because no other country's gonna be able to to provide the power, save maybe China.

我认为，我们需要对如何解决这些问题进行更多长远的考量。

This is where I think there needs to be a lot more long term consideration of how are we gonna solve these problems.

我个人认为，整个电力问题最终会成为一个烟雾弹，因为核聚变将在未来几年内实现商业化，尽管这并不被预期。

Personally, I think that the whole power thing is gonna turn out to be a red herring, simply because fusion is gonna arrive, you know, in the next few years on a commercial basis, even though it's not expected.

是的。

Yeah.

人工智能也同样没有被预期到。

AI was not expected either.

而且，任何情况下，人工智能都将加速这些意外事物的到来。

And if anything, the AI will only help accelerate the arrival of all these unexpected things.

我认为人们还没有真正理解这一点。

And I think this is the thing people are not really wrapping their heads around.

你知道，去年十月达雷·阿马德发表了他的论文，关于‘救赎之机’之类的，或者说是‘慈爱之机’。

You know, when Dare Amade wrote his paper in October a year ago, know, machines of saving grace or something, loving grace.

他指出，如果我们真的因为这些机器的出现而获得科学与工程能力的阶跃式提升——可能是10到20倍——那么人类用旧方法在本世纪所做的一切，都将到2035年之前完成。

You know, he he pointed out that if we actually get a step function increase in science and engineering capability courtesy of these machines arriving, that's maybe 10 or 20 x, then everything humans would have done by the old method this century will all be done by 2035.

我认为这基本是对的。

I think that's largely true.

因此，那些历史上在经济或技术层面被认为不可估量、或难以部署的事情，可能会被极大地加速，导致规划不足的问题变得越来越明显。

And so all these things that were historically imponderable from economic or technological terms or, you know, the ability to deploy may get so dramatically accelerated that that the failures to plan become more and more obvious.

所以，如果我们谈论的是如此非凡的事情，那么从经济角度看，AI的一个潜在作用就是消除劳动力短缺，创造你们所说的丰裕世界。

So, you know, if we're talking about something as extraordinary as that, then, you know, we're talking about the, you know, the the I guess, the potential of AI is to basically one of the potentials from an economic perspective is limit eliminating labor scarcity and creating this what what you guys call a world of abundance.

而这就引出了一个问题：这些产生的财富该如何分配？

And, you know, that then becomes a question of, you know, how does all this wealth that gets generated gets split up?

你在书中确实谈到了这一点，关于谁得到什么。

And you do talk about this in the book, who gets who gets what.