生命的物理，第1集：物理学能告诉我们关于自己的什么？

有一种水母，会变老，但当它厌倦了变老时，就会重新变年轻一段时间，然后又开始变老，除非被吃掉，否则它永远不会死。

There's a jellyfish that gets older and then when it gets tired of getting older, it gets younger for a good while and then it starts to get older again and it'll never die unless it's eaten.

那为什么我不能这么做呢？

Now why can't I do that?

所以，克里斯。

So, Chris.

阿巴？

Abha?

我们刚才听到的是维杰·巴拉苏布雷曼尼安，稍后还会听到他的更多发言。

The person we just heard from was Vijay Balasubramanian, and we'll hear more from him later on.

但他的话让我感到疑惑。

But what he said makes me wonder.

为什么人类不能像这种可以自主决定是否变老的水母呢？

Why aren't humans like this jellyfish that gets to just decide if it ages?

为什么我们不能都像本杰明·巴顿那样呢？

Why can't we all be Benjamin Buttons?

说实话，我对这种叫做灯塔水母的水母了解不多。

Well, to be honest, I don't know too much about this jellyfish, which is called Turritopsis dohrnii.

但我确实了解一些物理学的规律。

But I do know something about the laws of physics.

基本上，正是物理学让我们变老。

And basically, physics is what makes us age.

灯塔水母似乎找到了某种规避衰老的漏洞。

Turritopsis seems to have found some sort of loophole for aging.

但今天我们邀请的嘉宾将帮助我们理解我们其他人的情况。

But our guests today will help us understand what it's like for the rest of us.

像熵和体型这样的物理定律让我们变老，而不是变年轻。

Physical laws like entropy and size make us get older, not younger.

他们还将帮助我们理解为什么我们会进化成这样。

And they'll help us understand why we evolved to be this way.

长期收听《复杂性》节目的听众可能已经注意到，这个节目之前停播了一段时间。

Long term listeners of Complexity will have noticed that this show has been on a break.

我们以一种新的方式重新推出节目。

We are relaunching with a bit of a new approach.

我们将节目分成多个季度，每个季度深入探讨复杂性研究的一个新领域。

We are breaking the show into seasons, and in each season we'll dive into a new area of complexity research.

在本季度中，我们将探索我的研究领域——生命的物理学。

And in this season, we'll explore my field of research, the physics of life.

我是克里斯·肯佩斯，圣塔菲研究所的教授，我的兴趣是揭示宇宙中生命的规律。

I'm Chris Kempes, professor at the Santa Fe Institute, and I'm interested in uncovering the laws of life in the universe.

在本季度中，我们将听到的所有人，都是我曾经合作或以某种方式共事过的人。

In this season, everyone we'll hear from are people I've collaborated with or worked with in some way.

我是阿巴·埃利·菲博，圣塔菲研究所传播总监。

And I'm Abha Eli Phoboo, Director of Communications at the Santa Fe Institute.

今天，我们将从三个部分来探讨物理学能告诉我们关于生命和演化的哪些内容。

Today, we're going to look at what physics can tell us about life and evolution in three parts.

第一部分：打破学术的围墙。

Part one: Breaking the Walls of Academia.

克里斯，在我们邀请两位嘉宾之前，我想听听你在这方面的研究。

Chris, before we launch into our two guests, I want to hear a little bit about your work in this area.

你是如何对科学产生兴趣的？你一直对物理学和生命科学都感兴趣吗？

How did you become interested in science and were you always interested in both physics and life?

是的，我成长于新墨西哥州北部一个偏远小镇，那里夜空极其美丽，每晚都能看到银河，繁星点点。

Yeah, I grew up in a tiny town in rural Northern New Mexico, and that town had amazing night sky where you could see the Milky Way every night and tons and tons of stars were visible.

而且那里还有一个极其丰富的化石层，州恐龙腔骨龙就是在那里被发现的，此后还发现了许多其他恐龙。

And it was home to an amazingly rich fossil bed where the state dinosaur Coelophysis was found and many other dinosaurs have been found since.

对我来说，我对恒星和恐龙都着迷。

And for me, I was sort of fascinated by both stars and dinosaurs.

它们所体现的时间深度和宏大尺度，深深触动了我。

There was something about the depth of time, the largeness of what both of them conveyed to me.

这激发了我内心深处的某种东西，也正是因此我走上了科学之路。

It brought out something deep in me and that was really how I got into science.

我常常告诉别人，我想成为一名古生物学家，也想成为一名天文学家。

And I'd go around telling people I wanna be both a paleontologist and an astronomer.

然后到了申请研究生的时候，我意识到我想从事我称之为物理生物学的交叉领域。

And then when it went time to apply for grad school, I realized I wanted to do this intersection of things that I called physical biology.

进入麻省理工学院后，我实际上为自己设计了一套专注于这一领域的学习计划。

And I actually designed my own course of study once I was admitted to MIT focused on exactly that.

后来，我做的一些工作让我受邀参加了一次天体生物学会议。

And then I eventually did some work that got me invited to an astrobiology conference.

我去参加了会议，并对自己说：对，我就是一位天体生物学家，我就是那个古生物学家兼天文学家。

And I went there and I said, right, I am an astrobiologist, right, I am this paleontologist astronomer.

我一直都在做这些事。

And I've been doing it all along.

我只是忘了该怎么称呼它。

I just forgot what to call it.

你的工作一直非常跨学科，因为这正是我们在圣塔菲研究所的工作方式。

So your work has been very interdisciplinary because that's sort of how we work at SFI.

我想知道，作为一名专业不属于单一领域（如物理或生物学）的研究者，你的经历如何？

And I wonder, what's your experience been like as a researcher whose expertise doesn't fit squarely into one category, like physics or biology?

有一位社会学家和哲学家我很欣赏，他说学科本质上是一种实际需求。

So there's a sociologist and philosopher I really like who says, the disciplines are really a practical concern.

它们是组织人员和资金等方式的一种手段，但并不真正具有智力上的价值。

There are ways to organize people and funding and so forth, but they don't really have intellectual merit.

我认为这正是圣塔菲研究所看待世界的方式。

And I think that's very much the way SFI sees the world.

我们更关注的是问题本身，尤其是那些宏大的问题，而不是某个人的特定训练背景或他们所谓的‘成长’领域。

We're much more driven by questions, overarching questions, than we are by someone's particular history of training or the field that they might've quote unquote grown up in.

因为圣塔菲研究所没有院系，这是一个非常独特的特点。

Because SFI has no departments and it's a really unique feature.

对我来说，这让我感到轻松，因为我希望思考所有这些领域，并理解不同思维框架之间的联系。

And for me, that's a relief because I want to think about all of those things and understand where the connections are between different frameworks of thought.

事实上，除了我们有研究人员彼此交融、交流思想，并共享对世界的一种共同观点之外，并没有太多组织结构。

And in fact, there's not much of an organizational structure beyond just the fact that we have researchers who mix together and exchange ideas and who share sort of a common view on the world.

我认为，这在某种程度上正是科学曾经运作的方式。

And I think this is at one point how science was done.

更深入地思考这个问题，或许可以说，许多学科的发展方式纯粹是偶然所致，在很多方面都是任意的。

A deeper way to think about it might also be to say, a lot of the ways the disciplines have evolved are purely dependent on happenstance, and they're sort of arbitrary in lots of ways.

例如，生物学本可以对人类的社会现象产生更大的兴趣。

For example, biology could have become much more interested in the social phenomena of humans.

我们可能会发现，社会学从未成为独立的领域，而始终被视为生物学的一个子领域。

And we could find that sociology had never become its own field, and it always sort of situated itself as a subfield of biology.

这告诉我们，一个学科的许多发展轨迹都取决于历史的特殊性。

And that tells us that a lot of the trajectory of a discipline is based on the particularities of history.

因此，如果我们摆脱这种局限，就可能取得巨大的进展，因为我们摆脱了那些任意性的东西。

And so if we get away from that, we might be able to make huge amounts of progress because we get away from things that are sort of arbitrary.

这也是为什么人们觉得跨学科的交流如此有用的原因之一。

And that's part of why people find this exchange between disciplines so useful.

因此，我们正在论证物理学是生命的一个基本组成部分。

So we're making the case that physics is a fundamental part of life.

它是一个庞大而广泛的领域，今天我们将聚焦于几个物理学与生命相互交织的具体例子。

It's a huge, broad area of study, and today we'll zoom in and land on just a couple of examples of how physics and life are intertwined.

我们将探讨物理学如何限制我们人类的思维方式以及大脑如何运作和理解事物。

We'll explore how physics limits the way we humans think, how our brains work, comprehend.

我们还将研究物理学如何限制人类的寿命。

And we'll investigate the way physics limits our human lifespans.

如果你能先介绍一下自己的话。

If you could start by introducing yourself.

当然。

Sure.

我叫维杰·巴拉苏布拉马尼安，是一名理论物理学家。

My name is Vijay Balasubramanian, and I'm a theoretical physicist by trade.

我的研究领域广泛，涵盖从量子引力理论到生物物理学和复杂系统，还包括一些凝聚态物理，偶尔也会涉及宇宙学。

I work broadly in the field on everything from the quantum theory of gravity to biophysics and complex systems with, you know, bits of condensed matter physics and occasionally things involving cosmology on the way.

所以，这就是我的工作。

So, yeah, that's what I do.

维杰对他的研究采取了非常广泛的方法，他告诉我们，正是对世界的好奇心驱使他成为了一名科学家。

Vijay takes a pretty broad approach to his work, and he tells us how his curiosity about the world led him to be a scientist.

我七岁的时候，我们住在加尔各答，那里有一种非常浓厚的文化氛围，比如一些小型书商把书放在墙上的小壁橱里，诸如此类。

When I was seven, we were living in Calcutta at the time, and Calcutta has this very big culture of sort of, you know, small booksellers who have books in little closets in the wall, things like this.

我父母给我买了一本厚厚的书，作者叫阿卡迪·勒库姆，我记得他的名字，书名叫《告诉我为什么》。

And my parents got me a big thick volume written by somebody named Arkady Leokum, I remember his name, called Tell Me Why.

这本书包含了大量的问题，我随手翻开，第一个看到的问题是：为什么天空是蓝色的？

And the book consisted of a very large number of questions, and I flipped the thing open, And the first question I saw was, why is the sky blue?

我觉得，对，太准了。

And I thought, yeah, right on.

为什么天空是蓝色的？

Why is the sky blue?

作者用氮分子散射等原理给我做了解释。

And the guy explained it, you know, with scattering of nitrogen molecules and everything.

太神奇了。

Wow.

然后我随便翻到另一页，上面写着：为什么你开车时月亮总跟着你？

Then I flipped another page somewhere and it said, why does the moon follow you as you drive around?

我当时就说，是的，是的。

I was like, yeah, yeah.

为什么月亮会跟着你开车时走呢？

Why does the moon follow you as you drive around?

然后他解释说，因为月亮很大又很远，诸如此类的原因。

And then he explained it as, you know, because the moon's very big and far away and stuff like this.

我完全被吸引住了。

And I was completely hooked.

我想我当时就知道自己想当科学家，但你知道，七岁的时候，你很难清晰地表达出自己想研究的是物理、神经科学还是别的什么。

And I think I knew I would want to be a scientist, but you know, when you're seven, you can't articulate these things particularly clearly whether it's physics or neuroscience or whatever.

在那时看来，所有这些都是一回事，就是搞清楚世界是如何运行的。

It's all the same, you know, not to work out the way the world works.

他现在可能会说这些都一样，但维杰小的时候，他的关注点要狭窄得多。

He may say that it's all the same now, but when Vijay was younger his focus was much narrower.

所以，我就是这样的一个学生。

So I was one of these students.

所以你知道，在印度长大的时候，那个年代你并没有太多机会接触到生物实验室之类的东西。

So, you know, when you're growing up in India, you didn't have access as much in those days to sort of biological laboratories and things like that.

聪明的孩子通常都想学物理和数学。

Smart kids typically wanted to go into physics and math.

在很多情况下，我自己的兴趣也偏向物理和数学，因为我一直觉得这些领域有一种纯粹的美感——只需要记住很少的几个原理，就能推导出所有其他东西。

In many cases, my own inclinations lay towards physics and math because I always perceived those fields as having this sort of pristine beauty where there's a very small number of principles that you've got to remember, you can drive everything else.

维杰认为生物学更偏向定性。

Vijay saw biology as more qualitative.

需要记忆一大堆东西，而不是那种由少数核心定律带来的纯粹美感。

All these things to memorize rather than that pristine beauty of a few core laws.

他设法找各种理由逃掉能逃的每一节生物课，直到最后

He managed to argue his way out of every biology class he could until it was

在我物理学博士学业快结束时，我的导师说，你知道吗，还有一个叫系统神经科学的领域。

Towards the end of my PhD in physics that my PhD advisor said, well, you know, there's this whole field of systems neuroscience.

他一直研究计算机，热爱编程和机器。

He had been studying computers, and he loved programming and machines.

这些机器在能做什么和能计算多少方面都有局限，这让他开始思考。

The machines had limits to what they could do and how much they could compute, which got him thinking.

但我的大脑里那个思考机器，却限制了我思考的方式。

But there was something about the thinking machine inside my head that would constrain the way I thought.

也就是说，你知道，猫也会思考，但它不会微积分。

That is to say, you know, a cat, you know, thinks too, but it can't do calculus.

所以我假设，我的大脑能够实现的任何算法，都因为大脑这个‘计算机’的本性和限制，让我无法完成许多事情。

So I assume that whatever algorithms my brain can implement, there are many things I can't do because of the nature of the computer inside my head and its constraints.

我们究竟如何知道我们不知道什么？

Basically, how do we know what we don't know?

大脑的局限在哪里？

What are the limits of the brain?

突然间，对维杰来说，生物学看起来变得更有吸引力了。

Suddenly, for Vijay, biology was looking much more appealing.

但博士的第四年并不是转行去别的领域的合适时机。

But the fourth year of the PhD is not a good time to switch into something else.

你需要完成一件事。

You need to finish something.

于是我拿到了博士学位，之后作为博士后，我晚上开始在神经科学实验室兼职，阅读更多论文，学习更多知识。

So I got my PhD, and then as a postdoc, I began to moonlight in the evenings in neuroscience lab, read more papers, learn more.

你知道，这是一个非常庞大的领域，需要学习的东西非常多，尤其是对于像我这样当年错过了所有生物学课程的人。

You know, it's a very big field with an enormous amount to learn, especially for somebody who made the mistake of skipping all the biology classes all those years.

所以，维杰对科学的热爱始于童年，但当他作为一名博士后期学生意识到自己更喜欢生物学而非仅仅物理学时，他觉得自己落后了。

So Vijay's love of science started when he was a kid, but he felt like he was behind when he realized he liked biology, not just physics, as a late stage PhD student.

但他并不是唯一一个觉得自己来晚了的人。

But he's not the only one who felt like he arrived late.

你能告诉我，你原本是一名物理学家，为什么后来想研究生命科学吗？

Could you tell me about why you wanted to look into life when you were really a physicist to begin with?

是的。

Well, yes.

其实，我关注的并不是生命本身。

I it wasn't so much life.

说实话，更多是关于死亡。

It was more death, to be honest.

这是杰弗里·韦斯特，理论物理学家，曾任圣塔菲研究所所长，并创立了洛斯阿拉莫斯国家实验室的高能物理组。

That's Geoffrey West, theoretical physicist, former president of the Santa Fe Institute, and founder of the High Energy Physics Group at Los Alamos National Laboratory.

在90年代初，杰弗里正在研究一个叫做

In the early 90s Geoffrey was working on something called

一个超导超级对撞机，这是在德克萨斯州建造的一个大型粒子加速器。

A superconducting supercollider, this big particle machine being built in Texas.

它获得了包括美国政府在内的多个资金来源的支持。

It was receiving funding from multiple sources, including the US government.

但随着时间推移，成本不断飙升，原本预期的外国投资也从未实现。

But over time, the cost ballooned and foreign investment that had been expected never materialized.

最终，克林顿政府决定终止拨款。

Eventually, the Clinton administration decided to cut them off.

我们被彻底抛弃了，随之而来的是一个类似死亡的过程。

And we were left high and dry, and there was a sort of a death process associated with it.

但那时，高能物理、基本粒子物理领域开始面临威胁，而这也恰好发生在我五十岁出头的时候。我来自一个男性寿命较短的家族，人们通常早逝，我一直以为自己也会在六十岁左右去世。而那时我已年过五十，这台本是该领域命脉的机器却正在走向死亡——它最终真的死了。于是我开始意识到自己的死亡。

But it was starting you know, it was very threatening to the field of high energy physics, elementary particle physics, and it coincided with me being sort of in my early mid fifties, and it turns out that I come from a short lived line of men, people who die young, and I had always expected to die relatively young, meaning in my early sixties and here I was in my fifties and here was this machine that was the lifeblood of the field and it was dying and in fact died and, I began to be conscious of my own death.

但当时，不仅其他科学家，就连能源部和政府官员也普遍认为：物理学是十九世纪和二十世纪的科学，而生物学显然是二十一世纪的科学。因此，人们认为我们已经掌握了足够的物理学知识，再继续研究下去也没什么意义。

But at that time one of the big comments that was banded around not just by other scientists but by, people in the Department of Energy and Administration was that physics was the science of the nineteenth and twentieth century and biology is clearly going to be the science of the twenty first century and so so to speak we know enough physics was the corollary so there's not much point in doing any more.

他们并没有用这些原话，但那种态度确实是普遍存在的。

Those weren't the exact words they used but it was the general attitude.

我当时的反应是——顺便说一句，我对生物学一无所知——如果生物学真是一门科学，它就必须融合物理学的范式、文化和方法，才能取得真正的进展，从我所认为的定性研究转变为更严谨、更数学化、更计算化、更具预测性的科学。当然，我根本不懂生物学，这种说法完全是高能物理学家典型的傲慢言论，但它确实让我开始思考：也许我该言出必行。

I reacted saying, you know, without knowing any biology, by the way, saying if biology were a real science, it would have to incorporate, the paradigms, culture, and techniques of physics in order to make real progress and move from something that I perceived as being qualitative into something that was more principled, more mathematical, more computational and more predictive and of course I knew no biology and it was a very arrogant kind of high energy physics thing to say, but, it did get me thinking that, you know, maybe I should put money where my mouth is.

这正是这一切的开端，因为我开始想：从我的角度看，为什么我感到死亡迫近？

That's what started this because I thought, well, maybe, I should start thinking about why is it that from my viewpoint I had impending mortality.

为什么人类的寿命大约是一百年，而不是一千年、一百万年，或者只有十年？

Why is it that human lifespan is the order of a hundred years and not a thousand years, a million years or ten years?

到底是什么决定了这一切？

What what the hell determines it?

于是我开始查阅文献，发现当时关于衰老与死亡的整个问题在生物学领域几乎是个冷门。我想，也许这正是一个值得深入思考的好问题，能让我重新审视一些关于死亡的疑问。

And I started looking at the literature and discovered that the whole question of aging and mortality was at that time quite a backwater of biology and I thought well maybe this is a good problem to just start thinking about, get me thinking about some of these questions of death.

杰弗里和维贾伊都从物理学转向了生物学，因为他们想理解人类的极限——为什么我们的大脑在思考能力上存在局限，为什么我们的寿命即使幸运也仅限于一百年左右。

Both Geoffrey and Vijay made this transition from physics to biology because they wanted to understand human limits, why our brains are limited in how much they can think, Why our lifespans are limited to a hundred years if we're lucky.

是否存在解释这种局限性的根本规律？

Are there underlying laws that explain this?

第二部分：维贾伊、大脑与能量。

Part two: Vijay, the Brain, and Energy.

维贾伊对一个现象感到着迷：当信息变得过于庞大或复杂，超出了我们理解的范围时，我们会使用简化的术语来描述它。

Something that fascinated Vijay was the fact that when information becomes too big or too complex for us to wrap our heads around, we use shorthand to describe it.

在复杂性科学中，这被称为压缩、粗粒化或有效定律。

In Complexity Science, this is known as compression or coarse graining or effective laws.

它有几种不同的叫法。

It goes by a few different names.

比如，想象一群人和两个人坐在房间里的情景。

For example, think of a crowd versus two people sitting in a room.

你可以想象两个人在房间里交谈的场景。

You can picture two individual people having a conversation.

但如果你想象一群人

But if you think of a crowd

当然，我们无法从每个个体的角度去思考整个群体。

We're not capable, of course, of thinking about the crowd in terms of all of the individuals.

这太难了。

It's too difficult.

它需要太多的计算能力，因此你需要进行压缩。

It requires too much computational power, so you compress it.

我们进行压缩是因为我们必须这么做。

We compress it because we have to.

Vijay 用《博闻强记的富内斯》这个故事来说明这一点。

Vijay uses the story Funes the Memorious to show why.

这个故事出自豪尔赫·路易斯·博尔赫斯的《博闻强记的富内斯》。

It's from this story called Funes the Memorious by Jorge Luis Borges.

我不确定你是否知道这个故事。

I don't know if you know the story.

有个叫富内斯的人，被一头驴踢了头之类的，醒来后获得了完美理解并掌握所有细节的能力。

There's a guy, Funes, who gets kicked on the head by a donkey or something, and when he wakes up, he has this perfect ability to understand and know everything in complete detail.

我们看到一棵树。

So we see a tree.

他看到这棵树以及它所有的叶子，因此不明白为什么一棵树会被归为与另一棵树同类，因为它们其实并不相同，对吧？

He sees the tree and all of its individual leaves and therefore doesn't see why one tree is in the same category as the other tree because it isn't, right?

它的形状不一样。

It's shaped differently.

它的叶子也不同。

It's got different leaves.

他逐渐丧失了思考能力，因为思考和认知本质上是关于为不同事物建立有效理论的过程。

And he slowly loses the ability to think because the ability to think and cognition is really about this process of making effective theories about different things.

它是将所有细节的复杂性压缩成这些有效概念的过程。

It's about compressing the complexity of all the detail into these effective concepts.

某种程度上，这些有效概念、我们用来描述复杂事物的简略表达，是一种效率的形式。

In a way, these effective concepts, these shorthands we use to describe complex things are a form of efficiency.

我们能理解的东西是有限的，但压缩细节让我们依然能够在这个世界中前行，而大脑在能量使用方面出人意料地高效。

There's a limit on what we can comprehend, but compressing the details allows us to move through the world anyway, and the brain is surprisingly efficient in the way it uses energy.

所有生物过程都需要一定的能量才能运作。

So all biological processes require some amount of energy to function.

因此，能量预算——也就是食物如何转化为ATP，ATP又如何被用于各种功能——在我看来，是生物学必须应对的核心挑战之一，即生物体所获得的能量是有限的。

And so energy budgets, that is how food gets turned into ATP and how ATP eventually gets used for various things, to me is sort of one of the central challenges biology has to deal with is this finite energy budget that is given to an organism.

大脑也是如此。

And that's also true for the brain.

大脑从身体获得一定量的能量，并必须用这笔能量预算完成所有工作。

The brain gets a certain amount of energy from the body and has to do everything that it does with that energy budget.

因此，最近我深受大量研究的启发，这些研究表明，大脑在许多情况下似乎都以极高的能量效率运行。

And so I've been really inspired recently by lots of work showing that the brain, in many cases, seems to be operating with sort of enormous energetic efficiency.

大脑中有一些计算过程，其效率几乎接近物理极限。

There are certain computations that happen in the brain that seem to be pretty close to the physical limit.

所以我认为大脑，

So I think the brain,

就像生活中的其他一切一样，它是一个受到极大限制的器官。

like everything else in life, is a very constrained organ.

它只有这么大的空间。

It's got only so much space.

它只有这么多细胞，只有这么多能量。

It's got only so many cells, it's got only so much energy.

从能量消耗来看，大脑是我们体内最昂贵的器官，它必须节约所有这些资源。

The brain is the most expensive organ we own in terms of energy consumption, and, you know, it has to conserve all of these things.

它也只有这么少的时间，因为如果你要做一个决定，却花了一整天，那可能就太晚了。

It's also got only so much time because, you know, if I have to make a decision and I take a day to do it, it'll probably be too late.

事情都会迅速完成。

Things will be done quickly.

我认为，所有这些因素都限制了神经回路的结构。

So all of these things, I believe, constrain the architecture of circuits.

对吧？

Right?

你必须快速完成事情。

You have to do things quickly.

你必须用少量组件高效地完成事情，并消耗更少的电力。

You have to do things efficiently with few components and burn less power.

大脑受到物理限制，而我们的大脑却如此擅长利用现有资源，这真是令人惊叹。

The fact that there are physical limits constraining the brain and the fact our brains are so good at using what they've got is pretty amazing.

以这种方式研究大脑，也模糊了传统物理学与传统生物学之间的界限，因为大脑是一个始终在消耗能量的活体器官。

Studying the brain this way also blurs the line between old school physics and old school biology because it's a living organ that's always using energy.

你知道，历史上在物理学中容易研究的系统，通常是处于平衡或接近平衡状态的。

You know, historically, the systems that have been easy to study in physics, that have been historically studied, are either at equilibrium or close to equilibrium.

它们处于静止状态，稍微移动一下，如此等等。

You know, they're at rest and they move a little bit and so on and so forth.

现在，任何生命体都非常有趣的一点是，它们本质上是非平衡态的。

Now what's very interesting about anything living is it's inherently out of equilibrium.

如果你作为一个生命体处于平衡状态，那你就是死了。

If you are at equilibrium as a living thing, you're dead.

所以，生命运作的方式就是从一侧获取能量，通过系统将其引导，并利用这些能量做各种疯狂的事情，比如讨论物理学。

So really, the way life works is it brings in energy on one side and funnels it through the system and uses that energy to do all kinds of crazy things like, you know, like talk about physics.

因此，物理学中有一个叫做‘活性物质’的领域，其目标是理解这类系统——通过向其中注入能量，就能产生完全疯狂的动力学行为，甚至违反牛顿定律，因为它们在过程中消耗能量。

So there's this whole field of something called active matter in physics whose goal is to understand systems like that, where you funnel energy through it and you can get completely crazy dynamics, things that violate Newton's laws, for example, because they're burning energy on the way.

所以，这种灵感

So the inspiration

来自所有这些

from all of

这些生物系统的灵感，现在正推动着物理学中软物质和生命物质研究领域的一场小革命，去研究这些本质上远离平衡态的复杂系统。

these kinds of biological systems is now fueling, you know, a minor revolution in parts of physics, in soft matter and living matter physics, to study these kinds of complex systems which are inherently out of equilibrium.

物理定律是普适的。

The laws of physics are universal.

我的意思是，基本定律具有这种普适性和不变性，也就是说，人们假设它们在任何地方都适用，这一点最早由牛顿提出并发现：他所提出的关于球体滚下小山坡、马匹拉车等的运动定律，与决定月球绕地球、行星绕太阳运行的定律是相同的，适用于宇宙中的一切。

I mean, the fundamental laws have this kind of universality to them, this immutability, namely that, you know, it is assumed they apply everywhere, something that was first postulated, discovered by Newton to recognize that his laws of motion that deal with the boars rolling down little hills or horses pulling carts or whatever are the same laws that determine the motion of the moon around earth and the planets around the sun and everything else in the universe.

这正是非凡之处。

That's what's kind of extraordinary.

因此，它们显然也适用于生命。

And so therefore, they obviously apply to life.

我认为一个有趣的问题是，我们能在多大程度上用这种观点来理解生命和生物现象？

And I think one of the interesting questions is how far can you take that in understanding life and biological phenomena?

通过与维杰的合作，我们了解到大脑的大小及其能量使用方式限制了我们的思考能力，也就是真正的计算能力。

With Vijay, we learned that the size of the brain and the way it uses energy create limits on our ability to think, to literally compute.

我们还了解到，尽管生物体不像岩石那样处于平衡状态，但仍然适用物理定律。

And we also learned that although living organisms are not at equilibrium the way, for example, a rock is, there are still physical laws that apply.

接下来，我们将探讨生命中的限制：为什么人类活到100岁而不是1000岁？为什么树木不会无限长高直冲云霄？为什么睡眠如此重要。

Next up, we'll look at the limits in our lives: why humans die at age 100 and not 1,000 why trees don't grow forever and ever up to the sky and why sleep is so important.

这是第三部分：杰弗里与天平。

This is part three: Geoffrey and the Scales.

到目前为止，你可能已经注意到杰弗里的声音听起来像是在户外，而克里斯实际上和他在一起。

By now, you've probably noticed that Geoffrey sounds like he's outside, and Chris was actually there with him.

杰弗里·韦斯特和我已在南非一起待了一周。

Geoffrey West and I have been in South Africa together for a week.

我们俩都在复杂性全球学校任教。

We're both teaching at the Complexity Global School.

当我们坐下来进行这次对话时，我们正坐在露台上，俯瞰着一条干涸的河床，那里是稀树草原逐渐过渡到稍显茂密森林的地方。

When we sat down for this conversation, it was on the deck overlooking a dry riverbed where the savannah gives way to slightly denser forest.

就在这条干涸的河床对面，有一个饮水点。

And just across this dry river bed is a watering hole.

我们看到了各种各样的动物，斑马、羚羊、狒狒、猴子，还有一只鬣狗在附近，不过我们俩都没见过它。

We've seen all manner of animals, zebras, impalas, baboons, monkeys, and there's a hyena around although neither of us have seen it.

德克萨斯。

Texas.

抱歉。

And sorry.

我得停一下。

I have to stop.

听好了。

Listen.

你知道吗？

You know what?

我们能听到下面狮子的吼叫声。

We're hearing the roars of lions just below.

是的。

Yeah.

这儿确实有一只狒狒。

There's a, yeah, there's a baboon right here.

那就是吗？

Is that what it is?

是的。

Yeah.

那是一只狒狒。

It's a baboon.

是的。

Yeah.

它是一只獴。

It's a mongoose.

它们就是这样的。

That's what they are.

它们是小獴。

They're little mongoose.

太好了。

That's great.

哦，太棒了。

Oh, that's wonderful.

看它们。

Look at them.

它们吃什么？

What do they eat?

它们一定在吃小昆虫。

They must be eating little insects.

我认为它们是在追捕昆虫。

I think they're after the insects.

小小的昆虫。

Little insects.

但不管怎样，我们回到物理学的定律吧。

But anyway, back to the laws of physics.

这些定律如何应用于这些狒狒、獴以及周围所有的动物呢？

How do they apply to these baboons and mongoose and animals that are all around?

自从杰弗里开始研究生命以来，他深入研究的一个物理限制就是尺度定律的概念。

Since Geoffrey began studying life, one physical limit he's examined in-depth is the concept of scaling laws.

他写了一本名为《尺度》的书，并与克里斯合著了多篇论文，思考它如何应用于从动物到整个城市和社会的一切事物。

He's written a book called Scale and coauthored multiple papers with Chris, and he's thought about how it applies to everything, from animals to entire cities and societies.

但为什么这些规律性模式，比如尺度定律，会如此出现呢？

But why is it that these regular patterns emerge like scaling laws?

我想你和我以前聊过很多次了，为什么物理学会在进化或生物学中显现出来？

I think you and I have talked a lot about this, about why is it that physics should show up in evolution or biology?

规模定律基本上指出，关于老鼠的生理特征和生命史的几乎所有可测量方面，都可以通过一个简单的数学公式与大象、人类、长颈鹿等生物的相同生理现象和生命事件相关联，它们以一种简单的方式彼此联系。

Scaling law basically says that in terms of almost anything that you can measure about the physiology and life history of a mouse can be related by a simple mathematical formula to those same physiological phenomena and life history events in an elephant or a human being or a giraffe and so on, and they are connected all together in a simple way.

但重要的是，从这个角度看，生物学与物理学在概念上的区别在于，当这些定律应用于物理现象，如围绕太阳运行的行星时，这些定律具有我们所相信的某种精确性和确定性。

But the important thing is, the thing that distinguishes conceptually the biological from the physics from that viewpoint is that when it's applied to physical phenomena, planets around the sun, etc, those laws have a certain precision and exactness that we believe.

但在生物学中，情况则不同，因为生物实际上是一个不断进化的适应性系统。

That is a different character in biology because it is in fact an evolving adaptive system.

它持续不断地产生变异。

It's continually changing in variants.

我认为，关于规模定律的一个有趣问题是：你允许在多大程度上偏离这些规模定律？

One of the interesting questions, I think, about scaling is how much are you allowed to deviate from the scaling laws?

换句话说，你允许多大的变异，同时仍能保持功能性和竞争力？

How much variance, so to speak, are you allowed and still be functional and still be competitive?

如果你绘制所有不同动物的体型与器官功能的关系图，你会发现其中存在一种模式，一种贯穿所有生物的规律。

If you were to plot the size and organ functions of all different animals, you'd see that there is a pattern, a law underlying all organisms.

但随着体型和复杂性的增加，这种模式并非一条简单的直线。

But as you move up in size and complexity, the pattern isn't a simple straight line.

如果你取某个物体或系统并将其放大，比如将其尺寸翻倍、三倍，会发生什么？

What happens if you take some object or some system and you scale it up, meaning you double its size, triple its size and so on?

你知道，如果把一座城市规模翻倍，它的道路也会增加一倍吗？

You know, double a city, does it have twice as many roads?

如果将一只动物的体型翻倍，它的心跳也会加快一倍吗？

If you double the size of an animal, does it have twice as fast a heartbeat?

以此类推。

And so on.

你知道，系统是如何响应的？它们会怎样变化？

You know, what are the what how does it how do the systems respond?

这个问题是现代科学最早提出的问题之一，它打开了无数新的视野。

That question, which is one of the first questions in modern science to be asked, has opened up all kinds of vistas.

事实上，第一个提出这个问题的人是现代科学的奠基人伽利略，他问的是：为什么我们不能有无限高的树木或建筑？（虽然他没有用这些词）

And indeed, the first person to ask it was the founding the founding father of modern science, Galileo, who asked the question, why can't we have infinitely I mean, didn't use these words, but infinitely tall trees or buildings.

是什么阻止了我们？

What stops us?

他使用了比例论证。

And he used a scaling argument.

这个比例论证是说，如果你把尺寸翻倍，重量也会翻倍。

And the scaling argument was the scaling of, you know, if you double the size, double the weight.

但他意识到，如果你把尺寸翻倍，强度并不会随之增加。

But he realized if you double the size, don't strengthen strength.

梁、腿或一般肢体的强度并不会线性增长。

Strength of beams or legs or limbs in general do not increase linearly.

这就是关键所在。

And this is the point.

它们是非线性增长的，最终重量会压垮强度。

They increase non linearly, and eventually weight crushes the strength.

想想动物的新陈代谢，它消耗多少能量。

Think about the metabolism of an animal, how much energy it uses.

你知道，所有事物都是通过自然选择进化而来的，因此你可能会认为，一个体型是另一个动物两倍的生物——它们是不同的物种——由于所处的环境生态位不同、进化历史不同等等，各方面都会很不一样。

You know, everything is evolved by natural selection, And so you would think that an animal that is twice the size of another that is a different animal, you know, things would be quite different because they have a completely different environmental niche, they have a different history, and so on.

数据表明，大象实际上并不是一只被放大的老鼠，而更像是一只被缩小的鲸鱼和一只被放大的长颈鹿，而长颈鹿在生物学上相当于一个被放大的人类——这简直让人震惊。

And and one of the things that the data showed is that an elephant is actually not just a scaled up mouse, but a scaled down whale and a scaled up giraffe, which is a scaled up human being biologically, which was sort of mind blowing actually.

要从底层动力学的角度理解这一点，正是这一点让我，也最终让你对研究这些问题充满热情，并将其作为洞察底层原理的窗口。

And to understand that in terms of the underlying dynamics, that's what got me and ultimately you very excited about working on these issues and using that as a window onto underlying principles.

这个观点是，如果你把一只老鼠放大到大象的大小，它的行为会出人意料地与大象相似。

The idea is that basically if you took a mouse and blew it up to the size of an elephant, it would behave surprisingly similar to the elephant.

当然，它不会长出象鼻，也不会突然长出巨大的垂耳，但它的新陈代谢会变慢，睡眠时间会减少，这两者都会以可预测但非线性的方式发生。

Now obviously, it wouldn't grow a trunk or suddenly get giant floppy ears, but its metabolism would slow down and it would sleep less, and both would happen in a predictable but nonlinear way.

杰弗里和他的合作者萨维奇实际上建立了一套理论，能够根据动物新陈代谢的缩放规律预测其睡眠时间。

Geoffrey and one of his co authors, Savage, have actually created a theory that predicts how much an animal should sleep based on the way its metabolism scales up or down.

有一天我们得出了这个结果，当时我们非常兴奋。

And we got this result one day and we were very excited.

我们把它写在白板上，我说：‘这太棒了’，但接着我又说：‘这里其实有个大问题。’

And, we did we had it on the board and I said, that's great, but I said, you know, there's a real problem here.

如果按照我在黑板上写的数字，大象每天应该只睡三到四个小时，这显然荒谬至极。

If you put in the numbers as I did on the blackboard, an elephant should only sleep for about three or four hours, which is obviously nuts.

于是我离开了。

And so I went away.

我们离开后说，我们必须好好想想这个问题。

We went away and said, well, we have to think about this.

我们一定漏掉了什么。

There must be some something we're missing here.

然后我接到了范的电话，他说：嘿。

And then I got a call from Van who said, hey.

你猜怎么着？

Guess what?

我查了下数据。

I looked up the data.

大象每天只睡三到四个小时。

Elephants sleep for three or four hours.

事实上，它们只睡两到三个小时。

In fact, two to three hours, in fact, is what they do sleep.

所以这真是太棒了。

So that was fantastic.

因此，这解释了为什么大象只睡这么短的时间，为什么像老鼠这样的动物要睡十六到十七个小时，也解释了为什么你和我在婴儿时期也曾睡十六或十七个小时，随着我们成长，从个体发育的角度来看，我们需要的睡眠时间越来越少，最终稳定在八小时左右，而这一理论正好可以解释这一点。

And so this explained why it was that elephants sleep for such a short time and why, mice for example sleep from sixteen seventeen hours and also why you and me used to sleep for sixteen or seventeen hours, you know, when we were babies And, as we grew, ontogenetically, we needed less and less until we settled down at eight, which this theory explains.

还记得杰弗里在预期死亡时提出的原始问题吗？

Remember Geoffrey's original question as he was expecting death?

为什么人类的寿命是一百年，而不是一千年？

Why do humans live on a scale of one hundred years and not one thousand?

事实证明，睡眠是这个问题的重要答案。

It turns out, sleep is a big part of the answer.

睡眠就像死亡。

Sleep is like death.

或者，也许到了我这个年纪，可以安慰自己说，死亡就像睡眠。

Or maybe one can console oneself at my age that death is like sleep.

而睡眠对生命至关重要。

And sleep is crucial to life.

维持你生命的那些过程——新陈代谢，以及被称为ATP的生化能量分子的产生——当然会通过我们的各种网络，特别是心血管系统，将能量输送到细胞中。

The very processes that are keeping you alive metabolism, biochemical production of so called ATP, which is sort of the complex molecule that is our currency of energy, it gets of course sent through our various networks, particularly our cardiovascular system, to supply energy to cells.

新陈代谢会在我们的身体上造成磨损，就像汽车、洗衣机或家里的管道会随着时间推移而老化一样。

And metabolism produces wear and tear on our bodies, just like a car or a washing machine or the pipes in your home get worn out over time.

我们的身体并不像石头那样处于物理平衡状态。

Our bodies are not at physical equilibrium the way a rock is.

我们进化出了修复机制来应对这些损伤，但修复过程代价极高。

We have evolved repair mechanisms to deal with those, but repair is exceedingly costly.

自然选择让我们进化出足够的修复能力，使我们至少能活到三十五或四十岁，以便生育十几个孩子。

Natural selection has evolved us so that, the repair is good enough so that we live to at least 35 or 40 so that we can, have a dozen or so children.

然后，你知道，自然选择就不再关心了，于是我们就死了。

And and then, you know, natural selection doesn't care, and, we die.

这就是与死亡的关系。

So that's the relationship to death.

事实上，直到上个世纪之前，人类的预期寿命通常只有四十岁左右，在许多情况下甚至更短。

And indeed, you know, the expected lifespan of human beings until this last century or so was, you know, close to forty years or even less in many cases.

所以你并不在意肝脏或肺部等器官没有被完全修复，只要能撑过三十五到四十年就够了。

So you don't care that you don't exactly repair your liver or your maybe your lungs and so forth, because as long as it lasts for thirty five, forty years, great.

但有一个器官你必须忠实修复，那就是大脑，因为一旦受损，你很快就会失去自我，因此神经系统、小脑和大脑皮层等部位的修复机制非常强大。

But there's one organ that you better repair faithfully, and that's your brain, because that damage very quickly results in you not being you after a relatively short time, so that the repair mechanisms are very strong in the neural system in your cerebellum and cortex and so forth.

这就是为什么你的大脑消耗的能量比例远高于身体其他部分，因为它要对抗熵的力量——如果你不抵抗，最终就会死亡。

And, that's why your brain takes proportionately much more energy, metabolic energy, than the rest of your body, it's because fighting the forces of entropy that will eventually, if you didn't combat them, you'd die.

因此，规模定律最终决定了我们的新陈代谢、身体的磨损程度以及我们的睡眠量。

So scaling laws ultimately determine our metabolisms, the wear and tear in our bodies and how much we sleep.

而这种磨损最终会累积到一定程度，导致我们死亡。

And eventually that wear and tear accumulates so much that we die.

正如维杰所说，我们的大脑有物理极限，决定了我们能思考多少、能理解多少。

Like Vijay said, our brains have physical limits that determine how much thinking we can do and how much we can understand.

当然，到处都有例外。

There are outliers everywhere, of course.

事实上，生命最有趣的一点之一就是它惊人的多样性。

In fact, one of the interesting things about life is its incredible diversity.

但这些限制和规律以一种自然的方式将自然界中的一切联系在一起。

But these limits and laws do connect everything in the natural world in a natural way.

这是维杰又说了。

Here's Vijay again.

我并不真正认同物理学与其他科学之间存在明确界限。

I don't really recognize a distinction between physics and necessarily the other sciences.

我认为我们对此要谨慎对待。

I think we should be careful about that.

在牛顿的时代，还没有‘物理学’这个概念。

Back in the day of Newton, there was no physics.

当时只有自然哲学，其理念是自然哲学家试图定量且精确地描述世界上的所有现象。

There was natural philosophy, which was supposed to be the idea that natural philosophers could attempt to quantitatively and precisely describe all the phenomena of the world.

我完全相信，如果牛顿能接触到我们今天在生物学中所做的实验，他一定会深入思考这些问题，并写出一部《生物学原理》之类的作品。

And I'm totally confident that if Newton had access to the experiments we have today in biology, he would absolutely be thinking about these, and there'd be a Principia Mathematica, Biologica or something.

所以我对此很有把握。

So I'm sure of that.

我的意思是，物理学并没有发展出真正去思考那些看似有生命系统的手段。

I mean, physics didn't develop techniques to really think about systems that look like they're living.

但这种情况将很快改变。

But that's going to change very rapidly.

我认为，一些生物学家会发明出这些方法，然后物理学家会说，这么酷的东西怎么能不参与其中，于是他们就会加入进来。

I think, there are biologists who will just invent the techniques, and then there are physicists who will say this stuff is too cool not to be involved in, and they will come in.

事实上，这种情况已经发生了。

In fact, that's happening already.

许多人都来自这一传统，包括克里斯和我自己，还有很多人也是如此。

Many, I would say both Chris and myself come from that tradition, as do many others.

我们大脑使用能量的方式、寿命的极限以及塑造这一切的标度律，如果你不是科学家，这些都可能显得非常抽象。

The way our brains use energy, the limits of our lifespans, and the scaling laws that shape it all, it can all feel really abstract if you're not a scientist.

但我觉得，尽管我们的世界充满混乱，但看着周围的一切生命，知道它们背后有一个简单的结构在维系着一切，这令人无比安心。

But, I think despite the chaos of our world, it's incredibly comforting to look at all the life around us and know that there's a simple structure holding it all together.

我正看着这片森林，当然，每个视角都呈现出不同大小、不同物种的树木。

I'm just looking here at this forest, and, of course, it looks like every front looks like a bunch of different sized trees, different species.

看起来像一堆随意的杂乱。

Looks like some arbitrary mess.

而这项工作最了不起的地方在于，它揭示了表面上看似随机和混乱的事物中存在着一种非凡的系统性规律。

And the wonderful thing about having done that work was that it revealed an extraordinary kind of systematic regularity in what superficially appears to be random and chaotic.

我得说，我有点不愿意使用这个词。

And I have to say, it's don't I'm a little reluctant to use the word.

但你知道，有时我在森林里散步，甚至在城市里，我都会感受到一种近乎灵性的体验。

But, you know, sometimes when I'm walking in a forest situation and sometimes even in a city, I feel almost a spiritual experience.

也许这是一种傲慢，因为我在这里散步时，并没有把它看作只是一个具有独特历史的特定个体事物。

And maybe it's a bit of arrogance that here I am walking and not seeing it as just some very specific individual thing that has, of course, its own unique history.

但我能看到它演化出了遵循这些非凡数学方程的规律，而我有幸理解了其中的原因。

But I can see it's evolved to obey these extraordinary mathematical equations, and I'm blessed to understand why.

所以那里确实存在一种傲慢，但同时我也感受到自己是其中的一部分，我的意思是，我讨厌用这个词。

So it's a kind of arrogance that exists there, but it's also being part of it, being feeling that I'm mean, I hate to use the phrase.

我与它融为一体。

I'm sort of at one with it.

那是维杰·巴拉苏布雷曼尼安和杰弗里·韦斯特。

That was Vijay Balasubramanian and Geoffrey West.

接下来，我们将探讨生命是如何起源的，以及它在外星球上可能是什么样子。

Time, we'll ask how life began and what it might look like outside our own planet.

因此，信息、时间和物质在本质上是同一回事，它们以深度进化的产物形式呈现出来。

And so information and time and matter are all kind of the same thing in assembly, and they're manifest in objects that are deep products of evolution.

下一期《复杂性》将为您揭晓。

That's next time on Complexity.

《复杂性》是圣塔菲研究所的官方播客。

Complexity is the official podcast of the Santa Fe Institute.

本集由凯瑟琳·蒙库尔制作，主题曲由米奇·米尼亚诺创作。

This episode was produced by Katherine Moncure, and our theme song is by Mitch Mignano.

额外音效来自 Blue Dot Sessions、Pink House Music、Eardeer 和 Craig Smith。

Additional sounds from Blue Dot Sessions, Pink House Music, Eardeer, and Craig Smith.

我是阿巴，我们下期再见。

I'm Abha, and we'll see you next time.