Lex Friedman 访谈 Leonard Susskind 量子力学、弦理论和黑洞- 中英双语

伦纳德·萨斯坎德访谈 20个要点总结：

费曼的影响:

1. 费曼以深刻的直觉方式进行物理研究，通过可视化绕过复杂的数学论证。

2. 费曼的成功证实了萨斯坎德自己的直觉性思维方式的有效性。

物理直觉:

1. 萨斯坎德倾向于先可视化物理现象，然后才将其转化为数学语言。

2. 尽管现代物理学很多概念违反直觉，但随着时间的推移，人们可以发展出新的直觉。

量子力学与大脑:

1. 人脑的神经连接使我们能够理解经典物理世界，但不一定能自然理解量子力学。

2. 萨斯坎德怀疑人脑是否以量子力学的方式运作，但希望未来能有更多神经科学家的参与研究。

量子计算机:

1. 量子计算机是真正的量子系统，可以执行量子操作，并满足不确定性原理。

2. 量子计算机在模拟量子系统方面具有巨大潜力，尤其是在经典计算机难以模拟的领域。

学术界:

1. 萨斯坎德在学术界曾感到格格不入，直到 50 岁左右才突然成为物理学界的核心人物。

2. 他认为物理学家的思维方式对机器学习领域将有很大价值。

弦理论:

1. 萨斯坎德不喜欢被称为“弦理论家”，更愿意被视为研究基本物理问题的理论物理学家。

2. 弦理论的主要价值在于其数学上的严谨性，它证明了量子力学和引力可以共存。

现实的本质:

1. 萨斯坎德的友人 ’t Hooft 认为量子力学可能由更深层的经典、确定性结构涌现而来。

2. 萨斯坎德对自由意志、意识、自我等概念的幻觉感到困惑，认为物理系统如何产生这些幻觉是一个谜。

时间与宇宙:

1. 时间之箭是热力学概念，只在宏观系统中出现。

2. 在实验室中，可以逆转中等规模系统的熵增，但无法逆转大型系统。

3. 可以模拟反德西特空间的宇宙，但目前尚不清楚如何模拟德西特空间的宇宙。

4. 萨斯坎德倾向于认为宇宙在时间上是无限的。

黑洞与科学的未来:

1. 黑洞的图像证实了爱因斯坦引力理论的正确性，是一项伟大的科学成就。

2. 萨斯坎德希望未来科学能够解答意识的本质，并认为计算机科学家和神经科学家将在其中发挥重要作用

伦纳德·萨斯坎德访谈中英对照全文：

Lex: The following is a conversation with Leonard Susskind. He’s a professor of theoretical physics at Stanford University and the founding director of the Stanford Institute for Theoretical Physics. He’s widely regarded as one of the fathers of string theory and, in general, is one of the greatest physicists of our time, both as a researcher and an educator. This is the Artificial Intelligence Podcast. Perhaps you’ve noticed that the people I’ve been speaking with are not just computer scientists, but also philosophers, mathematicians, writers, psychologists, physicists, and soon, other disciplines too. To me, AI is much bigger than deep learning, bigger than computing. It is our civilization’s journey into understanding the human mind and creating echoes of it in the machine. If you enjoy the podcast, subscribe on YouTube, give it five stars on iTunes, support it on Patreon, or simply connect with me on Twitter, @lexfridman, spelled F-R-I-D-M-A-N. And now, here’s my conversation with Leonard Susskind.

莱克斯：接下来是对伦纳德·萨斯坎德的访谈。他是斯坦福大学的理论物理学教授，也是斯坦福理论物理研究所的创始主任。他被广泛认为是弦理论之父之一，总的来说，他是我们这个时代最伟大的物理学家之一，无论作为研究人员还是教育家。这是人工智能播客。也许你已经注意到，我一直与之交谈的人不仅是计算机科学家，还有哲学家、数学家、作家、心理学家、物理学家，以及很快，其他学科的专家。对我来说，人工智能远比深度学习、远比计算更大。这是我们文明理解人类思维并在机器中创造其回声的旅程。如果你喜欢这个播客，请在YouTube上订阅，在iTunes上给它五星好评，在Patreon上支持它，或者简单地在Twitter上联系我，@lexfridman，拼写为F-R-I-D-M-A-N。现在，这是我和伦纳德·萨斯坎德的对话。

Lex: You worked and were friends with Richard Feynman. How has he influenced you or changed you as a physicist and thinker?

莱克斯：你和理查德·费曼一起工作，并且是朋友。他作为物理学家和思想家，是如何影响你或改变你的？

Leonard: What I saw—I think what I saw—was somebody who could do physics in this deeply intuitive way. His style was almost to close his eyes and visualize the phenomena that he was thinking about, and through visualization, outflank the mathematical, highly mathematical, and very, very sophisticated technical arguments that people would use. I think that was also natural to me, but I saw somebody who was actually successful at it, who could do physics in a way that I regarded as simpler, more direct, more intuitive. And while I don’t think he changed my way of thinking, I do think he validated it. He made me look at it and say, “Yeah, that’s something you can do and get away with—practically even get away with it.”

伦纳德：我所看到的——我认为我所看到的——是一个能够以这种深刻直觉的方式进行物理研究的人。他的风格几乎是闭上眼睛，想象他正在思考的现象，并通过想象，绕过人们会使用的高度数学化、非常非常复杂的专业论证。我认为这对我来说也很自然，但我看到了一个在这方面真正成功的人，他能够以一种我认为更简单、更直接、更直观的方式进行物理研究。虽然我不认为他改变了我的思维方式，但我确实认为他证实了它。他让我看着它说，“是的，这是一件你可以做并且可以侥幸成功的事情——实际上甚至可以侥幸成功。”

Lex: So, do you find yourself, whether you’re thinking about quantum mechanics or black holes or string theory, using intuition as a first step or step throughout? Using visualization?

莱克斯：所以，无论你是在思考量子力学、黑洞还是弦理论，你是否发现自己都将直觉作为第一步或贯穿始终的步骤？使用可视化？

Leonard: Yeah, very much so. Very much so. I tend not to think about the equations; I tend not to think about the symbols. I tend to try to visualize the phenomena themselves, and then when I get an insight that I think is valid, I might try to convert it to mathematics. But I’m not a natural mathematician. I’m good enough at it, but I’m not a great mathematician. So, for me, the way of thinking about physics is first intuitive, first visualization, scribble a few equations maybe, but then try to convert it to mathematics. Other people are better at converting it into mathematics than I am.

伦纳德：是的，非常是这样。非常是这样。我倾向于不去思考方程式；我倾向于不去思考符号。我倾向于尝试想象现象本身，然后当我得到我认为有效的见解时，我可能会尝试将其转化为数学。但我不是一个天生的数学家。我足够擅长它，但我不是一个伟大的数学家。所以，对我来说，思考物理的方式首先是直观的，首先是可视化的，也许会草草写下几个方程式，然后尝试将其转化为数学。其他人比我更擅长将其转化为数学。

Lex: And yet you’ve worked with very counterintuitive ideas. So how—

莱克斯：然而，你一直在研究非常违反直觉的想法。那么如何——

Leonard: That’s true.

伦纳德：那是真的。

Lex: How do you do that? Is there a rewiring of your brain in new ways?

莱克斯：你是怎么做到的？你的大脑是否以新的方式重新连接？

Leonard: Yeah, quantum mechanics is not intuitive. Very little of modern physics is intuitive. What does “intuitive” mean? It means the ability to think about it with basic classical physics, the physics that we evolved with—throwing stones, splashing water, or whatever it happens to be. Quantum physics, general relativity, quantum field theory are deeply unintuitive in that way. But, you know, after time and getting familiar with these things, you develop new intuitions. I always said you rewire, and it’s to the point where many of my friends and I can think more easily quantum-mechanically than we can classically. We’ve gotten so used to it.

伦纳德：是的，量子力学不直观。现代物理学中很少有直观的东西。“直观”是什么意思？它指的是用基本的经典物理学来思考它的能力，也就是我们进化而来的物理学——扔石头、泼水，或者其他任何事情。量子物理学、广义相对论、量子场论在那种意义上是极不直观的。但是，你知道，随着时间的推移和对这些事物的熟悉，你会发展出新的直觉。我一直说你会重新连接，以至于我和我的许多朋友可以更容易地用量子力学的方式思考，而不是用经典的方式思考。我们已经非常习惯它了。

Lex: I mean, yes, our neural wiring in our brain is such that we understand rocks and stones and water and so on, sort of evolved for—

莱克斯：我的意思是，是的，我们大脑中的神经连接使我们能够理解岩石、石头和水等等，这些都是为——进化而来的。

Leonard: Right.

伦纳德：对。

Lex: Do you think it’s possible to create a wiring of neuron-like state devices that more naturally understand quantum mechanics, understand wave function, understand these weird things?

莱克斯：你认为有可能创造一种类似神经元的设备连接，更自然地理解量子力学，理解波函数，理解这些奇怪的东西吗？

Leonard: Well, I’m not sure. I think many of us have evolved the ability to think quantum mechanically to some extent. But that doesn’t mean you can think like an electron. That doesn’t mean—another example, forget for a minute quantum mechanics—just visualizing four-dimensional space or five-dimensional space or six-dimensional space. I think we’re fundamentally wired to visualize three dimensions. I can’t even visualize two dimensions or one dimension without thinking about it as embedded in three-dimensional space. If I want to visualize a line, I think of the line as being a line in three dimensions, right? Or I think of the line as being a line on a piece of paper, with the piece of paper being in three dimensions. I never seem to be able to, in some abstract and pure way, visualize in my head the one dimension, the two dimensions, the four dimensions, the five dimensions. And I don’t think that’s ever gonna happen. The reason is, I think, our neural wiring is just set up for that. On the other hand, we do learn ways to think about five, six, seven dimensions, and we learn mathematical ways, and we learn ways to visualize them, but they’re different. And so, yeah, I think we do rewire ourselves. Whether we can ever completely rewire ourselves to be completely comfortable with these concepts, I doubt—so that it’s completely natural, that it’s completely natural.

伦纳德：嗯，我不确定。我认为我们许多人在某种程度上已经进化出了量子力学思考的能力。但这并不意味着你可以像电子一样思考。这并不意味着——另一个例子，暂时忘记量子力学——仅仅是想象四维空间或五维空间或六维空间。我认为我们从根本上就被连接成可以想象三维空间。我甚至无法想象二维或一维，而不把它想象成嵌入在三维空间中。如果我想象一条线，我就会把它想象成三维空间中的一条线，对吧？或者我把它想象成一张纸上的一条线，而这张纸是在三维空间中的。我似乎从来没有能够以某种抽象和纯粹的方式，在我的脑海中想象一维、二维、四维、五维。我认为这永远不会发生。原因是，我认为，我们的神经连接就是为此而建立的。另一方面，我们确实学习了思考五维、六维、七维的方法，我们学习了数学方法，我们学习了想象它们的方法，但它们是不同的。所以，是的，我认为我们确实在重新连接自己。我们是否能够完全重新连接自己，以完全适应这些概念，我表示怀疑——以至于它是完全自然的，它是完全自然的。

Lex: So, I’m sure there’s some—you could argue, creatures that live in a two-dimensional space.

莱克斯：所以，我敢肯定，有一些——你可以争论，生活在二维空间的生物。

Leonard: Yeah.

伦纳德：是的。

Lex: And there are—

莱克斯：而且还有——

Leonard: Well, it’s romanticizing the notion. Of course, we’re all living, as far as we know, in three-dimensional space.

伦纳德：嗯，这是一种浪漫化的想法。当然，据我们所知，我们都生活在三维空间中。

Lex: But how do those creatures imagine 3D space?

莱克斯：但是那些生物是如何想象三维空间的呢？

Leonard: Well, probably the way we imagine 4D—by using some mathematics and some equations and some tricks.

伦纳德：嗯，可能就像我们想象四维空间一样——通过使用一些数学、一些方程式和一些技巧。

Lex: Okay, so jumping back to Feynman just for a second. He had a little bit of an ego.

莱克斯：好的，让我们回到费曼，就一会儿。他有点自负。

Leonard: Yes.

伦纳德：是的。

Lex: Do you think ego is powerful or dangerous in science?

莱克斯：你认为自负在科学中是强大的还是危险的？

Leonard: I think both. Both. Both. I think you have to have both arrogance and humility. You have to have the arrogance to say, “I can do this. Nature is difficult. Nature is very, very hard. I’m smart enough; I can do it. I can win the battle with nature.” On the other hand, I think you also have to have the humility to know that you’re very likely to be wrong on any given occasion. Everything you’re thinking could suddenly change. Young people can come along and say things you won’t understand, and you’ll be lost and flabbergasted. So I think it’s a combination of both. You better recognize that you’re very limited, and you better be able to say to yourself, “I’m not so limited that I can’t win this battle with nature.” It takes a special kind of person who can manage both of those.

伦纳德：我认为两者都有。两者都有。两者都有。我认为你必须既有傲慢又有谦逊。你必须有傲慢地说，“我可以做到这一点。自然界是困难的。自然界是非常非常艰难的。我足够聪明；我可以做到。我可以赢得与自然的战斗。”另一方面，我认为你也必须有谦逊地知道，你在任何特定情况下都很有可能犯错。你正在思考的一切都可能突然改变。年轻人可能会出现，说一些你无法理解的事情，而你会迷失方向，目瞪口呆。所以我认为这是两者的结合。你最好认识到自己非常有限，你最好能够对自己说，“我并没有那么有限，以至于我不能赢得这场与自然的战斗。”这需要一种特殊的人，能够同时驾驭这两种品质。

Lex: I would say there’s echoes of that in your own work. A little bit of ego, a little bit of outside-the-box humble thinking.

莱克斯：我想说，你自己的工作中也有这种体现。有点自负，有点跳出框框的谦逊思考。

Leonard: I hope so.

伦纳德：我希望如此。

Lex: So, was there a time where you felt—you looked at yourself and asked, “Am I completely wrong about this?”

莱克斯：那么，有没有那么一刻，你感到——你审视自己，问自己，“我在这件事上完全错了吗？”

Leonard: Oh yeah.

伦纳德：哦，是的。

Lex: The whole thing?

莱克斯：整件事？

Leonard: About specific things? The whole thing?

伦纳德：关于具体的事情？整件事？

Lex: The whole thing.

莱克斯：整件事。

Leonard: You mean me? Me and my ability to do this thing?

伦纳德：你是指我？我和我做这件事的能力？

Lex: Oh, those kinds of doubts?

莱克斯：哦，那种怀疑？

Leonard: Those kinds of doubts? First of all, did you have those kinds of doubts?

伦纳德：那种怀疑？首先，你有那种怀疑吗？

Lex: No.

莱克斯：没有。

Leonard: No? I had different kinds of doubts. I came from a very working-class background, and I was uncomfortable in academia for a long time. But they weren’t doubts about my ability or my—they were just the discomfort in being in an environment that my family hadn’t participated in and I knew nothing about. As a young person, I didn’t learn that there was such a thing called physics until I was almost 20 years old. So, I did have certain kinds of doubts, but not about my ability. I don’t think I was too worried about whether I would succeed or not. I never—I never felt this insecurity, “Am I ever gonna get a job?” That thought never occurred to me that I wouldn’t.

伦纳德：没有？我有不同种类的怀疑。我来自一个非常工薪阶层的背景，在很长一段时间里，我在学术界感到不舒服。但这并不是对我能力的怀疑，也不是对我——它们只是因为身处一个我的家庭从未参与过，而我一无所知环境中的不适。作为一个年轻人，我直到快20岁才了解到有物理学这回事。所以，我确实有一些疑问，但不是关于我的能力。我不认为我太担心自己是否会成功。我从来没有——我从来没有感到这种不安全感，“我还能找到工作吗？”我从来没有想过我不会找到工作。

Lex: Maybe you could speak a little bit to this sense of, what is academia for? Because I do feel a bit uncomfortable in it.

莱克斯：也许你可以谈谈这种感觉，学术界是干什么的？因为我确实在其中感到有些不自在。

Leonard: Mm-hmm.

伦纳德：嗯哼。

Lex: There’s something I can’t quite put into words.

莱克斯：有些事情我无法用语言表达。

Leonard: What do you have?

伦纳德：你有什么？

Lex: That’s not—

莱克斯：那不是——

Leonard: Doesn’t fit.

伦纳德：不合适。

Lex: If we call it music, you play a different kind of music than a lot of academia. How have you joined this orchestra? How do you think about it?

莱克斯：如果我们把它称为音乐，你演奏的音乐与许多学术界人士不同。你是如何加入这个乐队的？你是怎么想的？

Leonard: I don’t know that I thought about it as much as I just felt it. You know, thinking is one thing; feeling is another thing. I felt like an outsider until a certain age when I suddenly found myself the ultimate insider in academic physics. And that was a sharp transition.

伦纳德：我不知道我是否像我感受到的那样多地思考过它。你知道，思考是一回事；感觉是另一回事。我一直觉得自己像个局外人，直到某个年龄，我突然发现自己是学术物理学的终极局内人。那是一个急剧的转变。

Lex: In the world?

莱克斯：在世界上？

Leonard: I wasn’t a young man; I was probably 50 years old.

伦纳德：我不是一个年轻人；我当时可能50岁了。

Lex: You were never quite in the middle?

莱克斯：你从来没有真正处于中间位置？

Leonard: It was a phase transition.

伦纳德：那是一个相变。

Lex: You were never quite free milk in the middle?

莱克斯：你从来没有真正处于中间的免费牛奶？

Leonard: Yeah, that’s right. I wasn’t. I always felt a little bit of an outsider. In the beginning, a lot of an outsider. My way of thinking was different, my approach to mathematics was different, but also my social background that I came from was different. Now, these days, half the young people I meet, their parents were professors. That was not my case. So, yeah. But then all of a sudden, at some point, I found myself at very much the center of—maybe not the only one at the center, but certainly one of the people in the center of a certain kind of physics. And all that went away—I mean, it went away in a flash.

伦纳德：是的，没错。我没有。我一直觉得自己有点像个局外人。一开始，我感觉自己像个局外人。我的思维方式不同，我处理数学的方式不同，我的社会背景也不同。现在，我遇到的年轻人中，有一半的父母是教授。我的情况并非如此。所以，是的。但后来突然之间，在某个时刻，我发现自己处于——也许不是唯一一个处于中心的人，但肯定是某种物理学中心的人之一。所有这些都消失了——我的意思是，它在一瞬间就消失了。

Lex: So, maybe a little bit with Feynman, but in general, how do you develop ideas? Do you work through ideas alone? Do you brainstorm with others?

莱克斯：所以，也许和费曼有点像，但总的来说，你是如何发展想法的？你是独自思考想法，还是与他人一起 brainstorming？

Leonard: Oh, both. Both. Very definitely both. Earlier in my career, I spent more time with myself. Now, because I’m at Stanford, because I have a lot of ex-students and people who are interested in the same thing I am, I spend a good deal of time, almost on a daily basis, interacting, brainstorming, as you said. It’s a very important part. I spend less time probably completely self-focused, sitting at the paper and just staring at it.

伦纳德：哦，两者都有。两者都有。绝对是两者都有。在我职业生涯的早期，我更多地是独自一人度过。现在，因为我在斯坦福大学，因为我有许多以前的学生和对我感兴趣的事情感兴趣的人，我花了大量的时间，几乎每天都在互动、brainstorming，正如你所说的那样。这是一个非常重要的部分。我可能花更少的时间完全专注于自己，坐在论文前，只是盯着它看。

Lex: What are your hopes for quantum computers? Machines that are based on—that have some elements of leveraging quantum mechanical ideas?

莱克斯：你对量子计算机有什么希望？基于——利用量子力学思想的机器？

Leonard: Yeah, it’s not just leveraging quantum mechanical ideas. You can simulate quantum systems on a classical computer. Simulate them means solve the Schrödinger equation for them or solve the equations of quantum mechanics on a computer, on a classical computer. But the classical computer is not doing—it’s not a quantum mechanical system itself. Of course, it is; everything is made of quantum mechanics. But it’s not functioning as a quantum system; it’s just solving equations. The quantum computer is truly a quantum system which is actually doing the things that you’re programming it to do. You want to program a quantum field theory? If you do it in classical physics, that program is not actually functioning in the computer as a quantum field theory; it’s just solving some equations. Physically, it’s not doing the things that the quantum system would do. The quantum computer is really a quantum mechanical system which is actually carrying out the quantum operations. You can measure it at the end. It intrinsically satisfies the uncertainty principle. It is limited in the same way that quantum systems are limited by uncertainty and so forth, and it really is a quantum system. That means that what you’re doing when you program something for a quantum system is you’re actually building a real version of the system. The limits of a classical computer—classical computers are enormously limited when it comes to quantum systems. Enormously limited because—you’ve probably heard this before—but in order to store the amount of information that’s in a quantum state of 400 spins, that’s not very many. 400—I can put 400 pennies in my pocket. So, to be able to simulate the quantum state of 400 elementary quantum systems—qubits, we call them—to do that would take more information than can possibly be stored in the entire universe if it were packed so tightly that you couldn’t pack any more in. Right? 400 qubits. On the other hand, if your quantum computer is composed of 400 qubits, it can do everything 400 qubits can do.

伦纳德：是的，这不仅仅是利用量子力学思想。你可以在经典计算机上模拟量子系统。模拟它们意味着为它们求解薛定谔方程，或者在计算机上，在经典计算机上求解量子力学方程。但经典计算机并没有这样做——它本身不是一个量子力学系统。当然，它是；一切都是由量子力学构成的。但它并没有像量子系统那样运作；它只是在求解方程式。量子计算机是一个真正的量子系统，它实际上在做你编程让它做的事情。你想编写一个量子场论程序吗？如果你用经典物理学来做，那么这个程序实际上并没有在计算机中像量子场论那样运作；它只是在求解一些方程式。从物理上讲，它并没有做量子系统会做的事情。量子计算机实际上是一个量子力学系统，它实际上是在执行量子操作。你可以在最后测量它。它本质上满足不确定性原理。它与量子系统受不确定性等因素的限制方式相同，它确实是一个量子系统。这意味着当你为一个量子系统编写程序时，你实际上是在构建该系统的真实版本。经典计算机的局限性——当涉及到量子系统时，经典计算机的局限性非常大。局限性非常大，因为——你可能以前听过这个——但为了存储 400 个自旋的量子态中的信息量，这并不多。400——我可以把 400 枚便士放在我的口袋里。因此，为了能够模拟 400 个基本量子系统（我们称之为量子比特）的量子态，这将需要比整个宇宙中可能存储的信息还要多，如果它被紧紧地压缩在一起，你无法再压缩更多。对吧？400 个量子比特。另一方面，如果你的量子计算机由 400 个量子比特组成，它可以做 400 个量子比特可以做的所有事情。

Lex: What kind of space, if you just intuitively think about the space of algorithms, that that unlocks for us? So, there’s a whole complexity theory around classical computers, measuring the running time of things and P versus NP, and so on. What kind of algorithms, just intuitively, do you think it unlocks for us?

莱克斯：如果只是直观地思考算法的空间，它会为我们解锁什么样的空间？围绕经典计算机，有一整套复杂性理论，测量事物的运行时间，以及 P 与 NP 的比较等等。你认为它直观地为我们解锁了什么样的算法？

Leonard: Okay, so we know that there are a handful of algorithms that can seriously beat classical computers and which can have exponentially more power. And this is a mathematical statement. Nobody’s exhibited this in the laboratory; it’s a mathematical statement. We know that’s true. But it also seems, more and more, that the number of such things is very limited. Only very, very special problems exhibit that much advantage for a quantum computer. Others are standard problems. To my mind, as far as I can tell, the great power of quantum computers will actually be to simulate quantum systems. If you’re interested in a certain quantum system and it’s too hard to simulate classically, you simply build a version of the same system. You build a version of it; you build a model of it that’s actually functioning as the system. You run it, and then you do the same thing you would do with the quantum system—you make measurements on it, quantum measurements on it. The advantage is you can run it much slower. You could say, “Why bother? Why not just use the real system? Why not just do experiments on the real system?” Well, real systems are kind of limited. You can’t change them, you can’t manipulate them, you can’t slow them down so that you can poke into them, you can’t modify them in arbitrary kinds of ways to see what would happen if I change the system a little bit. So, I think that quantum computers will be extremely valuable in understanding quantum systems at the lowest fundamental laws. They’re actually satisfying the same laws as the systems that they’re simulating.

伦纳德：好的，所以我们知道有少数算法可以严重击败经典计算机，并且可以拥有指数级增长的能力。这是一个数学陈述。没有人能在实验室里展示这一点；这是一个数学陈述。我们知道这是真的。但似乎也越来越明显，这类东西的数量非常有限。只有非常非常特殊的问题才能体现出量子计算机的巨大优势。其他的都是标准问题。在我看来，据我所知，量子计算机的巨大威力实际上在于模拟量子系统。如果你对某个量子系统感兴趣，而它又太难用经典方法模拟，你只需构建同一个系统的版本。你构建它的一个版本；你构建它的一个模型，它实际上就像系统一样运作。你运行它，然后你做与量子系统相同的事情——你在它上面进行测量，量子测量。这样做的好处是你可以运行得慢得多。你可能会说，“为什么要麻烦呢？为什么不直接使用真实的系统？为什么不直接在真实的系统上做实验呢？”嗯，真实的系统是有局限性的。你不能改变它们，你不能操纵它们，你不能让它们慢下来以便你能探究它们，你不能以任意的方式修改它们，看看如果我稍微改变一下系统会发生什么。因此，我认为量子计算机在理解最低基本定律下的量子系统方面将非常有价值。它们实际上满足与它们正在模拟的系统相同的定律。

Lex: That’s right. Okay, so on the one hand, you have things like factoring. Factoring is the great thing of quantum computers—factoring large numbers. That doesn’t seem to have much to do with quantum mechanics, right? It seems to be almost a fluke that a quantum computer can solve the factoring problem in a short time. And those problems seem to be extremely special, rare, and it’s not clear to me that there’s gonna be a lot of them. On the other hand, there are a lot of quantum systems—chemistry, solid-state physics, material science, quantum gravity, quantum field theory. Some of these are actually turning out to be applied sciences as well as very fundamental sciences. So, we probably will run out of the ability to solve equations for these things. You know, solve equations by the standard methods of pencil and paper, and solve the equations by the method of classical computers. And so what we’ll do is we’ll build versions of these systems, run them, and run them under controlled circumstances where we can change them, manipulate them, make measurements on them, and find out all the things we want to know.

莱克斯：没错。好的，一方面，你有像分解质因数这样的东西。分解质因数是量子计算机的伟大之处——分解大数。这似乎与量子力学没有太大关系，对吧？量子计算机能够在短时间内解决分解质因数问题，这似乎几乎是一个侥幸。而这些问题似乎极其特殊、罕见，我不清楚是否会有很多这样的问题。另一方面，有很多量子系统——化学、固态物理学、材料科学、量子引力、量子场论。其中一些实际上正逐渐成为应用科学以及非常基础的科学。因此，我们可能会失去解决这些问题的能力。你知道，用铅笔和纸的标准方法求解方程，以及用经典计算机的方法求解方程。因此，我们将要做的是构建这些系统的版本，运行它们，并在我们可以改变它们、操纵它们、对它们进行测量并找出我们想知道的所有事情的可控制环境下运行它们。

Lex: So, in finding out the things we want to know about very small systems, right now—is there something we can also find out about the macro level, about something about the function, and forgive me, of our brain, biological systems, the stuff that’s about one meter in size versus much, much smaller?

莱克斯：所以，在找出我们想知道的关于非常小的系统的事情时，现在——我们是否也可以找到关于宏观层面的一些东西，关于功能的一些东西，请原谅我，关于我们的大脑、生物系统，那些大约一米大小的东西，而不是小得多得多？

Leonard: Well, the only excitement is about, among the people that I interact with, is understanding black holes.

伦纳德：嗯，在我与之互动的人中，唯一令人兴奋的事情是理解黑洞。

Lex: That falls on the side of—

莱克斯：那属于——

Leonard: Black holes are big things. There are many, many degrees of freedom. There is another kind of quantum system that is big—it’s a large quantum computer. And one of the things we’ve learned is that the physics of large quantum computers is, in some ways, similar to the physics of large quantum black holes. And we’re using that relationship now. You asked—you didn’t ask about quantum computers or systems; you didn’t ask about black holes; you asked about brains.

伦纳德：黑洞是大东西。它们有许多许多自由度。还有另一种量子系统也很大——它是一个大型量子计算机。我们学到的一件事是，大型量子计算机的物理学在某些方面与大型量子黑洞的物理学相似。我们现在正在利用这种关系。你问了——你没有问量子计算机或系统；你没有问黑洞；你问了大脑。

Lex: Yeah, about stuff that’s in the middle of the two.

莱克斯：是的，关于介于两者之间的东西。

Leonard: It’s different. So, but black holes are—

伦纳德：这是不同的。所以，但是黑洞是——

Lex: There’s something fundamental about black holes that feels to be very different than the brain.

莱克斯：黑洞有一些根本的东西，感觉与大脑非常不同。

Leonard: Yes, and they also function in a very quantum mechanical way, right? Okay, it is first of all unclear to me—but of course, it’s unclear to me. I’m not a neuroscientist. I don’t even have very many friends who are neuroscientists. I would like to have more friends who are neuroscientists; I just don’t run into them very often. Among the few neuroscientists I’ve ever talked to about this, they are pretty convinced that the brain functions classically. It is not intrinsically a quantum mechanical system or doesn’t make use of the special features—entanglement, coherent superposition. Are they right? I don’t know. I sort of hope they’re wrong just because I like the romantic idea that the brain is a quantum system, but I think that—probably not. The other thing, big systems can be composed of lots of little systems. Materials—the materials that we work with and so forth are big systems and a large piece of material, but they’re made out of quantum systems. Now, one of the things that’s been happening over the last a good number of years is we’re discovering materials and quantum systems which function much more quantum mechanically than we imagined—topological insulators, this kind of thing, that kind of thing. Those are macroscopic systems, but they—just superconductors. Superconductors have a lot of quantum mechanics in them. You can have a large chunk of superconductor, so it’s a big piece of material. On the other hand, it’s functioning, and its properties depend very, very strongly on quantum mechanics, and to analyze them, you need the tools of quantum mechanics.

伦纳德：是的，它们也以一种非常量子力学的方式运作，对吧？好的，首先我不清楚——当然，我不清楚。我不是神经科学家。我甚至没有很多神经科学家朋友。我希望有更多神经科学家朋友；我只是不经常遇到他们。在我与之谈论过这个问题的少数神经科学家中，他们非常确信大脑是以经典方式运作的。它本质上不是一个量子力学系统，或者没有利用特殊的功能——纠缠、相干叠加。他们是对的吗？我不知道。我有点希望他们是错的，只是因为我喜欢大脑是一个量子系统这个浪漫的想法，但我认为——可能不是。 另一件事，大型系统可以由许多小型系统组成。材料——我们使用的材料等等都是大型系统和一大块材料，但它们是由量子系统构成的。现在，在过去许多年中发生的事情之一是，我们正在发现比我们想象的更具量子力学功能的材料和量子系统——拓扑绝缘体，这类东西，那类东西。这些是宏观系统，但它们——只是超导体。超导体中包含很多量子力学。你可以有一大块超导体，所以它是一大块材料。另一方面，它正在运作，它的特性非常非常强烈地依赖于量子力学，要分析它们，你需要量子力学的工具。

Lex: If we can go on to black holes and looking at the universe as an information processing system, as a computer, as a giant computer, what’s the power of thinking of the universe as an information processing system? But what is perhaps its use, besides the mathematical use of discussing black holes and your famous debates and ideas around that, to human beings or life in general as information processing systems?

莱克斯：如果我们可以继续讨论黑洞，并将宇宙视为一个信息处理系统，一台计算机，一台巨型计算机，那么将宇宙视为信息处理系统的力量是什么？除了讨论黑洞及其周围的著名辩论和想法的数学用途之外，它对人类或一般生命作为信息处理系统的用途是什么？

Leonard: Well, all systems are information processing systems. You poke them, they change a little bit, they evolve. All systems are information processing systems.

伦纳德：嗯，所有系统都是信息处理系统。你戳它们，它们会发生一点变化，它们会进化。所有系统都是信息处理系统。

Lex: There’s no extra magic to us humans. It certainly feels—

consciousness, intelligence—feels like magic.

莱克斯：我们人类没有额外的魔力。它当然感觉——意识、智力——感觉像是魔法。

Leonard: Sure, though.

伦纳德：当然，不过。

Lex: Where does it emerge from? If we look at information processing, what are the emergent phenomena that come from viewing the world as an information processing system?

莱克斯：它是从哪里出现的？如果我们着眼于信息处理，将世界视为信息处理系统，会出现哪些涌现现象？

Leonard: Here is what I think. My thoughts are not worth much on this. If you ask me about physics, my thoughts may be worth something. If you ask me about this, I’m not sure my thoughts are worth anything. But as I said earlier, I think when we do introspection, when we imagine doing introspection and try to figure out what it is we’re doing when we’re thinking, I think we get it wrong. I’m pretty sure we get it wrong. Everything I’ve heard about the way the brain functions is so counterintuitive. For example, you have neurons which detect vertical lines, you have different neurons which detect lines at 45 degrees, you have different neurons—I never imagined that there were whole circuits which were devoted to vertical lines in the brain. It doesn’t seem to—when my brain works, my brain seems to work—put my finger up vertically, or if I put it horizontally, or if I put it this way or that way, it seems to me it’s the same circuits that are—but it’s not the way it works. The way the brain is compartmentalized seems to be very, very different than what I would have imagined if I were just doing psychological introspection about how things work. My conclusion is that we won’t get it right that way.

伦纳德：这是我的想法。我的想法对此没有什么价值。如果你问我物理学方面的问题，我的想法可能有些价值。如果你问我这个问题，我不确定我的想法是否有什么价值。但正如我之前所说，我认为当我们进行内省时，当我们想象进行内省并试图弄清楚我们在思考时在做什么时，我认为我们弄错了。我很确定我们弄错了。我所听到的关于大脑运作方式的一切都非常违反直觉。例如，你有检测垂直线的神经元，你有检测 45 度线的神经元，你有不同的神经元——我从未想象过大脑中会有专门用于垂直线的整个回路。它似乎不是——当我的大脑工作时，我的大脑似乎在工作——垂直举起我的手指，或者如果我水平举起它，或者如果我这样或那样举起它，在我看来，它是相同的回路——但这不是它的工作方式。大脑划分区域的方式似乎与我仅仅通过心理内省来思考事物如何运作时想象的非常非常不同。我的结论是，我们不会以这种方式得到正确的答案。

Lex: So how will we get it right?

莱克斯：那么我们将如何得到正确的答案？

Leonard: I think maybe computer scientists will get it right eventually. I don’t think that day is near. I don’t even think they’re thinking about it. But by—eventually, we will build machines, perhaps, which are complicated enough, and partly engineered, partly evolved—maybe evolved by machine learning and so forth. This machine learning is very interesting. By machine learning, we’ll evolve systems, and we may start to discover mechanisms that have implications for how we think and for what this consciousness thing is all about. And we’ll be able to do experiments on them and perhaps answer questions that we can’t possibly answer by introspection.

伦纳德：我认为也许计算机科学家最终会得到正确的答案。我不认为那一天会很快到来。我甚至不认为他们在考虑这个问题。但是通过——最终，我们可能会建造足够复杂的机器，部分是工程化的，部分是进化而来的——也许是通过机器学习等进化而来的。这种机器学习非常有趣。通过机器学习，我们将进化系统，我们可能会开始发现对我们如何思考以及这种意识到底是什么有影响的机制。我们将能够对它们进行实验，并可能回答我们不可能通过内省回答的问题。

Lex: So that’s a really interesting point. In many cases, if you look even at string theory, when you first think about a system, it seems really complicated, like the human brain. And through some basic reasoning, then trying to discover a fundamental low-level behavior of the system, you find out that it’s actually much simpler. One, is that generally the process? And two, do you have that also hope for biological systems as well, for all the kinds of stuff we’re studying at the human level?

莱克斯：这是一个非常有趣的观点。在许多情况下，即使你看看弦理论，当你第一次思考一个系统时，它看起来非常复杂，就像人脑一样。通过一些基本的推理，然后尝试发现系统的基本底层行为，你会发现它实际上要简单得多。第一，这通常是过程吗？第二，你是否也对生物系统抱有同样的希望，对我们在人类层面研究的所有类型的东西？

Leonard: Of course, physics always begins by trying to find the simplest version of something and analyzing it. Yeah, I mean, there are lots of examples where physics has taken very complicated systems, analyzed them, and found simplicity in them, for sure. I said superconductors before. It’s an obvious one. A superconductor seems like a monstrously complicated thing with all sorts of crazy electrical properties, magnetic properties, and so forth. And when it finally is boiled down to its simplest elements, it’s a very simple quantum mechanical phenomenon called spontaneous symmetry breaking, which we, in another context, learned about and were very familiar with. So yeah, I mean, yes, we do take complicated things, make them simple. But what we don’t want to do is take things which are intrinsically complicated and fool ourselves into thinking that we can make them simple. We don’t want to make—I don’t know who said this—but we don’t want to make them simpler than they really are. Right?

伦纳德：当然，物理学总是从尝试找到某事物的最简单版本并对其进行分析开始。是的，我的意思是，有很多例子表明，物理学已经研究了非常复杂的系统，对它们进行了分析，并肯定在其中发现了简单性。我之前说过超导体。这是一个明显的例子。超导体看起来像一个极其复杂的东西，具有各种疯狂的电学特性、磁学特性等等。当它最终被归结为其最简单的元素时，它是一个非常简单的量子力学现象，称为自发对称性破缺，我们在另一个背景下了解了它，并且非常熟悉它。所以是的，我的意思是，是的，我们确实把复杂的事情简单化了。但我们不想做的是，把本质上复杂的东西简单化，然后愚弄自己，认为我们可以把它们简单化。我们不想让——我不知道是谁说的——但我们不想让它们变得比它们实际更简单。对吧？

Lex: Right.

莱克斯：对。

Leonard: Okay. Is the brain a thing which ultimately functions by some simple rules, or is it just complicated?

伦纳德：好的。大脑是一个最终通过一些简单规则运作的东西，还是它只是复杂？

Lex: In terms of artificial intelligence, nobody really knows what are the limits of our current approaches. You mentioned machine learning. How do we create human-level intelligence? It seems that there are a lot of very smart physicists who perhaps oversimplify the nature of intelligence and think of it as information processing, and therefore, that it doesn’t seem to be any theoretical reason why we can’t artificially create a human-level or superhuman-level intelligence. In fact, the reasoning goes, if you create human-level intelligence, the same approach you just used to create human-level intelligence should allow you to create superhuman-level intelligence very easily, exponentially. So, what do you think that way of thinking that comes from physicists is all about?

莱克斯：就人工智能而言，没有人真正知道我们目前方法的局限性是什么。你提到了机器学习。我们如何创造人类水平的智能？似乎有很多非常聪明的物理学家可能过于简化了智能的本质，并将其视为信息处理，因此，似乎没有任何理论上的理由解释为什么我们不能人为地创造人类水平或超人水平的智能。事实上，推理是这样的，如果你创造了人类水平的智能，那么你刚刚用来创造人类水平的智能的相同方法应该可以让你非常容易地、指数级地创造超人水平的智能。那么，你认为这种来自物理学家的思维方式到底是什么？

Leonard: I wish I knew, but there’s a particular reason why I wish I knew. I have a second job. I consult for Google—not for Google, for Google X. I am the senior academic advisor to a group of machine learning physicists. Now, that sounds crazy because I know nothing about the subject. I know very little about the subject. On the other hand, I’m good at giving advice, so I give them advice on things. Anyway, I see these young physicists who are approaching the machine learning problem. There is a myth; there is a real machine learning problem, namely, why does it work as well as it does? Nobody really seems to understand why it is capable of doing the kind of generalizations that it does and so forth. And there are three groups of people who have thought about this. There are the engineers. The engineers are incredibly smart, but they tend not to think as hard about why the thing is working as much as they do how to use it. Obviously, they provided a lot of data, and it is they who demonstrated that machine learning can work much better than you have any right to expect. The machine learning systems are systems that the system is not too different than the kind of systems physicists study. There’s not all that much difference between quantum construction of mathematics—physically, yes, but in the structure of the mathematics—between a tensor network designed to describe a quantum system on the one hand and the kind of networks that are used in machine learning. So, there are more and more, I think, young physicists being drawn to this field of machine learning—some very, very good ones. I work with a number of very good ones—not on machine learning, but having lunch.

伦纳德：我希望我知道，但我希望知道有一个特殊的原因。我有第二份工作。我为谷歌做咨询——不是为谷歌，而是为谷歌 X。我是一组机器学习物理学家的高级学术顾问。现在，这听起来很疯狂，因为我对这个主题一无所知。我对这个主题知之甚少。另一方面，我擅长提建议，所以我给他们提建议。无论怎样，我看到这些年轻的物理学家正在研究机器学习问题。有一个神话；有一个真正的机器学习问题，即为什么它能如此出色地工作？似乎没有人真正理解为什么它能够进行那种概括等等。有三组人考虑过这个问题。有工程师。工程师非常聪明，但他们往往不会像思考如何使用它那样努力思考为什么它能工作。显然，他们提供了大量的数据，正是他们证明了机器学习可以比你预期的好得多。机器学习系统是系统，该系统与物理学家研究的系统类型并没有太大区别。在一方面设计用于描述量子系统的张量网络与机器学习中使用的网络类型之间，在量子数学结构——物理上，是的，但在数学结构上——没有那么大的区别。因此，我认为，越来越多的年轻物理学家被吸引到这个机器学习领域——一些非常非常优秀的物理学家。我和许多非常优秀的物理学家一起工作——不是在机器学习方面，而是在一起吃午饭。

Lex: On having lunch.

莱克斯：一起吃午饭。

Leonard: Yeah. And I can tell you, they are super smart. They don’t seem to be so arrogant about their physics backgrounds that they think they can do things that nobody else can do. But that physics way of thinking, I think, will add—I believe it will add great value to machine learning.

伦纳德：是的。我可以告诉你，他们非常聪明。他们似乎并没有因为自己的物理学背景而傲慢自大，认为自己可以做别人做不到的事情。但我认为，这种物理学的思维方式会增加——我相信它会为机器学习增加巨大的价值。

Lex: On what timescale do you think predicting the future becomes useless? In your long experience, in being surprised at new discoveries?

莱克斯：你认为在什么时间尺度上预测未来会变得无用？根据你长期以来的经验，你对新发现感到惊讶吗？

Leonard: Sometimes a day, sometimes 20 years. There are things which I thought we were very far from understanding, which practically in a snap of the fingers or a blink of the eye suddenly became understood—completely surprising to me. There are other things which I looked at and I said, “We’re not gonna understand these things for 500 years.” In particular, quantum gravity. The timescale for that was 20 years, 25 years. And we understand a lot. We don’t understand it completely by any means, but where I thought it was 500 years to make any progress, it turned out to be very, very far from that. It turned out to be more like 20 or 25 years from the time when I thought it was 500 years.

伦纳德：有时是一天，有时是 20 年。有些事情我以为我们还远未理解，但实际上，一眨眼之间，我们突然就理解了——这让我完全感到惊讶。还有其他的事情，我看了之后说，“我们 500 年内都不会理解这些事情。”特别是量子引力。它的时间尺度是 20 年、25 年。我们了解了很多。我们无论如何都没有完全理解它，但在我认为需要 500 年才能取得任何进展的地方，结果却与之相差甚远。事实证明，从我以为需要 500 年的时候算起，更像是 20 年或 25 年。

Lex: So, if we can jump around quantum gravity—some basic ideas in physics—what is the dream of string theory, mathematically? What is the hope? Where does it come from? What problem is it trying to solve?

莱克斯：所以，如果我们可以跳过量子引力——物理学中的一些基本概念——从数学上讲，弦理论的梦想是什么？希望是什么？它来自哪里？它试图解决什么问题？

Leonard: I don’t think the dream of string theory is any different than the dream of fundamental theoretical physics altogether—understanding a unified theory of everything. I don’t like thinking of string theory as a subject unto itself, with people called string theorists who are the practitioners of this thing called string theory. I much prefer to think of them as theoretical physicists trying to answer deep, fundamental questions about nature—in particular, gravity and its connection with quantum mechanics—who at the present time find string theory a useful tool, rather than saying there’s a subject called string theory.

伦纳德：我不认为弦理论的梦想与整个基础理论物理学的梦想有什么不同——理解一个统一的万物理论。我不喜欢把弦理论本身看作一个学科，把那些被称为弦理论家的人看作是这种叫做弦理论的东西的实践者。我更愿意把他们看作是试图回答关于自然的深刻、基本问题的理论物理学家——特别是引力及其与量子力学的联系——他们目前发现弦理论是一个有用的工具，而不是说有一个叫做弦理论的学科。

Lex: So, you don’t like being referred to as a string theorist?

莱克斯：所以，你不喜欢被称为弦理论家？

Leonard: No.

伦纳德：不。

Lex: But as a tool, is it useful to think about our nature in multiple dimensions, strings vibrating?

莱克斯：但是作为一种工具，在多维空间中思考我们的本质，弦的振动，有用吗？

Leonard: I believe it is useful. I’ll tell you what the main use of it has been up till now. Well, it has had a number of main uses. Originally, string theory was invented—and I know there; I was there, I was right at the spot where it was being invented, literally—and it was being invented to understand hadrons. Hadrons are sub-nuclear particles—protons, neutrons, mesons—and at that time, the late 60s, early 70s, it was clear from experiments that these particles called hadrons could vibrate, could rotate, could do all the things that a little closed string can do. And it was and is a valid and correct theory of these hadrons. It’s been experimentally tested, and that is a done deal. It had a second life as a theory of gravity. The same basic mathematics, except on a very, very much smaller distance scale. The objects of gravitation are 19 orders of magnitude smaller than a proton, but the same mathematics turned up. The same mathematics turned up. What has been its value? Its value is that it’s mathematically rigorous in many ways and enabled us to find mathematical structures which have both quantum mechanics and gravity with rigor. We can test out ideas. We can test out ideas—we can’t test them in the laboratory, they’re 19 orders of magnitude too small for the things that we’re interested in—but we can test them out mathematically and analyze their internal consistency. By now, 40 years ago, 35 years ago, and so forth, people very, very much questioned the consistency between gravity and quantum mechanics. Stephen Hawking was very famous for it, rightly so. Now, nobody questions that consistency anymore. They don’t because we have mathematically precise string theories which contain both gravity and quantum mechanics in a consistent way. So, it’s provided that certainty that quantum mechanics and gravity can coexist. That’s not a small thing.

伦纳德：我相信它是有用的。我会告诉你它到目前为止的主要用途是什么。嗯，它有很多主要用途。最初，弦理论被发明出来——我知道在那里；我就在那里，我就在它被发明出来的地方，毫不夸张地说——它被发明出来是为了理解强子。强子是亚核粒子——质子、中子和介子——在那个时候，60 年代末 70 年代初，从实验中可以清楚地看出，这些被称为强子的粒子可以振动，可以旋转，可以做所有小闭弦可以做的事情。它曾经是，现在仍然是关于这些强子的一个有效且正确的理论。它已经通过了实验检验，这是一个已经完成的交易。它作为引力理论获得了第二次生命。同样的基本数学，只是在非常非常小的距离尺度上。引力物体比质子小 19 个数量级，但出现了同样的数学。同样的数学出现了。它的价值是什么？它的价值在于它在许多方面在数学上是严格的，并且使我们能够找到严格地同时具有量子力学和引力的数学结构。我们可以检验想法。我们可以检验想法——我们不能在实验室里检验它们，因为它们比我们感兴趣的东西小 19 个数量级——但我们可以用数学方法检验它们，并分析它们的内部一致性。到目前为止，40 年前，35 年前等等，人们非常非常地质疑引力和量子力学之间的一致性。斯蒂芬·霍金因此而非常有名，这是理所当然的。现在，没有人再质疑这种一致性了。他们不再质疑，因为我们有数学上精确的弦理论，它以一致的方式包含了引力和量子力学。因此，它提供了量子力学和引力可以共存的确定性。这不是一件小事。

Lex: That’s a very huge thing.

莱克斯：那是一件非常大的事情。

Leonard: It’s a huge thing.

伦纳德：这是一件大事。

Lex: Einstein would be proud.

莱克斯：爱因斯坦会感到自豪的。

Leonard: Einstein might be appalled—I don’t know.

伦纳德：爱因斯坦可能会感到震惊——我不知道。

Lex: Or he might like it very much.

莱克斯：或者他可能会非常喜欢它。

Leonard: Yeah, he would certainly be struck by it. I think that maybe at this time, its biggest contribution to physics is illustrating almost definitively that quantum mechanics and gravity are very closely related and not inconsistent with each other.

伦纳德：是的，他肯定会对此感到震惊。我认为，也许在这个时候，它对物理学最大的贡献是几乎明确地说明了量子力学和引力之间有着非常密切的联系，并且彼此之间并不矛盾。

Lex: Is there a possibility of something deeper, more profound, that still is consistent with string theory but is deeper, that is to be found?

莱克斯：是否有可能存在更深层次、更深刻的东西，它仍然与弦理论一致，但更深层次，有待发现？

Leonard: Well, you could ask the same thing of quantum mechanics.

伦纳德：嗯，你可以对量子力学提出同样的问题。

Lex: Exactly.

莱克斯：没错。

Leonard: Yeah. I think string theory is just an example of a quantum mechanical system that contains both gravitation and quantum mechanics.

伦纳德：是的。我认为弦理论只是一个包含引力和量子力学的量子力学系统的例子。

Lex: So, is there something underlying quantum mechanics, perhaps something deterministic?

莱克斯：那么，量子力学背后是否存在某种东西，也许是某种确定性的东西？

Leonard: Perhaps something deterministic. My friend Gerard ’t Hooft, whose name you may know—he’s a very famous physicist, Dutch, not as famous as he should be, but the heart—

伦纳德：也许是某种确定性的东西。我的朋友 Gerard ’t Hooft，你可能知道他的名字——他是一位非常著名的物理学家，荷兰人，不像他应该的那样出名，但内心——

Lex: It’s because his name is hard to spell.

莱克斯：这是因为他的名字很难拼写。

Leonard: It’s hard to say his name. No, it’s easy to spell. He’s the only person I know whose name begins with an apostrophe. He’s one of my heroes in physics, and he’s a little younger than me, but he’s nonetheless one of my heroes. ’t Hooft believes that there’s some substructure to the world which is classical in character, deterministic in character, which somehow, by some mechanism that he has a hard time spelling out, emerges as quantum mechanics.

伦纳德：他的名字很难说。不，它很容易拼写。他是
我认识的唯一一个名字以撇号开头的人。他是我的物理学英雄之一，他比我年轻一点，但他仍然是我的英雄之一。’t Hooft 认为世界有一些亚结构，其特征是经典的，确定性的，它以某种方式，通过某种他很难解释的机制，涌现为量子力学。

Lex: So, the wavefunction is somehow emergent?

莱克斯：那么，波函数是以某种方式涌现出来的？

Leonard: The wavefunction, and not just the wavefunction, but the whole thing that goes with quantum mechanics—uncertainty, entanglement—all these things are emergent.

伦纳德：波函数，不仅仅是波函数，还有与量子力学相关的一切——不确定性、纠缠——所有这些都是涌现出来的。

Lex: Do you think quantum mechanics is the bottom of the well, as it is right now?

莱克斯：你认为量子力学是井底，就像现在这样吗？

Leonard: Here, I think, is where you have to be humble. Here’s where humility comes in. I don’t think anybody should say anything is the bottom of the well at this time. Yes, I think we can reasonably say, I can reasonably say, when I look into the well, I can’t see past quantum mechanics. I don’t see any reason for there to be anything beyond quantum mechanics. I think ’t Hooft is asking very interesting and deep questions. I don’t like his answers.

伦纳德：我认为，这就是你必须谦逊的地方。这就是谦逊的用武之地。我认为现在任何人都
不应该说任何事情是井底。是的，我认为我们可以合理地说，我可以合理地说，当我往井里看时，我无法看到量子力学之外的东西。我看不到任何理由认为量子力学之外还有其他东西。我认为 ’t Hooft 提出了非常有趣和深刻的问题。我不喜欢他的答案。

Lex: Again, let me ask: If we look at the deepest nature of reality, whether it’s deterministic or unobserved, it’s probabilistic. What does that mean for our human-level ideas of free will? Is there any connection whatsoever between this perception—perhaps illusion—of free will that we have and the fundamental nature of reality?

莱克斯：再次，让我问：如果我们看看现实最深层的本质，无论它是确定性的还是未被观察的，它都是概率性的。这对我们人类层面的自由意志观念意味着什么？我们对自由意志的这种感知——也许是幻觉——与现实的基本本质之间是否存在任何联系？

Leonard: The only thing I can say is I am as puzzled by that as much as you are. The illusion of it, the illusion of consciousness, the illusion of free will, the illusion of self—how can a physical system do that? And I am as puzzled as anybody.

伦纳德：我唯一能说的是，我和你一样对此感到困惑。它的幻觉，意识的幻觉，自由意志的幻觉，自我的幻觉——一个物理系统怎么能做到这一点？我和任何人都一样感到困惑。

Lex: There are echoes of it in the observer effect.

莱克斯：观察者效应中也有它的回声。

Leonard: Yeah.

伦纳德：是的。

Lex: So, do you understand what it means to be an observer?

莱克斯：那么，你理解作为观察者意味着什么吗？

Leonard: I understand it at a technical level. An observer is a system with enough degrees of freedom that it can record information and which can become entangled with the thing it’s measuring. Entanglement is the key. When a system, which we call an apparatus or an observer (same thing), interacts with the system that it’s observing, it doesn’t just look at it—it becomes physically entangled with it. And it’s that entanglement which we call an observation or measurement.

伦纳德：我从技术层面上理解它。观察者是一个具有足够自由度的系统，它可以记录信息，并且可以与它正在测量的事物纠缠在一起。纠缠是关键。当一个我们称为仪器或观察者（相同的东西）的系统与它正在观察的系统相互作用时，它不仅仅是看着它——它与它在物理上纠缠在一起。正是这种纠缠，我们称之为观察或测量。

Lex: Now, does that satisfy you personally as an observer?

莱克斯：现在，作为观察者，这是否让你个人感到满意？

Leonard: Hmm. Yes and no. I find it very satisfying that we have a mathematical representation of what it means to observe a system.

伦纳德：嗯。既是又不是。我发现我们有一个关于观察系统意味着什么的数学表示，这非常令人满意。

Lex: You are observing stuff right now.

莱克斯：你现在正在观察东西。

Leonard: Yeah.

伦纳德：是的。

Lex: At the conscious level.

莱克斯：在意识层面上。

Leonard: Right.

伦纳德：对。

Lex: Do you think there are echoes of that kind of entanglement at our macro scale?

莱克斯：你认为在我们的宏观尺度上，这种纠缠有回声吗？

Leonard: Yes, absolutely. For sure. We’re entangled—quantum mechanically entangled with everything in this room. If we weren’t, we wouldn’t be observing it. But on the other hand, you can ask, “Am I really comfortable with it?” And I’m uncomfortable with it in the same way that I can never get comfortable with five dimensions. My brain isn’t wired for it.

伦纳德：是的，绝对的。当然。我们与这个房间里的一切都纠缠在一起——量子力学上的纠缠。如果我们没有纠缠，我们就不会观察到它。但另一方面，你可以问，“我真的对它感到舒服吗？”我对此感到不舒服，就像我永远无法对五维空间感到舒服一样。我的大脑没有为此而连接。

Lex: Are you comfortable with four dimensions?

莱克斯：你对四维空间感到舒服吗？

Leonard: A little bit more because I can always imagine the fourth dimension as time.

伦纳德：稍微舒服一点，因为我总是可以把第四维想象成时间。

Lex: So, the arrow of time—are you comfortable with that arrow? Do you think time is an emergent phenomenon, or is it fundamental to nature?

莱克斯：那么，时间之箭——你对那支箭感到舒服吗？你认为时间是一种涌现现象，还是自然的基本现象？

Leonard: That is a big question in physics right now. All the physics that we do, or at least that the people that I am comfortable with talking to—my friends.

伦纳德：这是目前物理学中的一个大问题。我们所做的所有物理学，或者至少是我乐于与之交谈的人——我的朋友们。

Lex: Yes.

莱克斯：是的。

Leonard: My friends. We all ask the same question that you just asked. In space, we have a pretty good idea it’s emergent, and it emerges out of entanglement and other things. Time always seems to be built into our equations as just what Newton—pretty much what Newton modified a little bit by Einstein—would have called time. And mostly, in our equations, it is not emergent. Time in physics is completely symmetric—forward and backward symmetric. So, you don’t really need to think about the arrow of time for most physical phenomena.

伦纳德：我的朋友们。我们都问了你刚才问的同一个问题。在空间中，我们有一个很好的想法，那就是它是涌现出来的，它从纠缠和其他东西中涌现出来。时间似乎总是被构建到我们的方程式中，就像牛顿——几乎就是牛顿被爱因斯坦稍微修改了一下——会称之为时间的东西。而且在大多数情况下，在我们的方程式中，它不是涌现出来的。物理学中的时间是完全对称的——前后对称。因此，对于大多数物理现象，你并不真的需要考虑时间之箭。

Lex: For most microscopic phenomena.

莱克斯：对于大多数微观现象。

Leonard: No. It’s only when the phenomena involve systems which are big enough for thermodynamics to become important, for entropy to become important. For small systems, entropy is not a good concept. Entropy is something which emerges out of large numbers. It’s a probabilistic idea; it’s a statistical idea, and it’s a thermodynamic idea. Thermodynamics requires lots and lots and lots of little substructures. Okay, so it’s not until you emerge at the thermodynamic level that there’s an arrow of time. Do we understand it?

伦纳德：不。只有当现象涉及到足够大的系统，以至于热力学变得重要，熵变得重要时，时间之箭才会出现。对于小型系统，熵不是一个好概念。熵是从大量数字中涌现出来的东西。它是一个概率性的概念；它是一个统计性的概念，它是一个热力学概念。热力学需要许许多多的小型子结构。好的，所以直到你出现在热力学层面上，才会出现时间之箭。我们理解它吗？

Lex: Yeah.

莱克斯：是的。

Leonard: I think we understand it better than most people think we understand it. Yeah, I think we understand it. It’s just a statistical idea.

伦纳德：我认为我们对它的理解比大多数人认为的要好。是的，我认为我们理解它。它只是一个统计性的概念。

Lex: You mean like the second law of thermodynamics—entropy and so on?

莱克斯：你的意思是像热力学第二定律——熵等等？

Leonard: Yeah. The pack of cards, and you fling it in the air, and you look what happens to it.

伦纳德：是的。一副扑克牌，你把它扔到空中，然后看看会发生什么。

Lex: Yeah, but—

莱克斯：是的，但是——

Leonard: What’s random—we understand it doesn’t go from random to simple. It goes from simple to random.

伦纳德：什么是随机的——我们理解它不会从随机到简单。它从简单到随机。

Lex: But do you think it ever breaks down?

莱克斯：但是你认为它会崩溃吗？

Leonard: What I think you can do is, in a laboratory setting, you can take a system which is somewhere intermediate between being small and being large and make it go backward—a thing which looks like it only wants to go forward because of statistical mechanical reasons, because of the second law. You can very, very carefully manipulate it to make it run backward. I don’t think you can take an egg—Humpty Dumpty—who fell on the floor and reverse that. But you can, in a very controlled situation, you can take systems which appear to be evolving statistically toward randomness, stop them, reverse them, and make them go back.

伦纳德：我认为你可以在实验室环境中，取一个介于小和大之间的系统，并让它倒退——这个东西看起来只想向前走，因为统计力学的原因，因为热力学第二定律。你可以非常非常小心地操纵它，让它倒退。我不认为你可以把一个掉在地上的鸡蛋——矮胖子——倒过来。但是你可以在一个非常可控的环境中，取一个看起来正在向随机性统计演化的系统，停止它，反转它，并让它返回。

Lex: What’s the intuition behind that? How do we do that? How do we reverse it?

莱克斯：这背后的直觉是什么？我们是怎么做到的？我们如何反转它？

Leonard: A closed system?

伦纳德：一个封闭的系统？

Lex: Yeah, pretty much a closed system.

莱克斯：是的，几乎是一个封闭的系统。

Leonard: Yes.

伦纳德：是的。

Lex: Did you just say that time travel is possible?

莱克斯：你刚才说时间旅行是可能的吗？

Leonard: No, I didn’t say time travel is possible. I said you can make a system go backward in time.

伦纳德：不，我没有说时间旅行是可能的。我说你可以让一个系统在时间上倒退。

Lex: And you don’t like—

莱克斯：而且你不喜欢——

Leonard: You can make it go backward—you can make it reverse its steps. You can make it reverse its trajectory.

伦纳德：你可以让它倒退——你可以让它反转它的步骤。你可以让它反转它的轨迹。

Lex: Yeah. How do we do that? What’s the intuition there? Does it have—is it just a fluke thing that we can do at a small scale in the lab that doesn’t have—

莱克斯：是的。我们是怎么做到的？那里的直觉是什么？它有——它只是我们在实验室里小规模进行的侥幸行为，它没有——

Leonard: What I’m saying is you can do it a little bit better than a small scale. You can certainly do it with a simple small system. Small systems don’t have any sense of the arrow of time. Atoms have no sense of the arrow of time; they’re completely reversible. It’s only when you have—you know, the second law of thermodynamics is the law of large numbers. You can break the law, but it’s hard. It requires great care. The bigger the system is, the harder it is. You have to overcome what’s called chaos, and that’s hard. And it requires more and more precision. For 10 particles, you might be able to do it with some effort. For 100 particles, it’s really hard. For 1,000 or a million particles, forget it. But not for any fundamental reason—just because it’s technologically too hard to make the system go backward.

伦纳德：我的意思是，你可以做得比小规模好一点。你当然可以用一个简单的小型系统来做到这一点。小型系统没有任何时间之箭的感觉。原子没有时间之箭的感觉；它们是完全可逆的。只有当你拥有——你知道，热力学第二定律是大数定律。你可以打破这条定律，但这很难。这需要非常小心。系统越大，就越难。你必须克服所谓的混沌，这很难。而且它需要越来越精确。对于 10 个粒子，你可能需要一些努力才能做到。对于100个粒子，这真的很难。对于1000个或一百万个粒子，忘了吧。但这不是因为任何基本的原因——只是因为在技术上让系统倒退太难了。

Lex: So, no time travel for engineering reasons.

莱克斯：所以，由于工程方面的原因，没有时间旅行。

Leonard: No, no, no. What is time travel? Time travel to the future—that’s easy.

伦纳德：不，不，不。什么是时间旅行？到未来的时间旅行——这很容易。

Lex: Yes.

莱克斯：是的。

Leonard: You just close your eyes, go to sleep, and you wake up in the future.

伦纳德：你只要闭上眼睛，睡一觉，醒来时就已经在未来了。

Lex: Yeah, yeah, a good nap gets you there.

莱克斯：是的，是的，好好睡一觉就能到达那里。

Leonard: Yeah, a good nap gets you there, right. But in reversing the second law of thermodynamics, it’s a very difficult engineering effort. I wouldn’t call that time travel because it gets too mixed up with what science fiction calls time travel. This is just the ability to reverse a system. You take the system, and you reverse the direction of motion of every molecule in it. You can do it with one molecule. If you find a particle moving in a certain direction—let’s not say a molecule, let’s say a baseball—you stop it dead at some point, and you simply reverse its motion. In principle, that’s not too hard, and it’ll go back along its trajectory in the backward direction.

伦纳德：是的，好好睡一觉就能到达那里，对吧。但在逆转热力学第二定律方面，这是一项非常困难的工程工作。我不会称之为时间旅行，因为它与科幻小说中所说的时间旅行混淆得太厉害了。这只是逆转系统的能力。你拿走这个系统，然后逆转其中每个分子的运动方向。你可以用一个分子来做到这一点。如果你发现一个粒子在某个方向上运动——我们不要说分子，我们说一个棒球——你在某个时刻让它停下来，然后你简单地逆转它的运动。原则上，这并不太难，它会沿着它的轨迹向后倒退。

Lex: Just running the program backward.

莱克斯：只是倒着运行程序。

Leonard: Running the program backward, yeah. Okay, if you have two baseballs colliding—well, you can do it, but you have to be very, very careful to get it just right. Now, 10 baseballs? Really, really hard. Better yet, 10 billiard balls on an idealized frictionless billiard table, okay? So, you start the balls all in a triangle, right? And you whack them.

伦纳德：倒着运行程序，是的。好的，如果你有两个棒球碰撞——嗯，你可以做到，但你必须非常非常小心才能做到恰到好处。现在，10 个棒球？真的，真的很难。更好的是，10 个台球在一个理想化的无摩擦台球桌上，好吗？所以，你把球都放在一个三角形里，对吧？然后你猛击它们。

Lex: Yep.

莱克斯：是的。

Leonard: Depending on the game you’re playing, you whack them—where you’re really careful, but you whack them—and they go flying off in all possible directions. Okay, try to reverse that. Try to reverse that. Imagine trying to take every billiard ball, stopping it dead at some point, and reversing its motion so it was going in the opposite direction. If you did that with tremendous care, it would reassemble itself back into the triangle. Okay, that is a fact, and you could probably do it with two billiard balls, maybe with three billiard balls if you’re really lucky. But what happens is, as the system gets more and more complicated, you have to be more and more precise not to make the tiniest error, because the tiniest errors will get magnified, and you’ll simply not be able to do the reversal. So, yeah, you could do that, but I wouldn’t call that time travel.

伦纳德：根据你玩的游戏，你猛击它们——你真的很小心，但你猛击它们——它们会向所有可能的方向飞去。好的，试着逆转它。试着逆转它。想象一下，试着拿起每一个台球，在某个时刻让它停下来，然后逆转它的运动，让它朝着相反的方向运动。如果你非常小心地这样做，它会重新组装成三角形。好的，这是一个事实，你可能可以用两个台球做到这一点，如果你真的幸运的话，也许可以用三个台球做到这一点。但是，随着系统变得越来越复杂，你必须越来越精确，不能犯哪怕是最小的错误，因为最小的错误会被放大，你根本无法进行逆转。所以，是的，你可以做到这一点，但我不会称之为时间旅行。

Lex: Yeah, that’s something else. But if you think of it—it just made me think—if we think of the unrolling of state that’s happening as a program, if we look at the world, so the idea of looking at the world as a simulation, as a computer, but it’s not a computer—it’s just a single program. A question arises that might be useful: how hard is it to have a computer that runs the universe?

莱克斯：是的，那是另一回事。但如果你想一想——它只是让我想到——如果我们把正在发生的状态展开看作一个程序，如果我们看看这个世界，那么把世界看作一个模拟，一台计算机，但它不是一台计算机——它只是一个程序。一个可能有用问题出现了：拥有一台运行宇宙的计算机有多难？

Leonard: Okay, so there are mathematical universes that we know about. One of them is called anti-de Sitter space, where we understand its quantum mechanics well. I think we could simulate it in a computer. In a quantum computer or classical computer, all you can do is solve its equations. You can’t make it work like the real system. If we could build a quantum computer or a big enough one, robust enough one, we could probably simulate a universe—a small version of an anti-de Sitter universe. Anti-de Sitter is a kind of cosmology. So, I think we know how to do that. The trouble is, the universe that we live in is not the anti-de Sitter geometry. It’s the de Sitter geometry, and we don’t really understand the quantum mechanics of that. So, at the present time, I would say we wouldn’t have the vaguest idea how to simulate a universe similar to our own. You know, we could ask, “Oh, could we build in the laboratory a small version—a quantum mechanical version—of a collection of quantum computers entangled and coupled together, which would reproduce the phenomena that go on in the universe, even on a small scale?” Yes, if it were anti-de Sitter space; no, if it’s de Sitter space.

伦纳德：好的，所以我们知道有一些数学宇宙。其中一个叫做反德西特空间，我们对它的量子力学很了解。我认为我们可以在计算机中模拟它。在量子计算机或经典计算机中，你所能做的就是求解它的方程式。你不能让它像真实的系统那样工作。如果我们能够建造一台量子计算机，或者一台足够大、足够强大的量子计算机，我们可能可以模拟一个宇宙——一个反德西特宇宙的小型版本。反德西特是一种宇宙学。所以，我认为我们知道如何做到这一点。问题是，我们生活的宇宙不是反德西特几何。它是德西特几何，我们并不真正理解它的量子力学。所以，在目前，我想说我们对如何模拟一个类似于我们自己的宇宙没有任何模糊的概念。你知道，我们可以问，“哦，我们能否在实验室里建造一个小型版本——一个量子力学版本——一个由纠缠和耦合在一起的量子计算机集合，它可以重现宇宙中发生的现象，即使是在小规模上？”如果是反德西特空间，答案是肯定的；如果是德西特空间，答案是否定的。

Lex: Can you slightly describe de Sitter space and anti-de Sitter space? What are the geometric properties that make them different?

莱克斯：你能稍微描述一下德西特空间和反德西特空间吗？是什么几何特性使它们不同？

Leonard: They differ by the sign of a single constant called the cosmological constant. One of them is negatively curved, the other is positively curved. The anti-de Sitter space, which is the negatively curved one, you can think of as an isolated system in a box with reflecting walls. You could think of it as a quantum mechanical system in an isolated environment. De Sitter space is the one we really live in, and that’s the one that’s exponentially expanding—exponential expansion, dark energy, whatever you want to call it. And we don’t understand that mathematically.

伦纳德：它们的区别在于一个称为宇宙学常数的常数的符号不同。其中一个是负曲率的，另一个是正曲率的。反德西特空间，也就是负曲率的空间，你可以把它想象成一个孤立的系统，在一个有反射壁的盒子里。你可以把它想象成一个孤立环境中的量子力学系统。德西特空间是我们真正生活的空间，它正在呈指数级膨胀——指数级膨胀，暗能量，随便你怎么称呼它。我们从数学上不理解它。

Lex: Do we understand?

莱克斯：我们理解吗？

Leonard: Not everybody would agree with me, but I don’t understand it.

伦纳德：不是每个人都会同意我的看法，但我不理解它。

Lex: They would agree with you?

莱克斯：他们会同意你的看法？

Leonard: They definitely would agree with me that I don’t understand it.

伦纳德：他们肯定会同意我的看法，那就是我不理解它。

Lex: What about the understanding of the birth, the origin, the Big Bang?

莱克斯：那么对宇宙的诞生、起源、大爆炸的理解呢？

Leonard: There are theories.

伦纳德：有一些理论。

Lex: Are those—

莱克斯：那些是——

Leonard: There are theories. My favorite is the one called eternal inflation. The infinity can be on both sides, on one of the sides, and none of the sides.

伦纳德：有一些理论。我最喜欢的是一个叫做永恒膨胀的理论。无限可以存在于两边，一边，或者不存在于任何一边。

Lex: What’s your real opinion?

莱克斯：你真正的看法是什么？

Leonard: My real opinion? Infinity on both sides.

伦纳德：我真正的看法？两边都是无限的。

Lex: Oh boy.

莱克斯：哦，天哪。

Leonard: Yeah.

伦纳德：是的。

Lex: Yeah, that’s—

莱克斯：是的，那是——

Leonard: Why is that your favorite? Because it’s the most—just mind-blowing?

莱克斯：那是你最喜欢的吗？因为它最——令人震惊？

Lex: No, because—

莱克斯：不，因为——

Leonard: We want a beginning.

伦纳德：我们想要一个起点。

Lex: No. Why do we want a beginning? In practice, there was a beginning, of course. In practice, there was a beginning. But could it have been a random fluctuation in an otherwise infinite time? Maybe. In any case, the eternal inflation theory, I think, if correctly understood, it would be infinite in both directions.

莱克斯：不。我们为什么要一个起点？在实践中，当然有一个起点。在实践中，有一个起点。但它可能是无限时间中的随机波动吗？也许吧。无论如何，我认为，如果正确理解永恒膨胀理论，它在两个方向上都是无限的。

Lex: How do you think about infinity?

莱克斯：你是如何看待无限的？

Leonard: Oh God.

伦纳德：哦，天哪。

Lex: Yeah.

莱克斯：是的。

Leonard: So, okay, of course you can think about it mathematically. I just finished this discussion with my friend Sergey Brin.

伦纳德：所以，好的，当然你可以从数学上思考它。我刚刚和我的朋友谢尔盖·布林完成了关于这个的讨论。

Lex: Yes.

莱克斯：是的。

Leonard: How do you think about infinity? I say, well, Sergey Brin is infinitely rich. How do you test that hypothesis?

伦纳德：你是如何看待无限的？我说，嗯，谢尔盖·布林是无限富有的。你如何检验这个假设？

Lex: Yeah, so there’s really no way to visualize some of these things, like—

莱克斯：是的，所以真的没有办法想象其中的一些东西，比如——

Leonard: Yeah, no. This is a very good question. Does infinity have any place in physics?

伦纳德：是的，没有。这是一个非常好的问题。无限在物理学中占有一席之地吗？

Lex: Right.

莱克斯：对。

Leonard: And, well, all I can say is, very good question.

伦纳德：嗯，我只能说，这是一个非常好的问题。

Lex: What do you think of the recent first image of a black hole visualized from the Event Horizon Telescope?

莱克斯：你如何看待最近从事件视界望远镜中看到的第一个黑洞图像？

Leonard: It’s an incredible triumph of science in itself. The fact that there are black holes, which collide, is not a surprise. And they seem to work exactly the way they’re supposed to work. Will we learn a great deal from it? I don’t know. I—we might, but the kind of things we learn won’t really be about black holes. Why there are black holes in nature of that particular mass scale, and why they’re so common, may tell us something about the structure, evolution of structure in the universe. But I don’t think it’s going to tell us anything new about black holes. But it’s a triumph in the sense that you go back 100 years, and it was a continuous development—general relativity, the discovery of black holes, LIGO, the incredible technology that went into LIGO. It is something that I never would have believed was gonna happen 30, 40 years ago. And I think it’s a magnificent—this structure, magnificent thing—this evolution of general relativity, LIGO, high precision, ability to measure things on a scale of 10 to the minus 21.

伦纳德：这本身就是科学的一个不可思议的胜利。存在黑洞，而且它们会碰撞，这并不奇怪。它们似乎完全按照它们应该的方式工作。我们会从中學到很多东西吗？我不知道。我——我们可能会，但我们学到的东西不会真的与黑洞有关。为什么自然界中存在那种特定质量尺度的黑洞，以及为什么它们如此普遍，可能会告诉我们一些关于宇宙结构、结构演化的信息。但我认为它不会告诉我们任何关于黑洞的新东西。但它是一项胜利，因为你回顾 100 年，它是一个持续的发展——广义相对论，黑洞的发现，LIGO，以及进入 LIGO 的令人难以置信的技术。这是我 30 年、40 年前从未想过会发生的事情。我认为这是一个宏伟的——这个结构，宏伟的东西——广义相对论、LIGO、高精度、能够在 10 的负 21 次方尺度上测量事物的能力的演变。

Lex: So, you’re just astonished by all this?

莱克斯：所以，你只是对这一切感到惊讶？

Leonard: Just happy for us, too, this right picture.

伦纳德：也为我们感到高兴，这张正确的图片。

Lex: Is it different? You know, you’ve thought a lot about black holes. How did you visualize them in your mind, and is the picture different than your visualization?

莱克斯：它有什么不同吗？你知道，你对黑洞思考了很多。你在脑海中是如何想象它们的，这张图片与你的想象有什么不同吗？

Leonard: No, it simply confirmed—no, it’s a magnificent triumph to have confirmed a direct observation. Yeah, that Einstein’s theory of gravity at the level of black hole collisions actually works is awesome.

伦纳德：不，它只是证实了——不，直接观察的证实是一个伟大的胜利。是的，爱因斯坦的引力理论在黑洞碰撞的层面上 tatsächlich works is awesome.

Lex: That’s really awesome.

莱克斯：那真是太棒了。

Leonard: Yeah, I know some of the people who were involved in that. They just thought, “Merry people.”

伦纳德：是的，我认识一些参与其中的人。他们只是想，“快乐的人们。”

Lex: Yeah, just these little Homo sapiens.

莱克斯：是的，只是这些小智人。

Leonard: Yeah, just these little monkeys got together.

伦纳德：是的，只是这些小猴子聚在一起。

Lex: Slightly advanced lemurs.

莱克斯：稍微先进一点的狐猴。

Leonard: Right.

伦纳德：对。

Lex: I think. What kind of questions can science not currently answer, but you hope might be able to soon?

莱克斯：我想是的。科学目前无法回答哪些问题，但你希望很快就能回答？

Leonard: Well, you’ve already addressed them. What is consciousness, for example?

伦纳德：嗯，你已经提到了它们。例如，什么是意识？

Lex: Do you think that’s within the reach of science?

莱克斯：你认为这在科学的范围之内吗？

Leonard: I think it’s somewhat within the reach of science, but I think that now, I think it’s in the hands of the computer scientists and the neuroscientists. Not the physicists—perhaps with their help, perhaps at some point. But I think physicists will try to simplify it down to something that they can use their methods on, and maybe they’re not appropriate. Maybe we simply need to do more machine learning on bigger scales, evolve machines—machines not only that learn, but evolve their own architecture as a process of learning. Evolve an architecture, not under our control—only partially under our control, but under the control of machine learning.

伦纳德：我认为它在某种程度上是在科学的范围之内，但我认为现在，我认为它掌握在计算机科学家和神经科学家手中。不是物理学家——也许在他们的帮助下，也许在某个时候。但我认为物理学家会试图将它简化到他们可以使用他们的方法的东西，也许它们不合适。也许我们只需要在更大的规模上做更多的机器学习，进化机器——机器不仅可以学习，而且可以进化它们自己的架构作为学习过程。进化一个架构，不在我们的控制之下——只在我们部分的控制之下，但在机器学习的控制之下。

Lex: I’ll tell you another thing that I find awesome. You know this Google thing, that they taught the computers how to play chess?

莱克斯：我再告诉你一件我觉得很棒的事情。你知道谷歌的这件事，他们教计算机下棋？

Leonard: Yeah.

伦纳德：是的。

Lex: Yeah, okay, they taught the computers how to play chess, not by teaching them how to play chess, but just having them play against each other.

莱克斯：是的，好的，他们教计算机下棋，不是通过教它们如何下棋，而是让它们互相比赛。

Leonard: Against each other.

伦纳德：互相比赛。

Lex: This is a form of evolution. These machines evolved. They evolved an intelligence. They evolved an intelligence without anybody telling them how to do it. They were not engineered. They just played against each other and got better and better and better. That makes me think that machines can evolve intelligence.

莱克斯：这是一种进化形式。这些机器进化了。它们进化出了智力。它们进化出了智力，没有人告诉它们如何去做。它们不是被设计的。它们只是互相比赛，变得越来越好。这让我认为机器可以进化出智力。

Leonard: What exact kind of intelligence? I don’t know.

伦纳德：究竟是什么样的智力？我不知道。

Lex: But in understanding that better and better, maybe we’ll get better clues as to what goes on in human life and intelligence.

莱克斯：但通过越来越好地理解这一点，也许我们会更好地了解人类生活和智力中发生了什么。

Leonard: Right.

伦纳德：对。

Lex: Last question. What kind of questions can science not currently answer and may never be able to answer?

莱克斯：最后一个问题。科学目前无法回答，并且可能永远无法回答哪些问题？

Leonard: Yeah. Is there an intelligence out there that underlies the whole thing? You can call them with the “G” word if you want.

伦纳德：是的。是否存在一种智力支撑着整个事物？如果你愿意，你可以用“G”这个词来称呼它们。

Lex: I can say, are we a computer simulation?

莱克斯：我可以说，我们是一个计算机模拟吗？

Leonard: With a purpose, is there an agent, an intelligent agent that underlies or is responsible for the whole thing? Does that intelligent agent satisfy the laws of physics? Does it satisfy the laws of quantum mechanics? Is it made of atoms and molecules?

伦纳德：带着目的，是否存在一个代理人，一个支撑着或负责着整个事物的智能代理人？这个智能代理人是否满足物理定律？它是否满足量子力学的定律？它是由原子和分子构成的吗？

Lex: Yeah.

莱克斯：是的。

Leonard: There’s a lot of questions, and I don’t see this—it seems to me a real question.

伦纳德：有很多问题，我看不到这一点——在我看来这是一个真正的问题。

Lex: It’s an answerable question?

莱克斯：这是一个可以回答的问题吗？

Leonard: Well, it’s answerable—the questions have to be answerable to be real. Some philosophers would say that a question is not a question unless it’s answerable. This question doesn’t seem to me answerable by any known method, but it seems to me real.

伦纳德：嗯，它是可以回答的——问题必须是可以回答的，才是真实的。一些哲学家会说，除非问题是可以回答的，否则它就不是问题。这个问题在我看来无法用任何已知的方法来回答，但在我看来它是真实的。

Lex: There’s no better place to end. Leonard, thank you so much for talking.

莱克斯：没有比这更好的结束方式了。伦纳德，非常感谢你的谈话。

Leonard: Okay.

伦纳德：好的。

备注

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